dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and the  Worldwide LHC Computing Grid  (WLCG) collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the  ALICE detector: A. I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation (ANSL) ,  State Committee of Science and World Federation of Scientists  (WFS), Armenia;  Austrian Academy of Sciences and Nationalstiftung für Forschung ,  Technologie und Entwicklung, Austria; Ministry of Communications and High Technologies, National Nuclear Research Center, Azerbaijan ;  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Universidade Federal do Rio Grande do Sul (UFRGS) ,  Financiadora de Estudos e Projetos (Finep)  and  Fundacão de Amparo à Pesquisa do Estado de São Paulo (FAPESP), Brazil; Ministry of Science & Technology of China (MSTC) ,  National Natural Science Foundation of China (NSFC)  and  Ministry of Education of China (MOEC) , China; Ministry of Science and Education, Croatia ;  Centro de Aplicaciones Tecnológicas y s NN = 5.
The Environment for Visualizing Images (ENVI) and the Geographic Resources Analysis Support System (GRASS) software were used to process satellite images. The area's forest fire danger estimation and visual presentation of the results were carried out in ArcGIS Desktop software. Information on the vegetation was obtained using the analysis of the Landsat 8 Operational Land Imager (OLI) images for a typical forestry of the Lake Baikal natural area. The maps (schemes) of the Gilbirinskiy forestry were also used in the present article. The unsupervised k-means classification was used. Principal component analysis (PCA) was applied to increase the accuracy of decoding. The classification of forest areas according to the level of fire danger caused by the typical ignition source was carried out using the developed method.
Introduction Forest fires pose a global threat to natural systems and humans. Every year the number of forest fires in the world is growing. The problem of forest fire danger estimation in the Lake Baikal basin is of particular relevance  [1] [2] [3]. The anthropogenic impact on the forest is constantly increasing. On the other hand, the number of natural disasters is increasing. The forest fire danger classes of the territory were determined in the process of the basic forest management and are based on forest types, rock groups, age of forest stands, and proximity to local infrastructure (roads, settlements, and industrial objects). The use of satellite imagery data to determine or specify the probability of forest fires is one of the best possible solutions. Satellite remote sensing has become the main source of data for predicting fire risk, mapping of forest fuel, detecting and monitoring forest fires, and estimating the damage done to vegetation after a forest fire  [4] [5] [6] [7] [8] [9] [10].
In general, data on the greenness of vegetation, meteorological data, data on wetting the surface, and moisture content of forest fuel are necessary to forecast and to monitor forest fires  [28]. In addition, numerous studies showed the feasibility of geographic information system (GIS) and remote sensing data  [29, 30]. The GIS is a widely used tool for processing spatial data and displaying results. A model of a potential forest fire using satellite data and the GIS was developed in China to identify areas with a high probability of forest fire  [31]. In Sheriza et al. [32]  five fire danger classifications were identified in developed forest fuel maps and the degree of fire danger in peat-bog forests was assessed. The vegetation types in the study area were analyzed using digital classification systems, namely, two vegetation indices: the extended vegetation index (AVI) and the Tasseled Cap (TC) conversion.
The objectives of the research were (i) to process the satellite image of the Gilbirinskiy forestry located in the basin of Lake Baikal; (ii) to select homogeneous areas of forest vegetation on the basis of their spectral characteristics; (iii) to estimate the level of forest fire danger of the area by vegetation types. The paper presents an approach for estimation of forest fire danger depending on vegetation type and radiant heat flux influence using GIS and remote sensing data. The ENVI and the GRASS software were used to process the satellite images. The area's forest fire danger estimation and visual presentation of the results was carried out in the ArcGIS Desktop software. The final information product was the map displaying vector polygonal feature class, containing the type of vegetation and the level of fire danger for each forest quarter in the attribute table.
