Risk management of hydrogenation station PPP project based on 3D framework—A case study in China
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ABSTRACT:
SUBMITTER: Zhao H
PROVIDER: S-EPMC10727456 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:Traditional continuing medical education (CME) depended primarily on periodic courses and conferences. The cost-effectiveness of these courses has not been established, and often the content is not tailored to best meet the needs of the students. Internet training has the potential to accomplish these goals. Over the last 10 years, we have developed a Web site entitled "Orthochina.org," based upon the wiki concept, which uses an interactive, case-based format. We describe the development of online case discussions, and various technical and administrative requirements. As of December 31, 2007, there were 33,984 registered users, 9,759 of which passed the confirmation procedures. In 2007, an average of 211 registrants visited daily. The average number of first page clicks was 4,248 per day, and the average number of posts was 70 per day. All cases submitted for discussion include the patient's complaint, physical examination findings, and relevant images based on specific criteria for case discussion. The case discussions develop well professionally. No spam posting or unauthorized personal advertisement is permitted. In conclusion, online academic discussions proceed well when the orthopaedic surgeons who participate have established their identities.
| S-EPMC2584280 | biostudies-other
Project description:FAIR (Findability, Accessibility, Interoperability, and Reusability) next-generation sequencing (NGS) data analysis relies on complex computational biology workflows and pipelines to guarantee reproducibility, portability, and scalability. Moreover, workflow languages, managers, and container technologies have helped address the problem of data analysis pipeline execution across multiple platforms in scalable ways. Here, we present a project management framework for NGS data analysis called PM4NGS. This framework is composed of an automatic creation of a standard organizational structure of directories and files, bioinformatics tool management using Docker or Bioconda, and data analysis pipelines in CWL format. Pre-configured Jupyter notebooks with minimum Python code are included in PM4NGS to produce a project report and publication-ready figures. We present 3 pipelines for demonstration purposes including the analysis of RNA-Seq, ChIP-Seq, and ChIP-exo datasets. PM4NGS is an open source framework that creates a standard organizational structure for NGS data analysis projects. PM4NGS is easy to install, configure, and use by non-bioinformaticians on personal computers and laptops. It permits execution of the NGS data analysis on Windows 10 with the Windows Subsystem for Linux feature activated. The framework aims to reduce the gap between researcher in experimental laboratories producing NGS data and workflows for data analysis. PM4NGS documentation can be accessed at https://pm4ngs.readthedocs.io/.
| S-EPMC7788391 | biostudies-literature
Project description:Human activities are contributing to a global decline in biodiversity and ecosystem services. While national parks, rooted in sustainable development principles, aim to counteract this trend and have been successful in developed nations, their direct applicability to developing countries is debatable. In light of the challenges associated with coordinating environmental, social, economic, and cultural objectives, we have formulated a theoretical framework centered on the concept of "risk-value" for managing national park zoning in developing nations. This framework is designed to promote co-prosperity by simultaneously addressing biodiversity conservation, equity, and human well-being concerns. Our framework, when applied to China's Baishanzu National Park, entailed subdividing the park into four distinct zones, each managed by specific and tailored policies. Our research provides insights into the theoretical underpinnings of implementing the national park concept in developing countries and showcases effective strategies for enhancing ecological conservation in these regions.
| S-EPMC11617969 | biostudies-literature
Project description:ObjectiveHigh-risk pregnancy (HRP) conditions such as gestational diabetes mellitus (GDM), hypertension (HTN), and peripartum depression (PPD) affect maternal and neonatal health. Patient engagement is critical for effective HRP management (HRPM). While digital technologies and analytics hold promise, emerging research indicates limited and suboptimal support offered by the highly prevalent pregnancy digital solutions within the commercial marketplace. In this article, we describe our efforts to develop a portfolio of digital products leveraging advances in social computing, data science, and digital health.MethodsWe describe three studies that leverage core methods from Digilego digital health development framework to (1) conduct large-scale social media analysis (n = 55 301 posts) to understand population-level patterns in women's needs, (2) architect a digital repository to enable women curate HRP related information, and (3) develop a digital platform to support PPD prevention. We applied a combination of qualitative coding, machine learning, theory-mapping, and programmatic implementation of theory-linked digital features. Further, we conducted preliminary testing of the resulting products for acceptance with sample of pregnant women for GDM/HTN information management (n = 10) and PPD prevention (n = 30).ResultsScalable social computing models using deep learning classifiers with reasonable accuracy have allowed us to capture and examine psychosociobehavioral drivers associated with HRPM. Our work resulted in two digital health solutions, MyPregnancyChart and MomMind are developed. Initial evaluation of both tools indicates positive acceptance from potential end users. Further evaluation with MomMind revealed statistically significant improvements (P < .05) in PPD recognition and knowledge on how to seek PPD information.DiscussionDigilego framework provides an integrative methodological lens to gain micro-macro perspective on women's needs, theory integration, engagement optimization, as well as subsequent feature and content engineering, which can be organized into core and specialized digital pathways for women engagement in disease management.ConclusionFuture works should focus on implementation and testing of digital solutions that facilitate women to capture, aggregate, preserve, and utilize, otherwise siloed, prenatal information artifacts for enhanced self-management of their high-risk conditions, ultimately leading to improved health outcomes.
