Data Integration Research Articles

Implementasi Python-PPTX dan Dom-to-Image dalam Pembangunan Sistem Otomatisasi Laporan Berbasis Web di PT. XYZ

PT. XYZ is a leading retail company in Indonesia operating in a highly dynamic industry. Despite using PowerPoint (PPT) presentation reports as a key communication tool with its business partners, PT. XYZ's report creation process is still done manually. This leads to time wastage, the risk of errors, and disruptions to operational efficiency. The purpose of this research is to implement Python-pptx for the automation of report compilation and dom-to-image for the integration of visual data. The goal is to overcome the main challenges faced by PT. XYZ. The research methodology includes problem identification, literature review, system requirements analysis, system design, application development, and system testing. The results of this research include the successful implementation of Python-pptx and dom-to-image in building a web-based automated reporting system at PT. XYZ. System testing demonstrates the success of this technology, ensuring user satisfaction, and preparing the system for more efficient use.

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

Developing predictive models for HIV Drug resistance: A genomic and AI approach

 This paper proposes a novel approach to combating HIV drug resistance through the development of predictive models leveraging genomic data and artificial intelligence (AI). With the increasing prevalence of drug-resistant strains of HIV, there is a critical need for innovative strategies to predict and manage resistance mutations, thereby optimizing treatment outcomes and prolonging the efficacy of antiretroviral therapy (ART). Drawing on advances in genomics and AI, this study outlines a conceptual framework for the development of predictive models that can identify potential drug-resistance mutations in HIV genomes and inform clinical decision-making. The proposed framework integrates genomic data from HIV-infected individuals with AI algorithms capable of learning complex patterns within the data. By analyzing genomic sequences obtained from HIV-positive patients, the models aim to identify genetic variations associated with drug resistance, predict the likelihood of resistance development, and guide the selection of appropriate treatment regimens. This approach holds promise for personalized medicine in HIV care, enabling clinicians to tailor therapy based on an individual's genetic profile and risk of resistance. Key components of the conceptual framework include data preprocessing to extract relevant genomic features, model training using machine learning techniques such as deep learning and ensemble methods, and validation of predictive performance through cross-validation and independent testing. Furthermore, the integration of clinical data, such as treatment history and viral load measurements, enhances the predictive accuracy of the models and provides valuable insights into treatment response dynamics.The development of predictive models for HIV drug resistance represents a paradigm shift in HIV care, offering a proactive approach to treatment management and surveillance. By leveraging genomic and AI technologies, healthcare providers can anticipate and address emerging resistance mutations before they compromise treatment efficacy. Ultimately, the implementation of predictive models holds the potential to improve patient outcomes, reduce the transmission of drug-resistant HIV strains, and advance the global fight against HIV/AIDS. Keywords:  Developing, Predictive Models, HIV Drug Resistance, Genomic, AI Approach.

Environmental data in epidemic forecasting: Insights from predictive analytics

Epidemic forecasting plays a critical role in public health preparedness and response, enabling proactive measures to mitigate the impact of infectious diseases. Environmental data, encompassing factors such as temperature, humidity, air quality, and geographical features, holds valuable insights for predicting and identifying areas prone to epidemics. This paper explores the integration of predictive analytics with environmental data to enhance epidemic forecasting capabilities. By leveraging predictive analytics techniques, researchers and public health officials can analyze environmental data to identify regions at higher risk of experiencing epidemic outbreaks. Through statistical modeling, machine learning algorithms, and computational simulations, predictive analytics utilize environmental indicators to forecast the likelihood and spread of diseases. For example, areas with high temperatures and humidity may be conducive to mosquito-borne diseases, while regions with poor air quality may experience increased rates of respiratory infections. Case studies highlight the application of predictive analytics in various contexts, including forecasting mosquito-borne diseases in tropical regions and tracking respiratory infections in urban areas with poor air quality. Early warning systems, informed by environmental data, provide timely alerts to potential epidemic threats, enabling proactive interventions and resource allocation. While the integration of environmental data into epidemic forecasting offers significant benefits, challenges remain, including data quality, availability, and ethical considerations. Continued research and collaboration are essential to address these challenges and further enhance the effectiveness of predictive analytics in identifying and mitigating epidemic risks. In conclusion, this paper underscores the importance of leveraging environmental data and predictive analytics for epidemic forecasting, emphasizing their potential to improve public health outcomes and enhance preparedness efforts in the face of emerging infectious diseases and climate change. Keywords: Environmental Data, Epidemic Forecasting, Predictive Analytics.

