Asiatic acid (AA), a triterpenoid derivative of Centella asiatica, has shown significant biological effects of antioxidant and anti-inflammatory activities. Aim of this investigation was to evaluate ...the antihyperglycemic effect of AA on the activities of hepatic enzymes of carbohydrate metabolism in streptozotocin (STZ)-induced diabetic rats. To induce diabetes mellitus, rats were injected with streptozotocin intraperitoneally at a single dose of 40mg/kg b.w. Diabetic rats showed significant (p<0.05) increased in plasma glucose, glycosylated hemoglobin and significant (p<0.05) decreased in circulating insulin and hemoglobin. The altered activities of key enzymes such as glucose-6-phosphatase and fructose-1,6-bisphosphatase of carbohydrate metabolism significantly (p<0.05) increased whereas hexokinase, pyruvate kinase, glucose-6-phosphate dehydrogenase and glycogen content significantly (p<0.05) decreased in the liver of diabetic rats and also increased activities of aspartate transaminase (AST), alanine transaminase (ALT) and alkaline phosphatase (ALP). Oral administration of AA (5, 10 and 20mg/kg b.w.) and glibenclamide (600μg/kg b.w.) to diabetic rats for 45days prevented the above alteration and reverted to near normalcy. Protection of body weight loss of diabetic rats by AA was also observed. No significant effect was observed in normal rats treated with AA (20mg/kg b.w.). In this search, AA found to be potential bioactive compound to regulate the carbohydrate metabolism by modulating the key regulatory enzymes in diabetic rats. These findings merit further research in this field.
The saw-milling residues are biowaste generated during the sawing of logs into lumber, and they may be lucrative when turned into biowaste pellets. Using biowaste as a renewable fuel helps address ...the limited availability of fossil fuels, reduces pollution, and additionally decreases the need for landfill. This study investigated the thermal reaction kinetics and physical properties of oxidative torrefied sawdust (TSD) and waste medical plastic pyrolysis oil (WMPPO) blended pellets, TSD25 and TSD50, referring to 25% and 50% of WMPPO with TSD, respectively, using thermogravimetric analysis (TGA), whereas the pellets are prepared using a small-scale pellet processing system. The reported additional physicochemical properties like density, pellet durability index (PDI), moisture adsorption, and impact resistance (IR) were as per the American Society of Agricultural and Biological Engineers (ASABE) recommended tests for densified fuel pellets, proximate Fourier transform infrared spectroscopy (FTIR) were used for identifying the blend’s chemical composition active constituents. Ultimate analysis was carried out for the elemental analysis, and the morphological study was conducted using scanning electron microscopy. The samples have a moisture content of less than 10%, a volatile matter content that seems to be 75 to 82%, and an ash content that seems to be 1.05 to 6.65%. Oxidative torrefied sawdust scanning electron micrographs reveal micro, macro, and meso pores and improved the Brunauer-Emmett-Teller (BET) surface area. FTIR and TGA validate the addition of 50% WMPPO with TSD-controlled combustion zone temperature with the activation energy of 27.63 kJ/mol for an effective thermal conversion process. The physicochemical properties of density, pellet durability index (PDI), moisture adsorption, and impact resistance (IR) for sawdust (SD), TSD, and TSD blends with WMPPO are also reported. Both low-temperature torrefaction and waste medical plastic pyrolysis oil blending reduce volatile loss and improve fuel pellet physicochemical and thermochemical characteristics.
The aim of this study was to evaluate the protective effects of d‐limonene on the levels of lipid peroxidation by‐products and antioxidant defence systems in the plasma and tissues of normal and ...streptozotocin (STZ)‐induced diabetes rats. The experimental diabetes was induced in rats by a single dose of STZ (40 mg/kg i.p.) injection, and treatment with d‐limonene was continued for 45 days. After the treatment period, oxidative stress parameters such as lipid peroxidation by‐products; enzymatic antioxidants such as superoxide dismutase, catalase, glutathione peroxidase and glutathione‐S‐transferase; non‐enzymic antioxidants including reduced glutathione, Vitamins C and E were measured in the plasma and tissues of experimental rats. An increase in the levels of lipid peroxidation by‐products and significant decrease in antioxidant enzymes were observed in untreated diabetic rats. Administration of d‐limonene to diabetic rats for 45 days caused a significant reduction in the levels of lipid peroxidation by‐products and an increase in the activities of antioxidant enzymes, when compared with the untreated diabetic group. There was no significant difference in normal treated groups, when compared with normal rats. Biochemical observations were substantiated with the help of histopathological examinations through its antioxidant properties and thereby conferred protection against STZ‐induced diabetic rats. The result of this study indicates that d‐limonene has antioxidant potential in addition to its antidiabetic effect in experimental diabetes.
