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Owing to its high economic and nutritional value, Apostichopus japonicus is an important mariculture species in North China. Because of the rapid development of its aquaculture industry in recent years, the limitations of traditional aquaculture modes such as pond aquaculture, cofferdam aquaculture, and beach aquaculture have become increasingly prominent. Therefore, the high-efficiency and healthy northern suspension cage A. japonicus breeding model, with the best comprehensive benefits and the least management problems, came into being. Microorganisms, as an essential part of the aquaculture pond ecosystem, not only play an important role in the material circulation and energy flow of the ecosystem, but also have great significance in maintaining ecosystem balance. As a representative invertebrate, echinoderms such as A. japonicus have a simple digestive structure. Bacteria account for a large proportion of the gut microbiome of A. japonicus, providing more than 70% of their energy demand. The bacterial community is closely related to the healthy growth of the host and plays an important role in digestion and metabolism, defense against pathogens, and immune function. However, the complex bacterial community in the gut of aquaculture species depends on the culture environment, and there is a close symbiotic relationship between the environmental and gut bacterial community which affects the survival and growth of organisms, disease occurrence, and material circulation. Previous studies have shown that the complex bacterial community in the gut of A. japonicus primarily comes from their habitat and maintains a relatively stable dynamic balance with the external environmental community. In order to improve the growth capacity of A. japonicus and quality of the culture water, it is important to understand the structural characteristics of the bacterial communities of A. japonicus and their culture water to support the development of the A. japonicus aquaculture industry. This information will provide a theoretical reference for the healthy aquaculture of A. japonicus and assist with disease prevention and control. Clarifying the complex relationship between the structure and functional characteristics of bacterial communities and the aquaculture environment, as well as the important role of the bacterial community in growth, will support future research on the bacterial community mechanisms, explore ways to improve the ecological regulation of breeding yield, and promote the healthy development of the A. japonicus culture industry. To date, there have been limited studies on the correlation between the gut bacterial community structure of A. japonicus and its culture environment.At present, most of the existing studies are based on the traditional pure culture or separation and enrichment culture methods, which cannot accurately reflect natural bacterial communities. In recent years, high-throughput sequencing technology has introduced a new way to comprehensively analyze the structural and functional characteristics of bacterial communities by combining several bioinformatics methods. With the continuous development of molecular sequencing technology, 16S rRNA high-throughput sequencing technology has become a valuable tool to study the structure of bacterial communities. It has been widely used to study a variety of ecosystems and bacterial community diversity, providing a novel means to study the species diversity and quantity of bacterial communities, and the structural and functional characteristics of bacterial communities. Most of the relevant existing studies are based on the structure and diversity of the bacterial community of A. japonicus cultured in the south, whereas only a few studies have been conducted on the structure and functional characteristics of bacterial communities of A. japonicus cultured in suspension cages in the north. Therefore, in order to investigate the relationship between the bacterial community structure of A. japonicus and the culture water, this study analyzed their structural and functional characteristics in cage-cultured A. japonicus in North China using high-throughput sequencing technology, and preliminarily discussed the correlation between them. The results showed that the diversity and richness of the A. japonicus gut bacterial community were significantly higher than those of the culture water (P < 0.05). The dominant bacteria in the gut of A. japonicus and the culture water were Proteobacteria and Bacteroidetes. There were 13 common core bacteria with a relative abundance greater than 0.1%. In addition, the bacterial communities showed some independence; the specific phyla in the gut belonged to Firmicutes and Chloroflexi, represented by Bacillus, Lactobacillus, Halioglobus, Lutimonas, and Woeseia. Based on an analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway database, a total of 300 tertiary metabolic pathways was annotated, among which 146 tertiary metabolic pathways had highly significant differences (P < 0.001). The specific metabolic pathways in the gut of A. japonicus were mainly carbohydrate digestion and absorption, protein digestion and absorption, and sphingolipid metabolism. This study showed that the bacterial community in the gut of A. japonicus is similar to that of the culture water, but there were significant differences in the relative community abundance. The results of this study provide a theoretical basis for the healthy cultivation of northern A. japonicus in suspension cages.

Sulfamethoxazole (SMZ) is a frequently detected antibiotic in the environment, and there is a growing concern about its potential toxic effects on aquatic organisms. sea cucumber (Apostichopus japonicas) is a benthic invertebrate whose gut acts as a primary immune defense and serves critical protective barrier. In this study, growth performance, histology, gut microbiota, and metabolomics analyses were performed to investigate the toxic response in the intestine of sea cucumber effects caused by SMZ stress for 56 d by evaluating with different concentrations of SMZ (0, 1.2×10−3, and 1.2 mg/L). The weight gain rate of sea cucumbers under SMZ stress showed significant decrease, indicating that the growth of sea cucumbers was hindered. Analysis of the intestinal morphological features indicated that SMZ stimulation resulted in atrophy of the sea cucumber gut. In the 1.2×10−3 mg/L concentration, the thickness of muscle and mucosal layers was reduced by 12.40% and 21.39%, while in the 1.2 mg/L concentration, the reductions were 35.08% and 26.98%. The abundance and diversity of sea cucumber intestinal bacteria decreased significantly (P < 0.05) under the influence of SMZ. Notably, the intestinal bacteria of sea cucumber became homogenized with the increase in SMZ concentration, and the relative abundance of Ralstonia reached 81.64% under the stress of 1.2 mg/L concentration. The SMZ stress significantly impacted host metabolism and disrupted balance, particularly in L-threonine, L-tyrosine, neuronic acid, piperine, and docosapentaenoic acid. SMZ leads to dysregulation of metabolites, resulting in growth inhibition and potential inflammatory responses that could adversely affect the normal activities of aquatic organisms. Further metabolic pathway enrichment analyses demonstrated that impaired biosynthesis of unsaturated fatty acids and aminoacyl-tRNA biosynthesis metabolic pathway were major reasons for SMZ stress-induced intestinal bacteria dysbiosis. This research aims to provide some theoretical evidence for the ecological hazard assessment of antibiotics in water.

