dataesr/scientific-paragraphs-categorization huggingface dataset:
m' Cellprofiler module. This comprises 18 primary features in total, in which only 16 were used for the following analysis: including 1) closest distance to membrane; 2) distance to cell centroid; 3) distance to nuclear centroid; 4) distance to cell outline projecting from nuclear centroid; 5) each spot mean distance to remaining spots; 6) standard deviation of each spot distance to remaining spots; 7) radius in pixels to include 5%, 10%, 25%, 50% or 75% of all remaining spots in each cell; 8) number of spots within 20, 40, 80 and 120 pixels from a given spot center; 9) the mean and standard deviation of 7) and 8) features were calculated and normalized to the square root of the area of a given cell; Meanwhile, we also measured the cell features, including cell area shape, cell major and minor axis, nuclear area shape and nuclear axis, and Zernik cell features (Fig.
1 ). EIF4E-eIF4G PLA spot feature preparation for multivariate analysis. Primary eIF4E-eIF4G PLA spot features were obtained by the 'MeasureLocalizationOfSpots. m′ Cellprofiler module. Measurements of individual eIF4E-eIF4G PLA spots were normalized by z-scoring against random cytoplasmic pixels. The number of sampled pixels were the same as the number of spots, which were originally identified in the given cell. Sampling of pixels was iterated 100 times. Per iteration, the measurements of a single random pixel were used for the normalization of measurements of a given observed eIF4E-eIF4G PLA spot. To general each cell's eIF4E-eIF4G PLA spot features, cellular features were obtained by the 'MeasureChildren. m′ Cellprofiler module. For each parent objects (Cells), measurements robust to NaN (not a number) were derived from the normalized primary features of its corresponding children (eIF4E-eIF4G PLA spots).
The corresponding statistics measured were mean, median, standard deviation and variance as well was third, fourth, fifth and sixth central moments. However, for each cell, only eIF4E-eIF4G PLA spot measurements that were not NaN, was considered for the calculation of the central moments. Non-supervised single cell clustering. 1. 5×10 4 cells were randomly sampled from the initial pool of cells and the pairwise Euclidean distance to a randomly sampled 20% of the ∼1. 5×10 4 cells was computed in the full eIF4E-eIF4G PLA spot feature space after zscore normalization. The number of neighbors of every cell was calculated. We iterated this sampling process to achieve ∼ 2000 cell samplings and non-supervised hierarchical clustering was done using a Euclidean distance and Ward's linkage method. The nonsupervised clustering was implemented in SPSS software in order to determine the number groups, and followed K-means for the partition of the dataset.
Supervised classification of eIF4E-eIF4G localization patterns. Ensemble non-supervised clustering identified five eIF4E-eIF4G PLA spot localization patterns. Classification was based on this non-supervised clustering as references. The z-score normalized image analysis data were randomly divided into two part, including 70% of individual cells as training dataset and 30% of the remaining individual cells as validation dataset. We thus manually built a training dataset derived from A375 cell line from each of the five localization patterns, corresponding to the five clusters respectively. We trained the dataset by different machine learning methods, including K-means neighbor classifier, support vector machine (SVM, linear nuclear and Gaussian nuclear), random forest and gradient boosting classifier.
Acknowledgements We thank Ensemble contre le Mélanome; The Foundation Crédit Mutuel, The Foundation Carrefour, Olivier and  Christian Courtin  and the association  Vaincre le Mélanome  for their constant support (C. R. ). S. S. is supported by the  Natural Science Foundation of China (NSFC ,  82172794 ) and  National Clinical Research Center for Geriatrics ,  West China Hospital, Sichuan University  (grant no. Y2022JC002 ). S. S. thanks the constant support from  Sébastien Bazin. We acknowledge the editing support of  Life Science Editors.
Code availability. In this study we did not develop novel computational code for the analyses. The spot quantification pipeline could be found on the website: https://sites. google. com/site/ translationcomplexeif4f/home  [37]. For the MATLAB-based Cellprofiler pipeline, we used the Cellprofiler modules in combination with the modules developed by Lucas Pelkmans's lab  [3]. We shared the 'MeasureLocalizationOfSpots. m′ file in the supplemental files, however, please refer to the work from Battich et al. Battich et al. ,  [3]  when using this code.
