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Abstract Researchers and stakeholders have shown interest in heterogeneous composite biodiesel (HCB) due to its enhanced fuel properties and environmental friendliness (EF). The lack of high viscosity datasets for parent hybrid oils has hindered their commercialisation. Reliable models are lacking to optimise the transesterification parameters for developing HCB, and the scarcity of predictive models has affected climate researchers and environmental experts. In this study, basic fuel properties were analysed, and models were developed models for the yield of HCB and kinematic viscosity (KV) for composite biodiesel/neem castor seed oil methyl ester (NCSOME) using Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Statistical indices such as computed coefficient of determination (R2), root-mean-square-error (RMSE), standard error of prediction (SEP), mean average error (MAE), and average absolute deviation (AAD) were used to evaluate the effectiveness of the techniques. Emission models for NCSOME-diesel blends were also established. The study investigated the impact of optimised fuel types/NCSOME-diesel (10–30 vol%), ZnO nanoparticle dosage (400–800 ppm), engine speed (1100–1700 rpm), and engine load (10–30%) on emission characteristics and environmental friendliness indices (EFI) such as carbon monoxide (CO), Oxides of Nitrogen (NOx), and Unburnt Hydrocarbon (UHC) using Response Surface Methodology (RSM). The ANFIS model demonstrated superior performance in terms of R2, RMSE, SEP, MAE, and AAD compared to the ANN model in predicting yield and KV of HCB. The optimal emission levels for CO (49.26 ppm), NOx (0.5171 ppm), and UHC (2.783) were achieved with a fuel type of 23.4%, nanoparticle dosage of 685.432 ppm, engine speed of 1329.2 rpm, and engine load of 10% to ensure cleaner EFI. The hybrid ANFIS and ANN models can effectively predict and model fuel-related characteristics and improve the HCB process, while the RSM model can be a valuable tool for climate and environmental stakeholders in accurate forecasting and promoting a cleaner environment. The valuable datasets can also provide reliable information for strategic planning in the biodiesel and automotive industries.

The current paper aims to utilize non-Newtonian fluid and improve the cooling performance of turbine blades. To implement impinging fluid flow through a porous channel on a hot lower wall, in the first step, the rheology of non-Newtonian behavior is introduced. Then differential quadrature procedure is used to convert these highly nonlinear equations of motion to some simple algebraic expressions. There is a reasonable agreement between the present findings with previous research work. Finally, some vital parameters such as the cross-viscosity parameter and power law index are changed to evaluate how these factors improve the cooling performance of turbine blades. The findings show that a rising Prandtle number results in a 19% decrement in temperature pattern. For a constant cross-viscosity parameter, Reynolds number enhancement leads to wall friction augmentation of around 15%. Moreover, a 32% Nusselt number increment is observed by increasing the power law index for the same Reynolds number.

Researchers and stakeholders have shown interest in heterogeneous composite biodiesel (HCB) due to its enhanced fuel properties and environmental friendliness (EF). The lack of high viscosity datasets for parent hybrid oils has hindered their commercialisation. Reliable models are lacking to optimise the transesterification parameters for developing HCB, and the scarcity of predictive models has affected climate researchers and environmental experts. In this study, basic fuel properties were analysed, and models were developed models for the yield of HCB and kinematic viscosity (KV) for composite biodiesel/neem castor seed oil methyl ester (NCSOME) using Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Statistical indices such as computed coefficient of determination (R2), root-mean-square-error (RMSE), standard error of prediction (SEP), mean average error (MAE), and average absolute deviation (AAD) were used to evaluate the effectiveness of the techniques. Emission models for NCSOME-diesel blends were also established. The study investigated the impact of optimised fuel types/NCSOME-diesel (10–30 vol%), ZnO nanoparticle dosage (400–800 ppm), engine speed (1100–1700 rpm), and engine load (10–30%) on emission characteristics and environmental friendliness indices (EFI) such as carbon monoxide (CO), Oxides of Nitrogen (NOx), and Unburnt Hydrocarbon (UHC) using Response Surface Methodology (RSM). The ANFIS model demonstrated superior performance in terms of R2, RMSE, SEP, MAE, and AAD compared to the ANN model in predicting yield and KV of HCB. The optimal emission levels for CO (49.26 ppm), NOx (0.5171 ppm), and UHC (2.783) were achieved with a fuel type of 23.4%, nanoparticle dosage of 685.432 ppm, engine speed of 1329.2 rpm, and engine load of 10% to ensure cleaner EFI. The hybrid ANFIS and ANN models can effectively predict and model fuel-related characteristics and improve the HCB process, while the RSM model can be a valuable tool for climate and environmental stakeholders in accurate forecasting and promoting a cleaner environment. The valuable datasets can also provide reliable information for strategic planning in the biodiesel and automotive industries. [ABSTRACT FROM AUTHOR]

