Public Repositories Research Articles

Metagenomic investigation of 16S rRNA marker gene samples to analyze the role of race, ethnicity, and location in preterm birth: A comprehensive vaginal microbiome meta-analysis

The vaginal microbiome is essential to women's reproductive health and numerous studies have demonstrated that any microbial dysbiosis can increase the risk of Preterm Birth (PTB). The present study utilized an integrated computational methodology to conduct a meta-analysis of PTB vaginal microbiome samples from women of different racial, ethnic, and geographical locations, across multiple studies derived from public repositories. Taxonomic classification, functional profiling, and statistical analysis of the PTB samples revealed that Finegoldia, Haemophilus, and Lactobacillus showed the highest variation in their microbial abundances while the Sensory system pathway, Substance dependence pathway, and Circulatory system pathway showed the highest variation in their expression levels based on race, ethnicity, and locations respectively. All of these factors cause subsequent variations in the etiology of PTB across the aforementioned demographic attributes. LEfSE and ANCOM-BC approaches were performed to identify race, ethnicity, and geographic location-specific microbial biomarkers. To the best of our knowledge, our work is one of the first to provide a detailed analysis of the complex delineations between socioeconomic constructs of race and ethnicity at a global scale, considering the eight racial groups and the geographical location of the participants who faced PTB. We believe these findings will have significant implications for the development of more targeted and efficacious strategies for the prevention and treatment of PTB.

Current status of global conservation and characterisation of wild and cultivated Brassicaceae genetic resources.

The economic importance of the globally distributed Brassicaceae family resides in the large diversity of crops within the family and the substantial variety of agronomic and functional traits they possess. We reviewed the current classifications of crop wild relatives (CWRs) in the Brassicaceae family with the aim of identifying new potential cross-compatible species from a total of 1,242 species using phylogenetic approaches. In general, cross-compatibility data between wild species and crops, as well as phenotype and genotype characterisation data, were available for major crops but very limited for minor crops, restricting the identification of new potential CWRs. Around 70% of wild Brassicaceae did not have genetic sequence data available in public repositories, and only 40% had chromosome counts published. Using phylogenetic distances, we propose 103 new potential CWRs for this family, which we recommend as priorities for cross-compatibility tests with crops and for phenotypic characterisation, including 71 newly identified CWRs for 10 minor crops. From the total species used in this study, more than half had no records of being in ex situ conservation, and 80% were not assessed for their conservation status or were data deficient (IUCN Red List Assessments). Great efforts are needed on ex situ conservation to have accessible material for characterising and evaluating the species for future breeding programmes. We identified the Mediterranean region as one key conservation area for wild Brassicaceae species, with great numbers of endemic and threatened species. Conservation assessments are urgently needed to evaluate most of these wild Brassicaceae.

Refinement of prognostication for IDH-mutant astrocytomas using DNA methylation-based classification.

The 2021 World Health Organization (WHO) grading system of isocitrate dehydrogenase (IDH)-mutant astrocytomas relies on histological features and the presence of homozygous deletion of the cyclin-dependent kinase inhibitor 2A and 2B (CDKN2A/B). DNA methylation profiling has become highly relevant in the diagnosis of central nervous system (CNS) tumors including gliomas, and it has been incorporated into routine clinical diagnostics in some countries. In this study, we, therefore, examined the value of DNA methylation-based classification for prognostication of patients with IDH-mutant astrocytomas. We analyzed histopathological diagnoses, genome-wide DNA methylation array data, and chromosomal copy number alteration profiles from a cohort of 385 adult-type IDH-mutant astrocytomas, including a local cohort of 127 cases and 258 cases from public repositories. Prognosis based on WHO 2021 CNS criteria (histological grade and CDKN2A/B homozygous deletion status), other relevant chromosomal/gene alterations in IDH-mutant astrocytomas and DNA methylation-based subclassification according to the molecular neuropathology classifier were assessed. We demonstrate that DNA methylation-based classification of IDH-mutant astrocytomas can be used to predict outcome of the patients equally well as WHO 2021 CNS criteria. In addition, methylation-based subclassification enabled the identification of IDH-mutant astrocytoma patients with poor survival among patients with grade 3 tumors and patients with grade 4 tumors with a more favorable outcome. In conclusion, DNA methylation-based subclassification adds prognostic information for IDH-mutant astrocytomas that can further refine the current WHO 2021 grading scheme for these patients.

