dataesr/scientific-paragraphs-categorization huggingface dataset:
We also compared sparse LFMM to three non-sparse algorithms, all based on the generative model defined in equation (  1 ). First we implemented Surrogate Variable Analysis (SVA,  Leek and Storey (2007) ). SVA was introduced to overcome the problems caused by heterogeneity in gene expression studies. The algorithm starts with estimating the loading values of a principal component analysis for the residuals of the regression of the response matrix Y on X. In a second step, SVA determines a subset of response variables exhibiting low correlation with X, and uses this subset of variables to estimate the latent factors. SVA was implemented in the R package sva. Next, we implemented the Confounder Adjusted Testing and Estimation (CATE) method  (Wang et al. , 2017). CATE uses a linear transformation of the response matrix such that the first axis of this transformation is colinear to X and the other axes are orthogonal to X.
CATE was used without negative controls, and it was implemented in the R package cate. We eventually used the ridge version of LFMM implemented in the R package lfmm  (Caye et al. , 2019). Generative model simulations. We defined the confounding intensity as the percentage of variance of the primary variable X explained by the latent factors U. Following  Caye et al. (2019) , we performed simulations of a primary variable, X, with d = 1, and K = 6 independent latent factors, U, for two values of confounding intensity, R 2 = 0. 1 (lower) and R 2 = 0. 5 (higher). The joint distribution of (X, U) was a multivariate Gaussian distribution. Having defined primary variables and latent factors, we used the generative model defined in equation (  1 ) to simulate a response matrix, Y. To create sparse models, only a small proportion of effect sizes, around 0.
8%, were allowed to be different from zero. Non-null effect sizes were sampled according to a Gaussian distribution, N(B, 0. 2), where B could take two values, B = 0. 75 (lower value) and B = 1. 5 (higher value). Residual errors and loadings, V, were sampled according to a standard Gaussian distribution. The dimensions of the response matrix were set to n = 400 individuals and p = 10, 000 variables. Two hundred simulations were performed for each combination of parameters (800 simulations). Empirical simulations. We used the R package naturalgwas to simulate associations of phenotypes based on a matrix of sampled genotypes  (François and Caye, 2018). With this program, phenotypic simulations incorporate realistic features such as geographic population genetic structure and gene-by-environment interactions where environmental variables are derived from a bioclimatic database.
Candidate markers within a window of size 10kb around a causal marker were considered to be true discoveries (LD-r 2 < 0. 2,  François and Caye (2018) ). In addition to the F -score, we used the root mean squared error (RMSE) to evaluate the statistical errors of effect size estimates. We also used simulations from the generative model to assess the capability of the cross validation algorithm to estimate the number of latent factors in sparse LFMM. In program runs, the number of latent factors varied between K = 3 and K = 10 and the value estimated by the cross validation algorithm was compared with the true value (K = 6). GWAS of plant phenotype. Sparse LFMM and a set of other methods were used to perform association studies for two distinct types of genomic data including genotypic and epigenetic markers.
Acknowledgements. This article was developed in the framework of the Grenoble Alpes Data Institute, supported by the  French National Research Agency  under the  Investissements d'Avenir program  ( ANR-15-IDEX-02 ). It received support from  LabEx PERSYVAL Lab ,  ANR-11-LABX-0025-01 , and from the  French National Research Agency (Agence Nationale pour la Recherche) ETAPE ,  ANR-18-CE36-0005. We thank the participants of the EDEN cohort. We thank the midwife research assistants for data collection, the psychologists and the data entry operators. We also thank the EDEN mother-child cohort study group which includes  I Annesi-Maesano ,  JY Bernard ,  J Botton ,  M-A Charles ,  P Dargent-Molina ,  B de Lauzon-Guillain ,  P Ducimetière ,  M de Agostini ,  B Foliguet ,  A Forhan ,  X Fritel ,  A Germa ,  V Goua ,  R Hankard ,  B Heude ,  M Kaminski ,  B Larroque ,  N Lelong ,  J Lepeule ,  G Magnin ,  L Marchand ,  C Nabet ,  F Pierre ,  R Slama ,  MJ Saurel-Cubizolles ,  M Schweitzer ,  O Thiebaugeorges.
