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In the real world, economic covariates follow asymmetric and time-varying patterns. Therefore, it is imperative to integrate these effects while estimating environmental and economic relationships. Although prevailing literature reveals various emissions-deriving and eliminating factors, however, there is a dearth of empirical evidence that estimates the asymmetric and time-varying effect of globalization, natural resources, and financial development from a multidimensional perspective in China. In doing so, we employ the nonlinear autoregressive distributed lag (NARDL) and cross-wavelet modeling framework to explore the long- and short-run nonlinear and time-variant association between globalization, natural resources, financial development, and carbon emissions from 1980 to 2017. The NARDL method has the benefit of discriminating the long-term and short-term asymmetric carbon emission responses due to a positive and negative shock in our primary variables of interest. Mainly, the findings of NARDL estimations confirm that positive shocks in globalization and financial developments have a significant positive impact on carbon emissions, whereas negative shock in natural resources has a significant positive impact on carbon emissions. Similarly, the outcomes of continuous wavelet transformation and wavelet transformation coherence confirm the causal linkages between covariates; however, this effect varies across different time and frequency domains. These results imply that environmental researchers should consider asymmetric transmission channels and time–frequency associations among variables to devise long-term sustainable policies. [ABSTRACT FROM AUTHOR]

Today, China is the second-largest, fastest-growing economy in the world. This study analyzes asymmetric and time-varying impact of world energy prices (including world energy prices index, world coal prices, world crude oil prices and world natural gas prices) on China's CO2 emissions. We used a non-linear ARDL (NARDL) model and wavelet analysis using monthly data from 1992 to 2017. The results based on the NARDL estimate show that world energy prices have an asymmetric impact on CO2 emissions. However, the results of wavelet pairwise correlation and wavelet-transform coherence suggest that the relationship between world energy prices and CO2 emissions differs over time and across sectors (i.e. short-term, medium-term, long-term and very long-term). Evidence suggests that ignoring fundamental non-linearities can lead to misleading outcomes. Such empirical results are expected to have a high importance for the efficient design and implementation of world energy prices and Chinese environmental policies. [ABSTRACT FROM AUTHOR]

Energy consumption and increasing CO2 emissions in China are mainly indorsed to the industrial sector. The objective of this study was to explore the main factors driving CO2 emissions in China's industry throughout 1991–2016. Based on the log-mean Divisia index (LMDI) method, this study decomposes the change of industry-related CO2 emissions into energy structure effect, income effect, energy intensity effect, carbon emission, and labor effect. The core results indicate that CO2 emissions in China's industry experienced a significant increase from 738.5 to 7271.8 Mt during 1991–2013, while it decreased to 6844.0 Mt in 2016. The income effect and labor effect are the top two emitters, which accounted for increases of 351.8 Mt and 57.8 Mt in CO2 emissions respectively. Additionally, the energy structure effect also played a role in increasing CO2 emissions. Energy intensity and carbon emission effects are the most important factors in reducing CO2 emissions. The policy suggestions about the different period-wise analyses in terms of economic growth, energy structure, and energy intensity are provided. [ABSTRACT FROM AUTHOR]

• Critical discussion on gelatin in halal-related research and adulteration authentication. • Existing analytical methods for the authentication of gelatin sources. • Detection of marker peptides of various species through LC-MS. • Challenges in the reported approaches for the selective and sensitive detection of gelatin. Gelatin is a water-soluble protein obtained from the collagen of various animal origins (porcine, bovine, fish, donkey, horse, and deer hide) and has diverse applications in the food, pharmaceutical, and cosmetics industries. Porcine and bovine gelatins are extensively used in food and non-food products; however, their acceptance is limited due to religious prohibitions, whereas fish gelatin is accepted in all religions. In Southeast Asia, especially in China, gelatin obtained from donkey and deer skins is used in medicines. However, both sources suffer from adulteration (mixing different sources of gelatin) due to their limited availability and high cost. Unclear labeling and limited information about actual gelatin sources in gelatin-containing products cause serious concern among societies for halal and fraud authentication of gelatin sources. Therefore, authenticating gelatin sources in gelatin-based products is challenging due to close similarities between the composition differences and degradation of DNA and protein biomarkers in processed gelatin. Thus, different methods have been proposed to identify and quantify different gelatin sources in pharmaceutical and food products. To the best of our knowledge, this systematic and comprehensive review highlights different authentication techniques and their limitations in gelatin detection and quantification in various commercial products. This review also describes halal authentication and adulteration prevention strategies of various gelatin sources, mainly focussing on research gaps, challenges, and future directions in this research area. [ABSTRACT FROM AUTHOR]

