Early onset of aridity in the past millennium: Insights from vegetation dynamics and climate change in the alpine, cold-desert region of Trans Himalaya, India.

Palynological analysis of surface soil and sub-surface sediments from the outwash plain of Hamtah Glacier, Lahaul-Spiti, India, has brought out the vegetation and climatic changes in the area during the last 1580 years. The arboreal and non-arboreal pollen ratio (AP/NAP) has been used to demarcate the different vegetation and climatic zones, complemented by the frequencies of the broad-leaved taxa. Lower values of thermophilous, broad-leaved arboreal taxa, indicate that the region experienced cold-arid conditions between 1580 and 1330 yr BP (AD 370-620); which can be related to the Dark Ages Cold Period (DACP). Thereafter, between 1330 and 950 yr BP (AD 620-1000), a rejuvenation of the broad-leaved elements reflects the initiation of a comparatively warm and moist phase, marking the Medieval Climatic Anomaly (MCA) in the region. The warm-moist phase was, however, short-lived, and from 950 yr BP to the Present (AD 1000 onwards), the region saw a return to cold-arid conditions, as evidenced by a sharp fall in the AP/NAP ratio. This cold-arid phase was, nevertheless, punctuated by a warm-moist period during 790 to 680 yr BP (AD 1160-1270), which marks the terminal phase of the MCA. After the termination of the MCA, the Little Ice Age (LIA) is well-marked in the area. The culmination of the long cold-arid regime is characterized by warmer conditions over the last 160 years, which is the manifestation of the Current Warm Period (CWP). Magnetic susceptibility (χlf) and sediment geochemistry (Weathering Index of Parker) were also attempted to have a multi-proxy approach, and show a general compatibility with the palynological data. The palaeoclimatic evidences suggest shorter warm periods and extended colder phases during the last 1580 years; in this high-altitude, cold-desert, Trans Himalayan region.

Free fatty acid-induced miR-181a-5p stimulates apoptosis by targeting XIAP and Bcl2 in hepatic cells.

AIMS: Non-alcoholic fatty liver disease is one of the major health concerns in the World. The dietary free fatty acids (FFAs) affect the metabolic status of the hepatocytes by modulating cellular pathways. In this study, we showed that free fatty acids stimulate apoptosis by upregulating miR-181a-5p expression, which in turn targets XIAP and Bcl2. METHODS: Huh7 cells were incubated with FFAs for 72 h and the expression of XIAP, Bcl2, bax, pAkt, Akt, PTEN and ß-actin were determined by Western blots, and miR-181a-5p expression was determined using real-time RT-PCR. The Huh7 cells were transfected with either miR-181a-5p pre-miRs or anti-miR-181a-5p and the regulation of apoptosis and proliferation was studied. Three groups of C57BL/6 mice (n = 6 per group) were fed with standard diet, CSAA or CDAA diet for 6, 18, 32 and 54 weeks. Total protein and RNA were isolated from the liver tissues and used for Western blots and real-time RT-PCR respectively. KEY FINDINGS: FFAs inhibited Akt phosphorylation, expression of XIAP and Bcl2, while upregulating the expression of PTEN, bax, and miR-181a-5p in Huh7 cells. Similar results were observed when the Huh7 cells were transfected with miR-181a-5p premiRs, while these changes were reversed in anti-miR-181a-5p-transfected, FFA-treated Huh7 cells. The CDAA-fed mice showed a significant inhibition of Akt phosphorylation, XIAP and Bcl2, whereas PTEN and bax expression were upregulated. The expression of miR-181a-5p was also significantly higher in CDAA-fed mice. SIGNIFICANCE: These findings showed that free fatty acids induced apoptosis via upregulating miR-181a-5p in hepatic cells.

Source profiling of air pollution and its association with acute respiratory infections in the Himalayan-bound region of India.

The studies related to air pollutants and their association with human health over the mountainous region are of utmost importance and are sparse especially over the Himalayan region of India. The linkages between various atmospheric variables and clinically validated data have been done using various datasets procured from satellite, model reanalysis, and surface observations during 2013-2017. Aerosol optical depth, air temperature, and wind speed are significantly related (p < 0.001) to the incidence of acute respiratory infections with its peak during winter. Model-derived particulate matter (PM2.5) shows high contributions of black carbon, organic carbon, and sulfate during winter. The wind roses show the passage of winds from the south-west and southern side of the region. Back trajectory density plot along with bivariate polar plot analyses have shown that most of the winds coming from the western side are taking a southward direction before reaching the study area and may be bringing pollutants from the Indo-Gangetic Plain and other surrounding regions. Our study shows that the accumulation of pollutants in the Himalayan valley is owing to the meteorological stability with significant local emissions from burning of biomass and biofuels along with long-range and mid-range transport during the winter season that significantly correlated with the incidence of acute respiratory infections in the region.

