dataesr/scientific-paragraphs-categorization huggingface dataset:
A phylogenetic tree that is time-calibrated across all individuals of the six fish species is needed for BAMM. Firstly, we estimated the divergence time of the six fish species. Based on 2686 single-copy orthologues across 12 fish species (the protein sequence of longest transcript per gene was selected, the 12 species were six fish species in our study, and Japanese Medaka, Oryzias latipes; Fugu, Takifugu rubripes; Stickleback, Gasterosteus aculeatus; Zebrafish, Danio rerio; Platyfish, Xiphophorus maculatus; Spotted gar, Lepisosteus oculatus) detected by OrthoFinder v2. 3. 3  (Emms & Kelly, 2015). All orthologs were aligned using MUSCLE v. 3. 8. 31  (Edgar, 2004)  with default parameters and the regions with bad quality were trimmed using trimAl v1. 4. rev22  (Capella-Gutiérrez et al. , 2009)  with "-gt, 0.
This study was supported by the  Office of Competitive Research Funds   OSR-2015-CRG4-2541  from the  King Abdullah University of Science and Technology  (T. R. , P. L. M. , C. S. , J. L. R. ), the  University of Hong Kong  start-up grant to C. S. (C. S. , J. L. K. ), the  Australian Research Council (ARC)  and  ARC Centre of Excellence for Coral Reef Studies  (P. L. M. , J. L. R. ), and The  Okinawa Institute of Science and Technology Graduate University  (T. R. ). I. N. was supported by an  ARC   Future fellowship  (grant no. FT120100183 ). R. R-M was supported by the  French National Research Agency (ANR , project  CARIOCA  no. ANR15CE02-0006-01 , and  ANR-17-ERC2-0009 ). This project was completed under approval of the  James Cook University animal ethics committee  ( A2534 ) and according to the University's animal ethics guidelines.
Funding information the  King Abdullah University of Science and Technology , Grant/Award Number:  OSR-2015-CRG4-2541 ; The  Okinawa Institute of Science and Technology Graduate University ; the  French National Research Agency , Grant/Award Number:  ANR15CE02-0006-01  and  ANR-17-ERC2-0009 ; the  University of Hong Kong  start-up grant; the  Australian Research Council (ARC)  and  ARC Centre of Excellence for Coral Reef Studies , Grant/ Award Number: FT120100183.
I n r e v i e w Statistical analyses were performed in R version 4. 0. 0, using the R package lmerTest  (Kuznetsova et al. 2017 , R Core Tea 2021). A preliminary analysis exploring the effect of sampling date, hour, and breeding site on IGF-1 levels showed that only sampling date accounted for variation in IGF-1 levels; sampling date was therefore retained as a covariate when analysing variation in IGF-1. Firstly, we tested for an association between IGF-1 and wing length, sternum length and/or body mass by fitting a general linear model with IGF-1 as response variable and all three biometric traits, plus sampling date, as explanatory variables (model 1). Variance inflation factors in this first model were below 1. 24 indicating no collinearity issues between biometric traits. Secondly, we investigated how IGF-1 (response variable) was related to telomere length by including as explanatory variables telomere length while controlling for possible confounding effect of chronological age and traits identified as significant in our model 1.
Methods We identified suicide data from official public-sector sources for countries/areas-within-countries, searching websites and academic literature and contacting data custodians and authors as necessary. We sent our first data request on 22nd June 2021 and stopped collecting data on 31st October 2021. We used interrupted time series (ITS) analyses to model the association between the pandemic's emergence and total suicides and suicides by sex-, ageand sex-by-age in each country/area-within-country. We compared the observed and expected numbers of suicides in the pandemic's first nine and first 10-15 months and used meta-regression to explore sources of variation. Findings We sourced data from 33 countries (24 high-income, six upper-middle-income, three lower-middle-income; 25 with whole-country data, 12 with data for area(s)-within-the-country, four with both).
Research in context Evidence before this study We searched PubMed, Scopus, medRxiv, bioRxiv, the COVID-19 Open Research Dataset, and the WHO COVID-19 database using terms for suicide and suicidal behaviour (e. g. , "suicid*") and COVID-19 (e. g. , "coronavirus" OR "COVID*" or "SARS-CoV-2") from 1st January 2020 to 17th February 2022 and identified 86 studies investigating COVID-related suicide trends from 32 countries/ areas-within-countries, many of which did not use appropriate time series approaches. No change (and sometimes declines) in suicide frequencies/rates during the COVID-period were reported in most countries/ areas-within-countries, with the exceptions of rises (or slowing of declines) in Hungary, India, Japan, Nepal and Spain and in Vienna and Puerto Rico. There was no consistent evidence of disproportionate effects on suicide by specific groups based on sex or age.
