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Due to the scarcity of water supplies, coastal groundwater quality most importantly influences sustainable development in the coastal region. Rising groundwater pollution through heavy metal contamination is an intense health hazard and environmental concern worldwide. This study shows that 27%, 32%, and 10% of the total area come under the categories very high, high, and very low human health hazard index (HHHI) accordingly. This area's water quality is also much polluted; the study shows approximately 1% has very good water quality. High concentrations of Fe, As, TDS, Mg2+, Na, and Cl− are relatively noticed in the portion of the western part of this district. The concentration of heavy metals in coastal aquifers influences the groundwater pollution of that region. The average heavy metal concentration in this region is 0.20 mg/l (As) and 1.160 mg/l (TDS). The groundwater quality and hydrogeochemical properties are determined through the Piper diagram. The study stated that TDS, Cl− (mg/l), and Na+ (mg/l) are the most regulatory issues of vulnerability. In the present study region, a huge number of alkaline substances are present resulting in the water being unfit for drinking purposes. Lastly, it is clear from the study's findings that multiple risks exist there like As, TDS, Cl−, and other hydrochemical parameters in the groundwater. The proposed approach applied in this research work may be a pivotal tool for predicting groundwater vulnerability in other regions. [ABSTRACT FROM AUTHOR]

In recent years groundwater contamination through nitrate contamination has increased rapidly in the managementof water research. In our study, fourteen nitrate conditioning factors were used, and multi-collinearity analysis is done. Among all variables, pH is crucial and ranked one, with a value of 0.77, which controls the nitrate concentration in the coastal aquifer in South 24 Parganas. The second important factor is Cl−, the value of which is 0.71. Other factors like—As, F−, EC and Mg2+ ranked third, fourth and fifth position, and their value are 0.69, 0.69, 0.67 and 0.55, respectively. Due to contaminated water, people of this district are suffering from several diseases like kidney damage (around 60%), liver (about 40%), low pressure due to salinity, fever, and headache. The applied method is for other regions to determine the nitrate concentration predictions and for the justifiable alterationof some management strategies. [ABSTRACT FROM AUTHOR]

Determining the degree of high groundwater arsenic (As) and fluoride (F−) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F− concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F− contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination. [ABSTRACT FROM AUTHOR]

Groundwater is the most reliable source of freshwater for human well-being. Significant toxic contamination in groundwater, particularly in the aquifers of the Ganges delta, has been a substantial source of arsenic (As). The Sundarban Biosphere Reserve (SBR), located in the southwestern part of the world’s largest Ganges delta, suffers from As contamination in groundwater. Therefore, assessment of groundwater vulnerability is essential to ensure the safety of groundwater quality in SBR. Three data-driven algorithms, i.e. “logistic regression (LR)”, “random forest (RF)”, and “boosted regression tree (BRT)”, were used to assess groundwater vulnerability. Groundwater quality and hydrogeochemical characteristics were evaluated by Piper, United States Salinity Laboratory (USSL), and Wilcox's diagram. The result of this study indicates that among the applied models, BRT (AUC = 0.899) is the best-fit model, followed by RF (AUC = 0.882) and LR (AUC = 0.801) to assess groundwater vulnerability. In addition, the result also indicates that the general quality of the groundwater in this area is not very good for drinking purposes. The applied methods of this study can be used to evaluate the groundwater vulnerability of the other aquifer systems. [ABSTRACT FROM AUTHOR]

