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

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_v5.1.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_v5.1.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.

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

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.

Link Function: information

Genes encoding pathogenicity related factors derived from PHI database

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).

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.

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

Pathogen - host interaction phenotypes and pathogen phenotypes curated at PHI-base

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

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.

Effector candidates of E. arachidis in PHI database.

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)

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

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

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.

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.

Interactions between proteins underlie all aspects of complex biological mechanisms. Therefore, methodologies based on complex network analyses can facilitate identification of promising candidate genes involved in phenotypes of interest and put this information into appropriate contexts. To facilitate discovery and gain additional insights into globally important pathogenic fungi, we have reconstructed computationally inferred interactomes using an interolog and domain-based approach for 15 diverse Ascomycete fungal species, across nine orders, specifically Aspergillus fumigatus, Bipolaris sorokiniana, Blumeria graminis f. sp. hordei, Botrytis cinerea, Colletotrichum gloeosporioides, Colletotrichum graminicola, Fusarium graminearum, Fusarium oxysporum f. sp. lycopersici, Fusarium verticillioides, Leptosphaeria maculans, Magnaporthe oryzae, Saccharomyces cerevisiae, Sclerotinia sclerotiorum, Verticillium dahliae, and Zymoseptoria tritici. Network cartography analysis was associated with functional patterns of annotated genes linked to the disease-causing ability of each pathogen. In addition, for the best annotated organism, namely F. graminearum, the distribution of annotated genes with respect to network structure was profiled using a random walk with restart algorithm, which suggested possible co-location of virulence-related genes in the protein–protein interaction network. In a second ‘use case’ study involving two networks, namely B. cinerea and F. graminearum, previously identified small silencing plant RNAs were mapped to their targets. The F. graminearum phenotypic network analysis implicates eight B. cinerea targets and 35 F. graminearum predicted interacting proteins as prime candidate virulence genes for further testing. All 15 networks have been made accessible for download at www.phi-base.org providing a rich resource for major crop plant pathogens.