Added value of this study We synthesised sex-and age-specific suicide trend data from 33 countries over the first 9-15 months of the pandemic and used time-series models to account for prepandemic trends in suicide. There was no evidence of a change to pre-pandemic suicide trends in most countries/areas-within-countries, and no consistent evidence that any age/sex group was differentially affected by the pandemic. There were suggestions that proportionally more countries/areas-within-countries had greaterthan-expected numbers of suicide in analyses with longer follow-up periods, and that areas within lower-middle-income countries were faring less well than other settings.
Data inputs Suicide data sources. We sought suicide data from vital statistics systems and real-time suicide surveillance systems. The former are usually regarded as the official source of suicide statistics because they record deaths deemed to be suicides following investigations by coroners, medical examiners or other authorities. Because these investigations are often lengthy, however, real-time surveillance systems have been established to capture data in a timelier fashion. These use sources such as police reports and death certificates to classify deaths as suspected suicides. 14 They yield estimates that correspond closely with those from vital statistics systems. 14 clusion criteria. We sought data from countries and areas-within-countries, including the latter to generate as global a synthesis of the evidence as possible.
To be included, data had to: (1) come from a vital statistics or real-time surveillance system from an official public-sector source (e. g. , government department, national statistics agency, coroners court, medical examiners office, police department, university or other research setting); (2) cover a minimum period from 1st January 2019 to 31st December 2020 (and potentially a maximum period from 1st January 2016 to 30th June 2021); and (3) include total monthly counts of suicide (and ideally monthly counts by sex, age, and sex-by-age). Identifying and accessing suicide data. We searched health ministries', police agencies', and statistics agencies' websites using the (translated) terms "suicide" and "cause of death". We searched the academic literature, via our living systematic review.
We treated 1st April 2020 as the start of the COVID-19 period because April was the first full month after the World Health Organization's pandemic declaration (11th March 2020). We considered using different start-months for different countries, based on the first date of stay-athome orders, but this would have presented problems because there was often considerable within-country variability. Our primary analysis considered the pandemic's first nine months (1st April to 31st December 2020), and our secondary analysis considered its first 10-15 months (1st April 2020 through to the latest month for which data were available, from at least 31st January 2021 and potentially up until 30th June 2021; Figure  1 ). Each analysis examined total suicides and suicides by sex, age and sex-by-age. We conducted separate analyses for countries and areas-within-countries to avoid duplication in any one analysis and because the numbers of suicides in areaswithin-countries were generally smaller, creating more uncertainty around the RR estimates.
org/grapher/covid-stringencyindex); (4) level of economic support (composite measure based on indicators such as income support and debt relief; scored from 0-100 [100 = strong government support]; average of the daily index between 1st April 2020 and 31st December 2020) (https://ourworldindata. org/covid-income-supportdebt-relief); (5) interaction between (3) and (  4 ); and (6) presence of a national suicide prevention strategy (https://www. mindbank. info/collection/topic/suici de_prevention_). In presenting the data in tables, we have used red and green cells to indicate the direction and strength of the evidence based on RRs and 95%CIs. Red and green cells indicate that there was statistical evidence of suicide numbers being greater-or lower-than-expected in the COVID-19 period, respectively.
Results We sourced data from 33 countries (24 high-income, six upper-middle-income, three lower-middle-income; 25 with whole-country data, 12 with data for area(s)-withinthe-country, four with both). In total, we had 59 individual datasets (25 countries, 34 areas-within-countries; Figure  2 , Table  1 , appendix 1 [pp9-28]), with a total of 852,150 suicides. Data from England/Wales were provided to us in a combined form so we treated them as one country. See appendix 2 for raw data and appendix 3 for code that reads the data into Stata, labels the variables and performs an example analysis. Tables 2-5 present the findings from the primary and secondary analyses, for countries (Tables  2  and 3 ) and areas-within-countries (Tables  4  and 5 ); also see appendix 1 (pp 29-32 and pp33-50). Each cell shows the RR for the given country/area-within-country for total suicides and suicides by sex, age and sex-by-age groupings.
