DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders

This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images. The method uses a deep convolutional denoising autoencoder (CDAE) for generating an invariant, compact representation of in situ hybridization (ISH) images. While most existing methods for bio-imaging analysis were not developed to handle images with highly complex anatomical structures, the results presented in this paper show that functional representation extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Using this CDAE representation, our method outperforms the previous state-of-the-art classification rate, by improving the average AUC from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates on input images that were downsampled significantly with respect to the original ones to make it computationally feasible

Multi-population GWA mapping via multi-task regularized regression

Motivation: Population heterogeneity through admixing of different founder populations can produce spurious associations in genome- wide association studies that are linked to the population structure rather than the phenotype. Since samples from the same population generally co-evolve, different populations may or may not share the same genetic underpinnings for the seemingly common phenotype. Our goal is to develop a unified framework for detecting causal genetic markers through a joint association analysis of multiple populations

Automatic Annotation of Spatial Expression Patterns via Sparse Bayesian Factor Models

Advances in reporters for gene expression have made it possible to document and quantify expression patterns in 2D–4D. In contrast to microarrays, which provide data for many genes but averaged and/or at low resolution, images reveal the high spatial dynamics of gene expression. Developing computational methods to compare, annotate, and model gene expression based on images is imperative, considering that available data are rapidly increasing. We have developed a sparse Bayesian factor analysis model in which the observed expression diversity of among a large set of high-dimensional images is modeled by a small number of hidden common factors. We apply this approach on embryonic expression patterns from a Drosophila RNA in situ image database, and show that the automatically inferred factors provide for a meaningful decomposition and represent common co-regulation or biological functions. The low-dimensional set of factor mixing weights is further used as features by a classifier to annotate expression patterns with functional categories. On human-curated annotations, our sparse approach reaches similar or better classification of expression patterns at different developmental stages, when compared to other automatic image annotation methods using thousands of hard-to-interpret features. Our study therefore outlines a general framework for large microscopy data sets, in which both the generative model itself, as well as its application for analysis tasks such as automated annotation, can provide insight into biological questions

Mutual Information for Testing Gene-Environment Interaction

Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models

The diagnostic relevance of red cell rigidity

Red cell rigidity is an important hemorheological parameter determining the passage of erythrocyte through narrow capillaries and the reduction of blood viscosity under high shear rates. The changes in red cell rigidity in various diseases of altered blood flow - hypertension (HT), diabetes mellitus (DM), myocardial infarction (MI) and cerebrovascular accidents (CVA), using equal sample sizes of 25 each, have been analysed in this paper. One of the essential elements of red cell rigidity is the structural and functional properties of erythrocyte membrane which, in turn, is determined by the membrane biochemistry. Since cholesterol-rich erythrocytes have increased rigidity, the serum cholesterol and triglycerides levels have been monitored in order to detect the extent to which they affect red cell rigidity. No significant change in red cell rigidity have been found in CVA. RBC rigidity is found to be significantly increased in the other diseases. Significant increase in triglyceride levels have been found in all the diseases studied. Cholesterol levels were significantly increased in all diseases except CVA. Hence, increased cholesterol levels have been found to consistently cause a simultaneous increase in RBC rigidity. Triglycerides levels, on the other hand, have not shown a consistent change with changes in RBC rigidity, but have been shown to be a more sensitive marker for early detection of diseased status

Hematological aspects of biocompatibility - Review article

The development and improvement of medical devices and artificial organs is critically dependent on the realisation of the importance of the interactions between materials and body tissues. In this regard, the evaluation of biocompatibility assumes paramount importance. This paper reviews the hematological aspects of biocompatibility-the responses evoked by the material as well as the methods for their detection. The paper also mentions a few methods of improvement of material compatibility

Hemorheological parameters for biocompatibility evaluation

Biomaterials have been extensively used for various clinical applications. Since blood is very sensitive to the presence of any foreign substances, testing for hemocompatibility is a major part of biocompatibility evaluation. At present, blood viscosity parameters are being used as screening tests for biomaterial compatibility. Successful use of these parameters will help eliminate many incompatible materials from being subjected to extensive testing. In this study, we evaluated the blood viscosity parameters (n = 10)-whole blood viscosity, plasma viscosity, red cell rigidity, hematocrit, and biochemical parameters (total proteins and albumin). A significant increase in hemorheological parameters after incubation with material was found to be an indicator of incompatibility

Effects of exercise on rheological and microcirculatory parameters

The physiological changes occurring during exercise and its possible consequences have been receiving considerable attention lately. In this paper, we studied the changes in hemorheological and microcirculatory parameters, before and after the exercise, in the subjects undergoing mild exercise (n = 20). A cycle ergometer adjusted at 2.5 kilopounds was used for 15 minutes. The whole blood viscosity showed a significant increase after exercise at all shear rates (0.512-51.2/s) except at the high shear rate (94.5/s). However, the significant level was more (P < 0.005) at low shear rates (0.512-4.39/s). A significant elevation in plasma viscosity was observed after the exercise (P < 0.0008). Red cell rigidity showed a significant increase after the exercise (P < 0.001) while red cell aggregation and hematocrit failed to show any significant change. Microcirculatory studies showed a significant increase in the basal perfusion level after exercise (P < 0.0002) when compared to the resting state value. There was a significant decrease in reactive hyperaemia perfusion index after exercise (P < 0.0007). Hence, it is evident from this study that short-term exercise significantly alters hemorheological and microcirculatory parameters

Hemorheological changes in nephrotic syndrome

Hemorheological parameters of childhood nephrotic syndrome cases, in relapse and remission (n = 60 in each group), were studied and their results were compared with those of an equal number of age and sex matched normal children free from any renal disease. During relapse, it was noticed that the viscosity parameters, viz. plasma viscosity, red cell rigidity and whole blood viscosity, were deranged when compared to the values obtained in the remission period. These observations were statistically analyzed using t-test with the level of significance p = 0.05. It was also noted that serum/plasma biochemistry played an important role with regards to the fluidity of blood. During relapse period, fibrinogen level was significantly high, which persisted at a high level even during remission when compared to normal controls. The high cholesterol and triglyceride levels during relapse were responsible for a high plasma viscosity, increased red cell rigidity and thereby contributed directly to a marked increase in whole blood viscosity. Total protein and albumin levels were significantly decreased during relapse when compared to remission period. Hence, hemorheological parameters can be used for early detection of cases prone to relapse and could be of prognostic significance