Transforming equipment management in oil and gas with AI-Driven predictive maintenance

The oil and gas industry faces significant challenges in managing equipment maintenance due to the complexity and criticality of its assets. Traditional maintenance approaches are often reactive and inefficient, leading to costly downtime and safety risks. However, the emergence of artificial intelligence (AI) and predictive maintenance technologies offers a transformative solution to these challenges. This paper explores the role of AI-driven predictive maintenance in revolutionizing equipment management in the oil and gas sector. AI-driven predictive maintenance leverages machine learning algorithms to analyze equipment data and predict when maintenance is required before a breakdown occurs. By monitoring equipment performance in real-time, AI can identify potential issues early, allowing operators to take proactive maintenance actions. This approach helps minimize downtime, reduce maintenance costs, and improve overall equipment reliability and safety. The implementation of AI-driven predictive maintenance requires a comprehensive strategy that includes data collection, analysis, and integration with existing maintenance practices. Successful adoption of AI-driven predictive maintenance can lead to significant benefits for oil and gas companies, including increased equipment uptime, extended asset lifespan, and enhanced operational efficiency. This paper reviews the current landscape of equipment management in the oil and gas industry, highlighting the limitations of traditional maintenance practices and the need for a more proactive approach. It then examines the principles and benefits of AI-driven predictive maintenance, showcasing real-world examples of its successful implementation. Finally, the paper discusses the challenges and considerations for implementing AI-driven predictive maintenance and provides recommendations for oil and gas companies looking to transform their equipment management practices. Keywords: Transforming Equipment; Management; Oil and Gas; AI-Driven; Predictive Maintenance.

Challenges and directions for digital twin implementation in otorhinolaryngology.

Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes. These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems. Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues. Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research. These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.

What qualitative systems mapping is and what it could be: integrating and visualizing diverse knowledge of complex problems

AbstractResearchers in sustainability science deal with increasingly complex problems that cross administrative, geographical, disciplinary, and sectoral boundaries, and are characterized by high stakes and deep uncertainties. This in turn creates methodological challenges to frame, structure, and solve complex problems in science and practice. There is a long tradition in visualizing systems as diagrams, and concept and cognitive maps, but there is insufficient differentiation and comparison between these methods and no clear umbrella term has yet been established. Against this background, we systematically review three foundational methods from different academic disciplines—causal diagrams, concept mapping, and cognitive mapping. Comparing and contrasting them, we facilitate a coherent understanding of qualitative systems mapping (QSM) as an umbrella term. We then proceed to explore the evident intersections between these methods to showcase some of the inter- and transdisciplinary opportunities and challenges crystallizing in integrated QSM approaches. Finally, we share case study insights from the food–water–biodiversity nexus in Austria and elaborate on some of the methodological nuances to data integration in QSM. Overall, with this overview paper, we lay the groundwork for a systematic, transparent, and yet flexible development and application of QSM methods to support mixed-methods research design and clear case study documentation, as well as fostering effective inter- and transdisciplinary communication in sustainability science. Further research needs to explore these QSM applications in depth across alternative sustainability science contexts, particularly with respect to efficient and rigorous protocols for knowledge and data integration vis-a-vis complex problems and transdisciplinary research processes.

Challenges and Opportunities of ESG Integration in Financial Operations

In contemporary society, with the burgeoning concern for sustainable development among the populace, Environmental, Social, and Governance (ESG) criteria have emerged as a pivotal discourse within the global financial sector. Responsible investment is increasingly paramount, directing capital flows towards enterprises and ventures of greater sustainability. Against this backdrop, the integration of ESG criteria into financial operations poses both a significant challenge and opportunity for financial institutions. Architects and providers of financial products and services are gradually incorporating ESG factors into their decision-making processes to align with escalating regulatory demands and meet investors' expectations of social responsibility. Nevertheless, this transition is not devoid of challenges. Issues such as data acquisition and quality, regulatory uncertainty, and inadequate comprehension of ESG values among market participants constitute substantial obstacles that the financial realm must surmount in the pursuit of ESG integration. Nonetheless, through a series of strategies and recommendations, such as enhancing data transparency and quality, bolstering relevant education and training for market participants, and advocating for regulatory authorities to formulate clearer and more unified directives, financial institutions can harness technological advancements to optimize the analysis and integration of ESG data, thereby paving the way for new opportunities.

