dataesr/scientific-paragraphs-categorization huggingface dataset:
| Body mass estimates and phylogenetic generalised least squares Scaling describes how certain biological traits in individuals vary with body size, which may include allometry or disproportionate change of a trait with size  (Gould, 1966). Knowing this, it is important to consider this size effect when studying a biological trait in a large number of individuals that vary greatly in size. For this work, we considered body mass as a proxy for body size. We gathered mass estimates from the literature for each of the species in our sample. For extant species, we mainly relied on the database of  Myhrvold et al. (2015)  which compiled median mass values for a large proportion of amniote taxa. In some particular cases, when the species is not known, for example Gallus sp. , we calculated the average mass of the species in the genus.
In some cases, the species was absent from the database, and this is especially the case for extinct species. We used the cQE function from the R package MASSTIMATE   et al. , 2014; Hutchinson et al. , 2011). All mass estimates with their provenance are available in Table  1  and Supplementary Material 1. We used phylogenetic generalised least squares (PGLS) in R to study a potential influence of body size on the different geometric and microanatomical parameters retained in our models. We performed PGLS using the caper package  (Orme et al. , 2018). PGLS fits a linear regression between a dependent variable and one or more independent variables while accounting for phylogeny  (Symonds & Blomberg, 2014). This is done by adjusting branch length transformations with lambda  (Pagel, 1999). PGLS were performed with 100 phylogenetic trees.
G. collected the data, designed the study, performed the analyses, interpreted the results and wrote the manuscript. J. B. designed the analyses. M. G. designed the analyses. J. R. H. collected the data and interpreted the results. M. L. collected the data, designed the study and interpreted the results. All authors reviewed and approved the final manuscript. ACK N O WLE D G E M ENTS This work was supported by the doctoral programme Interfaces pour le vivant (IPV) with Sorbonne Université and by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [grant agreement #695517 to J. R. H]. The authors acknowledge the support of the Virtual Data initiative, run by the laboratoire d'excellence Physique des deux infinis et des Origines (LabEX P2IO) and supported by Université Paris-Saclay, for providing computing resources on its cloud infrastructure.
Results Genome Quality. Reference genomes for the leatherback and green turtles were generated using four genomic technologies following the VGP pipeline v1. 6  (39) , with minor modifications (see Methods). A total of 100% of the leatherback and 99. 8% of the green turtle assembled sequences were placeable within chromosomes. The assembled genomes were near full-length (~2. 1 GB), with annotations of all 28 known chromosomes for both species, composed of 11 macrochromosomes (>50 Mb) and 17 microchromosomes (<50 Mb) (SI Appendix, Table  S1  and Fig. S1 ). These genomes are among the highest quality genomes assembled for nonavian reptiles to date in terms of both contiguity and completeness (Dataset S1), with the leatherback turtle assembly representing the first reptile genome where all scaffolds were assigned to chromosomes.
Scaffold N50s were high for both genomes (SI Appendix, Table  S1 ). We annotated 18,775 protein-coding genes in the leatherback and 19,752 in the green turtle genomes (see below for analysis of these gene differences). For the leatherback and green turtles, 96. 9% and 97. 5% of these genes were supported at >95% of their length from experimental evidence and/or high-quality protein models from related species (see Methods). The numbers of protein-coding genes are within the range of other reptiles (Dataset S1) and include 97. 7% and 98. 2% complete BUSCO copies for leatherback and green turtles based on Sauropsida models  (50) , which are similar to or higher than all other assembled reptilian genomes to date (SI Appendix, Fig. S2 ). Genome Architecture. Despite diverging over 60 Mya (4), leatherback and green turtles show extremely high genome synteny and collinearity (Fig.
Eight chromosomes exhibited small RRCs (0. 1 to 3 Mb), and 11 contained RRCs that were between 3 and 18 Mb in length (Fig. 2  A-D and Dataset S3). Analyses of coding regions revealed a similar pattern of strong collinearity between the two species (Fig. 1C  and SI Appendix, Fig. S6 ), particularly within the macrochromosomes, which contain more than 80% of the total length of the genomes. The two genomes also displayed similar percentages of repetitive elements (REs), which were almost exclusively transposable elements (TEs) and unclassified repeats (SI Appendix, Fig. S8 ). The landscape of TE superfamily composition over evolutionary time was also similar between the two species, with the exception of REs with low Kimura values (<5%), which appeared at a higher frequency in the leatherback turtle genome (see SI Appendix, section I for full analyses).
