Yoxas/statistical_literacy huggingface dataset:
Genetic risk modifier testing (GRMT), an emerging form of genetic testing based on common single nucleotide polymorphisms and polygenic risk scores, has the potential to refine estimates of BRCA1/2 mutation carriers' breast cancer risks. However, for women to benefit from GRMT, effective approaches for communicating this novel risk information are needed. To evaluate patient preferences regarding risk communication materials for GRMT. We developed four separate presentations (panel of genes, icon array, verbal risk estimate, graphical risk estimate) of hypothetical GRMT results, each using varying risk communication strategies to convey different information elements including number of risk modifier variants present, variant prevalence among BRCA1/2 carriers, and implications and uncertainties of test results for cancer risk. Thirty BRCA1/2 carriers evaluated these materials (randomized to low, moderate, or high breast cancer risk versions). Qualitative and quantitative data were obtained through in-person interviews. Across risk versions, participants preferred the presentation of the graphical risk estimate, often in combination with the verbal risk estimate. Interest in GRMT was high; 76.7% of participants wanted their own GRMT. Participants valued the potential for GRMT to clarify their cancer susceptibility and provide actionable information. Many (65.5%) anticipated that GRMT would make risk management decisions easier. Women with BRCA1/2 mutations could be highly receptive to GRMT, and the minimal amount of necessary information to be included in result risk communication materials includes graphical and verbal estimates of future cancer risk. Findings will inform clinical translation of GRMT in a manner consistent with patients' preferences.
Genetic studies have identified associations between gene mutations and clear cell renal cell carcinoma (ccRCC). Since the complete gene mutational landscape cannot be characterized through biopsy and sequencing assays for each patient, non-invasive tools are needed to determine the mutation status for tumors. Radiogenomics may be an attractive alternative tool to identify disease genomics by analyzing amounts of features extracted from medical images. Most current radiogenomics predictive models are built based on a single classifier and trained through a single objective. However, since many classifiers are available, selecting an optimal model is challenging. On the other hand, a single objective may not be a good measure to guide model training. We proposed a new multi-classifier multi-objective (MCMO) radiogenomics predictive model. To obtain more reliable prediction results, similarity-based sensitivity and specificity were defined and considered as the two objective functions simultaneously during training. To take advantage of different classifiers, the evidential reasoning (ER) approach was used for fusing the output of each classifier. Additionally, a new similarity-based multi-objective optimization algorithm (SMO) was developed for training the MCMO to predict ccRCC related gene mutations (VHL, PBRM1 and BAP1) using quantitative CT features. Using the proposed MCMO model, we achieved a predictive area under the receiver operating characteristic curve (AUC) over 0.85 for VHL, PBRM1 and BAP1 genes with balanced sensitivity and specificity. Furthermore, MCMO outperformed all the individual classifiers, and yielded more reliable results than other optimization algorithms and commonly used fusion strategies.
Genetic variants that severely alter protein products (e.g. nonsense, frameshift) are often associated with disease. For some genes, these predicted loss-of-function variants (pLoFs) are observed throughout the gene, whilst in others, they occur only at specific locations. We hypothesised that, for genes linked with monogenic diseases that display incomplete penetrance, pLoF variants present in apparently unaffected individuals may be limited to regions where pLoFs are tolerated. To test this, we investigated whether pLoF location could explain instances of incomplete penetrance of variants expected to be pathogenic for Mendelian conditions. We used exome sequence data in 454,773 individuals in the UK Biobank (UKB) to investigate the locations of pLoFs in a population cohort. We counted numbers of unique pLoF, missense, and synonymous variants in UKB in each quintile of the coding sequence (CDS) of all protein-coding genes and clustered the variants using Gaussian mixture models. We limited the analyses to genes with ≥ 5 variants of each type (16,473 genes). We compared the locations of pLoFs in UKB with all theoretically possible pLoFs in a transcript, and pathogenic pLoFs from ClinVar, and performed simulations to estimate the false-positive rate of non-uniformly distributed variants. For most genes, all variant classes fell into clusters representing broadly uniform variant distributions, but genes in which haploinsufficiency causes developmental disorders were less likely to have uniform pLoF distribution than other genes (P < 2.2 × 10-6). We identified a number of genes, including ARID1B and GATA6, where pLoF variants in the first quarter of the CDS were rescued by the presence of an alternative translation start site and should not be reported as pathogenic. For other genes, such as ODC1, pLoFs were located approximately uniformly across the gene, but pathogenic pLoFs were clustered only at the end, consistent with a gain-of-function disease mechanism. Our results suggest the potential benefits of localised constraint metrics and that the location of pLoF variants should be considered when interpreting variants.
