dataesr/scientific-paragraphs-categorization huggingface dataset:
Automated segmentation could extend the use of CT for sarcopenia diagnosis to the clinical setting. ABACS (Automatic Body composition Analyzer using Computed tomography image Segmentation software) is a module of SliceOmatic (ABACS-SliceOmatic). 9  It contains an automated muscle segmentation solution, which is based on the a priori shape of skeletal muscle. 10, 11 3] [14]  Paris et al. developed an artificial intelligence (AI) solution termed Automated Muscle and Adipose Tissue Composition Analysis (AutoMATiCA), which they made freely available. 14 The reliability of ABACS-SliceOmatic is controversial  15, 16  and AutoMAT-iCA has not been externally validated. Few researchers have addressed the question of the validity of automated segmentation tools. The present study aimed to independently compare the reliability of ABACS-SliceOMatic and AutoMATiCA in a retrospective cohort of individuals hospitalized in a French University Hospital.
Ethics statement The ODIASP study was performed in accordance with the recommendations of the Helsinki Declaration. Ethical approval was obtained on 18 August 2021 (CECIC Rhône-Alpes-Auvergne, Clermont-Ferrand, IRB 5891). According to current French law for retrospective studies of clinical data, participants were individually informed that their data could be used for research (MR-004 CNIL reference methodology). People who objected and those with missing updated contact details were excluded. All the data was retrospectively collected in the electronic health records and de-identified.
Study cohort All consecutive individuals (1) admitted to Grenoble University Hospital (CHU Grenoble Alpes) between January and May 2018, (2) with abdominal CT images that included a complete image of the third lumbar vertebra and sagittal reconstruction, and (3) with their height recorded in the electronical health records, were retrospectively included. Exclusion criteria were (1) people under 19 years, (2) creatinine level above the upper limit, and (3) images of insufficient quality for manual skeletal muscle segmentation, for example, incomplete slices with muscle area missing, metal artefacts, psoas hematoma, or motion. Data was initially collected for another research project assessing the relationship between low level of serum creatinine and sarcopenia. For this previous project, to limit inclusion of subjects with advanced kidney disease, those with serum creatinine level beyond the upper limit (84 and 104 µmol/L for female and male respectively) were excluded.
Automated methods: ABACS-SliceOmatic and AutoMATiCA Skeletal muscle was automatically segmented with (1) ABACS (Voronoi Health Analytics Inc. ), a module of the SliceOmatic software solution (Tomovision, version 5. 0) and (  2 ) the AutoMATiCA software solution. ABACS-SliceOmatic is based on a priori information about muscle shape at the third lumbar vertebra level. More precisely, it is based on a finite element method deformable model that incorporates an a priori shaped model within the template-based segmentation framework. It was developed to avoid mislabeling parts of the neighboring organs that have an HU density within the range of muscle tissue HU density. 10, 11 utoMATiCA is based on a deep learning algorithm and, more precisely, on convolutional Neural Networks that have proven their effectiveness in the field of medical imaging.
Statistical analysis Descriptive data is presented as median and interquartile range (IQR). The Dice similarity coefficient (DSC) was used to measure the overlap between segmentation with the manual reference method and segmentation with each of the automated solutions. A value of 1 indicates perfect agreement, whereas 0 indicates no overlap. 21 Median values and the 5th percentile are presented for the DSC. The median DSC values for each solution were compared with the Mann-Whitney U test. The DSC value for the manual segmentation of 50 slices by both observers was also calculated. Inter-observer agreement was determined with the intra-class correlation coefficients two-way random effect model (ICCs) and 95% confidence intervals. A value < 0. 5 indicates poor reliability, a value between 0.
Reliability of the AutoMATiCA AI-automated software As far as we know, our study is the first to compare an AI-automated software solution to ABACS-SliceOmatic in an unselected cohort. The DSC values for the inter-observer comparison and between manual and automated segmentation with AutoMATiCA were in the same order of magnitude. Moreover, the DSC value for the external validation (0. 969) was also close to the value for the internal validation (0. 983) suggesting good generalizability of AutoMATiCA. 17, 23 AutoMATiCA is faster than both manual segmentation and ABACS-SliceOmatic (33 s vs. 29 and 19 min respectively for 50 slices), which is an important feature for clinical practice. These findings support the use of machine learning for automated skeletal muscle segmentation in clinical practice.
