dataesr/scientific-paragraphs-categorization huggingface dataset:
Sixteen climatic hazard classes were identified from the intersection of rainfall variability, heat stress, drought and flood risk. To characterize rainfall variability, we used the coefficient of variation in annual mean rainfall (15-30% = highly variable; >30% = extremely variable) derived from the Climate Hazards Group InfraRed Precipitation with Stations dataset  47. Areas of heat stress were defined as having thermal stress (projected for 2030 under the Representative Concentration Pathway 8. 5 scenario) with a of ≥79 (ref. 48). To define areas at risk of flooding, we used the UN Environment Programme-Division of Early Warning and Assessment-GRID-Europe dataset  49  , in which flooding risk is ranked from 0 (no risk) to 5 (extreme). Areas at risk of drought were defined as having more than 25 d without rain per month on average.
Analysis of mitigation We calculated total livestock emissions by geography by summing direct and estimated emissions from deforestation (Supplementary Figs. 10  and 11 ). We did not include changes in soil carbon stocks due to the difficulty in estimating these reliably at the scale of our study. Across the 132 countries included in this analysis, emissions were estimated to be 2,995 megatons (CO 2 e), which is lower than other recently published estimates  51. This difference arises primarily due to the exclusion of direct CO 2 emissions from cropland used for feed (see Supplementary Information) and the focus on LMICs only (compared with global estimates). Despite inherent limitations and uncertainties in the input datasets (see the corresponding references), our sources of emissions data are derived well-established sources (refs.
13,42,52,53)  and robust for the purposes of our prioritization analysis. Furthermore, because our analysis does not introduce specific equations or parameters, it is unlikely that it introduces new uncertainties or propagates any original uncertainties present in the input datasets, beyond what would be a simple addition of these original uncertainties. Direct emissions data come from ref. 42 and are for the year 2010. To update these data to 2019, we multiplied 2010 values by the proportional increase in national livestock emissions between 2000 and 2019, as reported by FAOSTAT 13,54. To estimate livestock emissions linked to deforestation, we first combined above-and below-ground carbon biomass data from 2010  55  and masked these values by areas with tree cover values ≥30%  56  , giving a layer of carbon biomass per hectare of forest.
Data availability The datasets analysed during the current study are available from the livestock_prioritization repository https://github. com/CIAT/ livestock_prioritization/tree/main/Data. No restrictions on data availability exist other than those related to refs. 42,52, the data from which are not publicly available. For use of these datasets, we strongly recommend contacting the first and/or corresponding authors of the studies. Access links for all datasets are provided in Supplementary Table  3.
Within the HBM4EU Initiative, health effects elicited by exposure to the prioritized chemical families were also selected, and biomarker data on exposure to pollutants of particular interest were combined with biomarkers of effect. A stepwise strategy (identification, prioritization, and validation) was also followed before the implementation of effect biomarkers in HBM4EU-aligned studies  (Gilles et al. , 2022 (Gilles et al. , , 2021)). The main process is detailed below; please see the HBM4EU project website https://www. hbm4eu. eu/for more information on deliverables and additional deliverables: D14. 1, D14. 2, D14. 3, D14. 4, AD14. 4, AD14. 6, D14. 7, and D14. 8  (Fernandez et al. , 2019a; Fernandez et al. , 2019b; Fernandez et al. , 2021; Mustieles et al. , 2018a Mustieles et al. , , 2018c Mustieles et al.
Literature searches and inventory of effect biomarkers Fourteen comprehensive literature searches were conducted in PubMed and Scopus databases, each covering one of the HBM4EU prioritized chemical substance groups  (Ougier et al. , 2021). Relevant health outcomes (reproduction, neurodevelopment, cancer, allergies, immune system, cardiovascular diseases, obesity, and oxidative stress) and prioritized chemical substance groups (bisphenols, cadmium, chromium IV, flame retardants, polycyclic aromatic hydrocarbons, per-/poly-fluorinated compounds, phthalates, acrylamide, arsenic, lead, mercury, mycotoxins, pesticides, and benzophenones) were chosen as key search terms. Boolean terms e. g. , 'AND', 'OR', were combined with key terms to complete the search strategy. Successively, the following PubMed filters a) Full-text articles and b) articles published in no more than 10 years, were used to gain precision.
