Confusing

Let’s say an investor is interested in investing in a startup. Here’s how the terms might look in a real example:

•   Price: $10 per share (the Original Purchase Price).
•   Fully Diluted Premoney Valuation: $50 million. This means that before the new investment, the company is valued at $50 million, considering all potential shares.
•   Fully Diluted Postmoney Valuation: $60 million. After the investment, the company is valued at $60 million, considering all potential shares.

might be good, considering most mitochdonrial CK is in heart

not good - means we need more creatine bound AAS to even get in skeletal muscle b/c o.w. it wont be carried in blood

what does this mean

What are rings

gene therapy materials seem to get contaminated very easily? why?

This means they order and receive supplies as they need them, rather than keeping large stocks on hand. While this can be cost-effective, it also means there's little room for error or delay in the supply chain.

Transfection is a laboratory method used to introduce foreign genetic material (like DNA or RNA) into cells.

They are transfecting these cells to produce the viral particles they want to use for gene therapy

Calculate percentages here maybe

Find costs for drugs in each of these settings? hmm

not really sure what this means. If the result when the neurons fail the Pong game is predictably 150mV &5Hz for 4 seconds, how is that unpredictable?

Are they just using a nicer word for punishment lol

$26 billion

kinda similar to sharpe?

this is the sharpe ratio, yo!

This is the covariance term. It accounts for the extent to which Asset A and Asset E move together.

drift of interest rate

volatility of interest rate

Direct Borrowing: If Company A borrows at the floating rate and Company B borrows at the fixed rate directly from lenders, their total interest is LIBOR + 8.5%.

Opportunity for Savings: They notice that if they could somehow work together, they could lower the total interest to LIBOR + 8%, saving 0.5%.

The Swap: Company A decides to borrow at a 7% fixed rate, and Company B borrows at LIBOR + 1% floating rate (opposite of what they want). They then agree to swap interest payments:

Company A agrees to pay LIBOR to Company B. Company B agrees to pay Company A a fixed rate (let's call it 'x'). Finding the Fixed Rate for Swap (x): They want to divide the 0.5% saving equally (0.25% for each). So they set 'x' in such a way that Company A’s net payment is LIBOR + 0.05%, and Company B’s net payment is 7.95% fixed. Through some calculations, they find that 'x' should be 6.95%.

Results:

Company A ends up effectively paying a floating rate of LIBOR + 0.05%, which is better than if they borrowed directly. Company B ends up effectively paying a fixed rate of 7.95%, which is also better than if they borrowed directly.

So, why are we integrating the spot rates? It's because we are dealing with continuously compounded interest rates, and the spot rate r(τ) can be a function that varies over time. The integral represents the accumulated value of the spot rates from time t to T. It's like asking, "if the interest rate varies continuously, what's the equivalent constant rate that, if applied over the same time period, would have the same effect?"

An example where using forward rates is more advantageous than using the yield to maturity (YTM) is in interest rate risk management, specifically in constructing an immunized bond portfolio.

Suppose you are a portfolio manager who has the obligation to pay a fixed amount of money at a specific future date. For example, a pension fund manager who needs to make payments to retirees 10 years from now.

Let’s see how using forward rates would be beneficial:

Using YTM: If you use the yield to maturity, you might invest in a 10-year bond believing that the YTM is representative of the average return you will earn annually over the next 10 years. However, YTM assumes that all the intermediate coupons are reinvested at the same yield, which may not be realistic, as interest rates fluctuate. If interest rates decrease in the future, you will not be able to reinvest the coupons at the same rate, and the realized return will be less than the YTM.

Using Forward Rates: Instead, you can use forward rates to immunize your bond portfolio. By understanding how forward rates change, you can construct a portfolio of bonds with different maturities in such a way that regardless of how interest rates change, your portfolio will be worth a specific amount at the time when you have to make the payments. This is achieved by matching the duration of the portfolio to the time horizon and ensuring that the portfolio's convexity and cash flows are appropriately managed.

By using forward rates, you can estimate more accurately the interest rates at which the future coupon payments can be reinvested. With this information, you can allocate assets in such a way that the total return over the time horizon matches the obligations, irrespective of the interest rate fluctuations.

