dataesr/scientific-paragraphs-categorization huggingface dataset:
0001). (C) Cells (5 3 10 3 ) were seeded in 24-well plates and treated with doxorubicin at different concentrations for 24 h, after which cells were released in standard growth medium. Confluence was determined by Incucyte S3. Data were fitted with a logistic model, and growth rate was measured as the slope at the middle point. Results of two experiments were used for the analysis. Details of the analysis and statistical significance analysis are reported in STAR Methods. N = 2. (D) Upper panels: we propose that the mitotic arrest induced by nocodazole results in mild caspase-3/7 activity and DNA damage (left). In cells that express higher levels of Triap1 (right), cytochrome c release from mitochondria is reduced, and cells experience less DNA damage and p21 activation. Lower panel: when cells undergo slippage, a few of them overexpress Triap1 (through an unknown process marked by ''?'').
0 Promega G9241 Trevigen Bio-techne comet assay kit Bio-techne 4250-050-K SYBR Green master mix Roche 04887352001 Maxwellâ RSC simplyRNA Tissue Kit Promega AS1340 SuperScript VILO cDNA Synthesis Kit Thermofisher 11754050 Deposited data Mass Spectrometry data ProteomeXchange Consortium via the PRIDE PXD037143 Experimental models: Cell lines hTERT-RPE1 ATCC CRL-4000 hTERT-RPE1 EmptyVector This study NA hTERT-RPE1 Triap1 OE This study NA hTERT-RPE1 FirstCycle shScramble This Study NA hTERT-RPE1 FirstCycle shTriap1 This study NA hTERT-RPE1 p53KO Burigotto et al. , EMBO J, 2021 32 NA (Continued on next page).
Impacts of COVID-19 will be assessed with a questionnaire based on other COVID-19 questionnaires  [72]. It consists of 16 items focusing on various aspects of the COVID-19 pandemic (e. g. COVID-19 infection, adherence to and consequences of COVID-19 regulations) and an additional item on vaccination status at baseline assessment. After the trial, various implementation indicators, e. g. reach, dose, resource use, and costs (training, staff, recruitment etc. ), will be assessed. Furthermore, based on the implementation indicators and fidelity data the incremental costs per change in the primary outcome and quality of life will be estimated. Hair cortisol concentrations (HCC), cortisone, dehydroepiandrosterone (sulphate) (DHEA(S)), testosterone, and progesterone levels will be analysed through hair samples.
Treatment fidelity Treatment fidelity will be assessed in various ways. First, helpers will fill in a session checklist after each DWM call and PM + session, and supervisors do so after each supervision. Second, 10% of the recordings of the DWM phone calls and PM + sessions will be assessed with fidelity checklists. This will be done for a random sample, stratified on helpers for DWM and PM + separately. Fidelity checks take place throughout the delivery of the programme, resulting in an iterative process of programme monitoring informing programme delivery. Third, metadata on participants' app usage, e. g. if a participant has completed a module, will be collected.
Quantitative data (RCT) Intention-to-treat (ITT) and per-protocol (PP) analysis will be conducted for both the primary and secondary outcomes. ITT analysis will include all randomized participants (N = 212). PP analysis will include participants in the intervention group only if they have (a) clicked through at least three (out of five) modules of the DWM web application, and (b), if they qualified for PM + in terms of K10-distress score at t 2, attended at least four (out of five) PM + sessions. ITT analysis of the primary outcome will be used to answer the main research question. To compare differences between the two treatment groups at baseline, t-tests (continuous variables) and chisquared tests (categorical variables) will be conducted for normally distributed data. For continuous non-normally distributed data, Mann-Whitney tests will be conducted.
Missing data will not be imputed but will be treated as missing at random (MAR) as linear mixed models can handle missing data. If some items of a particular scale are missing (i. e. < 50%), the Corrected Item Mean Substitution method will be used  [79]. This method uses the item mean across participants of the same study group and time point, weighted by the subject's mean of completed items. All the above-mentioned analyses will be performed using Jamovi version 2. 3. 26  [80]. Jamovi is an open-source graphical user interface for R. As an exploratory additional analysis, digital markers will be assessed using open-source tools in a Python environment, which is available on GitHub  [38, 74]. Digital markers will be assessed using open source tools in a Python environment, which is available on GitHub  [38, 74].
