Targeting NLRP3 inflammasome as a chief instigator of obesity,contributing to local adipose tissue inflammation and insulin resistance

Environmental Science and Pollution Research - Inflammasome activity plays a vital role in various non-microbial disease states correlated with prolonged inflammation. NLRP3 inflammasome function...     

Influence of microbial and synthetic surfactant on the biodegradation of atrazine

The present study reports the effect of surfactants (rhamnolipids and triton X-100) on biodegradation of atrazine herbicide by strain A6, belonging to the genus Acinetobacter. The strain A6 was able to degrade nearly 80 % of the 250-ppm atrazine after 6 days of growth. The bacterium degraded atrazine by de-alkylation process. Bacterial cell surface hydrophobicity as well as atrazine solubility increased in the presence of surfactant. However, addition of surfactant to the mineral salt media reduced the rate and extent of atrazine degradation by decreasing the bioavailability of herbicide. On the contrary, addition of surfactant to atrazine-contaminated soil increased the rate and extent of biodegradation by increasing the bioavailability of herbicide. As compared to triton X-100, rhamnolipids were more efficient in enhancing microbial degradation of atrazine as a significant amount of atrazine was removed from the soil by rhamnolipids. Surfactants added for the purpose of hastening microbial degradation may have an unintended inhibitory effect on herbicide degradation depending upon contiguous condition, thus highlighting the fact that surfactant must be judiciously used in bioremediation of herbicides.     

Essential oils and their bioactive compounds as eco-friendly novel green pesticides for management of storage insect pests: prospects and retrospects

Environmental Science and Pollution Research - The control of storage insect pests is largely based on synthetic pesticides. However, due to fast growing resistance in the targeted insects,...     

Sensitivity analysis and evaluation of microFacCO: a microscale motor vehicle emission factor model for CO emissions

This paper presents a sensitivity analysis of a microscale emission factor model (MicroFacCO) for predicting real-time site-specific motor vehicle CO emissions to input variables, as well as a limited field study evaluation of the model. The sensitivity analysis has shown that MicroFacCO emission estimates are very sensitive to vehicle fleet composition, speed, and ambient temperature. For the present U.S. traffic fleet, the CO emission rate (g/mi) is increased by more than 500% at 5 mph in comparison with a speed greater than 40 mph and by approximately 67% at ambient temperatures of 45 degrees F and > or = 95 degrees F in comparison with an ambient temperature of 75 degrees F. The input variable "emission failure standard rate" is more sensitive to estimating emission rates in the 1990s than in the 2000s. The estimation of emission rates is not very sensitive to relative humidity. MicroFacCO can also be applied to examine the contribution of emission rates per vehicle class and model year. The model evaluation is presented for tunnel studies at five locations. In general, this evaluation study found good agreement between the measured and the modeled emissions. These analyses and evaluations have identified the need for additional studies to update the high-speed (>35 mph) air conditioning (A/C) correction factor and to add effects due to road grades. MicroFacCO emission estimates are very sensitive to the emission standard failure rate. Therefore, the model performance can be greatly improved by using a local emission standard failure rate.     

Chromium induced lipid peroxidation in the plants of Pistia stratiotes L.: role of antioxidants and antioxidant enzymes

In the plant, Pistia stratiotes L., the effect of different concentrations of chromium (0, 10, 40, 80 and 160 microM) applied for 48, 96 and 144 h was assessed by measuring changes in the chlorophyll, protein, malondialdehyde (MDA), cysteine, non-protein thiol, ascorbic acid contents and superoxide dismutase (SOD), ascorbate peroxidase (APX) and guiacol peroxidase (GPX) activity of the plants. Both in roots and leaves, an increase in MDA content was observed with increase in metal concentration and exposure periods. In roots, the activity of antioxidant enzymes viz. SOD and APX increased at all the concentrations of Cr at 144 h than their controls. The GPX activity of the treated roots increased with increase in Cr concentration at 48 and 96 h of exposures, however, at 144 h its activity was found declined beyond 10 microM Cr. The level of antioxidants in the roots of the treated plant viz. cysteine and ascorbic acid was also found increased at all the concentrations of Cr at 144 h than their respective controls, however, an increase in the non-protein thiol content was recorded up to 40 microM Cr followed by decrease. The chlorophyll content decreased with increase in Cr concentrations and exposure periods. However, the protein content of both roots and leaves were found decreased with increase in Cr concentrations at all the exposure periods except an increase was recorded at 10 microM Cr at 48 h. In Cr treated plants, the no observed effect concentration (NOEC) and lowest observed effect concentration (LOEC) for leaves chlorophyll and protein contents were 40 and 80 microM Cr, respectively after 48 h exposure while NOEC and LOEC for root protein content were 10 and 40 microM, respectively after 48 h. The analysis of correlation coefficient data revealed that the metal accumulation in the roots of the plant was found positively correlated with antioxidant parameters except SOD after 48 h of exposure, however, negatively correlated with most of all the parameters studied at 144 h in both part of the plant. It may be suggested from the present study that toxic concentrations of Cr cause oxidative damage as evidenced by increased lipid peroxidation and decreased chlorophyll and protein contents. However, the higher levels of enzymatic and non-enzymatic antioxidants suggest the reason for tolerating higher levels of metals.     

