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The issue of nitrogen fertilizer contamination resulting from high-protein diets can be effectively solved by adopting low-protein diets. The objective of this study was to investigate the effects of inorganic zinc supplementation in low-protein diets on 42-day-old broilers across a multitude of parameters. To determine the optimal dose of inorganic zinc in broiler diets with a 1.5% reduction in crude protein, 1-day-old Arbor Acres broilers (n = 270) were randomly assigned to five groups, each containing 54 broilers. Our results revealed that inorganic zinc supplementation at levels of 130 mg/kg elevated growth performance and carcass traits (p < 0.05). It also significantly increased the ratio of intestinal villi heights to crypt depths (p < 0.001), changed intestinal morphology, and significantly increased albumin content in serum (p < 0.05). Furthermore, analysis of mRNA expression showed that 130 mg/kg and 150 mg/kg of inorganic zinc improved the myogenic differentiation involved in muscle development, as well as intestinal tight junction and liver metallothionein capacity (p < 0.001). Additionally, these groups exhibited lower zinc excretion compared with other treatments (p < 0.001). In summary, our findings suggest that inorganic zinc supplementation in low-protein diets holds the potential to support muscle and intestinal development in broilers, presenting a viable nutritional strategy.

During the perinatal period, the bovine mammary epithelial cells of dairy cows exhibit vigorous metabolism and produce large amounts of reactive oxygen species (ROS). The resulting redox balance disruption leads to oxidative stress, one of the main causes of mastitis. Puerarin (PUE) is a natural flavonoid in the root of PUE that has attracted extensive attention as a potential antioxidant. This study first investigated whether PUE could reduce oxidative damage and mastitis induced by hydrogen peroxide (H2O2) in bovine mammary epithelial cells in vitro and elucidated the molecular mechanism. In vitro, BMECs (Bovine mammary epithelial cells) were divided into four treatment groups: Control group (no treatment), H2O2 group (H2O2 stimulation), PUE + H2O2 group (H2O2 stimulation before PUE rescue) and PUE group (positive control). The growth of BMECs in each group was observed, and oxidative stress-related indices were detected. Fluorescence quantitative PCR (qRT–PCR) was used to detect the expression of tightly linked genes, antioxidant genes, and inflammatory factors. The expression of p65 protein was detected by Western blot. In vivo, twenty cows with an average age of 5 years having given birth three times were divided into the normal dairy cow group, normal dairy cow group fed PUE, mastitis dairy cow group fed PUE, and mastitis dairy cow group fed PUE (n = 5). The contents of TNF-α, IL-6, and IL-1β in milk and serum were detected. In BMECs, the results showed that the PUE treatment increased the activities of glutathione (GSH), superoxide dismutase (SOD), catalase (CAT), and total antioxidant capacity (T-AOC); ROS and malondialdehyde (MDA) levels were reduced. Thus, PUE alleviated H2O2-induced oxidative stress in vitro. In addition, the PUE treatment eliminated the inhibition of H2O2 on the expression of oxidation genes and tight junction genes, and the enrichment degree of NRF-2, HO-1, xCT, and tight junctions (claudin4, occludin, ZO-1 and symplekin) increased. The PUE treatment also inhibited the expression of NF-κB-associated inflammatory factors (IL-6 and IL-8) and the chemokine CCL5 in H2O2-induced BMECs. In vivo experiments also confirmed that feeding PUE can reduce the expression of inflammatory factors in the milk and serum of lactating dairy cows. In conclusion, PUE can effectively reduce the oxidative stress of bovine mammary epithelial cells, enhance the tight junctions between cells, and play an anti-inflammatory role. This study provides a theoretical basis for PUE prevention and treatment of mastitis and oxidative stress. The use of PUE should be considered as a feed additive in future dairy farming.

Aflatoxin B1 (AFB1), a typical fungal toxin found in feed, is highly carcinogenic. Oxidative stress is one of the main ways it exerts its toxicity; therefore, finding a suitable antioxidant is the key to reducing its toxicity. Astaxanthin (AST) is a carotenoid with strong antioxidant properties. The aim of the present research was to determine whether AST eases the AFB1-induced impairment in IPEC-J2 cells, and its specific mechanism of action. AFB1 and AST were applied to IPEC-J2 cells in different concentrations for 24 h. The AST (80 µM) significantly prevented the reduction in the IPEC-J2 cell viability that was induced by AFB1 (10 μM). The results showed that treatment with AST attenuated the AFB1-induced ROS, and cytochrome C, the Bax/Bcl2 ratio, Caspase-9, and Caspase-3, which were all activated by AFB1, were among the pro-apoptotic proteins which were diminished by AST. AST activates the Nrf2 signaling pathway and ameliorates antioxidant ability. This was further evidenced by the expression of the HO-1, NQO1, SOD2, and HSP70 genes were all upregulated. Taken together, the findings show that the impairment of oxidative stress and apoptosis, caused by the AFB1 in the IPEC-J2 cells, can be attenuated by AST triggering the Nrf2 signaling pathway.

Persona, a special means of image management by social media influencers (SMIs), has become a trending phenomenon and it is expected to substantially affect SMIs’ persuasiveness and ad effect. This study aims to explore the impact of SMIs’ personas constructed through personal values on the effectiveness of social media video ads. Adopting the Stimulus-Organism-Response (S-O-R) framework, this study validates how SMIs’ personas constructed through self-enhancement values and self-transcendence values affect consumer cognitive processes and responses. Consumer behavioral intentions are used to verify the effectiveness of ads derived by SMIs. The findings reveal that influencers’ personas constructed by values can promote consumer purchase intention and forwarding intention. Consumer perceptions of self (i.e., ideal self and actual self) have a mediating role. Results also show that SMIs’ personas with self-enhancement values are more effective in stimulating female consumers’ forwarding intention, whereas personas with self-transcendence values are more influential in female consumers’ purchase intention. This study enriches the influencer marketing literature by providing a more tailored understanding of the impact of SMIs’ personas and extending the applicability of value theory. It also provides insights for stakeholders involved in influencer marketing on how to significantly capitalize on the benefits and deploy marketing campaigns.

Since the first discovery of grapevine brown leaf spot disease in Turpan, Xinjiang, China, in 2007, it has been a common occurrence in grapevine-growing regions. Grapevine brown leaf spot seriously decreases fruit yield and has become one of the most important leaf diseases in the Turpan region. However, thus far, there have been no reports on the evaluation of grapevine germplasm resources for resistance to brown leaf spot disease. In addition, the response mechanism of grapevine leaves to brown leaf spot infection has not been revealed. To better understand the resistance of grapevine germplasm resources to brown leaf spot disease, we first evaluated resistance in grapevine varieties. On this basis, the most susceptible variety ‘Sultanina’ was selected as the experimental material for this study. Transcriptome analysis and carbohydrate content analysis were performed on ‘Sultanina’ leaves with different levels of disease susceptibility. As the severity of the disease increased, the content of fructose gradually decreased, while the content of trehalose gradually increased. Transcriptome data revealed that differentially expressed genes were enriched in the pentose and glucuronate interchange pathway. These results suggest that the sugar trehalose may play an important role in the response of ‘Sultanina’ leaves to brown leaf spot infection. In addition, the pentose and glucuronate interchange pathway may be involved in the response mechanism of brown leaf spot disease. Our work provides new insights into the mechanism of the ‘Sultanina’ leaf response to brown leaf spot infection.

