dataesr/scientific-paragraphs-categorization huggingface dataset:
2. Interpolation between 1970-1997: Both the N emission and N deposition maps are available between the years 1970-1997 but for different sets of years. N emissions maps are available annually from 1970 to 1997, while N deposition is only available for four years (i. e. , 1970, 1980, 1990 and 1997). Using the common years (i. e. , 1970, 1980, 1990 and 1997) considering both the datasets, a relationship between N emission and N deposition was established (Eq. 2). This relationship was used to interpolate values of N deposition for years other than 1970, 1980, 1990, and 1997 using N emission maps that are available annually. β β = + N N ( 2 ) deposition emission 0 1 3. Extrapolation after 2013: Between 1997-2018, both the N emission and N depositions maps are available. However, N deposition maps are available only until 2013.
Using the common years (i. e. , 1997-2013), a relationship between N emission and N deposition was established (Eq. 2). Then, N deposition for each grid was estimated from 2013 to 2018 using N emission map. 4. For years 2019 and 2020, due to unavailability of N emission data, the N deposition was assumed to be the same as in 2018. These steps were conducted at gridded level and the deposition rates were aggregated to national scale using the approach mentioned in section "Methods for estimating N deposition". Validation of the N deposition products. We used two approaches to validate the N deposition inputs from the four data products. First, we compared the N deposition data products in this study with the site-level observations. Second, we compared the four data products with the N deposition estimated by the other global N budget studies.
For the first approach, we collected observation records of N deposition flux over the agricultural lands across China, the UK, the USA, and East Asia including 268, 4, 357, and 7 stations, respectively (Fig. 3  and Table  2 ). Except the UK, the N deposition flux from the station networks in other regions is expressed similarly to the products in this study. In the United Kingdom, six stations of the Environmental Change Network (ECN: https://ecn. ac. uk) are located on agricultural land (i. e. , Drayton, Hillsborough, North Wyke, Porton, Rothamsted, and Wytham). Among them, only Drayton, North Wyke, Rothamsted, and Wytham have precipitation chemistry data available for ammonium, nitrate, and total nitrogen deposition (Fig. 3c ). The N deposition data from each station are available on a daily scale in mg L -1.
To convert the unit to kg N ha -1 yr -1 , the following approach was followed: 1. Total N (kg N ha -1 day -1 ) = Total N (mg L -1 ) × Precipitation (mm) × 0. 01 2. = × ∑ - - - - Total N(kg N ha yr ) 365 Total N(kg N ha day ) n 1 1 1 n 1 1 1 where n is the number of days with available data in a year from all stations. The daily precipitation data were obtained from ECMWF 38. Each station's precipitation corresponds to the precipitation value in the grid cell adjacent to the respective station. For the second approach, we compared the N deposition estimates in this study with the N deposition from 14 global nitrogen budget datasets represented collectively in Zhang et al. 14. The datasets include Zhang Reorganized 13 , Zhang 2015 13 , Lassaletta  39, 40  , Lu and Tian  41, 42  , Nishina with double cropping  43  , Nishina without double cropping  43  , Conant and Dorich  16  , Bodirsky without forage  11, 17  , Bodirsky with forage  11, 17  , IMAGE  12  , FAO  44  , Gerber and Mueller  [45] [46] [47]  , Chang et al.
Sensitivity analysis. The sensitivity analysis examined how N deposition estimates from the four different products affect the assessment of NUE for each country over the past decades. The NUE is defined as the ratio of N removal by the crop (CR) to the sum of N inputs from synthetic fertilizer (SF), manure (MN), biological N fixation (BNF), and atmospheric N deposition (AD) (Eq. 3). We used N fertilizer, N manure, N fixation from FAOSTAT cropland nutrient budget  26  , while varying the N deposition estimates based on the four products developed in this study. Since the FAOSTAT cropland nutrient budget dataset  26  already includes N deposition from WL, we estimated a reference NUE with WL, and compared this NUE with the NUE estimated by replacing the N deposition with other three products (i.