Forecasting daily maximum surface ozone concentrations in Brunei Darussalam--an ARIMA modeling approach

A time series approach using autoregressive integrated moving average (ARIMA) modeling has been used in this study to obtain maximum daily surface ozone (O3) concentration forecasts. The order of the fitted ARIMA model is found to be (1,0,1) for the surface O3 data collected at the airport in Brunei Darussalam during the period July 1998-March 1999. The model forecasts of one-day-ahead maximum O3 concentrations have been found to be reasonably close to the observed concentrations. The model performance has been evaluated on the basis of certain commonly used statistical measures. The overall model performance is found to be quite satisfactory as indicated by the values of Fractional Bias, Normalized Mean Square Error, and Mean Absolute Percentage Error as 0.025, 0.02, and 13.14% respectively.     

Genotoxic fungicide methyl thiophanate as an oxidative stressor inducing 8-oxo-7,8-dihydro-2′ -deoxyguanosine adducts in DNA and mutagenesis

Dimethyl 4,4′ -(O-phenylene)bis(3-thioallophanate), commonly known as methyl thiophanate (MT), is a systemic fungicide and suspected carcinogen to humans. In this study, the oxidative potential of this category-III acute toxicant has been ascertained based on its capacity of inducing reactive oxygen species (ROS) and promutagenic 8-oxo-7,8-dihydro-2′ -deoxyguanosine (8-oxodG) adducts in DNA. The discernible MT dose-dependent reduction in fluorescence intensity of a cationic dye rhodamine (Rh-123) in human lymphocytes and increased fluorescence intensity of 2′,7′-Dichlorodihydro fluorescein diacetate (DCFH-DA) treated cells signifies decreased mitochondrial membrane potential (Δ Ψ m) due to intracellular ROS generation. The ³²P-post-labeling assay demonstrated the MT-induced 8-oxodG adduct formation in calf thymus DNA. Thus, it is concluded that MT, as a potent oxidative stressor, produces ROS leading to mitochondrial dysfunction, oxidative DNA damage and mutagenesis.     

Agriculture,dairy and fishery farming practices and greenhouse gas emission footprint: a strategic appraisal for mitigation

Rising global population would force farmers to amplify food production substantially in upcoming 3–4 decades. The easiest way to increase grain production is through expanding cropping area by clearing uncultivated land. This is attained by permitting deadly loss of carbon (C) stocks, jeopardizing ecosystem biodiversity and deteriorating environmental quality. We aim to propose key agronomical tactics, livestock management strategy and advance approaches for aquaculture to increase productivity and simultaneously reduce the environmental impacts of farming sector. For this, we considered three major sectors of farming, i.e. agriculture, fishery and dairy. We collected literatures stating approaches or technologies that could reduce GHG emission from these sectors. Thereafter, we synthesized strategies or options that are more feasible and accessible for inclusion in farm sector to reduce GHG emission. Having comprehensively reviewed several publications, we propose potential strategies to reduce GHG emission. Agronomic practices like crop diversification, reducing summer fallow, soil organic carbon sequestration, tillage and crop residue management and inclusion of N2-fixing pulses in crop rotations are some of those. Livestock management through changing animals’ diets, optimal use of the gas produced from manures, frequent and complete manure removal from animal housing and aquaculture management strategies to improve fish health and improve feed conversion efficiency could reduce their GHG emission footprint too. Adapting of effective and economic practices GHG emission footprint reduction potential of farming sector could make farming sector a C neutral enterprise. To overcome the ecological, technological and institutional barriers, policy on trade, tax, grazing practice and GHG pricing should be implemented properly.     

Rapidly locating and characterizing pollutant releases in buildings

Releases of airborne contaminants in or near a building can lead to significant human exposures unless prompt response measures are taken. However, possible responses can include conflicting strategies, such as shutting the ventilation system off versus running it in a purge mode or having occupants evacuate versus sheltering in place. The proper choice depends in part on knowing the source locations, the amounts released, and the likely future dispersion routes of the pollutants. We present an approach that estimates this information in real time. It applies Bayesian statistics to interpret measurements of airborne pollutant concentrations from multiple sensors placed in the building and computes best estimates and uncertainties of the release conditions. The algorithm is fast, capable of continuously updating the estimates as measurements stream in from sensors. We demonstrate the approach using a hypothetical pollutant release in a five-room building. Unknowns to the interpretation algorithm include location, duration, and strength of the source, and some building and weather conditions. Two sensor sampling plans and three levels of data quality are examined. Data interpretation in all examples is rapid; however, locating and characterizing the source with high probability depends on the amount and quality of data and the sampling plan.