dataesr/scientific-paragraphs-categorization huggingface dataset:
As controls, medium or an irrelevant peptide were added. Brefeldin-A (10 mg/ml; Sigma Chemical Co, St. Louis, MO) was added after one hour. After five hours of stimulation, the cells were stained with  allophycocyanin-labeled anti-CD8 and peridinin chlorophyll protein-labeled anti-CD3 MoAb, fixed and permeabilized with Cytofix/Cytoperm (BD Bioscience, San Jose, CA) followed by phycoerytrin-labled anti-human IFN-c (Sigma) labeling. Intracellular IFN-c expressions in CD8 + CD3 + cells were analyzed on a FACS-Calibur flow cytometer using CELLQuest software (BD). Background IFN-c-positive CD8 + cells values in cells stimulated with controls (range 0%-0. 4%) were subtracted from the values of the restimulated cells. CD8 + T cells without detectable IFN-c expression were given a value of 0. 009% to be able to be visualized using a log 10 scale.
Statistical Analyses Mann Whitney-U test, Spearman correlation (r s ), Kruskal-Wallis test, x 2 test, receiver operating characteristic (ROC) calculating the area under the curve (AUC), and stepwise binary logistic regression analyses were used. The sCD26 concentration cut-off value was determined from the intersection of the sensitivity and specificity obtained in the ROC analysis using a two graph-ROC (TG-ROC) curve  [35]. All statistical analyses were performed using Prism (Version 5. 0c, GraphPad Software, La Jolla, CA) or SPSS (Version 20. 0. 0, IBM Corp, Armonk, NY, USA) software. All reported P-values are two-sided.
INTRODUCTION Novel mechanisms to escape therapy are constantly emerging among human pathogen populations, and this clearly urges the development, on one hand, of new drugs for the treatment of the diseases and, on the other hand, of rapid and effective methods to help expand the landscape of available treatment options  (Hopkins et al. , 2011). In this context, computational studies are called on to help identify novel therapeutic targets and characterize their interactions, and indeed a number of such efforts are described in the literature  (Aguero et al. , 2008; Kinnings et al. , 2010; Lepore et al. , 2011; Orti et al. , 2009). However, these are mostly devoted to the analysis of single targets or specific tropical disease pathogens. The TiPs database has been developed with the aim of facilitating the identification of new therapeutic targets in4150 organisms responsible for human infections.
METHODS More than 400 human pathogen species were obtained from 'The Approved List of Biological Agents' provided by the Advisory Committee on Dangerous Pathogens. To unambiguously assign an identifier (ID) to human pathogens, the names of the organisms were mapped onto the NCBI Taxonomy Database records (http://www. ncbi. nlm. nih. gov/Taxonomy/). Drug compounds and information on their molecular targets were obtained from DrugBank (http://www. drugbank. ca). The SMILE IDs of drugs annotated either as 'inhibitor', 'agonist' or 'antagonist' were used to associate them with ligands present in the PDB structure entries  (Berman et al. , 2012). Only identical compounds were considered (Tanimoto coefficient ¼ 1). A total of 308 distinct drugs were observed in complex with at least one PDB structure.
About 40% of these (119/308) occur in complex with their actual pharmaceutical target. These were used as starting points to predict potential drug targets in pathogens. The search for homologues in pathogens was performed using BLASTþ  (Camacho et al. , 2009)  with default parameters against the nr database (ftp://ftp. ncbi. nlm. nih. gov/ blast/db/). We only retained highly reliable hits, i. e. those showing at least 40% sequence identity to the original target and e-value510 À6. Pathogen taxonomic IDs were retrieved by matching the gi numbers of BLAST hits to the NCBI Taxonomy database. For each known drug-target complex, we defined the binding site as the subset of target residues having at least one atom within 3. 5 A ˚distance from any atom of the drug. The drug-binding site residues in the predicted pathogen sequences were retrieved through a multiple sequence alignment (MSA) of the original target sequence with its homologues generated with T-coffee  (Taly et al.
