On 2014 Dec 03, Rafael Najmanovich commented:

We predicted back in 2012 (Chartier M, 2012) as part of a large scale analysis of rare codon cluster that such clusters may play a role in the molecular recognition of the nascent protein for intracellular targeting and membrane insertion. It is unfortunate that the authors were not aware of our publication.

On 2015 Jan 03, William Grant commented:

Here are some papers not included in this review showing benefits of vitamin D testing. Peiris AN, Bailey BA, Grant WB, Mascitelli L. Vitamin D testing. Lancet 2012 May 5;379:1699-1701.

Der T, Bailey BA, Youssef D, Manning T, Grant WB, Peiris AN.,Vitamin D and prostate cancer survival in veterans. Military Med. 2014;179 (1) :81–84.

Peiris AN, Bailey BA, Manning T. Relationship of vitamin D monitoring and status to bladder cancer survival in veterans. South Med J. 2013 Feb;106(2):126-30.

Bailey BA, Manning T, Peiris AN. Vitamin D testing patterns among six Veterans Medical Centers in the Southeastern United States: links with medical costs. Mil Med. 2012 Jan;177(1):70-6.

Of course some may have been published after your literature search was completed. However, they do support the benefits of vitamin D testing among hospital patients.

Also, this paper is supportive in that it found that patients in the ICU with very low 25OHD concentrations benefited from vitamin D supplementation. Amrein K, Schnedl C, Holl A, Riedl R, Christopher KB, Pachler C, Urbanic Purkart T, Waltensdorfer A, Münch A, Warnkross H, Stojakovic T, Bisping E, Toller W, Smolle KH, Berghold A, Pieber TR, Dobnig H. Effect of High-Dose Vitamin D3 on Hospital Length of Stay in Critically Ill Patients With Vitamin D Deficiency: The VITdAL-ICU Randomized Clinical Trial. JAMA. 2014 Oct 15;312(15):1520-30.

One of the impediments to acceptance of vitamin D seems to be the lack of supportive trials. The trials have not supported the ecological and observational studies largely because the trials have not been properly designed. Too often, people with normal to high 25OHD concentrations are enrolled and given a small amount of vitamin D. The proper way to conduct such trials was outlined recently in this paper: Heaney RP. Guidelines for optimizing design and analysis of clinical studies of nutrient effects. Nutr Rev. 2014 Jan;72(1):48-54.

Disclosure I receive funding from Bio-Tech Pharmacal (Fayetteville, AR) and Medi-Sun Engineering, LLC (Highland Park, IL).

On 2014 Nov 25, Robert Eibl commented:

Sounds like a very good step forward in understanding these cells.

On 2015 Jan 12, Larry Parnell commented:

This paper was my selection for Paper of the Week for 10-14 Nov 2014.

On 2015 Mar 22, Sean Ekins commented:

And here is a post on the letter from editor http://www.collabchem.com/2015/03/22/what-warrants-an-erratum-and-why-the-old-publishing-model-must-change/

On 2015 Mar 22, Sean Ekins commented:

Here is the letter CAS sent to J Med Chem Editor (which was forwarded to me) regarding this paper http://figshare.com/articles/jm_2014_011308_CAS_report_of_error_2_pdf/1348274 alerting us to 20 document records for one patent which were not visible to us when the analysis was done in 2014.

On 2015 Feb 11, Christopher Southan commented:

None

On 2014 Dec 17, Sean Ekins commented:

Here are some slides on this work http://www.slideshare.net/ekinssean/medicinal-chemistry-due-diligence-computational-predictions-of-an-experts-evaluation-of-the-nih-chemical-probes

On 2014 Dec 17, Sean Ekins commented:

Here is a link to a post discussing this paper and also link to in the pipeline mention https://www.collaborativedrug.com/buzz/2014/12/16/beware-researchers-challenges-navigating-commercial-and-public-databases/

On 2014 Dec 15, Sean Ekins commented:

Also added in my summary of 2014 papers http://www.collabchem.com/2014/12/15/a-year-in-publications-2014/

On 2014 Dec 04, Sean Ekins commented:

Here is a link to a discussion of the peer review of this paper and it includes all reviewer comments http://www.collabchem.com/2014/12/04/chemical-probes-and-parallel-database-worlds-who-wants-to-know-more-publishing-fun/

On 2014 Dec 02, Nadia Litterman commented:

Here is a link to the related paper on using maching learning to model an expert's evaluations and due diligence: http://www.ncbi.nlm.nih.gov/pubmed/25244007

On 2014 Nov 29, Christopher Southan commented:

Comparative statistics updated and live links added at http://cdsouthan.blogspot.se/2014/11/pmid-25415348-back-story-on-bioactivity.html

On 2014 Nov 28, Paolo Tieri commented:

This article is one of the "Multi-omic Data Integration" Research Topic in Frontiers, which focuses on data integration approaches and methods of any type and extent, their application in understanding the pathogenesis of specific diseases or in identifying candidate biomarkers, in order to exploit the full benefit of multi-omic datasets and their intrinsic information content. For more information about multi-omic data integration check http://journal.frontiersin.org/ResearchTopic/2280

On 2015 Jan 29, Christopher Southan commented:

Please make the separate molecular classes (the numbers of which are specified in the abstact) available for download. This should have been a condition for publication. I cant even open the CFAM seed file.

On 2016 Aug 23, Ben Goldacre commented:

This trial has the wrong trial registry ID associated with it on PubMed: both in the XML on PubMed, and in the originating journal article. The ID given is NCT0160325. We believe the correct ID, which we have found by hand searching, is NCT01603251.

This comment is being posted as part of the OpenTrials.net project^[1] , an open database threading together all publicly accessible documents and data on each trial, globally. In the course of creating the database, and matching documents and data sources about trials from different locations, we have identified various anomalies in datasets such as PubMed, and in published papers. Alongside documenting the prevalence of problems, we are also attempting to correct these errors and anomalies wherever possible, by feeding back to the originators. We have corrected this data in the OpenTrials.net database; we hope that this trial’s text and metadata can also be corrected at source, in PubMed and in the accompanying paper.

Many thanks,

Jessica Fleminger, Ben Goldacre*

[1] Goldacre, B., Gray, J., 2016. OpenTrials: towards a collaborative open database of all available information on all clinical trials. Trials 17. doi:10.1186/s13063-016-1290-8 PMID: 27056367

* Dr Ben Goldacre BA MA MSc MBBS MRCPsych<br> Senior Clinical Research Fellow<br> ben.goldacre@phc.ox.ac.uk<br> www.ebmDataLab.net<br> Centre for Evidence Based Medicine<br> Department of Primary Care Health Sciences<br> University of Oxford<br> Radcliffe Observatory Quarter<br> Woodstock Road<br> Oxford OX2 6GG

On 2015 Apr 06, Jeffrey Ross-Ibarra commented:

Muñoz-Fuentes et al. offer an interesting look at the relationship between domestication and recombination rate in animals. A quick correction worth mentioning: while they cite my 2004 paper on recombination and domestication in plants (PMID: 14767840) several times, it's done somewhat out of context. They quote me as saying

'"recombination rate is likely of little importance" in relation to plant domestication (Ross-Ibarra 2004)'

whereas the actual quote from my paper clearly referred specifically to preadaptation:

"Other hypothesized genetic preadaptations, such as polyploidy (Hilu 1993), have been shown to be unimportant in determining the successful domestication of plant species, and the present analysis suggests that recombination rate is likewise of little importance."

and the abstract is fairly unambiguous:

"The results support the hypothesis that domestication selects for an increase in recombination..."

On 2014 Nov 30, Hilda Bastian commented:

Thanks for the helpful and informative reply, Tetyana.

While modeling can take account of some known variables, it can't overcome the limitations of measures based on these traditional theories. Other mechanisms that could explain the results remain. There are assumptions used to explain the results of these data simulations (such as that hiring and firing exposes people to conflict and hostility, but pay decisions do not) that remain open to question.

The results do not exclude the possibility that the women did not have enough authority in comparison with the men with whom they were compared, or other associated (in)tangible benefits that the men could take for granted with the "hire/fire/influence pay" status. Having equal status may indeed have brought similar benefits. Adequate markers for a particular status attainment for the original in-group from whom the measures were derived, may lack the power to discriminate unequal status for others. If so, then like is not necessarily being compared with like. It wasn't possible to "take all other job characteristics into account," because they weren't measured.

Using unreported modifications of measurement tools for the key outcome makes it difficult for others to be able to assess the validity of the data and its interpretation. It would be helpful if that were done within the larger project, and linked here. Depression implies an adverse mental health condition (both in the community and clinically), and the study's conclusions refer to health benefits, not happiness. The CESD has cut-offs for symptomatology that has no clinical relevance.

While there's no doubt that workplace circumstances for women and other traditional "out groups" must change, I don't believe on the basis of this data that people should believe that workplace authority over others per se makes women depressed. But the data are enormously valuable, and this work is indeed an important contribution to addressing an important social issue. Thank you for that, as well as the additional information in your reply.

On 2014 Nov 30, Tetyana Pudrovska commented:

Dear Hilda, thank you for your thoughtful and insightful comments and for engaging in this dialogue. It was a pleasure to ponder over the points you made and to consider our methodology, data, and findings from another perspective. Below are some of my thoughts.

1. The measure of depression does not have a clear clinical relevance. Different terms are used interchangeably. Each item is coded 0 or 1.

The most widely used term for the CES-D scale is depressive symptoms. We also use depression because it is a very general term that doesn’t refer to a clinical diagnosis. Depression is not a disease in the DSM, “major depressive disorder” is.

The CES-D items (like almost all other measures of self-reported physical and mental health) are highly skewed because most people report no depressive symptoms. Hence, the dichotomization of each item. We conducted a variety of sensitivity analyses using different coding approaches for the outcome, such as averaging all items and taking a natural log to reduce the positive skew, and the findings were remarkably similar.

The issue of using continuous scales vs binary diagnoses has received a lot of attention in sociology of mental health. Both approaches have strengths and weaknesses. Our findings hold in a variety of alternative models when we use a binary measure with a cutoff at the 75th percentile or at 10+ symptoms.

Sociologists typically prefer continuous scales because they are better for capturing the stressful consequences of social inequality. Unlike clinicians, sociologists are interested in the full spectrum of mental health, not only its negative extremes. To uncover the effects of social structures and social relationships on individual mental health, we need a continuum from mild to very severe that enables us to compare social groups on this scale. Binary diagnoses can obscure important differences because people who have, for example, 5 symptoms are in the same category as people who have no symptoms.

Because the effects documented in our study are large in magnitude and statistically significant after adjustment for many factors that are traditionally used to explain women’s higher depression, our findings provide important insights into the psychological consequences of social arrangements.

Ultimately, clinical relevance is not consistent with the brunt of our argument. One of the major implications of our study is that a higher level of depression among women in authority positions is not a clinical issue that can be addressed by diagnosing and treating specific individuals. It’s a social issue that should be addressed at the macro-level of society and the meso-level of organizations.

2. The observed differences in depression may reflect not the effect of job authority itself but the effects of many other job characteristics that differ between men and women with job authority.

The workplace situation is certainly not equal between men and women in authority positions. It is well-documented that in the same occupations and at the same levels of human capital characteristics, women have lower earnings, lower autonomy, and lower levels of many other desirable workplace characteristics than men.

Yet, the gender difference in depression documented in our study is not due to the differences in other job characteristics between men and women. Our models control for all these variables, and the effects of job authority are observed after we take all other job characteristics into account.

In addition to multiple regression, we use counterfactual approach to improve causal inference. It’s also called a “quasi-experimental” design because it simulates random assignment in an experiment. Our approach matches people with job authority (the “treatment” group) and people without job authority (the “control” group) in 1993 on many characteristics, including baseline depression, education, occupation, earnings, weekly hours, job characteristics and job satisfaction, marriage, parenthood, and early-life characteristics, especially parents’ socioeconomic resources. By matching people, we make the two groups as similar as possible with the exception of job authority and then see how depression changes over time based on people’s authority status in 1993.

It is also important that we conduct not only between-gender comparisons but also within-gender comparisons. Women with job authority have more depressive symptoms compared to women without job authority. In contrast, men with job authority have lower depression than men without job authority. What’s striking is that women with job authority in our study are socially advantaged in terms of most socioeconomic characteristics that are strong predictors of positive mental health. These women have more education, higher income, more prestigious occupations, and higher levels of job satisfaction and job autonomy than women without job authority. By all traditional models of socioeconomic status and health women with job authority should fare better than lower-status women. Yet we find the opposite.

3. Absence of direct measures of interpersonal stress, harassment, and prejudice.

Ideally we’d certainly like to have all possible measures of interpersonal stress and discrimination. But such a data set simply does not exist. The Wisconsin Longitudinal Study (WLS) that we use is currently among the best for our purposes. We are doing more work with other data sets, including the National Longitudinal Surveys, but all data have their advantages and limitations. So we are launching our own data collection. The current study, by documenting these patterns and providing a theoretically and empirically grounded interpretation, makes an important contribution and one of the first steps to address an important social issue.

The WLS has very rich array of measures of job characteristics. We include all variables that are considered main stressors in traditional theories of work stress. Yet, the effect of job authority persists net of these traditional explanations, which bolsters the indirect evidence for the mechanisms we propose.

The WLS started in 1957 and is still ongoing. We have information about our participants’ employment histories for the last 55 years. Job authority (but not depression) was measured for the first time in 1975 when our participants were 36 years old. We used this earlier measure of job authority in related articles that are all components of a larger project.

On 2014 Nov 29, Hilda Bastian commented:

This paper uses the terms "depressive symptoms" and "depression" interchangeably. However, the relationship between the screening questions asked and the "clinical" condition of depression is unclear. A modified form of an unspecified version of the CESD screening tool was used. It included an unspecified 10 of the 16 CESD questions, applied only for the last week. In a further variation to the CESD, answers were scored with only a dichotomous outcome.

The cut-off for determining "depression" was also not explained and the "clinical" relevance of the measure (and associated increase) is unclear. If there has been a validation of an association between the scores used here and depression, it was not referred to in the paper. More details on this would be helpful to people interested in interpreting the results of this study.