Quantitative data (RCT) Data collected through Castor EDC is automatically pseudonymised. Data is fully accessible to the VU research team. Participants give separate permission for their data (a) to be shared with partners in the RESPOND project, and (b) to be stored at the research location for 15 years, so it can be used for future scientific research into the health of IMWs. Audio and video recordings of DWM support calls and PM + sessions respectively are stored pseudonymously in an active but secured data folder in Surfdrive (i. e. , a cloud service for Dutch education and research) that can only be accessed by the members of the research team. At the end of the trial, when all hair samples are collected, they will be sent to the laboratory for analysis. Participants who submitted a hair sample will be informed of the analysis results of their sample.
Trial monitoring and adverse events reporting The project has an Ethics and Data Advisory Board (EDAB), that will monitor and advise on data management, and ethical, legal, and societal issues that arise within the project. (Serious) adverse events ((S)AEs) are defined as any undesirable experience occurring to a participant during the study, regardless of its connection to the study procedure or the DWM/PM + intervention. All SAEs will be recorded in Castor EDC and reported to the EDAB and the Medical Ethics Committee of the Amsterdam University Medical Center (UMC), location VU University Medical Center (VUmc). The research team will follow up with all SAEs until they are stabilized or have abated. If necessary, participants will be referred to a general practitioner. Participants can withdraw from the study at any time.
Funding The RESPOND project has received funding from the European Union's Horizon 2020 research and innovation programme Societal Challenges under Grant Agreement number 101016127. The funder has no role in study design; collection, management, analysis, and interpretation of data; writing of the report; and the decision to submit the report for publication. The content of this article reflects only the authors' views and the European Commision is not responsible for any use that may be made of the information it contains.
Machine learning methods are becoming increasingly popular to anticipate critical risks in patients under surveillance reducing the burden on caregivers. In this paper, we propose an original modeling that benefits of recent developments in Graph Convolutional Networks: a patient's journey is seen as a graph, where each node is an event and temporal proximities are represented by weighted directed edges. We evaluated this model to predict death at 24 hours on a real dataset and successfully compared our results with the state of the art.
Considering the aforementioned objectives and the identified limits of the approaches in the literature, we propose in this article a modeling of patient records as temporal graphs, input into GNNs. The contributions of the article are as follows: 1) We propose a new original modeling of temporal and heterogeneous data in the form of a graph where each node corresponds to a measurement and where temporal proximities are represented by weighted arcs. 2) We input this representation into a GCN (Graph Convolutional Network) that we evaluate on the MIMIC-III dataset described by  [6] , to predict the patient death at 24 hours, from real-world data collected during their journeys. We show through experiment that our approach offers results comparable to those of the state of the art. The rest of the article is organized as follows.
Experiments and Results To evaluate our approach, we focused on the 24-hour death prediction task described in Harutyunyan et al. [6]. Only patient journeys that last at least 4 hours are selected. For each of them, we seek to predict for each one-hour span whether a patient dies within the next 24 hours, based on data prior to the observation. The original data is available on Physionet  [8]  in the public MIMIC-III Clinical Database  [9, 10]. This database is often used in the literature to compare approaches for many medical supervision tasks because it is voluminous, heterogeneous and anonymized. Additionally, the data made available describe actual patient journeys that occurred during Beth Israel Deaconess Medical Center activity from 2001 to 2021. For their experiments, Harutyunyan et al.
Each trained layer is followed by an activation function tanh, except the last. The chosen optimizer is Adam with the learning rate set to 0. 003 and the weight decay to 0. 05. The temporal proximity parameter is fixed at 4. 5 and the width of the successive layers is fixed at 15. Learning is computed on a machine equipped with 40 computing cores, 126GB of RAM and 4 NVIDIA GeForce GTX 1080 graphics cards, each with 11GB of VRAM. To compare the approaches, we measure the area under the sensitivity/specificity curve, referred to as AUC, on the test set. This score is well suited for datasets with unbalanced label proportions. As seen in the Figure  2 , our modeling offers results similar to those of the state of the art, observed in Harutyunyan et al. [6] , namely logistic regression and the different variants of LSTM.
