Loose programming of GIS workflows with geo-analytical concepts.
Ontology highlight
ABSTRACT:
SUBMITTER: Kruiger JF
PROVIDER: S-EPMC7983927 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Representations of scientific knowledge must reflect the dynamic nature of knowledge construction and the evolving networks of relations between scientific concepts. In this article, we describe initial work toward dynamic, visual methods and tools that support the construction, communication, revision, and application of scientific knowledge. Specifically, we focus on tools to capture and explore the concepts that underlie collaborative science activities, with examples drawn from the domain of human-environment interaction. These tools help individual researchers describe the process of knowledge construction while enabling teams of collaborators to synthesize common concepts. Our visualization approach links geographic visualization techniques with concept-mapping tools and allows the knowledge structures that result to be shared through a Web portal that helps scientists work collectively to advance their understanding. Our integration of geovisualization and knowledge representation methods emphasizes the process through which abstract concepts can be contextualized by the data, methods, people, and perspectives that produced them. This contextualization is a critical component of a knowledge structure, without which much of the meaning that guides the sharing of concepts is lost. By using the tools we describe here, human-environment scientists are given a visual means to build concepts from data (individually and collectively) and to connect these concepts to each other at appropriate levels of abstraction.
| S-EPMC387308 | biostudies-other
Project description:PurposeForming accurate data models that assist the design of developability assays is one area that requires a deep and practical understanding of the problem domain. We aim to incorporate expert knowledge into the model building process by creating new metrics from instrument data and by guiding the choice of input parameters and Machine Learning (ML) techniques.MethodsWe generated datasets from the biophysical characterisation of 5 monoclonal antibodies (mAbs). We explored combinations of techniques and parameters to uncover the ones that better describe specific molecular liabilities, such as conformational and colloidal instability. We also employed ML algorithms to predict metrics from the dataset.ResultsWe found that the combination of Differential Scanning Calorimetry (DSC) and Light Scattering thermal ramps enabled us to identify domain-specific aggregation in mAbs that would be otherwise overlooked by common developability workflows. We also found that the response to different salt concentrations provided information about colloidal stability in agreement with charge distribution models. Finally, we predicted DSC transition temperatures from the dataset, and used the order of importance of different metrics to increase the explainability of the model.ConclusionsThe new analytical workflows enabled a better description of molecular behaviour and uncovered links between structural properties and molecular liabilities. In the future this new understanding will be coupled with ML algorithms to unlock their predictive power during developability assessment.
| S-EPMC9944381 | biostudies-literature
Project description:INTRODUCTION:As biomedical research becomes more data-intensive, computational reproducibility is a growing area of importance. Unfortunately, many biomedical researchers have not received formal computational training and often struggle to produce results that can be reproduced using the same data, code, and methods. Programming workshops can be a tool to teach new computational methods, but it is not always clear whether researchers are able to use their new skills to make their work more computationally reproducible. METHODS:This mixed methods study consisted of in-depth interviews with 14 biomedical researchers before and after participation in an introductory programming workshop. During the interviews, participants described their research workflows and responded to a quantitative checklist measuring reproducible behaviors. The interview data was analyzed using a thematic analysis approach, and the pre and post workshop checklist scores were compared to assess the impact of the workshop on the computational reproducibility of the researchers' workflows. RESULTS:Pre and post scores on a checklist of reproducible behaviors did not change in a statistically significant manner. The qualitative interviews revealed that several participants had made small changes to their workflows including switching to open source programming languages for their data cleaning, analysis, and visualization. Overall many of the participants indicated higher levels of programming literacy, and an interest in further training. Factors that enabled change included supportive environments and an immediate research need, while barriers included collaborators that were resistant to new tools, and a lack of time. CONCLUSION:While none of the workshop participants completely changed their workflows, many of them did incorporate new practices, tools, or methods that helped make their work more reproducible and transparent to other researchers. This indicates that programming workshops now offered by libraries and other organizations contribute to computational reproducibility training for researchers.
