Translocation(X;Y)(p22.33;p11.2) in XX males: Etiology of male phenotype

Summary Analysis of G-banded prometaphase chromosomes from three XX males revealed extra bands on the distal end of one X short arm. These bands were similar both in size and staining properties to the distal Y short arm of their fathers (in the two cases examined) and also to other chromosomally normal males. The extra material on the abnormal X chromosomes was not C-or G-11 positive in the two cases examined, suggesting that the proximal Y long arm was not present.Previous karyotype-phenotype correlations with structurally altered Y chromosomes provided evidence for localization of male determinants on the Y short arm. The present findings in XX males provide support for more precise localization, to bands p11.2pter of Y short arm.     

Plasmon-Assisted Crystalline Silicon Solar Cell with TiO2 as Anti-Reflective Coating

Plasmonics - The present study focuses on the employment of TiO2 (titanium dioxide) film as an anti-reflective coating (ARC) on thin crystalline silicon (Si)-based solar cells along with the...     

Comparing methods for metabolic network analysis and an application to metabolic engineering

Bioinformatics tools have facilitated the reconstruction and analysis of cellular metabolism of various organisms based on information encoded in their genomes. Characterization of cellular metabolism is useful to understand the phenotypic capabilities of these organisms. It has been done quantitatively through the analysis of pathway operations. There are several in silico approaches for analyzing metabolic networks, including structural and stoichiometric analysis, metabolic flux analysis, metabolic control analysis, and several kinetic modeling based analyses. They can serve as a virtual laboratory to give insights into basic principles of cellular functions. This article summarizes the progress and advances in software and algorithm development for metabolic network analysis, along with their applications relevant to cellular physiology, and metabolic engineering with an emphasis on microbial strain optimization. Moreover, it provides a detailed comparative analysis of existing approaches under different categories.     

An integrated pathway system modeling of Saccharomyces cerevisiae HOG pathway: a Petri net based approach

Biochemical networks comprise many diverse components and interactions between them. It has intracellular signaling, metabolic and gene regulatory pathways which are highly integrated and whose responses are elicited by extracellular actions. Previous modeling techniques mostly consider each pathway independently without focusing on the interrelation of these which actually functions as a single system. In this paper, we propose an approach of modeling an integrated pathway using an event-driven modeling tool, i.e., Petri nets (PNs). PNs have the ability to simulate the dynamics of the system with high levels of accuracy. The integrated set of signaling, regulatory and metabolic reactions involved in Saccharomyces cerevisiae’s HOG pathway has been collected from the literature. The kinetic parameter values have been used for transition firings. The dynamics of the system has been simulated and the concentrations of major biological species over time have been observed. The phenotypic characteristics of the integrated system have been investigated under two conditions, viz., under the absence and presence of osmotic pressure. The results have been validated favorably with the existing experimental results. We have also compared our study with the study of idFBA (Lee et al., PLoS Comput Biol 4:e1000–e1086, 2008) and pointed out the differences between both studies. We have simulated and monitored concentrations of multiple biological entities over time and also incorporated feedback inhibition by Ptp2 which has not been included in the idFBA study. We have concluded that our study is the first to the best of our knowledge to model signaling, metabolic and regulatory events in an integrated form through PN model framework. This study is useful in computational simulation of system dynamics for integrated pathways as there are growing evidences that the malfunctioning of the interplay among these pathways is associated with disease.     

Interdomain contacts control folding of transcription factor RfaH

Escherichia coli RfaH activates gene expression by tethering the elongating RNA polymerase to the ribosome. This bridging action requires a complete refolding of the RfaH C-terminal domain (CTD) from an α-helical hairpin, which binds to the N-terminal domain (NTD) in the free protein, to a β-barrel, which interacts with the ribosomal protein S10 following RfaH recruitment to its target operons. The CTD forms a β-barrel when expressed alone or proteolytically separated from the NTD, indicating that the α-helical state is trapped by the NTD, perhaps co-translationally. Alternatively, the interdomain contacts may be sufficient to drive the formation of the α-helical form. Here, we use functional and NMR analyses to show that the denatured RfaH refolds into the native state and that RfaH in which the order of the domains is reversed is fully functional in vitro and in vivo. Our results indicate that all information necessary to determine its fold is encoded within RfaH itself, whereas accessory factors or sequential folding of NTD and CTD during translation are dispensable. These findings suggest that universally conserved RfaH homologs may change folds to accommodate diverse interaction partners and that context-dependent protein refolding may be widespread in nature.     

Molecular Assortment of Lens Species with Different Adaptations to Drought Conditions Using SSR Markers

The success of drought tolerance breeding programs can be enhanced through molecular assortment of germplasm. This study was designed to characterize molecular diversity within and between Lens species with different adaptations to drought stress conditions using SSR markers. Drought stress was applied at seedling stage to study the effects on morpho-physiological traits under controlled condition, where tolerant cultivars and wilds showed 12.8–27.6% and 9.5–23.2% reduction in seed yield per plant respectively. When juxtaposed to field conditions, the tolerant cultivars (PDL-1 and PDL-2) and wild (ILWL-314 and ILWL-436) accessions showed 10.5–26.5% and 7.5%–15.6% reduction in seed yield per plant, respectively under rain-fed conditions. The reductions in seed yield in the two tolerant cultivars and wilds under severe drought condition were 48–49% and 30.5–45.3% respectively. A set of 258 alleles were identified among 278 genotypes using 35 SSR markers. Genetic diversity and polymorphism information contents varied between 0.321–0.854 and 0.299–0.836, with mean value of 0.682 and 0.643, respectively. All the genotypes were clustered into 11 groups based on SSR markers. Tolerant genotypes were grouped in cluster 6 while sensitive ones were mainly grouped into cluster 7. Wild accessions were separated from cultivars on the basis of both population structure and cluster analysis. Cluster analysis has further grouped the wild accessions on the basis of species and sub-species into 5 clusters. Physiological and morphological characters under drought stress were significantly (P = 0.05) different among microsatellite clusters. These findings suggest that drought adaptation is variable among wild and cultivated genotypes. Also, genotypes from contrasting clusters can be selected for hybridization which could help in evolution of better segregants for improving drought tolerance in lentil.     

Design‐to‐Device Approach Affords Panchromatic Co‐Sensitized Solar Cells

Data‐driven materials discovery has become increasingly important in identifying materials that exhibit specific, desirable properties from a vast chemical search space. Synergic prediction and experimental validation are needed to accelerate scientific advances related to critical societal applications. A design‐to‐device study that uses high‐throughput screens with algorithmic encodings of structure–property relationships is reported to identify new materials with panchromatic optical absorption, whose photovoltaic device applications are then experimentally verified. The data‐mining methods source 9431 dye candidates, which are auto‐generated from the literature using a custom text‐mining tool. These candidates are sifted via a data‐mining workflow that is tailored to identify optimal combinations of organic dyes that have complementary optical absorption properties such that they can harvest all available sunlight when acting as co‐sensitizers for dye‐sensitized solar cells (DSSCs). Six promising dye combinations are shortlisted for device testing, whereupon one dye combination yields co‐sensitized DSSCs with power conversion efficiencies comparable to those of the high‐performance, organometallic dye, N719. These results demonstrate how data‐driven molecular engineering can accelerate materials discovery for panchromatic photovoltaic or other applications.     

Refractive Index Sensor Using Long-Range Surface Plasmon Resonance with Prism Coupler

Plasmonics - Long-range surface plasmon resonance (LRSPR)-based sensors exhibit high sensitivity as compared to the conventional SPR sensors due to low losses. A high refractive index prism and low...