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Abstract Malignant mesothelioma is a rare tumour caused by asbestos exposure that originates mainly from the pleural lining or the peritoneum. Treatment options are limited, and the prognosis is dismal. Although immune checkpoint blockade (ICB) can improve survival outcomes, the determinants of responsiveness remain elusive. Here, we report the outcomes of a multi-centre phase II clinical trial (MiST4, NCT03654833) evaluating atezolizumab and bevacizumab (AtzBev) in patients with relapsed mesothelioma. We also use tumour tissue and gut microbiome sequencing, as well as tumour spatial immunophenotyping to identify factors associated with treatment response. MIST4 met its primary endpoint with 50% 12-week disease control, and the treatment was tolerable. Aneuploidy, notably uniparental disomy (UPD), homologous recombination deficiency (HRD), epithelial-mesenchymal transition and inflammation with CD68+ monocytes were identified as tumour-intrinsic resistance factors. The log-ratio of gut-resident microbial genera positively correlated with radiological response to AtzBev and CD8+ T cell infiltration, but was inversely correlated with UPD, HRD and tumour infiltration by CD68+ monocytes. In summary, a model is proposed in which both intrinsic and extrinsic determinants in mesothelioma cooperate to modify the tumour microenvironment and confer clinical sensitivity to AtzBev. Gut microbiota represent a potentially modifiable factor with potential to improve immunotherapy outcomes for individuals with this cancer of unmet need.

BackgroundAnkle sprains are prevalent in sports, often causing complex injuries to the lateral ligaments. Among these, anterior talofibular ligament (ATFL) injuries constitute 85%, and calcaneofibular ligament (CFL) injuries comprise 35%. Despite conservative treatment, some ankle sprain patients develop chronic lateral ankle instability (CLAI). Thus, this study aimed to investigate stress response and neural control alterations during landing in lateral ankle ligament injury patients.MethodThis study recruited twenty individuals from a Healthy group and twenty CLAI patients performed a landing task using relevant instruments to collect biomechanical data. The study constructed a finite element (FE) foot model to examine stress responses in the presence of laxity of the lateral ankle ligaments. The lateral ankle ligament was modeled as a hyperelastic composite structure with a refined representation of collagen bundles and ligament laxity was simulated by adjusting material parameters. Finally, the validity of the finite element model is verified by a high-speed dual fluoroscopic imaging system (DFIS).ResultCLAI patients exhibited earlier Vastus medialis (p < 0.001) and tibialis anterior (p < 0.001) muscle activation during landing. The FE analysis revealed that with laxity in the ATFL, the peak von Mises stress in the fifth metatarsal was 20.74 MPa, while with laxity in the CFL, it was 17.52 MPa. However, when both ligaments were relaxed simultaneously, the peak von Mises stress surged to 21.93 MPa. When the ATFL exhibits laxity, the CFL is subjected to a higher stress of 3.84 MPa. Conversely, when the CFL displays laxity, the ATFL experiences a peak von Mises stress of 2.34 MPa.ConclusionThis study found that changes in the laxity of the ATFL and the CFL are linked to shifts in metatarsal stress levels, potentially affecting ankle joint stability. These alterations may contribute to the progression towards CLAI in individuals with posterolateral ankle ligament injuries. Additionally, significant muscle activation pattern changes were observed in CLAI patients, suggesting altered neural control strategies post-ankle ligament injury.

There are several unresolved issues in the field of ship instance segmentation in synthetic aperture radar (SAR) images. First, in inshore dense ship area, the problems of missed detections and mask overlap frequently occur. Second, in inshore scenes, false alarms occur due to strong clutter interference. In order to address these issues, we propose a novel ship instance segmentation network based on dynamic key points information enhancement. In the detection branch of the network, a dynamic key points module is designed to incorporate the target's geometric information into the parameters of the dynamic mask head using an implicit encoding technique. In addition, we introduce a dynamic key points encoding branch, which encodes the target's strong scattering regions as dynamic key points. It strengthens the network's ability to learn the correspondence between local regions with strong scattering and overall ship targets, effectively mitigating mask overlap issues. Moreover, it enhances the discriminative ability of network between ship targets and clutter interference, leading to a reduction in false alarm rates. To further enhance the dynamic key points information, an instancewise attention map module is designed, which decodes the key points during the mask prediction period, generating instancewise attention maps based on 2-D Gaussian distribution. This module further enhances the sensibility of network to specific instances. Simulation experiments conducted on the Polygon Segmentation SAR Ship Detection Dataset and High-Resolution SAR Images Dataset demonstrate the superiority of our proposed method over other state-of-the-art methods in inshore and offshore scenes.

In this article, we explore a cutting-edge concept known as class incremental learning (CIL) in novel category discovery for synthetic aperture radar (SAR) targets (CNTs). This innovative task involves the challenge of identifying categories within unlabeled datasets by utilizing a provided labeled dataset as reference. In contrast to the conventional category discover approaches, our method introduces novel categories without relying on old labeled classes and effectively mitigates the issue of catastrophic forgetting. Specifically, to reduce the bias of the established categories toward unknown ones, CNT extracts representational information via self-supervised learning, gleaned directly from the SAR data itself to facilitate generalization. To retain the model's competence in classifying previously acquired knowledge, we employ a dual strategy incorporating the rehearsal of base category feature prototypes and the application of knowledge distillation. Our methodology integrates multiview and pseudolabeling strategies. In addition, we introduce a novel approach that focuses on enhancing the discernibility of class spaces. This strategy primarily ensures distinct separation of the unlabeled classes from base class prototypes, and imposes stringent constraints on the internal relationships among individual samples and their corresponding perspectives. To the best of our knowledge, this is the first study on category discovery in the CIL scenario. The experimental results show that our method significantly improves the performance on SAR images compared to the previous optimal method, which indicates the effectiveness of our method.

The purpose of this paper is to summarize the functional arguments for unstable footwear in the recent research literature and to explore the different effects of various unstable designs of footwear in enhancing muscle strength training, improving stability and loss prevention. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) criteria, to find all the relevant studies for this systematic review, a comprehensive electronic search was conducted. The following keyword combinations were used as part of a standardized electronic literature search strategy: ‘unstable OR bionic OR MBT’ AND ‘shoe OR shoes OR footwear’ AND ‘biomechanics OR kinetics OR kinematics OR muscle force’ from 2000 until November 2021 using the following databases: ScienceDirect, Web of Science and PubMed online. There were 17 articles included in this review, eight consisting of anterior-posterior (AP) unstable condition studies and nine consisting of medial-lateral (ML) unstable condition studies. It was also uncovered that AP unstable footwear is more suitable for fully developed adults, while for ML unstable footwear is perhaps more suitable for children and adolescents.

Abstract Aims To construct a quality evaluation index system for clinical drug trials nursing management and obtain the weight of all indicators. Design A mixed‐method research design with a quantitative component was used, primarily qualitative. Methods Through a literature review and semi‐structured interview, an expert consultation questionnaire on the quality of nursing evaluation indicators for clinical drug trials was developed in April 2021. Eighteen experts in clinical drug trial nursing, medical, and pharmacy conducted 2 rounds of consultation according to the Delphi method to determine the indicators for evaluating the quality of clinical drug trial nursing. The weights of each indicator were determined using analytic hierarchical analysis. Results The established quality evaluation system of clinical drug trial nursing mainly includes 3 first‐level indicators, 12 second‐level indicators, and 59 third‐level indicators. The positive coefficients of the two rounds of expert consultation were 90%–100%, and the authority coefficients were 0.831 and 0.885, respectively; the coordination coefficients were 0.189 and 0.214, respectively. The consulting results and weight settings are reliable. The evaluation index system we constructed is in line with the characteristics of the clinical drug trial nursing profession, with clear index levels and strong clinical operability, which can provide a reference for the assessment, monitoring and improvement of nursing quality in clinical drug trials. It will also clarify how nurses contribute to subjects' safety.

