dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison with other chemical profiles of PM 10 sources from a regional study It is interesting to evaluate whether a PMF study conducted at a city scale is leading to more similar source chemical profiles than a study using a database from a much larger area. Another question is whether PMF can produce more similar chemical source profiles with the help of additional organic tracers than a "classic" PMF run. Hence, the results obtained here are compared to those in the SOURCES program  (Weber et al. , 2019)  for the nine factors common in both studies (the secondary biogenic factor was not identified in the SOURCES program with the data sets not including its proper chemical tracers). This can be represented with the projection on a similarity plot of the distances between the factors for the three pairs of sites over the Grenoble basin, both for the "classic" and "orga" PMF.
Acknowledgements. The authors would like to kindly thank the dedicated efforts of many people from  Atmo-AuRA  at the sampling sites and in the lab at  IGE  ( Anthony Vella ,  Claire Vérin ,  Céline Voiron ) for collecting and analyzing the samples, respectively. Financial support. This work is supported by the  French National Research Agency  in the framework of the " Investissements d'avenir"  ( ANR-15-IDEX-02 ), for the  Mobil'Air program. It also received support from the program QAMECS funded by  ADEME  (convention  1662C0029 ) and from  LCSQA  and  French Ministry of Environment  for part of the analyses for the Les Frenes site within the  CARA program. Chemical analysis of the Air-O-Sol facility at IGE was made possible with the funding of some of the equipment by the  Labex OSUG@2020  ( ANR10 LABX56 ).
For neuroserpin-tPA colocalization study, pregnant C57BL/6J mice were in utero electroporated at E14 with pPlat-GFP and pCAG-RFP plasmids at the animal facility of the University of Caen, France, as previously published  (Pasquet et al. , 2019). The pCAG-RFP plasmid was purchased from Addgene. The pPlat-GFP plasmid (1. 2-kb sequence of the human Plat promoter, spanning from -1035 to +207 base pairs from the transcription start codon) was purchased from GeneCopoeia (pPlat-GFP; Catalog No. : HPRM12655-PF02). Both constructs were amplified in Escherichia coli JM109 cells and purified by a Nucleobond endotoxin-free plasmid DNA PC 2000 kit (Macherey-Nagel) according to the manufacturer's instructions. Mouse protocols were performed in accordance with the French (Decree 87/848) and the European Communities Council (Directive 86/609) guidelines.
Mass Spectrometric Measurements and Data Analysis The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (https:// www. ebi. ac. uk/pride/) partner repository with the dataset identifier PXD022371. Prior to mass spectrometric analyses, peptides were resuspended in 0. 1% FA to a final concentration of 1 µg/µl. LC-MS/MS measurements were performed on a quadrupoleiontrap-orbitrap mass spectrometer (Orbitrap Fusion, Thermo Fisher) coupled to a nano-UPLC (Dionex Ultimate 3000 UPLC system, Thermo Fisher). One micro gram of tryptic peptides were injected into the chromatographic system via an autosampler, purified and desalted using a reversed phase trapping column (Acclaim PepMap 100 C18 trap; 100 µm × 2 cm, 100 Å pore size, 5 µm particle size; Thermo Fisher) and transferred to a reversed phase column for chromatographic separation (Acclaim PepMap 100 C18; 75 µm × 50 cm, 100 Å pore size, 2 µm particle size, Thermo Fisher).
Trapping was done for 5 min at a flow rate of 15 µl/min with 99% solvent A (0. 1% FA) and 1% solvent B (0. 1% FA in ACN). Separation and elution of peptides were achieved by a linear gradient from 1 to 30% solvent B in 70 min at a flow rate of 3 µl/min. Eluted peptides were ionized using a nano-electrospray ionization source (nano-ESI) with a spray voltage of 1,800, transferred into the mass spectrometer and analyzed in data dependent acquisition (DDA) mode. For each MS1 scan, ions were accumulated for a maximum of 120 ms or until a charge density of 2 × 105 ions (AGC Target) was reached. Fourier-transformation-based mass analysis of the data from the orbitrap mass analyser was performed covering a mass range of 400-1,300 m/z with a resolution of 120,000 at m/z = 200. Peptides with charge states between 2+ and 5+ above an intensity threshold of 1,000 were isolated within a 1.