The workplace situation was not equal between the men and women bracketed in the same job authority categories in this sample. The women worked fewer hours per week, earned less than the men of the same age, and were supervised more often. It's women's job authority with less pay and less freedom than men's job authority that is being compared. That would also be a function of the gender inequality the authors identify as a clear problem here. But it raises a question about the level of emphasis given to the psychological impact of having supervisory authority, and, therefore, to know what to do about it.

The range of workplace factors addressed by this study include the traditional ones related to autonomy. Those questions don't address the kinds of gender-related issues the authors point to in the literature as constituting psychological workplace adversity for women in management: such as endemic social exclusion by peers and supervisors, frequent slights from all directions, being judged more frequently as socially disruptive, unequal opportunity and status attainment, and harassment. More sensitive tools (and relevant data from before the age of 54) would have been needed to unpack what made that generation of women unhappier than the men. The underlying point these authors show, though - that psychological aspects of the workplace experience have serious bearing on women's happiness - is a critical one.

The full text of this article is available here.

On 2015 May 31, Shin Lin commented:

This response discusses the manuscript submitted by Gilad and Mizrahi-Man at F1000Reseach, as well as our two responses at that journal. For details, we encourage interested individuals to read those various pieces.

The batch effect that Gilad and Mizrahi-Man present as confounders of our findings are not the result of experimental protocols. Our sequencing libraries were largely prepared in one sitting by the same person, and we used matched primer indices/barcodes to minimize variation as observed in 't Hoen PA, 2013. The potential batch effect to which Gilad and Mizrahi-Man are referring is from sequencing on different lanes/flow cells/sequencing machines. In our experience, these effects are small (also found in 't Hoen PA, 2013), and we did not observe any such effects in the original published data. However, to further settle the issue, we reconstructed the sequencing libraries under a different multiplexing scheme to address their concerns, and we found the same clustering pattern as originally presented by Lin et al. Thus, we emphatically disagree with the conclusion from Gilad and Mizrahi-Man that our conclusions are “not warranted,” but rather, we argue that objective normalization procedures allow the discovery of the clustering of transcriptomes by species.

Gilad and Mizrahi-Man found clustering by tissue after normalization, because in their attempt to account for lane/flow cell/sequencing machine effect, they normalized away the species effect. In that set of experiments, tissues of the same species were multiplexed on the same sequencing lane; accounting for primer indices would not have been possible otherwise. That normalization of the data by each species separately causes clustering by tissue was known to authors of Lin S, 2014, as this observation was presented in the Mouse ENCODE main paper Yue F, 2014.

Gilad and Mizrahi-Man's work focused on one particular dataset in Lin S, 2014. However, that paper contains a principal component analysis (PCA) on data from multiple sources: Stanford (human, mouse), Salk (human), HBM (human), LICR (mouse), and CSHL (mouse). There are undoubtedly many technical differences between the various sources. Yet, the clustering by species was seen in higher order principal components (PCs) (see Figure 1A Lin S, 2014); clustering by tissues, in lower components (Figure 1B in Lin S, 2014) or by normalizing species separately (Extended Data Fig. 1C of Yue F, 2014). The same behavior is seen in the Stanford-only data—both in Lin S, 2014, which minimizes primer index effect (Figure 1C & 1D) and now the newly generated results that account for lane effect. The latter are consistent with our earlier observation that experimental batch did not drive the species-specific clustering.

As for the latest criticisms concerning sample collection, these are issues outside the scope of the manuscript by Gilad and Mizrahi-Man. We state that our procurement practices are consistent with what other investigators have done and continue to do. When we limit our analyses to the small number of tissues examined by recent studies showing tissue specific-clustering (i.e. those with a large number of tissue-specific genes), we also find tissue specific clustering (see Figure 1F in Lin S, 2014). Thus, there are no inherent biases in our data which account for species-specific clustering. Rather, it is our complete dataset with many additional tissue types which results in the different clustering pattern. This evaluation of a broad tissue set is the critical difference which led to our finding of species-specific clustering. Indeed, when we examine the broad dataset of mouse and human CAGE expression data from the Riken Fantom 5 project (FANTOM Consortium and the RIKEN PMI and CLST (DGT)., 2014), we confirm species-specific clustering.

Finally, as stated in the F1000 comment, we reiterate our enthusiasm of the mouse as a vital model system for experimental research, because of its many similarities to humans, which we show in our PNAS paper. However, an appreciation of the differences which exist between human and mouse will allow investigators to better interpret the disparities which are encountered when applying findings in the mouse model to humans.

***New data mentioned herein is available for download at the Mouse ENCODE website.

Shin Lin^1,2 , Yiing Lin³ , Michael A. Beer⁴ , Thomas R. Gingeras⁵ , Joseph R. Ecker^6,7 , Michael Snyder¹

¹ Department of Genetics, Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305; ² Division of Cardiovascular Medicine, Stanford University, Falk Building, 870 Quarry Road Stanford, California 94304; ³ Department of Surgery, Washington University School of Medicine, 660 S. Euclid Ave., Campus Box 8109, St. Louis, Missouri 63110; ⁴ McKusick-Nathans Institute of Genetic Medicine and the Department of Biomedical Engineering, Johns Hopkins University, 733 N. Broadway, BRB 573 Baltimore, Maryland 21205; ⁵ Cold Spring Harbor Laboratory, Functional Genomics, 1 Bungtown Road, Cold Spring Harbor, New York 11742;⁶ Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037; and ⁷ Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA 92037.

Acknowledgement: We thank the other members of the Mouse ENCODE consortium in formulating this response.

On 2015 May 26, Steven Salzberg commented:

Serious technical questions have been raised about the main conclusion of this paper. Specifically, Yoav Gilad and Orna Mizrahi-Man described how the human and mouse samples were processed separately in several ways, any of which could lead to a significant "batch effect." They published some of their findings in F1000 Research, at http://f1000research.com/articles/4-121/v1. They showed that after removing the batch effects, the finding that human and mouse genes cluster separately completely disappeared. In the discussion of that paper on the F1000 site, further sources of batch effects were identified. Thus it appears that the main finding of this paper cannot be supported by the data, because the samples from human and mouse were handled and processed in distinct ways, confounding the batch effect and any possible biological effect.

On 2015 Jan 24, Wichor Bramer commented:

I think this article is a rathor open door article, in that its conclusions can be drawn beforehand, wihout executing the experiments. If you consider PICOS as PICO + S, surely then of course PICO will retrieve more relevant articles and PICOS sarches will be more specific. The searches used for testing PICOS were the exact searches for PICO, with an added element of S, which in this case was very basic (two or three tersm), compared to the exhaustive translation of the other elements (more than 10 terms). PICO, PICOS and SPIDER should not be used in the creation of search strategies. Not every element in those methods is necessary in a search query. Including specific outcomes and controls can introduce bias in the search results. For a good thorough systematic review, only P and I are used, and when necessary combined with a sensitive filter on study design, that consists of more than three terms and is verified (when possible). PICO, PICOS ad SPIDER can be useful in the process of evaluating the retrieved full text references for inclusion, but should not direct the search strategy.

On 2016 Aug 23, Ben Goldacre commented:

This trial has the wrong trial registry ID associated with it on PubMed: both in the XML on PubMed, and in the originating journal article. The ID given is NCT00696414. We believe the correct ID, which we have found by hand searching, is NCT00696514.

This comment is being posted as part of the OpenTrials.net project^[1] , an open database threading together all publicly accessible documents and data on each trial, globally. In the course of creating the database, and matching documents and data sources about trials from different locations, we have identified various anomalies in datasets such as PubMed, and in published papers. Alongside documenting the prevalence of problems, we are also attempting to correct these errors and anomalies wherever possible, by feeding back to the originators. We have corrected this data in the OpenTrials.net database; we hope that this trial’s text and metadata can also be corrected at source, in PubMed and in the accompanying paper.

Many thanks,

Jessica Fleminger, Ben Goldacre*

[1] Goldacre, B., Gray, J., 2016. OpenTrials: towards a collaborative open database of all available information on all clinical trials. Trials 17. doi:10.1186/s13063-016-1290-8 PMID: 27056367

* Dr Ben Goldacre BA MA MSc MBBS MRCPsych<br> Senior Clinical Research Fellow<br> ben.goldacre@phc.ox.ac.uk<br> www.ebmDataLab.net<br> Centre for Evidence Based Medicine<br> Department of Primary Care Health Sciences<br> University of Oxford<br> Radcliffe Observatory Quarter<br> Woodstock Road<br> Oxford OX2 6GG

On 2015 Oct 03, Torsten Seemann commented:

The source code is available at https://github.com/broadinstitute/pilon/wiki

On 2016 Sep 14, Peter Hajek commented:

There exists a simple test of whether the 'gateway effect' applies to humans. Over the past 60 years, the prevalence of smoking among young men in the US and UK declined about 4-fold. If nicotine use leads to increased use of cocaine and other drugs, the use of other drugs should decline as well. There is no sign of that.

On 2017 Jul 07, Wei Wang commented:

Several other problems have been reported: https://pubpeer.com/publications/DAA39CA8966C9B4DAB38EDAA343B4C

On 2014 Dec 15, Ivan Shatsky commented:

General comment: The phenomenon studied in this paper is very exciting. I read the article with a great interest. Unfortunately, I regret to say that the underlying mechanism remained uncovered. Although I agree that the authors identified some curious structures within the 5’UTRs of HOXA mRNAs which might be implicated in the regulation of these mRNAs by RPL38, I did not find sufficient evidence for existence of IRES-elements in these mRNAs. As in numerous other similar investigations, to identify IRES-elements the authors employed the method of DNA bicistronic constructs, the approach that had been repeatedly shown to be associated with almost unavoidable artifacts (see Jackson Cold Spring Harb Perspect Biol. 2013 Feb 1;5(2); Lemp et al. Nucleic Acids Res. 2012 Aug;40(15):7280-90.). And I suspect this paper is not free of those artifacts either (see below). Several crucial control experiments necessary to support or to exclude the IRES-mediated mechanism have been recently described (for references see Shatsky et al. Mol Cells. 2010 Oct; 30(4):285-93). One of them, for instance, is the ratio of translational activities for m7G capped versus uncapped (A-capped) monocistronic constructs. This value estimates contribution of the cap to the translational potential of a 5’UTR under selected conditions. If this contribution is very high (as is the case of cap-dependent mRNAs) one may exclude the presence of a true IRES. I think that this and other obligatory controls are feasible to perform with cells C3H10T1/2 used in this paper but they were not done.

Some specific points:

1. The authors regard the cap-independent and IRES-dependent modes of translation initiation as synonymous mechanisms and support this notion with reference 21. I should stress that not all specialists in eukaryotic translation would share this opinion since nobody has ever shown that a 5’end dependent translation initiation cannot be regulated by specific structures within the respective 5' UTR. The opposite has recently been demonstrated (Terenin et al. Nucleic Acids Res. 2013 Feb 1;41(3):1807-16.)
2. When listing examples of cellular IRESs identified to date (second paragraph), the authors mention c-myc, Apaf-1, XIAP. To the best of my knowledge, these cellular IRESs have been disproved (Andreev et al. Nucleic Acids Res. 2009 Oct;37(18):6135-47; Bert et al. RNA. 2006 Jun;12(6):1074-83; Baranick et al. Proc Natl Acad Sci U S A. 2008 Mar 25;105(12):4733-8; van Eden et al. RNA. 2004 Apr;10(4):720-30); Lemp et al. Nucleic Acids Res. 2012 Aug;40(15):7280-90. )
3. “Extended data Figure 1a” raises a great concern: the HCV IRES should not be used as a normalizing construct for testing bicistronic DNAs since the HCV IRES has been reported to harbor a cryptic promoter (Dumas, E. et al. 2003 Nucleic Acids Res. 31 (4): 1275-1281) and hence may produce capped monocistronic mRNAs . By the way, among viral IRESs characterized to date the HCV IRES is regarded as one of the weakest.
4. The control test with Rluc shRNA (Extended data Figure 1 b,c) strongly suggests that some significant amount of monocistronic (and therefore capped) Fluc mRNAs is present in transfected cells since the residual Fluc activity after RNA interference is too high. This may also be the case for the control bicistronic mRNA containing the HCV IRES (see point 3). Otherwise, the pictures 1b and 1c must be similar. At least, in the analogous test performed in our lab, the Rluc and Fluc activities fall down to the similar background levels (Fig. 2D in Dmitriev et al. Mol Cell Biol. 2007 Jul;27(13):4685-97).
5. The authors suggest that the IRES elements are mostly confined within ~300 nts proximal to the start codon. As a support to this conclusion, they note that some of HOXA mRNAs possess a 5’ UTR of that size. If so, why the activity of HOXA9 construct 944-1266 is much lower than that for the full length 5’UTR (Fig. 1d)?<br>
6. “Extended Figure 1d”. This control is useless. It only shows that the bicistronic mRNA of the expected size is present in transfected cells but unable to show the presence of monocistronic mRNAs starting within the intercistronic region. The corresponding bands won’t be seen.
7. On the base of pull-down experiments the authors claim that 80S ribosomes are specifically formed on their IRES-elements. The problem is that they use 10mM of magnesium in these expts, i.e. the concentration at which the assembly of translation initiation complexes in mammalian systems should not occur.
8. The mode of action of TIE element looks absolutely puzzling. It is even difficult to imagine any mechanism for its operation. My question: is it specific to these particular cells?

On 2015 Jan 12, Larry Parnell commented:

This paper was my selection for Paper of the Week for 15-19 Dec 2014.

On 2015 Feb 06, Ryan Radecki commented:

Post-publication commentary: "Social Media in Medicine – Useless!"

Or, might it be how you use it that matters?

This is a brief report from the journal Circulation, regarding a self-assessment of their social media strategy. The editors of the journal performed a prospective, block-randomization of published articles to either social media promotion on Facebook and Twitter, or no promotion, and compared 30-day website page views for each article. 121 articles were randomized to social media and 122 to control, and were generally evenly balanced between article types.

And, the answer – unfortunately, for their 3-person associate editor team – is: no difference....

http://www.emlitofnote.com/2014/11/social-media-in-medicine-useless.html

On 2015 Nov 25, S A Ostroumov commented:

This article is exploring new links between issues of ecology (trophic web, trophic chains) and medicine (malaria control). It is very interesting.