Development of an Integrated Computing Platform for Measuring, Predicting, and Analyzing the Profile-Specific Fixity of Railway Tracks

The current measures for railway track fixity in the UK’s railway remain at a relatively low level of granularity. This paper presents a pilot proof-of-concept study on the development of an integrated computing framework for improving the measurement, prediction, and analysis of profile-specific track fixity in the context of the UK’s rail network. The framework is aimed to produce a data integration and mining tool, which can determine track fixity parameters for any given section of track. In this study, we propose to measure track movement based on point cloud data and assess the track fixity by a set of parameters, such as the direction and rate of the track movement relative to the plane of the rail within a certain period. We seek to integrate a data mining algorithm into the framework to predict these parameters, given vast amounts of disparate and heterogeneous data of potential influencing factors in the area. From the study, we have developed a prototype framework, which allows the rapid implementation of data workflows with the necessary functionality. The feasibility of the prototype was demonstrated by training a random forest model on real data from an approximately 80-km section of the East Coast Main Line, southeast of Edinburgh, Scotland. The modeling results indicate that the curvature, cant, and maximum speed of trains are among the key factors that impact on, and are critical for predicting and analyzing, the profile-specific track fixity.

Integration of Seismic and Well Log Data for Reservoir Characterization in Offshore Niger Delta, Nigeria

The demand for fossil fuels is continually increasing due to the growing global population and industrialization. It is thus necessary to find hydrocarbon prospects in new fields and those that have previously been classified as marginal fields in order to maximize production. An integrated approach is required to effectively characterize hydrocarbon reservoirs and assess their ability to store and produce hydrocarbons. 3D seismic reflection data and well log data were combined to quantitatively estimate hydrocarbon reserves in a field, offshore Niger delta, Nigeria. The reservoirs were delineated on the gamma ray and resistivity logs which penetrate four wells selected from the field. The petrophysical analysis provided information about the net-to-gross thickness ratio, porosity, shale volume and water saturation of the reservoirs, deduced from well log suites comprising gamma, resistivity, density and neutron-porosity logs through four wells. The seismic interpretation involved mapping of horizons and faults across the wells on the seismic section. Check shot data were used to tie the seismic data to the well log data to generate the synthetic seismogram. The time structural and depth structural maps were generated. The volumetric analysis entailed derivation of the Gross rock volume from the depth structural map, and estimation of the Stock Tank Oil Initially In Place (STOIIP). Four reservoirs were delineated in each of the four wells. The average thickness of the reservoirs ranges from 56.6 m to 232.7 m while the water saturation varies from 0.29% to 0.57%. The average porosities of the reservoirs ranges from 0.18% to 0.22%. The structural interpretation of the eight faults mapped reveals synthetic and antithetic faults, and rollover anticlines. The time- and depth structural maps generated from the mapped horizons show that the reservoirs are penetrated by the different faults. The Stock Tank Oil Initially In Place (STOIIP) of the reservoirs range from 25 MMstb to 468 MMstb. The study shows that the integration of 3D reflection data and well log data can be used effectively to estimate hydrocarbon reserves. The results of the study are expected to contribute to the development of new exploration and production strategies in the Niger delta basin and similar sedimentary environments.

Designing and Analysing an APP based on "Internet+" for Integrating Health Data of University Physical Classes

INTRODUCTION: University physical education programs still largely use traditional methods without significant innovation in teaching or health evaluation. With the growing capabilities of Internet technology and artificial intelligence, there's a critical need to leverage these advancements to enhance the physical health assessments of college students.OBJECTIVES: The study proposes an integrated APP design for health data collection and analysis both inside and outside physical education classes, utilizing Internet technologies and intelligent learning algorithms. This is aimed at precisely analyzing and improving the health outcomes of university students by fostering more tailored and responsive physical education experiences..METHODS: The method involves constructing an app design analysis index system, integrating a vulture search heuristic optimization algorithm with a convolutional neural network (CNN). This setup uses smart sports APP behavioral data as input to refine and optimize health data integration, aiming to enhance the analysis and feedback mechanisms within university sports programs.RESULTS: The implementation of this method showed that it meets real-time requirements while significantly improving the accuracy and efficiency of integrated APP design analyses for health data. The use of smart algorithms allows for more precise adjustments and feedback in physical education programs, suggesting a substantial improvement over traditional physical health monitoring and evaluation methods.CONCLUSION: The proposed APP design successfully integrates and analyzes health data, enhancing the management and evaluation of physical education programs. It represents a significant step forward in utilizing modern technology to address the stagnation in physical education health monitoring, with potential implications for broader educational and health management practices in universities. Future iterations of the APP could incorporate more diverse data inputs and advanced analytical features to further refine its effectiveness and usability.

