Implementation of Steiner point of fuzzy set.
Ontology highlight
ABSTRACT:
SUBMITTER: Liang J
PROVIDER: S-EPMC4000674 | biostudies-other | 2014
REPOSITORIES: biostudies-other
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Project description:BackgroundIntegrating mental health services into primary healthcare platforms is an established health systems strategy in low-to-middle-income countries. In South Africa, this was pursued through the Programme for Improving Mental Health Care (PRIME), a multi-country initiative that relied on task-sharing as a principle implementation strategy. Towards better describing the implementation processes, qualitative comparative analysis was adopted to explore causal pathways in the intervention.ObjectiveThis study aimed to explore factors that could have influenced key outcomes of an integrated mental healthcare intervention in South Africa.MethodsDrawing from an embedded multiple case study design, the analysis used qualitative comparative analysis. Focusing on nine PHC clinics in the Dr Kenneth Kaunda District as cases, with depression reduction scores set as outcome measures, trial data variables were modelled in a hypothetical causal process. A fuzzy-set qualitative comparative analysis was performed by 1) developing the research questions, 2) developing the fuzzy set, 3) testing necessity and 4) testing sufficiency. These steps were undertaken collaboratively among the research team.ResultsThe data were calibrated during several meetings among team members to gain a degree of consensus. Necessity analyses suggested that none of the causal conditions exceeded the threshold of necessity and triviality, and confirmed the inclusion of relevant variables in line with the proposed models. Sufficiency analyses produced two configurations, which were subjected to standard and specific analyses. Ultimately, the results suggested that none of the causal conditions were necessary for a reduction in depression scores to occur, while programme fidelity was identified as a sufficient condition for a reduction in scores to occur.ConclusionsThe study highlights the importance of understanding implementation pathways to enable better integration of mental health services within primary healthcare in low-to-middle-income settings. It underlines the importance of programme fidelity in achieving the goals of implementation.
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Project description:BackgroundGene-gene interactions (GGIs) are a known cause of missing heritability. Multifactor dimensionality reduction (MDR) is one of most commonly used methods for GGI detection. The generalized multifactor dimensionality reduction (GMDR) method is an extension of MDR method that is applicable to various types of traits, and allows covariate adjustments. Our previous Fuzzy MDR (FMDR) is another extension for overcoming simple binary classification. FMDR uses continuous member-ship values instead of binary membership values 0 and 1, improving power for detecting causal SNPs and more intuitive interpretations in real data analysis. Here, we propose the fuzzy generalized multifactor dimensionality reduction (FGMDR) method, as a combined analysis of fuzzy set-based analysis and GMDR method, to detect GGIs associated with diseases using fuzzy set theory.ResultsThrough simulation studies for different types of traits, the proposed FGMDR showed a higher detection ratio of causal SNPs, compared to GMDR. We then applied FGMDR to two real data: Crohn's disease (CD) data from the Wellcome Trust Case Control Consortium (WTCCC) with a binary phenotype and the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) data from Korean population with a continuous phenotype. The interactions derived by our method include the pre-reported interactions associated with phenotypes.ConclusionsThe proposed FGMDR performs well for GGI detection with covariate adjustments. The program written in R for FGMDR is available at http://statgen.snu.ac.kr/software/FGMDR .
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Project description:Immunotherapy has achieved long-term disease control in a proportion of cancer patients, but determinants of clinical benefit remain unclear. A greater understanding of antitumor immunity on an individual basis is needed to facilitate a precision oncology approach. A conceptual framework called the "cancer-immune set point" has been proposed to describe the equilibrium between factors that promote or suppress anticancer immunity and can serve as a basis to understand the variability in clinical response to immune checkpoint blockade. Oesophageal cancer has a high mutational burden, develops from pre-existing chronic inflammatory lesions and is therefore anticipated to be sensitive to immune checkpoint inhibition. However, both tumour- and patient-specific factors including the immune microenvironment, the microbiome, obesity, and host genetics contribute to an immune set point that confers a lower-than-expected response to checkpoint blockade. Immunotherapy is therefore currently confined to latter lines of treatment of advanced disease, with no reliable predictive biomarker of response. In this review, we examine oesophageal cancer in the context of the cancer-immune set point, discuss factors that contribute to response to immunotherapeutic intervention, and propose areas requiring further investigation to improve treatment response.
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Project description:Several studies profile similar single cell RNA-Seq (scRNA-Seq) data using different technologies and platforms. A number of alignment methods have been developed to enable the integration and comparison of scRNA-Seq data from such studies. While each performs well on some of the datasets, to date no method was able to both perform the alignment using the original expression space and generalize to new data. To enable such analysis we developed Single Cell Iterative Point set Registration (SCIPR) which extends methods that were successfully applied to align image data to scRNA-Seq. We discuss the required changes needed, the resulting optimization function, and algorithms for learning a transformation function for aligning data. We tested SCIPR on several scRNA-Seq datasets. As we show it successfully aligns data from several different cell types, improving upon prior methods proposed for this task. In addition, we show the parameters learned by SCIPR can be used to align data not used in the training and to identify key cell type-specific genes.