As one of many fundamental sports techniques, the landing maneuver is also frequently used in clinical injury screening and diagnosis. However, the landing patterns are different under different constraints, which will cause great difficulties for clinical experts in clinical diagnosis. Machine learning (ML) have been very successful in solving a variety of clinical diagnosis tasks, but they all have the disadvantage of being black boxes and rarely provide and explain useful information about the reasons for making a particular decision. The current work validates the feasibility of applying an explainable ML (XML) model constructed by Layer-wise Relevance Propagation (LRP) for landing pattern recognition in clinical biomechanics. This study collected 560 groups landing data. By incorporating these landing data into the XML model as input signals, the prediction results were interpreted based on the relevance score (RS) derived from LRP. The interpretation obtained from XML was evaluated comprehensively from the statistical perspective based on Statistical Parametric Mapping (SPM) and Effect Size. The RS has excellent statistical characteristics in the interpretation of landing patterns between classes, and also conforms to the clinical characteristics of landing pattern recognition. The current work highlights the applicability of XML methods that can not only satisfy the traditional decision problem between classes, but also largely solve the lack of transparency in landing pattern recognition. We provide a feasible framework for realizing interpretability of ML decision results in landing analysis, providing a methodological reference and solid foundation for future clinical diagnosis and biomechanical analysis.

Introduction: The purpose of this study was to compare the changes in foot at different sole-ground contact angles during forefoot running. This study tried to help forefoot runners better control and improve their technical movements by comparing different sole-ground contact angles.Methods: A male participant of Chinese ethnicity was enlisted for the present study, with a recorded age of 25 years, a height of 183 cm, and a body weight of 80 kg. This study focused on forefoot strike patterns through FE analysis.Results: It can be seen that the peak von Mises stress of M1-5 (Metatarsal) of a (Contact angle: 9.54) is greater than that of b (Contact angle: 7.58) and c (Contact angle: 5.62) in the three cases. On the contrary, the peak von Mises stress of MC (Medial Cuneiform), IC (Intermediate Cuneiform), LC (Lateral Cuneiform), C (Cuboid), N (Navicular), T (Tarsal) in three different cases is opposite, and the peak von Mises stress of c is greater than that of a and b. The peak von Mises stress of b is between a and c.Conclusion: This study found that a reduced sole-ground contact angle may reduce metatarsal stress fractures. Further, a small sole-ground contact angle may not increase ankle joint injury risk during forefoot running. Hence, given the specialized nature of the running shoes designed for forefoot runners, it is plausible that this study may offer novel insights to guide their athletic pursuits.

Single-leg landing (SL) is often associated with a high injury risk, especially anterior cruciate ligament (ACL) injuries and lateral ankle sprain. This work investigates the relationship between ankle motion patterns (ankle initial contact angle [AICA] and ankle range of motion [AROM]) and the lower limb injury risk during SL, and proposes an optimized landing strategy that can reduce the injury risk. To more realistically revert and simulate the ACL injury mechanics, we developed a knee musculoskeletal model that reverts the ACL ligament to a nonlinear short-term viscoelastic mechanical mechanism (strain rate-dependent) generated by the dense connective tissue as a function of strain. Sixty healthy male subjects were recruited to collect biomechanics data during SL. The correlation analysis was conducted to explore the relationship between AICA, AROM, and peak vertical ground reaction force (PVGRF), joint total energy dissipation (TED), peak ankle knee hip sagittal moment, peak ankle inversion angle (PAIA), and peak ACL force (PAF). AICA exhibits a negative correlation with PVGRF (r = −0.591) and PAF (r = −0.554), and a positive correlation with TED (r = 0.490) and PAIA (r = 0.502). AROM exhibits a positive correlation with TED (r = 0.687) and PAIA (r = 0.600). The results suggested that the appropriate increases in AICA (30° to 40°) and AROM (50° to 70°) may reduce the lower limb injury risk. This study has the potential to offer novel perspectives on the optimized application of landing strategies, thus giving the crucial theoretical basis for decreasing injury risk.

Abstract The MECHANISMS study investigates how social norms for adolescent smoking and vaping are transmitted through school friendship networks, and is the first study to use behavioral economics methodology to assess smoking-related social norms. Here, we investigate the effects of selection homophily (the tendency to form friendships with similar peers) and peer influence (a social process whereby an individual’s behavior or attitudes are affected by peers acting as reference points for the individual) on experimentally measured smoking and vaping norms, and other smoking outcomes, in adolescents from high and middle-income settings. Full school year groups in six secondary schools in Northern Ireland (United Kingdom) and six secondary schools in Bogotá (Colombia) participated (n = 1344/1444, participation = 93.1%, target age 12–13 years). Over one semester, pupils received one previously tested school-based smoking prevention program (ASSIST or Dead Cool). Outcomes included experimentally measured smoking/vaping norms, self-report and objectively measured smoking behavior, and self-report smoking norms, intentions, susceptibility, attitudes, and psycho-social antecedents. We investigated selection homophily and peer influence using regressions and SIENA modeling. Regression results demonstrate lagged and contemporaneous selection homophily (odds ratios [ORs] = 0.87–1.26, p ≤ 0.01), and peer influence effects for various outcomes from average responses of friends, school classes, or school year groups (standardized coefficients [βs] = 0.07–0.55, ORs = 1.14–1.31, p ≤ 0.01). SIENA models showed that comparable proportions of smoking/vaping-based similarity between friends were due to selection homophily (32.8%) and peer influence (39.2%). A higher percentage of similarity between friends was due to selection homophily and/or peer influence for ASSIST schools compared to Dead Cool. Selection homophily was also more important in Bogotá, whilst peer influence was stronger in Northern Ireland. These findings support using social norms strategies in adolescent smoking prevention interventions. Future research should consider selection homophily and social influence jointly, and examine whether these findings translate to other high and low-middle-income settings with varying cultures and norms.

Background: Human locomotion involves the coordinated activation of a finite set of modules, known as muscle synergy, which represent the motor control strategy of the central nervous system. However, most prior studies have focused on isolated muscle activation, overlooking the modular organization of motor behavior. Therefore, to enhance comprehension of muscle coordination dynamics during multi-joint movements in chronic ankle instability (CAI), exploring muscle synergies during landing in CAI patients is imperative. Methods: A total of 22 patients with unilateral CAI and 22 healthy participants were recruited for this research. We employed a recursive model for second-order differential equations to process electromyographic (EMG) data after filtering preprocessing, generating the muscle activation matrix, which was subsequently inputted into the non-negative matrix factorization model for extraction of the muscle synergy. Muscle synergies were classified utilizing the K-means clustering algorithm and Pearson correlation coefficients. Statistical parameter mapping (SPM) was employed for temporal modular parameter analyses. Results: Four muscle synergies were identified in both the CAI and healthy groups. In Synergy 1, only the gluteus maximus showed significantly higher relative weight in CAI compared to healthy controls (p = 0.0035). Synergy 2 showed significantly higher relative weights for the vastus lateralis in the healthy group compared to CAI (p = 0.018), while in Synergy 4, CAI demonstrated significantly higher relative weights of the vastus lateralis compared to healthy controls (p = 0.030). Furthermore, in Synergy 2, the CAI group exhibited higher weights of the tibialis anterior compared to the healthy group (p = 0.042). Conclusions: The study suggested that patients with CAI exhibit a comparable modular organizational framework to the healthy group. Investigation of amplitude adjustments within the synergy spatial module shed light on the adaptive strategies employed by the tibialis anterior and gluteus maximus muscles to optimize control strategies during landing in patients with CAI. Variances in the muscle-specific weights of the vastus lateralis across movement modules reveal novel biomechanical adaptations in CAI, offering valuable insights for refining rehabilitation protocols.

