dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgment-The authors are all full-time employees of international pharmaceutical companies, working as environmental toxicologists, risk assessors, microbiologists, and engineers. Funding for the data presented herein was provided by the individual companies. The authors would like to acknowledge  Lisa Ziv  ( Merck & Co. , Inc. ) for collating all the company data,  Lisa Constantine  ( Pfizer ) for providing additional analysis, and  Allergan  for providing additional data. This manuscript is dedicated to the memory of  Frank Mastrocco, PhD. Frank  was a leader in advancing the science of pharmaceuticals in the environment while advocating for industry to align around common goals, promote science-based decision making, and implement solutions that drive real improvements to human health and the environment.
These clones were purified by repeated single colony isolation and repeatedly checked for their resistance to the corresponding phage. We also checked for absence of emerging phages able to grow on the original host strain to avoid pseudolysogenic or carrier-state cultures  26, 27. All the cultures obtained were then analyzed by rapid LPS SDS-PAGE profiling. The LPS profiles of all the phage resistant clones selected by DT57C phage were indistinguishable from the ones of the wild type (Fig. 2 ). It matched our expectations based on the data on the adsorption strategy used by this phage  17. We suggested that in this case the resistance was due to the modification of the phage secondary receptor BtuB protein. We sequenced btuB gene in 2 of our mutants and found that in both cases mutations that The only band present in the strains 4 sR, 4 sG and 4 sC (lines 4-6) corresponds to the lipid A moiety with or without core OS; the ladders observed in the O-antigen producing strains (lines 1-3) correspond to populations of LPS molecules that differ by the length of their OPS chain by one O-unit.
Strain ID, genotype and phenotype Ref. or accession number 4 s 4 sGTR 4 sI 4 sR 4 sG 4 sC wt gtrA-, gtrB-, gtrO22- wclK - wclH- waaG- waaC- O-acetylated and Non-glycosylated but Non-acetylated, but No OPS No OPS, no No OPS, no Phage Family/genus /note glycosylated OPS O-acetylated OPS glycosylated OPS synthesis outer core core (Kdo only) DT57C S/T5virus + + + + + + NC_027356. 1 17,23 DT571/2 S/T5virus - - + + + + KM979355. 1 17,23 BF23 S/T5virus - - + + + + AAZ03642. 1 46,47 MH203051. 1 Gostya9 S/T5virus - Inh. + + + + Golomidova et al. , 2018 -in press) phiKT P/NC/distantly related to T7 + + + - - - NC_019520. 1 48 G7C P/G7Cvirus/N4-related + + - - - - HQ259105. 1 25 Alt63 P/G7Cvirus/N4-related + + + - - - 25 Hf4s P/NC/P22-related + + Inh - - - NP 1. 2/Zh M/ND - - + + + + NP 3. 104/Zh9 P/NC/phiEco32-related - - + + + + NP 4c P/NC/phiEco32-related - - + + - - NP PEcoF P/NC/phiEco32-related + + Inh +/- - - NP LAMP P/NC/phiEco32-related + + + - - - MG673519.
Genes with similar expression profiles were clustered together with "Heatmap2" R script and the cluster delimited from the dendrogram by a black horizontal line. Each cluster of genes was analysed against the 86 genes with DAVID bioinformatics tools and significantly enriched pathways were identified. The summary of the enriched pathways is shown in the table (at the right of each cluster) with the associated significant P-value could reveal a contribution of hypothalamic lipid metabolism in the WSB/EiJ phenotype as Apoe plays a major role in lipid metabolism and transport from the astrocytes to the neurons  30. HFD is known to induce low-grade inflammatory responses (called meta-inflammation) in immune and metabolic cells from several tissues including the brain, and become chronic concomitant with the settlement of obesity  31, 32.
(B) Gene expression data for representative significantly differentially regulated genes involved in pathways defined in (A): Data were collected by microfluidic qRT-PCR from microdissected ARC of twelve week-old C57BL/6J (red) and WSB/EiJ (green) mice either maintained under control feeding (CTRL), or challenged with HFD for the last 3 days of the period (3D) or during the whole 8-week experiment duration (8WK). Irs1: Insulin receptor substrate 1; Mtor: mammalian target of rapamycin; Insr: insulin receptor; Foxo3: Forkhead box protein O3. Non parametric Two-way Anova analysis with permutations revealed a significant strain effect (independently of the diet) for Mtor and Irs1. Insr expression was influenced both by the strain and the diet, indicating a strain difference in the response to the diet, with no effect of the diet in WSB/EiJ, whereas HFD induced a high increase in Insr expression in C57BL/6J.
