dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of recombination regions To determine the genomic differences between ST838 and ST6521 isolates, a maximum-likelihood phylogenetic tree was constructed with the 68 isolates belonging to these two ST, 52 from this study and 16 from ENA, using RaxML  [31]. The 9V-ST838 (ERR2303060) draft assembled genome was concatenated by mapping the contigs against 4041STDY6836167 (NZ_LS483448), which is a closed serotype 9V, ST156 genome using Mauve  [32]. The 9V-ST838 concatenated genome was used as a reference and the 68 reads belonging to lineage 3 isolates were mapped using Snippy 3. 1 (https:// github. com/tseemann/snippy). An assembly-free core-SNP alignment was done with Snippy's core module (snippy-core). The alignment was used to identify the recombinant regions through the evaluation of the density of base substitution using Gubbins v2.
Acknowledgements This study was supported by grants from  Fondo de Investigaciones Sanitarias de la Seguridad Social  ( PI14/00627 ;  PI18/00339 ,  INT 15/0186 ;  INT16/0117 ) and from  Centro de Investigación Biomédica en Red (CIBER) de Enfermedades Respiratorias (CIBERES)   CB06/06/0037 ), an initiative of the  Instituto de Salud Carlos III, Madrid ,  Spain  and  ONEIDA  project ( LISBOA-01-0145-FEDER-016417 ) co-funded by  FEEI -'Fundos Europeus Estruturais e de Investimento' from 'Programa Operacional Regional Lisboa 2020' and by national funds from FCT -'Fundação para a Ciência e a Tecnologia'. Financial support was also provided by the  European Regional Development Fund (ERDF). We thank  CERCA Programme /Generalitat de Catalunya  for institutional support.
In the general population analyses, 2013 to 2017 constitute the comparison period, while corresponding data, during 2019-2023, constitutes the intervention period. Each person included in the follow-up will contribute a record (row) in the research database for each year this person is included in one of the two cohorts. In the row, in addition to each individual's consecutive number, variables such as date, sex, age, cohort (1 = OST, 2 = NEP), intervention (0 = comparison, 1 = intervention), person time, date in the event of death, and status (alive/dead) at the end of each year, will be registered. In addition, based on public registers of the number of patients in OST, in the analyses it will be possible to control for fluctuations in OST availability (number of patients in methadone and buprenorphine treatment, respectively) in the region, as OST availability is likely to affect mortality rates; for example, based on national register data from the present setting, it has been shown that the risk of overdose mortality varies depending on OST status  [90].
The trainer should also be able to answer questions raised by participants. If not, the project leaders are to be contacted without delay. Interested patients will then provide a written informed consent where upon they will be asked to fill in baseline questionnaire. Individuals declining study participation will access the same OPEND model as the individuals entering the study, and no compensation is given to the study subjects. The informed consent provided by included patients in the cohort study includes the follow-up in register data (national patient register and regional hospital and ambulance registers) for a three-year period, and register follow-up is carried out during the full three-year period, unless the patients contacts the researchers in order to withdraw consent. In case a patient ceases to show up in OST or NEP facilities in the region, only the self-report data regarding witnessed overdoses and naloxone administration will be dropped from further data collection.
Measures In order to distinguish acute drug-related deaths from deaths caused by diseases and organ damage caused by long-term use, and data from emergency and prehospital healthcare, collecting data on mortality, cause of death, and toxicological raw data, information will be gathered from the following registers: The National Board of Health's Death Causes Register stores diagnostic information on mortality and causes of death such as international disease classification codes, ICD codes. The National drug index (linked to the National Board of Health's death cause register), containing information on deceased persons, where drug addiction or drug poisoning is mentioned on the cause of death certificate, either as underlying or contributing cause. The National registry data for F1 diagnosis (ICD-10 -diagnoses related to harmful use or substance dependence)  [89] , for enabling interconnection of national mortality data with the presence of such diagnosis, in order to correctly identify deaths in individuals with substance use history The Public Health Agency's research register Toxreg is based entirely on toxicological raw data, which is taken by routine when investigated by forensic medicine.
