Web-based Tool Research Articles

The UCSC Genome Browser database: 2025 update.

The UCSC Genome Browser (https://genome.ucsc.edu) is a widely utilized web-based tool for visualization and analysis of genomic data, encompassing over 4000 assemblies from diverse organisms. Since its release in 2001, it has become an essential resource for genomics and bioinformatics research. Annotation data available on Genome Browser includes both internally created and maintained tracks as well as custom tracks and track hubs provided by the research community. This last year's updates include over 25 new annotation tracks such as the gnomAD 4.1 track on the human GRCh38/hg38 assembly, the addition of three new public hubs, and significant expansions to the Genome Archive[GenArk) system for interacting with the enormous variety of assemblies. We have also made improvements to our interface, including updates to the browser graphic page, such as a new popup dialog feature that now displays item details without requiring navigation away from the main Genome Browser page. GenePred tracks have been upgraded with right-click options for zooming and precise navigation, along with enhanced mouseOver functions. Additional improvements include a new grouping feature for track hubs and hub description info links. A new tutorial focusing on Clinical Genetics has also been added to the UCSC Genome Browser.

Dynamic web-based GIS tool for pre-feasibility evaluation of renewable energy projects

The high energy consumption and the large amount of emissions associated with roads life cycle makes it necessary for the administrations involved to invest in the implementation of renewable energy technologies on adjacent lands so that the net balance between CO2 emitted and CO2 saved is zero, thus helping to decarbonize the transport networks. To facilitate the first step in the long process leading up to the construction of wind and solar farms, a user-friendly tool is here presented that allows an early-stage evaluation of renewable energy projects. The ENROAD web-based GIS tool, through a functional, modern, intuitive, and ergonomic graphical user interface aimed at non-specialized users, optimizes the use of photovoltaic and wind technologies based on shading and wake losses, respectively, taking into account the solar radiation and wind conditions of the location and the geometry of the area selected by the user. In addition, the tool provides a thorough financial analysis, incorporating the effects of user-defined long-term interest rates, inflation rates, loan costs and efficiency losses on the Levelized Cost of Energy as well as traditional financial metrics, enabling users to conduct insightful if-then simulations. While this tool cannot have the precision of the in-depth study needed for the design of complex renewable energy facilities, it can facilitate the decision making by the administrations. Finally, a case study and a validation section have been included to demonstrate the technical and financial evaluation provided by ENROAD and ensure that the results provided are reasonable and accurate enough.

50 Co-designing an intervention with asylum seeking youth to improve access to healthcare in Canada using human-centered design

Abstract Background Asylum seeking youth in Canada and the United States face multiple barriers to accessing healthcare, leaving them at greater risk of worse health outcomes. In a previous study, we found that limited knowledge of the Canadian health care system and few navigation supports were a key barrier to care for asylum seeking youth, yet no youth-developed intervention existed to support this population. Human-centered design (HCD) develops meaningful interventions with communities who have been historically marginalized. Objectives This study sought to co-design an intervention to increase youth empowerment and access to health care for asylum seeking youth in Canada. Design/Methods This was a community-based participatory-action research (CBPAR) study, and combined human-centered design (HCD) methodologies. We obtained research ethics board approval for this study (#SMH REB 22-117). In partnership with two community organizations that supported asylum seeking youth in a large Canadian city, a youth advisory board was created. In multiple co-creation sessions over a calendar year, an intervention was developed in rapid prototyping sessions and refined with feedback, to develop a final pilot intervention. Results A total of 7 asylum-seeking youth participated in the youth advisory board, ranging in age from 12-19, representing 3 different languages and included youth who self-identified with chronic medical conditions. Over a 15-month period, they participated in 6 co-creation sessions to develop a multi-lingual, web-based tool; youth specifically stated they did not believe an app would be helpful, but rather a resource that could be easily accessed. The youth advisory board shared they had limited understanding of the Canadian healthcare system, as well as interim and provincial health insurance. Youth felt the tool should answer questions about how health insurance worked, be brightly colored, use dyslexia-friendly font and be easily saved on a smartphone as an image. A multi-lingual wallet card was developed for youth to easily present at healthcare encounters, particularly if they did not have provincial health insurance, to help youth communicate their coverage, language and needs. Conclusion To our knowledge, we developed the first co-designed tool in Canada for youth asylum seekers, by youth, to improve and empower youth to receive health care services they are entitled to through navigation support. Given the high degree of marginalization faced by asylum seeking youth, this intervention has the promise to improve health outcomes. Feasibility and acceptability of this study will be evaluated in subsequent studies.

