dataesr/scientific-paragraphs-categorization huggingface dataset:
Author Contributions: C. J. P. S. , investigation, formal analysis, and writing-original draft. S. A. M. , N. Q. and P. S. P. P. , investigation, formal analysis, and validation. C. M. (Carla Mayer), investigation. A. M. and C. M. (Claudio Migliaresi), methodology and investigation. M. J. C. , investigation. C. H. E. , conceptualization, methodology, validation, and resources. E. R. B. , conceptualization, methodology, validation, and writing-review and editing. M. v. G. , conceptualization, methodology, validation, writing-review and editing, and supervision. All authors have read and agreed to the published version of the manuscript. Funding: C. H. E. 's research is partly funded by the John and Posy Krehbiel Professorship in Orthopedics. This work was supported by the Province of Limburg, Limburg Invests in its Knowledge Economy (LINK).
Behavioral emotionality measurement Three behavioral tests (i. e. , EPM, NSF and ST) were used to measure components of animal behavioral emotionality. Z-score methodology was used to investigate the potential of combining results within and across the different behavior tests for depressive/anxious-like behaviors and investigate the treatment effects in the Cort model. The emotionality-related data was normalized as previously described  (Guilloux et al. , 2011; Mekiri et al. , 2017). Briefly, z scores are standardized scores (by the group mean and group standard deviation). They indicate how many standard deviations (σ) an observation (x) is above or below the mean of a control group (µ). z X -μ σ Z scores for behavioral measures were first averaged within the test, and then across the test for equal weighting of the three tests comprising the final emotionality score.
Data processing and bioinformatics analysis Peak lists were generated as mzXML files using the converter MSConvert (ProteoWizard). A database search was performed using X!TandemPipeline software developed by PAPPSO facility (version 3. 4. 3; http://pappso. inra. fr/bioinfo/xtandempipeline/. 3; http://pappso. inra. fr/bioinfo/xtandempipeline/) (Langella et al. , 2017)  with search parameters as followed: enzymatic cleavage by trypsin digestion with one possible miscleavage, fixed carbamidomethylation modification on cysteine and variable oxidation on methionine; precursor mass tolerance of ± 10 ppm and fragment mass tolerance of 0. 5 Da. Several databases were used: the Uniprot KB/SwissProt Mus musculus database (24,977 entries, version January 2017) and a homemade contaminant database (trypsin, keratine, etc.
This quantification relies on the more of a particular protein is present in a sample, the more MS spectra are detected for peptides of that protein. Statistical analysis was performed using MassChroqR package developed by PAPPSO team (http://pappso. inra. fr/bioinfo/ masschroq/) (R version 3. 3. 2). A generalized linear mixed model (GLM) with a Poisson distribution was applied. This model suits in the case of a counting like SC. The principal component analysis was obtained by simulating the kernel densities from group's means and variances assuming bivariate normal distributions. This distribution was generated using protein abundances as variables. Hierarchical bivariate clustering was performed using Euclidean distances and unweighted pair group averages as the aggregation method. All data analyses and graphical representations were performed using the R package MassChroq.
Objectives Compare costs associated with all-cause healthcare resource use (HCRU), stroke/systemic thromboembolism (STE) and major bleedings (MB) between patients with non-valvular atrial fibrillation (NVAF) initiating apixaban or other oral anticoagulants (OACs). Methods We performed a retrospective cohort study using the French healthcare claims database, including NVAF patients between 2014/01/01 and 2016/12/31, followed until 2016/12/31. We used 4 sub-cohorts of OAC-naive patients, respectively initiating apixaban, dabigatran, rivaroxaban or VKAs. We matched patients initiating apixaban with patients initiating each other OACs using 1:n propensity score matching. All-cause HCRU and event-related costs by OAC treatment were estimated and compared between matched patients using generalised-linear models with gamma-distribution and two-part models.