Abstract Background Circular RNAs (circRNAs) have been demonstrated to participate in the progression of osteoarthritis (OA). This study aimed to investigate the role and molecular mechanism of hsa_circ_0007292 in OA. Methods Hsa_circ_0007292 was identified by analyzing a circRNA microarray from the Gene Expression Omnibus (GEO) database, and its expression was detected by real-time PCR in OA cartilage tissues and interleukin (IL)-1β-induced two human chondrocytes (CHON-001 and C28/I2), the OA cell models. The effects of hsa_circ_0007292 knockdown and miR-1179 overexpression on IL-1β-induced chondrocyte injury were examined by CCK-8, BrdU, flow cytometry, ELISA, and western blot. RNA pull-down assay and dual-luciferase reporter gene assay were used to analyze the interaction between hsa_circ_0007292 and miR-1179. Rescue experiments were carried out to determine the correlations among hsa_circ_0007292, miR-1179 and high mobility group box-1 (HMGB1). Results Hsa_circ_0007292 expression was upregulated in OA tissues and IL-1β-induced chondrocytes. Both downregulation of hsa_circ_0007292 and miR-1179 overexpression increased the proliferation and Aggrecan expression, suppressed apoptosis, matrix catabolic enzyme MMP13 expression and inflammatory factor (TNF‐α, IL‐6, and IL‐8) levels. There was a negative correlation between hsa_circ_0007292 and miR-1179, and a positive correlation between hsa_circ_0007292 and HMGB1 in OA tissues. The mechanistic study showed that hsa_circ_0007292 prevented HMGB1 downregulation by sponging miR-1179. Upregulation of HMGB1 could reverse the influence of hsa_circ_0007292 downregulation on IL-1β-induced chondrocyte injury. Conclusions Downregulation of hsa_circ_0007292 relieved apoptosis, extracellular matrix degradation and inflammatory response in OA via the miR-1179/HMGB1 axis, suggesting that hsa_circ_0007292 might be a potential therapeutic target for OA treatment.

IntroductionAccurate identification of the myocardial texture features of fat around the coronary artery on coronary computed tomography angiography (CCTA) images are crucial to improve clinical diagnostic efficiency of myocardial ischemia (MI). However, current coronary CT examination is difficult to recognize and segment the MI characteristics accurately during earlier period of inflammation.Materials and methodsWe proposed a random forest model to automatically segment myocardium and extract peripheral fat features. This hybrid machine learning (HML) model is integrated by CCTA images and clinical data. A total of 1,316 radiomics features were extracted from CCTA images. To further obtain the features that contribute the most to the diagnostic model, dimensionality reduction was applied to filter features to three: LNS, GFE, and WLGM. Moreover, statistical hypothesis tests were applied to improve the ability of discriminating and screening clinical features between the ischemic and non-ischemic groups.ResultsBy comparing the accuracy, recall, specificity and AUC of the three models, it can be found that HML had the best performance, with the value of 0.848, 0.762, 0.704 and 0.729.ConclusionIn sum, this study demonstrates that ML-based radiomics model showed good predictive value in MI, and offer an enhanced tool for predicting prognosis with greater accuracy.

Fiber sensors, with their high sensitivity and flexibility, have contributed to the integration of wearable technologies into everyday clothing, enabling both comfortability and efficient data collection. Meanwhile, the rapid advancements in machine learning have substantially boosted the capability of wearable technology to process intricate information collected from the human body and environment. This work offers a comprehensive review of wearable devices that utilize both fiber sensors and machine learning. Various fiber sensors are discussed based on their working principles and dimensions, and an overview of the machine learning algorithms that have been applied with these fiber sensors is presented. Additionally, the advantages, limitations, and concerns of different approaches are highlighted, paving the way for the future development of intelligent wearable devices and systems.

