dataesr/scientific-paragraphs-categorization huggingface dataset:
Nucleotide Archive (ENA) This work was supported by  ERA-CAPS AUREATE (DFG)  and the  Max Planck Society  to (DW), an  European Research Council  ( ERC) Marie Skłodowska-Curie Fellowship  ( 751204-H2020-MSCA-IF-2016 ) and  Airlangga University Hibah Riset Mandat Grant  ( 387/UN3. 14/PT/2020 )to (ATW), and  BBSRC  grants ( BB/L003023/1 ,  BB/ N005279/1 ,  BB/N00194X/1  and  BB/P02601X/1 ) to (JGM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Nutritional Composition and Carbon Impact Data Individual dietary nutrient intake and carbon impact were estimated by matching food consumption data from the INCA2 study with the previously described SUStable database, which contains the nutritional composition and carbon impact of 212 frequently consumed French foods (covering 84% of the total adult energy intake)  [18]. The nutritional composition of SUStable foods was obtained from the French nutritional composition database (French Information Center on Food Quality, CIQUAL; n = 1300 foods) and their carbon impact was obtained from a database of 402 foods built previously by the environmental consultancy firm BIO by Deloitte  [19]. The SUStable database already contained four PBDL products; two others were added for this study in order to illustrate the recent diversification of the product offerings in this market compared with the survey period.
Composite mixed dishes were decomposed by ingredient for this analysis. Third, statistical analyses were performed between PBDL product tracking subpopulations (OptStillNoPB, OptAddedPB, OptStillPB and OptNoMorePB). Energy intake and observed and optimized food group quantities were compared between subpopulations using GLMs adjusted for age, family status, educational level, and physical activity level. Observed and optimized quantities were compared using a mixed model to account for repeated measurements. Comparisons were performed after summing dairy products (Milk, Yogurt, and Cheese) and all PBDL products (PBDAs, PB desserts, and PB drinks). Diet optimization and statistical analyses were performed using SAS version 9. 4 (SAS Institute, Cary, NC, USA), and an α of 5% was used for all statistical tests.
The strengths of this approach have been documented by Gazan et al. [14] , and are further emphasized in this paper. Individual diet modelling provides more relevant and more robust results because it enables taking individual preferences into account, can estimate the percentage of people for whom designing an optimized diet is feasible, and allows statistical analyses to be performed in order to test the variations between individual observed and optimized diets. To the best of our knowledge, only one other study has used multi-criteria individual linear programming to design nutritionally adequate diets while reducing carbon impact  [43]. That study was based on NDNS (National Diet and Nutrition Survey in the United Kingdom) data consisting of a four-day dietary diary. Nevertheless, the authors applied different optimization models from those used in this paper, thus making it difficult to compare with this study.
One limitation of this study is that certain characteristics of the sustainable diet concept were not considered. In particular, the economic dimension was not considered because of the lack of data on food prices. Including food prices in our models would have enabled controlling the cost of optimized diets, an important characteristic of sustainable diets  [30]. Moreover, within the environmental dimension we used only the carbon impact metric, which is not sufficient to represent the entire dimension. Other metrics, such as water footprint and impact on biodiversity, should complement it. In September 2020, a consolidated French database named Agribalyse was released  [44] ; it is the first free access database of environmental impact indicators for food items produced and consumed in France.
It would be valuable to repeat these analyses with the metrics provided in this new database (land use, acidification, water use, fine particles, etc. ) in addition to carbon impact. Food can have varying impacts depending on the environmental sustainability metrics selected (carbon impact, water footprint, nitrogen fixation, land use, eutrophication, etc. ), depending on location and agricultural practices  [45]. Therefore, other studies including other environmental metrics need to be performed in order to explore how PBDL products can play a role in improving sustainability of diets, as such metrics were not available at the time of the present study. Even if the acceptability of dietary changes was considered by minimizing the distance to individuals' observed food quantities and introducing constraints to force optimized diets to remain within the ranges of food quantity already observed, the acceptability of the proposed dietary changes by the models was not ensured, as INCA2 participants were not offered their own optimized diet in an intervention study.