The parameters of the electrospray interface were optimized as follows: capillary voltage 4 kV, source temperature 120 °C, desolvation temperature 350 °C, desolvation gas 900 L/hour. Steroid identification was based on an identical retention time and 2 identical mass transitions with authentic reference compounds (testosterone from Sigma-Aldrich, and progesterone from Vetranal). Quantification was performed relative to a calibration series (0, 0. 1 to 100 ng/mL of each steroid) with an appropriate internal standard steroid. All measurements were performed in duplicate. Data processing was ensured using MassLynx software (V 4. 1, SCN 805, Waters, Manchester, UK). A QuantLynx (V 4. 1, SCN 803, Waters) was used to detect and integrate the peaks. Accuracy checked in our unit did not differ from 100%.
Micro Computed Tomography Analysis Femurs were scanned at a resolution of 9 μm, using a SkyScan 1076 system (Bruker, Kontich, Belgium) in a blinded fashion. According to the published guidelines  (27) , the following settings were used: x-ray power and tube current were 40 kV and 250 μA, respectively. Beam hardening was reduced using a 1-mm aluminum filter, exposure time was 2. 3 seconds, and an average of 3 pictures was taken at each angle with steps of 0. 8° to generate final images. Segmentation of the reconstructed images was done on basis of global thresholding. Using software packages from Bruker (NRecon, CtAn, and Dataviewer), bone microarchitecture parameters were assessed in trabecular and cortical bones of all mice (n = 8-12 mice per genotype and sex). The trabecular bone parameters trabecular bone volume fraction (BV/TV), trabecular thickness (Tb.
Statistical Analysis Steroid hormone levels during the estrous cycle were analyzed by two-way analysis of variance (ANOVA) followed by Bonferroni post hoc analysis. All other parameters were analyzed by one-way ANOVA followed by Tukey HSD post hoc test. To determine an effect of aging linear regression analysis was performed. Analyses were performed using GraphPad Prism 9 (GraphPad Software, Inc. ). A P value <0. 05 was considered significant. Data are expressed as mean ± SEM.
The study was accepted for Voluntary Harmonisation Procedure (VHP673, VHP2015061) and approved by Ethics Committees and Regulatory Authorities (EudraCT No: 2015-001628-45; EC Sweden: Dnr 2015/279; EC Austria: 415-E/1544/20-2014). Written informed consent were obtained from participants and parents. The trial was conducted according to the Declaration of Helsinki (World Medical Association; Version 2013) and the E6 Guideline for Good Clinical Practice (International Conference on Harmonisation).
Statistical Analysis Statistical analyses were performed using Stata software (version 15, StataCorp, College Station, US). Continuous data were expressed as means and standard-deviations (SD). The normality of the distribution was checked with a Shapiro-Wilk test. Comparisons between groups were performed using Chisquared or Fisher's exact test for categorical data, and analysis of variance (ANOVA) or non-parametric Kruskal-Wallis test (when the ANOVA assumptions were not met) for continuous variables. Assumption of homoscedasticity was studied using Bartlett's test. Whan appropriate (omnibus p-value<0. 05), post-hoc test for two by two multiple comparison were applied: Tukey-Kramer after ANOVA, Dunn after Kruskal-Wallis test and Marascuilo for categorical data. Relationships between continuous data were explored with Pearson or Spearman correlation coe cient and a Sidak type I error correction.
In addition, in each collision system the neutron single spin asymmetries are extracted for inclusive neutrons, as well as in (anti) coincidence with hard-collision sensitive detector activity. These correlated results provide additional insights into the hadronic and UPC interactions as they either enhance or suppress either one of the contributions. This paper is organized as follows. Initially the various data sets, necessary Monte Carlo simulations and the PHENIX detector systems relevant for this analysis are presented in Sec. II. Then the event and neutron selection are explained in Sec. III and the asymmetry extraction and unfolding of the raw asymmetries are presented in Sec. IV. The results are discussed in Sec. V before the paper is summarized.
