Application Programming Interface Research Articles

Geographical accessibility to functional emergency obstetric care facilities in urban Nigeria using closer-to-reality travel time estimates: a population-based spatial analysis

Better accessibility for emergency obstetric care facilities can substantially reduce maternal and perinatal deaths. However, pregnant women and girls living in urban settings face additional complex challenges travelling to facilities. We aimed to assess the geographical accessibility of the three nearest functional public and private comprehensive emergency obstetric care facilities in the 15 largest Nigerian cities via a novel approach that uses closer-to-reality travel time estimates than traditional model-based approaches. In this population-based spatial analysis, we mapped city boundaries, verified and geocoded functional comprehensive emergency obstetric care facilities, and mapped the population distribution for girls and women aged 15-49 years (ie, of childbearing age). We used the Google Maps Platform's internal Directions Application Programming Interface to derive driving times to public and private facilities. Median travel time and the percentage of women aged 15-49 years able to reach care were summarised for eight traffic scenarios (peak and non-peak hours on weekdays and weekends) by city and within city under different travel time thresholds (≤15 min, ≤30 min, ≤60 min). As of 2022, there were 11·5 million girls and women aged 15-49 years living in the 15 studied cities, and we identified the location and functionality of 2020 comprehensive emergency obstetric care facilities. City-level median travel time to the nearest comprehensive emergency obstetric care facility ranged from 18 min in Maiduguri to 46 min in Kaduna. Median travel time varied by location within a city. The between-ward IQR of median travel time to the nearest public comprehensive emergency obstetric care varied from the narrowest in Maiduguri (10 min) to the widest in Benin City (41 min). Informal settlements and peripheral areas tended to be worse off compared to the inner city. The percentages of girls and women aged 15-49 years within 60 min of their nearest public comprehensive emergency obstetric care ranged from 83% in Aba to 100% in Maiduguri, while the percentage within 30 min ranged from 33% in Aba to over 95% in Ilorin and Maiduguri. During peak traffic times, the median number of public comprehensive emergency obstetric care facilities reachable by women aged 15-49 years under 30 min was zero in eight (53%) of 15 cities. Better access to comprehensive emergency obstetric care is needed in Nigerian cities and solutions need to be tailored to context. The innovative approach used in this study provides more context-specific, finer, and policy-relevant evidence to support targeted efforts aimed at improving comprehensive emergency obstetric care geographical accessibility in urban Africa. Google.

GestureFusion: A Gesture-based Interaction System

Abstract: In an era of increasingly sophisticated human-computer interaction, the ability to comprehend and interpret hand gestures has become a pivotal element for bridging the gap between humans and machines. This project, titled ”GestureFusion: A Gesture-based Interaction System,” seeks to contribute to this rapidly evolving field by developing a comprehensive system for recognizing and categorizing hand gestures through the application of computer vision techniques. The primary objective of this project is to create a versatile and extensible hand gesture recognition system that can be employed in various domains such as virtual reality, robotics, sign language interpretation, and human-computer interface design. To achieve this, the project employs state-of-the-art computer vision algorithms and deep learning techniques. Key components of this project include data collection and annotation, model training and optimization, the development of a user-friendly application programming interface (API) for integration into diverse applications.

PypKa server: online pKa predictions and biomolecular structure preparation with precomputed data from PDB and AlphaFold DB.

When preparing biomolecular structures for molecular dynamics simulations, pKa calculations are required to provide at least a representative protonation state at a given pH value. Neglecting this step and adopting the reference protonation states of the amino acid residues in water, often leads to wrong electrostatics and nonphysical simulations. Fortunately, several methods have been developed to prepare structures considering the protonation preference of residues in their specific environments (pKa values), and some are even available for online usage. In this work, we present the PypKa server, which allows users to run physics-based, as well as ML-accelerated methods suitable for larger systems, to obtain pKa values, isoelectric points, titration curves, and structures with representative pH-dependent protonation states compatible with commonly used force fields (AMBER, CHARMM, GROMOS). The user may upload a custom structure or submit an identifier code from PBD or UniProtKB. The results for over 200k structures taken from the Protein Data Bank and the AlphaFold DB have been precomputed, and their data can be retrieved without extra calculations. All this information can also be obtained from an application programming interface (API) facilitating its usage and integration into existing pipelines as well as other web services. The web server is available at pypka.org.