S I M U L AT I O N Horizon-AGN is a cosmological hydrodynamical simulation run with a standard flat CDM cosmology { m = 0. 272 , b = 0. 045 , = 0. 728 , σ 8 = 0. 81 , H 0 = 70. 4 kms -1 Mpc -1 , n s = 0. 967 } compatible with the constraints from WMAP7  (Komatsu et al. 2011 ). The box has a width of L box = 100 h -1 Mpc and 1024 3 dark matter particles, each having a mass of 8 × 10 7 M. Run with the adaptive mesh refinement code RAMSES  (Teyssier 2002 ) , and an initial gas mass resolution of 10 7 M , the mesh is adaptively refined down to x = 1 proper kpc. For more details on the simulation of gas cooling, star formation, and stellar and black hole feedback processes, see  Dubois et al. ( 2014 )  and  Dubois et al. ( 2016 ). At each redshift snapshot, galaxies are identified in the simulation by running the ADAPTAHOP subclump finder (Aubert,  Pichon & Colombi 2004  ) against the stellar particle distribution.
Larger data points denote more conserv ati ve signals measured for Masscut Fundamental Plane samples ( M * > 10 10. 5 M ; Section 2 ), whilst smaller points are measured using the Full Fundamental Plane samples featuring lower-mass galaxies. Pink solid (dotted) curves in the right-most panels give the results of fitting the 1-parameter phenomenological model (described in Section 4. 1. 2 ) separately to elliptical/spiral λλ correlations measured in the Masscut (Full) Fundamental Plane samples. Intrinsic size correlations are highly sensitive to galaxies' morphology and stellar mass range. poorer FP fits and induced asymmetry in λ (Section 2. 3 ); the steeply increasing stellar mass-function (as M * decreases from 10 10 M ) of simulated ellipticals (Fig. 1  ), which are known to be o v erproduced at the low-mass end  (Kaviraj et al.
AC K N OW L E D G E M E N T S The authors acknowledge useful discussions with  Andrej Dvornik ,  Robert Reischke ,  Corentin Cadiou , and  Christophe Saulder. This work is part of the  Delta ITP consortium , a program of the  Netherlands Organisation for Scientific Research (NWO)  that is funded by  Ministry of Education, Culture and Science, Netherlands (OCW). This work has made use of the HPC resources of CINES (Jade and Occigen supercomputer) under the time allocations  2013047012 ,  2014047012 , and  2015047012  made by  GENCI. This work is partially supported by the  Spin(e)  grants  ANR-13-BS05-0005  ( http://cosmicorigin. org ) of the  French Agence Nationale de la Recherche  and by the  ILP LABEX  (under reference  ANR-10-LABX-63  and  ANR11-IDEX-0004-02 ). Part of the analysis of the simulation was performed on the DiRAC facility jointly funded by  STFC ,  BIS , and the  University of Oxford.
Quantifying changes in umbilicus size Sixteen individual days-old neonates ('time zero' animals) caught between September 2016 and February 2022-of a total of 727 caught individuals-that were subsequently recaptured during the following weeks were considered for quantifying changes in umbilicus size over time. Neonatal blacktip reef sharks lose the remnants of their umbilical cord within the first couple of days post-parturition (S. Debaere, personal observation), and these remnants are therefore a good indicator of recent birth. To assess the size of the umbilicus of neonatal sharks, photographs were imported in the open-source image processing package Fiji by ImageJ (version 2. 0. 0-rc-69/1. 53c;  Schindelin et al. , 2012). Scale was set to 1 cm using the ruler in the photographs, and the circumference of the umbilical wound was carefully traced using the polygon selection tool from ImageJ to calculate umbilicus area and perimeter.
Morphometrics and body condition Morphometric data were collected to assess transitions in growth and body condition across UWCs to shed light on early-life growth patterns and energy reserve depletion rates. Morphometric measurements taken during collections (see preceding section) included total body mass (M, in kilograms) and precaudal length (PCL, distance from the tip of the snout to the origin of the caudal fin, in centimeters). In addition, Fulton's body condition factor (K, sensu  Ricker, 1975)  (1) Before statistical analyses, where applicable, data were checked for normality using the Shapiro-Wilk test, where the test statistic W < 0. 90 was considered as the critical value to reject the null hypothesis that the data come from a normal distribution. The critical value of statistical significance was set to α = 0.
