Ce label est décerné par l’Union européenne à des sites témoins de l'héritage européen et choisis pour leur valeur symbolique. Son objectif est d'aider les citoyens européens à mieux comprendre l’histoire de l’Europe et de la construction de l’Union ainsi que celle de leur patrimoine culturel commun.Actuellement 48 sites européens ont été labellisés dont 5 français : Cluny (Bourgogne), la maison de Robert Schuman (Lorraine), le quartier européen de Strasbourg (Alsace), l’ancien camp de concentration de Natzweiler et ses camps annexes (France-Allemagne), le lieu de Mémoire au Chambon-sur-Lignon (Haute-Loire). Pour en savoir plus.

Our dataset is composed of information about 7,769 posts on Instagram. The data collection was done over a two-week period in July 2017 using an InstaLooter API. We searched for posts mentioning 62 internationally renowned fashion brand names as hashtag.

Efficient and accurate quantitation of metabolites from LC-MS data has become an important topic. Here we present an automated tool, called iMet-Q (intelligent Metabolomic Quantitation), for label-free metabolomics quantitation from high-throughput MS1 data. By performing peak detection and peak alignment, iMet-Q provides a summary of quantitation results and reports ion abundance at both replicate level and sample level. Furthermore, it gives the charge states and isotope ratios of detected metabolite peaks to facilitate metabolite identification. An in-house standard mixture and a public Arabidopsis metabolome data set were analyzed by iMet-Q. Three public quantitation tools, including XCMS, MetAlign, and MZmine 2, were used for performance comparison. From the mixture data set, seven standard metabolites were detected by the four quantitation tools, for which iMet-Q had a smaller quantitation error of 12% in both profile and centroid data sets. Our tool also correctly determined the charge states of seven standard metabolites. By searching the mass values for those standard metabolites against Human Metabolome Database, we obtained a total of 183 metabolite candidates. With the isotope ratios calculated by iMet-Q, 49% (89 out of 183) metabolite candidates were filtered out. From the public Arabidopsis data set reported with two internal standards and 167 elucidated metabolites, iMet-Q detected all of the peaks corresponding to the internal standards and 167 metabolites. Meanwhile, our tool had small abundance variation (≤0.19) when quantifying the two internal standards and had higher abundance correlation (≥0.92) when quantifying the 167 metabolites. iMet-Q provides user-friendly interfaces and is publicly available for download at http://ms.iis.sinica.edu.tw/comics/Software_iMet-Q.html.

P and N refer to positive and negative respectively.

Nitroxides are stable organic radicals with exceptionally long lifetimes, which render them uniquely suitable as observable probes or polarising agents for spectroscopic investigation of biomolecular structure and dynamics. Spin labelling enables the study of biomolecules using electron paramagnetic resonance (EPR) spectroscopy. Here, we describe the synthesis of a spin label based on a spirocyclic pyrrolidinyl nitroxide containing an iodoacetamide moiety. The spin label was successfully used for double labelling of a calmodulin mutant, and was shown to be highly persistent under reducing conditions while maintaining excellent relaxation parameters up to a temperature of 180 K. Interspin distances measured by double electron-electron resonance (DEER) were in good agreement with the protein tertiary structure. This dataset contains raw files and analysis reports of 1HNMR, 13CNMR of all compounds synthesized for and used in work Sigmatropic rearrangement enables access to a highly stable spirocyclic nitroxide for protein spin labelling. Moreover, copies of ATR-FTIR spectroscopy and high resolution mass spectrometry are included for novel compounds. For all nitroxides raw files of X-band CW-EPR spectra. For spin label and labelled protein Q-band EPR relaxation measurements are included. Dataset is completed with DEER experiment results for labelled protein in various temperatures and its kinetic stability based on CW-EPR spectra .

Political scientists increasingly use supervised machine learning to code multiple relevant labels from a single set of texts. The current ``best practice'' of individually applying supervised machine learning to each label ignores information on inter-label association(s), and is likely to under-perform as a result. We introduce multi-label prediction as a solution to this problem. After reviewing the multi-label prediction framework, we apply it to code multiple features of (i) access to information requests made to the Mexican government and (ii) country-year human rights reports. We find that multi-label prediction outperforms standard supervised learning approaches, even in instances where the correlations among one's multiple labels are low. This repository replicates the figures and tables in the article and appendix. More information can be found in the "README.md" file.