Professional values gained in postgraduate nursing education from the perspectives of master's and doctorate graduates: A mixed-methods study.

To determine the professional values of graduates of master's and doctoral programs in nursing and their views on the contribution of postgraduate education to their professional values. In postgraduate education, which is an important catalyst of professional identity development, a professional is expected to specialize in a field, get into more depth professionally, internalize professional values, and reflect and strengthen these values. To shed light on this expectation, more information is needed to assess the impact of postgraduate education on professional values. A convergent parallel mixed-methods design was used. Data were collected for the quantitative part of the study for 385 graduates of postgraduate nursing education programs in Turkey who were selected using the snowball sampling method, while the maximum variation sampling method was used to collect data from 12 individuals for the qualitative part of the study. The Nurses Professional Values Scale (NPVS) and a Semi-Structured Interview Form were used to collect data in October-December 2022. The quantitative data obtained in the study were analyzed using the SPSS software, and the qualitative data were analyzed using the MAXQDA software. The mean total NPVS score of the participants was 130.32±21.34 (possible range: 31-155). Among all dimensions, the highest mean score of the participants was in the caring dimension, followed by professionalism and activism. There were significant differences in the NPVS total and subscale scores of the participants based on their views on the contributions of age, institution where they worked, marital status, position at work, and postgraduate education to the acquisition of professional values. In the qualitative dimension of the study, two main themes were identified. One of these themes was the theme of professional values in postgraduate nursing education that covered the categories of professional values gained through postgraduate education, professional values that a nurse who has completed postgraduate education should have, and professional values in postgraduate nursing education. The second theme was the theme named recommendations for providing nurses with professional values through postgraduate education. The integration of data from both components indicated that the qualitative interviews enriched the quantitative findings. It was concluded that postgraduate nursing education programs contribute greatly to the professional values of graduates and strengthen their professional values. Forthe future of nursing, increasing professional strengths and visibility is significantly associated with professional values. This situation requires nurses with strong professional values. Graduate education programs in nursing should be developed in this direction and focus on development of professional values.

Scalable Integration of Multiomic Single Cell Data Using Generative Adversarial Networks.

Single cell profiling has become a common practice to investigate the complexity of tissues, organs and organisms. Recent technological advances are expanding our capabilities to profile various molecular layers beyond the transcriptome such as, but not limited to, the genome, the epigenome and the proteome. Depending on the experimental procedure, these data can be obtained from separate assays or from the very same cells. Despite development of computational methods for data integration is an active research field, most of the available strategies have been devised for the joint analysis of two modalities and cannot accommodate a high number of them. We here propose a multiomic data integration framework based on Wasserstein Generative Adversarial Networks (MOWGAN) suitable for the analysis of paired or unpaired data with high number of modalities (>2). At the core of our strategy is a single network trained on all modalities together, limiting the computational burden when many molecular layers are evaluated. Source code of our framework is available at https://github.com/vgiansanti/MOWGAN. Supplementary data are available at Bioinformatics online.

Abstract PO5-10-09: Capturing Experiences: Navigating Breast Cancer Treatment of Nigerian Patients and Healthcare Providers Using Qualitative Interviews

