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In the new era of artificial intelligence (AI) and the Internet of Things (IoT), big data collection and analysis for intelligent sports are of great importance in monitoring human health. Herein, naturally, biodegradable triboelectric nanogenerators (NB‐TENGs) are developed based on low‐cost, recyclable, and environmentally friendly corn bracts, which are further applied in neck motion recognition. Three NB‐TENGs are integrated into an elastic collar to create a neck‐condition monitoring triboelectric sensor (NCM‐TS). An intelligent behavioral monitoring system is achieved by combining NCM‐TS with a deep learning model, which allows the recognition of four types of neck motion with an average accuracy of 94%. The developed neck motion monitoring sensor has broad potential applications in sports health monitoring, rehabilitation training, and healthcare. [ABSTRACT FROM AUTHOR]

Zhibai Dihuang pill (ZD), a traditional Chinese medicine nourishes Yin and reduces internal heat, is believed to have therapeutic effects on urinary tract infections (UTIs). To explore the effects and mechanism of modified ZD (MZD) on UTI induced by extended-spectrum β-lactamase (ESBLs) Escherichia coli. Thirty Sprague-Dawley rats were randomly divided into control, model (0.5 mL 1.5 × 108 CFU/mL ESBLs E. coli), MZD (20 g/kg MZD), LVFX (0.025 g/kg LVFX), and MZD + LVFX groups (20 g/kg MZD + 0.025 g/kg LVFX), n = 6. After 14 days of treatment, serum biochemical indicators, renal function indicators, bladder and renal histopathology, and urine bacterial counts in rats were determined. Additionally, the effects of MZD on ESBLs E. coli biofilm formation and related gene expression were analyzed. MZD significantly decreased the count of white blood cells (from 13.12 to 9.13), the proportion of neutrophils (from 43.53 to 23.18), C-reactive protein (from 13.21 to 9.71), serum creatinine (from 35.78 to 30.15), and urea nitrogen (from 12.56 to 10.15), relieved the inflammation and fibrosis of bladder and kidney tissues, and reduced the number of bacteria in urine (from 2174 to 559). In addition, MZD inhibited the formation of ESBLs E. coli biofilms (2.04-fold) and decreased the gene expressions of luxS, pfS and ompA (1.41–1.62-fold). MZD treated ESBLs E. coli-induced UTI inhibited biofilm formation, providing a theoretical basis for the clinical application of MZD. Further study on the clinical effect of MZD may provide a novel therapy option for UTI. [ABSTRACT FROM AUTHOR]

This research presents a comprehensive study of the dichotomous search iterative parabolic discrete time Fourier transform (Ds-IpDTFT) estimator, a novel approach for fine frequency estimation in noisy exponential signals. The proposed estimator leverages a dichotomous search process before iterative interpolation estimation, which significantly reduces computational complexity while maintaining high estimation accuracy. An in-depth exploration of the relationship between the optimal parameter p and the unknown parameter δ forms the backbone of the methodology. Through extensive simulations and real-world experiments, the Ds-IpDTFT estimator exhibits superior performance relative to other established estimators, demonstrating robustness in noisy conditions and stability across varying frequencies. This efficient and accurate estimation method is a significant contribution to the field of signal processing and offers promising potential for practical applications. [ABSTRACT FROM AUTHOR]

In this paper, an optimal planning strategy is proposed for EV charging stations by considering travel demand based on non‐cooperative game theory. In the proposed methodology, a realistic and stochastic charging demand distribution was obtained with the travel chain of an EV and the associated probability density function. Based on the charging demand of EV owners and a multi‐operator scenario, a two‐stage effective and practical charging station planning model was established. The first stage of the planning model optimized the location of charging stations to decrease the total deadhead mileage of the EVs with the cooperation of the charging station operators. In the second stage, the operators were committed to increasing their economic benefits by developing a charging station capacity strategy in the framework of the non‐cooperative game. Furthermore, the combination of an adaptive mutation particle swarm optimization algorithm (AMPSO) and approximate Nash equilibrium had a better effect in reducing the difficulty of solving the game model and obtaining an optimal planning strategy. Finally, the results of the case study demonstrated the effectiveness of the proposed methodology for improving the charging experience of the EV owners and achieving a balance of benefits among multiple charging station operators. [ABSTRACT FROM AUTHOR]

The antioxidant activity of protein-derived peptides was one of the first to be revealed among the more than 50 known peptide bioactivities to date. The exploitation value associated with food-derived antioxidant peptides is mainly attributed to their natural properties and effectiveness as food preservatives and in disease prevention, management, and treatment. An increasing number of antioxidant active peptides have been identified from a variety of renewable sources, including terrestrial and aquatic organisms and their processing by-products. This has important implications for alleviating population pressure, avoiding environmental problems, and promoting a sustainable shift in consumption. To identify such opportunities, we conducted a systematic literature review of recent research advances in food-derived antioxidant peptides, with particular reference to their biological effects, mechanisms, digestive stability, and bioaccessibility. In this review, 515 potentially relevant papers were identified from a preliminary search of the academic databases PubMed, Google Scholar, and Scopus. After removing non-thematic articles, articles without full text, and other quality-related factors, 52 review articles and 122 full research papers remained for analysis and reference. The findings highlighted chemical and biological evidence for a wide range of edible species as a source of precursor proteins for antioxidant-active peptides. Food-derived antioxidant peptides reduce the production of reactive oxygen species, besides activating endogenous antioxidant defense systems in cellular and animal models. The intestinal absorption and metabolism of such peptides were elucidated by using cellular models. Protein hydrolysates (peptides) are promising ingredients with enhanced nutritional, functional, and organoleptic properties of foods, not only as a natural alternative to synthetic antioxidants. [ABSTRACT FROM AUTHOR]

Progress in science and technology drives the continuous innovation of energy collection and utilization. In the field of sports, the information collection and analysis based on Internet of things have attracted particular attention. Moreover, triboelectric nanogenerator (TENG) has promising applications in the field of sports. Here, we introduce the working principle of the TENG then the progress of the TENG as a wearable energy sensor is examined in the two fields of basic human activities and sports, especially competitive sports. On this basis, it is considered that the stability of devices, the universality of materials, and the scientificity of application of the TENG in the future need to be improved. We provide a direction for further upgrading energy collection technology to promote the high-quality development of human mechanical energy sensing in the field of sports. [ABSTRACT FROM AUTHOR]

Lesion mimic mutants resembling the hypersensitive response without pathogen attack are an ideal material to understand programmed cell death, the defence response, and the cross‐talk between defence response and development in plants. In this study, mic, a lesion mimic mutant from cultivar Yunyin treated with ethyl methanesulphonate (EMS), was screened. By map‐based cloning, a short‐chain alcohol dehydrogenase/reductase with an atypical active site HxxxK was isolated and designated as SDR7‐6. It functions as a homomultimer in rice and is localized at the endoplasmic reticulum. The lesion mimic phenotype of the mutant is light‐dependent. The mutant displayed an increased resistance response to bacterial blight, but reduced resistance to rice blast disease. The mutant and knockout lines showed increased reactive oxygen species, jasmonic acid content, antioxidant enzyme activity, and expression of pathogenicity‐related genes, while chlorophyll content was significantly reduced. The knockout lines showed significant reduction in grain size, seed setting rate, 1000‐grain weight, grain weight per plant, panicle length, and plant height. SDR7‐6 is a new lesion mimic gene that encodes a short‐chain alcohol dehydrogenase with atypical catalytic site. Disruption of SDR7‐6 led to cell death and had adverse effects on multiple agricultural characters. SDR7‐6 may act at the interface of the two defence pathways of bacterial blight and rice blast disease in rice. [ABSTRACT FROM AUTHOR]

• The regional risk assessment is achieved through a prediction perspective. • Unsupervised clustering methods and deep learning based predictive models are combined to detect possible collision hazards. • A realistic distribution of the vessels' collision risks at specific moments is shown. A method based on the predictable Transformer network associated with a clustering method is introduced to build a framework for the regional collision risk assessment, which is an alternative to the traditional methods that have two problems: 1) The indicators are calculated based on the current navigation status of ships, not considering the dynamic characteristics and the variability of the ship's trajectory, which makes the calculated indicators inaccurate not allowing an accurate risk assessment. 2) Many deep learning–based algorithms used in ship trajectory prediction are not easy to be trained, as the model structure makes the features unable to be processed in parallel. First, the ships with potential collision risk are clustered by the Density–Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to divide the hotspots. Then, the possible locations of ships in the near future are calculated by a multi–step prediction model, i.e., the designed Transformer network. Finally, the ship pairs' collision risk and the regional collision risk are evaluated based on the predicted results. Based on the AIS data from the Yangtze River, the effectiveness of the proposed framework is verified through regional risk assessment for 41 moments and specific interpretation for three moments. [ABSTRACT FROM AUTHOR]