On 2014 Dec 30, Kausik Datta commented:

To add to Hilda Bastian's informative comment, the press release mentions the misleading statement not only in the title, but also in the first paragraph - stating definitively: "The study, published Nov. 17 by Proceedings of the National Academy of Sciences, shows that triclosan causes liver fibrosis and cancer in laboratory mice through molecular mechanisms that are also relevant in humans." (Emphasis mine.)

This is, at best, irresponsible journalism (and at worst, a terrible disservice to people living with cancer). What seems particularly galling is the fact that this sacrifice of scientific accuracy at the altar of needless sensationalism in the press release was perpetrated by none other than the University (UCSD) at which the work was done. This brings to mind once again the age-old tussle in science communication, between science and journalism.

At the same time, the authors cannot deflect the blame completely, especially since the lead author, quoted in the Press Release, didn't seem to emphasize at all the dosage effect of Triclosan administration and exposure route - which is rather odd, given that the Triclosan was either fed to the mice or injected directly into their peritoneal cavity at a high enough amount, none of which would apply to humans.

I hope the authors pay heed to the most germane points raised by Hilda about the further inclusion of the data; I'd be most interested in the actual experimental outcomes.

On 2014 Nov 21, Hilda Bastian commented:

The title and abstract of this article focuses on the positive finding in tumor promotion, without emphasizing that the findings were negative on causation, in a way that is clearly accessible for non-specialist readers. This is of particular importance, as a university press release issued for this study was headed with this misleading statement: "The dirty side of soap: Triclosan, a common antimicrobial in personal hygiene products, causes liver fibrosis and cancer in mice." This encouraged unwarranted alarm in the community (which I discuss further in this blog post).

A 2010 inventory of animal and clinical studies of triclosan safety (Rodricks JV, 2010) found that oncogenicity studies to that point had not found cancer-related increases in any species, except for liver cancer in mice. Without pre-registration of studies on this question, we are unaware of what the outcomes have been for all oncogenicity studies on this substance, and thus whether there is publication bias.

Further areas of uncertainty relate to the experiments here. The article does not report sufficient data and methodological information to enable adequate assessment of the level of uncertainty associated with the experiments (see the NIH's Proposed Principles and Guidelines for Reporting Preclinical Research). It would be helpful if the authors took the opportunity to include key data here, specifically:

• how the sample size was determined;
• the inclusion/exclusion criteria;
• exact data on the experiments' results (including confidence intervals);
• whether or not allocation of mice to the groups was random, and if so, details of the method of randomization (including whether or not there was blinding);
• whether there was blinding in outcome assessment.

On 2014 Nov 20, Neven Patrick commented:

Looks like estrogen removal reduced the proliferation significantly and probably the tumor has high ER mRNA score and very sensitive to estrogen deprivation. My thought is to regard this tumor as luminal A. Also important would be to check the probability of the class assignment by PAM50. Some cases surely will have lower probability than others and this case might be one of those. Because I presume single case PAM50 algorithm based on distance from centroid of model cases of intrinsic subtype will be forcing the class assignment for the cases which in the clustering would be gray zone cases. Does Prosigna report provide such QC measure? Soon Paik

Truly fascinating (and not so simple) questions! To my knowledge this has not been studied, and I agree that removal of estrogen could potentially alter the proliferative rate of the tumor (and proliferation is a dominant aspect of most MGS to date). For this patient I would base treatment on the higher risk result. Kathy Albain

If one measures Ki67 on the core it will be higher because pt is on E but if you measure it again 2 wks later on the surgical specimen it will be lower and won't agree with that done on the core. I would be surprised if that is not true. C.Kent Osborne

Just saw a patient with an ER+ cancer, ki67 30% on core biopsy. Stopped HRT. Had surgery for T2N0 IDC and oncotype 14. Oncologist recommended chemo due to Ki67. Repeated ki67 on surgical sample and it was less than 5%. There is clearly an education opportunity here. Presumably the lower ki67 after stopping HRT is predicative of outcome and impact of hormone therapy. Interesting.<br> Hope S. Rugo

On 2015 Feb 18, Christophe Dessimoz commented:

Note that this article was published as open access and is thus freely available from the publisher's website.

On 2015 Jan 05, NephJC - Nephrology Journal Club commented:

This study was discussed on Dec 2nd 2014 in the open online nephrology journal club, #NephJC, on twitter (along with a related article).

Introductory comments, written by Eoin O'Sullivan, are available at the NephJC website and cross-posted at the Renal Fellow Network blog.

The discussion was quite intense, with more than 50 participants, including nephrologists, emergency medicine physicians, fellows and residents as also two of the authors, Bhupinder Singh and Edgar Lerma.

A transcript and a curated (i.e. Storified) version of the tweetchat are available from the NephJC website.

The highlights of the tweetchat were:

• There is considerable interest in a drug such as ZS-9, given the incidence of hyperkalemia, both acute and chronic, sometimes resulting in the need to stop ACE-inhibitors and ARBs in CKD patients.

• There was considerable discussion about the choice of placebo (as against sodium polystyrene sultanate, or diuretics and/or low potassium diet) as a comparator; the authors provided insight that this was mainly to comply with FDA requirements.

• Though the results of the trial show that ZS-9 as studied was effective in lowering potassium levels, more information about the differential incidence of edema as also the details of diuretic use in both groups was sought.

Overall, though there is considerable enthusiasm about the availability of new agents for management of hyperkalemia, considerations of safety, cost and data from more studies will determine how widely this drug will be used once licensed.

Interested individuals can track and join in the conversation by following @NephJC or #NephJC, or visit the webpage at NephJC.com.

On 2016 Apr 20, Jennifer S Walsh commented:

It wasn't possible to get perfect matching for all the factors, so it was done in order of: gender, age, height, postcode and smoking. Because smoking was the lowest priority, there were a few pairs who were matched on the other factors but not on smoking. The number of smokers was low overall, and we don't think the imperfect matches for smoking had any influence on the study results.

On 2016 Apr 15, Jose M. Moran commented:

There is an issue that needs to be addressed. Controls were recruited to be individually matched to an obese participant by sex, age (±3 years), height (±5 cm), postcode and smoking status (current smoker or nonsmoker). If controls were matched 1:1 by the smoking status, how it is possible that the percentage of current smokers differs between normal and obese subjects across all the age groups (except in men aged 25-45 years n=6)?

On 2016 Oct 03, Morten Oksvold commented:

Please note that after an investigation at the University of Cologne, six articles where T. Wenz figures as first or senior author were found to contain questionable data due to scientific misconduct. This article is one of these six articles.

The conclusion from the report was ready June 28, 2016, please see the link (in German):

http://www.portal.uni-koeln.de/9015.html?&tx_news_pi1[news]=4335&tx_news_pi1[controller]=News&tx_news_pi1[action]=detail&cHash=1deb8399d7f796d65ca9f6ae4764a1ce

On 2014 Dec 04, David Keller commented:

Cause and effect cannot be distinguished in the observed associations

Dopamine levels are reduced in the brains of patients with Parkinson's disease (PD). Dopamine inhibits the secretion of prolactin, and prolactin, in turn, reduces the activity of the steroid sex hormones (estrogen, testosterone, etc.) Thus, untreated PD patients should have low brain dopamine levels, high prolactin levels and thus low sex steroid hormone levels. These hormone actions are well-documented in physiology texts.

The next clinical scenario to consider is the effect of treating PD with levodopa, which is metabolized to dopamine in the brain. Clearly, brain dopamine levels will rise, driving down prolactin levels, which, in turn, allows an increase in sex steroid levels, ameliorating, to a variable degree, the hypogonadism caused by untreated PD.

To what degree does the dose of levodopa, taken in amounts sufficient to control the movement disorder symptoms of PD, also treat the hypogonadism caused by PD? This question is not addressed in the abstract.

The authors conclude that lower sex steroid levels and higher prolactin levels "may result in a bigger susceptibility to the disease in men." This observational study cannot possibly prove the cause-and-effect mechanism implied in that statement. The observed associations are explained equally well by the opposite conclusion: that PD causes men to have lower dopamine levels, higher prolactin levels and consequently lower sex steroid levels (hypogonadism), in other words, that hypogonadism is an effect of PD, not a cause. It would require a randomized, controlled interventional study in which hypogonadal PD patients were repleted with administered exogenous sex steroids to prove that hypogonadism is a cause, rather than just an effect, of PD.

On 2015 Jan 12, Larry Parnell commented:

This paper was my selection for Paper of the Week for 17-21 Nov 2014.

On 2014 Dec 19, Scott Federhen commented:

I am Scott Federhen, author of this article, and would like to report some aspects of our genomes from type that were discovered too late to include in the manuscript. A visual inspection of our k-mer tree revealed two genomes from type for Bacillus subtilis subsp. spizizenii that are placed quite distantly from each other.

The average nucleotide identity (ANI) statistics from the alignments of the type genomes from the relevant subspecies of Bacillus subtilis are as follows:

96.7997 CP002905 ADGS01 89.7316 94.8698 – spizizenii vs. spizizenii

94.6427 CP002905 AMXN01 89.4248 86.6265 – spizizenii vs. inaquosorum

93.2854 CP002905 AL009126 88.7053 88.4756 – spizizenii vs. subtilis

89.7% of CP002905 aligns with 94.9% of ADGS01, and the parts that align share 96.8% average nucleotide identity. These two genomes a likely to be from the same species, but probably not from the same subspecies - and certainly not from co-identical strains.

To examine this problem systematically, we looked for all of the cases where we had more then one genome from type from the same species (or subspecies) and sorted them by pairwise ANI. These were usually from different culture collections and often sequenced in different labs. 274 pairwise combinations break down like this:

4 pairs of genomes < 90% identical.

5 pairs of genomes 96% - 99% identical.

8 pairs of genomes 99% - 99.9% identical.

112 pairs of genomes 99.9% - 99.99% identical.

135 pairs of genomes 99.99% - 99.999% identical.

10 pairs of genomes > 99.999% identical

This is a wide range for genomes from strains that are supposed to be co-identical. The four most diverse pairs are likely to be from different species. The problem could lie in many places - the annotation in the sequence entries, a strain mixup in the sequencing lab, or a contamination, misannotation or misidentification in the culture collection. We are working with submitters and culture collections to resolve the most egregious discrepancies and improve the reliability of our subset of genomes and sequences from type. At some point the community is going to have to come to a consensus as to what constitutes identity between co-identical strains.

We look forward to the day when every described species of prokaryote has a complete genome sequence, and a genome is included with every new species description. At that point it would be useful for each culture collection holding strains from type to sequence at least a low-coverage genome to verify the identity of the strain.

On 2017 Jul 13, Randi Pechacek commented:

Sean Gibbons, first author of this paper, provided some background information on microBEnet.

On 2015 Feb 26, Harri Hemila commented:

Missing contradictory findings and other problems in Curtis AJ, 2014

Curtis AJ, 2014 state in their abstract that “The evidence from pooled analysis of 18 randomised controlled trials undertaken in apparently healthy people shows no effect of vitamin E supplementation at a dose of 23-800 IU/day on all-cause mortality.” This conclusion was based on the pooling of results from 18 RCTs that obtained RR = 1.01 (95% CI: 0.97 to 1.05).

In their calculation of the average effect of vitamin E from the 18 studies, Curtis (2014) assume that there exists an overall uniform effect for all of those 18 RCTs, which can be estimated by pooling their findings. However, a subgroup analysis of the ATBC Study (1994) refutes the notion that the effect of vitamin E is uniform for all people. The combination of age and dietary vitamin C in the subgroup analysis modified the effect of vitamin E, which provides very strong evidence of heterogeneity over 6 subgroups (P = 0.0005) Hemilä H, 2009. Such heterogeneity refutes the assumption of a uniform effect in pooling the findings of the 18 RCTs by Curtis (2014).

Curtis AJ, 2014 also state “Subgroup analyses by ... duration of exposure ... showed no association with all-cause mortality.” However, the effect of time should not be analyzed by comparing RCTs at the study level, some of the RCTs being long in duration while the others being short in duration. In the ATBC Study, harm from vitamin E in the young participants was seen after 3.3 years of supplementation. Longer vitamin E supplementation increased mortality by 38% (17% to 63%), whereas vitamin E had no effect on mortality over the earlier period, see Hemilä H, 2009. This finding of a time-dependent effect modification refutes the claim that the duration of vitamin E supplementation does not modify the effects of the vitamin with regard to all-cause mortality. Comparing RCTs on vitamin E by the mean durations of the RCTs, ie study-level analysis, cannot capture changes in the vitamin E effect after lag periods. Instead the analysis of time-dependent effect modification requires individual-level analysis.

Ecological fallacy occurs when mean data about a group is used to impute identical values for all individuals of the group. Thus, the assumption by Curtis AJ, 2014 that the calculated average effect of vitamin E on mortality for the 18 RCTs (ie RR = 1.01) is valid for all participants in these 18 studies, or for other individuals in the community, is an example of ecological fallacy. The conclusion by Curtis AJ, 2014 that “duration of exposure ... showed no association with all-cause mortality” is another example of ecological fallacy.

We do not know how far the heterogeneity of the ATBC Study can be generalized. The participants in that study were middle-aged males, who were aged 50-69 years at the start of the trial in the 1980s, which indicates that they were born before WW-II. In addition, all were smokers and lived in Finland. We do not know if similar effect modification occurs in other populations. Nevertheless, the heterogeneity found in this particular cohort refutes the notion that there might be a universal vitamin E effect on mortality that would be valid for all groups of people. Highly significant heterogeneity was also found in the effect of vitamin E on pneumonia incidence in Hemilä H, 2011 and on the incidence of the common cold in Hemilä H, 2006. Thus the evidence of heterogeneity of the vitamin E effect is not restricted to overall mortality but exists to other outcomes as well.

Furthermore, in the Table 1 of Curtis AJ, 2014 it is stated that the “study quality” of the ATBC Study (1994) was “medium”. As a justification for this classification, in Appendix 2 (“quality of included studies”) Curtis AJ, 2014 inserted question marks next to “blinding of personnel” and “blinding of participants” and a cross (to indicate high risk of bias) to “blinding of outcome assessment”.