Genome Sciences Education Outreach (GSEO) has developed innovative programs that bring leading-edge science to teachers and students in K-12 schools. Disseminating educational materials equitably and accessibly to teacher stakeholders to maximize reach and impact is challenging for many programs. Traditionally, programs connect materials with teachers through local networks, in-person professional development sessions, and at regional and national conference presentations. The need for curricular changes in 2020 spurred the proliferation of online and digital educational materials and professional development opportunities. These digital materials-now available to a worldwide audience-require a shift in dissemination strategy to enhance the potential reach of these materials both locally and nationally. This manuscript reports a case study of a dissemination approach, to create a collaboration between GSEO and CommLead (the communications master's program at the University of Washington) to promote education materials developed by the publicly-funded Genes, Environment and Me Network (GEMNet) program. This manuscript describes the development and the ad hoc implementation and evaluation of a social media campaign to expand the reach of the GEMNet curricula. With a targeted social media campaign on Facebook, GSEO was able to dramatically and affordably increase the reach of the GEMNet curricula and expand the potential impact and utilization of educational materials to a nationwide teacher audience, highlighting the potential for other similar collaborations to efficiently enhance the dissemination strategy of other education outreach programs.
Genome-wide association studies (GWAS) have identified several genetic variants associated with major depression disorder (MDD). However, pinpointing the causal variants which are responsible for the association signal at a risk locus remains a major challenge. We used Summary data-based Mendelian Randomization (SMR) with Psychiatric Genomics Consortium (PGC) GWAS summary and brain expression quantitative trait loci (eQTL) data to identify genes whose expression levels are causally associated with MDD. Then we performed differential expression analysis, methylation quantitative trait loci analysis, and cognitive genetics analysis to investigate the potential roles of risk genes in the pathogenesis of MDD. Through SMR integrative analysis, we identified the SNP rs10789336 located in Neuronal growth regulator 1 (NEGR1) gene significantly affected the expression level of RPL31P12 in brain tissues and contributed to the risk of MDD (P = 1.96 × 10-6). Consistently, the SNP rs10789336 was associated with the methylation levels of three nearby DNA methylation sites, including cg09256413 (NEGR1, P=1.72 × 10-10), cg11418303 (prostaglandin E receptor 3 [PTGER3], P = 4.78 × 10-6), and cg23032215 (ZRANB2 antisense RNA 2 [ZRANB2-AS2], P = 1.23 × 10-4). Differential expression analysis suggested that the NEGR1 gene was upregulated in prefrontal cortex (P = 5.14 × 10-3). Cognitive genetics analysis showed that the SNP rs10789336 was associated with cognitive performance (P = 2.41 × 10-16), educational attainment (P = 1.75 × 10-14), general cognitive function (P = 2.65 × 10-12), and verbal numerical reasoning (P = 1.36 × 10-12). Collectively, our results revealed that the SNP rs10789336 in NEGR1 might confer risk to MDD. Further investigation of the roles of NEGR1 in the pathogenesis of MDD is warranted.
Genome-wide linkage and association studies of tens of thousands of clinical and molecular traits are currently underway, offering rich data for inferring causality between traits and genetic variation. However, the inference process is based on discovering subtle patterns in the correlation between traits and is therefore challenging and could create a flood of untrustworthy causal inferences. Here we introduce the concerns and show that they are already valid in simple scenarios of two traits linked to or associated with the same genomic region. We argue that more comprehensive analysis and Bayesian reasoning are needed and that these can overcome some of the pitfalls, although not in every conceivable case. We conclude that causal inference methods can still be of use in the iterative process of mathematical modeling and biological validation.
Genomic technologies such as genetic testing and precision treatments are rapidly becoming routine in oncology care, and nurses play an increasingly important role in supporting the growing demands for genomics-informed healthcare. Policy infrastructure such as competencies, standards, scope of practice statements, and education and curriculum frameworks are urgently needed to guide these practice and education changes. This study is part of a larger three-phase project to develop recommendations and catalyze action for genomics-informed oncology nursing education and practice for the Canadian Association of Nurses in Oncology and the Canadian Association of Schools of Nursing. This phase aimed to enhance understanding of policy needs and action drivers for genomics-informed oncology nursing education and practice through the perspectives of Canadian oncology nurses and patient partners. Interpretive description methodology guided the study. Twenty semi-structured virtual interviews were conducted; 17 with oncology nurses in various domains of practice, and three with patient partner representatives. Data collection and analysis occurred concurrently. Our analysis identified three themes: 1) nurses and patients recognize that it is time for action, 2) nurses and patients see advantages to executing intentional, strategic, and collaborative policy development, and 3) leadership and advocacy are required to drive action. Nursing policy infrastructure is required to increase genomic literacy, support nurses in providing safe patient care, and establish clear roles, responsibilities, and accountabilities within the interdisciplinary team. Strong leadership and advocacy at the practice, organizational, and systems levels are vital to accelerating action.