II. DATASETS AND MONTE CARLO SIMULATIONS In 2015 the PHENIX experiment has taken data colliding transversely polarized proton beams with also transversely polarized proton beams, as well as Al and Au beams. The PHENIX detector is described in detail in Ref. [10]. Here only the detectors relevant for the following results are presented. Forward neutrons are detected with the zero degree calorimeter (ZDC) which comprise three modules of Cu-W alloy absorbers that are layered with optical fibers. Each module corresponds to 1. 7 nuclear interaction lengths or 51 radiation lengths and has a projected transverse size of 10 cm by 10 cm with respect to the beam direction. Each of these layers is tilted 45 degrees upward to optimize the Čerenkov light collection. The ZDCs are located approximately 18 m up and downstream of the PHENIX interaction point but for the presented analysis only the results of the ZDC at the proton-going beam direction are reported.
Both results were found to be consistent with each other and no systematic uncertainty was assigned to it. Due to the energy and position smearing, the kinematic values need to be unfolded. Unlike the previous publication  [9] , for the inclusive neutron asymmetries in p þ p collisions, the unfolding was performed using the Bayes unfold method  [16]  as implemented in one (of various) methods in the RooUnfold  [17]  package of Root  [18]. The advantage of using this unfolding package is that threedimensional unfolding is directly available which is needed in this analysis. Also, the fact that the spin dependent yields (which implicitly contain the asymmetries) will not a priori be very similar to the (unpolarized) MC truth distributions is iteratively addressed in the Bayes unfolding. Therefore, one does not need to artificially create spin dependent true MC yields by reweighting the MC according to various asymmetry assumptions.
ACKNOWLEDGMENTS We thank the staff of the  Collider-Accelerator and Physics Departments at Brookhaven National Laboratory  and the staff of the other PHENIX participating institutions for their vital contributions. We acknowledge support from the  Office of Nuclear Physics in the Office of Science of the Department of Energy , the  National Science Foundation ,  Abilene Christian University Research Council, Research Foundation of SUNY, and Dean of the College of Arts and Sciences, Vanderbilt University (U. S. A) ,  Ministry of Education, Culture, Sports, Science, and Technology  and the  Japan Society for the Promotion of Science (Japan) ,  Natural Science Foundation of China (People's Republic of China) ,  Croatian Science Foundation and Ministry of Science and Education (Croatia) ,  Ministry of Education, Youth and Sports (Czech Republic) ,  Centre National de la Recherche Scientifique ,  Commissariat à l'Énergie Atomique , and  Institut National de Physique Nucléaire et de Physique des Particules (France) ,  J.
Patient characteristics and postoperative complications A total of 166 patients were initially eligible for inclusion in the study. Of these, 5 patients received 2 HIPEC and were therefore included twice. Forty-four patients were excluded: 34 due to a lack of data (no CRP values collected in 10 days), 5 because surgical exploration did not allow curative management and received intravenous chemotherapy (high PCI, metastasis, local invasion), and 5 patients who had CRS without HIPEC. Annex 1 A total of 127 patients who had undergone HIPEC were included. Table  1  The study population consisted of 88 women (69. 3%) and 39 men (30. 7%). All patients with colorectal and ovarian PMs received preoperative IV chemotherapy. Patients with primary peritoneal cancer received surgery treatment in front line.
Introduction In-silico predictive algorithms are now widely used for annotation of punctual variation in gene sequencing analysis. The American College of Medical Genetics and Genomics edited guidelines for proper use of these in-silico predictors  (Richards et al. , 2015). Initially, first prediction strategies were based on either sequence/evolutionary conservation, or protein sequence, or supervised learning methods  (Tavtigian et al. , 2008). More complex approaches are now developed by combining multiple single tools  (Kircher et al. , 2014). These combinations, which define meta-predictors algorithms, often offer a continuous scoring rather than a 2-side classification. The vast majority of the studies compare the precision, accuracy and efficiency of in-silico tools on large multigene variation datasets such as the ClinVar database  (Ghosh et al.
