dataesr/scientific-paragraphs-categorization huggingface dataset:
The same GLMM was used for the HBNTS without testing for the interactions as an almost complete segregation of the data was observed (no infectious mosquitoes collected in all sites except the forest during the dry season) preventing the model from converging. Mosquito host preference-The anthropophily index (AI) was expressed as the number of Anopheles sp. caught in the human-baited trap over the total number of mosquitoes caught in both human-and cow-baited traps. We assessed statistically the anthropophily indices only in the two species having a sufficient number of collected samples as well as a good agreement between morphological and molecular identifications (96. 5% and 98. 7% agreement for A. dirus and A. kochi respectively) for meaningful assessment since morphological identifications often encompass several species in the same group.
karwari (n = 3) were also present. There were 2 mosquitoes that had 99. 9% identical ITS2 and 99. 4% identical CO1 sequences but matched no species in the NCBI database. In addition to the random subset, 79 Plasmodium sp. infected samples were molecularly characterized for species which resulted in a total of 29 Anopheles species as determined by ITS2 and CO1. Day biting rate. Overall 20. 2 ± 1. 2% of the Anopheles females were captured during the daytime (between 06:00 and 18:00). Indeed, while the majority of Anopheles mosquitoes bite at night, an important proportion was active during the day (Fig. 2 ). Excluding species with extremely low sample sizes and unidentified samples, day biting behaviour was observed for all the Anopheles species and varied from 13 to 68% (Table  S3 ). The day biting rate in the HBNTs was not different across collection sites (19.
Acknowledgements This study is part of the  International Centres of Excellence for Malaria Research program  from the  National Institutes of Health  "Understanding, tracking and eliminating malaria transmission in the Asia- Pacific Region " (Grant  1U19AI129392-01 ). A. P. is funded by the  Institut Pasteur International Network Calmette  and  Yersin Ph. D. scholarship  ( MHL/MJ/N°212/18 ). This work was supported by  NIH   R01 AI145999-01A1  to MMR; and to KDV from the  European Commission ,  Horizon 2020 Infrastructures # 731060 Infravec2 ,  French Laboratoire d'Excellence  " Integrative Biology of Emerging Infectious Diseases " # ANR-10-LABX-62-IBEID , and  French Agence Nationale de la Recherche , # ANR-19-CE35-0004  ArboVec.
peditaeniatus. When resistant allele frequencies were compared between the A. peditaeniatus in the representation plates (n = 26) and all other A. peditaenitus (n = 82), there was no statistically significant difference (ace-1: χ 2 = 1. 69, df = 1, p = 0. 2, kdr: χ 2 = 0. 01, df = 1, p = 0. 91, rdl: χ 2 = 0. 78, df = 1, p = 0. 38), indicating that the representation plates accurately represented the larger dataset. Of the 108 total A. peditaeniatus, 106 carried the ace-1 G119S mutation (3 heterozygotes; 103 homozygotes), 78 carried the kdr L1014F mutation (22 heterozygotes; 56 homozygotes), and 104 carried the rdl A296S mutation (12 heterozygotes, 92 homozygotes; Fig.
Acknowledgements This work was supported by the  National Natural Science Foundation of China Grants  ( 41876039  and  42025602 ) to Z. L. Additional support was provided by the  US National Science Foundation  Grant  EAR-1502786  to G. B. The  ERA-Interim  reanalysis data are obtained from the  ECMWF  data server and the sea-ice concentration from the  NSIDC.
Data availability ERA-Interim reanalysis data can be obtained from the ECMWF public data sets web interface, http://apps. ecmwf. int/datasets; NSIDC sea ice satellite sea ice can be downloaded from https://nsidc. org/data/nsidc-0116/versions/3. All CMIP5 model output are available from http://esgf-index1. ceda. ac. uk. Other data are available from the corresponding author on request.
A fresh subsample of the composite soil was taken before drying for DNA extraction and stored at -20°C until DNA extraction. The three remaining cores were kept intact and used to measure bulk density of the ne soil (< 8 mm). Mean annual atmospheric temperature (MAT) data (1987-2017) were obtained for each sampling site using their GPS coordinates and the closest weather station in the NOAA database (https://www. noaa. gov/), through the R package 'rnoaa' (v. 0. 8. 4;  [35] ). The same GPS coordinates were also used to gather elevation data using the R package 'elevatr' (v. 0. 2. 0; [36]). DNA extraction, ampli cation and sequencing DNA was extracted on the homogenized soil samples using the DNeasy PowerSoil-htp 96 well DNA isolation kit (Qiagen, Hilden, Germany), according to the manufacturer's instructions.
