dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus parameterizations in ORCHIDEE-MICT should be calibrated, and atmospheric forcing should be carefully selected to reflect the situation of China. Several criteria were chosen and compared among the four simulations in China, YZRB, and YLRB. Results showed that GSWP3 and WFDEI, which had the best performances in correlation coefficients and RMSE, respectively, were ideal choices for hydrological study in China. However, higher MSD in the Yellow River basin reflected the complicated climate conditions in northern China, which might be significantly influenced by human activities as well. Finally, we used the differences of simulated soil moisture and meteorological variables to simply investigate the linkage between them. Results showed that the differences of simulated soil moisture were mainly explained by the differences of air humidity and precipitation frequency among the four atmospheric forcing datasets.
Acknowledgements. This study was supported by the  National Natural Science Foundation of China  (grant number  41561134016 ) and by the  CHINA-TREND-STREAM French national project (ANR grant  no. ANR-15-CE01-00L1-0L ). Matthieu Guimberteau  and www. hydrol-earth-syst-sci. net/22/5463/2018/ Hydrol. Earth Syst. Sci. , 22, 5463-5484, 2018  Z. Yin  et al. : Evaluation of  ORCHIDEE-MICT soil moisture driven by different forcings in China Philippe Ciais  acknowledge support from the  European Research Council Synergy  grant  ERC-2013-SyG-610028 IMBALANCE-P. Hyungjun Kim  was supported by  Japan Society for the Promotion of Science KAKENHI  ( 16H06291 ). We thank  Brecht Martens  and  Suxia Liu  for helpful discussion about GLEAM and ISMN datasets. We gratefully acknowledge two anonymous referees and the editor for their helpful comments and efforts.
Plasmids pSK108 (reference sequence number NC_013395) and pKH8 (GenBank accession number U50077) (2) are 0. 4% and 0. 5% divergent from pLUH01, respectively. More distant relatives include pSA1308 (reference sequence number NC_007928) (2), pWBG754 (reference sequence number NC_013350), pKH4 (GenBank accession number U81980), and pNVH01 (reference sequence number NC_004562). The last two of these have qacJ in place of qacC, at 27% amino acid divergence (3). Volunteers were recruited from a general practice surgery and a general hospital in Lancaster, United Kingdom (54. 05°N, 2. 80°W).
ACKNOWLEDGMENTS K. V. A. received a  Service Increment from Teaching (SIFT) studentship  from the  University Hospitals of Morecambe Bay (UHMB) National Health Service (NHS) Foundation Trust ,  United Kingdom , and performed this work as part of the requirements for the degree of Master of Science (M. Sc. ). Rosetrees Trust ,  United Kingdom , provided additional funding for deep sequencing (grant  M395 ).
| Plant genotyping Plants genotypes were fingerprinted using AFLP  (Meudt & Clarke, 2007)  by the procedure of  Vos et al. (1995)   AFLP profiles were analyzed using Peak Scanner software (Applied Biosystems); the profiles, for which the automated process failed to detect or attribute peak size, were processed manually. The binning and scoring of the profiles were performed using the RawGeno package  (Arrigo, Tuszynski, Ehrich, Gerdes, & Alvarez, 2009)  in R software  (R_Development_Core_Team, 2011). We kept individuals within the 5%-95% quantile confidence interval around the peak number mean and kept the peaks that corresponded to fragments longer than 100 bp to limit size homoplasy  (Arrigo et al. , 2009; Pompanon et al. , 2005). Bins that were not reproducible at least once within a group of replicates (four groups of replicates for each pair of primers and three to four replicates per group) were systematically removed.
We calculated the "bin content information" criterion  (Arrigo et al. , 2009; Pompanon et al. , 2005)  for various filtering strategies and found that our conservative strategy retained the most information. A total of 93 individual plants were reliably scored for the presence or absence of 345 reliable and polymorphic loci, which were coded in a binary matrix. The plant population genetic structure was inferred from AFLP data using STRUCTURE 2. 3. 2  (Pritchard, Stephens, & Donnelly, 2000) , implementing an admixture model without a priori knowledge of the geographic provenance of individuals. The likelihood of the number of genotypic clusters (K) was estimated for K ranging from 1 to 20 with five runs for each K value with 50,000 burn-in periods followed by 50,000-step Markov chain Monte Carlo (Supporting Information Figure  S1 ).