Abstract Computational super-resolution methods, including conventional analytical algorithms and deep learning models, have substantially improved optical microscopy. Among them, supervised deep neural networks have demonstrated outstanding performance, however, demanding abundant high-quality training data, which are laborious and even impractical to acquire due to the high dynamics of living cells. Here, we develop zero-shot deconvolution networks (ZS-DeconvNet) that instantly enhance the resolution of microscope images by more than 1.5-fold over the diffraction limit with 10-fold lower fluorescence than ordinary super-resolution imaging conditions, in an unsupervised manner without the need for either ground truths or additional data acquisition. We demonstrate the versatile applicability of ZS-DeconvNet on multiple imaging modalities, including total internal reflection fluorescence microscopy, three-dimensional wide-field microscopy, confocal microscopy, two-photon microscopy, lattice light-sheet microscopy, and multimodal structured illumination microscopy, which enables multi-color, long-term, super-resolution 2D/3D imaging of subcellular bioprocesses from mitotic single cells to multicellular embryos of mouse and C. elegans.

Abstract Detection noise significantly degrades the quality of structured illumination microscopy (SIM) images, especially under low-light conditions. Although supervised learning based denoising methods have shown prominent advances in eliminating the noise-induced artifacts, the requirement of a large amount of high-quality training data severely limits their applications. Here we developed a pixel-realignment-based self-supervised denoising framework for SIM (PRS-SIM) that trains an SIM image denoiser with only noisy data and substantially removes the reconstruction artifacts. We demonstrated that PRS-SIM generates artifact-free images with 20-fold less fluorescence than ordinary imaging conditions while achieving comparable super-resolution capability to the ground truth (GT). Moreover, we developed an easy-to-use plugin that enables both training and implementation of PRS-SIM for multimodal SIM platforms including 2D/3D and linear/nonlinear SIM. With PRS-SIM, we achieved long-term super-resolution live-cell imaging of various vulnerable bioprocesses, revealing the clustered distribution of Clathrin-coated pits and detailed interaction dynamics of multiple organelles and the cytoskeleton.

Straw cellulose is an abundant renewable resource in nature. In recent years, the conversion of cellulose from waste straw into biofuel by specific microorganisms’ fragmentation has attracted extensive attention. Although many bacteria with the ability to degrade cellulose have been identified, comprehensive bioinformatics analyses of these bacteria remain limited, and research exploring optimal fragmentation conditions is scarce. Our study involved the isolation and screening of bacteria from various locations in Yangzhou using carboxymethyl cellulose (CMC) media. Then, the cellulose-degrading bacteria were identified using 16S rRNA and seven candidate bacterial strains with cellulose degrading ability were identified in Yangzhou city for the first time. The cellulase activity was determined by the 3,5-dinitrosalicylic acid (DNS) method in different fragmentation conditions, and finally two bacteria strains with the strongest cellulose degradation ability were selected for whole genome sequencing analysis. Sequencing results revealed that the genome sizes of Rhodococcus wratislaviensis YZ02 and Pseudomonas Xanthosomatis YZ03 were 8.51 Mb and 6.66 Mb, containing 8,466 and 5,745 genes, respectively. A large number of cellulose degradation-related genes were identified and annotated using KEGG, GO and COG analyses. In addition, genomic CAZyme analysis indicated that both R. wratislaviensis YZ02 and P. Xanthosomatis YZ03 harbor a series of glycoside hydrolase family (GH) genes and other genes related to cellulose degradation. Our finding provides new options for the development of cellulose-degrading bacteria and a theoretical basis for improving the cellulose utilization of straw.

Abstract Background Cryptosporidium spp. are common protozoa causing diarrhea in humans and animals. There are currently only one FDA-approved drug and no vaccines for cryptosporidiosis, largely due to the limited knowledge of the molecular mechanisms involved in the invasion of the pathogens. Previous studies have shown that GP60, which is cleaved into GP40 and GP15 after expression, is an immunodominant mucin protein involved in the invasion of Cryptosporidium. The protein is highly O-glycosylated, and recombinant proteins expressed in prokaryotic systems are non-functional. Therefore, few studies have investigated the function of GP40 and GP15. Methods To obtain recombinant GP40 with correct post-translational modifications, we used CRISPR/Cas9 technology to insert GP40 and GP15 into the UPRT locus of Toxoplasma gondii, allowing heterologous expression of Cryptosporidium proteins. In addition, the Twin-Strep tag was inserted after GP40 for efficient purification of GP40. Results Western blotting and immunofluorescent microscopic analyses both indicated that GP40 and GP15 were stably expressed in T. gondii mutants. GP40 localized not only in the cytoplasm of tachyzoites but also in the parasitophorous vacuoles, while GP15 without the GPI anchor was expressed only in the cytoplasm. In addition, a large amount of recTgGP40 was purified using Strep-TactinXT supported by a visible band of ~ 50 kDa in SDS-PAGE. Conclusions The establishment of a robust and efficient heterologous expression system of GP40 in T. gondii represents a novel approach and concept for investigating Cryptosporidium mucins, overcoming the limitations of previous studies that relied on unstable transient transfection, which involved complex steps and high costs. Graphical Abstract

The study of genre preferences of different doctors by leveraging their handled computed tomography (CT) images from multiple hospital databases offers a compelling field of AI-based medical treatment, especially in terms of deciphering and categorizing different diseases and the diseased regions. This research introduces a technique to categorize a large number of medical images into defined groups or “circles” based on common genre preferences, such as “heart diseases” or “osteoarthritis”. Our findings underscore two primary factors: 1) the necessity for an adaptable genre model that can adjust to varied visual characteristics depending on the database, and 2) the inconsistency in the volume of CT images per hospital, with some possessing very few image collection for some particular diseases (for example, a heart hospital has very few joint fracture CT images). To tackle these issues, we propose a regularized latent probabilistic model to depict each doctor’s genre-relevant features as a distribution within a latent manifold space. We subsequently develop a graph that reflects the genre similarities among different doctors. Employing an advanced technique for densely connected graph discovery, we are able to cluster doctors with similar genre preferences into circles. Our experimental outcomes, based on a review of CT image datasets from over 6000 hospitals, validate the effectiveness of our method in precisely distinguishing doctors/users between different genre types. Based on this, various applications can be enhanced.

Image segmentation has demonstrated immense potential in computer vision. In particular, the U-Net architecture, built on fully convolutional networks, is highly suitable for image segmentation tasks. Its encoder-decoder structure effectively captures both local and global features. This approach has achieved remarkable outcomes across various sectors, most notably in medical diagnostics and industrial quality control. However, U-Net, by employing skip connections, fuses different low-level and high-level convolutional features between the encoder and decoder, limiting its ability to effectively integrate useful features and harness contextual information. To address these feature disparities between the encoder and decoder, this paper introduces a novel network structure named Multi-Convolutional Channel Residual Spatial Attention U-Net (MCRSAU-Net). Designed for industrial and medical image segmentation, this model is anchored on the U-Net architecture. It replaces the traditional skip connections with channel attention residual paths featuring multiple convolutions, retaining more low-level features. Moreover, spatial attention module is incorporated in the decoding path to ensure the model concentrates on crucial regions of the input space, enhancing its segmentation capability across varied tasks. The proposed method was subjected to 5-fold cross-validation and testing on three public datasets: Mvtec AD, CHASE DB1, and Kvasir SEG. MCRSAU-Net achieved average Dice coefficients of 0.7755, 0.7651, and 0.8958 for defect segmentation of bottles, woods, and tiles, respectively, with average accuracies reaching 0.9751, 0.9815, and 0.9841. For retinal blood vessel and colon polyp segmentation, it exhibited superior performance, achieving average Dice scores of 0.8540 and 0.7053, and average accuracies of 0.9465 and 0.9195, respectively. These results not only underscore MCRSAU-Net’s strong performance in image segmentation tasks but also demonstrate its significant potential in addressing specific challenges encountered in industrial and medical image segmentation.