AI chatbot: I want help, not the answer!

In recent years, the integration of artificial intelligence (AI) into educational settings has become a topic of great interest. As technology continues to evolve, educators and students are exploring how AI can enhance learning experiences. University of South-Eastern Norway (USN) and Kristiania University College (HK) are developing a web-based educational tool, DbPersist, where IT students can practice tasks within the database subject and receive automated feedback. The latest version now also offers students suggestions on how to utilize AI for learning while they are solving various database related tasks. In this study, we explore the students' experiences with getting automatically generated “prompts” for further use in their preferred AI tool, whenever they request assistance. Do they see the value in gaining experience in using prompts designed for learning? Do they believe it might help them using AI for learning purposes in other courses in their study? To answer these questions, we conducted surveys and interviews. Participants included bachelor students across various disciplines and multiple campuses at USN. The participants were all enrolled in database courses with learning goals involving the database query language SQL and ER modelling (drawing Entity-Relationship Diagrams). Early findings suggest that students appreciate the assistance provided by DbPersist’s AI prompts. They value the emphasis on AI-assisted learning and find the prompts useful for getting assistance in solving difficult tasks as opposed to getting complete answers. They also see the value in gaining experience in using AI chatbots using prompts designed for learning and understanding. As AI continues to shape education, tools like DbPersist offer exciting possibilities. DbPersist exemplifies how AI may be used to empower students, emphasizing learning and understanding alongside correctness.

Economic and environmental pollutant impact of Maternal-Fetal Telemedicine.

The global issue of greenhouse gas emissions has significant implications for the environment and human health. Telemedicine provides a valuable tool for delivering healthcare while reducing gas emissions by limiting the need for patient travel. However, the environmental effects of telemedicine in high-risk pregnancy populations remain unassessed. The aim of this study was to estimate the economic and environmental impact of an outpatient teleMFM program. This retrospective cohort study examined all visits at 3 teleMFM clinics more than 90 miles away from the nearest in-person MFM office between 10/1/2021 and 6/1/2022. Travel distances and times were calculated for each appointment between the patient's home, telemedicine clinic, and nearest in-person clinics, using zip code data and Google Maps web-based map calculator tools. Travel cost savings and environmental impact were calculated by determining differences in mileage reimbursement rate and emissions between those incurred in attending telemedicine appointments and those that would have been incurred if in-person using inflation-adjusted Internal Revenue Service annual standard mileage reimbursement rate ($0.58 per mile), and the U.S. Environmental Protection Agency Office of Transportation and Air Quality's average annual emissions and fuel consumption for gasoline-fueled passenger vehicles. During the study period a total number of 2,712 appointments were scheduled, of which 2,454 were kept (cancellations removed) and analyzed. Visiting a teleMFM clinic resulted in 204 miles, 200 minutes and 118.32 dollars saved per patient visit compared to visiting the nearest in-person clinic. Over a 7 month period a total of 96.6 metric tons of emissions were saved. This study demonstrates the positive economic and environmental impact of teleMFM utilization in communities remote from in-person care. Given the contribution of greenhouse gas emissions to climate change, such findings may provide strategies for our specialty make informed policy, advocacy, and business decisions.

Integrative prognostic modeling for stage III lung adenosquamous carcinoma post-tumor resection: machine learning insights and web-based implementation.

