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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 R 2 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The thick-shell mussel Mytilus coruscus serves as a common sessile intertidal species and holds economic significance as an aquatic organism. M. coruscus often endure higher temperatures than their ideal range during consecutive low tides in the spring. This exposure to elevated temperatures provides them with a thermal tolerance boost, enabling them to adapt to high-temperature events caused by extreme low tides and adverse weather conditions. This phenomenon is referred to as heat-hardening. Some related studies showed the phenomenon of heat-hardening in sessile intertidal species but not reported at the mechanism level based on transcriptome so far. In this study, physiological experiments, gene family identification and transcriptome sequencing were performed to confirm the thermotolerance enhancement based on heat-hardening and explore the mechanism in M. coruscus. A total of 2935 DEGs were identified and the results of the KEGG enrichment showed that seven heat-hardening relative pathways were enriched, including Toll-like receptor signal pathway, Arachidonic acid metabolism, and others. Then, 24 HSP70 members and 36 CYP2 members, were identified, and the up-regulated members are correlated with increasing thermotolerance. Finally, we concluded that the heat-hardening M. coruscus have a better thermotolerance because of the capability of maintaining the integrity and the phenomenon of vasodilation of the gill under thermal stress. Further, the physiological experiments yielded the same conclusions. Overall, this study confirms the thermotolerance enhancement caused by heat-hardening and reveals the survival strategy in M. coruscus. In addition, the conclusion provides a new reference for studying the intertidal species' heat resistance mechanisms to combat extreme heat events and the strategies for dealing with extreme weather in aquaculture under the global warming trend., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Soil organic matter (SOM) is an important index used to evaluate soil fertility and nutrient availability, and it is also an important component of precision agriculture. In this study, in order to quickly and efficiently estimate the SOM content of farmland soil, we took 190 farmland soil samples in Jingbian County and measureed the SOM content of the samples in the lab and collected the corresponding Vis-NIR spectroscopy data. Based on the six pretreatment methods, a competitive adaptive weighting algorithm (CARS) is used for characteristic wavelength selection. Random forest (RF) regression is used to establish the predictive SOM model. The results indicate that after the CARS algorithm screens the different spectral variables, the optimal variable sets of the seven spectral variables are 15, 40, 30, 23, 20, 26, and 23, respectively. The accuracy of the model is improved after the CARS algorithm screens the different spectral variables. A total of 15 characteristic variables from the 2151 spectral wavelengths were used as the optimal spectral variable subset; RF shortened the training time required during the SOM modeling process and dramatically improved the model's accuracy and predictive ability, and the R 2 of the validation set increased from 0.21 to 0.96, and the RPD increased from 0.46 to 3.02. The RPIQ increased from 1.25 to 4.41. Among the tested models, the CR-RF model produced the best results. The R 2 and RMSE values of the calibration set are 0.91 and 0.49, and the R 2 , RMSE, RPD, and RPIQ values of the validation set are 0.96, 0.51, 3.02, and 4.41, respectively. Accurate prediction of the SOM of the cultivated layer in the study area was realized., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The precise and nondestructive detection of leaf chlorophyll content is one key to assessing the health status of crops. The objective of this study was to develop a precision method for determining the leaf chlorophyll content in rape. A genetic algorithm (GA) combined with the partial least squares (PLS) method was used to establish a chlorophyll content PLS regression estimation model based on screening the characteristic spectral regions of chlorophyll. The results show that the characteristic bands of chlorophyll in rape are 510-535, 675-695, 905-965, 1025-1225, 1165-1175, 1295-1385, 1495-1765, 1875-1895, 1970-2145, and 2179-2185 nm. Based on the characteristics of each input spectrum, the Rv 2 and RPD values of the best model reached 0.97 and 5.41, respectively. This represented an increase of 0.20 and 3.42, respectively, over these values for the original full-spectrum model. The best model also achieved an RMSEP of 2.63 mg g -1 , which was only 3.59% of the total sample average and was 3.78 mg g -1 less than that of the original full-spectrum model. Therefore, the best model provided good prediction accuracy for the chlorophyll content of rape. The model based on the Log (1/R) spectral transformation performed best in terms of prediction accuracy. The genetic algorithm combined with the partial least squares method (GA-PLS) can effectively screen the characteristic bands of rape chlorophyll, reduce the number of variables in the model, and produce high estimation accuracy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Photoreceptors have high intrinsic metabolic demand and are exquisitely sensitive to metabolic perturbation. In addition, they shed a large portion of their outer segment lipid membranes in a circadian manner, increasing the metabolic burden on the outer retina associated with the resynthesis of cell membranes and disposal of the cellular cargo. Here, we demonstrate that deletion of both ABCA1 and ABCG1 in rod photoreceptors leads to age-related accumulation of cholesterol metabolites in the outer retina, photoreceptor dysfunction, degeneration of rod outer segments, and ultimately blindness. A high-fat diet significantly accelerates rod neurodegeneration and vision loss, further highlighting the role of lipid homeostasis in regulating photoreceptor neurodegeneration and vision., (Copyright © 2018 Ban et al.)