Simple Summary: The Wuzhishan pig breed exhibits three different coat colour phenotypes (white, black, and black-back/white-belly). The coat colour variation is worth studying. Sixty-one white, 96 black-back/white-belly, and 58 black Wuzhishan pigs were used for genome-wide association analysis. Ninety-seven genome-wide significant SNPs on the animal's chromosome 8 are related to its coat colour. The RAPGEF2, PDGFRA, and KIT genes were identified as candidate genes for coat colour variation in haplotype-sharing analysis and gene ontology analysis. SNP genotyping and mRNA expression analysis further confirmed RAPGEF2 and PDGFRA as potential candidate genes. KIT was confirmed as a potential candidate gene for white coat colour by duplication and splice mutation detection. Our study may also shed light on the complex genetic background of coat colour variation in animals. Despite identifying genes regulating the coat colour in Western pig breeds, the genetic basis of the coat colour in Chinese indigenous pigs is still not understood due to the diversity of indigenous breeds and their genetic differences from exotic pigs. In this study, 215 Wuzhishan pigs with three coat colour patterns (white, black, and black-back/white-belly) were used to conduct a genome-wide association analysis. We found that genes responsible for the coat colour in the Wuzhishan breed are located on chromosome 8. Ninety-seven genome-wide significant SNPs are related to the animal's coat colour. Using a haplotype-sharing analysis, we narrowed the potential candidate region to a 10.1 Mb interval encompassing only one gene, RAPGEF2, which participates in the regulation of melanogenesis. Two additional candidate genes, PDGFRA and KIT, are located within 1 Mb of the genome-wide significant SNPs. Gene ontology analysis and literature mining suggest that these candidate genes are associated with the animal's coat colour. mRNA expression results revealed that RAPGEF2 and PDGFRA had significantly higher expressions in black pigs than in white pigs and higher expressions in black skin than in white skin from the same black-back/white-belly pigs. These results suggest that RAPGEF2 and PDGFRA are potential candidate genes regulating the coat colour in Wuzhishan pigs. Interestingly, mutations of KIT (a gene duplication and a G to A substitution at the splicing site in intron 17) were detected in white Wuzhishan pigs but not in black-back/white-belly or black pigs, suggesting a close genetic relationship between white Wuzhishan pigs and Western white pig breeds. In summary, these results indicate that the expression of RAPGEF2 and PDGFRA may cause the coat colour variation by influencing the deposition of melanin, while the mutation of KIT causes the white coat colour. Our results may provide a theoretical basis for the breeding of white coat colour Wuzhishan pigs, and shed light on the complex genetic background of coat colour variations in indigenous Chinese pig breeds. [ABSTRACT FROM AUTHOR]

Although porcine liver contributes to their growth and development by nutrition production and energy supply, oxidative stress-induced hepatocyte damage is inevitable during metabolism. N-glycosylation is a common modification in oxidation; nevertheless, the effects of N-glycosylation on pig liver oxidative reactions remain undefined. In this study, liver proteins with N-glycosylation were detected in Wuzhishan (WZS) pigs between 4 and 8 months old and Large White (LW) pigs at 4 months old based on LC-MS/MS. The results showed that the number of differentially expressed proteins (DEPs) was larger between different pig cultivars than that between WZS pigs at various growth periods. The enriched pathways of DEPs were mainly related to oxidative reactions, and 10 proteins were finally selected that primarily consisted of CYPs, GSTs and HSPs with expressions significantly correlating to liver size and weight. The oxidative genes shared N-glycosylation-modified models of N-x-S and N-G. Five out of 10 proteins were upregulated in WZS pigs compared to LW pigs at 4 months old, while five proteins increased in WZS pigs from 4 to 8 months old. In conclusion, this research provides valuable information on the N-glycosylation motifs in liver oxidation genes of WZS pigs. [ABSTRACT FROM AUTHOR]

