dataesr/scientific-paragraphs-categorization huggingface dataset:
UK Biobank  is open to bona fide researchers anywhere in the world, including those funded by academia and industry. The medical research project is a non-profit charity that has received core funding of around £133 million. Core funding continues to be received from the  Wellcome Trust , the  MRC , and, more recently,  Cancer Research UK  and  NIHR. UK Biobank  has received additional funding for genotyping of all 500,000 participants (from the  Department of Health, Medical Research Council , and  British Heart Foundation ). We want to acknowledge the participants and investigators of  UK Biobank. This research has been conducted using the  UK Biobank Resource  under application number  51784. UFO cohort: We thank A ˚sa A ˚gren,  Hubert Sjo ¨din , and  Magnus Hellstro  ¨m for data processing and for help creating the UFO database and  Kerstin Enquist  and  Ann-Marie  A ˚hren for their help in preparing samples for genotyping.
Variables, data sources and measurements Demographic information, clinical findings and treatment details were retrieved from the patients' medical records. All operating notes and pathology reports were reviewed. Histology was defined by one of two pathologists specialising in bone and soft-tissue tumours from the resection specimen using the residual tumour (R) classification: R0, no residual tumour; R1, microscopic residual tumour; and R2, macroscopic residual tumour. Radiographic contamination was suspected based on the following criteria: an intra-articular fracture of the affected bone or direct visualisation of the tumour in the joint. Histological articular contamination was then confirmed, or not, from the resection specimen. The patients' status was that reported at the time of last review.
Statistical analysis and data collection Continuous data are presented as median with first and third quartile values (Q1-Q3); categorical data are presented as counts and proportions. The cumulative incidence rates of reoperation for infection, mechanical failure and any reason and the cumulative incidence rate of local recurrence were presented as defined by Prentice et al. (12)  The cumulative incidence rates of events were estimated from the date of EAR of the hip to either the event, competing event (death) or last news when neither event nor death was experienced (censored observation). Point estimates with 95% exact confidence intervals (CIs) are reported. Patient survival was estimated according to the Kaplan-Meier method (13) from the date of EAR of the hip to either death or last news for living patients (censored observation).
Data Collection We retrospectively compiled data from patients' electronic medical records and then standardized them in a report file. The collected data included epidemiological, clinical and biochemical characteristics. Symptom onset date was recorded. Patients' current treatments and medical history, including cardiovascular disease, diabetes, chronic kidney disease, immune and malignant diseases, were also collected. In this study, severity of disease was defined by admission to the ICU. Obesity was defined by a body mass index superior to 30 kg/m 2. Overweight was defined by a body mass index between 25 and 30 kg/m 2. Standard biochemical parameters, such as creatinine, C-reactive protein (CRP), total leukocytes and lymphocytes, were also recorded. We then studied the overall hospital length of stay: firstly, in the ICU, including the need for invasive support care (mechanical ventilation, dialysis, etc.
) and the occurrence of complications (pulmonary embolism); then, secondly in post-ICU departments. Severity of illness was assessed using the Simplified Acute Physiology Score II (SAPS II)  [10]. Autonomy was measured by the Knaus chronic health status score  [11]. We also recorded ICU and in-hospital mortality. Lastly, we studied the impact of age and comorbidities in ICU patients infected with SARS-CoV-2. Therefore, we divided population into three age ranges (<65 years, 65-75 years, >75 years). As for comorbidities, they were grouped into five, according to general systems (cardiovascular, respiratory, hepatic, renal and immune or onco-hematological). All collected data are summarized in the Results Section.
Clinical Characteristics of the Study Population During the study period, which lasted almost two months, 72,941 patients presented to the EDs of all study centers combined. Of these patients, 9296 were diagnosed with SARS-CoV-2 infection using RT-PCR on nasopharyngeal swab and 6346 were hospitalized for COVID-19. Among all COVID-19 patients, 5570 presented mild disease and 776 presented severe or critical disease and were admitted to the ICU. After excluding patients with missing data and those admitted to the ICU secondarily during their hospitalization, a total of 423 patients were included in our study (Figure  1 ). During the study period, which lasted almost two months, 72,941 patients presented to the EDs of all study centers combined. Of these patients, 9296 were diagnosed with SARS-CoV-2 infection using RT-PCR on nasopharyngeal swab and 6346 were hospitalized for COVID-19.