6 m/z isolation window in Top speed mode for 3 s from each precursor scan and fragmented with a normalized collision energy of 30% using higher energy collisional dissociation (HCD). MS2 scanning was performed using an orbitrap mass analyser, covering a mass range of 380-1,500 m/z with a orbitrap resolution of 15'000 at m/z = 200 and accumulated for 60 ms or to an AGC target of 1 × 105. Dynamic exclusion of fragmented peptides was applied for 15 s after precursor selection. Raw data obtained from LC-MS/MS measurements were processed with MaxQuant version 1. 6. 2. 10 (Max Plank Institute for Biochemistry, Version 1. 6. 2. 10) using the integrated Andromeda algorithm. For protein identification, measured MS2 spectra were searched against theoretical fragment-spectra of tryptic peptides, generated from a reviewed murine Swissprot FASTA database obtained in February 2020, containing 17,015 entries.
05% Tween-20 and Roti R -ImmunoBlock. Secondary antibodies conjugated with IRDye R 800CW or IRDye R 680RD (1:10'000) (LI-COR Biosciences) were incubated in the same buffer for 1 h at RT. Membranes were scanned using an Odyssey R Infrared Imaging System (LI-COR Biosciences). Densitometric quantification was performed with LI-COR R Odyssey Software, version 2. 0 (LI-COR Biosciences) and local background subtraction. Band intensity was normalized to betaactin expression, and values for the wt group were arbitrarily set to 1.
Statistical Analysis For quantification of Reelin, ADAMTS-4, PAI-1, and aggrecan expression and cleavage, soma size and dendrite length, statistical comparison among groups was determined using two-tailed Student's t-test (wt vs. Ns-/-). For statistical analysis of quantified cortical immmunofluorescence, signal detection fractions in matching bins were compared between control and mutant mice using two-tailed Student's t-test. p values were adjusted for multiple comparisons using the Holm-Sidak-method. Means +/-SD are reported, statistical significance was set at * p ≤ 0. 05, * * p ≤ 0. 01 and * * * p ≤ 0. 001. The following statistical softwares were used: GraphPad Prism version 5. 0 (GraphPad Software) and Excel (Microsoft-Office). For mass spectrometric analysis, the ProteinGroups. txt result file from MaxQuant was loaded into Perseus software (Max Plank Institute for Biochemistry, Version 1.
ACKNOWLEDGMENTS The authors thank  Antonio Virgilio Failla  and  Bernd Zobiak  of the  UKE microscopy imaging facility, Hamburg, Germany ;  Kristin Hartmann  of the  UKE mouse pathology facility, Hamburg, Germany ;  Chudamani Raithore  of the ZMNH morphology and electron microscopy facility, Hamburg, Germany; and  Yannick Hommet  and  Laurent Lebouvier  of the  Department of Physiopathology and Imaging of Neurological Disorders, Normandie Université, Caen, France  for technical assistance. A special thanks to  Renato Frischknecht ,  Department of Biology and Animal Physiology, Friedrich Alexander University Erlangen-Nürnberg, Germany , for his suggestions during the elaboration of this work.
B. Codec Structure, Atlases, and Patches Fig. 2  shows a high-level block diagram of an encoder for the MIV. A key function of an MIV encoder is to form one or more attribute and geometry atlases and generate metadata to describe the atlases, by compositing patches extracted from the input views. The attribute and geometry atlases are encoded as a video with a video encoder, while the metadata is encoded following the MIV standard, using the TMIV reference software encoder. As with the V3C standard, the MIV can be used with any video coding standard. HEVC has been used during the development process, because it has been widely deployed in products, and the HEVC HM reference software  [17]  is used in the CTCs  [24]. V3C explicitly defines codec profiles, which enables support for HEVC, AVC, and VVC, and provides a mechanism to indicate other codecs.