The remaining merged reads were assigned to their sample of origin by their terminal tags using ngsfilter, which trims primers and tags away from the sequence, and identical reads were counted within each sample using obiuniq. Finally, singletons were removed following Coissac, Riaz, and Puillandre (2012), together with sequences shorter than 66 nt (the smallest one in our taxonomic database, see below), and an abundance table was built using obitab. To cluster sequences into MOTUs, we first aligned them pairwise using a global Needleman-Wunsch algorithm implemented in the Sumatra package (https://metabarcoding. org/sumatra). Each pair yielded a similarity index equal to the longest common subsequence normalized to the alignment length. Indices were then used to build clusters sharing at least 98% similarity  (Lentendu et al.
, 2011)  using the nonhierarchical Markov clustering method (MCL;  van Dongen, 2000; Zinger, Shahnavaz, Baptist, Geremia, & Choler, 2009). The taxonomic assignment of MOTUs used a custom ITS1 database derived from the UNITE database (release V7, 2017-01) using ecoPCR, which extracts sequences flanked by two input primers. The most represented sequence of each MOTU was aligned against this ITS1 database using the fasta35 algorithm  (Pearson, 2000)  implemented in the ecotag program, and only environmental sequences identical at a minimum of 80% to a reference sequence were assigned. Possible lifestyles were deduced from the MOTU taxonomy using FUNGuild  (Nguyen et al. , 2016). Nine samples with fewer than 900 reads were removed, and MOTUs with less than 10 reads or unassigned to the Fungi kingdom were discarded.
Plant genetic dissimilarities were calculated using the Jaccard index on AFLP fingerprints. Additionally, Euclidian distances were used for soil abiotic environment (using reduced chemical data combining nitrogen, carbon, water, nitrate and ammonium contents, and pH values), both beneath and outside cushions and for geographic positions. Instead of using a traditional regression analysis of variance, we chose a generalized additive model (GAM) that deals with potential nonlinear relationships between pairwise beta-diversities and environmental distances  (Ferrier, Manion, Elith, & Richardson, 2007 ; see Supplemental data) and thus increases the predictive power. The pure and joint explained variance of each predictor (either plant dissimilarities or environmental distances) was calculated using Nagelkerke's R-square: The pure fraction represents the effect of a variable after accounting for the effects of all other predictors.
| D ISCUSS I ON We have studied the fungal communities of a foundation cushion plant in a microevolutionary context across contrasting environmental conditions, such as different bedrocks. Genotyping of S. acaulis cushions delineated two genetic clusters, one of which can be found both on siliceous and limestone bedrock, while the other one is restricted to limestone. This unique situation, despite asymmetries in the dataset, allows highlighting the key influence of plant genotypes on soil engineering, both in abiotic modifications and in the filtering of fungal species. F I G U R E 5 Predictors of variation of the fungal communities hosted by cushions. Pairwise dissimilarities in a set of 64 cushions were used to partition the variance of fungal communities using GAM (see Methods). The pure and joint effects of the different explanatory variables on fungal variation are indicated.
-Noëlle Binet, and two anonymous referees also provided a number of insightful comments. This research was conducted at the Long-Term Socio-Ecological Research (LTSER) Zone Atelier Alpes, a member of the eLTER-Europe network, and at the Station Alpine J. Fourier, a member of AnaEE France supported by the project Investissement d'Avenir: ANR-11-INBS-0001AnaEE-Services. The Laboratoire d'Écologie Alpine is part of Labex OSUG@2020 (ANR10 LABX56). We are also grateful to the Ecrins National Park for authorizing the fieldwork. We acknowledge Julien Renaud and Charline Giguet-Covex for their help in preparing the geographic database and Figure 1, respectively; Eric Coissac and Frederic Boyer for providing the unpublished Robitools R package; and Laure Gallien, LucieZinger and Damien Georges for their help with data analysis.