Limitations Our study presents some limitations such as its retrospective nature and the relatively small sample size. Given the overwhelming workload submerging healthcare systems during the first wave of the pandemic, some of the collected data could not be exhaustively detailed, such as certain parameters (especially during ICU stay) and medical history. This lack of statistical power probably caused insufficient precision when studying different comorbidities as predictive factors of mortality in our cohort. Moreover, data were collected at the very start of the pandemic. At that time, patients were mostly managed following current guidelines, which did not rely on any specific therapeutic strategy since no treatment was validated, vaccines had not yet been marketed and mechanical ventilatory support was almost exclusively invasive due to the fear of aerosolization of viral particles.
Then, the fitness f(x) and f ðyÞ are calculated according to the NK model with Eq 8. If f(y) > f(x), the mutated genotype is selected, otherwise other mutations on x are tested until the fitness increases. In Algorithm 1: Mutate, the loci [i, j]2{1,. , N} are drawn from a pseudo random number generator function. With this recipe, the evolutionary dynamics is simulated up to a local fitness maximum is reached, i. e. x 2 X satisfies: f ðyÞ < f ðxÞ; 8y 2 N n ðxÞ. In other words, we verify that all mutants for a given genotype do not have higher fitness values, if not, the simulation continues. The main routine to simulate adaptive walks on the Kauffman's NK-fitness landscape model, with point mutations (as usual) and inversions (as new) is available at https://gitlab. inria. fr/letrujil/getting-higher.
Acknowledgments We wish to acknowledge insightful conversations with members of the Beagle team, especially  David P. Parsons  and  Aoife O. Igoe  also for their suggestions on the manuscript. L. T. thanks the  Institut National des Sciences Appliquees (INSA) as well as the Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS)  for hospitality while part of this research was done and would like to thank  Anton Crombach ,  Harold P. de Vladar  and  Ivan Junier  for useful discussions and valuable support. P. B. is grateful to  Laurent Turpin  and  Nathan Quiblier  for stimulating discussions.
Background Long-term studies are key to understand the drivers of biodiversity erosion, such as landuse change and habitat degradation, climate change, invasive species or pollution. The long-term project SLAM (Long Term Ecological Study of the Impacts of Climate Change in the natural forest of Azores) started in 2012 and focuses on arthropod monitoring, using SLAM (Sea, Land and Air Malaise) traps, aiming to understand the impact of the drivers of biodiversity erosion on Azorean native forests (Azores, Portugal). This is the fourth contribution including SLAM project data and the second focused on the spider fauna (Arachnida, Araneae) of native forests on two islands (Pico and Terceira). In this contribution, we describe data collected between 2019 and 2021 and we analyse them together with a previously published database that covered the 2012-2019 period, in order to describe changes in species abundance patterns over the last ten years.
New information We present abundance data of Azorean spider species for the 2019-2021 period in two Azorean Islands (Terceira and Pico). We also present analyses of species distribution and abundance of the whole sampling period. In the period of 2019-2021, we collected a total of 5110 spider specimens, of which 2449 (48%) were adults. Most juveniles, with the exception of some exotic Erigoninae, were also included in the data presented in this paper, since the low diversity of spiders in the Azores allows a relatively precise specieslevel identification of this life-stage. We recorded a total of 45 species, belonging to 39 genera and 16 families. The ten most abundant species were composed mostly of endemic or native non-endemic species and only two exotic species (Tenuiphantes tenuis (Blackwall, 1852) and Dysdera crocata C.
General description Purpose: This publication is the fourth data-paper contribution to the long-term project SLAM (Long Term Ecological Study of the Impacts of Climate Change in the natural forest of Azores) that started in 2012 with the aim of understanding the impact of the drivers of biodiversity erosion on Azorean native forests (Azores, Portugal) (see  Costa and Borges 2021 , Borges et al. 2022a , Borges et al. 2022b ). This publication is also the second of the series that explores time-series data for the spider fauna in Pico and Terceira Islands (Azores Archipelago) (see the first contribution in Costa and Borges 2021). We used passive flight interception SLAM traps (Sea, Land and Air Malaise trap) (MegaView Science Co. Ltd. , Taichung City, Taiwan) (Fig. 1 ) to sample native forest plots in several Azorean islands, with one trap placed at each plot.