Image Processing Technique. The radiometric calibration was the first important step in Landsat 8 data processing. The Landsat 8 data set consists of normalized values called a digital number (DN) that represents multispectral image data. These numbers have no physical meaning. They may also be completely incompatible in each independent data set and even in different bands of the same set. DN values for digital processing of Landsat 8 image are usually converted into one of two physical parameters, reflectance or spectral radiance  [38]. It was chosen to convert DN pixel values into Top of Atmosphere (TOA) reflectance for data processing. These adjustments were made based on the coefficients presented in the metadata file (MTL. txt) that comes with the image set  [39]. The conversion of one unit to another was carried out using the radiometric calibration tool i.
The parameters for the model were received from the metafile supplied with the raster image. Peculiarities of each image shooting were taken into account. Popular models for the atmospheric correction are the Fast Line of Spectral Hypercubes (FLAASH), implemented in the ENVI software and Second Simulation of a Satellite Signal in the Solar Spectrum (6S) (Figure  2 ). This model is available in the GRASS software  [40]. Model 6S is located on the free access online platform that allows designing the model and downloading it as a file  [48]. The atmospheric correction was performed with the i. atcorr tool available in the GRASS software  [49]. The tool input data consisted of the configuration file of a specific band and the image of this band. Each spectral band was processed separately. The central coordinates of the image were latitude -51 ∘ 41  34.
94  N, longitude -107 ∘ 05  35. 41  E. The exposure time as well as the location was indicated on the source site  [50]  and in the metadata. The time was given on the source site as a set of numbers "2015: 233: 03: 45: 26. 9418490" in the format: "year: day in year: hours: minutes: decimal seconds", while the time alone was given in the SCENE CENTER TIME column of the metadata file. Since the image shooting was performed in August, the "atmospheric model" was chosen as the parameter: average latitudes (summer). The aerosol model depends on the latitude of the study area. In this case the continental model was used. The average height above sea level was obtained on the basis of the Shuttle Radar Topography Mission (SRTM) file  [51]  for the study area using the r. univar module of the GRASS software.
A total of five categories were used to determine the level of forest fire danger, from emergency to low. Elimination of forest areas with low fire danger, assessment of probability of the forest fires caused by the radiant heat flux and determination of the forest fire danger level were implemented in the ArcGIS Desktop software  [58]  using the forest taxation data and the vegetation type. The data on the composition of vegetation are either terrestrial taxation descriptions or the results of a satellite image processing. Operations were performed using the built model, in which the geoprocessing tools were connected to each other in sequence. The script was written in Python on the basis of standard processing tools  [59, 60]. The algorithm is shown in Figure  5. The result of all operations was a thematic map showing the fire danger levels of forest quarters.
The areas occupied by various land categories were defined for the territory of the forest stand. The classification of forest areas according to the level of fire danger caused by thermal radiation was carried out (Figure  15 ). The level of fire danger was determined by the calculated value of the probability of a fire caused by the action of the radiant flux. A total of five categories were used to determine the level of forest fire danger: extreme, high, mean, moderate, and low levels  [16]. The possibilities to estimate fire danger from the data on the vegetation and land use were demonstrated using the satellite image. The method was applied to the Gilbirinskiy forestry of the Republic of Buryatia. The paper discusses the method of pretreatment and correction of Landsat 8 satellite images, which can be applied almost without changes to images of the area obtained at another time.
Based on the corrected data, classes were identified and compared with the corresponding type of territory by spectral responses. Fire danger estimation was carried out according to the vectorised classified data. The fire danger estimation approach developed by the authors was applied to each separate quarter and showed realistic results. The method used may be applicable for other areas in which forest vegetation prevails. The obtained land use maps and the fire danger map can serve to prevent fires and their consequences in the territory of the Gilbirinskiy forestry. Decision making based on map data is a tool used in various industries. Given the characteristics and speed of change of forest areas, it can be noted that the composed maps will be relevant for a long period of time. This is due to the low rate of change in the area of forest plantations: the growth of coniferous trees and cutting down in a specially protected natural zone.