In the remaining regions, AL is almost always lower than the 14 other nitrogen deposition estimates. Overall, WL estimates compare favorably to the remaining 14 N budget datasets with N deposition. comparison with station networks. All four data products either slightly overestimated or were within the range of station records of N deposition. However, for most practical purposes these differences are probably acceptable. In the USA, all four products overestimated N deposition from the station network (Fig. 8a ). In fact, until 2002, the values fall outside the upper bounds of the station records. The four products appear to be within the range for years 2003, 2011, 2013, and 2018. They are, however, above the 75 th percentile. Hence, a clear indication of overestimation is seen in the USA, but, for most application purposes, the products' N deposition estimates are only few kg N per ha cropland higher than station records.
Similar to differences in NUE, the correlation between NUE WL and NUE estimated by using the other three products did not vary a lot. Despite replacement of N deposition from AH, AL, and WH in the NUE estimation, all products show high correlation (r > 0. 8) in the majority of countries when the reference NUE (i. e. , NUE WL ) for the period of 1961-2020 obtained from FAOSTAT's cropland nutrient budget dataset  26  were correlated with the other three N deposition products (Section "Sensitivity Analysis", and Fig. 11 ). This indicates less sensitivity of NUE and the general usability of any of these products for global scale NUE assessment.
Usage Notes Within this study, we developed four N deposition products at national scale; AH: ACCMIP + HYDE, AL: ACCMIP + LUH2, WH: Wang et al. + HYDE, and WL: Wang et al. + LUH2. We recommend using the WL product for most general studies of global and regional cropland N budgets and NUE. It has already been adopted in the Cropland Nutrient Budget database jointly released  26  by FAO and IFA in 2022, for the following reasons: (1) the Wang et al. estimates are closer to the remaining existing datasets for the majority of regions, particularly Asian countries, whereas the estimates obtained using ACCMIP are on the lower side of the distribution of N deposition compared to other datasets, with minimal change after 2005, (2) the LUH2 cropland map is available at an annual time scale, which serves the purpose of this study, while the HYDE map is only available    3 ) the WL product was within the range of ground estimates of N deposition from the station networks in multiple countries (i.
acknowledgements This work was supported by the  International Fertilizer Association (IFA) , the  UN Food and Agricultural Organization (FAO) , and the  National Science Foundation. X. Z. is supported by the  National Science Foundation  ( CNS-1739823 ,  CBET-2047165 , and  CBET-2025826 ) and the  Belmont Forum. F. Z. is supported by the  National Science Fund for Distinguished Young Scholars  ( 42225102 ). The views expressed in this publication are those of the authors and do not necessarily reflect the views or policies of the organizations that conducted or funded this research.
Data records Data are available at Vishwakarma et al. 54  , Quantifying nitrogen deposition inputs to cropland: A national scale dataset from 1961 to 2020, Dryad, Dataset, https://doi. org/10. 5061/dryad. msbcc2g1x. The data contain N deposition (kg N ha -1 yr -1 ) in cropland. Each file has rows for countries and columns for years. The NaN stands for "Not a Number". File description: 1_AH. xlsx: includes the nitrogen deposition (kg N ha -1 yr -1 ) data from ACCMIP and HYDE 2_AL. xlsx: includes the nitrogen deposition (kg N ha -1 yr -1 ) data from ACCMIP and LUH2 3_WH. xlsx: includes the nitrogen deposition (kg N ha -1 yr -1 ) data from Wang et al. and HYDE 4_WL. xlsx: includes the nitrogen deposition (kg N ha -1 yr -1 ) data from Wang et al. and LUH2.
Study design and participants We performed an analysis using data from the SHEWHART trial, which has been described at length (protocol available at http://shewhart. univ-lyon1. fr). 14  The SHEWHART trial was a nationwide parallel cluster-randomised trial, designed to determine the impact of using control charts to monitor surgical outcomes on patients' major adverse events. Following a 2-year pre-implementation period (1 January 2014-31 December 2015), participating hospitals were randomised into two cluster groups by the health data department of Hospices Civils de Lyon using a computer-generated randomisation schedule. Because this trial concerned an open-label intervention and involved local investigators, it was not possible to mask hospital staff, although patients were masked to study group allocation.
