dataesr/scientific-paragraphs-categorization huggingface dataset:
Redundancy In order to eliminate possible dataset bias, we performed a redundancy check at the contact level, by comparing -bead to -bead distances within 6Å in the native poses of the protein-fragment cases. If such distance sets were very similar for two cases, these cases were considered redundant, and one of them was removed from the dataset. The nal dataset consists of 217 RRM-fragment cases, with labelled docking poses per case. Its corresponding benchmark consists of 57 RRM-ssRNA complexes and can be found in Additional le 1: Table  S1.
Training and test sets We separated the dataset into pairs of training and test sets based on protein sequence similarity, in a leave-homology-out procedure. Our sequence similarity threshold was 40%. We selected a random protein-ssRNA complex from the benchmark along with all other complexes whose protein sequence similarity was greater than 40%. All data cases derived from these complexes (protein-fragment structures along with their docking poses) became the test set. The remaining data cases formed the corresponding training set. We repeated this procedure iteratively until each of the benchmark complexes was in one of the test sets. To prohibit repetitive and near-repetitive (training; test) pairs, we ensured that the rst randomly selected case in each iteration did not belong to any of the previous test sets.
Results In study, we developed a new protocol for deriving scoring parameters for molecular docking poses, based on distances between RNA and protein beads, in the form of a collection of 4 histogram sets. We applied it to create HIPPO, a novel scoring function speci cally for RRM-ssRNA fragment-based docking. To achieve this goal, we split every available RRM-ssRNA structure into RRM-fragment cases (fragments of 3 consecutive bound nucleotides), for each of which docking poses were generated using the ATTRACT docking engine. Our initial benchmark consisted of 479 fragments from 81 complexes. Out of these, 262 fragments were unusable for training because of a sampling problem (less than 100 near-native poses sampled) or because of redundancy between fragments on the contact level (6Å), resulting in a dataset of 217 well-sampled non-redundant cases, coming from 57 RRM-ssRNA complexes.
Within the resulting dataset, the average number of sampled near-native poses is 9112 and the median is 3145. To assess how HIPPO performance would generalise to new data cases, we used the leave-homology-out cross-validation strategy: 29 pairs of training and test sets were formed based on RRM sequence similarity. The size of the test set depended on the number of cases derived from each RRM-ssRNA complex of a given RRM and varied from 1 to 33 cases per set. For a given pair of test and training sets, for each case in the training set, we derived an by analysing the frequencies of bead-bead distances in the near-native (LRMSD < 5Å) vs non-native (LRMSD > 7Å) docking poses, and we applied it to each of the other cases in the training set. We selected the collection of 4 sets that maximised the number of training cases for which at least one ranks 60% of all nearnative poses in the 5% top-ranked poses.
Conclusions Our study provides guidance for researchers aiming to establish proof-of-concept approaches for preventive or curative perspectives in mouse models, to ultimately lead to future clinical trials for muscle disorders. body should be transduced. Indeed, skeletal muscle account for nearly half of dried body weight in mammals. Recently, ELEVIDYS, a rst AAV-based gene therapy product treating Duchenne muscular dystrophy (DMD) has been approved by the FDA 6. However, several obstacles remain for the development of such therapeutic strategy, such as (1) the huge amount of vector dose needed to transduce the entire muscular system 7 , (2) the toxicity induced in other organs and especially in the liver, (3) the pre-existence of immunity against AAVs 8. Indeed, while high dose (3E + 14vg/kg) of AAV8-MTM1 (AT132) in patients with the severe myotubular myopathy correlated with muscle amelioration, 4 patients died from hepatic complications including cholestasis following the treatment, leading to the halting of the clinical trial (NCT03199469)  9, 10.
Acknowledgements We thank  Paola Rossolillo  for AAV production. This work of the  Interdisciplinary Thematic Institute IMCBio , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  Inserm , was supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ), and by  SFRI-STRAT ' US  project ( ANR-20-SFRI-0012 ) and  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program. This work was also founded by  AFM-Téléthon  ( 23933 ).