Environment 3D modeling is critical for the development of future intelligent unmanned systems. This paper proposes a multi-sensor robotic system for environmental geometric-physical modeling and the corresponding data processing methods. The system is primarily equipped with a millimeter-wave cascaded radar and a multispectral camera to acquire the electromagnetic characteristics and material categories of the target environment and simultaneously employs light detection and ranging (LiDAR) and an optical camera to achieve a three-dimensional spatial reconstruction of the environment. Specifically, the millimeter-wave radar sensor adopts a multiple input multiple output (MIMO) array and obtains 3D synthetic aperture radar images through 1D mechanical scanning perpendicular to the array, thereby capturing the electromagnetic properties of the environment. The multispectral camera, equipped with nine channels, provides rich spectral information for material identification and clustering. Additionally, LiDAR is used to obtain a 3D point cloud, combined with the RGB images captured by the optical camera, enabling the construction of a three-dimensional geometric model. By fusing the data from four sensors, a comprehensive geometric-physical model of the environment can be constructed. Experiments conducted in indoor environments demonstrated excellent spatial-geometric-physical reconstruction results. This system can play an important role in various applications, such as environment modeling and planning.

Lunar exploration is of significant importance in the development and utilization of in situ lunar resources, water ice exploration, and astronomical science. In recent years, ground-based radar (GBR) has gained increasing attention in the field of lunar exploration due to its flexibility, low cost, and penetrating capabilities. This paper reviews the scientific research on lunar exploration using GBR, outlining the basic principles of GBR and the progress made in lunar exploration studies. Our paper introduces the fundamental principles of lunar imaging using GBR and systematically reviews studies on lunar surface/subsurface detection, the dielectric properties inversion of the lunar regolith, and polar water ice detection using GBR. In particular, the paper summarizes the current development status of the Chinese GBR and forecasts future development trends in China. This review will enhance the understanding of lunar exploration results using GBR radar, systematically demonstrate the main applications and scientific achievements of GBR in lunar exploration, and provide a reference for GBR radar in future lunar exploration missions.

In the field of computer-assisted medical diagnosis, developing medical image segmentation models that are both accurate and capable of real-time operation under limited computational resources is crucial. Particularly for skin disease image segmentation, the construction of such lightweight models must balance computational cost and segmentation efficiency, especially in environments with limited computing power, memory, and storage. This study proposes a new lightweight network designed specifically for skin disease image segmentation, aimed at significantly reducing the number of parameters and floating-point operations while ensuring segmentation performance. The proposed ConvStem module, with full-dimensional attention, learns complementary attention weights across all four dimensions of the convolution kernel, effectively enhancing the recognition of irregularly shaped lesion areas, reducing the model’s parameter count and computational burden, thus promoting model lightweighting and performance improvement. The SCF Block reduces feature redundancy through spatial and channel feature fusion, significantly lowering parameter count while improving segmentation results. This paper validates the effectiveness and robustness of the proposed SCSONet on two public skin lesion segmentation datasets, demonstrating its low computational resource requirements. https://github.com/Haoyu1Chen/SCSONet.

This investigation delves into elucidating the mechanism by which resveratrol (Res), a natural polyterpenoid renowned for its antimicrobial properties, exerts its effects on Aeromonas hydrophila, a ubiquitous waterborne pathogen. Our findings underscore the dose-dependent manifestation of resveratrol in exhibiting antibacterial and antibiofilm formation activities against A. hydrophila. Employing a Data-independent acquisition (DIA) based quantitative proteomics methodology, we systematically compared differentially expressed proteins in A. hydrophila subjected to varying concentrations of Res. Subsequent bioinformatics analyses revealed key proteins and pathways pivotal in resveratrol’s antimicrobial action, encompassing oxidative stress, energy metabolism, and cell membrane integrity. Validation of the proteomics outcomes was meticulously conducted using the qPCR method at the mRNA level. Dynamic trend analysis unveiled alterations in biological processes, notably the correlation between the cell division-related protein ZapC and resveratrol content. Furthermore, scanning electron microscopy corroborated a significant elongation of A. hydrophila cells, affirming resveratrol’s capability to inhibit cell division. In concert, resveratrol emerges as a participant in the cell membrane integrity pathway, biofilm formation, and potentially, the regulation of genes associated with cell division, resulting in morphological elongation. These revelations position resveratrol as a promising natural alternative to conventional antibiotics for treating A. hydrophila infections.

Purpose: This study aims to estimate the regional choroidal thickness from color fundus images from convolutional neural networks in different network structures and task learning models. Method: 1276 color fundus photos and their corresponding choroidal thickness values from healthy subjects were obtained from the Topcon DRI Triton optical coherence tomography machine. Initially, ten commonly used convolutional neural networks were deployed to identify the most accurate model, which was subsequently selected for further training. This selected model was then employed in combination with single-, multiple-, and auxiliary-task training models to predict the average and sub-region choroidal thickness in both ETDRS (Early Treatment Diabetic Retinopathy Study) grids and 100-grid subregions. The values of mean absolute error and coefficient of determination (R2) were involved to evaluate the models' performance. Results: Efficientnet-b0 network outperformed other networks with the lowest mean absolute error value (25.61 μm) and highest R2 (0.7817) in average choroidal thickness. Incorporating diopter spherical, anterior chamber depth, and lens thickness as auxiliary tasks improved predicted accuracy (p-value = 6.39×10−44, 2.72×10−38, 1.15×10−36 respectively). For ETDRS regional choroidal thickness estimation, multi-task model achieved better results than single task model (lowest mean absolute error = 31.10 μm vs. 33.20 μm). The multi-task training also can simultaneously predict the choroidal thickness of 100 grids with a minimum mean absolute error of 33.86 μm. Conclusions: Efficientnet-b0, in combination with multi-task and auxiliary task models, achieve high accuracy in estimating average and regional macular choroidal thickness directly from color fundus photographs.

Background: Ischemic heart failure (IHF) is a serious complication after acute myocardial infarction (AMI). Understanding the mechanism of IHF after AMI will help us conduct early diagnosis and treatment. Methods: We obtained the AMI dataset GSE66360 and the IHF dataset GSE57338 from the GEO database, and screened overlapping genes common to both diseases through WGCNA analysis. Subsequently, we performed GO and KEGG enrichment analysis on overlapping genes to elucidate the common mechanism of AMI and IHF. Machine learning algorithms are also used to identify key biomarkers. Finally, we performed immune cell infiltration analysis on the dataset to further evaluate immune cell changes in AMI and IHF. Results: We obtained 74 overlapping genes of AMI and IHF through WGCNA analysis, and the enrichment analysis results mainly focused on immune and inflammation-related mechanisms. Through the three machine learning algorithms of LASSO, RF and SVM-RFE, we finally obtained the four Hub genes of IL1B, TIMP2, IFIT3, and P2RY2, and verified them in the IHF dataset GSE116250, and the diagnostic model AUC = 0.907. The results of immune infiltration analysis showed that 8 types of immune cells were significantly different in AMI samples, and 6 types of immune cells were significantly different in IHF samples. Conclusion: We explored the mechanism of IHF after AMI by WGCNA, enrichment analysis, and immune infiltration analysis. Four potential diagnostic candidate genes and therapeutic targets were identified by machine learning algorithms. This provides a new idea for the pathogenesis, diagnosis, and treatment of IHF after AMI.

