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FCS-like zinc finger (FLZ) proteins are plant-specific regulatory proteins, which contain a highly conserved FLZ domain, and they play critical roles in plant growth and stress responses. Although the FLZ family has been systematically characterized in certain plants, it remains underexplored in Brassica species, which are vital sources of vegetables, edible oils, and condiments for human consumption and are highly sensitive to various abiotic stresses. Following the whole-genome triplication events (WGT) in Brassica, elucidating how the FLZ genes have expanded, differentiated, and responded to abiotic stresses is valuable for uncovering the genetic basis and functionality of these genes. In this study, we identified a total of 113 FLZ genes from three diploid Brassica species and classified them into four groups on the basis of their amino acid sequences. Additionally, we identified 109 collinear gene pairs across these Brassica species, which are dispersed among different chromosomes, suggesting that whole-genome duplication (WGD) has significantly contributed to the expansion of the FLZ family. Subcellular localization revealed that six representative BolFLZ proteins are located in the nucleus and cytoplasm. Yeast two-hybrid assays revealed that 13 selected BolFLZs interact with BolSnRK1α1 and BolSnRK1α2, confirming the conservation of the SnRK1α-FLZ module in Brassica species. Expression profile analysis revealed differential expression patterns of BolFLZ across various tissues. Notably, the expression levels of seven BolFLZ genes out of the fifteen genes analyzed changed significantly following treatment with various abiotic stressors, indicating that the BolFLZ genes play distinct physiological roles and respond uniquely to abiotic stresses in Brassica species. Together, our results provide a comprehensive overview of the FLZ gene family in Brassica species and insights into their potential applications for enhancing stress tolerance and growth in Chinese kale. [ABSTRACT FROM AUTHOR]

The purpose of crowd counting is to estimate the number of pedestrians in crowd images. Crowd counting or density estimation is an extremely challenging task in computer vision, due to large scale variations and dense scene. Current methods solve these issues by compounding multi-scale Convolutional Neural Network with different receptive fields. In this paper, a novel end-to-end architecture based on Multi-Scale Adversarial Convolutional Neural Network (MSA-CNN) is proposed to generate crowd density and estimate the amount of crowd. Firstly, a multi-scale network is used to extract the globally relevant features in the crowd image, and then fractionally-strided convolutional layers are designed for up-sampling the output to recover the loss of crucial details caused by the earlier max pooling layers. An adversarial loss is directly employed to shrink the estimated value into the realistic subspace to reduce the blurring effect of density estimation. Joint training is performed in an end-to-end fashion using a combination of Adversarial loss and Euclidean loss. The two losses are integrated via a joint training scheme to improve density estimation performance.We conduct some extensive experiments on available datasets to show the significant improvements and supremacy of the proposed approach over the available state-of-the-art approaches.

The purpose of crowd counting is to estimate the number of pedestrians in crowd images. Crowd counting or density estimation is an extremely challenging task in computer vision, due to large scale variations and dense scene. Current methods solve these issues by compounding multi-scale Convolutional Neural Network with different receptive fields. In this paper, a novel end-to-end architecture based on Multi-Scale Adversarial Convolutional Neural Network (MSA-CNN) is proposed to generate crowd density and estimate the amount of crowd. Firstly, a multi-scale network is used to extract the globally relevant features in the crowd image, and then fractionally-strided convolutional layers are designed for up-sampling the output to recover the loss of crucial details caused by the earlier max pooling layers. An adversarial loss is directly employed to shrink the estimated value into the realistic subspace to reduce the blurring effect of density estimation. Joint training is performed in an end-to-end fashion using a combination of Adversarial loss and Euclidean loss. The two losses are integrated via a joint training scheme to improve density estimation performance.We conduct some extensive experiments on available datasets to show the significant improvements and supremacy of the proposed approach over the available state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Due to the high price of whole-plant corn silage in southern China, many dairy farms are attempting to prepare whole-plant corn silage using corn stover and corn flour, but the mixing ratio has not yet been determined. Therefore, we mixed fresh corn stover and corn flour at the proportions of 100:0 (F0 group) to 80:20 (F20 group), using five groups with three replicates each. The optimal mixing ratio was determined by assessing the nutritional composition, fermentation quality, and bacterial community of silage after 45 days. The results showed that dry matter and water-soluble carbohydrates in silage increased linearly with the increasing ratio of corn flour (p < 0.01) while crude protein, true protein, neutral detergent fiber, acidic detergent fiber, and crude ash content decreased linearly (p < 0.01). The F0 group had the highest pH, and the mixing ratio quadratically affected ammonia nitrogen concentration (p < 0.05). Additionally, at the phylum level, the relative abundances of Proteobacteria and Cyanobacteria in the F15 group were significantly higher than in other groups (p < 0.05). At the genus level, Lactobacillus increased with corn flour inclusion compared to the F0 group. In conclusion, the silage quality is the best when the mixing ratio of fresh corn stover and corn flour is 85:15. [ABSTRACT FROM AUTHOR]

