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Abstract Background The accuracy of artificial intelligence (AI) and experts in diagnosing early esophageal cancer (EC) and its infiltration depth was summarized and analyzed, thus identifying the advantages of AI over traditional manual diagnosis, with a view to more accurately assisting doctors in evaluating the patients' conditions and improving their cure and survival rates. Methods The PubMed, EMBASE, Cochrane, Google, and CNKI databases were searched for relevant literature related to AI diagnosis of early EC and its invasion depth published before August 2023. Summary analysis of pooled sensitivity, specificity, summary receiver operating characteristics (SROC) and area under the curve (AUC) of AI in diagnosing early EC were performed, and Review Manager and Stata were adopted for data analysis. Results A total of 19 studies were enrolled with a low to moderate total risk of bias. The pooled sensitivity of AI for diagnosing early EC was markedly higher than that of novices and comparable to that of endoscopists. Moreover, AI predicted early EC with markedly higher AUCs than novices and experts (0.93 vs. 0.74 vs. 0.89). In addition, pooled sensitivity and specificity in the diagnosis of invasion depth in early EC were higher than that of experts, with AUCs of 0.97 and 0.92, respectively. Conclusion AI‐assistance can diagnose early EC and its infiltration depth more accurately, which can help in its early intervention and the customization of personalized treatment plans. Therefore, AI systems have great potential in the early diagnosis of EC.

Abstract Objective To evaluate mindfulness-based intervention for hypertension with depression and/or anxiety. Methods 10-week mindfulness-based intervention, including health education for hypertension, exclusively for the control group, was administered to the intervention group to assist sixty hypertension patients with depression/anxiety. Among them, the intervention group comprised 8 men and 22 women, with a mean age of 60.02 years and a mean duration of hypertension of 6.29 years. The control group consisted of 14 men and 16 women with a mean age of 57.68 years and a mean duration of hypertension of 6.32 years. The severity of depressive and/or anxiety symptoms was assessed using the 9-item Patient Health Questionnaire (PHQ-9) and the 7-item Generalized Anxiety Disorder scale (GAD-7), along with blood pressure (BP) measurements taken twice daily. The study utilized a self-made self-efficacy scale and awareness of physical and mental health to evaluate mental health and state. Results The depression PHQ-9 or GAD-7 scores reduced by 21.1% or 17.8% in the mindfulness-based intervention group, compared to the control (Z = -2.040, P = 0.041) post 10-week period, suggesting significant reduction in anxiety/stress. These results were consistent with a reduction in systolic BP of 12.24 mm Hg (t = 6.041, P = 0.000). The self-efficacy score of the mindfulness intervention group significantly improved compared to the control (t = 7.818, P

ObjectiveThe aim of this study was to explore the risk factors for different metabolic status in patients with schizophrenia.MethodsA total of 968 hospitalized patients with schizophrenia were recruited. Fasting blood glucose (GLU) and lipid profile, including total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) were measured. Schizophrenia patients were divided into four groups: normal metabolism and weight (NMNW), abnormal metabolism and normal weight (AMNW), normal metabolism and overweight/obesity (NMO), and abnormal metabolism and overweight/obesity (AMO).ResultsOur results showed that NMNW, AMNW, NMO, and AMO accounted for 25.3%, 12.7%, 25.4%, and 36.6%, respectively. There were significant differences in age, disease duration, body mass index (BMI), waist circumference, chronic disease, systolic blood pressure (SBP), diastolic blood pressure (DBP), GLU, TG, TC, HDL-C, and LDL-C among these four groups (all p < 0.05). With the NMNW group as the reference, the disordered multiple classification regression analysis showed that chronic disease was a significant risk factor for AMNW (OR = 5.271, 95% CI = 3.165 to 8.780, p < 0.001) and AMO (OR = 3.245, 95% CI = 2.004 to 5.254, p < 0.001), age was an important protective factor for NMO (OR = 0.968, 95% CI = 0.943 to 0.994, p = 0.015) and AMO (OR = 0.973, 95% CI = 0.948 to 0.999, p < 0.042), waist circumference was a significant risk factor for NMO (OR = 1.218, 95% CI = 1.180 to 1.257, p < 0.001) and AMO (OR = 1.252, 95% CI = 1.212 to 1.291, p < 0.001), and college education was an obvious protective factor for AMO (OR = 0.343, 95% CI = 0.123 to 0.953, p < 0.040) among patients with schizophrenia.ConclusionThe findings of our study underscored the importance of factors such as age, education level, chronic disease, and waist circumference when exploring the influencing factors and biological mechanisms of obesity-related metabolic problems in schizophrenia patients.

AimsTo evaluate the effects of acupuncture and/or nicotine patches on smoking cessation.MethodsEighty-eight participants were randomly allocated into four groups: acupuncture combined with nicotine patch (ACNP), acupuncture combined with sham nicotine patch (ACSNP), sham acupuncture combined with nicotine patch (SACNP), and sham acupuncture combined with sham nicotine patch (SACSNP). The primary outcome was self-reported smoking abstinence verified with expiratory Carbon Monoxide (CO) after 8 weeks of treatment. The modified Fagerstrom Test for Nicotine Dependence (FTND) score, Minnesota Nicotine Withdrawal Scale (MNWS), and the Brief Questionnaire of Smoking Urge (QSU-Brief) score were used as secondary indicators. SPSS 26.0 and Prism 9 software were used for statistical analyses.ResultsSeventy-eight participants completed the study. There were no significant differences in patient characteristics at baseline across the four groups. At the end of treatment, there was a statistically significant difference (χ2 = 8.492, p = 0.037) in abstaining rates among the four groups. However, there were no significant differences in the reduction in the number of cigarettes smoked daily (p = 0.111), expiratory CO (p = 0.071), FTND score (p = 0.313), and MNWS score (p = 0.088) among the four groups. There was a statistically significant difference in QUS-Brief score changes among the four groups (p = 0.005). There was no statistically significant interaction between acupuncture and nicotine patch.ConclusionAcupuncture combined with nicotine replacement patch therapy was more effective for smoking cessation than acupuncture alone or nicotine replacement patch alone. No adverse reactions were found in the acupuncture treatment process.Clinical trial registrationhttp://www.chictr.org.cn/showproj.aspx?proj=61969, identifier ChiCTR2100042912.

Abstract Deficit schizophrenia (DS) is a subtype of schizophrenia (SCZ). The polygenic effects on the neuroimaging alterations in DS still remain unknown. This study aims to calculate the polygenic risk scores for schizophrenia (PRS-SCZ) in DS, and further explores the potential associations with functional features of brain. PRS-SCZ was calculated according to the Whole Exome sequencing and Genome-wide association studies (GWAS). Resting-state fMRI, as well as biochemical features and neurocognitive data were obtained from 33 DS, 47 NDS and 41 HCs, and association studies of genetic risk with neuroimaging were performed in this sample. The analyses of amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo) and functional connectivity (FC) were performed to detect the functional alterations between DS and NDS. In addition, correlation analysis was used to investigate the relationships between functional features (ALFF, ReHo, FC) and PRS-SCZ. The PRS-SCZ of DS was significantly lower than that in NDS and HC. Compared to NDS, there was a significant increase in the ALFF of left inferior temporal gyrus (ITG.L) and left inferior frontal gyrus (IFG.L) and a significant decrease in the ALFF of right precuneus (PCUN.R) and ReHo of right middle frontal gyrus (MFG.R) in DS. FCs were widely changed between DS and NDS, mainly concentrated in default mode network, including ITG, PCUN and angular gyrus (ANG). Correlation analysis revealed that the ALFF of left ITG, the ReHo of right middle frontal gyrus, the FC value between insula and ANG, left ITG and right corpus callosum, left ITG and right PCUN, as well as the scores of Trail Making Test-B, were associated with PRS-SCZ in DS. The present study demonstrated the differential polygenic effects on functional changes of brain in DS and NDS, providing a potential neuroimaging-genetic perspective for the pathogenesis of schizophrenia.