IntroductionChanges in coronary artery luminal dimensions during the cardiac cycle can impact the accurate quantification of volumetric analyses in intravascular ultrasound (IVUS) image studies. Accurate ED-frame detection is pivotal for guiding interventional decisions, optimizing therapeutic interventions, and ensuring standardized volumetric analysis in research studies. Images acquired at different phases of the cardiac cycle may also lead to inaccurate quantification of atheroma volume due to the longitudinal motion of the catheter in relation to the vessel. As IVUS images are acquired throughout the cardiac cycle, end-diastolic frames are typically identified retrospectively by human analysts to minimize motion artefacts and enable more accurate and reproducible volumetric analysis.MethodsIn this paper, a novel neural network-based approach for accurate end-diastolic frame detection in IVUS sequences is proposed, trained using electrocardiogram (ECG) signals acquired synchronously during IVUS acquisition. The framework integrates dedicated motion encoders and a bidirectional attention recurrent network (BARNet) with a temporal difference encoder to extract frame-by-frame motion features corresponding to the phases of the cardiac cycle. In addition, a spatiotemporal rotation encoder is included to capture the IVUS catheter's rotational movement with respect to the coronary artery.ResultsWith a prediction tolerance range of 66.7 ms, the proposed approach was able to find 71.9%, 67.8%, and 69.9% of end-diastolic frames in the left anterior descending, left circumflex and right coronary arteries, respectively, when tested against ECG estimations. When the result was compared with two expert analysts’ estimation, the approach achieved a superior performance.DiscussionThese findings indicate that the developed methodology is accurate and fully reproducible and therefore it should be preferred over experts for end-diastolic frame detection in IVUS sequences.

Abstract Objective To explore the characteristics of renal artery variation in patients with renal cell carcinoma and to evaluate the predicting value of accessory renal artery in the pathological grading of renal cell carcinoma. Methods The clinicopathological data of patients with clear cell renal cell carcinoma diagnosed in the Department of Urology of the First Hospital of Shanxi Medical University from September 2019 to March 2023 were retrospectively analyzed. All patients underwent visual three-dimensional model reconstruction from computed tomography images. All kidneys were divided into two groups: the affected kidney and the healthy kidney, and the incidence of renal artery variation in the two groups was analyzed. Then, according to the existence of accessory renal artery in the affected kidney, the patients were divided into two groups, and the relationship between accessory renal artery and clinicopathological features of patients with clear cell renal cell carcinoma was analyzed. Finally, univariate and multivariate logistic regression analyses were performed to determine the predictors of Fuhrman grading of clear cell renal cell carcinoma, and the predictive ability of the model was evaluated by the receiver operating characteristic curve. Results The incidence of renal artery variation and accessory renal artery in the affected kidney was significantly higher than them in the healthy kidney. The patients with accessory renal artery in the affected kidney had larger tumor maximum diameter, higher Fuhrman grade and more exophytic growth. The presence of accessory renal artery on the affected kidney and the maximum diameter of tumor are independent predictors of high-grade renal cell carcinoma. The receiver operating characteristic curve suggests that the model has a good predictive ability. Conclusion The existence of accessory renal artery on the affected kidney may be related to the occurrence and development of clear cell renal cell carcinoma, and can better predict Fuhrman grade of clear cell renal cell carcinoma. The finding provides a reference for the future diagnostic evaluation of RCC, and provides a new direction for the study of the pathogenesis of RCC.

Abstract We know little about how smoking prevention interventions might leverage social network structures to enhance protective social norms. In this study we combined statistical and network science methods to explore how social networks influence social norms related to adolescent smoking in school-specific settings in Northern Ireland and Colombia. Pupils (12–15 years old) participated in two smoking prevention interventions in both countries (n = 1344). A Latent Transition Analysis identified three groups characterized by descriptive and injunctive norms towards smoking. We employed a Separable Temporal Random Graph Model to analyze homophily in social norms and conducted a descriptive analysis of the changes in the students’ and their friends’ social norms over time to account for social influence. The results showed that students were more likely to be friends with others who had social norms against smoking. However, students with social norms favorable towards smoking had more friends with similar views than the students with perceived norms against smoking, underlining the importance of network thresholds. Our results support the notation that the ASSIST intervention takes advantage of friendship networks to leverage greater change in the students' smoking social norms than the Dead Cool intervention, reiterating that social norms are subject to social influence.

Abstract Background Little is known about the association between the preoperative low-density lipoprotein cholesterol (LDL-C) level and prognosis in patients with renal cell carcinoma (RCC) after nephrectomy, and its prognostic value needs to be elucidated. Methods The clinical and follow-up data of 737 RCC patients who underwent nephrectomy were retrospectively analyzed. The optimal cut-off LDL-C level was determined using X-tile, and then patients were divided into low and high LDL-C groups. The association between LDL-C levels and survival of RCC patients was assessed using the Kaplan-Meier method and Cox regression analysis. Results The optimal cut-off LDL-C level was 1.93 mmol/L, and patients were divided into the low (≤ 1.93 mmol/L) and high LDL-C (> 1.93 mmol/L) groups. The Kaplan-Meier analysis showed that patients in the low LDL-C group had significantly shorter overall survival (OS), cancer-specific survival (CSS) and recurrence-free survival (RFS) than those in the high LDL-C group (P = 0.001, P = 0.001, and P = 0.003, respectively). The COX univariate analysis showed that the preoperative LDL-C level was closely associated with OS, CSS, and RFS in RCC patients (P = 0.002, P = 0.003, and P = 0.005, respectively). The multivariate analysis showed that the preoperative LDL-C level was an independent factor for predicting survival (OS, CSS and RFS) in RCC patients after nephrectomy. The low preoperative LDL-C levels predicted worse OS (hazard ratio [HR]: 2.337; 95% confidence interval [CI]: 1.192–4.581; P = 0.013), CSS (HR: 3.347; 95% CI: 1.515–7.392; P = 0.003), and RFS (HR: 2.207; 95% CI: 1.178–4.132; P = 0.013). Conclusions The preoperative LDL-C level is an independent factor for the prognosis of RCC patients after nephrectomy, and low preoperative LDL-C levels predict worse survival (OS, CSS, and RFS).

PEP06 is a novel endostatin-Arg-Gly-Asp-Arg-Gly-Asp (RGDRGD) 30-amino-acid polypeptide featuring a terminally fused RGDRGD hexapeptide at the N terminus. The active endostatin fragment of PEP06 directly targets tumor cells and exerts an antitumoral effect. However, little is known about the kinetics and degradation products of PEP06 in vitro or in vivo. In this study, we investigated the in vitro metabolic stability of PEP06 after it was incubated with living cells obtained from animals of different species; we further identified the degradation characteristics of its cleavage products. PEP06 underwent rapid enzymatic degradation in multiple types of living cells, and the liver, kidney, and blood play important roles in the metabolism and clearance of the peptides resulting from the molecular degradation of PEP06. We identified metabolites of PEP06 using full-scan mass spectrometry (MS) and tandem MS (MS2), wherein 43 metabolites were characterized and identified as the degradation metabolites from the parent peptide, formed by successive losses of amino acids. The metabolites were C and N terminal truncated products of PEP06. The structures of 11 metabolites (M6, M7, M16, M17, M21, M25, M33, M34, M39, M40, and M42) were further confirmed by comparing the retention times of similar full MS spectrum and MS2 spectrum information with reference standards for the synthesized metabolites. We have demonstrated the metabolic stability of PEP06 in vitro and identified a series of potentially bioactive downstream metabolites of PEP06, which can support further drug research.