The ATBC Study (1994), Alpha-Tocopherol, Beta Carotene Cancer Prevention Study Group., 1994, reported that “Participants and all study staff involved in the ascertainment of end points and the assignment of final diagnoses remained blinded to the participants' treatment assignments throughout the trial” (p. 1030). The same report also stated that “Deaths (n=3570) were identified from the National Death Registry, a branch of Statistics Finland” (p. 1030), which clearly indicates that the deaths in the ATBC study were recorded by an organization that had nothing to do with the administration of the tablets. Thus, the claims by Curtis (2014) about the blinding of participants and personnel in their Appendix 2 are inconsistent with the ATBC Study (1994) report.

Transparency in systematic reviews is important, so that the reader is correctly informed what the basis for the quality assessments is. Curtis AJ, 2014 do not give any explanations about why they dismiss the descriptions about blinding in the ATBC Study (1994) report. Therefore the readers are misled about the quality of the study that has the greatest weight in Fig. 2 of Curtis (2014).

Finally, the ATBC Study (1994) is wrongly cited in Curtis AJ, 2014. They write Virtamo (1998) in Fig. 2 ,Virtamo (2006) in Appendix 2, and Virtamo (2003) in the reference section. All of these three years are wrong. The ATBC Study was actually published in 1994, with the PubMed record Alpha-Tocopherol, Beta Carotene Cancer Prevention Study Group., 1994 (the correct year and reference). Virtamo (2003), which is listed in the reference section, does exist but it is a post-intervention follow-up report of the trial and not the correct reference for the main results of the ATBC Study (1994).

On 2017 Jan 09, Kausik Datta commented:

I came to this paper after reading about Dr. Hadiyah-Nicole Green on Social Media. Much plaudits to Dr. Green and her team. The principle - conversion of near Infra Red light to thermal energy by Gold nanorods - is exciting and full of possibilities, and the proof-of-concept is well-demonstrated both in vitro and in vivo. The question that I had about the AuNR-only group in Figure 2D is answered in the discussion, although I'd have loved to see confirmation of the visible light PTE hypothesis: because if visible light is able to show similar effects on AuNRs, it might make their therapeutic spectrum broader - but bring in problems of specificity as well. In cancer therapeutics, specificity/focus of treatment is an important issue in respect of preserving good cells and destroying rogue, tumorous ones. Ideally, visible or any other light should have minimal effect, while NIR should have maximal on rousing the AuNRs, so to speak.

Also in that respect, while intratumoral delivery of AuNRs takes care of stationary tumors, perhaps an antibody/ligand-based mechanism in conjunction with AuNRs may in future be able to target metastatic tumors as well? This is excellent work that I shall be much interested to follow. (I note that this paper is from 2014. Perhaps Dr. Green would be kind enough to provide a status update?)

On 2015 Mar 03, Dita Gratzinger commented:

We congratulate the authors on a comprehensive overview of this very complex and evolving field of research. Many of the crucial studies represent in vitro and mouse model research; we are characterizing the state of the intact bone marrow stroma in myelodysplastic syndromes. The authors state that

"Multiple data have shown that osteoprogenitors—MSPCs—exhibit normal morphology and frequency in the bone marrow of MDS patients, as well as undisturbed osteoblastic, adipocytic and chondrocytic differentiation potential in vitro."

We have recently published data, not available at the time this review was written, regarding the architecture and extent of the CD271+ mesenchymal stromal cell compartment in intact human bone marrow core biopsies Johnson RC, 2014, and in fact demonstrate an increase in the density of this compartment in higher grade MDS as compared to lower grade MDS and marrow from similarly aged cytopenic patients. We also found an association of high CD271+ mesenchymal stromal cell density with shorter overall survival among patients with MDS, independent of IPSS-R and history of transfusion. We acknowledge that it is not clear whether functionally described osteoprogenitors correspond to all or a subset of CD271+ mesenchymal stromal cells; however, as cited in your review, we have previously shown Flores-Figueroa E, 2012 that CD271+ mesenchymal stromal cells express CXCL12 and arborize extensively within marrow, in association with marrow vasculature, and are intimately associated with the majority of CD34+ hematopoietic stem/progenitor cells in intact human marrow, and are thus good candidates to represent a key functional component of the human bone marrow hematopoietic progenitor/stem cell niche.

On 2014 Nov 14, Hilda Bastian commented:

Very useful data on an important issue, given the high proportion of the population using contact lenses (Swanson MW, 2012). On the issue of the level of individual risk, readers might find a review of large-scale epidemiological studies helpful (Stapleton F, 2013).

The authors stress the importance of good lens hygiene to reduce the risk of infection. That's a critical issue, and people may well over-estimate the adequacy of their own lens care (Bui TH, 2010). Given the increased risk of extended wear (rising from 2-4 per 10,000 for daily use to about 20 for extended wear Stapleton F, 2013), users being better informed about reduced wear as a way of lessening risks may also help (covered along with social and historical aspects in this blog post.)

On 2014 Nov 24, Alexis Clapin commented:

Congratulation for your important work. In table 1, you state that the first approache to addressing attrition bias is intend-to treat analysis. Intend to treat analysis includes all data but when a clinical trial suffers from attrition bias, patients followed up for a longer duration are favouring one of the compared product. For a lot of outcome criteria, a longer duration means a more important impact on the criteria. Consequently, the intend to treat analysis is a biased evaluation of the difference between groups. An example of the impact of attrition bias on the intend-to-treat analysis is given in this article http://www.ncbi.nlm.nih.gov/pubmed/23662092. Without complete data, the best way to evaluate attrition bias is the comparison of intend-to-treat analysis and per-protocol analysis. If the per-protocol analysis provides us with a "better" result than intend-to-treat analysis, it means that patients followed up for the longer duration are favouring one of the compared product. This comparison should be done for all truncated trials. Unfortunately, it is not the case and a lot of clinical trials are truncated ; almost all based on survival analysis. To conclude, if intend-to-treat analysis is the only performed analysis, it is a good way to mask an attrition bias.

For french readers : a more complete evaluation of the advantages of performing both analysis : http://www.etudes-et-biais.com/per-protocole-ou-intention-de-traiter-les-deux-svp/

On 2014 Dec 31, Dennis Eckmeier commented:

We deposited an earlier version of the manuscript on the free pre-print repository bioRXiv: http://biorxiv.org/content/early/2014/08/12/002550

On 2013 Oct 25, DAVID SANDERS commented:

From abstract from Kikuchi A, 1989

"In this paper, two proteins stimulating the GTPase activity of smg p21 are partially purified from bovine brain cytosol. These proteins, designated as smg p21 GTPase-activating protein (GAP) 1 and 2, are separated from a c-ras p21 GAP described previously by column chromatographies. smg p21 GAP1 and -2 stimulate the GTPase activity of only smg p21 but not that of c-Ha-ras p21 or the rho and smg-25A GTP-binding proteins. The Mr values of smg p21 GAP1 and -2 are estimated to be 250-400 x 10(3) and 80-100 x 10(3) by gel filtration and sucrose density gradient ultracentrifugation, respectively. The activity of smg p21 GAP1 and -2 is killed by tryptic digestion or heat boiling. These results indicate that bovine brain contains two smg p21 GAPs in addition to c-ras p21 GAP.

On 2014 Nov 24, Guillaume Filion commented:

This article is one of the "CISCOM meta-analyses", which are very similar papers written by different authors. For more information about the CISCOM meta-analyses, check the blog post "A flurry of copycast on PubMed" at the following link http://blog.thegrandlocus.com/2014/10/a-flurry-of-copycats-on-pubmed

On 2017 Apr 05, Misha Koksharov commented:

None

On 2017 Apr 04, Misha Koksharov commented:

Great! Thank you! Apparently, in your field people normally don't describe the mutagenesis part much (in contrast to protein engineering field per se) and focus on characterization of the final version as I see from various papers.

On 2017 Mar 30, Yi Shen commented:

Thanks Misha for your interest in our work! We are happy to provide further detailed information regarding the experimental procedures. Please email me at yshen3@ualberta.ca.

On 2017 Mar 20, Misha Koksharov commented:

The developed FPs are nice. However, the experimental details (and intermediate results) of mutagenesis procedures are very limited and insufficient. It's impossible to understand what exactly you were doing at this part of the study.

On 2015 Nov 25, Uday Yanamandra commented:

Author name Uday Y should read as Yanamandra U

On 2015 Jul 23, thomas samaras commented:

Over 80 diverse populations show that weight increases as the cube of height increase. For example, the publication Advancedata, US Department of health, education, and welfare, Nov 19, 1976, Table 7 provided data for 18-24 year olds which allowed the following calculations.

Males: 1971 vs. 1960: (69.7"/68.7")cubed x 158 lb = 165 lb (predicted by cubed law) Actual weight given for taller cohort = 165 lb

For females, the results were 130 lb predicted and 132 lb actual.

Another publication, Secular growth and its harmful ramifications, 2002, confirmed the height cubed law (not height squared or BMI law)for five populations (Table 1):

Harvard male entrants (1958 vs. 1930) Wellesley female entrants (1958 vs. 1930) Malina's child data (1958 vs. 1934) Swedish males (n= 488,732) (1971 vs. 1960) US population (18-74 years)(1971 vs. 1960)

A number of more recent studies provide data that support the Ponderal Index or height cubed law.

On 2015 Mar 11, IRWIN FEINBERG commented:

Crawley et al 1 state that there have been few longitudinal studies of sleep/wake timing across adolescence. They fail to take note of our findings in a ten-year longitudinal study of 93 subjects aged 6-18 years. Sleep EEG was recorded twice yearly on habitual school-night schedules in subjects aged 6-18 years. In addition, weekend recordings with extended time in bed and a follow-up night, were obtained from subjects 9-18 years. Results published thus far could have added perspective to the findings of Cowley et al. In addition to defining the trajectories of NREM delta and theta EEG power 2, 3, their within-night dynamics 4, and their relations to puberty 5 and daytime sleepiness 6, we documented changes in NREM and REM bed schedules and sleep durations 7 that bear directly on the findings of Crowley et al. Our study demonstrated that school-night time in bed decreased by 13 min/year between ages 9-18 yrs (p<0.0001). From an average bedtime of 9:15 PM at age 9 yrs, bedtimes advanced by 13 min/year (p<0.0001), whereas rise times did not change (p=0.11). Sleep latency did not change (p=0.70), but sleep efficiency increased over this age range (p<0.0001). The net result was that total sleep time (TST) decreased by 10 min/yr from 515 min at age 9 to xx min at age 18 (p<0.0001). This TST reduction was not produced by shorter REM and NREM durations, as would be expected if it was the result of sleep deprivation due to restricted time in bed. Instead, TST declined because of a selective reduction of NREM sleep, which declined by 12 min/yr (p<0.0001). REM sleep durations actually increased significantly by 2 min/yr (p<0.0001). This pattern of change cannot be attributed to a phase advance. We interpret it, along with the massive decline in slow wave EEG power, as a manifestation of brain maturation driven by synaptic elimination. In our model, synaptic elimination during adolescence decreases the intensity of waking brain activity (shown also by declining cerebral metabolic rate 8) which decreases the need for NREM dependent recuperation of plastic neuronal systems.

1. Crowley SJ, Van Reen E, LeBourgeois MK, et al. A longitudinal assessment of sleep timing, circadian phase, and phase angle of entrainment across human adolescence. PLoS One 2014;9:e112199.
2. Campbell IG, Feinberg I. Longitudinal trajectories of non-rapid eye movement delta and theta EEG as indicators of adolescent brain maturation. Proc Natl Acad Sci U S A 2009;106:5177-80.
3. Feinberg I, Campbell IG. Longitudinal sleep EEG trajectories indicate complex patterns of adolescent brain maturation. Am J Physiol Regul Integr Comp Physiol 2013;304:R296-303.
4. Campbell IG, Darchia N, Higgins LM, et al. Adolescent changes in homeostatic regulation of EEG activity in the delta and theta frequency bands during non-rapid eye movement sleep. Sleep 2011;34:83-91.
5. Campbell IG, Grimm KJ, de Bie E, Feinberg I. Sex, puberty, and the timing of sleep EEG measured adolescent brain maturation. Proc Natl Acad Sci U S A 2012;109:5740-3.
6. Campbell IG, Higgins LM, Trinidad JM, Richardson P, Feinberg I. The increase in longitudinally measured sleepiness across adolescence is related to the maturational decline in low-frequency EEG power. Sleep 2007;30:1677-87.
7. Feinberg I, Davis NM, de Bie E, Grimm KJ, Campbell IG. The maturational trajectories of NREM and REM sleep durations differ across adolescence on both school-night and extended sleep. Am J Physiol Regul Integr Comp Physiol 2012;302:R533-R40.
8. Chugani HT, Phelps ME, Mazziotta JC. Positron emission tomography study of human brain functional development. Ann Neurol 1987;22:487-97.

On 2016 Aug 23, Ben Goldacre commented:

This trial has the wrong trial registry ID associated with it on PubMed: both in the XML on PubMed, and in the originating journal article. The ID given is NCT0150177. We believe the correct ID, which we have found by hand searching, is NCT01501773.

This comment is being posted as part of the OpenTrials.net project^[1] , an open database threading together all publicly accessible documents and data on each trial, globally. In the course of creating the database, and matching documents and data sources about trials from different locations, we have identified various anomalies in datasets such as PubMed, and in published papers. Alongside documenting the prevalence of problems, we are also attempting to correct these errors and anomalies wherever possible, by feeding back to the originators. We have corrected this data in the OpenTrials.net database; we hope that this trial’s text and metadata can also be corrected at source, in PubMed and in the accompanying paper.

Many thanks,

Jessica Fleminger, Ben Goldacre*

[1] Goldacre, B., Gray, J., 2016. OpenTrials: towards a collaborative open database of all available information on all clinical trials. Trials 17. doi:10.1186/s13063-016-1290-8 PMID: 27056367

* Dr Ben Goldacre BA MA MSc MBBS MRCPsych<br> Senior Clinical Research Fellow<br> ben.goldacre@phc.ox.ac.uk<br> www.ebmDataLab.net<br> Centre for Evidence Based Medicine<br> Department of Primary Care Health Sciences<br> University of Oxford<br> Radcliffe Observatory Quarter<br> Woodstock Road<br> Oxford OX2 6GG

On 2015 Jan 02, Meenakshisundaram Ananthanarayanan commented:

I would like to clarify that the title of our paper is misleading since what we are describing in this study is the post-transcriptional regulation of type 3 InsP3R3 by miR-506 at the mRNA level and not at the protein level.