[Display omitted] • A robust FOD detection method for rocket tank final assembly based on acoustic signal is proposed. • The FOD energy variation-driven adaptive WF is studied for signal denoising. • The FOD state probability of the front and rear frames is considered to improve GMM for FOD detection. • Long-time frames and abnormal percentage-related index is used to enhance the robustness of FOD detection. • The effectiveness of the proposed method is verified on a rocket tank experimental platform. The presence of Foreign Object Debris (FOD) during the assembly of rocket tanks poses a significant risk to the success of rocket launches. The current manual listening method which relies on the human ear for detecting FOD in rocket tanks is limited by its low level of automation and poor robustness. To overcome these limitations, this study presents a Robust Foreign Object Debris Detection (RFODD) method that utilizes an Adaptive Wiener Filter (EDAWF) based on energy variation and an Adaptive Forgetting Factor Gaussian Mixture Model (AFFGMM). EDAWF improves the input signal quality, while AFFGMM addresses the challenge of updating parameters under time-varying signals. FOD detection is then performed through a multi-segment counting method and a criterion based on the anomaly percentage index. The proposed RFODD method was evaluated through an experimental study on a rocket tank and was found to offer a more robust and automated solution for FOD detection with engineering applications. [ABSTRACT FROM AUTHOR]

Glycosylinositol phosphorylceramides (GIPCs) are the major sphingolipids in the plant plasma membrane. In Arabidopsis , mutations of genes involved in the synthesis of GIPCs affect many physiological aspects of plants, including growth, pollen fertility, defense, and stress signaling. Loss of function of the GIPC MANNOSYL-TRANSFERASE1 (AtGMT1) results in GIPC misglycosylation and induces plant immune responses accompanied by a severely dwarfed phenotype, thus indicating that GIPCs play important roles in plant immunity. Here, we investigated the enzymatic activity and phenotypes of transgenic lines of OsGMT1 , the ortholog of AtGMT1. Sphingolipidomic analysis indicated that OsGMT1 retained the enzymatic activity of GIPC hexose (Hex) glycosylation, but the knockout lines did not accumulate H 2 O 2. In contrast, the OsGMT1 overexpression lines showed significant down-regulation of several defense-associated or cell wall synthesis-associated genes, and enhanced sensitivity to rice blast. Furthermore, we first demonstrated the sensitivity of rice cells to MoNLP1 protein through calcein AM release assays using rice protoplasts, thus legitimizing the presence of MoNLPs in rice blast fungus. In addition, yeast two-hybrid screens using OsGMT1 as bait revealed that OsGMT1 may regulate heading time through the OsHAP5C signaling pathway. Together, our findings suggested clear physiological functional differentiation of GMT1 orthologs between rice and Arabidopsis. • Loss of function of OsGMT1 , the ortholog of AtGMT1 , did not result in accumulation of peroxidase and H 2 O 2 in homozygous knockout transgenic rice plants, thus indicating functional differentiation of GMT1 orthologs between rice and Arabidopsis. • Sphingolipidomic analysis of OsGMT1 overexpression lines indicated that OsGMT1 retained GIPC Hex glycosylation enzymatic activity. • The sensitivity of monocot rice cells to MoNLP1 toxin secreted by Magnaporthe grisea was demonstrated through calcein AM release assays using rice protoplasts. • OsGMT1 may regulate heading time through the OsHAP5C signaling pathway. [ABSTRACT FROM AUTHOR]

Dysregulation of the immune system is a cardinal feature of opioid addiction. Here, we characterize the landscape of peripheral immune cells from patients with opioid use disorder and from healthy controls. Opioid-associated blood exhibited an abnormal distribution of immune cells characterized by a significant expansion of fragile-like regulatory T cells (Tregs), which was positively correlated with the withdrawal score. Analogously, opioid-treated mice also showed enhanced Treg-derived interferon-γ (IFN-γ) expression. IFN-γ signaling reshaped synaptic morphology in nucleus accumbens (NAc) neurons, modulating subsequent withdrawal symptoms. We demonstrate that opioids increase the expression of neuron-derived C-C motif chemokine ligand 2 (Ccl2) and disrupted blood-brain barrier (BBB) integrity through the downregulation of astrocyte-derived fatty-acid-binding protein 7 (Fabp7), which both triggered peripheral Treg infiltration into NAc. Our study demonstrates that opioids drive the expansion of fragile-like Tregs and favor peripheral Treg diapedesis across the BBB, which leads to IFN-γ-mediated synaptic instability and subsequent withdrawal symptoms. [Display omitted] • An expansion of fragile-like Tregs is identified in heroin-associated blood • Opioid-induced global hypoxia triggers Treg fragility • Fabp7 protects BBB integrity from opioid-induced hyperpermeability • IFN-γ regulates opioid-induced NAc synaptic remodeling and withdrawal signs Opioids drive the peripheral expansion of fragile-like regulatory T cells that cross the blood-brain barrier and, through IFN-γ, mediate synaptic instability and subsequent withdrawal symptoms. [ABSTRACT FROM AUTHOR]

• A long-sequence multi-step prediction method based on multivariate Gaussian hypothesis and Gaussian process is proposed. • The reference trajectory prediction model is developed, and the uncertainty intervals are predicted. • The two parts are fused for a more accurate prediction to calculate the dynamic collision probability. • The Gaussian process and a Laplacian Eigenmaps-Self-Organizing Maps model are adopted for fast batch processing. • The experimental results demonstrate that the proposed model can achieve a more accurate dynamic risk assessment. A long-sequence multi-step prediction method based on multivariate Gaussian hypothesis and Gaussian process is proposed to model the uncertainty in the future ship path. This is a necessary step to predict the area where the ship is likely to be located at each future moment and to perform a dynamic risk assessment. Through data fusion, the uncertainty of the prediction is reduced, and more accurate support can be achieved for risk assessment. Firstly, from the current trajectory, the initial uncertainty intervals for the future trajectory are predicted based on the Gaussian process. Then, from the historical data, a reference trajectory set suitable for predicting the future path is generated based on a feature extracting process, named the reference trajectory prediction model in this paper, and the uncertainty intervals are also predicted. After that, the two parts are fused for a more accurate prediction to calculate the dynamic collision probability. The Gaussian process and a Laplacian Eigenmaps-Self-Organizing Maps model are adopted for fast batch processing. The experimental results demonstrate that the proposed model can combine the advantages of both and achieve a more accurate dynamic risk assessment. [ABSTRACT FROM AUTHOR]

• Game theory solves the problem of new energy power generation equipment capacity planning. • Electric vehicles connect to hybrid power generation systems as auxiliary equipment to replace energy storage batteries. • Establish a representative days selection model based on mixed integer multi-objective linear programming (MIMLP). • Consider the uncertainty of electric vehicle traffic attributes. In recent years, hybrid power systems (HPSs) with renewable energy sources, such as wind power and photovoltaic (PV) power, have been used as one of the most effective methods to address resource shortages and environmental pollution. In this paper, a model based non-cooperative game theory is developed to optimize the capacity configuration problem with wind power, PV power, and electric vehicles (EVs). The model is integrated with economic factors, such as electricity sales income, investments, and power supply reliability costs. An iterative search algorithm is used to solve the proposed game model, and the particle swarm optimization algorithm was used to optimize the income of each player. Mixed integer multi-objective linear programming (MIMLP) is used to establish the representative days selection model of resources and load demands. To verify the rationality of the model, the grid-connected hybrid power generation system with different game participants and line transmission capacities was studied. Furthermore, the representative days results of the daily load rate and peak valley ratio generated by MIMLP are compared with those obtained using k -means clustering and a scenario reduction method. The results reveal that the proposed model and related strategies can realize a reasonable allocation of resources and effectively improve the operation economy of power systems. [ABSTRACT FROM AUTHOR]

