Clustering based on Mixtures of Sparse Gaussian Processes

Creating low dimensional representations of a high dimensional data set is an important component in many machine learning applications. How to cluster data using their low dimensional embedded space is still a challenging problem in machine learning. In this article, we focus on proposing a joint formulation for both clustering and dimensionality reduction. When a probabilistic model is desired, one possible solution is to use the mixture models in which both cluster indicator and low dimensional space are learned. Our algorithm is based on a mixture of sparse Gaussian processes, which is called Sparse Gaussian Process Mixture Clustering (SGP-MIC). The main advantages to our approach over existing methods are that the probabilistic nature of this model provides more advantages over existing deterministic methods, it is straightforward to construct non-linear generalizations of the model, and applying a sparse model and an efficient variational EM approximation help to speed up the algorithm

Management of Students\' Happiness in Dormitories of Isfahan University of Medical Sciences

Introduction: Happiness is the most fundamental concept in positive-oriented psychology. Students are influential and generating forces that need happy environment and mood. This Study aimed to determine factors affecting happiness of students who live in dormitories of Isfahan University of Medical Sciences Methods: This Study was conducted on 120 male and female students living in dormitories using an open questionnaire. Data were coded and categorized and reported as frequency and percentage. Results: Results showed that the most important factors affecting students' happiness before entering the university, were sports (26/4%), television (13/05%), and walking (11/86%). the most recreation programs after entering university were sports (22/9 %), television and video room (12/84 %), computer and internet (8/33 %). Students suggested some programs to improve conditions and increase the happiness. They were providing some inside and outside camps (15/21%), improving beautify of campus (12/64%) and providing recreational facilities and sports competitions (10/5 %). Conclusion: The findings of this research aimed improving students' happiness can be considered by officials and policy makers of dormitories. This aim will be achieved through expansion of existing facilities and purposeful long-term plannin

Survey of predictive value of 4-hour urine collection for diagnosis of proteinuria in preeclampsia

Background: Measuring the 24-hour urine protein ≥300 mg is the standard threshold value for diagnosis of preeclampsia. Objective: This study was intended to determine if a patient’s 4-hour urine protein correlate with the 24-hour value for diagnosis of preeclampsia. Materials and Methods: This was a cross sectional study performed on 84 women with suspected preeclampsia due to positive urinary test strip with minimum protein content of 1+ and BP ≥140/90 at Al-zahra Educational Hospital in Rasht (Iran) from May 2007 to January 2008. Urine samples were collected within 24 hours in successive periods: The first 4-hour and the next 20-hours urine, in separate containers. The protein contents of 4-hour and 24-hour urine samples were calculated. Data were analyzed by intra-class correlation coefficient, and Receiver Operating Characteristic (ROC) curve. Results: The ROC curve showed the cut-off point of 55.5 for 4-hour urine protein. The correlation between 4- and 24-hour urine protein excretions identified that most women (about 85.1%) with protein excretion rate of 300 mg/24h or more (with preeclampsia) had the same amount of protein of 55.5 or more in their 4-hour urine excretion (p<0.001). Also, most of them (about 83.7%) with a total urinary protein excretion of less than 300 mg/24h (no preeclampsia) had a protein excretion rate of less than 55.5 mg/4h. Conclusion: This study showed 4-hour protein collection can be used as acceptable substitute for assessing the protein content of 24-hour urine samples as a more convenient, faster, and cheaper method for diagnosis of preeclampsia and the cut-off point for 4-hour urine protein is 55.5 mg

Brain matters: unveiling the distinct contributions of region, age, and sex to glia diversity and CNS function

Abstract The myelinated white matter tracts of the central nervous system (CNS) are essential for fast transmission of electrical impulses and are often differentially affected in human neurodegenerative diseases across CNS region, age and sex. We hypothesize that this selective vulnerability is underpinned by physiological variation in white matter glia. Using single nucleus RNA sequencing of human post-mortem white matter samples from the brain, cerebellum and spinal cord and subsequent tissue-based validation we found substantial glial heterogeneity with tissue region: we identified region-specific oligodendrocyte precursor cells (OPCs) that retain developmental origin markers into adulthood, distinguishing them from mouse OPCs. Region-specific OPCs give rise to similar oligodendrocyte populations, however spinal cord oligodendrocytes exhibit markers such as SKAP2 which are associated with increased myelin production and we found a spinal cord selective population particularly equipped for producing long and thick myelin sheaths based on the expression of genes/proteins such as HCN2. Spinal cord microglia exhibit a more activated phenotype compared to brain microglia, suggesting that the spinal cord is a more pro-inflammatory environment, a difference that intensifies with age. Astrocyte gene expression correlates strongly with CNS region, however, astrocytes do not show a more activated state with region or age. Across all glia, sex differences are subtle but the consistent increased expression of protein-folding genes in male donors hints at pathways that may contribute to sex differences in disease susceptibility. These findings are essential to consider for understanding selective CNS pathologies and developing tailored therapeutic strategies