dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, the optimization concerns the binding parameters, i. e. the gaps, tightening forces and friction coefficients between the different elements of the assembly. The objective functions studied were the transmissible torque M a and axial load F a between the pinion (or the hub) and the shaft. The strategy known as two step optimization consists in carrying out first a global optimization using the metamodel, then a local optimization based on that first optimum using the full FE model and a multiparametric strategy (see Figure  1 ). The global optimization based on the metamodel was performed at low cost using a genetic algorithm  (Goldberg, 1989 ) (the ga function in MATLAB) which generated n 1 evaluations of the objective function. This first step enabled us to localize the trough in which the global optimum of the objective function was to be sought.
The metamodel was built in a preliminary phase using n 0 evaluations of the full mechanical model. The settings of the GA algorithm were specified as follow : a population size equal to 20, a crossover rate of 0. 8, a mutation rate of 0. 2 and a number of generations equal to 100. The local optimization using the full mechanical model was performed with a sequential quadratic programming  (Han, 1977)  algorithm (the f mincon function in MATLAB) which generated n 2 evaluations of the objective function. The total optimization time t multilev+multipar associated with this two step / multiparametric strategy (  23 ) is the sum of: t 0 , the time for generating the metamodel using the multiparametric strategy, t 1 , the time for optimizing the metamodel, and t 2 , the time for optimizing the full FE model using the multiparametric LATIN technique.
768 S s (15) where, S s is the section area of the sensor. Based on the front analysis, we employ Mathcad ---a kind of general mathematical software to calculate and make some discussions on the influencing factors on the distribution of the solid-conveying pressure. Then, the following can express some operating conditions and some physical dimensions as:  15 ) is achieved, illustrated in Fig. (7). = 5000 r min , R N = 0. 2m , R n = 0. 19m , = 920kg / m 3 (LDPE), = 56. 8 o , f H = f B = 0. 3. So the result of Equation ( According of Equation (  15 ) and Fig. (7) , the pressure of the outlet (located at the edge of the rotor 1) can reach 2. 25 10 6 Pa. Thus it can be sure that centrifugal extruder can provide the sufficient conveying pressure, which provides the basis fortheory the industry feasibility of centrifugal extruder.
Given the available resources in the EFF this limits the time per event in the HLT to ∼30 ms. The HLT is divided into two stages. The first stage (HLT1) processes the full L0 rate and uses partial event reconstruction to reduce the rate to 43 kHz. At this rate the second stage (HLT2) performs a more complete event reconstruction. A "trigger line" is composed of a sequence of reconstruction algorithms and selections. The trigger line returns an accept or reject decision. An event will be accepted by L0, HLT1 or HLT2 if it is accepted by at least one of its trigger lines at the relevant stage. Combinations of trigger lines, together with a L0 configuration, form a unique trigger with its associated Trigger Configuration Key (TCK). The TCK is a 32 bit label pointing to a database that contains the parameters that configure the trigger lines.
This implies that a cut on IP, a powerful variable to reject combinatorial background, cannot be used. In order to enrich the B s candidates two dedicated neural networks based on the NeuroBayes neural network package  [10]  are used. In a first step, kinematic constraints such as the transverse momentum of the final state particles and the helicity angle in the rest frame of the B s candidate are used to reduce the rate. This allows running the comparatively slow particle identification algorithm using the RICH sub-detector on the events selected by the first neural network. This information is then included in a second neural network that uses both kinematic and particle ID information to make the final selection.
Substantial differences in trigger efficiency, however, are observed for true signal and background. The trigger performance on each channel is measured by determining the signal component using fits to the invariant mass distributions, hence avoiding any background contamination. In what follows, the term "signal" refers to a combination of tracks that form the off-line reconstructed and selected b or c-hadron candidate. To determine the trigger efficiency, trigger objects are associated to signal tracks. The trigger records all the information needed for such an association. All strips, straws, cells and pads of the sub-detectors have a unique identifier, and these identifiers are written in a trigger report in the data stream for every trigger line that accepts an event. The criteria used to associate a trigger object with a signal track are as follows: • L0-Calorimeter: the off-line track is extrapolated to the z-position of the calorimeter (ECAL or HCAL), and the cells intersecting with the track and its eight neighbours are considered signal cells.