Abstract Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We establish an uncertainty-inspired open set (UIOS) model, which is trained with fundus images of 9 retinal conditions. Besides assessing the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieves an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external target categories (TC)-JSIEC dataset and TC-unseen testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicts high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images. UIOS provides a robust method for real-world screening of retinal anomalies.

The youcai mosaic virus (YoMV) can infect a diverse array of crop species, such as Raphanus sativus, Brassica napus, Solanum nigrum, and Rehmannia glutinosa, causing substantial economic damage. This study aimed to develop a rapid, sensitive, and economical diagnostic method for YoMV. We successfully expressed and purified the recombinant His-CPYoMV-YZ protein in E. coli BL21, which was used to immunize New Zealand White rabbits, generating high-titer polyclonal antibodies (PAb-CPYoMV-YZ). Additionally, a serological-based reverse transcription loop-mediated isothermal amplification (S-RT-LAMP) assay was refined, combining serological and molecular detection techniques to enhance practicality. Utilizing PAb-CPYoMV-YZ, we developed four techniques for detecting YoMV: Western blot, dot immunoblotting assay, enzyme-linked immunosorbent assay (ELISA), and S-RT-LAMP. YoMV isolates from various regions and hosts were analyzed. The results indicated that PAb-CPYoMV-YZ was highly effective in detecting YoMV across a range of hosts and isolates from diverse regions. This study fills an important gap in the serological detection of YoMV and provides a practical tool for on-site diagnosis and control of YoMV infection.

Catch fish optimization algorithm (CFOA) is a newly proposed meta-heuristic algorithm based on human behaviors. CFOA shows better performance on multiple test functions and clustering problems. However, CFOA shows poor performance in some cases, and there is still room for improvement in convergence accuracy, getting rid of local traps, and so on. To further enhance the performance of CFOA, a multi-strategy improved catch fish optimization algorithm (MICFOA) is proposed in this paper. In the exploration phase, we propose a Lévy-based differential independent search strategy to enhance the global search capability of the algorithm while minimizing the impact on the convergence speed. Secondly, in the exploitation phase, a weight-balanced selection mechanism is used to maintain population diversity, enhance the algorithm’s ability to get rid of local optima during the search process, and effectively boost the convergence accuracy. Furthermore, the structure of CFOA is also modified in this paper. A fishermen position replacement strategy is added at the end of the algorithm as a way to strengthen the robustness of the algorithm. To evaluate the performance of MICFOA, a comprehensive comparison with nine other metaheuristic algorithms is performed on the 10/30/50/100 dimensions of the CEC 2017 test functions and the 10/20 dimensions of the CEC2022 test functions. Statistical experiments show that MICFOA has more significant dominance in numerical optimization problems, and its overall performance outperforms the CFOA, PEOA, TLBO, COA, ARO, EDO, YDSE, and other state-of-the-art algorithms such as LSHADE, JADE, IDE-EDA, and APSM-jSO.

Lane changing is a frequent traffic accident scenario. To improve the driving safety in lane changing scenarios, the blind spot display of lane changing is increased through human–machine interaction (HMI) interfaces in intelligent vehicles to improve the driver’s rate of risk perception with regard to the driving environment. However, blind spot information will increase the cognitive load of drivers and lead to driving distraction. To quantify the coupling relationship between blind spot display and drivers’ cognitive load, we proposed a method to quantify the cognitive load of the driver’s interaction by improving the AttenD algorithm, collecting feature data by carrying out a variety of real-vehicle road-testing experiments on three kinds of intelligent vehicles, and then establishing a model blind spot design and driver cognitive load correlation model using Bayesian Logistic Ordinal Regression (BLOR) and Categorical Boosting (CatBoost). The results show that the blind spot image display can reduce the driver’s cognitive load more effectively as it is closer to the driver, has a larger area, and occupies a higher proportion of the center control screen, especially when it is located in the middle and upper regions of the center control screen. The improved AttenD algorithm is able to quantify the cognitive load of the driver, which can be widely used in vehicle testing, HMI interface development and evaluation. In addition, the analytical framework constructed in this paper can help us to understand the complex impact of HMI in intelligent vehicles and provide optimization criteria for lane change blind spot design.

Integrated gate commutated thyristors (IGCTs) are critical components in high-voltage, high-current, and high-power conversion systems, particularly in offshore wind energy systems. However, the working environment of offshore wind energy conversion systems is extremely harsh. In this article, we propose an active damage control approach aiming at enhancing the reliability of the conversion system. By employing electro-thermal modeling for the equipment of the offshore wind energy conversion system, the junction temperature and fatigue damage of IGCT are simulated during the operation process. Using the improved model predictive current control (MPCC) method, active damage control effectively regulates the switching frequency of IGCT. IGCTs are symmetrically distributed on each leg of the converter, so the lifespan of the two IGCTs on each leg is also considered to be similar. This method balances the life of the IGCTs on the three legs of the converter and optimizes their utilization to the maximum extent. These measures effectively enhance the reliability of the conversion system and lower the operation and maintenance cost of high-power IGCT converters. The effectiveness of the proposed method is validated by co-simulation results by ANSYS and MATLAB/Simulink.

Abstract Previous studies have shown that the ventral medial prefrontal cortex (vmPFC) plays an important role in schema-related memory. However, there is an intensive debate to what extent the activation of subregions of the hippocampus is involved in retrieving schema-related memory. In addition, it is unclear how the functional connectivity (FC) between the vmPFC and the hippocampus, as well as the connectivity of the vmPFC with other regions, are modulated by prior knowledge (PK) during memory retrieval over time. To address these issues, participants learned paragraphs that described features of each unfamiliar word from familiar and unfamiliar categories (i.e., high and low PK conditions) 20 min, 1 day, and 1 week before the test. They then performed a recognition task to judge whether the sentences were old in the scanner. The results showed that the activation of the anterior-medial hippocampus (amHPC) cluster was stronger when the old sentences with high (vs. low) PK were correctly retrieved. The activation of the posterior hippocampus (pHPC) cluster, as well as the vmPFC, was stronger when the new sentences with high (vs. low) PK were correctly rejected (i.e., CR trials), whereas the cluster of anterior-lateral hippocampus (alHPC) showed the opposite. The FC of the vmPFC with the amHPC and perirhinal cortex/inferior temporal gyrus was stronger in the high (vs. low) PK condition, whereas the FC of the vmPFC with the alHPC, thalamus and frontal regions showed the opposite for the CR trials. This study highlighted that different brain networks, which were associated with the vmPFC, subregions of the hippocampus and cognitive control regions, were responsible for retrieving the information with high and low PK.

Abstract Background The identification of genomic regions affected by selection is one of the most important goals in population genetics. If temporal data are available, allele frequency changes at SNP positions are often used for this purpose. Here we provide a new testing approach that uses haplotype frequencies instead of allele frequencies. Results Using simulated data, we show that compared to SNP based test, our approach has higher power, especially when the number of candidate haplotypes is small or moderate. To improve power when the number of haplotypes is large, we investigate methods to combine them with a moderate number of haplotype subsets. Haplotype frequencies can often be recovered with less noise than SNP frequencies, especially under pool sequencing, giving our test an additional advantage. Furthermore, spurious outlier SNPs may lead to false positives, a problem usually not encountered when working with haplotypes. Post hoc tests for the number of selected haplotypes and for differences between their selection coefficients are also provided for a better understanding of the underlying selection dynamics. An application on a real data set further illustrates the performance benefits. Conclusions Due to less multiple testing correction and noise reduction, haplotype based testing is able to outperform SNP based tests in terms of power in most scenarios.

