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Shaker potassium channels play a crucial role in potassium (K+) nutrition and stress resistance in plants. However, systematic research on Shaker K+ channels in Chinese cabbage [Brassica rapa var. chinensis (L.) Kitamura] remains scarce. This study identified 13 Shaker K+ channel members within the cabbage genome, which are unevenly distributed across eight chromosomes. Notably, the number of Shaker K+ channel members in Chinese cabbage exceeds that found in the model plants Arabidopsis (9) and rice (10). This discrepancy is attributed to a higher number of homologous proteins in Groups II and V of Chinese cabbage, with gene segmental duplication in these two subgroups being a significant factor contributing to the expansion of the Shaker K+ channel gene family. Interspecies collinearity analysis revealed that the whole genome and the Shaker K+ channel family of Chinese cabbage show greater similarity to those of Arabidopsis than to those of rice, indicating that Shaker K+ channels from the Brassicaceae family have a closer relationship than that from the Poaceae family. Given that gene expansion occurs in Group II, we investigated whether a functional difference exists between BrKAT1.1 and BrKAT1.2 using yeast assays and promoter analysis. The expression of two BrKAT1 genes in the potassium uptake-deficient yeast mutant R5421 can restore growth under low potassium conditions, indicating their role in potassium absorption. Truncation of the N-terminal 63 amino acids of BrKAT1.2 resulted in the loss of potassium absorption capability, suggesting that the N-terminus is essential for maintaining the potassium absorption function of BrKAT1.2. Furthermore, the expression of the two BrKAT1 genes in the salt-sensitive yeast G19 enhances yeast tolerance to salt stress. These results demonstrate that BrKAT1.1 and BrKAT1.2 exhibit similar abilities in potassium uptake and salt tolerance. The difference between BrKAT1.1 and BrKAT1.2 lay in their promoter regulatory elements, suggesting that differences in transcriptional regulation contributed to the functional differentiation of BrKAT1.1 and BrKAT1.2. These findings provide a foundation for understanding the evolution and functional mechanisms of the Shaker K+ channel family in Chinese cabbage and for improving potassium nutrition and salt tolerance in this species through the manipulation of BrKAT1.

Hydrogen is now recognized as the primary alternative to fossil fuels due to its renewable, safe, high-energy density and environmentally friendly properties. Efficient hydrogen production through water splitting has laid the foundation for sustainable energy technologies. However, when hydrogen production is scaled up to industrial levels, operating at high current densities introduces unique challenges. It is necessary to design advanced electrocatalysts for hydrogen evolution reactions (HERs) under high current densities. This review will briefly introduce the challenges posed by high current densities on electrocatalysts, including catalytic activity, mass diffusion, and catalyst stability. In an attempt to address these issues, various electrocatalyst design strategies are summarized in detail. In the end, our insights into future challenges for efficient large-scale industrial hydrogen production from water splitting are presented. This review is expected to guide the rational design of efficient high-current density water electrolysis electrocatalysts and promote the research progress of sustainable energy.

In light of the satellite rejection environment and how aircraft can obtain high-precision positioning, this paper proposes a collaborative correction algorithm for aircraft based on the rank-defect network. Aiming at the problem of insufficient anchor points, which result in insufficient observations and the divergence of aircraft inertial navigation errors, this algorithm can effectively improve the navigation performance of cluster aircraft. On the basis of the observation information provided by the anchor aircraft, the observation information between aircraft is fully utilized to improve the observability of the aircraft cluster positioning method. At the same time, the pseudo-observation equation of heterogeneous aircraft cluster positioning is introduced, and the divergence of inertial navigation positioning errors caused by insufficient observations is suppressed by the pseudo-observation solution. On the basis of introducing the pseudo-observation equation, the inertial navigation error is solved and corrected by the Newton iterative method and the divergence of the inertial navigation position error is restrained. Compared with an aircraft cluster positioning method that does not use the inertial navigation error co-correction based on the pseudo-observation solution, this paper can achieve better overall cluster positioning accuracy when the available observations are insufficient, which is suitable for practical applications.

Temperature is an important factor in the physiological processes of aquatic organisms and can seriously affect several chemical and biological processes in their bodies, including respiratory metabolism, antioxidant capacity, immune capacity, and signal transduction. In this study, physiological, transcriptomic, and metabolomic analyses were used to investigate the response of Penaeus vannamei to cold stress. The results indicated that cold stress disrupted nucleotide metabolism and inhibited gluconeogenesis. However, shrimp exhibited response mechanisms in order to enhance cold tolerance, regulating changes in key genes and metabolites in amino acid and lipid metabolism to increase energy supply and protect cell membrane stability. In addition, the response included regulating Ca2+ pumps and ion channels to maintain intracellular ion homeostasis and osmotic balance. When the temperature dropped further, oxidative damage occurred due to overwhelming of the antioxidant defense system, and immune function was inhibited. This research provides some references regarding the molecular mechanisms involved in responding to cold stress and potential strategies to improve cold tolerance in P. vannamei; these are important references for studying the cold stress response of shrimp.