Ninety three per cent of all unnatural / violent deaths among persons under the age of 65 are examined medically in Sweden, which means that the majority of all acute drug-related deaths can be found in Toxreg (the Public Health Authority, 2015). Skåne's County medical and administrative registers for diagnoses and intervention codes indicating a condition of drug poisoning Data for all fatal and non-fatal overdose cases attended by ambulance in the region will be identified (including vital status of cases upon ambulance attendance) from a regional electronic documentation system for prehospital units in the Skåne County, where clinical data from ambulance-attended cases, and treatments given by ambulance staff, are documented. This data source covers the full intervention period in the study, but the electronic documentation system was introduced in early 2017, such that the comparison period can include this type of data only since then.
Data from emergency hospital departments at hospitals in the region, describing all cases of overdose events who attend hospital (identified through diagnostic codes). Regarding self-report data in the cohort study, surveys are administered to the research subjects during their visit to units, included in the project, distributing naloxone at six, 12, 18, 24, 30 and 36 months, respectively, and / or at re-supply (any cause). The enrolment data include information concerning demographics, own substance use during the last 30 days, previous experience of either witnessing an overdose and/or own experience of overdose and questions on recognition of, and handling of an overdose (Questionnaire 1 see Additional file 1). Data collected upon naloxone refill concerns what happened to the previous naloxone/kit, for instance if it was stolen, lost, or used on oneself or on someone else.
Data analysis Data storage and missing data Collected data from active intervention participants are registered in an electronic Case Report Form (e-CRF). A unique e-CRF is created for each active participant, separated from the identification of the study participant, and stored in the clinical trial software RedCap. Data are stored confidentially and cannot be accessed by anybody outside the study staff. All questions in the e-CRF are to be answered actively, if questions are not replied to, they shall be marked as NA = "Not applicable" or NK = "Not known". During the first 5 months data was collected on paper, as the use of the computerized application was delayed. In November 2018, each unit received their own iPad, which was provided for by the project, containing the RedCap application.
Patient information and consent is still provided on paper. The 5 months of data collected on paper will be entered in the e-CRF by a research administrator at Lund University. There will be no registration of individuals declining naloxone training. During the 3 years that the project will be running, individuals could have visited many of the sites, and have, most likely been asked on several occasions. However, aggregated data from OST and NEP facilities in the region will enable descriptions of whether included participants differ from these overall populations on a group level. Statistical data are drawn from available data registers. Each individual included by follow up will contribute to the research database with one entry (row) for each year, in either cohort. During the follow up period, persontime time after cohort regression, will not be taken into account.
Trial status In July 2018, 3 weeks into the project, 70% of targeted units (N = 24) were included, providing OPEND and including subjects into the study. Five weeks into the project 87% of targeted units had started, after 12 weeks, all units were including patients in the project. After inclusion and establishment of the first targeted 24 units, making sure the program is well established into daily management, the naloxone project has recently included another 3 units, and are planning on further expansion. So far, reports from included units are indicating that upon completion of training, approximately 80% of patients are giving their consent to study inclusion. The intervention period begins 1 July 2019 (with the first analyses conducted for general population data for the 18-month period of 1 July 2019-31 December 2020), based on the assumption that the naloxone distribution will be considered to have reached a satisfactory level from July 2019.
Funding The naloxone project, including the study, is funded by the Health Board in Skåne County as a result of a political decision. Financial support from Psychiatry Skåne and from the Southern Healthcare Region have contributed to completion of the report. The Southern Healthcare Region did not have any role in the study design, and will not have any role in the interpretation of data. As the study is carried out within the clinical structure of the hospital region of Skåne County, leading executives in this regional hospital organization have been involved in the organizational and regulatory preparations of the study, and has provided professional resources in clinical trial preparation and power calculations. The political and organizational leaders of the Skåne County will not be involved in the scientific interpretation of data and in the scientific publication of the results, although parts of the resources needed, such as statistical expertise and data storage, will be provided within this organization.