| S-EPMC10919928 | biostudies-literature
Project description:PurposeDuring gas station operation, unburned fuel can be released to the environment through distribution, delivery, and storage. Due to the toxicity of fuel compounds, setback distances have been implemented to protect the general population. However, these distances treat gasoline sales volume as a categorical variable and only account for the presence of a single gas station and not clusters, which frequently occur. This paper introduces a framework for recommending setback distances for gas station clusters based on estimated lifetime cancer risk from benzene exposure.MethodsUsing the air quality dispersion model AERMOD, we simulated levels of benzene released to the atmosphere from single and clusters of generic gas stations and the associated lifetime cancer risk under meteorological conditions representative of Albany, New York.ResultsCancer risk as a function of distance from gas station(s) and as a continuous function of total sales volume can be estimated from an equation we developed. We found that clusters of gas stations have increased cancer risk compared to a single station because of cumulative emissions from the individual gas stations. For instance, the cancer risk at 40 m for four gas stations each dispensing 1 million gal/year is 9.84 × 10-6 compared to 2.45 × 10-6 for one gas station.ConclusionThe framework we developed for estimating cancer risk from gas station(s) could be adopted by regulatory agencies to make setback distances a function of sales volume and the number of gas stations in a cluster, rather than on a sales volume category.Supplementary informationThe online version contains supplementary material available at 10.1007/s40201-020-00601-w.
| S-EPMC8172828 | biostudies-literature
Project description:The analytic procedures incorporated to facilitate the delivery of projects are often referred to as project analytics. Existing techniques focus on retrospective reporting and understanding the underlying relationships to make informed decisions. Although machine learning algorithms have been widely used in addressing problems within various contexts (e.g., streamlining the design of construction projects), limited studies have evaluated pre-existing machine learning methods within the delivery of construction projects. Due to this, the current research aims to contribute further to this convergence between artificial intelligence and the execution construction project through the evaluation of a specific set of machine learning algorithms. This study proposes a machine learning-based data-driven research framework for addressing problems related to project analytics. It then illustrates an example of the application of this framework. In this illustration, existing data from an open-source data repository on construction projects and cost overrun frequencies was studied in which several machine learning models (Python's Scikit-learn package) were tested and evaluated. The data consisted of 44 independent variables (from materials to labour and contracting) and one dependent variable (project cost overrun frequency), which has been categorised for processing under several machine learning models. These models include support vector machine, logistic regression, k-nearest neighbour, random forest, stacking (ensemble) model and artificial neural network. Feature selection and evaluation methods, including the Univariate feature selection, Recursive feature elimination, SelectFromModel and confusion matrix, were applied to determine the most accurate prediction model. This study also discusses the generalisability of using the proposed research framework in other research contexts within the field of project management. The proposed framework, its illustration in the context of construction projects and its potential to be adopted in different contexts will significantly contribute to project practitioners, stakeholders and academics in addressing many project-related issues.
| S-EPMC9463441 | biostudies-literature
Project description:Geological disaster could pose a great threat to human development and ecosystem health. An ecological risk assessment of geological disasters is critical for ecosystem management and prevention of risks. Herein, based on the "probability-loss" theory, a framework integrating the hazard, vulnerability, and potential damage for assessing the ecological risk of geological disasters was proposed and applied to Fujian Province. In the process, a random forest (RF) model was implemented for hazard assessment by integrating multiple factors, and landscape indices were adopted to analyze vulnerability. Meanwhile, ecosystem services and spatial population data were used to characterize the potential damage. Furthermore, the factors and mechanisms that impact the hazard and influence risk were analyzed. The results demonstrate that (1) the regions exhibiting high and very high levels of geological hazard cover an area of 10.72% and 4.59%, respectively, and are predominantly concentrated in the northeast and inland regions, often distributed along river valleys. Normalized difference vegetation index (NDVI), precipitation, elevation, and slope are the most important factors for the hazard. (2) The high ecological risk of the study area shows local clustering and global dispersion. Additionally, human activities have a significant influence on ecological risk. (3) The assessment results based on the RF model have high reliability with a better performance compared with the information quantity model, especially when identifying high-level hazard areas. Our study will improve research on the ecological risk posed by geological disasters and provide effective information for ecological planning and disaster mitigation.