Post Hoc tests results are indicated on the graph. (*, P ≤ 0. 05; ***, P ≤ 0. 001. N = 5-7 per group), FC: relative fold-change expression compared to C57BL/6J control group. for 86 genes related to inflammatory and mitochondrial pathways: Data were collected by microfluidic qRT-PCR from PVN of twelve week-old C57BL/6J (left panel) and WSB/EiJ (right panel) mice either maintained under control feeding (CTRL), or challenged with HFD for the last 3 days of the period (3D) or during the whole 8-week experiment duration (8WK). Color transition represents gene expression levels expressed as scaled log2 Fold Change values (scaled Log2(FC)): green for low expression, yellow for mean expression and red for high expression. Genes with similar expression profiles were clustered together with "Heatmap2" R script and the cluster delimited from the dendrogram by a black horizontal line.
Data acquisition of multiplex analyses was performed at the Cochin Cytometry and Immunobiology Facility (France) using lectors Bioplex 200 (Biorad) and Sector Imager 2400 (Meso Scale Discovery). Lipid profile, hydroxybutyrate and total antioxidant status (TAS) were assayed on trunk serum using an Olympus AU-400 multiparametric analyzer at 'Plateforme de Biochimie' (INSERM U1149, France). Values for triglycerides, total cholesterol, high-density lipoprotein-cholesterol (HDL) and non-esterified fatty acids (NEFA) allowed the calculation of very low density lipoprotein-cholesterol (VLDL) and low density lipoprotein-cholesterol (LDL) following the Friedewald's equation  39.
Hypothalamic immunohistochemistry. Frozen brains were post-fixed overnight (O/N) in paraformaldehyde (PFA) 4% in PBS, pH 7. 4, immersed O/N in 30% sucrose in PBS and embedded in Frozen Section Compound (FSC 22R Clear LEICA Biosystems). Hypothalamic frozen coronal sections (30 µm) were processed using a cryostat (Leica CM 3050S cryostat, Leica Mannheim, Germany) and floating sections stored at -20 °C in the percentage of genes in the cluster involved in each pathway, and the names of the involved genes. (B) Gene expression data for representative significantly differentially regulated genes involved in pathways defined in (A): Data were collected by microfluidic qRT-PCR from microdissected PVN of twelve week-old C57BL/6J (red) and WSB/ EiJ (green) mice either maintained under control feeding (CTRL), or challenged with HFD for the last 3 days of the period (3D) or during the whole 8-week experiment duration (8WK).
Washed sections were incubated with the secondary anti-rabbit antibody Alexa Fluor 488 (1/500, Invitrogen) for 2 hours at RT. After washing, sections were incubated with the chicken primary antibody against GFAP (1/300, Abcam), a marker of glial cells including astrocytes and tanycytes, O/N at 4 °C, followed by the secondary anti-chicken antibody Alexa Fluor 594 (1/500 Invitrogen) for 2 hours at RT. DAPI staining was used as a nucleus marker. Sections were mounted onto SuperFrost/Plus glass slides with ProLong antifade (Invitrogen) and stored in darkness at 4 °C. Slides were observed under SP5 Leica confocal microscopy at ImagoSeine Facility (Paris, France). Two independent observers quantified microglial density and microglial surface area (µm 2 ) in the PVN and ARC regions of interest (ROI) using the Image J software.
Electron microscopy analysis. PVN regions were dissected under binocular and samples were prepared according to  40  , fixed overnight in 2. 5% glutaraldehyde solution in Sörensen buffer, and post-fixed in 1% osmium tetroxide. After serial ethanol dehydration, PVN samples were embedded in epoxy mixture (Spurr's resin). Ultrathin sections were contrasted with uranyl acetate and observed with a transmission electron microscope (Hitachi H-7100). Pictures were taken using a Hamamatsu CCD camera, focusing in two zones of the PVN: the tanycyte region lining the third ventricle and the parvocellular region. In both regions of interest, a series of 10-12 photos were analysed per animal using Image J software, and two animals were used for each strain/treatment. Two independent observers blindly counted the presence (number and size) of lipid droplets in the tanycyte region.
Thermal conditions for qPCR were: 25 °C for 30 min and 70 °C for 60 min for thermal mix; 50 °C for 2 min and 95 °C for 10 min for hot start; 40 cycles at 95 °C for 15 s and 60 °C for 1 min. Microfluidic qPCR data analysis. data analysis was done using Fluidigm Real-time PCR Software (version 4. 1. 3), with manual determination of the fluorescence threshold (CT), the quality threshold which was set by default (0. 65) and Linear (Derivative) as a baseline correction. The three most stable housekeeping genes in PVN and ARC were selected from 10 housekeeping genes chosen from the bibliography using SlqPCR package (version 1. 42. 0) based on Vandesompele et al. method  41. A custom R tool was constructed to measure relative gene expression levels according to the ddCT method (DCT, DDCT, Fold Change (FC))  42  and to perform statistical tests (two-way non-parametric ANOVA with permutation).
