dataesr/scientific-paragraphs-categorization huggingface dataset:
CFigure 1. 1 Figure 1. PEHO syndrome variations in the evolutionarily conserved protein Hit1. A, domain organization of yeast Hit1 and human ZNHIT3 showing the conserved residues mutated in PEHO syndrome. The figure was generated using ConSurf (68). B, multiple sequence alignment of the Zf-Hit domains generated with Clustal Omega (69) and colored in Jalview (70). Shaded amino acids are conserved. Darker shades of gray show higher conservation. The PEHO-associated conserved cysteine and serine residues are in bold and marked with an arrow. C, the structure of the Zf-HIT domain of Hit1 (PDB ID: 2N95) and ZNHIT3 (PDB ID: 2YQQ) highlighting the conserved residues mutated in PEHO syndrome. PEHO, progressive encephalopathy with edema, hypsarrhythmia, and optic atrophy; Zf-HIT, zinc finger HIT; ZNHIT3, human zinc finger HIT-type containing protein 3.
| Seismic dataset and volume calculations We use widespread high-and low-resolution seismic reflection profiles to calculate the volumes of the MSC units in the CMD (Figure  2a ). This dataset has been interpreted, described and used in several previous studies (e. g. ,  Bellucci et al. , 2021; Maillard et al. , 2014; Raad et al. , 2021). Following the interpretation of the MSC units on the seismic profiles, a thickness map for each unit was created (Figure  2 ) using the internal velocities presented in Table  1  for the time to depth conversion. For the volume calculations, we consider 80% of the total volume of BU1 (=PLG) and BU2 as gypsum, since elsewhere around the Mediterranean the PLG cycles contain only thin non-evaporitic intercalations and much thicker gypsum beds (Table  1 ; e. g.
Unless a drastic decrease in the fluxes caused by a more sluggish circulation (e. g. , slowdown of the currents due to a base-level drop) of the Mediterranean currents took place, the CMD will have had almost the same salinity as the upper Mediterranean water layer, as is true for the present-day situation  (Barceló-Llull et al. , 2019). To our knowledge, until present, no studies showed or quantified such a decrease in the Mediterranean currents and its consequences during the MSC. Our calculations also show that gypsum precipitation could not have persisted for the whole duration of a precessional cycle. Instead, F I G U R E 1 2 Schematic representation of the depositional conditions in the Western Mediterranean during stages 1 and 2 of the MSC according to our modelling results. The non-silled Valencia Basin does not feature the presence of Halite on seismic dataset in its presentday setting  (Maillard et al.
| CONCLUSIONS We carried out numerical modelling of the Messinian Salinity Crisis (MSC) evaporites accumulation in the Central Mallorca Depression (CMD) using physics-based models built on conservation of mass of water and salt and a simplified model for the flow in sea straits. The interpretation of a widespread seismic dataset covering the CMD allowed the estimation of the volumes of the MSC evaporites that are used to constrain both our isostatic and evaporite precipitation models. According to the results and observations, we conclude the following: • During stage 1 of the MSC (5. 97-5. 60 Ma), the upper water layer of the Mediterranean had to be at gypsum saturation salinity to supply the CMD with Ca 2+ and SO 2- 4 ions needed to deposit the observed volume of PLG. Gypsum deposition likely occurred only during part of precessional cycles (maximum duration of ca.
Map of the MSC units over the Balearic Promontory (BP), Valencia Basin and Algerian Basin. Our study area focuses on the Central Mallorca Depression (CMD) located between the islands of Mallorca and Ibiza, which contains several Bedded Units (BUs) and a Halite unit geometrically/attitudinally separated from the deep basin's Mobile Unit (MU) and Upper Unit (UU). MSC units of the BP are modified from Raad et al. (2021). Onland geology of the Balearic Islands is modified from geological map of Spain 1:50000 (IGME). Thin white lines in the background represent the present-day Bathymetry taken from the European Marine Observation and Data network (EMODnet) database available online (www. emodn et-bathy metry. eu). Thin black lines represent the paleo-bathymetry at the start of the MSC, modified after Heida et al.