The occurrences of flash floods in sub-tropical climatic regions like India are ubiquitous phenomena, particularly during the monsoon season. This type of flood occurs within a short period of time and makes it distinctive from all-natural hazards, which causes huge loss of economy and causalities of life. Therefore, its prediction is crucial and one of the challenging tasks for researchers to mitigate this sustainably. Furthermore, identifying flash flood susceptible regions is the foremost responsibility in managing flood events, which helps the local administration take emergency relief operations in flood-prone regions. In September 2021, the flood in the Gandheswari river basin was the most severe compared to the past decade. The occurrences of flash floods in the lower course of the Gandheswari river has been affected riparian habitats rigorously. Thus, in this study, we proposed the bivariate logistic regression (LR) method to delineate this river basin's flash flood hazard (FFH) map. Here, sixteen flood conditioning factors were selected for modeling purposes with the help of a multicollinearity test, and a total of 71 flood points were identified from the historical dataset. The produced result was validated by six distinctive validating techniques, including receiver operating characteristics (ROC) analysis, specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and F-score. These techniques have shown that present modeling has high predictive performance in both training and testing dataset with the values of ROC (training—0.928, validating—0.892), specificity (training—0.911, validating—0.882), sensitivity (training—0.915, validating—0.885), PPV (training—0.912, validating—0.874), NPV (training—0.91, validating—0.875), and F-score (training—0.92, validating—0.89). Therefore, the proposed method in this and the outcome result will help the disaster manager make proper decisions to mitigate the hazardous situation and take sustainable emergency relief operations. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic enforced nationwide lockdown, which has restricted human activities from March 24 to May 3, 2020, resulted in an improved air quality across India. The present research investigates the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined. The selected seven air quality pollutant parameters (PM10, PM2.5, CO, NO2, SO2, NH3, and O3) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown. The results showed that the air quality has improved across the country and average temperature and maximum temperature were connected to the outbreak of the COVID-19 pandemic. This outcomes indicates that there is no such relation between climatic parameters and outbreak and its associated mortality. This study will assist the policy maker, researcher, urban planner, and health expert to make suitable strategies against the spreading of COVID-19 in India and abroad. [ABSTRACT FROM AUTHOR]

Worldwide, the ever-increasing population and urbanization have put tremendous pressure on groundwater (GW). It has provoked the over-exploitation of groundwater and hindered the percolation of rainwater beneath the earth's surface in another way. The uncertainty of rainfall occurrence and over-exploitation of groundwater created the problem of water scarcity and decreased groundwater height significantly in the hard rock-dominated semi-arid regions. This study is designed to delineate groundwater potential zone (GWPZ) in the hard-rock terrain controlled Bankura district of West Bengal with the help of a knowledge-driven statistical technique, analytical hierarchy process (AHP), in remote sensing and GIS environment. The GWPZ map has been prepared by compiling nine groundwater controlling parameters: geology, geomorphology, slope, land use–land cover (LULC), rainfall, drainage density, soil texture, topographical wetness index (TWI) and lineament density. Among all the parameters, rainfall, geology, slope and LULC have played a dominant role in groundwater availability in the Bankura district, and the weightage of these parameters is 0.28, 0.20, 0.14 and 0.11, respectively. These four layers have facilitated to accumulation of groundwater potentiality in the Bankura district and surroundings. Our study of the GWPZ map revealed that 13.36, 33.80, 21.05, 19.02 and 12.77% of the area of the Bankura district is under very good, good, moderate, poor and very poor condition, respectively, for targeting GW in the future. Very high to high groundwater yielding spots are observed in valley fills, flood plains, vegetation-covered areas, agricultural land and low-lying areas of the study area. The result is evaluated by employing the receiver operation characteristics (ROC) and area under curve (AUC) technique using the well's yield groundwater data, and this value (0.757) also displayed the reliability of this work. However, the GWPZ map of the hard rock-dominated Bankura district has been delineated successfully; the findings of this study can be useful to the planners, engineers and policymakers for proper utilization, evaluation and management of groundwater in this water-scarce semi-arid region to ensure its sustainable development. [ABSTRACT FROM AUTHOR]

Landslide and earthquake are two of the dangerous natural hazards in the Himalayan mountainous ranges. This study focused on small-scale landslide susceptibility (LS) maps as well as hotspot zones of earthquake and landslide on the Himalayas. Remote sensing, geological, and hydrological data were collected and processed using the geographic information system (GIS) for this purpose. Seventeen landslide influential causative factors (LICFs) were used, which included slope, aspect, elevation, profile curvature, plan curvature, topographic wetness index (TWI), stream power index (SPI), terrain ruggedness index (TRI), drainage density, distance to drainage, seismic zone, soil texture, geology, and land use and land cover (LULC). The machine learning (ML) neural network and deep learning models, namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), artificial neural network (ANN), deep learning neural network (DLNN), and Getis-Ord GI*, have been applied for landslide susceptibility (LS) maps, and earthquake and landslide hotspot maps respectively of the Himalayas. The LS maps were compared with the earthquake and landslide hotspot zones, and there is valid agreement among these. The results of the susceptibility mapping were validated by the confusion matrix table and sensitivity, specificity, accuracy, precession, F score, and kappa statistical indices. The validation result showed that all the landslide susceptibility models (LSMs) have a near 90% prediction rate. Among the four models, the DLNN models have the highest prediction accuracy followed by LDA, QDA, and ANN. The small-scale LS maps of the Himalayas may help planners and developers for macro-scale mitigation of landslide hazard and land use planning. [ABSTRACT FROM AUTHOR]

