dataesr/scientific-paragraphs-categorization huggingface dataset:
Study setting and location Data were collected from August to September 2018 in Moc Chau, a mountainous district of Son La province, Vietnam. As of 2009, the population in Moc Chau was about 152,172 (42), and the district has a total natural land area of 108,166 hectares  (43). By 2014, about 33% of communes in Moc Chau have met the national standard for healthcare with 3,5 doctors/10,000 people, 13 patients/10,000 people, and one district general hospital with a total bed capacity of 150 (42). We first prepared a list of Moc Chau districts by the number of households. After that 200 households from the list were randomly selected to participate in this study. Finally, one family member was randomly selected from each household by computer-based software to involve in a face-to-face interview and complete the questionnaire.
Measure and instruments A 10-min face-to-face interview was performed. Data collectors were researchers that were well-trained to use the questionniare. Private rooms in the commune health stations were used for the interviews to maintain privacy and confidentiality. We also informed participants about the merits and drawbacks of participating in this particular study. If agreed to enroll in the study, respondents would sign written informed consent. Respondents were informed that their decision would not influence their capacity to use health services. A structured questionnaire with four main components was designed in Vietnamese and utilized to collect data in this study, including (1) Socioeconomic characteristics; (2) Health status and psychological stress; (3) Health care utilization; and (4) Selfmedication behavior characteristics.
Factors associated with higher tendency of self-purchase were ethnic group, arthritis condition, distance from home to health facility and traveling conditions. Our results showed the insignificant difference between the rate of people who used health services utilization and who did not use (58. 9 and 41. 1%, respectively). This difference, though not substantial, was recorded through self-report and contributed to health system performance assessment considering the existing gap in data globally and especially in developing countries  (54, 55). Moreover, the utilization of health services was not only associated with service availability but also with the preference, trust, and socioeconomic characteristics of the community  (56, 57). Health care-seeking behavior also varied between demographics and income levels.
Short tandem repeat signature The identity of each PDX was periodically authenticated by profiling STRs. Patient tumors and corresponding PDX DNA samples were subjected to STR using PowerPlex ® 16 HS System (Promega, ref DC2101) that amplifies 16 STR loci and the amelogenin gender-determining marker, according to manufacturer's instructions. PDXs passed authenticity when >80% match in alleles was obtained. PCR products were separated by capillary electrophoresis on ABI prism 3500 and results were analyzed using GeneMapper software (v5).
Sequence alignment and variant calling Base calling was conducted using the Real-Time Analysis software sequence pipeline (2. 7. 7) from Illumina with default parameters. In order to remove contaminating mouse reads, raw reads were classified depending on their species of origin (graft or host) using the Xenome tool  (23). Raw human reads were aligned on human hg38 genome using the Burrows-Wheeler Aligner (BWA) tool  (24). Duplicated reads were removed using Sambamba  (25). Variant calling of somatic single nucleotide variants (SNVs) and small insertions/deletions (indels) was performed using the Broad Institute's GATK MuTect2 tool (2. 0, -max_alt_alleles_in_normal_ count=2; -max_alt_allele_in_normal_fraction=0. 04) against a Panel of Normals (PON) comprising 107 normal samples sequenced by IntegraGen following the same protocol  (26).
Ensembl' Variant Effect Predictor  (27)  (VEP, release 95) was used to annotate variants with respect to functional consequences (type of mutation and prediction of the functional impact on the protein by SIFT 5. 2. 2 and PolyPhen 2. 2. 2) and frequencies in public (dbSNP151, 1000 Genomes phase 3, gnomAD 17-02-28, COSMIC v86) and in-house databases.
Somatic variant analysis To keep/detect only reliable somatic variants, the following post-filtering steps were applied: -QSS score ≥ 20 (the average base quality of variant bases) -coverage ≥10 in the tumor -variant allele fraction in the tumor (VAF T )≥0. 05 with ≥5 mutated reads -gnomAD_Global_AF < 1e-5 -IntegraGen proprietary database AF < 0. 01.