Variation data and annotation All possible combinations of MED13L missense variations were generated, corresponding to 14,545 different nucleotide substitutions. Missense variations were annotated with Ensembl Variant Effect Predictor (VEP), using genome assembly GRCh37, and with Combined Annotation Dependent Depletion (CADD), using version 1. 3  (Kircher et al. , 2014; McLaren et al. , 2016). We considered data parameters extracted from VEP and CADD annotations. Molecular anomalies with reported evidence of a splice defect, substitutions in the first or last three bases of each exon, were excluded from the analysis to prevent the pathogenicity classification associated with splice effect rather than pathogenicity due to an amino-acid substitution (n = 398, 2. 73%). A comparison of conserved residues between MED13L and MED13 proteins was performed using Clustal Omega alignment with canonical transcript sequence of MED13L (Q71F56) and MED13 (Q9UHV7) obtained from Uniprot.
Clustering was performed in R (v4. 0. 1). For each aligned amino acid, a score ranging from 0 to 1 was applied, according to the conservation of residues between MED13L and MED13. Fully conserved residues were annotated with the higher score "1". Substitutions between two residues belonging to a group with strongly similar properties were annotated "0. 5" and "0. 25" respectively for groups with weakly similar properties. An unconserved substitution in the Gonnet PAM 250 matrix between MED13L and MED13 was annoted "0". Distance to phosphorylation sites of each neutral or pathogenic missense on MED13L was estimated from PhosphoSite database on the protein model THRAP2, the previous name of MED13L  (Hornbeck et al. , 2004). Variations were classified according to ACMG 2015 criteria  (Richards et al.
, 2015). Frequencies of known reported alleles were considered using gnomAD, BRAVO, FREXAC and HGVD databases. Likely pathogenic and pathogenic variations described in the MED13L syndrome were identified in ClinVar, DECIPHER, HGMD and LOVD databases. Likely pathogenic and pathogenic variations previously reported were merged in the pathogenic missense group. Benign and likely benign missenses reported in ClinVar and LOVD, and missense variations above or equal to one allele in gnomAD control subpopulation, were merged in the control/neutral group.
Statistical Analysis The repeatment was regarded as the experimental units for the animal growth performance. One gilt of a repeatment of three treatments had diarrhea or sick when younger, therefore, that repeatment was excluded from the present study. The gilt was regarded as the experimental units for the animal study except for the animal growth performance. Before using parametric analyses, all data were checked for normality and homogeneity of variance with Shapio-Wilk W test and Levene's test, respectively. All data were analyzed by one-way ANOVA using SPSS Statistics 22 (IBM R SPSS R Statistics, New York, NY, USA) and GraphPad Prism 6. 0 (GraphPad Inc. , La Jolla, CA, USA; figures). Then multiple comparison by DUNCAN analysis (Tukey's multiple range test was used in all figures) were used to determine statistical differences between treatment groups.
| Data collection The following variables were collected from a prospective database and analyzed: brain death (DBD) or circulatory death (DCD), cold ischemia duration. 3. Arterial reconstruction: all operative notes were systematically reviewed in order to identify the reconstruction method as well as the arterial anatomy of the graft. 4. Outcomes: intraoperative and postoperative outcomes, especially the occurrence of arterial complications, and patient and graft survival. The severity of complications during initial hospitalization was graded using the Clavien-Dindo classification.
| Ethics For University Medical Center Groningen, patient data was derived from a post-hoc analysis of an observational cohort study (www. trial regis ter. nl-Trial NL6334), which was approved by the Medical Ethics Committee (METc 2014/77). For Rennes university hospital, patient data were mostly retrieved from the database of the national agency of regulation of the procurements and transplantations ("Agence de la Biomédecine"). A formal approval from the local ethics committees was also obtained (avis n°22. 81). Data was retrieved from each center and made anonymous prior to analysis. The study adhered to the Declaration of Helsinki and the Declaration of Istanbul.
| Inverse probability weighting (IPW) ponderation In order to carry out efficient analyses and to correct for bias due to population heterogeneity, stabilized Inverse Probability Weighting (IPW)  15  was performed. Comparisons were made using an adjusted log-rank test. 16 nly the impacting variables (i. e. , variables significantly different across groups in the bivariate analysis) were selected and used in the propensity score calculation. A p-value <. 05 was considered significant. All statistical analyses were performed on R software version 3. 1. 3 using the "survival" v3. 1-12, "ipw" v0. 1. 0-11 and "Weightit" v0. 12. 0 packages.