, 2012)  (with default parameters) on the nonredundant Uniprot database. The MSA was used as HHsearch query to search for templates in the PDB70 database. We only selected templates with at least 40% sequence identity (and e-value510 À5 ) with the pathogen query sequence. If more than one template was found, the one with the highest coverage to the pathogen sequence was selected. Models were *To whom correspondence should be addressed. generated using the Modeller software. Note that the best template used to build the model corresponds to the original structure in the drug-target complex only in 153 cases, whereas in all the other cases, the best template was a different structure. The binding site residues of the original complex and of the predicted target were structurally superimposed using the LGA software  (Zemla, 2003).
RESULTS TiPs currently contains 4071 candidate pathogen target structures involved in 5153 different drug-target complexes in 150 pathogens. All entries are thoroughly annotated with both sequence and functional information. The database can be queried by organism name (genus or specie name), protein family or function (EC number, GO terms and Pfam), as well as UniProt ID. The query returns a sortable table providing information about both known and predicted drug-target pairs and links to visualize specific information on the drug(s) (physicochemical properties, structure, indication and side effects), the target(s) [UniProt annotation and PDB structure(s)] and to visually analyse or download their 3D complexes. Ligplot  (Laskowski and Swindells, 2011)  drawings of both the known and inferred binding sites in complex with the drug are available as well (Fig.
1 ). Fig. 1. The figure shows the results of 'all pathogens' filtered by the 'ATP binding' GO term query in the TiPs database. The output table lists all putative pathogen targets. Each table row reports the known and predicted target UniProt IDs, their overall sequence identity, their binding site identity and rmsd, whether there are clashes between the known drug and the predicted target, and whether there are insertions or deletions nearby the binding site in the alignment used to model the protein. For each hit, the system also shows details of the structure(s) and the binding site(s) in a Jmol window and the corresponding Ligplot drawings.
Microarray technology allows monitoring of gene expression profiling at the genome level. This is useful in order to search for genes involved in a disease. The performances of the methods used to select interesting genes are most often judged after other analyzes (qPCR validation, search in databases. ), which are also subject to error. A good evaluation of gene selection methods is possible with data whose characteristics are known, that is to say, synthetic data. We propose a model to simulate microarray data with similar characteristics to the data commonly produced by current platforms. The parameters used in this model are described to allow the user to generate data with varying characteristics. In order to show the flexibility of the proposed model, a commented example is given and illustrated.
One fluorescence color is used, and expression intensities are obtained for genes on the arrays used. These values should also be normalized  [3]. For a given gene, a ratio of values from two samples is used as a measurement of its expression change. Throughout this paper, we use logarithm two scale to transform the values. Hence, we use log 2 intensities and log 2 ratios for sample intensities and their ratios. In general, log 2 ratios are used for two-color microarray data, while log 2 intensities are used for one-color microarray data. In a screening study, the goal is to select genes with differential expression from data whose characteristics are unknown. To study the performance of gene selection methods, we often use synthetic data, which can also serve in developing new methods. To properly play their role, synthetic data must resemble as closely as possible the real data they represent.
This is achieved by simulating the physical phenomena through a model. The parameters of this model are approximations of the physical laws that govern the observed phenomenon, and a good knowledge of the physical laws is therefore required to obtain a good model. A complex model that takes into account several components of a phenomenon may become less flexible and may be difficult to modify in order to include new factors. In contrast, a simple model can be effective and easy to modify to take into account an unexpected situation. One way to generate synthetic data is to use real microarray values as seeds  [4] [5] [6] [7]. In  [4] , data were generated using a normal distribution and a real microarray dataset. This dataset was used to estimate some hyperparameters, and the percentage of the differentially expressed (DE) genes was fixed; see details in  [4].
