Meiotic Instability Generates a Pathological Condition in Mammalian Ovum.

Maintenance of metaphase-II (M-II) arrest in ovum is required to present itself as a right gamete for successful fertilization in mammals. Surprisingly, instability of meiotic cell cycle results in spontaneous exit from M-II arrest, chromosomal scattering and incomplete extrusion of second polar body (PB-II) without forming pronuclei so called abortive spontaneous ovum activation (SOA). It remains unclear what causes meiotic instability in freshly ovulated ovum that results in abortive SOA. We propose the involvement of various signal molecules such as reactive oxygen species (ROS), cyclic 3',5' adenosine monophosphate (cAMP) and calcium (Ca2+) in the induction of meiotic instability and thereby abortive SOA. These signal molecules through their downstream pathways modulate phosphorylation status and activity of cyclin dependent kinase (cdk1) as well as cyclin B1 level. Changes in phosphorylation status of cdk1 and its activity, dissociation and degradation of cyclin B1 destabilize maturation promoting factor (MPF). The premature MPF destabilization and defects in other cell cycle regulators possibly cause meiotic instability in ovum soon after ovulation. The meiotic instability results in a pathological condition of abortive SOA and deteriorates ovum quality. These ova are unfit for fertilization and limit reproductive outcome in several mammalian species including human. Therefore, global attention is required to identify the underlying causes in greater details in order to address the problem of meiotic instability in ova of several mammalian species icluding human. Moreover, these activated ova may be used to create parthenogenetic embryonic stem cell lines in vitro for the use in regenerative medicine.Graphical abstract.

An ab initio study of sensing applications of MoB2 monolayer: a potential gas sensor.

Using first principles density functional theory, we have studied the interaction mechanism of NO2 and SO2 gas molecules on an MoB2 monolayer, for gas sensing applications. The selectivity for a particular gas by the sensor has been analyzed through electronic structure calculations and adsorption studies. The calculations have been performed by considering the fact that the MoB2 monolayer as a sensing material encounters a change in its electrical properties, when gas molecules with different orientations get adsorbed on the surface. From the density of states study, we find better selectivity for NO2 as compared to SO2, as the latter leaves the electronic structure of the sensing material unaffected. Further, the adsorption curves support the above fact as the larger value of adsorption energy (Ead ∼ -1 eV) for NO2 indicates stronger adsorption. The chemisorptive nature for NO2, in contrast with the relatively weaker physisorption for SO2, additionally supports the fact that NO2 gas has a better perspective for MoB2 sensor application. Charge density plots for each case are in good agreement with the above conclusions. The faster recovery time attributes the MoB2 monolayer better as a sensor material for NO2 interaction.

MoB2 Driven Metallic Behavior and Interfacial Charge Transport Mechanism in MoS2/MoB2 Heterostructure: A First-Principles Study.

We have performed the density functional theory calculations on heterostructure (HS) of MoS2 and MoB2 monolayers. The aim of this study is to assess the influence of MoB2 on electron transport of adjacent MoS2 layer. In present investigation we predict that the electronic properties of MoS2 monolayer is influenced by 4d-states of Mo in MoB2 monolayer. Whereas, the B atoms of MoB2 and S atoms of MoS2 exhibit overlapping of intermediate atomic orbitals thereby collectively construct the interfacial electronic structure observed to be metallic in nature. From charge density calculations, we have also determine that the charge transfer is taking place at the interface via B-2p and S-3p states. The bonds at the interface are found to be metallic which is also confirmed by adsorption analysis. Thermoelectric performance of this HS is found be in good agreement with available literature. Low Seebeck coefficient and high electrical conductivity further confirms the existence of metallic state of the HS.

A Novel Hybrid Feature Selection Model for Classification of Neuromuscular Dystrophies Using Bhattacharyya Coefficient, Genetic Algorithm and Radial Basis Function Based Support Vector Machine.

An accurate classification of neuromuscular disorders is important in providing proper treatment facilities to the patients. Recently, the microarray technology is employed to monitor the level of activity or expression of large number of genes simultaneously. The gene expression data derived from the microarray experiment usually involve a large number of genes but a very few number of samples. There is a need to reduce the dimension of gene expression data which intends to find a small set of discriminative genes that accurately classifies the samples of various kinds of diseases. So, our goal is to find a small subset of genes which ensures the accurate classification of neuromuscular disorders. In the present paper, we propose a novel hybrid feature selection model for classification of neuromuscular disorders. The process of feature selection is done in two phases by integrating Bhattacharyya coefficient and genetic algorithm (GA). In the first phase, we find Bhattacharyya coefficient to choose a candidate gene subset by removing the most redundant genes. In the second phase, the target gene subset is created by selecting the most discriminative gene subset by applying GA wherein the fitness function is calculated using radial basis function support vector machine (RBF SVM). The proposed hybrid algorithm is applied on two publicly available microarray neuromuscular disorders datasets. The results are compared with two individual techniques of feature selection, namely Bhattacharyya coefficient and GA, and one integrated technique, i.e., Bhattacharyya-GA wherein the fitness function of GA is calculated using four other classifiers, which shows that the proposed integrated method is capable of giving the better classification accuracy.