Sequence processing and phylogenetic reconstruction All sequence analyses were performed using the R software (v. 3. 6. 4, R Core Team, 2019). The archaeal 16S rRNA gene amplicons were processed with the 'dada2' package (v. 1. 6. 0; [42] ) to infer amplicon sequence variants (ASVs), which allowed detection of ecological preferences at the nest phylogenetic scale  [43, 44]. Brie y, primer sequences were removed and the reads merged using default parameters. Chimeras were discarded using a denovo approach with the removeBimeraDenovo function ('consensus' method). The resulting ASVs were aligned to the SILVA reference database (SSU132 Ref NR) using the SINA aligner (v. 1. 6. 0; [45] ) and classi ed using SINA's least common ancestor algorithm. After elimination of the bacterial ASVs, 2 042 archaeal ASVs remained based on a total of 6 316 715 highquality 16S rRNA gene amplicons (corresponding to ca.
70 % of the original dataset). The reads were also clustered into OTUs at a similarity cut-off of 97 % (Additional le 1: Supplementary Methods) to compare the diversity of nitrosophaerial taxa based on ASVs and OTUs. Rarefaction curves of species richness were generated from the raw ASV table using the rarcurve function in 'vegan' (v. 2. 5. 5; [46]) (Additional le 1: Figure  S2 ) and a rare ed table (n = 1 079 ASVs) was obtained by averaging the ASV counts over 1 000 computations using the rrarefy function in 'vegan'. A phylogeny was built with the rare ed ASVs and a broad taxonomic selection of reference sequences extracted from SILVA. Sequences were aligned using the SINA aligner and the phylogeny generated using FastTreeMP (v. 2. 1. 10; [47] ) with the GTR + CAT model of nucleotide evolution.
Partitioning and transformation of sequence data The ASV abundance distributions were examined to further partition the rare ed table between frequent and rare community members. An index of dispersion corresponding to the ratio of the variance to the mean abundance multiplied by the occurrence was calculated  [48]  to split the dataset according to the frequency of occurrence of each ASV  [49]. This index was then used to model whether ASVs followed a stochastic (Poisson) distribution and those falling below the 2. 5 % con dence limit of the χ 2 distribution were discarded  [50]  (Additional le 1: Figure  S3 ). By focusing on the frequent community members, we minimized the risk of sampling artefacts that would bias the distribution of the ASVs and thus increase the likelihood to detect relevant ecological patterns.
Such partitioning remains rarely used in microbial ecology (but see  [51] [52] [53] ), despite being more statistically robust than traditional approaches using arbitrary cut-offs of local and regional abundances (e. g. [54] ). The resulting community included 718 ASVs representing 99. 6 % of the reads in the rare ed dataset. Zero count ASVs were replaced by an imputed value using the Bayesian-multiplicative replacement method available in the 'zCompositions' package (v. 1. 2. 0;  [55] ). An isometric log-ratio transformation [56], as implemented in the 'philr' package (default parameters, v. 1. 10. 0; [57] ), was then applied to the zero replaced dataset using the phylogenetic tree of the ASVs as the sequential binary partition. The output of this transformation consists of a matrix of sites x nodes containing the balances calculated on the internal nodes of the phylogeny.
Statistical analyses Alpha-diversity indices and taxonomic composition were calculated using the full rare ed table. Faith's phylogenetic diversity (PD; [60]) was calculated at both domain and class levels using the phylogenetic tree of the ASVs and the pd function in the 'picante' package (v. [31]. Brie y, variables were rst ranked according to a variable importance score, averaged across 50 RFs. The set of variables leading to the model with the smallest out-of-bag error, averaged across a nested collection of 25 RFs starting from one with only the most important variable, was selected. To account for the random nature of RFs, the algorithm was run with default parameters 100 times and only the variables selected in the second step in > 95 % of the runs were retained. Random forest analyses, as implemented in the 'randomForest' package (v.
size = 30) implemented in the 'iml' package (v. 0. 9. 0; [65]). These plots show how the prediction of a response variable in a given RF model (PD, evenness or abundance of AOA) changes on average over the range of each individual environmental variable, while accounting for potential correlations amongst explanatory variables  [66]. All statistical analyses on β-diversity were conducted on the philr-transformed  [57]  ASV table. Differences in community composition and structure were visualized with a principal component analysis (PCA) using the rda function in 'vegan'. Distance-decay curves were calculated as the linear regression relationship between geographical distance and Euclidean distance-based community similarity. The relative in uence of climatic, edaphic and spatial factors on the patterns of β-diversity was estimated by variation partitioning analysis (VPA; varpart function in 'vegan').