Copy-number analysis using genotype data Two complementary approaches were used to reconstruct the copy-number profiles of the tumors. * Copy-number analysis using genotype data We identified germline Single-Nucleotide Polymorphisms (SNPs) in each sample, and we calculated the coverage log-ratio (LRR) and B allele frequency (BAF) at each SNP site. Genomic profiles were divided into homogeneous segments by applying the circular binary segmentation algorithm, as implemented in the Bioconductor package DNAcopy, to both LRR and BAF values. We then used the Genome Alteration Print (GAP) method to determine the ploidy of each sample, the level of contamination with normal cells and the allele-specific copy number of each segment  (29). Ploidy was estimated as the median copy-number across the genome.
Chromosome aberrations were then defined using empirically determined thresholds as follows: gain, copy number > ploidy + 0. 5; loss, copy number < ploidy -0. 5. We considered a segment to have undergone Loss of Heterozygosity (LOH) when the copy number of the minor allele was equal to 0. * Copy-number analysis based on coverage We calculated the coverage log ratio in each bait of the exon capture kit between the tumor and a panel of normal. Log-ratio profiles were then smoothed using the circular binary segmentation algorithm as implemented in the Bioconductor package DNAcopy. The most frequent smoothed value was the zero level of each sample. Segments with a smoothed log ratio above zero + 0. 3 or below zero -0. 3 were considered to have gains and deletions, respectively. High-level amplification and homozygous deletion thresholds were defined as the mean +/-5 s.
First analysis Quality of reads was assessed for each sample using FastQC (V. 0. 11. 4; http://www. bioinformatics. babraham. ac. uk/projects/ fastqc/). RNA-SeQC provided key measures of data quality. These metrics were shown within Reporting and included yield, alignment, and duplication rates, rRNA content, regions of alignment (exon, intron and intragenic). Alignment was performed by STAR (https://github. com/alexdobin/STAR). The duplicate reads (e. g. , paired-end reads in which the insert DNA molecules have identical start and end locations in the Human genome) were removed using Sambamba tools (https:// github. com/biod/sambamba).
Second analysis Variant calling for the identification of SNVs (Single Nucleotide Variations) and small insertions/deletions (up to 20bp) was performed via the Broad Institute's GATK Haplotype Caller GVCF tool. Ensembl' VEP (Variant Effect Predictor, Release) program was used to process variants for further annotation. This tool annotates variants, determines the effect on relevant transcripts and proteins, and predicts the functional consequences of variants. This included considering data available in gnomAD, the 1000 Genomes Project and the Kaviar databases. Moreover, an in-house database enabled to filter out sequencing artefacts.
Third analysis Five bioinformatics algorithms for pathogenicity were used to predict the functional, molecular, and phenotypic consequences of coding and non-coding SNPs. This included DANN, FATHMM, MutationTaster, SIFT and Polyphen. The clinical and pathological significance was also added from the ClinVar database. Other information reported included quality score, homozygote/ heterozygote status, count of variant allele reads, and presence of the variant in the COSMIC and OncoKB databases. RegulomeDB was used to annotate SNPs in known and predicted regulatory elements in the intergenic regions.
Fusion transcript analysis To detect fusion-genes candidates in RNA-seq data, FusionCatcher (start with fastq files) and STAR-Fusion (start with alignment files) were used to achieve higher detection efficiency  (30, 31). These were run using default configurations. In silico validation of a list of fusion transcript predictions was then performed using FusionInspector, a component of the Trinity Cancer Transcriptome Analysis Toolkit (CTAT)  (32). FusionInspector assisted in fusion transcript discovery by performing a supervised analysis of fusion predictions, attempting to recover and re-score evidence for such predictions. Fusion-gene candidates were annotated according to several databases of known fusion genes found in healthy samples (known false positives) including the 1000 Genome Project, ChimerDB2, GTEx and cancer databases such as COSMIC, 18Cancers.
Internationally consistent definition of metropolitan areas Across 27 European OECD-member countries, 687 metropolitan areas are observed. Importantly, the definition of metropolitan areas is designed to be consistent across countries. The boundaries of these metropolitan areas are defined using the EU-OECD definition of Functional Urban Areas, whose details are provided by  Dijkstra et al. (2019). According to this definition, a Functional Urban Area is composed of two main parts. The first is a 'city' or 'urban core', which consists of a set of contiguous one-km 2 grid cells of high-population density (at least 1500 people per km 2 ) with a total population of at least 50,000 inhabitants (Fig. 1A ). Grid cells and their relative population count are obtained from GeoStat data sourced from Eurostat.