This work analyzes the heat transfer and fluid flow within a batch reactor for hydrothermal carbonization (HTC) of raw olive pomace (ROP). The autoclave is partially filled with a mixture of ROP and distilled water and hence it is considered as a dispersed medium. The reactor is heated through its lateral surface, whereas the bottom wall and the upper surface of the mixture are thermally insulated. Under the effect of heat and pressure, the fluid moves inside the reactor, while particles are subject to other forces. Additionally, the biomass (ROP) is decomposed into very fine particles to produce a solid product (hydrochar). COMSOL Multiphysics software is used for the analysis of heat transfer and fluid dynamics. Chemical kinetics of the reactions are modeled by a basic kinetics model. Numerical results are validated using experimental data carried out in similar operating conditions.
Effects of the residence time and the operating temperature on the hydrochar properties were analyzed using various characterization techniques. For the three carbonized organic wastes, the carbon content and the higher heating value (HHV) are close to those of black carbon. They also showed that the HTC process would allow up to 50% of energy savings compared with the torrefaction process. In 2019, Lucian et al. [11]  utilized olive trimmings to achieve an experimental study and an original HTC kinetics model. The biomass was carbonized at a temperature ranging from 180 and 250 • C for a residence time of up to 8 h. Their experimental data and the findings of two other agro-wastes (grape marc and spineless cactus) previously were compared with the obtained model. They concluded that the model predicts the carbon distribution correctly inside HTC products with respect to time, including the thermal unsteady heat period.
Nevertheless, Baratieri et al. [15]  simulated the transient thermal behavior of the HTC batch reactor containing grape marc at several operating conditions (imposed temperatures and residence times). They established a simplified thermal dynamic model, based on lumped capacitance method, which was combined with a kinetic model to evaluate the HTC process performance. These types of model are not overly abundant because of the lack in data regarding the thermodynamics of HTC and the change of physical properties of the mixture  [20]. As for the study of Alvarez-Murillon et al. [16] , it modeled kinetic of cellulose hydrothermal carbonization taking into account heat transfer within an autoclave. The heat balance equation including conduction, convection and radiation terms was solved by COMSOL Multiphysics software under appropriate boundary and initial conditions.
The authors showed the influence of the reactor (autoclave) temperature on the biomass decomposition represented by the pre-exponential and exponential factors evolving the reaction rate. In 2020, thermo-fluid dynamic performance of the HTC process of olive pomace in a batch reactor has been developed by means of ANSYS Fluent software  [17]. In this work, the authors considered the multiphase model composed of three phases (air, water and dried olive pomace). The mixture model, which is applicable for fluid-solid flow with dispersed phases, has been chosen to simulate the flow within the reactor. The conservation equations have been solved to simulate dynamic, thermal and kinetics behavior inside a batch reactor. This experimentally validated work is undoubtedly an important source of information on temperature, velocity and the spatial distribution of the mixture inside the reactor during the HTC process.
More recently (2021), Ischia and Fiori  [18]  published a review paper which examine and analyze some predicting tools such as kinetics, statistical, heat transfer and computational models, highlighting their potentialities and limits. A mathematical model for HTC has been developed by Heidari et al. [19] , in 2021, and comparison with experimental results was performed in the case of pine wood particles. In this study, heat transfer rate, reaction kinetics, and the porous structure of the biomass have been estimated by using COMSOL Multiphysics software. Then, the predicted results were validated and operating parameters Energies 2022, 15, 818 3 of 18 such as biomass to water ratio, temperature, residence time and power consumption were recorded. From the above brief state of the art, it is obvious that studies on modeling and simulation of the hydrothermal carbonization (HTC) process are lacking in the literature.
Materials A photo of a treated ROP sample is provided in Figure  1. This agro-food residue is a product obtained from the olive oil production process. This biomass, coming from a crushing unit located in Meknès region (Morocco), has an average particle size of 5 mm, and an initial humidity rate ranging between 30% and 40%. Also, it has been dried using an indirect forced convection solar dryer and then characterized  [4, [21] [22] [23] [24]. Table  1  summarizes ultimate and proximate analyses of the selected biomass. transfer rate, reaction kinetics, and the porous structure of the biomass have be mated by using COMSOL Multiphysics software. Then, the predicted results we dated and operating parameters such as biomass to water ratio, temperature, re time and power consumption were recorded.