The Pathogen–Host Interactions Database (PHI-base) is an online database that catalogues experimentally-verified pathogenicity, virulence and effector genes from fungal, oomycete, and bacterial pathogens, which infect animal, plant, fungal, and insect hosts. PHI-base is a valuable resource in the discovery of genes in medically and agronomically important pathogens, which may be potential targets for chemical intervention.

Information in PHI-base is manually curated by domain experts and is supported by strong experimental evidence (for example, gene disruption and gene complementation experiments), as well as references to the literature in which the original experiments are described. Annotations are made using terms from ontologies and controlled vocabularies, including the Gene Ontology (GO), Brenda Tissue Ontology (BTO), and the Pathogen--Host Interaction Phenotype Ontology (PHIPO).

PHI-base 5 includes data that was curated using a new curation process described in Cuzick et. al (2023). Data releases for PHI-base 5 do not use the same schema as data releases from PHI-base 4, but all data records from PHI-base 4 that can be made compatible with the new schema are included with this release. Data releases from PHI-base 4 and PHI-base 5 will occur in parallel until such time that all data from PHI-base 4 can be migrated to PHI-base 5. The PHI-base 4 data releases are available on Zenodo at https://zenodo.org/doi/10.5281/zenodo.5356870.

Data content

• phi-base_v5.0.xlsx: the PHI-base dataset as an Excel spreadsheet. This format follows the layout of the PHI-base 5 website, with sheets corresponding to the sections of gene pages on the website. This format is designed for use by non-technical users.

• phi-base_v5.0.json: the PHI-base dataset in JSON format. This format is closer to the data format that is exported by PHI-Canto, the curation tool used by PHI-base. This format is primarily intended for programmatic usage and has additional data (e.g. metadata for curation sessions) that is not included in the spreadsheet format.

• phi-base.schema.json: a JSON Schema file for the JSON format of the dataset. This is included as documentation for the fields in the JSON file, but can also be used to validate the dataset.

PHI-base is an online database (available at phi-base.org) that catalogues experimentally verified pathogenicity, virulence and effector genes from fungal, oomycete and bacterial pathogens, which infect animal, plant, fungal and insect hosts. PHI-base is a valuable resource in the discovery of genes in medically and agronomically important pathogens, which may be potential targets for chemical intervention.

Each entry in PHI-base is curated by domain experts and is supported by strong experimental evidence (for example, gene disruption and gene complementation experiments), as well as literature references in which the original experiments are described. Each gene in PHI-base is presented with its nucleotide sequence and deduced amino acid sequence (available in a FASTA file), as well as a detailed description of the predicted protein's function during the host infection process. To facilitate data interoperability, we have annotated genes using ontologies, controlled vocabularies, and links to external sources (including UniProt, Gene Ontology, Enzyme Commission, NCBI Taxonomy, EMBL, PubMed and FRAC).

This PHI-base dataset is a Frictionless Data Package that contains an export of the PHI-base database in CSV format (comma-separated values), plus a FASTA file with sequences for each gene in the database. This version of the dataset, version 4.17, contains 5,521 publications, covering 22,408 pathogen–host interactions and 9,973 pathogen genes across 296 pathogen species and 249 host species.

Erratum

Please note that the funding information included in the readme file for this dataset (specifically README.md and README.html) is incorrect. The correct funding sources are Growing Health [BB/X010953/1; BBS/E/RH/230003A] and Delivering Sustainable Wheat [BB/X011003/1; BBS/E/RH/230001B], both ultimately funded by the Biotechnology and Biological Sciences Research Council (BBSRC). The metadata for this dataset has been amended to use the correct funding sources (updated 16 September 2024).