1.Framework overview. This paper proposed a pipeline to construct high-quality datasets for text mining in materials science. Firstly, we utilize the traceable automatic acquisition scheme of literature to ensure the traceability of textual data. Then, a data processing method driven by downstream tasks is performed to generate high-quality pre-annotated corpora conditioned on the characteristics of materials texts. On this basis, we define a general annotation scheme derived from materials science tetrahedron to complete high-quality annotation. Finally, a conditional data augmentation model incorporating materials domain knowledge (cDA-DK) is constructed to augment the data quantity.2.Dataset information. The experimental datasets used in this paper include: the Matscholar dataset publicly published by Weston et al. (DOI: 10.1021/acs.jcim.9b00470), and the NASICON entity recognition dataset constructed by ourselves. Herein, we mainly introduce the details of NASICON entity recognition dataset.2.1 Data collection and preprocessing. Firstly, 55 materials science literature related to NASICON system are collected through Crystallographic Information File (CIF), which contains a wealth of structure-activity relationship information. Note that materials science literature is mostly stored as portable document format (PDF), with content arranged in columns and mixed with tables, images, and formulas, which significantly compromises the readability of the text sequence. To tackle this issue, we employ the text parser PDFMiner (a Python toolkit) to standardize, segment, and parse the original documents, thereby converting PDF literature into plain text. In this process, the entire textual information of literature, encompassing title, author, abstract, keywords, institution, publisher, and publication year, is retained and stored as a unified TXT document. Subsequently, we apply rules based on Python regular expressions to remove redundant information, such as garbled characters and line breaks caused by figures, tables, and formulas. This results in a cleaner text corpus, enhancing its readability and enabling more efficient data analysis. Note that special symbols may also appear as garbled characters, but we refrain from directly deleting them, as they may contain valuable information such as chemical units. Therefore, we converted all such symbols to a special token

Traditional differential expression genes (DEGs) identification models have limitations in small sample size datasets because they require meeting distribution assumptions, otherwise resulting high false positive/negative rates due to sample variation. In contrast, tabular data model based on deep learning (DL) frameworks do not need to consider the data distribution types and sample variation. However, applying DL to RNA-Seq data is still a challenge due to the lack of proper labeling and the small sample size compared to the number of genes. Data augmentation (DA) extracts data features using different methods and procedures, which can significantly increase complementary pseudo-values from limited data without significant additional cost. Based on this, we combine DA and DL framework-based tabular data model, propose a model TabDEG, to predict DEGs and their up-regulation/down-regulation directions from gene expression data obtained from the Cancer Genome Atlas database. Compared to five counterpart methods, TabDEG has high sensitivity and low misclassification rates. Experiment shows that TabDEG is robust and effective in enhancing data features to facilitate classification of high-dimensional small sample size datasets and validates that TabDEG-predicted DEGs are mapped to important gene ontology terms and pathways associated with cancer.

Dataset overview

Note: the preprint of the data paper is currently pending peer review.

Summary

The dataset is a collection of RANS simulations of reference cases where DNS or LES data are available, to enable training and testing of machine learnt turbulence models. The DNS/LES data are mapped onto the RANS grid, so that at each point, both RANS and DNS/LES fields are available. For each turbulence model, 895,640 points with RANS fields and corresponding DNS/LES fields are available. An example notebook that shows how to build a feature and label set, and train a tensor basis neural network can be found on the "code" page.