Selection of nitrogen responsive root architectural traits in spinach using machine learning and genetic correlations.

The efficient acquisition and transport of nutrients by plants largely depend on the root architecture. Due to the absence of complex microbial network interactions and soil heterogeneity in a restricted soilless medium, the architecture of roots is a function of genetics defined by the soilless matrix and exogenously supplied nutrients such as nitrogen (N). The knowledge of root trait combinations that offer the optimal nitrogen use efficiency (NUE) is far from being conclusive. The objective of this study was to define the root trait(s) that best predicts and correlates with vegetative biomass under differed N treatments. We used eight image-derived root architectural traits of 202 diverse spinach lines grown in two N concentrations (high N, HN, and low N, LN) in randomized complete blocks design. Supervised random forest (RF) machine learning augmented by ranger hyperparameter grid search was used to predict the variable importance of the root traits. We also determined the broad-sense heritability (H) and genetic (rg) and phenotypic (rp) correlations between root traits and the vegetative biomass (shoot weight, SWt). Each root trait was assigned a predicted importance rank based on the trait's contribution to the cumulative reduction in the mean square error (MSE) in the RF tree regression models for SWt. The root traits were further prioritized for potential selection based on the rg and SWt correlated response (CR). The predicted importance of the eight root traits showed that the number of root tips (Tips) and root length (RLength) under HN and crossings (Xsings) and root average diameter (RAvdiam) under LN were the most relevant. SWt had a highly antagonistic rg (- 0.83) to RAvdiam, but a high predicted indirect selection efficiency (- 112.8%) with RAvdiam under LN; RAvdiam showed no significant rg or rp to SWt under HN. In limited N availability, we suggest that selecting against larger RAvdiam as a secondary trait might improve biomass and, hence, NUE with no apparent yield penalty under HN.

Microplastics in polar regions: An early warning to the world's pristine ecosystem.

The menace of plastic which is polluting the ocean has emerged as a global problem. It is well-known to everyone that the ultimate end for most of the plastic debris is the ocean. The distribution of plastic rubbish in the oceans is strongly influenced by hydrodynamic properties of water. The continuous break down of plastic objects, as a consequence of thermal, chemical and biological processes along with various environmental factors, results into microplastics (MPs). The microplastics are those particles which are deriving pallets of plastic, having length of less than 5 mm or 0.2 in. Nowadays microplastics are everywhere in the waters all around the world. The high dispersion pattern of oceanic currents takes away microplastics in the entire ocean even to remote areas, like the Polar Regions. Microplastics are difficult to remove from the ocean and the ingestion of these particles by several consumers of different trophic levels like benthos, birds, and fishes is a threat to the diverse food webs and ecosystems. Different scientific investigations have ascertained that a significant concentration of MPs are present in various marine ecosystems globally including the Polar region (both Arctic and Antarctic), and in the upcoming future, the condition is expected to get worse. The objective of this review is to establish a baseline evidence for the availability of microplastics in the polar region. For this reason, the state of the art of knowledge on microplastics in Polar Regions was studied.

Defects in nanosilica catalytically convert CO2 to methane without any metal and ligand.

Active and stable metal-free heterogeneous catalysts for CO2 fixation are required to reduce the current high level of carbon dioxide in the atmosphere, which is driving climate change. In this work, we show that defects in nanosilica (E' centers, oxygen vacancies, and nonbridging oxygen hole centers) convert CO2 to methane with excellent productivity and selectivity. Neither metal nor complex organic ligands were required, and the defect alone acted as catalytic sites for carbon dioxide activation and hydrogen dissociation and their cooperative action converted CO2 to methane. Unlike metal catalysts, which become deactivated with time, the defect-containing nanosilica showed significantly better stability. Notably, the catalyst can be regenerated by simple heating in the air without the need for hydrogen gas. Surprisingly, the catalytic activity for methane production increased significantly after every regeneration cycle, reaching more than double the methane production rate after eight regeneration cycles. This activated catalyst remained stable for more than 200 h. Detailed understanding of the role of the various defect sites in terms of their concentrations and proximities as well as their cooperativity in activating CO2 and dissociating hydrogen to produce methane was achieved.

Forecasting Indian infant mortality rate: An application of autoregressive integrated moving average model.

