dataesr/scientific-paragraphs-categorization huggingface dataset:
35 Both the SD and OD datasets represent close-range proximity interactions as a temporally-ordered sequence of contact networks, where nodes are individuals and an edge between nodes indicates a close-range proximity relation. For both datasets, the spatial resolu-5 tion is of the order of 1-2 meters, making these datasets suitable for modeling at-risk contacts for droplet-transmitted pathogens such as SARS-CoV-2. We run simulations starting from one initially infectious individual chosen at random, letting the spread evolve until no infectious individuals are present in the population. We first calibrate the parameters of the model to obtain a reproduction number R 0 ≈ 3 in the absence of any containment measures. 5, 51 We also considered simulations with lower R 0 values, corresponding, e.
g. , to reemerging outbreaks during which a number of public health measures such as enhanced hygiene and mask-wearing are enforced to limit the spread. As a baseline intervention, used as a reference to assess the relative improvement of contact tracing, we then consider a simple strategy that does not rely on any contact information: isolation (Q) of all cases with severe symptoms as well as isolation of a fraction p md of the mildly symptomatic cases, until they recover. For p md = 50%, this intervention yields a relative decrease of the average epidemic size of, respectively, 14% for the OD data and 25% for the SD data. We then simulate manual contact tracing (MCT) for all detected infectious cases (i. e. , all severe cases I s and a fraction p md of the mild cases I m ): for each case, an interview is assumed to be performed to identify all individuals who have been in contact with the case for an interval longer than θ ct over the 48 hours before detection.
It is thus rather small at low app adoption and, in particular, improving MCT leads to stronger effects. However, even in the hypothetical scenario of perfect MCT, DCT still improves the results thanks to the shorter delay between detection and quarantine made possible by DCT, and the combination of DCT and MCT makes it possible to reach strong epidemic suppression effects even at intermediate parameter values. We note that the qualitative behavior of the epidemic size reduction is similar for both datasets, despite the differences in contexts and data collection methods. In the SI, we show that this behavior is robust for other datasets and other parameter values. Figure  1 , moreover, illustrates the crucial role of detecting cases with mild symptoms. Even in the absence of tracing (p app = p ct = 0), increasing the probability of case detection increases linearly the reduction in the average epidemic size.
While our results are obtained with simulations of a realistic compartmental model performed on state-of-the-art datasets describing human interactions, which include all the inherent complex features of such interactions, we can actually obtain a very similar phenomenology using analytical arguments on simplified models. To this end, we consider a Susceptible-Infectious-Recovered (SIR) model on a static random network. Using a well-known mapping of SIR models to percolation problems,  52  it is indeed possible to obtain an equation for the final epidemic size of such a process. In the Supplementary Information we show that it is possible to extend the percolation arguments to introduce both digital and manual contact tracing, and we show in Fig. 3  that the resulting shape of the epidemic size reduction as a function of the tracing parameters p ct and p app closely reflects the phenomenology observed in our numerical simulations.
g. , on-line social networks, mobile call networks, organizational networks, etc. ), increasing the probability of re-identification. The resulting privacy concerns might lead to lower app adoption in the public, and thus potentially to an actual loss of efficiency of the digital contact tracing efforts. Our study has a number of limitations which are important to make explicit. In particular, the data we use correspond to limited social environments (a university campus and a workplace) and we do not provide an overall general study that includes all possible multiple and differentiated contexts and their mutual interplay. These data moreover do not include interactions between the individuals participating in those studies and the general population. Nevertheless, these datasets correspond to the current state-of-the-art in terms of data describing human interactions, and the results show a robust qualitative behavior for data obtained in different contexts with different data collection infrastructures, both in terms of the epidemic size reduction and how the cost of the intervention depends on the manual contact tracing efficiency and on the app adoption.
a difference. In this study, we have provided a new quantification of the efficiency of contact tracing by leverag-11 ing state-of-the-art datasets describing contacts between individuals in different settings, namely among office workers and among students of a university. Indeed, most previous works have considered either homogeneous mixing hypothesis or model networks of interactions, while real contacts are known to display a wealth of structural and temporal heterogeneities having an impact on epidemic propagation. The use of real contact data in simulations is therefore a crucial step in the evaluation of interventions. We have considered a compartmental model drawn from the most recent literature on the propagation of the SARS-CoV-2 pathogen, and simulated several mitigation measures, focusing on the reduction in the final average epidemic size in the population.