File structure

There is a directory for each turbulence model (e.g. kepsilon), and a labels directory. The filenames for the RANS data (features) are given as:

MODEL/MODEL_FLOW_CASE_FIELD.npy

MODEL is one of - kepsilon - kepsilonphitf - komega - komegasst

FLOW_CASE is one of - DUCT_1100 - DUCT_1150 - DUCT_1250 - DUCT_1300 - DUCT_1350 - DUCT_1400 - DUCT_1500 - DUCT_1600 - DUCT_1800 - DUCT_2000 - DUCT_2205 - DUCT_2400 - DUCT_2600 - DUCT_2900 - DUCT_3200 - DUCT_3500 - PHLL_case_0p5 - PHLL_case_0p8 - PHLL_case_1p0 - PHLL_case_1p2 - PHLL_case_1p5 - BUMP_h20 - BUMP_h26 - BUMP_h31 - BUMP_h38 - BUMP_h42 - CNDV_12600 - CNDV_20580 - CBFS_13700

FIELD is one of - Ak - Akhat - Ap - Aphat - Cx - Cy - Cz - DUDtx - DUDty - DUDtz - epsilon - f - gradkx - gradky - gradkz - gradpx - gradpy - gradpz - gradU - I - k - Lambda - omega - p - phit - q - R - Rhat - S - Shat - T_k - T_t - Tensors - Ux - Uy - Uz - wallDistance

The filenames for the mapped DNS/LES data (labels) are given as: labels/FLOW_CASE_FIELD.npy

FLOW_CASE is from the list above.

FIELD is one of - um - vm - wm - uu - uv - uw - vv - vw - ww - tau - b - k

Please refer to the related reference for details of each field. Some of the fields are derived from others (e.g., k = 0.5*trace(tau)), and provided for convenience in construction feature and label sets. Note: only the kOmega models include omega, and only the kEpsilonPhitf model includes phit and f.

How to use this dataset

A set of features and labels for machine learning can be conveniently constructed using the provided arrays. Since the features and labels have been interpolated to the same grid, we can directly combine the arrays to form feature sets with corresponding labels. An example notebook is provided, which shows how to manipulate the arrays.

More information

The RANS data was collected using the four turbulence models above, in OpenFOAM v2006. The reference flow cases are a set of DNS and LES data from other studies, available online (see below). These fields were interpolated onto the RANS grid.

Reference cases 1. PHLL - Periodic hills, 5 cases with varying geometry, Xiao et al. (2020), available here 2. DUCT - Square duct, 16 cases with varying Reynolds number, Pinelli et al. (2010), available here 3. BUMP - Parametric bumps, 5 cases with varying geometry, Matai & Durbin (2019), available here 4. CNDV - Converging-diverging channel, two cases with varying Reynolds number, Re=12600: Marquillie et al. (2008), available here, Re=20580: Schiavo et al. (2015), available here 5. CBFS - Curved backward-facing step, one case, Bentaleb et al. (2011), available here

How the data was collected

For each reference case (29 total), and each turbulence model (4 total): 1. Set the RANS boundary conditions, fluid properties, etc. to match the DNS/LES reference case 2. Run the RANS case, and write the desired RANS fields 3. Interpolate the reference data onto the...