Abstract Decoding human action intention prior to motion onset with surface electromyograms (sEMG) is an emerging neuroengineering topic with interesting clinical applications such as intelligent control of powered prosthesis/exoskeleton devices. Despite extensive prior works in the related fields, it remains a technical challenge due to considerable variability of complex multi-muscle activation patterns in terms of volatile spatio-temporal characteristics. To address this issue, we first hypothesize that the inherent variability of the idle state immediately preceding the motion initiation needs to be addressed explicitly. We therefore design a hierarchical dynamic Bayesian learning network model that integrates an array of Gaussian mixture model – hidden Markov models (GMM-HMMs), where each GMM-HMM learns the multi-sEMG processes either during the idle state, or during the motion initiation phase of a particular motion task. To test the hypothesis and evaluate the new learning network, we design and build a upper-limb sEMG-joystick motion study system, and collect data from 11 healthy volunteers. The data collection protocol adapted from the psychomotor vigilance task includes repeated and randomized binary hand motion tasks (push or pull) starting from either of two designated idle states: relaxed (with minimal muscle tones), or prepared (with muscle tones). We run a series of cross-validation tests to examine the performance of the method in comparison with the conventional techniques. The results suggest that the idle state recognition favors the dynamic Bayesian model over a static classification model. The results also show a statistically significant improvement in motion prediction accuracy by the proposed method (93.83±6.41%) in comparison with the conventional GMM-HMM method (89.71±8.98%) that does not explicitly account for the idle state. Moreover, we examine the progress of prediction accuracy over the course of motion initiation and identify the important hidden states that warrant future research.

For continuous steel–concrete composite girder bridges based on the post-combined method, the conventional rectangular group studs contribute to the isolation of the steel girder and the concrete slab before prestressing, leading to the majority of prestress forces being introduced to the concrete slab. However, rectangular-group stud holes cause the prestress forces to be unevenly distributed. In this study, a new type of bellow-sleeved stud (BSS) was developed to mitigate the weakening effects of rectangular group stud holes on the slab. A steel corrugated sleeve with a diameter of 60 mm was employed to cover the stud, which served as an internal formwork to prevent the concrete from bonding with the root of the stud. After prestressing was complete, the steel sleeve was filled with ultra-high-performance concrete (UHPC) to create a reliable combination between the concrete slab and the steel girder. To investigate the shear performance of this new type of connection, eight push-out test specimens were designed, and finite-element models were built. This study drew a comparison between the BSS and the ordinary headed stud (OHS). The research findings suggested that the BSS is subjected to less bending–shear coupling and offers a 4.5% increase in shear strength and a 31.9% increase in shear stiffness compared with the OHS. The study also analyzed the structural parameters influencing the shear performance of the BSS. It is found that the steel sleeve of the BSS has a negative effect on shear performance, but this can be mitigated by infusing high-strength material into the sleeve. Furthermore, the study examined the effect of construction quality on shear performance and suggested that sleeve deviation and grout leakage considerably reduced the shear performance of the BSS. Accordingly, strict control over the construction quality of the BSS is necessary.

To solve the shuttling effect and transformations of LiPSs in lithium–sulfur batteries, heterostructures have been designed to immobilize LiPSs and boost their reversible conversions. In this paper, we have constructed AlN/InN heterojunctions with AlN with a wide band gap and InN with a narrow band gap. The heterojunctions show metallic properties, which are primarily composed of 2s, 2p N atoms and 5s, 5p In atoms. InN has relatively higher adsorptivity for LiPSs than AlN. Reaction profiles show that on the surface of AlN, there is a lower rate-limiting step than on that of InN, from S8 to Li2S6, and a higher rate-limiting step from Li2S4 to Li2S2, which is more favorable for InN during the reduction from Li2S4 to Li2S2. The heterojunction can realize the synergistic reaction of trapping–diffusion–conversion for LiPSs, in which AlN traps large Li2S8 and Li2S6, the heterojunction causes the diffusion of Li2S4, and InN completes the conversion of Li2S4 to Li2S.

Abstract Floor cleaning robots are widely used in public places like food courts, hospitals, and malls to perform frequent cleaning tasks. However, frequent cleaning tasks adversely impact the robot’s performance and utilize more cleaning accessories (such as brush, scrubber, and mopping pad). This work proposes a novel selective area cleaning/spot cleaning framework for indoor floor cleaning robots using RGB-D vision sensor-based Closed Circuit Television (CCTV) network, deep learning algorithms, and an optimal complete waypoints path planning method. In this scheme, the robot will clean only dirty areas instead of the whole region. The selective area cleaning/spot cleaning region is identified based on the combination of two strategies: tracing the human traffic patterns and detecting stains and trash on the floor. Here, a deep Simple Online and Real-time Tracking (SORT) human tracking algorithm was used to trace the high human traffic region and Single Shot Detector (SSD) MobileNet object detection framework for detecting the dirty region. Further, optimal shortest waypoint coverage path planning using evolutionary-based optimization was incorporated to traverse the robot efficiently to the designated selective area cleaning/spot cleaning regions. The experimental results show that the SSD MobileNet algorithm scored 90% accuracy for stain and trash detection on the floor. Further, compared to conventional methods, the evolutionary-based optimization path planning scheme reduces 15% percent of navigation time and 10% percent of energy consumption.