The simulation data obtained in  [4]  may be useful for statistical methods, but they differ from observed data, since the log 2 -intensities obtained vary between the unrealistic bounds 16 and 30. In  [5] , two samples are selected from the control and the test samples. The measurements associated with their genes are modified (exchange of values between the two samples. ) in order to obtain two statistically undistinguishable samples. Finally, a given number of down-and up-regulated genes is used in this dataset. This procedure necessitates a real microarray dataset, which cannot be available. The procedure proposed in  [6]  is a modular system, including all steps of the microarray technology. This includes slide layout, hybridization, scanning and image processing. Many models already available in the literature are used with new ones in  [6].
These components allow one to define the DE genes and the overall level of variability in the data. However, it lacks flexibility in choosing the number of DE genes and the number of genes over-and under-regulated. In this paper, we are interested in generating synthetic microarray data associated with two biological conditions. These conditions correspond to comparison of wild-type samples versus knock-out ones, treated samples versus non-treated ones, etc. For these situations, we use the generic terms of control versus test samples. Two condition biological data can be obtained using either one-color or two-color microarray technology leading to log 2 intensities or log 2 ratios, respectively. We assume that the same reference sample was used for log 2 ratio data. This paper is organized as follows.
Methods We propose here a model that does not require knowledge of the physical laws governing gene expression to produce data with similar characteristics to the data commonly produced by current platforms. The user provides a subset of parameters that control the behavior of the data generated. We assume the following characteristics for microarray data: 1. There are usually more genes with low intensities than genes with high intensities. One possible explanation for this observation is a non-response of all genes to given biological conditions. 2. The log 2 -intensities of microarray data usually vary between zero and 20. Expression values are the results of 16-or 20-bit float per pixel images, representing probes on arrays. 3. Under similar biological conditions, the expression level of a gene varies around an average value.
Discussion The choice of some setting parameters for the proposed model is easy and can be dictated by the experimental design. This applies to n, m 1 , m 2 and ratio. Parameters lb and ub and sym and pde concern the dynamic range of variation of the data to generate and define the DE genes. The intervals indicated for lb and ub are those observed for data from common platforms. Parameters, shape 2 , lb and ub, have no effect if real microarray data are used as a seed. The number of DE genes (pde) and the proportion of under-and over-regulated genes (sym) is at the discretion of the user. The parameter, rseed, allows one to produce the same data at different times using the same computer. This parameter is also useful for generating test data for different analysis algorithms. Settings, λ 1 , λ 2 , µ min de , σ de and σ n , control the global behavior of the data generated.
The results presented show a good agreement across-platforms in contrast to some results previously reported in the literature, see, for instance, references cited in  [12]. The microarray data generated for the study described in  [12]  are available from the Gene Expression Omnibus  [13]  under accession number GSE2521. These data can be used as seed for our model, which can then be integrated into user friendly data analysis software, such as Partek Genomics Suite, GeneSpring GX, etc. , for demonstration and/or teaching purposes.
R Code For immediate use of the proposed model, we provide an R code function madsim. R (MicroArray Data Simulation Model), which is deposed as a package on the Comprehensible R Archive Network (CRAN) server for download  [14]. The outputs of this function are the data generated (xdata) and the indexes of DE genes (xid). Real data can be used as seed for each gene. An example of such data is available in the data folder of the package. Further explanations are available from the package's help function.
This is then followed by experiment methods and results which are split into two main parts: simulation related work and validation by process: In the first section, based on computational lithography/RET; an approach is investigated for best focus shift mitigation by correcting the wavefront in order to compensate for the mask 3D effects with Brion Tachyon software. This work consists of three steps: a) simulation of the focus shift b) mitigation by wavefront modification and c) feasibility by using rigorous lithographic simulation with Panoramic® software. In the second section, based on exposed wafer analysis, simulation data is compared with scanning electron microscopy (SEM) measurements on proceeded wafers using FlexWave.