The exponential growth in the number of flash flood events is a global threat, and detecting a flood-prone area has also become a top priority. The flash flood-susceptibility mapping can help to mitigate the worst effects of this type of risk phenomenon. However, there is an urgent need to construct precise models for predicting flash flood-susceptibility mapping, which can be useful in developing more effective flood management strategies. In this present research, support vector regression (SVR) was coupled with two meta-heuristic algorithms such as particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA), to construct new GIS-based ensemble models (SVR–PSO and SVR–GOA) for flash flood-susceptibility mapping (FFSM) in the Gandheswari River basin, West Bengal, India. In this regard, 16 topographical and environmental flood causative factors have been identified to run the models using the multicollinearity (MC) test. The entire dataset was divided into 70:30 for training and validating purposes. Statistical measures including specificity, sensitivity, PPV, NPV, AUC–ROC, kappa and Taylor diagram have been employed to validate adopted models. The SVR-based factor importance analysis was employed to choose and prioritize significant factors for the spatial analysis. Among the three modeling approaches used here, the ensemble method of SVR–GOA is the most optimal (specificity 0.97 and 0.87, sensitivity 0.99 and 0.91, PPV 0.97 and 0.86, NPV 0.99 and 0.91, AUC 0.951 and 0.938 in training and validation, respectively), followed by the SVR–PSO (specificity 0.84 and 0.84, sensitivity 0.87 and 0.86, PPV 0.85 and 0.82, NPV 0.87 and 0.87, AUC 0.951 and 0.938 in training and validation, respectively) and SVR (specificity 0.80 and 0.77, sensitivity 0.93 and 0.89, PPV 0.82 and 0.77, NPV 0.91 and 0.89, AUC 0.951 and 0.938 in training and validation, respectively) model. The result shown that 40.10 km2 (10.99%) and 25.94 km2 (7.11%) areas are under very high and high flood-prone regions, respectively. This produced reliable results that can help policymakers at the local and national levels to implement a concrete strategy with an early warning system to reduce the occurrence of floods in a region. [ABSTRACT FROM AUTHOR]

Hot and humid subtropical plateau regions are susceptible to land degradation in the form of weathering and gully erosion. Here, we investigate chemical weathering, gully erosion and cohesiveness through field-based measurements with a view to understand the controlling factors of potential land degradation, in complex river basin of the Chotanagpur plateau region in Eastern India. The layers of controlling factors of gully erosion were developed and prioritized considering boosted regression tree (BRT), alternative decision tree (ADT), particle swarm optimization (PSO) and random forest (RF) algorithms in the R software, and the results of these methods were also validated using receiver operating characteristic (ROC) curves. The spectroscopic analysis was carried out of collected soil samples to measure the degree of chemical weathering and cohesiveness. Furthermore, the climatic elements like temperature and rainfall were also considered for estimating the chemical weathering. The results of the gully erosion models (i.e., BRT, ADT, PSO and RF) show remarkable accuracy with ROC values of 0.93, 0.89, 0.91 and 0.84, respectively. An advanced decision tree model was integrated with the results of degree of chemical weathering and cohesiveness in geographical information system platform. The land degradation map developed from this approach shows that 10.53% of the study area is highly affected, whereas 17.36% area is moderately affected and the rest of the 73.85% area is less affected by land degradation. Our results provide essential information for policy makers in adopting measures for minimizing and controlling the land degradation. Our novel approach is significant to assess land degradation to a large scale. [ABSTRACT FROM AUTHOR]