Open source and reproducible and inexpensive infrastructure for data challenges and education

Data sharing is necessary to maximize the actionable knowledge generated from research data. Data challenges can encourage secondary analyses of datasets. Data challenges in biomedicine often rely on advanced cloud-based computing infrastructure and expensive industry partnerships. Examples include challenges that use Google Cloud virtual machines and the Sage Bionetworks Dream Challenges platform. Such robust infrastructures can be financially prohibitive for investigators without substantial resources. Given the potential to develop scientific and clinical knowledge and the NIH emphasis on data sharing and reuse, there is a need for inexpensive and computationally lightweight methods for data sharing and hosting data challenges. To fill that gap, we developed a workflow that allows for reproducible model training, testing, and evaluation. We leveraged public GitHub repositories, open-source computational languages, and Docker technology. In addition, we conducted a data challenge using the infrastructure we developed. In this manuscript, we report on the infrastructure, workflow, and data challenge results. The infrastructure and workflow are likely to be useful for data challenges and education.

Streamlining remote nanopore data access with slow5curl.

As adoption of nanopore sequencing technology continues to advance, the need to maintain large volumes of raw current signal data for reanalysis with updated algorithms is a growing challenge. Here we introduce slow5curl, a software package designed to streamline nanopore data sharing, accessibility, and reanalysis. Slow5curl allows a user to fetch a specified read or group of reads from a raw nanopore dataset stored on a remote server, such as a public data repository, without downloading the entire file. Slow5curl uses an index to quickly fetch specific reads from a large dataset in SLOW5/BLOW5 format and highly parallelized data access requests to maximize download speeds. Using all public nanopore data from the Human Pangenome Reference Consortium (>22 TB), we demonstrate how slow5curl can be used to quickly fetch and reanalyze raw signal reads corresponding to a set of target genes from each individual in large cohort dataset (n = 91), minimizing the time, egress costs, and local storage requirements for their reanalysis. We provide slow5curl as a free, open-source package that will reduce frictions in data sharing for the nanopore community: https://github.com/BonsonW/slow5curl.

The Role of Public and Private Interventions on the Evolution of Green Innovation in China

China's future economy is in jeopardy due to climate change, zero COVID policy, supply chain disruption, surging inflation, and the energy crisis due to Russia Ukraine war. Additionally, the government pledges to the Paris agreement and COP27: The UN Climate Change Conference to combat with climate change crisis through the reduction of carbon and greenhouse gases emission. In these uncertain conditions, climate pledges, and shifts to a greener economic system, it is essential to investigate the public and private interventions on the evolution of green innovation in China. Accordingly, we relied on public repositories, such as the World Bank, World Intellectual Property Organization, PatSnap, SIPR, China City Statistical Yearbook 2021, and the Peking University Digital Financial Inclusion Index of China from 2009 to 2021 in 280 Chinese cities, to investigate the role of public and private interventions in the growth of green innovation. Our findings indicate that one interquartile range increase in government intervention, imperative environment regulations, gross regional domestic product, investment in environmental pollution, actual foreign investment, market pull, and material increase the green innovation growth at the Chinese city level. At the same time, we did not find a significant relationship between energy and green innovation. Moreover, the study paves the path for policymakers to remodify and develop policies on the different authoritative levels, which can reshape the Chinese green economy in the long run.

A Distributed Software Architecture for IoT: Container Orchestration Impact and Evaluation

This paper proposes a Distributed Software Architecture (DSA) for Smart Building (SB) based on the Reactive Manifesto (RM) principles. To follow the RM principles, we analyze the usage of different deployment approaches, particularly the impact of using a container orchestrator on the application layer. After running performance tests on the different configurations, the container orchestrator usage led to enhanced distributed processing, lowering the latency, increasing flexibility, enhancing security, and providing cost-effectiveness and scalability. We introduce the implementation of a modern DSA, developed following state-of-the-art cloud patterns and compliant with the RM for the SB context. Furthermore, we have ensured the reproducibility of this implementation by making the initial tests and overall architecture code available in public repositories. The research follows the Design Science Research (DSR) methodology for elaborating each phase until we get the artifact (DSA) and, with this, contribute to the Knowledge Base. The architecture was properly tested, considering the performance as the principal test layer. This solution is tailored for application in domains of the Internet of Things (IoT), focusing on the SB and a case study involving the Laboratory of Distributed Systems and Concurrent Programming (LaSDPC) at São Paulo University. Moreover, its applicability extends to IoT domains like smart home, smart campus, smart city, and health-related applications.

