Investigating the relationship of government revenue and expenditure on economic growth using a generalized method of moments: Does state-level panel ensure sustainable growth?

The current research project investigates the correlation between economic growth, government spending, and public revenue in seventeen Indian states spanning the years 1990 to 2020. An analysis of the relationship between key fiscal policy variables and economic growth was conducted utilising a panel data approach, the Generalised Method of Moments (GMM), and fully modified Ordinary Least Squares (FMOLS & DOLS) estimation. In our investigation, we assessed the impacts of non-tax revenue, development plan expenditure, tax revenue, and development non-plan expenditure on (i) the net state domestic product (NSDP) and (ii) the NSDP per capita. The findings indicate that the selected fiscal variables are significantly related. The results indicate that expeditious expansion of the fiscal sector is obligatory to stimulate economic growth in India and advance the actual development of the economies of these states.

Inequalities in cardiovascular disease among elderly Indians: A gender perspective analysis using LASI wave-I (2017-18).

BACKGROUND: While Cardiovascular disease (CVD) affects both men and women, emerging evidence suggests notable gender differentials in disease prevalence. This study aims to explore and analyse the gender differentials in CVD disease prevalence in India. METHODS: The present study utilizes data from first wave of the nationally representative survey "Longitudinal Ageing Study in India" (LASI, WAVE-I, 2017-18) with the eligible sample size of 31,464 individuals aged 60 years and above. Logistic regression analysis was used to understand risk of CVD by demographic characteristics. Factors contribution to gender differences in CVD prevalence was examined using a non-linear Fairlie decomposition. RESULTS: The prevalence of CVD was lower in men (31.06%) compared to women (38.85%). Women have a 33% higher likelihood of CVD compared to men (OR: 1.33; 95% CI: 1.25-1.42). Lack of education also confers a lower risk, more pronounced in women with no schooling (OR: 0.81; 95% CI: 0.7-0.94) compared to men (OR: 0.52; 95% CI: 0.47-0.58). Morbidity influences CVD presence more among women than men, with individuals suffering from three or more diseases having markedly increased odds (Men: OR: 3.89; 95% CI: 3.54-4.3, Women: OR: 6.97; 95% CI: 6.48-10.11). Smoking accounted increase in (20.52%) the gender gap while years of schooling dramatically lessened the gender gap (-46.30%). CONCLUSION: Result show gender differential in CVD prevalence and underlying risk factors, underscoring the need for gender-specific preventive strategies and interventions. Our findings highlight the importance of refined approach to cardiovascular health that considers the complex interplay of biological, social, and environmental determinants.

Spatio-temporal epidemiology and associated indicators of COVID-19 (wave-I and II) in India.

The spatio-temporal distribution of COVID-19 across India's states and union territories is not uniform, and the reasons for the heterogeneous spread are unclear. Identifying the space-time trends and underlying indicators influencing COVID-19 epidemiology at micro-administrative units (districts) will help guide public health strategies. The district-wise daily COVID-19 data of cases and deaths from February 2020 to August 2021 (COVID-19 waves-I and II) for the entire country were downloaded and curated from public databases. The COVID-19 data normalized with the projected population (2020) and used for space-time trend analysis shows the states/districts in southern India are the worst hit. Coastal districts and districts adjoining large urban regions of Mumbai, Chennai, Bengaluru, Goa, and New Delhi experienced > 50,001 cases per million population. Negative binomial regression analysis with 21 independent variables (identified through multicollinearity analysis, with VIF < 10) covering demography, socio-economic status, environment, and health was carried out for wave-I, wave-II, and total (wave-I and wave-II) cases and deaths. It shows wealth index, derived from household amenities datasets, has a high positive risk ratio (RR) with COVID-19 cases (RR: 3.577; 95% CI: 2.062-6.205) and deaths (RR: 2.477; 95% CI: 1.361-4.506) across the districts. Furthermore, socio-economic factors such as literacy rate, health services, other workers' rate, alcohol use in men, tobacco use in women, overweight/obese women, and rainfall have a positive RR and are significantly associated with COVID-19 cases/deaths at the district level. These positively associated variables are highly interconnected in COVID-19 hotspot districts. Among these, the wealth index, literacy rate, and health services, the key indices of socio-economic development within a state, are some of the significant indicators associated with COVID-19 epidemiology in India. The identification of district-level space-time trends and indicators associated with COVID-19 would help policymakers devise strategies and guidelines during public health emergencies.

EEG-Based Emotion Classification in Financial Trading Using Deep Learning: Effects of Risk Control Measures.