Lipopolysaccharides (LPS) confer resistance against harsh conditions, including antibiotics, in Gram-negative bacteria. The lipopolysaccharide transport (Lpt) complex, consisting of seven proteins (A-G), exports LPS across the cellular envelope. LptB2FG forms an ATP-binding cassette transporter that transfers LPS to LptC. How LptB2FG couples ATP binding and hydrolysis with LPS transport to LptC remains unclear. We observed the conformational heterogeneity of LptB2FG and LptB2FGC in micelles and/or proteoliposomes using pulsed dipolar electron spin resonance spectroscopy. Additionally, we monitored LPS binding and release using laser-induced liquid bead ion desorption mass spectrometry. The β-jellyroll domain of LptF stably interacts with the LptG and LptC β-jellyrolls in both the apo and vanadate-trapped states. ATP binding at the cytoplasmic side is allosterically coupled to the selective opening of the periplasmic LptF β-jellyroll domain. In LptB2FG, ATP binding closes the nucleotide-binding domains, causing a collapse of the first lateral gate as observed in structures. However, the second lateral gate, which forms the putative entry site for LPS, exhibits a heterogeneous conformation. LptC binding limits the flexibility of this gate to two conformations, likely representing the helix of LptC as either released from or inserted into the transmembrane domains. Our results reveal the regulation of the LPS entry gate through the dynamic behavior of the LptC transmembrane helix, while its β-jellyroll domain is anchored in the periplasm. This, combined with long-range ATP-dependent allosteric gating of the LptF β-jellyroll domain, may ensure efficient and unidirectional transport of LPS across the periplasm. Methods DEER/PELDOR experiments were conducted on a Bruker Elexsys E580 Q-Band (34 GHz) pulsed ESR spectrometer equipped with an arbitrary waveform generator (SpinJet AWG, Bruker), a 50 W solid-state amplifier, a continuous-flow helium cryostat, and a temperature control system (Oxford Instruments). Measurements were carried out at 50 K using a 10 – 20 µL frozen sample containing 15 – 20% glycerol-d8 in a 1.6 mm quartz ESR tube (Suprasil, Wilmad LabGlass) with a Bruker EN5107D2 dielectric resonator. The phase memory time (TM) measurements were performed with a 48 ns π/2–t–π Gaussian pulse sequence with a two-step phase cycling after incrementing t in 4 ns steps. A dead-time free four-pulse sequence with a 16-step phase cycling (x[x][xp]x) was used for DEER measurements. A 38 ns Gaussian pump pulse (with a full width at half maximum (FWHM) of 16.1 ns) was employed, along with a 48 ns observer pulse (FWHM of 20.4 ns). The pump pulse was placed at the maximum of the echo-detected field-swept spectrum, and the observer pulses were set at 80 MHz lower. Deuterium modulations were averaged by progressively increasing the first interpulse delay by 16 ns over 8 steps.

AbstractIntroductionX-SRL: Parallel Cross-lingual Semantic Role Labeling was developed by Heidelberg University, Department of Computational Linguistics and the Leibniz Institute for the German Language (IDS). It consists of approximately three million words of German, French and Spanish annotated for semantic role labeling. The texts are translations of the English portion of 2009 CoNLL Shared Task Part 2 (LDC2012T04). All sentences have annotations for verbal predicates and share the original English Propbank label set across the four languages.DataThe 2009 CoNLL Shared Task developed syntactic dependency annotations, including the semantic dependency model roles of both verbal and nominal predicates. The following English data was used in the shared task: Treebank-2 (LDC95T7): over one million words of annotated English newswire and other text developed by the University of Pennsylvania Proposition Bank I (LDC2004T14): semantic annotation of newswire text from Treebank-2 developed by the University of Pennsylvania NomBank v 1.0 (LDC2008T23): argument structure for instances of common nouns in Treebank-2 and Treebank-3 (LDC99T42), developed by New York University For X-SRL, the English source data was automatically translated using DeepL. Automatic tokenization, lemmatization, part-of-speech tagging and syntactic parsing were then applied to the text. The data was divided into train, development and test partitions. Semantic labels were transferred for the train and development sections, and the test sentences were validated for translation quality, alignment, label transfer, and filtering. More information on the development process and tools used is available in the included documentation. Annotated data is in the Universal CoNLL format and encoded in UTF-8.

The Mindboggle-101 dataset is part of the Mindboggle project (http://mindboggle.info) and includes anatomically labeled brain surfaces and volumes derived from magnetic resonance images of 101 healthy individuals. The manually edited cortical labels follow sulcus landmarks according to the Desikan-Killiany-Tourville (DKT) labeling protocol: "101 labeled brain images and a consistent human cortical labeling protocol" Arno Klein, Jason Tourville. Frontiers in Brain Imaging Methods. 6:171. DOI: 10.3389/fnins.2012.00171 Data and License All labeled data, including nifti volumes (nii), vtk surfaces (vtk), and FreeSurfer files (mgh, etc.) for each scanned "GROUP" (OASIS-TRT-20, NKI-TRT-20, NKI-RS-22, MMRR-21, HLN-12, etc.) are licensed under a Creative Commons License. These brains are in their original space as well as affine-registered to "MNI152space": [GROUP]_volumes.tar.gz SurfaceLabels_[GROUP].tar.gz The manually labeled subcortical portions of the "WholeBrain" OASIS-TRT-20 labels are licensed under a similar Creative Commons License: WholeBrain_VolumeLabels_OASIS-TRT-20.tar.gz WholeBrain_VolumeLabels_OASIS-TRT-20_MNI152space.tar.gz The following code and documentation files are also included: CHANGELOG.txt: log of changes code_prep_WholeBrain_OASIS-TRT-20_labels.txt: preprocessing code for volumes* code_[re]postprocess_Mindboggle101_data.txt: postprocessing code code_resample2mm.txt: resampling code label_definitions.txt: labeling protocol (see above article) labels_on_fsaverage_surfaces.png: example labels subject_list_Mindboggle101.txt: list of subjects subject_scans_info_Mindboggle101.tar.gz: information about the scans subject_sources_Mindboggle101.txt: scan sources subject_table_Mindboggle101.pdf: table of subjects ShapeTables_mindboggle_20141017.tar.gz: features and shapes output by Mindboggle software