Acknowledgments We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  NSFC (China) ;  CNRS/IN2P3  and  Region Auvergne (France) ;  BMBF ,  DFG ,  HGF  and  MPG (Germany) ;  SFI (Ireland) ;  INFN (Italy) ;  FOM  and  NWO  (The Netherlands); SCSR (Poland);  ANCS/IFA (Romania) ;  MinES, Rosatom ,  RFBR  and  NRC "Kurchatov Institute" (Russia) ;  MinECo, XuntaGal and GENCAT (Spain) ;  SNSF  and  SER (Switzerland) ;  NAS Ukraine (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA). We also acknowledge the support received from the  ERC  under  FP7.
Xca5 is an American race 1 X. campestris pv. campestris strain originally classified as pathovar armoraciae by M. Daniels (Sainsbury Laboratory, Norwich, United Kingdom). Yet, pathogenicity tests (3), as well as multilocus sequence analyses (data not shown), clearly identified this strain as a bona fide X. campestris pv. campestris strain. Xca5 genome shotgun sequencing was performed on a GAIIx Illumina platform. A total of 28,569,308 76-bp paired-end reads corresponding to 4,342,534,816 bp and 868-fold coverage were obtained. Genome assembly was performed using a combination of Short Oligonucleotide Analysis Package (SOAP) de novo (4) and Velvet (5) assemblers and yielded 130 contigs that were Ͼ500 bp and had an N 50 of 121,265 bp. The average contig size was 38,385 bp, and the largest was 447,475 bp long, for a total genome size of 4,990,056 bp.
campestris pv. campestris reference genome sequences, this genome sequence represents the first X. campestris pv. campestris strain with TAL protein genes and plasmids, suggesting that much more genomic diversity might be expected at the intraspecific level than is anticipated from the available X. campestris pv. campestris genome sequences  (1, 2, 11). Nucleotide sequence accession numbers. The Whole Genome Shotgun project has been deposited at EMBL under the accession no. CAOR01000001 to CAOR01000130.
ACKNOWLEDGMENTS This study was funded by the  Laboratoire d'Excellence (LABEX)   TULIP  ( ANR-10-LABX-41 ), Genoscope grant,  AP10/11-N°28_Rott , a Ph. D. grant from the  French Ministry of National Education and Research and French Guiana  to E. G. , and an  Agence Nationale de la Recherche-Jeunes Chercheurs  grant (Xopaque  ANR-10-JCJC-1703-01 ) to L. D. N.
LASER SCANNING CONFOCAL MICROSCOPY The microscope imaging was performed in Montpellier RIO Imaging Platform (http://www/mri/cnrs. fr) with a confocal microscope (LSM 510, Meta; Carl Zeiss MicroImaging, http:// www. zeiss. de). An Argon laser at 488 nm provided excitation for the Alexa 488, and 405 nm was used for DAPI staining. The fluorescence emission signals were detected in Multi Track using a band-pass filter of 505-530 nm for the Alexa Fluor 488, a long-pass filter of 650 nm for the far-red autofluorescence of the chloroplast and a band-pass filter of 420-480 nm for DAPI. The sections were observed with a x63 oil Zeiss objective. Pictures were processed using the Zeiss LSM Image Browser software.
Predator-prey interactions play an important role for species composition and community dynamics at local scales, but their importance in shaping large-scale gradients of species richness remains unexplored. Here, we use global range maps, structural equation models (SEM), and comprehensive databases of dietary preferences and body masses of all terrestrial, non-volant mammals worldwide, to test whether (1) prey bottom-up or predator top-down relationships are important drivers of broad-scale species richness gradients once the environment and human influence have been accounted for, (2) predator-prey richness associations vary among biogeographic regions, and (3) body size influences large-scale covariation between predators and prey. SEMs including only productivity, climate, and human factors explained a high proportion of variance in prey richness (R 2 ¼ 0.