In this study, a novel bacteriocin Lactocin C-M2 produced by Lactobacillus panis C-M2, combined with dielectric barrier discharged cold plasma (DBD-CP), was used to evaluate the antibacterial effect on aquatic foods. After the purification procedures of ethyl acetate extraction, cation exchange chromatography and semi-preparative liquid phase, the stability of Lactocin C-M2 under DBD-CP environment was determined, and the preservation effect of these two joint treatments was investigated on fresh white fish samples. As revealed by LC-MS/MS and BLAST analysis, Lactocin C-M2 is a new type of class II bacteriocin, with a molecular weight of 863.52 Da and the N -terminal sequence MVKKTSAV. Application of Lactocin C-M2 showed significantly stronger inhibitory effect on bacteria than on yeasts and mold. All the tested 10 Gram positive bacteria and 3 Gram-negative bacteria, including Staphylococcus aureus, Shigella flexneri, Bacillus spp, Lactobacillus spp, Escherichia coli, Pseudomonas aeruginosa, and so on, were inhibited. Lactocin C-M2 also presented stable antibacterial activity after exposure to DBD-CP, with the 95% residual activity against Staphylococcus aureus under the 40∼80 kV voltage for 30∼180 s, indicating the possibility of synergistic application. Combined with addition of 0.9 mg/g Lactocin C-M2, the treatment of DBD-CP with voltage at 60 kV for 90 s on fresh white fish (Culter alburnus) could significantly inhibit the microbial growth, the accumulation of volatile nitrogen and histamine during the storage. Therefore, the Lactocin C-M2, used together with the DBD-CP, is effective in food preservation. [ABSTRACT FROM AUTHOR]

The natural distribution of industrial data is imbalanced, which deteriorates the performance of intelligent fault diagnostic models. Although cost-sensitive learning is an effective method for solving the data imbalance problem, it suffers from the difficulty of setting optimal costs. Therefore, this paper proposes a strategy that considers the number distribution of samples, the convergence trend of classes, and the convergence trend of samples to calculate sample costs adaptively. Using costs to weigh the sample losses and applying them to different models and different loss functions, the diagnostic results under different sample sets show that the weighted losses can significantly improve the model's performance when using imbalanced data. By further analysing the training loss of the modified model, the angles between deep features, and the angles between deep features and classification weight vectors, it can be found that the dominance of majority classes in imbalanced data is suppressed in training, which is attributed to the loss of each class being coordinated by the proposed strategy. The comparison with various imbalanced learning methods demonstrates the advantages of the proposed method under conditions of large imbalance ratios and complex tasks. • A novel cost-adaptive calculation method is proposed. • The cost varies adaptively with the sample distribution, the sample convergence trend, and the category convergence trend. • By applying the sample cost to the loss function, the losses of each class are coordinated. • Results with different models, different loss functions, and different datasets show the flexibility of the proposed method. [ABSTRACT FROM AUTHOR]

Transparent stretchable wearable hybrid nano-generators present great opportunities in motion sensing, motion monitoring, and human-computer interaction. Herein, we report a piezoelectric-triboelectric sport sensor (PTSS) which is composed of TENG, PENG, and a flexible transparent stretchable self-healing hydrogel electrode. The piezoelectric effect and the triboelectric effect are coupled by a contact separation mode. According to this effect, the PTSS shows a wide monitoring range. It can be used to monitor human multi-dimensional motions such as bend, twist, and rotate motions, including the screw pull motion of table tennis and the 301C skill of diving. In addition, the flexible transparent stretchable self-healing hydrogel is used as the electrode, which can meet most of the motion and sensing requirements and presents the characteristics of high flexibility, high transparency, high stretchability, and self-healing behavior. The whole sensing system can transmit signals through Bluetooth devices. The flexible, transparent, and stretchable wearable hybrid nanogenerator can be used as a wearable motion monitoring sensor, which provides a new strategy for the sports field, motion monitoring, and human-computer interaction. [ABSTRACT FROM AUTHOR]

Because the fault characteristic frequencies of a rolling bearing are submerged in strong noise when it fails early, the fault feature in the original signal is relatively weak to allow the diagnosis of the bearing. Consequently, the method to extract a weak fault feature is becoming a challenging research topic in fault diagnosis. Traditional diagnostic networks are typically trained by the time series or the frequency spectrum of the acquired discrete signal fragment, whereas the connection of the local fragments (quasi-periodicity) is neglected, resulting in low diagnostic accuracy for the bearing under strong noise conditions. To solve this problem, a novel weak fault feature extraction and diagnosis method, composed of two parts, is proposed in this paper. The first part is a multi-channel continuous wavelet transform (MCCWT), by which the original temporal signals can be more easily transformed into a new representation with several channels and fewer network parameter requirements than those required by the traditional methods. The second part is a convolution-feature-based recurrent neural network (CFRNN) that is based on a traditional recurrent neural network (RNN). In the latter, a recurrent unit combining several residual blocks and a long short term memory (LSTM) block is proposed to mine the temporal features and the local vibration characteristics simultaneously. The efficiency of the proposed diagnosis method is validated respectively by the datasets collected by simulating fault bearings with strong noise and using real fault bearings containing faults at an early stage. • The quasi-periodicity is considered in the proposed weak fault diagnosis method. • A multi-channel representation method is proposed to keep the details in signal. • Residual blocks were used to interpret the local time–frequency characteristics. • The efficiency was validated by simulated fault bearings and real fault bearings. [ABSTRACT FROM AUTHOR]

A self-powered portable triboelectric nanogenerator (TENG) is used to collect biomechanical energy and monitor the human motion, which is the new development trend in portable devices. We have developed a self-powered portable triboelectric nanogenerator, which is used in human motion energy collection and monitoring mobile gait and stability capability. The materials involved are common PTFE and aluminum foil, acting as a frictional layer, which can output electrical signals based on the triboelectric effect. Moreover, 3D printing technology is used to build the optimized structure of the nanogenerator, which has significantly improved its performance. TENG is conveniently integrated with commercial sport shoes, monitoring the gait and stability of multiple human motions, being strategically placed at the immediate point of motion during the respective process. The presented equipment uses a low-frequency stabilized voltage output system to provide power for the wearable miniature electronic device, while stabilizing the voltage output, in order to effectively prevent voltage overload. The interdisciplinary research has provided more application prospects for nanogenerators regarding self-powered module device integration. [ABSTRACT FROM AUTHOR]

Whiskers have been extensively used in ceramics as excellent reinforcing phases during the past decades. Despite in-depth investigation of whisker-reinforced ceramics (WRC), however, the time-consuming, environmentally unfriendly, content design blindness of experimental method as well as pathogenicity of whiskers force people to explore quick and green research approaches. Therefore, this work aims at developing a modeling approach of WRC and predicting the mechanical properties of WRC, thereby compensating for the disadvantages of traditional experimental method. Initially, a modeling approach of WRC based on Voronoi tessellation was presented. Then, taking β-Si 3 N 4w and Si 3 N 4 as research objects, the prediction models of mechanical properties of 0–6 wt% β-Si 3 N 4w -reinforced Si 3 N 4 ceramics (SWRSC) were established. Finally, the effect of β-Si 3 N 4w content on mechanical properties of Si 3 N 4 -based ceramics was discussed, and the enhancement mechanism of β-Si 3 N 4w was revealed. Results indicated that the flexural strength of SWRSC was significantly advanced with increasing β-Si 3 N 4w content. Crack deflection and intergranular fracture induced by β-Si 3 N 4w as well as load-bearing effect of β-Si 3 N 4w were the mainly responsible for the reinforcement of Si 3 N 4 -based ceramics. The enhancement mechanisms, previously observed experimentally, were effectively simulated. The fracture toughness reached a maximum of 6.17 MPa m1/2 when β-Si 3 N 4w amount was 1 wt%, which was approximately 36.50% higher than monolithic Si 3 N 4 ceramic. The predicted value of β-Si 3 N 4w optimal content was consistent with relevant experimental conclusions. When β-Si 3 N 4w content was 3 wt%, the hardness of Si 3 N 4 -based ceramics reached the maximum value of 23.21 GPa. The effectiveness of prediction results and the presented modeling method were confirmed by comparing with related test values. [ABSTRACT FROM AUTHOR]