Ground truth reference on urban land use Urban land use maps that serve as ground truth reference in training the deep learning model and evaluating its classification performance are sourced from the Copernicus Urban Atlas database. These data offer a comprehensive classification of urban land use, as well as further land cover, as measured by granular vectors  (Montero et al. , 2014). In specific, the vectors observe any patches of land use that are at least 0. 25 ha in size as per 2018, which is the latest year of coverage of these maps, across the study area. Details about accuracy and uncertainty are given in Copernicus (2021). Furthermore, land use is defined on an objectbasis rather than at the scale of individual pixels. This adopts the Fig. 1. Consistent delineation of metropolitan areas, as illustrated here for Graz, Austria, which comprise of a city, or cities, and a commuting zone (adapted from  Dijkstra et al.
Fusion of Sentinel-1 and Sentinel-2 satellite imagery The imagery observed in this study come from the Sentinel-1 and Sentinel-2 satellite constellations operated by the European Commission and the European Space Agency. Following other studies of urban land use or land cover the radar-based and multi-spectral imagery from these satellites is fused to increase the variation in information that can be observed in the classification model  (Li et al. , 2020; Zhang et al. , 2021; Zhu et al. , 2022). In specific, selected Sentinel-1 and Sentinel-2 image bands area are fused by stacking these bands such that the tensors that serve as input data for the deep learning model are composed of 8 bands, as discussed in more detail next. Sentinel-2's multi-spectral optical imagery is this study's base source of remotely sensed information.
Acknowledgements We would like to extend our gratitude to  Rudiger Ahrend ,  Mikaela Rambali ,  Katia Karousakis ,  Ivan Hascic , and  Mikael Maes  at the  OECD  as well as  Lewis Dijkstra  and  Joachim Maes  at the  European Commission  for discussions and their insights. This project was produced within the  OECD-EU project on Global State of Cities , financially supported by the  European Commission, Directorate-General for Regional and Urban Policy. The views expressed herein are those of the authors and do not necessarily correspond to the views of the OECD or of its member countries.
The hBCI is also used for the categorization and identification of arithmetic activities and mental workloads. In  [24]  authors have processed the EEG-fNIRS signals for an effective identification of the mental workload tasks. They used the Gramian Angular Summation Field (GASF) for conversion of time series data in image format. Onward these images are processed with Convolutional Neural Networks (CNNs) for the identification of different mental workloads. In  [29] , authors have identified the mental stress by using the Deep Belief Network (DBN). The changes occur in EEG and fNIRS signals are detected to identify between rest and mental stress states. The timeseries data is directly processed by the proposed DBN for classification. In  [30] , authors used the Point-Biserial Correlation Coefficient (PBCC) for features extraction.
The dataset In this research, a dataset from 18 healthy participants (eight women and ten men) with an average age between (21. 3, 26. 3) years is considered for evaluating the performance of devised method  [19]. The EEG and fNIRS signals are respectively acquired at the sampling rates of 2048 Hz and 13. 3 Hz respectively. Details about the placement of EEG and fNIRS sensors on the brain are detailed in  [19]. The data is categorized in three classes namely, the ''Idle State" (IS), ''Mental Arithmetic" (MA) and ''Motor Imagery" (MI). We focused on four classification problems: i) MI vs. MA (P1), ii) MI vs. IS (P3), iii). MA vs. IS (P3) and iv). MI vs. MA vs. IS (P4). We hypothesized that these classifications findings would be suitable for implementation of the BCIs. Data size: It contains EEG + fNIRS signals from 18 participants.
Fig. 3  -The Fitness curve of GA. In this study, in order to get a higher accuracy of classification, we employed stacking technique as an ensemble learning model for our system. In addition, we employed several classifiers to train on the same dataset, which gives high performance of the system when using stacking with this case. This technique improves classification accuracy over individual classifiers, due to the usage of multiple classification models that have different abilities in solving the classification issues. In this case, we employed several classifiers such as SVM, KNN, ''Naı ¨ve Bayes" and ''random forest" (see Table  8  to find the parameters of each classifier) to obtain the probability of each class which is called meta-features. After that, we fed these meta-features to a meta-classifier  [37]  to perform the final classification.