Outcomes and data sources The economic outcomes analysed for this analysis were the number of hospital bed-days per patient within 30 days following surgery, and the hospital costs reimbursed for this care per patient by the insurer. Hospital bed-days included both the index stay associated with the surgery and any acute care readmissions related to the occurrence of major adverse events within the 30 days following the initial surgical procedure, regardless of the hospital. Major adverse events were defined as inpatient death, extended intensive care stay (at least two nights in intensive care or five nights in critical care), reoperation (open or laparoscopic digestive tract procedure) or severe complications (cardiac arrest, pulmonary embolism, sepsis or surgical site infection). Hospital costs reimbursed for this care by the insurer was determined based on the total amount of expenditures reimbursed by France's health insurance system (Assurance Maladie) for each patient stay.
This study was based on data from the French Medical Information System (Programme de Médicalisation des Systèmes d'Information (PMSI), source Agence Technique de l'Information sur l'Hospitalisation (ATIH)). The PMSI is a nationwide database routinely implemented for care reimbursement, and updated weekly with data from all hospitals in France. The data are prospectively collected, and the database relies on a coding system with strict variable definitions. A subset of records is audited on a regular basis to avoid coding errors. Due to its accuracy and exhaustive data collection, no patients were lost to follow-up during the study period. Inpatient stays were recorded as standard discharge abstracts containing compulsory information about the patient and their primary/ secondary diagnoses using the International Classification of Diseases, 10th revision, and detailed procedural codes associated with the care provided.
The corresponding 95% CIs were computed from non-parametric bootstrap based on 1000 replications. The total number of avoided hospital bed-days within 30 days following surgery, and hospital costs reimbursed for this care by the insurer were estimated from the difference of relative rates or costs difference for surgical patients in intervention hospitals during the implementation period. All of these models were adjusted for potential confounders based on a patient resource consumption score introduced as a categorical variable (quartiles), death status within 30 days following surgical procedure and their interaction. The patient resource consumption score predicted the mean expected amount of resource consumption, and was previously developed from patient data from the randomisation period.
RESULTS Among the 40 participating hospitals across the country, 17 (43%) were academic, 14 (35%) were non-profit and 9 (23%) were private-for-profit. Of the 159 688 patients who underwent operative procedures during the pre-implementation and implementation study periods, 97. 8% (156 133) were eligible for trial inclusion, of whom 99. 5% (155 362) were included in analyses (figure  1 ). After hospital randomisation, 75 047 patients were included in the intervention group and 80 315 were included in the control group. Patient characteristics between study groups and periods are presented in table 1. Detailed data reporting the compliance of each hospital with the implementation of the programme, and examples of control charts from the study with improvement actions made or corrective actions following a special cause variation can be found in the online supplemental appendix.
However, hospitals could also automatize control chart feedback by integrating data into local computerised decision support systems that trigger monitoring alerts for abnormal surgical outcomes. Future studies could evaluate how the automation of control charts in-house could facilitate their implementation and transform the way surgeons monitor patients. When applying a control chart intervention, healthcare providers should recognise that the feedback alone will not result in improvement. In our intervention, control charts were actively reviewed by surgical teams during regularly scheduled meetings. Discussion of the control charts encouraged behavioural and organisational changes, and helped surgical teams determine actions that could be taken to improve their outcomes. Therefore, achieving the desired results depends on the participation of surgical teams, and requires dedicated leaders on-site, regular meetings, productive communication and a willingness to identify and implement new solutions.
Original research robust statistical analysis approach enabled us to adjust for potential confounding factors that were measured, including median household income, which was significantly different between study groups. The results of sensitivity analysis including patients with missing household incomes were consistant with our intial results. Nevertheless, this trial does have several limitations. First, we cannot rule out the possibility that residual confounders influenced our findings due to potential inherent inaccuracies or unmeasured variables (linked to patient case-mix, surgeon, surgical team and the operating room) in the medico-administrative data. However, as most of the information used are critical for billing purposes, we expect they are accurately collected in hospital claims databases.