To this end, cartesian coordinates of each sampling site were obtained from the GPS data (geoXY function, package 'SoDA' v. 1. 0. 6) and used to construct a matrix of distance-based Moran's eigenvectors maps (dbMEM). The edaphic factors were selected following a procedure similar to Power et al. [67]. First, individual permutational multivariate analyses of variance (PERMANOVA) and Mantel tests were conducted between overall β-diversity and each variable using the adonis and mantel functions implemented in 'vegan' (number of permutations = 9 999), respectively (Additional le 1: Table  S2 ). Collinearity among edaphic factors was assessed by pairwise Spearman correlations; only the variable with the highest mantel statistic was retained in each collinear group (|r| > 0. 7). Thereby, the selected edaphic factors were calcium, clay, C:N and C:P ratios, magnesium, moisture, pH, silt, SOC and total C.
Finally, a forward selection step (p < 0. 05) was applied to select the nal set of variables before running the variation partitioning analysis (forward. sel function, package 'adespatial' v. 0. 3. 4). The signi cance of each component of the VPA was estimated by a permutation test (adonis function). Elevation, although signi cant, only improved the explanatory power by 0. 4 % and was not included in the nal analysis. Since Nitrososphaeria represent putative AOA [6], their ecological preferences were predicted at a ner taxonomic scale by recursive partitioning of the corresponding philr-transformed ASV table with the entire set of metadata (Table  1 ). First, a subset of the full phlir-transformed ASV table was generated by extracting the nodes corresponding to Nitrososphaeria. Then, multivariate regression trees (MRT) were computed using the 'mvpart' package (v.
Funding The Digging Deeper project was funded through the 2015-2016 BiodivERsA call, with national funding from the  Swiss National Science Foundation  (grant  31BD30-172466 ), the  Deutsche Forschungsgemeinschaft  (grant  317895346 ), the  Swedish Research Council Formas  (grant  2016-0194 ), the  Spanish Ministerio de Economía y Competitividad  (grant  PCIN-2016-028 ) and the  Agence Nationale de la Recherche  (grant  ANR-16-EBI3-0004-01 ). Sequencing was performed by the  SNP&SEQ Technology Platform in Uppsala. The facility is part of the  National Genomics Infrastructure (NGI) Sweden and Science for Life Laboratory. The  SNP&SEQ Platform  is also supported by the  Swedish Research Council  and the  Knut and Alice Wallenberg Foundation.
Background: Although several studies explored the association between ADHD and suicidality in the pediatric population, there is a lack of studies focusing on adults. It is particularly unclear whether comorbid mood disorders confer an increased risk of suicidal ideation and attempt among individuals with high ADHD symptoms. Methods: Participants were 111 inpatients admitted to the Department of Psychiatry of Sant'Andrea Hospital between July 2017 and July 2018. Data were collected using self-report questionnaires and semi-structured interviews. Lifetime and last-month suicidal ideation and attempts were rated with The Columbia-Suicide Severity Rating Scale (C-SSRS). Symptoms of ADHD were rated using The Adult ADHD Self-Report Scale Version 1. 1 (ASRS-V1. 1). Results: Of the 111 participants, 40.
Participants Participants were 111 inpatients consecutively admitted to the Department of Psychiatry and Suicide Prevention Center of Sant'Andrea Hospital between July 2017 and July 2018. Their participation in the study was conditional on signing a written informed consent, drafted according to the new European rules 679/2016 (GDPR). Inclusion criteria were: meeting the DSM-5 criteria for any mental disorder, understanding the instructions and answer the questions, age of 18 or older, and signing the informed consent. Exclusion criteria were: severe neuropsychiatric disorders (e. g. , epilepsy, cognitive impairment, genetic syndromes) and the presence of cognitive deficits causing language problems. All patients gave their informed consent. Data are part of a broader study on mental pain and suicide risk, approved by the local institutional review board.