Results Our model is developed using the ''MATLAB" 2019a. The ''Microsoft Windows 10" Pro 64-bit is the ''operating system". The proposed ensemble learning model is trained on ''Intel Core i5-7300HQ 2. 5 GHz" with 16 GB of memory. In total 22 non-linear attributes were retrieved from the incoming signals (Table  1 ). The graphical depiction of the 10 selected attributes, contributed in attaining the best accuracy, is shown in Fig. 4. The data is categorized in three classes namely, the ''Idle State" (IS), ''Mental Arithmetic" (MA) and ''Motor Imagery" (MI). We focused on four classification problems: i) MI vs. MA (P1), ii) MI vs. IS (P3), iii). MA vs. IS (P3) and iv). MI vs. MA vs. IS (P4). We hypothesized that these classifications findings would be suitable for implementation of the BCIs.
However, multiclassification is more complex and also needs more processing time. In addition, sometimes the normal cases may affect with several noise as discussed in  [39] , which may affect the classification accuracy in some cases. Therefore, we recommend using binary classification (MI Vs. MA) for the implementation of our BCI system, which is the most widely used classification problem in previous works. Fig. 5  shows that the MI EEG signal is more homogeneously and periodic compared to the MA EEG signal, which is more random and inconsistent. It proves that the extracted nonlinear features can be efficaciously used for the categorization of MI and MA tasks. Comparing the BCI to state-of-the-art performance is a tedious task since the BCI domain has been well explored. The combination of datasets, pre-processing, feature extraction, dimension reduction, and classification approaches used in earlier research is the fundamental reason for their success.
To make it more comprehensive, Table  12  compares the proposed framework to state-of-the-art methods utilizing hBCI datasets. In  [22]  the authors have employed the wavelet generated coefficients with slope information for the features mining. Onward, the PCA is used for dimension reduction and the selected feature set is classified with the LDA classifier. The highest reported accuracy is of 86. 00%. In  [23] , the phase-space reconstruction-based Hurst exponents are mined and used to prepare the feature set. The highest categorization accuracy of 81. 20% is reported for the case of SVM classifier. The GASF is used for converting the time series data in images  [24]. Onward, these images are processed by the CNN classifier for an automated categorization of mental tasks. The highest categorization accuracy of 89.
2% is reported while identifying between the mental arithmetic and baseline tasks. The Engagement ratio plus wavelet coherence-based features are mined in  [31]. Onward, the sLDA is sued for the categorization purpose while securing a classification accuracy of 87. 60%. The above presented facts confirmed that compared with previously presented methods, the proposed method outperforms. The findings suggest that the pre-processing, nonlinear feature selection, dimension reduction, and stacking ensemble learning technique proposed has a significant impact on the system's accuracy and performance. Ensemble classifiers have the benefit of being able to adapt to a specific training dataset. By incorporating the event-driven tools, it may be feasible to improve the computational performance of the described technique  [40].
Indeed, this term was necessary to control for the additional mortality of Dmel larvae caused by bleaching eggs in the axenic treatment (Fig. S3b ). The presence of this term in the analysis ensures the significant effect of axeny was not an artifact due to reduced Dmel larvae numbers. The model also included a block term (treated as random). Differences among treatments were tested with independent contrasts and pairwise Student's tests. All analyses were carried out with the software JMP 14. 0 (SAS Institute Inc. 2018). Throughout the manuscript, stars in figures indicate the significance of onetailed statistical tests: * p < 0. 05; ** p < 0. 01; ***p < 0. 001; n. s. p > 0. 05. All data is available on the Zenodo platform under the reference: https:// doi. org/ 10. 5281/ zenodo. 39707 37.
Funding This work received financial support from  French ANR ' s " Investissements d'avenir"  ( ANR-10-LABX-0001-01 ),  Labex Agro ,  CIVC ,  BIVB ;  ANR   SWING  ( ANR-16-CE02-0015 ). INRA 's department " Santé des Plantes et Environnement ";  Eranet LEAP Agri project Pest Free Fruit  ( ANR-18-LEAP-0006-02 ).