Investigation of Cnidium monnieri compounds as phosphodiesterase-5 antagonists in erectile dysfunction via molecular docking and dynamic simulation

This study aimed at evaluating the inhibitory potential of bioactive compounds from C. monnieri and predicting lead compounds for achieving and maintaining penile erection through computational models. A total of 79 compounds were retrieved for the plant through public repositories, phosphodiesterase-5 (PDE-5) was retrieved from the data bank for proteins (PDB). The molecular docking, free binding energy calculation, pharmacophore modeling and molecular dynamic simulation were performed using Schrodinger suite (2017–1). The quantum chemical calculation was also carried out with Spartan 14 computational software. Among the 79 compounds virtually screened against Phosphodiesterase-5 (PDE-5), eight (8) bioactive compounds showed potent antagonist against PDE-5 with varying binding scores comparable to that of Sildenafil (co-crystalized ligand). Monnieriside (−10.625 kcal/mol), Diosmetin (−10.041 kcal/mol and Isogosferol (−10.038 kcal/mol) were observed to have the best docking score comparable with Sildenafil (−11.872 kcal/mol). The fitness score range of 0.982–1.941 confirmed the validation of the docking scores using e-pharmacophore hypothesis. The result of the MD simulation reveals better stability of hit compound-protein complex (Monnieriside A-PDE-5) compared with the Sildenafil-PDE-5 complex. None of the hit compounds violated more than one rule of five and within the reference range for pharmacokinetics parameters. The quantum mechanics estimation revealed the hit molecules possess proton accepting and donating potential therefore, possess antagonistic potential. Based on the results from this study, the three top binding compounds may be predicted as potent inhibitors of PDE-5 and could use as lead compound for discovery of potent drugs for erectile dysfunction.

A machine learning based framework for IoT devices identification using web traffic.

Identification of the Internet of Things (IoT) devices has become an essential part of network management to secure the privacy of smart homes and offices. With its wide adoption in the current era, IoT has facilitated the modern age in many ways. However, such proliferation also has associated privacy and data security risks. In the case of smart homes and smart offices, unknown IoT devices increase vulnerabilities and chances of data theft. It is essential to identify the connected devices for secure communication. It is very difficult to maintain the list of rules when the number of connected devices increases and human involvement is necessary to check whether any intruder device has approached the network. Therefore, it is required to automate device identification using machine learning methods. In this article, we propose an accuracy boosting model (ABM) using machine learning models of random forest and extreme gradient boosting. Featuring engineering techniques are employed along with cross-validation to accurately identify IoT devices such as lights, smoke detectors, thermostat, motion sensors, baby monitors, socket, TV, security cameras, and watches. The proposed ensemble model utilizes random forest (RF) and extreme gradient boosting (XGB) as base learners with adaptive boosting. The proposed ensemble model is tested with extensive experiments involving the IoT Device Identification dataset from a public repository. Experimental results indicate a higher accuracy of 91%, precision of 93%, recall of 93%, and F1 score of 93%.

Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis

BackgroundPsychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state functional magnetic resonance imaging (rs-fMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains. Methodsrs-fMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron emission tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients. ResultsCompared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta-power. Exploratory analyses revealed a close statistical relationship between LEN and positive symptom severity in patients. ConclusionOur entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs.

Local trade, spatial occurrence and conservation of hypostomus soniae (siluriformes, loricariidae), an ornamental fish endemic to the tapajos river, Brazil.

Hypostomus soniae is a small sized armored catfish endemic to the Tapajos River basin and ranked as one of the most exploited ornamental fish in the Santarem export marketplace. This study aims to evaluate distributional patterns of Hypostomus soniae and contribute to the species conservation in the face of development of the ornamental fish trade in the Amazon region. We compiled data associated with geographic coordinates in public repositories, supplemented with original field records. We compared our data to published records in the literature and museum collections to check for accuracy. To investigate the fishery and commercialization of H. soniae, we conducted interviews with ornamental fish stakeholders from the local trade. We also made direct observations in the fishing sites and export facilities in Santarem. A cluster analysis of the geolocation data was carried out to explore the spatial distribution patterns. The volume of captures and exportation of H. soniae decreased during the period 2020-2023. The occurrence of H. soniae was associated with annual rainfall ranging from 2,000 mm to 2,500 mm and concentrated in two municipalities of the State of Mato Grosso and two of the Para State. The species distribution area has been threatened, unfortunately, by fishermen who do not respect the laws that support artisanal fishing in the Amazon.

