Epithelial-mesenchymal transition or EMT is an extremely dynamic process involved in conversion of epithelial cells into mesenchymal cells, stimulated by an ensemble of signaling pathways, leading to change in cellular morphology, suppression of epithelial characters and acquisition of properties such as enhanced cell motility and invasiveness, reduced cell death by apoptosis, resistance to chemotherapeutic drugs etc. Significantly, EMT has been found to play a crucial role during embryonic development, tissue fibrosis and would healing, as well as during cancer metastasis. Over the years, work from various laboratories have identified a rather large number of transcription factors including the master regulators of EMT, with the ability to regulate the EMT process directly.  In this review, we put together these EMT transcription factors and discussed their role in the process. We have also tried to focus on their mechanism of action, their inter-dependency, and the large regulatory network they form. Subsequently, it has become clear that the composition and structure of the transcriptional regulatory network behind EMT probably varies based upon various physiological and pathological contexts, or even in a cell/tissue type dependent manner.

Hypergolic or self ignition delays of unsymmetrical dimethylhydrazine (UDMH) and several amine fuels, mixed with three fuming nitric acid oxidizers, have been determined, at room temperature, in a highly sensitive “Cup Test” apparatus. Ignition delay (ID) variations have been studied with respect to the chemical structure of fuel, oxidizer composition, and oxidizer‐to‐fuel (O/F) ratio. Probable preignition reactions and structure‐hypergolicity correlations have been suggested. Some non‐hypergolic hydrocarbons and petroleum fractions have been hypergolized by addition of UDMH, and ID variations have been studied with respect to UDMH‐content in fuel and catalytic additives (ammonium metavanadate, ammonium dichromate, and cuprous oxide) in the red fuming nitric acid oxidizer (RFNA). Increment in UDMH‐content improves the hypergolicity of fuels towards RFNA. For example, kerosene + UDMH 60:40 blend ignites with RFNA with a remarkably low ID of 6 ms. However, the catalytic effect of the additive in RFNA varies widely with the fuel‐blend composition.

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Abstract. Buried nodules from siliceous sediments in the central Indian Basin are morphologically variable and mineralogically consist of d-MnO~ and incipient todorokite. Compositionally they are weakly diagenetic. The sediment coarse fractions (>63 ~m) at different depths show variable abundances of micronodules, volcanic glass shards and biodebris. Dissolution of biodebris increases and abundance of micronodules decreases with increasing depth. Enrichment in Mn, Fe, Cu, Ni, Co, together with a decrease in organic carbon in the sediment column, may result from diagcnetic metal remobilization. Diagenetically remobilized trace metals might have been utilizcd for the growth of micronodules over the buried nodules.

Mathematical models in seismo-geochemical monitoring offer powerful tools for the study and exploration of complex dynamics associated with discharge of radon as the indicator of change of intense-deformed conditions of seismogenic layers or blocks within the lithosphere. Seismic precursory model of radon gas emanation in the process of earthquake prediction research aims to find out the distinct anomaly variation necessary to correlate radon gas with processes of preparation and realization of tectonic earthquakes in long-term and short-term forecasts tectonic earthquakes. The study involves a radon gas volume analytic model to find the correlation of radon fluctuations to stress drop under compression and dilatation strain condition. Here, we present a mathematical inference by observing radon gas emanation prior to the occurrence of earthquake that may reduce the uncertainties in models and updating their probability distributions in a Bayesian deterministic model. Using Bayesian melding theorem, we implement an inferential framework to understand the process of preparation of tectonic earthquake and concurrent occurrence of radon discharge during a tectonic earthquake phenomena. Bayesian melding for deterministic simulation models was augmented to make use of prior knowledge on correlations between model inputs. The background porosity is used as a priori information for analyzing the block subjected to inelastic strain. It can be inferred that use of probabilistic framework involving exhalation of radon may provide a scenario of earthquake occurrences on recession of the curve that represents a qualitative pattern of radon activity concentration drop, indicating associated stress change within the causative seismogenic fault. Using evidence analysis, we propose a joint conditional probability framework model simulation to understand how a single fracture may be affected in response to an external load and radon anomaly change that can be used to detect the slip, a predictable nature of the causative fault in the subsurface rock

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The proposed work describes research into the behavior of radon (VAR-volume activity of radon) excreted from the array. Radon migration and its selection from the array depends on the porosity, permeability and fractures in the array. A drastic change in the strength of an array and reset the elastic stresses by external forces (earthquake) occurs when certain changes in the structure of the array as the compressive and tensile strength of the array

Sensors deployed in underground tunnels found that radio frequency signals suffer significant signal strength attenuation which can result in considerable variation of link quality on the receiving end. This study analyzes the received signal strength index (RSSI) based on the development of a theoretical wireless sensor model for data collection by enabling sensors to determine the location from which each data packet is obtained. To improve positioning accuracy, the complex radio wave propagation environment requires the use of a voronoi cell to minimize signal attenuation. A relatively simple calculation is used to predict the intensity and perception range of the received wireless signals to measure the extent of signal reduction in the attenuating rock medium. Simulation results show that RSSI-based localization and wireless network lifetime and throughput measurements are more accurate when the node concept is applied to the self-locating rupture zones than the maximum likelihood estimation method. The proposed minimum energy relay routing technique based on beacon node chain deployment is found to help correct localization errors resulting from interference caused by the underground tunnel environment. The extent of localization and power of the sensor nodes are determined based on the beacon node chain deployment of tunnel wireless sensor networks. The algorithm accounts for the distance and the corresponding RSSI between adjacent beacon nodes to calculate the actual path loss parameter in the tunnel. The proposed model can serve as the theoretical basis for locating ruptures in underground wireless sensor network nodes, thus maximizing the monitoring range of large scale tectonic environments while minimizing equipment cost. We recommend that this model can be field tested through a series of experiments by researchers and engineers working in seismology, telecommunication, and information technology.