Background and Aims: Few studies have been conducted on the long-term evolution of gambling disorder (GD). The aim of this study was to identify factors that could predict GD relapse. Methods: Data were part of a dataset from a large 5-year cohort of gamblers who were assessed at inclusion and each year thereafter. Participants were recruited from an outpatient addiction treatment center, from various gambling places and through the press. For this specific study, inclusion criteria included (i) transitioning from GD to recovery at a follow-up time and (ii) undergoing at least one follow-up visit afterwards. Participants were evaluated using a structured clinical interview and self-report questionnaires assessing sociodemographic, gambling and clinical characteristics. "Relapse" was defined as the presence of GD (according to the DSM-5) at the Nþ1 th visit following the absence of GD at the N th visit.
Statistical analyses All available follow-up visits performed after the first observed recovery (first transition from GD to no GD, which determined inclusion in the analysis) for a given gambler were taken into account to screen for a potential relapse. We converted the dataset such that each transition between two consecutive follow-up visits (presence/absence of a GD at the N th visit→presence/absence of a GD at the Nþ1 th visit) represents an observation. Transitions were then classified into two categories: (i) "sustained recovery" (no GD→no GD) and (ii) "relapse" (no GD→GD). Transitions "GD→GD" and "GD→no GD" were not taken into account because they did not correspond to transitions through which gamblers had or could have relapsed. We should note that a power analysis was not conducted due to the exploratory nature of the study.
Strengths and weaknesses These results must be viewed within the context of several limitations. First, participants were not assessed strictly at the time when relapse occurred. Indeed, data were collected during the previous follow-up visit. Second, we used several self-reports, which are a source of recall and social desirability bias, although this was reduced through the complementary use of standardized clinical interviews. Third, 40. 7% of the eligible sample refused to participate in the follow-up assessment. Dropout is a crucial problem in interventional studies by destroying the comparability of groups but is less problematic in observational studies, in which groups are not comparable at baseline. Statistical analyses took into account differences in the composition of the groups, so dropout had a weak impact on the results.
We voluntarily used a concept that differs slightly from "sustained remission" defined by the DSM-5 ("after full criteria for GD were previously met, none of the criteria for GD have been met during a period of 12 months or longer"). In research, the term "recovery" is often used synonymously with the term "remission" (White, 2012). However, the latter seemed too restrictive for our purposes. Moreover, our sample consisted of a broad range of gamblers at baseline (with and without GD, with or without treatment, and engaged in various types of gambling activities). Our study was designed to ensure a follow-up period that was long enough to capture key-state changes such as relapse. We were also able to collect data on gambling, personality traits and psychiatric disorders over a relatively long period (6 years), which is quite rare in the literature.
PRE REGISTRATION ON A PUBLICLY AVAILABLE PLATFORM PRIOR TO UNDERTAKING THE ANALYSIS The study was preregistered in the ClinicalTrials. gov database (ClinicalTrials identifier: NCT01207674). Our team has published a "study protocol" article, which describes the data collection and the statistical analysis plan. This article is in open access  (Challet-Bouju et al. , 2014//PMID: 25141820).
Objectives: In face of the COVID-19 health emergency, France has allowed medical abortions to be performed by teleconsultation until 9 weeks of gestation. In an attempt to understand the demand and main drivers of telemedicine abortion, we analysed the requests that Women on Web (WoW), an online telemedicine abortion service operating worldwide, received from France throughout 2020. Methods: We conducted a parallel convergent mixed-method study among 809 consultations received from France at WoW between 1 January and 31 December 2020. We performed a cross-sectional study of data obtained from the WoW consultation survey and a manifest content analysis of anonymised email correspondence of 140 women consulting with WoW helpdesk from France. Results: We found that women encounter macro-level, individual-level, and provider-level constraints while trying to access abortion in France.