BACKGROUND: The Infant Mortality Rate (IMR) reflects the socioeconomic development of a nation. The IMR was reduced by 28% between 2015 and 2016 (National Family Health Survey-4 [NFHS-4]) as compared to 2005-2006 (NFHS-3), from 57/1000 to 41/1000 live births. The target fixed by the Government of India for IMR in 2019 is 28/1000 live births (National Health Policy, 2017). One of the most common methods of forecasting this is the autoregressive integrated moving average (ARIMA) model. A forecast of IMR can help implementation of interventions to reduce the burden of infant mortality within the target range. MATERIALS AND METHODS: The objective of the study was to give a detailed explanation of ARIMA model to forecast the IMR (2017-2025). Secondary data analysis and forecast were done for the available year and IMR data extracted from "open government data platform India" website. RESULTS: The forecast of the sample period (1971-2016) showed accuracy by the selected ARIMA (2, 1, 1) model. The postsample forecast with ARIMA (2, 1, 1) showed a decreasing trend of IMR (2017-2025). The forecast IMR for 2025 is 15/1000 live births. CONCLUSION: In the current study, long-time series IMR data were used to forecast the IMR for 9 years. The data showed that IMR would decline from 33/1000 live births in 2017 to 15/1000 live births in 2025. When the actual data for another year (2017) are available, the model can be checked for validity and a more accurate forecast can be performed.

Butyrate inhibits HBV replication and HBV-induced hepatoma cell proliferation via modulating SIRT-1/Ac-p53 regulatory axis.

Butyrate, a histone deacetylase inhibitor, has several therapeutic applications, including cancer. However, the effect of butyrate in HBV replication is not known so far. It was hypothesized that butyrate might inhibit HBV replication and host cell proliferation via SIRT-1. It was found that the increased expression of SIRT-1 in Hep G2.2.15 cells (HBV expressing cells) than Hep G2 cells. Next the expression of SIRT-1 and Acetylated p53 (Ac-p53) were measured in the liver biopsy samples of chronic hepatitis B (CHB) patients with high viral load and compared to CHB patients with low viral load and found that there was a high SIRT-1 expression and a low Ac-p53 levels in CHB patients with high viral load compared to CHB patients with low viral load. Incubation of butyrate inhibited SIRT-1 expression and cell proliferation. Inhibition of SIRT-1 by butyrate or SIRT-1 siRNA increased the levels of Ac-p53. The elevated Ac-p53 decreased p-akt, cyclin D1, and thereby inhibited cell proliferation. Incubation of butyrate with Hep G2.2.15 cells also inhibited HBx protein expression, HBV-DNA and hepatitis B surface antigen (HBsAg). Taken together, the data showed that butyrate inhibited HBV replication and cell proliferation by inhibiting SIRT-1 expression in hepatoma cells.

A fully automated algorithm under modified FCM framework for improved brain MR image segmentation.

Automated brain magnetic resonance image (MRI) segmentation is a complex problem especially if accompanied by quality depreciating factors such as intensity inhomogeneity and noise. This article presents a new algorithm for automated segmentation of both normal and diseased brain MRI. An entropy driven homomorphic filtering technique has been employed in this work to remove the bias field. The initial cluster centers are estimated using a proposed algorithm called histogram-based local peak merger using adaptive window. Subsequently, a modified fuzzy c-mean (MFCM) technique using the neighborhood pixel considerations is applied. Finally, a new technique called neighborhood-based membership ambiguity correction (NMAC) has been used for smoothing the boundaries between different tissue classes as well as to remove small pixel level noise, which appear as misclassified pixels even after the MFCM approach. NMAC leads to much sharper boundaries between tissues and, hence, has been found to be highly effective in prominently estimating the tissue and tumor areas in a brain MR scan. The algorithm has been validated against MFCM and FMRIB software library using MRI scans from BrainWeb. Superior results to those achieved with MFCM technique have been observed along with the collateral advantages of fully automatic segmentation, faster computation and faster convergence of the objective function.

Methane emission and heavy metals quantification from selected landfill areas in India.

In this study, an attempt has been made to study methane flux and quantification of heavy metals from Municipal Solid Waste (MSW) landfill areas of selected cities in India. During the period of study, the average value of methane flux was estimated from these landfill areas varied from 146-454 mg/m2/h. Methane emission from landfill is of serious environmental global concern as it accounts for approximately 15 percentages of current Greenhouse gas emissions. It has been estimated that methane emission, from landfill areas in the world, in next two decades would be same as that what is emitted from paddy fields presently. Besides, the estimation of methane flux, quantification of some heavy metals was conducted to analyse the suitability of using MSW as compost. The average values for metals were observed to be both within the range of USEPA and Indian standards for MSW disposal in landfill areas and to be used as compost respectively.