All data analyses are currently in progress and only very preliminary results are available. Some examples of ongoing work using this approach are described below. The HBM4EU Aligned group of children included analyses of biomarkers of exposure such as phthalates, HEXAMOLL® DINCH, and flame retardants as well as others from the second list of HBM4EU priority substances  (Gilles et al. , 2021). Information on neurodevelopmental status (specifically behavioral function, assessed with the Child Behavior Check List, and cognitive function, assessed with the Wechsler's Intelligence Scale for Children) was also available for this population. Epigenetic biomarkers, such as DNA methylation and BDNF protein levels, were measured in blood and serum samples, respectively, alongside additional clinical markers (e.
g. , thyroid and reproductive hormone levels) to cover a broader toxicity pathway. For the adolescent population, biomarkers of exposure to phthalates, HEXAMOLL® DINCH, and PFAS were measured in biological samples. Information on sexual maturation (Tanner stage scale) and metabolic function (lipid, glucose markers, and BMI) was also available for this population. The biomarkers of effect analyzed included DNA methylation (KISS1 gene) and kisspeptin protein levels (kisspeptin 54, kiss54) in blood and serum samples, respectively. Data analyses are ongoing, with some promising results  (Rodriguez-Carrillo et al. , 2023). Information on health outcomes demonstrates the presence of tangible adverse effects and may indicate converging effects of different chemical families. Exposure-health outcome associations alone may not demonstrate a mechanistic or biological relation; however, when combined with molecular and clinical biomarkers, these endpoints may confirm the occurrence of disease pathways.
Conclusion The progressive strategy developed within HBM4EU has followed a reproducible, systematic, and innovative approach for identifying biomarkers of effect. Besides allowing its solid implementation in the HBM4EU project, it offers a useful point of departure for future HBMs and European Initiatives, including the European Partnership for the Assessment of Risks from Chemicals (PARC). The work done under the HBM4EU initiative confirms the added value of including effect biomarkers in HBM, epidemiologic, and occupational studies, especially when selected and prioritized based on toxicological and clinical information. The advantages of implementing effect biomarkers together with exposure biomarkers are: i) the evaluation of potential adverse effects at an early and reversible stage (especially relevant for new chemicals/substitutes and for diseases with long-latency periods) and in cross-sectional studies; ii) the potential to align toxicological and epidemiological knowledge, thereby improving the biological plausibility of the associations found and increasing the likelihood of regulatory acceptance; iii) a higher degree of causal inference through mediation analyses in longitudinal studies; iv) the assessment of dose-response relationships; and even v) the identification of human groups at greater health risk, Further efforts are needed to promote the utilization of effect biomarker data as consolidated scientific evidence to support risk assessment and regulatory decision-making in the framework of EU chemical legislation and in occupational safety and health legislation.
Acknowledgements The authors acknowledge the financial support from the  European Union  ( ERC Advanced Grant   MOL-2D 788222  and  FET-OPEN SIN-FONIA 964396 ), the  Spanish MCIN  ( PID2020-117264GB-I00 ,  PID2020-117152RB-100  and  PID2020-119748GA-I00  co-financed by the  FEDER and Excellence Unit "María de Maeztu" ,  CEX2019-000919-M ), and the  Generalitat Valenciana (PROMETEO program ,  GV/2021/027  and  iD-iFEDER/2018/061 ). This study forms part of the Advanced Materials programme and was supported by  MCIN  with funding from the  European Union NextGenerationEU  ( PRTR-C17. I1 ) and by  Generalitat Valenciana. Proteomic measurements were performed in the proteomics facility of  SC-SIE University of Valencia. This proteomics laboratory is a member of Proteored. The authors acknowledge the Italian MIUR, through the  Project ″Dipartimenti di Eccellenza 2018-2022  (ref.