Introduction: Tenofovir alafenamide (TAF) is approved for paediatric use in fixed-dose combination tablets, but efficacy and safety data in children are limited. We conducted a systematic review on the efficacy/effectiveness and safety of TAF in infants, children and adolescents living with HIV. Methods: We searched MEDLINE, Embase, the Cochrane Library, clinical trial registries, reference lists and relevant conferences to identify literature published January 2009-March 2021. We included clinical trials and observational studies assessing the efficacy/effectiveness or safety of TAF through ≥6 months of treatment in participants aged 0-19 years. Results and discussion: Overall 3626 abstracts and 371 full papers were screened. Four single-arm, innovator-funded trials (341 participants) and a pooled analysis of those trials were identified.
Data sources and search strategies We searched MEDLINE, MEDLINE In-Process, MEDLINE Epub ahead of print (via Ovid), Embase (via Ovid) and the Cochrane Library using free-text terms and index terms (File S1). We also searched abstract databases of selected conferences occurring between 2018 and March 2021. Reference lists from key HIV treatment guidelines and from all included papers were screened, and clinical trial registries were searched to identify ongoing studies. We screened English-and French-language publications reporting on the use of TAF in treatment-naïve orexperienced infants, children and/or adolescents aged 0-19 years living with HIV (Table  1 ). Observational studies and clinical trials published 1 January 2009-21 March 2021 were included, to cover the period when TAF and other drugs included in the review were approved for use in children and adolescents.
Article screening and data extraction All abstracts and papers were screened independently, in duplicate, by three reviewers (JOR, CT and EM) against the eligibility criteria, and decisions were recorded in a Microsoft Excel spreadsheet. Any disagreements were discussed between the reviewers and resolved with another member of the project team if necessary. Data from all selected articles were extracted into a standard Microsoft Excel form by one reviewer and independently checked by another reviewer. The risk of bias was assessed independently by two reviewers using the Cochrane Risk of Bias Tool (Version 2) for randomized controlled trials, the National Institute of Health quality assessment tool for non-randomized interventional studies and the CLARITY tool for cohort studies. The following data were extracted: study characteristics (design, country, time period, duration of follow-up, inclusion criteria and number of participants); participant characteristics (age, sex, treatment experience, baseline viral load and CD4 cell count/percent); efficacy/effectiveness and safety data (File S1).
Primary efficacy/effectiveness outcomes were HIV RNA viral load and change in CD4 counts. Primary safety outcomes were mortality, grade 3/4 adverse events  [19]  and treatment discontinuation. Secondary outcomes included hypersensitivity reactions, growth, and measures of bone health and renal function. For binary and categorical variables, we extracted the number and proportion of participants with an event. For studies reporting interim results for viral load outcomes, the denominator used was the number of participants with data available at that time point. For continuous variables, we extracted the mean, median, range, interquartile range (IQR), change from baseline, regression coefficients and 95% confidence intervals where available. Data that were only reported in a figure were extracted using the online tool "Web Plot Digitizer"  [20].
Safety outcomes Figure  3  shows the proportion of participants with safety outcomes reported (all-cause mortality; grade 3/4 adverse events: clinical, laboratory, both and drug-related; and discontinuations due to adverse events of any grade). For each study, data are presented separately for each sub-population (different age groups) and the time point at which events were reported. Mortality was reported in at least one subgroup within all four studies (Figure  3 ); there was one death and the cause was accidental  [26]. Three studies reported data on grade 3/4 clinical and laboratory adverse events in five sub-groups of treatmentexperienced children and/or adolescents (GS-US-292-0106, GS-US-380-1474 and GS-US-311-1269)  [23, 24, 28, 32]. No events were reported in two sub-groups of children on F/TAF with boosted protease inhibitor in one study (n = 3 and n = 9) at 48 weeks (GS-US-311-1269)  [32] , while events were reported in 25% (3/12) of children on B/F/TAF after 24 weeks (GS-US-380-1474)  [28] , and in 4% (1/27) and 27% (14/52) of children on EVG/COBI/FTC/TAF after 48 weeks (GS-US-292-0106) (Figure  3 )  [23, 24] ; all were laboratory events (mostly low neutrophils and haematuria).
7%) experienced a grade 3/4 clinical event (gastrointestinal disorders [n = 2] and syncope [n = 1])  [31]. Grade 3/4 laboratory abnormalities occurring in more than one participant were reported in this study and included amylase (n = 5, 18%), fasting lowdensity lipoprotein (n = 2, 7%) and haematuria (n = 2, 7%, all in female participants with normal menses). In both studies, the total number of participants with grade 3/4 adverse events was not reported. All four studies reported data on drug-related grade 3/4 adverse events (Figure  3 ). One event was considered related to TAF: a case of intermediate uveitis in an ART-naïve adolescent on EVG/COBI/FTC/TAF that resolved without treatment interruption (GS-US-292-0106)  [21]. No other drug-related adverse events were reported. All four trials reported on treatment discontinuations due to adverse events of any grade.