V. A L I G N M E N T W I T H V 3 C / V -P C C S P E C I F I C A T I O N A. Alignment With V-PCC Specification The MIV bitstream syntax is an extension to the V3C bitstream format  [3]  that is in common with V-PCC. At the highest level, there is a V3C unit stream where each V3C unit is composed of an integer number of bytes. The standard specifies the V3C sample stream to concatenate V3C units into a single bitstream. Every V3C unit is composed of a header and a payload, where the first element of the header is the unit type. The current text of the standard specifies five unit types, namely the V3C parameter set (VPS), AD, GVD, attribute video data (AVD), and occupancy video data (OVD). The VPS is an essential unit which signals the start of a new sequence and provides information that allows the decoder to setup, such as the atlas count, frame sizes, presence of unit types, map count (i.
e. , number of geometry layers), attribute count and definition, and so on. The VPS can either be provided in-band or out-of-band. For other V3C units, the header links the unit to VPS and atlas by ID. In addition, GVD and AVD link to a map, and AVD also links to an attribute (e. g. , texture) and attribute partition (i. e. , set of video planes) by index. The payload of GVD, AVD, and OVD units is a video sub-bitstream. The AD unit contains an AD sub-bitstream that is structured as a network abstraction layer (NAL) unit stream, which is a generic format suitable for use in both packet-oriented and bitstream-oriented systems. Every NAL unit is either an atlas coding layer (ACL) NAL containing patch data or a non-ACL NAL. The atlas sequence parameter set (ASPS), atlas frame parameter set (AFPS), and atlas adaptation parameter set (AAPS) are non-ACL NAL units that carry infrequently changing information of the coded atlases in the V3C bitstream.
For example, there are specular and translucent objects in Kitchen, heavy noise in ClassroomVideo, fast movement in Fencing, and imperfect depth maps for natural sequences. 2) Coding Setup: A challenging subset of 97 frames is selected for each test sequence, corresponding to three Group of Pictures plus one I-Frame at the end. The content is encoded using HEVC reference software HM16. 16  [17]. In Section II-B, two operating modes of the standard are compared: MIV Atlas and MIV View. While in the MIV Atlas mode, views are pruned as described in Section II-B, in the MIV View mode a subset of views has been manually selected for each sequence, and the atlases are made of basic views only (no pruning is applied), with one view per atlas. Five rate points are considered, corresponding to a set of QPs for the attributes QP = {22, 27, 32, 37, 42} and a set of QPs for the geometry QP d = {4, 7, 11, 15, 20} for the MIV Atlas mode, and QP d = {9, 9, 14, 17, 21} for the MIV View mode.
Reports of any study design and conducted in any country, at any date, reported in English, were eligible for inclusion in the review, except for doctoral dissertations. The literature search was carried out using online databases (PubMed, PsycInfo and Web of Science). The following filters were applied: humans, adults, articles and English. In order to check the adequacy of the search terms, forward and backward searching were performed. The search terms used for PubMed were MeSH Terms arranged according to the following algorithm: (alcohol craving OR craving for alcohol OR alcohol urge OR alcohol cues OR alcohol stimuli) AND (inhibition psychology OR impulsivity OR impulsive behaviour OR thought suppression). This was used for each database. A pilot test from a sample including 30% of the articles was performed in order to verify the reliability of the interpretation of the selection criteria and to ensure that the application of the selection criteria was carried out systematically and consistently by all evaluators.
Characteristics of included studies Of all documents found, 1379 were imported to an online reference manager, Mendeley. After checking for duplicates, 1016 articles were retrieved. Then, relevant articles were selected in three stages. Preliminary scrutiny of the titles was undertaken to remove articles irrelevant to the review. Next, articles deemed irrelevant by abstract content were discarded and full texts of the remaining potentially relevant articles were obtained. Data from included studies were extracted and summarized in Table  1. Forty-two articles deemed relevant by titles and abstracts were identified for further consideration and full texts of these articles were obtained. Ultimately, 13 articles that conformed to the eligibility criteria were included in this review (Figure  2  and Table  2 ).
Finally, in this study HRV responsivity was also measured and data showed that high trait thought suppression was associated with lower HRV compared to state suppression of thoughts. HRV was considered as reflecting executive functions and could explain a rebound effect of chronic suppressed thoughts involved in craving  (Garland et al. , 2012). The two studies reporting an association between the inhibition process and craving used an alcohol exposure paradigm via photographs or alcoholic beverages  (Garland et al. , 2012; Ingjaldsson et al. , 2003) , one used a control group  (Ingjaldsson et al. , 2003). Of the three studies reporting evidence of an association between thought suppression and craving  (Garland et al. , 2012; Ingjaldsson et al. , 2003; Kavanagh et al. , 2009) , one included participants who were not currently inpatients in residential treatment for alcohol use disorder but in correspondence-based treatment trials for alcohol abuse or dependence  (Kavanagh et al.