Using global range maps, structural equation modeling, and comprehensive databases of dietary preferences and body masses of all terrestrial, non-volant mammals worldwide, we provide the first global-scale analysis of mammalian predator and prey species richness to test the relative roles of direct bottom-up and top-down mechanisms while simultaneously accounting for shared responses to environmental gradients and human influence. We further examine how these associations vary among biogeographic regions, and how body size constraints influence the co-variation between predator and prey richness. For the latter, we specifically test the links between predator-prey richness for small-and large-bodied species and expect trophic relationships within mass categories (e. g. , large prey to large predator) to be stronger than between them (e.
Species distribution data We used global species distribution maps for all 4091 terrestrial mammal species, excluding bats (IUCN 2010). Polygon range maps were converted to rasters on a Behrmann projection (a cylindrical equal area projection) with a resolution of 28 equivalents (;220 km). A species was counted as present in a cell if any part of the cell was covered by the species' range polygon. We then added up all species presences for each grid cell to quantify species richness. Grid cells with ,50% land area were excluded, resulting in 3523 cells for our analysis. All data handling and plotting was performed in the R statistical program, version 2. 15 (R Development Core Team 2012), using the raster, rgdal, and maptools packages (available online). 8.
Diet and mass data Mammal diet descriptions were derived from a comprehensive new database (compiled by the authors, unpublished data) based on dietary information in  Nowak (1999). This database describes dietary preferences of all mammal species using predefined food categories (e. g. , mammals, birds, herptiles, fishes, invertebrates, seeds, fruits, leaves, etc. ). For a given taxon, each food category in the database is ranked in importance according to whether it is a primary food item (rank 1), secondary food item (rank 2), or occasional food item (rank 3). For the purpose of our study, we defined predators (n ¼ 125 species) as species that primarily feed on other mammals (see Plate 1), i. e. , scoring rank 1 in the food category ''mammals'' and not rank 1 in the food category ''plants'' (thus avoiding species that mainly feed on both mammals and plants).
All other species that did not meet this criterion were classified as prey (n ¼ 3966 species). Body mass data were gathered from  Smith et al. (2003). Species-level mass data were available for 2845 species (70%). A further 1144 species (28%) were filled by taking the mean of species' masses in the same genus, leaving 102 species (2%) without mass data that were excluded from the mass analysis. Species diet, body mass, and distribution data were matched using the International Union for Conservation of Nature (IUCN) taxonomy and recognized synonyms. Body size data were log 10 -transformed for further analysis to achieve normal distributions.
We included the first three PCA components as predictors of predator and prey richness, capturing 97. 5% of the variation in our global dataset (PC1¼44. 5%, representing MAP and negative SEAS; PC2 ¼ 33. 7%, representing WB and negative MAT; PC3 ¼ 19. 3%, representing TOPO). To characterize net primary productivity, we used the normalized difference vegetation index (NDVI), downloaded from the European Distributed Institute of Taxonomy in the original resolution of 0. 18 and calculated as mean per grid cell (data available online). 10 In all analyses, we included direct effects of climate and productivity as well as indirect effects of climate via productivity on predator and prey species richness. Human influence was included via the human influence index (HII;  Sanderson et al. 2002)  with mean values calculated for each grid cell.
All three categories of predictor variables (climate via PC1-3, productivity via NDVI, and human influence via HII) were included in all structural equation models. The following transformations were performed to approximate normal distributions: MAP, SEAS, HII, prey richness and predator richness were square-root transformed and TOPO was log 10 -transformed. All data layers were converted to a Behrman projection to create equal-area grid cells with the same resolution as the species richness grids.
Structural equation models at global and regional scales Structural equation models (SEMs) were implemented with the R package lavaan  (Rosseel 2012). SEMs are particularly well suited for evaluating multivariate hypotheses because direct and indirect effects of predictor variables can be tested  (Grace 2006). We first built a priori theoretical SEMs reflecting hypothesized relationships among all variables, including direct effects of climate (PC1-PC3), productivity (NDVI), and human influence (HII) on species richness of predators and prey, and indirect effects of climate via productivity. Trophic interactions were tested with direct links from prey to predators (bottom up) and predators to prey (top down). For each of the theoretical SEMs, we then evaluated residual correlations, modification indices, and model fits to test whether important paths between variables were missing  (Grace et al.
