dataesr/scientific-paragraphs-categorization huggingface dataset:
The region between these markers must be partially sex-linked, as it shows intermediate properties between the fully sex-linked and pseudoautosomal genes. In particular, the genes in this partially sex-linked region show full sex-linkage in the genetic cross df108, and also show evidence of occasional recombination in male meiosis as some of the wild females contain the alleles that should be Y-linked based on segregation in df108 family. Interestingly, the genes proximally to contig6406 (mapped to 63 cM  [47] ) contained zero or one Y-SNPs in females (Table  3  and Table S1 ), perhaps because of lower recombination rate compared to the genes from 63. 4 to 63. 6 cM, or because of the action of gene conversion between the Xand Y-linked gametologs. To compare our results with the previous work  [33, 44, [49] [50] [51] , we identified 14 markers (listed in Table  2 ) in common between our dataset and the markers in the most detailed map delimiting the PAR published by others (Figure  1  in  [49] ).
The patterns of polymorphism in the pseudoautosomal genes are consistent with balancing selection maintaining excess of intermediate frequency polymorphisms at the genes in the vicinity of the NRY/PAR-boundary. This is evidenced by elevated F st and the difference in polymorphism in males and females, as well as the negative regression of Tajima's D with distance from the NRY/PAR-boundary (Figure  1C ). The latter result is consistent with the previous report of inflated Tajima's D in pseudoautosomal genes adjacent to the PAR-boundary  [49]. However, that study reported positive Tajima's D only in the two genes immediately adjacent to the PAR boundary, E559 and cs3297, while more distally located genes between E523 and E241 showed negative Tajima's D (Figure  2B  in  [49] ). Although we found no homologs for the markers E559 and cs3297 in our dataset, they are likely to be located in the fuzzy boundary region (proximally to contig16617) rather than in the PAR (Table  2 ).
IMPORTANCE On October 1, 2015, the US transitioned to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for recording diagnoses, symptoms, and procedures. It is unknown whether this transition was associated with changes in diagnostic category prevalence based on diagnosis classification systems commonly used for payment and quality reporting. OBJECTIVE To assess changes in diagnostic category prevalence associated with the ICD-10-CM transition. DESIGN, SETTING, AND PARTICIPANTS This interrupted time series analysis and cross-sectional study examined level and trend changes in diagnostic category prevalence associated with the ICD-10-CM transition and clinically reviewed a subset of diagnostic categories with changes of 20% or more. Data included insurance claim diagnoses from the IBM MarketScan Commercial Database from January 1, 2010, to December 31, 2017, for more than 18 million people aged 0 to 64 years with private insurance.
Data and Study Sample We used the IBM MarketScan Commercial Database from January 1, 2010, to December 31, 2017, including individuals ages 0 to 64 years enrolled in noncapitated commercial insurance plans with both medical and pharmacy benefits. The MarketScan database contains deidentified health care claims information from US employers, health plans, and hospitals. Data on all enrollment records and inpatient, outpatient, ancillary, and drug claims are collated and linked to individuals. The age and sex distribution of the eligible population varied little over the 96 months studied. Boston University's institutional review board exempted this study from review and informed consent owing to the use of deidentified data. Data were analyzed from December 1, 2018, to January 21, 2020.
11 e HHS Hierarchical Condition Categories (HHS-HCCs) system was developed for the Patient Protection and Affordable Care Act (ACA) marketplace risk adjustment payment model and is used to allocate funds among competing health plans. 12 The HHS-HCC system used 127 condition categories for the ACA population when it was implemented from 2014 through 2017, and its mappings were updated to accommodate ICD-10-CM diagnoses in 2015. 13 To ensure that recently added diagnostic codes were recognized, we applied the 2017 HHS-HCC software to the full range of study data for 2010 to 2017. For sensitivity analysis, we evaluated the effects of the HHS filtering logic and the 2015 HHS software with the original ICD-10-CM mappings.
Limitations This study has several limitations. First, we only examined the association of diagnostic category prevalence with the ICD-10-CM transition for individuals younger than 65 years who were privately insured by employer-sponsored insurance, a population mostly enrolled in relatively generous health plans. Coding patterns and discontinuities in frequencies of diagnostic categories may not generalize to individuals covered by other insurers (eg, the ACA marketplace, Medicare, and Medicaid). Many health care practitioners have been encouraged by their institutions to maximize the capture of patient complexity in their billing. However, our study data came from commercial plans whose payments are rarely risk adjusted for disease prevalence. Specifically, their payments do not generally rely on HHS-HCCs, reducing the explicit incentive to increase the prevalence of well-reimbursed HHS-HCC categories.