Gene Families and Gene Functional Analysis. Gene function analysis of localized RRCs revealed that most contained genes with higher copy numbers in the green turtle compared with the leatherback (Fig. 2  A-D and Dataset S3). Nineteen chromosomes had RRCs with higher gene copy numbers in the green turtle, and of these, ten contained genes associated with immune system, olfactory reception, and/or zinc-finger protein-coding genes. Many of the same gene families were also detected as high-diversity exonic regions via separate, independent analyses (SI Appendix, section I), reinforcing their importance in the divergent evolution of these species. In addition to localized RRCs, higher gene copy numbers in the green turtle occurred in many gene orthologous groups (orthogroups) across the entire genome, and generally in variable multicopy genes (Fig.
S10 ), and is the only divergent region across the very large chromosome 1 in the turtles analyzed. In contrast, OR class II genes were spread across several chromosomes in both sea turtle species, with higher copy numbers again in the green turtle found within RRCs (Fig. 2 B-D ). The instability and rapid evolution of OR gene numbers in turtles is further illustrated in the expansion-contraction analysis of orthogroups (Fig. 2E  and Dataset S6 A-D), which showed that OR class I genes underwent a modest contraction in the ancestral sea turtle lineage, followed by an expansion in the green turtle but a further contraction in the leatherback turtle. Similar trends were detected for OR class II genes, but with a greater magnitude of contraction in the ancestral sea turtle lineage followed by a further contraction for the leatherback turtle and only a small expansion for the green turtle (Fig.
2E ). Another important RRC (RRC14) encompassed the major histocompatibility complex (MHC), which plays a critical role in vertebrate immunity and is particularly relevant to sea turtle conservation due to the threat of FP and other diseases  (32). In addition to the MHC region, this RRC includes several copies of OR class II genes, zinc-finger protein-coding genes and other genes involved with immunity, such as butyrophilin subfamily members and killer cell lectin-like receptors (Fig. 2D  and Dataset S3). Invariably, the green turtle carried higher numbers of all the multicopy genes present in RRC14. RRCs on other chromosomes similarly showed increased levels of zinc-finger protein genes in the green turtle, including the RRCs labeled 6A, 11A, 14A, and 28 (Dataset S3). In particular, zinc-finger protein genes were highly prevalent on chromosomes 14 and 28 in both sea turtles, representing more than 50% of all the protein domains present on these chromosomes (SI Appendix, Fig.
S11 ). Finally, all but three genes with known roles in TSD in reptiles (Dataset S7) were located as single-copy genes within both sea turtle genomes, with homologous copies located in the same region of the chromosomes in both species (see SI Appendix, section I for full analyses). Macro and Microchromosomes. Microchromosomes contained significantly higher proportions of genes than macrochromosomes (Fig. 3  A and B; green turtle: F (2, 25) = 16. 46, P < 0. 01; leatherback turtle: F (2, 25) = 16. 35, P < 0. 01), and gene content was strongly positively correlated with GC content (SI Appendix, Fig. S13 ; green turtle R 2 = 0. 81, P < 0. 01; leatherback turtle R 2 = 0. 87, P < 0. 01). These patterns were particularly apparent in small (<20 Mb) microchromosomes, where GC content reached 50%, compared with the 43 to 44% genome-wide averages.
Runs of Homozygosity (ROH). In addition to lower genomewide heterozygosity, leatherbacks had a greater total number of ROHs (>50 kb) than green turtles (mean N ROH = 4,510 and Fig. 2. (A-D ) Dotplots (identity values; dark green = 1 to 0. 75, green = 0. 75 to 0. 5, orange = 0. 5 to 0. 25 and yellow = 0. 25 to 0) showing four of the regions with reduced collinearity (RRC) identified within chromosomes and associated with higher copy numbers of immune system (IS), ORs, or zinc finger domain genes in the green turtle relative to leatherback turtle (see also SI Appendix, Fig. S6  and Tables S3-S5 and Dataset S3 for full details of all RRCs). RRC positions are marked with gray squares on the dot plots (Left; with leatherback turtle on the X-axes and green turtle on the Y-axes) and gene collinearity maps (Right) for each chromosome highlighting the connections among specific gene families in different colors.