Human Biomonitoring Data The used human biomonitoring data are part of the HBM4EU Aligned studies which have been described in detail by Gilles et al. [11]. Biobanked urine samples of children (6-12 years) from Belgium (3xG study), Denmark (OCC study), France (Esteban study), Germany (GerES V sub sample), Slovenia (SLO CRP study) and Slovakia (PCB cohort study) were collected and analysed for its bis (2-chloroethyl) phosphate (BCEP), bis (1-chloro-2-propyl) phosphate (BCIPP) and bis (1,3-dichloro-2-propyl) phosphate (BDCIPP) concentration to determine the exposure to TCEP, TCIPP and TDCIPP. A detailed description of the used HBM studies is given in the Supplementary Materials Table  S1. In this risk assessment, when referring to a country, the HBM-study listed above is meant. However, the HBM-study does not always reflect the general exposure of the whole population of the country, since some of the studies were not population representative or only performed on a regional level.
Estimation of the Daily Intake (EDI) The HBM data which were used for calculating the daily intake were not stratified and were volume based [µg/L] and non-creatinine adjusted. The estimated daily intake (EDI) from the urinary excretion of the OPFR was estimated by using the following equation: EDI = C Urine * UV excr Fue × MW d MW m where C Urine is the concentration of the metabolite in µg/L and UV excr is the daily excreted urinary volume of 22. 2 mL/kg bw/d for children  [12]. F ue is the molar fraction of the urine excreted metabolite with respect to its parent compound, and MW d and MW m are the molecular weights of the parent compound and its metabolite. The following molecular weights were used [g/mol]: TCEP (285. 49), BCEP (222. 99), TCIPP (327. 55), BCIPP (251. 04), TDCIPP (430. 9) and BDCIPP (319.
Dietary Exposure A deterministic approach was used to estimate the dietary intake of Belgian, Danish, French, German, Slovenian and Slovakian children. Since the toxicokinetic data are limited, it was assumed that 100% of the detected OPFRs concentration in the contaminated food is absorbed through the dietary intake. The provided occurrence data (see Table  1 ) originated from a Belgium food basket study published by  Poma et al. [7] , where samples were drawn from supermarkets, discount retailers and specialized meat and fish stores in two Belgian cities Brussels and Antwerp and in one village (Dessel) from spring 2015 to autumn 2016. In total, 165 samples belonging to 14 food categories were collected and analysed on its TDCIPP, TCIPP and TCEP concentration. The food category "animal and vegetable fats and oils and primary derivatives thereof" also included fish oil food supplement, which was the highest contaminated sample.
This sample was excluded, since it is not regularly consumed by children and could distort the outcome. The edited occurrence data in regard to Lower Bound (LB), Medium Bound (MB) and Upper Bound (UB) were provided by Giulia Poma (2021; personal communication) and were not modified in this risk assessment. Since, the occurrence data were left-censored, the LB-, MB-and UB approach was used  [15]. Occurrence data below the limit of quantification (LOQ) were set equal to zero (LB), to the half of the LOQ (MB) and equal to the LOQ (UB). The used food consumption data, which were surveyed by food recording are given in Table  2. In general, the mean occurrence data were multiplied with the available mean food consumption data for children provided by the EFSA Comprehensive European Food Consumption Database  [16].
This was conducted for the overall population (=all subjects) as well as for those who actually consumed the relevant food (=consumers) as reported in the respective national food consumption survey. Subsequently, the food category with the highest exposure of consumer was determined. For this food category, the P95 of the consumers and for the other food categories the mean of the overall population was used in order to calculate the exposure of high consumers. No food consumption data for Slovenian and Slovakian children were available, therefore the Austrian data were used, because these were the closest geographically data available.
Hazard and Risk Characterization As no HBM4EU HBM-GV, neither a HBM-I nor a HBM-II value from the German Human biomonitoring Commission nor a biomonitoring equivalent (BE) were available, a health-based guidance value (HBGV) was derived on the basis of data for a toxicologically acceptable uptake. For each study population and exposure scenario (LB-UB), the estimated daily intake (EDI) and dietary intake were set in relation to the available HBGV which was derived from external uptake data. In the European Union Risk Assessment Report of TCIPP, a lowest observed adverse effect level (LOAEL) of 52 mg/kg bw/d was derived from a 13-week rat study. The LOAEL was based on the increased liver weight observed in male rats. A minimal margin of safety (MOS) for repeated dose toxicity of 50 was considered sufficient  [22].