We classified 7-18 yrs as <20 yrs, and 19-39 yrs as 20-39 yrs. Table  3 : Rate ratios (RRs) for observed versus expected suicides in the first 10-15 months of the pandemic, by country (n=11). 1. The COVID-19 period was defined as 1st April to the latest date for which data were available (up to 30th June 2021), and the pre-COVID-19 period as at least 1st January 2019 to 31st March 2020 (with data included from as early as 1st January 2016, if available). 2. Countries with a latest-available date of 31st December 2020 were excluded from the analysis because their RRs were the same as those in Table  2. 3. Red and green cells indicate that there was statistical evidence of suicide numbers being greater-or lower-than-expected in the COVID-19 period, respectively. As noted in the legend, the red and green cells are graduated, with pale red/green indicating weak evidence, mid red/green indicating moderate evidence, and dark red/green indicating strong evidence.
We classified 7-18 yrs as <20 yrs, and 19-39 yrs as 20-39 yrs. Table  5 : Rate ratios (RRs) for observed versus expected suicides in the first 10-15 months of the pandemic, by area-within-country (n=25). 1. The COVID-19 period was defined as 1st April to the latest date for which data were available (up to 30th June 2021), and the pre-COVID-19 period as at least 1st January 2019 to 31st March 2020 (with data included from as early as 1st January 2016, if available). 2. Areas-within-countries with a latest-available date of 31st December 2020 were excluded from the analysis because their RRs were the same as those in Table  4. 3. Red and green cells indicate that there was statistical evidence of suicide numbers being greater-or lower-than-expected in the COVID-19 period, respectively. As noted in the legend, the red and green cells are graduated, with pale red/green indicating weak evidence, mid red/green indicating moderate evidence, and dark red/green indicating strong evidence.
There is a need to maintain and strengthen suicide prevention activities. The way that the pandemic confers risk for suicide may be changing. Initially, much of the concern related to potential consequences of lockdowns (e. g. , feelings of isolation/entrapment). Many countries have now moved to "living with COVID-19", but the pandemic is still having far-reaching impacts. Many individuals have suffered financially, experienced high levels of stress, and been bereaved through COVID-19 deaths, and many still fear the future. Ongoing suicide prevention activities will need to respond to the major impact that COVID-19 has had and will continue to have on people's lives; continued economic and mental health supports will be key. Our study had many strengths. It included data from 33 different countries.
Contributors JP, DG, AJ and MJS conceptualised, designed and led the study. SS, MDP-B, and VA conducted the internet searches for data and JP, DG and AJ followed up leads through the ICSPRC network. Additional data were sourced or provided by the following authors: PAA, AB, CB, GB, PB, RC, VC, GC, S-SC, DCo, MC-G, MC, RD, EDJ, EAD, JD, AE, JF, MF, SF, AG, GG, RGe, SKG, FH, IK, AK, KK, SCK, HK, SL, FM, AMa, EM-R, PN, NN, TN, MN, HO, RP, TP, GPo, GPs, PQ, DR, AR, CR-L, MR, NR-V, SR, VR, GS, KS, BS, NS, MS, ST, MU, DW, RTW, PW, SFPY, and RZ. MJS, JP, SS, MDP-B and VA were responsible for data verification, management and storage. MJS did the analysis. JP prepared the first draft of the manuscript with input from DG, AJ and MJS. JP, DG, AJ, MJS, SS, MDP-B, VA, PAA, LA, SMYA, EA, JLA-M, YPSB, JBa, AB, CB, JBe, GB, MB, PB, EC, RC, VC, GC, LFC, S-SC, DCo, MC-G, DCr, MC, RD, EDJ, DDL, EAD, JD, AE, JSF, MF, SF, AG, GG, RGe, RGi, MG, SKG, KH, FH, IK, NK, AK, MK, OJK, DK, KK, SCK, HK, SL, FM, AMa, AMe, EM-R, PN, NN, TN, EN, MN, HO, RCOC, RP, TP, MRP, SP, GPo, GPs, PQ, DR, AR, CR-L, MR, NR-V, SR, VR, GS, KS, BS, NS, MS, ST, ET, MU, DW, RTW, PW, SFPY, GZ and RZ interpreted data and made critical intellectual revisions to the manuscript.