The combination of these data enabled us to compute the final size spectrum (Figure  2C ), that was used to estimate abundance, biomass and metabolic rates for each ESD size class, and then for the whole sample (sum of all the size classes) and for the total mesozooplankton (sum of the size classes C 200-300 , C 300-500 , C 500-1000 and C 1000-2000 ). For the FlowCAM analyses, the sample was concentrated in a given water volume. Then, an aliquot of each sample was analyzed using FlowCAM in auto-image mode. For the fraction N 100 F <200 , a 4X magnification and 300 µm flow cell were used and the analysis was carried out up to3000 counted particles. For the fraction N 100 F 200-1000 a 2X magnification and 800 µm flow cell were used and the analysis was carried out up to 1500 counted particles.
𝐴 = 𝑝 𝑎 × 𝑉 𝑐 𝑉 𝑎 × 𝑉 𝑠 Where A is the abundance (ind m -3 ); P a is the number of particles in the analyzed aliquot; V c is the given volume in the concentrated sample and V a is the volume of the analyzed aliquot and V s is the volume of sea water sampled by the zooplankton net (m 3 ). For the ZOOSCAN analyses, the sample was homogenized and split using a Motoda box until a minimum of 1000 particles were obtained. Then, for the digitalization, the subsample was placed on the glass slide of the ZOOSCAN and the organisms were manually separated using a wooden spike to avoid overlapping. After scanning, the images were processed with the ZooProcess using the image analysis software Image J. Particles were classified automatically into taxonomic categories. Then the classification was manually verified to ensure that every vignette is in the correct category.
Normalized biomass size spectrum The size spectra were computed for each station using combined FlowCAM and ZOOSCAN data, following  Suthers et al. (2006). Firstly, the data were classified in size categories of 0. 1 µm of ESD from 0. 2 to 2. 0 µm. Zooplankton biovolume (mm 3 ) was estimated for each category following the equation: 𝐵𝑖𝑜𝑣𝑜𝑙𝑢𝑚𝑒 = 1 6 × 𝜋 × (𝐸𝑆𝐷) 3 With ESD expressed in mm. The X-axis of the normalized biomass sizespectrum (NBSS) was calculated by dividing the biovolume by the abundance of each category and transformed into Log10. For the Y-axis, the biovolume of each category was divided by the difference in biovolume between two consecutive categories and transformed into Log10. NBSS slope and intercept were determined using linear regression model.
Data analysis Principal component analysis (PCA) was used to explore spatial patterns of the environmental variables temperature, salinity, dissolved oxygen, using mean values of the layer 0-300 m depth, plus the estimated MLD. Averaged Chl-a values from fluorescence sensor coupled with CTD were also included in this analysis. The data were first normalized and then analyzed using Primer 6. 0 software. A taxonomic group-station matrix with the abundance values was created and then square-root transformed to estimate station similarity using Bray Curtis similarity. The similarity matrix was then ordinated using Nonmetric Multidimenstional Scaling (NMDS). These analyses were performed using Primer 6. 0 software. Rank frequency diagrams (RFD) were created using the data from N 200 to see differences in taxonomic composition between the samples.
Acknowledgments This study is a contribution to the  PEACETIME  project (http://peacetime-project. org), a joint initiative of the MERMEX and ChArMEx components supported by  CNRS-INSU ,  IFREMER ,  CEA , and  Météo-France  as part of the programme MISTRALS coordinated by  INSU. The PEACETIME cruise (https://doi. org/10. 17600/17000300) was managed by C. Guieu https://doi. org/10. 5194/bg-2020-126Preprint. Discussion started: 4 May 2020 c Author(s)  2020. CC BY 4. 0 License.
Background: We aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data. Methods: We accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary endpoint was defined as scoring zero at diagnosis and higher than quartile 3 at 1 year after primary treatment completion on European Organization for Research and Treatment of Cancer quality-of-life questionnaires for Overall Fatigue and on the multidimensional questionnaire for Physical, Emotional, and Cognitive fatigue. First, we tested univariate associations of each endpoint with clinical variables and genome-wide variants. Then, using preselected clinical (false discovery rate < 0. 05) and genomic (P <.