The long-term lockdown due to COVID-19 has beneficial impact on the natural environment. India has enforced a lockdown on 24th March 2020 and was subsequently extended in various phases. The lockdown due to the sudden spurt of the COVID-19 pandemic has shown a significant decline in concentration of air pollutants across India. The present article dealt with scenarios of air quality concentration of air pollutants, and effect on climatic variability during the COVID-19 lockdown period in Kolkata Metropolitan Area, India. The result showed that the air pollutants are significantly reduced and the air quality index (AQI) was improved during the lockdown months. Aerosol concentrations decreased by − 54.94% from the period of pre-lockdown. The major air pollutants like particulate matters (PM2.5, PM10), sulphur dioxide (SO2), carbon monoxide (CO) and Ozone (O3) were observed the maximum reduction (− 40 to − 60%) in the COVID-19 lockdown period. The AQI has been improved by 54.94% in the lockdown period. On the other hand, Sen's slope rank and the Mann–Kendal trend test showed the daily decreased of air pollutants rate is − 0.051 to − 1.586 μg /m3. The increasing trend of daily minimum, average, and maximum temperature from the month of March to May in this year (2020s) are 0.091, 0.118, and 0.106 °C which is lowest than the 2016s to 2019s trend. Therefore, this research has an enormous opportunity to explain the effects of the lockdown on air quality and climate variability, and it can also be helpful for policymakers and decision-makers to enact appropriate measures to control air pollution. [ABSTRACT FROM AUTHOR]

The modeling and prediction of land movement susceptibility hazards, i.e., debris flow, landslide, and rock fall, can assist in controlling and preventing a variety of societal and environmental damages. The purpose of this study was to develop a land movement susceptibility hazard model of debris flow, landslide, and multiple land movement, i.e., combination of debris flow and landslide in the Saveh city of Markazi Province, Iran, using an ensemble of Bayesian generalized linear model (BGLM), sparse partial least squares (SPLS), boosted tree (BT), and random forest (RF) algorithms. For this purpose, 167 debris flow points, 261 landslide points, and 257 multiple (debris flow and landslide) points were identified based on field visits and available information, and 15 suitable conditioning factors were prepared as independent variables for this study. The accuracy and efficiency of the models were assessed using the receiver operating characteristic (ROC) and other statistical indices. The variable importance result indicates that slope is the most important factor in debris flow (25.53), landslide (31.39), and multiple hazard (41.90) occurrences. The accuracy assessment results in the validation phase revealed that the RF is the most optimal among the applied algorithms, with area under the curve (AUC) values of 0.90, 0.94, and 0.89 for debris flow, landslide, and multiple (D + L) hazard modeling. The findings of this study indicated that the use of a Bayesian and tree-based ensemble model in preparing a land movement-related disaster map could be useful among policymakers and land use planners for sustainable land use management and practices. [ABSTRACT FROM AUTHOR]

The deficiency of freshwater has become a global issue in the recent era, especially in water-scarce hard rock region including India. Groundwater (GW) as a natural resource is decreasing at an alarming rate in West Bengal, India. Sustainable use and planning for better management of groundwater resources are essential; thus, spatial modelling of GW distribution requires proper assessment to conserve and manage the groundwater resource. Machine learning algorithms in RS-GIS environment plays a crucial role in exploration, assessing, monitoring and conserving groundwater resource in this regard. Logistic regression (LR), support vector machine (SVM) and random forest (RF) were used to develop groundwater potential zone (GWPZ) of water-stressed district Purulia with the help of 10 GW controlling factors including geology, geomorphology, lineament density, slope, soil texture, drainage density, GW level, rainfall, NDVI and NDWI. Multi-collinearity analysis was also used to eliminate collinearity issues among all controlling factors. In this study, the total area has been divided into five groups under the very low, low, moderate, high and very high groundwater potential zone categories. It has been calculated that most of the area has moderate groundwater potentiality, which is 29% of the total regions. About 8%, 19%, 25% and 18% of the study area fall under the very low, low, high and very high zones. Finally, all the adopted models were validated through ROC–AUC using GW depth data from CGGB and 484 validating point datasets in this area. The AUC values of adopted LR, SVM and RF models are 0.801, 0.849 and 0.878, respectively; implies that RF is a more reliable algorithm with better predictive ability than other models in the study area. This study's findings will help decision-makers take proper strategies and planning of groundwater resource management for this particular water-scare hard rock region. [ABSTRACT FROM AUTHOR]