Day traders in the financial markets are under constant pressure to make rapid decisions and limit capital losses in response to fluctuating market prices. As such, their emotional state can greatly influence their decision-making, leading to suboptimal outcomes in volatile market conditions. Despite the use of risk control measures such as stop loss and limit orders, it is unclear if these strategies have a substantial impact on the emotional state of traders. In this paper, we aim to determine if the use of limit orders and stop loss has a significant impact on the emotional state of traders compared to when these risk control measures are not applied. The paper provides a technical framework for valence-arousal classification in financial trading using EEG data and deep learning algorithms. We conducted two experiments: the first experiment employed predetermined stop loss and limit orders to lock in profit and risk objectives, while the second experiment did not employ limit orders or stop losses. We also proposed a novel hybrid neural architecture that integrates a Conditional Random Field with a CNN-BiLSTM model and employs Bayesian Optimization to systematically determine the optimal hyperparameters. The best model in the framework obtained classification accuracies of 85.65% and 85.05% in the two experiments, outperforming previous studies. Results indicate that the emotions associated with Low Valence and High Arousal, such as fear and worry, were more prevalent in the second experiment. The emotions associated with High Valence and High Arousal, such as hope, were more prevalent in the first experiment employing limit orders and stop loss. In contrast, High Valence and Low Arousal (calmness) emotions were most prominent in the control group which did not engage in trading activities. Our results demonstrate the efficacy of our proposed framework for emotion classification in financial trading and aid in the risk-related decision-making abilities of day traders. Further, we present the limitations of the current work and directions for future research.

Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks.

Bitcoin is a volatile financial asset that runs on a decentralized peer-to-peer Blockchain network. Investors need accurate price forecasts to minimize losses and maximize profits. Extreme volatility, speculative nature, and dependence on intrinsic and external factors make Bitcoin price forecast challenging. This research proposes a reliable forecasting framework by reducing the inherent noise in Bitcoin time series and by examining the predictive power of three distinct types of predictors, namely fundamental indicators, technical indicators, and univariate lagged prices. We begin with a three-step hybrid feature selection procedure to identify the variables with the highest predictive ability, then use Hampel and Savitzky-Golay filters to impute outliers and remove signal noise from the Bitcoin time series. Next, we use several deep neural networks tuned by Bayesian Optimization to forecast short-term prices for the next day, three days, five days, and seven days ahead intervals. We found that the Deep Artificial Neural Network model created using technical indicators as input data outperformed other benchmark models like Long Short Term Memory, Bi-directional LSTM (BiLSTM), and Convolutional Neural Network (CNN)-BiLSTM. The presented results record a high accuracy and outperform all existing models available in the past literature with an absolute percentage error as low as 0.28% for the next day forecast and 2.25% for the seventh day for the latest out of sample period ranging from Jan 1, 2021, to Nov 1, 2021. With contributions in feature selection, data-preprocessing, and hybridizing deep learning models, this work contributes to researchers and traders in fundamental and technical domains.

Quantification of field-incurred residues of cyantraniliprole and IN-J9Z38 in cabbage/soil using QuEChERS/HPLC-PDA and dietary risk assessment.

Cyantraniliprole is an anthranilic diamide insecticide used for the effective management of diamondback moth in cabbage. Dietary risk assessment of pesticides in food is a major concern now. This study developed a QuEChERS/HPLC-PDA-based highly efficient and reliable method, registering 89.80-100.11% recoveries of cyantraniliprole and its metabolite IN-J9Z38 from cabbage and soil with a relative standard deviation of 0.43-5.77%. Field experiment was conducted to study the residue dissipation of cyantraniliprole in cabbage and soil. Two foliar treatments of 10.26% formulation (Benevia) at 60 (T1 ) and 120 (T2 ) gram active ingredient/hectare were applied. The dissipation half-lives of cyantraniliprole in cabbage and soil were determined to be 3.5-4.2 and 3.8-5.3 days at T1 and 3.9-4.8 and 4.1-4.7 days at T2 , respectively. The maximum concentrations of IN-J9Z38 at T1 and T2 were 0.819 and 1.061 mg/kg, respectively, on the fifth day. A risk quotient value of <1 indicates no dietary risk to the consumers. The residues in the harvested cabbage were below the tolerance level of 2.0 mg/kg established by the regulatory body in India.

Assessment of the efficiency of U-tube continuous bioreactor and immobilized enzyme beads for dye decolourization.