Abstract Deficit schizophrenia (DS) is a subtype of schizophrenia characterized by the primary and persistent negative symptoms. Previous studies have identified differences in brain functions between DS and non-deficit schizophrenia (NDS) patients. However, the genetic regulation features underlying these abnormal changes are still unknown. This study aimed to detect the altered patterns of functional connectivity (FC) in DS and NDS and investigate the gene expression profiles underlying these abnormal FC. The study recruited 82 DS patients, 96 NDS patients, and 124 healthy controls (CN). Voxel-based unbiased brain-wide association study was performed to reveal altered patterns of FC in DS and NDS patients. Machine learning techniques were used to access the utility of altered FC for diseases diagnosis. Weighted gene co-expression network analysis (WGCNA) was employed to explore the associations between altered FC and gene expression of 6 donated brains. Enrichment analysis was conducted to identify the genetic profiles, and the spatio-temporal expression patterns of the key genes were further explored. Comparing to CN, 23 and 20 brain regions with altered FC were identified in DS and NDS patients. The altered FC among these regions showed significant correlations with the SDS scores and exhibited high efficiency in disease classification. WGCNA revealed associations between DS/NDS-related gene expression and altered FC. Additionally, 22 overlapped genes, including 12 positive regulation genes and 10 negative regulation genes, were found between NDS and DS. Enrichment analyses demonstrated relationships between identified genes and significant pathways related to cellular response, neuro regulation, receptor binding, and channel activity. Spatial and temporal gene expression profiles of SCN1B showed the lowest expression at the initiation of embryonic development, while DPYSL3 exhibited rapid increased in the fetal. The present study revealed different altered patterns of FC in DS and NDS patients and highlighted the potential value of FC in disease classification. The associations between gene expression and neuroimaging provided insights into specific and common genetic regulation underlying these brain functional changes in DS and NDS, suggesting a potential genetic-imaging pathogenesis of schizophrenia.

With the maturity of satellite imaging technology, satellite video has attracted more and more attention because of high spatial resolution and temporal resolution. Thus, object tracking as the main application task of satellite videos also becomes a popular research topic. Compared to natural videos, the difficulties of satellite videos object tracking mainly are caused by feature deficiency of small objectives, surroundings, occlusion (OCC), etc. In this article, based on a dual correlation filter (DCF) tracking framework, a new object tracking method for satellite videos is proposed to deal with the above-mentioned problems. For feature deficiency problem, the proposed super-resolution feature enhancement module could improve the feature discriminative ability of objects utilizing the prior information learned by a super-resolution network. For the OCC problem, a multilayer perceptron motion prediction network is designed to predict the position of objects when OCC occurs, which uses an online training strategy. Besides, KNN background subtraction also is introduced to reduce the interference of surroundings. Finally, these above-mentioned processes are combined together appropriately on the DCF tracking framework for better tracking results. Aiming to verify the performance of the proposed method, abundant experiments have been conducted on satellite video datasets. The experimental results show that the design of the proposed tracking method is effective and it also has a conspicuous advantage compared with some state-of-the-art methods.

Cross-scene hyperspectral image (HSI) classification using transfer learning can effectively address the challenge of insufficient labeled samples in classification. The majority of existing transfer learning methods assume consistency in feature labels between the source and target HSI. In practical situations, the target-domain hyperspectral data are likely to contain some categories that do not exist in the source-domain hyperspectral data. In order to solve this problem, this article proposes an unsupervised open-set cross-domain manifold mapping aligned method for target-domain HSI classification. This method designs manifold embedding maps to align the spectral–spatial features of hyperspectral in different scenes. The spectral–spatial feature data are mapped to the subspace using both the source-domain and target-domain mapping matrices. Gradually, target-domain samples are chosen for pseudolabeling within the shared subspace, while samples from unknown classes are rejected. This method achieves subspace learning and pseudolabeled sample updating through iteration, reducing the intraclass distance of all the classes and pushing rejected target data away from known classes. Experiments on the three cross-domain hyperspectral data pairs demonstrate that the proposed method outperforms related state-of-the-art methods.

In recent years, constrained by the challenges associated with expensive data annotation and poor generalization ability in supervised models, domain adaptation has been proposed to effectively mitigate domain gaps, which has gained significant attention in the field of remote sensing. However, most existing methods do not consider the inherent characteristics of remote sensing images when formulating adaptive strategies. In addition, there has been limited research on addressing class-imbalanced data situations, leading to undesirable performance in domain adaptation tasks involving long-tailed datasets. To overcome the aforementioned limitations, based on the analysis of intraclass diversity and intradomain style differences of remote sensing images, we propose a novel prototype contrastive learning framework called general-specific prototype contrastive learning (GSPCL). Due to the unreliability of clustering samples in conventional prototype-based clustering methods, the bidirectional weighted prototype strategy is proposed to optimize this loophole. Consequently, more robust prototypes are constructed in both domains, serving as mediators to reduce domain discrepancies bidirectionally. Particularly, often overlooked in most methods, we incorporate low-confidence sample features into the contrastive learning process alongside these prototypes to further guide the model to address feature alignment and long-tail issues effectively. Finally, in order to verify the superiority of our proposed method, we adhere to two existing experimental settings and construct an extra optical remote sensing domain adaptation dataset with class-imbalanced scenarios. In the first two experimental settings, GSPCL outperforms the second-ranked approach by 5.0% and 4.0% in average accuracy. Furthermore, our approach exhibits highly competitive results in handling long-tailed data scenarios.

Semantic segmentation is an important issue in intelligent interpretation of remote sensing, playing an important role in applications such as Earth observation and land data update. However, remote sensing images often contain complex ground objects and the boundaries between them are blurred, which poses a huge challenge to the semantic segmentation task of remote sensing images. This article proposes a dual stream hybrid network (DSHNet) model, which focuses on parallel extraction of semantic and boundary features in remote sensing images, and improves the performance of semantic segmentation by fully integrating dual stream information. In the semantic stream, the ViT model pretrained on remote sensing images is used as the backbone network for feature extraction. In the boundary stream, the boundary detection operator Sobel is used to capture the boundaries of different ground objects in the image, and a boundary enhancement mechanism is taken to optimize and enhance the feature representation of ground object boundaries. In addition, DSHNet designs a feature fusion module to cross-aggregate features from both semantic and boundary streams. Compared with the state-to-art semantic segmentation methods, DSHNet model has achieved the best performance on two datasets of Yellow River Estuary Wetland and Gaofen image dataset.

Abstract Immune inflammation has long been implicated in the pathogenesis of schizophrenia. Despite as a rapid and effective physical therapy, the role of immune inflammation in electroconvulsive therapy (ECT) for schizophrenia remains elusive. The neutrophils to lymphocytes (NLR), platelets to monocytes (PLR) and monocytes to lymphocytes (MLR) are inexpensive and accessible biomarkers of systemic inflammation. In this study, 70 schizophrenia patients and 70 age- and sex-matched healthy controls were recruited. The systemic inflammatory biomarkers were measured before and after ECT. Our results indicated schizophrenia had significantly higher peripheral NLR, PLR and MLR compared to health controls at baseline, while lymphocytes did not differ. After 6 ECT, the psychiatric symptoms were significantly improved, as demonstrated by the Positive and Negative Syndrome Scale (PANSS). However, there was a decline in cognitive function scores, as indicated by the Mini-Mental State Examination (MMSE). Notably, the neutrophils and NLR were significantly reduced following ECT. Although lymphocytes remained unchanged following ECT, responders had significantly higher lymphocytes compared to non-responders. Moreover, the linear regression analyses revealed that higher lymphocytes served as a predictor of larger improvement in positive symptom following ECT. Overall, our findings further highlighted the presence of systemic inflammation in schizophrenia patients, and that ECT may exert a therapeutic effect in part by attenuating systemic inflammation. Further research may therefore lead to new treatment strategies for schizophrenia targeting the immune system.