Recently, preconditioned random forest regression (PRFR), proposed by Oh et al. (23)  as means to prioritize the SNPs with predictive benefits, led to discovery of SNP panels of high relevance to radiotherapy-related toxicities  (24). To address the limitations of the previous studies on fatigue in breast cancer survivors, we applied a machine learning approach on data from the Cancer Toxicity (CANTO) cohort, consisting of large prospective, longitudinal, clinical, patientreported outcomes and genomic data of survivors of early-stage breast cancer, to search the genome for a panel of fatigueassociated SNPs that could help predict severe fatigue 1 year after completion of primary breast cancer therapy and potentially suggest putative biological mechanisms of cancer-related fatigue.
Study Procedures The CANTO study (NCT01993498) is a prospective cohort study that enrolled 12 012 patients between 2012 and 2018. Patients were evaluated at diagnosis (baseline) and then for 5 years following completion of primary treatment, including surgery, adjuvant chemotherapy, or radiation therapy, whichever came last. For this study, data on diagnosis and 1 year after completion of primary treatment were used. Clinical data were prospectively collected by dedicated nurse practitioners. Socioeconomic characteristics and validated patientreported outcome data including European Organization for Research and Treatment of Cancer quality-of-life questionnaires (EORTC QLQ-C30 and EORTC-QLQ-FA12 [fatigue-specific module])  (11, 25) , Global Physical Activity Questionnaire-16, 12  and Hospital Anxiety and Depression Scale were also collected  (23).
Clinical Cohort We accessed clinical data from 5007 patients enrolled in CANTO between March 2012 and December 2014. The main exclusion criteria to define a study group for each fatigue domain included absence of cancer-directed surgery to include only patients treated with curative intent; death, secondary cancer, or breast cancer recurrence to focus on a population disease free; withdrawn consent; missing baseline or follow-up scores for each fatigue endpoint; and nonzero baseline scores for the respective fatigue domain, because we were interestedin isolating the fatigue events that developed after breast cancer diagnosis and therefore more likely associated with treatment (Figure  1 ). The resulting clinical sample sizes were 989, 763, 1274, and 2128 for Overall, Physical, Emotional, and Cognitive Fatigues, respectively (Supplementary Tables  1  and 2 , available online, detail cohort characteristics).
GWAS Data By July 2018, 3895 patients from the entire CANTO cohort were genotyped at study inclusion and had available information for 687 572 germline SNPs (Illumina InfiniumExome24 version 1. 1 and Illumina GSA24 v1. 0). Standard quality control  (14)  was applied, filtering 1) 68 individuals with high genetic similarity, non-European origin, and low X chromosome heterozygosity (<0. 15); and 2) 177 746 SNPs due to minor allele frequency less than 0. 01, missing rate greater than 0. 05, and Hardy-Weinberg Equilibrium P less than 10 -5. Finally, 2 patients with an SNP missing rate greater than 0. 05 were removed. Thus, 3825 patients with 509 826 SNPs passed the quality control. The genomic study cohort was defined by an overlap between the 3825 genotyped patients and the clinical overall cohorts as described above (N ¼ 2799).
Multivariate Modeling of Fatigue Using Genetic and Clinical Variables Using machine-learning techniques, predictive modeling on severe fatigue at 1 year after primary treatment completion was built based on patterns in patients' permutations of SNPs. To this end, a multivariable prediction model, based on PRFR methods, was built as described by Oh et al. (23). First, the data were randomly split into the training and validation setswith matching event rate and distribution for the clinical variables with statistically significant univariate associations (Table  2 ). The PRFR model was built and validated separately on these 2 disjoint subsets (a holdout approach) (28). To reduce modeling computational complexity, an independent screening  (29, 30)  was performed on the GWAS training data to filter likely irrelevant predictors: the SNPs with univariate correlation (P <.