Independent component analysis (ICA) is a multivariate approach that has been widely used in analyzing brain imaging data. In the field of functional near-infrared spectroscopy (fNIRS), its promising effectiveness has been shown in both removing noise and extracting neuronal activity-related sources. The application of ICA remains challenging due to its complexity in usage, and an easy-to-use toolbox dedicated to ICA processing is still lacking in the fNIRS community. In this study, we propose NIRS-ICA, an open-source MATLAB toolbox to ease the difficulty of ICA application for fNIRS studies. NIRS-ICA incorporates commonly used ICA algorithms for source separation, user-friendly GUI, and quantitative evaluation metrics assisting source selection, which facilitate both removing noise and extracting neuronal activity-related sources. The options used in the processing can also be reported easily, which promotes using ICA in a more reproducible way. The proposed toolbox is validated and demonstrated based on both simulative and real fNIRS datasets. We expect the release of the toolbox will extent the application for ICA in the fNIRS community.

The mirror neuron system (MNS), mainly including the premotor cortex (PMC), inferior frontal gyrus (IFG), superior parietal lobule (SPL), and rostral inferior parietal lobule (IPL), has attracted extensive attention as a possible neural mechanism of social interaction. Owing to high ecological validity, functional near-infrared spectroscopy (fNIRS) has become an ideal approach for exploring the MNS. Unfortunately, for the feasibility of fNIRS to detect the MNS, none of the four dominant regions were found in previous studies, implying a very limited capacity of fNIRS to investigate the MNS. Here, we adopted an experimental paradigm in a real-life situation to evaluate whether the MNS activity, including four dominant regions, can be detected by using fNIRS. Specifically, 30 right-handed subjects were asked to complete a table-setting task that included action execution and action observation. A double density probe configuration covered the four regions of the MNS in the left hemisphere. We used a traditional channel-based group analysis and also a ROI-based group analysis to find which regions are activated during both action execution and action observation. The results showed that the IFG, adjacent PMC, SPL, and IPL were involved in both conditions, indicating the feasibility of fNIRS to detect the MNS. Our findings provide a foundation for future research to explore the functional role of the MNS in social interaction and various disorders using fNIRS.

Enterocytozoon bieneusi is an intestinal pathogen that infects a wide range of species, including humans. Cattle constitute an important host for E. bieneusi; however, there is a scarcity of information on the prevalence and genotyping of E. bieneusi in cattle in the Hainan Province of China. In this study, PCR analysis of 314 fecal samples from cattle in six cities of Hainan was performed for genotype identification. The average prevalence of E. bieneusi in these animals was 9.9% (31/314), and ranged from 0.0% (0/12) to 20.5% (8/39). Five known genotypes – EbpC (n = 14), BEB4 (n = 12), J (n = 2), I (n = 1), and CHG5 (n = 1) – and a novel genotype: HNC-I (n = 1) – were identified. Genotypes EbpC and HNC-I were placed in zoonotic Group 1, and the remaining four genotypes (BEB4, J, I, and CHG5) were placed in Group 2. Since 93.5% of the genotypes found in the cattle (29/31) (EbpC, BEB4, J, and I) have previously been found in humans, these genotypes are probably involved in the transmission of microsporidiosis to humans.

Enterocytozoon bieneusi is an important pathogen commonly found in humans and animals. Farmed animals with close contact to humans are important hosts of E. bieneusi. The role of goats in the transmission of E. bieneusi, however, remains unclear. In this study, 341 fresh fecal samples of black goats were collected from five locations in Hainan Province, China. Enterocytozoon bieneusi was identified and genotyped by sequences of the internal transcribed spacer (ITS) region. Phylogenetic analysis was performed by constructing a neighbor-joining tree of the ITS gene sequences. The average prevalence of E. bieneusi in black goats was 24.0% (82/341) with rates ranging from 6.3% (4/63) to 37.2% (32/86) across the locations (χ2 = 17.252, p