This publication aims to document the most recent data (from winter 2020 to autumn 2021 on Terceira and from winter 2019 to Autumn 2021 on Pico) as an extension of the previous database published by  Costa and Borges (2021)  following the same sampling strategy. Besides, we also incorporate a recent taxonomic change over the database, where Sancus acoreensis  (Wunderlich, 1992)  becomes Leucognatha acoreensis  Wunderlich, 1992 ( Ceccolini and Cianferoni 2022).
Additional information: The year 2012 marks the beginning of the survey of arthropods in Terceira Island through SLAM traps, within the Project NETBIOME ISLANDBIODIV. In Pico Island, the study started in September 2013. Since 2020, the SLAM project has been financed within the project LIFE-BEETLES. Samples were collected by the University of the Azores team members in Terceira Island and by Pico Nature Park rangers in Pico Island. The statistical analyses presented and commented in the Discussion are based on the complete dataset on spiders from 2012-2021, including all samples since the beginning of the study, using also the data published by  Costa and Borges (2021). In a few cases, it was impossible to collect the SLAM traps on some sites and seasons (22 EventID out of 665 recorded) and sampling periods extended over three months.
Sampling description: In the laboratory, specimen sorting and spider identification followed standard procedures, using morphologic and copulatory features for species identification. A reference collection was made for all collected specimens (whether or not identified at species level) by assigning them a morphospecies code and depositing them at the Dalberto Teixeira Pombo Insect Collection (DTP), University of Azores (Terceira Island). Spider juvenile identification is very important in spider studies  (Domènech et al. 2022). Most juveniles, with the exception of some exotic Erigoninae, were also included in the data presented in this paper, since the low diversity of spiders in the Azores allows a relatively precise species-level identification of this life-stage.
Description: The dataset was published in the Global Biodiversity Information Facility platform, GBIF (Lhoumeau and Borges 2022). The following data table includes all the records for which a taxonomic identification of the species was possible. The dataset submitted to GBIF is structured as a sample event dataset that has been published as a Darwin Core Archive (DwCA), which is a standardised format for sharing biodiversity data as a set of one or more data tables. The core data file contains 155 records (eventID). This GBIF IPT (Integrated Publishing Toolkit, Version 2. 6. 2) archives the data and, thus, serves as the data repository. The data and resource metadata are available for download in the Portuguese GBIF Portal IPT (Lhoumeau and Borges 2022 ).
Column label Column description id Unique identification code for sampling event data. eventID Identifier of the events, unique for the dataset. The ten most abundant species are composed mostly of endemic or native non-endemic species and only two exotic species (Tenuiphantes tenuis (Blackwall, 1852) and Dysdera crocata C. L. Koch, 1838). The most abundant species were the endemic linyphiid Tenuiphantes miguelensis with 950 specimens (776 [81%] adults) (Fig. 5 ) and the endemic theridiid Rugathodes acoreensis with 780 specimens (293 [38%] adults) (Fig. 6 ). Linyphiidae was the richest and most abundant family with 15 (33%) species and 2630 (51%) specimens (Table  2 ).
Data set name: Occurrence Cheiracanthium mildei L. Koch, 1864 is a new record for Pico Island (Fig. 7 ). Both on Terceira and Pico Islands, the abundance of adults gradually increased from autumn to summer, with the highest abundance occurring in summer and the lowest in autumn. On Terceira Island, we collected more juveniles than adults in all seasons, except Spring and there were two peaks of abundance in winter and in summer (Fig. 8 ). On Pico Island, the abundance of juveniles is lower than that of the adults for each season. We observed a slight increase in the overall abundance of the number of specimens during years of sampling, for both Islands. Such increase was more evident on Pico Island than on Terceira Island where we found a peak of abundance in 2013 followed by a drop until 2015.