Microvascular free tissue transfer surgery is a frequently used technique for head and neck reconstruction involving anticoagulants, and the present study aimed to analyse the postoperative outcomes of aspirin use in conjunction with this procedure. We searched databases for articles published between 2007 and 2022 on microvascular free tissue transfer surgery using aspirin and assessed them for primary and secondary outcomes. Odds ratios (ORs) and 95 % confidence intervals (CIs) were determined through analyses, followed by constructing a forest plot for complication rates. A total of 617 articles were retrieved from the databases, including 14 original full-text articles. Overall complication rates ranged from 0. 7-38 % (95 % CI, 17. 85 ± 0. 503 ( ±2. 8 %) [17. 347-18. 353]), while flap survival rates ranged from 95-99.
Search strategy Only full-text articles published in English were included in the present systematic review and meta-analysis. We searched the following databases for articles published between January 2007 and today (January 2023): PubMed, PubMed Central, Cochrane databases, Google Scholar, MEDLINE, EM-BASE, SCOPUS databases, and Web of Sciences. The search terms and strings used for the analysis were ("aspirin" or "acetylsalicylic acid") AND ("microvascular free tissue transfer" or "free tissue transfer" or "free flap" or "flap loss"). To find and include more eligible studies, a reference list was manually compiled from the retrieved studies and related reviews, and the process was repeated until no further articles were identified.
Study selection and data extraction Following the initial screening process, 2 researchers independently read the entire texts of the articles that met the inclusion criteria. A third researcher reviewed the articles only in the event of a disagreement between the 2 researchers. Relevant data, such as article type, first author name, year of publication, number of patients, age and sex distribution, type of surgical procedure, comparison of aspirin with other compounds, primary and secondary outcomes, associated complications, and study limitations, were extracted from each included article.
Statistical analysis We used IBM SPSS Statistics for Windows (version 26. 0; IBM Corp. , Armonk, NY, USA) for statistical analysis. The incidence rate (odds ratio [OR]) was calculated by analysing dichotomous variables, and the interval estimation was expressed using a 95 % confidence interval (CI), with P < 0. 05 indicating statistically significant differences. The size of the effect was calculated as the standardised mean deviation. We calculated the OR for complication rates and subsequently created a forest plot, using Microsoft Excel, to represent the overall complication rates associated with the use of an aspirin in conjunction with microvascular free tissue transfer surgery.
Literature selection A total of 617 articles were retrieved from PubMed, PubMed Central, EMBASE, Web of Science, Cochrane, MEDLINE, and SCOPUS databases, which were manually reviewed for inclusion/exclusion criteria. After the initial screening, 311 articles were excluded because they had unrelated data, small sample sizes, nonhuman participants, non-English abstracts or texts, poster and oral presentations, and/or systematic reviews and meta-analyses. After the final screening, a total of 14 full-text original articles that met all the inclusion criteria were included in the present study. The PRISMA chart for the present study, which provides detailed data on the literature collection and screening, is shown in Figure  1. Among the 14 included articles, 7 were retrospective analyses, 2 were prospective studies, 2 were interventional and therapeutic studies, and 3 were comparative studies, randomised control trials, or case series.
Geostatistical analysis We resampled the spatial resolution of all covariate layers at 2 km to match the scale of potential within-natal site movements of juvenile Eurasian Reed Warblers and extracted covariate values for each sample at each sampling location. We calculated average δ 34 S values at sampling sites or took the only measurement for further analysis (n = 69). To identify and quantify relationships between covariates and average feather δ 34 S at the sites, we employed a random forest regression model using the R package caret  (Kuhn 2008). Random forest models are based on large number of regression trees grown using a randomly sampled subset of the training dataset  (Breiman 2001, Belgiu and Dragut 2016). Random forest models can accommodate different data types including categorical covariates.
Acknowledgments We are obliged to following bird ringers who helped us collect feathers samples in Europe:  A. Leprêtre ,  A. Ożarowska ,  A. Trnka ,  A. Sponga ,  A. Pranaitis ,  B. Vollot ,  B. Fontaine ,  B. Bargain ,  C. Coleiro ,  R. Galea ,  C. Z. Martínez ,  C. Heroguel ,  C. Ion ,  D. Vigour ,  D. Dimitrov ,  M. Ilieva ,  D. Ragyov ,  D. Zhuravlev ,  D. Leoke ,  E. Baltag ,  F. G. Lopez ,  G. Goujon ,  J. R. Álvarez ,  J. Hlaváček ,  J. J. Baptiste ,  J. Kralj ,  J. Reif ,  J. Beier ,  J. M. Neto ,  K. and  F. Breek ,  K. Bedev ,  K. Bairaktaridau ,  L. F. P. da Silva ,  M. Haluzík , M. Šćiban,  M. Leconte ,  M. Olekšák ,  O. Zakala ,  P. S. Ranke ,  P. Matyjasiak ,  R. Thomas ,  R. Patapavičius ,  R. Slobodník ,  S. Gautier ,  S. Scebba ,  S. Capasso ,  S. Bräger ,  V. Cohez ,  V.