In this example, the forward rate approach allows for a more sophisticated and accurate risk management strategy than simply relying on YTM, making it preferable for ensuring that the portfolio is adequately immunized against interest rate risks. This is particularly important for institutional investors such as pension funds, insurance companies, or any entity with fixed future liabilities.

This equation gives the value of a zero-coupon bond at time t, which matures at time T, in terms of the spot interest rate r(t). The integral represents the accumulated interest rate from time t to T.

It tells you that duration can be thought of as the average time it takes to receive the bond's cash flows (coupon, return of principal). In the formula for duration, each time of coupon payment is weighted by its present value, so the more a coupon contributes to the bond's value, the more it affects the duration.

Bonds typically pay interest to their holders periodically (e.g., every 6 months). When you buy a bond between these payment dates, you have to compensate the seller for the interest that has built up since the last payment. This compensation is called accrued interest. It's calculated by taking the interest amount that the bond pays in a full period and multiplying it by the fraction of the period that has passed since the last payment.

For example, if a bond pays $20 in interest every 6 months and you are buying it 3 months after the last payment, the accrued interest would be $20 * (3/6) = $10.

Now let's go through the example provided step by step:

Buying the Treasury bill: Suppose you buy a six-month Treasury bill (T-bill). A T-bill is basically a short-term government bond that you can buy at a discount, and the government will pay you the face value upon maturity (in this case, in six months).

Repo it out for three months: Instead of holding onto the T-bill, you enter into a repurchase agreement, which means you sell the T-bill to someone else with an agreement to buy it back at a future date (in this case, three months) at a specified price. The amount you receive from this repurchase agreement is the same as what you paid for the T-bill. This means that initially, you haven't gained or lost any money since what you paid to buy the T-bill equals what you received when you entered the repo.

Buying back the T-bill: In three months, you have to repurchase the T-bill as per the agreement. This means you pay back the amount you initially received plus a little extra (this extra amount is effectively the interest on the 'loan'). This is an outflow of cash.

Receiving the principal at maturity: In six months (when the T-bill matures), the government pays you the face value of the T-bill. This is an inflow of cash.

the premium

Regarding the term D2 −D1, think of D1 and D2 as dividend yields for assets 1 and 2, respectively. The term can be interpreted as the relative net dividend yield when considering the ratio ξ. When you construct a ratio of two assets, you effectively look at the performance of one asset relative to another. The difference in the dividends reflects how dividends from one asset erode or increase the value relative to the other asset.

The risk-free rate seems to be absorbed into the terms D1 and D2. Generally, in option pricing, the risk-free rate comes into play as a discount factor. However, in the transformation, the risk-free rate can be considered as part of the general constants D1 and D2. Essentially, these terms can represent the net effect of dividends and the risk-free rate. Note that this representation is not directly the Black-Scholes model but is an adapted version for the particular problem.

By converting V(S1, S2, t) to a function composed of ξ and t, where ξ = S1/S2 (giving us some idea of the relationship of how S1 changes to S2, which is what we're trying to get at anyway), we reduce a lot of pain by making it 2 dimensional problem again!

This expression is a mathematical representation of the value of an option taking into account multiple underlying assets and their statistical properties. It integrates over all possible paths the underlying assets can take to calculate the expected payoff of the option, discounted to present value. This kind of model is commonly used in financial mathematics to price complex derivatives.

This term inside the integral represents the weighting for each path in the multivariate normal distribution.

ρ = Σ( (X - mean(X)) * (Y - mean(Y)) ) / [(n-1) * std(X) * std(Y)]

Downside here ofc is that this is historical correlation between two assets. That is always subject to change

Bruh

ρ = Σ( (X - mean(X)) * (Y - mean(Y)) ) / [(n-1) * std(X) * std(Y)]

Σ = M * Rho * M

This equation essentially scales the correlations by the respective volatilities of the assets. In the resulting covariance matrix:

The diagonal elements Σ_ii represent the variance of asset i, which is σ_i^2 (the volatility squared). The off-diagonal elements Σ_ij represent the covariance between asset i and asset j, which is σ_i * σ_j * ρ_ij. This covariance matrix is essential in finance, especially in portfolio theory, for understanding and managing the risks associated with a portfolio of multiple assets.

allow you to choose whichever is more valuable

yes, really

The fact that the Black-Scholes equation has a second derivative with respect to asset price but only a first derivative with respect to time illustrates that the model considers changes in the asset price to have a more complex effect on the option's value compared to the passage of time.