At present, flood is the most significant environmental problem in the entire world. In this work, flood susceptibility (FS) analysis has been done in the Dwarkeswar River basin of Bengal basin, India. Fourteen flood causative factors extracted from different datasets like DEM, satellite images, geology, soil and rainfall data have been considered to predict FS. Three heuristic models and one statistical model fuzzy Logic (FL), frequency ratio (FR), multi-criteria decision analysis (MCDA) and logistic regression (LR) have been used. The validating datasets are used to validate these models. The result shows that 68.71%, 68.7%, 60.56% and 48.51% area of the basin is under the moderate to very high FS by the MCDA, FR, FL and LR, respectively. The ROC curve with AUC analysis has shown that the accuracy level of the LR model (AUC = 0.916) is very much successful to predict the flood. The rest of the models like FL, MCDA and FR (AUC = 0.893, 0.857 and 0.835, respectively) have lesser accuracy than the LR model. The elevation was the most dominating factor with coefficient value of 19.078 in preparation of the FS according to the LR model. The outcome of this study can be implemented by local and state authority to minimize the flood hazard. [ABSTRACT FROM AUTHOR]

Floods are common and recurring natural hazards which damages is the destruction for society. Several regions of the world with different climatic conditions face the challenge of floods in different magnitudes. Here we estimate flood susceptibility based on Analytical neural network (ANN), Deep learning neural network (DLNN) and Deep boost (DB) algorithm approach. We also attempt to estimate the future rainfall scenario, using the General circulation model (GCM) with its ensemble. The Representative concentration pathway (RCP) scenario is employed for estimating the future rainfall in more an authentic way. The validation of all models was done with considering different indices and the results show that the DB model is most optimal as compared to the other models. According to the DB model, the spatial coverage of very low, low, moderate, high and very high flood prone region is 68.20%, 9.48%, 5.64%, 7.34% and 9.33% respectively. The approach and results in this research would be beneficial to take the decision in managing this natural hazard in a more efficient way. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic forced India as a whole to lockdown from 24 March 2020 to 14 April 2020 (first phase), extended to 3 May 2020 (second phase) and further extended to 17 May 2020 (third phase) and 31 May 2020 (fourth phase) with only some limited relaxation in non-hot spot areas. This lockdown has strictly controlled human activities in the entire India. Although this long lockdown has had a serious impact on the social and economic fronts, it has many positive impacts on environment. During this lockdown phase, a drastic fall in emissions of major pollutants has been observed throughout all the parts of India. Therefore, in this research study we have tried to establish a relationship among the fall in emission of pollutants and their impact on reducing regional temperature. This analysis was tested through the application of Mann–Kendall and Sen's slope statistical index with air quality index and temperature data for several stations across the country, during the lockdown period. After the analysis, it has been observed that daily emissions of pollutants (PM10, PM2.5, CO, NO2, SO2 and NH3) decreased by − 1– − 2%, allowing to reduce the average daily temperature by 0.3 °C compared with the year of 2019. Moreover, this lockdown period reduces overall emissions of pollutants by − 51– − 72% on an average and hence decreases the average monthly temperature by 2 °C. The same findings have been found in the four megacities in India, i.e., Delhi, Kolkata, Mumbai and Chennai; the rate of temperature fall in the aforementioned megacities is close to 3 °C, 2.5 °C, 2 °C and 2 °C, respectively. It is a clear indicator that a major change occurs in air quality, and as a result it reduced lower atmospheric temperature due to the effect of lockdown. It is also a clear indicator that a major change in air quality and favorable temperature can be expected if the strict implementations of several pollution management measures have been implemented by the concern authority in the coming years. [ABSTRACT FROM AUTHOR]