4B ), suggesting long-term low population sizes in the leatherback turtle. Within species, overall ROH distributions were generally similar between samples representative of different populations for leatherback turtles, although individuals from the Northwest Atlantic and East Pacific populations displayed slightly higher total aggregate lengths of ROHs than those from the West Pacific population, primarily due to greater aggregate lengths of medium and long ROHs (Fig. 4B ). Among green turtles, the aggregate length of ROHs in all categories were generally small and similar across individuals, with the clear exception of the genome reference sample that originated from the Mediterranean population. This individual displayed higher numbers and lengths of long ROHs (>1 Mb) compared with all other green turtles (n = 50 compared with <5, and aggregate length = 74 Mb compared with <4 Mb), suggesting higher levels of recent inbreeding relative to the other green turtle populations represented in our dataset.
0, P < 0. 01; modifier variants: t (5. 33) = 31. 8, P < 0. 001). The missense-to-silent mutation ratio was also higher in leatherbacks than green turtles (t (7. 19) = -72. 3, P < 0. 001; mean = 0. 99 and 0. 70), further suggesting that genetic load is higher in the leatherback turtles. Within species, there was limited variation between individuals for all variant categories (Fig. 4C ). Demographic History. Pairwise Sequential Markovian Coalescence (PSMC) analyses indicated different historical effective population sizes (N e ) between the two sea turtle species (Fig. 4D ). N e for all leatherback turtle populations represented in our dataset have been relatively small and sustained over time, ranging in size from approximately 2,000 to 21,000 over the last 10 My, up until the pnas. org Last Glacial Maximum (LGM) and at the lower end of this range for most of the last 5 My.
Finally, with global distributions and longdistance migratory connectivity, sea turtle conservation requires international collaboration that has been previously hampered by difficulty comparing datasets between laboratories. Existing anonymous markers (e. g. , microsatellites and restriction-site based SNP markers) can now be anchored to these genomes, and new ones can be optimized for conservation-focused questions and shared across the global research community, facilitating large-scale syntheses and equitable capacity building for genomics research. While ongoing anthropogenic impacts continue to threaten the viability of sea turtles to persist, combined with the critical work of reducing major threats such as fisheries bycatch and habitat loss, these genomes will enable research that make critical contributions to recovering imperiled populations.
Methods Reference Sample Collections, Genome Assembly, and Annotation. Ultrahigh molecular weight DNA was isolated from blood collections, and biopsies of internal organs for RNA were collected opportunistically from recently deceased or euthanized animals. Raw data were deposited into the VGP Genome Ark and NCBI Short-Read Archive (SRA; see Data Accessibility Statement). We assembled both genomes using four genomic technologies following the VGP pipeline v1. 6  (39)  with minor modifications. Short-and long-read transcriptome data (RNA-Seq and Iso-Seq) were generated from tissues known for their high transcript diversity in each species to enable accurate, species-specific annotations. These data, plus homology-based mapping from other species, were used to annotate the genomes using the standardized NCBI pipeline (117).
We performed annotation as previously described  (39, 118) , using the same RNA-Seq, Iso-Seq, and protein input evidence for the prediction of genes in both species. Full details for all methods are provided in SI Appendix, section I. Genome Quality Analysis. We used the pipeline assembly-stats from https:// github. com/sanger-pathogens/assembly-stats to estimate scaffold N50, size distributions, and assembly size. BUSCO analysis (115) and QV value estimations (116) were conducted to assess the overall completeness, duplication, and relative quality of the assemblies. We used D-GENIES (118) with default parameters to conduct dot plot mapping of the entire genomes and each individual chromosome to evaluate the synteny between leatherback and green turtle genomes, and Haibao Tang JCVI utility libraries following the MCScan pipeline (119) to verify their contiguity.
