Natural resources, fiscal decentralization, and environmental quality in China: an empirical analysis from QARDL approach.

It is generally believed that fiscal decentralization is an essential way to increase the overall effectiveness and efficiency of the government through the transfer of financial autonomy to the local governments. On the same lines, this study attempts to confabulate the influence of two important economic indicators, (i) fiscal decentralization and (ii) natural resource rent in validation of the environmental Kuznets curve hypothesis. Our current analysis is grounded upon a developing economy - China - and will serve as a stepping stone for the similar economies. The time period for the empirical estimation is from 1990 to 2020. The study applied an advanced econometrics approach, called the quantile autoregressive distributed lag (QARDL), which has many advantages as compared to any conventional approach. The empirical outcomes, after estimations, illustrate that in the long run, FDE is having an unfavorable connotation with CO2 emissions. NRR is another essential factor influencing the CO2 emissions in the long run in the selected economy. The estimated outcomes are revealing the presence of the EKC. Furthermore, the present research elucidates the existence of the bi-directional causality between selected economic indicators, FDE and CO2 emissions, and GDP square and CO2 emissions. But there is a unidirectional causality between GDP and CO2 emissions. Thus, policymakers should encourage the transfer of powers to the lower tiers of government to ameliorate environmental quality in the Chinese economy.

Modelling wellbeing of farmers by using nexus of climate change risk perception, adaptation strategies, and their drivers on irrigation water in Pakistan.

This study aimed to determine the farmers' perceived impact of climate change on irrigation water and the adaptation measure adopted to mitigate its adverse effects. A binary logistic regression model was used to identified factors affecting the selection of adaptation measures. Partial Least Square-Structural Equation Modelling (PLS-SEM) was employed to compute the benefits of adaptation strategies. The study was conducted in two major cropping systems, i.e., the Cotton Wheat Cropping System (CWCS) and Rice Wheat Cropping System (RWCS) of Punjab, Pakistan, using primary data of 1080 farmers collected through a multistage sampling technique. Due to climate change there was deterioration in surface water and groundwater quality in CWCS than in RWCS. The farmer uses different adaptation strategies like water harvesting, crop diversification, increasing use of irrigation, laser land leveling to save water, making ridges, building a water harvesting scheme, changing irrigation time, high-efficiency irrigation system and water-saving technologies. Adaptation strategies used by farmer were affected by different socioeconomic, demographic and agronomic factors. Results of the binary logistic regression showed that age, farming experience, education, household size, farm size, tenancy status of owner, access to farm credit, information on weather forecasting, soil quality, tube well ownership, remittances, off-farm income, agricultural extension services provided for irrigation water, and information on climatic and natural hazards played a significant role in the selection of adaptation strategies for irrigation water. Results of PLS-SEM showed that adaptation strategies mitigate the adverse effects of climate change on irrigation water. Farmers' awareness regarding the impact of climatic variability on irrigation water should be enhanced. Availability of credit to farmers should be improved on easy terms to facilitate the adoption of interventions for better irrigation water management. It is high time for policymakers to design effective, affordable, and workable policies to mitigate climate change vulnerabilities against irrigation water to improve the wellbeing of the farmers.

Enhancement of healthful novel sugar contents in genetically engineered sugarcane juice integrated with molecularly characterized ThSyGII (CEMB-SIG2).

