jats:titleAbstract/jats:title jats:pThe pathogen–host interactions database (PHI-base) is available at www.phi-base.org. PHI-base contains expertly curated molecular and biological information on genes proven to affect the outcome of pathogen–host interactions reported in peer reviewed research articles. PHI-base also curates literature describing specific gene alterations that did not affect the disease interaction phenotype, in order to provide complete datasets for comparative purposes. Viruses are not included, due to their extensive coverage in other databases. In this article, we describe the increased data content of PHI-base, plus new database features and further integration with complementary databases. The release of PHI-base version 4.8 (September 2019) contains 3454 manually curated references, and provides information on 6780 genes from 268 pathogens, tested on 210 hosts in 13,801 interactions. Prokaryotic and eukaryotic pathogens are represented in almost equal numbers. Host species consist of approximately 60% plants (split 50:50 between cereal and non-cereal plants), and 40% other species of medical and/or environmental importance. The information available on pathogen effectors has risen by more than a third, and the entries for pathogens that infect crop species of global importance has dramatically increased in this release. We also briefly describe the future direction of the PHI-base project, and some existing problems with the PHI-base curation process./jats:p

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).

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).

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, 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 controlled vocabularies and links to external sources (UniProt, Gene Ontology, Enzyme Commission, NCBI Taxonomy, EMBL, PubMed and FRAC).

The PHI-base dataset is published as a single table in CSV format (comma-separated values). The data contains information from 4,387 publications, covering 18,190 pathogen–host interactions and 8,411 genes across 279 pathogen species and 228 host species.

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

Link Function: information

Approved curation sessions from the PHI-Canto curation tool, as of 13 December 2022. PHI-Canto is used to curate literature on pathogen–host interactions, and supplies data to PHI-base, the Pathogen–Host Interactions Database.

The curated data is exported in JSON format, and contained in a single JSON object. The object's keys are the identifiers for individual curation sessions, where each curation session corresponds to one publication. The export contains the raw data exported by PHI-Canto: no further processing has been applied.

There is a JSON Schema file included that describes the data fields used in the export file.

Textbooks Are All You Need

Leveraging Large Language Models (LLMs), there's an opportunity to create a comprehensive open-source repository reminiscent of the historic Library of Alexandria. This initiative represents a preliminary attempt at producing high-quality books covering an extensive range of subjects. The source of these samples varies:

Some generated using the RAG model, referencing Wikipedia or other search data. Some are completely synthetically generated. Some created… See the full description on the dataset page: https://huggingface.co/datasets/open-phi/textbooks.

Effector candidates of E. arachidis in PHI database.

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

The HIPAA compliance services market is experiencing robust growth, driven by increasing regulatory scrutiny, the rising adoption of cloud-based healthcare solutions, and a growing awareness of data breach consequences. The market's expansion is fueled by a need for robust security measures to protect sensitive patient health information (PHI). This demand is particularly strong in North America, which holds a significant market share due to the stringent regulations and advanced healthcare infrastructure. The market is segmented by service type (consulting, software, training), deployment model (cloud, on-premise), and organization size (small, medium, large). Companies such as TECEZE, Comforte, and others offer a range of solutions, from risk assessments and compliance audits to security software and employee training programs. The market is characterized by a competitive landscape with established players and emerging technology providers. Continuous innovation in areas like AI-powered security and blockchain technology is expected to further propel market growth. The forecast period (2025-2033) suggests a sustained upward trajectory, influenced by factors such as increasing healthcare data volumes, the growing prevalence of telehealth, and advancements in data security technologies. While restraints such as the high cost of implementation and the complexities of HIPAA compliance exist, the potential financial and reputational risks associated with non-compliance significantly outweigh these challenges. The market is expected to witness consolidation and strategic partnerships as companies strive to expand their service offerings and cater to diverse customer needs. Geographical expansion into emerging markets with burgeoning healthcare sectors will also play a significant role in shaping future market dynamics. The overall growth outlook remains positive, presenting significant opportunities for both established players and new entrants to this crucial market segment.

