Measurement of Heat and Pressure Induced Denaturation of Whey Protein Isolate Using Reversed-Phase HPLC and FTIR-Spectroscopy

The protein molecules experience various external stresses leading to denaturation of protein during the process of transforming original whey to the concentrated whey proteins or while the concentrated proteins are used in the protein-enriched food formulation. This study was designed for a comparative assessment of the denaturation of whey protein isolate (WPI) under an important thermal stress, isothermal heat treatment (IHT), and high hydrostatic pressure treatment (HPT). The type and extent of denaturation were determined using reversed-phase HPLC and FTIR spectroscopy. The HPLC results demonstrated that the isothermal heat treatment caused higher denaturation of protein due to IHT at 80oC for 600s (88.38%) than that of HPT (58.5%). However, the infra-red spectroscopic analyses suggested that the HPT caused severe destruction of the structural conformation of WPI. The state of protein has a great impact on food formation; hence, the findings of this study would alert the concentrate protein producers and protein-enriched food manufacturers to prepare more active functional foods. HIGHLIGHTS  Heat (IHT) and pressure (HPT) stresses caused 88% and 58% WPI to denature, respectively.  Reversed phase-HPLC determined the denatured protein through aggregation. FTIR together with HPLC is required for better characterization of denatured protein

Role of Secondary Metabolites to Attenuate Stress Damages in Plants

Plants are constantly facing various threats posed by the biotic and abiotic stressors. To survive in these challenged environment, plants evolve a variety of defense mechanism. Among the various phytochemicals, secondary metabolites (SMs) accumulate higher amount under stressful conditions and initiate signaling functions to up-regulation of defense responsive genes. SMs ensures the survival, persistence and competitiveness of the plant against the threat generated under stressful conditions. Therefore, the signaling functions of SMs to protect the plant from biotic and abiotic stressors are getting importance in the recent times. In this chapter the contribution of SMs to protect the plant from specific environmental stresses has been discussed

Biofilm Formation, Production of Matrix Compounds and Biosorption of Copper, Nickel and Lead by Different Bacterial Strains

Bacterial biofilms play a key role in metal biosorption from wastewater. Recently, Enterobacter asburiae ENSD102, Enterobacter ludwigii ENSH201, Vitreoscilla sp. ENSG301, Acinetobacter lwoffii ENSG302, and Bacillus thuringiensis ENSW401 were shown to form air–liquid (AL) and solid–air–liquid (SAL) biofilms in a static condition at 28 and 37°C, respectively. However, how environmental and nutritional conditions affect biofilm formation; production of curli and cellulose; and biosorption of copper (Cu), nickel (Ni), and lead (Pb) by these bacteria have not been studied yet. In this study, E. asburiae ENSD102, E. ludwigii ENSH201, and B. thuringiensis ENSW401 developed the SAL biofilms at pH 8, while E. asburiae ENSD102 and Vitreoscilla sp. ENSG301 constructed the SAL biofilms at pH 4. However, all these strains produced AL biofilms at pH 7. In high osmolarity and ½-strength media, all these bacteria built fragile AL biofilms, while none of these strains generated the biofilms in anaerobic conditions. Congo red binding results showed that both environmental cues and bacterial strains played a vital role in curli and cellulose production. Calcofluor binding and spectrophotometric results revealed that all these bacterial strains produced significantly lesser amounts of cellulose at 37°C, pH 8, and in high osmotic conditions as compared to the regular media, at 28°C, and pH 7. Metal biosorption was drastically reduced in these bacteria at 37°C than at 28°C. Only Vitreoscilla sp. ENSG301 and B. thuringiensis ENSW401 completely removed (100%) Cu and Ni at an initial concentration of 12.5 mg l–1, while all these bacteria totally removed (100%) Pb at concentrations of 12.5 and 25 mg l–1 at pH 7 and 28°C. At an initial concentration of 100 mg l–1, the removal of Cu (92.5 to 97.8%) and Pb (89.3 to 98.3%) was the highest at pH 6, while it was higher (84.7 to 93.9%) for Ni at pH 7. Fourier transform infrared spectroscopy results showed metal-unloaded biomass biofilms contained amino, hydroxyl, carboxyl, carbonyl, and phosphate groups. The peak positions of these groups were shifted responding to Cu, Ni, and Pb, suggesting biosorption of metals. Thus, these bacterial strains could be utilized to remove Cu, Ni, and Pb from aquatic environment

Comparative Study of Integrated Pest Management and Farmers Practices on Sustainable Environment in the Rice Ecosystem

Integrated pest management (IPM) is an environmentally friendly technology. IPM is a multifaceted approach to pest management that seeks to minimize negative impacts on the environment. This technique is an important step towards providing healthy, viable food for a growing global population. The focus of this study was to examine the impact of integrated pest management in a rice agroecosystem. Currently, more than 80% of farmers rely on pesticides. IPM methods employed in our study had an impact on the number of healthy tillers and hills and grain weight. The lowest percentage of dead heart (1.03) and white head (2.00) was found in the IPM treated plots. These plots had an average yield of 7.4 tonne/ha. We found that there were significant differences between the treatment and the observed percentage of dead heart, grain weight, and yield. We conclude that IPM practices are an effective strategy for obtaining high rice yields while protecting the environment and creating a more sustainable agroecosystem. Furthermore, the need for ongoing research and training on IPM methods will be essential for creating a sustainable rice agroecosystem

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