ACKNOWLEDGEMENTS SALTGIANT ESRs and PIs are all thanked for the numerous exchanged discussions and comments during workshops, courses and fieldtrips. We are grateful to  Andrew Madof  and  Francesco dela Pierre  for their thorough and instructive review that improved a lot the quality of the manuscript. Fadl Raad  and  Ronja Ebner  shared the primary responsibility for the modelling and writing of the manuscript. Paul Meijer oversaw and supervised the modelling procedure. Hanneke Heida ,  Johanna Lofi ,  Agens Maillard  and  Daniel Garcia-Castellanos  helped in writing, reviewing and discussing the results of this work. All authors contributed to and made edits on the manuscript.
F Map of the MSC units over the Balearic Promontory (BP), Valencia Basin and Algerian Basin. Our study area focuses on the Central Mallorca Depression (CMD) located between the islands of Mallorca and Ibiza, which contains several Bedded Units (BUs) and a Halite unit geometrically/attitudinally separated from the deep basin's Mobile Unit (MU) and Upper Unit (UU). MSC units of the BP are modified from Raad et al. (2021). Onland geology of the Balearic Islands is modified from geological map of Spain 1:50000 (IGME). Thin white lines in the background represent the present-day Bathymetry taken from the European Marine Observation and Data network (EMODnet) database available online (www. emodn et-bathy metry. eu). Thin black lines represent the paleo-bathymetry at the start of the MSC, modified after Heida et al.
The French land parcel identification system To identify where crops are grown, the French Land Parcel Identification System (LPIS) was used. This database provides spatially explicit information of crops grown in French field parcels (i. e. field geometry and crop type) over the whole French territory, and has been used in previous research to describe the spatial organization of crops at different scales  [19, 20]. The French LPIS has been set up in 2002 and is updated every year on the basis of the farmers' Common Agricultural Policy (CAP) declarations. Data for the year 2018 were used, which was the most recent year available at the time of the study. This represents over 6 million field parcels covering about 27 Mha. Cultivated crops are classified into 23 groups (Table  A1 ). A group of crops may correspond to a single crop like wheat, or to several crops (apples, plums, and pears belong to the "orchards" group).
Map of residential buildings in France The location of residential buildings in France was provided by the BD TOPO® database  [22]. This database provides a map of the whole French territory in 2019 where each building is represented by a polygon, with attributes describing the nature of the building, its use, state, date of creation and modification. The term "building" encompass all "constructions above the ground, used to shelter humans, animals, objects, for the production of economic goods or for the provision of services and refers to any structures built or erected permanently on its site"  [22]. The "use" attribute has the following levels: agricultural, commercial and services, industrial, religious, sports, annex, residential or undifferentiated. However, due to a lack of interoperability of databases used in the development of the BD TOPO®, the "use" attribute of buildings could not be filled in some municipalities, where all buildings are then reported as "undifferentiated" use.
Buffer zones around residential buildings Buffer zones of 10 m, 50 m, 100 m, and 150 m were created around residential buildings using the st_buffer() function of the sf R package  [23]. These values were chosen to be representative of the pesticide-free buffer zones width range proposed by the French government (3 m-20 m) and advocated by environmental NGOs (up to 150 m). The smaller width was set to 10 m because the spatial resolution of the BD TOPO® ranges between 2. 5 and 10 m  [22]. This procedure likely underestimates the size of buffer zones as defined by the French government because it considers distances to residential buildings and not distances to the outside limit of the unbuilt amenity space surrounding buildings (Fig. 1 ). A lack of interoperability of available databases prevented us to use the distance to the outside limit of the unbuilt amenity space surrounding buildings.
6•10 1 c R (s m -2 ) 3. 7•10 -3 ± 1. 2•10 -3 6. 7•10 -3 ± 2. 0•10 -3 1. 1•10 -2 ± 1. 6•10 -1 3. 0•10 -3 ± 4. 5•10 -3 p R 1. 1•10 -2 ± 8. 0•10 -3 1. 2•10 -2 ± 3. 6•10 -3 4. 3•10 -1 ± 1. 1•10 -1 6. 5•10 -1 ± 1. 6•10 -1 β 9. 9•10 -1 ± 1. 9•10 -1 6. 3•10 -1 ± 1. 4•10 -1 2. 4•10 -1 ± 1. 2•10 -1 1. 6•10 -1 ± 5. 6•10 -2 Temporally averaged rates and residence time estimates Λ D baseflow (s -1 ) 2. 9•10 -3 2. 2•10 -3 3. 9•10 0 7. 6•10 -1 Note. Rates and residence time estimates were calculated based on average best-fit model parameters. data with minimum error θ = ∼0. 17 for the artificial floods and θ = ∼0. 21 for the natural storm events (Figures S7 and S8 in Supporting Information S1, respectively). This result showed that for a flashy event, microbes are resuspended back to the water column during both the rising and falling limb, but only during the rising limb for a subdued natural event.