Solid-state lithium metal batteries (LMBs) have been extensively investigated owing to their safer and higher energy density. In this work, we prepared a novel elastic solid-state polymer electrolyte based on an in situ-formed elastomer polymer matrix with ion-conductive plasticizer crystal embedded with Li6.5La3Zr1.5Ta0.5O12 (LLZTO) nanoparticles, denoted as LZT/SN-SPE. The unique structure of LZT/SN-SPE shows excellent elasticity and flexibility, good electrochemical oxidation tolerance, high ionic conductivity, and high Li+ transference number. The role of LLZTO filler in suppressing the side reactions between succinonitrile (SN) and the lithium metal anode and propelling the Li+ diffusion kinetics can be affirmed. The Li symmetric cells with LZT/SN-SPE cycled stably over 1100 h under a current density of 5 mA cm−2, and Li||LiFePO4 cells realized an excellent rate (92.40 mAh g−1 at 5 C) and long-term cycling performance (98.6% retention after 420 cycles at 1 C). Hence, it can provide a promising strategy for achieving high energy density solid-state LMBs.

Ballistocardiography (BCG) provides an unobtrusive, mechanical measurement of heartbeat for early detection and longitudinal monitoring of cardiovascular conditions, although it is plagued by poor signal to noise ratio. Despite tremendous efforts over the last decade in related advanced signal processing and deep learning research, automated and precise heartbeat detection in BCG remains a major technical challenge. In this work, we design and study a sequential deep neural network based approach that addresses three key issues in automated BCG learning and detection: auto-labeling of BCG samples in the raw training data; design of a recursive neural network that can learn the association between the continuous BCG waves and the label sequence in the form of sample-by-sample predictions; and finally, heartbeat detection from the output sequence. We evaluate the proposed method using our data set comprising 8 human subjects under varying cardiac output conditions: pre-exercise and post-exercise. Using multiple performance metrics, we report that the proposed method can achieve a heart rate accuracy of 98.68% and root-mean-square-error of 1.37 bpm with 98.85% coverage, which compare favorably to two state-of-the-art methods in the same test.

Anomaly detection tasks involving time-series signal processing have been important research topics for decades. In many real-world anomaly detection applications, no specific distributions fit the data, and the characteristics of anomalies are different. Under these circumstances, the detection algorithm requires excellent learning ability of the data features. Transformers, which apply the self-attention mechanism, have shown outstanding performances in modelling long-range dependencies. Although Transformer based models have good prediction performance, they may be influenced by noise and ignore some unusual details, which are significant for anomaly detection. In this paper, a novel temporal context fusion framework: Temporal Context Fusion Transformer (TCF-Trans), is proposed for anomaly detection tasks with applications to time series. The original feature transmitting structure in the decoder of Informer is replaced with the proposed feature fusion decoder to fully utilise the features extracted from shallow and deep decoder layers. This strategy prevents the decoder from missing unusual anomaly details while maintaining robustness from noises inside the data. Besides, we propose the temporal context fusion module to adaptively fuse the generated auxiliary predictions. Extensive experiments on public and collected transportation datasets validate that the proposed framework is effective for anomaly detection in time series. Additionally, the ablation study and a series of parameter sensitivity experiments show that the proposed method maintains high performance under various experimental settings.

To break the three lockings during backpropagation (BP) process for neural network training, multiple decoupled learning methods have been investigated recently. These methods either lead to significant drop in accuracy performance or suffer from dramatic increase in memory usage. In this paper, a new form of decoupled learning, named decoupled neural network training scheme with re-computation and weight prediction (DTRP) is proposed. In DTRP, a re-computation scheme is adopted to solve the memory explosion problem, and a weight prediction scheme is proposed to deal with the weight delay caused by re-computation. Additionally, a batch compensation scheme is developed, allowing the proposed DTRP to run faster. Theoretical analysis shows that DTRP is guaranteed to converge to crical points under certain conditions. Experiments are conducted by training various convolutional neural networks on several classification datasets, showing comparable or better results than the state-of-the-art methods and BP. These experiments also reveal that adopting the proposed method, the memory explosion problem is effectively solved, and a significant acceleration is achieved.

Abstract Fucosterol, a sterol isolated from brown algae, has been demonstrated to have anti-cancer properties. However, the effects and underlying molecular mechanism of fucosterol on non-small cell lung cancer remain to be elucidated. In this study, the corresponding targets of fucosterol were obtained from PharmMapper, and NSCLC related targets were gathered from the GeneCards database, and the candidate targets of fucosterol-treated NSCLC were predicted. The mechanism of fucosterol against NSCLC was identified in DAVID6.8 by enrichment analysis of GO and KEGG, and protein–protein interaction data were collected from STRING database. The hub gene GRB2 was further screened out and verified by molecular docking. Moreover, the relationship of GRB2 expression and immune infiltrates were analyzed by the TIMER database. The results of network pharmacology suggest that fucosterol acts against candidate targets, such as MAPK1, EGFR, GRB2, IGF2, MAPK8, and SRC, which regulate biological processes including negative regulation of the apoptotic process, peptidyl-tyrosine phosphorylation, positive regulation of cell proliferation. The Raf/MEK/ERK signaling pathway initiated by GRB2 showed to be significant in treating NSCLC. In conclusion, our study indicates that fucosterol may suppress NSCLC progression by targeting GRB2 activated the Raf/MEK/ERK signaling pathway, which laying a theoretical foundation for further research and providing scientific support for the development of new drugs.