THE FLEXWAVE MANIPULATOR A possible solution to handle some of the mask 3D effects can be the application of the FlexWave lens manipulator available in ASML scanners. The latest generation NXT: 1950i systems have the FlexWave optical element manipulator (see Figure  1 ). This advanced actuator allows the adjustment of the wavefront to compensate for process induced effects, like M3D effects, which can be responsible for focus and pattern shifts. This module is composed of two optical elements placed near the projection lens pupil plane. They consist of individually heated segments translating into a change in optical path length as a function of the position. It allows the adjustment of higher order aberration terms and tuning the phase of the wavefront. This phase wavefront change in the projection pupil can thus mitigate M3D effects.
SEM measurements analysis on hotspots Finally, in order to explore the impact of wavefront tuning by FlexWave on metal 28 nm hotspots, a set of hotspots structures is chosen from production full-chip verification using Tachyon Lithographic Manufacturing Check (LMC) simulations. Tachyon LMC is a RET verification software solution that can provide the most critical spots including bridging and necking defects. Note that these LMC simulations are not the same for Annular and C-Quad illumination sources, thus different hotspots are checked for the two different illumination sources. These experiments consist of bridging and necking defects with overall about ten hotspots for each case. At the chip level measurements are performed by SEM on the whole wafer. Focus shift analysis is addressed first to assess whether applying the correction map using FlexWave re-centers best focus.
Materials and methods From 2007 to 2010, epidemiological data on dog-related injuries and associated rabies PEP treatment were prospectively collected from patients attending the Marseille rabies treatment center (RTC) using standardized forms. At the Marseille RTC, Background: Dog bites are a serious public health concern. Besides injuries and the adverse psychological impacts, dog bites can be complicated by infections including rabies, which has the highest case-fatality rate of all infectious diseases. Methods: Dog-associated injuries occurring in the city of Marseille, France were investigated in 245 individuals among patients presenting to the rabies treatment center over a 4-year period. Results: Male patients were more likely to report dog bites compared to female patients (66. 5% vs.
Only patients who had been injured within the boundaries of the city of Marseille were selected. Demographic and clinical data and rabies prophylaxis details are routinely documented at the RTC. Additional data were obtained retrospectively by telephone, when possible, regarding the detailed circumstances of the dog attack: characteristics of the dog's owner, place of exposure within the city, environment, and reason for the bite. Interviews were all conducted by one person. The city of Marseille is located in the south of France and comprises 16 districts. Human demographic data (total population and distribution according to sex and age range) were extracted from the 2007 official census (http://www. insee. fr). At that time, the population totaled 852 396 inhabitants. Climatic data were obtained from the Wunderground database (http:// www.
wunderground. com). Place of exposure and place of residence were mapped using Quantum GIS 1. 6. 0 software (Open Source Geospatial Foundation). The spatial distribution of cases according to the season, the day of the week (weekend vs. working days), and the category of owner (street beggars or not) was investigated using SaTScan software. To detect clusters, this software systematically moves a circular scanning window of increasing diameter over the studied region and compares observed numbers inside the window to the numbers that would be expected under the null hypothesis (random distribution of the studied features). The maximum allowed cluster size corresponded to 50% of the study population. The statistical significance for each cluster was obtained through Monte Carlo hypothesis testing, i.
e. , results of the likelihood function were compared with 999 random replications of the dataset generated under the null hypothesis. Differences in proportions (categorical variables) were tested by Pearson tests using SPSS (SPSS, Inc. ) software package. A p-value of <0. 05 was considered significant. Odds ratios (ORs) were estimated by logistic regression. All statistical tests were twosided. Comparisons of percentages and ORs with 95% confidence intervals (95% CI) were carried out using R 2. 8. 1 environment (www. r-project. org).
2 (A  and B ). Experimental and interpolated statistical maps are represented in the top and bottom half of Fig. 2 C , respectively. If SNR6 shows a good agreement with interpolation, mild or strong discrepancies are observed, respectively, for tA(UGC), tL(UAA), or tP(UGG). Such data argue for a local effect of Pol III genes on chromosome structure, and differ from colocalization assays, in which Pol III-transcribed genes were organized in clusters close to CEN or NUC  (Thompson et al. , 2003; Duan et al. , 2010). We suggest that the distribution of genes is primarily influenced by their genomic position, and only locally by sequence-specific physical interactions (Fig. 2, A  and B ). Note that we checked that the FROS insertion did not affect chromatin structure by analyzing the inner structure of SNR6 using in situ indirect-end labeling (IEL; Fig.