The emulsions prepared by three non-meat proteins, sodium caseinate (SC), soy protein isolate (SPI) and egg white protein (EPI), were individually added to the continuous phase of myofibrillar protein (MP) sol to form MP composite gels to simulate meat products. The research aimed to investigate the effects of Transglutaminase (TGase) on the physicochemical properties, microstructure and water phase distribution of non-meat protein emulsion MP composite gels. The results of this study revealed that TGase played a crucial role in forming a tight gel network structure in the composite gels. This enhanced their ability to retain water and improved their overall gel strength. Additionally, TGase increased the gel formation temperature of myofibrillar proteins. Electrophoresis analysis showed that when catalyzed by TGase, there was a lighter band compared to those not catalyzed by TGase. This indicated that the addition of TGase facilitated cross-linking interactions between meat proteins and non-meat proteins in the composite gels. Furthermore, microscopy observations demonstrated that composite gels treated with TGase exhibited a more uniform microstructure. This could be attributed to an acceleration in relaxation time T2. The uniform network structure restricted the movement of water molecules in the gel matrix, thereby improving its water-holding capacity. Overall, these findings highlight how incorporating non-meat proteins into myofibrillar systems can be effectively achieved through enzymatic treatment with TGase. Such modifications not only enhanced important functional properties but also contributed towards developing alternative meat products with improved texture and moisture retention abilities.

Brandt’s vole (Lasiopodomys brandtii) is a small herbivorous mammal that feeds on plants rich in secondary metabolites (PSMs), including tannins. However, plant defense mechanisms against herbivory by Brandt’s voles are not clearly established. This study aimed to investigate the effects of dietary tannic acid (TA) on the growth performance, intestinal morphology, digestive enzyme activities, cecal fermentation, intestinal barrier function, and gut microbiota in Brandt’s voles. The results showed that TA significantly hindered body weight gain, reduced daily food intake, changed the intestinal morphology, reduced digestive enzyme activity, and increased the serum zonulin levels (p < 0.05). The number of intestinal goblet and mast cells and the levels of serum cytokines and immunoglobulins (IgA, IgG, TNF-α, IL-6, and duodenal SlgA) were all reduced by TA (p < 0.05). Moreover, TA altered β-diversity in the colonic microbial community (p < 0.05). In conclusion, the results indicate that TA could damage the intestinal function of Brandt’s voles by altering their intestinal morphology, decreasing digestive ability and intestinal barrier function, and altering microbiota composition. Our study investigated the effects of natural PSMs on the intestinal function of wildlife and improved our general understanding of plant–herbivore interactions and the ecological role of PSMs.

Axonal varicosities or swellings are enlarged structures along axon shafts and profoundly affect action potential propagation and synaptic transmission. These structures, which are defined by morphology, are highly heterogeneous and often investigated concerning their roles in neuropathology, but why they are present in the normal brain remains unknown. Combining confocal microscopy and cryo-electron tomography (Cryo-ET) with in vivo and in vitro systems, we report that non-uniform mechanical interactions with the microenvironment can lead to 10-fold diameter differences within an axon of the central nervous system (CNS). In the brains of adult Thy1-YFP transgenic mice, individual axons in the cortex displayed significantly higher diameter variation than those in the corpus callosum. When being cultured on lacey carbon film-coated electron microscopy (EM) grids, CNS axons formed varicosities exclusively in holes and without microtubule (MT) breakage, and they contained mitochondria, multivesicular bodies (MVBs), and/or vesicles, similar to the axonal varicosities induced by mild fluid puffing. Moreover, enlarged axon branch points often contain MT free ends leading to the minor branch. When the axons were fasciculated by mimicking in vivo axonal bundles, their varicosity levels reduced. Taken together, our results have revealed the extrinsic regulation of the three-dimensional ultrastructures of central axons by the mechanical microenvironment under physiological conditions.

The effective estimation of mixed-layer depth (MLD) plays a significant role in the study of ocean dynamics and global climate change. However, the methods of estimating MLD still have limitations due to the sparse resolution of the observed data. In this study, a hybrid estimation method that combines the K-means clustering algorithm and an artificial neural network (ANN) model was developed using sea-surface parameter data in the Indian Ocean as a case study. The oceanic datasets from January 2012 to December 2019 were obtained via satellite observations, Argo in situ data, and reanalysis data. These datasets were unified to the same spatial and temporal resolution (1° × 1°, monthly). Based on the processed datasets, the K-means classifier was applied to divide the Indian Ocean into four regions with different characteristics. For ANN training and testing in each region, the gridded data of 84 months were used for training, and 12-month data were used for testing. The ANN results show that the optimized NN architecture comprises five input variables, one output variable, and four hidden layers, each of which has 40 neurons. Compared with the multiple linear regression model (MLR) with a root-mean-square error (RMSE) of 5.2248 m and the HYbrid-Coordinate Ocean Model (HYCOM) with an RMSE of 4.8422 m, the RMSE of the model proposed in this study was reduced by 27% and 22%, respectively. Three typical regions with high variability in their MLDs were selected to further evaluate the performance of the ANN model. Our results showed that the model could reveal the seasonal variation trend in each of the selected regions, but the estimation accuracy showed room for improvement. Furthermore, a correlation analysis between the MLD and input variables showed that the surface temperature and salinity were the main influencing factors of the model. The results of this study suggest that the pre-clustering ANN method proposed could be used to estimate and analyze the MLD in the Indian Ocean. Moreover, this method can be further expanded to estimate other internal parameters for typical ocean regions and to provide effective technical support for ocean researchers when studying the variability of these parameters.

