dataesr/scientific-paragraphs-categorization huggingface dataset:
Model setup and compression test Bead Positions. The initial bead locations are taken from the relative positions of particles as measured in either of the two microtomograms. The natural dispersion in bead sizes and voxel resolution of the tomography data leads to a small uncertainty in these positions, which could result in slightly overlapping beads. We avoided this issue by randomly varying the diameter of each particle with the Box-Muller method  [29]  using a Gaussian distribution with a mean diameter of D = 200. 9 µm and a standard deviation of 5%, while constraining the sizes of the particles to prevent overlap. Ensembles of at least 5 different realisations of particle sizes were used, and the error bars in Figs. 4  and 5  give the standard deviations of measurements on such ensembles.
Background: Switzerland's maternity protection legislation aims to protect the health of pregnant employees and their unborn children by regulating their potential occupational exposure to hazards and strenuous activities. This legislation provides a role for obstetricians, but not for midwives. Aims: Identify the practices of Switzerland's French-speaking midwives that favour the implementation of maternity protection legislation and reflect on the profession's role in supporting pregnant employees. Methods: 356 midwives answered an online questionnaire. The analysis focuses on the 205 midwives who perform pregnancy consultations in their practice. Data were analysed in two stages using STATA software: 1) simple descriptive and correlational statistics and 2) hierarchical cluster analysis to identify typologies of practices by grouping similar responses.
Data collection An online questionnaire was generated using Sphinx Online (v. 4. 8) software. Questions were built with regard to the literature and to the authors' clinical experiences. Three external evaluators tested the French version of the survey -notably in order to check the readability of the questionnaire. The electronic format of the survey was chosen to allow an ample response time (almost three months). From April to June 2017, we sent four reminders. Data collection was closed on the 30th of June 2017. The questionnaire covered the themes of midwives' knowledge and perceptions about the legal dispositions for maternity protection in Switzerland, their practices, the difficulties they faced and the resources they had with regard to maternity protection at work. The response rate to the 657 email questionnaires sent out in April 2017 was 54% (n = 356).
Acknowledgments We thank the  "Swiss Federation of Midwives "  for their support and all the midwives who participated in the study. This work is supported by the  Swiss National Science Foundation , by  Vaud Public Health Service  and by a research fund of the  University of Applied Sciences and Arts of Western Switzerland (HES-SO). Center for Primary Care and Public Health (Unisanté) ,  University of Lausanne, Switzerland  and the  School of Health Sciences (HESAV)  of the  University of Applied Sciences and Arts of Western Switzerland (HES-SO ) contribute to the salaries. Acknowledgements and Disclosures We thank the  "Swiss Federation of Midwives "  for their support and all the midwives who participated in the study. We also thank the  Swiss National Science Foundation , for the support provided to the research project.
Such 19 in silico laboratory was made available in a Shiny R-based platform called CARTmath. It is an 20 open-source, easy to run simulator, available at github. com/tmglncc/CARTmath or directly on 21 the webpage cartmath. lncc. br, containing this manuscript results as examples and documentation. 22 The developed model, together with the CARTmath platform, provides potential use for assessing 23 different CAR-T cell immunotherapy protocols and associated efficacy, becoming an accessory 24 towards in silico trials. 25.
For 187 the RAJI tumor, inference of the CAR-T cell inhibition due to interaction with tumor 188 (α) was performed based on data from  [11]. All used data were extracted using the 189 free software G3Data Graph Analyzer  [45]. For completeness, these data and details We perform a mathematical analysis of the model long-term dynamics, finding the 195 steady states and characterizing their stability. In order to simplify the calculations, we dY dτ = uX -qZY -wY , (4) dZ dτ = Z(1 -Z) -XZ , (5) where p = ρ-φ r , q = θ br , s = α br , u = r , and w = µ r ; note that these parameters are 201 positive, due to the conditions imposed on the original parameters (see Table  1 P 0 = (0, 0, 0). Another equilibrium point, representing the tumor escape, given by 205 P 1 = (0, 0, 1). Finally, there are also two nontrivial equilibria corresponding to the coexistence between 206 tumor cells, effector, and long-term memory CAR-T cells, given by 207 P i = (X i , Y i , Z i ) = X i , [ϑ -s(1 -X i )]X i w , 1 -X i , i = 2, 3 , where ϑ = up, with up positive due to the first condition imposed on the original 208 parameters, and X 2 and X 3 are the roots of the second-degree equation aX 2 + bX + c = 0 , with coefficients 210 a = qs > 0, b = qr -2qs -sw, c = pw -qr + qs + sw.
