dataesr/scientific-paragraphs-categorization huggingface dataset:
contemporary levels of genetic diversity and infer their demographic histories over time. Using coalescent-and identity-by-descent-based modelling to estimate ancestral effective population sizes from genomic data, we demonstrate that North Atlantic right whales have lived with smaller effective population sizes (N e ) than southern right whales in the Southwest Atlantic since their divergence and describe the decline in both populations around the time of whaling. North Atlantic right whales exhibit reduced genetic diversity and longer runs of homozygosity leading to higher inbreeding coefficients compared to the sampled population of southern right whales. This study represents the first comprehensive assessment of genome-wide diversity of right whales in the western Atlantic and underscores the benefits of high coverage, genome-wide datasets to help resolve long-standing questions about how historical changes in effective population size over different time scales shape contemporary diversity estimates.
, 2022). This study revealed some interesting findings related to changing ecologies of many baleen whales that could have been initiated by the LGM, focusing on the Pleistocene-Holocene transition 1 to 30 kya  (Cabrera et al. , 2022). The demography history results presented by  Cabrera et al. (2022)  are strengthened by both consistent findings across species within ocean basins and the use of genotype likelihood to account for uncertainty due to the very low coverage. However, their analyses were limited to the use of short mitochondrial markers for both Northern and southern right whales. In contrast, the nuclear dataset was limited to low-coverage RADseq data for only a few baleen whale species and included the southern right whale, but not the North Atlantic right whale. Additionally, the confidence in the site frequency spectrum (SFS) derived from RADseq data was further limited by the use of folded SFS not exploiting the full range of the SNP frequency spectrum and by low sequencing coverage, both factors which bring important limitations to the resolution that can be achieved with such a dataset  (Mona et al.
11  (Danecek et al. , 2021)  to remove sites missing more than 25% of genotype calls and sites with mapping quality of less than 30 and applied a maximum depth threshold for each dataset that was calculated as two times the median depth of variant sites summed across all individuals (INFO field 'DP' of the VCF file) after repeat masking (NARW alone: 790X, SRW alone: 558X, all on NARW: 1370X, SRW with bowhead: 636X; Figure  S2 ). We identified the sex chromosomes using the D-Genies pipeline  (Cabanettes & Klopp, 2018)  to map our reference genomes to the well-annotated blue whale genome assembly (GCA_009873245. 3) using minimap2  (Li, 2018)  and visualized the similarities with a dot-plot. This allowed us to identify the scaffold representing the X-chromosome and confirm that the Y-chromosome was excluded when we only retained long scaffolds for our reference genomes.
, 2020). In order to understand the extent of LD in the right whale genomes to identify the best strategy for pruning linked sites, LD decay was calculated with PopLDdecay v3. 41  (Zhang et al. , 2019; Figure S4). Using PLINK v1. 9  (Purcell et al. , 2007) , we pruned the datasets to remove linked SNPs with a mean r 2 threshold determined for each population (NARW: r 2 =. 2, SRW: r 2 =. 1). Coalescent-based models of demographic history can also be influenced by population structure. While there is only one extant population of North Atlantic right whale, there are known subpopulations in the southern hemisphere. Most of our southern right whale samples were collected from a known, distinct population near Peninsula Valdés, Argentina, with the exception of one sample that was collected on the feeding grounds in South Georgia where whales aggregate from Argentina and less frequently South Africa  (Carroll et al.
| Demographic history We estimated the divergence dynamics of the species using MSMC-IM  (Wang et al. , 2020). First, we ran MSMC2 on four North Atlantic right whale haplotypes and four southern right whale haplotypes that were mapped to the North Atlantic right whale genome and on the 16 pairwise comparisons between the two species. We combined these within-and between-species outputs from MSMC2 and ran MSMC-IM to generate cumulative migration probabilities over time. We repeated these steps to run 10 MSMC-IM replicates using different combinations of samples/haplotypes. We also used MSMC2 v2. 1. 3  (Wang et al. , 2020)  to estimate changes in effective population size after the species diverged. We generated a list of accessible genomic regions for variant calling for each species and we removed indels and sites with missing data and phased biallelic SNPs using SHAPEIT2  (Delaneau et al.