Lancé en 2021 par le Haut Conseil de l’éducation artistique et culturelle (HCEAC), le label 100% EAC reconnaît l’engagement d’un territoire en faveur de la généralisation de l’éducation artistique et culturelle (EAC).Décerné pour une durée de 5 ans renouvelables, ce label valorise les collectivités et les intercommunalités qui proposent une éducation artistique et culturelle à l’ensemble des jeunes de leur territoire, de la petite enfance à l’âge adulte. Accompagné d’outils méthodologiques permettant d’élaborer un état des lieux et une stratégie, il aide à renforcer la cohérence de l‘action, fédérer les acteurs, mobiliser d’autres partenaires, pérenniser et développer les dispositifs. Les ministres de la culture et de l’éducation nationale, qui co-président le HCEAC, ont confié aux préfets et aux recteurs l’attribution de ce label, après avis des services déconcentrés des deux ministères.Dès la première session en 2022, 79 territoires, répartis dans toutes les régions, ont été labellisés 100% EAC ; 78 (dont deux d’outre-mer) l’ont été en 2023. A l'issue des deux premiers appels à candidature, 157 territoires sont labellisés 100% EAC. NB : - La carte indique les surfaces des départements, ceux-ci peuvent inclure une ou plusieurs collectivités labellisées, dans ce cas les flèches permettent de toutes les visualiser.- Les données concernant les partenariats et les dispositifs ne sont valables que l’année de labellisation, puisqu’ils peuvent évoluer dans le temps. Pour en savoir plus : https://www.culture.gouv.fr/catalogue-des-demarches-et-subventions/appels-a-projets-candidatures/Label-100-EAC