The following R packages were used: RVAideMemoire (version 0. 9-61), ezPerm (version 4. 4-0) and coin (version 1. 1-3). DCT values were used for two-way non-parametric ANOVA tests with permutations (1000 permutations for ANOVA, 10000 permutations for post-tests) with FDR correction (p < 0. 05). Graphical representations (as Tukey whiskers) were performed using FC values (fold-changes compared to C57BL/6J control group) on GraphPad Prism (version 6. 01). Outliers were removed using Dixon test from the Outliers package in R (version 0. 14).
Bioinformatic data analysis. A custom R tool was designed to perform Heatmap using R packages: gplots (version 3. 0. 1), R-color-Brewer (version 1. 1-2), and HEATMAP. 2 function. Fold Change values (FC) for mitochondria and inflammation genes were used and transformed to log2  43. Normalisation was applied for each gene (horizontally) starting from the median of the log2FC of each group of treatment using the "SCALE" function in R to rescale the log2FC in order to have a mean of zero and a standard deviation of one for each raw (i. e. each gene). A row dendrogram was then obtained (HEATMAP. 2 function), and gene clustered according to this dendrogram. The colour code was defined based on the normalized values around 0 (yellow color), going from -2 (full green; value of the row with the lower expression) to +2 (full red; value of the row with the higher expression).
Statistical analyses. Unless specifically mentioned, all data are presented as medians with ranges (GraphPad Prism 6 software). Unless for qPCR data analysis (see above), statistical-parametric tests were applied by using linear model (after data transformation when necessary), followed by post-hoc analysis (in case of normal distribution and homoscedasticity) with the R software 3. 1. 3. When data normalisation was not possible or when group numbers were small (for electron microscopy and immunohistochemistry analyses), non-parametric permutation tests were performed using the 'lmp' or 'aovp' functions (package 'LmPerm' 2. 1. 0). These analyses were run on all samples as a function of diet, strain, diet:strain and time (when appropriate), Data correction for body weight was applied when convenient according to Speakman et al.
Genes with similar expression profiles were clustered together with "Heatmap2" R script and the cluster delimited from the dendrogram by a black horizontal line. Each cluster of genes was analysed against the 86 genes with DAVID bioinformatics tools and significantly enriched pathways were identified. The summary of the pathways is shown in the table (at the right of each cluster) with the associated significant P-value (P ≤ 0. 1),.
Acknowledgements Authors wish to thank the valuable technical support from the  following French scientific services: Cytometry and Immunobiology Facility (Cybio ,  INSERM U1016 , Paris) and the  Plateforme de Biochimie (CRI ,  INSERM UMR1149 , Paris). We also acknowledge the  ImagoSeine Facility  (member of the  France BioImaging infrastructure  supported by the  French National Research Agency ,  ANR-10-INSB-04 , "Investments for the future"). The expert animal care provided by  S. Sosinski  and  F. Uridat  is gratefully acknowledged. This work was supported by  European Union contracts Nina  ( GA-238665 ),  Switchbox  ( GA-259772 ) and  Human  ( GA-602757 ). L. Chamas received a PhD Fellowship from  Ile de France Region. The authors declare there is no conflict of interest that would prejudice the impartiality of this scientific work.
Eq. (  10 )) and m V the vector leptoquark mass. The matching of the model parameters with the Wilson coefficients is performed at the scale of leptoquark mass and the Wilson coefficients are subsequently run down to the b-quark mass scale  [115]. In particular, it is interesting to note that due to RG-running a large K iτ L coupling can potentially induce a nonnegligible lepton-universal contribution to b → s transitions via a log-enhanced anapole photon penguin contribution, as noted in Ref. [127]. The dominant log-enhanced contribution is given by ∆C ij;univ. 9 ≈ - =e,µ,τ √ 2 G F V tb V * ts m 2 V 1 6 K i L K j * L log(m 2 b /m 2 V ). (16) In our analysis, the running from the scale of leptoquark mass to the b-quark mass scale, and to any other relevant process (observable) scale, is taken into account using the wilson package  [120]  in association with the flavio package  [121].