For comparison, the enrichment analysis was also done using the initial SNP set with univariate correlation P less than. 001 without the VIM-based filtering. In addition, an interactome analysis searched for a network of genes that are connected through previously known interactions. MetaCore (Thompson Reuters, New York, NY) was used for the enrichment and interactome analyses. Analyses were performed using SAS (v. 9. 4) and R (v. 3. 6. 0) packages GenABEL  (21). All statistical tests were 2-sided. 1 ). No genome-wide significant SNPs (P < 5. 00 Â 10 -8 ) were found to be associated with any of the endpoints. There was no notable genomic inflation for any of the 4 endpoints (Supplementary Figure  2 , available online). These results were consistent for the genome-wide scan within the training subcohorts.
Discussion In this large multicentric, prospective, clinico-genomic longitudinal dataset of breast cancer survivors, we deployed machine learning techniques to investigate if high-dimensional genomic data could be used to build and validate a predictive model for the different known dimensions of fatigue. Although the ability of our models to identify clinic and genomic contributors of fatigue differed by fatigue domain, a group of SNPs and clinical variables was suggested to be associated with the cognitive domain. Cancer-related fatigue is known to be complex in etiology, with possibly many clinical, bio-behavioral, and genetic contributors  (1). Prior studies had several limitations. First, comprehensive integration of clinical, behavioral, and genetic information was lacking. Second, prior studies focused on candidate gene approaches mostly targeting proinflammatory cytokine activity that were largely not independently validated.
Fifth, the study included individuals only of European origin, and thus the results are generalizable only to this population. Last, we acknowledge that the methodology and results reported in this article are mainly exploratory. Particularly, it is important to stress that the predictive power of the genomic variants identified as associated with fatigue is not sufficient to justify their use in clinical decision-making. Importantly, although our data point at pathways that may be worthy of further investigation, external validation of our findings is needed. This study analyzed combined clinical and GWAS data from a large group of breast cancer survivors, suggesting a small genetic role for development of Cognitive Fatigue. This study broadens our understanding of cancer-related cognitive fatigue and informs further studies focused on identifying those patients with high risk of cognitive fatigue.
The most statistically significantly enriched gene groups from the genes associated with the SNPs with GWAS scan P less than. 001(unfiltered)and the subset of those SNPs with top 50% VIM for the Cognitive Fatigue endpoint, obtained using the bioinformatics tool GeneGO a (ANR), grant no ANR-10-COHO-0004. This research was supported by the Laboratory of Excellence GENMED (Medical Genomics) grant no. ANR-10-LABX-0013 managed by the ANR part of the Investment for the Future program. In addition, it also received support by National Institutes of Health/National Cancer Institute Cancer Center Support Grant P30 CA008748, U01 CA083178, R01 CA097397, R01 CA114236, P01 CA19656, and Breast Cancer Research Foundation grant BCRF-17-193. NotesRole of the funders: The study sponsors had no role in the design, collection, analysis, data interpretation, writing of the manuscript or decision to submit the manuscript.
Funding This study was supported by a  Career Catalyst Research grant  from  Susan G. Komen  ( CCR17483507 ) to  Ines Vaz-Luis ,  Odyssea  to  Ines Vaz-Luis , and  Fondation ARC  pour la recherche sur le cancer grant ( CANTO-WORK-programme labillis e)  to  Ines Vaz-Luis  and  Gwenn Menvielle ,  Foundation Gustave Roussy  to  Ines Vaz-Luis. This research was supported by the  French government  under the " Investment for the Future" program  managed by the  National Research.
Assignment of the individual parallel factor analysis components The contour plots of the 7 individual components determined by parallel factor analysis decomposition are presented in Fig. 1. Components C1 to C5 have been successfully matched in the OpenFluor database with similarity scores higher than 0. 95. Component C1 (λ exc = 300 and < 250) nm/λ em = 390 nm) is a widespread component generally found in marine and terrestrial aquatic environments and attributed to low molecular weight substances  (Osburn et al. 2011; Coble 2007). Component C2 (λ exc = 280 nm/λ em = 300, 340 nm) seems to gather the protein-like fluorophores with emission at 300 nm for tyrosine-like and the emission at 330-350 nm for tryptophane-like fluorophores  (Coble 2007; Parlanti et al. 2000). (1) R TMP = R 3DOM * × TMP = R a × Φ 3DOM * × TMP (2) R P = 0.