, 2013). We ran 10 iterations of MSMC2 for each species, including a different combination of three samples (six haplotypes) in each run (therefore the sample from South Georgia was only included in three iterations). We plotted the results using different estimates of mutation rate to assess the robustness of our results to small changes in this parameter (Figure  S8 ). We generated estimates of effective population size over time based on the unfolded SFS in STAIRWAY PLOT v2. 1. 1  (Liu & Fu, 2020). First, we polarized the ancestral versus derived allelic states of the SNP calls for North Atlantic right whale and southern right whales setting the bowhead whale sample as the ancestral allele with a custom python script. We calculated the unfolded SFS of biallelic variant sites without missing data using SCIKIT-ALLEL v1.
, 2014) , especially in datasets such as ours where lower values of r 2 max base the analyses on very few variants. We present the results for r 2 max = 1. 0 in the main text and include a range of values in the supplementary material (Figure  S9 ). We used filtered variant call files for each population alone, pruned to remove sites with missing data. We then used IBDNe  (Browning & Browning, 2015)  to generate population size estimates using a constant recombination rate of 1. 0 cM/Mbp and a minimum IBD block length of 2 cM. As no recombination map is currently available, we also ran the program with a range of constant recombination rates (0. 8-1. 2 cM/Mbp) Time(generations) = 50 ROH Length(Mb) x recombination rate(cM ∕ Mb) to reflect a range of recombination rates reported for mammals  (Dumont & Payseur, 2008 ; Figure  S9 ).
| DISCUSS ION Using whole genome sequence data from nine unrelated individuals from both North Atlantic and southern right whales from the Southwest Atlantic, we found lower levels of genetic diversity and higher inbreeding coefficients in North Atlantic right whales compared to southern right whales. We also described the changes in effective population size over time in each of these species since their divergence. We estimate a divergence time of ~ 124-480 kya, which is more recent than has been described in other studies they usually lead to one-or two-order-magnitude differences in split time estimates  (Henn et al. , 2008; Ho, Shapiro, et al. , 2007; Howell et al. , 2003). This discrepancy between the CCR decay approach implemented in MSMC-IM and more traditional phylogenetic approaches comes from the fact that phylogenetic approaches as used in  McGowen et al.
After diverging, southern right whales had effective population sizes nearly 10 times greater than North Atlantic right whales for thousands of years. We also detected a rapid and recent decline in both populations over the past thousand years, as expected based on the history of whaling  (Aguilar, 1986; Rastogi et al. , 2004; Reeves et al. , 1999). The recent decline in Southwest Atlantic southern right whales was only detected by the most sensitive analysis of IBDNe relying on IBD tract lengths and was not detected based on site frequency spectrum data used by STAIRWAY PLOT2. This analysis suggests the bottleneck occurred more recently than in the North Atlantic, as predicted by the onset of industrial whaling beginning a couple of centuries later into the 1700s  (International Whaling Commission, 2013).
The estimates of demographic history are based on coalescent models using site frequency spectrum or runs of homozygosity and are influenced not only by the data themselves but also by factors such as generation time, mutation rate, recombination rate and selection. An inherent assumption of many biological models is non-overlapping generations, which is violated for most mammalian species. In right whales, a female can be reproductively active for decades  (Hamilton et al. , 1998)  and therefore be calving at the same time as her granddaughters. This assumption is largely F I G U R E 5 Nucleotide diversity reported for other cetacean species based on genomic datasets. Raw data and sources for the non-right whale species can be found in Table  S4. unavoidable with long-lived mammalian studies but should be recognized as a potential source of bias where N e is likely underestimated and bottlenecks may be detected earlier than they occurred  (Charbonnel et al.