The trial-and-error method used in ceramics research has certain limitations such as the high blindness of material component design. Moreover, calculations of the toughness of ceramics using the extended finite element method, which is the most broadly applied technique, are complicated. To overcome these issues, in this study, multilayer graphene (MLG)/Si 3 N 4 whisker (Si 3 N 4w)-reinforced Si 3 N 4 ceramics (MWSCs) were used as the model material, and the modeling of MWSCs was conducted using Voronoi tessellation. Additionally, a more concise novel approach was applied for the prediction of the fracture toughness of MWSCs. Furthermore, the optimal MLG and Si 3 N 4w contents were predicted, and then they were verified by fabricating MWSCs using spark plasma sintering (SPS). Simulation results indicated that the optimum MLG and Si 3 N 4w contents to enable the toughness and hardness to reach the maximum values (9.87 MPa·m1/2 and 23.19 GPa) were 1 wt% and 3 wt%, which were consistent with the experimental results. Consequently, the effectiveness of the proposed method was verified. Moreover, the experimental values of the maximum fracture toughness and hardness were 11.04 MPa·m1/2 and 20.29 GPa, which were 47.20% and 12.10% higher than those of Si 3 N 4 ceramics reinforced with 1 wt% MLG, respectively. The synergistic toughening effects of MLG and Si 3 N 4w were significantly reflected. The load-bearing effect, bridging, and crack deflection induced by MLG and Si 3 N 4w were the key reasons for the improvement in the mechanical properties of MWSCs. [ABSTRACT FROM AUTHOR]

Imbalance is a typical characteristic of data in the field of intelligent fault diagnosis. As a data augmentation method that both balances data and extends information, the generative adversarial network has aroused widespread concern in the data imbalance problem. However, it still suffers from high difficulty in model training and poor quality of generated samples. To address this issue, this paper proposes a progressive generative adversarial network, which is a strategy able to gradually generate complex signals from low-frequency signals to wide-frequency signals. In the developed network, on the one hand, decomposing signals into different frequency bands explicitly reduces the difficulty of directly generating high-dimensional and wide-frequency signals. On the other hand, different generative models can focus on different frequency bands, avoiding the interference of high-energy signals on the fault feature frequencies. In order to achieve good generative performance, the generators and training method for the progressive generative adversarial network are elaborately designed. The correlation shortcut avoids information suppression due to the poor selection of noise amplitudes, and the focusing shortcut forces the body of the generator to focus on key feature bands. Ultimately, the superiority of the progressive generative network is verified by evaluating the quality of the generated samples on three datasets and applying the generated samples to solve the data imbalance problem. • A progressive generation framework is proposed to improve the quality of the generated signal. • The proposed generator retains the correlation between low and high frequencies. • The proposed generator can focus on specific frequency bands. • Model at each level is trained independently to avoid the accumulation of errors. [ABSTRACT FROM AUTHOR]

Polyploid is considered an advantage that has evolved to be more environmentally adaptable than its diploid. To understand if doubled chromosome of diploid rice can improve drought tolerance, we evaluated the diploid (2X) and autotetraploid (4X) plants of three indica and three japonica varieties. Drought stress in the plastic bucket of four-leaf stage revealed that the drought tolerance of 4X plants was lower than that of its diploid donor plants. The assay of photosynthetic rate of all varieties showed that all 4X varieties had lower rates than their diploid donors. The capacity for reactive oxygen species production and scavenging varied among different 2X and 4X varieties. Further, transcriptomic analysis of 2X and 4X plants of four varieties under normal and drought condition showed that the wide variation of gene expression was caused by difference of varieties, not by chromosome ploidy. However, weighted gene co-expression network analysis (WGCNA) revealed that the severe interference of photosynthesis-related genes in tetraploid plants under drought stress is the primary reason for the decrease of drought tolerance in autotetraploid lines. Consistently, new transcripts analysis in autotetraploid revealed that the gene transcription related with mitochondrion and plastid of cell component was influenced most significantly. The results indicated that chromosome doubling of diploid rice weakened their drought tolerance, primarily due to disorder of photosynthesis-related genes in tetraploid plants under drought stress. Maintain tetraploid drought tolerance through chromosome doubling breeding in rice needs to start with the selection of parental varieties and more efforts. [ABSTRACT FROM AUTHOR]

Summary Although thousands of genes have been identified or cloned in rice (Oryza sativa) in the last two decades, the majority of them have only been separately characterized in specific varieties or single‐gene modified backgrounds, thus limiting their practical application. We developed an optimized multiplex genome editing (MGE) toolbox that can efficiently assemble and stably express up to twelve sgRNA targets in a single plant expression vector. In this study, we established the MGE‐based Rapid Directional Improvement (MRDI) strategy for directional improvement of complex agronomic traits in one small‐scale rice transformation. This approach provides a rapid and practical procedure, encompassing sgRNA assembly, transgene‐free screening and the creation of promising germplasm, by combining the precision of gene editing with phenotype‐based field breeding. The MRDI strategy was used to generate the full diversity of twelve main agronomic genes in rice cultivar FXZ for the directional improvement of its growth duration and plant architecture. After applying the MRDI to FXZ, ideal plants with the desired traits of early heading date reduced plant height, and more effective panicles were generated without compromising yield, blast resistance and grain quality. Furthermore, the results of whole‐genome sequencing (WGS), including the analysis of structural variations (SVs) and single nucleotide variations (SNVs) in the MGE plants, confirmed the high specificity and low frequency of unwanted mutations associated with this strategy. The MRDI breeding strategy would be a robust approach for exploring and applying crucial agronomic genes, as well as for generating novel elite germplasm in the future. [ABSTRACT FROM AUTHOR]

Alcohol use disorder is characterized by compulsive alcohol-seeking behavior, which is associated with dysregulation of afferent projections from the medial prefrontal cortex to the basolateral amygdala (BLA). However, the contribution of the cell type–specific mechanism in this neuronal circuit to alcohol-seeking behavior remains unclear. Mice were trained with 2-bottle choice and operant alcohol self-administration procedures. Anterograde and retrograde viral methods traced the connection between dopamine type 1 receptor (D1R) neurons and BLA neurons. Electrophysiology and in vivo optogenetic techniques were used to test the function of neural circuits in alcohol-seeking behavior. Chronic alcohol consumption preferentially changed the activity of posterior BLA (pBLA) neurons but not anterior BLA (aBLA) neurons and overexcited D1R neurons in the medial prefrontal cortex. Interestingly, we found that 2 populations of D1R neurons, anterior and posterior (pD1R) neurons, separately targeted the aBLA and pBLA, respectively, and only a few D1R neurons innervated both aBLA and pBLA neurons. Furthermore, pD1R neurons exhibited more excitability than anterior D1R neurons in alcohol-drinking mice. Moreover, we observed enhanced glutamatergic transmission and an increased NMDA/AMPA receptor ratio in the medial prefrontal cortex inputs from pD1R neurons to the pBLA. Optogenetic long-term depression induction of the pD1R-pBLA circuit reduced alcohol-seeking behavior, while optogenetic long-term depression or long-term potentiation induction of the anterior D1R–aBLA circuit produced no change in alcohol intake. The pD1R-pBLA circuit mediates chronic alcohol consumption, which may suggest a cell type–specific neuronal mechanism underlying reward-seeking behavior in alcohol use disorder. [ABSTRACT FROM AUTHOR]

Summary: The biological synthesis of functionalised nanoparticles using plant materials is considered more cost‐effective and eco‐friendly than chemical and physical methods. Hence, this study demonstrated the synthesis of silver nanoparticles (AgNPs) using Houttuynia cordata Thunb rhizome (HCR). The extract, prepared with distilled water, provided the required phytochemical components, such as flavonoids, terpenoids, phenols, proteins and other essential bio‐reducing agents. The AgNPs were exposed to reaction conditions, such as temperatures, pH values and AgNO3 concentrations, to identify the optimum synthesis condition and explore their impacts on particle size and antibacterial action against Gram‐positive (Staphylococcus aureus) and Gram‐negative (Salmonella enterica and Shigella dysenteriae) foodborne pathogens. The formation of AgNPs was indicated by specific surface plasmon resonance (SPR) intensity at 440–450 nm and colour changes under different reaction conditions. The structure and size distribution characterised by SEM and TEM revealed that the AgNPs exhibited a spherical‐shaped structure with irregular contours, regular distribution and aggregation. Although the average particle size ranged from 2 to 100 nm, relatively smaller sizes were detected at 10 mM (54.30), 80 °C (49.50 nm) and pH 6 (60.16 nm) conditions. The FTIR spectrum and EDX spectral signals affirmed the presence of reducing and stabilising agents in HCR extract, following the reduction of AgNPs from Ag+ to Ag0. AgNPs also exhibited excellent antibacterial activity against S. aureus, S. enterica and Sh. dysenteriae, indicating a broad spectrum for various applications in the food and pharmaceutical industries. [ABSTRACT FROM AUTHOR]