Acknowledgements The authors wish to acknowledge the  County of Skåne ,  Psychiatry Skåne  and the  Southern Healthcare Region of Sweden  for substantial organizational support,  Henrik Thiesen & Marian Jørgensen  at  RedLiv (OPEND), Denmark , for sharing your material, time and knowledge, and  Desirée Madah-Amiri ,  Norwegian Centre for Addiction Research , for sharing information regarding OPEND implementation Norway. Also, we would like thank all members of staff and users at targeted units, making implementation and distribution possible.
Results Demographic and clinical data of the study population. In total, 92 UHR participants were evaluated. Among them, 36 participants developed psychotic disorders during the follow-up (converters) and 56 did not (non-converters). The demographic and clinical characteristics (age, sex ratio, cannabis and tobacco use, psychotropic treatment intake, duration of the follow-up, severity of the symptoms, and RNA-sample quality) are reported in Table  1. At study start, there were no significant differences between converter and non-converter groups for daily use of tobacco, cannabis use within the last month, and psychotropic medication. There were no significant differences between groups in RNA quality from the samples obtained at the beginning and the end of the follow-up. The follow-up was longer for non-converters (p = 0.
Comparison of baseline expression of YWHA genes between converters and non-converters. In the logistic-regression analysis, the baseline expression levels for YHWAB, YWHAE, YWHAG, YWHAQ and YWHAZ were significantly higher in converters than in non-converters (Table  2 , Fig. 1a-g , Supplementary Fig. 1 ). By contrast, the baseline expression levels of YWHAH and YWHAS were not significantly different between converters and non-converters. The baseline expression levels of YWHAB, YWHAE, YWHAQ and YWHAS were significantly correlated with several clinical scores at the end of the follow-up, especially with the score of negative symptoms (Supplementary Table  1 ). comparison of longitudinal changes in expression of YWHA genes between converters and non-converters. YWHA mRNA expression data at the end of the study were obtained for 67 patients (29 converters and 38 non-converters).
Genome-wide DNA methylation was assessed using Illumina Infinium HumanMethylation450 BeadChip (Illumina, San Diego, CA, USA), which interrogates the DNA methylation profile of >485 000 CpG loci across the genome at single-nucleotide resolution. Illumina GenomeStudio software (Illumina) was used to extract signal intensities for each probe. For details about preprocessing and clean up steps, please refer to Kebir et al. 4. Methylation and expression data were available for 25 patients (12 converters and 13 non converters) at both baseline and at the end of follow-up. A total of 122 CpGs were located in the promoter or the body of one of the seven YWHA genes. Methylation levels on these CpG were correlated with their corresponding gene expressions using Pearson's test.
The reported P-value was calculated as the ratio of this number to the total number of tests done (10,000). In order to account for multiple comparison, p-value were adjusted using a false discovery rate (FDR) correction. Graphical representations were produced using Python and the ggplot2 R package  60. Statistics and visualization of the correlation matrices were done with the R software, using the Hmisc and Corrplot packages  [61] [62] [63] [64]. Correlations between mRNA expression levels used Pearson's correlation analysis. Correlations between mRNA expression levels and clinical scores used Spearman's correlation analysis. PCA on the YWHA mRNA expression data was performed using the R package FactoMineR package  65. The PCA determines the principal components of the correlated expression of the YWHA genes.