In simpler terms, the model pays more attention to the twists and turns of the asset's price than to how much time is passing.

Imagine if this wasn’t true. Let’s say, for example, the paper was worth $12 just before the money was paid and remained $12 just after. You could make free money by buying this paper for $12, instantly getting $10, and still having the paper worth $12. This is what's called an "arbitrage opportunity", meaning you could make risk-free profit. But in real life, such opportunities are usually not there because prices adjust to avoid them.

It means that the profit you make is sensitive to the specific movements of the underlying asset's price over time (the path it takes). The gamma will change as the asset price moves, affecting the profit.

Having a directional view that the actual volatility is greater or less than the implied volatility involves analyzing various factors and employing different strategies. Here are a few ways in which traders and investors might develop such a view:

Historical Volatility Analysis: By analyzing the historical volatility of the underlying asset, one might be able to identify patterns or trends. If the historical volatility has generally been higher than the current implied volatility, one might expect that trend to continue.

Market Events and News: Upcoming events such as earnings reports, economic data releases, or other significant news can cause the actual volatility to change. If an investor anticipates that an upcoming event will cause the stock price to move more than what is currently priced in by the implied volatility, they might have a view that the actual volatility will be higher.

Volatility Term Structure: By looking at the term structure of implied volatility (i.e., implied volatilities of options with different expiration dates), one can sometimes infer market expectations. If short-term implied volatilities are much higher than long-term implied volatilities, it might be an indication that the market expects high volatility in the near term.

Global Market Conditions: Broader market conditions can impact volatility. For example, during periods of market stress or financial crises, actual volatility tends to spike. If an investor believes that current market conditions are calm but are likely to become turbulent, they might expect actual volatility to be higher than implied.

Relative Value Analysis: By comparing the implied volatility of the asset in question to the implied volatilities of similar assets or to a volatility index like the VIX, an investor might identify discrepancies. For example, if the implied volatility of an asset is low relative to its sector or the overall market, an investor might expect actual volatility to be higher.

Technical Analysis: Some traders use technical analysis and charting techniques to predict future movements in volatility. Certain patterns or indicators, such as Bollinger Bands, can sometimes give traders insights into future volatility.

Sentiment Analysis: Analyzing the sentiment of market participants through surveys, social media analysis, or other means can sometimes give insights into expected market movements. For example, if there is a lot of pessimism and fear in the market, this might be an indication that volatility could increase.

Arbitrage Opportunities: Sophisticated traders might look for arbitrage opportunities between options and other derivatives. If, for example, variance swaps are priced with a different volatility than options, there might be an arbitrage opportunity which suggests a discrepancy between implied and actual volatility.

It's important to note that predicting the future is inherently uncertain and there's always a risk involved. This is why risk management and diversification strategies are crucial when actively trading or investing based on volatility views.

this is for the hedge. we are still long the option. the trick here is that we're shorting the option as if it were priced with the actual volatility

isn't volatility traded though through some ETFs?

At the expiry of an option contract, it should be very low, no? Volatility crush?

Edit: big difference here between implied volatility and real volatility. Oops!

Meaning: both the delta and gamma of your portfolio (which is composed of multiple options) are zero.

This is basically how the derivative is classically derived - but in this case, for random variables

since this is a Riemann sum, we can only use the left side of the discrete quantity we're summing

The conditional expectation E[S_6|R_1, ..., R_5] represents this expectation. Knowing the outcomes of the first five tosses, our best estimate for the sum after the sixth toss is simply the sum after five tosses (S_5) plus the expected outcome of the sixth toss. Since the sixth toss is still random and has an expected value of 0 (equally likely to be heads or tails), the best estimate for S_6 given the outcomes of the first five tosses is S_5.

The variance represents how spread out the particle's position can be around its starting point. Since each tiny movement is random and independent, the "spread" of positions increases as more time passes, and this increase is linear with time, just like in the discrete random walk.

