dataesr/scientific-paragraphs-categorization huggingface dataset:
Main Figures (https://github. com/nextstrain/nextclade) and (v) a tab-separated file with Pango lineages assigned to sequences by pangolin (https://github. com/cov-lineages/pangolin). Our script to update the MAT daily is available at https://github. com/ucscGenomeBrowser/kent/blob/master/src/hg/utils/otto/sarscov2phylo/update CombinedTree. sh. matUtils: Design Overview matUtils is implemented using the C++ programming language and is developed and maintained within the phylogenetic placement package of UShER  (Turakhia et al. 2021) , since matUtils shares the core mutation-annotated tree (MAT) data structure with UShER, which helps us ensure cross-compatibility of both tools. matUtils complements UShER through its ability to analyze and manipulate the MAT output but can be used as a standalone phylogenetics tool independent of UShER.
matUtils and UShER can be installed together via (i) a Docker container (https://hub. docker. com/repository/docker/yatisht/usher), (ii) the Conda package manager using the bioconda  (Grüning et al. 2018 ) channel (http://bioconda. github. io/recipes/usher/README. html) or (iii) the installation scripts that we provide on our GitHub repository (https://github. com/yatisht/usher) for some recent Linux and MacOS releases. Detailed installation and usage instructions are available on our wiki: https://usher-wiki. readthedocs. io/en/latest/matUtils. html. Several matUtils functions have multi-threaded parallel implementations through Intel's Thread Building Blocks library (https://github. com/oneapi-src/oneTBB).
matUtils: Implementation details Annotate: matUtils annotate is designed to annotate clades on the internal branches of the MAT. Our MAT format (specified in https://github. com/yatisht/usher/blob/master/parsimony. proto) provides an ability to annotate internal branches with an array of clade names, one for each clade nomenclature. Each run of matUtils annotate extends the clade name array size in a MAT by one to accommodate a new nomenclature. Only the node corresponding to a clade root is labeled with its clade name, as descendants of that node can be automatically inferred to belong to that clade. Clades can be nested, so that each sequence can be assigned to a clade corresponding to the lowest-level clade root to which it is a descendant. matUtils annotate provides two different ways to annotate clades in a MAT.
Both ways, by design, ensure that all clades remain monophyletic. In the first, a user can directly provide the internal node identifiers corresponding to the root of each clade. In the second, a user can provide a list of representative sequences for each clade, such as training data for Pango lineages (https://github. com/cov-lineages/pango-designation), from which the clade root can be inferred in the tree. Not all sequences in the tree need to be designated by a clade. Since the training data is imperfect, and the representative sequences for lineages are sometimes non-monophyletic in our tree, we have found the simple approach of using the most recent common ancestor (MRCA) does not yield accurate results. The matUtils annotate inference method works instead by first building a "consensus" sequence (where, by default, the consensus sequence requires an allele to be present in at least 80% of representative sequences, with lower frequency alleles marked as ambiguous) for each clade and finding its phylogenetic placement using UShER's placement module to obtain the clade root.
An EPP score of 1 indicates a high placement certainty of a sample in its local neighborhood in that there is a single most parsimonious placement location for that sample on the entire tree, and a higher EPP score suggests the sample placement is less certain. This metric is calculated by computing the number of most parsimonious placements after remapping the input sample(s) against the same tree (disallowing it from mapping to itself) with UShER's optimized placement module. About 85% of samples in our SARS-COV-2 MAT database have an EPP score of 1. The second metric is "neighborhood size score" (NSS), which is the longest distance (in number of edges) between any two equally parsimonious placement locations for a given sample. This metric is complementary to EPPs -when multiple EPPs are possible for a sample, NSS indicates whether the placement uncertainty is restricted to a small neighborhood (small NSS value) or spans a large portion of the tree (large NSS value).