Contains:World HillshadeWorld Street Map (with Relief) - Base LayerLarge Scale International Boundaries (v11.3)World Street Map (with Relief) - LabelsDoS Country Labels DoS Country LabelsCountry (admin 0) labels that have been vetted for compliance with foreign policy and legal requirements. These labels are part of the US Federal Government Basemap, which contains the borders and place names that have been vetted for compliance with foreign policy and legal requirements.Source: DoS Country Labels - Overview (arcgis.com)Large Scale International BoundariesVersion 11.3Release Date: December 19, 2023DownloadFor more information on the LSIB click here: https://geodata.state.gov/ A direct link to the data is available here: https://data.geodata.state.gov/LSIB.zipAn ISO-compliant version of the LSIB metadata (in ISO 19139 format) is here: https://geodata.state.gov/geonetwork/srv/eng/catalog.search#/metadata/3bdb81a0-c1b9-439a-a0b1-85dac30c59b2 Direct inquiries to internationalboundaries@state.govOverviewThe Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.3 (published 19 December 2023). The 11.3 release contains updates to boundary lines and data refinements enabling reuse of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control.National Geospatial Data AssetThis dataset is a National Geospatial Data Asset managed by the Department of State on behalf of the Federal Geographic Data Committee's International Boundaries Theme.DetailsSources for these data include treaties, relevant maps, and data from boundary commissions and national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process involves analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground.Attribute StructureThe dataset uses thefollowing attributes:Attribute NameCC1COUNTRY1CC2COUNTRY2RANKSTATUSLABELNOTES These attributes are logically linked:Linked AttributesCC1COUNTRY1CC2COUNTRY2RANKSTATUS These attributes have external sources:Attribute NameExternal Data SourceCC1GENCCOUNTRY1DoS ListsCC2GENCCOUNTRY2DoS ListsThe eight attributes listed above describe the boundary lines contained within the LSIB dataset in both a human and machine-readable fashion. Other attributes in the release include "FID", "Shape", and "Shape_Leng" are components of the shapefile format and do not form an intrinsic part of the LSIB."CC1" and "CC2" fields are machine readable fields which contain political entity codes. These codes are derived from the Geopolitical Entities, Names, and Codes Standard (GENC) Edition 3 Update 18. The dataset uses the GENC two-character codes. The code ‘Q2’, which is not in GENC, denotes a line in the LSIB representing a boundary associated with an area not contained within the GENC standard.The "COUNTRY1" and "COUNTRY2" fields contain human-readable text corresponding to the name of the political entity. These names are names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the list of Independent States in the World and the list of Dependencies and Areas of Special Sovereignty maintained by the Department of State. To ensure the greatest compatibility, names are presented without diacritics and certain names are rendered using commonly accepted cartographic abbreviations. Names for lines associated with the code ‘Q2’ are descriptive and are not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS are names of independent states. Other names are those associated with dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user.The following fields are an intrinsic part of the LSIB dataset and do not rely on external sources:Attribute NameMandatoryContains NullsRANKYesNoSTATUSYesNoLABELNoYesNOTESNoYesNeither the "RANK" nor "STATUS" field contains null values; the "LABEL" and "NOTES" fields do.The "RANK" field is a numeric, machine-readable expression of the "STATUS" field. Collectively, these fields encode the views of the United States Government on the political status of the boundary line.Attribute NameValueRANK123STATUSInternational BoundaryOther Line of International Separation Special Line A value of "1" in the "RANK" field corresponds to an "International Boundary" value in the "STATUS" field. Values of "2" and "3" correspond to "Other Line of International Separation" and "Special Line", respectively.The "LABEL" field contains required text necessarily to describe the line segment. The "LABEL" field is used when the line segment is displayed on maps or other forms of cartographic visualizations. This includes most interactive products. The requirement to incorporate the contents of the "LABEL" field on these products is scale dependent. If a label is legible at the scale of a given static product a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field is not a line labeling field but does contain the preferred description for the three LSIB line types when lines are incorporated into a map legend. Using the "CC1", "CC2", or "RANK" fields for labeling purposes is prohibited.The "NOTES" field contains an explanation of any applicable special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, any limitations regarding the purpose of the lines, or the original source of the line. Use of the "NOTES" field for labeling purposes is prohibited.External Data SourcesGeopolitical Entities, Names, and Codes Registry: https://nsgreg.nga.mil/GENC-overview.jspU.S. Department of State List of Independent States in the World: https://www.state.gov/independent-states-in-the-world/U.S. Department of State List of Dependencies and Areas of Special Sovereignty: https://www.state.gov/dependencies-and-areas-of-special-sovereignty/The source for the U.S.—Canada international boundary (NGDAID97) is the International Boundary Commission: https://www.internationalboundarycommission.org/en/maps-coordinates/coordinates.phpThe source for the “International Boundary between the United States of America and the United States of Mexico” (NGDAID82) is the International Boundary and Water Commission: https://catalog.data.gov/dataset?q=usibwcCartographic UsageCartographic usage of the LSIB requires a visual differentiation between the three categories of boundaries. Specifically, this differentiation must be between:- International Boundaries (Rank 1);- Other Lines of International Separation (Rank 2); and- Special Lines (Rank 3).Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary.Additional cartographic information can be found in Guidance Bulletins (https://hiu.state.gov/data/cartographic_guidance_bulletins/) published by the Office of the Geographer and Global Issues.ContactDirect inquiries to internationalboundaries@state.gov.CreditsThe lines in the LSIB dataset are the product of decades of collaboration between geographers at the Department of State and the National Geospatial-Intelligence Agency with contributions from the Central Intelligence Agency and the UK Defence Geographic Centre.Attribution is welcome: U.S. Department of State, Office of the Geographer and Global Issues.Changes from Prior ReleaseThe 11.3 release is the third update in the version 11 series.This version of the LSIB contains changes and accuracy refinements for the following line segments. These changes reflect improvements in spatial accuracy derived from newly available source materials, an ongoing review process, or the publication of new treaties or agreements. Notable changes to lines include:• AFGHANISTAN / IRAN• ALBANIA / GREECE• ALBANIA / KOSOVO• ALBANIA/MONTENEGRO• ALBANIA / NORTH MACEDONIA• ALGERIA / MOROCCO• ARGENTINA / BOLIVIA• ARGENTINA / CHILE• BELARUS / POLAND• BOLIVIA / PARAGUAY• BRAZIL / GUYANA• BRAZIL / VENEZUELA• BRAZIL / French Guiana (FR.)• BRAZIL / SURINAME• CAMBODIA / LAOS• CAMBODIA / VIETNAM• CAMEROON / CHAD• CAMEROON / NIGERIA• CHINA / INDIA• CHINA / NORTH KOREA• CHINA / Aksai Chin• COLOMBIA / VENEZUELA• CONGO, DEM. REP. OF THE / UGANDA• CZECHIA / GERMANY• EGYPT / LIBYA• ESTONIA / RUSSIA• French Guiana (FR.) / SURINAME• GREECE / NORTH MACEDONIA• GUYANA / VENEZUELA• INDIA / Aksai Chin• KAZAKHSTAN / RUSSIA• KOSOVO / MONTENEGRO• KOSOVO / SERBIA• LAOS / VIETNAM• LATVIA / LITHUANIA• MEXICO / UNITED STATES• MONTENEGRO / SERBIA• MOROCCO / SPAIN• POLAND / RUSSIA• ROMANIA / UKRAINEVersions 11.0 and 11.1 were updates to boundary lines. Like this version, they also contained topology fixes, land boundary terminus refinements, and tripoint adjustments. Version 11.2 corrected a few errors in the attribute data and ensured that CC1 and CC2 attributes are in alignment with an updated version of the Geopolitical Entities, Names, and Codes (GENC) Standard, specifically Edition 3 Update 17.LayersLarge_Scale_International_BoundariesTerms of

