Segment- and cell-specific expression of D-type cyclins in the postnatal mouse epididymis

Sperm transport, maturation and storage are the essential functions of the epididymis. The epididymis in the mouse is structurally characterized by regional and segmental organization including caput, corpus and cauda epididymis that are comprised of 10 segments. Although several growth factor signaling pathways have been discovered in the epididymis, how these converge onto the cell cycle components is unknown. To begin to elucidate the growth factor control of cell cycle events in the epididymis, we analyzed the expression of D-type cyclins at different postnatal ages. At 7d, cyclin D1 was mainly expressed in the cauda epithelium, by 14d its expression occurred in the epithelium of caput, corpus and cauda that persisted up to 21d. By 42d, cyclin D1 was mostly detectable in the principal cells of the caput and corpus (segments 1–7) but not in the cauda epididymis. Expression of cyclin D2, unlike that of cyclin D1, was evident only at 42d but not earlier, and was mostly confined to corpus and cauda epithelium. In contrast to both cyclins D1 and D2, cyclin D3 was expressed primarily in the interstitium at 7d and by 21d its expression was localized to the epithelium of the corpus and cauda epididymis. By 42d, expression of cyclin D3 peaked in segments 6–10 and confined to basal and principal cells of the corpus and apical cells of the cauda epithelium. Ki67 immunoreactivity confirmed absence of cell proliferation despite continued expression of D-type cyclins in the adult epididymis. Collectively, on the basis of our immunophenotyping and protein expression data, we conclude that the D-type cyclins are expressed in a development-, segment-, and cell-specific manner in the postnatal mouse epididymis.     

Validation of computational approach to study monomer selectivity toward the template Gallic acid for rational molecularly imprinted polymer design

Gallic acid (GA) is important for pharmaceutical industries as an antioxidant. It also finds use in tanning, ink dyes and manufacturing of paper. Molecularly imprinted polymers (MIP), which are tailor made materials, can play an excellent role in separation of GA from complex matrices. Molecular recognition being the most important property of MIP, the present work proposes a methodology based on density functional theory (DFT) calculations for selection of suitable functional monomer for a rational design of MIP with a high binding capacity for GA. A virtual library of 18 functional monomers was created and screened for the template GA. The prepolymerization template-monomer complexes were optimized at B3LYP/6-31G(d) model chemistry and the changes in the Gibbs free energy (ΔG) due to complex formation were determined on the optimized structures. The monomer with the highest Gibbs free energy gain forms most stable complex with the template resulting in formation of more selective binding sites in the polymeric matrix in MIPs. This can lead to high binding capacity of MIP for GA. Amongst the 18 monomers, acrylic acid (AA) and acrylamide (AAm) gave the highest value of ΔG due to complex formation with GA. 4-vinyl pyridine (4-Vp) had intermediate value of ΔG while, methyl methacrylate (MMA) gave least value of ΔG due to complex formation with GA. Based on this study, the MIPs were synthesized and rebinding performance was evaluated using Langmuir-Freundlich model. The imprinting factor for AA and AAm based MIPs were 5.28 and 4.80 respectively, 4-Vp based MIP had imprinting factor of 2.59 while MMA based MIP exhibited an imprinting factor of 1.95. The experimental results were in good agreement with the computational predictions. The experimental data validated the DFT based computational approach.     

Automated diagnosis of epileptic EEG using entropies

Epilepsy is a neurological disorder characterized by the presence of recurring seizures. Like many other neurological disorders, epilepsy can be assessed by the electroencephalogram (EEG). The EEG signal is highly non-linear and non-stationary, and hence, it is difficult to characterize and interpret it. However, it is a well-established clinical technique with low associated costs. In this work, we propose a methodology for the automatic detection of normal, pre-ictal, and ictal conditions from recorded EEG signals. Four entropy features namely Approximate Entropy (ApEn), Sample Entropy (SampEn), Phase Entropy 1 (S1), and Phase Entropy 2 (S2) were extracted from the collected EEG signals. These features were fed to seven different classifiers: Fuzzy Sugeno Classifier (FSC), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Probabilistic Neural Network (PNN), Decision Tree (DT), Gaussian Mixture Model (GMM), and Naive Bayes Classifier (NBC). Our results show that the Fuzzy classifier was able to differentiate the three classes with a high accuracy of 98.1%. Overall, compared to previous techniques, our proposed strategy is more suitable for diagnosis of epilepsy with higher accuracy.     