In summary, this study is the first to propose an age classification for shark neonates based on quantitative changes in umbilicus size. Our temporal regression relationships of umbilicus area and perimeter allow the relative age of neonatal blacktip reef sharks to be estimated and grouped into distinct UWCs. The accuracy of our umbilical wound classification is supported by the minimal error rates observed between predicted and actual TALs of recaptured neonates. Nevertheless, considering the rapid development of neonatal sharks, we encourage the use of distinct UWCs, rather than their actual age, to minimize the impact of these error rates. Neonatal age estimates are essential data for accurate lengthat-age descriptions that are fundamental for understanding productivity, fisheries stock status and population extinction risks.
Acknowledgements The authors wish to acknowledge that this work was partly conducted on the land of the  Maohi people on Moorea, French Polynesia. The authors thank the staff and Physioshark students and volunteers at the  CRIOBE  who were involved in collecting field data, including  A. Aqua ,  E. Duncan ,  I. Ender ,  E. Jacquesson ,  E. Milanesi ,  A. Schoen ,  G. Schwieterman ,  S. Winter , and  M. Zanaboni. We also thank the two anonymous reviewers who provided constructive feedback and suggestions that have greatly improved the quality of this manuscript.
Data Sources At the state level: from the New York Times,  1  we obtained numbers of adult (aged 18+) COVID-19 deaths (https://github. com/nytimes/covid-19-data); from the Centers for Disease Control (CDC),  2  we obtained numbers of adults (aged 18+) who had had at least two COVID-19 vaccination injections (https://data. cdc. gov/Public-Health-Surveillance/Rates-of-COVID-19-Casesor-Deaths-by-Age-Groupand/3rge-nu2a). No administrative permissions are required to access the data used in this study. The data were aggregated and therefore anonymized before use. As the data were publicly available and aggregated, no human subjects review was required.
Acknowledgements This work was partly supported by the  DFG-ANR  project  REDBIRD  ( DFG  project number  316877802 ,  ANR-16-CE92-0021 ) and the  DFG  project  MAID  (project number  466331904 ). The authors would like to thank  A. Destrieux  (currently affiliated with  Université Laval, Canada ) for supporting the measurements,  U. Nehmzow  (glassblower,  INP ) for manufactoring the discharge cell, and  D. Loffhagen  ( INP ) for providing electron transport and rate coefficients for the model and for fruitful discussions.
The prophages of LF82 encode various morons Previous analysis of the E. coli LF82 genome had identified four putative complete prophages and one plasmid, pLF82, subsequently found to be homologous to the Salmonella SSU5 phage  [21]. Our analysis of the E. coli LF82 genome did not detect any other complete or degraded prophage using PHASTER. Here we named the five prophages Gally (previously prophage 1), Perceval (prophage 2), Tritos (prophage 3), Cartapus (prophage 4) and Cyrano (pLF82), and upon updated re-annotation, these were introduced into the European Nucleotide Archive database (see Material and Methods). The phage-plasmid Cyrano closely resembles the phage SSU5 (81% identity, 65% coverage, see S1 Fig panel  A ), and is predicted to have a siphovirus morphology. Among integrated prophages, Gally is partially homologous to the podophage HK620 (97% identity on 44% coverage, Fig  1 top ,  [29] ), and follows the general genetic organization of P22 (Lederbergvirus genus,  [30] ).
Sequencing of the virome as well as the phage attachment sites (attP) allowed to characterize the boundaries of the four chromosome-integrated prophages (Table  2 , see the Materials and Methods section). In silico reconstruction of the bacterial attachment sites and their inspection did not reveal ORFs restoration (>300 bp) after prophage excision, or modification of pre-existing ORFs in the genome of the lysogenized strain, indicating that the integrated prophages do not alter the bacterial gene content after excision. Notably however, Gally was placed between the divergent torS (the sensor of the torS/R system, pointing leftward) and torT (a periplasmic protein pointing rightward). This site is the target of prophage insertion in approximately 5% of E. coli strains  [35]. The presence of the HK022 prophage at this site increases the expression of torS and consequently inhibits the expression of the torCAD operon when cells are grown aerobically  [35].