Methods We conducted a parallel convergent mixed-method study among the 809 consultations received from France at WoW between 1 January and 31 December 2020. Within the framework of this research, we analysed two main data components. The first component is the data obtained from WoW consultation questionnaire and the second is the data obtained from women's anonymised email correspondence with the WoW helpdesk. Both components were collected simultaneously and analysed independently. They were then merged to validate data and results and to interpret the findings better.
Cross-Sectional Analysis of the Survey Data We performed a cross-sectional study of the survey data obtained from WoW consultation survey that participants completed while requesting help online. The survey consists of 25 questions and is available online on WoW homepage  [15]. The WoW consultation process has been described previously  [16]. As part of the survey, participants gave their informed consent for their data to be used anonymously for research purposes  [17]. The survey consists of both categorical and continuous data; continuous data was summarised in median and interquartile range, and categorical data in frequencies. We distinguished categorical data as per age groups to map demographic patterns. Moreover, we also distinguished between consultations where COVID-19 was mentioned and not mentioned among reasons, to suggest a counterfactual effect for reasons within and outside the pandemic context.
Strengths and Limitations of the Study The WoW dataset consists of self-reported data. The research made use of all available data WoW had from France in 2020; the analysed sample is not representative and the data was not randomized. The data for France was collected only for 2020, and therefore it does not allow a longitudinal multi-year analysis. The content analysis could have been enriched with in-depth qualitative interviews. Despite these limitations, the study offers a significant contribution to the literature on abortion access in France during the pandemic. To our knowledge, there is no study on the demand for and drivers of telemedicine abortion in France. Given the ongoing health emergency, the study has a significant potential to inform policy makers on telemedicine abortion care provision within the French context but also in other similar contexts.
Study design and patients Data from two studies were used in this post hoc analysis: (a) the RAID study, a cross-sectional data set used to develop the RAID, 12 which included 563 outpatients with RA with definite diagnosis from 12 European countries (Estonia, Germany, Finland, France, Greece, Italy, The Netherlands, Norway, Spain, Romania, Turkey and the UK) and (b) the Rainbow study, a 12-week, open-label, French multicentre, single-arm trial of etanercept in RA (Clinical Trials NCT 00768053). 16 This trial included 108 patients fulfilling the ACR criteria for RA,  19  naïve to biological agents, with active disease, as defined by a Disease Activity Score 28-erythrocyte sedimentation rate (DAS28-ESR) >3. 2, and at least one of the following: ≥4 swollen joints or C reactive protein (CRP) ≥10 mg/L or ESR ≥28 mm/first hour.
Construct validity Construct validity was calculated by Spearman's correlation between each single RAID domain and the respective instrument of reference (reversed, if necessary). Correlations from 0. 3 to 0. 5 were considered weak, moderate, from 0. 5 to 0. 7, and strong if >0. 7. 22 asibility Feasibility was calculated by the percentage of missing data for each of the seven questions. The floor and the ceiling effect of each NRS were evaluated through the proportion of patients who scored 0 and 10, respectively.
Responsiveness Responsiveness was calculated by the standardised response means (SRM between baseline and 12 week visits, and the respective intervals of confidence were obtained through bootstrapping procedures. 25 26 SRMs >0. 8 were considered large. Responsiveness was also assessed by the correlation between change in DAS28(3v)-ESR and each domain through the Pearson's coefficient of correlation. Comparison between responders and nonresponders was performed through independent samples t-test. Additionally, individual domains were compared between patients in remission/low disease activity and patients with moderate/high disease activity at the end of the study through independent sample t-test. Quantitative data were expressed as means (SD) and categorical data as frequencies and percentages.
MOS Sleep 50 Coping Considering your arthritis overall, how well did you cope (manage, deal, make do) with your disease during the last week; Very Well (0) to Very poorly (10)   Helplessness mHAQ, modified Health Assessment Questionnaire; MOS-SF36, Medical Outcome Study Short Form -36 Item; NRS, numeric rating scale; RAID, rheumatoid arthritis impact of disease. imputation of missing data was performed for any statistical analysis. Statistical analyses were performed through SPSS IBM for Windows V. 24 and Medcalc software V. 18. 11. 3.