Incongruences in gene synteny blocks were manually investigated using Artemis Comparison Tool (120), identifying possible regions that could be caused by artifacts during assembly, and correcting these. The final curated assemblies were analyzed using the genome evaluation pipeline (https://git. imp. fu-berlin. de/cmazzoni/GEP) to obtain all final QC plots and summary statistics. Identification and Analysis of RRCs. Using dot plots, 20 Mb windows were visually screened to identify regions of reduced collinearity (RRCs; SI Appendix, Fig. S5 ). Several genomic features (e. g. , GC content, repeat elements) were compared between RRCs and equisized regions directly up-and down-stream to determine whether these were influencing collinearity (Dataset S5). Interproscan (119) was used to identify the functions of genes found within RRCs, and overall GO-term proportions for each chromosome were estimated using PANTHER (120); SI Appendix, Fig.
S25 ). The two sea turtle genomes were aligned using Progressive Cactus (121, 122) to examine whether RRCs presented patterns of sequence divergence and/or gene duplication between the species. Gene Families and Gene Functional Analysis. To estimate the timing of OR gene family evolution in sea turtles, we used computational analysis of gene family evolution (CAFEv5; (123). CAFE uses phylogenomics and gene family sizes to identify expansions and contractions. We used a dataset containing 8 species of turtle, 4 nonturtle reptiles, 3 mammals, and 1 amphibian using OrthoFinder (124, 125). OR orthogroups were grouped based on subfamily (class I and class II; see ref. 73) , and an ultrametric phylogeny was generated by gathering 1:1 orthologs. We then aligned OrthoFinder amino acid sequences for each orthogroup and generated a phylogenetic tree.
See SI Appendix, section I for searches of other specific genes. Genetic Diversity and Demographic History. The halSnps pipeline (126) was used to estimate genetic distance between species by computing interspecific single variants based on alignments obtained with Progressive Cactus (121, 122). Genetic distances were calculated for 10,000-bp windows across the genome, where each window included only single alignments in the Cactus output. Differences in genetic distance, gene content, GC content, and heterozygosity between macro-, intermediate micro-, and small microchromosomes were tested using one-way ANOVAs for each species. Regression analyses were used to test for correlations between these measures across chromosomes. For genome diversity, ROH, demographic history, and genetic load analyses, we included whole-genome resequencing data for additional individuals representing multiple global populations in each species (SI Appendix, Table  S6  and section I).
We calculated genome-wide heterozygosity using a method adapted from (127) using 100-kb nonoverlapping windows. Heterozygosity was calculated for the entire genome, repeat-masked genome, exons, and nonexons. Statistical comparisons between species were made using t tests. We subsequently applied the heterozygosity pipeline to generate genome-wide heterozygosity for additional reptilian species sourced from NCBI SRA, where species-specific reference genomes were available (SI Appendix, section I). ROHs were identified by generating a SNP-list using the analysis of next generation sequencing data [ANGSD; (128)] pipeline. ANGSD was parameterized to output files configured for use as input for the PLINK ROH analysis (129). ROHs were then further characterized into size classes approximately based on (130).
ACKNOWLEDGMENTS. We thank the  St. Croix Sea Turtle Program, USFWS ,  NOAA-SWFSC California  in-water leatherback research team, and the  New England Aquarium  for assistance with leatherback sample collection, and the  Israel National Sea Turtle Rescue Centre ,  NOAA PIFSC-MTBAP , and  Thierry Work  ( USGS ) for assistance with green turtle sample collection. We thank  Estefany Argueta  and  Jamie Adkins  for assistance with literature searches and library preparations, and  Phillip Morin ,  Andrew Foote ,  Anna Brüniche-Olsen ,  Annabel Beichman ,  Morgan McCarthy ,  David Adelson , and  Yuanyuan Cheng  for invaluable discussions and comments on the manuscript. Green turtle sequencing was performed by the  Long Read Team  of the  DRESDENconcept Genome Center, DFG NGS Competence Center , part of the  Center for Molecular and Cellular Bioengineering (CMCB) ,  Technische Universität Dresden  and  MPI-CBG.
For green turtle resequenced samples, we thank  Jessica Farrell ,  Whitney Crowder ,  Brooke Burkhalter ,  Nancy Condron , and the veterinary and rehabilitation staff and volunteers of the  University of Florida's (UF) Sea Turtle Hospital at Whitney Laboratories , staff of the  South Carolina Aquarium, UF's Interdisciplinary Center for Biotechnology Research Core  for sequencing services, and to the  Florida FWC and South Carolina DNR  for assistance with permitting. We thank  Erin LaCasella  for help procuring leatherback samples for resequencing from the  NOAA-SWFSC Marine Mammal and Sea Turtle Research Collection. This work was completed in part with resources provided by the  University of Massachusetts' Green High Performance Computing Cluster (GHPCC). Funding was provided by the  University of Massachusetts Amherst ,  NSF-IOS  (grant # 1904439  to L.