Within-site variation in δ 34 SThe average within-site variation (SD) in feather δ 34 S was 2. 1 ‰ (median = 1. 3, min = 0. 1, 1 st quartile = 0. 7, 3 rd quartile = 2. 5, max = 9. 1). Overall, the highest within-site variation was detected in some inland parts of Europe (i. e. , France, Germany, Hungary, southern Poland) while the lowest values appeared in coastal regions(Fig 5). The correlation between the within-site averages and within-site standard deviations was -0. 21 (Pearson's correlation; 95% CI[-0. 44, 0. 05]) and sites close to the coastline tended to show low within-site variability (ρ = 0. 29 95% CI [0. 05, 0. 51]). The random forest regression model of within-site variability explained 87% of variance in the training dataset (slope = 2. 3 [SE = 0. 1], intercept = -2. 2 [0. 2]) but the cross-validation explained no variance (slope = 0.
The DEM model To study rock slope stability, we used the discrete element method (DEM) implemented in the open-source software Yade Open DEM  ( Šmilauer et al. , 2021). The rock material was simulated by spherical discrete elements (DE) bonded to one another through elastic-cohesivefrictional interaction laws governed by a modified Mohr-Coulomb criterion, as defined in the bonded particle model (BPM) of  Scholtès and Donzé (2013)  (see also Fig. S1 ). In the present study, we built upon previous works dedicated to investigating slope stability using the same BPM  (Scholtès and Donzé, 2012, 2015; Bonilla-Sierra et al. , 2015 , 2017). The mechanical properties of the interparticle bonds dictate the overall emergent behavior of the DE assembly. The bonds can break to give place to purely frictional inter-particle interactions.
Acknowledgements We thank  M. Zakari  very much for his help in handling computing environments. Additionally, we would like to thank  Sam McColl  and one anonymous reviewer for inspiring and thorough reviews which helped improve our article. This study was funded by the  Université de Lorraine (AMI Future leader  grant) and  Otelo program AAP interdisciplinaires. M. Huber 's PhD grant has been funded by the  Région Grand Est  and the  CRPG.
A pair of flat platinum electrodes, connected to an external stimulator set at 1 A, were placed on each side of the muscle. A SI610A Dynamics Muscle Control v5. 420 software (Aurora Scientific, Ontario, Canada) was used to control the displacement of the lever arm to stretch the muscle at different velocities. Prior to performing the passive mechanical tests, the muscle was placed at its optimal length (L o ), i. e. the length where the muscle developed its maximal isometric force. Optimal length was obtained for each muscle as follows. First, the muscle was set to its resting length (L r ), which corresponds to the length where muscle develops a stable force of 10 mN  (Canon et al. , 2008; Toscano et al. , 2010) , by applying three successive tetanus and three successive twitch contractions.
Statistical analysis The SystatTM V11 software (Systat Software Inc. , CA, USA) was used for all statistical analyses. Non-parametric two-sample Mann-Whitney tests were used to compare all parameter values at a given scale between the WT and KLF10 KO genotypes. Results were considered significant for p < 0. 05. At the macroscopic scale (Fig. 3 ), all passive parameters (σ i_Ramp , E, σ d , σ s_Relax , t 1/2 ) obtained for WT controls and Klf10 KO soleus muscle were within the same range (Fig. 3A , Table  1A ). However, none of these reached statistical significance (Table  2 ). At the microscopic scale, a significant difference (P < 0. 01) was observed for all of the parameters analyzed (Fig. 3B , Table  1B , Table  2 ), excepted for the half relaxation time, between fibers extracted as a function of mouse genotype.