Management of any uncertain state or situations allows everything to be backed up to its normal position. Disaster planning has been important for environmental planning in recent times. In every world, researchers, geographers, climatologists and regional planners are going to detect and minimize the impact of natural disasters. The recent work presents flood-prone areas in the Ajoy River basin of India, considering the "Support Vector Machine", "Extremely Randomize Trees" and "Biogeography Based Optimization" (BBO) method in GIS platform. Various geo-environmental variables are known to determine the final outcome. "Area Under Curve" (AUC) values of "Receiver Operating Characteristics" were used to validate and determine the accuracy of the predicted models. AUC shows the state or nature of the flooding. If the outcome of AUC values indicates more than 0.95, then the likelihood of flooding will be high BBO gave us the most optimal outcomes in this research out of these models (AUC = 0.98) and assured us that BBO is an influential tool for assessing flood-prone areas in eastern India and helps to take measures for strategic management and preparing for progress. [ABSTRACT FROM AUTHOR]

Groundwater being an essential resource is not easily available in some parts of the world. The present study, aimed at procuring better prediction maps for groundwater potential zones, is based on a novel approach combining the use of k-fold cross-validation method and the implementation of four scenarios, each comprising of six machine learning models, ANFIS (Adaptive Neuro Fuzzy Inference System) and five other ensembles of it, ANFIS-Firefly, ANFIS-Bees, ANFIS-GA, ANFIS-DE and ANFIS-ACO. Ada Boost Model has played a vital role in determining the collinearity among the fourteen conditioning factors, which are, Lithology, Slope, TST, TRI, LULC, HAND, Curvature, Distance to Stream, Distance to Fault, Rainfall, Fault Density, Drainage Density, Elevation and Aspect. The AUCROC (Area Under Curve – Receiver Operating Characteristics) approach was employed as a model evaluation metric along with Accuracy, Sensitivity and Specificity. Among the models, ANFIS-DE showed the most promising results, acquiring the highest average values among the four scenarios for AUC (0.934), Accuracy (0.987), Sensitivity (0.985) and Specificity (0.985). Promising results of this study gives the necessary incentive for further applying this approach for groundwater zonation of other areas of the world as well as other areas of hydrogeological studies. [ABSTRACT FROM AUTHOR]

'Consolidation in Indian banking' has been on and off in the realm of deliberations of the policy planners of the country. SBI, the largest commercial bank in the country was ranked 64th in the list of Top 1000 Banks in the world by Banker in 2014 and it moved up to the position of 54th rank in 2015. Present paper aims to search for possible justification for consolidation in the Indian banking space by evaluating the efficiency performances of banks including its drivers. Using DEA technique, the efficiency performances of 54 commercial banks in India during the period 2001-2012 were evaluated. Tobit regression analysis using bootstrap corrected efficiency scores was adopted to decipher drivers of efficiency. Findings clearly indicate that 'size' does matter significantly in determining the efficiency profile of banks in India. [ABSTRACT FROM AUTHOR]

Blocking mitotic progression has been proposed as an attractive therapeutic strategy to impair proliferation of tumour cells. However, how cells survive during prolonged mitotic arrest is not well understood. We show here that survival during mitotic arrest is affected by the special energetic requirements of mitotic cells. Prolonged mitotic arrest results in mitophagy-dependent loss of mitochondria, accompanied by reduced ATP levels and the activation of AMPK. Oxidative respiration is replaced by glycolysis owing to AMPK-dependent phosphorylation of PFKFB3 and increased production of this protein as a consequence of mitotic-specific translational activation of its mRNA. Induction of autophagy or inhibition of AMPK or PFKFB3 results in enhanced cell death in mitosis and improves the anti-tumoral efficiency of microtubule poisons in breast cancer cells. Thus, survival of mitotic-arrested cells is limited by their metabolic requirements, a feature with potential implications in cancer therapies aimed to impair mitosis or metabolism in tumour cells. [ABSTRACT FROM AUTHOR]