Abstract This paper studies an optimization problem of antenna placement for multiple heading angles of the target in a distributed multiple-input multiple-output (MIMO) radar system. An improved method to calculate the system’s coverage area in light of the changing target heading is presented. The antenna placement optimization problem is mathematically modelled as a sequential decision problem for compatibility with reinforcement learning solutions. A reinforcement learning agent is established, which uses the long short-term memory (LSTM)-based proximal policy optimization (PPO) method as the core algorithm to solve the antenna placement problem. Finally, the experimental findings demonstrate that the method can enhance the coverage area of antenna placement and thus has reference value for providing new ideas for the antenna placement optimization of distributed MIMO radar.

Cross-domain scene classification requires the transfer of knowledge from labeled source domains to unlabeled target domain data to improve its classification performance. This task can reduce the labeling cost of remote sensing images and improve the generalization ability of models. However, the huge distributional gap between labeled source domains and unlabeled target domains acquired by different scenes and different sensors is a core challenge. Existing cross-domain scene classification methods focus on designing better distributional alignment constraints, but are under-explored for fine-grained features. We propose a cross-domain scene classification method called the Frequency Component Adaptation Network (FCAN), which considers low-frequency features and high-frequency features separately for more comprehensive adaptation. Specifically, the features are refined and aligned separately through a high-frequency feature enhancement module (HFE) and a low-frequency feature extraction module (LFE). We conducted extensive transfer experiments on 12 cross-scene tasks between the AID, CLRS, MLRSN, and RSSCN7 datasets, as well as two cross-sensor tasks between the NWPU-RESISC45 and NaSC-TG2 datasets, and the results show that the FCAN can effectively improve the model’s performance for scene classification on unlabeled target domains compared to other methods.

Hyperspectral (HS) data, encompassing hundreds of spectral channels for the same area, offer a wealth of spectral information and are increasingly utilized across various fields. However, their limitations in spatial resolution and imaging width pose challenges for precise recognition and fine classification in large scenes. Conversely, multispectral (MS) data excel in providing spatial details for vast landscapes but lack spectral precision. In this article, we proposed an adaptive learning-based mapping model, including an image fusion module, spectral super-resolution network, and adaptive learning network. Spectral super-resolution networks learn the mapping between multispectral and hyperspectral images based on the attention mechanism. The image fusion module leverages spatial and spectral consistency in training data, providing pseudo labels for spectral super-resolution training. And the adaptive learning network incorporates spectral response priors via unsupervised learning, adjusting the output of the super-resolution network to preserve spectral information in reconstructed data. Through the experiment, the model eliminates the need for the manual setting of image prior information and complex parameter selection, and can adjust the network structure and parameters dynamically, eventually enhancing the reconstructed image quality, and enabling the fine classification of large-scale scenes with high spatial resolution. Compared with the recent dictionary learning and deep learning spectral super-resolution methods, our approach exhibits superior performance in terms of both image similarity and classification accuracy.

ObjectiveTo investigate brain structural and functional characteristics of three brain functional networks including default mode network (DMN), central executive network (CEN), and salience network (SN) in persistent negative symptoms (PNS) patients.MethodsWe performed an activation likelihood estimation (ALE) meta-analysis of functional connectivity (FC) studies and voxel-based morphometry (VBM) studies to detect specific structural and functional alterations of brain networks between PNS patients and healthy controls.ResultsSeventeen VBM studies and twenty FC studies were included. In the DMN, PNS patients showed decreased gray matter in the bilateral medial frontal gyrus and left anterior cingulate gyrus and a significant reduction of FC in the right precuneus. Also, PNS patients had a decrease of gray matter in the left inferior parietal lobules and medial frontal gyrus, and a significant reduction of FC in the bilateral superior frontal gyrus in the CEN. In comparison with healthy controls, PNS patients exhibited reduced gray matter in the bilateral insula, anterior cingulate gyrus, left precentral gyrus and right claustrum and lower FC in these brain areas in the SN, including the left insula, claustrum, inferior frontal gyrus and extra-nuclear.ConclusionThis meta-analysis reveals brain structural and functional imaging alterations in the three networks and the interaction among these networks in PNS patients, which provides neuroscientific evidence for more personalized treatment.Systematic Review RegistrationThe PROSPERO (https://www.crd.york.ac.uk/PROSPERO/, registration number: CRD42022335962).

The foundation model (FM) has garnered significant attention for its remarkable transfer performance in downstream tasks. Typically, it undergoes task-agnostic pretraining on a large dataset and can be efficiently adapted to various downstream applications through fine-tuning. While FMs have been extensively explored in language and other domains, their potential in remote sensing has also begun to attract scholarly interest. However, comprehensive investigations and performance comparisons of these models on remote sensing tasks are currently lacking. In this survey, we provide essential background knowledge by introducing key technologies and recent developments in FMs. Subsequently, we explore essential downstream applications in remote sensing, covering classification, localization, and understanding. Our analysis encompasses over 30 FMs in both natural and remote sensing fields, and we conduct extensive experiments on more than 10 datasets, evaluating global feature representation, local feature representation, and target localization. Through quantitative assessments, we highlight the distinctions among various FMs and confirm that pretrained large-scale natural FMs can also deliver outstanding performance in remote sensing tasks. After that, we systematically presented a brain-inspired framework for remote sensing foundation models (RSFMs). We delve into the brain-inspired characteristics in this framework, including structure, perception, learning, and cognition. To conclude, we summarize 12 open problems in RSFMs, providing potential research directions. Our survey offers valuable insights into the burgeoning field of RSFMs and aims to foster further advancements in this exciting area.

Transformer has shown excellent performance in remote sensing field with long-range modeling capabilities. Remote sensing video (RSV) moving object detection and tracking play indispensable roles in military activities as well as urban monitoring. However, transformers in these fields are still at the exploratory stage. In this survey, we comprehensively summarize the research prospects of transformers in RSV moving object detection and tracking. The core designs of remote sensing transformers and advanced transformers are first analyzed. It mainly includes the attention mechanism evolution for specific tasks, the fitting ability design of input mapping, diverse feature representation, model optimization, etc. The architectural characteristics of RSV detection and tracking are then described across two aspects. One is moving object detection for motion-based traditional background subtractions and appearance-based deep learning models. The other is object tracking for single and multiple targets. The research difficulties mainly include the blurred foreground in RSV data, the irregular object movement in traditional background subtraction, and the severe object occlusion in object tracking. Following that, the potential significance of transformers is discussed according to some thorny problems in RSV. Finally, we summarize ten open challenges of transformers in RSV, which may be used as a reference for promoting future research.

Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This article summarizes and analyzes the essential properties of brain cognize learning and the recent advance of remote sensing interpretation. First, this article introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this article summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation, and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.

Ventilator-induced lung injury (VILI) during mechanical ventilation (MV) has been attributed to airway remodeling involving increased airway smooth muscle cells (ASMCs), but the underlying mechanism is not fully understood. Thus, we aimed to investigate whether MV-associated high stretch (>10% strain) could modulate mechanosensitive Piezo1 expression and thereby alter cell migration of ASMCs as a potential pathway to increased ASMCs in VILI. C57BL/6 mice and ASMCs were subjected to MV at high tidal volume (VT, 18 mL/kg, 3 h) and high stretch (13% strain, 0.5 Hz, 72 h), respectively. Subsequently, the mice or cells were evaluated for Piezo1 and integrin mRNA expression by immunohistochemical staining and quantitative PCR (qPCR), and cell migration and adhesion by transwell and cell adhesion assays. Cells were either treated or not with Piezo1 siRNA, Piezo1-eGFP, Piezo1 knockin, Y27632, or blebbistatin to regulate Piezo1 mRNA expression or inhibit Rho-associated kinase (ROCK) signaling prior to migration or adhesion assessment. We found that expression of Piezo1 in in situ lung tissue, mRNA expression of Piezo1 and integrin αVβ1 and cell adhesion of ASMCs isolated from mice with MV were all reduced but the cell migration of primary ASMCs (pASMCs) isolated from mice with MV was greatly enhanced. Similarly, cell line mouse ASMCs (mASMCs) cultured in vitro with high stretch showed that mRNA expression of Piezo1 and integrin αVβ1 and cell adhesion were all reduced but cell migration was greatly enhanced. Interestingly, such effects of MV or high stretch on ASMCs could be either induced or abolished/reversed by down/up-regulation of Piezo1 mRNA expression and inhibition of ROCK signaling. High stretch associated with MV appears to be a mechanical modulator of Piezo1 mRNA expression and can, thus, promote cell migration of ASMCs during therapeutic MV. This may be a novel mechanism of detrimental airway remodeling associated with MV, and, therefore, a potential intervention target to treat VILI.