Parameter choice is extremely important in modelbased studies and can create many biases in results, however, we are fairly confident we mitigated these biases as best we could and we caution against over-interpretation of exact timing and population sizes, and rather encourage focusing on the broader trends presented by the data. In addition to mitigating biases, our study design provides a rich and highly informative genomic dataset capable of tackling questions regarding the impact of whaling that were unable to be addressed in previous work (see  Taylor et al. , 2021). We used highcoverage, genome-wide data, polarized SFSs, identity-by-descent tracts and runs of homozygosity to both describe diversity and investigate changes in effective population size, with fine scale resolution thanks to the use of dedicated methods to analyse demographic changes from the times of divergence to the near present (~4 generations ago).
Whole genome datasets and the increasing availability of reference genomes are opening doors to help answer new and long-standing questions important for conservation  (Formenti et al. , 2022; Theissinger et al. , 2023). When tackling real-world conservation issues and trying to save a species from the brink of extinction, there are often obvious (while extremely difficult to achieve) goals including stopping habitat destruction, preventing anthropogenic mortalities and protecting critical habitat, among others. What is less clear, however, is when do we consider a species or population to be 'recovered'. The identification of clear and specific goals in conservation is often a challenge. Genomic data are proving to be an invaluable tool for conservation management providing more accurate measures of genetic diversity, a better understanding of the genetics of small populations and estimates of demographic history of wildlife populations  (Formenti et al.
1365294x, 0 , 0 Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/mec. 17099 by Institut De Recherche Pour Le Developpement, Wiley Online Library on [16/08/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License DNA databank housed at Saint Mary's University (Halifax, Nova Scotia) from 12 North Atlantic right whales and 10 southern right whales that had been previously extracted using standard phenolchloroform methods (as explained in Wang et al. , 2008) and stored at -20°C. A total of 1-5 μg of genomic DNA was sent to the McGill Applied Genomics Innovation Core (Montreal, Quebec, Canada) for PCR-free library preparation using an NxSeq AmpFREE kit (Lucigen).
16S rRNA gene amplicon sequencing DNA was extracted from the granules using the DNeasy UltraClean Microbial kit (Qiagen, Venlo, The Netherlands), using the manufacturer's protocol. The extracted DNA was quantified using a Qubit 4 (Thermo Fisher Scientific, Waltham, MA). Samples were sent to Novogene Ltd. (Hong Kong, China)  for amplicon sequencing of the V3-4 hypervariable region of the 16S rRNA gene (position 341-806) on a MiSeq desktop sequencing platform (Illumina, San Diego, CA) operated under paired-end mode. The raw sequencing reads were processed by Novogene Ltd. (Hong Kong, China)  and quality filtered using the QIIME software  (Caporaso et al. 2010). Chimeric sequences were removed using UCHIME  (Edgar et al. 2011 ) and sequences with ≥97% identity were assigned to the same operational taxonomic units (OTUs) using UPARSE  (Edgar 2013).
Metagenome sequencing The previous DNA samples were sent to Novogene Ltd. (Hong Kong, China)  for shotgun metagenome sequencing. DNA sample was fragmented by sonication to a size of 350 bp and the fragmented DNA was used for libraries preparation using NEB Next Ultra DNA Library Prep Kit, following manufacturer's recommendations. Libraries were sequenced on an Illumina NovaSeq platform (Illumina, San Diego, CA) as 2 × 150 bp paired-end reads. The raw sequencing reads were processed by Novogene Ldt. (Hong Kong, China)  for quality-trimming, adapter removal, and contaminant-filtering. The trimmed reads were assembled into scaffolds using MEGAHIT  (Li et al. 2015). Scaffolds (≥500 bp) were used for open reading frame (ORF) prediction by MetaGeneMark  (Zhu et al. 2010). KEGG, EggNog, and CAZy databases were used for functional annotation.