In addition, situation is possible when concentrated solar radiation focused by glass containers or their fragments is affected to a forest fuel layer  [62]. In general, the types of vegetation in the Gilbirinskiy forestry were obtained by the computerized classification and the satellite image interpretation using the ENVI, GRASS, and ArcGIS software. The proposed approach allows estimating the localization and composition of vegetation, as well as identifying the areas with highest fire danger. Thus, for the marked areas with a high level of risk, it is recommended to conduct further detailed studies, which should include study of the territory on the ground and creation of reference zones. It is also necessary to develop recommendations for reforestation and minimizing forest fire risks.
This permitted the retrieval of the optimal values of the elastic modulus and Poisson ratio that were recovered by the second model. Precisely, the computations were done in three dimensions (3D) using an elastodynamic finite-element method (FEM)  [25, 26]  implemented in commercial software Abaqus (version 6. 14, Dassault Systèmes Simulia Corporation, Providence, RI, USA)  [27]. This constituted the first interaction model.
For titanium, density functional theory using the projector augmented-wave (PAW) method in Quantum Espresso associated with the Exciting  [30, 31]  package was employed. The mechanical parameters were then injected into the 3D finite-element model to calculate the eigenfrequencies and plot mode shapes/deformation. The identification of mode types was also theoretically confirmed. A 3D elastodynamic finite-element direct-solution steady-state dynamic analysis was used to calculate the steady-state dynamic linearized response of the disc submitted to a concentrated harmonic-force excitation  [32]. The concentrated force was applied at the center of the disc to only excite longitudinal modes as confirmed in the computed response  [33]. In a second step, force was applied to the edge of the disc to excite all modes.
The utilization of different carbon sources and sensitivity to different compounds (5 mM H 2 O 2 , 1. 2 M KCl, 0. 05% SDS, 5 mM caffeine) was monitored in liquid YPG at 30 • C by spectrophotometry (Tecan Infinity 200 Pro Serie). Five ml of YPG inoculated with stationary phase cells were cultivated to an optical density at 600 (OD 600 ) = 0. 6. Ten µl of culture were used to inoculate 180 µl of YP medium containing different carbon sources or the required additives distributed in the wells of a plate. Controls lacking carbon source or specific compounds were performed. Plates were sealed with gas-permeable plastic film. OD 600 was measured every 30 min during 48 h, with shaking at 380 rpm. Growth data were based on three independent experiments, each of which consisted of assays performed in triplicate.
GFP Detection by Microscopy Yeast strain expressing the CaHxk2::GFP and CaGlk1::GFP fusion proteins were grown to exponential phase (OD 600 ≈ 0. 8) in YP containing 0. 05, 0. 1, or 2% glucose or 2% lactate. Nuclei were stained by addition of DAPI to 10 µg/ml to the cultures and incubated at 28 • C, 180 rpm for 60 min. Cells were washed twice with phosphate buffer saline (PBS) (10 mM Na 2 HPO 4 , 1. 76 mM KH 2 PO 4 , 137 mM NaCl, and 2. 7 mM KCl), collected by centrifugation and resuspended in 20 µl of PBS. GFP and DAPI localization were monitored in live cells cultures using a Zeiss Axioskop 2 Plus fluorescence microscope. Images were taken with a Zeiss AxioCam MR camera using AxioVision software and processed using LiveQuartz Images Editor.
Statistical Analysis All experiments were performed at least three times independently. All statistical data were calculated with GraphPad Prism 7. 0 software. For comparisons of multiple groups one-way ANOVA method was used. Significance of mean comparison is annotated as follow: ns, not significant; * P = 0. 033; * * P = 0. 002; * * * P = 0. 0002; * * * * P < 0. 0001.
Deciphering the Hexose Kinase Activity in C. albicans One hypothetical hexokinase (CaHXK2, GenBank XM_712312) and two hypothetical glucokinase genes (CaGlK1, GenBank XM_705084 and CaGLK4, GenBank XM_707231) were found in the genomic sequence of C. albicans 3. Analysis of the C. albicans HXK2, GLK1 and GLK4 sequences revealed the presence of a classical hexose kinase conserved domain organized in two regions 4  : a small and a large subdomain. The small subdomain contains the sugar-binding site of typical hexose kinases: -LGFTFSF/YP-  (Bernardo et al. , 2007). Protein function prediction 5  revealed that CaHxk2, CaGlk1 and CaGlk4 may be involved in phosphate-containing compound metabolic processes, transferase activity and ATP-binding. The localization of these conserved domains is provided in Supplementary Table  S3   (Bork et al.