The electrocatalytic hydrogen evolution activity of transition metal sulfide heterojunctions are significantly increased when compared with that of a single component, but the mechanism behind the performance enhancement and the preparation of catalysts with specific morphologies still need to be explored. Here, we prepared a Co9S8/MoS2 heterojunction with microsphere morphology consisting of thin nanosheets using a facile two-step method. There is electron transfer between the Co9S8 and MoS2 of the heterojunction, thus realizing the redistribution of charge. After the formation of the heterojunction, the density of states near the Fermi surface increases, the d-band center of the transition metal moves downward, and the adsorption of both water molecules and hydrogen by the catalyst are optimized. As a result, the overpotential of Co9S8/MoS2 is superior to that of most relevant electrocatalysts reported in the literature. This work provides insight into the synergistic mechanisms of heterojunctions and their morphological regulation.

In this paper, we propose a novel cross-spectral matching system for identity verification based on the palm-vein and the palmprint acquired from the visible (RGB) and the near infrared (NIR) image spectral bands. Considering the vast availability of the visible library, the red and the blue spectrums are treated as sources of gallery samples and the NIR spectral band is utilized as the probe source without loss of generality. Apart from the extraction of palm-vein and palmprint features, the discriminative power of the palmprint templates is enhanced using a simplified Local Binary Pattern (LBP) encoding scheme. The similarity scores obtained by matching the NIR palm-vein templates against the registered RGB palm-vein templates is finally fused with scores obtained from matching the NIR palmprint codes against the registered RGB palmprint codes. Our empirical results on two publicly available multi-spectral palm databases show that the proposed system consistently achieves promising verification performance.

It is commonly known that Global Positioning System (GPS) can alleviate travelling difficulties of automobile drivers, and generally we hold the view that it reduces the driver's stress when they are in unfamiliar road conditions. In this research, an in-laboratory experiment and an in-car experiment are conducted to find out whether GPS instructions can reduce or may induce additional mental stress of drivers. Electrocardiography (ECG) signals are collected in the experiments and the extracted heart rate variability (HRV) features are used for analysis. Three binary classifiers, specifically Support Vector Machine, k-Nearest Neighbor (k-NN) and Random Forest, are trained based on the data collected in the in-laboratory experiment, where the stress state is elicited by the Stroop color word Test. The k-NN classifier outperforms the other two classifiers, and thus is applied to the data collected in the in-car experiment, to identify drivers' stress state under different driving events, such as waiting for traffic lights, turning, under GPS instructions, and traffic conditions like overtaking, or changing lanes. During each event, whether the driver is in stress or relaxed state for each time instant is predicted based on the trained classifier. The percentages of time that the driver is in stress state for each type of events are computed. It shows that GPS instructions cause the second largest time-percentage of stress state, lower than that caused by the turning event, but higher than that caused by the events of waiting for traffic lights and other traffic conditions.

Item recommendation has become a significant means to help people discover interesting items. Meanwhile, plenty of reviews and ratings in recommender system can be utilized to relieve data sparsity problem. However, existing review-based approaches ignore the influence of static preference of user and the static characteristics of item, which could reflect long-term and stationary property, and guide feature extraction from reviews. Moreover, adaptive property, i.e., the importance of the historical records to each user and item, is not fully exploited in previous works. In this paper, we propose an Attention- based Adaptive Memory Network (AAMN) model to leverage historical reviews and ratings systemically. Specifically, we propose an attention mechanism guided by the static features to learn the importance of different historical records, for modeling the adaptive features of users and items. Notably, this paper is the first to bring static features into adaptively extracting semantic information from reviews, which can not only characterize user and item from a global view, but also assist to distinguish the importance of different reviews. In addition to the attention mechanism, we propose a non-linear feature fusing layer and a deep interaction layer to combine the static features and adaptive features, which capture underlying interactions among these features. To further improve prediction accuracy and training efficiency, we propose a dynamic sampling strategy for model training. We conduct extensive experiments on 16 benchmark datasets from Amazon and Yelp. The results demonstrate that our model outperforms the state-of-the-art models.

Learning from minority class has been a significant and challenging task which has many potential applications. Weather classification is such a case of imbalanced label distribution. This is because in places like Beijing, some types of weather, such as rain and snow, are relatively rare compared to sunny and haze days. Existing methods are primary to classify the weather conditions relying on expensive sensors or human assistance, which however usually are expensive and time-consuming. In this paper, we propose a new ensemble framework based on the advanced generative adversarial network and an effective data cleaning way to address the class imbalance problem for weather classification. The proposed method not only generates new and reliable samples for the minority class to restore balance, but also filters those generated samples which are unreliable. Experiments show that our approach outperforms the state-of-the-art methods by a huge margin for imbalanced weather classification on several benchmark data sets.