Assessment Data were collected using self-report questionnaires and semistructured interviews. Demographic and clinical information: (1) A Socio-Anamnestic form gathering information about socio-demographic variables (sex, age, ethnicity, educational level, occupation, etc. ) (2) Clinical data, including diagnosis, assessed according to Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) criteria (American Psychiatric Association, 2013) and supported by the Mini International Neuropsychiatric Interview (MINI)  (Sheehan et al. , 1998). The MINI has good validity, with median kappa coefficients greater than 0. 63 against other interviews and interrater reliability ranging from kappas of 0. 79 to 1. 00. The MINI has been updated for DSM-IV/MINI 6. 0 (version10/10/10) and DSM-5/MINI 7.
Statistical analyzes All analyzes were performed with the statistical package for social sciences (IBM SPSS Statistics for Windows. Version 25. 0. Armonk. NY) and PROCESS macro  (Hayes, 2017). Descriptive statistics are presented as percentages, means, and standard deviations. Independent sample t-tests and chi-square tests were used to test differences in the measured variables between participants without a history of a suicide attempt. We investigated the association between high ADHD symptoms and suicide-related outcomes using binary logistic regression, adjusted for age and sex. Finally, we conducted moderation analyzes by introducing an interaction term (high ADHD symptoms by mood disorders diagnosis) to test the potential moderating role of mood disorder diagnosis in the association between high ADHD symptoms and suicide-related outcomes.
Acknowledgements Acknowledgements seminars and workshops. I extend particular thanks to  Caroline Goodson ,  Colin Harding ,  Laura King ,  Laura Kounine ,  Claire Langhammer ,  Edward Madigan ,  Maria Margaronis ,  Lucy Noakes ,  Lyndal Roper ,  Michael Roper ,  Penny Summerfield ,  Corinna Peniston-Bird  and  Wendy Ugolini  for their insights and encouragement. Finally, all writers rely heavily on the tolerant kindness of friends and families. Special thanks to  Nick Fearn  for his careful reading of an early draft. My greatest love and appreciation goes to  Pavi Warwicker  for providing an almost unquantifiable abundance of loving support.
Abstract. Although snow depth on sea ice is a key parameter for sea ice thickness (SIT) retrieval, there currently does not exist reliable estimations. In the Arctic, nearly all SIT products use a snow depth climatology (the modified Warren-99 climatology, W99m) constructed from in situ data obtained prior to the first significant impacts of climate change. In the Antarctic, the lack of information on snow depth remains a major obstacle in the development of reliable SIT products. In this study, we present the latest version of the altimetric snow depth (ASD) product computed over both hemispheres from the difference of the radar penetration into the snow pack between the Ka-band frequency SARAL/Altika and the Ku-band frequency CryoSat-2. The ASD solution is compared against a wide range of snow depth products including model data (Pan-Arctic Ice-Ocean Modelling and Assimilation System (PIOMAS) or its equivalent in the Antarctic the Global Ice-Ocean Modeling and Assimilation System (GIOMAS), the MERCATOR model, and NASA's Eulerian Snow On Sea Ice Model (NESOSIM, only in the Arctic)), the Advanced Microwave Scanning Radiometer-2 (AMSR2) passive radiometer data, and the Dual-altimeter Snow Thickness (DuST) Ka-Ku product (only in the Arctic).
Finally, we have characterised the SIT uncertainty due to the snow depth from an ensemble of SIT solutions computed for the Arctic by using the different snow depth products previously used in the comparison with the ASD product. During the period of 2013-2019, we found a spatially averaged SIT mean standard deviation of 20 cm. Deviations between SIT estimations due to snow depths can reach up to 77 cm. Using the ASD data instead of W99m to estimate SIT over this time period leads to a reduction in the average SIT of about 30 cm. Published by Copernicus Publications on behalf of the European Geosciences Union.
, 2015; Kwok et al. , 2011). The climatology of  Forsström et al. (2011)  provides snow depths in areas outside the central Arctic where the W99m climatology is not working properly. More recently,  Shalina and Sandven (2018)  have created an improved snow depth climatology taking into account snow depth observations obtained during campaigns over seasonal ice. However, it is still based on data collected mainly in the 1960s, 1970s and 1980s. Apart from the data used to construct these climatologies, several campaigns have provided snow depth measurements in the Arctic. Between 1997 and 1998 , the SHEBA (Surface HEat Budget of the Arctic Ocean) project has highlighted the complex temporal and spatial snow depth variations  (Perovich et al. , 1999; Sturm et al. , 2002). The ice mass balance (IMB) buoys, originally deployed by the US Army CRREL-Dartmouth Mass Balance Buoy Program during the SHEBA project have measured snow depths since the 2000s.