This should, in turn, assist with assessing waterborne microbial hazards. (NIF\R1\180935), Marie Curie Individual Fellowship (833702-MICROPATH) and NIWA Strategic Science Investment Funding visitors grant awarded to J. D. Drummond, the Leverhulme Trust research grant "PlasticRivers" (RPG-2017-377), and the European Commission grant HiFreq: Smart high-frequency environmental sensor networks for quantifying nonlinear hydrological process dynamics across spatial scales (project ID 734317) to S. Krause and T. Aquino was supported by a Marie Skłodowska Curie Individual Fellowship, funded by the European Union's Horizon 2020 research and innovation programme under the project ChemicalWalks 838426.
PD-L1 IHC was measured centrally at Ventana using the SP-142 clone. CD8 IHC was measured at Histo-GeneX (Antwerp, Belgium) using the CD8A (clone C8/144B from Dako [Santa Clara, CA, USA]). Both PD-L1 and CD8 are reported as the percentage of positive cells in the tumor area. IFN-γ was measured from plasma at Myriad RBM (Austin, TX, USA) using the SIMOA (single-molecule array) technology and is reported as pg/ml. The primary analysis was based on patient data collected through study discontinuation or the end of study. All analyses were based on the safety-evaluable population, defined as all patients who received any amount of either study drug. Response was reported by PET-CT or CT scan and time-to-event data were estimated using Kaplan-Meier methods. Duration of response (DOR) was defined as the time from the BOR to the time of disease progression (PD) or death, whichever occurred first.
Patients The clinical data cut-off date was January 21, 2020. A total of 49 patients (FL, n = 26; DLBCL, n = 23 [none with PMBCL]) were enrolled to receive atezolizumab in combination with obinutuzumab. For the FL cohort, the median age of patients was 59. 5 years (range: 41-83) with 8 patients (31%) being ≥ 65 years of age. There were 16 males (62%) and 10 females (38%). The median number of prior lines of therapy was 3 (range: 1-7), and the majority of patients had an ECOG PS of 0 or 1 (ECOG PS 0, n = 12 [46%]; ECOG PS 1, n = 13 [50%]). Based on the Follicular Lymphoma International Prognostic Index (FLIPI), 12 of 26 patients (46%) were classified as high-risk at study entry. For the DLBCL cohort, the median age of patients was 69 years (range: 26-90) with 15 patients (65%) being ≥ 65 years of age.
Conclusions We analyzed several years of snow survey data at two sites that were located less than 1 km apart in the forest-tundra ecotone. One site was covered with sparse forest (FOREST) and one was an Arctic tundra (TUNDRA). We compared the snow properties of the two sites in terms of snow height, stratigraphy, density, thermal conductivity and snow temperature. Additionally, we ran simulations with the snow model Crocus to explore model performance in both environments. All the observed snow properties revealed marked differences between the two sites. Snow height was up to twice as high at FOREST than at TUNDRA, due to wind-induced snow transport. This strongly affected the temperatures at the bottom of the snowpack, as they were at times more than 10 • C colder at TUNDRA than at FOREST.
Complex systems: structure, function and interrelationships in the North". The authors are grateful to Denis Sarrazin for technical support of the CEN weather stations and the community of Umiujaq for permission to conduct our research. This research has been supported by the Natural Sciences and Engineering Research Council of Canada (Discovery Grant), the French Polar Institute (IPEV) (grant no. 1042) and the Horizon 2020 program of the European Commission (INTAROS; grant no. 727890). CNRM/CEN is part of Labex OSUG@2020 (ANR-10-LABX-0056). 445 31 https://doi. org/10. 5194/tc-2022-19 Preprint. Discussion started: 10 February 2022 c Author(s) 2022. CC BY 4. 0 License.
Code availability. The source files of SURFEX code are provided at the Git repository (http://git. umr-cnrm. fr/git/Surfex_Git2. git, last access: 15 November 2021) with several code management tools (history management, bug fixes, documentation, interface for technical support, etc. ). Registration is required; a description of the procedure is described at https://opensource. cnrm-game-meteo. fr/projects/snowtools/ wiki/Procedure_for_new_users (last access: 15 January 2022). Data availability. Data sets allowing the driving and testing of snow models at the TUNDRA and FOREST sites are being prepared and will be available soon.