Participants This study was approved by the ethics commission Vaud (protocol CER-VD #120/12) and registered at  Clinical-Trials. gov (NCT02666547). Each participant signed a written informed consent form. Inclusion criteria consisted of biopsy-proven esophageal or EGJ cancer, age ≤ 85 years, Karnofsky index ≥ 80%, and signed consent form. Exclusion criteria consisted of pregnancy, lactation period, and age < 18 years. TNMp or TNMyp (yp denotes the pathological stage after neoadjuvant therapy) stages and DFS (times from the date of scans to the first date of disease recurrence or death) were recorded, according to the criteria of the seventh edition of the Cancer Staging Manual of the American Joint Committee on Cancer. Recurrence was defined as the appearance of one or more new lesions confirmed by imaging or by cytologic or pathological evaluation.
PET/CT acquisitions All the enrolled participants underwent 68 Ga-NODAGA-RGD and 18 F-FDG PET/CT using a single dedicated PET/CT scanner (Discovery 690 TOF; GE Healthcare, Waukesha, WI, USA). The same procedure for both 68 Ga-NODAGA-RGD and 18 F-FDG PET/ CT was used for data acquisition. A pregnancy test was done before the scan in women of childbearing age. Acquisitions were performed with 3 min per bed position. PET data were reconstructed using OSEM (3 iterations, 16 subsets). Vertex to mid-thigh unenhanced CT was acquired for attenuation correction (120 kV, 60 mA, 0. 8 s/rotation, pitch 0. 9, CTDI 4. 54 mGy). The axial resolution was full width at half maximum of 4. 7 mm, at 1 cm from the center of the field of view. The mean positron ranges of 18 F and of 68 Ga are 0. 6 mm and 2. 9 mm, respectively.
Image analysis PET images were analyzed based on standardized uptake value (SUV) measurements in both data sets ( 68 Ga-NODAGA-RGD and 18 F-FDG), using a workstation equipped with dedicated analysis software (Syngo. via, VB30, Siemens Healthineers, Erlangen, Germany). Scans were evaluated by two experienced nuclear medicine physicians (JOP and MD), blinded to participant's clinical and histologic information. Any difference of opinion was resolved by a consensus. Through visual analysis, positive uptake was identified as areas of focal increase in contrast to the surrounding normal tissue. For the calculation of maximum SUV (SUV max ) and of metabolic tumor volumes (MTV), circular regions of interest were drawn around tumor lesions with focally increased uptakes in transaxial slices and automatically adapted to 3-D volumes of interest (VOI) delineated around lesions using 60% SUV max thresholds.
Lesion-to-background ratios were computed. For the definition of the background, 10-mm-radius circular volumes of interest were drawn in the right atrium (blood pool activity), and the SUV mean was recorded. The locations of the maximum uptake pixel within primary sites were visually identified in both data sets ( 68 Ga-NODAGA-RGD and 18 F-FDG), and the distance in millimeter (mm) between them was measured.
Statistical analysis The statistical analysis was performed using R version 4. 0. 3 (R Foundation for Statistical Computing, Vienna, Austria). We assessed the distribution of data with the Shapiro-Wilk test. Continuous parametric variables were expressed as mean ± SD. Nonparametric data were presented as median [interquartile range] and compared using the Mann-Whitney U test. Spearman correlation analysis (ρ) was used to evaluate potential interrelation between tracers uptake parameters. Cox's proportional hazards regression analysis was used to assess the effects of covariates on survival times. A p value of < 0. 05 was considered statistically significant.
Funding Dr. Dietz  was supported by  Research Fellowship Awards  from the  Société Française de Radiologie, Paris, France , and from the  Agence Régionale de Santé Auvergne-Rhone-Alpes, Lyon, France. The authors are indebted to  Swiss Heart Foundation (Bern, Switzerland)  for their financial support in developing the 68 Ga-NODAGA-RGD radiopharmaceutical.
Phenotypic and Genotypic Records The phenotypic and genotypic data were previously collected by the BAIF Development Research Foundation (https: / / baif. org. in), a nongovernmental organization established in 1967 with a mission to enhance livelihoods of Indian rural families, using smallholder dairy production as one of the tools to achieve this objective. We used monthly test day (TD) milk records (L/d) collected from Indian smallholder dairy farms. Existing records from 4,655 cows with 106,109 monthly TD milk records and 689,750 autosomal imputed SNPs (Al  Kal-aldeh et al. , 2021)  were used here for our analyses. Cows were crossbreds between local indigenous Bos indicus cattle and exotic dairy breeds, mainly Holstein/Friesian and Jersey  (Strucken et al. , 2021). The average breed composition of these animals was estimated to be 0.