15 In contrast, Cespedes Feliciano et al. concluded that the reliability of automated segmentation with ABACS-SliceOmatic was similar to that of manual segmentation. 16 One explanation for this discrepancy is the difference in quality of the two CT-scan databases. We did not exclude individuals with photon starvation artefacts relating to the body pressing against the sides of the scanner, whereas Cespedes Feliciano et al. did. Moreover, cases with anatomic abnormalities were estimated at <2% in the study by Cespedes Feliciano et al. We believe that the proportion was much higher in our study. Finally, some of the CT-scans in our study were carried out in emergency situations, which often result in lower image quality.
Study limitations Several potential limitations need to be considered. First, there is no gold standard available in clinical practice for the assessment of muscle mass, even if CT is considered a reference standard. 8 In this study, we compared automated segmentation to manual segmentation. We demonstrated that the DSC for two observers was high, which suggests that the manual method was of good quality and reliable. Furthermore, we double-checked the segmentation of the 676 CT slices to improve the quality of "the ground truth. "  23 Alternatively, a phantom study could have been performed. Second, we collected data and CT images retrospectively. Nevertheless, we included all consecutive individuals with abdominal CT images and sagittal reconstruction during a 5-month period. Thus, we limited selection bias and ensured that the database was representative of hospitalized individuals.
001 and LAR318) ins the USA and Nigeria were also shared. These shared genotypes were almost identical genetically, with few SNPs between samples (Table  S4 ). The minimum spanning network (MSN) showed a similar relationship among these genotypes. The European cluster displayed an inner position with genetically closer MLGs, while the Brazilian and North American clusters were placed on the edges of the network, likely due to their separate evolutionary histories. To estimate genetic diversity, we computed the number of segregating sites within genetic groups. Genetic diversity was twice as high in the North American clade compared to the Brazilian and European clades (Table  1 ). Based on all the SNPs from the full data set, the North American clade showed almost all sites segregating when all isolates belonging to this clade were considered.
Monosporic cultures were stored at 280°C from suspension conidia in 7. 5% skim milk (Merck, Darmstadt, Germany) mixed on silica gel (S/0730/53; Thermo Fisher Scientific, Waltham, MA, USA)  (128). Isolates were verified as C. graminicola by inspection of colony morphology, spore size, and shape, and by sequencing of the internal transcribed spacer (ITS) region of rDNA using the primers ITS4 and ITS5  (129) , following the PCR amplification conditions described by Rech et al. (97). The amplified ITS fragments were purified using NucleoSpin gel and PCR Clean-Up kits (Macherey-Nagel, Düren, Germany) and sequenced using Sanger technology by the University of Salamanca (Spain). Species identity was confirmed by BLAST searches performed against GenBank (NCBI). All materials used in this study were used in accordance with the rights and obligations of the recipient as described in the International Treaty on Plant Genetic Resources for Food and Agriculture adopted by the Food and Agriculture Organization (FAO) on 3 November 2001.
DNA extraction, library preparation, and sequencing. Mycelia from monosporic cultures were incubated on an orbital shaker in potato dextrose broth (PDB) (Difco, Becton, Dickinson and Company, Franklin Lakes, NJ, USA) for 3 days at 25°C and 150 rpm under continuous light  (52). Genomic DNA was extracted using DNeasy Plant Mini Kits (Qiagen Inc. , Valencia, CA, USA) and quantified using a Qubit 3. 0 fluorimeter (Invitrogen, Waltham, MA, USA). DNA for C. graminicola isolates used for whole-genome sequencing was extracted using the cetyltrimethylammonium-bromide (CTAB) protocol adapted from Baek and Kernerley  (130). New DNA sequence data sets were generated from 97 C. graminicola isolates using either RAD-seq or WGS; sequences for an additional 11 isolates were also included in the analyses (97) (Tables  S1  and S2 ).