Enhancement of sugar contents and yielding healthful sugar products from sugarcane demand high profile scientific strategies. Previous efforts to foster manipulation in metabolic pathways or triggering sugar production through combating abiotic stresses fail to yield high sugar recovery in Saccharum officinarum L. Novel sucrose isomers trehalulose (TH) and isomaltulose (IM) are naturally manufactured in microbial sources. In pursuance of novel scientific methodology, codon optimized sucrose isomerase gene, Trehalulose synthase gene II(CEMB-SIG2) cloned under dual combined stem specific constitutive promoters in pCAMBIA1301 expression vector integrated with Vacuole targeted signal peptide (VTS) to concentrate gene product into the vacuole. The resultant mRNA expression obtained by Real Time PCR validated extremely increased transgene expression in sugarcane culms than leaf tissues. Overall sugar estimation from transgenic sugarcane lines was executed through refractometer. HPLC based quantifications of Trehalulose (TH) alongside different internodes of transgenic sugarcane confirmed the enhancement of boosted sugar concentrations in mature sugarcane culms. Trehalulose synthase gene II receptive sugarcane lines indicated the unprecedented impressions of duly combined constitutive stem regulated promoters. Transgenic sugarcane lines produce highest sugar recovery percentages, 14.9% as compared to control lines (8.5%). The increased sugar recovery percentage in transgenic sugarcane validated the utmost performance and expression of ThSyGII gene .High Profile Liquid chromatography based sugar contents estimation of Trehalulose (TH) and Isomaltulose (IM) yielded unprecedented improvement in the whole sugar recovery percentage as compared to control lines.⁠.

Silencing of multiple target genes via ingestion of dsRNA and PMRi affects development and survival in Helicoverpa armigera.

RNA interference (RNAi) triggered by exogenous double-stranded RNA (dsRNA) is a powerful tool to knockdown genetic targets crucial for the growth and development of agriculturally important insect pests. Helicoverpa armigera is a pest feeding on more than 30 economically important crops worldwide and a major threat. Resistance to insecticides and Bt toxins has been gradually increasing in the field. RNAi-mediated knockdown of H. armigera genes by producing dsRNAs homologous to genetic targets in bacteria and plants has a high potential for insect management to decrease agricultural loss. The acetylcholinesterase (AChE), ecdysone receptor (EcR) and v-ATPase-A (vAA) genes were selected as genetic targets. Fragments comprising a coding sequence of < 500 bp were cloned into the L4440 vector for dsRNA production in bacteria and in a TRV-VIGS vector in antisense orientation for transient expression of dsRNA in Solanum tuberosum leaves. After ingesting bacterial-expressed dsRNA, the mRNA levels of the target genes were significantly reduced, leading to mortality and abnormal development in larva of H. armigera. Furthermore, the S. tuberosum plants transformed with TRV-VIGS expressing AChE exhibited higher mortality > 68% than the control plants 17%, recorded ten days post-feeding and significant resistance in transgenic (transient) plants was observed. Moreover, larval lethality and molting defects were observed in larva fed on potato plants expressing dsRNA specific to EcR. Analysis of transcript levels by quantitative RT-PCR revealed that larval mortality was attributable to the knockdown of genetic targets by RNAi. The results demonstrated that down-regulation of H. armigera genes involved in ATP hydrolysis, transcriptional stimulation of development genes and neural conduction has aptitude as a bioinsecticide to control H. armigera population sizes and therefore decreases crop loss.

Prediction of Site Directed miRNAs as Key Players of Transcriptional Regulators Against Influenza C Virus Infection Through Computational Approaches.

MicroRNAs (miRNAs) are small non-coding RNAs that play critical roles in gene expression, cell differentiation, and immunity against viral infections. In this study, we have used the computational tools, RNA22, RNAhybrid, and miRanda, to predict the microRNA-mRNA binding sites to find the putative microRNAs playing role in the host response to influenza C virus infection. This computational research screened the following four miRNAs: hsa-mir-3155a, hsa-mir-6796-5p, hsa-mir-3194-3p and hsa-mir-4673, which were further investigated for binding site prediction to the influenza C genome. Moreover, multiple sites in protein-coding region (HEF, CM2, M1-M2, NP, NS1- NS2, NSF, P3, PB1 and PB2) were predicted by RNA22, RNAhybrid and miRanda. Furthermore, 3D structures of all miRNAs and HEF were predicted and checked for their binding potential through molecular docking analysis. The comparative results showed that among all proteins, HEF is higher in prevalence throughout the analysis as a potential (human-derived) microRNAs target. The target-site conservation results showed that core nucleotide sequence in three different strains is responsible for potential miRNA binding to different viral strains. Further steps to use these microRNAs may lead to new therapeutic insights on fighting influenza virus infection.

