Maternal overnutrition programs changes in the expression of skeletal muscle genes that are associated with insulin resistance and defects of oxidative phosphorylation in adult male rat offspring.

Children of obese mothers have increased risk of metabolic syndrome as adults. Here we report the effects of a high-fat diet in the absence of maternal obesity at conception on skeletal muscle metabolic and transcriptional profiles of adult male offspring. Female Sprague Dawley rats were fed a diet rich in saturated fat and sucrose [high-fat diet (HFD): 23.5% total fat, 9.83% saturated fat, 20% sucrose wt:wt] or a normal control diet [(CD) 7% total fat, 0.5% saturated fat, 10% sucrose wt:wt] for the 3 wk prior to mating and throughout pregnancy and lactation. Maternal weights were not different at conception; however, HFD-fed dams were 22% heavier than controls during pregnancy. On a normal diet, the male offspring of HFD-fed dams were not heavier than controls but demonstrated features of insulin resistance, including elevated plasma insulin concentration [40.1 ± 2.5 (CD) vs 56.2 ± 6.1 (HFD) mU/L; P = 0.023]. Next-generation mRNA sequencing was used to identify differentially expressed genes in the offspring soleus muscle, and gene set enrichment analysis (GSEA) was used to detect coordinated changes that are characteristic of a biological function. GSEA identified 15 upregulated pathways, including cytokine signaling (P < 0.005), starch and sucrose metabolism (P < 0.017), inflammatory response (P < 0.024), and cytokine-cytokine receptor interaction (P < 0.037). A further 8 pathways were downregulated, including oxidative phosphorylation (P < 0.004), mitochondrial matrix (P < 0.006), and electron transport/uncoupling (P < 0.022). Phosphorylation of the insulin signaling protein kinase B was reduced [2.86 ± 0.63 (CD) vs 1.02 ± 0.27 (HFD); P = 0.027] and mitochondrial complexes I, II, and V protein were downregulated by 50-68% (P < 0.005). On a normal diet, the male offspring of HFD-fed dams did not become obese adults but developed insulin resistance, with transcriptional evidence of muscle cytokine activation, inflammation, and mitochondrial dysfunction. These data indicate that maternal overnutrition, even in the absence of prepregnancy obesity, can promote metabolic dysregulation and predispose offspring to type 2 diabetes.

Biomedical sensing analyzer (BSA) for mobile-health (mHealth)-LTE

The rapid expansion of mobile-based systems, the capabilities of smartphone devices, as well as the radio access and cellular network technologies are the wind beneath the wing of mobile health (mHealth). In this paper, the concept of biomedical sensing analyzer (BSA) is presented, which is a novel framework, devised for sensor-based mHealth applications. The BSA is capable of formulating the Quality of Service (QoS) measurements in an end-to-end sense, covering the entire communication path (wearable sensors, link-technology, smartphone, cell-towers, mobile-cloud, and the end-users). The characterization and formulation of BSA depend on a number of factors, including the deployment of application-specific biomedical sensors, generic link-technologies, collection, aggregation, and prioritization of mHealth data, cellular network based on the Long-Term Evolution (LTE) access technology, and extensive multidimensional delay analyses. The results are studied and analyzed in a LabView 8.5 programming environment.

Departure fuel loads in time-minimizing migrating birds can be explained by the energy costs of being heavy

Lindstrom and Alerstam presented a model that predicts optimal departure fuel loads as a function of the rate of fuel deposition in time-minimizing migrants. The basis of the model is that the coverable distance per unit of fuel deposited, diminishes with increasing fuel load. This is an effect of the increasing flight costs associated with increasing body mass. Lindstrom and Alerstam (1992) found that birds left at lower fuel loads than their model predicted for which they considered various ecological explanations. Alternatively, we hypothesize that the difference between prediction and empirical data might be a result of extra resting metabolic and transport costs associated with an increase in fuel load during stopover. We develop a new version of the Lindstrom and Alerstam (1992) model taking fuel load associated costs during stopover into account. We fit empirical data from rufous hummingbirds Selasphorus rufus and bluethroats Luscinia svecica to this new model. Estimated fuel-load costs are discussed in relation to knowledge presently available on variations in basal metabolic costs and transport costs with body mass. We show that fuel-load costs within a reasonable range can explain the observed departure fuel loads when migrating birds are time minimizers.

Enhancing predictive accuracy of cardiac autonomic neuropathy using blood biochemistry features and iterative multitier ensembles

Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigate the role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progression. Blood biochemistry contributes to CAN, and so it is a causative factor that can provide additional power for the diagnosis of CAN especially in the absence of a complete set of Ewing tests. We introduce automated iterative multitier ensembles (AIME) and investigate their performance in comparison to base classifiers and standard ensemble classifiers for blood biochemistry attributes. AIME incorporate diverse ensembles into several tiers simultaneously and combine them into one automatically generated integrated system so that one ensemble acts as an integral part of another ensemble. We carried out extensive experimental analysis using large datasets from the diabetes screening research initiative (DiScRi) project. The results of our experiments show that several blood biochemistry attributes can be used to supplement the Ewing battery for the detection of CAN in situations where one or more of the Ewing tests cannot be completed because of the individual difficulties faced by each patient in performing the tests. The results show that AIME provide higher accuracy as a multitier CAN classification paradigm. The best predictive accuracy of 99.57% has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitioners can use these findings to increase the accuracy of CAN diagnosis.

Lung cancer prediction from microarray data by gene expression programming

Lung cancer is a leading cause of cancer-related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)-based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP-based prediction models. Prediction performance evaluations and comparisons between the authors' GEP models and three representative machine learning methods, support vector machine, multi-layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross-data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.

A computational framework for nucleic acid sub-sequence identification

Identification of nucleic acid sub-sequences within larger background sequences is a fundamental need of the biology community. The applicability correlates to research studies looking for homologous regions, diagnostic purposes and many other related activities. This paper serves to detail the approaches taken leading to sub-sequence identification through the use of hidden Markov models and associated scoring optimisations. The investigation of techniques for locating conserved basal promoter elements correlates to promoter thus gene identification techniques. The case study centred on the TATA box basal promoter element, as such the background is a gene sequence with the TATA box the target. Outcomes from the research conducted, highlights generic algorithms for sub-sequence identification, as such these generic processes can be transposed to any case study where identification of a target sequence is required. Paths extending from the work conducted in this investigation have led to the development of a generic framework for the future applicability of hidden Markov models to biological sequence analysis in a computational context.