Discussion We analysed all available data on the two target islands -the new data collected between 2019-2021 and those from Costa and Borges (2021) -to obtain a long-term view on their forest spider assemblages. As specified previously, we considered only Event ID that were linked to only one season of sampling (about 90 days). The use of SLAM traps for long term monitoring in native forest provides good abundance data for spiders amongst a wide variety of families (Table  3 ). Previous analysis of the sampling strategy revealed a sample completeness of almost 100% for the overall arthropod communities between 2013 and 2018  (Borges et al. 2020). So far, we recorded 36 introduced, 14 endemic and nine native non-endemic species in both Terceira and Pico Islands (see Table  3 ). Although the indigenous/non-indigenous species ratio is in favour of introduced species, in terms of abundance, indigenous species are the most abundant group in native forests, with 75% of total specimens in Pico and 89% in Terceira.
Accumulation curves computed with all our data show a global increase in the number of species through time (Fig. 10 ), also observed in Fig. 9. It is mainly due to the rise of introduced species. According to these curves, the majority of indigenous species were recorded in the two first years of the SLAM project, reaching an asymptote. However, the number of exotic species recorded continues to increase. It is in accordance with a recent study focusing on all arthropods across the Azores  (Borges et al. 2020) , which indicates that exotic species are one of the major causes of biodiversity erosion on islands  (Borges et al. 2006 , Borges et al. 2019). Further investigation through time series analyses is needed to assess the rate and detection of new introduced species and to properly adapt the conservation management of these areas.
2018). Finally, Tenuiphantes tenuis, the only dominant exotic species in the dataset, exhibits a hump-shaped variation of abundance in Pico Island, where a peak of abundance was observed in 2017, when an average of circa 15 specimens per site were sampled. Its abundance is now decreasing, which is a positive observation. Particular attention should be given to this species to determine whether the trend will persist over time, as this species has already been able to successfully colonise other Macaronesia islands  (Cardoso and Crespo 2008, Nuria 2010) , being one of the dominant species of epigean spiders found in Madeira native forests  (Boieiro et al. 2018). The SLAM Trap sampling method is fully in accordance with the need of improving arachnofauna knowledge in terms of seasonal abundance and distribution.
The dataset was published in the Global Biodiversity Information Facility platform, GBIF (Lhoumeau and Borges 2022). The following data table includes all the records for which a taxonomic identification of the species was possible. The dataset submitted to GBIF is structured as an occurrence table that has been published as a Darwin Core Archive (DwCA), which is a standardised format for sharing biodiversity data as a set of one or more data tables. The core data file contains 978 records (occurrenceID). This GBIF IPT (Integrated Publishing Toolkit, Version 2. 6. 2) archives the data and, thus, serves as the data repository. The data and resource metadata are available for download on the Portuguese GBIF Portal IPT (Lhoumeau and Borges 2022).
Acknowledgements Numerous students (many of them financed by the  EU Programmes ERASMUS)  sorted the samples prior to species assignment by one of us (PB) and we are grateful to all of them:  Adrian Fernandez Marinez ,  Emanuela Cosma ,  Jonne Bonnet ,  Joel Martin Aye ,  Loic Navarro ,  Magí Ramon Martorell ,  Marco Canino ,  Natalia Fierro Frerot ,  Sébastien Lhoumeau  and  Valentin Moley. AMCS is supported by the  Ramón y Cajal program  ( RYC2020-029407-I ), financed by the  Spanish Ministerio de Ciencia e Innovación. IRA and MB were supported by  FCT -DL57/2016/CP1375/CT0004  and /CT0001, respectively.
Number of data sets: 2 Data set name: Event Table Character set: UTF Usage licence: Creative Commons Public Domain Waiver (CC-Zero) Data resources Data package title: Long-term monitoring of Azorean Forest Spiders -Part 2 Resource link: http://ipt. gbif. pt/ipt/resource?r=spiders_azores_2021 Alternative identifiers: https://www. gbif. org/dataset/f8b3ed49-f65d-4989- add0-9a726b1e745a -8 Download URL: http://ipt. gbif. pt/ipt/resource?r=spiders_azores_2021 Data format: Darwin Core Archive format Data format version: Version 1. 3.
A total of fourteen species collectionCode were endemic to the Azores Archipelago (2416 specimens; 1114 adults), nine species were The code of the collection where the specimens are conserved. datasetName native non-endemic (1793 specimens; 1006 adults) and twenty-two species were Name of the dataset. introduced (901 specimens, 329 adults) (Table 2). basisOfRecord The nature of the data record. Family Species Colonis. Pico Terceira Total Abundance Agelenidae Tegenaria domestica (Clerck, 1757) I 2 0 2 Agelenidae Textrix caudata L. Koch, 1872 I 0 5 5 Araneidae Gibbaranea occidentalis Wunderlich, 1989 E (NT) 81 265 346 eventID Cheiracanthiidae Cheiracanthium erraticum (Walckenaer, 1802) Identifier of the events, unique for the dataset. I 9 8 17 identifiedBy Cheiracanthiidae Cheiracanthium mildei L.