Few-shot learning (FSL) describes the challenge of learning a new task using a minimum amount of labeled data, and we have observed significant progress made in this area. In this paper, we explore the effectiveness of the FSL theory by considering a real-world problem where labels are hard to obtain. To assist a large study on chimpanzee hunting activities, we aim to classify various animal species that appear in our in-the-wild camera traps located in Senegal. Using the philosophy of FSL, we aim to train an FSL network to learn to separate animal species using large public datasets and implement the network on our data with its novel species/classes and unseen environments, needing only to label a few images per new species. Here, we first discuss constraints and challenges caused by having in-the-wild uncurated data, which are often not addressed in benchmark FSL datasets. Considering these new challenges, we create two experiments and corresponding evaluation metrics to determine a network’s usefulness in a real-world implementation scenario. We then compare results from various FSL networks, and describe how factors may affect a network’s potential real-world usefulness. We consider network design factors such as distance metrics or extra pre-training, and examine their roles in a real-world implementation setting. We also consider additional factors such as support set selection and ease of implementation, which are usually ignored when a benchmark dataset has been established.

Abstract Silicon (Si)‐based solid‐state batteries (Si‐SSBs) are attracting tremendous attention because of their high energy density and unprecedented safety, making them become promising candidates for next‐generation energy storage systems. Nevertheless, the commercialization of Si‐SSBs is significantly impeded by enormous challenges including large volume variation, severe interfacial problems, elusive fundamental mechanisms, and unsatisfied electrochemical performance. Besides, some unknown electrochemical processes in Si‐based anode, solid‐state electrolytes (SSEs), and Si‐based anode/SSE interfaces are still needed to be explored, while an in‐depth understanding of solid–solid interfacial chemistry is insufficient in Si‐SSBs. This review aims to summarize the current scientific and technological advances and insights into tackling challenges to promote the deployment of Si‐SSBs. First, the differences between various conventional liquid electrolyte‐dominated Si‐based lithium‐ion batteries (LIBs) with Si‐SSBs are discussed. Subsequently, the interfacial mechanical contact model, chemical reaction properties, and charge transfer kinetics (mechanical–chemical kinetics) between Si‐based anode and three different SSEs (inorganic (oxides) SSEs, organic–inorganic composite SSEs, and inorganic (sulfides) SSEs) are systemically reviewed, respectively. Moreover, the progress for promising inorganic (sulfides) SSE‐based Si‐SSBs on the aspects of electrode constitution, three‐dimensional structured electrodes, and external stack pressure is highlighted, respectively. Finally, future research directions and prospects in the development of Si‐SSBs are proposed.

This paper proposed a precast concrete pavement structure with the composite base layer comprised of the concrete beam and the filling low strength materials (B-PCP). The mechanical responses of the B-PCP induced by moving wheel loads coupled with temperature loads were analysed using the finite element method. Then the optimal geometry of the concrete beam was determined based on the sensitivity analysis, considering the tensile stress of both the slab and the beam of the base layer, the slab curling, and the faulting. Furthermore, the performance evaluation of the B-PCP was conducted by the numerical analysis and the scale experiment testing, respectively. The results showed that the optical concrete beam was 0.8 ∼ 1.0 m wide and 0.2 ∼ 0.3 m thick. The numerical results showed that the optimal B-PCP structure had a good fatigue resistance performance, and the corresponding slab curling decreased by almost 40% compared with that of the traditional precast concrete pavement. Meanwhile, the scale experiment testing verified that the optimal B-PCP structure exhibited an excellent ability to resist the deformation. The results also showed that the bearing capacity of the optimal B-PCP structure was larger than 350kN, indicating a good bearing capacity.

Abstract Although the power conversion efficiency values of perovskite solar cells continue to be refreshed, it is still far from the theoretical Shockley-Queisser limit. Two major issues need to be addressed, including disorder crystallization of perovskite and unbalanced interface charge extraction, which limit further improvements in device efficiency. Herein, we develop a thermally polymerized additive as the polymer template in the perovskite film, which can form monolithic perovskite grain and a unique “Mortise-Tenon” structure after spin-coating hole-transport layer. Importantly, the suppressed non-radiative recombination and balanced interface charge extraction benefit from high-quality perovskite crystals and Mortise-Tenon structure, resulting in enhanced open-circuit voltage and fill-factor of the device. The PSCs achieve certified efficiency of 24.55% and maintain >95% initial efficiency over 1100 h in accordance with the ISOS-L-2 protocol, as well as excellent endurance according to the ISOS-D-3 accelerated aging test.

In this study, a novel nonfragile deep reinforcement learning (DRL) method was proposed to realize the finite-time control of switched unmanned flight vehicles. Control accuracy, robustness, and intelligence were enhanced in the proposed control scheme by combining conventional robust control and DRL characteristics. In the proposed control strategy, the tracking controller consists of a dynamics-based controller and a learning-based controller. The conventional robust control approach for the nominal system was used for realizing a dynamics-based baseline tracking controller. The learning-based controller based on DRL was developed to compensate model uncertainties and enhance transient control accuracy. The multiple Lyapunov function approach and mode-dependent average dwell time approach were combined to analyze the finite-time stability of flight vehicles with asynchronous switching. The linear matrix inequalities technique was used to determine the solutions of dynamics-based controllers. Online optimization was formulated as a Markov decision process. The adaptive deep deterministic policy gradient algorithm was adopted to improve efficiency and convergence. In this algorithm, the actor–critic structure was used and adaptive hyperparameters were introduced. Unlike the conventional DRL algorithm, nonfragile control theory and adaptive reward function were used in the proposed algorithm to achieve excellent stability and training efficiency. We demonstrated the effectiveness of the presented algorithm through comparative simulations.

Non-small cell lung cancer (NSCLC) is one of the main causes of cancer-related death worldwide, with a serious impact on human health and life. The identification of NSCLC at an early stage is a formidable task that frequently culminates in a belated diagnosis. LncRNA is a kind of noncoding RNA with limited protein-coding capacity, and its expression is out of balance in many cancers, especially NSCLC. A large number of studies have reported that lncRNA acts a vital role in regulating angiogenesis, invasion, metastasis, and the proliferation and apoptosis of tumor cells, affecting the occurrence and development of NSCLC. Abundant evidence demonstrates that lncRNAs may serve as potential biomarkers for NSCLC diagnosis and prognosis. In this review, we summarize the latest progress in characterizing the functional mechanism of lncRNAs involved in the development of NSCLC and further discuss the role of lncRNAs in NSCLC therapy and chemotherapy resistance. We also discuss the advantages, limitations, and challenges of using lncRNAs as diagnostic or prognostic biomarkers in the management of NSCLC.