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Project description:Behavioural flexibility allows ectotherms to exploit the environment to govern their metabolic physiology, including in response to environmental stress. Hydrogen sulfide (H2S) is a widespread environmental toxin that can lethally inhibit metabolism. However, H2S can also alter behaviour and physiology, including a hypothesized induction of hibernation-like states characterized by downward shifts of the innate thermal set point (anapyrexia). Support for this hypothesis has proved controversial because it is difficult to isolate active and passive components of thermoregulation, especially in animals with high resting metabolic heat production. Here, we directly test this hypothesis by leveraging the natural behavioural thermoregulatory drive of fish to move between environments of different temperatures in accordance with their current physiological state and thermal preference. We observed a decrease in adult zebrafish (Danio rerio) preferred body temperature with exposure to 0.02% H2S, which we interpret as a shift in the thermal set point. Individuals exhibited consistent differences in shuttling behaviour and preferred temperatures, which were reduced by a constant temperature magnitude during H2S exposure. Seeking lower temperatures alleviated H2S-induced metabolic stress, as measured by reduced rates of aquatic surface respiration. Our findings highlight the interactions between individual variation and sublethal impacts of environmental toxins on behaviour.
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Project description:Many Black men suffer symptoms of traumatic stress in the aftermath of traumatic injury, and they also often carry social concerns, including experiences of discrimination and stigma, and a lack of financial resources. The objective of the present study was to understand how traumatic symptoms and social factors combine in complex ways toward the outcome of psychological help-seeking. We analyzed qualitative and quantitative data from 32 injured Black men admitted to a Level 1 trauma center. Qualitative interviews explored their attitudes toward seeking professional psychological help. We analyzed quantitative data, collected using validated instruments, on posttraumatic stress and depression symptoms, financial worry, and discrimination/stigma. Fuzzy set qualitative comparative analysis (fsQCA) was conducted by calibrating each condition to fuzzy set membership scores based on our knowledge of the causal conditions and the cases. We then constructed truth tables for QCA analysis using fsQCA software. Three causal pathways for psychological help-seeking were identified: Two pathways showed that severe trauma symptoms in the absence of financial worry were sufficient for seeking help, whereas the third showed that financial worry and discrimination in the absence of trauma symptoms were sufficient for help-seeking. We identified two causal pathways for negated help-seeking, in which low posttraumatic symptom severity and low levels of discrimination or financial worry were sufficient for not seeking psychological help. The QCA analysis revealed multiple pathways for psychological help-seeking among Black men who suffer trauma. These findings highlight the need for further research to understand complex pathways toward psychological help-seeking in this population.
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Project description:With the development of Internet technology, e-commerce platforms have emerged one after another, and the competition of the fruit e-commerce market is increasingly intensified. How to boost customer demand by improving their purchase intention has become a key issue. The study identified the critical conditions leading to high purchase intention of fruits through fuzzy-set qualitative comparative analysis. The empirical analysis was conducted based on an online questionnaire of 344 Internet users. The results reveal that high purchase intention comes from high fruit quality, high fruit price concessions and supply service, and low perceived risk (peripheral conditions). In addition, high purchase intention can also be realized from high fruit quality, high fruit price concessions, high supply service, and high website information quality (peripheral conditions). This study provides more nuanced thinking of how to improve online purchase intention of fruits.
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Project description:Before pushbroom hyperspectral imaging (HSI) data can be applied in remote sensing applications, it must typically be preprocessed through radiometric correction, atmospheric compensation, geometric correction and spatial resampling procedures. After these preprocessing procedures, HSI data are conventionally given as georeferenced raster images. The raster data model compromises the spatial-spectral integrity of HSI data, leading to suboptimal results in various applications. Inamdar et al. (2021) developed a point cloud data format, the Directly-Georeferenced Hyperspectral Point Cloud (DHPC), that preserves the spatial-spectral integrity of HSI data more effectively than rasters. The DHPC is generated through a data fusion workflow that uses conventional preprocessing protocols with a modification to the digital surface model used in the geometric correction. Even with the additional elevation information, the DHPC is still stored with file sizes up to 13 times smaller than conventional rasters, making it ideal for data distribution. Our article aims to describe the DHPC data fusion workflow from Inamdar et al. (2021), providing all the required tools for its integration in pre-existing processing workflows. This includes a MATLAB script that can be readily applied to carry out the modification that must be made to the digital surface model used in the geometric correction. The MATLAB script first derives the point spread function of the HSI data and then convolves it with the digital surface model input in the geometric correction. By breaking down the MATLAB script and describing its functions, data providers can readily develop their own implementation if necessary. The derived point spread function is also useful for characterizing HSI data, quantifying the contribution of materials to the spectrum from any given pixel as a function of distance from the pixel center. Overall, our work makes the implementation of the DHPC data fusion workflow transparent and approachable for end users and data providers.•Our article describes the Directly-Georeferenced Hyperspectral Point Cloud (DHPC) data fusion workflow, which can be readily implemented with existing processing protocols by modifying the input digital surface model used in the geometric correction.•We provide a MATLAB function that performs the modification to the digital surface model required for the DHPC workflow. This MATLAB script derives the point spread function of the hyperspectral imager and convolves it with the digital surface model so that the elevation data are more spatially consistent with the hyperspectral imaging data as collected.•We highlight the increased effectiveness of the DHPC over conventional raster end products in terms of spatial-spectral data integrity, data storage requirements, hyperspectral imaging application results and site exploration via virtual and augmented reality.
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