Two bioreactors (column and U-tube) were compared for continuous dye decolourization efficiency using a laccase-producing white rot fungus, Phlebia radiata. Column bioreactor containing immobilized crude enzyme beads and U-tube continuous bioreactor containing actively growing fungal biomass were established. Synthetic dye (coracryl blue C5G) solution treated with immobilized crude enzyme on alginate beads showed a maximum net decolourization up to 55% (flow rate 1 ml/min). The U-tube bioreactor was more efficient in decolorizing the dye, which showed a net decolourization up to 64% at faster flow rate (2.5 ml/min). The decolorization efficiency in both the systems was positively influenced by the slower flow rate. Thus, the study presents designing and operations of two continuous small-scale bioreactors one with immobilized enzyme while the another one with direct fungal contact.

Formulation of 99mTechnetium-labeled leuprolide loaded liposomes and its biodistribution study in New Zealand white female rabbits for assessment of its uterine targeting efficiency.

Leuprolide acetate (LPA), a GnRH analogue, is drug of choice for treatment of uterine fibroids and endometriosis. The current marketed formulations of LPA show severe systemic side effects. This project aims to formulate LPA loaded liposomes to be administered by vaginal route for uterine targeting. Liposomes were prepared by thin film hydration method using 1:1 M ratio of DSPC: Cholesterol and characterized for vesicle size, zeta potential, entrapment efficiency, and loading. Radiolabeling of LPA was performed by direct labeling with reduced technetium-99m. Binding affinity of 99mTc-labeled complexes was assessed by diethylenetriaminepentaacetic acid (DTPA) challenge test. Biodistribution study was done in New Zealand white female rabbits by administering the formulation via vaginal route. Spherical and discrete vesicles of size 189 nm were seen in TEM results with entrapment efficiency and loading of 74.36% and 9.29%w/w, respectively. Liposomes were able to sustain the drug release for 5 days. 99mTc-labeled complexes showed high labeling efficiency and stability both in saline and serum. DTPA challenge test confirmed low transchelation of 99mTc-labeled complexes. Biodistribution study by gamma scintigraphy revealed the preferential uptake of the formulation by uterus when administered vaginally. Compared to plain drug, liposomes concentrated and were retained within the uterus for a longer period of time. Uterine targeting of liposomal LPA indicates its potential to overcome the limitations of presently available formulations. Hence, this seems to be a promising approach for targeting the drugs, whose site of action is uterus.

Assessment of aquo-ethanolic extract of Camellia sinensis against Carbapenem Resistant Escherichia coli: In Vivo Trials in a Murine Model.

The prevalence of Carbapenem Resistant Escherichia coli (CRE) has increased considerably during the last decade, which can be ascribed to relative scarcity of effective non toxic antimicrobial agents. The present study was conducted to evaluate the antimicrobial activity of aquo-ethanolic (1:1) extract of leaves of Camellia sinensis (PTRC-31911-A) against Carbapenem Resistant Escherichia coli at preclinical level using peritonitis infection model in Sprague Dawley rats. Efficacy analysis of PTRC-31911-A involved enumeration of CRE colonies in blood and urine samples of test animals for a period of 5 days from infection. A reduction in microbial count of biological fluids was considered as the primary endpoint of the selected murine model. Physical, biochemical, hematological and histological indices of toxicity were employed as secondary relative indicators of the induced disease. Physical manifestations of infected rats included significantly high body temperature (TempInfected=103.18°F, ∼5% increase) and noteworthy reduction in weight (WeightInfected=126.83g, ∼15% decrease) as compared to control. Significant (P<0.05) increase in total white blood cells, eosinophil and monocyte counts as well as a significant decrease (P<0.05) in erythrocytes count, hematocrit volume, red blood cell distribution width and hemoglobin concentration were observed in the infected group as compared to the control group. Furthermore, noteworthy increase in liver and kidney function test parameters were observed in case of infected groups. All the hematological and biochemical parameters were found to be within optimum range in case of treatment group, indicating restoration of homeostasis. Histopathological studies also presented symptoms of hemorrhage and glomerular damage with structural distortion in glomerular capillary loops of infected groups, which were later recovered in treated groups, indicating the nephro-protective potential of PTRC-31911-A. The study clearly points out that Camellia sinensis extract (PTRC-31911-A; single dose of 5mg/Kg bwt; oral,+24h) is highly effective against Carbapenem Resistant Escherichia coli owing mainly to the presence of flavonoids and polyphenolic compounds, identified by LCMS. Ongoing studies are expected to further unravel the mechanism of action and bioactivity determinants of this broad spectrum plant extract.