6 ng/kg bw/d, which is comparable to our estimation. In their duplicate diet study, only 0. 39 ng/g ww TCIPP was measured in the consumed food. In that study, air, dust and hand wipes were also analysed and based on the low concentration in food, they concluded the main exposure source was inhalation of personal ambient air  [10, 31]. Moreover, the provided biomarker might not reflect a realistic TCIPP exposure. This can also explain the higher estimated dietary exposure compared to the EDI. The occurrence data were left-censored and for e. g. , TDCIPP the detection frequency was quite low, it was only frequently detected in fish (df 39%) and cheese (df 44%). TCIPP and TCEP were more frequently detected in the range of 25-71% and 11-57% across the analysed food categories. In the LB-MB-UB approach, the food category "animal fats and vegetable fats and oils and primary derivatives of" contributes most to the total OPFRs exposure, which might not reflect a realistic exposure scenario because it was not detected at all.
Acknowledgments: We would like to thank  Giulia Poma  for her cooperation regarding the occurrence data in food and  Kirsten Baken ,  Lisa Melymuk ,  Selma Mahiout  and  Tiina Santonen  for providing comments on this risk assessment. We would also like to thank  Margaux Riou  for her work on the ESTEBAN Study and in general we thank everyone who was involved in conducting the HBM studies.
This maximal temperature was maintained during 7 min to clean to column. For the inflorescence terpene content, compounds were separated and analysed by GC (model 6850, Agilent technology, USA) using a 30 m long capillary DB-5MS coupled with a MSD (model 5973, Agilent Technology, USA). Chromatographic conditions were as follows: helium was used as the carrier gas at a flow rate of 1. 2 mL min -1 ; oven was set at 40 • C after injection followed by a 3 • C min -1 temperature ramp to 245 • C. Temperature was then hold at 245 • C for 4 min; the injection volume was 2 µL in split mode with a 2:1 split ratio. Compound identification was checked through the injection of analytical standards (Sigma-Aldrich, USA), the calculation of Kovats index (using the alkane series C 8 -C 40 ) and the comparison of experimental mass spectra to the NIST and Wiley databases and with MSeasy (R package,  Nicolè et al.
Statistical analyses Statistical data analyses were performed with R (version 3. 3. 2). First, we compared the species (with merged varieties within each species) to determine their global strategy to cope with water stress. Second, we analysed the differences among varieties to highlight the most resistant to water stress. The effect of phenology (T1 and T2), treatment (control and stress), species (varieties merged) and their interaction were tested on all primary and specialized plant traits using Redundancy Analysis (RDA) and associated permutation test with 999 permutations (vegan package) on all measured plant traits. To go further and to determine the major differences between species to cope with water stress, we tested the effect of species and treatment on each parameter separately and within each phenological stage using a two-ways ANOVA (after validating normality and homoscedasticity) to highlight the main differences between L.
angustifolia and L. x intermedia to cope with water stress. These tests allowed to determine the effect of a prolonged period of water stress. When pairwise comparisons were needed, a Tuckey post hoc test was performed. If interactions were occurred, Student tests were performed to the for the effect of each factor separately. Moreover, the chemical data were processed separately for each species to highlight the potential role of terpenoids as defense compounds. The effect of phenology, treatment, and their interaction were tested on each dataset of terpene composition (leaf, inflorescence, and emission) using RDA. To determine the effect of water stress on total terpene content (floral and foliar) and total emission, two ways ANOVA (watering treatment and phenological stages as factors) were performed followed by a Tuckey post hoc for pairwise comparison.
Acknowledgments We thank  Sandrine Boucher  for her help with cultivation and samplings, Eric Chaisse (CRIEPPAM) and  Cédric Yvin (ITEPMAI)  for their expertise in lavenders cultivation, discussion about cultivar sensitivity to yellow disease, and having prepared and supplied the experimental plants. This study was funded by the program CASDAR of the  French Ministry of Agriculture and the department council of Bouches du Rhône.