Semisupervised learning is an effective technique to represent the intrinsic features of a hyperspectral image (HSI), which can reduce the cost to obtain the labeled information of samples. However, traditional semisupervised learning methods fail to consider multiple properties of an HSI, which has restricted the discriminant performance of feature representation. In this article, we introduce the hypergraph into semisupervised learning to reveal the complex multistructures of an HSI, and construct a semisupervised discriminant hypergraph learning (SSDHL) method by designing an intraclass hypergraph and an interclass graph with the labeled samples. SSDHL constructs an unsupervised hypergraph with the unlabeled samples. In addition, a total scatter matrix is used to measure the distribution of the labeled and unlabeled samples. Then, a low-dimensional projection function is constructed to compact the properties of the intraclass hypergraph and the unsupervised hypergraph, and simultaneously separate the characteristics of the interclass graph and the total scatter matrix. Finally, according to the objective function, we can obtain the projection matrix and the low-dimensional features. Experiments on three HSI data sets (Botswana, KSC, and PaviaU) show that the proposed method can achieve better classification results compared with a few state-of-the-art methods. The result indicates that SSDHL can simultaneously utilize the labeled and unlabeled samples to represent the homogeneous properties and restrain the heterogeneous characteristics of an HSI.

The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.

BackgroundBreast cancer (BRCA) is the most common cancer in women, while the bones are one of the most common sites of metastasis. Although new diagnostic methods or radiation or chemotherapies and targeted therapies have made huge advances, the occurrence of bone metastasis is also linked with poorer survival. Enhancer RNAs (eRNAs) have been demonstrated to participate in the progression of tumorigenesis and metastasis. However, the role of eRNAs in BRCA bone metastasis remains largely unclear.MethodGene expression profiling of 1,211 primary BRCA and 17 bone metastases samples were retrieved from The Cancer Genome Atlas (TCGA) database, and the significant prognostic eRNAs were identified by Cox regression and least absolute shrinkage and selection operator (LASSO) regression. The acceptable accuracy and discrimination of the nomogram were indicated by the receiver operating characteristic (ROC) and the calibration curves. Then target genes of eRNA, immune cell percentage by CIBERSORT analysis, immune genes by single-sample gene set enrichment analysis (ssGSEA), hallmark of cancer signaling pathway by gene set variation analysis (GSVA), and reverse phase protein array (RPPA) protein chip were used to build a co-expression regulation network and identified the key eRNAs in bone metastasis of BRCA. Finally, Cell Counting Kit-8 (CCK8) assay, cell cycle assay, and transwell assay were used to study changes in cell proliferation, migration, and invasiveness. Immunoprecipitation assay and Western blotting were used to test the interaction and the regulation signaling pathways.ResultsThe 27 hub eRNAs were selected, and a survival-related linear risk assessment model with a relatively high accuracy (area under curve (AUC): 0.726) was constructed. In addition, seven immune-related eRNAs (SLIT2, CLEC3B, LBPL1, FRY, RASGEF1B, DST, and ITIH5) as prognostic signatures for bone metastasis of BRCA were further confirmed by LASSO and multivariate Cox regression and CIBERSORT analysis. Finally, in vitro assay demonstrated that overexpression of SLIT2 reduced proliferation and metastasis in BRCA cells. Using high-throughput co-expression regulation network, we identified that SLIT2 may regulating P38 MAPK/c-Fos signaling pathway to promote the effects of metastasis.ConclusionBased on the co-expression network for bone metastasis of BRCA, we screened key eRNAs to explore a prognostic model in predicting the bone metastasis by bioinformatics analysis. Besides, we identified the potential regulatory signaling pathway of SLIT2 in BRCA bone metastasis, which provides a promising therapeutic strategy for metastasis of BRCA.

Mask-face detection has been a significant task since the outbreak of the COVID-19 pandemic in early 2020. While various reviews on mask-face detection techniques up to 2021 are available, little has been reviewed on the distinction between two-class (i.e., wearing mask and without mask) and three-class masking, which includes an additional incorrect-mask-wearing class. Moreover, no formal review has been conducted on the techniques of implementing mask detection models in hardware systems or mobile devices. The objectives of this paper are three-fold. First, we aimed to provide an up-to-date review of recent mask-face detection research in both two-class cases and three-class cases, next, to fill the gap left by existing reviews by providing a formal review of mask-face detection hardware systems; and to propose a new framework named Out-of-distribution Mask (OOD-Mask) to perform the three-class detection task using only two-class training data. This was achieved by treating the incorrect-mask-wearing scenario as an anomaly, leading to reasonable performance in the absence of training data of the third class.

Abstract Background It is very important to dynamically evaluate the functional outcome in the knee after anterior cruciate ligament (ACL) reconstruction under physiological weight bearing. The objective of the current study is that we would like to compare the patellofemoral joint kinematics in three ACL status: ACL intact, ACL deficiency, ACL reconstruction. Methods Twenty patients with unilateral ACL deficient knees were recruited as preoperative group. Six months after ACL reconstruction, these ten subjects were included as postoperative subjects. Ten normal subjects with healthy knees as the control group. Each subject was asked to walk up a custom set of stairs and a single-plane fluoroscopic imaging system was used to determine the 6DOF kinematics of the injured knees, ACL reconstructed knees, and intact knees. Results ACL deficient knees showed reduced patellar flexion angle and reduced distal patellar translation during knee flexion. ACL reconstructed knees showed abnormal patellofemoral joint kinematics compared to ACL intact and ACL deficient knees, exhibiting increased patellar external rotation, lateral tilt, lateral translation during knee flexion. Conclusion These findings imply that some alterations persist after ACL deficiency and ACL reconstruction. These abnormal changes will be the onset of degeneration in patellofemoral joint even if the ACL is reconstructed in a way that restores the clinical anteroposterior stability of the knee. Some biomechanical changes should be made to improve the outcome of intervention especially in surgical treatment like ACL reconstruction.