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.

ObjectiveTo explore the pattern of empathy characteristics in male patients with schizophrenia (SCH) and to examine whether empathy deficit is associated with impulsivity and premeditated violence.MethodsOne hundred and fourteen male SCH patients were enrolled in this study. The demographic data of all patients were collected and the subjects were divided into two groups, namely, the violent group, including 60 cases, and the non-violent group, comprising 54 cases, according to the Modified Overt Aggression Scale (MOAS). The Chinese version of the Interpersonal Reactivity Index-C (IRI-C) was used to evaluate empathy and the Impulsive/Predicted Aggression Scales (IPAS) was employed to assess the characteristics of aggression.ResultsAmong the 60 patients in the violent group, 44 patients had impulsive aggression (IA) and 16 patients had premeditated aggression (PM) according to the IPAS scale. In the violent group, the scores of the four subfactors of the IRI-C, i.e., perspective taking (PT), fantasy (FS), personal distress (PD), and empathy concern (EC), were significantly lower than in the non-violent group. Stepwise logistic regression showed that PM was independent influencing factor for violent behaviors in SCH patients. Correlation analysis revealed that EC of affective empathy was positively correlated with PM but not with IA.ConclusionSCH patients with violent behavior had more extensive empathy deficits compared with non-violent SCH patients. EC, IA and PM are independent risk factors of violence in SCH patients. Empathy concern is an important index to predict PM in male patients with SCH.

Remote sensing (RS) scene classification has always attracted much attention as an elemental and hot topic in the RS community. In recent years, many methods using convolutional neural networks (CNNs) and other advanced machine-learning techniques have been proposed. Their performance is excellent; however, they are disabled when there are noisy labels (i.e., RS scenes with incorrect labels), which is inevitable and common in practice. To address this problem, some specific RS classification models have been developed. Although feasible, their behavior is still limited by the complex contents of RS scenes, excessive noise filtering schemes, and intricate noise-tolerant learning strategies. To further enhance the RS classification results under the noisy scenario and overcome the above limitations, in this paper we propose a multiscale information exploration network (MIEN) and a progressive learning algorithm (PLA). MIEN involves two identical sub-networks whose goals are completing the classification and recognizing possible noisy RS scenes. In addition, we develop a transformer-assistive multiscale fusion module (TAMSFM) to enhance MIEN’s behavior in exploring the local, global, and multiscale contents within RS scenes. PLA encompasses a dual-view negative-learning (DNL) stage, an adaptively positive-learning (APL) stage, and an exhaustive soft-label-learning (ESL) stage. Their aim is to learn the relationships between RS scenes and irrelevant semantics, model the links between clean RS scenes and their labels, and generate reliable pseudo-labels. This way, MIEN can be thoroughly trained under the noisy scenario. We simulate noisy scenarios and conduct extensive experiments using three public RS scene data sets. The positive experimental results demonstrate that our MIEN and PLA can fully understand RS scenes and resist the negative influence of noisy samples.

Abstract Introduction Tobacco hazard is one of the most serious public health problems, accounting for up to 6 million deaths worldwide p.a. We aim to determine the efficacy and safety of acupuncture and/or nicotine replacement therapy on smoking cessation. Methods We will recruit 96 participants who are willing to quit smoking by acupuncture and/or nicotine replacement therapy in Chengguan, Xigu and Heping Districts, Lanzhou city, for multicenter randomized, double-blind, double-dummy controlled clinical trial. Following obtained the informed consent forms, all eligible participants will be randomly divided into 4 groups: (1) acupuncture combined with nicotine patch, (2) acupuncture combined with sham nicotine patch, (3) sham acupuncture combined with nicotine patch, and (4) sham acupuncture combined with sham nicotine patch. These participants will be treated with different intervention modalities for 8 weeks and then will be followed-up for 8 weeks. The SPSS 26.0 software will be applied to analyze the clinical effects and adverse reactions of different intervention measures for smoking cessation. Discussion This trial is a prospective, pragmatic, randomized, multicenter trial study protocol. The outcomes will illustrate the efficacy and safety of acupuncture and/or nicotine patches for smoking cessation. Provide smokers with a superior smoking cessation program. Trial registration Chinese Clinical Trial Registry ChiCTR2100042912 . Registered on January 31, 2021.

ObjectiveSchizophrenia can be divided into deficient schizophrenia (DS) and non-deficient schizophrenia (NDS) according to the presence of primary and persistent negative symptoms. So far, there are few studies that have explored the differences in functional connectivity (FC) between the different subtypes based on the region of interest (ROI) from GMV (Gray matter volume), especially since the characteristics of brain networks are still unknown. This study aimed to investigate the alterations of functional connectivity between DS and NDS based on the ROI obtained by machine learning algorithms and differential GMV. Then, the relationships between the alterations and the clinical symptoms were analyzed. In addition, the thalamic functional connection imbalance in the two groups was further explored.MethodsA total of 16 DS, 31 NDS, and 38 health controls (HC) underwent resting-state fMRI scans, patient group will further be evaluated by clinical scales including the Brief Psychiatric Rating Scale (BPRS), the Scale for the Assessment of Negative Symptoms (SANS), and the Scale for the Assessment of Positive Symptoms (SAPS). Based on GMV image data, a support vector machine (SVM) is used to classify DS and NDS. Brain regions with high weight in the classification were used as seed points in whole-brain FC analysis and thalamic FC imbalance analysis. Finally, partial correlation analysis explored the relationships between altered FC and clinical scale in the two subtypes.ResultsThe relatively high classification accuracy is obtained based on the SVM. Compared to HC, the FC increased between the right inferior parietal lobule (IPL.R) bilateral thalamus, and lingual gyrus, and between the right inferior temporal gyrus (ITG.R) and the Salience Network (SN) in NDS. The FC between the right thalamus (THA.R) and Visual network (VN), between ITG.R and right superior occipital gyrus in the DS group was higher than that in HC. Furthermore, compared with NDS, the FC between the ITG.R and the left superior and middle frontal gyrus decreased in the DS group. The thalamic FC imbalance, which is characterized by frontotemporal-THA.R hypoconnectivity and sensory motor network (SMN)-THA.R hyperconnectivity was found in both subtypes. The FC value of THA.R and SMN was negatively correlated with the SANS score in the DS group but positively correlated with the SAPS score in the NDS group.ConclusionUsing an SVM classification method and based on an ROI from GMV, we highlighted the difference in functional connectivity between DS and NDS from the local to the brain network, which provides new information for exploring the neural physiopathology of the two subtypes of schizophrenic.

The super-resolution (SR) reconstruction method based on deep learning can significantly improve the spatial SR of remote sensing images. However, the current methods make insufficient use of the remote context information and channel information in shallow feature extraction, resulting in the limited effect of SR reconstruction. This article proposed a new SR reconstruction model, SIEGAN, which uses generative adversarial network with shallow information enhancement to improve the effect of SR reconstruction of remote sensing images. Similar to other generative adversarial models, SIEGAN is composed of generator and discriminator. But SIEGAN enhances the generator's ability to extract shallow information by using three different scale convolution operations. Specifically, a depthwise convolution is used to extract the local context information of each band of the image. A depthwise dilation convolution is used to capture the remote context information in the image. Finally, a 1×1 convolution is used to extract the correlation features between different channels in remote sensing images. In addition, SIEGAN uses U-Net network as its discriminator to provide detailed feedback per pixel to the generator to improve the model's ability to identify image details. And the spectral–spatial total variation loss function is introduced to ensure the spectral–spatial reliability of the reconstructed images. The experimental results on Gaofen-1 data proved that compared with the state-of-the-art models, SIEGAN has achieved better SR reconstruction performance. Furthermore, the reconstructed images by SIEGAN demonstrate better performance in land cover classification.