The expected value of dX is 0 (mean zero):

This reflects the fact that, over an infinitesimally small time interval, the Wiener process does not have any directional bias. In other words, it is equally likely to go up or down. This is similar to the random walk theory where each step is independent and has no tendency to favor any direction. In the context of asset prices, this models the unpredictability of very short-term movements.

The expected value of dX squared is equal to the infinitesimal time step dt:

This reflects the variance of the Wiener process over the infinitesimal time interval dt. Recall that variance is a measure of how spread out the values in a distribution are. In the case of the Wiener process, the variance grows linearly with time. Over a small time interval dt, the change dX in the Wiener process can be thought of as having a normal distribution with mean 0 and variance dt. This is why E[dX^2] = dt.

Imagine ϕ as a raw measure of randomness and σ as a scaling factor that adjusts this randomness to reflect the inherent volatility of the specific asset. Together, σ * ϕ represents the random component of the asset’s returns, properly scaled to the asset’s volatility.

sigma scales with square root of time

Intuitively, over very short periods, the price doesn't have much time to move, so the fluctuations should be smaller. Over longer periods, there's more time for various factors to affect the price, so the fluctuations can be more significant.

Mathematically, this behavior is captured by scaling the standard deviation by sqrt(δt), that is σ * δt^(1/2). This is because the variance (square of standard deviation) of a sum of random variables is the sum of their variances. When you're looking over a longer period, you're essentially summing more variables, and thus, you're adding more variances. Since standard deviation is the square root of variance, it naturally scales with the square root of time.

go from discreet changes in time step -> continuous change

the formula for the standard normal distribution is crafted in such a way that it meets the requirements of being a probability distribution, exhibits the properties of the normal distribution (bell shape, symmetry, etc.), and has mathematical properties that make it useful and practical for statistical analysis.

this is a Taylor expansion of f(E[S]), which helps us approximate it

Taylor expansion looks like:

f(a + x) = f(a) + xf'(a) + (1/2)x²f''(a) + ...

Where:

f(a) is the value of the function at point 'a'. f'(a) is the first derivative of the function at point 'a'. f''(a) is the second derivative of the function at point 'a'. x is the deviation from point 'a'.

recall the payoff function for calls = max(S(T) - E, 0)

and for puts = max(E - S(T), 0)

i.e. We can construct a risk-free portfolio by combining the option and the stock in a certain way ("hedging"). Conversely, we can replicate the payoff of the option by combining the stock and the risk-free asset in a certain way.

on left hand, ? - 0.5 * 100 (our call option price minus our short hedge) = that future portfolio value of -99/2 * discount factor

252 = number of days trading risk free interest rate = 0.1, or 10% per year

remember: this is the discount factor of the portfolio looking backwards from the future predicted risk free portfolio price of -99/2 from the previous example.

In a delta hedging strategy, we adjust the quantity of the underlying asset we hold in order to offset the sensitivity of the options in our portfolio. This is done by setting delta equal to the ratio of the range of option payoffs to the range of stock prices.

Let's break this down:

Range of option payoffs: This represents the possible gains or losses from the option. For a call option, the payoff is the difference between the stock's price and the strike price if the stock's price is above the strike price, and zero otherwise.

Range of stock prices: This is the range of values the stock price can take over the relevant period.

By setting delta equal to the ratio of these two ranges, we're saying that we want the change in the value of our option position to be offset by the change in value of our position in the underlying asset. If the stock's price goes up, the increase in the value of our option position is offset by a corresponding decrease in the value of our short position in the stock, and vice versa if the stock's price goes down.

In this way, delta hedging helps us create a portfolio where the value doesn't depend on the direction of the stock's price. This is useful for traders who want to isolate certain risks (like the risk of the stock's price changing) and focus on others (like the risk of changes in the volatility of the stock's price).

True value = 0.5, for now is a bit of "just trust me bro"

If there's low liquidity in the market, it could potentially lead to arbitrage opportunities. Low liquidity means that there are fewer buyers and sellers in the market, which can lead to larger price discrepancies and potentially a divergence from the theoretical price.

In the scenario you're referencing, where the theoretical price of the option is 0.5, competition between sellers in a liquid market would typically drive the price towards this level. However, if there's low liquidity, sellers might be able to sell the option for a higher price, say 0.55, without immediately being undercut by competitors. Meanwhile, an arbitrageur who knows the theoretical price could theoretically create a risk-free profit by taking the opposite position in a different market or using a different financial instrument.