• Uncertainty analysis and a weighted bayesian fusion method on the decision-making side. • A semi-supervised fault diagnosis method for the simultaneous presence of a wide range of variable speed and sparsely labeled abnormal samples. • Method construction considering typical model failures. • Bearing experiments involving 20 different rotational speeds. The wide variety of rotational speeds and the small sample dataset make it challenging to mine the fault-related features in the signal, thereby limiting the generalization capability of the model. To address this problem, a semi-supervised method for fault diagnosis through feature perturbation and decision fusion is proposed in this paper. Firstly, a dual correlation model is constructed, and then the model's structural parameters are adjusted to increase the diversity and uncertainty of the diagnostic results. Finally, the conditional probability and penalty term of the sub-models are analyzed based on high-confidence samples, enabling the achievement of the final fusion diagnosis. To verify the effectiveness of the proposed method, variable speed experiments containing 20 kinds of speeds were conducted based on the Bearing Prognostics Simulator test bench. The validity of the proposed method is demonstrated through the constructed dataset. [ABSTRACT FROM AUTHOR]

Numerical studies have been performed to analyze the fluid flow and heat transfer characteristics of nine microchannel heat sinks (MCHS) with different shapes and different arrangements of the ribs and cavities on the sidewalls, using three common shapes (square, triangle, and circular) of ribs or cavities as the basic structure in this work. The boundary conditions, governing equations, friction factor (ƒ), Nusselt number (Nu), and performance evaluation criteria (ξ) were considered to determine which design was the best in terms of the heat transfer, the pressure drop, and the overall performance. It was observed that no matter how the circular ribs or cavities were arranged, its heat sink performance was better than the other two shapes for Reynolds number of 200–1000. Therefore, circular ribs or cavities can be considered as the best structure to improve the performance of MCHS. In addition, the heat sink performance of the microchannel heat sink with symmetrical circular ribs (MCHS-SCR) was improved by 31.2 % compared with the conventional microchannel heat sink at Re = 667. This was because in addition to the formation of transverse vortices in the channel, four symmetrical and reverse longitudinal vortices are formed to improve the mixing efficiency of the central fluid (low temperature) and the near-wall fluid (high temperature). Then, as the Reynolds number increases, the heat sink performance of MCHS-SCR dropped sharply. The heat sink performance of microchannel heat sinks with staggered ribs and cavities (MCHS-SCRC, MCHS-STRC, and MCHS-SSRC) exceeded that of MCHS-SCR. This indicated that the microchannel heat sink with staggered ribs and cavities was more suitable for high Reynolds number (Re > 800). [ABSTRACT FROM AUTHOR]

This study explored the effect of HPPM treatment (200 Hz for 3, 5, 7, 9, and 11 min) on P. aeruginosa PAO1 inactivation using tandem mass tag-based quantitative proteomics. HPPM demonstrated marked nucleic acid and protein constituents (p < 0.05) and inhibited ATPase activity and intercellular ATP concentration time-dependently. Hence, 9 min was selected for proteomic profiling to avoid any potential thermal effect. A total of 452 proteins (309 = down-regulated; 143 = up-regulated) were identified as differentially accumulated proteins (DAPs). The DAPs were primarily related to energy production and conversion; DNA replication, transcription, and translation, cell membrane composition; amino acid (AA) transport and metabolism, and biofilm formation and quorum sensing (QS). HPPM significantly disrupted cell membrane composition, altered AA metabolism, impeded cell energy generation, and drastically inhibited biofilm formation in P. aeruginosa. This study provided insights into the underlying mechanisms of HPPM against P. aeruginosa at the proteomic level. Pseudomonas aeruginosa is a key spoiler implicated in food contamination. High-power pulsed microwave (HPPM) is a potential non-thermal inactivation technology. However, the underlying mechanism to validate its efficacy remains unclear. This study provides the industry with the inactivation efficacy of HPPM against P. aeruginosa , highlighting insights into the molecular and biochemical changes after treatment and its remarkable potential to control cross-contamination in food processing, food services, and clinical environments. • High-power pulse microwave (HPPM) demonstrated marked leakage of nucleic acid and protein constituents. • Differentially accumulated proteins (DAPs) enabled molecular and biochemical expressions. • Most DAPs were involved in energy metabolism, membrane composition, DNA repair, and biofilm formation. • HPPM damaged cell membranes, disrupted energy production and inhibited biofilm formation. [ABSTRACT FROM AUTHOR]

Exploring ceramic-based self-lubricating materials with low coefficient of friction (COF) has become one of the hot directions to advance the service performance and life of high-end equipment. In this work, the tribological properties of multilayer graphene (MLG)/β-Si 3 N 4 whisker (β-Si 3 N 4w)/Si 3 N 4 ceramics were investigated. The effects of load and linear speed on the dry sliding COF were discussed. The friction-reducing and self-lubricating mechanisms of MLG and β-Si 3 N 4w were deeply analyzed according to the wear morphology and mechanical properties of Si 3 N 4 -based ceramics. The friction frequency was proposed to analyze the wear mechanism. Results indicated that MLG and β-Si 3 N 4w led to a breakthrough reduction in COF. At the load of 5–20 N, the COF reached an ultra-low level of 0.04–0.07, which was a further 89.86% lower than the reported studies. The comprehensive enhancement of mechanical properties and the existence of MLG lubricating film were the mainly responsible for the ultra-low COF. The COF of MLG/β-Si 3 N 4w /Si 3 N 4 ceramics raised to 0.24–0.31 at 50–200 mm/s, but it was still 33.88–47.64% lower than that of graphene/Si 3 N 4 ceramics. The increase in COF was attributed to the high wear induced by the raised friction frequency. The furrow wear and adhesive wear were the main wear forms of MLG/β-Si 3 N 4w /Si 3 N 4 ceramics. [ABSTRACT FROM AUTHOR]

The performance of intelligent fault diagnosis models is often hindered by the lack of available samples, a common issue in both the few-shot learning and imbalanced learning problems. While data generation has been shown to be an effective strategy for addressing this issue, existing methods tend to focus solely on the similarity of the generated samples, overlooking their diversity. In this regard, a self-reasoning training strategy that enables the participation of highly reliable generated samples in the training process is proposed. By augmenting the training samples, the strategy provides the generation model with diverse input information, effectively enhancing the diversity of the samples generated by the model. To assess the reliability of the generated samples, Pearson correlation coefficient between the generated and real samples in the amplitude–frequency domain is utilized. To this end, an amplitude–frequency domain generation model is constructed. In order to avoid issues such as gradient disappearance and convergence degradation during the generation process in the amplitude–frequency domain, a phase-frequency domain generation model as well as a time domain discriminator model are developed. While the phase-frequency domain generation model enhances the diversity of the generated samples in that domain, the time domain discriminator model ensures sample similarity by correlating the amplitude–frequency domain generation model with the phase-frequency domain generation model. Through experiments, it is demonstrated that the proposed method can effectively address the overfitting problem of generative adversarial networks with few samples, improving the diversity of the generated samples. Moreover, the improvement in fault diagnosis performance achieved by the samples generated by the proposed method further underscores its potential and superiority in practical applications. • A self-reasoning training strategy is proposed. • A generation model driven by multi-domain information is developed. • The quality (similarity and diversity) of the generated samples is enhanced. [ABSTRACT FROM AUTHOR]

