File: Unravel Two.zip ...
FTX's unraveling is causing ripple effects. Already companies that backed FTX are writing down their investments. Politicians and regulators are ramping up calls for stricter oversight of the crypto industry. And this latest crisis has put pressure on the prices of bitcoin and other digital currencies. The total market value of all digital currencies dropped by about $150 billion in the last week, according to CoinMarketCap.com.
File: Unravel Two.zip ...
The unravel tool is described in a two volume technical report (NISTIR 5691) is available by anonymous ftp. The first volume covers the requirements, design and evaluation of unravel while the second volume is an unravel user manual.
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It should be noted that mammalian genomes possess a complex structure with a diverseness of repetitive elements that complicates extensive genome-wide analyses . To better acknowledge this result, there is still the need for using mate pair sequences or merging long-insert mate pair and short-insert paired-end sequences to analyze the dog and wolf genomes and elucidate the difference of the distribution and impact of the genomic variations between dog and wolf during dog domestication. More work using a larger sample size is needed to more clearly unravel genome changes during dog domestication and selective breeding.
Citation: Claerhout S, Verstraete P, Warnez L, Vanpaemel S, Larmuseau M, Decorte R (2021) CSYseq: The first Y-chromosome sequencing tool typing a large number of Y-SNPs and Y-STRs to unravel worldwide human population genetics. PLoS Genet 17(9): e1009758.
In-depth chrY genotyping helps to unravel family history by providing more detail in patrilineal relatedness between relatives. Genealogists could make their expanded chrY-profile public or available in a database hoping to find an unexpected patrilineal relation. For population genetics, the link between a surname and patrilineage provides the opportunity to detect signals of past or recent population stratification and migrations which are still undetectable within genomic analysis of the limited number of markers . Additionally, the fixed set of Y-markers sequenced with the CSYseq could avoid problems with Y-SNP nomenclature and dataset differences as is currently observed in different population studies. For molecular biology, this panel will contribute to the knowledge concerning the Y-STRs mutation rate and the molecular mechanism of mutations, together with the general understanding of Y-STR evolution in the human genome. For evolutionary biology, the CSYseq enables deeper or equal subhaplogroup determination than CE in 98% of our samples, making Y-SNP haplogroup identification through the CSYseq panel definitely more convenient. Additionally, typing a large number of Y-STRs allows to study haplogroup phylogenetics in more detail and provide valuable information for tMRCA estimations and evolutionary dating to reconstruct phylogenetic trees in more detail. Furthermore, increasing Y-STR diversity by including sequence variation in the repeat region or in the flanking regions is beneficial for molecular, evolutionary and population genetic studies as this will increase their dataset resolution.
The analysis of protein carbonylation appears to be a very sensitive method to detect possible adverse effects of NPs. Furthermore, we observed a good correlation between ESR and carbonyl results, suggesting that most NPs used in our study induce oxidative stress mostly due to surface reactivity. In addition, we found a good correlation between the results obtained in the carbonyl assay and the overall NP toxicity as tested by WST-1 assay. Thus, we propose that the analysis of protein carbonylation after 1D immunoblotting can be used as a predictive screening method to identify NPs of concern. An advantage of this approach is that in a second step the identification of specifically altered proteins is possible via 2D separation. The more detailed proteomic analysis appears to be a promising tool to unravel underlying toxicity mechanisms. Hence, redox profiling might prove useful for NP classification according to their mode of action.
In this post on Python's syntactic sugar, I want to try to tackle generator expressions. If you look at the language definition for generator expressions you will see that it says, "[a] generator expression yields a new generator object" for what is specified (which is essentially a compact for loop with an expression for the body). So what does that look like if you take away the Python "magic" and unravel it down to its core Python semantics?
Let's start with (c for b in a) where c can be some expression. To unravel this we need to make a generator which takes in a as an argument to guarantee it is eagerly evaluated where the generator expression is defined.
We end up with a generator function which takes a single argument for the leftmost iterator. We call iter() outside of the generator to control for scoping. We should also technically unravel the for loop, but for readability I'm going to leave it in place. Now let's see what this looks like in some code that would use the generator expression:
Now we could just add a nonlocal statement to our unravelled generator expression and assume we are done, but there is one issue to watch out for: has the variable previously been defined in the enclosing scope? If the variable doesn't exist when the scope with the nonlocal is defined (technically the compiler walking the AST has not seen the variable yet), Python will raise an exception: SyntaxError: no binding for nonlocal 'b' found.
I actually wrote this entire post thinking I had solved the unravelling of generator expressions, and then I realized assignment expressions thwarted me in another way. Consider the following example: 041b061a72