Acknowledgements This work was supported by  Idex Sorbonne University   SU-19-3-EMRG-12  (S. F. B), the  Research Department of UTC  within the framework of AMI International (S. F. B) and the  National Institutes of Health (USA) , Grant No. R01 DE14036  (J. R. H. ). This work was also supported by the  Interdisciplinary Thematic Institute ITI HealthTech , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  INSERM , through  IdEx Unistra  ( ANR-10-IDEX-0002 ) (S. C) and  SFRI  ( STRAT ' US  project,  ANR-20-SFRI-0012  (S. C). We would also like to thank  Frank Corl  of the  Mayo Clinic  for the artistic rendering provided in Fig. 1.
acknowledged. Sarra Derroucih, from the Itodys lab (Université Paris Cité) is also thanked for her support with SEM and EDX experiments. ANR (Agence Nationale de la Recherche) and CGI (Commissariat à l'Investissement d'Avenir) are gratefully acknowledged for their financial support of this work through Labex SEAM (Science and Engineering for Advanced Materials and devices) ANR-10-LABX-0096 et ANR-18-IDEX-0001. We acknowledge the ImagoSeine facility (Jacques Monod Institute, Paris, France), and the France BioImaging infrastructure supported by the French National Research Agency (ANR-10-INBS-04, « Investments for the future »). This research was supported in part by the Agence Nationale de la Recherche under the contracts ANR-17-CE09-0017 (AlveolusMimics), ANR-20-CE18-0022 (Stric-On), ANR-21-CE19-0058-1 (MucOnChip) and by Solvay.
Study design and participants We retrospectively analyzed data from three international groups involved in the management of hematologic oncology patients: The Groupe de Recherche Respiratoire en Réanimation Onco-Hématologique (GRRR-OH), a French and Belgium research network on critical respiratory diseases in patients with malignancies; the Critical Care Oncologic Investigative Network (COIN), a Canadian research group focused on the care of critically-ill cancer patients and the Australian and New Zealand Intensive Care Society (ANZICS), a binational study group which collects data on all adult patients admitted in the participating ICUs. Details on each database are provided in the Supplemental Appendix. The corresponding author of each database was contacted with a request for anonymized individual data sets.
Study outcome and de nitions The primary outcome was day-28 mortality. Our primary exposure was the partial pressure of arterial oxygen on the rst day of ICU admission. This variable was extracted from each dataset from an arterial blood gas (ABG) on the day of ICU admission. When several ABG were available, the lowest value of the day was taken with the corresponding FiO 2 and ventilation /oxygenation strategy at this time. The PaO 2 was rst analyzed as a continuous variable and then dichotomized. We also considered excess oxygen use de ned according to previous studies as any use of FiO 2 ≥ 0. 6 in patients with a PaO 2 > 100 mmHg  [30]. The underlying active hematological malignancy was categorized into three pre-speci ed groups: acute leukemia, lymphoma/myeloma, and other malignancies (eg.
An estimate of the effect of excess oxygen use on the matched dataset was performed using Cox models with a random effect taking into account the paired observations (Supplemental appendix). Finally, we ran the analyses by restricting our dataset to patients with invasive mechanical ventilation only (IMV), after the exclusion of patients with severe Acute Respiratory Distress Syndrome (ARDS), those without severe hypoxemia (i. e. PaO 2 < 60 mmHg), and patients with cardiac arrest. Main analyses were performed on complete cases and then, as sensitivity analysis, computed after using multiple imputations with a chained equation, based on M = 30 imputed complete datasets taking into account the database cluster, to handle missing data (Mice and Micemd R packages). Longitudinal data were imputed according to the Last Observation Carry Forward method.
The time interval between each frame, i. e. , between laser pulses, was 22 ms. The image pairs were processed using the opensource software PIVlab  (Thielicke and Stamhuis, 2014 ). An FFT deformation algorithm (direct Fourier transform correlation with multiple passes and deforming windows) was used within the PIVlab software to derive the velocity field. The data were analyzed by this algorithm in 3 passes with 128, 64, and 32-pixel interrogation areas  (Thielicke and Stamhuis, 2014). To track the path of MPs and measure their velocity, 40-47 μm spherical and fluorescent PE particles (UVPMS-BR, Cospheric) with a density of 995 kg m -3 were injected into the flume, and their trajectories tracked. The laser sheet for illuminating the fluorescent particles was approximately 2 cm deep. The particles emitted light in a red color when excited by the laser, clearly distinguishing them from other particles in the flow.
Each measurement was repeated 6 times, for a total of 3 min per experiment. The sequences of captured images were processed to identify and track individual particles over time using algorithms developed by Standford University to track the trajectories of particles moving in fluids  (Particle Tracking, Standford). The image processing toolbox of MATLAB (MathWorks) was used to pre-process images, for instance, to increase image intensities or to draw the bed topography.