| S-EPMC10002115 | biostudies-literature
Project description:With the optional setting of multiple stepped collisional energies (NCEs), higher-energy collisional dissociation (HCD) as available on Orbitrap instruments is a widely adopted dissociation method for intact N-glycopeptide characterization, where peptide backbones and N-glycan moieties are selectively fragmented at high and low NCEs, respectively. Initially, a dependent setting of a central value plus minus a variation is available to the users to set up NCEs, and the combination of 30±10% to give the energies 20%/30%/40% has been mostly adopted in the literature. With the recent availability of independent NCE setup, we found that the combination of 20%/30%/30% is better than 20%/30%/40%; in the analysis of complex intact N-glycopeptides enriched from gastric cancer tissues, total IDs with spectrum-level FDR≤1%, site-specific IDs with site-determining fragment ions and structure-specific IDs with structure-diagnostic fragment ions were increased by 42% (4,767->6,746), 57% (599->942), and 97% (1771->3495), respectively. This finding will benefit all the coming N-glycoproteomics studies using HCD as the dissociation method.
2020-05-11 | PXD016865 | Pride
Project description:PurposeRisk management (RM) is a key component of patient safety in radiation oncology (RO). We investigated current approaches on RM in German RO within the framework of the Patient Safety in German Radiation Oncology (PaSaGeRO) project. Aim was not only to evaluate a status quo of RM purposes but furthermore to discover challenges for sustainable RM that should be addressed in future research and recommendations.MethodsAn online survey was conducted from June to August 2021, consisting of 18 items on prospective and reactive RM, protagonists of RM, and self-assessment concerning RM. The survey was designed using LimeSurvey and invitations were sent by e‑mail. Answers were requested once per institution.ResultsIn all, 48 completed questionnaires from university hospitals, general and non-academic hospitals, and private practices were received and considered for evaluation. Prospective and reactive RM was commonly conducted within interprofessional teams; 88% of all institutions performed prospective risk analyses. Most institutions (71%) reported incidents or near-events using multiple reporting systems. Results were presented to the team in 71% for prospective analyses and 85% for analyses of incidents. Risk conferences take place in 46% of institutions. 42% nominated a manager/committee for RM. Knowledge concerning RM was mostly rated "satisfying" (44%). However, 65% of all institutions require more information about RM by professional societies.ConclusionOur results revealed heterogeneous patterns of RM in RO departments, although most departments adhered to common recommendations. Identified mismatches between recommendations and implementation of RM provide baseline data for future research and support definition of teaching content.
| S-EPMC10033570 | biostudies-literature
Project description:Objective: We aimed to investigate the clinical and genetic risk factors associated with neonatal severe unconjugated hyperbilirubinemia. Methods: This was a retrospective, 1:1 matched, case-control study. We included 614 neonates diagnosed with severe unconjugated hyperbilirubinemia (serum total bilirubin level ≥425 μmol/L or serum total bilirubin concentration that met exchange transfusion criteria) from the China Neonatal Genomes Project in Children's Hospital of Fudan University. Clinical exome sequencing data were analyzed using a data analysis pipeline of Children's Hospital of Fudan University. The factors associated with severe unconjugated hyperbilirubinemia were assessed using univariable and multivariable logistic regression analyses. Interaction analyses were examined between clinical and genetic risk factors. Results: ABO/Rh incompatibility hemolysis (odds ratio [OR] 3.36, 95% confidence interval [CI] 2.32-4.86), extravascular hemorrhage (OR 2.95, 95% CI 2.24-3.89), weight loss (OR 5.46, 95% CI 2.88-10.36), exclusive breastmilk feeding (OR 3.56, 95% CI 2.71-4.68), and the homozygous mutant of UGT1A1 211G>A (OR 2.35, 95% CI 1.54-3.59) were all identified as factors significantly associated with severe unconjugated hyperbilirubinemia. The presence of UGT1A1 211G>A mildly increased the risk of severe unconjugated hyperbilirubinemia caused by ABO/Rh incompatibility hemolysis (OR 3.98, 95% CI 2.19-7.23), although the effect is not statistically significant. Conclusion: ABO/Rh incompatibility hemolysis, extravascular hemorrhage, weight loss, exclusive breastmilk feeding, and the homozygous mutant of UGT1A1 211G>A were found to be risk factors for severe unconjugated hyperbilirubinemia. Clinical factors remain the most crucial and preventable determinants in managing severe unconjugated hyperbilirubinemia, with a minimal genetic contribution. The establishment of preconception care practices and the reinforcement of screening for the aforementioned risk factors are essential steps for preventing severe unconjugated hyperbilirubinemia.
| S-EPMC10808734 | biostudies-literature