RAID dataset: validity and feasibility assessment The mean of the NRSs of each domain of RAID varied from 3. 8 to 4. 8, at baseline, with pain-NRS presenting the highest score (table 3). A moderate floor effect was observed in the majority of the NRS: 4. 5%-16. 5% reported a score of 0, which was more pronounced for the sleep NRS (16. 5% scores <1). A ceiling effect was present but less pronounced in all, with only 1. 8% (coping NRS) to 4. 7% (sleep NRS) of the patients scoring 10. Feasibility was excellent for all individual items, with a much lower percentage of missing data (0. 7%-1. 2%) than observed in the responses to the reference instruments (5. 4%-21. 9%) (online supplemental table S1). All NRS were moderately to strongly correlated with the respective reference Patient Reported Outcome Measures (PROMs) (table  3 ): the highest correlations were observed, as expected, for pain vs MOS-SF36 bodily pain (r=0.
Rainbow dataset: reliability and responsiveness to change assessment Reliability The ICC from screening to baseline was excellent for fatigue (0. 80, 95% CI: 0. 72 to 0. 86) and sleep (0. 83,     A correlation, although weak, was observed between change in DAS28(3 v)-ESR and change in individual domains, ranging between 0. 3 (sleep disturbance, coping, physical and emotional well-being) and 0. 4 (pain, fatigue, and function). Patients who achieved low disease activity/remission at week 12 reported a lower impact in all domains compared with patients who remained in moderate to high disease activity (figure  2 ). Additionally, responders with an improvement ≥1. 2 in DAS28(3 v)-ESR between baseline and week 12, showed a higher change in all domains, except for sleep disturbance, compared with non-responders (figure  3 ).
Acknowledgements We gratefully acknowledge the RAID investigators, who in addition to authors LG, MdW, MD and TKK are the following:  Andra Balanescu  ( Bucharest, Romania ),  Dimitrios Boumpas  ( Heraklion, Greece ),  Loreto Carmona  ( Madrid, Spain ),  Een A. C. Dijkmans  ( Amsterdam, the Netherlands ),  Matthias Englbrecht  ( Erlangen, Germany), Feride Gogus (Ankara, Turkey), Turid Heiberg (Oslo, Norway ),  John Kirwan  ( Bristol, UK ),  Emilio Martin Mola  ( Madrid, Spain ),  Marco Matucci Cerinic  ( Firenze, Italy ),  Kati Otsa  ( Tallinn, Estonia), Georg Schett (Erlangen, Germany ) and  Tulikki Sokka (Jyväskylä, Finland). This publication is based on research using data from  Pfizer  that has been made available through  Vivli. Vivli  has not contributed to or approved, and is not in any way responsible for, the contents of this publication.
1A ). All four cases had anti-IFN-2 antibodies at the earliest time of sampling, and the concentration of anti-IFN-2 antibodies remained stable for three of four cases across their disease course (Fig. 1E ). To profile the circulating immune response, we collected about 200 PBMC samples from up to four longitudinal time points: 0, 4, 7, and 14 days since hospitalization. Multiplexed single-cell epitope and transcriptome sequencing (muxCITE-seq) was performed across nine pools of genetically distinct samples to simultaneously measure mRNA abundances transcriptome-wide and surface protein abundances of 189 markers from the same cell (fig. S2  and data file S5). A total of 971,550 cell-containing droplets were sequenced, and 600,929 cells remained in the final dataset after quality control and removal of doublets, platelets, and red blood cells.
026) (data file S8). Given the heterogeneous expression of the IFN signaling cluster (cluster 1) within COVID-19 samples, we further compared the expression of type I-specific (ISG-I) and type II-specific IFNstimulated genes (ISG-II) between COVID-19 + cases and healthy controls and within COVID-19 + cases of varying severity. To differentiate ISG-I and ISG-II, we compared healthy donor PBMCs stimulated with recombinant IFN- or IFN- from an independent single-cell RNA sequencing (scRNA-seq) dataset to identify genes specifically up-regulated by either IFN (Fig. 3B ). In myeloid cells (cM, ncM, cDC, and pDC), the average expression of ISG-I, and to a lesser extent ISG-II, in critical cases on day 0 of hospitalization was significantly lower compared to moderate and severe cases [ISG-I: log 2 FC = -0.