Research in context Evidence before this study We searched PubMed from database inception to December 7, 2022 using the terms "scleroderma" or "systemic sclerosis" with "symptom cluster" or "symptom clusters" or "cooccurring symptoms" or "concurrent symptoms" or "multiple symptoms" or "pain" or "fatigue" or "sleep disturbance" or "anxiety" or "depression" and "latent profile analysis" or "latent class analysis" or "cluster analysis. " We identified several studies that have examined patterns of single symptoms (e. g. , pain) over time in systemic sclerosis or that have used latent profile analysis for other purposes but only one study that attempted to identify classes of co-occurring patient-reported symptom outcomes. That study, which was published in 2022, analysed data from 587 participants at a single United States academic medical centre and used cluster analysis to identify three subgroups, or classes, of people with "no/minimal," "mild," or "moderate" symptoms of dyspnoea, pain, fatigue, sleep disturbance, anxiety, and depression.
Study design and participants In this multi-centre cross-sectional study, we assessed baseline data collected at the time of enrolment in the ongoing Scleroderma Patient-centered Intervention Network (SPIN) Cohort. 24  The SPIN Cohort sample is a convenience sample. Eligible participants at SPIN recruiting sites who are classified as having systemic sclerosis by a SPIN physician according to 2013 American College of Rheumatology/European League Against Rheumatism criteria,  25  are 18 years of age or older, and are fluent in English, French, or Spanish are invited to participate by a physician or nurse coordinator. 24  Written informed consent is obtained, and an online medical data form is completed by the SPIN physician or nurse coordinator to initiate registration. Participants then receive an email with instructions on how to complete patient-reported outcome measures online at enrolment and every three months thereafter.
The SPIN Cohort study was approved by the Research Ethics Committee of the Centre intégré universitaire de santé et de services sociaux du Centre-Ouest-de-l'Île-de-Montréal (#MP-05-2013-150) and by the ethics committees of all recruiting sites. This study used baseline data collected between April 2014 and July 2020 from 48 SPIN centres in Australia, Canada, France, Mexico, Spain, the United Kingdom, and the United States of America. SPIN Cohort participants are comparable to those of other major systemic sclerosis cohorts. 26  In the present study, data were included for participants who completed the Patient-Reported Outcomes Measurement Information System-29 version 2. 0 (PROMIS-29 v2. 0) measure 27 at baseline and were not missing data for the anxiety, depression, fatigue, and sleep disturbance domains and the pain intensity item.
All medical variables were assessed by physicians. Participants reported sociodemographic information (including race or ethnicity) and completed patientreported outcome measures. These measures were completed at the time the medical data form was completed. The PROMIS-29 v2. 0 measure  27  was used to assess the five symptoms in the symptom cluster. Domain scores were used for anxiety symptoms, depression symptoms, fatigue, and sleep disturbance. Raw subscale scores for the domains were derived by summing the four subscale item scores rated on a 5point scale (1 = "not at all" to 5 = "very much"). Higher scores indicated greater symptom severity (e. g. , more severe anxiety symptoms). The raw subscale scores (range 4-20) were converted to subscale T-scores standardized for the USA general population (mean = 50, standard deviation = 10).
There were also several limitations. While the SPIN Cohort is comparable to other large systemic sclerosis cohorts,  26  we acknowledge that the SPIN Cohort is a convenience sample of participants with access to the internet and specialized systemic sclerosis care, which may limit the generalizability of our findings. We also recognize that socioeconomic status data were not available for SPIN Cohort participants, and that additional factors (e. g. , co-existing fibromyalgia or arthritis) that may contribute to the symptom outcomes were not collected. As a largely descriptive study, we did not correct for multiple hypothesis testing, and we used cross-sectional data which provided only a single snapshot of participants' symptoms. While the criteria used for pain severity was based on previously established cut-off scores, we recognize that this might not accurately reflect how individuals experience their symptoms.
71073 Å and were deposited at the Cambridge Crystallographic Data Centre as CCDC 2252606 (Supplementary Materials). The data can be obtained free of charge from the Cambridge Crystallographic Data Centre via http://www. ccdc. cam. ac. uk/getstructures, accessed on 3 February 2023. The structure was solved by direct methods and refined by full-matrix least-squares calculation against F 2 (SHELXL, 2015)  [29] [30] [31].