Remote sensing (RS) scene classification plays an essential role in the RS community and has attracted increasing attention due to its wide applications. Recently, benefiting from the powerful feature learning capabilities of convolutional neural networks (CNNs), the accuracy of the RS scene classification has significantly been improved. Although the existing CNN-based methods achieve excellent results, there is still room for improvement. First, the CNN-based methods are adept at capturing the global information from RS scenes. Still, the context relationships hidden in RS scenes cannot be thoroughly mined. Second, due to the specific structure, it is easy for normal CNNs to exploit the heterogenous information from RS scenes. Nevertheless, the homogenous information, which is also crucial to comprehensively understand complex contents within RS scenes, does not get the attention it deserves. Third, most CNNs focus on establishing the relationships between RS scenes and semantic labels. However, the similarities between them are not considered deeply, which are helpful to distinguish the intra-/interclass samples. To overcome the limitations mentioned previously, we propose a homo–heterogenous transformer learning (HHTL) framework for the RS scene classification in this article. First, a patch generation module is designed to generate homogenous and heterogenous patches. Then, a dual-branch feature learning module (FLM) is proposed to mine homogenous and heterogenous information within RS scenes simultaneously. In the FLM, based on vision transformer, not only the global information but also the local areas and their context information can be captured. Finally, we design a classification module, which consists of a fusion submodule and a metric-learning module. It can integrate homo–heterogenous information and compact/separate samples from the same/different RS scene categories. Extensive experiments are conducted on four public RS scene datasets. The encouraging results demonstrate that our HHTL framework can outperform many state-of-the-art methods. Our source codes are available at the below website.

Image-based geo-localization focuses on predicting the geographic information of query images by matching them with annotated images in a database. To facilitate relevant studies, researchers collect numerous images to build the datasets, which explore many challenges faced in real-world geo-localization applications, significantly improving their practicability. However, a crucial challenge that often arises is overlooked, named the cross-time challenge in this paper, i.e., if query and database images are taken from the same landmark but at different time periods, the significant difference in their image content caused by the time gap will notably increase the difficulty of image matching, consequently reducing geo-localization accuracy. The cross-time challenge has a greater negative influence on non-real-time geo-localization applications, particularly those involving a long time span between query and database images, such as satellite-view geo-localization. Furthermore, the rough geographic information (e.g., names) instead of precise coordinates provided by most existing datasets limits the geo-localization accuracy. Therefore, to solve these problems, we propose a dataset, MTGL40-5, which contains remote sensing (RS) satellite images captured from 40 large-scale geographic locations spanning five different years. These large-scale images are split to create query images and a database with landmark labels for geo-localization. By observing images from the same landmark but at different time periods, the cross-time challenge becomes more evident. Thus, MTGL40-5 supports researchers in tackling this challenge and further improving the practicability of geo-localization. Moreover, it provides additional geographic coordinate information, enabling the study of high-accuracy geo-localization. Based on the proposed MTGL40-5 dataset, many existing geo-localization methods, including state-of-the-art approaches, struggle to produce satisfactory results when facing the cross-time challenge. This highlights the importance of proposing MTGL40-5 to address the limitations of current methods in effectively solving the cross-time challenge.

Abstract Background The cognitive impairment pattern of deficit schizophrenia (DS) is centered on an impaired attention function. Previous studies have suggested that the exploratory eye movement (EEM) tests reflect attention deficits in patients with schizophrenia. However, no study has investigated the characteristics of eye movement in DS in the Chinese Han population. This study aimed to investigate the pattern of eye movement characteristics in DS patients and to examine whether eye movement characteristic is associated with serious negative symptoms and cognitive decline in this schizophrenia subtype. Methods A total of 86 male patients [37 DS and 49 non-deficit schizophrenia (NDS)] and 80 healthy controls (HC) participated in this study. Clinical symptoms were assessed using the Scale for the Assessment of Positive Symptoms (SAPS) and Scale for the Assessment of Negative Symptoms (SANS). Cognitive function was assessed using the Mattis Dementia Rating Scale (MDRS-2). Eye movement data of subjects were collected using an eye movement tracking analyzer. Results There were significant differences in the overall eye movement data and cognitive test scores among the three groups (all P

Abstract Objective This study aims to explore the difference in anhedonia between Major Depressive Disorder (MDD) and Bipolar Disorder II (BD-II), and attempt to distinguish the two diseases through Snaith-Hamilton Pleasure Scale (SHAPS). Methods A total of 164 drug-free depressive patients (98 MDD patients, 66 BD-II patients) completed the investigation. 17-item Hamilton Depression Scale (HAMD-17) and Hamilton Anxiety Scale (HAMA) and SHAPS were assessed in all participants. Results Our results showed that BD-II patients had higher SHAPS scores than MDD patients. The stepwise logistic regression analysis further revealed that SHAPS score, drinking habit, and extroversion as influencing factors for the identification of BD-II. The ROC curve analysis indicated that SHAPS could differentiate BD-II from MDD patients (AUC = 0.655, P = 0.001, 95% CI = 0.568 to 0.742), with the best screening cutoff at 26, and the corresponding sensitivity and specificity was 0.788 and 0.520, respectively. Conclusion Our results suggest that BD-II patients had more severe anhedonia compared to MDD patients, and the difference in anhedonia may help clinicians preliminary identify BD patients from MDD patients. The preliminary findings are worthly of further exploration.

BackgroundPersistent negative symptoms (PNS) include both primary and secondary negative symptoms that persist after adequate treatment, and represent an unmet therapeutic need. Published magnetic resonance imaging (MRI) evidence of structural and resting-state functional brain abnormalities in schizophrenia with PNS has been inconsistent. Thus, the purpose of this meta-analysis is to identify abnormalities in structural and functional brain regions in patients with PNS compared to healthy controls.MethodsWe systematically searched PubMed, Web of Science, and Embase for structural and functional imaging studies based on five research methods, including voxel-based morphometry (VBM), diffusion tensor imaging (DTI), functional connectivity (FC), the amplitude of low-frequency fluctuation or fractional amplitude of low-frequency fluctuation (ALFF/fALFF), and regional homogeneity (ReHo). Afterward, we conducted a coordinate-based meta-analysis by using the activation likelihood estimation algorithm.ResultsTwenty-five structural MRI studies and thirty-two functional MRI studies were included in the meta-analyses. Our analysis revealed the presence of structural alterations in patients with PNS in some brain regions including the bilateral insula, medial frontal gyrus, anterior cingulate gyrus, left amygdala, superior temporal gyrus, inferior frontal gyrus, cingulate gyrus and middle temporal gyrus, as well as functional differences in some brain regions including the bilateral precuneus, thalamus, left lentiform nucleus, posterior cingulate gyrus, medial frontal gyrus, and superior frontal gyrus.ConclusionOur study suggests that structural brain abnormalities are consistently located in the prefrontal, temporal, limbic and subcortical regions, and functional alterations are concentrated in the thalamo-cortical circuits and the default mode network (DMN). This study provides new insights for targeted treatment and intervention to delay further progression of negative symptoms.Systematic review registration[https://www.crd.york.ac.uk/prospero/], identifier [CRD42022338669].