Our model 288 framework is implemented in the CARTmath  [50] , whereby the results presented in 289 Section 3 may be easily reproduced using the predefined datasets as explained in the 290 CARTmath manual. We first simulate the scenario presented in  [7] , which consists of CAR-T 123 therapy 308 against HDLM-2 cells. Ruella et al. [7]  reported that 2 × 10 6 cells of Hodgkin lymphoma.
model is implemented as an open-source program, called CARTmath, available at 93 github. com/tmglncc/CARTmath or directly on the webpage cartmath. lncc. br. Using 94 the CARTmath virtual laboratory, a researcher without a mathematical background can 95 test the proposed model, reproducing our results, and performing new tests. We use 96 CARTmath to complement the present study by investigating, in silico, the occurrence of 97 different therapy outcomes depending on the relationship between the tumor burden 98 and CAR-T cell number; the intensity of immunosuppression mechanisms in the tumor 99 microenvironment, and intrinsic individual specificities. Our model simulations provide 100 insights on the role of these critical mechanisms on the effectiveness of CAR-T cell 101 immunotherapy, showing that CARTmath can be used for assessing different CAR-T 102 cell immunotherapy protocols and associated efficacy, complementing and potentially 103 avoiding further in vivo experiments.
92 model is implemented as an open-source program, called CARTmath, available at 93 github. com/tmglncc/CARTmath or directly on the webpage cartmath. lncc. br. Using 94 the CARTmath virtual laboratory, a researcher without a mathematical background can 95 test the proposed model, reproducing our results, and performing new tests. We use 96 CARTmath to complement the present study by investigating, in silico, the occurrence of 97 different therapy outcomes depending on the relationship between the tumor burden 98 and CAR-T cell number; the intensity of immunosuppression mechanisms in the tumor 99 microenvironment, and intrinsic individual specificities. Our model simulations provide 100 insights on the role of these critical mechanisms on the effectiveness of CAR-T cell 101 immunotherapy, showing that CARTmath can be used for assessing different CAR-T 102 cell immunotherapy protocols and associated efficacy, complementing and potentially 103 avoiding further in vivo experiments.
Research in context Evidence before this study We did a systematic search of the MEDLINE and PubMed databases on Dec 5, 2020, using the search terms ("in-hospital" OR "hospital") AND ("SARS-CoV-2" OR "COVID" OR "COVID-19") AND "complications". We limited dates of searches from Jan 1, 2020, to the date the search was conducted. No language restrictions applied. Data from other areas of health care, such as surgery, suggest that patients with COVID-19 are at greater risk of subsequent complications, but systematic characterisation of complications in these patients has not yet been undertaken in large multicentre studies of patients admitted to hospital. Most COVID-19 studies have focused on mortality and respiratory support outcomes. Characterising the burden of complications is important for health-care system preparedness for further waves of infection, determining future population morbidity, understanding the full repercussions of COVID-19 for society, and for informing future research and clinical guidelines.
Procedures Data collected by research nurses and volunteer medical students were entered into a standardised electronic case report form within a secure research electronic data capture database. 16 Multiple timepoints were captured, including admission, hospital stay at days 1, 3, and 9, and discharge or status at 28 days if not discharged. Data were collected according to a detailed protocol, which was updated to reflect developments over the course of the pandemic. Participant characteristics including age, sex at birth, physiological parameters at presentation, and comorbidities were also recorded. Comorbidities included asthma, chronic cardiac disease, chronic haematological disease, chronic kidney disease, chronic neurological disease, chronic pulmonary disease, HIV/AIDS, history of malignancy, liver disease, clinician-defined obesity, rheumatological disorders, and smoking.