Of note, cobicistat is not a recommended treatment for children living with HIV  [8]. In GS-US-311-1269, most children were not receiving cobicistat and the changes in eGFR were considered not clinically significant  [32]. Data on weight gain in children and adolescents on TAF were reported in the pooled analysis, with slight increases in WAZ observed by 48 weeks. There was also an increase in the proportion of participants classified as overweight or obese in this population  [33]. Continued monitoring of weight gain and associated metabolic outcomes in children and adolescents on TAF is critical in light of findings in adults, showing increased weight gain with TAF compared with other NRTIs  [42].
TAF is currently available as part of oncedaily co-formulations, which provide advantages in terms of reduced pill burden and improved adherence. However, the currently available associated drugs (bictegravir and emtricitabine, or elvitegravir, cobicistat and emtricitabine) are not recommended as WHO-preferred nor alternative regimens. F/TAF may be of use in combination with dolutegravir, as a WHO-recommended alternative first-line regimen for children  [8] , and paediatric fixed-dose combinations of F/TAF alone and F/TAF plus dolutegravir are highlighted as priority formulations for research and development  [15]. Three of the four identified studies aimed to assess pharmacokinetics, effectiveness and safety, and sample sizes were, therefore, relatively small (≤100). Furthermore, three studies were ongoing at the time of this review, and viral load was estimated using data from participants with follow-up at the time of reporting rather than from the total study population.
A U T H O R S ' C O N T R I B U T I O N S CLT, JOR, EM, IJC, AJ, MV, JJ, VL, FR and MP contributed to developing the study protocol. JOR conducted the searches. JOR, CLT and EM contributed to the screening of abstracts and full papers, extracted the data from selected studies, and assessed the studies for risk of bias. CLT and JOR drafted the paper. JOR, CLT and HC developed the figures. All authors reviewed the manuscript and approved the final draft.
Reuben Harris and obtained through the NIH AIDS Reagent Program, Division of AIDS, NIAID, NIH  [50] ) used at a dilution of 1:1000 in TBS supplemented with 0. 1% Tween 20 and 5% BSA. Hsp90 was used as loading control and was detected with an anti-HSP90AB1 antibody (Sigma-Aldrich) at a dilution of 1:1000 in TBS supplemented with 0. 1% Tween 20 and 5% BSA. An HRP-coupled anti-rabbit IgG secondary antibody (Dako) was used at a dilution of 1:2000 in TBS with 0. 1% Tween 20 and 5% BSA or 4% milk. Membranes were incubated with the chemoluminescent SuperSignal West Femto Maximun Sensitivity Substrate (ThermoFisher Scientific) and chemoluminescence was read using an ImageQuant LAS4000 (GE Healthare Life Sciences). Densitometry analysis was done using ImageJ software  [51].
Reaction mixture was treated with 50 mM NaOH and heated to 95˚C for 10 minutes to cleave DNA probes at the abasic site. Reaction mixture was then neutralized with 50 mM HCl and mixed with 1. 25x formamide buffer. Substrates (43 bases-long) were separated from products (30 bases-long) on a 15% tris-borate-EDTA (TBE)-urea gel. The Cy5-labeled substrates and deamination products were detected using ImageQuant LAS4000 mini (GE Healthcare Life Sciences). Densitometry analysis was done using ImageJ software  [51].
Acknowledgments We thank  Prof. Jerry W. Shay  for the HBEC3-KT cell line,  Prof. Slobodan Paessler  for the PKR-deficient A549 cell line,  Prof. Lieve Naesens  for the HAdV-B3 and -C2 isolates,  Prof. Warner C. Greene  for the pSicoR-MS2 plasmids and  Prof. Reuben Harris  for the pLenti4_A3B and pLenti4-A3B-E68Q-E255Q expression plasmid. The following reagent was obtained through the  NIH AIDS Reagent Program, Division of AIDS, NIAID, NIH: Anti-Human APO-BEC3B Monoclonal  ( 5210-87-13 ) from  Prof. Reuben Harris  (cat#  12397 ). We thank the  UNamur MORPH-IM  platform for flow cytometry and fluorescent microscopy imaging. We thank  Prof. Henri-Francois Renard  for his microscopy advice.