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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)

Pear black spot (PBS) disease, which is caused by Alternaria alternata (Aa), is one of the most serious diseases affecting sand pear (Pyrus pyrifolia Nakai) cultivation worldwide. To investigate the defense mechanisms of sand pear in response to Aa, the transcriptome of a sand pear germplasm with differential resistance to Aa was analyzed using Illumina paired-end sequencing. Four libraries derived from PBS-resistant and PBS-susceptible sand pear leaves were characterized through inoculation or mock-inoculation. In total, 20.5 Gbp of sequence data and 101,632,565 reads were generated, representing 44717 genes. Approximately 66% of the genes or sequenced reads could be aligned to the pear reference genome. A large number (5213) of differentially expressed genes related to PBS resistance were obtained; 34 microsatellites were detected in these genes, and 28 genes were found to be closely related to PBS resistance. Using a transcriptome analysis in response to PBS inoculation and comparison analysis to the PHI database, 4 genes (Pbr039001, Pbr001627, Pbr025080 and Pbr023112) were considered to be promising candidates for sand pear resistance to PBS. This study provides insight into changes in the transcriptome of sand pear in response to PBS infection, and the findings have improved our understanding of the resistance mechanism of sand pear to PBS and will facilitate future gene discovery and functional genome studies of sand pear.

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...

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Dataset Summary

This dataset is created for training the phi-1 model, based on the paper "Textbooks are All You Need". It contains high-quality data derived from various textbooks, transformed and synthesized using OpenAI's GPT-3.5 and GPT-4 models. For optimal results, it is recommended to train models with the following parameters and sequence lengths:

For a model with 350M parameters, use a sequence length of 2048. For a model with 700M parameters, use a sequence length of 4096.… See the full description on the dataset page: https://huggingface.co/datasets/teleprint-me/phi-1.

The Polarimetric and Helioseismic Imager is a magnetograph based on two telescopes, the Full Disc Telescope (FDT) and the High Resolution Telescope (HRT). The instrument carries out narrow-band imaging spectro-polarimetry of the Fe I 617.3nm absorption line, obtaining four polarization states at each of the six spectral scanning position. In their more extended version, the scientific data provided by SO/PHI consist of the Stokes vector, the full magnetic field vector, the Doppler velocity as well as the continuum intensity at each pixel in the field of view. The 24 polarimetric images per dataset obtained by SO/PHI are demodulated into the Stokes vector and subsequently processed by inverting the radiative transfer equation (RTE). SO/PHI has the unique option to perform the RTE inversion also onboard. SO/PHI scientific data usually consist of fully calibrated maps of the RTE inverted physical quantities, namely the magnetic field strength, the inclination angle of the magnetic field vector with respect to the line of sight, the angle of the magnetic field azimuth, the line-of-sight flow velocity as well as the continuum intensity in the 617nm band. When the RTE inversion is performed on ground, the calibrated maps of the Stokes vector are also available. The SO/PHI-FDT telescope has a plate scale of 3.7 arcsec and it is designed to image the full solar disk at each position of the orbit of Solar Orbiter, where the distance from the Sun varies from 0.29 to 1.1AU. FDT data are usually provided at lower cadence (typically hours) for synoptic purposes. The SO/PHI-HRT telescope has a plate scale of 0.5 arcsec and it is meant for high-resolution observations at closer approach. The helio-projective pixel size at perihelion (0.29AU) is about 106km. HRT data are mainly obtained for shorter periods but at higher cadence (typically from a minute to hours). Dedicated observing programs for helioseismology contain only a single polarization state in order to provide the Doppler velocity and the continuum intensity at the highest possible cadence. More information on SO/PHI and its data products can be found at https://www.mps.mpg.de/solar-physics/solar-orbiter-phi

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