The apparent K m of Glk1 was estimated in the Cahxk2 / by subtracting the effect of CaGlk4. Data revealed that hexokinase 2 and glucokinase 1 have much lower K m values (K m 104. 87 ± 7. 05 µM and K m 84. 86 ± 6. 23 µM, respectively) than glucokinase 4 (K m 3900 ± 400 µM), using glucose as a substrate. The low glucose affinity of CaGlk4 could partially explain the poor contribution of this protein to glucose and mannose phosphorylation. (Ca22chrRA_C_albicans_SC5314:994, 376. 997, 830). Glucokinase genes are oriented in opposite directions and bordered by highly homologous regions (light green). (B) Hexose phosphorylation rates in C. albicans wild type and hexose kinase mutant cell extracts. For each strain, the amount of glucose-6-phosphate produced was measured and expressed as a percentage of the wild type strain.
1%, or 0. 05% glucose or 2% lactate as carbon source. GFP and DAPI localization was monitored in live cell cultures using a Zeiss Axioskop 2 Plus fluorescence microscope. Images were taken with a Zeiss AxioCam MR camera using AxioVision software. (B) Expression of glucose permeases, controlled by the Sugar Receptor Repressor pathway (CaHGT12, CaHXT10, and CaHGT7) or not (CaHGT8) was measured by qPCR in each strain. Cells were cultivated in the presence of 2% lactate and then transferred for 1 h in 2% glucose before RNA was extracted. Expression levels were normalized to the expression of CaACT1. For each permease, the mRNA levels were expressed relatively to their abundance in the wild type strain, which was set at 1. Results represent a mean (+ standard deviation) of three independent experiments performed on three different biological samples, in duplicate; (n = 6), * * P = 0.
To eventually highlight a specific role in pathogenic behavior for C. albicans hexose kinases, we compared hexokinase and glucokinase gene expression levels during the early steps of the morphological switch. Our data did not reveal any particular transcriptional response of one gene or another (Figure  6B ). Both (A) Growth of the wild type and hexose kinase mutant strains exposed to various stresses in YPG was expressed as a percentage of the growth of each strain in absence of stress which was set to 100% (black line). Data correspond to the OD values reached after 24 h of growth in microplates. (B) Growth of the wild type and hexose kinase mutant strains under hypoxic conditions in 2% glucose YPG was expressed as a percentage of growth in normoxia which was set to 100% (black line). (C) Relative expression of CaHXK2 or CaGLK1/4 measured in normoxia (N) or hypoxia (H) during growth in YPG 2% glucose.
The Cahxk2 / c/c complemented strain revealed a partially restored virulence. These data suggest that strains lacking the hexokinase or both glucokinases show an affected virulence in the G. mellonella model. Secondly, we analyzed the ability of the mutant to kill macrophages at different interaction times using an in vitro model assay (Figure  7B ). Flow cytometer analysis showed that hexokinase and glucokinase gene deletions did not modify macrophages association with yeast for any strains, except for the Cahxk2glk1 / double mutant which shows a slightly decreased number of recruited macrophages at the early time of infection Photographs present in the lower panel were obtained using Zeiss Axioskop 2 Plus microscope with dark field and show the microscopic aspect. (B) Relative expression of CaHXK2 and CaGLK1/4 in the wild type strain during filamentation after transfer from 0.
ACKNOWLEDGMENTS We are grateful to  Jade Ravent  for technical assistance. We thank  J. Morschhäuser  and  S. Bates  for providing the pSFS2A and pGFP-NAT1 plasmids, respectively. The RT-qPCR experiments were performed thanks to the  DTAMB (Développement de Techniques et Analyse Moléculaire de la Biodiversité, Université Lyon 1 ). RL was the recipient of a fellowship from the  Ministère de la Recherche of France. This work was released as a preprint at BioRxiv, https://doi. org/10. 1101/448373 ,(Laurian et al. , 2018).