A Comparative Analysis of Malware Detection Methods Traditional vs. Machine Learning

Mobile devices have been the target of malicious software since their beginnings. Two known types
 of malware threats can intrude into mobile, independently injected applications and fraudulent
 applications that are developed to breach the security of mobile devices. Mostly these fraudulent
 applications access data using API calls and permission requests. API calls and permission requests
 are important for smooth conversation between mobile devices and database servers. This research
 proposes an efficient classification model that concatenates API calls and permission requests to
 detect malicious applications. We have used a dataset that contained more than 15 thousand Android
 devices’ malware. We have divided data into three groups to differentiate between the malicious
 permission requests and malicious API calls with normal permission requests and normal API calls.
 To increase the probability of recognizing Android malware applications, we develop three distinct
 grouping strategies for selecting the most valuable API calls that are obscure, critical, and obstreperous and are chosen because Android apps extensively use several application programming interfaces (APIs). According to the results, malware applications require authorizations to access confidential information very frequently than normal Android applications do. Also, malicious Android
 applications raise a diverse set of API calls to access sensitive data, evidenced by malware applications making a distinct set of API calls. Our proposed method attains an F-score of 94.04%, which suggests that it is efficient at discovering mobile malware applications. Our model can be of significant assistance in conducting mobile application analysis and forensic investigations into malware.

HEIDI: an experiment-management platform enabling high-throughput fragment and compound screening.

The Swiss Light Source facilitates fragment-based drug-discovery campaigns foracademic and industrial users through the Fast Fragment and Compound Screening (FFCS) software suite. This framework is further enriched by the option to utilize the Smart Digital User (SDU) software for automated data collection across the PXI, PXII and PXIII beamlines. In this work, the newly developed HEIDI webpage (https://heidi.psi.ch) is introduced: a platform crafted using state-of-the-art software architecture and web technologies for sample management of rotational data experiments. The HEIDI webpage features a data-review tab for enhanced result visualization and provides programmatic access through a representational state transfer application programming interface (REST API). The migration of the local FFCS MongoDB instance to the cloud is highlighted and detailed. This transition ensures secure, encrypted and consistently accessible data through a robust and reliable REST API tailored for the FFCS software suite. Collectively, these advancements not only significantly elevate the user experience, but also pave the way for future expansions and improvements in the capabilities of the system.

SKHASH: A Python Package for Computing Earthquake Focal Mechanisms

Abstract We introduce a Python package for computing focal mechanism solutions. This algorithm, which we refer to as SKHASH, is largely based on the HASH algorithm originally written in Fortran over 20 yr ago. HASH innovated the use of suites of solutions, spanning the expected errors in polarities and takeoff angles, to estimate focal mechanism uncertainty. SKHASH benefits from new features with flexible input formats and allows users to take advantage of recent advances in constraining focal mechanisms for small magnitude or poorly recorded earthquakes. The 3D locations of earthquakes and the velocity models used are varied when finding acceptable solutions. As a result, source–receiver azimuths are reflective of errors from the earthquake locations and velocity models, in addition to the takeoff angles. Users can consider weighted P-wave first-motion polarities derived from traditional or machine-learning picks, cross-correlation consensus, and/or imputation techniques using SKHASH. Focal mechanism solutions can also be further constrained using traditional, machine learning, and/or cross-correlation consensus S/P amplitude ratios. With improved reporting of individual and collective P polarity and S/P amplitude misfits, users can better evaluate the success of the solutions and the quality of the measurements. The reporting also makes it easier to identify potential issues with metadata, including incorrectly reported station polarity reversals. In addition, by leveraging vectorized operations, taking advantage of an efficient backend Python C Application Programming Interface, and the use of a parallel environment, the Python SKHASH routine may compute mechanisms quicker than the HASH routine.