Study design and data sources We conducted a retrospective cohort study from the French National Health System healthcare claims database (Système National des Données de Santé, SNDS), which contains anonymous individual information on patients' sociodemographic characteristics, non-hospital reimbursed healthcare expenditures (e. g. , drug dispensing, visits to physicians), and all hospital discharge summaries, including associated costs. Although no clinical, paraclinical, or biological data are recorded in the SNDS, the outcome events of interest for our study (STE and major bleedings) are available  [15].
As gamma models are defined for positive values only, two-part models were used for items with no strictly positive costs and to account for the non-negligible part of the patient population without any event: the first part modeled the occurrence of the cost/event using a logistic regression, and the second part modeled the costs related to each HCRU/event, conditional on the HCRU/event occurrence, using a GLM with gamma distribution and log-link function. For all types of costs, we used the modified Park test to confirm that the data followed the gamma distribution. All statistical analyses were performed using SAS (SAS Institute, North Carolina, US), version 9. 4.
Results may slightly differ because of differences in patient populations: indeed, some US studies were performed in elderly, or not fully representative populations due to the specific nature of US claims databases. For instance, in elderly populations, apixaban was associated with significantly lower hospitalization-related costs compared with other OACs (VKAs or other DOACs). Apixaban showed significantly lower all-cause HCRU costs and major bleeding-related medical costs  [19]. In another study performed in the US Department of Defense population, overall, all-cause HCRU costs and event-related costs were significantly lower in patients initiating apixaban than in patients initiating rivaroxaban; and event-related costs were significantly lower in patients initiating apixaban than in those initiating VKAs or dabigatran  [11].
Consequently, our results would have been stronger if we would have indexed all costs on a specific year. As this study was requested by French health authorities, the associated protocol as well as the data collection and extraction have been validated by public institutions, ensuring the excellent quality of the data used  [20]. Results are based on a large claims database, ensuring very high representativeness (> 98% of the population covered) and including comprehensive information on treatments and use of reimbursed healthcare resources. In addition, as the French covering system is universal, the data covers all types of populations, regardless of their age, social condition, or economic resources. Studies based on claims also provide Fig. 5  Comparison of costs (in €, PPPM) associated to allcause HCRU, stroke/systemic embolism, major bleedings and any NVAF event over the follow-up period between apixaban and rivaroxaban matched cohorts Fig.
6  Comparison of costs (in €, PPPM) associated to allcause HCRU, stroke/systemic embolism, major bleedings and any NVAF event over the follow-up period between apixaban and dabigatran matched cohorts large study populations with high statistical power and high-quality data not impacted by study conduct. Finally, contrary to other claims databases, the French claims database provides detailed information on different types of hospitalizations (distinguishing MCO, HAD, SSR) and types of physicians visited (public vs. private), allowing this study to provide crucial information to the payers on which type of costs are driving management costs in patients with NVAF. However, the SNDS database only provides information on drug delivery, i. e. , not on patients' actual use, and no direct information about the daily prescribed dosages is available.
The SNDS also does not contain data on drugs dispensed during hospital stays (except for very costly medication); however, the associated costs are considered in the hospitalization cost. Finally, as no diagnoses are associated to emergency visits, hospital-related costs may be underestimated, but this should be limited, as complications of NVAF often require in-patient hospitalizations. As limited clinical information was available in the database, it was not possible to compare the distribution of risk factors between each cohort compared (e. g. , smoking status, obesity, alcohol consumption, clearance of creatinine, chronic kidney disease). Matching using a large number of covariates in the PS improved control of confounding as these variables could collectively be proxies for non-collected factors.
increase the spatial, temporal, and taxonomic coverage of monitoring programmes. They also allow new questions to be asked about insect population dynamics, phenology, and biotic interactions (Box 1). At the same time, these technologies come with their own sets of limitations, and are in parallel development in different projects and countries. To ensure efficient progress, there is a need for large-scale collaboration to develop international databases and metadata standards, and open communication on hardware and software development, to ensure adherence to FAIR data principles. This review aims to evaluate four emerging tools and technologies (computer vision, acoustic sensors, radar, and molecular methods) for insect monitoring, and outline ways to harness their potential. We review (i) the state of the art of these technologies, their advantages, current limitations, and future potential, (ii) how the data collected using these technologies can adhere to modern standards of data curation and transparency, (iii) how citizens can participate in projects using these new technologies, and (iv) the potential for integration and synergies among technologies.