| S-EPMC7343163 | biostudies-literature
Project description:BackgroundThe recent development and enormous application of parallel sequencing technology in oncology has produced immense amounts of cell-specific genetic information. However, publicly available cell-specific genetic variants are not explained by well-established guidelines. Additionally, cell-specific variants interpretation and classification has remained a challenging task and lacks standardization. The Association for Molecular Pathology (AMP), the American Society of Clinical Oncology (ASCO), and the College of American Pathologists (CAP) published the first consensus guidelines for cell-specific variants cataloging and clinical annotations.MethodsAMP-ASCO-CAP recommended sources and information were downloaded and used as follows: relative knowledge in oncology clinical practice guidelines; approved, investigative or preclinical drugs; supporting literature and each gene-tumor site correlation. All information was homogenized into a single knowledgebase. Finally, we incorporated the consensus recommendations into a new computational method.ResultsA subset of cancer genetic variants was manually curated to benchmark our method and well-known computational algorithms. We applied the new method on freely available tumor-specific databases to produce a clinically actionable cancer somatic variants (CACSV) dataset in an easy-to-integrate format for most clinical analytical workflows. The research also showed the current challenges and limitations of using different classification systems or computational methods.ConclusionCACSV is a step toward cell-specific genetic variants standardized interpretation as it is readily adaptable by most clinical laboratory pipelines for somatic variants clinical annotations. CACSV is freely accessible at ( https://github.com/tsobahytm/CACSV/tree/main/dataset ).
| S-EPMC9036759 | biostudies-literature
Project description:A single laboratory result actually represents a range of possible values, and a given laboratory result is impacted not just by the presence or absence of disease, but also by biological variation of the measurand in question and analytical variation of the equipment used to make the measurement. Biological variation refers to variability in measurand concentration or activity around a homeostatic set point. Knowledge of biological and analytical variation can be used to facilitate interpretation of patient clinicopathologic data and is particularly useful for interpreting serial patient data and data at or near reference limits or clinical decision thresholds. Understanding how biological and analytical variation impact laboratory results is of increasing importance, because veterinarians evaluate serial data from individual patients, interpret data from multiple testing sites, and use expert consensus guidelines that include decision thresholds for clinicopathologic data interpretation. The purpose of our report is to review current and emerging concepts in biological and analytical variation and discuss how biological and analytical variation data can be used to facilitate clinicopathologic data interpretation. Inclusion of veterinary clinical pathologists having expertise in laboratory quality management and biological variation on research teams and veterinary practice guideline development teams is recommended, to ensure that various considerations for clinicopathologic data interpretation are addressed.
| S-EPMC7694803 | biostudies-literature
Project description:BackgroundDeep brain stimulation (DBS) of the subthalamic nucleus (STN) is a preferred treatment for parkinsonian patients with severe motor fluctuations. Proper targeting of the STN sensorimotor segment appears to be a crucial factor for success of the procedure. The recent introduction of directional leads theoretically increases stimulation specificity in this challenging area but also requires more precise stimulation parameters.ObjectiveWe investigated whether commercially available software for image guided programming (IGP) could maximize the benefits of DBS by informing the clinical standard care (CSC) and improving programming workflows.MethodsWe prospectively analyzed 32 consecutive parkinsonian patients implanted with bilateral directional leads in the STN. Double blind stimulation parameters determined by CSC and IGP were assessed and compared at three months post-surgery. IGP was used to adjust stimulation parameters if further clinical refinement was required. Overall clinical efficacy was evaluated one-year post-surgery.ResultsWe observed 78% concordance between the two electrode levels selected by the blinded IGP prediction and CSC assessments. In 64% of cases requiring refinement, IGP improved clinical efficacy or reduced mild side effects, predominantly by facilitating the use of directional stimulation (93% of refinements).ConclusionsThe use of image guided programming saves time and assists clinical refinement, which may be beneficial to the clinical standard care for STN-DBS and further improve the outcomes of DBS for PD patients.
| S-EPMC10836544 | biostudies-literature
Project description:BACKGROUND:Among emerging circulating biomarkers, miRNA has the potential to detect lung cancer and follow the course of the disease. However, miRNA analysis deserves further standardization before implementation into clinical trials or practice. Here, we performed international ring experiments to explore (pre)-analytical factors relevant to the outcome of miRNA blood tests in the context of the EU network CANCER-ID. METHODS:Cell-free (cfmiRNA) and extracellular vesicle-derived miRNA (EVmiRNA) were extracted using the miRNeasy Serum/Plasma Advanced, and the ExoRNeasy Maxi kit, respectively, in a plasma cohort of 27 NSCLC patients and 20 healthy individuals. Extracted miRNA was investigated using small RNA sequencing and hybridization platforms. Validation of the identified miRNA candidates was performed using quantitative PCR. RESULTS:We demonstrate the highest read counts in healthy individuals and NSCLC patients using QIAseq. Moreover, QIAseq showed 15.9% and 162.9% more cfmiRNA and EVmiRNA miRNA counts, respectively, in NSCLC patients compared to healthy control samples. However, a systematic comparison of selected miRNAs revealed little agreement between high-throughput platforms, thus some miRNAs are detected with one technology, but not with the other. Adding to this, 35% (9 of 26) of selected miRNAs in the cfmiRNA and 42% (11 of 26) in the EVmiRNA fraction were differentially expressed by at least one qPCR platform; about half of the miRNAs (54%) were concordant for both platforms. CONCLUSIONS:Changing of (pre)-analytical methods of miRNA analysis has a significant impact on blood test results and is therefore a major confounding factor. In addition, to confirm miRNA biomarker candidates screening studies should be followed by targeted validation using an independent platform or technology.