Introduction: The harmful use of alcohol is a growing global public health concern, with Sub-Saharan Africa at particular risk. A large proportion of adults in Uganda consume alcohol and the country has a high prevalence of alcohol use disorders (AUD), almost double that for the African region as a whole. Bwindi Community Hospital, in rural western Uganda, recently introduced a program of screening, diagnosis and management of AUD and we assessed how this worked. Methods: This was a cross-sectional study in three departments (out-patients, adult in-patients and sexual & reproductive health) of Bwindi Community Hospital assessing numbers of patients screened, diagnosed and treated with AUD between January 2014 and June 2017. Data sources included the hospital electronic data base and departmental case files.
Data Variables and Sources of Data Data variables included: hospital department (out-patient, adult in-patient, sexual and reproductive health); date; age, sex, screened for AUD; AUD score; diagnosed with AUD (score on AUDIT = 8 or more); clinical diagnostic category; medical and supportive intervention given; type of intervention (brief guidance and counselling, social support from community members, referral to mental health team, detoxification, referral to AA group). Sources of data included (a) the hospital electronic database for out-patients and for adult in-patients between April and June 2017, and (b) hospital case files for adult inpatients (before April 2017) and patients admitted to sexual & reproductive health. Data were collected into a structured proforma using EpiData software (version 4.
ACKNOWLEDGMENTS This research was conducted through the  Structured Operational Research and Training Initiative (SORT IT ), a global partnership led by the  Special Program for Research and Training in Tropical Diseases at the World Health Organization (WHO/TDR ). The training model is based on a course developed jointly by the  International Union Against Tuberculosis and Lung Disease  ( The Union ) and Medécins sans Frontières (MSF). The specific  SORT IT program  and the mentorship and coordination of the three SORT IT Workshops which resulted in this publication were implemented by authors affiliated with:  Bwindi Community Hospital, Bwindi, Uganda; The University of Chester, Chester, UK ;  University of Exeter , Exeter, UK; and  The Union, Paris, France.
Validation of the research tool A total of 1,008 children and adolescents from the target group participated in the validation study. In outpatient examination AR was diagnosed in 46. 4% and BA in 11. 2%. The BA and AR risk assessment based on the original version of the algorithm is presented in Figure  2. On the basis of the data obtained during the validation study, the algorithm was calibrated to better match the specificity of the target group. The standard screening test division into two groups: probably healthy (negative result) and probably sick (positive result) was also adopted. Therefore, it was possible to determine the sensitivity and specificity of the new version of the algorithm. In the case of AR, the sensitivity of the diagnostic test was 0. 852 and the specificity was 0.
Limitations and strengths of the project The limitations of the project include using mainly new media for the purposes of the information and education campaign, which due to their nature may constitute a barrier for people who do not use the Internet or do it rarely. However, according to Polish Public Opinion Research Center estimates, in 2017, 67% of Polish citizens used the Internet at least once a week. Among people aged between 18 and 44 years, the percentage of Internet users ranged from 87% to 100% (21). The recruitment system is also a limitation of the study. All students from a given educational institution are invited, but the participation is completely voluntary. Due to the fact that no data is collected on people who refused to participate in the study, it is impossible to determine.
Nat. Hazards Earth Syst. Sci. Discuss. , https://doi. org/10. 5194/nhess-2018-230 Manuscript under review for journal Nat. Hazards Earth Syst. Sci. Discussion started: 17 September 2018 c Author(s) 2018. CC BY 4. 0 License. Code and data availability. Information about the availability of the COSMO-CLM source code can be found on https://www. clm-community. eu. The simulations generated as part of this work are not publicly available. Researchers interested in scientific collaboration and data usage are asked to contact the authors.