On 2015 Feb 10, Pieter-Jan Volders commented:

At the time of submission, we were unaware of a specific RefSeq lncRNA subset. As a result, we initially used the NR_* records larger than 200 nucleotides. When dr. Kim D. Pruitt contacted us regarding this issue, we repeated the analysis for the suggested RefSeq subset. This subset contains 4774 transcripts and was obtained through the UCSC table browser. As expected, the percentage of transcripts passing the PhyloCSF cutoff has decreased from 48% to 14% of the RefSeq subset. The online manuscript was updated and the current Figure 3 (online as of January 15, 2015) represents these new results. Additionally, LNCipedia.org was updated as well, and the RefSeq records that do not represent lncRNAs were removed from the database version 3.1.

It is worth noting that we could not find any information on the keyword that was used in the query suggested by dr. Kimm D. Pruit (biomolncrnalncrna), neither on the RefSeq website, nor in the cited manuscript. It is unclear to us how many researchers are aware of this and we would like to suggest to RefSeq to indicate this subset on their website as it is of great value to the lncRNA research community.

On 2014 Dec 03, Kim D Pruitt commented:

The RefSeq results that are presented in Figure 3 do not accurately reflect the high quality of the RefSeq lncRNA dataset.

The article does not describe how this dataset was defined but the authors kindly provided this information as well as a file listing all of the RefSeq accessions and PhyloCSF results. The authors’ definition of RefSeq lncRNA was too simple as it was primarily based on the RefSeq accession prefix (‘NR_’). However, this accession prefix is used by the RefSeq project for several types of noncoding transcripts (PMID:18927115). Roughly 50% of the RefSeq dataset analyzed represent transcribed pseudogenes or noncoding transcripts for protein-coding genes.

On 2016 Feb 05, Kristina Hanspers commented:

Figure 8 is available as a pathway in the "Open Access Publication" Collection at WikiPathways: http://wikipathways.org/index.php/Pathway:WP2886. This pathway can be used for data visualization and network analysis in tools like Cytoscape and PathVisio.

On 2015 Sep 08, CREBP Journal Club commented:

This study gives an excellent insight into GPs’ experiences of two contrasting interventions – training in communication skills (including use of a patient booklet) and the use of a point of care test (CRP). We found it encouraging that the clinicians reported gaining new knowledge from the interventions. Information, such as expected duration of illness and the benefits and harms of antibiotic treatment of acute respiratory infections should preferably be part of any intervention aimed at either GPs or patients. Both interventions achieved important reductions in antibiotic prescribing for acute respiratory infections – and combining the interventions was associated with an even greater reduction(1). The group discussed if future interventions should be multi-faceted. Anthierens et al. found that the GPs reported that the two interventions were complementary and often used for different situations, i.e. the CRP test when there was uncertainty about the severity of the infection, and the communication skills/booklet when an explanation was required. However, it was mentioned that in some countries, such as Australia, the CRP test is still not routinely used as a point of care test in general practice. The group found the information in the booklet very useful. However, the sections about “Helping your immune system fight infection” and “How you can care for your cough” were debated. E.g. a Cochrane Review on Echinacea products did not find any benefits for treating colds(2) and also in the booklet it is stated that the advice on fluids, rest and stress is based on evidence about how the immune system works. Preferably, information used in interventions to enhance the quality of antibiotic prescribing for acute respiratory infections should be based on solid evidence about the group of patients being examined – i.e. in this case patients with acute respiratory infections. In addition, high quality, primary care-based studies are needed to further explore alternatives such as probiotics, zinc and vitamin C and to develop and test new non-antibiotic treatments. See CREBP Journal Club for more information.

References: (1) Little P, Stuart B, Francis N, et al. Effects of internet-based training on antibiotic prescribing rates for acute respiratory-tract infections: a multinational, cluster, randomised, factorial, controlled trial. Lancet 2013; 382(9899): 1175-82. (2) Karsch-Volk M, Barrett B, Kiefer D, Bauer R, Ardjomand-Woelkart K, Linde K. Echinacea for preventing and treating the common cold. The Cochrane database of systematic reviews 2014; 2: Cd000530.

On 2015 Feb 27, Geriatric Medicine Journal Club commented:

This clinical trial of of Problem Adaptation Therapy (PATH) was critically appraised at the January 2015 Geriatric Medicine Journal Club (follow #GeriMedJC on Twitter). A transcript of the discussion can be found here: http://gerimedjc.blogspot.com/2015/01/gerimedjc-january-30-2015.html?spref=tw Highlights include the question of how feasible it is to deliver this type intervention in our current health care climate.

On 2015 Jan 05, NephJC - Nephrology Journal Club commented:

This trial was discussed on Dec 16th and Dec 18th (in the first transatlantic version) in the open online nephrology journal club, #NephJC, on twitter. Introductory explanatory comments, written by Rheumatologist, Dr Paul Sufka, are available at the NephJC website, and Dr Sufka’s blog. It had more than 50 participants, including nephrologists, rheumatologists and nephrology and rheumatology fellows. Transcripts and curated (i.e. Storified) versions of the tweetchats are available from the NephJC website.

The salient highlights of the discussion included:

• The authors and the funding agency (the French Ministry of Health) should be commended for performing this trial to find better ways of minimizing relapses in ANCA associated vasculitis.

• There was significant discussion around the Azathioprine dose chosen (tapered down to levels below that used in the CYCAZAREM trial in later part of this trial); as also the finding of early separation between arms, suggesting some patients in the control arm were azathioprine non-responders.

• The Rituximab dosing strategy seemed quite astute, and was quite successful in reducing relapses without an increase in adverse events. The discussants looked forward to publication of more data from this trial, especially on B cell populations and ANCA titres, that could shed more light for a deeper understanding of the results.

Overall, there was significant enthusiasm for using Rituximab in this setting, though the results of more trials, especially RITAZAREM and MAINRITSAN-2 are now keenly awaited.

Interested individuals can track and join in the conversation by following @NephJC or #NephJC, or visit the webpage at NephJC.com.

On 2014 Nov 12, KEVIN BLACK commented:

I think the effects of levodopa itself on brain activity may be of interest when interpreting the effects of levodopa on movement-related brain activity. Examples from my colleagues include the 4 following articles: PubMed Central IDs PMC21757 and PMC1738560, doi: 10.1038/sj.npp.1300632, and doi: 10.1006/exnr.2000.7522 .

On 2014 Nov 19, David Keller commented:

A landmark paper with important implications for melanoma patients

The results obtained by combining high-dose ipilimumab (10 mg/kg) with GM-CSF (sargramostim) are remarkable. The combination increased median overall survival by 4.8 months compared with high-dose ipilimumab alone, an increase of over 37%, and the combination also reduced the rate of serious adverse events significantly compared with high-dose ipilimumab monotherapy. Patients with advanced melanoma and poor response to existing treatments will want to discuss these results with their oncologists, particularly since GM-CSF is currently available in the U.S.

Since GM-CSF is already approved for other indications, there will be great pressure on oncologists to prescribe ipilimumab in combination with GM-CSF on the basis of this study, particularly for patients whose tumor burden worsens despite approved therapy. Questions which will be encountered include:

1) Would the pairing of GM-CSF with the approved dose of ipilimumab (3 mg/kg) have similar benefits, despite the fact that the ipilimumab dose used in the study was more than 3 times larger than the approved dose?

2) Given the reduction in toxicity of the combination compared with ipilimumab monotherapy, can the approved dose of ipilimumab be safely tripled to 10 mg/kg if used in combination with GM-CSF?

3) Is there any reason to believe that PD-1 checkpoint inhibitors, such as pembrolizumab, given in combination with GM-CSF, would not afford similar improvements in survival and adverse events?

Patients with advanced melanoma who do not respond to currently approved treatments have little to lose. There is no reason to force these patients to wait for the results of confirmatory studies which they may have little hope of surviving to see published. Those that can be accommodated into clinical trials should be encouraged to participate, while the others should be offered the option of adding GM-CSF to their checkpoint inhibitor, if the latter fails to control their disease.

On 2014 Nov 05, Dan Laks commented:

These data support earlier findings that chronic Hg exposure results in depletion of LH:

http://www.ncbi.nlm.nih.gov/pubmed/19914008 http://www.ncbi.nlm.nih.gov/pubmed/19697139

On 2015 May 04, Christopher Southan commented:

This paper is an good target mapping example, see http://cdsouthan.blogspot.se/2015/05/antimalarial-dot-joining-for-mmv008138.html for database mapping of compounds and forward pointer to http://www.ncbi.nlm.nih.gov/pubmed/25754494

On 2014 Nov 17, Alexandre Chigaev commented:

After the Nobel Prize in Physiology or Medicine in 1998 nitric oxide became a widely accepted signaling messenger. Studies of carbon monoxide are still largely limited to the fields of pollution, carbon monoxide poisoning, and vascular biology. This paper provides insight into possible role of carbon monoxide in the rapid regulation of cell adhesion that is crucial for cell mobilization and migration, ischemia-reperfusion injury and transplantation, immune response modulation and evasion of host defence employed by haemolytic pathogens.

On 2014 Nov 10, Serge Ahmed commented:

This is an interesting series of experiments on choice between nicotine and sucrose in rats. In experiments 1, 3, 4 & 5, hungry rats were first trained to respond for sucrose or nicotine on alternate days before being provided with a choice between the two options. Overall, virtually all rats responded more for sucrose than for nicotine under a variety of choice conditions. Thus, all else being (approximately) equal, sucrose surpasses nicotine reward in rats!

In experiment 2, rats were first trained to respond for nicotine before being provided with a choice between nicotine and sucrose. In this condition, about 50% of the rats responded more for nicotine than for sucrose, suggesting that “nicotine self-administration does not only occur in the absence of alternative reinforcement options”, at least in some rats.

Though the results of experiment 2 are promising, they are difficult to interpret univocally. Experiment 2 lacks an important control group that controls for the difficulty in learning to respond for sucrose during choice testing. Briefly, rats were asked to respond on a novel lever under a random ratio 4 schedule of sucrose reinforcement, WITHOUT ANY PRIOR PROGRESSIVE TRAINING on this lever and after a long period of operant extinction. It is likely that many rats will fail to learn to respond for sucrose under these specific conditions, even in the absence of the opportunity to self-administer nicotine! Future research should resolve this important issue.

On 2014 Nov 07, Stephen Strum commented:

I have looked at the full article in JCO and will re-read it, but it seems to me that the issue of salvage RT in the context of men with persistent PC after a local or local-regional therapy could be focused on additional key issues along with stronger advice. Issues that come to mind are the many papers that demonstrate that the first PSA post-RP taken at about 5-6 weeks post-op should use an ultrasensitive PSA. Papers by Doherty et al, Witherspoon and others showed that those with ultrasensitive values ≤ 0.01 had outstanding prognoses e.g., Only 2 patients with an undetectable prostate-specific antigen after radical retropubic prostatectomy biochemically relapsed (3%),compared to 47 relapses out of 61 patients (75%) who did not reach this level. More importantly, in 31 years of focused work caring for men with PC at all stages of illness, I rarely (< 1%) of the time see any diligence regarding use of nomograms and/or ANNs(artificial neural nets)done prior to initial therapy or at the time of so-called PSAR. Nomograms using PSAV + pathologic findings at RP are very helpful in risk assessment for men likely to be helped by salvage IMRT vs not.

Another key issue not discussed in this paper is the RT treatment field and again, the use of nomograms, and ANNs to get a risk assessment for which patients are at risk for nodal spread. Too many men are being treated with RT fields that are not inclusive of where the disease is.

To this end, I will say that ODAC blew it badly when they rejected a simple iron contrast nanoparticle (Combidex) to identify nodal mets at a level of sensitivity and specificity that is dramatically superior to the lousy sensitivity of CT abdomen and pelvis exams. The latter studies involve a half billion dollars globally on an annual basis but even more importantly MISdirect the use of RT and give the RadOnc and patient a false sense of where the active PC is.

We have some wonderful tools that remain in the proverbial Al Gore "lockbox", often discussed in academic meetings but rarely used in the day in and day out care of men faced with prostate cancer.

Stephen B. Strum, MD, FACP Member ASCO since 1973, AUA since 1998, ASTRO since 2002 PCRI (Prostate Cancer Research Institute) First Medical Director and Co-Founder 1997

On 2014 Nov 30, Christopher Southan commented:

See http://www.ncbi.nlm.nih.gov/pubmed/25415348 for the assesment of an expanded probe set

On 2015 Jun 18, NephJC - Nephrology Journal Club commented:

This guideline document was discussed on June 9th and 10th 2015 in the open online nephrology journal club, #NephJC, on twitter.

Introductory comments are available at the NephJC website and blog. The discussion was very detailed, with more than 50 participants, including nephrologists, urologists and residents. Notable were nephrolithiasis experts, David Goldfarb and John Asplin.

A transcript and a curated (i.e. Storified) version of the tweetchat are available at the NephJC website.

The highlights of the tweetchat were:

• Though the ACP follows stringent guideline development process, the absence of high quality data made the end result not quite useful for practical use, especially when compared to other competing publications, such as from the American Urological Association and the European Association of Urology. There was also concern that these guidelines may be (mis)used by third party payers to deny coverage for tests not endorsed in these guidelines.

• From a physiological rationale, there was near unanimity that testing for stone composition is essential, especially in recurrent stone-formers. There was concern that the guideline developers were interpreting absence of evidence of benefit as evidence of absence of benefit.

• Additional online comments that were thought to provide useful context to the discussion, written by Drs Coe, Goldfarb and Topf, are available here, here, here and here.

Interested individuals can track and join in the conversation by following @NephJC or #NephJC, or visit the webpage at NephJC.com.