MetSim: Integrated Programmatic Access and Pathway Management for Xenobiotic Metabolism Simulators.

Xenobiotic metabolism is a key consideration in evaluating the hazards and risks posed by environmental chemicals. A number of software tools exist that are capable of simulating metabolites, but each reports its predictions in a different format and with varying levels of detail. This makes comparing the performance and coverage of the tools a practical challenge. To address this shortcoming, we developed a metabolic simulation framework called MetSim, which comprises three main components. A graph-based schema was developed to allow metabolism information to be harmonized. The schema was implemented in MongoDB to store and retrieve metabolic graphs for subsequent analysis. MetSim currently includes an application programming interface for four metabolic simulators: BioTransformer, the OECD Toolbox, EPA's chemical transformation simulator (CTS), and tissue metabolism simulator (TIMES). Lastly, MetSim provides functions to help evaluate simulator performance for specific data sets. In this study, a set of 112 drugs with 432 reported metabolites were compiled, and predictions were made using the 4 simulators. Fifty-nine of the 112 drugs were taken from the Small Molecule Pathway Database, with the remainder sourced from the literature. The human models within BioTransformer and CTS (Phase I only) and the rat models within TIMES and the OECD Toolbox (Phase I only) were used to make predictions for the chemicals in the data set. The recall and precision (recall, precision) ranked in order of highest recall for each individual tool were CTS (0.54, 0.017), BioTransformer (0.50, 0.008), Toolbox in vitro (0.40, 0.144), TIMES in vivo (0.40, 0.133), Toolbox in vivo (0.40, 0.118), and TIMES in vitro (0.39, 0.128). Combining all of the model predictions together increased the overall recall (0.73, 0.008). MetSim enabled insights into the performance and coverage of in silico metabolic simulators to be more efficiently derived, which in turn should aid future efforts to evaluate other data sets.

Social media for palliative and end-of-life care research: a systematic review

BackgroundSocial media with real-time content and a wide-reaching user network opens up more possibilities for palliative and end-of-life care (PEoLC) researchers who have begun to embrace it as a complementary...

APIs in the Metaverse—A Systematic Evaluation

One of the most critical challenges for the success of the Metaverse is interoperability amongst its virtual platforms and worlds. In this context, application programming interfaces (APIs) are essential. This study analyzes a sample of 15 Metaverse platforms. In the first step, the availability of publicly accessible APIs was examined. For those platforms offering an API, i.e., Decentraland, Second Life, Voxels, Roblox, Axie Infinity, Upland, and VRChat, the available API contents were collected, analyzed, and presented in the paper. The results show that only a few Metaverse platforms offer APIs at all. In addition, the available APIs are very diverse and heterogeneous. Information is somewhat fragmented, requiring access to several APIs to compile a comprehensive data set. Thus, standardized APIs will enable better interoperability and foster a more seamless and immersive user experience in the Metaverse.