jats:titleAbstract/jats:title jats:pSince 2005, the Pathogen–Host Interactions Database (PHI-base) has manually curated experimentally verified pathogenicity, virulence and effector genes from fungal, bacterial and protist pathogens, which infect animal, plant, fish, insect and/or fungal hosts. PHI-base (www.phi-base.org) is devoted to the identification and presentation of phenotype information on pathogenicity and effector genes and their host interactions. Specific gene alterations that did not alter the in host interaction phenotype are also presented. PHI-base is invaluable for comparative analyses and for the discovery of candidate targets in medically and agronomically important species for intervention. Version 4.12 (September 2021) contains 4387 references, and provides information on 8411 genes from 279 pathogens, tested on 228 hosts in 18, 190 interactions. This provides a 24% increase in gene content since Version 4.8 (September 2019). Bacterial and fungal pathogens represent the majority of the interaction data, with a 54:46 split of entries, whilst protists, protozoa, nematodes and insects represent 3.6% of entries. Host species consist of approximately 54% plants and 46% others of medical, veterinary and/or environmental importance. PHI-base data is disseminated to UniProtKB, FungiDB and Ensembl Genomes. PHI-base will migrate to a new gene-centric version (version 5.0) in early 2022. This major development is briefly described./jats:p

The Pathogen–Host Interactions Database (PHI-base) is an online database that catalogues experimentally-verified pathogenicity, virulence and effector genes from fungal, oomycete, and bacterial pathogens, which infect animal, plant, fungal, and insect hosts. PHI-base is a valuable resource in the discovery of genes in medically and agronomically important pathogens, which may be potential targets for chemical intervention.

Information in PHI-base is manually curated by domain experts and is supported by strong experimental evidence (for example, gene disruption and gene complementation experiments), as well as references to the literature in which the original experiments are described. Annotations are made using terms from ontologies and controlled vocabularies, including the Gene Ontology (GO), Brenda Tissue Ontology (BTO), and the Pathogen–Host Interaction Phenotype Ontology (PHIPO).

PHI-base 5 includes data that was curated using a new curation process described in Cuzick et. al (2023). Data releases for PHI-base 5 do not use the same schema as data releases from PHI-base 4, but all data records from PHI-base 4 that can be made compatible with the new schema are included with this release. Data releases from PHI-base 4 and PHI-base 5 will occur in parallel until such time that all data from PHI-base 4 can be migrated to PHI-base 5. The PHI-base 4 data releases are available on Zenodo at https://zenodo.org/doi/10.5281/zenodo.5356870.

For more information about the planned transition from PHI-base 4 to PHI-base 5, see the Help and Announcements page on the PHI-base 5 website.

Release statistics

This version of the PHI-base 5 dataset contains the following types of information:

File contents

• phi-base_v5.3.xlsx: the PHI-base dataset as an Excel spreadsheet. This format follows the layout of the PHI-base 5 website, with sheets corresponding to the sections of gene pages on the website. This format is designed for use by non-technical users.

• phi-base_v5.3.json: the PHI-base dataset in JSON format. This is modelled on the export format used by PHI-Canto, the curation tool used by PHI-base. This format is primarily intended for programmatic usage and has additional information (e.g. metadata for curation sessions) that is not included in the spreadsheet format.

• phi-base.schema.json: a JSON Schema file for the JSON format of the dataset. This is included as documentation for the fields in the JSON file, but can also be used to validate the dataset.

Open access or shared research data must comply with (HIPAA) patient privacy regulations. These regulations require the de-identification of datasets before they can be placed in the public domain. The process of image de-identification is time consuming, requires significant human resources, and is prone to human error. Automated image de-identification algorithms have been developed but the research community requires some method of evaluation before such tools can be widely accepted. This evaluation requires a robust dataset that can be used as part of an evaluation process for de-identification algorithms.

We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM image information objects were selected from datasets published in TCIA. Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM data elements to mimic typical clinical imaging exams. The evaluation dataset was de-identified by a TCIA curation team using standard TCIA tools and procedures. We are publishing the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (result of TCIA curation) in advance of a potential competition, sponsored by the National Cancer Institute (NCI), for de-identification algorithm evaluation, and de-identification of medical image datasets. The evaluation dataset published here is a subset of a larger evaluation dataset that was created under contract for the National Cancer Institute. This subset is being published to allow researchers to test their de-identification algorithms and promote standardized procedures for validating automated de-identification.