Chemical composition and heat treatments of the (0. 9 C -13. 95 Mn) steel The as-received steel contains essentially 0. 9 % carbon (weight percentage) and 13. 95 % manganese. The chemical elements composition constituting the (0. 9 C -13. 95 Mn) steel is given in Table  1. The phase diagram of the (0. 9 C -13. 95 Mn) steel was calculated by ProCast® software. Figure  1  shows the fraction of liquid, austenite and M7C3 carbides for a range temperature from 600 to 1400 °C. From phase fractions versus temperature diagram, the high-manganese steel is biphasic composed of the austenite and the M7C3 carbides at temperatures 700, 750, 800 and 850 °C. First, the (0. 9 C -13. 95 Mn) steel is aged at four different temperatures 700, 750, 800 and 850 °C for different times. Then, the samples (15 * 15 * 10 mm 3 ) after each heat treatment are water quenched.
X-ray diffraction X-ray diffraction spectra of the (0. 9 C -13. 95 Mn) steel samples are obtained using a BRUKER D8 Advance diffractometer of BRAGG-BRENTANO geometry. Monochromatic radiations are produced from copper tubes (Kα=1. 5406 Å) operating at 40 kV and 40 mA. The scan interval (2θ) is between 10 and 100 ° with an angular step of 0. 06 ° per second. The International Center for Diffraction Data (ICDD) cards used to identify the peaks are n° (31-0619) for the austenite and n° (5-720) for the M7C3 carbides.
9 C -13. 95 Mn) steel. Significant interactions between the M7C3 carbides are observed for 60 minutes of aging (Figure  3(d) ). The softening that occurs in the overaging is caused by the coarsening of the M7C3 carbides. The fine precipitates observed earlier will progressively disappear in favor of the larger ones and they will end up being few, large and widely spaced. Figure  4  shows a scanning electron microscope image of the (0. 9 C -13. 95 Mn) steel aged at 800 °C for 10 minutes. The results obtained corroborate with the phase fraction versus temperature diagram calculated by JMatPro® software (Figure  1 ). According to the EDS analysis (spectrum 1), the intergranular carbides are M7C3 type composed mainly of 39. 06 % iron, 54. 23 % chromium and 8. 2 % carbon. We also observe that the area of the austenitic matrix adjacent to the M7C3 carbides are depleted in iron and chromium.
This dataset presents the chemical characteristics of plant biomass and crop residues from agrosystems in European areas (carbon and nitrogen contents and biochemical composition). These data have been collected from the scientific literature. The specific data and their origins are presented. The mean values from these data are also provided by major production type (main crops, forage and pasture crops, green manure and cover crops, vegetable crops and energy crops), species and litter type. These data were collected as part of the framework of the European project ResidueGas (ERA-GAS, 2017-2021), which aims to improve the estimation of greenhouse gas emissions associated with crop residues.
Value of the Data • This dataset is based on the collection of a large amount and diversity of data on the amount of plant biomass and its elemental and gross biochemical composition from crops at different stages of growth (young to full maturity) in the European areas; • These data are primarily intended for researchers working on the agricultural and environmental impacts of agricultural activities (soil carbon storage, fertilization management, and greenhouse gas emissions) because the composition of residues remaining on the soil is one of the factors, in addition to climate, soil geophysical environment and management (e. g. , tillage), that determines the consequences of crop residue management; • This collection of data made it possible to generate average values by plant species and type of use, which should correspond to stakeholder needs, such as for national GHG inventory agencies in the European area and Intergovernmental Panel on Climate Change (IPCC) experts; • These data are rarely available to users because they are expensive to acquire and mostly distributed across a wide range of scientific publications as well as unpublished data sources; this dataset is freely available to all users through the Data INRAE repository; and • This dataset is intended to be updated regularly based on new data published in the literature.
Data Description This dataset is positioned in the open Data INRAE repository. It is described in English language. It is made up of twelve files: a file named "List_of_literature_references_v1. tab" produces the origin of the primary data and access link; a "List_of_Terms_and_Abbreviations_v1. tab" file produces the correspondences of the terms and abbreviations used in the dataset; and ten data files, described below, comprising five "Literature_data_xx_crops_v1. tab" files and five "Mean_data_xx_crops_v1. tab" files. The twelve files are displayed into two pages. Each file can be downloaded. The dataset contains data collected in 177 documents, including 158 scientific articles, 3 books, 4 PhD theses, 2 Master theses, 7 reports and 3 conference presentations. This literature was published between January 1985 and January 2021.