Nitroxides are stable organic radicals with exceptionally long lifetimes, which render them uniquely suitable as observable probes or polarising agents for spectroscopic investigation of biomolecular structure and dynamics. Spin labelling enables the study of biomolecules using electron paramagnetic resonance (EPR) spectroscopy. Here, we describe the synthesis of a spin label based on a spirocyclic pyrrolidinyl nitroxide containing an iodoacetamide moiety. The spin label was successfully used for double labelling of a calmodulin mutant, and was shown to be highly persistent under reducing conditions while maintaining excellent relaxation parameters up to a temperature of 180 K. Interspin distances measured by double electron-electron resonance (DEER) were in good agreement with the protein tertiary structure. This dataset contains raw files and analysis reports of 1HNMR, 13CNMR of all compounds synthesized for and used in work Sigmatropic rearrangement enables access to a highly stable spirocyclic nitroxide for protein spin labelling. Moreover, copies of ATR-FTIR spectroscopy and high resolution mass spectrometry are included for novel compounds. For all nitroxides raw files of X-band CW-EPR spectra. For spin label and labelled protein Q-band EPR relaxation measurements are included. Dataset is completed with DEER experiment results for labelled protein in various temperatures and its kinetic stability based on CW-EPR spectra .

El tamaño del mercado mundial de etiquetado y recopilación de datos sanitarios superó los 1.110 millones de dólares en 2024 y se prevé que crezca a un ritmo constante del 25,8% CAGR, alcanzando los 21.940 millones de dólares en 2037. Se estima que la industria de América del Norte representará la mayor participación en los ingresos del 37,8% para 2037, debido a la utilización de herramientas de última generación como la inteligencia artificial (IA) y el aprendizaje automático para mejorar la eficiencia y precisión en el etiquetado de datos. y anotación.

