Potentiating Effects of MPL on DSPC Bearing Cationic Liposomes Promote Recombinant GP63 Vaccine Efficacy: High Immunogenicity and Protection

Visceral leishmaniasis (VL), a vector-transmitted disease caused by Leishmania donovani, is potentially fatal if left untreated. Vaccination against VL has received limited attention compared with cutaneous leishmaniasis, although the need for an effective vaccine is pressing for the control of the disease. Earlier, we observed protective efficacy using leishmanial antigen (Ag) in the presence of either cationic liposomes or monophosphoryl lipid A-trehalose dicorynomycolate (MPL-TDM) against experimental VL through the intraperitoneal (i.p.) route of administration in the mouse model. However, this route of immunization is not adequate for human use. For this work, we developed vaccine formulations combining cationic liposomes with MPL-TDM using recombinant GP63 (rGP63) as protein Ag through the clinically relevant subcutaneous (s.c.) route. Two s.c. injections with rGP63 in association with cationic liposomes and MPL-TDM showed enhanced immune responses that further resulted in high protective levels against VL in the mouse model. This validates the combined use of MPL-TDM as an immunopotentiator and liposomes as a suitable vaccine delivery system

Unraveling a 146 Years Old Taxonomic Puzzle: Validation of Malabar Snakehead, Species-Status and Its Relevance for Channid Systematics and Evolution

The current distribution of C. diplogramma and C. micropeltes is best explained by vicariance. The significant variation in the key taxonomic characters and the results of the molecular marker analysis points towards an allopatric speciation event or vicariant divergence from a common ancestor, which molecular data suggests to have occurred as early as 21.76 million years ago. The resurrection of C. diplogramma from the synonymy of C. micropeltes has hence been confirmed 146 years after its initial description and 134 years after it was synonymised, establishing it is an endemic species of peninsular India and prioritizing its conservation value

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Prioritization of drivers of corporate social responsibility in the footwear industry in an emerging economy: A fuzzy AHP approach

Corporate social responsibility (CSR) is gaining popularity among researchers and practitioners due to its strong influence on the global market. Recently, the decision-makers of footwear companies have given special attention on CSR issues due to increased stakeholders’ awareness on social and environmental issues. In this study, the fuzzy analytical hierarchy process (FAHP) has been used to identify and evaluate drivers to CSR-based sourcing in the context of the footwear industry of Bangladesh. A total of 20 drivers are identified through a literature review and experts’ opinions. The results indicate that financial drivers are paramount toward CSR-based sourcing into existing supply chains followed by environmental drivers. This study offers some managerial implications that may assist companies to incorporate CSR-based sourcing into existing supply chains. The identified drivers may guide footwear companies in strategic planning to create a sustainable business structure in the competitive market. © 2018 Elsevier Lt

Modelling the Drivers of Solar Energy Development in an Emerging Economy: Implications for Sustainable Development Goals

Energy demand in Bangladesh is consistently rising due to the country's rapid population growth and economic expansion. As a result, solar energy holds substantial potential in the Bangladeshi energy portfolio. This study aims to identify and evaluate the key drivers behind the sustainable development of solar energy in Bangladesh, an emerging economy in South Asia. We do this by adopting an integrated methodology. First, through a literature review and expert feedback, we identify 12 drivers of solar energy development. We then employ the best-worst method (BWM) to rank the drivers based on their significance and use the ISM-MICMAC to analyze the interrelationships among them. The findings indicate that favourable geographical location in terms of solar irradiation, government policy toward sustainable renewable energy, the need to reduce greenhouse gas emissions, and large bodies of water constitute the most significant drivers behind the sustainable development of solar energy in Bangladesh. This research is expected to contribute to the literature on sustainable solar energy development in a systematic way that can benefit both decision-makers and end-users

Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm.