OpenMP-based parallel MLPG solver for analysis of heat conduction

PurposeIn the present work, we focus on developing an in-house parallel meshless local Petrov-Galerkin (MLPG) code for the analysis of heat conduction in two-dimensional and three-dimensional regular as well as complex geometries.Design/methodology/approachThe parallel MLPG code has been implemented using open multi-processing (OpenMP) application programming interface (API) on the shared memory multicore CPU architecture. Numerical simulations have been performed to find the critical regions of the serial code, and an OpenMP-based parallel MLPG code is developed, considering the critical regions of the sequential code.FindingsBased on performance parameters such as speed-up and parallel efficiency, the credibility of the parallelization procedure has been established. Maximum speed-up and parallel efficiency are 10.94 and 0.92 for regular three-dimensional geometry (343,000 nodes). Results demonstrate the suitability of parallelization for larger nodes as parallel efficiency and speed-up are more for the larger nodes.Originality/valueFew attempts have been made in parallel implementation of the MLPG method for solving large-scale industrial problems. Although the literature suggests that message-passing interface (MPI) based parallel MLPG codes have been developed, the OpenMP model has rarely been touched. This work is an attempt at the development of OpenMP-based parallel MLPG code for the very first time.

Abstract LB141: Harmonization and integration of regional based prospective cohorts: A preliminary report of the African Caribbean Cancer Consortium

Abstract Introduction: The promise of achieving cancer control both nationally and globally rest on rigorous data collection, data harmonization and data sharing to catalyze scientific knowledge and understanding to more quickly advance our progress to addressing cancer disparities. In response, the LIFE Project aimed to establish a cancer-focused robust, functional platform for data harmonization and sharing across national and international studies with a focus on low-resourced settings and LMIC. Methods: Established in 2022, the integrated database platform hosts data collected from the ongoing Jamaican LIFE and US CAP3 projects. Both studies focus on investigating biologic and socioecologic risk factors for non-communicable chronic diseases (NCDs) and cancers. Participants self-reported data via REDCap. A one-to one mapping of the variables from both questionnaires was conducted and stored in the integrated REDCap file. An automated REDCap Application Programming Interface (API) script weekly imports the new entries into the integrated database. To ensure data accuracy, on a weekly schedule, data importation and data integration are monitored and assessed by random sampling of original source data entries and the corresponding entries in the integrated database. Any additional errors in data integration are resolved via consultations with the programming team. Results: As of June 2023, 3,369 participants were enrolled - 2,561 enrolled from the LIFE study and 808 from the CAP3 study. Overall, the combined participants were 89% Afro-Caribbean or African American, and comprised 40% males with a median age of 54 years and 60% females with median age being 51 years. For the LIFE study, median age is 50 years, and for the CAP3 the median age is 55. The prevalence of self-reported health conditions are as follows: diabetes - 13% LIFE, 13% CAP3; hypertension - 37% LIFE, 19% CAP3; dyslipidemia - 15% LIFE, 21% CAP3. Conclusion: With the continuing need for precision public health, there is a dire need to address disparities through innovative ways that promote comprehensive data linkage and analyses. This platform can be used in mapping individual cohort data with demonstrated success for the LIFE and CAP3 studies. Our web-based query tool to interface that interfaces with our REDCap database also allows us to query the available variables in both the separate and combined cohort databases and will facilitate hypothesis development. The impact of our study goes beyond the data harmonization, and robust data integration platform that links the LIFE and CAP3 studies. This allows for US comparative studies. Novel. We will also have the ability to harmonize with large US cohort studies, as well as interrogate research questions examining commonalities and differences among African Americans, Afro-Caribbean Americans and Afro-Caribbeans in the Caribbean. These comparative studies will help to elucidate the causes increased health risks, and accelerate the science to remedy the global Black NCD and cancer disparities. Citation Format: Camille C. R. Ragin, Janeil Williams, Olga Tchuvatkina, Joette Mckenzie, Kimlin Ashing, Marshall Tulloch-Reid. Harmonization and integration of regional based prospective cohorts: A preliminary report of the African Caribbean Cancer Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB141.