Database that catalogs experimentally verified pathogenicity, virulence and effector genes from fungal, Oomycete and bacterial pathogens, which infect animal, plant, fungal and insect hosts. It is an invaluable resource in the discovery of genes in medically and agronomically important pathogens, which may be potential targets for chemical intervention. In collaboration with the FRAC team, it also includes antifungal compounds and their target genes. Each entry is curated by domain experts and is supported by strong experimental evidence (gene disruption experiments, STM etc), as well as literature references in which the original experiments are described. Each gene is presented with its nucleotide and deduced amino acid sequence, as well as a detailed description of the predicted protein's function during the host infection process. To facilitate data interoperability, genes have been annotated using controlled vocabularies and links to external sources (Gene Ontology terms, EC Numbers, NCBI taxonomy, EMBL, PubMed and FRAC).

Get the latest USA Phi import data with importer names, shipment details, buyers list, product description, price, quantity, and major US ports.

Pi Beta Phi has been supporting and empowering women since 1867. The organization has grown into a global community with a lifelong membership experience rooted in timeless values, promoting friendship, leadership potential, and community service. Pi Phi aims to build confident women leaders who are equipped to make a difference in their communities and beyond. The sorority's literacy program, Read > Lead > Achieve, has been a cornerstone of its philanthropic efforts for over a century, inspiring a love of reading and learning among its members and in the communities it serves.

With its headquarters based in Town and Country, Missouri, Pi Beta Phi has a presence on college campuses and in communities across the United States and Canada. The organization is dedicated to providing its members with opportunities for personal growth, leadership development, and community engagement. Pi Phi's alumni network is a key part of the sorority's identity, with many sisters going on to become leaders in their communities and professions. Through its various programs and initiatives, Pi Beta Phi aims to create a lasting impact on the lives of its members and their communities.

The vascular plant pathogen Verticillium nonalfalfae causes Verticillium wilt in several important crops. VnaSSP4.2 was recently discovered as a V. nonalfalfae virulence effector protein in the xylem sap of infected hop. Here, we expanded our search for candidate secreted effector proteins (CSEPs) in the V. nonalfalfae predicted secretome using a bioinformatic pipeline built on V. nonalfalfae genome data, RNA-Seq and proteomic studies of the interaction with hop. The secretome, rich in carbohydrate active enzymes, proteases, redox proteins and proteins involved in secondary metabolism, cellular processing and signaling, includes 263 CSEPs. Several homologs of known fungal effectors (LysM, NLPs, Hce2, Cerato-platanins, Cyanovirin-N lectins, hydrophobins and CFEM domain containing proteins) and avirulence determinants in the PHI database (Avr-Pita1 and MgSM1) were found. The majority of CSEPs were non-annotated and were narrowed down to 44 top priority candidates based on their likelihood of being effectors. These were examined by spatio-temporal gene expression profiling of infected hop. Among the highest in planta expressed CSEPs, five deletion mutants were tested in pathogenicity assays. A deletion mutant of VnaUn.279, a lethal pathotype specific gene with sequence similarity to SAM-dependent methyltransferase (LaeA), had lower infectivity and showed highly reduced virulence, but no changes in morphology, fungal growth or conidiation were observed. Several putative secreted effector proteins that probably contribute to V. nonalfalfae colonization of hop were identified in this study. Among them, LaeA gene homolog was found to act as a potential novel virulence effector of V. nonalfalfae. The combined results will serve for future characterization of V. nonalfalfae effectors, which will advance our understanding of Verticillium wilt disease.

Emerging evidence suggests that amino acid homorepeats (HRs) in proteins (HRPs) contribute to protein interactability. What is the role of HRs in human-pathogen protein interactions? We find that pathogens engage physiologically important human HRPs, thereby affecting diverse host physiological processes. From the pathogen standpoint, (i) eukaryotic pathogens engage more HRPs but with host-sparse HRs, leading to disparate and discriminate interactions, (ii) prokaryotic pathogens engage less HRPs but with host-abundant non-polar HRs via host protein proxies bringing about discriminate or promiscuous interactions and (iii) viral pathogens engage more HRPs with host-abundant polar uncharged HRs affecting promiscuous interactions using host-partner HR tract mimicry. To propel further research, we introduce a resource Hi-PHI (http://hiphi.iisertirupati.ac.in/) cataloging critical information about human and pathogen HRPs and HRs. We propose mechanisms to (i) repurpose drugs targeting human HRPs engaged by pathogens for treating different infections and (ii) exploit HRs and their flanks as targets for pathogen-targeted anti-infectives.