Spatial autocorrelation To test for potential confounding effects of spatial autocorrelation (SAC), we first assessed the degree of SAC in model residuals by building our SEM models as a series of independent ordinary least squares (OLS) regressions. Standardized coefficients from OLS models are equivalent to path coefficients from SEMs and allow a direct comparison of residual spatial autocorrelation between spatial and nonspatial models (see  Kissling et al. 2008). We computed correlograms of OLS model residuals using the ncf package in R, with distance classes of 1000 km (package available online). 11 All OLS models showed substantial SAC (see Appendix A: Fig. A1 ), so we reran these models using simultaneous autoregressive (SAR) models of the error type  (Kissling and Carl 2008). These SAR models were implemented in the R package spdep (available online).
ACKNOWLEDGMENTS We thank  J. B. Grace  for advice on how to implement structural equation models. We gratefully acknowledge economic support from  Villum Kann Rasmussen Foundation  (grant  VKR09b-141  to J. -C. Svenning),  Danish Council for Independent Research-Natural Sciences  (starting independent researcher grant  11-106163  to  W. D. Kissling  and individual postdoc grant  10-093843  to  C. Fløjgaard ),  MADALGO-Center for Massive Data Algorithmics ,  Aarhus University Research Foundation's Center for Interdisciplinary Geospatial Informatics Research (CIGIR)  (grant to  J. -C. Svenning ), 15  Juni Fonden  (grant to  J. -C. Svenning , and  R. Ejrnaes ),  Center for Informatics Research on Complexity in Ecology (CIRCE) , funded by the  Aarhus University Research Foundation  under the  AU Ideas program  (grant to  J.
Study design and data collection We followed a previously described design and protocol for data collection  [7]. Briefly, "non-infected" patients had no bacteriological or clinical signs of nosocomial infections and "infected" patients were those with ICUacquired, therefore nosocomial, infection. For analysis, only the first episode of nosocomial infection was considered. Day 0 (D0) was defined as the day of nosocomial infection diagnosis for infected patients or the day of ICU discharge for non-infected patients. Daily values of PCT, CRP, temperature, WBC and SOFA score from four days prior to day 0 (D-4) up to day 0 (D0) ware collected and analysed comparing infected and noninfected patients. For comparisons, daily values on D0 and D-4 were considered as well as biomarker variations over time (ΔPCT and ΔCRP) calculated as the difference between D0 value minus the lowest PCT and CRP value over the previous 4 days.
Collected data included age, gender, admission diagnosis, systemic inflammatory response syndrome (SIRS), Simplified Acute Physiology Score II (SAPS II), Sequential Organ Failure Assessment (SOFA) score, central vein catheterization and dwell time, mechanical ventilation and duration, anti-infectious treatment, ICU length of stay and patient outcome  [8]. PCT, CRP and WBC were measured at admission and daily until ICU discharge or death in all patients. Body temperature was measured every three hours and daily lowest and highest values were recorded. Patients were assessed daily for clinical evidence of infection, and appropriate samples for bacteriological cultures were collected whenever infection was suspected.
Gene expression in monocytes is associated to smoking and plaques The analysis workflow of expression data is outlined in Figure  1. Expression of 18,364 genes was detected in total RNA from circulating monocytes by 23,214 probes in the Illumina Human HT-12 BeadChip (Methods). Association of probe expression level with smoking or plaques (log-transformed) was assessed by linear model adjusted for age and sex, as well as for the 6 first singular value decomposition (SVD) components of the expression matrix taken as surrogate variables for technical sources of variability (Methods and Text S1). In a first step, we identified genes whose expression was associated to either smoking or plaques by univariate analysis. At FDR #0. 1, we found 3,774 probes (3,062 distinct genes) associated to smoking.