Motivating example To illustrate the problem, we used data from an observational prospective multicenter cohort of a French national network, focusing on rare head and neck cancers, the Réseau d'expertise français sur les cancers ORL rares (REFCOR) database. Patients were included between 2009 and 2021 at the time of diagnosis and then followed prospectively. Inclusion was carried out by each center using a standardized questionnaire. In accordance with French law, their data were anonymized, and all patients signed an informed consent form. Only patients diagnosed with a primary histologically proven parotid cancer who were surgically treated and included in the REFCOR database were included. To address the objectives of this work, we selected patients with a tumor that was in close contact with the nerve.
Simulation study We aim to evaluate from observational data a subsetby-treatment interaction on right-censored data using propensity score methods. To specifically evaluate the empirical performances of the two "across" and "within" subsets strategies, we performed a Monte-Carlo simulation study. We generated data close to the REFCOR setting, where patients with facial palsy, the smaller group of the sample, received mostly (in 80% of cases) a facial nerve resection and may have benefit more to that resection than those without (the majority of the sample but who only received a nerves resection in 40% of cases). We thus considered a population partitioned into two subsets (S = 1, 2) of different sizes, with a potential heter- ogeneity in treatment effect across the subsets. Similarly to the REFCOR study, we considered the treatment effect possibly restricted to the smaller subset, but where the treatment has been widely preferred.
Methods In looking for treatment-by-subset interaction, two strategies of analysis regarding the PS estimation were considered and applied to each dataset. First, the "across subsets" strategy was used, which consisted of estimating a single PS from the whole sample but incorporating the subset indicator and potential interaction terms into the PS. Second, we applied the "within subsets" strategy, which consisted of estimating the PS in each subset separately. Regardless of the strategy, several PS methods were applied, targeting the average treatment effect in the treated group (ATT) and then the average treatment effect in the overlap (ATO). Thus, we first performed PS matching without replacement using a 1-to-1 nearest neighbor matching algorithm, with a caliper set to 0. 2, then to 0. 1 standard deviations of the logit of the PS  [19].
We imputed 33 datasets, with 20 iterations, using an imputation model including important variables, the estimated cumulative baseline hazard based on the Nelson-Aalen estimator and interaction terms between the Nelson-Aalen estimator and covariates  [28]  (details are provided in Additional file 2). To account for multiple imputations, variances in estimated treatment effects were calculated by bootstrap  [29] , except for matching without replacement  [30].
164. 8 and 14. 6 for the "within subsets" strategy), 62% more patients with the PS matching method (means of 22. 4 vs. 13. 8 patients) and 85% more patients with PS matching with replacement (means of 152. 8 vs. 82. 4 patients) (Fig. 8 ). The treatment effects in both subsets obtained with the different PS-based methods are summarized in Fig. 8  for OS and DFS. No treatment-by-subset interaction was Fig. 6  REFCOR data: Connect-S-plot with the "across subsets" approach. Connect-S plot representing standardized mean differences (SMDs) between treatment groups in the "across subsets" approach, in the original dataset ("naive estimation") and according to the PS-based method found regardless of the PS estimation strategy and the PS method with the recommended methods, but the interaction was significant when using the biased "across" method without an interaction term, using PS weighting methods.
This strategy can therefore lead to a poor balance between treatment groups in the covariates within subsets or even to a worsening of this imbalance  [15]. In our illustrative case, this strategy afforded a good balance of covariates overall, although some imbalances persisted across treatment groups in the subsets (Figs. 6  and 7 ). Expectedly, most persisted differences were found in the small subset of patients with facial palsy (ranging from 93 original observations down to 17 in the overlap population with the "across subsets" strategy and 15 with the "within subsets" strategy). Fig. 7  REFCOR data: Connect-S-plot with the "within subsets" approach. Connect-S-plot representing standardized mean differences (SMDs) between treatment groups in the "within subsets" approach in the original dataset (first two lines) and according to the PS-based method Within subsets Across subsets Fig.
Background A randomised, double-blind, placebo-controlled, multicentre phase 3 trial (PAN-AMO, NCT04333420) showed that in addition to standard of care (SOC), vilobelimab (formerly IFX-1), a monoclonal antibody which specifically binds complement 5a (C5a), improved survival of invasively mechanically ventilated patients with coronavirus disease 2019 (COVID-19)  [1]. Vilobelimab, in addition to SOC, reduced all-cause mortality at day 28 from 42 to 32% and protected against renal replacement therapy  [1]. C5a is a potent anaphylatoxin attracting inflammatory cells to the site of infection, leading to tissue damage  [2]. Increased activation of the complement system, in particular C5a, is associated with poor disease outcomes  [3] [4] [5] [6]. Previous research suggests that C5a inhibition decreases the inflammatory response and hypercoagulability in severe COVID-19 patients  [7].