The prognostic landscape of stage III Lung Adenosquamous Carcinoma (ASC) following primary tumor resection remains underexplored. A thoughtfully developed prognostic model has the potential to guide clinicians in patient counseling and the formulation of effective therapeutic strategies. Utilizing data from the Surveillance, Epidemiology, and End Results database spanning 2000 to 2018, this study identified independent prognostic factors influencing Overall Survival (OS) in ASC using Boruta analysis. Employing Gradient Boosting, Random Forest, and Neural Network algorithms, predictive models were constructed. Model performance was assessed through key metrics, including Area Under the Receiver Operating Characteristic Curve (AUC), calibration plot, Brier score, and Decision Curve Analysis (DCA). Among 241 eligible patients, seven clinical parameters-age, sex, primary tumor size, N stage, primary tumor site, chemotherapy, and systemic therapy-were identified as significant predictors of OS. Advanced age, male gender, larger tumor size, absence of chemotherapy, and lack of systemic therapy were associated with poorer survival. The Random Forest model outperformed others, achieving 3- and 5-year AUCs of 0.80/0.79 (training) and 0.74/0.65 (validation). It also demonstrated better calibration, lower Brier scores (training: 0.189/0.171; validation: 0.207/0.199), and more favorable DCA. SHAP values enhanced model interpretability by highlighting the impact of each parameter on survival predictions. To facilitate clinical application, the Random Forest model was deployed on a web-based server for accessible prognostic assessments. This study presents a robust machine learning model and a web-based tool that assist healthcare practitioners in personalized clinical decision-making and treatment optimization for ASC patients following primary tumor resection.

Bayesian optimization of one-dimensional convolutional neural networks (1D CNN) for early diagnosis of Autistic Spectrum Disorder

Autistic Spectrum Disorder (ASD) is a challenging neurological development disorder, which involves poor social interaction, communication, and repetitive behaviours. If autism is identified early enough it can be treated with better outcomes but present diagnostic tests are dependent on subjective opinion, consume a lot of time, and are vague. This study is aimed at optimizing one-dimensional convolutional neural networks (1D CNN) to improve the precision and speed of early ASD diagnosis. Four ASD datasets representing different age groups — toddlers, children, adolescents, and adults were modelled using one-dimensional convolutional neural networks (1D CNN). These datasets are accessible to the public on the UCI Machine Learning Repository and Kaggle, they consist of behavioural features relevant to ASD diagnosis. Each dataset underwent feature selection, categorical encoding, and missing value handling. Then, baseline 1D CNN with predefined hyperparameters was modelled on each of the datasets. Subsequently, the baseline models were optimized using the Tree-structured Parzen Estimator (TPE). An interactive web-based ASD diagnostic tool was developed, where user inputs are processed through age-specific pre-trained optimized models to determine ASD probability. The optimized 1D CNN models significantly outperformed the baseline models across all age groups and achieved scores of 100% in accuracy, precision, recall, F1-score, MCC, and AUC ROC. This implies that the optimized models can reliably identify people in various age groups who have and do not have ASD. The development of an interactive web-based diagnostic tool extends the practical utility of the models, making them accessible for clinical and at-home use.

Evaluating User Experiences and Preferred Features of a Web-Based 24-Hour Dietary Assessment Tool: Usability Study.

Intake24, a web-based 24-hour dietary recall tool developed in the United Kingdom, was adapted for use in New Zealand (Intake24-NZ) through the addition of a New Zealand food list, portion size images, and food composition database. Owing to the customizations made, a thorough evaluation of the tool's usability was required. Detailed qualitative usability studies are well suited to investigate any challenges encountered while completing a web-based 24-hour recall and provide meaningful data to inform enhancements to the tool. This study aims to evaluate the usability of Intake24-NZ and identify improvements to enhance both the user experience and the quality of dietary intake data collected. We used a mixed methods approach comprising two components: (1) completion of a single 24-hour dietary recall using Intake24-NZ with both screen observation recordings and collation of verbal participant feedback on their experience and (2) a survey. A total of 37 participants aged ≥11 years self-completed the dietary recall and usability survey (men and boys: 14/37, 38% and women and girls: 23/37, 62%; Māori: 10/37, 27% and non-Māori: 27/37, 73%). Although most (31/37, 84%) reported that Intake24-NZ was easy to use and navigate, data from the recorded observations and usability survey revealed challenges related to the correct use of search terms, search results obtained (eg, type and order of foods displayed), portion size estimation, and associated food prompts (eg, did you add milk to your tea?). This comprehensive usability study identified challenges experienced by users in completing a dietary recall in Intake24-NZ. The results informed a series of improvements to enhance user experience and the quality of dietary data collected with Intake24-NZ, including adding new foods to the food list, optimizing the search function and ordering of search results, creating new portion size images, and providing clearer instructions to the users.