All the tests were done using Python 3. 8. For more information and experiments not presented here we invite the reader to look into the github repository https://github. com/agussomacal/ROMHighContrast. • Greedy H 1 0 : The y k are picked incrementally, y k+1 maximizing the relative H 1 0 projection error u(y) -P V k u(y) H 1 0 / u(y) H 1 0. • Greedy Galerkin: The y k are picked incrementally, y k+1 maximizing the relative H 1 0 error of the Galerkin projection u(y)-P y V k u(y) H 1 0 / u(y) H 1 0. Figure  5   for the same parameter selection strategies. Here, we notice that the inclusion of the limit solution u ∞ is not anymore critical for reaching good accuracy. Nevertheless, these errors still decay faster for the greedy strategies. Remark 5. 1. As the diffusion coefficient is piecewise constant on the partition Ω A ∪Ω c A , the parameter space dimension is d = 2 in this numerical example.
Similarity metrics and differential gene expression profiling including SASP of sham-irradiated and UVB-induced senescent melanocytes (UVSM) detected by RNA seq analysis (E, left and middle panels). Expression levels of the mRNA of SASPs as analyzed by real-time PCR (E, right panel). (F) GSEA was performed using the "FRIDMAN senescence," "KEGG melanogenesis" and "GOBP melanocyte differentiation" gene sets to demonstrate the sequential transition in gene expression from sham-irradiated to UVB-induced senescent melanocytes. Statistical significances were assessed using chi-square test for categorical data (A and B) and Mann-Whitney U test for numerical data (C, D, E, and F) in independent two group comparison. changes in gene expression levels from nearly normal melanocytes to senescent melanocytes after UV irradiation.
Although the senescence markers of CDKN2A and CDKN1A were downregulated, there were no significant changes in the expression levels of melanosome transport-related genes and melanogenesis-associated genes (Figure  S2 ). UVB-induced senescent melanocytes with or without 2-DG. Melanosome transport-related genes (MYO5A, RAB27A and MLPH) expression (F, left panel) and protein levels (F, right panel), melanogenesis-related genes (MITF and TYR) mRNA expression (G) and the total melanin content (H) were analyzed in UVB-induced senescent melanocytes with or without 2-DG. (I) UVB-induced senescent melanocytes and 2-DG treated senescent melanocytes were analyzed by EM (left panel). The melanosome distribution was analyzed and is presented here as a bar graph (right panel). Statistical significances were assessed using chi-square test for categorical data (A lower left and E; SA-β-Gal) and Mann-Whitney U test for numerical data (G and I) in independent two group comparison.
Single-cell transcriptomic analysis Single-cell suspensions were obtained from live UV-stimulated melanocytes as described above. Single-cell RNA sequencing was performed using a Chromium Single Cell 3' V3 Reagent kit (10X Genomics, Pleasanton, CA) according to the manufacturer's protocol. Next-generation sequencing was performed using an Illumina HiSeq X Ten System (San Diego, CA USA) for 10,000 cells per sample. Sequencing results were converted into FASTQ files using Cell Ranger (10X Genomics). Samples were aligned using the human genome GRCh38 as a reference. Matrices were loaded into Seurat (v. 3) for data analysis, and R (v. 3. 6. 1) was used for statistical analyses  [38]. Cells with an RNA count too low < 200 or mitochondrial gene expression too high (> 15%) were excluded. Data were normalized using the NormalizeData function.
Variance of gene expression was checked using the FindVariableFeature function. Data dimensions were reduced using principal component (PC) analysis (PCA) to 20 significant PCs for each sample. Data were clustered using the FindNeighbors and FindClusters functions at various resolutions. RunUMAP was used to visualize the selected PCs. Data were aligned and normalized for the number of genes per cell and mitochondrial gene content using the Seurat workflow. Cluster markers were identified using the FindMarkers function. Pseudotime-trajectory calculation was performed using Monocle3 (v. 0. 2. 3. 0) with ribosomal genes excluded removed from the gene expression matrix.
Gene set enrichment analysis (GSEA) GSEA was performed using the GSEA v. 4. 3. 2 software. Rank data was extracted from all the DEG between sham and UVSMA in bulk RNA sequencing. In single cell analysis data, we used the DEG with more than 0. 25 of log2FC as the rank data. All gene set files for this analysis were obtained from molecular signatures database of GSEA website (http://www. gsea-msigdb. org/gsea/msigdb). Enrichment score (ES), normalized ES (NES), and adjusted P-value were extracted for each gene set after 1,000 times gene set permutations, and enrichment map was visualized to show the GSEA results.