We experimentally demonstrate a spectrometer, which consists of an arrayed waveguide grating (AWG), tunable microring resonator (MRR) array, and waveguide-coupled Ge-on-Si photodetector, all integrated onto a single chip. The spectrometer achieves high resolution (0.1 ± 0.029 nm) and large bandwidth (25.4 nm) in 9 channels with a small footprint of 3 × 3 mm2. It has high potential to enable a high-performance spectrometer for applications such as environmental monitoring, spectroscopy, biological and chemical sensing, etc.

The media access control (MAC) layer of the vehicular ad-hoc network (VANET) is designed 3 to provide fast and reliable vehicles to vehicles (V2V) or vehicles to infrastructures (V2I) access. With the development of autonomous driving and intelligent transportation, many sensor information in large-scale vehicles require efficient and reliable transmission with V2V and V2I technologies. In recent years, the demand for various services in VANETs is growing rapidly, which poses great challenges to the design of the MAC protocol. An adaptive scheme for channel frame structure can enhance channel access flexibility to cope with the changing network topologies in vehicular environments. In this paper, we adopt an adaptive frame scheme on the control channel and the service channels, which mainly improves two aspects of channel access performance: improved time slot utilization of the broadcasting period as much as possible while ensuring the vehicle can access the control channel quickly; and increasing the throughput of the service channels without generating excessive overhead. Analysis and simulation results demonstrate the performance of the proposed mechanism.

Animals are not only regulated by their own genes but also influenced by symbiotic bacteria, most of which are colonized in the gut. The gut bacterial community is involved in plenty of physiological processes; therefore, intestinal colonization by commensal microbiota is essential to the health of the host animal. Here, metagenome sequencing of the A. japonicus gut, surrounding water, and feed was performed to explore the structural and functional characteristics of the colonized bacteria in the gut of A. japonicus. Results showed that Bacteroidetes and Proteobacteria were the main dominant phyla of the A. japonicus gut, and Formosa, Vibrio, and Lactobacillus were the dominant genera. There was asymmetry between the A. japonicus gut and its surrounding environment in the bacterial community. In terms of the top 50 abundant genera, those colonized in the gut shared a similarity of 26% with those colonized in the surrounding water and a similarity of 30% with those colonized in the feed. According to KEGG annotation, the dominant metabolic pathways in the gut of A. japonicus were glycan biosynthesis and metabolism, nitrogen metabolism, and cysteine and methionine metabolism. This implies that the gut-colonized bacteria of A. japonicus are influenced by the surrounding water and the feed. In addition, the gut-colonized bacteria might be related to the growth and metabolism of A. japonicus.

Summary: Pulse laser has been widely used in both fundamental science and practical technologies. In this perspective, we highlight the employment of pulse laser ablation in air (LAA) in energy-related catalytic reactions. With LAA, samples are directly ablated in ambient air, which makes this technology facile to conduct. Materials can be modified by LAA in multiple aspects, such as morphology modulation, heterojunction fabrication, or defects engineering, which are desired features for energy-related catalytic reactions. We begin this perspective with a brief introduction of this technology, including the mechanism, the experimental setup, and the characteristic of laser-ablated materials. The recent works utilizing LAA are then summarized to prove the promising prospects of LAA in the energy field. Finally, several opportunities about the future usage of LAA are proposed and discussed.

It has attracted growing attention that the role of serine hydroxy methyl transferase 2 (SHMT2) in various types of cancers. However, the prognostic role of SHMT2 in lung adenocarcinoma (LUAD) and its relationship with immune cell infiltration is not clear. In this study, the information of mRNA expression and clinic data in LUAD were, respectively, downloaded from the GEO and TCGA database. We conducted a biological analysis to select the signature gene SHMT2. Online databases including Oncomine, GEPIA, TISIDB, TIMER, and HPA were applied to analyze the characterization of SHMT2 expression, prognosis, and the correlation with immune infiltration in LUAD. The mRNA expression and protein expression of SHMT2 in LUAD tissues were higher than in normal tissue. A Kaplan-Meier analysis showed that patients with lower expression level of SHMT2 had a better overall survival rate. Multivariate analysis and the Cox proportional hazard regression model revealed that SHMT2 expression was an independent prognostic factor in patients with LUAD. Meanwhile, the gene SHMT2 was highly associated with tumor-infiltrating lymphocytes in LUAD. These results suggest that the SHMT2 gene is a promising candidate as a potential prognostic biomarker and highly associated with different types of immune cell infiltration in LUAD.