Nitric oxide (NO) plays an important role in plant stress responses. However, the mechanisms underlying NO-induced stress resistance to cadmium (Cd) stress in rice remain elusive. In this study, rat neuron NO synthase (nNOS)-overexpressing rice plants with higher endogenous NO level showed higher cadmium stress tolerance than the wild-type plants. The results showed that nNOS-overexpressing rice plants accumulated less cadmium in the roots and shoots by downregulating the expression of Cd uptake and transport related genes including OsCAL1, OsIRT2, OsNramp5, and OsCd1. Moreover, nNOS-overexpressing rice plants accumulated less hydrogen peroxide (H2O2), accompanying with higher expression of antioxidant enzyme genes (OsCATA, OsCATB, and OsPOX1) and corresponding higher enzyme activities under cadmium stress. Furthermore, the transcription of melatonin biosynthetic genes, including OsASMT1, OsTDC1, OsTDC3, and OsSNAT2, was also upregulated in nNOS-overexpressing plants, resulting in increased content of melatonin under cadmium treatment compared with the wild-type controls. Taken together, this study indicates that nNOS overexpression improves Cd tolerance of rice seedlings through decreasing cadmium accumulation and enhancing the antioxidant capacity and melatonin biosynthesis of the plants. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective robust optimization method for a drive system consisting of a permanent magnet transverse flux machine with soft magnetic composite cores and a field-oriented controller. Unlike existing research work, the torque ripple is considered an optimization objective. Several machine uncertainties caused by manufacturing tolerances are investigated in the robust optimization model under the framework of the design for six-sigma. Since this is a system-level optimization problem, two approximation models are employed to decrease the computational cost. First, a Kriging model is used to approximate the steady-state electromagnetic performances of the motor, such as the output power and efficiency. Second, a Taylor series approximation is employed to estimate the dynamic performances of the control system, such as the speed overshoot and settling time. Furthermore, a sampling selection method is proposed to reduce the computational cost of the Monte Carlo analysis in robust optimization. To show the effectiveness of the proposed method, both deterministic and robust Pareto solutions are presented and discussed. It can be seen that the system-level multi-objective design optimization based on robust approach can produce optimal Pareto solutions with a high manufacturing quality for the whole drive system. This is valuable for the batch production of electrical drive systems. [ABSTRACT FROM AUTHOR]

High-efficiency quantum cutting materials have great application prospects in improving the photoelectric conversion efficiency of silicon-based photoelectric devices. Herein, La3+ and Yb3+ ions co-doped CsPbCl 3 perovskite quantum dots (PQDs) were prepared successfully via the modified hot-injection method. La3+ and Yb3+ ions codoping makes the PLQY of CsPbCl 3 PQDs increase from 5% to 168%. This is because Yb3+ ions doping makes the effective quantum cutting process happen, as well as La3+ ions doping effectively reduces non-radiative transition in CsPbCl 3 PQDs and further improves the quantum cutting efficiency. Finally, La3+ and Yb3+ ions co-doped CsPbCl 3 PQDs film were integrated on silicon-based photodetectors. The spectral range of silicon-based photodetectors is extended to the ultraviolet (UV) region (200–400 nm) besides the visible spectral region. The external quantum efficiency (EQE) is around 67%, and the responsivity is 0.14 A/W, which is comparable to the highest value previously reported. This work provides a new strategy to improve the quantum cutting efficiency and the performance of silicon-based photodetectors. • The PLQY of CsPbCl 3 PQDs increase from 5% to 168% via La3+ and Yb3+ codoping. • La3+/Yb3+ co-doped CsPbCl 3 PQDs were integrated on silicon photodetectors. • The response spanning of silicon photodetectors is extended to the ultraviolet. [ABSTRACT FROM AUTHOR]

Lead-free perovskites are a new type of luminescent material with non-toxic, stability and excellent photoelectric properties, which have great application in the field of photoelectric detection, solar cells, bio-imaging, etc. Here, we reported a new kind of uniform spherical rare-earth based double perovskite Cs 2 NaYCl 6 nanocrystals (NCs) as versatile hosts to accommodate ionic dopants for improving photoelectric properties especially the luminescent properties. In contrast to the low quantum yield of only 5.62%, the luminescent intensity of Cs 2 NaYCl 6 NCs can be impressively enhanced via doping Sb3+ and Mn2+ ions in the rare earth-based double perovskite lattices. For the Sb3+-doping, the photoluminescence quantum yield (PLQY) increases from 5.62% to 22.48%. On this basis, the luminescence colors can be tuned from blue to white and red via Mn2+ ions doping and the maximum PLQY can be further improved to 38.66%. Finally, the Sb3+, Sb3+/Mn2+ ions doped Cs 2 NaYCl 6 NCs based blue, red and white LEDs were explored, which indicates that these are potential materials in multicolor LEDs applications. [ABSTRACT FROM AUTHOR]

Plants usually keep resistance (R) proteins in a static state under normal conditions to avoid autoimmunity and save energy for growth, but R proteins can be rapidly activated upon perceiving pathogen invasion. Pib , the first cloned blast disease R gene in rice, encoding a nucleotide-binding leucine-rich repeat (NLR) protein, mediates resistance to the blast fungal (Magnaporthe oryzae) isolates carrying the avirulence gene AvrPib. However, the molecular mechanisms about how Pib recognizes AvrPib and how it is inactivated and activated remain largely unclear. In this study, through map-based cloning and CRISPR-Cas9 gene editing, we proved that Pib contributes to the blast disease resistance of rice cultivar Yunyin (YY). Furthermore, an SH3 domain-containing protein, SH3P2, was found to associate with Pib mainly at clathrin-coated vesicles in rice cells, via direct binding with the coiled-coil (CC) domain of Pib. Interestingly, overexpression of SH3P2 in YY compromised Pib -mediated resistance to M. oryzae isolates carrying AvrPib and Pib-AvrPib recognition-induced cell death. SH3P2 competitively inhibits the self-association of the Pib CC domain in vitro , suggesting that binding of SH3P2 with Pib undermines its homodimerization. Moreover, SH3P2 can also interact with AvrPib and displays higher affinity to AvrPib than to Pib, which leads to dissociation of SH3P2 from Pib in the presence of AvrPib. Taken together, our results suggest that SH3P2 functions as a "protector" to keep Pib in a static state by direct interaction during normal growth but could be triggered off by the invasion of AvrPib -carrying M. oryzae isolates. Our study reveals a new mechanism about how an NLR protein is inactivated under normal conditions but is activated upon pathogen infection. Plants usually keep resistance (R) proteins in a static state under normal conditions to avoid autoimmunity and save energy for growth, but R proteins can be rapidly activated upon perceiving pathogen invasion. In this study, the authors find that SH3P2, an SH3 domain-containing protein that interacts with both Pib and AvrPib, suppresses effector-triggered, Pib-mediated immunity in rice, revealing a new mechanism in which an NLR protein is inactivated under normal conditions but is activated upon pathogen infection. [ABSTRACT FROM AUTHOR]

• Three groups of glycosyltransferase 64 (GT64) family are widely conserved in Angiospermae. • Four rice GT64 genes all localize to the Golgi apparatus and show distinct tissue-specific expression patterns. • Genomic variation analysis of OsGT64s revealed that the indica – japonica or latitudinal differentiation exists in their coding sequences. • Promoter element and expression analysis of OsGT64s suggested that they may play a role in different stress responses and hormone signaling pathways. The glycosyltransferase 64 (GT64) family is widely conserved in many species, including animals and plants. The functions of GT64 family genes in animals have been well characterized in the biosynthesis of extracellular heparan sulfate, whereas two GT64 members in Arabidopsis thaliana are involved in the glycosylation of plasma membrane glycosylinositol phosphorylceramides (GIPCs). GIPCs are the main components of plant sphingolipids and serve as important signal molecules in various developmental processes and stress responses. Rice (Oryza sativa), a model monocot plant, contains four GT64 members in its genome. Using phylogenetic analysis, 73 GT64s from 19 plant species were divided into three main groups. Each group can be represented by the three members in Arabidopsis and show a trend of monocot-eudicot divergence. A promoter and genomic variation analysis of GT64s in rice showed that various stress-related regulatory elements exist in their promoters, and many sequence variations were found between the two main rice subspecies, japonica and indica. Additionally, transmembrane domain and subcellular localization analyses revealed that these genes all encode membrane-bound glycosyltransferases and are localized to the Golgi apparatus. Finally, expression analysis of the four GT64 genes in rice, as assessed by quantitative real-time PCR, showed that they have distinct tissue-specific expression patterns and respond to different hormone treatments or abiotic stresses. Our results indicated that this family of genes may play a role in different stress responses and hormone signaling pathways in rice, which will provide fundamental information for further investigation of their functions in future. [ABSTRACT FROM AUTHOR]

A self-powered triboelectric nanogenerator (SPTENG) based on triboelectric effect and an intelligent interactive system are fabricated for monitoring shooting training and virtual training. The SPTENG is composed of latex and PTFE and an intelligent system. Based on triboelectric effect, the SPTENG can be used to monitor the progress of trigger pressing without a power supply (this is supplied by trigger movements). Because of the flexible properties, it can be attached to a trigger conveniently to monitor the progress of trigger pressing, such as trigger time, trigger stability, etc. Meanwhile, as part of an intelligent shooting system, police can formulate a standard scheme according to signals to improve their skills. Furthermore, they can use it to train between reality and virtuality. Therefore, it has a wide development space in human–computer interaction and real-time information processing. [ABSTRACT FROM AUTHOR]