Although the 2021 Western North America (WNA) heat wave was predicted by weather forecast models, questions remain about whether such strong events can be simulated by global climate models (GCMs) at ...different model resolutions. Here, we analyze sets of GCM simulations including historical and future periods to check for the occurrence of similar events. High‐ and low‐resolution simulations both encounter challenges in reproducing events as extreme as the observed one, particularly under the present climate. Relatively stronger amplitudes are observed during the future periods. Furthermore, high‐ and low‐resolution short initialized GCM simulations are both able to reasonably predict such strong events and their associated high‐pressure ridge over the WNA with a 1 week forecast lead time. Moisture sensitivity experiments further indicate a drier atmospheric moisture condition results in substantially higher near‐surface temperatures in the simulated heat events.
Plain Language Summary
During June 2021, an extraordinarily strong heat wave occurred over parts of Western North America (WNA). It obliterated the high temperature record there and caused severe societal and ecological impacts, and is believed to be exacerbated by climate change. Low‐resolution Global Climate Models (GCMs) are currently used to quantify the atmospheric responses to climate change, even though they exhibit significant biases in their simulated climate. Their ability to predict extraordinarily strong heat wave events remains unassessed. In this study, low‐ and high‐resolution GCM simulations are shown to be able to reproduce extraordinarily strong heat wave events in the near future, but face challenges in reproducing the events as extreme as the observed one. This highlights the role of climate change in such events. A suite of short GCM simulations initialized from observations is used to show that even low‐resolution GCMs can forecast observed extreme strong heat wave events and their high‐pressure ridge over WNA at short lead times. We further show that in these short‐term forecasts, drier atmospheric moisture initial condition can lead to significantly higher near‐surface temperature.
Key Points
Global climate models (GCMs) climate simulations can reproduce extreme heat events, but face challenges in capturing the observed extremity
Low‐ and high‐resolution GCMs predict the 2021 Western North America heat wave similarly within a 1‐week forecast lead time
Dry atmospheric conditions in GCMs can lead to significantly higher near‐surface temperatures in the simulated heat waves
Previous studies have noticed that the Coupled Model Intercomparison Project Phase 6 (CMIP6) models with a stronger cooling from aerosol‐cloud interactions (ACI) also have an enhanced warming from ...positive cloud feedback, and these two opposing effects are counter‐balanced in simulations of the historical period. However, reasons for this anti‐correlation are less explored. In this study, we perturb the cloud ice microphysical processes to obtain cloud liquid of varying amounts in two Earth System Models (ESMs). We find that the model simulations with a larger liquid water path (LWP) tend to have a stronger cooling from ACI and a stronger positive cloud feedback. More liquid clouds in the mean‐state present more opportunities for anthropogenic aerosol perturbations and also weaken the negative cloud feedback at middle to high latitudes. This work, from a cloud state perspective, emphasizes the influence of the mean‐state LWP on effective radiative forcing due to ACI (ERFACI).
Plain Language Summary
Since the preindustrial era, emissions of greenhouse gases (GHGs) and aerosols have both increased substantially. Planetary warming from the elevated GHGs causes changes in cloud distribution and properties, thereby imposing feedbacks on the climate system. At the same time, aerosols from air pollution exert a cooling effect by modifying cloud properties and lifetime. Cloud feedback and aerosol‐cloud interactions (ACI) are two critical factors for understanding the past and projecting the future climate change. In this study, we examine the relationships of ACI and cloud feedback with the mean‐state cloud liquid water amount predicted from simulations with two different Earth system models. We find that both the effective radiative forcing due to aerosol‐cloud interactions (ERFACI) and the cloud feedback are modulated by mean‐state liquid water path (LWP). The warming induced by cloud feedback and cooling by ACI counteracts each other. Our study suggests that the mean‐state LWP is an important factor influencing both ERFACI and the cloud feedback, and is a useful index for the future climate projection.
