Sparse Statistical Learning Techniques for Analysis of High-Dimentional Gene Expression Data

With the advent of high-throughput biological data in the past twenty years there has been significant amount of effort in the scientific community to devise new techniques to analyze and make sense of these data. The effort can be categorized into two categories. One category deals with coming up more accurate and efficient techniques for acquisition, storage and organization of the data. The second category deals with advanced methods to dig into the collected data and make valuable predictions. In this work we focus on methods suited for analysis of one type of such biological data, namely gene expression data. Due to the nature of the gene expression data obtained in typical biological experiments we have to deal with expression values of thousands of genes across a much smaller size of the samples. This feature instantly poses great difficulty in statistical analysis of gene expression data. Main difficulty here is overfitting of models due to large number of predictors (genes) compared to number of samples. As such, we look at methods to overcome this difficulty and they all include some type of regularization of the model. We look in detail how different regularization techniques work and introduce a class of regularization methods that will shrink the parameter space to arbitrarily smaller sets. The ability to make sparse statistical models can potentially alleviate the overfitting problem as well as making the resulting model more interpretable and able to make better predictions on future input data. We start with a basic regression setting and add the nonsparse ridge penalty as our starting point to show how regularization can overcome overfitting. Sparse penalties starting with the lasso are introduced following elastic net penalty, group lasso and sparse group lasso. Finally, we apply these techniques to a real gene expression dataset showing how shrinkage of parameter space can help prediction accuracy

Examining the effect of mindfulness-based art therapy (MBAT) on stress and lifestyle of Iranian pregnant women

The purpose of this study was to determine the effect of mindfulness-based art therapy (MBAT) in decreasing stress and improving on pregnant women’s life style in Neyshabur, Iran 2018. The participants of the present quasi-experimental study were 84 pregnant women. The findings showed that the MBAT group demonstrated a significant decrease in symptoms of distress and significant improvements in key aspects of the health-related style of life as measured by Health Promoting Lifestyle Profile-II questionnaire. Moreover, it was found that the MBAT intervention had a significant effect on improving lifestyle behaviours (p < .05). The highest mean score of lifestyle was for the sub-domain of nutrition (31.35 ± 5.34), while the lowest score was achieved by the sub-domains of physical activity (13.55 ± 1.89).The mean (SD) score of stress management was (19.12 ± 1.54). This investigation of MBAT provides initial encouraging data that support a possible future role for the intervention as a psychosocial option for decreasing symptoms of distress in pregnant women and improving their lifestyle.Impact statement What is already known on this subject? Mindfulness-based art therapy is a blend of basic meditation principles and art therapy. The results of this study showed that mindfulness-based art therapy (MBAT) could decrease the stress and improve lifestyle behaviours in pregnant women. What do the results of this study add? The results of the present study showed that mindfulness-based art therapy (MBAT) during pregnancy decreased the stress and improved life style. It is believed that changing lifestyle to include mind-body medicine such as MBAT in pregnant women will greatly reduce stress responses, and help protect pregnant women from disease during pregnancy. What are the implications of these findings for clinical practice and/or further research? The study showed the important role of mindfulness-based art therapy (MBAT) during pregnancy in reducing stress and improving life style

Numerical Investigation of phase transition in different latent heat storage systems in the presence of natural convection and porous media

Securing a reliable supply of energy is critical given the ever-increasing demand for energy and the challenges posed by a growing population. Latent energy storage is being introduced as one of the most efficient solutions to harness renewable and waste energy and ensure a constant supply. The phase change material (PCM) in this type of energy storage suffers from low responsiveness, which slows down the industrialization of latent storage. This study succinctly identifies the influencing factors that affect the efficiency of PCM-based latent energy storage, including fluid temperature, the role of natural convection and the benefits of porous media. It also provides new insights through comprehensive configuration analysis, ultimately contributing to the understanding of the field and addressing sustainable energy demands. For the numerical computations, enthalpy-porosity methodology via ANSYS Fluent 18.2 is employed a rigid grid for precise dual-phase simulation. The working fluid temperature is of similar importance for three types of units, as an improvement of 22% has been obtained by increasing the inlet temperature by 5 degrees, while the pipe model has benefited from an improvement of 26% under the same condition. The Triplex Tube Heat Exchanger (TTHX) outperforms the other units in all scenarios, as the heat transfer surface of this type is greater than that of its counterparts. Natural convection phenomenon is most effective in the pipe model, as its absence slows the melting rate by 236%. The inclusion of porous media produced on average 93% faster systems due to the additional heat transfer surface, although it suppressed the gravitational movement

Brain proton magnetic resonance spectroscopy in patients with Parkinsons disease.