In silico identification of sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virus.

Sugarcane bacilliform virus (SCBV) is considered one of the most economically damaging pathogens for sugarcane production worldwide. Three open reading frames (ORFs) are characterized in the circular, ds-DNA genome of the SCBV; these encode for a hypothetical protein (ORF1), a DNA binding protein (ORF2), and a polyprotein (ORF3). A comprehensive evaluation of sugarcane (Saccharum officinarum L.) miRNAs for the silencing of the SCBV genome using in silico algorithms were carried out in the present study using mature sugarcane miRNAs. miRNAs of sugarcane are retrieved from the miRBase database and assessed in terms of hybridization with the SCBV genome. A total of 14 potential candidate miRNAs from sugarcane were screened out by all used algorithms used for the silencing of SCBV. The consensus of three algorithms predicted the hybridization site of sof-miR159e at common locus 5534. miRNA-mRNA interactions were estimated by computing the free-energy of the miRNA-mRNA duplex using the RNAcofold algorithm. A regulatory network of predicted candidate miRNAs of sugarcane with SCBV-ORFs, generated using Circos-is used to identify novel targets. The predicted data provide useful information for the development of SCBV-resistant sugarcane plants.

Target prediction of candidate miRNAs from Oryza sativa for silencing the RYMV genome.

In order to maintain a consistent supply of rice globally, control of pathogens affecting crop production is a matter of due concern. Rice yellow mottle virus(RYMV) is known to cause a variety of symptoms which can result in reduced yield. Four ORFs can be identified in the genome of RYMV encoding for P1 (ORF1), Polyprotein (processed to produce VPg, protease, helicase, RdRp4) (ORF2), putative RdRp (ORF3) and capsid/coat protein (ORF4). This research was aimed at identifying genome encoded miRNAs of O. sativa that are targeted to the genome of Rice Yellow Mottle Virus (RYMV). A consensus of four miRNA target prediction algorithms (RNA22, miRanda, TargetFinder and psRNATarget) was computed, followed by calculation of free energies of miRNA-mRNA duplex formation. A phylogenetic tree was constructed to portray the evolutionary relationships between RYMV strains isolated to date. From the consensus of algorithms used, a total of seven O. sativa miRNAs were predicted and conservation of target site was finally evaluated. Predicted miRNAs can be further evaluated by experiments involving the testing of the success of in vitro gene silencing of RYMV genome; this can pave the way for development of RYMV resistant rice varieties in the future.

In silico MCMV Silencing Concludes Potential Host-Derived miRNAs in Maize.

Maize Chlorotic Mottle Virus (MCMV) is a deleterious pathogen which causes Maize Lethal Necrosis Disease (MLND) that results in substantial yield loss of Maize crop worldwide. The positive-sense RNA genome of MCMV (4.4 kb) encodes six proteins: P32 (32 kDa protein), RNA dependent RNA polymerases (P50 and P111), P31 (31 kDa protein), P7 (7 kDa protein), coat protein (25 kDa). P31, P7 and coat protein are encoded from sgRNA1, located at the 3'end of the genome and sgRNA2 is located at the extremity of the 3'genome end. The objective of this study is to locate the possible attachment sites of Zea mays derived miRNAs in the genome of MCMV using four diverse miRNA target prediction algorithms. In total, 321 mature miRNAs were retrieved from miRBase (miRNA database) and were tested for hybridization of MCMV genome. These algorithms considered the parameters of seed pairing, minimum free energy, target site accessibility, multiple target sites, pattern recognition and folding energy for attachment. Out of 321 miRNAs only 10 maize miRNAs are predicted for silencing of MCMV genome. The results of this study can hence act as the first step towards the development of MCMV resistant transgenic Maize plants through expression of the selected miRNAs.