Acknowledgements We acknowledge the help that the International COVID-19  Suicide Prevention Research Collaboration (ICSPRC)  has received from the  International Association for Suicide Prevention (IASP)  in establishing and supporting its activities. This study was supported by the  ADP , which is funded by  MQ Mental Health Research Charity  (grant reference MQBF/3 ADP). ADP and the authors acknowledge the data providers who supplied the datasets enabling this study. The views expressed are entirely those of the authors and should not be assumed to be the same as those of ADP or MQ Mental Health Research Charity. The authors would also like to thank the team working on the living systematic review of COVID- 19 and suicidal behaviour:  Emily Eyles ,  Luke McGuinness ,  Babatunde K Olorisade ,  Lena Schmidt ,  Catherine MacLeod Hall , and  Julian Higgins  ( University of Bristol );  Chukwudi Okolie ,  Dana Dekel , and  Amanda Marchant  ( University of Swansea );  Faraz Mughal  ( University of Keele );  Lana Bojanic  ( University of Manchester ).
JP is funded by a  National Health and Medical Research Council   Investigator Grant  ( GNT1173126 ). AJ is funded by  MQ  ( MQBF/3 ) and the  Medical Research Council  ( MC_PC_17211 ). MDPB is funded by  Health and Care Research Wales  ( CA04 ). VA is supported by  Australian   Government Research Training Program Scholarship. AB is supported by the  European Union 's  Erasmus+ Strategic Partnership Programme  ( 2019-1-SE01-KA203-060571 ). OJK is supported by a  Senior Postdoctoral Fellowship  from  Research Foundation Flanders  ( FWO 1257821N ). MRP funded in part by a  Global Alliance for Chronic Diseases -National Natural Science Foundation of China  grant ( NSFC. No. 81761128031 ). AK is supported by the project " Sustainability for the National Institute of Mental Health ",  LO1611 ,  Ministry of Education, Youth and Sports of the Czech Republic  under the  NPU I programme  and by the  Charles University, Prague  ( SVV 260 596  and  GA UK 552119 ).
We designed two sets of 96 probes, one against the positive strand and one against the negative strand of SARS-CoV-2 (Fig  1C ). For more details on the probe design workflow, see the Materials and Methods section and the source code (https://github. com/ muellerflorian/corona-fish). In brief, we identified an initial list of more than 600 potential probe sequences with our previously described method Oligostan  (Tsanov et al, 2016). We then further screened these probes to be robust to known genomic variations of SARS-CoV-2 (as of April 2020), while removing probes with affinity to other known β-coronaviruses or viruses frequently causing similar respiratory diseases in humans such as Influenza. Last, we removed probes overlapping with the transcriptome of several relevant host organisms (human, mouse, African green monkey, hamster, and ferret).
We then imaged infected Vero cells at 18 h p. i. These images clearly show the presence of both positive and negative RNA strands in the same cells and the same subcellular regions, although the relative abundance of both strands appeared to vary from cell to cell and colocalization was only partial  (Figs 1G and S1C). By allowing visualization of positive and negative RNA together, CoronaFISH opens the door to analyzing the intracellular organization and dynamics of viral transcription and replication. Our data thus show that CoronaFISH probes can sensitively and specifically detect the positive and negative strands of SARS-CoV-2 in infected Vero cells.
Probe design The entire code for probe design is available on GitHub at: https:// github. com/muellerflorian/corona-fish. The probe design involves several steps to ensure high sensitivity for the detection of SARS-CoV-2 RNA, while minimizing false-positive detection of other β-coronaviruses, other pathogens provoking similar symptoms, and transcripts of the host organism. Below, we list in parentheses how many probes remain after each selection step for probes targeting positive and negative strands, respectively. The initial list of probes for the positive and negative strands was generated with Oligostan (N = 615 and 608, respectively)  (Tsanov et al, 2016). We then selected all probes with a GC content between 40% and 60% and probes satisfying at least two out of five previously established criteria for efficient oligo design  (Xu et al, 2009)  (N = 385/362).
Image analysis of infected cell lines Nuclei were automatically segmented in 2D images with an ImJoy  (Ouyang et al, 2019)  plugin using the CellPose model  (Stringer et al, 2021). Source code for segmentation is available here: https:// github. com/fish-quant/segmentation. Equidistant regions with a width of 50 pixels were calculated around each nucleus. Overlapping regions from different nuclei were removed. 3D FISH images were transformed into 2D images with a maximum intensity projection along z. Signal intensity for each cell was determined as the 99% quantile of all pixels in the equidistant region around its nucleus.