Genomic testing is increasingly employed in clinical, research, educational, and commercial contexts. Genomic literacy is a prerequisite for the effective application of genomic testing, creating a corresponding need for validated tools to assess genomics knowledge. We sought to develop a reliable measure of genomics knowledge that incorporates modern genomic technologies and is informative for individuals with diverse backgrounds, including those with clinical/life sciences training. We developed the GKnowM Genomics Knowledge Scale to assess the knowledge needed to make an informed decision for genomic testing, appropriately apply genomic technologies and participate in civic decision-making. We administered the 30-item draft measure to a calibration cohort (n = 1,234) and subsequent participants to create a combined validation cohort (n = 2,405). We performed a multistage psychometric calibration and validation using classical test theory and item response theory (IRT) and conducted a post-hoc simulation study to evaluate the suitability of a computerized adaptive testing (CAT) implementation. Based on exploratory factor analysis, we removed 4 of the 30 draft items. The resulting 26-item GKnowM measure has a single dominant factor. The scale internal consistency is α = 0.85, and the IRT 3-PL model demonstrated good overall and item fit. Validity is demonstrated with significant correlation (r = 0.61) with an existing genomics knowledge measure and significantly higher scores for individuals with adequate health literacy and healthcare providers (HCPs), including HCPs who work with genomic testing. The item bank is well suited to CAT, achieving high accuracy (r = 0.97 with the full measure) while administering a mean of 13.5 items. GKnowM is an updated, broadly relevant, rigorously validated 26-item measure for assessing genomics knowledge that we anticipate will be useful for assessing population genomic literacy and evaluating the effectiveness of genomics educational interventions.
Genomic variation can impact normal biological function in complex ways and so understanding variant effects requires a broad range of data to be coherently assimilated. Whilst the volume of human variant data and relevant annotations has increased, the corresponding increase in the breadth of participating fields, standards and versioning mean that moving between genomic, coding, protein and structure positions is increasingly complex. In turn this makes investigating variants in diverse formats and assimilating annotations from different resources challenging. ProtVar addresses these issues to facilitate the contextualization and interpretation of human missense variation with unparalleled flexibility and ease of accessibility for use by the broadest range of researchers. By precalculating all possible variants in the human proteome it offers near instantaneous mapping between all relevant data types. It also combines data and analyses from a plethora of resources to bring together genomic, protein sequence and function annotations as well as structural insights and predictions to better understand the likely effect of missense variation in humans. It is offered as an intuitive web server https://www.ebi.ac.uk/protvar where data can be explored and downloaded, and can be accessed programmatically via an API.
Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations.
Geographic information system (GIS) is a versatile tool that assists in health education, planning, research, monitoring, and evaluation of programs related to health. One of the epidemics which threaten the overall human welfare is human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS). In Manipur, the cases of HIV/AIDS have been reported at significant level. The study aimed to detect the hotspot regions of HIV/AIDS prevalence in Manipur and to identify the significant factors which influence the HIV prevalence. This study evaluates the spatial variations of HIV/AIDS prevalence in the state of Manipur, India, from 2011 to 2018. In this study, Getis-Ord Gi* statistic was used to detect the HIV/AIDS prevalent regions. The ordinary least square (OLS) spatial statistics embedded in the ArcGIS were employed for exploring the spatial relation between HIV/AIDS occurrence and the predictors. It was observed from the hotspot results that Churachandpur, Ukhrul, and Thoubal are the blocks where HIV/AIDS is more prevalent. Six factors associated with the prevalence of HIV/AIDS were found to be significant. The most obvious factor influencing HIV in the region is illiteracy. The constructed OLS model has the highest value of adjusted R2 statistic equals to 0.67 and the lowest value of the Akaike Information Criterion statistic equals to 474.55. The use of hotspot analysis, regression analysis, spatial autocorrelation, and GIS can aid health planners in properly assessing and identifying spatial prevalence of diseases among the masses to better guide evidence-based health planning decisions.