| S-EPMC7281602 | biostudies-literature
Project description:Smoking is the leading cause of lung cancer development and several genes have been identified as potential biomarker for lungs cancer. Contributing to the present scientific knowledge of biomarkers for lung cancer two different data sets, i.e. GDS3257 and GDS3054 were downloaded from NCBI?s GEO database and normalized by RMA and GRMA packages (Bioconductor). Diffrentially expressed genes were extracted by using and were R (3.1.2); DAVID online tool was used for gene annotation and GENE MANIA tool was used for construction of gene regulatory network. Nine smoking independent gene were found whereas average expressions of those genes were almost similar in both the datasets. Five genes among them were found to be associated with cancer subtypes. Thirty smoking specific genes were identified; among those genes eight were associated with cancer sub types. GPR110, IL1RN and HSP90AA1 were found directly associated with lung cancer. SEMA6A differentially expresses in only non-smoking lung cancer samples. FLG is differentially expressed smoking specific gene and is related to onset of various cancer subtypes. Functional annotation and network analysis revealed that FLG participates in various epidermal tissue developmental processes and is co-expressed with other genes. Lung tissues are epidermal tissues and thus it suggests that alteration in FLG may cause lung cancer. We conclude that smoking alters expression of several genes and associated biological pathways during development of lung cancers.
| S-EPMC4464538 | biostudies-literature
Project description:Simple Summary Prenatal nutrition can reshape an animal’s developmental trajectory, with persistent long-term consequences for the progeny. In prenatal life, the effects induced by the intrauterine conditions can be expressed as changes in offspring growth and meat quality. Thus, there are diverse sources of variations that contribute to the variations reported for fetal-programming responses in beef cattle, making it difficult to interpret and apply the results. In this sense, finding a common factor among the studies with which to group them may offer an opportunity to quantify the fetal-programming effects holistically and to obtain more applicable responses. With the increasing number of publications, it is important to summarize the quantitative measurements available in the scientific literature. In the present study, data from 35 publications were used. We verified that there is a gap related to the effects of maternal nutrition to females, at the beginning of gestation, and in zebu and crossbred animals, indicating new perspectives for future fetal-programming studies. In summary, our findings highlight an association between prenatal energy and protein supply and its effects on the offspring birth weight, weaning weight, and the daily weight gain of pregnant beef cows during pregnancy. Abstract This meta-analysis aimed to identify knowledge gaps in the scientific literature on future fetal-programming studies and to investigate the factors that determine the performance of beef cows and their offspring. A dataset composed of 35 publications was used. The prenatal diet, body weight (BW), average daily gain (ADG) during pregnancy, and calf sex were elicited as possible modulators of the beef cows and their offspring performance. Then, the correlations between these variables and the outcomes of interest were investigated. A mixed multiple linear regression procedure was used to evaluate the relationships between the responses and all the possible explanatory variables. A knowledge gap was observed in studies focused on zebu animals, with respect to the offspring sex and the consequences of prenatal nutrition in early pregnancy. The absence of studies considering the possible effects promoted by the interactions between the different stressors’ sources during pregnancy was also detected. A regression analysis showed that prenatal diets with higher levels of protein improved the ADG of pregnant beef cows and that heavier cows give birth to heavier calves. Variations in the BW at weaning were related to the BW at birth and calf sex. Therefore, this research reinforces the importance of monitoring the prenatal nutrition of beef cows.
| S-EPMC9404886 | biostudies-literature
Project description:Adsorbed species and its diffusion behaviors in GeO(2)∕Ge stacks, which are future alternative metal-oxide-semiconductor (MOS) materials, have been investigated using various physical analyses. We clarified that GeO(2) rapidly absorbs moisture in air just after its exposure. After the absorbed moisture in GeO(2) reaches a certain limit, the GeO(2) starts to absorb some organic molecules, which is accompanied by a structural change in GeO(2) to form a partial carbonate or hydroxide. We also found that the hydrogen distribution in GeO(2) shows intrinsic characteristics, indicative of different diffusion behaviors at the surface and at the GeO(2)∕Ge interface. Because the impurity absorbability of GeO(2) has a great influence on the electrical properties in Ge-MOS devices, these results provide valuable information in realizing high quality GeO(2)∕Ge stacks for the actual use of Ge-MOS technologies.
| S-EPMC3206894 | biostudies-other