INFORMED CONSENT Authors declare as follows. • The study has been carried out in compliance with the current applicable ethical-legal framework (EU Charter of Fundamental Rights, Declaration of Helsinki, EU Reg. 2016/679 General Data Protection Regulation ''GDPR''). Ethics committee approval is not required as it is an observational study. • Participation of volunteers was conditioned to the collection of freely-given informed consent. The information sheet included transparent and exhaustive information regarding the project, partners involved in the observational study, benefits, possible risks, incidental findings procedure, insurance details, information privacy under article 13 GDPR. Only adults and healthy participants have been recruited. • The collected personal data have been pseudonymized and stored in a limited accessed folder.
6, 7 The current analyses examine four subgroups of patients from ICARIA-MM and IKEMA: 1q21+ (≥3 copies with or without HRCA), isolated 1q21+ (≥3 copies without HRCA), gain(1q21) (3 copies with or without HRCA), and amp(1q21) (≥4 copies with or without HRCA). The analyses show a clear benefit of Isa-based combinations in 1q21+ disease. Full methodologic details for the randomized, open-label, phase 3 ICARIA-MM (ClinicalTrials. gov, identifier NCT02990338) and IKEMA (ClinicalTrials. gov, identifier NCT03275285) studies were previously described. 4, 5 The primary endpoint in both trials was PFS, as assessed by an independent response committee. Secondary endpoints included overall survival (OS) and overall response rate, assessed according to International Myeloma Working Group response criteria.
PFS was defined as the time from randomization to first documentation of progressive disease before initiation of anti-myeloma therapy or death from any cause, whichever came first. PFS data was analyzed as per the ICARIA-MM primary analysis cut-off date (October 11, 2018). OS data was analyzed at the second interim cut-off date (October 1, 2020). Efficacy analyses were performed on the intention-to-treat population and summarized by assigned treatment. Confidence intervals are 95% for all Kaplan-Meier plots. 1q21+ definition: ≥3 copies, 30% cutoff, with or without high-risk chromosomal abnormalities. *99% confidence intervals were stratified on number of prior lines of therapy (1 versus >1) and Revised International Staging System stage (I or II versus III versus not classified) according to interactive response technology.
To do so, we studied 125 patients with acute non-idiopathic TTP presenting with miscellaneous pathological contexts (autoimmunity, cancer, liver insufficiency, infection, drug treatment) including both cases of uTTP (n=76, group of interest) and iTTP (n=49, control group). The uTTP patients had an older median age, lower female/male ratio, more frequent renal disorders, and higher platelet count, ADAMTS13 antigen level and mortality. Interestingly, ADAMTS13 conformation was closed in 86. 4% of uTTP cases but open in 76. 7% of iTTP cases. Adult patients with non-idiopathic TTP from the French Thrombotic Microangiopathy Registry (inclusion period 01. 01. 2012 -12. 31. 2016) were enrolled if they met the following inclusion criteria: age >18 years old, inaugural acute thrombotic microangiopathy event associated with both an ADAMTS13 activity <10 IU/dL and another clinical situation at presentation (excluding pregnancy), available medical data and citrated plasma samples.
These physico-chemical mechanisms were indeed not considered in the original ASMs, as they were not limiting for the considered sludge ages (>3 days in ASM1). To model high-rate processes without increasing the complexity of the model,  Smitshuijzen et al. (2016)  modified ASM1 by introducing a factor (f settler ) that accounts for the fraction of biodegradable particulate matter that is captured by the A-Stage system. This factor accounts for both the efficiency of biosorption and the efficiency of separation in the settler in one term. It was estimated from data from the full-scale plant Dokhaven (Rotterdam, The Netherlands) including daily and seasonal variations to investigate the impact of rain events and changes in water temperature. The results show that the model was able to predict effluent total COD with a 10% error under steady-state conditions.