Here, we have uploaded the assembled and curated human-pathogen protein interactome (HPI), which has 19,535 interactions between human and pathogen proteins. We have also provided the source code to facilitate repetition of this work and address other fundamental systems- and molecular-level questions. The instructions regarding usage of the codes are provided in individual scripts. All the datasets assembled, curated, generated and used in this study is available as a resource, Hi-PHI database (http://hiphi.iisertirupati.ac.in/).

Table provides RRES v5 gene ID, PHI identifier ID from PHI-base, Uniprot protein ID, gene function, mutant phenotype, experimental technique, author reference, and year published. (XLSX)

The production of $\mathrm{K}^{*}(\mathrm{892})^{0}$ and $\mathrm{\phi(1020)}$ resonances has been measured in p-Pb collisions at $\sqrt{s_{\rm NN}}$ = 8.16 TeV using the ALICE detector. Resonances are reconstructed via their hadronic decay channels in the rapidity interval $-$0.5 $<\(y\)<$ 0 and the transverse momentum spectra are measured for various multiplicity classes up to $p_{\rm T}$ = 20 GeV/$c$ for $\mathrm{K}^{*}(\mathrm{892})^{0}$ and $p_{\rm T}$ = 16 GeV/$c$ for $\mathrm{\phi(1020)}$. The $p_{\rm T}$ -integrated yields and mean transverse momenta are reported and compared with previous results in pp, p-Pb and Pb-Pb collisions. The $x_{\mathrm{T}}$ scaling for $\mathrm{K}^{*}(\mathrm{892})^{0}$ and $\mathrm{\phi(1020)}$ resonance production is newly tested in p-Pb collisions and found to hold in the high-$p_{\rm T}$ region at LHC energies. The nuclear modification factors ($R_{\rm pPb}$) as a function of $p_{\rm T}$ for $\mathrm{K}^{*0}$ and $\mathrm{\phi}$ at $\sqrt{s_{NN}}$ = 8.16 TeV are presented along with the new $R_{\rm pPb}$ measurements of $\mathrm{K}^{*0}$, $\mathrm{\phi}$ , $\Xi$, and $\Omega$ at $\sqrt{s_{\rm NN}}$ = 5.02 TeV. At intermediate $p_{\rm T}$ (2-8 GeV/$c$), $R_{\rm pPb}$ of $\Xi$, $\Omega$ show a Cronin-like enhancement, while $\mathrm{K}^{*0}$ and $\mathrm{\phi}$ show no or little nuclear modification. At high $p_{\rm T}$ ($>$ 8 GeV/$c$), the $R_{\rm pPb}$ values of all hadrons are consistent with unity within uncertainties. The $R_{\rm pPb}$ of $\mathrm{K}^{*}(\mathrm{892})^{0}$ and $\mathrm{\phi(1020)}$ at $\sqrt{s_{\rm NN}}$ = 8.16 and 5.02 TeV show no significant energy dependence.

Abstract The Medical Information Mart for Intensive Care (MIMIC)-IV database is comprised of deidentified electronic health records for patients admitted to the Beth Israel Deaconess Medical Center. Access to MIMIC-IV is limited to credentialed users. Here, we have provided an openly-available demo of MIMIC-IV containing a subset of 100 patients. The dataset includes similar content to MIMIC-IV, but excludes free-text clinical notes. The demo may be useful for running workshops and for assessing whether the MIMIC-IV is appropriate for a study before making an access request.

