This archive contains raw data of visual and acoustic mapping of perforations in Utah FORGE well 16A(78)-32 acquired during the August 2024 circulation program. The dataset includes downhole images captured by EV, a downhole visual analytics company, providing visual records of each perforation. Images are organized in two folders: one set with perforation visualization overlays and one without. An included Excel spreadsheet provides the organized raw data.

This is the raw data supporting the findings (both main text and supplementary) for our manuscript "Staphylococcus aureus cell wall structure and dynamics during host-pathogen interaction". Each excel file contains the raw data for each figure. Murine work was carried out according to UK law in the Animals (Scientific Procedures) Act 1986, under Project License P3BFD6DB9 (Staphylococcus aureus and other pathogens, pathogenesis to therapy, University of Sheffield Review Board).

Excel spreadsheet containing raw data, organized by figure.

The data used for each figure is included on a separate tab, organized by figure number. (XLSX)

The raw data were organized in one Microsoft Excel file, which can be opened using Microsoft Excel. The raw images were organized in one Microsoft Word file, which can be opened using Microsoft Word.

General information This item containst data sets for Schlegel et al, Nano Letters, 2023. DOI: https://doi.org/10.1021/acs.nanolett.2c04884 It contains confocal images, lattice light sheet images, flow cytometry data, compiled data as excle sheet and raw figure files. Abstract Speed is key during infectious disease outbreaks. It is essential, for example, to identify critical host binding factors to pathogens as fast as possible. The complexity of host plasma membrane is often a limiting factor hindering fast and accurate determination of host binding factors as well as high-throughput screening for neutralizing antimicrobial drug targets. Here, we describe a multiparametric and high-throughput platform tackling this bottleneck and enabling fast screens for host binding factors as well as new antiviral drug targets. The sensitivity and robustness of our platform were validated by blocking SARS-CoV-2 particles with nanobodies and IgGs from human serum samples. Data usage Researchers are welcome to use the data contained in the dataset for any projects. Please cite this item upon use or when published. We encourage reuse using the same CC BY 4.0 License. Data Content Excel files for graphs Microscopy Images Flow cytometry data Software to open files: .csv: Fiji (https://imagej.net/software/fiji/downloads) or Microsoft Excel .xlsx: Microsoft Excel .tif, .lsm: Fiji (https://imagej.net/software/fiji/downloads) .pzfx: GraphPad Prism .svg: Inkscape (https://inkscape.org/) .fcs: FCS Express .pdf: AdobeAcrobat or Mozilla Firefox .ijm: Fiji (https://imagej.net/software/fiji/downloads)

The proposed dataset, termed PC-Urban (Urban Point Cloud), is captured with an Ouster LiDAR sensor with 64 channels. The sensor is installed on an SUV that drives through the downtown of Perth, Western Australia (WA), Australia. The dataset comprises over 4.3 billion points captured for 66K sensor frames. The labelled data is organized as registered and raw point cloud frames, where the former has a different number of registered consecutive frames. We provide 25 class labels in the dataset covering 23 million points and 5K instances. Labelling is performed with PC-Annotate and can easily be extended by the end-users employing the same tool.The data is organized into unlabelled and labelled 3D point clouds. The unlabelled data is provided in .PCAP file format, which is the direct output format of the used Ouster LiDAR sensor. Raw frames are extracted from the recorded .PCAP files in the form of Ply and Excel files using the Ouster Studio Software. Labelled 3D point cloud data consists of registered or raw point clouds. A labelled point cloud is a combination of Ply, Excel, Labels and Summary files. A point cloud in Ply file contains X, Y, Z values along with color information. An Excel file contains X, Y, Z values, Intensity, Reflectivity, Ring, Noise, and Range of each point. These attributes can be useful in semantic segmentation using deep learning algorithms. The Label and Label Summary files have been explained in the previous section. Our one GB raw data contains nearly 1,300 raw frames, whereas 66,425 frames are provided in the dataset, each comprising 65,536 points. Hence, 4.3 billion points captured with the Ouster LiDAR sensor are provided. Annotation of 25 general outdoor classes is provided, which include car, building, bridge, tree, road, letterbox, traffic signal, light-pole, rubbish bin, cycles, motorcycle, truck, bus, bushes, road sign board, advertising board, road divider, road lane, pedestrians, side-path, wall, bus stop, water, zebra-crossing, and background. With the released data, a total of 143 scenes are annotated which include both raw and registered frames.