Expanding reference libraries could be done by developing apps for local experts and citizens to submit training image data of species from different angles  [32]. However, undescribed species will remain a challenge, since by definition they will not be present in the training dataset. An approach called open-set classification may to some extent solve this problem, but remains to be tested for insect monitoring  [7]. Another challenge is camera power consumption and data transfer. This difficulty may be reduced by using solar panels (Box 2), but this increases logistical challenges and risk of theft. Edge computing (local data processing) enables classification directly on the device (e. g. , the Seek app by iNaturalist, https://www. inaturalist. org/pages/seek_app) with the potential for realtime monitoring.
Glossary Acoustic sensor: a device that detects and records sounds. Artificial intelligence (AI): scientific field of computer science involved in (partially) reproducing human skillssuch as thinking, acting, or interpreting datawith computational algorithms. Often used as a synonym for machine learning (q. v. ). Citizen science: the participation of the general public in scientific processes. Participation can occur at different levels of involvement and expertise, and at different stages of the process (study design, data collection and/or interpretation). Computer vision: scientific field of computer science that develops algorithms for analysing image or video data to produce descriptions of the depicted content, for example, a categorisation via numerical representations. Convolutional neural networks (CNNs): group of machine learning methods that require large datasets for training, often used for image analysis and pattern recognition, where each network consists of connected nodes and layers that process input data to obtain desired outputs.
Edge computing: data processing done on the site of data collection, instead of transferring the data to a central location for processing and analysis. eDNA (environmental DNA): DNA obtained from environmental samples such as water, soil, air, faeces, and stomach contents. This term is sometimes also used to refer to DNA derived from the preservative of insect bulk samples (e. g. , ethanol). FAIR data: data that are findable, accessible, interoperable, and reusable. Machine learning: scientific field of computer science for developing predictive algorithms that learn patterns in data to make predictions. The algorithms learn from example training data rather than being programmed explicitly. Metabarcoding: identification of taxa from mixed samples using high-throughput DNA sequencing of one or multiple genes (DNA barcodes).
Some citizen science platforms already use crowd-sourced expert identification for validation of observation (e. g. , iNaturalist); however, manual validation is unable to keep pace with the rapidly growing number of submissions. Technologies could help by using active learning AI algorithms that select only a subset of images for human validation for (i) groundtruthing or training of the AI classifier, and (ii) where the AI classifier was most uncertain in its decision. Citizens with taxonomic expertise may also help to compile the training datasets by identifying species on the basis of images or sounds. New technologies have the potential to increase the accessibility and diversity of entomological citizen science. For instance, citizen science activities could be extended to volunteers with expertise in joint software development and data visualisation.
The primary outputs of metabarcoding are amplicon sequence variants (ASVs) and/or operational taxonomic units (OTUs), depending on the bioinformatics used. To link with existing species knowledge, these units must be mapped to reference databases, such as the Barcode of Life Data System (BOLD) or GenBank. BOLD now genetic data on 214 390 publicly available insect species, which, nevertheless, represents only about 4% of the expected 5. 5 million species of insects on earth  [1]. When using these reference libraries, sequencing errors, synonymy, misidentifications, and missing species can cause misclassifications. Nevertheless, international, national, and taxon-specific initiatives are improving the taxonomic coverage of such reference libraries  [71, 81].