Anomaly detection is a crucial task for hyperspectral image processing. Most popular methods detect anomalies at the pixel level, while a few algorithms for anomaly detection only utilize subpixel level unmixing technology to extract features without fundamentally analyzing the anomalies. To better detect and separate the anomalies from the background, this paper proposes a dual-view hyperspectral anomaly detection method by taking account of the anomaly analysis at both levels mentioned. At the pixel level, the spectral angular distance is adopted to calculate the similarities between the central pixel and its neighbors in order to further mine the spatial consistency for anomaly detection. On the other hand, from the aspect of the subpixel level analysis, it is considered that the difference between the anomaly and the background usually arises from dissimilar endmembers, where the unmixing will be fully implemented. Finally, the detection results of both views are fused to obtain the anomalies. Overall, the proposed algorithm not only interprets and analyzes the anomalies from dual levels, but also fully employs the unmixing for anomaly detection. Additionally, the performance of multiple data sets also confirmed the effectiveness of the proposed algorithm.

This study presents a novel algorithm to establish a numerical model for the study of mechanical behavior of multi-component material such as particle reinforced metal matrix composites, concrete, particle filled polymers, etc. The algorithm consists of two steps, one is to divide the given region into sub-regions of a specified size distribution, and the other is to fill each sub-region with particles of specified shapes. During the filling process, the contact states between particles can be controlled by the number and positions of reference points. Taking the quasi-static uniaxial tensile simulation of concrete as a verification example, two concrete models with the same particle size distribution but different shapes have been established. Through the comparison of elastoplastic responses of different aggregate shape models in different tensile directions, it is concluded that the models meet the requirements of macroscopic uniformity, meso-scale randomness and statistical rationality. This verifies the feasibility of modeling method and its advantages in precise control of particle shape, distribution and other parameters.

Remote sensing (RS) image scene classification is an important research topic in the RS community, which aims to assign the semantics to the land covers. Recently, due to the strong behavior of convolutional neural network (CNN) in feature representation, the growing number of CNN-based classification methods has been proposed for RS images. Although they achieve cracking performance, there is still some room for improvement. First, apart from the global information, the local features are crucial to distinguish the RS images. The existing networks are good at capturing the global features since the CNNs' hierarchical structure and the nonlinear fitting capacity. However, the local features are not always emphasized. Second, to obtain satisfactory classification results, the distances of RS images from the same/different classes should be minimized/maximized. Nevertheless, these key points in pattern classification do not get the attention they deserve. To overcome the limitation mentioned above, we propose a new CNN named attention consistent network (ACNet) based on the Siamese network in this article. First, due to the dual-branch structure of ACNet, the input data are the image pairs that are obtained by the spatial rotation. This helps our model to fully explore the global features from RS images. Second, we introduce different attention techniques to mine the objects' information from RS images comprehensively. Third, considering the influence of the spatial rotation and the similarities between RS images, we develop an attention consistent model to unify the salient regions and impact/separate the RS images from the same/different semantic categories. Finally, the classification results can be obtained using the learned features. Three popular RS scene datasets are selected to validate our ACNet. Compared with some existing networks, the proposed method can achieve better performance. The encouraging results illustrate that ACNet is effective for the RS image scene classification. The source codes of this method can be found in https://github.com/TangXu-Group/Remote-Sensing-Images-Classification/tree/main/GLCnet.

As a matrix for melt-cast explosives, 3,4-dinitropyrazole (DNP) is a promising alternative to 2,4,6-trinitrotoluene (TNT). However, the viscosity of molten DNP is considerably greater compared with that of TNT, thus, requiring the viscosity of DNP-based melt-cast explosive suspensions to be minimized. In this paper, the apparent viscosity of a DNP/HMX (cyclotetramethylenetetranitramine) melt-cast explosive suspension is measured using a Haake Mars III rheometer. Both bimodal and trimodal particle-size distributions are used to minimize the viscosity of this explosive suspension. First, the optimal diameter ratio and mass ratio (two crucial process parameters) between coarse and fine particles are obtained from the bimodal particle-size distribution. Second, based on the optimal diameter ratio and mass ratio, trimodal particle-size distributions are used to further minimize the apparent viscosity of the DNP/HMX melt-cast explosive suspension. Finally, for either the bimodal or trimodal particle-size distribution, if the original data between the apparent viscosity and solid content are normalized, the resultant plot of the relative viscosity versus reduced solid content collapses to a single curve, and the effect of the shear rate on this curve is further investigated.

Objective: This study aims to compare the cognitive function and social functioning in male patients with deficit syndrome (DS) and non-DS, and to explore whether cognitive function serves as a mediator in the relationship between the two factors of negative symptoms (motivation and pleasure (MAP) and expressivity (EXP) deficits, and social functioning in schizophrenia patients. Methods: One hundred and fifty-six male patients with schizophrenia and 109 age- and education-matched normal controls were enrolled in the current study. The Chinese version of a Schedule for Deficit Syndrome (SDS) was used for DS and non-DS categorization. The Brief Psychiatric Rating Scale (BPRS) and the Brief Negative Symptoms Scale (BNSS) were used to assess psychotic and negative symptoms in patients. The Social-Adaptive Functioning Evaluation (SAFE) was adopted to evaluate patients’ social functioning, and a battery of classical neurocognitive tests was used to assess cognition, including sustained vigilance/attention, cognitive flexibility, ideation fluency, and visuospatial memory. Results: We found that male patients with DS performed worse in all four cognitive domains and social functioning compared to non-DS patients. Both total negative symptoms and its two factors were significantly associated with all four domains of cognition and social functioning in male patients. Interestingly, our results indicate that only cognitive flexibility mediates the relationship between negative symptoms and social functioning in schizophrenia patients, but there were no differences between EXP and MAP negative factors in this model. Conclusion: Our findings suggest that DS patients may represent a unique clinical subgroup of schizophrenia, and the integrated interventions targeting both negative symptoms and cognition, especially cognitive flexibility, may optimally improve functional outcomes in schizophrenia patients.

The aim of remote sensing image captioning (RSIC) is to describe a given remote sensing image (RSI) using coherent sentences. Most existing attention-based methods model the coherence through an LSTM-based decoder, which dynamically infers a word vector from preceding sentences. However, these methods are indirectly guided through the confusion of attentive regions, as (1) the weighted average in the attention mechanism distracts the word vector from capturing pertinent visual regions and (2) there are few constraints or rewards for learning long-range transitions. In this paper, we propose a multi-source interactive stair attention mechanism that separately models the semantics of preceding sentences and visual regions of interest. Specifically, the multi-source interaction takes previous semantic vectors as queries and applies an attention mechanism on regional features to acquire the next word vector, which reduces immediate hesitation by considering linguistics. The stair attention divides the attentive weights into three levels—that is, the core region, the surrounding region, and other regions—and all regions in the search scope are focused on differently. Then, a CIDEr-based reward reinforcement learning is devised, in order to enhance the quality of the generated sentences. Comprehensive experiments on widely used benchmarks (i.e., the Sydney-Captions, UCM-Captions, and RSICD data sets) demonstrate the superiority of the proposed model over state-of-the-art models, in terms of its coherence, while maintaining high accuracy.

BACKGROUND Major depressive disorder (MDD) and posttraumatic stress disorder (PTSD) are highly comorbid and exhibit strong correlations with one another. We aimed to investigate mechanisms of underlying relationships between PTSD and 3 kinds of depressive phenotypes, namely, MDD, depressed affect (DAF), and depression (DEP, including both MDD and the broad definition of depression).METHODS Genetic correlations between PTSD and the depressive phenotypes were tested using linkage disequilibrium score regression. Polygenic overlap analysis was used to estimate shared and trait-specific causal variants across a pair of traits. Causal relationships between PTSD and the depressive phenotypes were investigated using Mendelian randomization. Shared genomic loci between PTSD and MDD were identified using cross-trait meta-analysis.RESULTS Genetic correlations of PTSD with the depressive phenotypes were in the range of 0.71–0.80. The estimated numbers of causal variants were 14,565, 12,965, 10,565, and 4,986 for MDD, DEP, DAF, and PTSD, respectively. In each case, causal variants contributing to PTSD were completely or largely covered by causal variants defining each of the depressive phenotypes. Mendelian randomization analysis indicated that the genetically determined depressive phenotypes confer a causal effect on PTSD (b = 0.21–0.31). Notably, genetically determined PTSD confers a causal effect on DEP (b = 0.14) and DAF (b = 0.15), but not MDD. Cross-trait meta-analysis of MDD and PTSD identified 47 genomic loci, including 29 loci shared between PTSD and MDD.CONCLUSION Evidence from shared genetics suggests that PTSD is a subtype of MDD. This study provides support to the efforts in reducing diagnostic heterogeneity in psychiatric nosology.FUNDING The National Key Research and Development Program of China and the National Natural Science Foundation of China.