Performance benchmarking of matUtils and other phylogenetics software packages All performance benchmarking experiments were carried out on a Google Cloud Platform (GCP) instance n2d-standard-16 with 16 vCPUs (Intel Xeon CPU E7-8870 v. 4, 2. 10 GHz) with 64 GB of memory using our public SARS-CoV-2 MAT dated June 9, 2021. matUtils does not have direct counterparts for its ability to work with the mutation-annotated tree (MAT) format, but we compared the performance of matUtils with state-of-the-art tools that offer some comparable functionality on Newick or VCF formats. Specifically, we compared the most recent version of matUtils (version 0. 3. 1) to newick_utils version 1. 6  (Junier and Zdobnov 2010) , tree_doctor (from version 1. 5 of the phast package;  (Hubisz et al. 2011) , ape version 5.
5  (Paradis and Schliep 2019) , and bcftools version 1. 7  (Danecek et al. 2011). The exact commands used for each comparison can be found in Supplementary Tables  S2-S9 , and the input data used for each comparison can be found at https://github. com/bpt26/matutils_benchmarking/. Supplementary Figure  2 : matUtils uncertainty statistics reveal low-quality sample placements. This Auspice view of an example subtree is annotated with both equally parsimonious placements (in color) and neighborhood size (branch label integers). 18 of our 23 samples in the subtree have a single placement and a neighborhood size of 0, indicating high placement certainty for those samples. Of the five samples with multiple equally parsimonious placements, one sample has 5 equally parsimonious placements with an NSS value of 19, indicating a high level of placement uncertainty for this sample spanning a relatively large neighborhood.
Funding J. M. , B. T. and R. C. -D. were supported by  R35GM128932  and by an Alfred P. Sloan foundation fellowship to R. C. -D. J. M. and B. T. were funded by  T32HG008345  and  F31HG010584. The  UCSC Genome Browser  is funded by  NHGRI , currently with grant  5U41HG002371. The SARS-CoV-2 database is funded by generous individual donors including  Eric and Wendy Schmidt by recommendation of the Schmidt Futures program. N. D. M. and N. G. are funded by the  European Molecular Biology Laboratory  (EMBL). Y. T is funded through  Schmidt Futures Foundation   SF 857  and  NIH  grant  5R01HG010485.
Maintaining a daily-updated mutation-annotated tree database of global SARS-CoV-2 sequences We are maintaining a daily-updated mutation-annotated tree (MAT) database of global SARS-CoV-2 sequences at http://hgdownload. soe. ucsc. edu/goldenPath/wuhCor1/UShER_SARS-CoV-2/. Our database is organized into sub-directories sorted by year, month and date. To update the MATs daily, we have set up a CRON job on a server at UCSC which downloads SARS-CoV-2 sequences daily from GenBank  (Clark et al. 2007 ) and COG-UK  (Nicholls et al. 2020 ) (see https://github. com/ucscGenomeBrowser/kent/blob/master/src/hg/utils/otto/sarscov2phylo/update Public. sh which calls other scripts in the same directory). We also include 253 sequences downloaded from the China National Center for Bioinformation (https://bigd.
big. ac. cn/ncov/release_genome) in October 2020 that are not associated with GenBank IDs. New sequences are added to the previous day's MAT using the UShER placement tool  (Turakhia et al. 2021 ) with options to place the samples in the order of the fewest ambiguous bases and exclude sequences with 5 or more equally parsimonious placements. Previously excluded sequences are reconsidered for placement during each build. We also use matUtils extract to prune samples with 30 or more private mutations and those internal branches longer than 30 mutations, as these are highly indicative of error-containing sequences  (Mai and Mirarab 2018). The trees are rooted to Wuhan/Hu-1 (GenBank MN908947. 3, RefSeq NC_045512. 2), and nodes with no associated mutations are collapsed  (Turakhia et al. 2021 ).
Our first MAT was created by starting with the last Newick tree release (dated November 13, 2020) of Rob Lanfear's sarscov2phylo (Lanfear and Mansfield 2020) containing 82,358 public sequences, adding the later additional public sequences using UShER. Each MAT is then annotated with Nextstrain clade and Pango lineage annotations using matUtils annotate -c with a file containing representative sequences for each clade/lineage. For Nextstrain clades, Nextclade assignments (https://github. com/nextstrain/nextclade) for all sequences are used. For Pango lineages, designated lineage representative sequences from https://github. com/cov-lineages/pango-designation/ are mapped to the corresponding public sequence IDs where possible. In addition to MATs, we provide in each sub-directory: (i) a Variant Call Format (VCF) file containing the genotypes of public sequences, generated from the corresponding MAT with matUtils extract such that missing or ambiguous bases have been imputed by UShER using maximum parsimony  (Turakhia et al.