Geographical terrains of Indonesia pose a major hindrance to transportation. The difficulty of transportation affects the provision of acute time-dependent therapy such as percutaneous coronary intervention (PCI). Also, Indonesia's aging population would have a significant impact on the prevalence of acute coronary syndrome in the next decade. Therefore, the analysis and enhancement of cardiovascular care are crucial. The catheterisation laboratory performs PCI procedures. In the current study, we mapped the number and distribution of catheterisation laboratories in Indonesia. A direct survey was used to collect data related to catheterisation laboratory locations in July 2022. The population data was sourced from the Ministry of Home Affairs. The recent growth of catheterisation laboratories was examined and evaluated based on geographical areas. The main instruments for comparing regions and changes throughout time are the ratio of catheterisation laboratories per 100,000 population and the Gini index (a measure of economic and healthcare inequality. Gini index ranges from 0 to 1, with greater values indicating more significant levels of inequality). Regression analysis was carried out to see how the number of catheterisation laboratories was affected by health demand (prevalence) and economic capacity (Gross Domestic Regional Product [GDRP] per Capita). The number of catheterisation laboratories in Indonesia significantly increased from 181 to 310 during 2017-2022, with 44 of the 119 new labs built in an area that did not have one. Java has the most catheterisation laboratories (208, 67%). The catheterisation laboratory ratio in the provinces of Indonesia ranges from 0.0 in West Papua and Maluku to 4.46 in Jakarta; the median is 1.09 (IQR 0.71-1.18). The distribution remains a problem, as shown by the high catheterisation laboratory Gini index (0.48). Regression shows that distribution of catheterisation laboratories was significantly affected by GDRP and the prevalence of heart disease. The number of catheterisation laboratories in Indonesia has increased significantly recently, however, maldistribution remains a concern. To improve Indonesia's cardiovascular emergency services, future development of catheterisation laboratories must be better planned considering the facility's accessibility and density. Airlangga Research Fund - Universitas Airlangga.
Geospatial data literacy is of paramount importance in an increasingly digital business world. Especially in economic decision-making processes, the ability to judge the trustworthiness of pertinent data sets is inevitable for reliable decisions. Thus, geospatial competencies need to supplement the university's teaching syllabus of economic degree programmes. Even if these programmes already have a lot of content, it is worth adding geospatial topics to educate students as skilled young experts, being geospatially literate. This contribution shows an approach on how to sensitise students and teachers with an economics background to understand the origin of geospatial data sets, their specific nature, their quality and how to gain geospatial data sets with a particular focus on sustainable economics applications. It proposes a teaching approach for educating students on geospatial characteristics of data, making them aware of spatial reasoning and spatial thinking. Especially it is vital to give them an impression of the manipulating nature of maps and geospatial visualisations. The aim is to show them the power of geospatial data and map products for research in their specific thematic field. The presented teaching concept originates from an interdisciplinary data literacy course geared to students other than geospatial sciences. It incorporates elements of a flipped classroom and a self-learning tutorial. This paper shows and discusses the results of the implementation of the course. Positive exam results imply that the teaching concept provides a suitable way to impart geospatial competencies to students belonging other than geo-related subjects.
Geothermal energy exploitation in urban areas necessitates robust real-time seismic monitoring for risk mitigation. While surface-based seismic networks are valuable, they are sensitive to anthropogenic noise. This study investigates the capabilities of borehole Distributed Acoustic Sensing (DAS) for local seismic monitoring of a geothermal field located in Munich, Germany. We leverage the operator's cloud infrastructure for DAS data management and processing. We introduce a comprehensive workflow for the automated processing of DAS data, including seismic event detection, onset time picking, and event characterization. The latter includes the determination of the event hypocenter, origin time, seismic moment, and stress drop. Waveform-based parameters are obtained after the automatic conversion of the DAS strain-rate to acceleration. We present the results of a 6-month monitoring period that demonstrates the capabilities of the proposed monitoring set-up, from the management of DAS data volumes to the establishment of an event catalog. The comparison of the results with seismometer data shows that the phase and amplitude of DAS data can be reliably used for seismic processing. This emphasizes the potential of improving seismic monitoring capabilities with hybrid networks, combining surface and downhole seismometers with borehole DAS. The inherent high-density array configuration of borehole DAS proves particularly advantageous in urban and operational environments. This study stresses that realistic prior knowledge of the seismic velocity model remains essential to prevent a large number of DAS sensing points from biasing results and interpretation. This study suggests the potential for a gradual extension of the network as geothermal exploitation progresses and new wells are equipped, owing to the scalability of the described monitoring system.