The bacterial community is an essential component of the aquaculture pond ecosystem, which not only improves and restores the aquaculture environment but also maintains a stable ecological equilibrium with the external environment. Here, Illumina 16S rRNA sequencing was conducted to characterize the bacterial community in the ecosystem of sea cucumber Apostichopus japonicus culture ponds, as well as their correlation with overall community structures. The alpha-diversities of bacterial community among water, sediment, and the gut of A. japonicus were consistent across culture ponds from different areas. Specifically, the richness and diversity of bacterial communities were the highest in sediment, followed by the gut, and the lowest in water. The dominant bacterial community among multiple media was Proteobacteria, which occupies a large proportion of the bacterial community structure, followed by Bacteroidetes and Verrucomicrobia. Highly similar bacterial community structures were present in multiple media among different areas, which provides evidence for deterministic natural evolution. Meanwhile, there was a significant difference (p < 0.05) in the specific bacterial communities across the multiple media. The specific functions of the multiple media in the ecosystem are the main reason for the formation of different bacterial communities. This work demonstrates that bacterial communities are the result of natural evolution within the ecosystem during adaptation to the required environment.

Advances in our understanding of the role of magnetic resonance imaging (MRI) for the detection of prostate cancer have enabled its integration into clinical routines in the past two decades. The Prostate Imaging Reporting and Data System (PI-RADS) is an established imaging-based scoring system that scores the probability of clinically significant prostate cancer on MRI to guide management. Image fusion technology allows one to combine the superior soft tissue contrast resolution of MRI, with real-time anatomical depiction using ultrasound or computed tomography. This allows the accurate mapping of prostate cancer for targeted biopsy and treatment. Machine learning provides vast opportunities for automated organ and lesion depiction that could increase the reproducibility of PI-RADS categorisation, and improve co-registration across imaging modalities to enhance diagnostic and treatment methods that can then be individualised based on clinical risk of malignancy. In this article, we provide a comprehensive and contemporary review of advancements, and share insights into new opportunities in this field.

A series of β-FeSe nanorods composited g-C3N4 were prepared. The structure, morphology, chemical state, photocatalytic activity, electrochemical impedance and photoluminescence of β-FeSe/g-C3N4 composites were well characterized. It is found that the decolourization rate of 3 wt% β-FeSe/g-C3N4 composites reaches 4.4 times than that of g-C3N4. The improved photocatalytic properties could be ascribed to the reduced recombination of photogenerated electrons and holes, which is derived from the excellent ability of β-FeSe to capture and transfer electrons. This work provides an alternative co-catalyst for decolourizing organic matter.

Endosomes are subcellular organelles which serve as important transport compartments in eukaryotic cells. Fluorescence microscopy is a widely applied technology to study endosomes at the subcellular level. In general, a microscopy image can contain a large number of organelles and endosomes in particular. Detecting and annotating endosomes in fluorescence microscopy images is a critical part in the study of subcellular trafficking processes. Such annotation is usually performed by human inspection, which is time-consuming and prone to inaccuracy if carried out by inexperienced analysts. This paper proposes a two-stage method for automated detection of ring-like endosomes. The method consists of a localization stage cascaded by an identification stage. Given a test microscopy image, the localization stage generates a voting-map by locally comparing the query endosome patches and the test image based on a bag-of-words model. Using the voting-map, a number of candidate patches of endosomes are determined. Subsequently, in the identification stage, a support vector machine (SVM) is trained using the endosome patches and the background pattern patches. Each of the candidate patches is classified by the SVM to rule out those patches of endosome-like background patterns. The performance of the proposed method is evaluated with real microscopy images of human myeloid endothelial cells. It is shown that the proposed method significantly outperforms several state-of-the-art competing methods using multiple performance metrics.

NaxMnO2 (NMO) is treated by a wet chemical method in this paper. The treated NMO can form a copper oxide coating layer, and some of the coating layer can be peeled off, smoothing the surface of particles. Electrochemical measurement shows that treated NMO can maintain 72.6% of its specific capacity after 300 cycles, which is better than the 58.7% specific capacity of untreated NMO materials. Additionally, the ratio of capacity remaining rate can be improved from an initial 87% to 99.5%. So, this wet chemical method is available to smooth the electrode surface and reduce the internal impedance, and thus to effectively improve electrochemical performance during the battery cycle.

Cesium lead halide perovskite nanocrystals (NCs) have attracted enormous interest in light-emitting diode, photodetector and low-threshold lasing application in terms of their unique optical and electrical performance. However, little attention has been paid to other structures associated with CsPbBr3, such as CsPb2Br5. Herein, we realize a facile method to prepare dual-phase NCs with improved stability against polar solvents by replacing conventional oleylamine with cetyltrimethyl ammonium bromide (CTAB) in the reprecipitation process. The growth of NCs can be regulated with different ratios of toluene and ethanol depending on solvent polarity, which not only obtains NCs with different sizes and morphologies, but also controls phase transition between orthorhombic CsPbBr3 and tetragonal CsPb2Br5. The photoluminescence (PL) and defect density calculated exhibit considerable solvent polarity dependence, which is ascribed to solvent polarity affecting the ability of CTAB to passivate surface defects and improve stoichiometry in the system. This new synthetic method of perovskite material will be helpful for further studies in the field of lighting and detectors.