Additionally, BLASTp from the NCBI website (blast. ncbi. nlm. nih. gov/ Blast. cgi) for functional and taxonomic annotation. SignalP5. 0 was employed for prediction of secreted proteins (referred as extracellular proteins) (Almagro  Armenteros et al. 2019). Sequences obtained are deposited under the Bioproject accession number PRJNA874531 in the NCBI database.
The gradient program began with 97% B, then ramped to 89% B at 12 min, for decreased to 80% B in 8 min, and to 30% in 5 min returned to the initial conditions and kept constant for 3 min. The flow-rate was 0. 500 mL/min and injection volume was 5 μL. All compound responses were measured in ESIand calibrated externally. The ESI Gas Temp is 200 °C, Vcap -3000V, Drying Gaz was set at 10 L/min, and Nebulizer at 30 psig. Fragmentor was set at 100 V. HRMS spectrum was registered at 2 Hz in the mass range of 60 to 1100 m/z with internal calibration. MassHunter software was used for data processing. The isotopic ions abundances were extracted from the extracted ions chromatogram calculate from the monoisotopic ions [M-H] -or [M-COO] -peak. The average of both values was used for the analysis.
2021). Raw data were analyzed using PEAKS Studio (Bioinformatics Solutions Inc. , Waterloo, Canada) for determining the community composition and MetaProSIP (OpenMS, University of Tubingen, Germany)  (Sachsenberg et al. 2015)  integrated into the KNIME 4. 0. 1 analytics platform as described recently  (Lawson et al. 2021 ) was used to determine 13 C stable isotope incorporation into the proteome. The most abundant 13 C enriched isotope peak cluster (compared to the native peptide peak) was used to represent the labeling ratio of each time point. All peptide spectra were matched against a protein database generated from predicted open reading frames from the total metagenomic assembly. Proteomic raw data are deposited under the project PXD036522 in the ProteomeXchange Consortium.
S1. Although glycogen accumulating organisms (GAOs) are typically seen in EBPR systems, they were not present in this microbial community. FISH results showed a large predominance of "Ca. Accumulibacter" relative to Dechloromonas. FISH was not quantified exactly but the ratio of "Ca. Accumulibacter" to Dechloromonas was far higher than suggested by 16S rRNA amplicon sequencing, while other eubacteria had a very low fraction in the community (Fig. 2B ). Both populations of "Ca. Accumulibacter" and Dechloromonas have been described as PAO  (Goel et al. 2012 ). After the 13 C labeling experiment, the bacterial community composition was analyzed and no changes were observed. Together with the reactor performance described in Fig. 1 , the molecular data collectively demonstrate that a high PAO enrichment was achieved.
Recently, there has been significant interest in applying machine-learning (ML) techniques to the automated analysis of X-ray scattering experiments, due to the increasing speed and size at which datasets are generated. ML-based analysis presents an important opportunity to establish a closed-loop feedback system, enabling monitoring and real-time decision-making based on online data analysis. In this study, the incorporation of a combined one-dimensional convolutional neural network (CNN) and multilayer perceptron that is trained to extract physical thin-film parameters (thickness, density, roughness) and capable of taking into account prior knowledge is described. ML-based online analysis results are processed in a closed-loop workflow for X-ray reflectometry (XRR), using the growth of organic thin films as an example.
Introduction X-ray user facilities are amongst the largest scientific data producers in the world  (Yanxon et al. , 2023). While experiments performed at these facilities cover an extensive range of multi-disciplinary sciences, they typically share a common data generation pattern, namely precisely positioning a specimen in the path of the X-ray beam and recording data (e. g. radiation scattered by the sample) captured by dedicated detectors (e. g. diffraction, imaging, spectroscopy) further downstream. Recent advances in accelerator development such as fourthgeneration synchrotron light sources  (Raimondi, 2016)  and innovative detector technology (e. g. higher acquisition rates and larger area detector dimensions) lead to continuously increasing data volume of these datasets that are more and more difficult to handle.