SecureLaw Portal: Empowering Citizens with Transparent Legal Records via Blockchain and Cloud eVault

In the digital age, where data integrity is essential, the management of legal data requires innovative solutions. SecureLaw is a new project that combines the immutable power of blockchain, the flexibility of Cloud. The agility of the Cloud, Express.js, React.js, Node .js suite. This integration forms the backbone of the transformation of data protection laws, providing unparalleled security, transparency and easy access. Highlights of this project: SecureLaw is instrumental in redefining information law in India. Traditional systems often face security breaches and lack of transparency, undermining the integrity of the legal process. SecureLaw solves these problems by integrating blockchain technology, which provides immutable data that ensures legal documents are not tampered with and ensure their authenticity. The introduction of smart contracts automates the process, controls permissions and makes data immutable, thus increasing efficiency and reducing errors. Leveraging Cloud services such as S3 for storage, API gateways for seamless communications, Lambda for serverless computing, and DynamoDB for scalable databases provides a powerful, scalable, and cost-effective infrastructure. Scalability is important to accommodate legal system growth, and Cloud’s Dynamic Services provide a solution. The AERN group (AWS, Express.js, React.js, Node.js) strengthens the foundation of the project. Express.js is a simple and easy backend framework that seamlessly integrates for efficient data processing and processing. React.js is a front-end library with many features that make it user responsive and intuitive. Node.js is known for its performance that enables dynamic recovery and makes data fast and recoverable. SecureLaw's user centric approach includes managing access to personal and legal personal details. Personal auditors, regional directors, directors (DGs), judges, etc. It deals with many user roles, personal control panels, and access rights. This change promotes cooperation, transparency and communication in the legal system. Features of a self-explanatory legal system encourage people to understand the legal process, fostering transparency, trust, and collaborative knowledge. Keywords: Blockchain, Application Programming Interface (API), Cloud , Express.js, React.js, Node.js

Data Processing Engine (DPE): data analysis tool for particle tracking and mixed radiation field characterization with pixel detectors Timepix

Hybrid semiconductor pixelated detectors from the Timepix family are advanced detectors for online particle tracking, offering energy measurement and precise time stamping capabilities for particles of various types and energies. This inherent capability makes them highly suitable for various applications, including imaging, medical fields such as radiotherapy and particle therapy, space-based applications aboard satellites and the International Space Station, and industrial applications. The data generated by these detectors is complex, necessitating the development and deployment of various analytical techniques to extract essential information.For this purpose, and to aid the Timepix user community, it was designed and developed the“Data Processing Engine” (DPE) as an advanced tool for data processing designed explicitly for Timepix detectors. The functionality of the DPE is structured into three distinct processing levels: i) Pre-processing: this phase involves clusterization and the application of necessary calibrations and corrections. ii) Processing: this stage includes particle classification, employing machine learning algorithms, and the recognition of radiation fields. iii) Post-processing: involves various analyses, such as directional analysis, coincidence analysis, frame analysis, Compton directional analysis, and the generation of physics products, are performed. The core of the DPE is supported by an extensive experimental database containing calibrations and referential radiation fields of typical environments, including protons, ions, electrons, gamma rays and X rays, as well as thermal and fast neutrons. To enhance accessibility, the DPE is implemented into various user interface platforms such as a command-line tool, an application programming interface, and as a graphical user interface in the form of a web portal. The DPE's broad utility is exemplified through its integration into various applications and developments.

Developing a smart system for infant incubators using the internet of things and artificial intelligence

This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R<sup>2</sup> value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R<sup>2</sup> value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.