Acknowledgements We thank members of the  4D Nucleome community  and the laboratories of J. E. P. -C. and D. M. G. for helpful discussions. J. E. P. -C. is a  New York Stem Cell Foundation -Robertson Investigator  and an Alfred P. Sloan Foundation Fellow. J. D. is an investigator of the  Howard Hughes Medical Institute. This research was supported by  National Institute of Mental Health  grants ( 1R011MH120269  and  1DP1MH129957 ; J. E. P. -C. ), 4D  Nucleome Common Fund  grants ( 1U01HL12999801 ,  1U01DK127405  and  1U01DA052715 ; J. E. P. -C. ), a  National Science Foundation (NSF)   CAREER Award  ( CBE-1943945 ; J. E. P. -C. ), an  NSF   Emerging Frontiers in Research Innovation grant  ( 1933400 ; J. E. P. -C. ), 4D  Nucleome Common Fund  grants ( DK107980  and  HG011536 ; J. D. ) and  National Institutes of Health  grants ( R01HG010658  and  U54DK107965 ; D.
Data availability All new raw data created in this manuscript have been uploaded to the 4D Nucleome portal and will be freely released for full distribution to the public (see specific details below). Processed data files for all figures and extended data figures are provided as Supplementary Tables  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19. ORM data have been uploaded to the National Center for Biotechnology Information, BioProject database accession number PRJNA788726 (http://genome. ucsc. edu/s/dsaulebe/ORM%20data%20 HCT116). Two-fraction Repli-seq data for Blobel engineered lines (raw data and processed log 2 [early/late] from three conditions) were obtained from https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE190117. Group 1 data (16-fraction Repli-seq data for H1 human ES cells) are available from the 4D Nucleome portal as follows: H1 human ES raw fastq, https://data.
Code availability We freely release all custom code for loop, TAD and subTAD detection at the following bitbucket links: TAD/subTAD detection, https://bitbucket. org/creminslab/cremins_lab_tadsubtad_calling_pipeline_11_6_2021; loop detection, https://bitbucket. org/creminslab/cremins_lab_loop_ calling_pipeline_11_6_2021/src/initial/.
Data analysis Domain calls were made with 3dnetmod from https://bitbucket. org/creminslab/cremins_lab_tadsubtad_calling_pipeline_11_6_2021/. loops on hic were called from custom python code based on HICCUPs approach from Aiden and colleagues available https://bitbucket. org/ creminslab/cremins_lab_loop_calling_pipeline_11_6_2021/. bowtie version 0. 12. 7 and samtools version 1. 2 were used for Chipseq and Cut&Run fastq processing. MACS2 2. 1. 1. 20160309 was used for peak calls. bedtools 2. 15. 0 was used for intersection of peaks with domain boundaries. opencv-python 4. 2. 0. 32 (cv2 4. 2. 0) was used for APA pileup of domains. linalg class of numpy (numpy 1. 16. 6) was used for eigenvector decomposition and compartment calling in hic matrices. Custom scripts in python were used to access domain layer hierarchy and domain intersection with compartment and dots and subsequent boundary classification as well as IZ to closest boundary permutation test.
Statistical Analysis The VNSE pathological results were compared with the mean value of three skin lesions measurements (as quantitative data) and skin lesion score (as qualitative data). Statistical analysis was performed using 9. 4 SAS software (SAS Institute Inc. , Cary, NC, USA). As the mean number of skin lesions presented neither a normal distribution nor homoscedasticity, the Wilcoxon two-sample test and the Kruskal-Wallis one way ANOVA were used using the npar1way procedure. The skin lesion score was compared to pathological variables using the Fisher exact test due to the small size of the samples using the freq procedure. Statistical significance was based on a 5% (0. 05) significance level.
tropica requires at least one week and usually 2-8 months  [17]. Most published research papers focus on localized geographical areas in a restricted time range. Here, we collected data on the proven vector of ZCL, P. papatasi, whose seasonal activity causes seasonal outbreaks at the North African region scale. To our knowledge, no studies dealing with these aspects were undertaken on such a broad geographic scale. We further analyzed the interplay between seasonal ZCL cases number, P. papatasi density, and climatic data.