Radiation-induced biomarkers for the detection and assessment of absorbed radiation doses.

Radiation incident involving living organisms is an uncommon but a very serious situation. The first step in medical management including triage is high-throughput assessment of the radiation dose received. Radiation exposure levels can be assessed from viability of cells, cellular organelles such as chromosome and different intermediate metabolites. Oxidative damages by ionizing radiation result in carcinogenesis, lowering of the immune response and, ultimately, damage to the hematopoietic system, gastrointestinal system and central nervous system. Biodosimetry is based on the measurement of the radiation-induced changes, which can correlate them with the absorbed dose. Radiation biomarkers such as chromosome aberration are most widely used. Serum enzymes such as serum amylase and diamine oxidase are the most promising biodosimeters. The level of gene expression and protein are also good biomarkers of radiation.

Influenza a (H1N1) outbreak and challenges for pharmacotherapy.

Influenza A (H1N1) virus, a genetic reassortment of endemic strain of human, avian flu and swine flu, with an inherent ability to mutate continuously has developed a subtype which is causing present flu in humans. As on 10th May, 2009, twenty nine countries are affected with officially reported 4379 cases with Mexico--1626 affected (45 deaths), US 2254 affected (02 deaths); Canada 280 (01 deaths) and Costa Ricia -8 cases (01 death) respectively. Rest of 15 countries have reported less than 100 officially confirmed cases of H1N1 infection. WHO has already declared Pandemic Alert V on 29th April, 2009. If the present flu achieves equivalent virulence to that of 1918-19 pandemic flu, expected deaths will be 62 million people. Travel advisory, stockpiling of antiviral drugs--Tamiflu & Relenza; vaccine development, activation of business continuity planning for maintenance of essential serives etc., are some of the important mitigation approaches, being followed all over the world. WHO has a regional reserve of 10,000 million doses of anti-viral drugs. National Disaster Management Authority (NDMA), Government of India, an apex body for disaster management, in active coordination with Ministry of Health & other stakeholders/service providers is maintaining a constant state of vigil on the present Influenza A (H1N1) outbreak. In collaboration with UNDMT, NDMA has outlined a strategy for Pandemic Preparedness beyond Health in April, 2008. Various non-pharmaceutical interventions like detection, isolation and quarantine are required to contain the situation. Accordingly, stockpiling of 10 million doses of anti viral drugs, surveillance at airports, isolation with strict enforcement of quarantine procedures, sustained supply of respiratory masks & other personal protective equipment; deployment of rapid response teams are some of the activities being undertaken by Indian Government proactively. As situation goes to Phase VI, there will be a shift in strategy from active surveillance, detection and quarantine to containment, treatment, prevention of spread of disease and maintenance of business continuity beyond health sectors. The major concern is to utilise this latency period, between phase V and VI, to fill the gaps in state of preparedness. It is also essential to focus on development/procurement of appropriate vaccine to manage the situation arising from any further mutation of the existing causative virus to be resistant against existing anti viral therapies. It is a continued effort which can save many lives around the world and everyone has to play its assigned role effectively.

Radiomodulatory and free-radical scavenging activity of the fractionated aquo-alcoholic extract of the adaptogenic nutraceutical (Rhodiola imbricata)--a comparative in vitro assessment with ascorbate.

OBJECTIVE: Immense interest has been generated in recent years for the development of drugs of herbal origin for the mitigation of deleterious effects of environmental pollutants like ionizing radiation, mainly to protect against radiation leakages resulting from mishaps in nuclear reactors, deliberate use of dirty bombs, etc. METHOD: The radio modifying effects of a fractionated extract of the high-altitude Himalayan plant species Rhodiola imbricata, along with its electron-donation potential, super-oxide ion scavenging (IC50 ≤ 0.025 mg/ml), nitric oxide (NO) scavenging potential (IC(50) = 0.5 mg/ml), and antihemolytic activity were evaluated in the present study. Reducing power, superoxide ion (O(2)(•-)), and nitric oxide scavenging ability of the fractionated extract increased in a dose-dependent manner. Rhodiola imbricata also exhibited antihemolytic potential preventing radiation-induced membrane degeneration of human erythrocytes. CONCLUSION: Thus, it can be stated that Rhodiola imbricata provides protection against gamma radiation via multifarious mechanisms that act in a synergistic manner. Rhodiola imbricata is widely used as a nutraceutical supplement in the trans-Himalayan region nations, and the current study shows that Rhodiola has immense potential for alleviation of biological damage in a radiation environment.