Abstract Little is known about the personality and cognitive traits that shape adolescents’ sensitivity to social norms. Further, few studies have harnessed novel empirical tools to elicit sensitivity to social norms among adolescent populations. This paper examines the association between sensitivity to norms and various personality and cognitive traits using an incentivised rule-following task grounded in Game Theory. Cross-sectional data were obtained from 1274 adolescents. Self-administered questionnaires were used to measure personality traits as well as other psychosocial characteristics. Incentivised rule-following experiments gauged sensitivity to social norms. A series of multilevel mixed effects ordered logistic regression models were employed to assess the association between sensitivity to norms and the personality and cognitive traits. The results highlighted statistically significant univariate associations between the personality and cognitive traits and sensitivity to norms. However, in the multivariate adjusted model, the only factor associated with sensitivity to norms was gender. The gender-stratified analyses revealed differences in the personality and cognitive traits associated with sensitivity to norms across genders. For males need to belong was significantly negatively associated with sensitivity to norms in the multivariate model. By comparison, emotional stability was negatively associated with sensitivity to norms for females. This study reinforced the findings from an earlier study and suggested female adolescents had higher levels of sensitivity to norms. The results indicated no consistent pattern between sensitivity to norms and the personality and cognitive traits. Our findings provide a basis for further empirical research on a relatively nascent construct, and bring a fresh perspective to the question of norm-following preferences among this age group.

Abstract Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions. In recent years, deep learning methods have shown promising performance in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. Considering the remarkable breakthroughs made by state-of-the-art technologies, we provide this survey with a comprehensive study of recently developed deep learning based brain tumor segmentation techniques. More than 150 scientific papers are selected and discussed in this survey, extensively covering technical aspects such as network architecture design, segmentation under imbalanced conditions, and multi-modality processes. We also provide insightful discussions for future development directions.

The mutual influence between information and infectious diseases during the spreading process is becoming increasingly prominent. To elucidate the impact of factors such as higher-order interactions, interpersonal distances, and asymptomatic carriers on the coupled propagation of information and infectious diseases, a novel coupled spreading model is constructed based on a two-layer complex network, where one layer is a higher-order network and another layer is a weighted network. The higher-order interactions in information propagation are characterized using a 2-simplex, and a sUARU (simplicial unaware-aware-removed-unaware) model is employed to articulate information propagation. The inter-individual social distances in disease propagation are represented by the weights of a weighted network, and an SEIS (susceptible-exposed-infected-susceptible) model is utilized to describe disease propagation. The dynamic equations of coupled spreading are formulated utilizing the microscopic Markov chain approach. An analytical expression for the epidemic threshold is obtained by deriving it from the steady-state form of the dynamic equations. Comprehensive simulations are conducted to scrutinize the dynamic characteristics of the coupled spreading model. The findings indicate that enhancing the effects of higher-order interactions in information propagation and increasing inter-individual social distances both lead to higher outbreak thresholds and greater spreading of diseases. Additionally, a stronger infectivity among asymptomatic carriers and an extended incubation period are favorable for the outbreak and spread of an epidemic. These findings can provide meaningful guidance for the prevention and control of real-world epidemics.

Abstract Cell line authentication is important in the biomedical field to ensure that researchers are not working with misidentified cells. Short tandem repeat is the gold standard method, but has its own limitations, including being expensive and time-consuming. Deep neural networks achieve great success in the analysis of cellular images in a cost-effective way. However, because of the lack of centralized available datasets, whether or not cell line authentication can be replaced or supported by cell image classification is still a question. Moreover, the relationship between the incubation times and cellular images has not been explored in previous studies. In this study, we automated the process of the cell line authentication by using deep learning analysis of brightfield cell line images. We proposed a novel multi-task framework to identify cell lines from cell images and predict the duration of how long cell lines have been incubated simultaneously. Using thirty cell lines’ data from the AstraZeneca Cell Bank, we demonstrated that our proposed method can accurately identify cell lines from brightfield images with a 99.8% accuracy and predicts the incubation durations for cell images with the coefficient of determination score of 0.927. Considering that new cell lines are continually added to the AstraZeneca Cell Bank, we integrated the transfer learning technique with the proposed system to deal with data from new cell lines not included in the pre-trained model. Our method achieved excellent performance with a precision of 97.7% and recall of 95.8% in the detection of 14 new cell lines. These results demonstrated that our proposed framework can effectively identify cell lines using brightfield images.

The blockchain, with its key characteristics of decentralization, persistence, anonymity, and auditability, has become a solution to overcome the overdependence and lack of trust for a traditional public key infrastructure on third-party institutions. Because of these characteristics, the blockchain is suitable for solving certain open problems in the service-oriented social network, where the unreliability of submitted reviews of service vendors can cause serious security problems. To solve the unreliability problems of submitted reviews, this paper first proposes a blockchain-based identity authentication scheme and a new trusted service evaluation model by introducing the scheme into a service evaluation model. The new trusted service evaluation model consists of the blockchain-based identity authentication scheme, evaluation submission module, and evaluation publicity module. In the proposed evaluation model, only users who have successfully been authenticated can submit reviews to service vendors. The registration and authentication records of users’ identity and the reviews for service vendors are all stored in the blockchain network. The security analysis shows that this model can ensure the credibility of users’ reviews for service vendors, and other users can obtain credible reviews of service vendors via the review publicity module. The experimental results also show that the proposed model has a lower review submission delay than other models.

Abstract Running gait patterns have implications for revealing the causes of injuries between higher-mileage runners and low-mileage runners. However, there is limited research on the possible relationships between running gait patterns and weekly running mileages. In recent years, machine learning algorithms have been used for pattern recognition and classification of gait features to emphasize the uniqueness of gait patterns. However, they all have a representative problem of being a black box that often lacks the interpretability of the predicted results of the classifier. Therefore, this study was conducted using a Deep Neural Network (DNN) model and Layer-wise Relevance Propagation (LRP) technology to investigate the differences in running gait patterns between higher-mileage runners and low-mileage runners. It was found that the ankle and knee provide considerable information to recognize gait features, especially in the sagittal and transverse planes. This may be the reason why high-mileage and low-mileage runners have different injury patterns due to their different gait patterns. The early stages of stance are very important in gait pattern recognition because the pattern contains effective information related to gait. The findings of the study noted that LRP completes a feasible interpretation of the predicted results of the model, thus providing more interesting insights and more effective information for analyzing gait patterns.