Besides the intensity and wavelength, the ability to analyze the optical polarization of detected light can provide a new degree of freedom for numerous applications, such as object recognition, biomedical applications, environmental monitoring, and remote sensing imaging. However, conventional filter-integrated polarimetric sensing systems require complex optical components and a complicated fabrication process, severely limiting their on-chip miniaturization and functionalities. Herein, the reconfigurable polarimetric photodetection with photovoltaic mode is developed based on a few-layer MoS2/PdSe2 heterostructure channel and a charge-trap structure composed of Al2O3/HfO2/Al2O3 (AHA)-stacked dielectrics. Because of the remarkable charge-trapping ability of carriers in the AHA stack, the MoS2/PdSe2 channel exhibits a high program/erase current ratio of 105 and a memory window exceeding 20 V. Moreover, the photovoltaic mode of the MoS2/PdSe2 Schottky diode can be operated and manipulable, resulting in high and distinct responsivities in the visible broadband. Interestingly, the linear polarization of the device can be modulated under program/erase states, enabling the reconfigurable capability of linearly polarized photodetection. This study demonstrates a new prototype heterostructure-based photodetector with the capability of both tunable responsivity and linear polarization, demonstrating great potential application toward reconfigurable photosensing and polarization-resolved imaging applications. [ABSTRACT FROM AUTHOR]

Lung adenocarcinoma (LUAD) is the most common and aggressive subtype of lung cancer, and coronavirus disease 2019 (COVID-19) has become a serious public health threat worldwide. Patients with LUAD and COVID-19 have a poor prognosis. Therefore, finding medications that can be used to treat COVID-19/LUAD patients is essential. Bioinformatics analysis was used to identify 20 possible metformin target genes for the treatment of COVID-19/LUAD. PTEN and mTOR may serve as hub target genes of metformin. Metformin may be able to cure COVID-19/LUAD comorbidity through energy metabolism, oxidoreductase NADH activity, FoxO signalling pathway, AMPK signalling system, and mTOR signalling pathway, among other pathways, according to the results of bioinformatic research. Metformin has ability to inhibit the proliferation of A549 cells, according to the results of colony formation and proliferation assays. In A549 cells, metformin increased glucose uptake and lactate generation, while decreasing ATP synthesis and the NAD+/NADH ratio. In summary, PTEN and mTOR may be potential targets of metformin for the treatment of COVID-19/LUAD. The mechanism by which metformin inhibits lung adenocarcinoma cell proliferation may be related to glucose metabolism regulated by PI3K/AKT signalling and mTOR signalling pathways. Our study provides a new theoretical basis for the treatment of COVID-19/LUAD. [ABSTRACT FROM AUTHOR]

This is a list of reviewers who served the Journal of Electronic Imaging in 2019. [ABSTRACT FROM AUTHOR]