Alteration of cornification in RHEs treated with Cl-amidine The consequence of 800 mM Cl-amidine treatment was first analyzed using hematoxylin and eosin staining of RHEs (Figure  2a ). Control and treated RHEs (n ¼ 5) presented a similar morphology. Cl-amidine did not significantly decrease the cell viability (Figure  2b ) and did not induce caspase-3 activation (data not shown). Moreover, the inhibitor did not affect keratinocyte proliferation, as evaluated using Ki67 immunodetection (21. 13 AE 8. 05 immunolabeled cells per RHE length in mm vs. 16. 92 AE 6. 25 in controls; n ¼ 3; P ¼ 0. 0719). To study the effect of Cl-amidine on RHEs at the ultrastructural level, transmission electron microscopy analysis was performed. In RHEs treated with Cl-amidine, we observed frequent unusual cells located between the granular and the cornified layers (Figures  2c, 2d ).
ACKNOWLEDGMENTS We are indebted to  Pascal Descargues  ( Genoskin, Toulouse, France ) for the human skin samples,  Yves Poumay  ( University of Namur, Belgium ), and  Valerie Pendaries-Rahoul  ( UniteDifferenciation Epitheliale et Autoimmunite Ŕhumatoı ¨de ) for their useful advice on RHE production, and to all members of the electron microscopy ( Toulouse University ), histopathology, and cellular imaging ( Toulouse Rio Imagerie ,  Institut National de la SanteEt de la Recherche Medicale U1043 ) facilities. We thank  Candide Alioli ,  Carole Pons , and  Geraldine Gasc  for their helpful assistance. This work was supported in part by  National Institutes of Health  grant  R35 GM118112  (PRT) and by grants from  Centre National de la Recherche Scientifique, Toulouse University, and Institut National de la Santeet de la Recherche Medicale.
Transcriptional profiling of pituitary mRNA expression In order to investigate differences in expression which could be contributing to altered pituitary hormone production and, ultimately, divergent growth and body composition, global gene expression profiling using the Del-Mar 14 K Chicken Integrated Systems Microarray was conducted in anterior pituitary glands of HG and LG broilers at weeks 1, 3, 5, and 7 post-hatch. A summary of the microarray data is given in Table  2. The array contains 19,200 spots representing 14,053 unique genes, over half of the known chicken genome. There were a total of 2312 cDNA probes that were significantly different across all  DEGs were organized into gene ontology (GO) categories in order to identify cellular biological processes and molecular functions that could contribute to differences in growth and body composition between the genetically divergent lines (Fig.
Identification of gene interaction networks To identify important pathways and gene networks associated with differences in pituitary mRNA expression between birds with different genetic growth potential, the functionally annotated gene list was submitted to Ingenuity Pathway Analysis (IPA) software for further functional annotation and analysis. Of the initial 260 DEGs, 249 had identifiers that mapped to the IPA annotated database and were eligible for incorporation into gene interaction networks and use in biological function identification. The top five gene interaction networks identified by IPA are listed in Table  3. The top network was associated with "Molecular Transport" (Fig. 6 ), and differential expression patterns of several genes within each network were confirmed by RT-qPCR (Fig.
Also within this network and the dataset as a whole are genes for several calcium binding proteins involved in intracellular vesicular trafficking and protein synthesis quality control [annexin A2 (ANXA2), copine 1 (CPNE1), calreticulin (CALR), and heat shock protein family A, member 5 (HSPA5)], as well as membrane trafficking [ankyrin repeat domain-containing protein 27 (ANKRD27) and microtubule associated protein RP/EB family member 2 (MAPRE2). Three of the pituitary hormones (GH, LH, and FSH) in our dataset were also included, and differences in their expression levels between lines either switches or increases in intensity over time. This indicates that alterations in cellular signaling, membrane trafficking components, and vesicular secretion may contribute to differences in hormone expression and secretion between HG and LG birds.