By taking advantage of our candidate genes, the isolation of pathogenic heterozygous variants will be accelerated through mining of next generation sequencing (NGS) databases of individuals with delayed or absent pubertal development. In addition, given the extensive phenotypic heterogeneity of mental disorders, our candidate genes in this genomic region will further help identify disease-associated variants from VUS (variants of unknown signi cance) databases of individuals with neurodevelopmental disorders including autism.
This microdeletion encompassing 29 genes is likely to harbor the disease genes involved in the common set of neurodevelopmental phenotypes shared with our seven studied CNV patients and eight unpublished CNV cases from the DECIPHER database (Fig. 2A , Tables  1  and 3 ). Among 15 heterozygous CNVs at 12p11. 2, duplications account for ten cases, whereas deletions represent the remaining ve cases (Fig. 2A , Tables  1  and 3 ). At least one neurodevelopmental phenotype was seen in each case. Some of the CNVs were inherited from one parent with an unknown phenotype, whereas the inheritance of the remainders is unknown (Tables  1  and 3 ). This suggests that some genes in the CNVs located at 12p11. 2 may have incomplete penetrance or epigenetic imprinting if a carrier parent is asymptomatic. We interpret the pathogenicity of deletion and duplication cases similarly since they both exhibit the similar neurological condition.
Slides were counterstained with 4',6'-diamidino-2-phenylindole hydrochloride (DAPI). Representative metaphase images were recorded using the CytoVision image analysis system (Applied Imaging) database. Using the relative STS positions on the UCSC map, BAC clones were chosen to cross the relevant regions on chromosomes 7 and 12. FISH analysis of each clone was then used to identify clones that mapped proximal or distal to each chromosome breakpoint. By this way, physical maps of chromosomes 7q21 and 12q24 were constructed, and the breakpoint regions narrowed and de ned  13.
Acknowledgements: We thank all the subjects and their families for their participation in this study. We are also indebted to  Robert E. Eisenman  for technical assistance and to  Amy Bosco  for clinical information. We are also grateful to the phenotypic assistance from  Elise David  and  Gerard Marion  at the  Department of Genetics, Reference Center for Rare Diseases of Developmental Anomalies and Polymalformative syndrome, CHU de Caen Normandie, France. We also thank  Lynn Chorich  and  Shahad Sabaawi Ibrahim AlHassan  for their assistance in proofreading the nal version of the manuscript.
Therefore, stellar atmospheres record the past in their chemical abundances, allowing a look-back time that varies between a few hundred million years and the age of the first stars in the Universe. In this framework, previous intermediate Gaia data releases had to be complemented with chemical data from ground-based observations. However, ground-based spectroscopic surveys like GALAH (e. g. Buder et al. 2021) , APOGEE  (Abdurro'uf et al. 2021) , Gaia-ESO Survey  (Gilmore et al. 2022; Randich et al. 2022) , and RAVE  (Steinmetz et al. 2020) , despite the recent improvement of multiplex capabilities, are still hampered by spatially biased samples. In addition, the inhomogeneity induced by different analysis procedures, targeted stellar types, and spectral configurations blur the collected chemical information.
2022). With continuous data collection for 34 months outside the Earth's atmosphere, and a large volume coverage reaching distances of about 8 kpc from the Sun (thanks to the population of giant stars), the Gaia DR3 spectroscopic survey provides stellar parameters and chemical abundances in all major Galactic populations, sharpening our global view of the Milky Way. In addition to the sky coverage advantage, it is worth comparing this Gaia DR3 GSP-Spec catalogue with high-resolution ground-based surveys in other crucial characteristics for Milky Way studies, as the number of analysed stars, the limiting magnitude, and the explored chemical diagnostics. For magnitudes brighter than G=13. 6 3  , there are more stars in the Gaia DR3 GSP-Spec catalogue than in any other ground-based survey (with both GALAH and APOGEE representing only ∼8% of Gaia GSP-Spec).