While schizophrenia (SCZ) is a devastating psychiatric disorder that detrimentally affects a significant portion of the worldwide population, its diagnosis is traditionally based on a relatively subjective assessment of current symptoms and medical history, devoid of an objective diagnostic modality. Antipsychotic medications are commonly used in the treatment of SCZ; however, some patients have low remission rates or forsake treatment due to the associated multiple side effects, resulting in recurrent episodes of the disease and poor prognosis. These situations imply that the diagnosis, treatment, and prognosis of SCZ need to be improved to increase the odds of a better outcome. Mounting studies have found that extracellular vesicles (EVs) play essential roles in the central nervous system. They are implicated in several mechanisms closely associated with SCZ such as cellular communication and synaptic plasticity. They can additionally exhibit neuroprotective and therapeutic effects. Since they possess distinct constituents, are readily available, easily detectable, and dependent on the internal environment, they can potentially serve as reliable biomarkers for disease diagnosis. Moreover, their biological configuration along with their ability to increase the bioavailability of their constituents and modulate intricate intracellular reactions in target cells, propel EVs as new targets for treatment. This review paper summarizes relevant research pertaining to the roles of EVs in SCZ, with the aim of improving insights into SCZ pathogenesis and evaluating EVs as potential biomarkers in the diagnosis and treatment of SCZ.

Abstract Background To investigate bacterial flora and antibiotics susceptibility of Streptococcus pneumoniae isolated from the conjunctival sac of heathy children. Methods Bacteria were isolated from the secretions of conjunctival sac of healthy children between 2015 and 2018. Antimicrobial susceptibility of isolated S. pneumoniae strains were determined using microbroth dilution method. Results The sac secretions were collected from a total of 6440 children. 1409 samples presented bacterial growth, accounting for 21.8% of the samples. Among the 22 bacterial species isolated, 528 samples presented Gram-positive Staphylococcus spp. growth, accounting for 37.4% of the isolates, followed by Corynebacterium spp., counting for 30% of the isolates and Streptococcus pneumoniae, counting for 21.4% of the isolates. Antibiotics susceptibility tests showed that the majority of S. pneumoniae isolates were sensitive to most antibiotics tested. However, 72.8 and 81.2% of the isolates were resistant to erythromycin and tetracycline, respectively, and over 10% of them were resistant to gentamicin, tobramycin and rifampicin. Conclusions The bacterial flora of healthy children is mainly consisted of Gram-positive bacteria belonging to Corynebacterium spp. and Streptococcus spp.; most of S. pneumoniae isolates were sensitive to antibiotics except erythromycin and tetracycline.

Abstract Background Semantic resources such as knowledge bases contains high-quality-structured knowledge and therefore require significant effort from domain experts. Using the resources to reinforce the information retrieval from the unstructured text may further exploit the potentials of such unstructured text resources and their curated knowledge. Results The paper proposes a novel method that uses a deep neural network model adopting the prior knowledge to improve performance in the automated extraction of biological semantic relations from the scientific literature. The model is based on a recurrent neural network combining the attention mechanism with the semantic resources, i.e., UniProt and BioModels. Our method is evaluated on the BioNLP and BioCreative corpus, a set of manually annotated biological text. The experiments demonstrate that the method outperforms the current state-of-the-art models, and the structured semantic information could improve the result of bio-text-mining. Conclusion The experiment results show that our approach can effectively make use of the external prior knowledge information and improve the performance in the protein-protein interaction extraction task. The method should be able to be generalized for other types of data, although it is validated on biomedical texts.

This paper presents an unsupervised fuzzy clustering based on evolutionary algorithm for image segmentation. It needs no prior information about exact numbers of segments. Local and nonlocal spatial information derived from observed images are incorporated into fuzzy clustering process. It consists of three major procedures. First, a multi-objective evolutionary sampling is proposed to locate image pixels with a variety of image information. Secondly, optimizing fuzzy compactness and fuzzy separation, a multi-objective evolutionary fuzzy clustering with spatial information is performed on sampling pixels. The particular numbers of segments and balances of spatial information can be obtained. Then fuzzy clustering segmentation on whole image is carried out by two fuzzy clustering approaches, which are depended on fuzzy c-means and evolutionary algorithm respectively. To enhance qualities of final segmentation results, a label correction based on entropy and local spatial information is introduced. Experiments on different types of images demonstrate the effectiveness of our approach for image segmentation.

The topic of one article reflects its main semantic content, which is also the main guidance for researchers to choose reference literature. In order to explore whether the topic of an article will affect its citation trend in future, this paper establishes a machine learning framework to study the role of topic characteristics in the prediction of future high influential articles. Articles from four different disciplines are collected as experimental samples to verify whether the framework proposed in this paper can be applied to the prediction task in different disciplines. The Latent Dirichlet Allocation (LDA) is used to determine the topic characteristics of sample articles. LDA can map sample articles to current hot topics and generate the mapping probability of sample articles under different hot topics. The maximum mapping probability of the sample article under the hot topics is extracted as the topic feature of the article. Then the feature space for the prediction task is constructed by combining the topic feature and some bibliometrics indices of articles. Three feature selection algorithms, Fisher Score, Relief-F and Spectral Feature Selection (SPEC), are taken to select the important features in the feature space. The prediction performance of these features is finally tested by three classifiers, SVM, KNN and Bagging. The experimental results show that the topic characteristics of article, the early citation characteristics of article, and the reputation of the author are the key factors that determine whether an article can grow into a highly influential one. The important value of topic characteristics in articles' citation activities shows that the content of the article is an important factor in attracting more citations.

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.

Background: Ginsenosides have been widely used clinically for many years and were regarded as very safe. However, a few researches on the toxicities of these kinds of agents showed that some ginsenosides may have side-effect on the rats or dogs. So it is extremely necessary to further clarify the potential toxicity of ginsenosides. This study was carried out to investigate long-term toxicity and genotoxicity of 25-methoxydammarane-3, 12, 20-triol (25-OCH3-PPD), a new derivative of ginsenoside, in beagle dogs. Methods: Twenty-four beagle dogs were divided randomly into four treatment groups and repeatedly orally administered with 25-OCH3-PPD capsule at 60, 120, and 240 mg/kg/day for 91 consecutive days. Ames, micronucleus, and chromosomal aberration tests were established to analyze the possible genotoxicity of 25-OCH3-PPD. Results: There was no 25-OCH3-PPD–induced systemic toxicity in beagle dogs at any doses. The level of 25-OCH3-PPD at which no adverse effects were observed was found to be 240 mg/kg/day. The result of Ames test showed that there was no significant increase in the number of revertant colonies of 25-OCH3-PPD administrated groups compared to the vehicle control group. There were also no significant differences between 25-OCH3-PPD administrated groups at all dose levels and negative group in the micronucleus test and chromosomal aberration assay. Conclusion: The highest dose level of 25-OCH3-PPD at which no adverse effects were observed was found to be 240 mg/kg per day, and it is not a genotoxic agent either in somatic cells or germs cells. 25-OCH3-PPD is an extremely safe candidate compound for antitumor treatment. Keywords: Beagle dog, Subchronic toxicity, Ginsenoside