Open science Insect data collected by traditional monitoring schemes or derived from museum specimens are becoming increasingly accessible via data discovery platforms such as the Global Biodiversity Information Facility (www. GBIF. org). However, for data collected using the discussed technologies, the norms and practices of open science, as well as standards for data publishing, have yet to evolve and to be agreed upon. To make these new technologies open and reproducible, both the underlying data and processing steps must be FAIR: findable, accessible, interoperable, and reusable  [82]. Data openness for DNA-based technologies has been fostered through International Nucleotide Sequence Database Collaboration (www. INSDC. org) data portals such as the Sequence Read Archive (https://submit.
Acknowledgments The workshop that this review resulted from, and part of the open access costs were funded by the  Volkswagen Stiftung  to D. E. B. and R. v. K. R. v. K. , A. B. , and D. E. B. also acknowledge support by iDiv funded by the  German Research Foundation  ( DFG-FZT 118 ,  202548816 ), in particular the sMon project for A. B. and D. E. B. A. B. also acknowledges funding by  EU   Horizon 2020 Coordination and Support Action  [Grant agreement No. 101003553  (EuropaBON)]. The  Leibniz Institute  for.
BLUPs values were used for correlation estimation and QTL detection. As several traits did not follow a normal distribution, phenotypic correlations among traits were calculated using the non-parametric Spearman correlation coefficient. Histograms, box-plots and Spearman correlation tests were performed using XLSTAT. The different trait distributions were tested for normality using Shapiro-Wilk  32 (1965)  and JarqueBera tests  33. Linkage maps. New LG16 linkage maps were constructed for each parent for detailed analysis of the structure of this chromosome using JoinMap 4. 1 software 34. These maps were generated using a larger number of progenies than those used to develop the reference genetic map. Indeed, when the reference genetic map was constructed, several progenies from populations A and B presented a missing data rate exceeding the set threshold (< 40% per individual) and could not be included in the analysis  19.
QTL mapping. The QTL analyses were performed on the previously published reference genetic map and generated from these populations. This map spanned 2613. 5 cM and contained 1579 SNP markers  19. The two population phenotyping datasets obtained for the two years combined with the D. alata reference map were used to identify regions associated with different tuber quality traits. QTL analysis was performed for each population separately on the best linear unbiased prediction (BLUP) values of clone effects using Map QTL version 6  35. QTLs were initially detected using the interval mapping (IM) approach. Then the loci with the highest LOD scores were used as cofactors in analyses using the composite interval mapping (MQM) model to detect additional QTLs that might be masked by major QTLs. Confidence intervals of QTL positions were determined as two-LOD support intervals.
Significance LOD score thresholds were calculated through permutation of 1000 iterations at the 0. 05 probability level of significance. QTL validation. QTL validation was performed on a diversity panel of 28 varieties. SNP genotyping data for these varieties was generated by Sharif et al. 36. A simple linear model that associates each phenotype with a SNP was tested using the Pearson correlation test and a 5% significance level. The Pearson R 2 coefficient of determination and the p-value (Pearson) of each SNP marker thus were determined. All SNPs included in the QTL confidence intervals were tested including those outside the closest ones. In addition, an ANOVA analysis was performed for each of the validated SNP markers to analyses whether the observed genotypic classes were significantly different at p < 0.
Identification of potential candidate genes. Putative genes located around the validated SNPs were identified by searching in the NCBI database, which contains all 35,078 genes that were anotated on Dioscorea rotunda reference genome  37. We found two candidate genes for trait tuber size, one near SNP 16. 1_23694121 (a cell division cycle 202 gene, a cofactor of the APC complex) and a second near SNPs 16. 1_22716126 and 16. 1_22746342 (a cell number regulator 6 gene). The APC complex is an E3 ubiquitin ligase and a critical regulator of cell cycle progression  38. While the family of CNR (cell number regulator) genes is known to affect plant and organ size  39. Regarding the presence of tuberous roots, a candidate gene was found near SNP 16. 1_21653548 (a Squamosa promoter binding like protein 8 gene).