Nasal swab patient samples Respiratory specimens (nasal swabs) have been collected from patients with respiratory symptoms as part of routine care at the Hospital Ambroise Par é in late March 2020. No additional samples were collected in the course of this work. Patients were contacted, informed about the research project, and given the possibility to oppose the use of their samples for this project. Lack of opposition to participate in clinical research was verified in the records of all patients whose samples were used here. This project has been recorded in the French public register Health-data-Hub (no. F20200717122429). Processing of personal data for this study complies with the requirements of the "reference methodology MR-004" established by the French Data Protection Authority (CNIL) regarding data processing in health research.
Acknowledgements We would like to thank  Edouard Bertrand , who originally developed smiFISH and  Hans Johansson  for insightful discussions. We also thank  Guillaume Dumenil  ( Ultrastructural Bioimaging UTechS of Institut Pasteur ) for having suggested the application of CoronaFISH to EM and for follow-up discussions. We thank  Nathalie Jolly  and  Nathalie Cl ément (Center for Translational Science, Institut Pasteur)  and  Clo é Giquel  ( Legal department, Institut Pasteur ) for their help in obtaining authorization to use patient samples. E  Simon-Loriere  acknowledges funding from the  French Government 's  Investissement d'Avenir program , " INCEPTION'  ( ANR-16-CONV-0005 ). G Barba-Spaeth  acknowledges funding by the  Institut Pasteur Coronavirus task force (don AXA   COVID-19  project  COVID-SPREAD ).
None of these initiatives fully addresses the management of children with hypoparathyroidism. Due to the huge phenotypic variability of hypoparathyroidism, physicians involved in the care and followup of patients range from pediatricians to endocrinologists and work in very different settings, from family medicine offices to tertiary-care hospitals. To increase awareness of the guidelines, it is important to target the right audience  8  , but very little is known about who these physicians are and what their daily practice is regarding the treatment and follow-up of patients with chronic hypoparathyroidism. The present study aimed to address this point. We analyzed practice pattern data with the following objectives: i) to describe the profile of physicians along with their practice pattern in terms of treatment and follow-up, ii) to identify their objectives for care and follow-up, and iii) to study the strategies used to meet them.
Methods Data sources and collection. Épi-Hypo (NCT02838927) is a cohort study that aimed to describe the natural history of chronic hypoparathyroidism in France. Data were collected online via a secure website. Physicians had to practice in France and provide care to one or more patient(s) with chronic hypoparathyroidism. They were solicited through national professional societies. At the time of registration, physicians were requested to fill out an online questionnaire (see Supplementary material) regarding how they monitor and provide care for patients with chronic hypoparathyroidism: the data were collected from September 2016 to November 2019 and are presented as the 'Physicians data'. Of note, 2 items (assessment of brain calcifications and bone mineral density) were added later to the study (May 2019); therefore, as mentioned, these data from a subset of physicians are missing.
We developed another online questionnaire (see Supplementary material) aimed to describe the practice patterns as reported by patients (Patient-Reported Outcomes). Volunteers registered online (therefore called 'ePatients') following an invitation sent by the Hypoparathyroidisme France association to its members. We collected 'ePatients data' during November 2019 and excluded those who also participated in Épi-Hypo. Finally, we also used data collected by investigators from the medical records of patients participating in Épi-Hypo who had at least one follow-up visit in 2019 (referred to as 'Épi-Hypo 2019 patients') to obtain data from the same period of time as that of the ePatients. Chronic hypoparathyroidism was defined as per the latest guidelines  4, 5, 7  : the association of hypocalcemia and inappropriately low concentrations of circulating PTH for at least 6 months.
Their duration of hypoparathyroidism was shorter than that of Épi-Hypo 2019 patients (5. 0 vs. 10. 0 years), whereas the delay to diagnosis was similar. We collected data from 2 children, in ePatients only. The various causes of hypoparathyroidism did not differ between the two cohorts of patients, with surgery being the most prominent cause. Two patients (belonging to the Épi-Hypo 2019 patients) had end-stage kidney disease (one treated by hemodialysis and one by kidney transplantation). The medical specialty of physicians following up with patients in both cohorts differed, as more endocrinologists were involved in the care and follow-up of ePatients (65 vs. 57%), whereas more nephrologists were involved in the care and follow-up of Épi-Hypo 2019 patients (42 vs. 10%). ePatients were more frequently followed in a for-profit office than Épi-Hypo 2019 patients (51 vs.