Acknowledgements We would like to thank all the patients and parents who participated in the ICAAR study, as well as all the staff from the  C'JAAD team (Centre d'Evaluation pour Jeunes Adultes et Adolescents)  and the staff from the  Clinical Evaluation and Research Center at the Service Hospitalo-Universitaire ,  Centre Hospitalier Sainte-Anne  ( GHU Paris Psychiatrie et Neurosciences ). We also thank the  URC Paris Centre Descartes  ( AP-HP ), INSERM, and  DRCI  for regulatory and technical assistance. We are grateful to  Bertrand Ducos  and  Juliette Pouch  from the  qPCR-HD-Genomic Paris Centre platform  for the qPCR. We thank  Matthew J. Morgan  ( MG Science Communications ) for the English editing. This work was supported by a grant by the  French Government's Agence Nationale pour la Recherche (ANR ,  08-MNP-007 ), by a grant from the  French Ministry of Health's Programme Hospitalier de Recherche Clinique (PHRC ,  AOM-07-118 ), and by the grant  ANR-13-SAMA-0010  (SAMENTA 2013 -Projet CERBAIS).
This work is supported by the  French Government , managed by the  Agence Nationale de la Recherche (ANR)  under the  programme "Investissements d'Avenir " with the reference  ANR-18-RHUS-0014. The  Centre Hospitalier Sainte-Anne  sponsored the ICAAR study. Additional financial support was obtained from the  Institut National de la Santé et de la Recherche Médicale (INSERM) , and  Université Paris Descartes  (recurrent funding). Boris Chaumette receives a grant from the  Fondation Bettencourt-Schueller. Fanny Demars  and  Anton Iftimovici  receive a funding from the  Fondation pour la Recherche Médicale (FRM). The sponsors had no role in the design and conduct of the study, in the collection, management, analysis or interpretation of the data, in the preparation, review or approval of the manuscript, or in the decision to submit the manuscript for publication.
Coalescent Simulations of Feature Vector Images for partialS/HIC Training To train partialS/HIC, we deployed the program discoal  (Kern and Schrider 2016)  to perform coalescent simulations of completed and partial as well as hard and soft selective sweeps, along with simulations without sweeps, in a manner analogous to  Schrider and Kern (2016)  (supplementary fig. S1 , Supplementary Material online). Individual simulations were converted into 2D matrices, or feature vector images, built from 89 rows corresponding to different summary statistics, and 11 columns corresponding to adjacent subwindows. The 89 statistics include, along with 14 that were previously implemented in S/HIC and/or diploS/HIC, 3 genomic region variants of the SNP-specific iHS statistic  (Voight et al. 2006 ) and 72 derivatives of the SAFE score  (Akbari et al.
We then applied the CNN that was trained given the BFS population history and measured overall accuracies. These accuracy measurements are generally similar to that of our baseline, that is, the corresponding original experiment whereby the testing data were simulated under the inferred demography for BFS, thus the demography was correctly specified with respect to the training set underlying this CNN (supplementary fig. S6 , Supplementary Material online). However, performance gradually worsens with decreasing bottleneck severity across the test sets, but this may be largely an effect from the number of polymorphisms. To address this, we generated another six test sets that identically replicated the specifications for the BFS training simulations except with h fixed in value yet varying between the six data sets.
Notably, the range we utilized here for h exceeded the h prior distribution for the original BFS training and testing simulations, hence further evaluating model misspecification. We find no loss in overall accuracy except in the case with the least genetic diversity simulated, where there is a moderate decrease (supplementary fig. S7 , Supplementary Material online). This is unsurprising given that low levels of genetic diversity lead to noisy estimates of the statistics employed by partialS/HIC. It is encouraging, however, that sensitivity is maintained throughout most of our h prior distribution range and accuracy does not fall below that of our baseline experiment until h is at a low value toward the bounds (h < 5,000 for the full genomic window size of 55,000 bp). To put this into perspective, genetic diversity measures for the corresponding empirical BFS data set are consistent with simulations generated with h ! 5,000, and likewise are beyond nearly the entirety of the h ¼ 1,000 simulated values; among the subwindows that passed our filtering thresholds (supplementary fig.