Prevalence of Cardiovascular Disease and Comparison of Risk Category Predictions of Systemic Coronary Risk Evaluation Score-2 and 4 Other Cardiovascular Disease Risk Assessment Tools Among People Living with Human Immunodefficiency Virus in Türkiye.

Cardiovascular disease (CVD) is a major cause of mortality among people living with HIV (PLWH). We aimed to assess the prevalence of diagnosed CVD and the risk of CVD among PLWH using 5 different tools. This retrospective, cross-sectional study was conducted in 20 tertiary centers in Türkiye between October 2021 and March 2022, among 1425 PLWH aged 40-75 years. About 82.7% were male, with a median age of 51. Web-based tools for each score were used for CVD risk calculations. Of 1425 PLWH enrolled, 10.8% had confirmed CVD, and 1132 had their risk scores evaluated. Of those participants, 42.8% had a higher risk of CVD (10-year risk of atherosclerotic CVD risk score (ASCVD) above 7.5%), and according to the European Society of Cardiology systemic coronary risk evaluation 2 (SCORE2), 71.7% had a high- to very high-risk rate. The agreement between various CVD risk tools varied, with Framingham heart study risk score (FRS), modified FRS, data collection on adverse effects of anti-HIV drugs (DAD), and SCORE2 for high-risk countries showing overall agreement rates of 82%, 94%, 91%, and 36%, respectively, compared to ASCVD. According to the 2021 European and 2019 American Cardiology guidelines, 75.3% and 47.1% of PLWH would be eligible for lipid-lowering agents, respectively. The diagnosed CVD prevalence highlighted the importance of monitoring cardiovascular health and comorbidities in this population. SCORE2 identified a greater number of individuals at high/very high risk compared to other prediction tools. The implementation of CVD prevention through lipid-lowering therapy was far from desired levels in our cohort.

Web-based vs. conventional: a comprehensive analysis of visual acuity assessment using the PocDoc tool in a tertiary eye care centre.

Visual acuity (VA) represents a fundamental measure of visual function. The significant prevalence of underdiagnosed ocular disorders underscores the importance of effective VA assessment. This study evaluates the efficacy of a web-based VA assessment tool ("PocDoc") versus conventional VA testing. Prospective observational study including 353 participants recruited from various eye clinics in a tertiary referral centre. Age, diagnosis, and VA related information (i.e. VA measurements from PocDoc and conventional VA test [Snellen chart], test type, etc) were collected. Spearman's rank correlation, Intraclass Correlation, and Bland-Altman plot compared outcomes of both tests. One-way ANOVA and paired-T test were used to compare means. Most patients were males (59.2%) with a mean age of 52.2 ± 20.6 years. PocDoc had moderate positive correlation to conventional testing (rho = 0.50, p < 0.001). PocDoc led to higher logMAR scores compared to conventional testing (mean logMAR 0.19 and 0.13 respectively, p < 0.01). Moreover, PocDoc demonstrated a sensitivity of 82.8% and specificity of 79% for detecting visual impairment. The discrepancy between PocDoc and conventional VA testing increased with higher logMAR values, indicating greater inconsistency between the tests for patients with poorer VA. Age, test type, and disease type contributed to this variability. The concordance between PocDoc and conventional testing for VA measurement across various ages and conditions makes it a suitable screening tool. Future technological inventions should consider age, test type, and disease type as critical factors related to the level of agreement and correlation between digital and conventional VA testing methods.