M. K. ),  NOAA-Fisheries ,  National Research Council postdoctoral  fellowship program to L. M. K. , VGP,  Rockefeller University , to E. D. J. ,  HHMI  to E. D. J. , the  Sanger Institute, Max-Planck-Gesellschaft , and grant contributions from  Tom Gilbert ,  Paul Flicek ,  R. W. M. ,  Karen A. Bjorndal ,  Alan B. Bolten ,  Ed Braun , N. J. G. , T. M. -B. , and A. F. S. We acknowledge  CONICYT-DAAD  for scholarship support to T. C. -V. , the  São Paulo Research Foundation  to E. K. S. R. -FAPESP (grant # 2020/10372-6 ). BeGenDiv  is partially funded by the  German Federal Ministry of Education and Research  (BMbF,  Förderkennzeichen 033W034A ). The work of F. T. -N. and P. M. was supported by the  Intramural Research Program  of the  National Library of Medicine ,  NIH. The work of M. P. was partially funded through the  Federal Ministry of Education and Research  (grant  01IS18026C ).
This study addresses the inherent difficulties in the creation of neuroengineering devices for closed-loop stimulation, a task typically characterized by intricate and technically demanding processes. Beneath the substantial hardware advancements in neurotechnology, there is often rather complex low-level code that poses challenges in terms of development, documentation, and long-term maintenance. To overcome these obstacles, we adopted an alternative strategy centered on Model-based Design (MBD) as a means to simplify the creation of closed-loop systems and reduce the entry barriers. MBD offers distinct advantages by streamlining the development workflow and facilitating the implementation of intricate systems. In this study, we applied MBD techniques to implement a spike detection algorithm on a Field-Programmable Gate Array (FPGA) using commercially available hardware that combines neural probe electronics with programmable FPGA-based hardware.
g. reading, processing, and writing the neural code  [14] ) can be daunting, especially for not expert scientists. To this end, it is necessary to provide simpler and faster ways to perform FPGA-based processing. Indeed, lowering the entry barrier for developing closed-loop systems is crucial to meet the growing demand and accelerate research progress. The primary objective of this work is to address the existing technological gap by introducing a rapidprototyping Model-Based Design architecture for accelerating the implementation of closed-loop systems on FPGAs. The focus is on testing this architecture on a commercially available system for in vivo closedloop stimulation experiments. To enhance the capabilities of the system, a spike detection algorithm has been developed thanks to the custom modification of the original code.
General philosophy of the approach A Model-Based Design approach, utilizing the Simulink® environment in conjunction with the HDL coder, could be a game-changer in the neuroscientific and neuroengineering research. Simulink® is a wellestablished tool for Model-Based Design provided by MathWorks that enables simulation of system behavior and troubleshooting of models before hardware implementation. Furthermore, the HDL code can be automatically generated from the model, speeding up the implementation process. This architecture aims to streamline the development process and enhance the efficiency of implementing closed-loop systems on FPGA platforms. The work has achieved this goal through the following steps (see Fig. 1 Panel A):  1. Creating a Simulink® model for processing neural data. Simulink®, being a graphical programming environment, allows for the modeling and simulation of dynamic systems in a faster and more intuitive way with respect to traditional methodologies.
By creating a comprehensive Simulink® model, the project establishes a foundation for implementing closed-loop systems on FPGAs; 2. Using HDL Coder to implement the model on FPGA: HDL Coder is employed to generate synthesizable Verilog and VHDL code from the Simulink model which can then be programmed onto the FPGA; 3. Establishing use case for commercially available systems. This use case provides real-world examples of how the architecture can be leveraged in various domains, highlighting its versatility and potential benefits. The Intan Stimulation/Recording system has been selected as the appropriate platform for the study. 3. Host PC: the host PC serves as a general-purpose computer that runs software for real-time data visualization and facilitates custom parameter communication. It acts as the central control unit for the overall system.