However, it's important to note that arbitrage opportunities are often difficult to exploit in practice, especially for individual investors. Transaction costs, bid-ask spreads, and other market frictions can eat into arbitrage profits. Plus, arbitrage opportunities tend to be fleeting, as other market participants are also on the lookout for them and will quickly act to close the price discrepancy.

Moreover, low liquidity itself can be a risk: if you need to unwind your position and there aren't enough buyers or sellers, you might have to do so at a less favorable price. So while low liquidity can theoretically lead to arbitrage opportunities, exploiting these opportunities can be risky and challenging.

Since the outcome of a binary option is either +$1 or $0, buying both a binary call and put would always yield $1. For this to be priced in, it would mean that the cost of purchasing a binary call and put would need to equal that risk free, current valuation of $1.

***Keep note of this! Basically means that the discrepancy in pricing between a call and put for a given strike price equals the discrepancy in price between the underlying stock value and the current "time value" of that strike price

If C - P > S - E^-r(t-t), you could buy the underlying asset, buy the put option, and sell the call option to make a risk-free profit. If C - P < S - E^-r(t-t), you could sell the underlying asset, sell the put option, and buy the call option to make a risk-free profit.

This is looking backwards - for however much money I want in the future, tied to X% interest rate, what money would I need now to get to that dollar amount?

If you wanted to do the reverse (look at how much money you would get with current $ and current X% interest rate, you would multiply current $ by e^r(T-t)

why not both? Modeling stock like linear regression. y = mx + b

Imagine trying to guess the future price of something, like a stock, but you’re making this guess based on certain conditions or information. This is a fancy way of saying your best educated guess under those conditions.

This tells you how the prices of two different things (like two stocks) are moving in relation to each other. If they both tend to go up and down together, they have a high correlation.

This is a measure of how wildly the price of something (like a stock) is swinging around. Higher standard deviation means the price is jumping around more.

Imagine a type of loan where you don’t get interest payments, but instead, it grows over time and you get all the money at once at the end. This notation is talking about how much it’s grown over a certain period.

This refers to interest rates for another currency in a currency exchange deal, or the regular payments (dividends) that a company makes to its shareholders.

This is like setting two barriers, one high and one low. If you’re betting on the price of gold, you might have barriers at $1,800 and $1,700. If the price hits either, something happens with your bet.

low overhead cost, and predefined "risk" cost later

because the time value is low with tons of time left (and people will pay higher prices for longer time values since theres a higher chance they'll hit their price strike with a longer timeframe), it's a shit deal

how many shares traded per option

wack that 10%, even with resubmissions considered, are denied!??!

bruh. there are so many other confounding factors at play. many unknown unknowns

weak af

would love to know spearman correlation of individual vs. population. better? worse?

interesting that mTORC2 was also enhanced in liver.. coincedence?

So the main takeaway of this paper is that these drugs inhibit mTOR and effect all these other pathways? seems kinda redundant huh. just say that they inhibit mTOR and call it a day. all the other mTOR downstream stuff is already known, right?

why? This bothers me

so less of this maigc gene, and i get bigger balls? awesome. wait i die of cancer now? damn

Enhancers are short nucleotide sequences that enhance the transcription rate in the genome. Promoters are fairly large nucleotide sequences that initiate the process of transcription.

basically the mrna copy was turned back into a gene through retrotranscription

so they had partial L2AKO and full L2AKO

KFRQ activity is dependent on phosphorylation / dephosphorylation events, which is maybe a reason why CMA is time dependent

liver homogenate levels of protein at different circadian times (CT)

lysosome protein levels at different circadian times (CT)

hypothesis: L2AKO knockout mice should have different lysosome fluorescence from WT mice

but the images are weird

dexamethazone is a steroid synchronizrs mouse circadian rhythm

like figure B but now looking with respect to mRNA than to proteins

Just because something is a CMA substrate, doesnt mean its singly degraded by CMA. This figure is trying to figure out if that relationship is true. You can see about that in two ways:

1) amplify CMA and see if the effect is exaggerated 2) amplify compensatory mechanisms like macroautophagy

doing knockout in germ cells of male mice, those mice have knockout babies

The bigger the gap, the more protein degraded relatively

Inject mice with leupeptin (protease inhibitor) in ZT. Want to check if these things are 1) working cyclically and 2) if CMA is actually playing a role.