This process is generally encouraged as the value of freely available, complete, augmented and documented FAIR datasets  (Wilkinson et al. , 2016; Scheffler et al. , 2022)  is recognized. These datasets are specifically valuable for ML activities and enable novel, previously impossible experiments that fully utilize modern infrastructure. This paper introduces an approach for integrating real-time X-ray scattering and diffraction data analysis into a closedloop control system that actively adjusts sample environment parameters. This approach unlocks exciting possibilities for conducting experiments using synchrotron radiation that unveil new insights into the underlying physics. While several approaches focusing on automatic and optimized data acquisition based on, for example, Gaussian processes defining an acquisition function have been reported  (Teixeira Parente et al.
, 2023; McDannald et al. , 2022; Kusne et al. , 2020; Yager et al. , 2023; Noack et al. , 2021) , in this study we describe a feedback mechanism relying on actual online data analysis, extracting physical parameters on-the-fly to control an in situ experiment. We demonstrate the seamless integration of user-developed ML code with the beamline control infrastructure, enabling real-time data analysis and integrated archiving of the analyzed results improving the dataset quality with respect to FAIR principles. Additionally, focusing on reflectometry as a case study, we provide a concise overview of a ML-based approach for predicting thin-film parameters in both singlelayer and multilayer structures. This use case convincingly demonstrates the main advantages of using ML in this context. The ML approach gives reliable fit results both for simple two-to three-layer models as well as for complex multilayer models in the millisecond regime.
Distributed online data analysis and closed-loop beamline integration We identified different 'hooks' that typically exist in a modern beamline environment to achieve this inter-process communication between the beamline control software and user-supplied data analysis code, focusing on various levels of integration and portability between different light sources targeting universal concepts to make user-developed ML analysis available from the beamline control system. Furthermore, we aim at increasing the reusability of the produced datasets by enriching facility-produced datasets with user-provided real-time data analysis results (data + metadata), working towards datasets in facility data catalogs that are FAIR 'immediately' after acquisition without manually revisiting the dataset. Modern beamline control systems, such as BLISS at the ESRF  (Guijarro et al.
, 2018)  and Bluesky  (Allan et al. , 2019) , offer dedicated frameworks to access data in the beamline environment, e. g. through 'publishing' the data through an integrated in-memory database or via direct access to data-and event-streams produced by the acquisition process. Evidently, these are hooks that could be used to integrate online data analysis (see the example in Section SI-3 of the supporting information); however, these are usually tightly coupled to a specific facility and typically introduce a number of critical dependencies into the user-supplied code. Another relatively simple option for external users to handle and test prior to the experiment is to integrate on the level of the SCADA (supervisory control and data acquisition) used at the respective facility. The two most spread systems in this field are TANGO controls (Go ¨tz et al.
, 2022; see also https://www. tango-controls. org) and EPICS (https://epicscontrols. org/). In the context of this study, we evaluated the integration via TANGO that is used at a sizable fraction of, mostly European, synchrotron facilities such as ESRF, DESY, SOLEIL, MAX IV and ALBA, but also in the broader scientific community such as the square kilometre array telescope. Data transfer from the beamline control system via the available SCADA system forms part of the most common operations performed on the control system level and thus does not induce any additional software dependencies for the beamline control software environment. To our knowledge this study represents a first attempt to combine ML/AI and TANGO. Further, there is also the possibility to rely on workflow engines offered by facilities; for example, at the ESRF documented by De Nolf et al.
(2022). However, care must be taken with respect to the online analysis capabilities of these systems since they inherently rely on queue systems (job schedulers for batch processing) and thus may introduce additional delays under heavy load. When it comes specifically to ML/AI pipelines, there is also potential to rely on standardized solutions that specifically fit the needs of handling larger ML models (e. g. NVIDIA Triton Inference Server) and thus abstract even beyond the specificity of beamline environments. Splitting beamline control and ML-based online data analysis in terms of infrastructure makes sense also when looking at the different hardware requirements of the two processes. While the beamline control process links to beamline instrumentation, it is physically tied to hardware that is available on the beamline itself.