Their distribution of the publications over the years, except 2021, which is in progress, is presented in Fig. 1  (raw data in Supplementary Material SM 1). The 158 scientific articles used were published in 44 journals. These studies were carried out in 17 European countries. The journals Plant and Soil, Soil Biology and Biochemistry, Nutrient Cycling in Agroecosystems, Agriculture, Ecosystems and Environment, and European Journal of Agronomy accounted for 56% of the data (raw data presented in SM 2). The dataset includes approximately 2300 individual data records (see SM 3). It is structured into five categories according to the crop type and crop agricultural use: main crops (24% of occurrences), pasture and forage crops (21% of occurrences), green manure and cover crops (30% of occurrences), vegetable crops (5% of occurrences), and energy crops (21% of occurrences).
While field crops are well represented, this is not the case for vegetable crops for which data are still scarce. On the other hand, many references are available for new crops, such as energy crops. The list of species, Latin names and botanical families of the five crop types identified in the dataset are presented in  SM 3. This effort allowed us to generate, for each category of crop type, a literature data file and a mean data file. The secondary data files (literature and unpublished data) provide the following information for each plant species: Species: common name, Latin name, cultivar, and family name; Agricultural conditions of growth: plant part, harvest stage, sowing date, harvest date, N fertilization (kg. ha -1 yr -1 ), country (region or town), and origin (field, glasshouse, lysimeter); Dry matter: ton ha -1 and percentage of fresh matter (% FM); Chemical composition: neutral detergent soluble (NDS), hemicellulose + cellulose, hemicellulose, cellulose, lignin, LCI (lignocellulose index = lignin/[hemicellulose + cellulose + lignin])  [4 , 5]  , water soluble C (WSC), total C, total N, and C:N ratio (carbon:nitrogen ratio = total C/total N); Soil properties: texture characteristics, bulk density, layer, pH, total N, organic C, and soil classification according to authors; and References: authors (year published), journal, issue (volume), pages or report number, and access link.
An example of extraction of the available data is produced by Fig. 2  (raw data in SM 4) for the total N concentration and C:N ratio of the species of the "main crop" type. For the mean data files, they provide us with the following aggregated information: Species: common name, Latin name, and family name; Agricultural conditions of growth: plant part and harvest stage; Dry matter: ton ha -1 and % FM; Chemical composition: NDS, hemicellulose + cellulose, hemicellulose, cellulose, lignin, LCI, WSC, total C, total N, C:N ratio. An example of data extraction is produced by Fig. 3  (raw data in SM 5) for the Van Soest chemical composition of species of the "main crop" type shown in Fig. 2. They allow us to compare species and reveal missing information ( Fig. 3  , raw data in SM 5). The dataset includes a file that describes the list of terms and abbreviations used and a file with literature references: author name, year published, journal name and access link.
Experimental Design, Materials and Methods The literature survey was conducted on studies carried out in the European area using the following keywords: "crop residue" OR "biomass" OR "aerial dry matter" OR "straw" OR "root" OR "root dry matter" OR "main crops" OR "forage crops" OR "meadow" OR "cover crop" OR "catch crop" OR "green manure" OR "legume", AND "N content" OR/AND "C content" OR/AND "biochemical composition". Several data retrieval directions were pursued and included in English and without limit of publication year and a query of several search engines: Web of Science, ResearchGate and Google Scholar; in addition, we systematically explored the reference list of the relevant articles, which allowed us to identify additional articles/reviews with which the initial keywords had not been associated; we also contacted the authors to obtain the raw data for their published works.
During this collection phase, we collected all available elementary data (e. g. , biomass, % N, % C, and lignin content), even partial, and we calculated values where possible, based on the information given in the articles. For example, dry biomass per hectare and corresponding N quantity allow the N concentration of the residues to be calculated; the residue N concentration and C:N ratio made it possible to calculate the C concentration. However, many articles were not retained because they presented aggregated data and/or modelled plant growth adjustments without presenting sufficient information on the initial data. We classified the data into five crop categories corresponding to the main agricultural production types: main crops, forage and pasture crops, green manure and cover crops (typical soil incorporated in an immature state rather than harvested), vegetable crops, and energy crops.