112 Flight Tracker: A REDCap Tool to Streamline Career Development Grant Preparation and Reporting

OBJECTIVES/GOALS: Compiling information about characteristics and progress of scholars is required for career development applications and progress reports. The range of information is substantial, and preparation is onerous. We sought to create a tool to facilitate gathering key data about trainees and mentors who participate in programs like NIH K- and T-awards. METHODS/STUDY POPULATION: Using the REDCap platform, we developed forms and surveys to support intake of applicants, updates from scholars, and information about their participation in activities and use of resources. We deployed application programming interfaces (APIs) to automate capture of publicly available data about publications, impact metrics, and federal grant funding. Similar tools capture descriptions of mentor expertise including grant funding, prior trainees, and publications with mentees We also built modules to 1) allow connection to institutional grant and contract data to capture foundation and other funding; 2) pre-populate follow-up surveys to update information about career trajectories with minimal scholar effort; and 3) support mentee-mentor agreements as living documents. RESULTS/ANTICIPATED RESULTS: After a pilot period at our institution, we disseminated Flight Tracker to more than 50 academic institutions, most of whom are CTSA hubs. They track scholars in TL1/T32s, KL2/K12s, MSTP programs, and academic groups. Beyond federal reporting, uses now include publication impact factors (relative citation ratios, Altmetrics scores), grant funding of groups, maps of network relationships among investigators, scholar receipt of internal pilot awards, and statistics about transition to independence and time-to-promotion. Scholars can be separated into smaller cohorts by demographics, training dates, and funding dates. Over 34,000 scholars are tracked nationally among over 260 programs. Having structured data supports program evaluation, continuous improvements, and documents program strengths. DISCUSSION/SIGNIFICANCE: We informally estimate Flight Tracker reduces staff and leadership effort in preparation of program data by 75%, preserving time to focus on service to scholars. Ready access to data over time and within and across institutions creates new opportunities for collaborative data analysis to support evidence-based career development.

Social Network Analysis of e-Cigarette-Related Social Media Influencers on Twitter/X: Observational Study.

An e-cigarette uses a battery to heat a liquid that generates an aerosol for consumers to inhale. e-Cigarette use (vaping) has been associated with respiratory disease, cardiovascular disease, and cognitive functions. Recently, vaping has become increasingly popular, especially among youth and young adults. The aim of this study was to understand the social networks of Twitter (now rebranded as X) influencers related to e-cigarettes through social network analysis. Through the Twitter streaming application programming interface, we identified 3,617,766 unique Twitter accounts posting e-cigarette-related tweets from May 3, 2021, to June 10, 2022. Among these, we identified 33 e-cigarette influencers. The followers of these influencers were grouped according to whether or not they post about e-cigarettes themselves; specifically, the former group was defined as having posted at least five e-cigarette-related tweets in the past year, whereas the latter group was defined as followers that had not posted any e-cigarette-related tweets in the past 3 years. We randomly sampled 100 user accounts among each group of e-cigarette influencer followers and created corresponding social networks for each e-cigarette influencer. We compared various network measures (eg, clustering coefficient) between the networks of the two follower groups. Major topics from e-cigarette-related tweets posted by the 33 e-cigarette influencers included advocating against vaping policy (48.0%), vaping as a method to quit smoking (28.0%), and vaping product promotion (24.0%). The follower networks of these 33 influencers showed more connections for those who also post about e-cigarettes than for followers who do not post about e-cigarettes, with significantly higher clustering coefficients for the former group (0.398 vs 0.098; P=.005). Further, networks of followers who post about e-cigarettes exhibited substantially more incoming and outgoing connections than those of followers who do not post about e-cigarettes, with significantly higher in-degree (0.273 vs 0.084; P=.02), closeness (0.452 vs 0.137; P=.04), betweenness (0.036 vs 0.008; P=.001), and out-of-degree (0.097 vs 0.014; P=.02) centrality values. The followers who post about e-cigarettes also had a significantly (P<.001) higher number of followers (n=322) than that of followers who do not post about e-cigarettes (n=201). The number of tweets in the networks of followers who post about e-cigarettes was significantly higher than that in the networks of followers who do not post about e-cigarettes (93 vs 43; P<.001). Two major topics discussed in the networks of followers who post about e-cigarettes included promoting e-cigarette products or vaping activity (55.7%) and vaping being a help for smoking cessation and harm reduction (44.3%). Followers of e-cigarette influencers who also post about e-cigarettes have more closely connected networks than those of followers who do not themselves post about e-cigarettes. These findings provide a potentially practical intervention approach for future antivaping campaigns.