Chromatin motion of chromosome XII We investigated the dynamics of chromosome XII and compared these data to predictions of polymer models. The motion of 10 loci (Fig. 1  A, asterisks) was studied by recording their trajectories in 2D rather than in 3D to increase the acquisition speed and reduce phototoxicity. Normal cell growth was observed during up to 2 h of acquisition (Video 3). A broad temporal range (from 0. 2 to 400 s) was probed using five distinct inter-frame intervals of 190 ms, 360 ms for all loci, and 1, 1. 5, and 10 s for three loci. Dedicated software was developed to perform systematic trajectory analysis (Fig. S2, A-C ; See Materials and methods). This software allowed us to extract the mean square displacement (MSD), which is the mean of the squared travel distances after a given time lag, for every locus, and to compare the dynamics in the case of central, peripheral, or nucleolar-localized loci (Fig.
(bottom) The gene maps are represented with the genomic position indicated on top (N represents the number of nuclei analyzed). Note that the position along the genome of the three rDNA loci has been determined by I-SceI cleavage. The data shown are from a single representative experiment out of three repeats. were treated with rapamycin, an inhibitor of the conserved protein kinase complex TORC1 (target of rapamycin;  Loewith et al. , 2002) , which induces a global modification of transcription, mimicking nutrient deprivation characterized by transcriptional repression of Pol II ribosomal protein genes, rRNA 5S genes transcribed by Pol III, and a repression of Pol I activity  (Hardwick et al. , 1999; Wullschleger et al. , 2006). After 20 min of rapamycin treatment, the mean nucleolar volume decreased by 37%, but the nucleus remained nearly constant in size (4% increase in volume), in agreement with previous studies  (Therizols et al.
, 2008; Mekhail and Moazed, 2010). This problem should be considered in future molecular dynamics simulations. To conclude, our systematic analysis of position and motion of loci along chromosome XII showed that its inner structure mainly consists of constrained chromosome arms anchored to nuclear elements (SPB, NUC, and NE), as expected from recent computational models  (Tjong et al. , 2012; Wong et al. , 2012). We also demonstrated that the Rouse polymer model accurately describes the motion of chromatin loci except for the rDNA  (Rosa and Everaers, 2008) , the biophysical properties of which remain to be investigated. We may finally speculate on whether the properties of chromosomes observed in yeast are relevant to metazoans. Our fit to the dataset with a power-law scaling of 0. 55. (B) The plot represents the anomaly parameter versus the genomic position, and shows the different dynamics in NUC for the 10 analyzed loci.
(C) Spatial fluctuations of the 10 analyzed loci are compared by measuring the amplitude of the power-law scaling response using a model with t 0. 5. (D) The gene territory, defined by the volume occupied by 50% of the gene population (the green isocontour in gene maps) expressed in cubic micrometers is measured as a function of the genomic position. Gene territory experimental errors were determined by measuring gene territories from three samplings of the full dataset.
Image analysis for locus position Confocal images were processed and analyzed with a MATLAB script nucloc, available at http://www. nucloc. org/ (MathWorks;  Berger et al. , 2008). The box plots of nuclear radius and nuclear-nucleolar center distances were generated using the boxplot function in MATLAB (Math-Works). The box plot of median distances from nuclear center to nucleolar center were computed in two steps, first MATLAB calculating the median distances for each 100 nuclei, and then by plotting the box plot of the median values obtained. Image analysis for gene trajectory Automatic particle tracking and spatial registration in time-lapse analysis was then performed with our custom-made software run in MATLAB (Supplemental data). Single particle-tracking software built on the MTT platform  (Sergé et al.