Participatory multi-criteria decision making for optimal siting of multipurpose artificial reservoirs

Water resources management is a crucial activity due to climate change concerns and increasing water scarcity. The increase of water storage by building new artificial reservoirs is one of the most common solutions to fulfill the community’s needs for potable water, energy, irrigation, and prevention of flood risk. The siting of the reservoirs is usually assessed based on topographical and hydrological considerations. However, there are additional crucial factors, often disregarded in common engineering top-down design approaches, that should be considered to determine suitable locations, such as bio-physical, socio-economic, regulatory, and environmental factors, especially where there are competing interests in the use of territory. Involving the communities in the initial stages of the decision-making process is therefore crucial. In this work, we present a methodology, based on multi-criteria decision-making analysis (MCDA), for identifying the optimal locations for new reservoirs while simultaneously addressing all the aforementioned factors through community engagement. The presented method aims at overcoming two major challenges in existing works, i.e., retrieving quantitative information from the participatory approach and going beyond standard hydro-morphological suitability of a dam site. In a first step, an algorithm assesses a large number of sites using a Digital Elevation Model (DEM). Then, MCDA is conducted to rank all potential locations. The selection criteria are defined quantitatively, based on a territorial analysis combined with hydrological modelling. The criteria include: geometric and morphological aspects (reservoir volume, etc.), hydrological indicators (water balance, potential flood mitigation), anthropization (population, infrastructures, etc.), landscape, archaeological heritage, ecology, environmental components and natural hazards. A web-based survey tool based on the pairwise comparison is developed to engage the community in the decision-making process, allowing them to express their perspectives on the relative significance of each criterion. The application of this methodology is demonstrated through a case study in the Pesa river watershed, Italy, showcasing its effectiveness in identifying the most suitable reservoir locations while targeting the highest environmental preservation and community acceptance. The results show that sole engineering criteria might not reflect the sensibility of the community and that variations of the size of the dam can lead to distinct trade-offs between costs and benefits where interference with impact elements occurs.

GlycoMaple: recent updates and applications in visualization and analysis of glycosylation pathways.

Post-translational modifications including glycosylation, phosphorylation, and lipidation expand the functionality and diversity of proteins. Protein glycosylation is one of the most abundant post-translational modifications in mammalian cells. The glycosylation process is regulated at multiple steps, including transcription, translation, protein folding, intracellular transport, and localization, and activity of glycosyltransferases and glycoside hydrolases. The glycosylation process is also affected by the concentration of sugar nucleotides in the lumen of the Golgi apparatus. Unlike the synthesis of other biological macromolecules, such as nucleic acids and proteins, glycan biosynthesis is not a template-driven process. In addition, the chemical complexity of glycan structures makes the glycosylation network extraordinarily intricate. We previously developed a web-based tool specially focused on glycan metabolic pathways known as GlycoMaple, which is able to easily visualize and estimate glycosylation pathways based on gene expression data. We recently updated GlycoMaple to incorporate the new genes and glycosylation pathways. Here, we introduce and discuss the uses and upgrades of GlycoMaple.

Quantification of Carbon Flux Patterns in Ecosystems: A Case Study of Prince Edward Island

Mitigating climate change by reducing heat-trapping greenhouse gas (GHG) emissions in the Earth’s atmosphere is a critical global challenge. In response to this urgency, the Canadian government has set a target of achieving zero emissions by 2050. The Government of Prince Edward Island (PEI) has committed to becoming Canada’s first net-zero province by 2040. Achieving this goal requires an extensive knowledge of emissions arising from ecosystem dynamics in PEI. Therefore, this study aims to quantify the carbon fluxes of these ecosystems, offering insights into their role in GHG emissions and removals. Through an extensive literature review and analysis, this research provides a detailed assessment of the potential carbon flux contributions from various ecosystems across PEI. High-resolution maps for carbon emissions, removals, and flux for the years 2010 and 2020 were developed, highlighting key findings on carbon dynamics. Additionally, a web-based tool was developed to allow decision makers and the general public to explore these carbon flux maps interactively. This work aims to inform policy decisions and enhance strategies for effective climate change mitigation in PEI.

BBBper: A Machine Learning-based Online Tool for Blood-Brain Barrier (BBB) Permeability Prediction.