Visual Question Generation from Remote Sensing Images Using Gemini API

Abstract: Visual Question Generation Extracting Information from Remote Sensing Images Remote Sensing Images plays a vital role in understanding and extracting information from aerial and satellite images. Utilizing Bidirectional Encoder Representation from Transformers (BERT) for extracting valuable insights from remote sensing images. Gemini Application Programming Interface(API), and Convolution Neural Networks (CNNs) are used. First, The proposed methodology employs CNN to extract high-level features from remote sensing images, capturing spatial data and generatingquestions. Similarly, the Gemini Application Programming Interface(API) integrates contextual understanding into the question-generation process by providing relevant environmental data. Lastly, BERT functions as a language model in which employees enhance and refine the generated questions by taking into account both the syntax and semantics. Hence, by combining all these techniques we are capable of generating required relevant questions from remote sensing images in an enhanced and efficient way.

Open-source large language models in action: A bioinformatics chatbot for PRIDE database.

We here present a chatbot assistant infrastructure (https://www.ebi.ac.uk/pride/chatbot/) that simplifies user interactions with the PRIDE database's documentation and dataset search functionality. The framework utilizes multiple Large Language Models (LLM): llama2, chatglm, mixtral (mistral), and openhermes. It also includes a web service API (Application Programming Interface), web interface, and components for indexing and managing vector databases. An Elo-ranking system-based benchmark component is included in the framework as well, which allows for evaluating the performance of each LLM and for improving PRIDE documentation. The chatbot not only allows users to interact with PRIDE documentation but can also be used to search and find PRIDE datasets using an LLM-based recommendation system, enabling dataset discoverability. Importantly, while our infrastructure is exemplified through its application in the PRIDE database context, the modular and adaptable nature of our approach positions it as a valuable tool for improving user experiences across a spectrum of bioinformatics and proteomics tools and resources, among other domains. The integration of advanced LLMs, innovative vector-based construction, the benchmarking framework, and optimized documentation collectively form a robust and transferable chatbot assistant infrastructure. The framework is open-source (https://github.com/PRIDE-Archive/pride-chatbot).

Updates to the Alliance of Genome Resources Central Infrastructure.

The Alliance of Genome Resources (Alliance) is an extensible coalition of knowledgebases focused on the genetics and genomics of intensively-studied model organisms. The Alliance is organized as individual knowledge centers with strong connections to their research communities and a centralized software infrastructure, discussed here. Model organisms currently represented in the Alliance are budding yeast, C. elegans, Drosophila, zebrafish, frog, laboratory mouse, laboratory rat, and the Gene Ontology Consortium. The project is in a rapid development phase to harmonize knowledge, store it, analyze it, and present it to the community through a web portal, direct downloads, and Application Programming Interfaces (APIs). Here we focus on developments over the last two years. Specifically, we added and enhanced tools for browsing the genome (JBrowse), downloading sequences, mining complex data (AllianceMine), visualizing pathways, full-text searching of the literature (Textpresso), and sequence similarity searching (SequenceServer). We enhanced existing interactive data tables and added an interactive table of paralogs to complement our representation of orthology. To support individual model organism communities, we implemented species-specific "landing pages" and will add disease-specific portals soon; in addition, we support a common community forum implemented in Discourse software. We describe our progress towards a central persistent database to support curation, the data modeling that underpins harmonization, and progress towards a state-of-the art literature curation system with integrated Artificial Intelligence and Machine Learning (AI/ML).

Information Extraction from Product Labels: A Machine Vision Approach

This research tackles the challenge of manual data extraction from product labels by employing a blend of computer vision and Natural Language Processing (NLP). We introduce an enhanced model that combines Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in a Convolutional Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition (OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food Facts API (Application Programming Interface) for database population and text-only label prediction. The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set. Importantly, our approach enables visually impaired individuals to access essential information on product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep learning and OCR in automating label extraction and recognition.