Soil extracellular enzymes play an important role in the biochemical cycling in the coastal tidal wetland. Previous results revealed the factors affecting the soil extracellular enzyme activity (EEA) in the terrestrial ecosystems with acid soils, while it is still unknown what factors mainly affect the EEAs in the tidal wetland with neutral or alkaline soils. To reveal the factors affecting the variations of soil EEAs along the tidal wetland with mosaic distributions of vegetation types, the activities of nine types of soil enzymes, whose functions closely related to the cycling of soil C, N and P, respectively, were investigated in the Chongming Dongtan tidal wetland, East China. The soil samples with different layers were collected from the unvegetated area (S1), Spartina alterniflora area (S2), Scirpus mariqueter area (S3), and Phragmites australis area (S4), respectively, over the high tidal zone (HT), mid-tidal zone (MT) and low tidal zone (LT), respectively. The results showed that the soil EEA profiles were observably (p < 0.05) among the vegetation types, soil layers and tidal levels, based on the multi-response permutation procedure (MRPP) method. Variation partitioning analysis revealed that soil properties, soil layer, tidal level and vegetation type explained 28.2% variation of EEAs. Soil properties explained 17.5% variation of EEAs. The most variations of EEAs explained by soil layer, tidal level and vegetation type were soil characteristics, and 3.6% variations of EEAs were explained by the others three variables. NH 3 –N, C/N and BD were the most important factors affecting the variations of EEAs. Therefore, the most variations of EEAs were well explained by the soil properties in the Dongtan tidal wetland, East China. [ABSTRACT FROM AUTHOR]

Background: Since the SARS-CoV-2 outbreak in December 2019 in Wuhan, China, the virus has infected more than 153 million individuals across the world due to its human-to-human transmission. The USA is the most affected country having more than 32-million cases till date. Sudden high fever, pneumonia and organ failure have been observed in infected individuals., Objectives: In the current situation of emerging viral disease, there is no specific vaccine, or any therapeutics available for SARS-CoV-2, thus there is a dire need to design a potential vaccine to combat the virus by developing immunity in the population. The purpose of present study was to develop a potential vaccine by targeting B and T-cell epitopes using bioinformatics approaches., Methods: B- and T-cell epitopes are predicted from novel M protein-SARS-CoV-2 for the development of a unique multiple epitope vaccine by applying bioinformatics approaches. These epitopes were analyzed and selected for their immunogenicity, antigenicity scores, and toxicity in correspondence to their ability to trigger immune response. In combination to epitopes, best multi-epitope of potential immunogenic property was constructed. The epitopes were joined using EAAAK, AAY and GPGPG linkers., Results: The constructed vaccine showed good results of worldwide population coverage and promising immune response. This constructed vaccine was subjected to in-silico immune simulations by C-ImmSim. Chimeric protein construct was cloned into PET28a (+) vector for expression study in Escherichia coli using snapgene., Conclusion: This vaccine design proved effective in various computer-based immune response analysis as well as showed good population coverage. This study is solely dependent on developing M protein-based vaccine, and these in silico findings would be a breakthrough in the development of an effective vaccine to eradicate SARS-CoV-2 globally., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Energy consumption and increasing CO 2 emissions in China are mainly indorsed to the industrial sector. The objective of this study was to explore the main factors driving CO 2 emissions in China's industry throughout 1991-2016. Based on the log-mean Divisia index (LMDI) method, this study decomposes the change of industry-related CO 2 emissions into energy structure effect, income effect, energy intensity effect, carbon emission, and labor effect. The core results indicate that CO 2 emissions in China's industry experienced a significant increase from 738.5 to 7271.8 Mt during 1991-2013, while it decreased to 6844.0 Mt in 2016. The income effect and labor effect are the top two emitters, which accounted for increases of 351.8 Mt and 57.8 Mt in CO 2 emissions respectively. Additionally, the energy structure effect also played a role in increasing CO 2 emissions. Energy intensity and carbon emission effects are the most important factors in reducing CO 2 emissions. The policy suggestions about the different period-wise analyses in terms of economic growth, energy structure, and energy intensity are provided.