Learning where to find nutrients while at the same time avoiding toxic food is essential for survival of any animal. Using Drosophila melanogaster larvae as a study case, we investigate the role of gustatory sensory neurons expressing IR76b for associative learning of amino acids, the building blocks of proteins. We found surprising complexity in the neuronal underpinnings of sensing amino acids, and a functional division of sensory neurons. We found that the IR76b receptor is dispensable for amino acid learning, whereas the neurons expressing IR76b are specifically required for the rewarding but not the punishing effect of amino acids. This unexpected dissociation in neuronal processing of amino acids for different behavioural functions provides a study case for functional divisions of labour in gustatory systems.

Following a request from the European Commission, in 2018 EFSA released a renovated database of host plant species of Xylella spp. (including both species X. fastidiosa and X. taiwanensis) together with a scientific report (EFSA, 2018). EFSA was tasked to maintain and update this database periodically. In May 2021, EFSA released the fourth update of the Xylella spp. host plant database (VERSION 4) with information retrieved from literature search up to December 2020, Europhyt outbreak notifications up to 18 March 2021, and communications of research groups and national authorities (EFSA, 2021). The protocol applied for the extensive literature review, data collection and reporting, as well as results and lists of host plants are described in detail in the related scientific report (EFSA, 2021). The overall number of Xylella spp. host plants determined with at least two different detection methods or positive with one method (between: sequencing, pure culture isolation) reaches now 385 plant species, 179 genera and 67 families (category A – see section 2.4.2 of EFSA (2021)). Such numbers rise to 638 plant species, 289 genera and 87 families if considered regardless of the detection method applied (category E, see section 2.4.2 of EFSA (2021). The Excel files here attached represent the VERSION 4 of the Xylella spp. host plants database. For a detailed description of the information included in the database, please consult the related scientific report (EFSA, 2021). The Excel file “Xylella spp. host plants database – VERSION 4” contains several sheets: the LEGENDA (with extensive description of each table), the full detailed raw data of the Xylella spp. host plant database (sheet “observation”) and several examples of data extraction. Additional Excel files contain the lists of host plant species of X. fastidiosa (subsp. unknown (i.e. not reported), fastidiosa, multiplex, pauca, morus, sandyi, tashke, fastidiosa/sandyi) and X. taiwanensis infected naturally, artificially and in not specified conditions, and according to different categories (A,B,C,D,E – see section 2.4.2 of EFSA (2021)). The Excel file “new_host_plant_species_v4” contain the list of new host plant species added to the database in this fourth update. Question number: EFSA-Q-2017-00221 Correspondence: alpha@efsa.europa.eu Bibliography: EFSA (European Food Safety Authority), 2018. Scientific report on the update of the Xylella spp. host plant database. EFSA Journal 2018;16(9):5408, 87 pp. https://doi.org/10.2903/j.efsa.2018.5408 EFSA (European Food Safety Authority), Delbianco A, Gibin D, Pasinato L and Morelli M, 2021. Scientific report on the update of the Xylella spp. host plant database – systematic literature search up to 31 December 2020. EFSA Journal 2021;19(6):6674, 70 pp. https://doi.org/10.2903/j.efsa.2021.6674

This dataset contains examples and raw data related to the cited publication (doi: 10.1113/jp284244). Computational models derived from histological images are provided. Raw values in the data spreadsheet are given as fraction of simulations for a particular configureation that resulted in graft-initiated host excitation., , Microsoft Excel or another open source should be used to view Gibbs_et_at_ExcelData.xlsx. Raw data for Figure 7 and 8 are provided. Example files are provided for: 1) All five histology-derived models described in the paper, in fully coupled and 10% coupled states. 2) A parameter file for openCARP (tested version v8.2) to conduct simulations of spontaneous graft-host excitation in 10% coupled models. 3) Output from these test simulations (large data files removed for ease of sharing). 4) Meshalyzer save states and transformations used to visualize activation maps and voltage over time data (tested version v2.2). 5) Videos showing voltage over time and images showing activation sequence for breakthrough wavefronts only for all five simulations A README file has also been provided with example code on how to run the simulations for the Linux terminal.