Background: The accuracy of current laboratory and imaging studies for diagnosis and monitoring of Parkinsons disease (PD) severity is low and diagnosis is mainly dependent on clinical examination. Proton magnetic resonance spectroscopy (MRS) is a non-invasive technique that can assess the chemical profile of the brain. In this study, we evaluated the utility of proton MRS in diagnosis of PD and determination of its severity. Methods: Patients with PD and healthy age-matched controls were studied using proton MRS. The level of N-acetylaspartate (NAA), total creatine (Cr), and total choline (Cho), and their ratios were calculated in substantia nigra (SN), putamen (Pu), and motor cortex. PD severity was assessed by the Unified Parkinsons Disease Rating Scale (UPDRS) and the Hoehn and Yahr scale. Results: Compared to 25 healthy controls (18 men, age: 59.00 ± 8.39 years), our 30 patients with PD (24 men, age: 63.80 ± 12.00 years, 29 under treatment) showed no significant difference in the metabolite ratios in SN, Pu, and motor cortex. Nigral level of NAA/Cr was significantly correlated with total UPDRS score in patients with PD (r = -0.35, P = 0.08). Moreover, patients with PD with Hoehn and Yahr scale score ≥ 2 had a lower NAA/Cr level in SN compared to patients with a lower stage. Conclusion: This study shows that 1.5 tesla proton MRS is unable to detect metabolite abnormalities in patients with PD who are under treatment. However, the NAA/Cr ratio in the SN might be a useful imaging biomarker for evaluation of disease severity in these patients

Comparison of Machine Learning Algorithms Using Manual/Automated Features on 12-Lead Signal Electrocardiogram Classification: A Large Cohort Study on Students Aged Between 6 to 18 Years Old.

PROPOSE An electrocardiogram (ECG) has been extensively used to detect rhythm disturbances. We sought to determine the accuracy of different machine learning in distinguishing abnormal ECGs from normal ones in children who were examined using a resting 12-Lead ECG machine, and we also compared the manual and automated measurement using the modular ECG Analysis System (MEANS) algorithm of ECG features. METHODS Altogether, 10745 ECGs were recorded for students aged 6 to 18. Manual and automatic ECG features were extracted for each participant. Features were normalized using Z-score normalization and went through the student's t-test and chi-squared test to measure their relevance. We applied the Boruta algorithm for feature selection and then implemented eight classifier algorithms. The dataset was split into training (80%) and test (20%) partitions. The performance of the classifiers was evaluated on the test data (unseen data) by 1000 bootstrap, and sensitivity (SEN), specificity (SPE), AUC, and accuracy (ACC) were reported. RESULTS In univariate analysis, the highest performance was heart rate and RR interval in the manual dataset and heart rate in an automated dataset with AUC of 0.72 and 0.71, respectively. The best classifiers in the manual dataset were random forest (RF) and quadratic-discriminant-analysis (QDA) with AUC, ACC, SEN, and SPE equal to 0.93, 0.98, 0.69, 0.99, and 0.90, 0.95, 0.75, 0.96, respectively. In the automated dataset, QDA (AUC: 0.89, ACC:0.92, SEN:0.71, SPE:0.93) and stack learning (SL) (AUC:0.89, ACC:0.96, SEN:0.61, SPE:0.99) reached best performances. CONCLUSION This study demonstrated that the manual measurement of 12-Lead ECG features had better performance than the automated measurement (MEANS algorithm), but some classifiers had promising results in discriminating between normal and abnormal cases. Further studies can help us evaluate the applicability and efficacy of machine-learning approaches for distinguishing abnormal ECGs in community-based investigations in both adults and children

Harnessing indigenous knowledge for climate change-resilient water management – lessons from an ethnographic case study in Iran

Through an in-depth ethnographic case study, we explore water management practices within the Jiroft County province in Iran and discuss the applicability of indigenous knowledge of regional water management to the resource governance of arid regions across the world. We explore, through qualitative analysis, the relationship between community social structure, indigenous knowledge, water management technologies and practices, and water governance rules under conditions of anthropogenic climate change. From participant observational and interview data (n = 32), we find that historically-dependent community roles establish a social contract for water distribution. Cultural conventions establish linked hierarchies of water ownership, profit-sharing and social responsibility; collectively they construct an equitable system of role-sharing, social benefit distribution, socio-ecological resilience and adaptive capacity in the face of climate change-induced drought. We conclude that the combination of hierarchical land ownership-based water distribution and what we term ‘bilateral compensatory mutual assistance’ for the lowest-profit agricultural water users, provides a model of spontaneous common pool resource management that bolsters community drought resilience. We use this case to proffer recommendations for adapting other centralized, grey infrastructure and regulatory models of water management from lessons learned from this spontaneous adaptive management case