In our analysis, the running from the scale of leptoquark mass to the b-quark mass scale, and to any other relevant process (observable) scale, is taken into account using the wilson package  [120]  in association with the flavio package  [121]. A. 1 Exclusive d j → d i -+ decays The effective Hamiltonian for d j → d i -+ transitions, including the LFV operators, can be cast as  [157] [158] [159] [160] [161] [162] H eff = - 4G F √ 2 V 3j V * 3i k = 7, 9, 10, S, P C k (µ)O k (µ) + C k (µ)O k (µ) + C T (µ)O T (µ) + C T 5 (µ)O T 5 (µ). (21) In the above, we recall that V ij denotes the CKM matrix; the operators are defined as O ij 7 = em d j (4π) 2 ( di σ µν P R d j )F µν , O ij; 9 = e 2 (4π) 2 ( di γ µ P L d j )( ¯ γ µ ) , O ij; 10 = e 2 (4π) 2 ( di γ µ P L d j )( ¯ γ µ γ 5 ) , O ij; S = e 2 (4π) 2 ( di P R d j )( ¯ ) , O ij; P = e 2 (4π) 2 ( di P R d j )( ¯ γ 5 ) , O ij; T = e 2 (4π) 2 ( di σ µν d j )( ¯ σ µν ) , O ij; T 5 = e 2 (4π) 2 ( di σ µν d j )( ¯ σ µν γ 5 ) , (22) with the primed operators O 7,9,10,S,P following from the replacement P L ↔ P R.
dip angle and the lateral earth pressure coefficient have a considerable impact on the tunnel behavior. They concluded that in the case of horizontal layered joints, the failure mode is of ''rock beam'' type; for joints with dip angle between 30 degrees and 45 degrees, the failure mode is sliding of sidewall and detaching, flexing and breaking of the layered rock mass near the tunnel shoulder; for joints with a larger dip angle, the failure mode is sliding of the rock mass along the joints interface. In their study, the gravity of rock mass was however not considered. In addition, it is impossible to make a general recommendation of the effect of joints on the tunnel behavior due to the limited number of performed calculations. He et al. (2012)  adopted the distinct element method (UDEC software) to highlight the behavior of a tunnel under the effect of bedding planes.
Numerical model FEM was used in this study to model the influence of rock joint on the stability of tunnel and subsequent estimation of parameters which influence on the deformation and development of yielded zone induced in rock mass. FEM has been used previously by many researchers  (Fortsakis 2012; Małkowski 2015; Panthee et al. 2016). Firstly, monitoring data (displacements) induced in the surrounding rock mass due to the Tunnel N-6-8 excavation in the Duonghuy coal mine (Quangninh, Vietnam) was used to validate the numerical model. The tunnel shape is presented in Fig. 1. The tunnel dimensions are 4. 03 m in width and 3. 24 m in height. This tunnel was excavated along a coal seam (Fig. 2 ). This tunnel was used to exploit the coal and transport it to outside. The tunnel is located at the depth of 150 meters from the ground surface.
Properties of the rock mass and of the discontinues surrounding the Tunnel N-6-8 are described in Table  2  and Table 3. It should be mentioned that parameters JCS and JRC of joint are determined on the basis of properties of the weaker rock contact. They were determined through both in-site and laboratory tests. This tunnel was supported by steel ribs, their properties are given in Table  4. The steel ribs are installed right after each excavation cycle and at the maximum distance of 0. 7m from the tunnel face. Both 2D and 3D numerical models built using the RS2 and the RS3 softwares (Rocscience 2016), respectively, were used to make a comparison with monitored data obtained from the tunnel site (Fig. 2 ). In the 2D model, a relaxation process using the softening method was applied to take into account of the pre-displacements in rock mass surrounding the tunnel after excavation and before the steel rib installation  (Do et al.
Response to High N Provision In order to examine the role of HNI9 in the response to high N provision, we compared the transcriptomic data of WT and hni9-1 mutant plants under low nitrate (0. 3 mM KNO 3 ) and high N (10 mM NH 4 NO 3 ) provision  (Widiez et al. , 2011). These distinct conditions were selected for their large differences in N availability, leading to the identification of the hni9 phenotype  (Widiez et al. , 2011). As HNI9 is a positive regulator of gene expression, we selected genes that were induced by high N in WT, and looked for those that were not induced in the hni9-1 mutant line. Using these criteria, we obtained a list of 108 genes induced under high N in an HNI9-dependent manner (Supplemental Table  S1 ). In order to determine which biological functions are affected by the HNI9 mutation under high N provision, we performed a gene ontology analysis using this list of 108 genes.