Abstract Introduction A notable shift has occurred in medical relationships, placing greater emphasis on the role of patients within the healthcare system. A nascent body of literature in the US captures the experiences of patients following a diagnosis, but this is lacking in Nigeria. We used Systems Engineering Initiative for Patient Safety (SEIPS 2.0) as a theoretical framework from the discipline of human factors that serves as a guide for characterizing complex sociotechnical systems comprised of factors, work processes, and outcomes - with the patient and clinicians at the center. We specifically focused on the patient work network, which refers to individuals and communities that play role in supporting patient work. The objective of this study was to elicit the experiences of Nigerian breast cancer patients and healthcare providers by qualitative interviews and characterize productive and counterproductive activities of the patient work network. Methods The study was approved by the IRB. We used snowball sampling and social media to recruit Nigerian breast cancer patients and health care providers with experience treating breast cancer. One researcher conducted interviews that lasted 30–60 min using teleconferencing technology and an interview guide developed for this study. Questions for both patients and healthcare providers covered topics such as patient response to diagnosis, barriers and facilitators to care including people and communities within the patient’s network. We calculated descriptive statistics for demographics. Using thematic analysis, we carried out the analysis in the following stages: data review, category coding and data extraction, and synthesis and integration of findings. Results We interviewed 18 female patients (29-67 yrs old) and 6 healthcare providers (31-42 yrs old; 3 males). Twelve patients received treatment in urban areas and 6 in rural. Four patients had a bachelor's degree. Healthcare providers included primary, specialty, palliative, treatment, diagnostic care. We highlight a subset of the dataset with focus on productive versus unproductive work conducted by family and by religious community, with unproductive defined as potentially having negative impact on the patient and their experience (Table 1). Providers stated that patients who receive family support exhibit better health outcomes compared to those who do not. Providers shared that religious community can perpetuate patient denial in their diagnosis. Both patients and providers discussed an inclination towards seeking traditional care. Despite recognizing the challenges and pressures faced by patients, providers lacked in-depth understanding of the specific experiences patients encountered. Conclusion Our findings highlight the role of family and religious community in conducting activities that are both productive and unproductive facilitating productive activities and its influence on the decision-making process of patients. Alternatively, both family and religious community engage in activities counterproductive to the patient and their illness. Findings emphasize the importance of comprehensive support systems, education, and targeted interventions to improve the experiences and outcomes of Nigerian breast cancer patients. Effective strategies should address cultural beliefs, empower patients to make informed decisions, and enhance provider knowledge regarding the unique challenges faced by patients within the identified barriers. Table. Productive versus unproductive work conducted by family and by religious community Citation Format: Blessing Nwachukwu, Elizabeth Papautsky. Capturing Experiences: Navigating Breast Cancer Treatment of Nigerian Patients and Healthcare Providers Using Qualitative Interviews [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-10-09.

Well-Candidate-Recognition Solution Offers Time Savings, Production Enhancement

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 213329, “Data-Driven Well-Candidate-Recognition Solution: Case Study in a Digital Oil Field in Abu Dhabi,” by Erismar Rubio, SPE, Nagaraju Reddicharla, SPE, and Mayada Ali Sultan Ali, ADNOC, et al. The paper has not been peer reviewed. _ A well-candidate-recognition (WCR) data-analytics solution was developed to expedite the process of identifying unhealthy wells that may require rig or rigless interventions based on data integration, automation, and advanced data-driven models. The solution expedites the well-performance-review process to pinpoint candidates for stimulation, nitrogen lift, gas lift conversion, and water or gas shutoff, providing a flexible visualization platform to highlight hidden well-performance insight. Introduction Mature oil fields will face challenges in terms of increasing water cut, lack of pressure support, and the requirement of artificial lift. The critical concern is prioritization of remedial actions that are economically attractive with high returns and low risk. In the past, the asset was prioritizing the rig and rigless intervention candidates on a manual basis with no technical framework, mostly relying on engineers’ backgrounds and experience. Lack of understanding and failure to apply lessons learned from previous jobs were frequent outcomes because no single, comprehensive database or documentation existed that effectively captured well-intervention history. Every year, the operator requested a well-performance-review report for every reservoir containing hundreds of diagnostic plots that required massive manual updates, although the subject asset is a digital oil field fully instrumented with real-time data streaming. However, no tools were available to integrate periodic and nonperiodic required data. The complete paper addresses these challenges of integrating huge amounts of data and model frameworks and developing a systematic workflow to identify opportunities for production enhancement. Data-driven models, combined with the integration of static and dynamic data, enable proactive surveillance routines that allow engineers to focus on problematic wells and opportunity generation in a timely manner. Objective The WCR data-analytics solution was developed as part of the operator’s digital transformation strategy to streamline the process of improving diagnostics, identifying unhealthy wells, and reviewing value gain. The solution aims to help achieve the following objectives: - Facilitate well-performance review to pinpoint candidates for rig and rigless intervention - Provide a flexible visualization platform to highlight hidden well performance insight - Integration of real-time data and official databases - Create a collaborative environment for improved decision-making - Improve rigless success factor for the most-expensive activities - Optimize and prioritize the reservoir-monitoring plan (RMP)

RCPA: An Open-Source R Package for Data Processing, Differential Analysis, Consensus Pathway Analysis, and Visualization.