ABSTRACTTo evaluate the effectiveness of smartphone ophthalmoscope (SO) in teaching ophthalmoscopy, compared with direct ophthalmoscope (DO). In this cross-over study, 45 final-year medical students attending sessions at a single institution were randomly allocated to two groups (A and B). Both groups attended two training sessions. In the first session, Group A students were taught ophthalmoscopy using DO and Group B students using SO. In the second session, the training sessions were crossed over. A series of eye models with 10 letters placed on the inner surface were designed to assess the students’ skill on ophthalmoscopy. Students performed ophthalmoscopy on the eye models, recorded their findings, and completed a questionnaire of feedback on DO and SO. The main outcome measure was the score of ophthalmoscopy, assessed by the student correctly recording each letter (score 1 for each letter). For Group A, the mean score of ophthalmoscopy on the eye model using DO and SO was 3.9±2.4 and 8.2±2.2, respectively. For Group B, the mean score of ophthalmoscopy on the eye model using SO and DO 8.7±1.8 and 5.7±3.5 . Students scored significantly higher in ophthalmoscopy when using SO than DO (P

Abstract Introduction Chronic erosive gastritis (CEG) is closely related to gastric cancer, which requires early diagnosis and intervention. The invasiveness and discomfort of electronic gastroscope have limited its application in the large-scale screening of CEG. Therefore, a simple and noninvasive screening method is needed in the clinic. Objectives The aim of this study is to screen potential biomarkers that can identify diseases from the saliva samples of CEG patients using metabolomics. Methods Saliva samples from 64 CEG patients and 30 healthy volunteers were collected, and metabolomic analysis was performed using UHPLC-Q-TOF/MS in the positive and negative ion modes. Statistical analysis was performed using both univariate (Student’s t-test) and multivariate (orthogonal partial least squares discriminant analysis) tests. Receiver operating characteristic (ROC) analysis was conducted to determine significant predictors in the saliva of CEG patients. Results By comparing the saliva samples from CEG patients and healthy volunteers, 45 differentially expressed metabolites were identified, of which 37 were up-regulated and 8 were down-regulated. These differential metabolites were related to amino acid, lipid, phenylalanine metabolism, protein digestion and absorption, and mTOR signaling pathway. In the ROC analysis, the AUC values of 7 metabolites were greater than 0.8, among which the AUC values of 1,2-dioleoyl-sn-glycoro-3-phosphodylcholine and 1-stearoyl-2-oleoyl-sn-glycoro-3-phospholine (SOPC) were greater than 0.9. Conclusions In summary, a total of 45 metabolites were identified in the saliva of CEG patients. Among them, 1,2-dioleoyl-sn-glycoro-3-phosphorylcholine and 1-stearoyl-2-oleoyl-sn-glycoro-3-phosphorine (SOPC) might have potential clinical application value.

Abstract Flight risk evaluation based on data-driven approach is an essential topic of aviation safety management. Existing risk analysis methods ignore the coupling and time-variant characteristics of flight parameters, and cannot accurately establish the mapping relationship between flight state and loss-of-control risk. To deal with the problem, a flight state deep clustering network (FSDCN) model was proposed to mine latent loss-of-control risk information implicating in raw flight parameters. FSDCN integrates the feature extraction and clustering into an end-to-end deep hybrid network to extract latent risk features from multivariate time-series flight parameters and cluster them. In the FSDCN model, a sequential multi-attention encoder–decoder network is designed to extract embedded risk features, and the feature clustering layer is designed to iteratively refine clustering effects and feature extraction. Besides, a loss-of-control classifier is added to optimize the risk feature vector expression and ensure sufficient dividing feature for facilitate clustering. The multi-task joint learning strategy is adopted to improve the clustering performance of the model further. According to extracted risk features and similarity metrics, the optimal clusters number of flight states is set as 5. Comparative experiments show that FSDCN significantly performs better than other clustering models with performance percentage error below $$6\%$$ 6 % . Through statistical analysis of clustering results, the risk level is quantified for each cluster. Three high-difficulty maneuver cases are presented to demonstrate FSDCN for flight risk evaluation. The flight parameter sequences of the maneuver cases are input into the well-trained FSDCN to obtain the risk prediction results. The spatiotemporal distribution characteristics of the risk-quantized results are consistent with flight parameters over-limit situations, which demonstrates the effectiveness of FSDCN on clustering flight states. The experimental results on flight maneuver cases show that FSDCN can find potential loss-of-control risk features according to multivariate time-series flight data and provide support for in-flight risk warnings.

Abstract Flexible perovskite solar cells (PSCs) have drawn increasing attention due to their promising applications for wearable electronics and aerospace applications. However, the efficiency and stability of flexible PSCs still lag behind their rigid counterparts. Here, we use N,N-dimethyl acrylamide (DMAA) to in situ synthesize cross-linking polymer for flexible Sn–Pb mixed PSCs. DMAA can gather at grain boundary as a scaffold to regulate the crystallization of perovskite and reduce defects. The rigid and flexible Sn–Pb mixed PSCs showed efficiencies of 16.44% and 15.44%, respectively. In addition, the flexible Sn–Pb mixed PSCs demonstrated excellent bending durability, which retained over 80% of the original efficiency after 5000 bending cycles at a radius of 5 mm.

Certain microalgal species can grow with different trophic strategies depending on the availability of nutrient resources. They can use the energy from light or an organic substrate, or both, and can therefore be called autotrophs, heterotrophs, or mixotrophs. We recently isolated a microalgal strain from the microplastic biofilm, which was identified as Auxenochlorella protothecoides, AS-1. Strain AS-1 grew rapidly in bacterial culture media and exhibited different growth rates and cell sizes under different trophic conditions. We compared the growth performance of AS-1 under the three different trophic modes. AS-1 reached a high biomass (>4 g/L) in 6 days under mixotrophic growth conditions with a few organic carbons as a substrate. In contrast, poor autotrophic growth was observed for AS-1. Different cell sizes, including daughter and mother cells, were observed under the different growth modes. We applied a Coulter Counter to measure the size distribution patterns of AS-1 under different trophic modes. We showed that the cell size distribution of AS-1 was affected by different growth modes. Compared to the auto-, hetero- and mixotrophic modes, AS-1 achieved higher biomass productivity by increasing cell number and cell size in the presence of organic substrate. The mechanisms and advantages of having more mother cells with organic substrates are still unclear and warrant further investigations. The work here provides the growth information of a newly isolated A. protothecoides AS-1 which will be beneficial to future downstream applications.

Global climate change is shifting the temperate climate zone towards higher latitudes, altering crop adaptability and potentially impacting cotton (Gossypium hirsutum L.) cultivation's climate suitability distribution. However, our understanding of these impacts is often based on one-sided suitability assessments, leading to potential biases when optimizing cotton planting spatial distribution and adaptability. In this study, we utilized a cotton cultivation spatial optimization framework, combining Geographical Information System (GIS) and Remote Sensing (RS) techniques, to accurately identify locations with ongoing land-use disputes. We then analyzed the extent of discrepancy between cotton suitability and planting zones under the influence of climate change. The results demonstrate the stability of suitable cotton cultivation areas in the southern (19%), northern (4%), and eastern (1%) regions, indicating a comparative advantage in climatic resources for the southern region. The climate-suitable areas for cotton were mainly concentrated on the south side of the Tianshan Mountains and near the Tarim River, covering the regions of Aksu, Hetian, Kashgar, and Bazhou. However, with the notable increase in effective accumulated temperature, precipitation, sunshine hours, and minimum temperature, the proportion of very highly suitable and unsuitable areas decreased, while the proportion of less suitable areas increased. Additionally, climate change led to the expansion of cotton suitable areas towards North Xinjiang. Moreover, the cotton cultivation center shifted from South Xinjiang to North Xinjiang during 2000–2020 and then returned to South Xinjiang, with the cotton cultivation areas in South and North Xinjiang being nearly balanced by 2021 (49% each). Despite a decrease in the conflict rate between cotton cultivation zones and suitable areas from 60% to 33% during 2000–2020, the conflict rate remained relatively high. It is reasonable to attribute this discrepancy to the significant influence of the target price policy and agricultural technological advancement in both South and North Xinjiang. Overall, our study effectively identifies land-use conflicts and suggests optimizing the spatial layout of cotton planting in Xinjiang. This can leverage agricultural resource advantages, reduce production costs, and provide valuable policy references for the decision makers.