Multidisciplinary approach to the diagnosis of Contracaecum magnipapillatum infections in Australian black noddies, Anous minutus (Charadriiformes: Laridae)

We provide the incidental necropsy findings associated with anisakid nematode infections of black noddy terns, Anous minutus Boie, 1844 (Charadriiformes: Laridae), from offshore islands in the southern Great Barrier Reef, Queensland, Australia. Specimens collected from the proventriculi were identified morphologically as Contracaecum magnipapillatum Chapin, 1925 (Rhabditida: Anisakidae), using light and scanning electron microscopy (SEM). The entire nuclear ribosomal DNA internal transcribed spacer (ITS) region (ITS1-5.8S-ITS2) was amplified by polymerase chain reaction (PCR) and sequenced to provide reference sequences for morphologically well-identified voucher specimens. Interestingly, after an alignment with closely related taxa using BLAST, sequences of the ITS1 and ITS2 were 100% identical to the sequences assigned to Contracaecum septentrionale Kreis, 1955, from a razorbill, Alca torda Linnaeus, 1758 (Charadriiformes: Alcidae), from Spain. These results either raise questions about the ITS as a genetic marker for some members of Contracaecum, or the identity of the specimens assigned to C. septentrionale, given that no supporting morphological data was associated with them. We highlight the need for a combined morphological and molecular approach to parasite diagnostics and the use of multiple genetic loci to resolve the molecular taxonomy of cryptic species. Morphological identifications should be taxonomically robust, transparent and precede the deposition of molecular barcodes in public repositories. The gross and histopathological findings of our investigation concur with previous reports of widespread Contracaecum infections in black noddies and support the contention that Contracaecum spp. are an unlikely primary cause of mortality.

ИССЛЕДОВАНИЕ МЕТОДОВ И АЛГОРИТМОВ РЕДУКЦИИ НЕЧЕТКИХ ПРАВИЛ В МОДЕЛЯХ ОЦЕНКИ ДИСКРЕТНОГО СОСТОЯНИЯ ОБЪЕКТОВ

The article describes the stages and results of the study of methods and genetic algorithms used to reduce fuzzy rules in models for assessing the discrete state of objects. Such models are fuzzy knowledge bases and fuzzy logical inference algorithms. A study was conducted of the influence of the parameter values of genetic algorithms on the results of reduction of the initial and intermediate knowledge bases (on the reduction of the number of rules in the knowledge bases and on the values of classification quality metrics). The following stages of research were implemented: selection of data sets from publicly available sources, formation of research knowledge bases, study of the influence of genetic algorithm parameter values on the results of knowledge base reduction, analysis of research results and development of recommendations for choosing optimal values of genetic algorithm parameters to achieve the best reduction results. Data for analysis was obtained from the public source UCI Machine Learning Repository. The selected sets are: Iris, Banknote Authentication and User Knowledge Modeling. To create research knowledge bases, the software package “Neuro-fuzzy system for generating fuzzy models for assessing the discrete state of objects” was used. Based on the analysis of the Iris data set, 3 research knowledge bases were formed, including 269, 339 and 542 fuzzy rules. For the Banknote Authentication data set, corresponding knowledge bases with 335, 643 and 1038 fuzzy rules were generated. For the User Knowledge Modeling set - with 877, 2094 and 2797 rules. For each of the knowledge bases, the values of the classification quality metrics Accuracy, Precision, Recall and F1-Score were calculated on the training and test samples. The reduction of knowledge bases was carried out in two successive stages, at each of which the corresponding genetic algorithm was used. Based on the results of the research, recommendations were developed for choosing the optimal values of the parameters of each of the genetic algorithms. The reduction of research knowledge bases based on the developed methods and algorithms made it possible to significantly reduce the number of their fuzzy rules (by an average of 85%) and at the same time slightly increase the generalizing ability of fuzzy models (by an average of 1.5%). In general, this made it possible to reduce the complexity of fuzzy models and their constituent knowledge bases, increase the accuracy of estimating the discrete state of objects and improve the interpretability of the generated solutions.

