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Summary: The fraudulent practice of beef adulteration is a growing concern, as it violates consumer rights. Electrical impedance spectroscopy (EIS) combined with machine learning has emerged as a widely used approach to identify low‐quality meat. Unlike traditional biochemical methods that require expensive instruments, complex sample preparation, and chemical reagents, EIS is a cost‐effective alternative. However, EIS data are susceptible to temperature fluctuations, requiring a waiting period under consistent temperature conditions for data stabilisation before measurements. This process becomes impractical when dealing with a large number of samples. To overcome this limitation, standardisation, normalisation, and smoothing methods were introduced in the meat quality detection based on EIS data. A recognition model for detecting carrageenan adulteration in beef was established. Under an inconsistent temperature condition, by applying the spectra pre‐processing methods to the prediction dataset, the model accuracy reached 84%, whereas the accuracy of the unprocessed prediction dataset dropped to 54%. This study demonstrates that acquiring EIS data under consistent temperature conditions is unnecessary if proper spectra pre‐processing methods are applied. By eliminating the waiting time for data stabilisation, this practical approach enhances the efficiency and accuracy of meat quality detection. [ABSTRACT FROM AUTHOR]

Summary: Unscrupulous merchants would sell cheap, low‐nutrition formula milk powder as pasteurised milk, or use it as a raw material in dairy products, for the purpose of making a profit. Currently, biochemical methods are utilised to identify the type of processed milk, which could involve chemical reagents, sample preparation and costly instruments. This paper investigates the utility of electrical impedance spectroscopy (EIS) in distinguishing different types of processed milk. Ultra‐high temperature sterilised milk, pasteurised milk and formula milk powder were studied. The one‐factor analysis of variance revealed that milk type had a significant influence on electrical properties, whereas sensory analysis, pH and total soluble solids showed poor discrimination of milk type. Recognition models were developed using Support Vector Classification, Partial Least Squares‐Discriminant Analysis (PLS‐DA), Gaussian Naive Bayes and Random Forest (RF). The nested cross‐validation and the external validation set (N = 45) indicated the RF model had the highest predicting accuracy of 97.8%; whereas PLS‐DA had a misclassification rate of 33.3% on positive samples and was, therefore, unsuitable for the authentication. The results demonstrated that the EIS data was featured enough for a small sample modelling (N = 75), and the established method was simple, fast and effective for liquid food classification. [ABSTRACT FROM AUTHOR]

Summary: Divergent selection of populations in contrasting environments leads to functional genomic divergence. However, the genomic architecture underlying heterogeneous genomic differentiation remains poorly understood. Here, we de novo assembled two high‐quality wild barley (Hordeum spontaneum K. Koch) genomes and examined genomic differentiation and gene expression patterns under abiotic stress in two populations. These two populations had a shared ancestry and originated in close geographic proximity but experienced different selective pressures due to their contrasting micro‐environments. We identified structural variants that may have played significant roles in affecting genes potentially associated with well‐differentiated phenotypes such as flowering time and drought response between two wild barley genomes. Among them, a 29‐bp insertion into the promoter region formed a cis‐regulatory element in the HvWRKY45 gene, which may contribute to enhanced tolerance to drought. A single SNP mutation in the promoter region may influence HvCO5 expression and be putatively linked to local flowering time adaptation. We also revealed significant genomic differentiation between the two populations with ongoing gene flow. Our results indicate that SNPs and small SVs link to genetic differentiation at the gene level through local adaptation and are maintained through divergent selection. In contrast, large chromosome inversions may have shaped the heterogeneous pattern of genomic differentiation along the chromosomes by suppressing chromosome recombination and gene flow. Our research offers novel insights into the genomic basis underlying local adaptation and provides valuable resources for the genetic improvement of cultivated barley. [ABSTRACT FROM AUTHOR]

Abstract: The circadian clock enables organisms to rapidly adapt to the ever‐changing environmental conditions that are caused by daily light/dark cycles. Circadian clock genes universally affect key agricultural traits, particularly flowering time. Here, we show that OsPRR37, a circadian clock gene, delays rice flowering time in an expression level‐dependent manner. Using high‐throughput mRNA sequencing on an OsPRR37 overexpressing transgenic line (OsPRR37‐OE5) and the recipient parent Guangluai4 that contains the loss‐of‐function Osprr37, we identify 14,992 genes that display diurnal rhythms, which account for 52.9% of the transcriptome. Overexpressing OsPRR37 weakens the transcriptomic rhythms and alters the phases of rhythmic genes. In total, 3,210 differentially expressed genes (DEGs) are identified, among which 1,863 rhythmic DEGs show a correlation between the change of absolute amplitudes and the mean expression levels. We further reveal that OsPRR37 functions as a transcriptional repressor to repress the expression levels and amplitudes of day‐phased clock genes. More importantly, OsPRR37 confers expanded regulation on the evening‐phased rhythmic DEGs by repressing the morning‐phased rhythmic DEGs. Further study shows that OsPRR37 expands its regulation on flowering pathways by repressing Ehd1. Thus, our results demonstrate an expanded regulation mechanism of the circadian clock on the diurnal rhythms of the transcriptome. [ABSTRACT FROM AUTHOR]