Aiming at mitigating the fluctuation of distributed photovoltaic power generation, a segmented compensation strategy based on the improved seagull algorithm is proposed in this paper. In this regard, a hybrid energy storage system comprising a lithium battery and supercapacitor is utilized. The internal power distribution of the hybrid energy storage system is adjusted using wavelet packet decomposition, and the state of charge is employed to adapt the primary power distribution. The start and end times for charging and discharging are determined by combining the time of use, electricity price, state-of-charge information, and load size at night to realize the economic operation of the system. The opposing search operator strategy and mutation operation are used to improve the seagull algorithm, optimize the controller parameters of the DC/DC converter, and improve its response time. Combined with the historical measured data of a distributed photovoltaic in Hubei Province, simulation results show that the proposed strategy can effectively smoothen the fluctuation of distributed photovoltaic generated power while reducing the charging and discharging frequencies of the energy storage system, hence improving its stability and service life.

Missing data presents a challenge to clustering algorithms, as traditional methods tend to pad incomplete data first before clustering. To combine the two processes of padding and clustering and improve the clustering accuracy, a generalized fuzzy clustering framework is proposed based on optimal completion strategy (OCS) and nearest prototype strategy (NPS) with four improved algorithms developed. Feature weights are introduced to reduce outliers’ influence on the cluster centers, and kernel functions are used to solve the linear indistinguishability problem. The proposed algorithms are evaluated regarding correct clustering rate, iteration number, and external evaluation indexes with nine datasets from the UCI (University of California, Irvine) Machine Learning Repository. The results of the experiment indicate that the clustering accuracy of the feature weighted kernel fuzzy C-means algorithm with NPS (NPS-WKFCM) and feature weighted kernel fuzzy C-means algorithm with OCS (OCS-WKFCM) under varying missing rates is superior to that of seven conventional algorithms. Experiments demonstrate that the enhanced algorithm proposed for clustering incomplete data is superior.

A deep exploration was conducted on the evolution law of the mechanical properties of salt rock fillers under the influence of different forming parameters in the Lop Nor area to promote the engineering application process of salt rock. The accuracy of primary regression, square regression, and support vector machine (SVM) regression algorithms in predicting the mechanical properties of salt rock fillers was compared, and the most accurate prediction model for the California bearing ratio (CBR) and rebound modulus of salt rock fillers were recommended. The results showed that the optimal brine content of SC-40, SC-20, and SC-10 salt rock fillers was between 8.2% and 9.3%, with a dry density of approximately 1.69–1.76 g/cm3. The CBR of salt rock samples gradually decreased with an increase in brine content, and the rebound modulus was higher than 90.6 MPa. As the compaction degree increased, the CBR value increased significantly, and the rebound modulus increased by approximately 28.1 MPa. As the immersion time increased, the mechanical properties of salt rock gradually decreased. Among the various regression models, the SVM prediction model had the highest accuracy index coefficient of determination (R2), whereas the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) were the smallest. Therefore, the SVM prediction model was recommended to accurately estimate the mechanical properties of salt rock roadbed fillers and provide a reference for the regulation of compaction parameters and the guarantee of bearing capacity characteristics of salt rock roadbeds.

Abstract Honeypot is an active defense mechanism, which attracts attackers to interact with virtual resources in the honeypot mainly by simulating real working scenarios and deploying decoy targets, so as to prevent real resources from being damaged and collect attackers’ attack processes and analyze potential system vulnerabilities to proactively respond to similar attacks. Because of the existing honeypot system has defects such as the inability to deploy specific honeypots to induce attacks based on complex attacks, the inability to select the best honeypot for dynamic response based on honeypot deployment and maintenance costs during attack interactions, and insufficient ability to identify variants of known attack methods. Although hybrid honeypots can solve some of these problems by deploying low-interaction honeypots and high-interaction honeypots, they cannot really be applied to real production scenarios because of their slow TCP connection switching speed and inability to efficiently identify encrypted malicious traffic. In this paper, we propose a new dynamic security defense system based on the combination of TCP_REPAIR-based dynamic honeypot selection architecture and a deep learning-based intelligent firewall. The system accurately distributes encrypted or non-encrypted attack traffic and its variants through the intelligent firewall. The normal traffic is sent to the actual system, and the marked malicious traffic dynamically selects honeypots to respond according to the attack process.The experimental result indicated that the system can select honeypots for targeted responses according to the actual network situation quickly and dynamically and covertly, effectively improving the utilization rate of honeypot clusters as well as the ability to decoy.

In this paper, we propose a permutation index differential chaos shift keying with the noise reduction (NR-PI-DCSK) to alleviate the negative effect of the additive noise and fading interference. Instead of sending a $\beta $ -length chaotic sequence as the reference in the conventional PI-DCSK, the transmitter of NR-PI-DCSK generates $\beta /R$ chaotic samples and repeats $R$ times, which serve as the reference sequence. The permuted copy of the reference is used to carry the data given the predefined permutation index. After the correlation and averaging of these $R$ chaotic segments at the receiver, the variance of noise and fading can be greatly reduced and the performance is improved. Then, we derive the analytical BER expressions for the multipath fading channels, which are verified by the consistent simulations. The results show that the proposed NR-PI-DCSK can achieve up to 3 dB performance gain over the conventional PI-DCSK in different wireless fading channels. In addition, the performance advantage of the proposed NR-PI-DCSK is also demonstrated in a typical UWB channel by simulations. More importantly, the proposed scheme maintains almost the same low transceiver complexity as the conventional ones, since it only requires the correlators. Thus, the NR-PI-DCSK can be seen as a promising candidate for low-cost short-range IoT communications.

In order to deal with the increasingly severe energy situation and climate change, reducing global carbon emissions and developing new energy have become a universal consensus among countries in the world. The design of clean energy heating systems such as solar collectors (SC) and air source heat pumps (ASHP) has also received widespread attention. However, optimizing multiple parameters that interact with each other in the hybrid heating systems such as solar-air hybrid source heat pumps (HSHP) is still challenging, and the optimization of the parameters remains to be studied. By using the TRNSYS simulation platform, modify the performance parameters to decrease the system’s annual cost with particle swarm optimization (PSO) and coordinate search method (CSM), respectively. The results show that two algorithms can significantly enhance the system performance, where it is the easier for PSO to find global optimum, and the average performance index COPsysof the system is about 15% higher than that of the CSM, and the system’s annual power consumption could be lowered by 27.75%; In addition, the matching principle of the key parameters of the hybrid heating system is proposed and the sensitivity ranking of the optimized parameters is derived. These results offer theoretical foundations for optimal design of the solar-air HSHP heating system.