In the screening assay, all study samples are screened for ADA positive signals with an acceptance of 5% false positive results. All screened positive samples are measured in the ADA confirmatory assay with an acceptance of 1% false positive results. All confirmed positive samples are characterized in the ADA titration assay for semi-quantification with an acceptance of 0. 1% false positive results. Actual PK sampling times were determined and the plasma concentration of vilobelimab was assessed by time point. After unblinding, the sponsor reviewed sampling dates and vilobelimab concentrations and flagged samples in the following two cases: (i) implausible vilobelimab/C5a concentration; (ii) timing of sample not pre-dose or other issue with timing. Data points were only omitted from the analysis in these two cases.
Data are expressed by descriptive statistics, including mean with standard deviation (SD), geometric mean and medians with interquartile range  [IQR]. C5a levels and their changes from baseline at day 8 were compared between both groups applying the Wilcoxon rank-sum test. Correlations between continuous variables were assessed with Spearman's rank correlation coefficient. A p-value below 0. 05 was considered as statistically significant. All statistical analyses were performed in SAS (version 9. 4).
001). Beyond day 8, although plasma sampling was sparse, C5a levels remained elevated in the placebo group (Fig. 2 ). In the vilobelimab group, C5a levels began to rise after end of treatment for patients discharged from day 30, but were still lower than levels recorded at baseline. Vilobelimab concentration at hospital discharge by time from last vilobelimab infusion is illustrated in Fig. 3. The PK data of vilobelimab and C5a is shown in Table  2. The relationship between vilobelimab and C5a levels for both groups was also assessed (Fig. 4 ). Treatment-emergent ADAs were observed in one patient in the vilobelimab group at day 40 on hospital discharge and in one patient in the placebo group at day 25 on hospital discharge.
Acknowledgements PANAMO study group:  Alexander P. J. Vlaar ,  Martin Witzenrath ,  Pieter van Paassen ,  Leo M. A. Heunks ,  Bruno Mourvillier ,  Sanne de Bruin ,  Endry H. T. Lim ,  Matthijs C. Brouwer ,  Pieter R. Tuinman ,  José Francisco K. Saraiva ,  Gernot Marx ,  Suzana M. Lobo ,  Rodrigo Boldo ,  Jesus A. Simon-Campos ,  Alexander D. Cornet ,  Anastasia Grebenyuk ,  Johannes M. Engelbrecht ,  Murimisi Mukansi ,  Philippe G. Jorens ,  Robert Zerbib ,  Simon Rückinger ,  Korinna Pilz ,  Renfeng Guo ,  Diederik van de Beek ,  Niels C. Riedemann ,  Pierre Bulpa ,  Fabio S. Taccone ,  Greet Hermans ,  Marc Diltoer ,  Michael Piagnerelli ,  Nikolaas De Neve ,  Antonio T. Freire ,  Felipe D. Pizzol ,  Anna Karolina Marinho ,  Victor H. Sato ,  Clovis Arns da Cunha ,  Mathilde Neuville ,  Jean Dellamonica ,  Djillali Annane ,  Antoine Roquilly ,  Jean Luc Diehl ,  Francis Schneider ,  Jean Paul Mira ,  Jean Baptiste Lascarrou ,  Luc Desmedt ,  Claire Dupuis ,  Carole Schwebel ,  Guillaume Thiéry ,  Matthias Gründling ,  Marc Berger ,  Tobias Welte ,  Michael Bauer ,  Ulrich Jaschinski ,  Klaus Matschke ,  Roberto Mercado-Longoria ,  Belinda Gomez Quintana ,  Jorge Alberto Zamudio-Lerma ,  Juan Moreno Hoyos Abril ,  Angel Aleman Marquez ,  Peter Pickkers ,  Luuk Otterspoor ,  Luis Hercilla Vásquez ,  Carlos Rafael Seas Ramos ,  Alejandro Peña Villalobos ,  Gonzalo Gianella Malca ,  Victoria Chávez ,  Victor Filimonov ,  Vladimir Kulabukhov ,  Pinak Acharya ,  Sjoerd A.