Semisupervised learning in synthetic aperture radars (SARs) is one of the research hotspots in the field of radar image automatic target recognition. It can efficiently deal with challenging environments where there are insufficient labeled samples and large unlabeled samples in the SAR dataset. In recent years, consistency regularization methods in semisupervised learning have shown considerable improvement in recognition accuracy and efficiency. Current consistency regularization approaches suffer from two main shortcomings: first, extracting all of the relevant information in the image target is difficult owing to the inability of conventional convolutional neural networks to capture global relational information; second, the standard teacher–student regularization methodology causes confirmation biases due to the high coupling between teacher and student models. This article adopts an innovative trimodel semisupervised method based on attention-augmented convolutional networks to address the aforementioned obstacles. Specifically, we develop an attention mechanism incorporating a novel positional embedding method based on recurrent neural networks and integrate this with a standard convolutional network as a feature extractor, to improve the network's ability to extract global feature information from images. Furthermore, we address the confirmation bias problem by introducing a classmate model to the standard teacher–student structure and utilize the model to impose a weak consistency constraint designed on the student to weaken the strong coupling between the teacher and the student. Comparative experiments on the Moving and Stationary Target Acquisition and Recognition dataset show that our method outperforms state-of-the-art semisupervised methods in terms of recognition accuracy, demonstrating its potential as a new benchmark approach for the deep learning and SAR research community.

In this article, a new method for seismic ionospheric Global Navigation Satellite System (GNSS) total electron content (TEC) based anomaly detection using a deep learning framework is proposed. The performance of the proposed encoder–decoder long short-term memory extended model is compared with those of five other benchmarking predictors. The proposed model achieves the best performance (R2 = 0.9105 and root-mean-square error (RMSE) = 2.6759) in predicting TEC time series data, demonstrating a 20% improvement in R2 and 39.1% improvement in the RMSE over the autoregressive integrated moving average model. To detect the pre-earthquake ionospheric disturbances more accurately, a reasonable strategy for determining anomaly limits is also proposed. Finally, the method is applied to analyze the pre-earthquake ionospheric TEC disturbance of the 2016 Xinjiang Ms 6.2 earthquake. By excluding the effects of solar activity and geomagnetic activity, obvious ionospheric anomalies could be observed, occurring during 4–8 days prior to, and on 1 day before, the earthquake. Negative anomalies tended to occur in the earlier period, whereas positive anomalies occurred closer to the earthquake time, with increasing anomaly intensity with temporal proximity. Furthermore, confusion analysis is used in this article to verify the reliability of the proposed model. The proposed model achieves significant improvements in predicting GNSS TEC time series and is shown to advance the performance of earthquake anomaly detection technology.

Road extraction from synthetic aperture radar (SAR) images has attracted much attention in the field of remote sensing image processing. General road extraction algorithms, affected by shadows of buildings and trees, are prone to producing fragmented road segments. To improve the accuracy and completeness of road extraction, we propose a neural network-based algorithm, which takes the connectivity and direction features of roads into consideration, named RoadSeg-CD. It consists of two branches: one is the main branch for road segmentation; the other is the auxiliary branch for learning road directions. In the main branch, a connectivity array is designed to utilize local contextual information and construct a connectivity loss based on the predicted probabilities of neighboring pixels. In the auxiliary branch, we proposed a novel road direction map, which is used for learning the directions of roads. The two branches are connected by specific feature fusion process, and the output from the main branch is taken as the road extraction result. Experiments on real radar images are implemented to validate the effectiveness of our method. The experimental results demonstrate that our method can obtain more continuous and more complete roads than several state-of-the-art road extraction algorithms.

Remote sensing (RS) scene classification plays an important role in a wide range of RS applications. Recently, convolutional neural networks (CNNs) have been applied to the field of scene classification in RS images and achieved impressive performance. However, to classify RS scenes, most of the existing CNN methods either utilize the high-level features from the last convolutional layer of CNNs, missing much important information existing at the other levels, or directly fuse the features at different levels, bringing redundant and/or mutually exclusive information. Inspired by the attention mechanism of the human visual system, in this article, we explore a novel relation-attention model and design an end-to-end relation-attention network (RANet) to learn powerful feature representations of multiple levels to further improve the classification performance. First, we propose to extract convolutional features at different levels by pretrained CNNs. Second, a multiscale feature computation module is constructed to connect features at different levels and generate multiscale semantic features. Third, a novel relation-attention model is designed to focus on the critical features whilst avoiding the use of redundant and even distractive ones by exploiting the scale contextual information. Finally, the resulting relation-attention features are concatenated and fed into a softmax layer for the final classification. Experiments on four well-known RS scene classification datasets (UC-Merced, WHU-RS19, AID, and OPTIMAL-31) show that our method outperforms some state-of-the-art algorithms.

Segmentation of marine oil spills in synthetic aperture radar (SAR) images is a challenging task because of the complexity and irregularities in SAR images. In this work, we aim to develop an effective segmentation method which addresses marine oil spill identification in SAR images by investigating the distribution representation of SAR images. To seek effective oil spill segmentation, we revisit the SAR imaging mechanism in order to attain the probability distribution representation of oil spill SAR images, in which the characteristics of SAR images are properly modelled. We then exploit the distribution representation to formulate the segmentation energy functional, by which oil spill characteristics are incorporated to guide oil spill segmentation. Moreover, the oil spill segmentation model contains the oil spill contour regularization term and the updated level set regularization term which enhance the representational power of the segmentation energy functional. Benefiting from the synchronization of SAR image representation and oil spill segmentation, our proposed method establishes an effective oil spill segmentation framework. Experimental evaluations demonstrate the effectiveness of our proposed segmentation framework for different types of marine oil spill SAR image segmentation.

Object detection is one of the most fundamental yet challenging research topics in the domain of computer vision. Recently, the study on this topic in aerial images has made tremendous progress. However, complex background and worse imaging quality are obvious problems in aerial object detection. Most state-of-the-art approaches tend to develop elaborate attention mechanisms for the space-time feature calibrations with arduous computational complexity, while surprisingly ignoring the importance of feature calibrations in channel-wise. In this work, we propose a simple yet effective calibrated-guidance (CG) scheme to enhance channel communications in a feature transformer fashion, which can adaptively determine the calibration weights for each channel based on the global feature affinity correlations. Specifically, for a given set of feature maps, CG first computes the feature similarity between each channel and the remaining channels as the intermediary calibration guidance. Then, rerepresenting each channel by aggregating all the channels weighted together via the guidance operation. Our CG is a general module that can be plugged into any deep neural networks, which is named as CG-Net. To demonstrate its effectiveness and efficiency, extensive experiments are carried out on both oriented object detection task and horizontal object detection task in aerial images. Experimental results on two challenging benchmarks (i.e., DOTA and HRSC2016) demonstrate that our CG-Net can achieve the new state-of-the-art performance in accuracy with a fair computational overhead. The source code has been open sourced at https://github.com/WeiZongqi/CG-Net.

Abstract The semantic representation of deep features is essential for image context understanding, and effective fusion of features with different semantic representations can significantly improve the model's performance on salient object detection. This paper proposes a novel method called multi‐receptive and parallel integration, for salient object detection. Firstly, a multi‐receptive enhancement module is designed to effectively expand the receptive fields of features from different layers and generate features with different receptive fields. Multi‐receptive enhancement module can enhance the semantic representation and improve the model's perception of the image context, which enables the model to locate the salient object accurately. Secondly, in order to reduce the reuse of redundant information in the complex top‐down fusion method and weaken the differences between semantic features, a relatively simple but effective parallel fusion strategy is proposed. It allows multi‐scale features to better interact with each other, thus improving the overall performance of the model. Experimental results on multiple datasets demonstrate that the proposed method outperforms state‐of‐the‐art methods under different evaluation metrics.