Analyzing and characterizing the differences between networks is a fundamental and challenging problem in network science. Most previous network comparison methods that rely on topological properties have been restricted to measuring differences between two undirected networks. However, many networks, such as biological networks, social networks, and transportation networks, exhibit inherent directionality and higher-order attributes that should not be ignored when comparing networks. Therefore, we propose a motif-based directed network comparison method that captures local, global, and higher-order differences between two directed networks. Specifically, we first construct a motif distribution vector for each node, which captures the information of a node’s involvement in different directed motifs. Then, the dissimilarity between two directed networks is defined on the basis of a matrix, which is composed of the motif distribution vector of every node and the Jensen–Shannon divergence. The performance of our method is evaluated via the comparison of six real directed networks with their null models, as well as their perturbed networks based on edge perturbation. Our method is superior to the state-of-the-art baselines and is robust with different parameter settings.

Virus infections cause devastative economic losses for various plant species, and early diagnosis and prevention are the most effective strategies to avoid the losses. Exploring virus genomic evolution and constructing virus infectious cDNA clones is essential to achieve a deeper understanding of the interaction between host plant and virus. Therefore, this work aims to guide people to better prevent, control, and utilize the youcai mosaic virus (YoMV). Here, the YoMV was found to infect the Solanum nigrum under natural conditions. Then, an infectious cDNA clone of YoMV was successfully constructed using triple-shuttling vector-based yeast recombination. Furthermore, we established phylogenetic trees based on the complete genomic sequences, the replicase gene, movement protein gene, and coat protein gene using the corresponding deposited sequences in NCBI. Simultaneously, the evolutionary relationship of the YoMV discovered on S. nigrum to others was determined and analyzed. Moreover, the constructed cDNA infectious clone of YoMV from S. nigrum could systematically infect the Nicotiana benthamiana and S. nigrum by agrobacterium-mediated infiltration. Our investigation supplied a reverse genetic tool for YoMV study, which will also contribute to in-depth study and profound understanding of the interaction between YoMV and host plant.

Ice accretion is inevitable on fix-wing UAVs (unmanned aerial vehicles) when they are applied to surveillance and mapping over colder climates and arctic regions. Subsequent aerodynamic profile changes have caused the current interest in the better prediction of the effect of icing shapes/sizes/distribution patterns on the aerodynamic performances of an aircraft. This study employs a numerical model which investigates the RG-15 aerofoil’s response to various icing scenarios at a Reynolds number of Re=2×105. Under icing conditions, compared to a clean aerofoil, a reduction in the lift coefficient and an increase in the drag coefficient are observed. Lower temperatures and reduced liquid water content lead to a decrease in the maximum thickness of ice accretion on the RG-15 aerofoil. Particularly noteworthy is the 10.85% reduction in the lift coefficient at a 10° angle of attack, which is in the icing condition at −10 °C with a mean volume diameter of 15 μm. Power consumption increases in the range of 0.46% to 26.5% under various icing conditions, showing synchronization with the rise in drag coefficient. This study underscores the need for future research to investigate various cloud conditions comprehensively and deeply in the context of aerofoil icing.

Abstract Background Capillary non-perfusion is an important characteristic for diabetic retinopathy (DR) indicating microvascular damage and ischemia. Data on the description and treatment outcomes of DR with large area of non-perfusion are lacking to date. We aim to describe the characteristics and treatment outcomes in a series of patients with DR who presented extensively large area of capillary non-perfusion (LACNP). Methods Fundus fluorescein angiograms from medical charts in patients diagnosed with DR between Jan 2017 and Dec 2019 were retrospectively reviewed. Clinical data in eyes with LACNP including imaging and laboratory findings at the first presentation were analyzed. The LACNP was defined as over 70% area of capillary non-perfusion throughout the whole image retina. The mean follow-up duration was 12.4 ± 16.7 months. Follow-up data including extensive pan-retinal photocoagulation and surgical intervention and treatment outcomes were evaluated. Results A total of 43 eyes in 24 patients with LACNP were included, accounting for 3.3% of DR populations in the same period. The overall percentage of non-perfusion area was 79.1 ± 8.1%. All patients received proper control of diabetes and hypertension, and extensive pan-retinal laser photocoagulation. During the follow-up periods, 20 eyes (46.5%) developed severe neovascular complications, of which 15 eyes (34.9%) underwent vitrectomy and/or anti-glaucoma surgeries. Conservative therapies including glycemic control and supplemental laser photocoagulation were conducted in 23 eyes (53.5%) without neovascular complications. In the final follow-up, best corrected visual acuity improved or maintained stable in 19 eyes (44.2%) while deteriorated in 24 eyes (55.8%). Conclusions The presence of LACNP is the hallmark of advanced DR and often indicates a poor visual outcome, although aggressive treatments may slow DR progression and maintain central vision for some time.

BackgroundClinical practice guidelines recommend early identification of cognitive impairment in individuals with hypertension with the help of risk prediction tools based on risk factors.ObjectiveThe aim of this study was to develop a superior machine learning model based on easily collected variables to predict the risk of early cognitive impairment in hypertensive individuals, which could be used to optimize early cognitive impairment risk assessment strategies.MethodsFor this cross-sectional study, 733 patients with hypertension (aged 30–85, 48.98% male) enrolled in multi-center hospitals in China were divided into a training group (70%) and a validation group (30%). After least absolute shrinkage and selection operator (LASSO) regression analysis with 5-fold cross-validation determined the modeling variables, three machine learning classifiers, logistic regression (LR), XGBoost (XGB), and gaussian naive bayes (GNB), were developed. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and F1 score were used to evaluate the model performance. Shape Additive explanation (SHAP) analysis was performed to rank feature importance. Further decision curve analysis (DCA) assessed the clinical performance of the established model and visualized it by nomogram.ResultsHip circumference, age, education levels, and physical activity were considered significant predictors of early cognitive impairment in hypertension. The AUC (0.88), F1 score (0.59), accuracy (0.81), sensitivity (0.84), and specificity (0.80) of the XGB model were superior to LR and GNB classifiers.ConclusionThe XGB model based on hip circumference, age, educational level, and physical activity has superior predictive performance and it shows promise in predicting the risk of cognitive impairment in hypertensive clinical settings.

Accurate and reliable prediction of air pollutant concentrations is important for rational avoidance of air pollution events and government policy responses. However, due to the mobility and dynamics of pollution sources, meteorological conditions, and transformation processes, pollutant concentration predictions are characterized by great uncertainty and instability, making it difficult for existing prediction models to effectively extract spatial and temporal correlations. In this paper, a powerful pollutant prediction model (STA-ResConvLSTM) is proposed to achieve accurate prediction of pollutant concentrations. The model consists of a deep learning network model based on a residual neural network (ResNet), a spatial–temporal attention mechanism, and a convolutional long short-term memory neural network (ConvLSTM). The spatial–temporal attention mechanism is embedded in each residual unit of the ResNet to form a new residual neural network with the spatial–temporal attention mechanism (STA-ResNet). Deep extraction of spatial–temporal distribution features of pollutant concentrations and meteorological data from several cities is carried out using STA-ResNet. Its output is used as an input to the ConvLSTM, which is further analyzed to extract preliminary spatial–temporal distribution features extracted from the STA-ResNet. The model realizes the spatial–temporal correlation of the extracted feature sequences to accurately predict pollutant concentrations in the future. In addition, experimental studies on urban agglomerations around Long Beijing show that the prediction model outperforms various popular baseline models in terms of accuracy and stability. For the single-step prediction task, the proposed pollutant concentration prediction model performs well, exhibiting a root-mean-square error (RMSE) of 9.82. Furthermore, even for the pollutant prediction task of 1 to 48 h, we performed a multi-step prediction and achieved a satisfactory performance, being able to achieve an average RMSE value of 13.49.