Neuronal disorders have affected more than 15% of the world's population, signifying the importance of continued design and development of drugs that can cross the Blood-Brain Barrier (BBB). BBB limits the permeability of external compounds by 98% to maintain and regulate brain homeostasis. Hence, BBB permeability prediction is vital to predict the activity of a drug-like substance. Here, we report about developing BBBper (Blood-Brain Barrier permeability prediction) using machine learning tool. A supervised machine learning-based online tool, based on physicochemical parameters to predict the BBB permeability of given chemical compounds was developed. The user-end webpage was developed in HTML and linked with back-end server by a python script to run user queries and results. BBBper uses a random forest algorithm at the back end, showing 97% accuracy on the external dataset, compared to 70-92% accuracy of currently available web-based BBB permeability prediction tools. The BBBper web tool is freely available at http://bbbper.mdu.ac.in.

Analysis of the Effects of Securing Baseflow and Improving Water Quality through the Introduction of LID Techniques

Rapid climate change and increasing water use have led to various problems in small- and medium-sized urban streams during dry periods, such as stream drying, water pollution, and ecological degradation, reducing their physical and ecological functions. Ensuring adequate baseflow and improving water quality during these critical periods are essential for maintaining urban stream health. While previous studies have explored the effects of Low Impact Development (LID) techniques (e.g., green roof, rainwater harvesting system, permeable pavement, infiltration trench) on infiltration and groundwater recharge, they have primarily focused on general flow regimes rather than dry and low-flow periods. This study specifically evaluates the effects of LID techniques on securing baseflow and improving water quality during dry periods, utilizing the SWAT-MODFLOW model and the Web-based Hydrograph Analysis Tool (WHAT) system. The results show that LID techniques reduce peak flow by an average of 27% and secure an additional 43% of baseflow during dry periods. Suspended solids (SS) and total phosphorus (T-P) concentrations were reduced by 15% and 41%, respectively. These findings demonstrate the effectiveness of LID techniques not only in managing stormwater runoff during flood events but also in maintaining baseflow and water quality during dry periods, thus providing valuable insights for sustainable urban watershed management.

The continuous improvement of digital assistance in the radiation oncologist's work: from web-based nomograms to the adoption of large-language models (LLMs). A systematic review by the young group of the Italian association of radiotherapy and clinical oncology (AIRO).

Recently, the availability of online medical resources for radiation oncologists and trainees has significantly expanded, alongside the development of numerous artificial intelligence (AI)-based tools. This review evaluates the impact of web-based clinical decision-making tools in the clinical practice of radiation oncology. We searched databases, including PubMed, EMBASE, and Scopus, using keywords related to web-based clinical decision-making tools and radiation oncology, adhering to PRISMA guidelines. Out of 2161 identified manuscripts, 70 were ultimately included in our study. These papers all supported the evidence that web-based tools can be transversally integrated into multiple radiation oncology fields, with online applications available for dose and clinical calculations, staging and other multipurpose intents. Specifically, the possible benefit of web-based nomograms for educational purposes was investigated in 35 of the evaluated manuscripts. As regards to the applications of digital and AI-based tools to treatment planning, diagnosis, treatment strategy selection and follow-up adoption, a total of 35 articles were selected. More specifically, 19 articles investigated the role of these tools in heterogeneous cancer types, while nine and seven articles were related to breast and head & neck cancers, respectively. Our analysis suggests that employing web-based and AI tools offers promising potential to enhance the personalization of cancer treatment.

A systematic review upon the online content provided for self-management of diabetes

Background: Diabetes is a major illness that requires appropriate education in order to increase one's understanding of self-management. With the advancement of the internet, the proliferation of online health-related content in supporting the management of chronic illness like diabetes has been ongoing; however, little is understood on the sort of acceptable and/or effective information attainable online. Objective: The aim of this review is to evaluate the current literature in hindsight of online management in diabetes whilst establishing the effectiveness of online management tools, the quality of the online information, and the methods of obtaining online information. Methods: A series of systematic searches of online databases were conducted in accordance with strictly defined inclusion and exclusion criteria relevant to the research question. Additionally, an evidence-based literature (EBL) checklist tool was used to assess the methodological and reporting quality of each study. Each article was peer-reviewed and screened for any biases. Results: A result of 11 electronic searches were critically appraised using the EBL appraisal checklist tool and all were found to have overall validity. The results indicate an increase use of online technology to empower an understanding of one's own health. Web-based tools like social media were useful in promoting the management of diabetes but were not able to provide quality information. Conclusion: Current research indicates the need for more investigation and future development of readily accessible and qualitative information in order to support the self-management of diabetes.