Outcomes The primary outcome of this study was the incidence of in-hospital complications, defined as organ-specific diagnoses occurring alone or in addition to any hallmarks of COVID-19 illness (appendix p 1-2). All complications were recorded so that total morbidity could be described, not just those directly attributable to COVID-19. Although COVID-19 is a multisystem disease, severe respiratory infection was considered characteristic of COVID-19 and was not regarded as a complication. Data were collected on organ-specific complications including complex respiratory (bacterial pneumonia, acute respiratory distress syndrome [ARDS], empyema, pneumothorax, and pleural effusion), neurological (meningitis, encephalitis, seizure, and stroke), cardio vascular (thromboembolism, heart failure, myocarditis, endocarditis, arrhythmia, cardiomyopathy, myocardial ischaemia, and cardiac arrest), acute kidney injury, gastrointestinal (acute liver injury, pancreatitis, and gastrointestinal haemorrhage), and other systemic complications (coagulopathy, disseminated intravascular coagulation, anaemia, and bloodstream infection).
We only included patients who had completed outcomes, with at least 2 months of follow-up. There were low rates of missing data and therefore multiple imputation was not used. To explore if the number of complications and which specific complications were associated with mortality (dependent variable), complication variables were entered independently into Cox proportional hazards models and adjusted for other potentially confounding factors. These data were described using Kaplan-Meier plots and modelled using Cox proportional hazards regression. Reported date of symptom onset was taken as day 0. Discharge from hospital was considered an absorbing state (once discharged, patients were considered no longer at risk of death); thus discharge did not compete with death. The proportional hazards assumption was checked.
To observe whether complications were associated with increased severity of initial disease, we used the ISARIC 4C Mortality Score, quick sequential organ failure assessment (qSOFA), and National Early Warning Score 2 (NEWS2) on admission or time of symptom start to examine the relationship between severity and presence of any inhospital complications. 18 These scores are commonly used in clinical practice to identify patients with deteriorating or critical illness and risk of subsequent death in general adult hospital popula tions (NEWS2 and qSOFA) or in COVID-19 patients (4C Mortality Score). We calculated the score for each adult patient in the dataset and plotted each score against the observed incidence of complications in each score group. Multilevel logistic regression models were constructed to identify associations between patient characteristics (potential confounders, including patient demographics and existing comorbidities) and the development of specific complications, worse self-care ability on discharge, and the requirement for ongoing hospital care.
Our data provide the most comprehensive, multicentre, systematic analysis of the effect of COVID-19 on shortterm clinical outcomes in a hospitalised population, including patient groups from both ward level and critical care. Data were collected prospectively and capture most people hospitalised with COVID-19 in the UK. Recruitment to our study con tinues, enabling us to capture trends and incidence of complications in near real time. 5] [36] [37] [38]  Our study identifies high rates of com plications and the risk factors for developing these, and describes severity, which previous studies have been unable to do at scale. In particular, we find that in the short term, respiratory and cardiovascular complications have the strongest association with mortality. A further strength is that our study includes patients in both critical care and in ward-level areas, whereas other groups have just studied intensive care populations.
For example, further characterisation of thromboembolic complications and stroke can help to identify optimal anti coagulation strategies in patients with COVID-19. 40 We found initial disease severity, measured using the 4C Mortality Score, qSOFA, and NEWS, were associated with the presence of complications, and could therefore be useful tools to stratify those at the highest risk of developing complications in clinical practice and interventional trials. There are several limitations to our study, which relate to the design and current unknowns in COVID-19 research. First, this dataset focuses on in-hospital complications during the index admission for COVID-19 and does not provide longer-term outcome data or data on quality of life. Nevertheless, our results suggest that complications of COVID-19 might affect all survivor groups, rather than just those who are older and have comorbidities.
However, the risk of this is reduced by the multicentre design of our study, as peaks in hospital admissions varied in the UK over time. 3] [44] [45]  The reasons for this are multifactorial, and could relate to differences in testing strategy, thresholds for hospital admission, pre-existing population morbidity, and health-care system preparedness. Finally, our data were collected from real-world observed clinical practice and patients did not undergo any additional tests to detect the presence of complications. Therefore, the true burden of complications is likely to be higher. However, doing large numbers of invasive tests might not be acceptable for patients, particularly in patients who are unlikely to survive or cannot tolerate investigations, and would be logistically challenging in a study of this size.