Linear and non-linear analysis of cardiac health in diabetic subjects

Diabetes is a chronic disease characterized by hyperglycaemia, which leads to specific long-term complications: retinopathy, neuropathy, nephropathy and cardiomyopathy. Analysis of cardiac health using heart rate variation (HRV) has become a popular method to assess the activities of the autonomic nervous system (ANS). It is beneficial in the assessment of cardiac abnormalities, because of its ability to capture fast fluctuations that may be an indication of sympathetic and vagal activity.This paper documents work on the analysis of both normal and diabetic heart rate signals using time domain, frequency domain and nonlinear techniques. The study is based on data from 15 patients with diabetes and 15 healthy volunteers. Our results show that non-linear analysis of HRV is superior compared to time and frequency methods. Non-linear parameters namely,correlation dimension (CD), approximate entropy (ApEn), sample entropy (SampEn) and recurrence plot properties (REC and DET), are clinically significant.     

A comparative study of solvent-assisted pretreatment of biodiesel derived crude glycerol on growth and 1,3-propanediol production from Citrobacter freundii

The rapidly growing biodiesel industry has created a scenario, where it is both important and challenging to deal with the enormous amount of crude glycerol generated as an inherent by-product. With every 100 gallons of biodiesel produced, 5-10 gallons of the crude glycerol is left behind containing several impurities which makes its disposal difficult. The objective of the present investigation was to evaluate the impact of biodiesel-derived crude glycerol upon microbial growth and production of 1,3-propanediol by Citrobacter freundii. Five different grades of crude glycerol (obtained from biodiesel preparation using jatropha, soybean, sunflower, rice bran and linseed oils) were used. Crude glycerol caused significant inhibition of microbial growth and subsequently 1,3-propanediol production as compared to pure glycerol. Therefore, a process was developed for the treatment of crude glycerol using solvents before fermentation wherein four different non-polar solvents were examined yielding different grades of pretreated glycerol. Subsequently, the potential toxic effects of pretreated glycerol on the growth and 1,3-propanediol production by C. freundii was evaluated. In case of petroleum ether-treated crude glycerol obtained from jatropha & linseed and hexane-treated crude glycerol obtained from rice bran, the yields obtained were comparable to the pure glycerol. Similarly, soybean-derived glycerol gave comparable results after treatment with either hexane or petroleum ether.     

Brain Imaging Investigation of the Impairing Effect of Emotion on Cognition

Emotions can impact cognition by exerting both enhancing (e.g., better memory for emotional events) and impairing (e.g., increased emotional distractibility) effects (reviewed in ¹). Complementing our recent protocol ² describing a method that allows investigation of the neural correlates of the memory-enhancing effect of emotion (see also ^{1, 3-5}), here we present a protocol that allows investigation of the neural correlates of the detrimental impact of emotion on cognition. The main feature of this method is that it allows identification of reciprocal modulations between activity in a ventral neural system, involved in ''hot'' emotion processing (HotEmo system), and a dorsal system, involved in higher-level ''cold'' cognitive/executive processing (ColdEx system), which are linked to cognitive performance and to individual variations in behavior (reviewed in ¹). Since its initial introduction ⁶, this design has proven particularly versatile and influential in the elucidation of various aspects concerning the neural correlates of the detrimental impact of emotional distraction on cognition, with a focus on working memory (WM), and of coping with such distraction ^7,11, in both healthy ^8-11 and clinical participants ^12-14.     

Androgen-Responsive MicroRNAs in Mouse Sertoli Cells

Although decades of research have established that androgen is essential for spermatogenesis, androgen's mechanism of action remains elusive. This is in part because only a few androgen-responsive genes have been definitively identified in the testis. Here, we propose that microRNAs - small, non-coding RNAs - are one class of androgen-regulated trans-acting factors in the testis. Specifically, by using androgen suppression and androgen replacement in mice, we show that androgen regulates the expression of several microRNAs in Sertoli cells. Our results reveal that several of these microRNAs are preferentially expressed in the testis and regulate genes that are highly expressed in Sertoli cells. Because androgen receptor-mediated signaling is essential for the pre- and post-meiotic germ cell development, we propose that androgen controls these events by regulating Sertoli/germ cell-specific gene expression in a microRNA-dependent manner.