Anticonvulsant activity of gap-junctional blocker carbenoxolone in albino rats

Background: Gap junctions (GJs) are clusters of channels that connect the interiors of adjoining neurons and mediate electrical/electrotonic coupling by transfer of ions and small molecules. Electrotonic coupling between principal neurons via GJs is increasingly recognized as one of the mechanisms in the pathogenesis of the abnormal neuronal synchrony that occurs during seizures. Carbenoxolone the succinyl ester of glycyrrhetinic acid obtained from liquorice has been shown to have the property of blocking gap junctional intercellular communication. The objectives were to study if carbenoxolone has in-vivo anticonvulsive activity in pentylenetetrazole (PTZ) and maximal electroshock (MES) seizure models and to probe the functional role of GJs in seizures.Methods: Carbenoxolone was tested for anticonvulsive effect in albino rats subjected to seizures by the PTZ and MES at three doses 100 m/kg, 200 m/kg, 300 m/kg. In the PTZ model parameters observed were seizure protection, seizure latency and seizure duration. In the MES model parameters observed were seizure protection and seizure duration.Results: The results showed that the carbenoxolone has anticonvulsant activity in both PTZ and MES induced seizures with better protection in the PTZ induced seizures. In the PTZ model carbenoxolone produced a statistically significant increase in seizure latency, decrease in seizure duration and seizure protection. In the MES model carbenoxolone produced a statistically significant decrease in seizure duration.Conclusions: Carbenoxolone has in-vivo anticonvulsive effect and could be useful in both petitmal (absence) seizures and grand mal (generalized tonic-clonic epilepsy) seizures. The protective effect of carbenoxolone could be due to blockade of GJ channels that mediate electro tonic coupling and thereby prevent the neural synchronization that is characteristic of seizures. The study also supports the view that GJs have a functional role in the electrophysiology of seizures and GJ blockers have potential as a new class of antiepileptic drugs

Marker-Assisted Improvement of the Elite Maintainer Line of Rice, IR 58025B for Wide Compatibility (S5n) Gene

The degree of heterosis in different hybrid rice varieties is reported to be at the highest in indica/japonica cross combination, however, there is a problem of sterility and semi-sterility in such inter sub specific hybrids. To overcome this problem, it is essential to develop parental lines having wide compatibility (S5n) gene. In this study, a functional marker S5-InDel was used for marker-assisted backcrossing (MABB) to introgress S5n gene from Dular into the genetic background of a widely grown recurrent parent IR 58025B, a maintainer line of wild-abortive (WA) cytoplasmic male sterile line, IR 58025A. Further, a closely linked marker nksbadh2 was used for the identification of plants devoid of aroma in backcross population to develop hybrids with no aroma. The stringent phenotypic selection followed by background selection of BC3F4 identified plants with 94.51–98.90% of the recurrent parent genome recovery of lines carrying S5n gene. Subsequently, at 10 promising BC3F5 lines possessing S5n gene with high yielding and long-slender grain type were validated for their maintainer behavior through test crosses with IR 58025A. Also the improved lines showed significantly improved spikelet fertility performance while crossed with japonica and javanica testers in comparison to the original recurrent parent. The improved lines developed in the present study, are being converted to CMS lines through marker-assisted backcross breeding to facilitate precise and improved hybrid breeding program in rice

Authentication of damaged hand vein patterns by modularization

As security is a major concern in present times, reliable authentication systems are in great demand. A biometric trait like the vascular pattern on the back of the hand of a person is unique and secure. A biometric system working on this principle often fails to authenticate a person either because of the varying hand posture or due to an injury altering the vein pattern. In this paper we propose an authentication system to overcome these disadvantages by modularizing the image and then comparing the features. This method of authentication reduces the False Rejection Ratio (FRR) and also False Acceptance Ratio (FAR) of the system

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

Self-learning Neuromorphic Integrated Circuits for Autonomous Drone Navigation

Artificial intelligence (AI) systems typically do not have the ability to execute inference and learning algorithms concurrently in real-time like the human brain. Instead, they typically execute these algorithms in series, which can be inefficient and lack adaptability in complex and unpredictable real-world situations. In this work, we present an intelligent system that combines a drone and a synaptic resistor (synstor) circuit to concurrently execute inference and reinforcement learning algorithms in real time. The synstor circuit's conductance matrix is able to adapt and optimize in real-time learning processes, allowing the drone to navigate towards its target positions even in chaotic aerodynamic conditions. In learning experiments involving synstor circuits, human neurobiological circuits, and artificial neural network (ANN), the synstor circuit's real-time learning outperformed both human real-time learning and ANN’s iterative offline learning process in terms of adaptability, learning speed, accuracy, power consumption, and energy efficiency. The use of synstor circuits to bypass the limitations of traditional computers offers the potential for the development of AI systems with brain-like real-time learning abilities, high energy efficiency, and adaptability in complex real-world environments for a wide range of applications

Self-learning Neuromorphic Integrated Circuits for Autonomous Drone Navigation

Artificial intelligence (AI) systems typically do not have the ability to execute inference and learning algorithms concurrently in real-time like the human brain. Instead, they typically execute these algorithms in series, which can be inefficient and lack adaptability in complex and unpredictable real-world situations. In this work, we present an intelligent system that combines a drone and a synaptic resistor (synstor) circuit to concurrently execute inference and reinforcement learning algorithms in real time. The synstor circuit's conductance matrix is able to adapt and optimize in real-time learning processes, allowing the drone to navigate towards its target positions even in chaotic aerodynamic conditions. In learning experiments involving synstor circuits, human neurobiological circuits, and artificial neural network (ANN), the synstor circuit's real-time learning outperformed both human real-time learning and ANN’s iterative offline learning process in terms of adaptability, learning speed, accuracy, power consumption, and energy efficiency. The use of synstor circuits to bypass the limitations of traditional computers offers the potential for the development of AI systems with brain-like real-time learning abilities, high energy efficiency, and adaptability in complex real-world environments for a wide range of applications

eTransform: Transforming Enterprise Data Centers by Automated Consolidation

Abstract—Modern day enterprises have a large IT infrastructure comprising thousands of applications running on servers housed in tens of data centers geographically spread out. These enterprises periodically perform a transformation of their entire IT infrastructure to simplify, decrease operational costs and enable easier management. However, the large number of different kinds of applications and data centers involved and the variety of constraints make the task of data center transformation challenging. The state-of-the-art technique for performing this transformation is simplistic, often unable to account for all but the simplest of constraints. We present eTransform, a system for generating a transformation and consolidation plan for the IT infrastructure of large scale enterprises. We devise a linear programming based approach that simultaneously optimizes all the costs involved in enterprise data centers taking into account the constraints of applications groups. Our algorithm handles the various idiosyncrasies of enterprise data centers like volume discounts in pricing, wide-area network costs, traffic matrices, latency constraints, distribution of users accessing the data etc. We include a disaster recovery (DR) plan, so that eTransform, thus provides an integrated disaster recovery and consolidation plan to transform the enterprise IT infrastructure. We use eTransform to perform case studies based on real data from three different large scale enterprises. In our experiments, eTransform is able to suggest a plan to reduce the operational costs by more than 50 % from the “as-is ” state of these enterprise to the consolidated enterprise IT environment. Even including the DR capability, eTransform is still able to reduce the operational costs by more than 25 % from the simple “as-is ” state. In our experiments, eTransform is able to simultaneously optimize multiple parameters and constraints and discover solutions that are 7x cheaper than other solutions. I