" Complex systems: structure, function and interrelationships in the North". The authors are grateful to Denis Sarrazin for technical support of the CEN weather stations and the community of Umiujaq for permission to conduct our research. This research has been supported by the Natural Sciences and Engineering Research Council of Canada (Discovery Grant), the French Polar Institute (IPEV) (grant no. 1042) and the Horizon 2020 program of the European Commission (INTAROS; grant no. 727890). CNRM/CEN is part of Labex OSUG@2020 (ANR-10-LABX-0056). 445 31 https://doi. org/10. 5194/tc-2022-19 Preprint. Discussion started: 10 February 2022 c Author(s) 2022. CC BY 4. 0 License.
Background. Suicide is one of the main preventable causes of death. Artificial intelligence (AI) could improve methods for assessing suicide risk. The objective of this review is to assess the potential of AI in identifying patients who are at risk of attempting suicide. Methods. A systematic review of the literature was conducted on PubMed, EMBASE, and SCOPUS databases, using relevant keywords. Results. Thanks to this research, 296 studies were identified. Seventeen studies, published between 2014 and 2020 and matching inclusion criteria, were selected as relevant. Included studies aimed at predicting individual suicide risk or identifying at-risk individuals in a specific population. The AI performance was overall good, although variable across different algorithms and application settings.
Several scales can be used in the suicide risk assessment, but their accuracy seems insufficient  [10]. In their meta-analysis, Franklin et al. [11]  found that the ability to predict suicide had not improved over the past 50 years. The ability to predict a suicide attempt lack accuracy. Advances in suicide risk assessment are needed  [11]. Artificial intelligence (AI) and machine learning (ML) have emerged as ways to improve risk detection  [12]. These techniques require a large database (big data) to extract a patient's profile or significant risk factors  [13]. AI platforms can identify patterns in the dataset to generate risk algorithms and determine the effect of risk and protective factors on suicide  [9]. AI has already been successfully applied to other medical disciplines (imagery, pathology, dermatology, etc.
Search strategy We went through the bibliographic databases PubMed, SCOPUS, and EMBASE until April 2020. We based the keywords list on two fields: suicide and AI. A search strategy was built by using the Booleans operator "AND" and "OR" and applied to titles and abstracts. The keywords and the search strategy were (suicid* [Title]) AND (artificial intelligence [Title/Abstract] OR AI[Title/ Abstract] OR neural network[Title/Abstract] OR deep learning [Title/Abstract] OR machine learning[Title/Abstract]) in any language, but referenced in the selected databases. To limit the selection bias, we did not apply any restriction in terms of the type of article or population. Studies that were not written in English were excluded.
Data used Data used to supply ML models were mainly those from health systems. A study  [29]  compared a model using only data from patient records from a health system database with several models using the same data combined with additional data, particularly clinical data (sociodemographic data, results from a questionnaire, data from an index consultation). The aim was to predict suicidal risk in the 90 days following a consultation for suicidal ideation. This study found approximately equivalent performance between the models (AUC 0. 843 vs. 0. 850)  [29]. Data collected during the medical visit provided a statistically significant improvement in the prediction of suicidal risk, but with a low effect size. A Korean team  [30]  sought to identify patients at risk of suicide among those who expressed suicidal ideations in a self-administered questionnaire, by analyzing retrospective data from a national database.
This team reported good overall performance, with an accuracy of 88. 9% and an AUC of 0. 947. Among the included studies, four studies used a prospective design. Zheng et al. [28]  used a deep NN to prospectively predict the one-year suicide risk and identify people at risk of suicide. The data used were solely from a health database. Performance was acceptable with an AUC of 0. 769 (95% CI: 0. 721-0. 817). This deep learning model significantly improved performance, compared to other algorithms tested on the same cohort in this study (XGB: AUC 0. 702; LR: AUC 0. 604). A study  [33]  sought to prospectively identify patients with a major depressive disorder and suicidal patients by analyzing blood markers (methylomes and transcriptomes) on a small sample. Their random forest model found an accuracy of 92.