Microscopy Confocal imaging of Arabidopsis root cells expressing GRX1-roGFP2 was performed using a Leica SP8 Confocal Microscope (Leica, Germany). GFP quantifications were done with an Axiovert 200 M microscope (Zeiss, Germany) and images were analyzed using ImageJ software. The data represent the fluorescence quantification values measured in the root tip.
ACKNOWLEDGMENTS We thank members of the lab for discussion. This work was supported by a grant from the  National Agency for Research  ( ANR14-CE19-0008 IMANA  to A. G. , An. M. , and G. K. ). F. B. was the recipient of a PhD fellowship from the  INRA BAP Department. We thank  Andreas Meyer  and  Alexandre Martinière  for the GRX1-roGFP2 line,  Fredy Barneche  for the hy5-215 line, and  Jean-François Briat  for the vtc2 line. We thank the Montpellier Rio Imaging platform for microscopy observations.
Study design and participants for prediction tool development We used data from the STAMP ('rapid urine-based screening for tuberculosis in HIV-positive patients admitted to hospital in Africa') trial for the clinical risk score derivation  [5, 21]. The STAMP trial recruited HIV-positive adults (aged 18 years or more), irrespective of symptoms or clinical presentation, who were admitted to medical wards of 2 hospitals in Malawi and South Africa between 26 October 2015 and 19 September 2017. On admission, patients were screened for TB using Xpert MTB/RIF (Xpert; Cepheid) on sputum in both study arms, and Xpert and Determine TB LAM Ag (TB-LAM; Alere) assays on urine in the intervention arm. Exclusion criteria in the trial were already taking TB treatment and inability to give consent. The clinical teams managing the patients were masked to which TB tests were positive; therefore, management of TB patients should not have differed between arms.
The management of HIV/TB in the study hospitals was representative of their local settings and followed local and national guidelines, with no input from the study team (beyond TB diagnostic tests). Patients diagnosed with TB in the standard-of-care arm had stored urine tested with Xpert and TB-LAM retrospectively. Data were collected at baseline (at or close to admission) on demographics and clinical characteristics, and subsequently on TB investigations and treatment, and clinical events, including death or discharge from hospital. Patients discharged alive were followed up at 2 months by outpatient attendance, home visit, or telephone for vital status. The derivation cohort included all patients (from both trial arms) with laboratory-confirmed TB. The outcome was mortality risk at 2 months after admission.
Analyses were planned prospectively (see S1 Appendix) except where indicated as post hoc. Continuous variables were assessed for non-linearity using fractional polynomials, and categorised based on previously established cutoffs (e. g. , CD4 cell count and haemoglobin) or associations with mortality (e. g. , age and weight, using the fp plot command in Stata). Complete case analysis was chosen for the derivation score as few data (<5%) were missing. We first performed univariable analyses assessing the association of each variable with mortality risk using logistic regression. We then used a backward elimination, stepwise approach to create a multivariable predictive model, starting with all candidate variables, and excluding variables sequentially if p > 0. 1 using likelihood ratio tests and the Akaike information criterion.
External validation To externally validate the clinical risk score, we used data collected independently from 2 studies: (1) a multicentre diagnostic clinical trial of adjunctive urine TB-LAM testing in HIV-positive patients with TB symptoms who were admitted to hospitals in 4 sub-Saharan African countries (South Africa, Zambia, Zimbabwe, and Tanzania) (LAM-RCT)  [31]  and (2) a prospective cohort study assessing the diagnostic yield of TB-LAM in HIV-positive patients with TB symptoms in Kenya (Medecins Sans Frontières [MSF] cohort study)  [32]. Patients were included in the validation cohort if they were adults and had laboratory-confirmed TB (as previously defined). Patients from the LAM-RCT in the 'no TB-LAM' arm were excluded, as were outpatients (i. e. , patients not admitted to hospital) from the MSF cohort study.
The validation cohort sites were all in settings in sub-Saharan Africa with high HIV prevalence and TB incidence, but differed from the derivation cohort in that all patients had at least 1 TB symptom (cough, fever, weight loss, or night sweats). The LAM-RCT recruitment occurred between 1 January 2013 and 2 October 2014, and the MSF cohort recruitment between 22 October 2013 and 20 August 2015. Mortality outcomes were assessed at 2 months in both studies. The clinical risk score for mortality was calculated by assigning the same 'points' to variables as for the derivation cohort, and the same cutoffs were used to define high-, medium-, and low-risk groups for mortality. Patients with missing observations were excluded, for a complete case analysis. However, sensitivity analyses were done for score performance using multivariate multiple imputation with chained equations for missing data as 42% of patients had missing data in the validation cohort.