Supplementary Figures and Tables Supplementary Figure  1 : Our global phylogeny contains 840,343 samples as of June 10. Our database, containing all high-quality publically available SARS-CoV-2 whole-genome sequences and their clade assignments, is updated daily. As of June 10, our phylogeny contains 840,343 sequences with a total parsimony score of 733,211. Sequences that have 5 or more equally parsimonious placements on the tree are removed at each build (see Supplementary Methods), so the total samples sometimes drop during successive builds.
A key feature of PBR and RLA is to ensure conservation objectives are met in the face of the multiple uncertainties or biases that plague real-world data on marine mammals. We built a package named RLA in the R software to carry out MSE of control rules to set removal limits in marine mammal conservation. The package functionalities are illustrated by two case studies carried out under the auspices of the Oslo and Paris convention (OSPAR) (the Convention for the Protection of the Marine Environment of the North-East Atlantic) Marine Mammal Expert Group (OMMEG) in the context of the EU Marine Strategy Framework Directive. The first case study sought to tune the PBR control rule to the conservation objective of restoring, with a probability of 0. 8, a cetacean population to 80% of carrying capacity after 100 years.
The second case study sought to further develop a RLA to set removals limit on harbor porpoises in the North Sea with the same conservation objective as in the first case study. Estimation of the removals limit under the RLA control rule was carried out within the Bayesian paradigm. Outputs from the functions implemented in the package RLA allows the assessment of user-defined performance metrics, such as time to reach a given fraction of carrying capacity under a given level of removals compared to the time needed given no removals.
In addition, to minimize duplication of effort by research groups, and to enhance reproducibility, a common tool is desirable. This is precisely our goal in developing the RLA package for statistical software R which has become the lingua franca of statistical computing for a wide community including many scientists and managers. Recently, tools to easily run MSE for marine mammals have been developed in the context of the US so-called "import rule"  (Williams et al. , 2016). These new regulations of the MMPA Import Provision require any nation exporting seafood products to the USA to establish a comparability finding for fisheries that have incidental or intentional mortality and serious injury of marine mammals  (Wade et al. , 2021). A comparability finding is a demonstration of equivalence in marine mammal conservation effectiveness to those governing bycatch in US fisheries.
This justifies the need for an applied tool to easily re-run simulations when needed, and to possibly consider control rules other than PBR. We describe below our RLA package which includes a set of functions to carry out MSE using contemporary population dynamics models for marine mammals species  (Punt, 2016). Documentation on MSE for managing marine mammal removals is abundant, yet there is a comparatively dearth of applied tools to carry out MSE (but see  Brandon et al. , 2017). This gap motivated the development of the package. The manuscript format will be unusual in meshing together in the main text equations and R command lines. This choice is motivated to ease the mapping from the principles of MSE to its application for readers not yet familiar with MSE in practice. Our contribution is to illustrate its use via two cases studies on cetaceans, building on the work of  Wade (1998)  and  Winship (2009)  among others.
The first case study considers a management strategy under the PBR control rule, then called modified PBR (mPBR), and illustrates tuning it to another CO than the US MMPA, namely the ASCOBANS short-term practical subobjective "to restore and/or maintain stocks/populations to 80% or more of the carrying capacity" (Res. 3. 3). The second case study focuses on furthering the development of a Removals Limit Algorithm (RLA) to set limits to anthropogenic mortality of harbor porpoises (Phocoena phocoena) in the North Sea  (Winship, 2009; Hammond et al. , 2019). The RLA is similar in concept to the Catch Limit Algorithm (CLA) of the International Whaling Commission (IWC)'s Revised Management Procedure (RMP 3  ;  Boyce, 2000). We first introduce notations, then detail of the data simulators currently implemented in the RLA package before carrying out the tuning of management strategies with respect to the quantitative interpretation of the ASCOBANS CO made by the Oslo and Paris convention (OSPAR) (Convention for the Protection of the Marine Environment of the North-East Atlantic 4  ) expert group on marine mammals (OMMEG).