A total of 87 isolates were genotyped with RAD-seq by Floragenex Inc. (Beaverton, OR, USA). Briefly, RAD-seq libraries with sample-specific barcode sequences were constructed from DNA digested with the restriction enzyme PstI, then single-end sequenced (1 Â 100 bp) on one lane using a TrueSeq PE150 kit (Illumina, Inc. , San Diego, CA, USA) on an Illumina HiSeq 2000 instrument. WGS data were obtained from seven C. graminicola isolates (CBS252. 59, F-64330-2, F-64330-7, F-40330-1, SW-8046-2, CRO-I-35, CR-10370-50) from libraries prepared using a Nextera XT DNA kit (Illumina, Inc. ) and sequenced on an Illumina HiSeq 4000 (2 Â 150 bp). WGS data were obtained from six C. graminicola isolates (ARG-2700-2, CA-CHAT-1, CA-N8H, CRO-I-41, P-7565-072-1, SW-8046-1) from paired-end libraries prepared using an Illumina TruSeq DNA Nano kit and sequenced on an Illumina MiSeq (2 Â 300 bp).
8S rRNA gene, and internal transcribed spacer 2, complete sequence; and 28S rRNA gene, partial sequence). Matching contigs were also removed. The assembly completeness was assessed using Busco v. 3  (133)  and the sordariomyceta_odb9 lineage data set. Read mapping and SNP calling. The RAD-seq reads were inspected with FastQC v0. 11. 7 (Babraham Bioinformatics, Cambridge, UK) to confirm quality and were demultiplexed and quality-filtered with the process_radtags program in Stacks v. 2. 10 (134). The demultiplexed reads were trimmed using Trimmomatic v0. 3. 8 (131) using the parameters 'leading:15 trailing:15 slidingwindow:4:15 minlen:36' and aligned to the C. graminicola reference genome M1. 001 (NCBI accession no. SAMN02953757) using BWA-MEM v0. 7. 8  (135)  with the default settings. Genetic variants were identified with FreeBayes v0.
Analysis of molecular variance was performed with the hypothesis of no genetic differentiation using the package Poppr v2. 8. 6  (139). Weir and Cockerham's genetic differentiation index (F ST ) was calculated with the R package hierfstat v0. 5-11 using the WC84 method  (140). We used three clustering methods to examine the C. graminicola population structure. First, we used a principal-component analysis with a priori geographical knowledge from sampling (by continent). This analysis was conducted in R with the package adegenet v2. 0. 1 (141) using the dudi. pca function. Second, we performed a discriminate analysis of principal components with adegenet v2. 0. 1  (141). The find. cluster function was used to identify the most likely grouping in the data based on the Bayesian information criterion calculated for 1 to 10 clusters.
Third, to confirm the pattern of population subdivision inferred by the PCA and DAPC, we used the fast maximum-likelihood genetic clustering method snapclust (142) in adegenet v2. 0. 1  (141). The function snapclust. choosek was used as an initial step to determine the optimal number of clusters (K) based on the BIC. Additionally, we implemented a neighbor network using the software SplitsTree v4  (143)  to visualize phylogenetic signals, indicated by the presence of reticulations in the network, from a distance matrix with default parameters. The number of multilocus genotypes (MLGs) was calculated using the R package Poppr v2. 8. 6  (139). We used the mgl. filter function to determine the true number of MLGs from a genetic distance obtained by the diss. dist function using the threshold determined in the cutoff_predictor tool.
ACKNOWLEDGMENTS We thank  Maria Elena Bayón Sánchez  for her assistance in the isolation of samples and pathogenicity tests, and  Lisa J. Vaillancourt  and the  Genetic Resources Center (NARO)  for providing some isolates. This research was supported by grants  AGL2015-66362-R ,  RTI2018-093611-B-100 , and  PID2021-125349NB-100 , funded by the  Ministry of Science and Innovation (MCIN) of Spain   AEI/10. 13039/501100011033 ; and by grant  SA165U13  funded by the  Junta de Castilla y Léon. F. R. was supported by grant  FJC2020-043351-I  financed by  MCIN/AEI /10. 13039/501100011033  and by the  European Union NextGenerationEU/PRTR. R. B. was supported by the postdoctoral program of USAL (Program II). F. B. C. -F. was supported by grant  BES-2016-078373 , funded by  MCIN/AEI/10. 13039/501100011033.