Deficit schizophrenia (DS), which is marked by stable negative symptoms, is regarded as a homogeneous subgroup of schizophrenia. While DS patients have structurally altered nucleus accumbens (NAcc) compared to non-deficit schizophrenia (NDS) patients and healthy individuals, the investigation of NAcc functional connectivity (FC) with negative symptoms and neurocognition could provide insights into the pathophysiology of schizophrenia. 58 DS, 93 NDS, and 113 healthy controls (HCs) underwent resting-state functional magnetic resonance (rsfMRI). The right and left NAcc were respectively used as seed points to construct the functional NAcc network in whole-brain FC analysis. ANCOVA compared the differences in NAcc network FC and partial correlation analysis explored the relationships between altered FC of NAcc, negative symptoms and neurocognition. Compared to HCs, both DS and NDS patients showed decreased FC between the left NAcc (LNAcc) and bilateral middle cingulate gyrus, and between the right NAcc (RNAcc) and right middle frontal gyrus (RMFG), as well as increased FC between bilateral NAcc and bilateral lingual gyrus. Moreover, the FC between the LNAcc and bilateral calcarine gyrus (CAL) was lower in the DS group compared to NDS patients. Correlation analysis indicated that FC value of LNAcc-CAL was negatively correlated to negative symptoms. Furthermore, aberrant FC values within the NAcc network were correlated with severity of clinical symptoms and neurocognitive impairments in DS and NDS patients. This study demonstrated abnormal patterns of FC in the NAcc network between DS and NDS. The presence of altered LNAcc-CAL FC might be involved in the pathogenesis of negative symptoms in schizophrenia.

Background Schizophrenia is a severe and complex psychiatric disorder that needs treatment based on extensive experience. Antipsychotic drugs have already become the cornerstone of the treatment for schizophrenia; however, the therapeutic effect is of significant variability among patients, and only around a third of patients with schizophrenia show good efficacy. Meanwhile, drug-induced metabolic syndrome and other side-effects significantly affect treatment adherence and prognosis. Therefore, strategies for drug selection are desperately needed. In this study, we will perform pharmacogenomics research and set up an individualised preferred treatment prediction model. Aims We aim to create a standard clinical cohort, with multidimensional index assessment of antipsychotic treatment for patients with schizophrenia. Method This trial is designed as a randomised clinical trial comparing treatment with different kinds of antipsychotics. A total sample of 2000 patients with schizophrenia will be recruited from in-patient units from five clinical research centres. Using a computer-generated program, the participants will be randomly assigned to four treatment groups: aripiprazole, olanzapine, quetiapine and risperidone. The primary outcomes will be measured as changes in the Positive and Negative Syndrome Scale of schizophrenia, which reflects the efficacy. Secondary outcomes include the measure of side-effects, such as metabolic syndromes. The efficacy evaluation and side-effects assessment will be performed at baseline, 2 weeks, 6 weeks and 3 months. Results This trial will assess the efficacy and side effects of antipsychotics and create a standard clinical cohort with a multi-dimensional index assessment of antipsychotic treatment for schizophrenia patients. Conclusion This study aims to set up an individualized preferred treatment prediction model through the genetic analysis of patients using different kinds of antipsychotics.

When describing the relation between the flow stress and plastic strain of a material under a wide range of strain rates and temperatures, the original Johnson–Cook model generally requires a complicated modification, resulting in a loss of simplicity and clear physical interpretation. In this paper, without modification, the original Johnson–Cook model was calibrated separately for the static and dynamic compression of a DNAN-based melt-cast explosive. The stress–strain curves for static and dynamic compression of this explosive were experimentally measured with a universal testing machine and a split-Hopkinson pressure bar, respectively. Based on the stress–strain curves, the flow stress vs. plastic strain data were extracted and used to calibrate the Johnson–Cook model. The calibration process is described. The parameters for the strain term, strain rate term, and temperature term were fitted sequentially. One set of model parameters was not able to fully describe the relationship between flow stress and plastic strain for both the static and dynamic compression of the DNAN-based melt-cast explosive. Two sets of model parameters were separately calibrated and compared for the static and dynamic compression of this explosive. The effects of the adiabatic temperature rise and the definition of the yield point on this calibration were also investigated.

Naringin is the major polyphenol in bitter orange peel with antioxidant property. However, its pH sensitivity, low solubility, and bitter taste limit its application in food. In this study, naringin–sodium alginate–silk fibroin microspheres were prepared by the ionic gel method. The loading capacity and encapsulation efficiency of naringin in microspheres were 13.2% and 77.6%, respectively. The morphology of microspheres was characterized by scanning electron microscopy. The X-ray diffractometry and differential scanning calorimetry results showed naringin was amorphous after encapsulation. Fourier-transform infrared spectroscopy and molecular docking analysis confirmed the intermolecular hydrogen bonds between naringin and sodium alginate. Naringin could release from the microspheres continuously under different pH conditions. Compared with free naringin, the 2,2-diphenyl-1-picrylhydrazyl scavenging activity and the stability of naringin microspheres were significantly improved. The application of naringin microspheres in yogurt indicated the precipitation of whey could be effectively reduced and the decline rate of pH was inhibited. The study suggested that naringin encapsulated microspheres were beneficial for improving the shelf life of this bioactive product as well as providing a new idea for functional yogurt.

Abstract Background To measure the serum levels of brain-derived neurotrophic factor (BDNF) and glial cell line-derived neurotrophic factor (GDNF) in deficit schizophrenia (DS), in order to examine the association between these two neurotrophic factors (NFs) and cognitive performance. Methods A total of 109 male patients [51 DS and 58 non-deficit schizophrenia (NDS)] with schizophrenia and 40 sex and age matched healthy controls (HC) participated in this study. Processing speed, attention, executive function, and working memory of all subjects were assessed by means of a battery of classical neuropsychological tests. Serum BDNF and GDNF levels were measured simultaneously using a double-antibody sandwich enzyme-linked immunosorbent assay (ELISA). Results There were significant differences in the overall cognitive test scores between three groups (all p

Background: Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are regarded as part of the pre-clinical Alzheimer's disease (AD) spectrum. The insular subregional networks are thought to have diverse intrinsic connectivity patterns that are involved in cognitive and emotional processing. We set out to investigate convergent and divergent altered connectivity patterns of the insular subregions across the spectrum of pre-clinical AD and evaluated how well these patterns can differentiate the pre-clinical AD spectrum.Method: Functional connectivity (FC) analyses in insular subnetworks were carried out among 38 patients with SCD, 56 patients with aMCI, and 55 normal controls (CNs). Logistic regression analyses were used to construct models for aMCI and CN, as well as SCD and CN classification. Finally, we conducted correlation analyses to measure the relationship between FCs of altered insular subnetworks and cognition.Results: Patients with SCD presented with reduced FC in the bilateral cerebellum posterior lobe and increased FC in the medial frontal gyrus and the middle temporal gyrus. On the other hand, patients with aMCI largely presented with decreased FC in the bilateral inferior parietal lobule, the cerebellum posterior lobe, and the anterior cingulate cortex, as well as increased FC in the medial and inferior frontal gyrus, and the middle and superior temporal gyrus. Logistic regression analyses indicated that a model composed of FCs among altered insular subnetworks in patients with SCD was able to appropriately classify 83.9% of patients with SCD and CN, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.876, 81.6% sensitivity, and 81.8% specificity. A model consisting of altered insular subnetwork FCs in patients with aMCI was able to appropriately classify 86.5% of the patients with aMCI and CNs, with an AUC of 0.887, 80.4% sensitivity, and 83.6% specificity. Furthermore, some of the FCs among altered insular subnetworks were significantly correlated with episodic memory and executive function.Conclusions: Patients with SCD and aMCI are likely to share similar convergent and divergent altered intrinsic FC patterns of insular subnetworks as the pre-clinical AD spectrum, and presented with abnormalities among subnetworks. Based on these abnormalities, individuals can be correctly differentiated in the pre-clinical AD spectrum. These results suggest that alterations in insular subnetworks can be utilized as a potential biomarker to aid in conducting a clinical diagnosis of the spectrum of pre-clinical AD.