Ultimately, this quantile corresponds to a number of animals, but selecting a quantile allows a dispassionate assessment as the user need not work directly on a number of animals: the number will only be revealed at a later stage. The model code in Stan syntax is stored as text data in a dataframe within the RLA package and can be accessed with: library(rstan) data(rlastan_models) # use uniform priors cat(rlastan_models$uniform) # compile model rlastan <-rstan::stan_model (model_code = rlastan_models$uniform, model_name = "Removals Limit Algorithm" ) The rstan library (Stan Development Team, 2020) is required for the RLA but is not included among the dependencies of the RLA package so that the user must load the library themself. The model code currently uses uniform priors on both parameters (r, D T ).
(2019) : they are available as data in the RLA package with data("north_sea_hp"). The frequency with which survey estimates were assumed to be collected was set to 6 years to match the MSFD reporting cycle. Carrying capacity during the simulations was set to K = 500, 000. The IPL was set to 0. 54, that is in a population estimated to be depleted to less than 54% of carrying capacity, the removals limit was automatically set to 0. The weight w was set to 1 16. The upper bound for the uniform prior on parameter r was set to 0. 1 given recent evidence on the maximum growth rate of harbor porpoise populations  (Forney et al. , 2021). Tuning was achieved by evaluating the same base case scenario as  Hammond et al. (2019)  and some "robustness" trials. For each scenario/trial, 1, 200 simulations were run, and the final depletion level of the population was monitored after 100 years of using equation (2) to set limits to anthropogenic removals.
Removals Limit Algorithm All results can be accessed via a shiny application  (Chang et al. , 2021) , available at https://gitlab. univ-lr. fr/pelaverse/ rlascenarioviz: remotes::install_gitlab( host = "https://gitlab. univ-lr. fr", repo = "pelaverse/rlascenarioviz" ) library(rlaScenarioViz) run_app().
A practical hurdle to using either the PBR or RLA control rule was the lack of tools to carry out a MSE tailored to the European context where the ASCOBANS interim conservation objective is "to restore and/or maintain stocks/populations to 80% or more of the carrying capacity. " We addressed this gap by building an R package to provide scientists with the means of carrying out MSE for setting thresholds on anthropogenic removals of marine mammals. A clear motivation was to remedy the situation seen in the OSPAR Intermediate Assessment 2017 where the recommended approach to setting removals limit could not be implemented. The RLA package for software R implements utilities to perform the MSE of anthropogenic removals on marine mammal populations (Figure  6 ). The core functions are two data simulators, pellatomlinson_pbr() and pellatomlinson_rla(), coupled with functions implementing specific control rules: forward_pbr() and forward_rla() for projecting the population forward in time.
This could lead to a more accurate and realistic MSE. This would also need to potentially consider different removals limits for different populations, either to reflect the sink-source distinction or to account for transboundary differences in management if populations are managed by different parties/states or subjected to several fisheries. Albeit currently limited to single-population, single-species, and single-control rule, the RLA package provides a convenient implementation of population dynamics simulators, which can be leveraged to design a precautionary management strategy by means of robustness trials  (Wade, 1998). These robustness trials include consideration of • systematic bias in abundance or removal estimates; • random errors in removal estimates, either before or after the implementation of a control rule; • random catastrophic mortality events killing off a fraction of the population (e.
g. , epizootics;  Aguilar and Raga, 1993 ); • a decline in carrying capacity K over time; • environmental stochasticity on population dynamics with a correlated, first order, random walk model; • differential vulnerabilities to the removal with respect to age. All these trials can currently be run with the RLA package as exemplified by the two case studies presented herein (Tables  3, 4 ). They use different control rules, either PBR or RLA, reflecting a difference in the data assumed available for management. The output of simulations is, however, the same as the whole population trajectory under the chosen control rule is available to the user. From this output, additional work is required from the user to analyze the strategy with respect to performance metrics. Performance metrics with respect to the CO are left to user discretion: there are no special functions in the RLA package to compute specific performance metrics.