Zeitgeber time: a rhythmically occurring natural phenomenon which acts as a cue in the regulation of the body's circadian rhythms.

Basically the circadian rhythm the mice have in an environment without light/dark cues

BMALL1 and CLOCK significantly down in these three rats during CMA-

significantly more accumulated with CLOCK during CMA+

CMA+ and CMA- cells are separated by density. nitrisomine is used to do this (tho not anymore since its super cytotoxic)

positive, negative, and glutamine– when this combo is exposed to cytoplasm, its prone to causing the protein with this motif to aggregate. hence why they may be CMA substrates

In this figure membrane potential is kind of the same?? Despite more mitochondria in PTEN mice?

Oxygen consumption rate

Oligomycin blocks oxygen consumption

FCCP is a decoupler, or a protonophore. It allows protons to freely cross membranes. Screws up the proton pump and rapidly speeds up the ETC. This makes oxygen consumption go brrrrrr

Rotenone stops complex I in mitochondria. Which makes oxygen consumption down down down

lactate is built up during anaerobic activity. We see much less lactate in PTEN

the RER tells us if:

1) aeorbic or anaerobic respiration 2) usage preference between fat or carbs

in this figure, PTEN mice have higher RER. So they burn more fat than WT mice

there is an age effect and genotype effect and an interaction between the two

is the transgene modifying the age effect? -> two way anova

thats is not what they did here...

very drunk answer from a scientist friend of joe endicott: 25% of myc is the proper amount. snell mice have this amount

flow cytometry.

you break up an organ, load up the bits of tissue onto flow cytometer. send light beams at cells, measure how light interacts with cells

If cells scatter same amount of light, then they are the same size! That's what we saw here

what is PTEN in these images??? what is the scale??? who knows lol

It would be nice if we knew how much of each protein (PTEN, Hsp90, Lamin B) was loaded onto western blot

We don't know why PTEN is localized in the nucleus?? It's mostly studied for its enzymatic function

lol i find it funny that they use glucose instead of galactate but even then use glucose less! they truly built different tho

basically more FAO -> more breakdown of fat to turn into energy

whats the difference between this growth curve and the 2 other growth curves shown in the prior figure?

oh nvm i just realized– this is for the in vitro MEFs!!!

measures the ability of a candidate oncogene to induce malignant transformation,

so they just copied the enitre boilerplate of PTEN into mice?

this catches my eye... what does it mean?

the observation that most cancer cells produce energy predominantly not through the 'usual' citric acid cycle and oxidative phosphorylation in the mitochondria as observed in normal cells, but through a less efficient process of 'aerobic glycolysis' consisting of high level of glucose uptake and glycolysis followed by lactic acid fermentation taking place in the cytosol, not the mitochondria, even in the presence of abundant oxygen.

development

Down the line, doing this with planaria genes could be really useful for seeing how translational their rejuvenation abilities are!!

The copies weren't inactive, they were supplementing the original copies in their respective functions!!

More amplifications in these aging related genes compared to the randomly annotated genes?

So basically they looked at a bunch of homologous genes related to human aging in cnidarians, validated their presence, then divided them up to their relation to specific hallmarks/pathways related to aging

mobile genetic elements which are transcribed into RNA, then reverse-transcribed into DNA and inserted into a new site in the genome

big difference in genome size!

the ability to produce an abundance of offspring or new growth; fertility.

Controls for proteosome inhibition; these things should go down when proteosome is inhibited

CMA indirectly lowers Myc because it causes proteosome to degrade Myc

silenced Lamp2A has higher lipofuscin buildup than other KO groups

GHKO mice have significantly more gene expression change than control mice. (550 vs 323 for increased expression, 700 vs 363 for decreased expression, etc)

What this volcano plot is saying is basically that a lot of protein expression in both genotypes (red) was significantly changed by leupeptin interference. In volcano plots, anything above the horizontal line in the graph is p<0.05, meaning significant

i wonder what they mean by both genotypes tho?? like theres only 1 control genotype mouse right

KO lysosomes, see which proteins start building up as a conseuquence

CMA doesn't exclusively destroy proteins that aggregate. Just degrades proteins susceptible to degradation

More "flux" in knockouts, meaning more variability in color in the band.