Chloe Donohue ,  Jo Dalton ,  Michelle Girvan ,  Egle Saviciute ,  Stephanie Roberts ,  Janet Harrison ,  Laura Marsh ,  Marie Connor ,  Sophie Halpin ,  Clare Jackson ,  Carrol Gamble  ( Liverpool Clinical Trials Centre, University of Liverpool ). Gary Leeming  ( Centre for Health Informatics, Division of Informatics, Imaging  and  Data Science, School of Health Sciences, University of Manchester). William Greenhalf (Department of Molecular and Clinical Cancer Medicine, University of Liverpool ). Victoria Shaw  ( Institute of Translational Medicine, University of Liverpool, Liverpool, Merseyside, United Kingdom ). Seán Keating  ( Intensive Care Unit, Royal  Acknowledgments This work is supported by grants from: the  NIHR  (award  CO-CIN-01 ),  MRC  (grant  MC_PC_19059 ),  NIHR Imperial Biomedical Research Centre  (grant  P45058 ), HPRU in Respiratory Infections at  Imperial College London , and  NIHR HPRU in Emerging and Zoonotic Infections at the University of Liverpool , in partnership with  Public Health England  ( NIHR award   200907 ),  Wellcome Trust ,  Department for International Development  ( 215091/Z/18/Z ),  Bill & Melinda Gates Foundation  ( OPP1209135 ),  Liverpool Experimental Cancer Medicine Centre  (grant  C18616/A25153 ),  NIHR Biomedical Research Centre at Imperial College London  ( IS-BRC-1215-20013 ),  EU Platform for European Preparedness Against (Re-) Emerging Epidemics  ( PREPARE ;  FP7  project  602525 ).
Common variants in Alzheimer's disease and risk stratification by polygenic risk scores Genetic discoveries of Alzheimer's disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer's disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5. 5 years difference in median age at onset of Alzheimer's disease patients in APOE ɛ4 carriers.
T hus far, multiple loci associated with Alzheimer's disease (AD) have been described next to causal mutations in two subunits of γ-secretases, membrane-embedded aspartyl complexes (PSEN1, PSEN2 genes), and the gene encoding one target protein of these proteases, the amyloid precursor protein gene (APP). The most prominent locus, APOE, was detected almost 30 years ago using linkage techniques  1. In addition, genome-wide association studies (GWAS) of AD case-control datasets and by-proxy AD case-control studies have identified 30 genomic loci that modify the risk of AD  [2] [3] [4] [5] [6] [7]. These signals account for ~31% of the genetic variance of AD, leaving most of the genetic risk as yet uncharacterized  8. Further disentangling the genetic constellation of common genetic variations underlying AD can drive our biological insights of AD and can point toward novel drug targets.
Using this strategy, we identified a genome-wide significant (GWS) association (p < 5 × 10 -8 ) for 36 independent genetic variants in 35 genomic regions (the APOE region contains signals for ɛ4 and ɛ2). As a sensitivity analysis, we removed the AD-by-proxy study and compared the resulted effect estimates with and without this dataset. We found a high correlation between the effect estimates from the case-control and by-proxy Fig. 1  Flow chart of analysis steps. Discovery meta-analysis in GR@ACE, IGAP stage 1 + 2 and UKBiobank followed by a replication in 16 independent cohorts. The genome-wide significant signals found in meta-GWAS were used to perform a Polygenic Risk Score in a clinical and pathological AD dataset. See Supplementary Methods to more information about the cohorts included and methods to the PRS generation.
a Extended dataset (Moreno-Grau et al. 19  ), b StageI + StageII (Kunkle et al. 20  ), c By proxy AD: Meta-analysis of maternal and paternal history of dementia (Marioni et al. 21 ), d Extra and independent GR@ACE dataset incorporated only for replication purposes, e Pathologically confirmed AD cases, f AD cases diagnosed based on clinical criteria, g Controls participants aged 55 years and younger. N = Total of individuals within specified data. approaches for the significant loci (R 2 = 0. 994, p = 8. 1 × 10 -37 ; Supplementary Fig. 1e ). Four genomic regions were not previously associated with AD (see Manhattan Plot, Fig. 2a ). Next, we aimed at replicating the associated loci in 16 cohorts (19,087 AD cases and 39,101 controls in total), many of them collected and analyzed by the European Alzheimer's Disease Biobank (JPND-EADB) project.