S. B. was supported by a fellowship program from the regional government of Castilla y León. W. B. was supported by a productivity fellowship from the  Conselho Nacional de Desenvolvimento Científico e Tecnológico  ( CNPq   307855/2019-8 ). Genome sequencing was funded by the  UNC Microbiome Core , which is funded in part by the  Center for Gastrointestinal Biology and Disease  ( CGIBD P30 DK034987 ) and the  UNC Nutrition Obesity Research Center  ( NORC P30 DK056350 ). P. D. E. was partially supported by the  USDA National Institute of Food and Federal Appropriations  under Project  PEN04660  and accession no. 1016474. We declare that we have no competing interests. S. A. S. and M. R. T. conceived the study and designed the project; S. A. S. performed fungal isolation and DNA extraction; M.
Marketing research on online reviews has attempted to understand the antecedents and consequences of review manipulation. Building on the elaboration likelihood model (ELM), this study deploys a rare dataset that allows distinguishing credible from less credible (and likely fake) online reviews by means of the online review posting policy adopted by the movie review website Naver. com. We use text analysis entailing word embedding and topic modelling techniques such as Latent Dirichlet Allocation, to capture content depth across different types of online reviews (credible vs manipulated). Furthermore, we explore how differences in the textual content of credible vs manipulated online reviews affect customer purchase decisions. Our results highlight that less credible reviews tend to contain more superficial information compared to more credible reviews, and that different levels of source credibility lead to distinctively different impacts of online reviews on box office revenue.
The central route is based on relatively high degrees of thinking, while the peripheral route occurs with relatively little thinking. This study explores how individuals process information in online reviews based on the levels of source credibility determined by fake reviews that they choose to evaluate. To distinguish between credible and less credible (and likely fake) online reviews, we examine the extent to which the design of the review posting policy on a website can discourage or encourage manipulation. The empirical setting is similar to that used by  Mayzlin et al. (2014) , which exploited the differences in review website designs to identify unbiased (credible) vs fake (less credible) reviews. More specifically, this study deploys a rare dataset that allows distinguishing credible and less credible (and likely fake) online reviews by means of the online review posting policy adopted by a movie online review website, namely Naver.
Data description To explore the textual characteristics and consequences of fake reviews, we collected online reviews for movies from Naver. com, the leading portal website in South Korea. The search engine market share of Naver. com in 2020 is about 63 % in Korea. 2  We selected the top 50 financially successful movies in South Korea every year from 2017 to 2019. Then, we collected movie information, including online reviews written in Korean for 150 movies from Naver. com. Each online review consists of review ratings and textual content. We excluded movies that had run for 5 weeks in movie theaters to analyze the impact of the promotional periods in the movie industry. So, the final dataset includes 139 movies. Based on the online review data, we merged daily box office revenue data from the Korean Film Council.
Summary statistics of weekly online reviews To merge individual online review data with daily box office data, we aggregate the individual online review data into daily data. The average daily rating of netizen-type reviews was 7. 50, while that of viewer-type reviews was 8. 44. The average of daily posted netizen-type reviews was 47. 11, but that of daily posted viewer-type reviews was 137. 53. The review volume of netizen-type considerably exceeds that of viewer-type and this might be due to the fact that Naver. com allows anyone to post their online reviews for movies without purchasing movie tickets on the site and those reviews are categorized as netizentype reviews. According to prior literature  (Ma et al. , 2019) , review manipulation concentrates on the first two weeks after movie release in South Korea.