Autofluorescence is stuff in tissue that lights up when you shine a light at it. Blue is nucleus. Green is lipofuscin, which is basically molecular trash that accumulates with age.

this helps mobilize fat to get it away from liver. but for some reason it's up in L2A????

H & E stain measures for difference in images

they drugged the mice with a muscle relaxant (which gets processed by the liver) and they wanted to measure liver health by seeing which mouse can recover from the muscle relaxant later

• is CMA high, - is CMA low

and cancer is why most aged mice die. interesting

significantly higher in L2AKO in youth but not in age??

L2AKO have significantly less of this healthy fat(?) compared to control

what is the significance here

L2AKO mice take significantly longer in youth and then even longer in age to clear out hepatotoxin/damaged proteins than control

hepatotoxic

this is a known biomarker for age iirc

LC3-II has higher presence in L2AKO mice than control mice during HFD

L2AKO has tougher time using compensatory autophagy mechanisms when facd with hepatotoxic substance acetimophen

fatty liver disease

so when young they actually are better at keeping blood sugar low, but when older they are worse at it than control?

cellular viability decreases more significantly in L2AKO than Ctr under hepatoxic stress

these proteins that should be destroyed under normal conditions (control) aggregate a lot more in L2AKO

ubiquitin compensatory response is higher here in L2AKO mice ???

inhibits proteosome, therefore inhibiting protein degradation

less ubiquinated proteins in L2AKO mice

Immunoblot = IB

confused, shouldnt CMA biomarkers be more present during starvation? i.e. L2A

also looks like TFEB is higher in L2AKO mice compared to control during starvation

prevents cell division

?

?

LC3, a biomarker for macroautophagy, goes up in both groups post leupetin injecition but significantly in L2AKO

L2A levels low in L2AKO, Ubiquitin levels higher in Leu injected mice (compensatory?)

overall the trend for these graphs is that in control, L2A levels go down with age, while in L2AKO mice, the L2A levels stay low consistently even with age

what does this mean

liver homogenate = slurry "soup" of degraded tissue in suspension

what does this mean

inhibits breakdown of proteins

ok looks like i answered my last question lol

is there an update to this?

so the proteins sent off to be destroyed aren't put into little packages and shipped off to their demise, instead a transmembrane protein will attach and accompany them to the lysosome for destruction

proteins in cytoplasm

tbh a lot of this went over my head... could use an ELI5

planaria open wound emits mucus gunk that helps cells stick to the opening ?

this is very good!!! i think

the extent to which a particular gene or set of genes is expressed in the phenotypes of individuals carrying it, measured by the proportion of carriers showing the characteristic phenotype.

a small molecule that selectively binds to a protein and regulates its biological activity

a bit shocking

good to know

even less known than the sexual genes!!!!

less than 50% of these genes related to sex in planaria were known!!!! what the fuck

could the sexualization/asexualization of planaria be polygenic?

these are useful methods and tools to keep an eye on

this is an important gene locus to investigate

could these cells be stimulated to generate organs by feeding them sexual planria like Kobayashi did in Dugesia ryukyuensis?

uses fluorescent DNA probes to target specific chromosomal locations within the nucleus, resulting in colored signals that can be detected using a fluorescent microscope.

a grid of DNA segments of known sequence that is used to test and map DNA fragments, antibodies, or proteins.

maybe not suuuuper important; it might be a downstream gene of sexualization, not the end all be all gene that causes it

this is super important

what does this mean

wtf

This is what we wish to know!!!

Is this true for S. mediteranea?

which shows signs of advanced aging, right?

interesting!!!

this is the stuff lost with increased frequency of double strand DNA breaks

***so they claim...

does this relate to information theory??? (randomly) incompleted restoration of information leads to further mistakes and eventual decline and then DEATH...

***Main claim of paper

rebuttal to previous claim

since cloned sheep dont come out benjamin button like, somatic mutations must not cause aging according to sinclair

we dunno if double strand DNA breaks are purely messing with the epigenome or if they have mutagenic effects too

aging regulatory network i think theyre referring to

yup that is what i was trying to get at