This is in contrast to the φ10403S prophage of Listeria monocytogenes 10403S, which performs active lysogeny. This mechanism blocks the lytic cycle of the phage after the excision of its genome and is required for the survival and multiplication of bacteria within macrophages  [45]. However, it is possible that Gally provides some benefits to its host in other environments. Indeed, the Gally prophage is inserted between the torT and torS genes involved in the regulation of the torCAD operon coding for TMAO reductase  [36]. Prophage integration at this site is known to affect the regulation of torS and consequently of the torCAD operon  [35]. We found a putative promoter in Gally, positioned at a place similar to the one characterized at the left boundary of the prophage HK022, which regulates the expression of torS in E.
coli MG1655  [35]. This suggests that Gally, as HK022, modulates the expression of the torCAD operon. However, the transcription profile of the Gally prophage neighboring genes, torS and torT, was not affected upon macrophage infection with LF82 (S4 Table ), in line with the observed low excision of Gally prophage in this environment. The lack of a role in macrophages does not exclude a role of Gally in a different setting, such as in the gut lumen or in external environments where TMAO is also available. TMAO respiration is performed by E. coli under anaerobic and aerobic conditions  [46]. In the latter case, only part of the bacterial population expresses torCAD, leading to the proposal that TMAO respiration under aerobic conditions may facilitate bacterial adaptation to anaerobic conditions  [47].
The insertion of HK022, and most likely Gally in LF82, between torT and torS represses torCAD expression under aerobic conditions, and thus should inhibit this adaptive advantage  [35]. In this latter study, the authors proposed that the phage-dependent repression of torCAD could increase the growth rate of the bacterial host in the presence of oxygen and TMAO, and thus the dissemination of the phage, most probably outside the gut. Remarkably, the HK022 prophage does not inhibit torCAD induction under anaerobic growth conditions  [35] , indicating that phage insertion at this site should not affect the physiology and competitiveness of a lysogenic strain in the gut. Thus, Gally could increase, when not induced, the competitiveness of LF82 under certain aerobic growth conditions. The subpopulation sacrificed by the prophage induction would allow the dispersion of LF82 genes by lateral transduction to recipient strains, yet to be identified.
Prophage detection and annotation Prophage region predictions on the E. coli LF82 genome (NC_011993,  [22] ) were initiated with PHASTER (http://phaster. ca [52] ). Regions containing phage genes from replication, capsid, lysis and lysogeny modules were confirmed as complete prophages; we further verified the absence of genes specific of integrative plasmids or insertion sequences. To annotate hypothetical genes, a BLASTP search against the viruses taxid 10239 database was performed with default values, and the annotation of sequences producing significant alignments were transferred to the query when either experimental evidence of function or conserved domains were detected. For hypothetical proteins without BLASTP hit, sequences were analyzed for Pfam matches  [53].
Transcriptome analyses RNA-Seq raw data from GEO accession GSE154648 (10 samples with bacteria from 5 conditions) were reanalyzed (S4 Table ). Reads were mapped onto E. coli LF82 and Cyrano genomes (Genbank accession numbers CU651637. 1 and OV696614. 1) using "Bowtie2" (v2. 4. 4, [54] ). Based on properly paired and mapped reads ("samtools view -f2 -q30", v1. 14,  [55] ), the RNA--Seq fragments overlapping each gene were counted with "featureCounts" (v2. 0. 1,  [56] ). Fpkm (fragments per kilobase of transcript per million mapped fragments) values reflecting the expression level of each gene in each sample were computed with library sizes estimated using the robust method implemented in R library "DESeq2" (v1. 34. 0,  [57] ). Differential gene expression analysis between condition MB6 and BLB was also performed with "DESeq2", whose p-values were converted into q-values using R library "fdrtool" (v1.
Homology between E. coli LF82 prophages and reference phages Related phages were searched in the nr/nt nucleotide collection of the NCBI by BLASTn, within the Viruses taxid 10239 as of March 2020, using the megaBLAST parameters. For each prophage, the type phage of the viral species (as defined by the ICTV, https://ictv. global/) and the closest phage were retained for comparison through genomic alignments. Alignments were realized using the R package GenoPlotR  [61] , based on tBLASTx to generate the comparison files and using a filter length of 50. Images were generated with the plot_gene_map function with the blue_red global color scheme.