This function calls another function get_streaks which will identify streaks of 0 and 1 in a vector. The output of a simulation includes a vector named depletion which can be used to compute the time to reach the CO as follows: time2CO(ifelse(depletion > 0. 8, 1, 0)) As each simulation output reports an identifier, which is also a seed that the user can set, it is possible to match the result from a removals limit implementation with the counterfactual outcome under no anthropogenic removals (by using the same seed in both cases). This feature allows performance comparisons under counterfactuals as random number generation remains under user control. One strength of the RLA package is the enhanced flexibility for users. In addition to defining CO and performance metrics, advanced users familiar with Stan syntax can code their own model, or modify the ones stored as text in a list available in the package, to implement different control rules to set a removals limit.
Depending on the results, an alternative model specification may be needed. The Stan engine for Bayesian inference using Hamiltonian Monte Carlo  (Carpenter et al. , 2017)  is versatile and allows to fit a large set of models with efficient algorithms  (Monnahan et al. , 2017). This versatility may be leveraged by advanced users: function forward_rla() needs an object of class "stanmodel" to run. This object is a compiled model that will be repeatedly used within function forward_rla(), thus minimizing model compilation time for a faster run. For example, with a time horizon of 100 years and an assumed frequency of 6 years, function forward_rla() calls internally ⌈ 100 6 ⌉ = 17 times the function sampling from package rstan (Stan Development Team, 2020) in a single simulation. While a single simulation with forward_rla() takes a couple of minutes on a laptop, running a large number of simulations quickly becomes prohibitively long.
Further work on the weight w should also be undertaken to assess the sensitivity of RLA to the current choice inherited from the IWC's CLA. The RLA as currently implemented in the package RLA differs from the IWC's CLA: these differences may have consequences that deserve more scrutiny. In particular, we found that increasing the time horizon from 100 to 200 years actually decreased the removals limit, while the reverse was found with the CLA  (Aldrin et al. , 2008). While surprising and requiring a more in-depth investigation with respect to its cause, this result may currently provide a disincentive to unambitious CO with a long-time horizon to address the issue of unsustainable anthropogenic impacts on marine mammals. The RLA package is primarily geared toward MSE for setting precautionary limits to anthropogenic removals in marine mammal conservation.
A modified PBR tuned to OMMEG's interpretation of the ASCOBANS CO, namely "a population should be able to recover to or be maintained at 80% of carrying capacity, with probability 0. 8, within a 100-year period" will address a current misalignment between management and conservation objectives in the salient context of small cetacean conservation in the Northeast Atlantic (ICES, 2020c). It is our hope that the RLA package will enable easier MSE, in particular in the current EU MSFD context of achieving "Good Environmental Status. " This hope crucially hinges on users' feedback and involvement in further developing and expanding the package to the benefit of improved management of the impact of human activities on marine mammals.
ACKNOWLEDGMENTS AG thanks the  German Federal Agency for Nature Conservation  for funding her time as the lead of the OSPAR expert group on marine mammals. MA and MG thank  Mikaël Guichard  and the dedicated staff at the  Mésocentre de Calcul de Poitou Charentes. We thank reviewers,  Finn Larsen ,  Lotte Kindt-Larsen , and OMMEG members for discussion and critical comments.
T is the solution temperature, which can be calculated as  [52] : T ¼ T Dew Point X 3 n¼0 A n Xm n þ X 3 n¼0 B n Xm n (45) A n and B n are constants that are shown in Table  9. To validate the DEACH, it is run based on Gebreslassie et al. [53]  conditions shown in Table  10 , and the results are presented in Table  11. Also, the COP of the Gebreslassie et al. [53]  model was 1. 655, while the present study has a COP of 1. 6012. As can be seen, the   present model of the DEACH is valid and presents almost the same results as Gebreslassie et al. [53]  study. The model of the DEACH is developed in MATLAB ®.