To capture this aspect, we examine the semantic similarity between online reviews and movie summaries to distinguish the characteristics of manipulated reviews. To examine the similarity, we use textual embedding techniques. First, we apply a pre-trained word embedding technique, the fastText word embedding technique  (Facebook I, 2016; Bojanowski et al. , 2017; Joulin et al. , 2016)  for Korean to vectorize all words. fastText is a neural network-based learning model that is relatively novel in the marketing field  (Alantari et al. , 2021). It was originally created by Facebook's AI research lab, and it allowed us to build a machine-learning algorithm to obtain a vector representation of tokens in the text for diverse languages, including Korean. By using fastText word embedding, words in both reviews and movie summaries are converted into 300-dimensional vectors.
Therefore, we apply the LDA model to extract different movie review topics in the online reviews analyzed. The LDA model assumes that there are a limited number of latent topics that appear across textual documents that are represented by online reviews in our dataset. Each document is a mixture of topics, and each topic is considered as a discrete probabilistic distribution of multiple words. In other words, there exists a dictionary of words for all documents, and the distribution of words used characterizes each topic. In particular, the words with higher probability are used to define the latent topics. Topic modeling is applied to each review as a document. For each document, we constructed a dictionary reporting how many words and how many times such words appear. Then, we identified the words occurring in each topic and their relative weights.
2019; Tuia et al. 2022). Since the beginning, machine learning approaches, and in particular deep learning models, have held the promise to solve this issue. Recent works have confirmed their power: for instance, deep-learning models developed by different research teams  (Willi et al. 2019; Tabak et al. 2019; Whytock et al. 2021 ) have obtained > 0. 90 recall and precision for a number of mammal species in North American ecosystems, African savannas and tropical forests. The recurring leading approach, as assessed in recent iWildcam competitions  (Beery et al. 2021) , consists in two steps: (step 1) detecting animals, humans and vehicles and filtering out empty images using a robust detection model such as MegaDetector (https://github. com/microsoft/CameraTraps/;  Beery et al. 2019 ) and (step 2) using a CNN classification model to identify the species in the images when an animal was detected.
Although this can lead to highly accurate models when tested on data collected in the same sites or applied to similar systems, such models may fall short from generalizing to other fauna or contexts  (Beery et al. 2018). For the European fauna, we believe there is potential in aggregating multiple small datasets from a large number of partners to build sufficiently large training, validation and test datasets to develop efficient classification models. The pros and cons of a cross-partner image aggregation strategy, and its ability to lead to successful models, have yet to be investigated. Recent initiatives such as Wildlife Insights (www. wildlifeinsights. org) or Agouti (www. agouti. eu), online platforms to manage and classify camera trap images, have embraced this strategy. Naturally, each initiative will have its own terms of participation, which may not be acceptable for certain institutional partners.
2023). Therefore, we advocate that releases of species-classification models for the ecological community must be accompanied by a reliable performance analysis to help ecologists understand how well the proposed classification model is expected to perform in practical situations (e. g. on new camera trap images). We report here on the DeepFaune initiative (https://www. deepfaune. cnrs. fr). This initiative aims at (1) aggregating camera trap images from many French institutions or research groups to create a collective and large-scale dataset, (2) developing a deep-learning-based species classification model, using a clearly explained methodology to circumvent the issues mentioned previously, (3) performing a rigorous performance analysis on out-of-sample datasets to assess how well our model performs on novel images from sites that are not included in the training set, (4) releasing the model to the community as a software component that can be integrated in other tools and (5) providing a free, open-source, user-friendly and efficient software for practitioners to run the identification pipeline on self-hosted images with standard personal computers.
Original dataset We gathered and sorted more than 2 million annotated pictures and 20 thousand annotated videos, after removal of corrupted or other problematic files. These pictures and videos included either one of the animal species originally identified by the partners, a human, a vehicle, or were empty (less than 1%), i. e. had no animal, human or vehicle visible. In some instances, annotations had been made at a higher taxonomic level than the species level (e. g. bird, rodent). The amount of data provided by each partner varied a lot, ranging from 20 videos to hundreds of thousands of images. Many partners shared data from camera traps set to take a series of images for a single detection event. We thereafter defined these consecutive images as 'sequences', considering that two images taken within 10s, at the same site, belonged to the same sequence.