Thyroid hormones have widespread cellular effects; however it is unclear whether their effects on the central nervous system (CNS) contribute to global energy balance. Here we demonstrate that either whole-body hyperthyroidism or central administration of triiodothyronine (T3) decreases the activity of hypothalamic AMP-activated protein kinase (AMPK), increases sympathetic nervous system (SNS) activity and upregulates thermogenic markers in brown adipose tissue (BAT). Inhibition of the lipogenic pathway in the ventromedial nucleus of the hypothalamus (VMH) prevents CNS-mediated activation of BAT by thyroid hormone and reverses the weight loss associated with hyperthyroidism. Similarly, inhibition of thyroid hormone receptors in the VMH reverses the weight loss associated with hyperthyroidism. This regulatory mechanism depends on AMPK inactivation, as genetic inhibition of this enzyme in the VMH of euthyroid rats induces feeding-independent weight loss and increases expression of thermogenic markers in BAT. These effects are reversed by pharmacological blockade of the SNS. Thus, thyroid hormone–induced modulation of AMPK activity and lipid metabolism in the hypothalamus is a major regulator of whole-body energy homeostasis. [ABSTRACT FROM AUTHOR]

Sirtuins are NAD+-dependent protein deacetylases. They mediate adaptive responses to a variety of stresses, including calorie restriction and metabolic stress. Sirtuin 3 (SIRT3) is localized in the mitochondrial matrix, where it regulates the acetylation levels of metabolic enzymes, including acetyl coenzyme A synthetase 2 (refs 1, 2). Mice lacking both Sirt3 alleles appear phenotypically normal under basal conditions, but show marked hyperacetylation of several mitochondrial proteins. Here we report that SIRT3 expression is upregulated during fasting in liver and brown adipose tissues. During fasting, livers from mice lacking SIRT3 had higher levels of fatty-acid oxidation intermediate products and triglycerides, associated with decreased levels of fatty-acid oxidation, compared to livers from wild-type mice. Mass spectrometry of mitochondrial proteins shows that long-chain acyl coenzyme A dehydrogenase (LCAD) is hyperacetylated at lysine 42 in the absence of SIRT3. LCAD is deacetylated in wild-type mice under fasted conditions and by SIRT3 in vitro and in vivo; and hyperacetylation of LCAD reduces its enzymatic activity. Mice lacking SIRT3 exhibit hallmarks of fatty-acid oxidation disorders during fasting, including reduced ATP levels and intolerance to cold exposure. These findings identify acetylation as a novel regulatory mechanism for mitochondrial fatty-acid oxidation and demonstrate that SIRT3 modulates mitochondrial intermediary metabolism and fatty-acid use during fasting. [ABSTRACT FROM AUTHOR]

Introduction: Energy deficiency is a cause for myocardial dysfunction during septic shock. In rodents, septic shock decreases the oxidation of long-chain fatty acids and glucose in the myocardium causing energy deficiency. However, the effect of septic shock on myocardial energy metabolites in large animals and human is unknown.Objectives: Investigate the effects of septic shock on myocardial energy metabolites in domestic pigs.Methods: Seventeen female pigs divided into control and lipopolysaccharide (LPS)-induced septic shock groups. Myocardial metabolites were analyzed ex vivo by 1H nuclear magnetic resonance spectroscopy and liquid chromatography-tandem mass spectrometry. Gene and protein expression analysis were analyzed by real-time PCR and western blot.Results: Septic shock was associated with an increase in myocardial levels of short- and medium-chain acylcarnitines, lactate, alanine, and pyruvate dehydrogenase kinase 4 gene expression. COX-2 and prostaglandin E4 receptor gene expression also increased in the septic myocardium, although the only elevated eicosanoid in the septic animals was thromboxane B2. Myocardial levels of niacin, taurine, glutamate, glutamine, and glutathione were higher, and hypoxanthine levels lower in septic pigs than controls.Conclusions: In pigs, septic shock induced by LPS caused myocardial changes directed to decrease the oxidation of medium- and short-chain fatty acid without an effect on long-chain fatty acid oxidation. The increase in myocardial levels of lactate, alanine, and pyruvate dehydrogenase kinase 4 gene expression suggest that septic shock decreases pyruvate dehydrogenase complex activity and glucose oxidation. Homeostasis of niacin, taurine, glutamate, glutamine, glutathione, hypoxanthine and thromboxane B2 is also affected in the septic myocardium. [ABSTRACT FROM AUTHOR]