Felines are generally acknowledged to have natural athletic ability, especially in jumping and landing. The adage “felines have nine lives” seems applicable when we consider its ability to land safely from heights. Traditional post-processing of finite element analysis (FEA) is usually based on stress distribution trend and maximum stress values, which is often related to the smoothness and morphological characteristics of the finite element model and cannot be used to comprehensively and deeply explore the mechanical mechanism of the bone. Machine learning methods that focus on feature pattern variable analysis have been gradually applied in the field of biomechanics. Therefore, this study investigated the cat forelimb biomechanical characteristics when landing from different heights using FEA and feature engineering techniques for post-processing of FEA. The results suggested that the stress distribution feature of the second, fourth metacarpal, the second, third proximal phalanx are the features that contribute most to landing pattern recognition when cats landed under different constraints. With increments in landing altitude, the variations in landing pattern differences may be a response of the cat's forelimb by adjusting the musculoskeletal structure to reduce the risk of injury with a more optimal landing strategy. The combination of feature engineering techniques can effectively identify the bone's features that contribute most to pattern recognition under different constraints, which is conducive to the grasp of the optimal feature that can reveal intrinsic properties in the field of biomechanics.

Background: Although numerous studies have been conducted to investigate the acute effects of shoe drops on running kinematics and kinetic variables, their effects on muscle forces remain unknown. Thus, the primary aim of this study was to compare the muscle force, kinematics, and kinetic variables of habitually rearfoot runners with heel-to-toe drops of negative 8 mm shoes (minimalist shoes) and positive 9 mm shoes (normal shoes) during the running stance phase by using musculoskeletal modeling and simulation techniques. Methods: Experimental data of lower limb kinematics, ground reaction force, and muscle activation from 16 healthy runners with rearfoot strike patterns were collected and analyzed in OpenSim. Using Matlab, the statistical parameter mapping paired t-test was used to compare the joint angle, moment, and muscle force waveform. Results: The results revealed differences in the sagittal ankle and hip angles and sagittal knee moments between the different heel-to-toe drops of running shoes. Specifically, it showed that the negative 8 mm running shoes led to significantly smaller values than the positive 9 mm running shoes in terms of the angle of ankle dorsiflexion, ankle eversion, knee flexion, hip flexion, and hip internal and hip external rotation. The peak ankle dorsiflexion moment, ankle plantarflexion moment, ankle eversion moment, knee flexion moment, knee abduction moment, and knee internal rotation also decreased obviously with the minimalist running shoes, while the lateral gastrocnemius, Achilleas tendon, and extensor hallucis longus muscles were obviously greater in the minimalist shoes compared to normal shoes. The vastus medialis, vastus lateralis and extensor digitorum longus muscles force were smaller in the minimalist shoes. Conclusions: Runners may shift to a midfoot strike pattern when wearing negative running shoes. High muscle forces in the gastrocnemius lateral, Achilleas tendon, and flexor hallucis longus muscles may also indicate an increased risk of Achilleas tendonitis and ankle flexor injuries.

Image captioning is the task of automatically generating a description of an image. Traditional image captioning models tend to generate a sentence describing the most conspicuous objects, but fail to describe a desired region or object as human. In order to generate sentences based on a given target, understanding the relationships between particular objects and describing them accurately is central to this task. In detail, information-augmented embedding is used to add prior information to each object, and a new Multi-Relational Weighted Graph Convolutional Network (MR-WGCN) is designed for fusing the information of adjacent objects. Then, a dynamic attention decoder module selectively focuses on particular objects or semantic contents. Finally, the model is optimized by similarity loss. The experiment on MSCOCO Entities demonstrates that IANR obtains, to date, the best published CIDEr performance of 124.52% on the Karpathy test split. Extensive experiments and ablations on both the MSCOCO Entities and the Flickr30k Entities demonstrate the effectiveness of each module. Meanwhile, IANR achieves better accuracy and controllability than the state-of-the-art models under the widely used evaluation metric.

Abstract With increasing application of cloud computing and big data technologies, a large amount of personal information is stored on the Internet, which raises the issue of privacy leakage. To protect people’s data privacy, this paper firstly presents a new anonymous Identify-Based Encryption (IBE) scheme and gives the proof of its security under the Bilinear Diffie–Hellman Security Assumption. Then, by introducing the anonymous IBE scheme into anonymous communication fields, this paper introduces a new lightweight anonymous communication model for cloud computing, which guarantees the anonymity of system users and the security of messages in small groups. Our analysis shows that, the proposed communication model cannot only reduce memory consumption and improve message transmission efficiency, but also effectively resist traffic-analysis attacks, node eavesdropping, and finally achieve secure anonymous communication in cloud computing.

Felines have significant advantages in terms of sports energy efficiency and flexibility compared with other animals, especially in terms of jumping and landing. The biomechanical characteristics of a feline (cat) landing from different heights can provide new insights into bionic robot design based on research results and the needs of bionic engineering. The purpose of this work was to investigate the adaptive motion adjustment strategy of the cat landing using a machine learning algorithm and finite element analysis (FEA). In a bionic robot, there are considerations in the design of the mechanical legs. (1) The coordination mechanism of each joint should be adjusted intelligently according to the force at the bottom of each mechanical leg. Specifically, with the increase in force at the bottom of the mechanical leg, the main joint bearing the impact load gradually shifts from the distal joint to the proximal joint; (2) the hardness of the materials located around the center of each joint of the bionic mechanical leg should be strengthened to increase service life; (3) the center of gravity of the robot should be lowered and the robot posture should be kept forward as far as possible to reduce machine wear and improve robot operational accuracy.

Airport detection in synthetic aperture radar (SAR) images has attracted much concern in the field of remote sensing. Affected by other salient objects with geometrical features similar to those of airports, traditional methods often generate false detections. In order to produce the geometrical features of airports and suppress the influence of irrelevant objects, we propose a novel method for airport detection in SAR images. First, a salient line segment detector is constructed to extract salient line segments in the SAR images. Second, we obtain the airport support regions by grouping these line segments according to the commonality of these geometrical features. Finally, we design an edge-oriented region growing (EORG) algorithm, where growing seeds are selected from the airport support regions with the help of edge information in SAR images. Using EORG, the airport region can be mapped by performing region growing with these seeds. We implement experiments on real radar images to validate the effectiveness of our method. The experimental results demonstrate that our method can acquire more accurate locations and contours of airports than several state-of-the-art airport detection algorithms.

Common horizontal bounding box-based methods are not capable of accurately locating slender ship targets with arbitrary orientations in synthetic aperture radar (SAR) images. Therefore, in recent years, methods based on oriented bounding box (OBB) have gradually received attention from researchers. However, most of the recently proposed deep learning-based methods for OBB detection encounter the boundary discontinuity problem in angle or key point regression. In order to alleviate this problem, researchers propose to introduce some manually set parameters or extra network branches for distinguishing the boundary cases, which make training more difficult and lead to performance degradation. In this article, in order to solve the boundary discontinuity problem in OBB regression, we propose to detect SAR ships by learning polar encodings. The encoding scheme uses a group of vectors pointing from the center of the ship target to the boundary points to represent an OBB. The boundary discontinuity problem is avoided by training and inference directly according to the polar encodings. In addition, we propose an intersect over union (IOU)-weighted regression loss, which further guides the training of polar encodings through the IOU metric and improves the detection performance. Comparative experiments on the benchmark Rotating SAR Ship Detection Dataset (RSSDD) demonstrate the effectiveness of our proposed method in terms of enhanced detection performance over state-of-the-art algorithms and other OBB encoding schemes.