Statistical analysis Data were presented as the mean ± standard deviation (SD) of independent measurements. Statistical significances were assessed using chisquare test for categorical data and Mann-Whitney U test for numerical data in independent two group comparison. One-way analysis of variance (ANOVA) was used to compare means between three groups. Furthermore, Tukey's post hoc analysis was conducted to determine which specific group means differ from each other when the significant result from ANOVA were found, and only the significant results of post-hoc test between two group were indicated in the Figure. The results were considered statistically significant when the two-tailed p-value was < 0. 05. IBM SPSS ver. 25 (IBM, Armonk, NY) was used for all statistical analyses. obtained funding support.
Acknowledgements This research was supported by the  National Research Foundation of Korea  ( 2020R1A6A1A0304 3539 ,  2020M3A9D8037604 ), by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Science, ICT  ( NRF-2022R1A2C2004487 ), and by the  Basic Science Research Capacity Enhancement Project  through the  Korea Basic Science Institute (National Research Facilities and Equipment Center)  in the form of a grant funded by the  Ministry of Education  to  Hee   Young Kang at Ajou University  ( 2019R1A6C1010003 ).
Overall Study design Our study is a longitudinal multi-centric cohort study, using mixed-methods. Quantitative data will be collected based on questionnaires repeated in time covering up to a year after the SA, and qualitative data will be based on semi-structured interviews with members of the cohort, people who experienced SA but who are not included in the cohort, and actors professionally involved in their psycho-socio-judicial care. As most medical and epidemiological studies (1), we only focused on acts of sexual violence involving force or threat of physical violence, therefore excluding harassment and exhibitionism.
Non-cohort participants The data collected through the cohort are completed by qualitative interviews with people outside of the cohort (see Fig. 1 ). We are interviewing people who experienced SA but who are recruited by other means and professionals involved in the psycho-socio-judicial care of people who experienced SA. Participants recruitment. We are recruiting non-cohort participants from investigators' networks and by advertising for our study in different organisms that receive people who experienced SA (e. g. victim support associations, police stations, forensic centers, Regional Psychotrauma Centers, etc. ). Our contact in each organization is responsible for distributing a recruitment poster containing investigators' contact information in the organization and to its network (where applicable), via their website, mailing lists, in their o ces or on their social media.
Quantitative measures Quantitative measures at each time points are presented in Table  1. Initial data are collected during the forensic examination. The physician has to complete a questionnaire (see Additional File 1) based on a questionnaire constructed for a previous study (46) that is still used today by forensic practitioners in the Department of Forensic Medicine of the Jean Verdier Hospital (Bondy, France) as part of their routinely collected information. The participants also have to complete two self-assessed questionnaires on peritraumatic dissociation and distress. Investigators call the participants back at 6 weeks, 3 months, 6 months and 1 year of the SA so they can answer follow-up questionnaires on their health, support and on the status of their judicial proceedings. Those follow-ups do not exceed 40 minutes.
Individual factors Sociodemographic variables. Sociodemographic data are rst collected during initial examination: participants' age (in year) and gender (1: female; 2: male; 3: other), the name of the forensic center in which the examination took place, the anonymized identity of the practitioner, and time between the assault and the examination (in hour). Complementary sociodemographic variables are collected during follow-ups. Marital status (0: never married; 1: married; 2: separated; 3: divorced; 4: widowed; 5: cohabitation) and employment status (1: employee; 2: independent worker; 3: unpaid worker; 4: student; 5: housewife/husband; 6: retired; 7: unemployed due to health reasons; 8: unemployed for another reason; 9: other) are based on sociodemographic information measured in the World Health Organization Disability Assessment Schedule 2.