The list of smoking-associated genes was enriched in three ''biological processes'' in the Gene Ontology (GO) database: platelet activation, interferon-gamma-mediated signaling pathway, and Toll signaling pathway (Table  3 ). Association between plaques and gene expression was much less prevalent than with smoking, with only 258 probes (236 distinct genes) associated to plaques at FDR #0. 1. No GO terms were significantly enriched for genes associated to plaques. Table  4  shows the 10 genes with strongest association to smoking or plaques, respectively. The whole list of associated genes to either phenotype is given in Table  S2. There were 2 members common to both top 10 gene lists, SASH1 and PTGDS. In addition to SASH1 and PTGDS, 4 genes of the top 10 list for smoking were ranked among the top 100 genes for plaques: FUCA1, LOC157627, MMP25 and PID1 (Table  S2 ).
A pattern is a linear combination of gene expressions whose level varies among individuals. A signature is a vector of the contributions of a component to each gene expression that can be characterized by the genes that are most affected by that component (see module below). In the following, the terms pattern, signature or module are used for referring to a component according to the context where it applies (i. e. individuals or genes). The number of components to extract was determined by a permutation test, which indicated that 59 components could be detected in this dataset (see Methods). Components that did not meet pre-specified quality control criteria were discarded, leaving 29 components for analysis (see Methods and Text S1 for details). Each component was associated to a specific module of genes characterizing its signature.
Inference of the gene network underlying ICA expression patterns To further characterize the ICA patterns showing some support for causality, we inferred the topology of the networks underlying patterns. For each pattern, the network was constructed from the subset of genes composing the module specific to that pattern. We applied the PC algorithm 1 to discover the skeleton of conditional independencies (algorithm 1 in  [22] ; see Methods). The network was represented as an undirected graph. To decrease the possibility of hidden variables for the network, we considered in these analyses all cardiovascular risk factors that were associated to each pattern by stepwise regression (Table  S7 ). This is important to avoid spurious edges resulting from untested confounding variables. To assess uncertainty in the inference, the process was repeated 1000 times by bootstrapping individuals and the proportion of data samples that recovered an edge was recorded.
Because ICA was performed on a subset of genes pre-selected by their association with smoking or atherosclerosis, we cannot exclude the possibility that we missed some genes that could be important nodes in the networks subsequently identified. However, not doing this pre-selection would have led to a larger number of ICA components (91 components were actually detected in our previous ICA application in the complete GHS expression dataset  [21] ), most of them being irrelevant for the problem under study. Also, we cannot exclude the possibility of spurious edges in networks resulting from untested confounding variables. Networks discovered in this study may represent diverse mechanisms of gene-by-gene co-expression. For instance, expression of genes may be co-regulated by a signaling pathway in a single cell type or they may represent coordinated variation in the proportion of cell population subclasses of monocytes.
Both possibilities are equally interesting since causal effects may be mediated by either mechanism. For instance, HDL levels modulate monocytes proliferation and activation, changing the composition of the myeloid cell lineage, which is thought to explain in part its anti-inflammatory and athero-protective effects  [45, 46]. Further studies are needed to determine what specific molecular mechanisms underlie the correlation patterns reported here. Additionally, replication of candidate gene networks in independent datasets should be performed before laborious functional studies are undertaken. A word of caution is in place about inferring causal associations from observational data. Direct associations in causal graphs are appropriate only in the context of the variables that are included in the system, i.
Subjects Study participants of both sexes aged 35-74 yr, were successively enrolled into the GHS, a community-based cohort study in the Rhein-Main region in western mid-Germany. Participants were of European origin. More details about the GHS study are provided in  [9]. There were 1,536 individuals with microarray expression data that passed quality control tests. Nine individuals with missing number of plaques were removed. Smoking status was recorded by interview at recruitment. Current smokers (individuals smoking $1 cigarette per day since at least 6 months before recruitment, n = 248) and nonsmokers (individuals who had never been smoking over a period of at least 6 months, n = 688) were used for analyses. Occasional smokers (n = 42), ex-smokers (n = 547) and individuals with missing information on smoking status (n = 2) were excluded.