Acknowledgements We thank the  MRI facility  for their assistance and  Daouda Abba Moussa  from  Metamontp  for his help in the Seahorse experiment. We thank the  Réseau d'Histologie Expérimentale de Montpellier (Plateau RHEM) histology facility  for tissue processing and the animal facility  RAM-Neuro  for his help in animal breeding and handling. All the illustrations were created with Biorender. com.
2019). The final sponge transcriptomes included only genes with metazoan annotations or no annotations. For a detailed description of all filtering and assembly steps, see  Strehlow et al. (2020)  and (https://zenodo. org/badge/latestdoi/573040118) for all scripts used in the current paper. Sponge reference transcriptomes fit within the expected range of size, gene number, and N50 (Supplemental Table  1 ) of published sponge transcriptomes  (Strehlow et al. 2020; Riesgo et al. 2014). Additionally, sponge reference transcriptomes were effectively complete based on Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis (Supplemental Table  1 ,  (Simão et al. 2015) ) and were therefore suitable for use for subsequent differential expression analysis (see below). Sponge reference transcriptome assemblies are available through NCBI under BioProject number PRJNA89319.
Differential gene expression analysis was performed with the package DESeq2 v. 1. 22. 2 (Love, Huber, and Anders 2016) in R (R Core Team 2016) with (RStudio Team 2020). Three pairwise comparisons of oxygen condition (normoxia vs. hypoxia, normoxia vs. anoxia, and anoxia vs. hypoxia) were computed from DESeq2 results. Significantly differentially expressed genes between each of these pairwise comparisons were identified using a false discovery rate (FDR) adjusted p-value threshold of 0. 1. The ReportingTools software package  (Huntley et al. 2013 ) was used to create HTML pages displaying4plots of filtered expressed genes and their annotations in various oxygen conditions (https://zenodo. org/badge/latestdoi/573040118). We note that comparisons to H. stellifera under normoxia were made with only one normoxic sample due to sampling restrictions.
2013)  were added using delta rank tables calculated by  Strader et al. (2016). Correlations between the KOG delta ranks in different datasets were performed using Pearson's Correlation in KOGMWU. Within the significantly enriched KOGs 'replication, recombination and repair' and 'energy production and conversion,' genes that were significantly differentially expressed genes were plotted in heatmaps to further investigate sponge transcriptomic responses to deoxygenation at the gene level in Lough Hyne sponges. KOG enrichment analysis was performed as above for each of the two major symbionts within the two sponge species; however, Clusters of Orthologous groups (COGs) were used since they are designed for prokaryotes rather than eukaryotic KOGs and are fewer in number.
Phylogenetic positions of nitroreductase proteins Nitroreductase proteins from the Thaumarchaeota symbionts of H. stellifera and E. sp. 2 were extracted using annotations assigned by egg-NOG. Phylogenetic position of nitroreductase proteins from H. stellifera and E. sp. 2 were investigated by aligning them to other known nitroreductase proteins from Genbank using Mafft v7  (Katoh and Standley 2013). The ModelTest-NG v. 0. 1. 7 function  (Edler et al. 2020 ) implemented within RaxmlGUI 2. 0 was used to calculate the evolutionary model. According to AIC, LG+I+G was chosen as the best-fitting model. Maximum Likelihood (ML) phylogeny was then reconstructed using raxmlGUI v. 2. 0. 6  (Edler et al. 2020)  with 100 (rapid) bootstraps. The resulting protein tree was rendered by FigTree v. 1. 4. 4 (http://tree.
Anox' columns, i. e. upregulated KOGs (red delta ranks) are upregulated in anoxia with respect to hypoxia. Within each column on the heatmap, KOGs that are significantly enriched (FDRadjusted p <. 05) are marked with asterisks. KOG enrichments from the sponge Tethya wilhelma after four days exposure to hypoxia (Tw Hypox Long) and one hour exposure to anoxia (Tw Anox Short), the coral Acropora millepora after three days of exposure to heat stress (Am Coral Heat), Caenorhabditis elegans in a dauer state (Ce Dauer), and the midge Sitodiplosis mosellana in diapause (Sm Diapause) from external studies were included for comparative purposes. B-J. Significant (p < 0. 05) correlations between the KOG enrichments of different treatments or datasets (columns in A). All other possible comparisons between the various datasets were not significant.