Retrieving biodiversity data from multiple sources: making secondary data standardised and accessible.

Biodiversity data, particularly species occurrence and abundance, are indispensable for testing empirical hypothesis in natural sciences. However, datasets built for research programmes do not often meet FAIR (findable, accessible, interoperable and reusable) principles, which raises questions about data quality, accuracy and availability. The 21st century has markedly been a new era for data science and analytics and every effort to aggregate, standardise, filter and share biodiversity data from multiple sources have become increasingly necessary. In this study, we propose a framework for refining and conforming secondary biodiversity data to FAIR standards to make them available for use such as macroecological modelling and other studies. We relied on a Darwin Core base model to standardise and further facilitate the curation and validation of data related including the occurrence and abundance of multiple taxa of a region that encompasses estuarine ecosystems in an ecotonal area bordering the easternmost Amazonia. We further discuss the significance of feeding standardised public data repositories to advance scientific progress and highlight their role in contributing to the biodiversity management and conservation.

Historical archaeological collections in Argentina: open science, digital conservation and public access repositories

Resumen La globalización, la internet y la expansión de las Tecnologías de la Información y la Comunicación modificaron los vínculos de la ciencia con la sociedad e impactaron notablemente en las prácticas científicas. Tanto la divulgación como la producción científica necesitan sumarse a las modalidades virtuales de comunicación en el contexto de las crecientes políticas de ciencia abierta que, por un lado, establecen el deber de transmitir a la sociedad los resultados de las investigaciones y, por otro, impulsan su difusión en forma digital. En este marco, se detallan las tareas realizadas para la divulgación y el acceso abierto a las colecciones arqueológicas del sitio Arroyo Nievas 2. Se trata de un sitio arqueológico localizado en una zona de frontera interétnica, donde se recuperaron objetos vinculados a la población indígena del siglo XIX y materiales de origen europeo novedosos en Argentina. Entre otras actividades, se amplió y estandarizó la base de datos existente, se hizo un inventario definitivo y la catalogación completa de los objetos, así como se digitalizó la documentación gráfica y las fichas analíticas. Los objetos digitales fueron cargados en el repositorio arqueológico de acceso libre Suquía (Universidad Nacional de Córdoba y CONICET, Argentina). Con esta experiencia de trabajo se establecieron novedosos parámetros específicos para estandarizar datos, hasta ahora inexistentes en la disciplina arqueológica y relevantes para profesionales que trabajan en estas colecciones. La exposición de los procedimientos, dificultades y resultados obtenidos contribuye a mejorar las prácticas de procesamiento y curación de datos, tanto como a la administración de colecciones científicas.

Linking coronary artery disease to neurodegenerative diseases through systems genetics.

Coronary artery disease (CAD) is still a leading cause of death worldwide despite the extensive research and the considerable progresses made through the years. As other cardiovascular diseases, CAD is the result of the complex interaction between genetic variants and environmental factors. Currently identified genetic loci associated to CAD revealed the contribution of multiple molecular pathways to its pathogenesis, suggesting the need for a systemic approach to understand the role of genetic determinants. In this study we wanted to investigate how GWAS variants associated to CAD interact with each other and with nearby genes in the context of the coronary artery molecular interactome. GWAS variants associated to CAD were selected from GWAS Catalog, then, a tissue-specific interactome was constructed integrating protein-protein interactions (PPI) from multiple public repositories and computationally inferred co-expression relationships. To focus on the part of the network most relevant for CAD, we selected the interactions connecting the genes carrying a variant associated to the disease. A functional enrichment analysis conducted on the subnetwork revealed that genes carrying genetic variants associated to CAD closely interact with genes related to relevant biological processes, such as extracellular matrix organization, lipoprotein clearance, arterial morphology and inflammatory response. These results confirm that the identified subnetwork reflects the molecular pathways altered in CAD and intercepted by the selected variants. Interestingly, the most connected nodes of the network included amyloid beta precursor protein (APP) and huntingtin (HTT), both implicated in neurodegenerative disorders. In recent years the interest in investigating the common processes between cardiovascular diseases and neurodegenerative disorders is increasing, with growing evidence of a link between CAD and Alzheimer's disease. The results obtained in this work support the association between such apparently unrelated diseases and highlight the necessity of a systems biology approach to better elucidate shared pathological mechanisms.