Serotonin (5-HT) is implicated in the reward processes underlying substance use disorder. Epigenetic and transcriptional mechanisms contribute to the development of addictive states. To examine the potential mechanisms of 5-HT receptor genes in opioid use disorder, we first determined the associations between several single-nucleotide polymorphism (SNPs) in three representative 5-HT receptor genes (HTR1B, HTR2A, and HTR3B) and susceptibility to heroin use disorder in 1731 participants. Gene–gene interactions among these genes were analyzed. After identifying the susceptibility genes and SNPs for heroin use disorder, DNA methylation in the promoter region of these susceptibility genes was compared between 111 healthy controls and 120 patients with heroin use disorder. In addition, associations between the susceptibility SNPs and methylation of the CpG sites and gene promoters with differential methylation between groups were examined. Finally, the function of the susceptibility SNPs in the expression of the corresponding genes was screened. Our results demonstrated that rs6296 in the HTR1B gene was correlated with susceptibility to heroin use disorder. Gene–gene interactions between the HTR1B and HTR2A genes were identified. The CpG sites HTR1B_07 and HTR1B_26 and the promoter region of the HTR1B gene were hypermethylated in patients with heroin use disorder compared with healthy controls. Notably, rs6296 correlated in an allele-specific manner with methylation in the HTR1B gene promoter in the blood and gene expression of the HTR1B gene in the frontal cortex and hypothalamus. SNP rs6296 was associated with opioid use disorder by involving mechanisms of DNA methylation and expression of the HTR1B gene. [ABSTRACT FROM AUTHOR]

Wearable motion-monitoring systems have been widely used in recent years. However, the battery energy storage problem of traditional wearable devices limits the development of human sports training applications. In this paper, a self-powered and portable micro-structure triboelectric nanogenerator (MS-TENG) has been made. It consists of micro-structure polydimethylsiloxane (PDMS) film, fluorinated ethylene propylene (FEP) film, and lithium chloride polyacrylamide (LiCl-PAAM) hydrogel. Through the micro-structure, the voltage of the MS-TENG can be improved by 7 times. The MS-TENG provides outstanding sensing properties: maximum output voltage of 74 V, angular sensitivity of 1.016 V/degree, high signal-to-noise ratio, and excellent long-term service stability. We used it to monitor the running skills of speed skaters. It can also store the biomechanical energy which is generated in the process of speed skating through capacitors. It demonstrates capability of sensor to power electronic calculator and electronic watch. In addition, as a flexible electrode hydrogel, it can readily stretch over 1300%, which can help improve the service life and work stability of MS-TENG. Therefore, MS-TENG has great application potential in human sports training monitoring and big data analysis. [ABSTRACT FROM AUTHOR]

Foodborne pathogens and biofilm formation pose a critical threat to food safety. Pseudomonas aeruginosa is a pathogen of interest in the food industry. High-power pulsed microwave (HPPM) is a non-thermal technology with proven efficacy towards bacterial inactivation. This study investigated the inactivation mechanisms and performance of different HPPM treatments (50, 100, 150, 200, and 250 Hz for 3, 5, 7, 9, and 11 min, respectively) to identify the optimum conditions to inactivate P. aeruginosa PAO1 biofilms. The results showed significant cell inactivation at increasing frequency/time (p < 0.05). Hence, HPPM treatment (200 Hz, 9 min) was selected following its efficacy and limited thermal sublethal effects to elucidate further the inactivation mechanisms against P. aeruginosa and its biofilm-forming ability. Cell viability, scanning (SEM) and transmission electron microscopy (TEM) results demonstrated severe morphological disruptions, cell disintegration, and cell lysis in the treated cells. From the confocal laser scanning micrographs (CLSM), biofilm thickness was calculated, showing a significant reduction after treatment (17.56 vs. 42.16 μm in the treated and untreated groups, respectively), indicating that HPPM effectively compromised the integrity of the biofilm envelope. This study outlines useful insights into better understanding HPPM's efficacy on microbial inactivation and biofilm proliferation in the food industry. • High-power pulse microwave (HPPM) demonstrated significant cell inactivation at increasing frequency/time (p < 0.05). • Treated cells demonstrated severe cell deformation, membrane shriveling, and cell lysis. • It showed a marked reduction in biofilm thickness than untreated cells (17.56 ± 1.51vs. 42.16 ± 1.23 μm). • This study provides useful insights into validating HPPM's efficacy against bacterial biofilm formation. [ABSTRACT FROM AUTHOR]

Exploring the natural, safe, and effective antimicrobial is one of the preferable ways to control foodborne bacteria. In this work, novel oil-in-water nanoemulsions were formulated with sophorolipids and eugenol without any co-surfactant using a self-assembling strategy. These nanoemulsions showed high stability with sizes less than 200 nm when exposure to low concentrations of salt ions, various pH values (5.0, 7.0, 10.0), storage temperature and time. The synergistic antibacterial effects against both Gram-negative Escherichia coli and Gram-positive Bacillus cereus were determined with a minimum inhibitory concentration (MIC) value of 0.5 mg/mL and 0.125 mg/mL, respectively. Further microscopy (SEM, TEM, LCSM) examination and ATP/Na+-K+-ATPase assay results showed that the morphological changes, intensive cell membrane permeability, leakage of ATP, and decreased Na+-K+-ATPase contributed to the antibacterial effects. Moreover, the bonding mechanism between nanoemulsions and cell membranes were further evaluated by FTIR and ITC using a DPPC vesicle model, which demonstrated that the nanoemulsions adsorbed on the surface of bilayer, interacted with the hydrophobic chains of DPPC membrane mainly through the hydrophobic interaction, and altered the structural integrity of the lipid bilayer. These results not only provide a facile green strategy for fabricating stable nanoemulsions, but also highlight a new perspective for stabilizing essential oils for their widely application in food industry. • Novel nanoemulsions were formulated with SLs and eugenol without any co-surfactant. • The nanoemulsions were stable exposure to various pH values. • Nanoemulsions showed excellent inhibition effects against gram-positive bacteria. • Nanoemulsions adsorbed on the bilayer surface, alerting the cell membrane integrity. [ABSTRACT FROM AUTHOR]

The goal of aspect-based sentiment classification (ASC) is to predict the corresponding emotion of a specific target of a sentence. In neural network-based methods for ASC, various sophisticated models such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) are widespread. Recently, ongoing research has integrated syntactic structures into graph neural networks (GNN) to deal with ASC tasks. However, these methods are limited due to the noise and inefficient use of information of syntactic dependency trees. This paper proposes a novel GNN based deep learning model to overcome the deficiencies of prior studies. In the proposed model, to exploit the information in the syntactic dependency trees, a novel part-of-speech (POS) guided syntactic dependency graph is constructed for a relational graph attention network (RGAT) to eliminate the noises. Further, a syntactic distance attention-guided layer is designed for a densely connected graph convolutional network (DCGCN), which can fully extract semantic dependency between contextual words. Experiments on three public datasets are carried out to evaluate the effectiveness of the proposed model. Comparing to the baselines, our model, as a best alternative, achieves state-of-arts performance. [ABSTRACT FROM AUTHOR]

Rationale: Opioid use disorder is a complicated brain disease with high heritability. The underlying mechanisms of the genetic underpinnings in the susceptibility and treatment response of opioid use disorder remain elusive. Objectives: To reveal the potential associations of genotypes and gene methylations of dopaminergic system genes, as well as roles of them in opioid use disorder. In the present study, we detected the DNA methylation in the promoter regions of five representative dopaminergic system genes (DRD1, DRD2, SLC6A3, TH, and COMT) between 120 patients with heroin use disorder in methadone maintenance treatment (MMT) program and 111 healthy controls. The associations of 25 SNPs in the above genes and methylation of 237 CpG sites, known as methylation quantitative trait loci (mQTLs), were determined. Then, the correlations of the above mQTLs and traits of heroin use disorder were analyzed in a sample set of 801 patients with heroin use disorder and 930 healthy controls. Results: Our results demonstrated that several mQTLs in the DRD1 and DRD2 genes were identified both in the heroin use disorder and healthy control groups. Interestingly, rs4867798-CpG_174872884 and rs5326-CpG_174872884 in the DRD1 gene were the unique SNP-CpG pairs in the patients with heroin use disorder. Furthermore, mQTL rs5326 was associated with the susceptibility and effective dosage of MMT for heroin use disorder, and demonstrated allele-specific correlation with the expression of the DRD1 gene in the human caudate. Conclusions: Our findings suggest that some mQTLs may be associated with traits of opioid use disorder by implicating the DNA methylation and gene expression. [ABSTRACT FROM AUTHOR]