Evidence that persistent environmental pollutants may target the male reproductive system is increasing. The male reproductive system is regulated by secretion of testosterone by testicular Leydig cells, and perturbation of Leydig cells function may have ultimate consequences. 3-methylsulfonyl-DDE (3-MeSO2-DDE) is a potent adrenal toxicants formed from the persistent insecticide DDT. Although studies have revealed endocrine disruptive effect of 3-MeSO2-DDE, the underlying mechanisms at cellular level in steroidogenic Leydig cells remains to be established. The current study addresses the effect of 3-MeSO2-DDE viability, hormone production and proteome response of primary neonatal porcine Leydig cells. The AlamarBlue™ assay was used to evaluate cell viability. Solid phase radioimmunoassay was used to measure concentration of hormones produced by both unstimulated and luteinizing hormone (LH)-stimulated Leydig cells following 48 h exposure. Protein samples from Leydig cells exposed to a non-cytotoxic concentration of 3-MeSO2-DDE (10µM) were subjected to nano-LC-MS/MS and analyzed on a Q Exactive mass spectrometer and quantified using label-free quantitative algorithm. Gene Ontology (GO) and Ingenuity Pathway Analysis (IPA) were carried out for functional annotation and identification of protein interaction networks. 3-MeSO2-DDE regulated Leydig cell steroidogenesis differentially depending on cell culture condition. Whereas its effect on testosterone secretion at basal condition was stimulatory, the effect on LH-stimulated cells was inhibitory. From triplicate experiments, a total of 7540 proteins were identified in which the abundance of 87 proteins in unstimulated Leydig cells and 146 proteins in LH-stimulated Leydig cells were found to be significantly regulated in response to 3-MeSO2-DDE exposure. These proteins not only are the first reported in relation to 3-MeSO2-DDE exposure, but also display small number of proteins shared between culture conditions, suggesting the action of 3-MeSO2-DDE on several targeted pathways, including mitochondrial dysfunction, oxidative phosphorylation, EIF2-signaling, and glutathion-mediated detoxification. Further identification and characterization of these proteins and pathways may build our understanding to the molecular basis of 3-MeSO2-DDE induced endocrine disruption in Leydig cells.

This Dataset contains all the images used for the three different experiments referenced in this paper, aswell as the code used to run the AI algorithms. In all three cases, images are labeled in order to classify them in one of the groups that are the matter of the present study, that is, cut marks done with or without meat. For that matter, images labeled as NM refer to cut marks done without any meat (No-Meat); and images labeled as WM refer to cut marks done during butchery (With-Meat). The type of cut mark of each experiment are referred in the paper. The code is presented in .ipynb format, accesible throught JupiterNotebook app. The code is also presented as used in the experiments.

Labeling peptides with isobaric tags is a popular strategy in quantitative bottom-up proteomics. In this study, we labeled six breast tumor cell lysates (1.34 mg proteins per channel) using 10-plex tandem mass tag reagents and analyzed the samples on a Q Exactive HF Quadrupole-Orbitrap mass spectrometer. We identified a total of 8,706 proteins and 28,186 phosphopeptides, including 7,394 proteins and 23,739 phosphosites common to all channels. The majority of technical replicates correlated with a R2 ≥ 0.98, indicating minimum variability was introduced after labeling. Unsupervised hierarchical clustering of phosphopeptide data sets successfully classified the breast tumor samples into Her2 (epidermal growth factor receptor 2) positive and Her2 negative groups, whereas mRNA abundance did not. The tyrosine phosphorylation levels of receptor tyrosine kinases, phosphoinositide-3-kinase, protein kinase C delta, and Src homology 2, among others, were significantly higher in the Her2 positive than the Her2 negative group. Despite ratio compression in MS2-based experiments, we demonstrated the ratios calculated using an MS2 method are highly correlated (R2 > 0.65) with ratios obtained using MS3-based quantitation (using a Thermo Orbitrap Fusion mass spectrometer) with reduced ratio suppression. Given the deep coverage of global and phosphoproteomes, our data show that MS2-based quantitation using TMT can be successfully used for large-scale multiplexed quantitative proteomics.

Replication data for the 4 different studies in this paper.