On 2014 Nov 28, Paolo Tieri commented:

This article is one of the "Multi-omic Data Integration" Research Topic in Frontiers, which focuses on data integration approaches and methods of any type and extent, their application in understanding the pathogenesis of specific diseases or in identifying candidate biomarkers, in order to exploit the full benefit of multi-omic datasets and their intrinsic information content. For more information about multi-omic data integration check http://journal.frontiersin.org/ResearchTopic/2280

On 2014 Nov 28, Paolo Tieri commented:

This article is part of the "Multi-omic Data Integration" Research Topic in Frontiers, which focuses on data integration approaches and methods of any type and extent, their application in understanding the pathogenesis of specific diseases or in identifying candidate biomarkers, in order to exploit the full benefit of multi-omic datasets and their intrinsic information content. For more information about multi-omic data integration check http://journal.frontiersin.org/ResearchTopic/2280

On 2014 Nov 28, Paolo Tieri commented:

This article is part of the "Multi-omic Data Integration" Research Topic in Frontiers, which focuses on data integration approaches and methods of any type and extent, their application in understanding the pathogenesis of specific diseases or in identifying candidate biomarkers, in order to exploit the full benefit of multi-omic datasets and their intrinsic information content. For more information about multi-omic data integration check http://journal.frontiersin.org/ResearchTopic/2280

On 2014 Nov 28, Paolo Tieri commented:

This article is part of the "Multi-omic Data Integration" Research Topic in Frontiers, which focuses on data integration approaches and methods of any type and extent, their application in understanding the pathogenesis of specific diseases or in identifying candidate biomarkers, in order to exploit the full benefit of multi-omic datasets and their intrinsic information content. For more information about multi-omic data integration check http://journal.frontiersin.org/ResearchTopic/2280

On 2014 Nov 28, Paolo Tieri commented:

This article is one of the "Multi-omic Data Integration" Research Topic in Frontiers, which focuses on data integration approaches and methods of any type and extent, their application in understanding the pathogenesis of specific diseases or in identifying candidate biomarkers, in order to exploit the full benefit of multi-omic datasets and their intrinsic information content. For more information about multi-omic data integration check http://journal.frontiersin.org/ResearchTopic/2280

On 2014 Nov 10, Klaas Vandepoele commented:

A dedicated supplementary data page incl. a pre-configured GenomeView browser is available at http://bioinformatics.psb.ugent.be/cig_data/RegNet/.

On 2015 Jan 05, NephJC - Nephrology Journal Club commented:

This study was discussed on Dec 2nd 2014 in the open online nephrology journal club, #NephJC, on twitter (along with a related article: the HARMONIZE trial).

Introductory comments, written by Eoin O'Sullivan, are available at the NephJC website and cross-posted at the Renal Fellow Network blog.

The discussion was quite detailed, with more than 50 participants, including nephrologists,emergency medicine physicians, fellows and residents, with great insight provided by the senior author, David Juurlink.

A transcript and a curated (i.e. Storified) version of the tweetchat are available from the NephJC website.

The highlights of the tweetchat were:

• The authors have leveraged administrative datasets to undertake numerous drug-interaction studies in the real world setting with important clinical outcomes.

• The study reports a robust association between use of co-trimoxazole and sudden death amongst elderly patients already using a renin-angiotensin system blocker. While this remains an association, and causation is elusive in observational studies such as these, the consensus was that this is quite likely to be a true effect given the physiological basis, and prior work from this group reporting greater hospitalization for hyperkalemia from the same combination.

• The final takeaway message was to be careful and prescribe co-trimoxazole specifically, and indeed all antibiotics generally, only when truly necessary.

Interested individuals can track and join in the conversation by following @NephJC or #NephJC, or visit the webpage at NephJC.com.

On 2015 Mar 13, Gerhard Nebe-von-Caron commented:

treat with care. Unless sample data are either submitted as supplimentary data or idealy at the flow repository to be checked, they data presented do not give confidence with regards to their relevance as the discriminator settings and gates used cannot be verified.

On 2015 Apr 14, FREDERICK DOMANN commented:

For prior work related to SOD3 and EcSOD in breast cancer please see the following:

http://www.ncbi.nlm.nih.gov/pubmed/19602586

http://www.ncbi.nlm.nih.gov/pubmed/23318435

On 2014 Nov 07, Wenxian Sun commented:

THank you for your interest in this work and reminding me to cite the preprinted paper. I will communicate with the editor and try to add the citation in the official publication.

On 2014 Nov 04, Benjamin Schwessinger commented:

An interesting paper about the transfer of the dicot immune receptor EFR into the monocot crop rice. The authors demonstrate full functionality of the immune receptor in rice and its ability to contribute to bacterial immunity. For readers interested in the topic I would suggest to also read up on the following pre-print EFR in rice leads to ligand dependent activation of immune responses, which was posted June 11th 2014 well before initial submission of this manuscript. We regret that the author's did not cite our pre-print in their here presented work.

On 2014 Nov 11, Olga Novikova commented:

Hm, I guess, it is Arabidopsis, not Afrabidopsis...

On 2015 Dec 03, Joshua L Cherry commented:

None

On 2015 Nov 30, John P A Ioannidis commented:

Dear Joshua,

thank you again for your comments. I am worried that you continue to cut and paste to distort my sentences.

1. The headline over my text was written by the Nature editors as their introduction to the paper, so perhaps you should blame them and ask them to replace it with "Here follows a horrible paper by Ioannidis". Yet, I think you would still be unfair to blame them, because their headline says "most innovative and influential", not just "most innovative". The terms "influential", "influence", "major influence" pervade my paper multiple times, but you pick one sentence with "innovative" instead, and interpret it entirely out of its context.<br>
2. The phrases "the most important" and "very important" are not identical. Very important papers may not necessarily be THE most important. But they are very important - and influential. [As an aside, honestly, this repeated cross-examining quotation-comment style makes me feel as if I am answering the Spanish Inquisition. Am I going to be burnt at the stake now (please!) or there is one more round of torture?]
3. We agree we need evidence, more evidence - evidence is good, on everything, including the current NIH funding system, which has practically no evidence that it better than other options, but still distributes tens of billions of dollars per year. Wisely, I am sure.<br>
4. "your list contains...". This is not my list. This is the Scopus list. Right or wrong, I preferred not to manipulate it. Your colleagues did manipulate it and did not even share the data on how exactly they manipulated it.
5. You continue to use the term "innovative thinker" out of its context. I scanned again carefully my paper and I can't find the word "excellent". In my mind, a student who has authored as first author a paper that got over 1000 citations (and the paper is not wrong/refuted) is already worthy to be given a shot as a principal investigator. If you disagree, what can I say, feel free not to fund him/her. And please don't worry, most of these guys are not funded anyhow currently, many of them even quit science. Hundreds of principal investigators who publish absolutely nothing or publish nothing with any substantial impact get funded again and again. Hurray!<br>

I am afraid it is unlikely there will be more convergence in our views at this point. A million thanks once again, I have learnt a lot from your comments.

John

On 2015 Nov 03, Joshua L Cherry commented:

John,

I, too, am wary of an endless discussion. In my view, my straightforward original point remains unscathed by our exchange. Your latest reply does compel me to make or repeat a few points.

1) It is perplexing that you seemingly deny that your earlier work "claimed that NIH does a poor job of funding innovators, based on the assumption that the highly cited authors that you identified were innovators". Nicholson and Ioannidis say of these authors that

Such innovative thinkers should not have so much trouble obtaining funding as principal investigators.

The claim that the papers are highly innovative even appears in a headline above your text. This has nothing to do with how I might interpret "innovative"; you unambiguously asserted that these authors were highly innovative, according to your own meaning, based solely on their authorship of a very highly cited article.

2) As already noted, the two publications are at odds with each other even if we consider "importance" rather than innovativeness. The discrepancy is reflected in your reply, where you confirm your belief that

It is an open question whether “the most highly cited papers are the most important ones”

and yet you write that

My assumption was that papers with over 1000 citations (i.e. in the top 0.01% of citations) are very important

I would add that one may rationally believe that there is a correlation between citation and importance while doubting that every primary author of every one of these papers should be funded as a principal investigator. (I add this because of your subtle replacement of "whether" with "the exact extent to which".)

3) Again, I have not asserted, much less insisted, that anybody should not be funded. I have merely questioned whether a certain criterion is a reliable indicator that a scientist is among those most worthy of funding. Those who assert that it is bear the burden of proof.

4) Much of your latest reply is an attack on others that has nothing to do with my comments here or with anything that I have written or done. I speak only for myself. I will note as a bystander that the letter to the editor that you criticize clearly does not say what you claim it does. Among other things, it characterizes only 11% of the papers as unrelated to human health. (I imagine that you have noticed that your list of "life sciences" papers includes some that clearly appear to belong to other disciplines.)

5)

Let us please collect more empirical evidence and fewer opinions.

Indeed. Let us also acknowledge that the leap from "author of a very highly cited paper" to "innovative thinker", "excellent scientist", or "person who should certainly be a principal investigator" is based largely on opinion rather than empirical evidence, as your later statements about what we do not know would suggest.

On 2015 Oct 31, John P A Ioannidis commented:

Dear Joshua,

Thank you for your additional insights, I suspect this topic can be hotly debated ad infinitum. Let me please try again to convey what I think:

You claim that my earlier paper was “based on the assumption that the highly cited authors identified were innovators.” I think that either your interpretation of my assumption is misleading and/or we understand the term “innovators” differently. My assumption was that papers with over 1000 citations (i.e. in the top 0.01% of citations) are very important and thus typically their leading authors merit support (unless the papers were wrong). Importance could include disruptive innovation narrowly defined, but also other equally major qualities that merit recognition and funding. I certainly do not believe that NIH should fund only “disruptive innovators” if the definition of “disruptive innovators” excludes influential experimental studies, randomized trials, other forms of evidence-based research, and interdisciplinary research – the types of work that were excluded by the letter-to-the-editor which was coauthored by David Lipman from your team whom I greatly admire but who threw out of the NIH-relevant agenda almost all health research that is important and almost everything that matters for health. I should also confess that I am disappointed by the stance of the authors of that letter. Their lead author had asked for my raw data and I had shared everything with him within 10 minutes of his request. Then, when I saw in his re-analysis that he had excluded two-thirds of the most extremely-cited papers with the excuse that they are not within the remit of NIH (even though they are objectively categorized by Scopus as belonging to life and health sciences), I asked him to share his raw data to understand his subjective arbitrations. I was very curious to see how leading NIH officials determined that the majority of the most influential medical and health-relevant research is not within the remit of NIH. Almost three years later, I still have not had the pleasure to see their raw data. Perhaps they did not want to reveal that their re-analysis was embarrassing: the re-analysis was based on the untenable assumption that the National Institutes of Health have almost nothing to do with health and with the majority of the most influential health-related research!

I think our inability to converge in our views lies in our difficulty to agree on what is “innovative” and “important”. For example, I argue that randomized trials and other experimental studies, meta-analyses, guidelines, implementation research, team science, and interdisciplinary science can be extremely innovative and important to fund by NIH, while probably much/most of the funded R01 type of bench work and so-called “mechanistic” research currently funded by NIH is neither "innovative" nor "important", no matter how you want to define these terms within the confines of common sense.

The exact extent to which “the most highly cited papers are the most important ones” is indeed an open question and the 2014 Nature paper tried to contribute towards answering this question. I hope that other scientists will revisit this question and improve on what we did. I do not expect a perfect correlation between citations and importance, but this does not mean that we know nothing about citations or that they have no value. When selecting papers in the top 0.01% of citations, it is hard to claim that they would not typically be even in the top 10-15% of importance so as to be worth funding. As I said already in my previous response, the papers assessed in the 2014 Nature analysis were less cited than the ones analyzed in the 2012 Nature analysis. The median number of citations in the papers analyzed in the 2014 Nature paper was 180. Only 39 of these papers (3%) had over 1000 citations, i.e. in the same range as the extremely cited papers evaluated in the 2012 Nature analysis. All of these 39 papers actually scored well in at least one of the 5 dimensions of importance assessed (excluding publication difficulty), with an average maximum score of 83/100. This means that practically all of these papers were considered to be important; thus it is fair to assume that the work would be worth funding by NIH. Nevertheless, if you still insist that NIH should not fund the people behind papers that are so extremely influential (provided they are not wrong), I am afraid I have run out of arguments and there is no way that I will ever convince you.

I trust both you and me and the previous letter writers, we all want to support science and celebrate the best science possible. It is fine to disagree on how to achieve this noble goal. Let us please collect more empirical evidence and fewer opinions. I would cherish to have more robust evidence, even if it were to prove me wrong. Thank you for giving me an opportunity to discuss again this interesting topic.

On 2015 Oct 28, Joshua L Cherry commented:

Thank you, John, for your response. As I see it, the apparent inconsistencies between the two publications remain unresolved.

The earlier work claimed that NIH does a poor job of funding innovators, based on the assumption that the highly cited authors that you identified were innovators. The later work not only provides evidence suggesting otherwise, but explicitly states that the relationship between citations and innovation is unknown. Would you agree, then, that the earlier claim about funding of innovators is unfounded? I am asking about the particular case presented there, not about other arguments or claims that might or might not involve innovativeness.

I would note, since it seems to be necessary, that the emphasis on innovativeness does not originate with me, but with your earlier publication. Your reply warns against a focus on disruptive innovativeness, but Nicholson and Ioannidis (2012) focused on innovativeness. I am merely responding to your argument. We can certainly consider whether highly cited authors necessarily have other qualities, but the shift in argument should be acknowledged.

What, then, of the argument that your list of highly cited authors can be assumed to be among the best of the best on grounds other than innovativeness? According to your later piece there is much that we do not know about citation patterns and it is an open question whether "the most highly cited papers [are] the most important ones". How, then, can you be so certain that these authors are all exceptionally good scientists who should undoubtedly have been funded as principal investigators?

The final paragraph of your response seems to suggest that by pointing out inconsistencies between these two publications I have laid the groundwork for an argument against funding of biomedical research. If this were correct, it is not clear how it would be relevant. (Surely you, of all people, are not suggesting self-censorship on those grounds.) But it is incorrect, and in fact backwards. I have never suggested, any more than you have, that anybody is unworthy of funding. Rather, we are discussing how to identify those most worthy of funding. Your remarks rely on the very point that we are debating: your conviction that your list of highly cited authors reliably identifies extraordinarily good scientists. Unlike my comment, your Conform and Be Funded piece, which claimed to have demonstrated empirically that NIH spends its funds unwisely, might make it difficult to argue for greater NIH funding. By pointing out that this claim was based on an unfounded assumption, I do, if anything, the opposite.