Precision Medicine in Treatment: Based on Multiomics or Clinical Data

Huge amounts of global data (genomic, epigenomic, transcriptomic, proteomic, and metabolomic) generated from a broad spectrum of specimens collected from human patients have accumulated in public repositories, together with electronic health records and drug treatment information. Accordingly, there is a need for bioinformatic methods that can effectively extract useful information for precision medicine. Here, we present two precision medicine approaches using multiomics data and clinical big data, respectively.

DeSci Advancement and Gamification – Virtual University as A Model

The existing model for knowledge dissemination that universities and research centers adopt relies on central funding and central repositories of publications and books which are for the most part guarded by pay walls. These central repositories manifest as for-profit Publishing houses. While the editing and peer review processes are fully voluntarily performed by experts in the scientific field, the outcome of the scientific work i.e. the publication remains under the publishing house ownership accessible only by paying an exorbitant fee. In conjunction, conventional brick and mortar universities nowadays stand as the main knowledge creation flocked by students. Again, access to these educational institutions and/or to research funding require substantial effort and/or cost from students and scientists alike. In this work we present a new model for the creation and dissemination of knowledge relying on the DeSci approach and crowd funding. The model circumvents many if not all of the deficiencies of the existing centralized approach.

ProGlycProt V3.0: updated insights into prokaryotic glycoproteins and their glycosyltransferases.

ProGlycProt is a comprehensive database of experimentally validated information about protein glycosylation in prokaryotes, including the glycoproteins, glycosyltransferases, and their accessory enzymes. The first release of ProGlycProt featured experimentally validated information on glycoproteins only. For the second release in 2019, the size and scope of the database were expanded twofold, and experimental data on cognate glycosyltransferases and their accessory proteins was incorporated. The growing research and technology interest in microbial glycoproteins and their enzymes is evident from the steady rise in academic publications and patents in this area. Accordingly, the third update comprises a new section on patents related to glycosylation methods, novel glycosyltransferases, and technologies developed therefrom. The structure gallery is reorganized, wherein the number and quality of the models are upgraded with the help of AlphaFold2. Over the years, the influx of experimental proteomics data into public repositories like PRIDE has surged. Harnessing this legacy data for in-silico glycoprotein identification is a smart approach. Version 3.0 adds 45N-glycoprotein entries annotated from MS datasets available on PRIDE and reviewed by independent research groups. With a 67% rise in entries corresponding to 119 genera of prokaryotes, the ProGlycProt continues to be the exclusive database of experimentally validated comprehensive information about protein glycosylation in prokaryotes.

ChatReview: A ChatGPT-enabled natural language processing framework to study domain-specific user reviews

Intelligent search engines including pre-trained generative transformers (GPT) have revolutionized the user search experience. Several fields including e-commerce, education, and hospitality are increasingly exploring GPT tools to study user reviews and gain critical insights to improve their service quality. However, massive user-review data and imprecise prompt engineering lead to biased, irrelevant, and impersonal search results. In addition, exposing user data to these search engines may pose privacy issues. Motivated by these factors, we present ChatReview, a ChatGPT-enabled natural language processing (NLP) framework that effectively studies domain-specific user reviews to offer relevant and personalized search results at multiple levels of granularity. The framework accomplishes this task using four phases including data collection, tokenization, query construction, and response generation. The data collection phase involves gathering domain-specific user reviews from public and private repositories. In the tokenization phase, ChatReview applies sentiment analysis to extract keywords and categorize them into various sentiment classes. This process creates a token repository that best describes the user sentiments for a given user-review data. In the query construction phase, the framework uses the token repository and domain knowledge to construct three types of ChatGPT prompts including explicit, implicit, and creative. In the response generation phase, ChatReview pipelines these prompts into ChatGPT to generate search results at varying levels of granularity. We analyze our framework using three real-world domains including education, local restaurants, and hospitality. We assert that our framework simplifies prompt engineering for general users to produce effective results while minimizing the exposure of sensitive user data to search engines. We also present a one-of-a-kind Large Language Model (LLM) peer assessment of the ChatReview framework. Specifically, we employ Google’s Bard to objectively and qualitatively analyze the various ChatReview outputs. Our Bard-based analyses yield over 90% satisfaction, establishing ChatReview as a viable survey analysis tool.