Soft and Partial Sweeps Are Commonplace among A. gambiae Populations Turning our attention to the Ag1000G phase I data, we applied our nine-state CNNs to the corresponding A. gambiae population data sets, classifying 5 kb segments using a 55 kb full sliding window throughout the whole genome (supplementary fig. S1 , Supplementary Material online). Each of the eight mosquito populations contains a large number of subwindows identified as completed soft sweeps (median fraction of total calls genome wide ¼ 5. 01%) as well as partial sweeps (median fraction of total calls genome wide for partial hard sweep ¼ 2. 84%; median fraction of total calls genome wide for partial soft sweep ¼ 7. 24%), coupled with only a handful of completed hard sweep predictions (median fraction of total calls genome wide ¼ 0.
Constructing 2D Feature Vector Images of Summary Statistics The eight training and test data sets (including an additional 11 BFS data sets for testing demographic misspecification), as well as empirical data sets, were converted into 2D feature vector matrices for downstream deep learning (supplementary fig. S1 , Supplementary Material online); this was performed within the Python environment and required usage of the module numpy. Prior to this 2D transformation, the simulated data were modified to better account for uncertainty within the empirical data, specifically: 1) sites that were missing any individual calls or could not be polarized against the outgroup were excluded; and 2) incorrect identification of the derived allele. For the former, each simulation randomly drew from a distribution of 1,552 masking profiles (with test simulations drawing without replacement per selection class of 1,000 simulations), which determined the exact sites to be omitted from further analysis; a masking profile consisted of the site positions within a single full 55 kb window on the A.
2006) , variance of pairwise genotype distances  (Kern and Schrider 2018) , skewness of pairwise genotype distances  (Kern and Schrider 2018) , and kurtosis of pairwise genotype distances  (Kern and Schrider 2018 ). These were previously implemented in S/ HIC  (Schrider and Kern 2016)  and/or diploS/HIC  (Kern and Schrider 2018)  except for H 1 (though a multilocus genotype equivalent was used in diploS/HIC) and the iHS-based statistics. We employed the Python package scikit-allel to calculate p, h W , Tajima's D, h H , Fay-Wu's H, number of unique haplotypes, H 1 , H 12 , H 2 /H 1 , and the statistics related to iHS. Values for iHS were standardized within 50 derived allele frequency bins, following mispolarization in the case of simulated data. Outlier iHS values were defined as within either 2.
g. , 1,800 simulations from the total nine-state training data set) randomly removed as a validation set during optimization (supplementary fig. S1 , Supplementary Material online). Training was performed for every population demography under three experimental settings: 1) given the full set of training data distributed across nine selection states; 2) exploiting a subset of the training data from only five of the selection states, specifically those involving neutral regions or completed sweeps; 3) deploying the entire training data, but with binary classification between selective sweeps in the focal subwindow and all unselected classes (i. e. , neutral class together with every linked class). The three training regimes were then applied to the test data set that corresponded in underlying simulated history, resulting in predictions of selection state given the default Keras threshold parameters for the first two training schemes, whereas the softmax probability of a focal sweep was instead exploited under training on binary classification.
046985). After simulation, we leveraged the ninestate CNN trained under the BFS specifications against each of the 11 testing data sets to obtain overall accuracies for comparison among these test data sets and to the original test data set whereby the BFS history was correctly modeled. Regarding the binary classification experiment, the Python module sklearn  (Pedregosa et al. 2011 ) was used for building the ROC curves to evaluate accuracy and sensitivity. Additionally, sklearn was utilized for conducting PCA on the training simulations, on either the focal subwindow or across the entire full-scale genomic window, respectively, with subsequent projection of the testing simulations into independent sets of PC1 and PC2 values, to obtain a total of four individual ROC curves. To calculate the Composite of Multiple Signals  (Grossman et al.
RNA isolation and miRNA expression assays RNA quantity and quality were assessed using the NanoDrop TM 2000 spectrophotometer (Thermo Fisher Scientific). For RT-qPCR experiments, 5 µL of isolated RNA were mixed with 10 µL of RT master mix. 1. 33 µL of cDNA were mixed with 18. 7 µL of qPCR master mix (Universal Master Mix II without UNG). PCR amplification was performed using a LightCycler ® 480 Instrument II Software Version 1. 5 (Roche).