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The hard-shell mussel (Mytilus coruscus) is widespread in the temperate coastal areas of the northwest Pacific and holds a significant position in the shellfish aquaculture market in China. However, the natural resources of this species have been declining, and population genetic studies of M. coruscus are also lacking. In this study, we conducted whole-genome resequencing (WGR) of M. coruscus from eight different latitudes along the Chinese coast and identified a total of 25,859,986 single nucleotide polymorphism (SNP) markers. Our findings indicated that the genetic diversity of M. coruscus from the Zhoushan region was lower compared with populations from other regions. Furthermore, we observed that the evolutionary tree clustered into two primary branches, and the Zhangzhou (ZZ) population was in a separate branch. The ZZ population was partly isolated from populations in other regions, but the distribution of branches was not geographically homogeneous, and a nested pattern emerged, consistent with the population differentiation index (FST) results. To investigate the selection characteristics, we utilized the northern M. coruscus populations (Dalian and Qingdao) and the central populations (Zhoushan and Xiangshan) as reference populations and the southern ZZ population as the target population. Our selection scan analysis identified several genes associated with thermal responses, including Hsp70 and CYP450. These genes may play important roles in the adaptation of M. coruscus to different living environments. Overall, our study provides a comprehensive understanding of the genomic diversity of coastal M. coruscus in China and is a valuable resource for future studies on genetic breeding and the evolutionary adaptation of this species. [ABSTRACT FROM AUTHOR]

[Display omitted] • SOC prediction in the agro-pastoral transitional zone of northwest China was realised using VIS-NIRS and chemometrics. • Ten spectral transforms coupled with correlation analysis and the Boruta algorithm for feature wavelength selection. • SOC content prediction model based on Boruta-FD-RF was developed. • Integrating spectral preprocessing, feature selection and chemometrics strategies has good prediction accuracy and stability. Rapidly and accurately grasp the change of soil organic carbon content in farmland, which is of great significance in guiding the timely and effective mastery of farmland soil fertility and improvement of soil physical properties. In this study, an ASD FieldSpec 4 spectrometer was used to collect spectral reflectance data on 128 agricultural soil samples taken from Jingbian County, Yulin City, Shaanxi Province, China. Firstly, descriptive statistics of the SOC in the study area were performed, and secondly, after 10 spectral transformations were performed, the correlation analysis and the Boruta algorithm were used to extract the characteristic wavebands of soil organic carbon, respectively, in order to reduce the redundancy of the data. Finally, by comparing the accuracies of different strategies, we constructed a spectral prediction model of soil organic carbon in farmland of the Northwest Agricultural and Animal Husbandry Intertwined Zone that integrates the optimal preprocessing, feature selection strategy and modelling method. The results indicate that: 1) The mean SOC content of the farmland in the study area was low and at the nutrient deficient level, with the standard errors and coefficients of variation for the modelling and validation sets were 1.596 g kg−1, 1.457 g kg−1, 54 % and 52 %, respectively; 2) The shape and trend of spectral special curves with different SOC contents show consistency, and the SOC content is negatively correlated with spectral reflectance; 3) CA selects more feature bands, but the feature bands are more homogeneous, while the Boruta algorithm can effectively remove irrelevant variables and improve the SOC feature selection effect; 4) The SOC prediction model based on Boruta-FD-RF can be better for soil organic carbon estimation, with R2 of 0.899 and 0.748 for the training set and validation set, respectively, RMSE of 1.432 g kg−1 and 1.967 g kg−1, and RPD of 2.557 and 1.647, respectively. The results show that the SOC model established by integrating optimal spectral pre-processing, feature selection strategy and chemometrics strategy has obvious improvement in prediction accuracy and stability, and this study provides an important reference for the fast and accurate estimation of SOC content in farmland of Agro-pastoral Transitional zone in northwest China. [ABSTRACT FROM AUTHOR]

Critical factors like electric vehicle market penetration, battery chemistry, and battery capacity determine future critical material demand and recycling potential from electric vehicles. However, current studies largely ignored the heterogeneity among vehicle segments. This study introduces a vehicle-model-based big-data driven approach to unveil electric vehicle characteristics at fleet-wide and vehicle-segment levels. Further, a bottom-up model was built to project the battery material flow dynamics in 8 scenarios, identifying middle and large vehicle segments as primary contributors. In the Business-As-Usual (BAU) scenario, demand for lithium, nickel, cobalt, and manganese by 2050 will reach 182, 447, 61, and 44 kt, respectively, with recycled materials meeting 59 %, 58 %, 60 %, and 57 % of demand. The demand and recycling of critical materials have significant inherent uncertainty, with demand potentially fluctuating from one-quarter to twice the BAU level. Policies can greatly reduce primary resources for critical materials by guiding battery usage preferences and promoting recycling. [Display omitted] [ABSTRACT FROM AUTHOR]

Purpose: Visible-near-infrared spectroscopy (VIRS) is one of the most promising alternative techniques for soil organic matter (SOM) due to its direct response. In this study, partial least squares regression (PLSR), support vector machine (SVM), artificial neural networks (ANNs), and Cubist combined with VIRS were utilized to develop the calibration model and evaluate the ability of machine learning models to predict soil organic matter content. Materials and methods: A total of 190 surface soil samples (earth-cumulic-orthic anthrosols) were collected from the Weihe Plain of Shaanxi Province, China. The Kennard–Stone (KS) algorithm was employed to divide them into calibration and validation data. Moreover, the successive projections algorithm (SPA), competitive adaptive weight weighting algorithm (CARS), and their combination (SPA + CARS) were utilized to select characteristic wavelengths and improve the predictive ability of the model. Different evaluation indices, including root mean square error (RMSE), coefficient of determination (R2), the ratio of the performance to deviation (RPD), and the ratio of performance to interquartile range (RPIQ), were adopted to evaluate the accuracy of the model. Results and discussion: In all cases, the AFS-SPA + CARS-Cubist method outperformed the PLSR, SVM, and ANN. For the Cubist model, the Rv2, RPD, and RPIQ ranged from 0.8629 to 0.9782, 0.8720 to 3.0203, and 2.005 to 4.4164, respectively. According to the results, combining VIRS with Cubist could accurately determine the SOM of earth-cumulic-orthic anthrosol soils of the Weihe Plain, China. Furthermore, SPA + CARS provided more precise calibration–validation models than SPA and CARS. [ABSTRACT FROM AUTHOR]