Key Points
Effective radiative forcing due to aerosol‐cloud interactions strengthens with the increase of mean‐state LWP in two Earth System Models
Cloud radiative feedback increases monotonically with the increase of mean‐state LWP
Mean‐state LWP is a good predictor for both ERFACI and cloud radiative feedback
Oxidative stress is a common pathogenesis of diabetes mellitus and asiatic acid (AA) plays an important role in ameliorating those difficulties. The present study was designed the protective effects ...of AA on altered lipid peroxidation products, enzymic and nonenzymic antioxidants in streptozotocin (STZ)-induced diabetic rats. Diabetes was induced in experimental rats by single dose STZ (40 mg/kg b.w.) injection. Diabetic rats showed significantly increased levels of plasma glucose, thiobarbituric acid reactive substances, lipid hydroperoxides, aspartate aminotransferase, alanine aminotransferase, bilirubin, creatine kinase, urea, uric acid, creatinine and decreased levels of plasma insulin. The activities of enzymatic antioxidants such as superoxide dismutase, catalase, glutathione peroxidase and glutathione-S-transferase and the levels of non-enzymatic antioxidants such as vitamin C, vitamin E and reduced glutathione were decreased in diabetic rats. Oral treatment with AA (20 mg/kg b.w.) showed near normalized levels of plasma glucose, insulin, lipid peroxidation products, enzymatic and nonenzymatic markers in diabetic rats. The results demonstrate that AA possesses potent antioxidant effect comparable with glibenclamide in improving antihyperglycemia and attenuating antioxidant status in diabetic rats.
Atmospheric rivers (ARs), referring to long and narrow filamentary bands of intense water vapor transport in the atmosphere, can cause extreme precipitation, floods, and drought events. Their ...variability has been linked to various climate modes, such as El Niño/Southern Oscillation, Pacific Decadal Oscillation, and Pacific‐North America pattern. Understanding and improving simulation of this linkage can provide the potential to predict ARs at subseasonal‐to‐decadal timescales. Up to now, the extent to which climate model simulations of ARs are dependent on model resolutions has not been fully assessed. Here, we compare and evaluate ARs in a pair of high‐resolution (HR) and a low‐resolution (LR) Community Earth System Model (CESM) simulations against the observations. The results show that within this CESM framework, climatological AR strength and associated precipitation are severely underestimated by LR compared to the observations, and their relationships with major modes of climate variability are also poorly reproduced. These deficiencies in LR are alleviated to a significant extent in HR. Using a linear regression analysis, we show that HR is able to capture with high fidelity the observed relationships between ARs and major climate modes in the Northern Hemisphere at seasonal‐to‐decadal time scales. These results suggest that the use of HR CESM may potentially lead to an improved forecast skill of ARs at season‐to‐decadal time scales.
Plain Language Summary
Atmospheric rivers (ARs) can trigger extreme rainfall and snowfall events, causing high risk of flooding. Meanwhile, they are sometimes responsible for the occurrence and break of drought events. So, it is of great importance for weather and climate forecast models to accurately represent ARs. Studies have shown that AR variability is linked to various climate modes, such as El Niño/Southern Oscillation, Pacific Decadal Oscillation, Pacific‐North America pattern, providing the potential for predicting ARs at subseasonal‐to‐decadal timescales. In this study, we examine the impact of climate model resolution on AR simulations and their relationship with major climate modes by comparing a pair of high‐resolution and a low‐resolution (LR) Community Earth System Model (CESM) simulations against the observations. The results reveal major deficiencies in LR that include underestimated climatological AR strength and associated precipitation, and inaccurate representations of ARs relationships with climate modes. Significant improvements are observed by increasing CESM horizontal resolution, suggesting a path for more accurate prediction of ARs.