In-silico analysis predicts disruption of normal angiogenesis as a causative factor in osteoporosis pathogenesis

Angiogenesis-osteogenesis coupling is critical for proper functioning and maintaining the health of bones. Any disruption in this coupling, associated with aging and disease, might lead to loss of bone mass. Osteoporosis (OP) is a debilitating bone metabolic disorder that affects the microarchitecture of bones, gradually leading to fracture. Computational analysis revealed that normal angiogenesis is disrupted during the progression of OP, especially postmenopausal osteoporosis (PMOP). The genes associated with OP and PMOP were retrieved from the DisGeNET database. Hub gene analysis and molecular pathway enrichment were performed via the Cytoscape plugins STRING, MCODE, CytoHubba, ClueGO and the web-based tool Enrichr. Twenty-eight (28) hub genes were identified, eight of which were transcription factors (HIF1A, JUN, TP53, ESR1, MYC, PPARG, RUNX2 and SOX9). Analysis of SNPs associated with hub genes via the gnomAD, I-Mutant2.0, MUpro, ConSurf and COACH servers revealed the substitution F201L in IL6 as the most deleterious. The IL6 protein was modeled in the SWISS-MODEL server and the substitution was analyzed via the YASARA FoldX plugin. A positive ΔΔG (1.936) of the F201L mutant indicates that the mutated structure is less stable than the wild-type structure is. Thirteen hub genes, including IL6 and the enriched molecular pathways were found to be profoundly involved in angiogenesis/endothelial function and immune signaling. Mechanical loading of bones through weight-bearing exercises can activate osteoblasts via mechanotransduction leading to increased bone formation. The present study suggests proper mechanical loading of bone as a preventive strategy for PMOP, by which angiogenesis and the immune status of the bone can be maintained. This in silico analysis could be used to understand the molecular etiology of OP and to develop novel therapeutic approaches.

A higher-order transformation approach to the formalization and analysis of BPMN using graph transformation systems

The Business Process Modeling Notation (BPMN) is a widely used standard notation for defining intra- and inter-organizational workflows. However, the informal description of the BPMN execution semantics leads to different interpretations of BPMN elements and difficulties in checking behavioral properties. In this article, we propose a formalization of the execution semantics of BPMN that, compared to existing approaches, covers more BPMN elements while also facilitating property checking. Our approach is based on a higher-order transformation from BPMN models to graph transformation systems. To show the capabilities of our approach, we implemented it as an open-source web-based tool.

POLQ immunostaining behaves as a prognostic factor for pancreatic carcinoma.

DNA polymerase theta (POLQ) is a translesion synthesis polymerase essential for the repair of double strand breaks by the error-prone TMEJ (Theta Mediated End Joining) pathway. Although POLQ participates in maintaining genome stability, several studies have shown that its overexpression correlates with cancer progression and poor prognosis. Due to the fact that its role as a biomarker in pancreatic cancer remains unexplored, we aimed to study the usefulness of POLQ H-score as a prognostic factor in a pancreatic cancer patient cohort. We evaluated POLQ gene expression using a web-based tool to deliver gene expression profiling and interactive analyses based on TCGA and GTEx (GEPIA) and we examined the POLQ immunostaining in 152 biliopancreatic cancer surgical specimens using tissue microarrays. Association with survival was evaluated by Kaplan Meier curves and uni-multivariate Cox regression. GEPIA analysis showed statistical differences according to POLQ mRNA levels in Disease Free Survival (DFS) (log rank 0.023, HR 2.8, p=0.029) and Overall Survival (OS) (log rank 0.011, HR 3.1, p=0.016). For immunohistochemistry (IHC) evaluation, POLQ H-score was calculated, and showed statistical differences for OS in Kaplan Meier curves (log rank 0.001) and uni-multivariate analysis (HR 2.27; 95% CI 1.24-4.15, p=0.008). Our results indicate that POLQ is an independent prognostic factor in pancreatic cancer when analyzed by immunostaining, which is in agreement with the results shown by the POLQ gene expression analysis (GEPIA).