COVID-19 is caused by SARS-CoV-2 which has so far affected more than 500 million people worldwide and killed over 6 million as of 1 May 2022. The approved emergency-use vaccines were lifesaving in such a devastating pandemic. Inflammation-related pathways have been well documented to be upregulated in the case of SARS-CoV-2 in rodents, non-human primates and human samples. We reanalysed a previously published dataset to understand if certain molecular components of inflammation could be higher in infected samples. Mechanistically, viroporins are important players in the life cycle of SARS-CoV-2 and are primary to its pathogenesis. We studied the two prominent viroporins of SARS-CoV-2 (i) Orf3a and (ii) envelope (E) protein from a sequence and structural point of view. Orf3a is a cation-selective viral ion channel which has been shown to disrupt the endosomal pathways.
Ion channelling activity is one such feature which encompasses viroporins. These viroporins' counterbalance host cellular responses range from opposite directional ion flow to downstream disruptions of the host cell signalling pathways  [23] [24] [25]. The ion channelling activity of SARS-CoV-2 is prominently maintained by two proteins: (i) E protein  [13, 25]  and (ii) Orf3a protein  [26, 27]. The structure of the pentameric E protein from SARS-CoV-2 has been elucidated by solidstate NMR (PDB id: 7K3G)  [26]  and homology modelling  [13]  which gives insights into its structure-function relationship and selectivity towards cations  [28]. Cryo-EM microscopy has given us insights into the dimeric structure of Orf3a (PDB id: 7KJR) which has two well-defined aqueous channels fit for cation-selective channelling  [29].
The viroporins of SARS-CoV-2 have been shown to induce inflammatory responses via the TLR pathway for E protein  [36]  and NLRP3 inflammasome activation for Orf3a  [37, 38]. SARS-CoV-2 infection led to a wide spectrum of inflammatory events and responses which are as follows: (i) severe pneumonia, (ii) eosinopenia, (iii) severe lymphopenia, (iv) lung tissue damage, (v) cytokine storm leading to acute respiratory distress syndrome (ARDS), and ultimately multiorgan failure  [39]. Pro-inflammatory cytokines have been widely analysed from COVID-19 infected patients which have been identified as the precursor to this unforeseen severity and pathogenicity of SARS-CoV-2  [40] [41] [42]. We found significant upregulation in immunomodulatory genes such as CD40, IFNL1, IFNL2, IFNL3, IL12A, IL33, IL6 and NFkB1 after analysing the RNA sequencing dataset from Katsura et al.
Transcriptomic Analysis Transcriptomic data were reanalysed from the following source: Katsura et al. , 2020 Cell Stem Cell  [43]. The data are derived from whole genome RNA sequencing from a modular alveolo-sphere culture system of human alveolar type 2 cells/pneumocytes derived from primary lung tissue  [43]. Data were downloaded from Gene Expression Omnibus library (ID: GSE152586) and pre-processing of the fastq files was performed. Details of data extraction and experimental procedures are available in the original publication: Katsura et al. , 2020 Cell Stem Cell  [43]. The DESeq2 package in R BioConductor (http://www. bioconductor. org/packages/release/bioc/html/DESeq2. html, accessed on 3 May 2022) was used to analyse the data. The normalised data were used for visualisation and differential analysis of the count data  [44].
The DESeq2 data class consists of a count matrix with rows corresponding to genes and columns denoting experimental samples (control and COVID-19). For dimensional reduction and outlier identification, we performed a principal component analysis (PCA) on the DESeq2 data class of count reads. The details of the DESeq2 pipeline are discussed in detail in  Love et al. , 2014  Genome Biology  [44]. Briefly, DESeq2 package models the data counts on the count matrix using a Gamma-Poisson distribution with mean (normalised concentration of cDNA fragments from the gene in a sample). The size factors are determined by the median-of-ratios method. For each gene, a generalised linear model (GLM), which returns overall expression strength of the gene, log 2 of the fold change (LFC) between the two groups compared.