Systematic Review The current study was based on a systematic review following the guidelines of the PRISMA initiative (2020)  [18]  (Figure  1 ) and meta-analyses  [19]. Monthly data on ZCL case number (Table  1 ) and P. papatasi density (Table  2 ) were extracted from selected publications. The keywords used in this study are "leishmaniasis", "ZCL seasonal transmission", "cutaneous leishmaniasis", "L. major", "sand fly", "vectors of Leishmaniasis", "P. papatasi" combined with "North Africa", and the countries of the area, including from north to east "Morocco", "Algeria", "Tunisia", and "Libya". The searches were performed on 11-14 July 2022 using PubMed, Web of Science, Scopus, and Google Scholar. Only studies with available monthly data were included from an initial panel of 585 scientific publications published between 1990 and July 2022.
Data Set Collection and Sites Localization Quantitative and qualitative information from the selected publications were extracted and presented in two tables; a map of the extracted data is shown in Figure  2. The geographic information, including altitude, latitude, and longitude, was extracted from the literature (Tables  1  and 2 , and Figure  1 ), and climatic variables (monthly maximum temperature, minimum temperature, precipitation, and relative humidity) in the relevant areas were collected from the Tutiempo Network, S. L. Data were processed using the Arc-GIS software (Figure  2 , Tables  1  and 2 ) and combined with the regional annual precipitation (Figure  2 ). Most sites with available published data are localized in semi-arid to arid areas where the rainfall is low and irregular in time and space, and the temperature ranges from moderate to high.
Normalization of ZCL Case Number and/Vector Density (Nj) Data on monthly ZCL cases number and P. papatasi density were extracted from tables or digitized using 'digitizelt' software and represented by country. However, the original values of these different sources are heterogeneous, which requires the normalization of data to be comparable. Therefore, equation 1 was used to calculate the normalized incidence value of ZCL standardizing extracted values for monthly ZCL cases and P. papatasi activity on a scale ranging from 0 to 1, where 0 refers to the absence of data or no recorded activity, and 1 indicates the highest activity. For example, the normalized values of a month's incidence and vector activity are a ratio between the extracted values for that month and the month with the maximal value.
Annual Distribution of ZCL Cases in the North African Region We compiled annual data on published ZCL case numbers in North African countries for 26 years (from 1995 to 2020) (Figure  3 ). In addition, data from four oasis sites (Ouarzazate, Zagora, Errachidia, and Figuig) were considered for Morocco. As a result, three principal peaks were recorded in 2003, 2010, and 2017. In Algeria, data from two sites point to two prominent peaks in 1997 and 2003, while data from Tunisian sites depict three primary peaks in 1999, 2004, and 2013. Finally, data published from Libya shows two major peaks in 2004 and 2008 (Figure  3 ).
Annual Distribution of ZCL Cases in the North African Region We compiled annual data on published ZCL case numbers in North African countries for 26 years (from 1995 to 2020) (Figure  3 ). In addition, data from four oasis sites (Ouarzazate, Zagora, Errachidia, and Figuig) were considered for Morocco. As a result, three principal peaks were recorded in 2003, 2010, and 2017. In Algeria, data from two sites point to two prominent peaks in 1997 and 2003, while data from Tunisian sites depict three primary peaks in 1999, 2004, and 2013. Finally, data published from Libya shows two major peaks in 2004 and 2008 (Figure  3 ). An overall decreasing trend in ZCL incidence was observed during the period studied and in the sites we reviewed (Supplementary Figure  S1 ). However, if the average ZCL case number followed a decreasing trend in Algeria and Tunisia, the opposite is reported in Morocco and Libya; overall, the decreasing trend was recorded in North Africa, which can be related to interventions aimed at combating the disease.
Monthly Distribution of P. papatasi in the North African Region Data collected on the seasonal P. papatasi dynamic depicted bi-to tri-modal activity from March to November (Figure  4 ). They point to a higher P. papatasi density during the hot period in the Moroccan sites. In the Algerian sites studied, El Honda recorded a monomodal activity, with the maximum activity in August. In M'Sila, a bi-modal distribution with a maximal peak in June is recorded. Sidi Bouzid, Tunisia, displays seasonal activity with a density peak in September. The two sites studied in Libya display An overall decreasing trend in ZCL incidence was observed during the period studied and in the sites we reviewed (Supplementary Figure  S1 ). However, if the average ZCL case number followed a decreasing trend in Algeria and Tunisia, the opposite is reported in Morocco and Libya; overall, the decreasing trend was recorded in North Africa, which can be related to interventions aimed at combating the disease.