Identifying impacted pathways is important because it provides insights into the biology underlying conditions beyond the detection of differentially expressed genes. Because of the importance of such analysis, more than 100 pathway analysis methods have been developed thus far. Despite the availability of many methods, it is challenging for biomedical researchers to learn and properly perform pathway analysis. First, the sheer number of methods makes it challenging to learn and choose the correct method for a given experiment. Second, computational methods require users to be savvy with coding syntax, and comfortable with command-line environments, areas that are unfamiliar to most life scientists. Third, as learning tools and computational methods are typically implemented only for a few species (i.e., human and some model organisms), it is difficult to perform pathway analysis on other species that are not included in many of the current pathway analysis tools. Finally, existing pathway tools do not allow researchers to combine, compare, and contrast the results of different methods and experiments for both hypothesis testing and analysis purposes. To address these challenges, we developed an open-source R package for Consensus Pathway Analysis (RCPA) that allows researchers to conveniently: (1) download and process data from NCBI GEO; (2) perform differential analysis using established techniques developed for both microarray and sequencing data; (3) perform both gene set enrichment, as well as topology-based pathway analysis using different methods that seek to answer different research hypotheses; (4) combine methods and datasets to find consensus results; and (5) visualize analysis results and explore significantly impacted pathways across multiple analyses. This protocol provides many example code snippets with detailed explanations and supports the analysis of more than 1000 species, two pathway databases, three differential analysis techniques, eight pathway analysis tools, six meta-analysis methods, and two consensus analysis techniques. The package is freely available on the CRAN repository. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Processing Affymetrix microarrays Basic Protocol 2: Processing Agilent microarrays Support Protocol: Processing RNA sequencing (RNA-Seq) data Basic Protocol 3: Differential analysis of microarray data (Affymetrix and Agilent) Basic Protocol 4: Differential analysis of RNA-Seq data Basic Protocol 5: Gene set enrichment analysis Basic Protocol 6: Topology-based (TB) pathway analysis Basic Protocol 7: Data integration and visualization.

Bridging Modalities: A Multimodal Machine Learning Approach for Parkinson’s Disease Diagnosis Using EEG and MRI Data

Parkinson’s disease (PD) is a slowly progressing neurological disorder with symptoms that overlap with those of other conditions, making early detection and accurate diagnosis vital for effective treatment and a patient’s quality of life. Symptoms such as tremors, stiffness, slow movements, and balance issues, along with psychiatric manifestations, are typical of PD. This study introduces a groundbreaking approach to PD diagnosis, utilizing a multimodal machine learning framework that integrates Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) data. Focusing on the early detection and accurate classification of PD, the proposed research leverages the distinct yet complementary nature of EEG and MRI datasets to enhance diagnostic precision. We employed a robust algorithmic strategy, including LightGBM and machine learning techniques, to analyze the complex patterns inherent in neurological data. The key steps of the proposed research are preprocessing and feature extraction from both EEG and MRI modalities, followed by their fusion using Principal Component Analysis (PCA) for dimensionality reduction. The fused dataset was then analyzed using a LightGBM model and validated through a 10-fold cross-validation process to ensure reliability and stability. The model’s efficacy was further tested on independent datasets, demonstrating its robustness across diverse patient demographics. The obtained results showcased an accuracy of 97.17%, sensitivity of 96.58%, and specificity of 96.82% in PD classification, outperforming traditional multimodal as well as single-modality diagnostic methods. The integration of EEG and MRI data provided a more comprehensive view of the neurophysiological and neuroanatomical changes associated with PD. Additionally, the use of advanced machine learning algorithms allowed for a nuanced analysis, capturing subtle patterns indicative of early PD stages.