Targets located at the same distance are easily neglected in most through-wall multiple targets detecting applications which use the single-input single-output (SISO) ultra-wideband (UWB) radar system. In this paper, a novel multiple targets vital signs tracking algorithm for through-wall detection using SISO UWB radar has been proposed. Taking advantage of the high-resolution decomposition of the Variational Mode Decomposition (VMD) based algorithm, the respiration signals of different targets can be decomposed into different sub-signals, and then, we can track the time-varying respiration signals accurately when human targets located in the same distance. Intensive evaluation has been conducted to show the effectiveness of our scheme with a 0.15 m thick concrete brick wall. Constant, piecewise-constant and time-varying vital signs could be separated and tracked successfully with the proposed VMD based algorithm for two targets, even up to three targets. For the multiple targets’ vital signs tracking issues like urban search and rescue missions, our algorithm has superior capability in most detection applications.

The application of physical processing technologies in fermentation is an effective way to improve the quality of substrates. The purpose of the study was to evaluate the feasibility of enhancing the polypeptides of rapeseed meal (RSM) by a low-intensity alternating magnetic field (LF-MF)-assisted solid-state fermentation. A protease-producing strain B16 from RSM was isolated and identified as Bacillus velezensis by analyzing its morphology and 16S rDNA sequencing. Then, it was employed in solid-state fermentation for polypeptide production. The results showed that the neutral protease activity could reach 147.48 U/mL when B.velezensis was cultured under suitable conditions. The protease activity increased rapidly on the 2.5th day of traditional fermentation, while the polypeptide yield reached the maximum on the third day. The highest polypeptides content was achieved by LF-MF-assisted fermentation at magnetic field intensity 140 Gs, treatment 4 h, magnetic field intervention after 16 h of inoculation, and rotation speed 50 rpm/min, which increased by 18.98% compared with traditional fermentation. Therefore, LF-MF-assisted fermentation effectively enhanced the polypeptide yield. The results suggested that LF-MF technology would be widely used to produce bioactive components from agro-industrial by-products.

Detection of the Fusarium head blight (FHB) is crucial for wheat yield protection, with precise and rapid FHB detection increasing wheat yield and protecting the agricultural ecological environment. FHB detection tasks in agricultural production are currently handled by cloud servers and utilize unmanned aerial vehicles (UAVs). Hence, this paper proposed a lightweight model for wheat ear FHB detection based on UAV-enabled edge computing, aiming to achieve the purpose of intelligent prevention and control of agricultural disease. Our model utilized the You Only Look Once version 4 (YOLOv4) and MobileNet deep learning architectures and was applicable in edge devices, balancing accuracy, and FHB detection in real-time. Specifically, the backbone network Cross Stage Partial Darknet53 (CSPDarknet53) of YOLOv4 was replaced by a lightweight network, significantly decreasing the network parameters and the computing complexity. Additionally, we employed the Complete Intersection over Union (CIoU) and Non-Maximum Suppression (NMS) to regress the loss function to guarantee the detection accuracy of FHB. Furthermore, the loss function incorporated the focal loss to reduce the error caused by the unbalanced positive and negative sample distribution. Finally, mixed-up and transfer learning schemes enhanced the model’s generalization ability. The experimental results demonstrated that the proposed model performed admirably well in detecting FHB of the wheat ear, with an accuracy of 93.69%, and it was somewhat better than the MobileNetv2-YOLOv4 model (F1 by 4%, AP by 3.5%, Recall by 4.1%, and Precision by 1.6%). Meanwhile, the suggested model was scaled down to a fifth of the size of the state-of-the-art object detection models. Overall, the proposed model could be deployed on UAVs so that wheat ear FHB detection results could be sent back to the end-users to intelligently decide in time, promoting the intelligent control of agricultural disease.

Language has been one of the most effective ways of human communication and information exchange. To solve the problem of non-contact robust speech recognition, recovery, and surveillance, this paper presents a speech recovery technology based on a 24 GHz portable auditory radar and webcam. The continuous-wave auditory radar is utilized to extract the vocal vibration signal, and the webcam is used to obtain the fitted formant frequency. The traditional formant speech synthesizer is selected to synthesize and recover speech, using the vocal vibration signal as the sound source excitation and the fitted formant frequency as the vocal tract resonance characteristics. Experiments on reading single English characters and words are carried out. Using microphone records as a reference, the effectiveness of the proposed speech recovery technology is verified. Mean opinion scores show a relatively high consistency between the synthesized speech and original acoustic speech.