Key Points
Simulated atmospheric rivers (ARs) are dependent on horizontal resolutions of coupled Community Earth System Model (CESM)
High‐resolution (HR) CESM simulates more realistic ARs' strength and associated rainfall than low‐resolution (LR)
ARs' response to large‐scale climate modes is better simulated in HR CESM than in LR CESM
The specific objective of the present is to evaluate the human health issue due to the continuous consumption of nitrate-contaminated groundwater among the various age groups of people. In the study, ...40 groundwater samples were collected during the post-monsoon season, and the major ions were analysed in a laboratory. Chadha plot revealed that weathering of parent rocks, ion exchange process and leaching of salts from the rocks are primary sources of groundwater contamination. Nitrate concentration varied from 24 to 78 mg/L with a mean of 46.45 mg/L. Nitrogen pollution index (NPI) value divulged that 40% and 17.5% of sample locations are moderately and significantly polluted due to elevated nitrate concentration in groundwater. The human health risk assessment model revealed that health issues are among the various age groups which are infants > kids > children > aged peoples > adults. The nitrate’s identified sources are leaching of salts from the rocks, using synthetic fertilizers, uncovered septic tanks and improper disposal of household waste from the residential area. Therefore, periodic inspection of water supply, health check-up and inspection of underground pipelines are the remedial measures that should be taken to reduce the severe effects of nitrate-contaminated drinking water in the study area.
Smart metering is a hot research topic and has gained significant attention since the electromechanical metering is not reliable and requires more energy and time. All the existing methods are ...focused only on how to deal with data rather than how to do efficiently. Prediction of electricity consumption is essential to gain intelligence to the smart gird. Precise electricity prediction allows a service provided in resource planning and also controlling actions for the demand and supply balancing. The users are beneficial from the smart metering solution by effective interpretation of their energy utilization, and labelling them to efficiently handle the utilization cost. With this motivation, the paper presents intelligent energy consumption analytics using smart metering data (ECA-SMD) model to determine the utilization of energy. The presented ECA-SMD model involves three major processes namely data pre-processing, feature extraction, classification, and parameter optimization. The presented ECA-SMD model uses Extreme Learning Machine (ELM) based classification to determine the optimum class labels. Besides, shell game optimization (SGO) algorithm is applied for tuning the parameters involved in the ELM and boosts the classification efficiency. The efficacy of the ECA-SMD model is validated using an extensive set of smart metering data and the results are investigated based on accuracy and mean square error (MSE). The proposed model exhibited supremacy with the maximum accuracy of 65.917 % and minimum MSE of 0.096.
Oxidative stress, imbalanced antioxidants, and dysregulated renal lipids are closely linked with diabetic nephropathy and eventual cause of end-stage renal failure. This study was performed to ...investigate the protective effect of bacoside-A on markers of lipid peroxidation, renal lipids, and markers of renal function in diabetic rats. Experimental diabetes was induced in Wistar rats by a single dose of streptozotocin 40 mg/kg body weight (BW) via intraperitoneal injection. Oral administration of bacoside-A (10 mg/kg BW) and glibenclamide, a reference drug, continued for 45 days. Diabetic rats showed a significant increase in the levels of plasma glucose, renal lipids, markers of renal lipid peroxidation, and plasma biomarkers of renal function such as urea, uric acid, and creatinine. A significant decrease in the levels of plasma insulin, nonenzymatic antioxidants, and the activity of enzymatic antioxidants was seen compared with the normal controls. Bacoside-A (10 mg/kg BW) and glibenclamide (600 μg/kg BW) administered to diabetic rats resulted in a significant decrease in plasma glucose and renal lipids but a significant increase in the plasma insulin level. In addition, bacoside-A achieved a remarkable increase in the activity of enzymatic antioxidants and the levels of nonenzymatic antioxidants in the renal tissue of diabetic rats, along with significant decreases in the markers of lipid peroxidation and those of renal function, consequently substantiating the protecting effectiveness of bacoside-A in a diabetic state. These biochemical observations were supported by a histopathological study of the renal tissue. The present study suggested that bacoside-A, a triterpenoid, offers a higher renoprotective effect to counter abnormal parameters of renal function in diabetes-induced renal injury.
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