Robust estimation of the number, location, and activity of multiple correlated brain sources has long been a challenging task in electromagnetic brain imaging from M/EEG data, one that is significantly impacted by interference from spontaneous brain activity, sensor noise, and other sources of artifacts. Recently, we introduced the Champagne algorithm, a novel Bayesian inference algorithm that has shown tremendous success in M/EEG source reconstruction. Inherent to Champagne and most other related Bayesian reconstruction algorithms is the assumption that the noise covariance in sensor data can be estimated from "baseline" or "control" measurements. However, in many scenarios, such baseline data is not available, or is unreliable, and it is unclear how best to estimate the noise covariance. In this technical note, we propose several robust methods to estimate the contributions to sensors from noise arising from outside the brain without the need for additional baseline measurements. The incorporation of these methods for diagonal noise covariance estimation improves the robust reconstruction of complex brain source activity under high levels of noise and interference, while maintaining the performance features of Champagne. Specifically, we show that the resulting algorithm, Champagne with noise learning, is quite robust to initialization and is computationally efficient. In simulations, performance of the proposed noise learning algorithm is consistently superior to Champagne without noise learning. We also demonstrate that, even without the use of any baseline data, Champagne with noise learning is able to reconstruct complex brain activity with just a few trials or even a single trial, demonstrating significant improvements in source reconstruction for electromagnetic brain imaging

Modelling of supply chain disruption analytics using an integrated approach: An emerging economy example

The purpose of this paper is to develop a framework to identify, analyze, and to assess supply chain disruption factors and drivers. Based on an empirical analysis, four disruption factor categories including natural, human-made, system accidents, and financials with a total of sixteen disruption drivers are identified and examined in a real-world industrial setting. This research utilizes an integrated approach comprising both the Delphi method and the fuzzy analytic hierarchy process (FAHP). To test this integrated method, one of the well-known examples in industrial contexts of developing countries, the ready-made garment industry in Bangladesh is considered. To evaluate this industrial example, a sensitivity analysis is conducted to ensure the robustness and viability of the framework in practical settings. This study not only expands the literature scope of supply chain disruption risk assessment but through its application in any context or industry will reduce the impact of such disruptions and enhance the overall supply chain resilience. Consequently, these enhanced capabilities arm managers the ability to formulate relevant mitigation strategies that are robust and computationally efficient. These strategies will allow managers to take calculated decisions proactively. Finally, the results reveal that political and regulatory instability, cyclones, labor strikes, flooding, heavy rain, and factory fires are the top six disruption drivers causing disruptions to the ready-made garment industry in Bangladesh

Behavioural factors for Industry 4.0 adoption: implications for knowledge-based supply chains

Industry 4.0 (I4.0) is a relatively new and still emerging concept. Due to its novelty, companies find it extremely difficult to adopt I4.0 and reap the full benefit of the digital transformation of the fourth industrial revolution. Even though challenges to I4.0 adoption are well explored, the extant literature has hardly investigated the numerous human-based behavioural factors that are fundamental for I4.0 adoption. Human experience, engagement, and dedication to I4.0 adoption are crucial due to the complex nature of human behaviour and can significantly affect the success of I4.0 adoption. To address the gap, this paper aims to unveil the indispensable behavioural factors for I4.0 adoption and portray a hierarchical relationship among these factors. An extensive literature review is conducted to identify behaviour critical for I4.0 adoption to operationalise this research. Then, a decision support framework based on the Delphi technique and a revised rough DEMATEL method is used to map the relationships among the behavioural factors. The results reveal that the most critical behavioural factor to I4.0 adoption is “communication,” which is followed by “I4.0 training” and “resistance to I4.0 initiatives”. This study substantiates the research on I4.0 adoption and assists in I4.0 adoption. I4.0 adoption is also essential for a country’s competitiveness; therefore, the paper will support relevant policy formulation

Room-temperature, mid-infrared (λ=4.7 μm) electroluminescence from single-stage intersubband GaAs-based edge emitters

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