Study population This study was based on data from the REIN registry. This registry started in 2002, and progressively extended to the 22 metropolitan French regions and 5 overseas territories. All French patients with ESRD are registered, including those who undergo preemptive kidney transplantation. Patients with acute kidney injury requiring dialysis are not included in the registry. If unclear, chronic kidney failure is defined by a persistent requirement of dialysis after 45 days of RRT (REIN guidelines). All incident patients aged 18 years or older who started long-term RRT in one of the 22 metropolitan regions in 2012 (or in the 16 regions included in 2006) were included, if the dialysis initiation status (PS or ES) was described. Patients who underwent preemptive kidney transplantation and patients on dialysis after loss of a functional transplant were not included because they were not considered as incident patients (REIN guidelines).
Collected data The proportion of ES among the included patients was calculated, globally and for each region. ES was defined as a first dialysis session within 24 h after a nephrology visit, for life threatening conditions, including acute pulmonary edema, severe hyperkalemia or acidosis, uremic confusion or pericarditis. This applied also to patients with a previous follow-up and presenting with an acute complication. However, the exact cause of dialysis start was not recorded in the registry. For both groups (ES or PS), the following baseline (i. e. , at dialysis initiation) data were collected: age, sex, primary renal disease, nutritional status, comorbidities, walking disability, and ESRD management (place of care, modalities). The number of previous nephrology visits within 1 year before dialysis start was also registered.
Primary renal diseases were grouped in three categories, depending on the form of renal function impairment: acute nephropathy (including CKD exacerbation or flare), slowly progressive nephropathy, or unknown (see Additional file 1). Then, the same data were compared between ES patients who started dialysis in 2006 and in 2012 (in the same 16 regions included in the REIN registry in 2006). Only, the number of nephrology visits in the last year before dialysis was not compared because this item was not recorded in the registry in 2006.
Comparison between the 2012 and 2006 data Next, data on 6119 patients who started dialysis in 2006 (92. 6% of all incident patients in the 16 regions included in the REIN registry at that time) were compared with the data on the 7084 patients from the same regions who started dialysis in 2012. ES proportion was similar in both years: 29. 2% in 2012 and 28. 4% in 2006 (p = 0. 282). Nevertheless, the detailed comparison by region showed large local differences (Additional file 1: Table  S3 ). Some regions did manage to reduce the ES percentage from 2006 to 2012 (e. g. , in Lorraine, the ES rate dropped from 49. 8% in 2006 to 19. 5% in 2012). The clinical profile of patients with ES remained similar, with high comorbidity level (Table  4 ). The proportion of some comorbidities were even higher in 2012 than in 2006: 17.
Discussion This large epidemiologic study, based on prospectively collected data from the REIN registry shows that in France, the ES rate is very high (about 30% of all incident dialysis patients in 2012) and remained stable between 2006 and 2012. This result is consistent with studies on other European and North-American cohorts  [15, 16]  that highlighted the difficulty in reducing ES rate, despite the development of multidisciplinary management for ESRD  [17]. Our study shows unequivocally that ES is associated with worse prognosis than PS (three-year survival: 57% for ES vs 68. 2% for PS, p < 0. 001), indicating that ES demonstrated in a monocentric study that ES was the major confounding factor explaining the over-mortality of hemodialysis compared with peritoneal dialysis as first RRT modality, and ES was the only factor strongly associated with early mortality in dialysis  [16].
This should be a priority of the oceanographic community; data not taken today is lost forever. A proper climate record of Earth's largest reservoirs of heat, water, and carbon is the least we owe to future generations. The recent report by the National Academies of Sciences, Engineering, and Medicine (2017) recommends the formation of Ocean Climate Partnerships between academia, industry, and philanthropy to find a way to provide the continuity of observations that is absolutely essential to climate research. These could build on the success of ocean research efforts like the Geochemical Ocean Sections Study (GEOSECS), the World Ocean Circulation Experiment (WOCE), and Argo. Oceanographers of all disciplines have a good record of cooperative action to accomplish significant scientific advancements on both the national and international levels.