Thank you again for engaging in this discussion.

On 2015 Oct 25, John P A Ioannidis commented:

Dear Joshua,

thank you for trying to make a connection between these two papers. I welcome further brainstorming and investigation on these topics. My interpretation of our results in the current paper is that highly-cited papers may be important for various reasons. Disruptive innovation is one of them, but continued progress, broader interest, and greater synthesis are more prominent features of these influential papers. This does not mean that extremely highly-cited papers are not important (even if some are more important than others), or that I would feel happy if the largest biomedical funding agency in the world does not have sufficient funds to support even the leading authors of extremely highly-cited papers. Also of note, the articles evaluated in the 2012 Nature analysis were far more cited, on average, than the papers evaluated in the 2014 Nature analysis and the sampling was very different, so the connection between the two analyses is even more tenuous.

I continue to think that if someone has been a leader in a paper that has reached the top 0.01% of citation impact in the scientific literature (as in the articles in the 2012 analysis), that person warrants to have his/her research funded, unless this research has been clearly proven wrong and a dead end in the meanwhile. I never argued that the authors of the top-0.01% of cited papers should be the only researchers to be funded, that only disruptive innovative research should be supported, or that all great work is extremely highly-cited. I believe that it is important to support research that is innovative, but it is also important to support research that achieves continued progress, broader interest, and greater synthesis. Scientific excellence has many faces, and focusing only on disruptive innovation may even limit scientific progress and may lead to exaggerated expectations and exaggerated claims by researchers and funders who try to justify their existence in a societal environment that is unfortunately not very supportive of science.

There is also a wider issue to be discussed in your criticism. I have always tried to make the strongest case for public support for science, I never tire to say that science is the best thing that has happened to human beings. In my humble opinion, the 2012 analysis should have offered strong support that the funding budget of NIH should be increased, since currently funds are so limited that NIH cannot even fund many of the people standing behind papers with the utmost extreme citation impact. I really do not see how it helps to make a case for more support for science, when one claims that even people behind the top-0.01% of cited work in the biomedical literature do not merit funding because their extremely high-impact work is unimportant or not relevant to NIH.

Thank you again for your comments.

On 2015 Oct 16, Joshua L Cherry commented:

This piece is quite astounding in light of earlier claims made by the first author in the pages of the same periodical. In the earlier piece, Nicholson and Ioannidis (Nicholson JM, 2012) based a harsh indictment of NIH funding decisions, along with a recommendation for a new policy, on a questionable assumption: that a scientist's authorship of a very highly cited article is a reliable indicator of excellence or innovativeness. This more recent work by Ioannidis et al. suggests that this assumption is false, or at best unsubstantiated, seemingly undermining the earlier work, while creating the impression that the authors never had any definite opinion on the matter.

In a piece with the provocative title "Research grants: Conform and be funded", Nicholson and Ioannidis (Nicholson JM, 2012) analyzed the pattern of subsequent NIH funding of the primary authors (first, last, or sole authors) of very highly cited articles. Because the fraction funded as NIH principal investigators was lower than they believed it should be, they concluded that NIH does a poor job of funding innovative research. They also suggested that such authors, whom they regarded as having demonstrated exceptional innovativeness or excellence, be automatically funded as principal investigators. Several people, myself among them, argued that such authors are not necessarily innovative or exceptional scientists, but Nicholson and Ioannidis staunchly maintained their position (http://tinyurl.com/npojxk2; http://tinyurl.com/ozme26j).

This more recent piece paints a very different picture. It begins by telling us how little we know about the meaning of citation patterns, posing such questions as "Are the most highly cited papers the most important ones?" If, as Ioannidis et al. have it, these were open questions, what basis could there have been for the conclusions of Nicholson and Ioannidis? Furthermore, to the extent that the evidence presented here tells us anything about what citation patterns actually mean, it tells us that very highly cited publications do not tend to be highly innovative, contrary to the assertions of Nicholson and Ioannidis. Strikingly, in the concluding section Ioannidis et al. tell us that

It would be particularly useful to know whether successful out-of-the-box ideas are generated and defended largely by the most influential scientists or by colleagues lower on the citation rankings.

Such knowledge would, indeed, be useful. It would, in fact, seem to be a prerequisite for the arguments of Nicholson and Ioannidis, a prerequisite that Ioannidis et al. tell us was unfulfilled.

This piece makes no mention of the earlier arguments that it appears to undermine. This leaves one wondering whether Ioannidis maintains his earlier conclusions. If so, it is not clear how this can be reconciled with the present publication. If not, an indication of the change in position would be helpful.

On 2015 Jan 14, William Grant commented:

Differences in 25-hydroxyvitamin D concentrations may explain some of the racial disparities in noncardia gastric cancer incidence rates

The recent paper by Bautista and colleagues presented findings regarding risk modifying factors for noncardia gastric cancer related to racial disparities [1]. When averaged over the period 2000-2010, rates were highest for blacks, intermediate for Asians and Hispanics, and lowest for whites. However, median survival times were highest for Asians, followed by blacks, Hispanics, then whites. This letter proposes two factors to explain some of the findings.

First, it is noted that gastric cancer incidence and/or mortality rates have been found significantly inversely correlated with solar UVB doses in ecological studies in Australia, China, Japan, Nordic countries, Spain, and the United States [2]. The most likely explanation for these findings is that UVB raises 25-hydroxyvitamin D [25(OH)D] concentrations [2]. 25(OH)D concentrations are correlated with skin pigmentation in the United States, with whites having the highest mean concentrations, Hispanics intermediate concentrations, and blacks the lowest concentrations [3]. Based on this information as well as many black-white health disparities that cannot be explained by socioeconomic status, stage or condition at time of diagnosis, and treatment, it has been proposed that black-white health disparities and cancer survival rates in the United States are related to the disparities in 25(OH)D concentrations [4,5]. Thus, disparities in 25(OH)D concentrations may explain the disparities in noncardia gastric cancer incidence rates reported in Ref. 1. They may also explain the higher rate of diabetes mellitus in blacks and Hispanics compared to whites, and hypertension in blacks compared to whites [4]. They may also help explain why blacks are diagnosed at younger ages than whites. In addition, Asians, blacks, and Hispanics had higher Heliocobacter pylori infection rates than whites, which would also contribute to risk of developing noncardia gastric cancer at a younger age.

Second, age at time of diagnosis seems to affect survival rates. The data in Table 1 of Ref. 1 show that there are significant disparities in age at time of diagnosis, with whites having the highest fraction diagnosed after the age of 70 years and Asians and Hispanics the lowest fractions. A plot of median survival as a function of the percentage diagnosed after the age of 70 years yields a slope of -6.5 days/percent with r = 0.77, p = 0.23. While this regression is not significant at the 95% confidence level due to the large difference in survival times for Asians and Hispanics for similar fraction diagnosed over the age of 70 years, which could be due to differences in other factors such as diet, it does suggest that age is an important factor affecting survival rate.

Thus, if blood samples from near or before the time of diagnosis are available, 25(OH)D concentrations could be measured to evaluate the role of vitamin D in noncardia incidence and survival rates. The effect of age at time of diagnosis can be studied using the existing data.

References 1. Bautista MC, Jiang SF, Armstrong MA, Kakar S, Postlethwaite D, Li D. Significant racial disparities exist in noncardia gastric cancer outcomes among Kaiser Permanente's patient population. Dig Dis Sci. 2014 Oct 30. [Epub ahead of print] 2. Moukayed M, Grant WB. Molecular link between vitamin D and cancer prevention. Nutrients. 2013;5:3993-4023. 3. Ginde AA, Liu MC, Camargo CA Jr. Demographic differences and trends of vitamin D insufficiency in the US population, 1988-2004. Arch Intern Med. 2009;169:626-632. 4. Grant WB, Peiris AN. Possible role of serum 25-hydroxyvitamin D in Black–White health disparities in the United States. J Am Med Directors Assoc. 2010;11:617-628. 5. Grant WB, Peiris AN. Differences in vitamin D status may account for unexplained disparities in cancer survival rates between African and White Americans. Dermatoendocrinol. 2012;4:85-94.

Disclosure I receive funding from Bio Tech Pharmacal (Fayetteville, AR) and Medi-Sun Engineering, LLC (Highland Park, IL).

On 2015 Jan 01, Prashant Sharma, MD, DM commented:

The most interesting part of this case was realizing that co-inheritance of an alpha as-well-as a beta globin chain variant results in three abnormal hemoglobins on HPLC. One each corresponds to the variants as they combine with the corresponding normal chains (i.e. abnormal alpha with normal beta, normal alpha with abnormal beta), and a third when they combine with each other (abnormal alpha with abnormal beta).

The other learning point was that abnormal alpha globin chains will result in not just a variant adult (A0) hemoglobin, but also a tiny peak of a variant HbA2 as they will combine with normal delta chains. This little spike is useful in hinting towards the fact that we are dealing with an alpha chain variant.

Would be happy to share the full paper with anyone who requires it for personal reading.

On 2015 Nov 04, Carl de Boer commented:

After publication, we found that our proposed mechanism was unclear, so we have published on figshare [1] a detailed model describing how a poly-dA:dT can be asymmetrically read by chromatin remodelers (http://dx.doi.org/10.6084/m9.figshare.1515926). Further, motivated by recent work by the Kornberg group, which showed that RSC can further decrease the nucleosome occupancy at poly-dA:dT sites [2], and unpublished results from Frank Pugh’s group [3], we checked for enrichment of chromatin modifiers surrounding poly-dA:dT tracts. RSC was specifically enriched in the 5’ offset NFR relative to the poly-dA:dT (http://dx.doi.org/10.6084/m9.figshare.1515927) [4], consistent with RSC recognizing and becoming stalled specifically at poly-dA:dT sequences in a strand-specific manner.

[1] de Boer, C; Hughes, TR (2015): Model for how poly-dA:dT sites act as nucleosome turnstiles. figshare. http://dx.doi.org/10.6084/m9.figshare.1515926 Retrieved 18:47, Aug 28, 2015 (GMT)

[2] Lorch Y, Maier-Davis B, Kornberg RD. Role of DNA sequence in chromatin remodeling and the formation of nucleosome-free regions. Genes Dev. 2014 Nov 15;28(22):2492-7. doi: 10.1101/gad.250704.114.

[3] Wal M, Krietenstein N, Watanabe S, Peterson CL, Korber P, Pugh BF. Unpublished results.

[4] de Boer, C; Hughes, TR (2015): The RSC complex may be the poly-A nucleosome turnstile mechanism. figshare. http://dx.doi.org/10.6084/m9.figshare.1515927 Retrieved 18:47, Aug 28, 2015 (GMT)

On 2014 Dec 02, Alison Harrill commented:

This is a much-needed review of mechanisms of acetaminophen toxicity as they relate to pediatric populations. Thank you for citing our paper in which we first identified a pharmacogenetic risk allele in CD44 in a genetically diverse mouse population and then subsequently validated the finding in two independent clinical trial cohorts. In those studies, sensitive subjects exhibited sub-acute elevations in ALT due to acetaminophen exposure at the maximum recommended therapeutic dose (which has since been lowered). Court et al. examined acetaminophen-induced acute liver failure cases and found indications that the same polymorphism in CD44 was enriched in cases of chronic acetaminophen use versus controls (PMID:24104197). To my knowledge, enrichment of this polymorphism in pediatric cases has not been evaluated.

On 2014 Nov 20, Daniel J Simons commented:

This paper reports data from the same training study that was previously reported in PLoS One: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092269#pone-0092269-g002. It reports results from different outcome measures, but it also reports the results from the earlier paper (including the same statistics and the same data figure: Figure 3b is nearly identical to Figure 2b in the PLoS paper). The paper does not acknowledge that these data were previously published. The training data and results also are presented in both places without acknowledgment of the overlap. The paper does not explicitly state that these are results from the same intervention that was published previously.

I have posted this comment on the Frontiers website as well, and I have written about these issues in more depth at http://blog.dansimons.com/2014/11/hi-bar-more-benefits-of-lumosity.html. I also wrote about problems with the PLoS paper in an earlier post-publication review (which is why I noticed the overlap): http://blog.dansimons.com/2014/04/hi-bar-benefits-of-lumosity-training.html. The Frontiers paper suffers from the same problems as the earlier paper, given that it was the same intervention.

On 2014 Nov 24, Guillaume Filion commented:

This article is one of the "CISCOM meta-analyses", which are very similar papers written by different authors. For more information about the CISCOM meta-analyses, check the blog post "A flurry of copycast on PubMed" at the following link http://blog.thegrandlocus.com/2014/10/a-flurry-of-copycats-on-pubmed

On 2015 Jun 18, Bill Cayley commented:

A great example of "less is more" in medicine: https://lessismoreebm.wordpress.com/2015/06/18/antibiotics-for-prelabour-rupture-of-membranes-at-or-near-term/

On 2016 Aug 22, Anthony Jorm commented:

The Royal Australian and New Zealand College of Psychiatrists (RANZCP) has recently published clinical practice guidelines on eating disorders Hay P, 2014, mood disorders Malhi GS, 2015 and schizophrenia Galletly C, 2016. These guidelines contain a mixture of evidence-based recommendations where there were relevant intervention studies, and consensus-based recommendations where relevant studies did not exist. The consensus-based recommendations comprised a substantial proportion of the guidelines for mood disorders (59%) and schizophrenia (46%), but less so for eating disorders (10%), indicating that expert consensus is an important source of guidance on best practice in psychiatry.

Given the substantial contribution of expert consensus to these guidelines, it is important that the methods for establishing this consensus are adequate. The Australian National Health and Medical Research Council (NHMRC) has published requirements for development of clinical practice guidelines, but these do not give much guidance on how this should be done, simply mandating that “The method used to arrive at consensus-based recommendations or points (e.g. voting or formal methods, such as Delphi) is documented”. (National Health and Medical Research Council. Procedures and requirements for meeting the 2011 NHMRC standard for clinical practice guidelines. Melbourne: National Health and Medical Research Council; 2011.)