Instance segmentation methods for synthetic aperture radar (SAR) ship imaging have certain unsolved problems. 1) Most of the anchor-based detection algorithms encounter difficulties in tuning the anchor-related parameters and high computational costs. 2) Different tasks share the same features without considering the differences between tasks, leading to mismatching of the shared features and inconsistent training targets. 3) Common loss functions for instance segmentation cannot effectively distinguish the positional relationships between ships with the same degree of overlap. In order to alleviate these problems, we first adopt a lightweight feature extractor and an anchor-free convolutional network, which effectively help to reduce computational consumption and model complexity. Second, to fully disseminate feature information, a dynamic encoder–decoder is proposed to dynamically transform the shared features to task-specific features in channel and spatial dimensions. Third, a novel loss function based on centroid distance is designed to make full use of the geometrical shape and positional relationship between SAR ship targets. In order to better extract features from SAR images in complex scenes, we further propose the dilated convolution enhancement module, which utilizes multiple receptive fields to take full advantage of the shallow feature information. Experiments conducted on the SAR ship detection dataset prove that the method proposed in this article is superior to the other state-of-the-art algorithms in terms of instance segmentation accuracy and model complexity.

Abstract Machine learning has been widely exploited in developing new materials. However, challenges still exist: small dataset is common for most tasks; new datasets, special descriptors and specific models need to be built from scratch when facing a new task; knowledge cannot be readily transferred between independent models. In this paper we propose a general and transferable deep learning (GTDL) framework for predicting phase formation in materials. The proposed GTDL framework maps raw data to pseudo-images with some special 2-D structure, e.g., periodic table, automatically extracts features and gains knowledge through convolutional neural network, and then transfers knowledge by sharing features extractors between models. Application of the GTDL framework in case studies on glass-forming ability and high-entropy alloys show that the GTDL framework for glass-forming ability outperformed previous models and can correctly predicted the newly reported amorphous alloy systems; for high-entropy alloys the GTDL framework can discriminate five types phases (BCC, FCC, HCP, amorphous, mixture) with accuracy and recall above 94% in fivefold cross-validation. In addition, periodic table knowledge embedded in data representations and knowledge shared between models is beneficial for tasks with small dataset. This method can be easily applied to new materials development with small dataset by reusing well-trained models for related materials.

The adversarial attack is crucial to improving the robustness of deep learning models; they help improve the interpretability of deep learning and also increase the security of the models in real-world applications. However, existing attack algorithms mainly focus on image classification tasks, and they lack research targeting object detection. Adversarial attacks against image classification are global-based with no focus on the intrinsic features of the image. In other words, they generate perturbations that cover the whole image, and each added perturbation is quantitative and undifferentiated. In contrast, we propose a global-to-local adversarial attack based on object detection, which destroys important perceptual features of the object. More specifically, we differentially extract gradient features as a proportion of perturbation additions to generate adversarial samples, as the magnitude of the gradient is highly correlated with the model’s point of interest. In addition, we reduce unnecessary perturbations by dynamically suppressing excessive perturbations to generate high-quality adversarial samples. After that, we improve the effectiveness of the attack using the high-frequency feature gradient as a motivation to guide the next gradient attack. Numerous experiments and evaluations have demonstrated the effectiveness and superior performance of our from global to Local gradient attacks with high-frequency momentum guidance (GLH), which is more effective than previous attacks. Our generated adversarial samples also have excellent black-box attack ability.

Pulmonary fibrosis (PF) is one of the sequelae of Corona Virus Disease 2019 (COVID-19), and currently, lung transplantation is the only viable treatment option. Hence, other effective treatments are urgently required. We investigated the therapeutic effects of an approved botanical drug, cepharanthine (CEP), in a cell culture model of transforming growth factor-β1 (TGF-β1) and bleomycin (BLM)-induced pulmonary fibrosis rat models both in vitro and in vivo. In this study, CEP and pirfenidone (PFD) suppressed BLM-induced lung tissue inflammation, proliferation of blue collagen fibers, and damage to lung structures in vivo. Furthermore, we also found increased collagen deposition marked by α-smooth muscle actin (α-SMA) and Collagen Type I Alpha 1 (COL1A1), which was significantly alleviated by the addition of PFD and CEP. Moreover, we elucidated the underlying mechanism of CEP against PF in vitro. Various assays confirmed that CEP reduced the viability and migration and promoted apoptosis of myofibroblasts. The expression levels of myofibroblast markers, including COL1A1, vimentin, α-SMA, and Matrix Metallopeptidase 2 (MMP2), were also suppressed by CEP. Simultaneously, CEP significantly suppressed the elevated Phospho-NF-κB p65 (p-p65)/NF-κB p65 (p65) ratio, NOD-like receptor thermal protein domain associated protein 3 (NLRP3) levels, and elevated inhibitor of NF-κB Alpha (IκBα) degradation and reversed the progression of PF. Hence, our study demonstrated that CEP prevented myofibroblast activation and treated BLM-induced pulmonary fibrosis in a dose-dependent manner by regulating nuclear factor kappa-B (NF-κB)/ NLRP3 signaling, thereby suggesting that CEP has potential clinical application in pulmonary fibrosis in the future.

Cloud computing has emerged as a promising paradigm for the Internet of Things (IoT) and Cyber-Physical-Social Systems (CPSS). However, the problem of how to ensure the security of data transmission and data storage in CPSS is a key issue to address. We need to protect the confidentiality and privacy of users' data and users' identity during the transmission and storage process in CPSS. In order to avoid users' personal information leakage from IoT devices during the process of data processing and transmitting, we propose a certificateless encryption scheme, and conduct a security analysis under the assumption of Computational Diffie-Hellman(CDH) Problem. Furthermore, based on the proposed cryptography mechanism, we achieve a novel anonymous communication protocol to protect the identity privacy of communicating units in CPSS. In the new protocol, an anonymous communication link establishment method and an anonymous communication packet encapsulation format are proposed. The Diffie-Hellman key exchange algorithm is used to construct the anonymous keys distribution method in the new link establishment method. And in the new onion routing packet encapsulation format, the session data are firstly separated from the authentication data to decrease the number of cryptography operations. That is, by using the new onion routing packet we greatly reduces the encryption operations and promotes the forwarding efficiency of anonymous messages, implementing the privacy, security and efficiency in anonymous communication in cyber-physical-social systems.

With the advent of the era of big data, the storage and retrieval of data have become a research hotspot. Hashing methods that transform high-dimensional data into compact binary codes have received increasing attention. Recently, with the successful application of convolutional neural networks in computer vision, deep hashing methods utilize an end-to-end framework to learn feature representations and hash codes mutually, which achieve better retrieval performance than conventional hashing methods. However, deep hashing methods still face some challenges in image retrieval. Firstly, most existing deep hashing methods preserve similarity between original data space and hash coding space using loss functions with high time complexity, which cannot get a win-win situation in time and accuracy. Secondly, few existing deep hashing methods are designed for fine-grained image retrieval, which is necessary in practice. In this study, we propose a novel semantics-preserving hashing method which solves the above problems. We add a hash layer before the classification layer as a feature switch layer to guide the classification. At the same time, we replace the complicated loss with the simple classification loss, combining with quantization loss and bit balance loss to generate high-quality hash codes. Besides, we incorporate feature extractor designed for fine-grained image classification into our network for better representation learning. The results on three widely-used fine-grained image datasets show that our method is superior to other state-of-the-art image retrieval methods.