Search strategy The search strategy was formulated in broader terms voluntarily, in order to ensure exhaustivity. The Mesh terms "transcranial magnetic stimulation" AND "stroke" were combined. We searched the following databases from inception until June 2018: Medline and EMBASE. The language was restricted to English. The number of articles corresponding to these Mesh terms was 1798. Studies were then included if (i) TMS was used to investigate ipsilesional rMT in participants with a confirmed diagnosis of stroke of any type, with or without comparison to healthy controls, (ii) rMT was collected in hand or forearms muscles (if rMT was recorded from multiple muscle groups, only the distal arm muscle data were included), (iii) motor upper limb or hand function was evaluated at the time of the TMS session or later and, (iv) individual patient data (with rMT and motor scores) were available even though the primary aim of the publication was not to investigate rMT but rather other TMS parameters.
Studies were excluded if (i) rMT was recorded from the proximal arm muscles (i. e. , biceps brachii) or from lower limb/pharyngeal/trunk muscles, (ii) motor threshold was collected under active condition (i. e. , during a contraction of the target muscle), (iii) rMT was collected after an intervention (i. e. , novel rehabilitation techniques, after non-invasive brain stimulation such as repetitive TMS. ) and, (iv) the sample size was less than 5 patients, including case reports. Two researchers ran each database search independently and then compared findings. Search results duplicates were removed. The same two researchers screened the search findings for eligibility, using article titles and abstracts, for the inclusion of appropriate participants, and measurements. When it was unclear if the study met all of the inclusion criteria on the initial title/abstract screening, the full text was obtained and assessed for eligibility.
Data extraction and management One author extracted data from the included studies using a standardized data extraction form specifically designed for this review. Extracted data included the following information from the methods section of the articles: aim of the study (UI, PO, PR), detailed description of the participants (age, sex, type and location of stroke, time since post-stroke onset, motor scores), research methods (type and size of the coil, target muscle) and type of the motor score. The correlation coefficient between rMT and motor scores, the R2 and the statistical significance were recorded when available.
Analysis of endocytic structures Quality control of automated imaging Datasets that were generated by automatic superresolution imaging typically consisted of 100 to 500 fields of view, and were first subjected to quality control. All individual back focal plane (BFP) images were inspected for air bubbles. If single BFPs showed bubbles, the corresponding fields of view were discarded. If multiple BFPs showed bubbles, the entire dataset was discarded. Subsequently, statistics from the individual superresolved images were compared. We analyzed the median localization precision, median fitted photon background, total number of localizations and number of frames for each experiment. If any of those parameters did not remain approximately constant over time, this indicated changes in the sample or imaging conditions, e.
fðX; YÞ = A erf r out À RðX; YÞ ffiffiffi 2 p s À erf r out À dr À RðX; YÞ ffiffiffi 2 p s (1) RðX; YÞ = ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðX À x 0 Þ 2 + ðY À y 0 Þ 2 q (2) As we used a low threshold during automatic site segmentation to avoid biases from overly stringent segmentation, the dataset contained a comparably small set of structures with a low number of localizations and a small size on the order of the resolution. Because these structures were too small to obtain geometric information, we excluded structures with outer radii below 30 nm and below 30 localizations, i. e. , with less than approximately 10 mMaple proteins, from the analysis. Because structures in the coat and scission modules (Clc1, Chc1, End3, Ent1, Pan1, Rvs167, Sla1, Sla2) mostly formed small structures, we only excluded structures with less than 30 localizations, and did not apply a size threshold.
ACKNOWLEDGMENTS We thank  Jan Ellenberg  for critically reading the manuscript; the entire Ries and  Kaksonen labs  for fruitful discussions and support of this work;  Rohit Prakash  and  Sunil Dogga ;  Bernd Klaus  from the  EMBL Centre  for Statistical Data Analysis; all contributors to Cytosim and the  EMBL IT  support team; the life science editors for editing assistance;  Richard van der Oost  for support with Blender; and  Effigos AG  for an early version of the 3D graphics. This work was funded by the  European Molecular Biology Laboratory  (to M. M. , J. A. v. d. B. , J. D. , S. D. , P. H. , J. L. M. , F. N. , and J. R. ), the  EMBL International PhD Programme  (to M. M. , J. D. , and P. H. ), the Centre for Modelling in the Biosciences (https://www. bioms. de/; to S. D. and F. N. ), the  Deutsche Forschungsgemeinschaft (DFG)  ( RI 2380/2 ) (to J.