Outcomes in teenagers and young adults Four studies have investigated the prediction of suicidal risk or the identification of at-risk patients in adolescents and young adults. Miché et al. [32]  studied four ML algorithms in suicide attempt risk assessment. They found close performance between these (AUC between 0. 824 and 0. 829) for patients aged from 14 to 24 years. The highest AUC was obtained with ridge regression. Hill et al. [34]  sought to identify patients at risk for suicide attempts among a large cohort of 4,834 teenagers during 12 months. Two classification trees had reached a higher risk prediction, with a sensitivity/ specificity profile of 69. 8%/85. 7% for the first and 90. 6%/70. 9% for the second. A Korean study  [35]  aimed to retrospectively identify at-risk patients in a national database, including 59,084 teenagers.
Poulin et al. [38]  sought to identify veterans at risk for suicide through analysis of each patient's medical observations. With a supervised ML algorithm retrospectively analyzing medical records of veterans who died by suicide, they obtained an accuracy of 67-69%. Gradus et al. [31]  sought to predict suicide risk according to gender by using several Danish databases, thus including a very large sample of patients. With a random forest algorithm, they obtained a good predictive performance of suicide risk (AUC of 0. 80 in men and 0. 88 in women).
Limitations of this study This preliminary study has several limitations. Firstly, it includes a low number of studies. The number of studies using AI for suicide prevention is increasing exponentially, but the number of published studies is still low to date. However, this study does not aim at a precise evaluation of the performance of AI in a given situation, but at an assessment of the potential of this technology. We have listed the performance of the different algorithms tested to better assess this potential. Secondly, the selected studies were published in a small number of countries. Most studies (88%) were published in the USA, Canada, or Korea. These countries have significant health databases, and AI could be more challenging to implement in countries or regions where patient data is less accessible or less accurate  [43].
Clinical and genetic information Data were obtained from medical files (n ¼ 450), registries (n ¼ 235), and/or questionnaires (n ¼ 128; Supplementary Figure  1 , available online). Information on vital status, cancer, clinical characteristics, surgeries, and genetic testing was collected from medical files using a standardized data dictionary. Similar information was collected from questionnaires completed by patients. For Dutch patients, information on cancer diagnoses, precursor lesions, and surgeries was additionally collected via the Dutch Nationwide Pathology Databank (PALGA)  (14). Ninety-eight percent of patients had validated genetic test reports. Of primary cancers, 75. 0% were pathology confirmed, maximally 10. 0% were self-reported, and the remainder was based on clinical reports.
Statistical analyses Descriptive statistics were performed using appropriate measures depending on data distribution. Cancer risks were calculated using Kaplan-Meier Analyses. Standardized incidence ratios (SIRs) were calculated using cancer, age, sex, birth cohort, and country-specific population incidence data  (15) (16) (17). Rightcensoring was applied at moment of site-relevant surgery, last follow-up, age 70 years or death, whichever came first. Siterelevant surgeries included bilateral mastectomy, hysterectomy, total thyroidectomy, total colectomy, bilateral tubectomy, or ovariectomy, irrespective of prophylactic or elective intent. Relative risks associated with variant coding effect and domain were analyzed using multivariable Cox regression. The proportionality assumption was verified by assessing log-minus-log plots and Schoenfeld residuals.
To correct for ascertainment bias, we performed 1) prevalent case analyses excluding index patients (ie, first identified patient in a family) who had cancer before genetic testing (abbreviated as ca-index patients); and 2) incident case analyses by prospective analyses from the age of genetic testing onward  (18). Surveillance bias was addressed by composite endpoints combining in situ with invasive BC and adenomatous polyps (APs) with invasive CRC. CRC risk was also modelled by AP-to-CRC progression using 10% and 20% progression rates and 3-, 5-, and 10year dwell time  (19) (20) (21). Survival bias was addressed by sensitivity analyses per data source (data not shown). value less than. 05 was considered statistically significant.
Acknowledgements: This research is supported (not financially) by the European Reference Network on Genetic Tumour Risk Syndromes (ERN GENTURIS)-Project  ID No 739547. ERN GENTURIS is partly cofunded by the  European Union  within the framework of the  Third Health Programme "ERN-2016-Framework Partnership Agreement 2017-2021 ". We thank the  Dutch Nationwide Pathology Databank (PALGA)  for retrieval of the pathology data and contribution to this study, and  Iris D. Nagtegaal ,  department of Pathology, Radboudumc, Nijmegen  for her contribution.