The raw data was collected by methods outlined in the methods section (2. Methods). Data was recorded into an Excel spreadsheet, cleaned, then uploaded into JMP Pro 14 and Prism for statistical analysis and figure creation.

Macros for organizing raw data from LabView software that logs timestamps for every rotation of a flight mill arms and then summarizes the time of individual flight bouts.

The “Source Data” file, containing experimental raw data in an organized Excel format, includes data for each figure on separate sheets, covering electrical and optical characterization, commercial spectrometer data, and the learning and testing data for our spectrometer devices.

This dataset supports the manuscript "Host ZAP Activity Correlates with the Levels of CpG Suppression in Primate Lentiviruses" (PNAS) and is provided for transparency and reproducibility. The specific figures referenced in the manuscript are indicated within the file. It includes:Excel File: Raw experimental data on CpG calculations in primate lentiviral genomes and host CpG suppression.PDF File: Raw Western blot membranes and full electrophoresis gels corresponding to figures presented in the manuscript. Word File: All ZAP sequences obtained and analyzed in this study, used for sequence alignments.

The type I interferon (IFN-I) response, which includes IFN-I induction and signaling, plays an important role in a host’s defence against viral infections. Many pathogenic viruses target it to evade the host immunity. Crimean-Congo hemorrhagic fever virus (CCHFV), the causative agent of Crimean-Congo hemorrhagic fever, which features high mortality in humans, has been reported in southeastern Europe, Africa, the Middle East, and Asia. Although a previous study reported that CCHFV antagonizes IFN-I signaling in human cell lines, it is unclear how it inhibits IFN-I signaling. Here we demonstrated that the non-structural protein of CCHFV, NSm, suppresses IFN-I signaling in human cell lines. Furthermore, we discovered that NSm binds to STAT2, an important host protein in IFN-I signaling, and induces its degradation within cells. Taken together, our results imply that NSm suppresses IFN-I signaling by targeting human STAT2.

Crimean-Congo Hemorrhagic Fever virus (CCHFV) is a tick-borne pathogen that causes severe acute fever disease in humans and requires a biosafety level 4 laboratory for handling. Hazara virus (HAZV), belonging to the same virus genus as CCHFV, does not exhibit pathogenesis in humans. To investigate host RNA-binding proteins (RBPs) that regulate CCHFV replication, we generated a series of mutant RAW264.7 cells by CRISPR/Cas9 system and these cells were infected with HAZV. The viral titers in the supernatant of these cells was investigated, and HuR (ELAVL1) was identified. HuR KO RAW264.7 cells reduced HAZV replication. HuR is an RBP that enhances mRNA stability by binding to adenyl-uridine (AU)-rich regions in their 3′ non-coding region (NCR). HuR regulates innate immune response by binding to host mRNAs of signaling molecules. The expression of cytokine genes such as Ifnb, Il6, and Tnf was reduced in HuR KO cells after HAZV infection. Although HuR supports the innate immune response during HAZV infection, we found that innate immune activation by HAZV infection did not affect its replication. We then investigated whether HuR regulates HAZV genome RNA stability. HAZV RNA genome was precipitated with an anti-HuR antibody, and HAZV genome RNA stability was lowered in HuR KO cells. We found that HuR associated with HAZV RNA and stabilized it to enhance HAZV replication. Furthermore, HuR-deficiency reduced CCHFV minigenome replication. CCHFV is a negative-strand RNA virus and positive-strand RNA is produced during replication. HuR was associated with positive-strand RNA rather than negative-strand RNA, and AU-rich region in 3’-NCR of S segment was responsible for immunoprecipitation with anti-HuR antibody and minigenome replication. Additionally, HuR inhibitor treatment reduced CCHFV minigenome replication. Our results indicate that HuR aids replication of the CCHFV minigenome by associating with the AU-rich region in the 3′-NCR.

The excel spreadsheet contains the underlying data for the experiments presented in each of the figures. (XLSX)