Understanding the dynamics of enhanced light non-aqueous phase liquids (lnapl) remediation at a polluted site: Insights from hydrogeophysical findings and chemical evidence

This study intricately unfolds a pioneering methodology for remediating contaminants in a persistent light non-aqueous phase liquids (LNAPL)-contaminated site. The remediation strategy seamlessly integrates enhanced desorption and in-situ chemical oxidation (ISCO), orchestrating the injection of PetroCleanze® (a desorbent) and RegenOx® (an oxidizer) through meticulously designed wells. These injections, based on detailed geological and hydrogeological assessments, aim at mobilizing residual contaminants for subsequent extraction. Real-time subsurface dynamics are investigated through geophysical monitoring, employing electrical resistivity tomography (ERT) to trace reagent migration pathways via their effect on bulk electrical conductivity. The integration of groundwater sampling data aims at providing additional insights into the transformations of contaminants in the spatiotemporal context. Vivid two-dimensional time-lapse ERT sections showcase the evolution of resistivity anomalies, providing high-resolution evidence of the heterogeneity, dispersion pathways of desorbent and oxidant, and residual LNAPL mobilization. Hydrochemical analyses complement this, revealing effective mobilization processes with increasing aqueous concentrations of total petroleum hydrocarbons (TPH) over time. Speciation analysis unveils the intricate interplay of desorption and oxidation, portraying the dynamic fractionation of hydrocarbon components. The hydrogeophysical and data-driven framework not only delivers qualitative and quantitative insights into reagent and contaminant distribution but also enhances understanding of spatial and temporal physio-chemical changes during the remediation process. Time-lapse ERT visually narrates the reagent's journey through time, while chemical analyses depict the unfolding processes of desorption and oxidation across space and time. The coupling of hydrogeophysical and chemical findings pictures the transformations of pollutants following the sequence of product injection and the push and pull activities, capturing the removal of mobilized contaminants through hydraulic barrier wells. This enhanced understanding proves instrumental towards optimizing and tailoring remediation efforts, especially in heterogeneous environmental settings. This study establishes a new standard for a sophisticated and innovative contaminant remediation approach, advancing environmental practices through the harmonized analysis of geophysical and chemical data.

From papers to RDF-based integration of physicochemical data and adverse outcome pathways for nanomaterials

Adverse Outcome Pathways (AOPs) have been proposed to facilitate mechanistic understanding of interactions of chemicals/materials with biological systems. Each AOP starts with a molecular initiating event (MIE) and possibly ends with adverse outcome(s) (AOs) via a series of key events (KEs). So far, the interaction of engineered nanomaterials (ENMs) with biomolecules, biomembranes, cells, and biological structures, in general, is not yet fully elucidated. There is also a huge lack of information on which AOPs are ENMs-relevant or -specific, despite numerous published data on toxicological endpoints they trigger, such as oxidative stress and inflammation. We propose to integrate related data and knowledge recently collected. Our approach combines the annotation of nanomaterials and their MIEs with ontology annotation to demonstrate how we can then query AOPs and biological pathway information for these materials. We conclude that a FAIR (Findable, Accessible, Interoperable, Reusable) representation of the ENM-MIE knowledge simplifies integration with other knowledge.Scientific contributionThis study introduces a new database linking nanomaterial stressors to the first known MIE or KE. Second, it presents a reproducible workflow to analyze and summarize thisknowledge. Third, this work extends the use of semantic web technologies to the field of nanoinformatics and nanosafety.

Comprehensive proteogenomic characterization of rare kidney tumors.

Non-clear cell renal cell carcinomas (non-ccRCCs) encompass diverse malignant and benign tumors. Refinement of differential diagnosis biomarkers, markers for early prognosis of aggressive disease, and therapeutic targets to complement immunotherapy are current clinical needs. Multi-omics analyses of 48 non-ccRCCs compared with 103 ccRCCs reveal proteogenomic, phosphorylation, glycosylation, and metabolic aberrations in RCC subtypes. RCCs with high genome instability display overexpression of IGF2BP3 and PYCR1. Integration of single-cell and bulk transcriptome data predicts diverse cell-of-origin and clarifies RCC subtype-specific proteogenomic signatures. Expression of biomarkers MAPRE3, ADGRF5, and GPNMB differentiates renal oncocytoma from chromophobe RCC, and PIGR and SOSTDC1 distinguish papillary RCC from MTSCC. This study expands our knowledge of proteogenomic signatures, biomarkers, and potential therapeutic targets in non-ccRCC.