Study design This study was designed to identify immunological features in the blood from hospitalized patients presenting with COVID-19 symptoms associated with disease status and severity. For this purpose, PBMCs and serum samples were collected from participants enrolled in the COMET cohort, which partially includes patients recruited to the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study (data file S4). The materials collected from each of the participants in this study were coded by donor ID numbers such that the experimentalist could not define the disease status of each participant. Hence, in each experiment, we randomized the processing of PBMCs from 22 to 25 participants, including 16 to 23 patients and 2 to 8 healthy individuals. All patients hospitalized for symptomatic COVID-19 infection at both the tertiary care center and the safety-net county hospital associated with the University of California San Francisco (UCSF) were eligible to participate in the COMET cohort study.
Biospecimens may have been collected under an Institutional Review Board (IRB)-approved initial waiver of consent with subsequent attempts to consent surrogates and study participants for full study participation. We selected samples from 69 of 101 of participants enrolled in COMET between 8 April 2020 and 20 June 2020 (Table  2  and data file S1), which resulted in at least 15 to 20 donors per COVID-19 severity group. According to previous publications of similar cohort sizes, our study should have sufficient power to detect COVID-19-associated cell type compositional and gene expression changes of similar effect sizes as described  (29, 38). The healthy anonymized donors were chosen within the age range of patients with COVID-19. Sample selection was prioritized in the patients that were hospitalized with longitudinal blood collections, and therefore, PBMCs were available across a 14-day period.
This study is approved by the IRB of the UCSF Human Research Protection Program (IRB no. A total of 231 patients with COVID-19 and 96 healthy controls were enrolled in the ZSFG cohort (Table  3  and data file S2). All ZSFG patients were collected between 1 March 2020 and 21 July 2020 and had positive results by SARS-CoV-2 reverse transcription PCR (RT-PCR) in nasopharyngeal swabs. Clinical data were extracted from electronic health records and included demographic information, major comorbidities, patient-reported symptom onset date, symptoms, and indicators of disease severity. Healthy, pre-COVID-19 control plasma samples were obtained as deidentified samples from the New York Blood Center. These samples were part of retention tubes collected at the time of blood donations from volunteer donors who provided informed consent for their samples to be used for research.
Details of the community-based cohort including 4041 participants were previously described by Chamie et al. (27)  (Table  4  and data file S3). The four APS-1 patients in the study were collected at the NIH under protocol #11-I-0187 and were previously published by Ferre et al. (44, 45). CCPs were collected in the Vitalant system following U. S. Food and Drug Administration (FDA) guidance for donor eligibility. These criteria evolved throughout the study period because of testing availability and evolution of the pandemic in the United States. Evidence of COVID-19 was required in the form of a documented positive SARS-CoV-2 molecular or serologic test and complete resolution of symptoms initially at least 14 days before donation, but then, a minimum of 28 days was implemented. All CCP donors were also required to meet traditional allogeneic blood donor criteria.
Cell counts and viability were determined using a Vi-CELL XR automated cell counter (Beckman Coulter Life Sciences). An equal number of cells were aliquoted from each sample to create a pool of 1. 5 × 10 6 cells with an average viability of 85% or higher. Pooled PBMCs were blocked with Human TruStain FcX (BioLegend) in cell staining buffer (BioLegend) for 10 min on ice, followed by staining with a customized TotalSeq-C human cocktail for 45 min on ice (data file S5). Cells were washed three times, resuspended in PBS with 0. 04% bovine serum albumin, filtered through a 40-m filter, and counted with Countess II Automated Cell Counter (Thermo Fisher Scientific). Single-cell suspensions were loaded into a Chromium Single Cell Chip A for single cell encapsulation using the 10X Chromium Controller according to the manufacturer's user guide (Document CG000186 Rev D, 10X Genomics) and as previously described  (47).