Another potential source of criteria for evaluating the quality of methods for developing consensus-based recommendations comes from research on ‘wisdom of crowds’ Lorenz J, 2011 Kattan MW, 2016 Baumeister RF, 2016. Based on such research, Surowiecki has proposed four conditions necessary for a group to make good decisions (Surowiecki J. The wisdom of crowds: why the many are smarter than the few. London: Abacus; 2004.): 1. Diversity of expertise. A heterogeneous group of experts will produce better quality decisions than a homogeneous one. For guidelines developers, this may mean that the experts should come from a range of relevant disciplines, including consumer experts where appropriate. 2. Independence. The experts must be able to make their decisions independently, so that they are not influenced by others. For guidelines developers, this means that voting on consensus-based recommendations is carried out privately so that strong individuals cannot dominate the group. 3. Decentralization. Expertise is held by autonomous individuals working in a decentralized way. For guidelines developers, it is important to specify what sources of information the experts had available to them. 4. Aggregation. There is a mechanism for coordinating and aggregating the group’s expertise. For guideline developers, this could involve an independent person who runs the voting and gives feedback to the group.

If we take these four conditions as appropriate for judging the quality of methods for developing consensus-based recommendations, how well do the RANZCP guidelines meet them?

An indication of diversity of expertise is the disciplinary composition of the guideline working groups. There was limited diversity for all working groups, with non-psychiatrists comprising 3 of the 8 members for eating disorders, 4 out of the 12 members for mood disorders and 2 out of the 10 members for schizophrenia working group. There were no consumer or carer members of any of the working groups. While the mood disorder and schizophrenia guidelines included consensus-based recommendations s for indigenous peoples, it is not stated whether any of the working groups included indigenous members.

The mood disorders and schizophrenia working groups did not appear to involve independent decision making. Both groups had discussions until consensus was reached. The eating disorders guidelines did not give relevant information about whether there was independence.

All three guidelines state that consensus-based recommendations were based on collective clinical and research knowledge and experience. The eating disorder guidelines additionally state that level IV articles were considered where higher-level evidence was lacking and this informed the consensus-based recommendations.

After drafting, all guidelines had input from a broader group of expert advisers with a wide diversity of expertise. However, it is not clear whether these advisers had the potential to persuade working group members to change consensus-based recommendations.

Where the guidelines included consensus-based recommendations relevant to indigenous peoples, it is not clear what sources of cultural expertise these were based on.

None of the guidelines state how judgements were aggregated to determine consensus. The mood disorders guidelines state that agreement on consensus-based recommendations was “in most cases unanimous but allowed one committee member to abstain”. The other guidelines did not define what constituted consensus.

In conclusion, there are major weaknesses in the procedures used to determine consensus-based recommendations for all three guidelines. These are lack of independence in decision making by experts, a lack of a formal mechanism for aggregating judgments, and a lack of diversity of expertise, particular in areas where consumers and carers could contribute and where cultural expertise is relevant.

While NHMRC gives quite detailed guidance on how to develop evidence-based recommendations, there is little guidance on best practice for developing consensus-based recommendations. While many of these weaknesses would be overcome by using formal consensus methods such as the Delphi process, there is a need for NHMRC and similar agencies to produce more rigorous quality standards for development of consensus-based recommendations.

On 2014 Oct 31, Wayne Butler commented:

The ABS does not recommend that the CTV be defined as prostate only. The consensus guidelines specify a target volume margin of 5 mm in all directions about the prostate except posteriorly. Refer to the second paragraph on page 10 of the paper by BJ Davis et al., "American Brachytherapy Society consensus guidelines for transrectal ultrasound-guided permanent prostate brachytherapy." Brachytherapy, 11:6-19, 2012.

On 2016 Sep 19, Saurabh Gupta commented:

More than 100 cases and variety of clinical presentation.

But developmental biologists & embryologists are having tough time to match these numbers.

Where are all fifth arteries in the human or mouse embryos?

On 2015 Jan 12, Larry Parnell commented:

This paper was my selection for Paper of the Week for 3-7 Nov 2014.

On 2014 Nov 17, Mick Watson commented:

Hi Robert, and thanks for the response. The results from your qPCR are inconclusive, really the only way to rule out contamination is to sequence the negative control, which has ideally been prepared using the same batch of kits and reagents. Also, the fact that the virus has an effect in mice is negated if the virus is a contaminant, as it may never get into mice in a natural environment.

May I ask why you didn't sequence the negative controls?

On 2014 Nov 06, Robert H Yolken commented:

We actively considered contamination as the source of the sequences we obtained since these viruses may be common in the environment. However, we believe that contamination is rendered unlikely by the fact that, in many cases, we were able to document the presence of DNA homologous to ATCV-1 by 2 independent methods, library generation and quantitative PCR. In the quantitative PCR the reagent controls gave consistently negative results. We also believe that the plausibility of our findings in humans is supported by the mouse experiments presented in the publication.

On 2014 Nov 04, Mick Watson commented:

I'd like to politely suggest that the authors need to rule out contamination from reagents and kits used in the experiments (e.g. see http://biorxiv.org/content/early/2014/07/16/007187)

On 2015 Feb 09, Peter Csermely commented:

Authors of the paper are happy to highlight the available additional information regarding the localization tree, manual mapping and revision, as well as the updates of the ComPPI database http://comppi.linkgroup.hu.

Localization tree: ComPPI uses the GeneOntology (GO) cellular component terms for standardizing the different subcellular localization nomenclature in the source databases. The localization tree was needed for the unambiguous classification of minor localizations to the major subcellular compartments. Mappings of the original subcellular localization names to the relevant GO cellular component term for all input sources are available on our GitHub repository https://github.com/erenon/ComPPI/wiki/LOC-databases, while the whole localization tree and the mapping of minor localizations to major cellular components is accessible via our respective Help page http://comppi.linkgroup.hu/help/subcell_locs#structure, and was included in the Supplement of the paper http://nar.oxfordjournals.org/content/43/D1/D485/suppl/DC1 as Figure S2 and Table S4.

Manual mapping of proteins: As we described it in our published paper (Figure 1) we manually built a mapping table to map 30% of proteins in ComPPI when 1.) the original gene ID (e.g. HGNC gene symbol, EnsemblGeneId etc.) was translated to more than one UniProt accessions (~20% of proteins total, e.g. "HTT" gene symbol could be mapped to both P31645 and P42858 UniProt accessions, where our manual decision of P42858 was based on the name description, "Huntingtin" found in the source dataset of OrganelleDB); or 2.) the original gene ID had no automatic translation to UniProt ID (~10% of proteins total, e.g. manual mapping of the OrganelleDB source dataset "p53AIP1" gene name to Q9HCN2 UniProt accession having the official gene ID "TP53AIP1"). We note that the process was not entirely manual, as we used online ID cross-reference tools (such as PICR http://www.ebi.ac.uk/Tools/picr/) and the UniProt ID mapping http://www.uniprot.org/uploadlists/ as gold standard validation of gene ID to UniProt mappings.

Manual revision: Our manual revision process besides the construction of the localization tree and the ID mapping included 1.) the manual testing of the interfaces connecting the source databases to the ComPPI dataset during the database building process including the check for false-entries (e.g. non-existing UniProt accessions) and data content errors (e.g. minor subcellular localizations without GO terms) for randomly chosen 200 proteins, and 2.) six experts tested the integrated dataset for false-entries, data inconsistency and protein name mapping errors. Additionally, two of the six experts tested randomly chosen 200 proteins for exact matches between the entries in the source databases compared to the ComPPI dataset (this manual revision process is described in Figure 1 of the paper and in Supplementary Table S3). In Supplementary Table S3 of the publication we showed how many proteins, subcellular localizations and interactions were included from the source databases to the ComPPI dataset. The amount of data 'missing' in ComPPI compared to the sources is due to mapping differences between different namespaces (e.g. the integrated CCSB dataset contained 3,881 interactions, while only 3,733 interactions were included to the ComPPI dataset due to the removal of non-protein coding genes), data inconsistencies, or protein name mapping errors.

Database updates: The manual steps, such as the inclusion of new GO terms to the localization tree, or mapping of missing IDs, can and will be included to all future ComPPI updates. In these updates we will document our manual curation process in more detail. We schedule the release of ComPPI 2.0 before May 2016. The new version will include the update of the source databases (such as the 2014 update of the CCSB dataset), the revision of our mapping system with the update of the UniProt dataset based on the latest release, the inclusion of new input sources, such as LocDB, as well as an upgraded network visualization and extended export options (such as SIF or PSI-MI).

On 2015 Jan 27, Robert M Flight commented:

Commended because the issues they raise are valid. It makes sense that protein-protein interactions (PPI's) should be localization specific, and that many false PPI may be removed by considering localization information.

However, the solution implemented by ComPPI is not an ideal solution. This is largely due to the "manual" curation steps required in its construction. These manual steps are described below, followed by a comment on why this is such a problem for this type of resource.

Localization tree: the authors' state that 1600 of the ~3600 gene ontology (GO) cellular compartment (CC) terms were used to "manually" construct a localization tree whereby each GO term has a single path to one of the six defined compartments of Cytosol, Nucleus, Mitochondrion, Secretory-Pathway, Membrane, and Extracellular. There is no justification for the requirement of a tree with single paths to root compartments beyond the multi-parent nature of the directed-acyclic-graph of the normal GO. The description in the paper implies that the generation of the tree from the GO terms was done fully by hand. No description of how terms are assigned to compartments are provided, or if any attempts at automated assignment methods were made.

Manual mapping of proteins: The authors state that 30% of protein IDs were un-mappable to UniProt IDs using databases, and had to be mapped manually. Although protein-protein ID mapping across databases is known to be a difficult problem (I will point to one possible solution: http://bioinformatics.louisville.edu/abid/), this seems like a rather large percentage to map by hand.

Manual revision of database: 200 entries were checked by six experts, and based on their findings, revisions of the database made. No description of the type of checks made is provided, nor numbers on the rates for true, false / positives / negatives, and what the corrective efforts involved.

Each of these manual steps is a possible point of introducing bias that is hard to quantify and replicate, and in fact makes it highly unlikely that the database will be updated at regular intervals in the future or expanded to other model organisms beyond yeast, c elegans, drosophila and human. This severely limits the reliability and future utility of the database in my opinion.

On 2014 Nov 01, Abduzhappar Gaipov commented:

well

On 2015 Aug 12, Jim Woodgett commented:

This manuscript would be improved by systematic removal of "beta" throughout as the inhibitor primarily used, GIN, is not selective for GSK-3beta vs the related isoform, GSK-3alpha. A direct quote from reference 17 (referring to the source of the GIN inhibitor used here):

"A similar assay measuring inhibition of GSK3-alpha was also routinely run, but 7-12 showed no significant ability to discriminate between the two isoforms of GSK3 (data not shown). To the best of our knowledge, no isoform specific inhibitors of GSK3 have been reported, probably due to their high sequence homology (vida supra)."

This inhibitor (GIN) acts equally on both isoforms of GSK-3 (alpha and beta) and should not be referred to as a GSK-3beta selective inhibitor. This makes sense given that inhibition of GSK-3beta alone is insufficient for deregulation of beta-catenin (PMID: 17543867).

On 2014 Dec 02, Mikkel Wallentin commented:

On the behavioral effects, the authors write: "Compared with the low-flavanol intervention, subjects who received the high-flavanol intervention showed a mean improved cognitive performance of 630 ms." However, visual inspection of the supplementary figure 3 clearly shows that the 630 ms difference at follow up is due to a modest improvement of approx. 200 ms between baseline and follow up and a much more pronounced DECLINE in the control group (approx. 400ms). Needless to say, such a difference cannot be interpreted as "a mean improved cognitive performance of 630 ms", if it can be interpreted at all.

On 2014 Oct 27, David Colquhoun commented:

For all the reasons given by Hilda Bastian (and a few more, like P = 0.04 provides lousy evidence) it astonishes me that this study should have been trumpeted as though it represented a great advance. That's the responsibility of Nature Neuroscience (and, ultimately, of the authors).

I wonder whether what happens is as follows. Authors do big fMRI study. Glamour journal refuses to publish without functional information. Authors tag on a small human study. Paper gets published. Hyped up press releases issued that refer mostly to the add on. Journal and authors are happy. But science is not advanced.

I certainly got this impression in another recent fMRI paper in Science. Brain stimulation was claimed to improve memory (P = 0.043)

I guess these examples are quite encouraging for those who think that expensive glamour journals have had their day. Open access and open comments are the way forward.

On 2014 Oct 27, Hilda Bastian commented:

This report of a very small, short-term trial in healthy adults does not meet the CONSORT standards for trial reporting in several key respects. It does not provide sufficient data on the cognitive outcomes assessed, nor an adequate flow chart of outcomes (despite considerable attrition). There is also very little detail provided in the record of this trial at ClinicalTrials.gov.

The abstract does not make it clear that this is a dietary supplement and exercise trial (partially funded by a manufacturer). There were apparently two cognitive outcome measures on a ModBent task (an adapted test not elsewhere validated): immediate matching and delayed retention. Both relate to very specific functions, not an overall rating of cognitive abilities.

No effect was found for the exercise component in the trial, and out of the two cognitive measures, some effect was found for one, but not the other. That this is a chance finding surely can't be ruled out.

This report describes low vs high supplement groups. The study in ClinicalTrials.gov for the trial number they provide, however, was for a supplement and a placebo comparator.

Despite the major limitations of this single trial to address the question, the "Newsroom" report for the trial claims that it shows that "dietary flavanols reverse age-related memory decline."

It's good to see claims about dietary supplements tested. However, the results here rely on a chain of yet-to-be-validated assumptions that are still weakly supported at each point. In my opinion, the immodest title of this paper is not supported by its contents.

On 2015 Jul 12, Guo-Liang Jiang commented:

Evaluation of the value of ENI in radiotherapy for cervical and upper thoracic esophageal cancer: a retrospective analysis (Radiat Oncol. 2014 Oct 25;9:232.) We correct the first author Liu M's affiliation as the following: 1. Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; 2.Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; Current address: Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China