0 questionnaire  (47). Level of education (from 0: no diploma; to 10: Master's Degree; and 11: Other) is based on the sociodemographic questions asked in the 2019 Health Barometer Study (48). Sexual identity (1: heterosexual, 2: gay or lesbian, 3: bisexual or pansexual, 4: other) is based on an item recommended by the UCLA SMART team  (49). We created an item to collect information on the identi cation as part of the gender diversity (0: no; 1: yes). The last sociodemographic variable collected is ethnicity/race (1: White, 2: Black; 3: Middle Eastern; 4: Hispanic/Latinx; 5: Asian; 6: other) and is based on recommendation from Public Health Ontario  (50)  and adapted to a French context. Medical and psychiatric history and treatments. We collect data gathered by the practitioners during the initial examination on history of serious or chronic disease and personal and familial psychiatric history, as well as actual medical treatment that could lead to a psychic tropism (thymoregulator, anxiolytic, hypnotic, antidepressant, neuroleptic, analgesic, etc.
State at the time of examination. We collect data on the physical complaints and lesions, psychological state, and duration of "total or temporary work incapacity" (a French legal notion used by judges to assess the seriousness of the SA) reported by the physician during the consult. PTSD symptoms. We assess PTSD symptomatology at each follow-up (6 weeks, 3 months, 6 months and 1 year) using the French version of the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5), which has been validated both in French and in its original version (63). This 20-item instrument assesses participants PTSD symptoms in the past month on a 5-point Lickert scale ranging from (0) Not at all to (4) Extremely. A total score of 31-33 or higher indicates that the participant might meet criteria for PTSD and might bene t from PTSD treatment.
Qualitative Semi-directive interviews are conducted with cohort participants and non-cohort people who have experienced SA in the past (see Additional File 2). These interviews allow us to collect complementary data to our quantitative data. The rst contact consists of a one-hour semi-directive interview on perception of the event, formal and informal support, coping methods and on the impacts of the event. Once a good contact is established between the participant and the interviewer, a second and/or third interview can be conducted to come back on what was said during the previous interview and to discuss in more depth the participant's value system and perceptions on how they integrate the event to their lifecourse and how they perceive themselves and the SA in relation to the notions of "trauma" and "victim".
Indeed, data collected during the rst forensic exam will allow us to develop initial AI algorithms to identify people at risk of developing PTSD. This study will be the basis for a second study, which will be integrated into the project, that will aim to investigate the contribution of AI in the early detection of PTSD as well as the reception and integration of the tool by medico-social services. We therefore hope to build tools to better orient people who experienced SA from the rst forensic examination, in particular in terms of psychotrauma. This could lead to novel ndings as there is promising evidence that AI can be of great help for medical services  (42, 43) , but has never been tested for forensic examinations after SA to our knowledge. Concerning the qualitative side of the project, this is one of the few projects studying the qualities and challenges of French infrastructures and the needs of people who experienced SA in France.
, 1985). Twenty-five thousand events of one distinct phycoerythrin/chlorophyllpositive population (Pop1; Fig. S6a  and b in the Supplement) were sorted directly on 13 mm, 0. 4 µm pore size polycarbonate filters (MerckMillipore, Germany) using the most accurate sorting mode "single-cell purity". The filters were prepared and observed by SEM as described below. For DNA extraction, 20 cells were sorted under the same conditions, directly into a 0. 2 mL microtube (Eppendorf), and stored at -20 • C. Cytometric data were processed using the BD FACSDIVA V9. 0. 1 software (Becton, Dickinson and Company, San Jose, California, USA). We also isolated cyanobacterial cells by micromanipulation from samples collected in March 2018 using an Eppendorf PatchMan NP2 micromanipulator equipped with 6 µm diameter microcapillaries (Eppendorf) mounted on a Leica Dlll3000 B inverted microscope.
Because of the possible presence of more than one species in the samples, we constructed 16S rRNA gene clone libraries with the PCR amplicons using the Topo TA cloning system (Invitrogen) following the instructions provided by the manufacturer. After plating, positive transformants were screened by PCR amplification using the M13R and T7 flanking vector primers. Amplicons were sequenced by Sanger sequencing (Genewiz, Essex, UK). All forward and reverse Sanger sequences were qualitycontrolled and merged using CodonCode Aligner (http:// www. codoncode. com/aligner/, last access: 22 May 2022, version v11. 0. 1). Then, we utilized Mafft  (Katoh and Standley, 2013)  to produce multiple sequence alignments including our sequences and the closest blast  (Altschul et al. , 1997)  hits identified in GenBank.