, 2006). Since 2009, and until their final campaign in 2020, the Operation Ice Bridge (OIB; see Sect. 3. 3) campaigns have provided airborne snow depth measurements each year between March and April in the Arctic  (Kurtz et al. , 2013; Koenig et al. , 2010)  using a frequency modulated continuous wave (FMCW) snow radar  (Kurtz and Farrell, 2011). Note also that  Webster et al. (2014)  assess spring snow depth distribution on the Arctic sea ice from 2009-2013 airborne OIB radar observations. In parallel, in situ snow thickness data were collected in 2009 at the Danish GreenArc 2009 ice camp in the north of Greenland  (Farrell et al. , 2011). Between February and March 2015, snow depth was measured in the Nansen Basin during the N-ICE expedition  (Granskog et al. , 2016) , demonstrating among others the impact of heavy precipitation on thin ice growth  (Merkouriadi et al.
, 2017). From September 2019 to October 2020, the international MOSAIC expedition monitored the central Arctic  (Shupe et al. , 2020)  provid-ing, among others, data on snow precipitation, snow water equivalent and Magnaprobe snow depth measurements (e. g. Munoz-Martin et al. , 2020). Hence, several campaigns have been conducted during the last two decades in the Arctic, all of which have provided crucial information and proved invaluable as reference data. Compared to the Arctic, snow depth measurements, and more generally sea ice in situ data, are very limited in the Antarctic. Actually, several CryoVEx and OIB missions have been conducted in the Antarctic, but the data are still only available in raw L1b level and are difficult to interpret. To the best of our knowledge, only the meereis data portal (https://seaiceportal.
(2018)  argued that assimilating snow depth products could considerably increase sea ice model performances. Therefore, a solution allowing us to provide large-scale information on the spatial and temporal variation in snow depth and to improve the performance of seasonal sea ice forecasts is to use satellite data. For now, only a few datasets exist. The first snow depth estimates come from passive radiometric data. Among the various sensors that have been deployed (e. g. the SMMR,  Chang et al. , 1987 , or the SSM/I,  Grody, 1991) , the most relevant estimations of snow depth over sea ice have been calculated from spectral vertical gradients of brightness temperatures  (Comiso et al. , 2003; Worby et al. , 2008b)  of AMSR-E and its successor AMSR2  (Lee et al. , 2015). In spite of the relevant long-term availability of data, the commonly used snow depth retrieval algorithm is not adapted for multi-year ice (MYI) zones  (Brucker and Markus, 2013).
Consequently, AMSR's official data in the Arctic are only calculated in first year ice (FYI) regions. As far as we know, the AMSR2 product version of the University of Bremen (AMSR2B;  Rostosky et al. , 2018)  and very recently the SPICES project snow depth retrieval method  (Mäkynen et al. , 2020)  are the only attempts to compute snow depths in the Arctic MYI zones. The dataset developed from the work of  Rostosky et al. (2018)  is available but has the inconvenience of being re-calibrated from OIB data, which limits their availability to the months of March and April. With the same disadvantage of depending on OIB,  Maaß et al. (2013)  and  Zhou et al. (2018)  have proposed algorithms to retrieve snow depth from SMOS (Soil Moisture Ocean Satellite), with mitigated results. Recently, Braakmann-Folgmann and Donlon (2019)  proposed to compute snow depth by combining AMSR2 and SMOS data with a neural network approach.
Studies have also estimated snow depth on sea ice in the Antarctic (e. g. Kacimi and Kwok, 2020; Maksym and Markus, 2008). AMSR-E and AMSR2 also provide snow depths in the Antarctic. Compare to the Arctic it covers the whole domain since all sea ice is considered as seasonal ice. The recent work of  Kacimi and Kwok (2020)  estimated the Antarctic snow depth from the difference between the total freeboard obtained from ICESat-2 lidar measurements and sea ice freeboards of CS-2. They show a very low variability in CS-2 freeboards compared to ICESat-2 and that ICESat-2 freeboards explained >90 % of the variance in snow depth. They also highlight that the validation sea ice parameters in the Antarctic remains a challenge since no seasonally or regionally diverse datasets from field records can be used to assess the large-scale satellite retrievals.