No abstract

JNK pathway regulates various physiological processes including inflammatory responses, cell differentiation, cell proliferation, cell death, cell survival and expression of proteins. Deregulation of JNK is linked with various diseases including neurodegenerative disease, autoimmune disease, diabetes, cancer, cardiac hypertrophy and asthma. Three distinct genes JNK1, JNK2 and JNK3 have been identified as regulator of JNK pathway. JNK1 and JNK2 have broad tissue distribution and play a potential role in insulin resistance, inflammation and cell signaling. JNK3 is predominantly found in the CNS neurons, making it an attractive target for neurodegenerative disorders. In this review, we summarize the evidence supporting JNK as a potent therapeutic target, and small molecules from various chemical classes as JNK inhibitors.

Autophagy, a catabolic process, is activated by conditions of stress and nutrient deprivation, which occurs to maintain metabolic homeostasis by performing catabolic lysis of excessive or unnecessary proteins, and injured or aged organelles. Autophagy is regulated by various signaling pathways. Main regulators of autophagy are the PI3K-Akt-mTOR pathway, Beclin1, Bcl-2, Ras and p53. Autophagy plays dual role in cancer, shows both tumor suppressive and oncogenic activity. It is accepted that drug resistance in cancer cells can be overcome by inhibition of autophagy. Herein, we summarize autophagy as a potential target of anticancer drugs and targeting autophagy provides a promising therapeutic strategy to circumvent resistance and enhance the effect of anticancer therapies for cancer patients.

No abstract

No abstract

Mahananda River is an important river in India and Bangladesh, as the people of both the countries use the water extensively, without sufficient and reliable information about water qualities and pollution status. The purpose of this study is to evaluate the water quality of the river and to analyse the suitability for drinking, agricultural and industrial uses. This is why this study on the Mahananda River is extremely important for the region. For this study, samples from fourteen sampling stations were collected in pre-monsoon and post-monsoon seasons in 2016 and water quality index (WQI), agriculture and industry-related indices were computed. WQI values designated two sampling stations out of fourteen sampling stations as ‘very bad’ category and another two sampling stations as ‘bad’ category. The pH values of some sampling stations slightly exceeded the upper permissible limit. USSL diagram analysis classified two samples of pre-monsoon season in C2S1 category which indicates a medium salinity and low sodium water. Magnesium hazard values of four sampling stations are above 50% suggesting not suitable for irrigation. However, some indices like sodium per cent, residual sodium carbonate and residual sodium bicarbonate, Kelly’s index, permeability index and potential salinity allow the water for use in irrigation purposes. Langelier Saturation Index and aggressive index values designate the water as moderately aggressive or non-aggressive. Ryznar Stability Index values designate the water as ‘aggressive’ or ‘very aggressive’ indicating unsuitability for industrial uses. Sampling stations S-1, S-2, S-8 and S-14 need special attention.

In present study focus has been given on estimating quality and toxicity of waste with respect to heavy metals and its impact on groundwater quality, using statistical and empirical relationships between different hydrochemical data, so that easy monitoring may be possible which in turn help the sustainable management of landfill site and municipal solid waste. Samples of solid waste, leachate and groundwater were analyzed to evaluate the impact of leachates on groundwater through the comparison of their hydrochemical nature. Results suggest the existence of an empirical relationship between some specific indicator parameters like heavy metals of all three above mentioned sample type. Further, K/Mg ratio also indicates three groundwater samples heavily impacted from leachate contamination. A good number of samples are also showing higher values for NO À 3 and Pb than that of World Health Organization (WHO) drinking water regulation. Predominance of Fe and Zn in both groundwater and solid waste samples may be due to metal plating industries in the area. Factor analysis is used as a tool to explain observed relation between numerous variables in term of simpler relation, which may help to deduce the strength of relation. Positive loading of most of the factors for heavy metal clearly shows landfill impact on ground water quality especially along the hydraulic gradient. Cluster analysis, further substantiates the impact of landfill. Two major groups of samples obtained from cluster analysis suggest that one group comprises samples that are severely under the influence of landfill and contaminated leachates along the groundwater flow direction while other assorted with samples without having such influence.

This paper presents a detailed analysis of the climate of the last interglacial simulated by two climate models of different complexities, CCSM3 (Community Climate System Model 3) and LOVECLIM (LOch-Vecode-Ecbilt-CLio-agIsm Model). The simulated surface temperature, hydrological cycle, vegetation and ENSO variability during the last interglacial are analyzed through the comparison with the simulated pre-industrial (PI) climate. In both models, the last interglacial period is characterized by a significant warming (cooling) over almost all the continents during boreal summer (winter) leading to a largely increased (reduced) seasonal contrast in the Northern (Southern) Hemisphere. This is mainly due to the much higher (lower) insolation received by the whole Earth in boreal summer (winter) during this interglacial. The Arctic is warmer than PI through the whole year, resulting from its much higher summer insolation, its remnant effect in the following fall-winter through the interactions between atmosphere, ocean and sea ice and feedbacks from sea ice and snow cover. Discrepancies exist in the sea-ice formation zones between the two models. Cooling is simulated by CCSM3 in the Greenland and Norwegian seas and near the shelves of Antarctica during DJF but not in LOVECLIM as a result of excessive sea-ice formation. Intensified African monsoon is responsible for the cooling during summer in northern Africa and on the Arabian Peninsula. Over India, the precipitation maximum is found further west, while in Africa the precipitation maximum migrates further north. Trees and grassland expand north in Sahel/Sahara, more clearly seen in LOVECLIM than in CCSM3 results. A mix of forest and grassland occupies continents and expands deep into the high northern latitudes. Desert areas reduce significantly in the Northern Hemisphere, but increase in northern Australia. The interannual SST variability of the tropical Pacific (El-Niño Southern Oscillation) of the last interglacial simulated by CCSM3 shows slightly larger variability and magnitude compared to the PI. However, the SST variability in our LOVECLIM simulations is particularly small due to the overestimated thermocline's depth