Background The increasing adoption of digital electronic health records has led to the existence of large datasets that could be used to carry out important research across many areas of medicine. Research progress has been limited, however, due to limitations in the way that the datasets are curated and made available for research. The MIMIC datasets allow credentialed researchers around the world unprecedented access to real world clinical data, helping to reduce the barriers to conducting important medical research. The public availability of the data allows studies to be reproduced and collaboratively improved in ways that would not otherwise be possible.

Methods First, the set of individuals to include in the demo was chosen. Each person in MIMIC-IV is assigned a unique subject_id. As the subject_id is randomly generated, ordering by subject_id results in a random subset of individuals. We only considered individuals with an anchor_year_group value of 2011 - 2013 or 2014 - 2016 to ensure overlap with MIMIC-CXR v2.0.0. The first 100 subject_id who satisfied the anchor_year_group criteria were selected for the demo dataset.

All tables from MIMIC-IV were included in the demo dataset. Tables containing patient information, such as emar or labevents, were filtered using the list of selected subject_id. Tables which do not contain patient level information were included in their entirety (e.g. d_items or d_labitems). Note that all tables which do not contain patient level information are prefixed with the characters 'd_'.

Deidentification was performed following the same approach as the MIMIC-IV database. Protected health information (PHI) as listed in the HIPAA Safe Harbor provision was removed. Patient identifiers were replaced using a random cipher, resulting in deidentified integer identifiers for patients, hospitalizations, and ICU stays. Stringent rules were applied to structured columns based on the data type. Dates were shifted consistently using a random integer removing seasonality, day of the week, and year information. Text fields were filtered by manually curated allow and block lists, as well as context-specific regular expressions. For example, columns containing dose values were filtered to only contain numeric values. If necessary, a free-text deidentification algorithm was applied to remove PHI from free-text. Results of this algorithm were manually reviewed and verified to remove identified PHI.

Data Description MIMIC-IV is a relational database consisting of 26 tables. For a detailed description of the database structure, see the MIMIC-IV Clinical Database page [1] or the MIMIC-IV online documentation [2]. The demo shares an identical schema and structure to the equivalent version of MIMIC-IV.

Data files are distributed in comma separated value (CSV) format following the RFC 4180 standard [3]. The dataset is also made available on Google BigQuery. Instructions to accessing the dataset on BigQuery are provided on the online MIMIC-IV documentation, under the cloud page [2].

An additional file is included: demo_subject_id.csv. This is a list of the subject_id used to filter MIMIC-IV to the demo subset.

Usage Notes The MIMIC-IV demo provides researchers with the opportunity to better understand MIMIC-IV data.

CSV files can be opened natively using any text editor or spreadsheet program. However, as some tables are large it may be preferable to navigate the data via a relational database. We suggest either working with the data in Google BigQuery (see the "Files" section for access details) or creating an SQLite database using the CSV files. SQLite is a lightweight database format which stores all constituent tables in a single file, and SQLite databases interoperate well with a number software tools.

Code is made available for use with MIMIC-IV on the MIMIC-IV code repository [4]. Code provided includes derivation of clinical concepts, tutorials, and reproducible analyses.

Release Notes Release notes for the demo follow the release notes for the MIMIC-IV database.

Ethics This project was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). Requirement for individual patient consent was waived because the pr...

These datasets are part of the California Health and Human Services Agency (CalHHS) Data De-Identification Guidelines (DDG) in the "State and County Population Projections" Appendix. The DDG assists CalHHS departments in evaluating data for public release while ensuring the privacy of individuals represented in the data. California population estimates serve as a foundation for the population-based scoring assessments outlined in the DDG.

Genes encoding pathogenicity related factors derived from PHI database

Dataset

This dataset was created by Mizanur Rahman

Released under Apache 2.0

Contents

Comprehensive dataset containing 46 verified Pi Beta Phi locations in United States with complete contact information, ratings, reviews, and location data.

1024m/PHI-4-Hindi-Instruct-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

Observed and expected 95% CL upper limits on B(H $\rightarrow$ Z$\phi$), for different polarizations.

Comprehensive dataset containing 41 verified Alpha Phi locations in United States with complete contact information, ratings, reviews, and location data.