Sleep recognition refers to detection or identification of sleep posture, state or stage, which can provide critical information for the diagnosis of sleep diseases. Most of sleep recognition methods are limited to single-task recognition, which only involves single-modal sleep data, and there is no generalized model for multi-task recognition on multi-sensor sleep data. Moreover, the shortage and imbalance of sleep samples also limits the expansion of the existing machine learning methods like support vector machine, decision tree and convolutional neural network, which lead to the decline of the learning ability and over-fitting. Self-supervised learning technologies have shown their capabilities to learn significant feature representations. In this paper, a novel self-supervised learning model is proposed for sleep recognition, which is composed of an upstream self-supervised pre-training task and a downstream recognition task. The upstream task is conducted to increase the data capacity, and the information of frequency domain and the rotation view are used to learn the multi-dimensional sleep feature representations. The downstream task is undertaken to fuse bidirectional long-short term memory and conditional random field as the sequential data recognizer to produce the sleep labels. Our experiments shows that our proposed algorithm provide promising results in sleep identification and can further be applied in clinical and smart home environments as a diagnostic tool. The source code is provided at: “https://github.com/zhaoaite/SSRM”.

With the widespread application of drone in commercial and industrial fields, drone detection has received increasing attention in public safety and others. However, due to various appearance of small-size drones, changeable and complex environments, and limited memory resources of edge computing devices, drone detection remains a challenging task nowadays. Although deep convolutional neural network (CNN) has shown powerful performance in object detection in recent years, most existing CNN-based methods cannot balance detection performance and model size well. To solve the problem, we develop a drone detection network with tiny iterative backbone named TIB-Net. In this network, we propose a structure called cyclic pathway, which enhances the capability to extract effective features of small object, and integrate it into existing efficient method Extremely Tiny Face Detector (EXTD). This method not only significantly improves the accuracy of drone detection, but also keeps the model size at an acceptable level. Furthermore, we integrate spatial attention module into our network backbone to emphasize information of small object, which can better locate small-size drone and further improve detection performance. In addition, we present massive manual annotations of object bounding boxes for our collected 2860 drone images as a drone benchmark dataset, which is now publicly available1. In this work, we conduct a series of experiments on our collected dataset to evaluate TIB-Net, and the result shows that our proposed method achieves mean average precision of 89.2% with model size of 697.0KB, which achieves the state-of-the-art results compared with existing methods.

Multi-layer feature integration has demonstrated its superiority in salient object detection. However, the salient regions generated by most models still suffer from inconsistencies and coarse region boundaries. In this paper, to solve these two problems, we proposed a wider and finer network named WFNet. Firstly, a wider feature enhancement module (WFE) is designed to expand the receptive fields of deep semantic features, which makes the network look wider and more accurate while locating salient regions. Secondly, to improve the regional continuity and reduce background noise, we introduce a finer feature fusion module (F3M) which consists of scale-invariant average pooling and detailed feature integration module with channel-wise attention. Finally, we propose an edge-region complementary strategy (ERC) and an edge-focused loss (EL), which can supplement the diluted deep semantics and let the network pay more attention to boundary pixels of salient objects. Benefit from rich deep semantics and more detailed edge features, WFNet can predict saliency maps with clear boundaries under the guidance of edge-focused loss. Experimental results prove that the proposed method outperforms state-of-the-art methods on five benchmarks without any post-processing.

Crop segmentation is a fundamental step of extracting the guidance line for an automated agricultural machine with a visual navigation system. However, the segmentation quality of green crop is seriously affected by the outdoor lighting conditions. To improve the accuracy of crop segmentation under complex lighting conditions, a color-index-based crop segmentation method with luminance partition correction and adaptive thresholding is proposed in this article. The method extracts the luminance component from the given RGB image and employs two adaptive thresholds to divide the luminance image into the dark, normal and bright areas. Then, a partition Gamma function is adaptively selected to improve the brightness levels of the dark and bright regions in which the Gamma correction parameter is adaptively determined based on the local distribution characteristics of illumination, and the corrected image is converted to the RGB counterpart through color saturation restoration. Finally, the ExG (excess green index) color index with Otsu thresholding is used to perform pre-segmentation in order to calculate the segmentation threshold for the final segmentation. Experimental results show that the proposed approach can effectively increase the brightness levels of the dark region and decrease the brightness levels in the bright region. In addition, compared with the traditional Otsu method employed in before and after luminance correction, the proposed method can achieve better segmentation results.

Hyperspectral image anomaly detection is an increasingly important research topic in remote sensing images understanding and interpretation. Recently, low-rank representation-based methods have attracted extensive attention and achieved promising performances in hyperspectral anomaly detection. These methods assume that the hyperspectral data can be decomposed into two parts: the low-rank component representing the background and the residual part indicating the anomaly. In order to improve the separability of the background and anomaly, we propose a novel hyperspectral anomaly detection based on low-rank representation with dictionary construction and data-driven projection. To construct a robust dictionary that contains all categories of the background objects whilst excluding the anomaly's influence, we adopt a superpixel-based tensor low-rank decomposition method to generate a comprehensive and pure background dictionary. Considering the spectral redundancy in the hyperspectral data, data-driven projection is introduced to the low-rank representation to project the original data to a low-dimensional feature space to better separate the anomaly and the background. Experimental results on four real hyperspectral datasets show that the proposed anomaly detection method outperforms the other anomaly detectors.

As an important step of synthetic aperture radar image interpretation, synthetic aperture radar image segmentation aims at segmenting an image into different regions in terms of homogeneity. Because of the deficiency of the labeled samples and the existence of speckling noise, synthetic aperture radar image segmentation is a challenging task. We present a new method for synthetic aperture radar image segmentation in this article. Due to the large size of the original synthetic aperture radar image, we first divide the input image into small slices. Then the image slices are input to the attention-based fully convolutional network for obtaining the segmentation results. Finally, the fully connected conditional random field is adopted for improving the segmentation performance of the network. The innovations of our method are as follows: 1) The attention-based fully convolutional network is embedded with the multiscale attention network which is capable of enhancing the extraction of the image features through three strategies, namely, multiscale feature extraction, channel attention extraction, and spatial attention extraction. 2) We design a new loss function for the attention fully convolutional network by combining Lovasz-Softmax and cross-entropy losses. The new loss allows us to simultaneously optimize the intersection over union and the pixel classification accuracy of the segmentation results. The experiments are performed on two airborne synthetic aperture radar image databases. It has been proved that our method is superior to other state-of- the-art image segmentation approaches.

During the lithospheric buildup to an earthquake, complex physical changes occur within the earthquake hypocenter. Data pertaining to the changes in the ionosphere may be obtained by satellites, and the analysis of data anomalies can help identify earthquake precursors. In this paper, we present a deep-learning model, SeqNetQuake, that uses data from the first China Seismo-Electromagnetic Satellite (CSES) to identify ionospheric perturbations prior to earthquakes. SeqNetQuake achieves the best performance [F-measure (F1) = 0.6792 and Matthews correlation coefficient (MCC) = 0.427] when directly trained on the CSES dataset with a spatial window centered on the earthquake epicenter with the Dobrovolsky radius and an input sequence length of 20 consecutive observations during night time. We further explore a transferring learning approach, which initially trains the model with the larger Electro-Magnetic Emissions Transmitted from the Earthquake Regions (DEMETER) dataset, and then tunes the model with the CSES dataset. The transfer-learning performance is substantially higher than that of direct learning, yielding a 12% improvement in the F1 score and a 29% improvement in the MCC value. Moreover, we compare the proposed model SeqNetQuake with other five benchmarking classifiers on an independent test set, which shows that SeqNetQuake demonstrates a 64.2% improvement in MCC and approximately a 24.5% improvement in the F1 score over the second-best convolutional neural network model. SeqNetSquake achieves significant improvement in identifying pre-earthquake ionospheric perturbation and improves the performance of earthquake prediction using the CSES data.