Component C5 (λ exc = 270 and 325 nm/λ em = 345 nm) was assigned to 2-naphthoxyacetic acid by comparison with an authentic sample (SI). 2-Naphthoxy-acetic acid is generally used for controlling of preharvest fruit drop on strawberries and tomatoes. The detection of this highly fluorescent anthropogenic compound in the cut-off meander water samples is explained by the presence of vegetable farming near the site. Component C6 (λ exc < 245 nm/λ em = 345 nm) did not find matches in OpenFluor-database. At last, C7 had strong analogies with component labelled SQ3 (λ exc = 265 and 345 nm/λ em = 412 nm) assigned once again to a reduced quinone-like component  (Cory and McKnight 2005).
The circuit board, contains an amplifier AD847J  37  , used as a trans-impedance amplifier. The acquired signals were then digitally filtered using IGOR Pro software. A 5000 Hz low pass filter was therefore employed (order 10). Transit time, amplitude of blockade, and phase shift are extracted from this signal as explained here after (see Fig. 6C )  38. The signal provided by the trans-impedance amplifier expresses: where VI is the amplitude of the signal, f s its frequency, φ s is the phase shift introduced by the presence of the cell (φ = 0 s when there is no cell within the restriction). To achieve the synchronous detection, V(t) I is multiplied by the reference signal: = π + φ V(t) V sin(2 f t ) (3) = π V(t) sin(2 f t) (4) r s We get: = φ - π V(t)V(t) V 2 (cos( ) cos(4 (f )t)) (5) I r I s s using a ten-order low-pass filter, the (2f ) s frequency component is highly alternated, and we get a filtered signal that can be approximated to: = φ V V 2 cos( ) (6) f I s finally from V f , one can estimate i) the blockade duration Δτ which corresponds to the time duration where the signal V f is attenuated by the presence of the cell within the restriction, ii) the blockade amplitude φ ˆ(1 cos( )) V 2 s I.
Acknowledgements The authors acknowledge the  Labex LaSIPS  ( ANR-10-LABX-0040-Lasips ), the  "Ecole Doctorale EOBE" of Université Paris-Saclay , the  "Institut d' Alembert" and the "CNRS"  for project fundings. The work was supported by the  Institut National de la Santé et de la Recherche Médicale (Inserm) , the  Institut National de la Transfusion Sanguine , the  Laboratory of Excellence GR-Ex , reference  ANR-11-LABX-0051 , and the  Laboratory of Excellence LaSIPS  ( ANR-10-LABX-0040-Lasips ). The labex GR-Ex is funded by the  IdEx program  " Investissements d'avenir " of the  French National Research Agency , reference  ANR-18-IDEX-0001. Maria Alejandra Lizarralde Iragorri  was funded by the  Ministère de l'Enseignement Supérieur et de la Recherche  ( Ecole Doctorale BioSPC ); she received financial support from:  Club du Globule Rouge et du Fer  and  Société Française d'Hématologie.
Elastase served as a positive control and sterile, deionized water served as a negative control. Data were plotted as relative fluorescence units versus time for each sample. Three individual experiments were performed, and all samples were performed in triplicate. Statistical analysis consisted of one-way ANOVA followed by Tukey's multiple comparisons post-test with significance set at p < 0. 05 Fig. 3  Hemolytic potential of vAh extracellular proteins secreted under biofilm and planktonic growth. The hemolytic ability of vAh secreted proteins was measured using channel catfish erythrocytes as the substrate. 2. 5 μg secreted proteins from each culture condition was incubated with 25 μl catfish blood diluted 1:10 in sterile PBS. at 30 °C with shaking. Sterile, deionized water served as positive control and sterile PBS served as a negative control.
Methods and materials Bacterial strain vAh strain ML09-119 was isolated from a diseased channel catfish from a MAS outbreak in a West Alabama aquaculture facility in 2009. Molecular characterization and genome sequencing of vAh ML09-119 have been performed  [19]  and the complete genome sequence deposited in GenBank (Accession CP005966). Aliquots of vAh ML09-119 were cryogenically stored in 10% glycerol freeze medium at -80 °C.
Histology Formalin-fixed tissues were paraffin-embedded and 4 μm sections were prepared and stained with hematoxylin and eosin according to standard methods  [65]. Slides were evaluated and photographed using an Olympus BX53 microscope with an Olympus UPlanFL N 20X/ 0. 50 objective, fitted with an Olympus DP26 digital camera, and captured with Olympus cellSens Entry Imagining software (Olympus Corporation). No further imaging processing or manipulation was performed on photomicrographs.