We tested all variants with suggestive association (p < 10 -5 ) located within a 200 kb region from the sentinel SNP. Overall, 384 variants were tested in the replication datasets (Supplementary Data 2). Discovery and replication were combined, and we identified associations in six variants comprising five genomic loci annotated using FUMA  23  (Table  1 , Fig. 2b -f, Supplementary Fig. 2  and Supplementary Results). In APP, we identified a common (MAF = 0. 46) intronic variant associated with a reduced risk of AD (rs2154481, OR = 0. 95 [0. 94-0. 96], p = 1. 39 × 10 -11 , Fig. 2f ). In SHARPIN (SHANK Associated RH Domain Interactor) gene, we found two missense mutations (rs34173062/p. Ser17Phe and rs34674752/p. Pro294Ser) that are in linkage equilibrium (R 2 = 1. 3 × 10 -6 , D′ = 0. 014, p = 0.
4b ). Last, we investigated the effect of sex and AAO (Fig. 4c ). Our analysis revealed that the effect of the PRS was the same in both sexes (Fig. 4c ) and was consistent with both early-onset (onset before 65 years; OR = 1. 58, 95% CI [1. 22-2. 05], p = 5. 8 × 10 -4 ) as well as with late-onset AD (onset later than 85 years; OR = 1. 29, 95% CI [1. 10-1. 51], p = 1. 5 ×  10 -3  ). PRSs has the potential to early identify subjects at risk of complex diseases  24. To identify people at the highest genetic risk of AD based on the PRS, we used the validated 39-variants PRS in the large GR@ACE dataset. The PRS was associated with a 1. 27-fold (95% CI [1. 23-1. 32]) increased risk for every standard deviation increase in the PRS (p = 7. 3 × 10 -39 ) and with a gradual risk increase when we stratified the dataset into 2% percentiles of the PRS (Fig.
5a , Supplementary Data 6). Next, we stratified the dataset in APOE genotype risk groups. The PRS percentiles were associated with AD within the APOE genotype groups (Fig. 5b , Supplementary Data 7). Finally, we compared the risk extremes and found a 16. 2-fold (95% CI [8. 84-29. 5], p = 1. 5 × 10 -19 ) increased risk for the highest-PRS group (APOE ɛ4ɛ4) compared with the lowest-PRS group (APOE ɛ2ɛ2/ɛ2ɛ3; Supplementary Data 8). When we compared the median AAO in AD patients in these extreme risk groups we found a 9-year difference in the median age (p Wilcoxon = 1. 7 × 10 -6 ) (Fig. 5c ). Lastly, we studied the effects on AAO of the PRS in the APOE genotype groups. The PRS differentiated AAO only within APOE ɛ4 carriers. In APOE ɛ4 heterozygotes the PRS determined a 4year difference in median AAO and in APOE ɛ4 homozygotes (p Wilcoxon = 6.
Our results directly support APP production or processing as a causal pathway not only in familial AD but in common sporadic AD. The SNP is in a DNase hypersensitive area of 295 bp (chr21:27473781-27474075) possibly involved in the transcriptional regulation of the APP gene. rs2154481 is an eQTL for the APP mRNA and an antisense transcript of the APP gene named AP001439. 2 in public eQTL databases  25  (Supplementary Fig. 4 ). Functional evidence supports a modified APP transcription  26  as an LD block of 13 SNPs within the APP locus Results obtained with a fixed effects inverse-variance-weighted meta-analysis on the discovery and follow-up stages. Freq A1 is from GR@ACE discovery dataset. P value for significance <5 × 10 -8. Effect allele: A1. (including rs2154481) increased the TFCP2 transcription factor avidity to its binding site and increased the enhancer activity of this specific intronic region  26.