The porous oil-containing cage achieves the storage, spillage, and suction of lubricating oil by its micro-pore structure, thus ensuring the self-lubricating performance of the bearing. Carrying out fast and accurate modeling of the cage microscopic pore structure is the key to the analysis of the self-lubricating mechanism of bearings. In response to the issues where current modeling methods of porous materials have a low similarity of pore distribution, morphology, structure, and size characteristics, and the transition of pore surfaces is sharp, this paper proposed a modeling method of a highly similar micro-pore structure based on the idea of median filtering, the quartet structure generation set (QSGS), and the slice method. By extracting and analyzing the pore characteristics of the porous model and comparing them with the experimental results of CT scanning, the advantages of the modeling method in terms of morphology and pore connectivity were verified. Finally, by carrying out simulation analysis of the centrifugal force of oil splashing and capillary oil absorption on the constructed model by combining the parameters of porous structures such as porosity and tortuosity, the advantages of the modeling method in the construction of the porous model and multi-physical field analysis were further verified. [ABSTRACT FROM AUTHOR]

Self-powered piezoelectric sensor can achieve real-time and harmless monitoring of motion processes without external power supply, which can be attached on body skin or joints to detect human motion and powered by mechanical energy. Here, a sensor for monitoring emergent motion is developed using the PVDF as active material and piezoelectric output as sensing signal. The multi-point control function enables the sensor to monitor the sequence of force order, angle change, and motion frequency of the "elbow lift, arm extension, and wrist compression" during shooting basketball. In addition, the sensor shows can simultaneously charge the capacitor to provide more power for intelligence, typically Bluetooth transmission. The sensor shows good performance in other field, such as rehabilitation monitoring and speech input systems. Therefore, the emerging application of flexible sensors have huge long-term prospects in sport big data collection and Internet of Things (IoT). [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to analyze the temperature field and the heat transfer performance of the counter rotating dual rotor bearings (CRDRB) based on the air phase flow field at different speeds to provide effective support for the lubrication and the thermal design of CRDRB. Design/methodology/approach: In this study, taking H7006C angular contact ball bearing as an example, based on the flow visualization technique and the thermal analysis methods, the effects of outer ring speed on the air phase flow field, the temperature field and the heat transfer in bearing cavity were investigated. Findings: Results indicated that there were more complex turbulent air vortices in CRDRB cavity. Turbulent cyclones in critical contact zone reduced the heat dissipation capacity of air. Compared with single rotor bearing with a static outer ring, the average heat transfer coefficient reduced by 11.78% and the average temperature raised by 3.06 K inside CRDRB cavity. Under the influence of outer ring rotation, the high temperature area in ball-inner raceway contact zone and pocket raised and reduced by 160.13% and 30.48%, respectively. The outer ring rotation had opposite effect on the heat dissipation of raceway contact zone and pocket. Originality/value: The air phase flow field characteristics and the heat transfer performance of CRDRB were revealed and analyzed from the mechanism. An area quantification method was presented as an auxiliary mean of the thermal analysis to evaluate the heat transfer performance of bearing. [ABSTRACT FROM AUTHOR]

Lead-free halide double perovskites are widely used for phosphors converted light emitting diode (pc-LED) devices. However, it remains a challenge to modulate their spectra to meet different application requirements. Herein, Cs 2 NaInCl 6 :Sb3+ double perovskite crystals with excellent blue emission of self-trapped excitons (STEs) were prepared and a lanthanide ions (Ln3+) doping strategy was employed to enable multicolor emissions in this system. The emission colors can be continuously adjusted in a wide range by altering the Ln3+ ions doping levels. Especially, white light emissions with tunable correlated color temperature were obtained in Tb3+, Ho3+ ions codoped samples. Outstanding photoluminescence quantum yields of the samples are ascribed to the sensitization effect of Sb3+ ions and the efficient energy-transfer from STEs to Ln3+ ions. LED devices were further fabricated to demonstrate the great application potentials of the prepared phosphors. These findings provide deep insights for the designing of multicolor double perovskite phosphors. • Sb3+ and different Ln3+ ions were successfully doped in Cs 2 NaInCl 6 by a hydrothermal method. • The emission colors can be continuously adjusted in a wide range by altering the Ln3+ ions doping levels. • Multi-color light-emitting diodes were designed with single component Cs 2 NaInCl 6 phosphors. [ABSTRACT FROM AUTHOR]

Opioid withdrawal generates extremely unpleasant physical symptoms and negative affective states. A rapid relief of opioid withdrawal-induced anxiety has obvious clinical relevance but has been rarely reported. We have shown that injection of ketamine metabolite (2 R ,6 R)-hydroxynorketamine (HNK) leads to a rapid alleviation of anxiety-like behaviors in male mice undergoing chronic morphine withdrawal. Here we investigated the contribution of nucleus accumbens shell (sNAc) parvalbumin (PV)-neurons to this process. Chronic morphine withdrawal was associated with higher intrinsic excitability of sNAc PV-neurons via reduced voltage-dependent potassium currents. Chemogenetic inhibition of sNAc PV-neurons reversed the enhanced excitability of PV-neurons and anxiety-like behaviors in these morphine withdrawal male mice, while activation of sNAc PV-neurons induced anxiety-like behaviors in naive male mice. (2 R ,6 R)-HNK reversed the altered potassium currents and intrinsic excitability of sNAc PV-neurons. Our findings demonstrate an important contribution of sNAc PV-neurons to modulating morphine withdrawal-induced anxiety-like behaviors and rapid relief of anxiety-like behaviors by (2 R ,6 R)-HNK, this newly identified target may have therapeutic potentials in treating opioid addiction and anxiety disorders. • sNAc PV-neuron excitability is higher in mice undergoing morphine withdrawal. • Inhibiting sNAc PV-neurons improves morphine withdrawal-induced anxiety-like behaviors. • Activating sNAc PV-neurons elicits anxiety-like behaviors in naïve mice. • (2 R ,6 R)-HNK alleviates morphine withdrawal-induced anxiety-like behaviors in mice. • (2 R ,6 R)-HNK increases K+ currents and reduces excitability of sNAc PV-neurons. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless technology, the deep coupling of cyber–physical power system (CPPS) is accelerated, and the openness of communication network makes the importance of secure information transmission increasingly prominent. As the communication module of CPPS, smart devices play an important role in data transmission, to ensure reliable and confident communication between the two layers. This paper presents a design for a secure and efficient smart devices utilizing anonymous security authentication and negotiation protocol supported by the identity-based cryptograph, elliptic curve cryptography, and digital signature algorithm are mainly used for that purpose. The security of the protocol is proved and validated under the random oracle model. The analysis show that the protocol has security performance such as bidirectional authentication, privacy protection, and replay attacks resistance; moreover, it has less computation and communication cost. [ABSTRACT FROM AUTHOR]

• An improved dynamic model of ball bearing is proposed. • This model considers bearing deformations and ball-inner raceway separation. • A prediction method for the initial values is applied to solve dynamic model. • The influence of deformations and ball-inner raceway separation is discussed. In actual operation conditions, the bearing deformations caused by the change of assembly state play a major role in bearing dynamic performance, and the ball-inner raceway separation due to the insufficient preload or excessive external loads is also a key issue that limits the bearing service. In this paper, an improved nonlinear dynamic model of ball bearing considering bearing deformations and ball-inner raceway separation is proposed. Firstly, the relationships between the bearing deformations and bearing-shaft assembly state are directly derived according to the deformation compatibility condition, and the interaction and relative motions among various bearing components under the ball-inner raceway separation conditions are analyzed. Then a prediction method for the initial iterative values is used to calculate the dynamic model, and the calculation results are compared with the experimental data for verification. Finally, the variations of bearing deformations, contact properties, and cage skid under different factors are investigated. It is found that the bearing deformations affect the contact properties of ball-raceway, and the appropriate preload effectively improves the bearing service characteristics, especially under the ball-inner raceway separation conditions. [ABSTRACT FROM AUTHOR]