The study employed event-related potential (ERP), time-frequency analysis, and functional connectivity to comprehensively explore the influence of male's relative height on third-party punishment (TPP) and its underlying neural mechanism. The results found that punishment rate and transfer amount are significantly greater when the height of the third-party is lower than that of the recipient, suggesting that male's height disadvantage promotes TPP. Neural results found that the height disadvantage induced a smaller N1. The height disadvantage also evoked greater P300 amplitude, more theta power, and more alpha power. Furthermore, a significantly stronger wPLI between the rTPJ and the posterior parietal and a significantly stronger wPLI between the DLPFC and the posterior parietal were observed when third-party was at the height disadvantage. These results imply that the height disadvantage causes negative emotions and affects the fairness consideration in the early processing stage; the third-party evaluates the blame of violators and makes an appropriate punishment decision later. Our findings indicate that anger and reputation concern caused by height disadvantage promote TPP. The current study holds significance as it underscores the psychological importance of height in males, broadens the perspective on factors influencing TPP, validates the promoting effect of personal disadvantages on prosocial behavior, enriches our understanding of indirect reciprocity theory, and extends the application of the evolution theory of Napoleon complex.

IntroductionPrevious studies have shown disrupted effective connectivity in the large-scale brain networks of individuals with major depressive disorder (MDD). However, it is unclear whether these changes differ between first-episode drug-naive MDD (FEDN-MDD) and recurrent MDD (R-MDD).MethodsThis study utilized resting-state fMRI data from 17 sites in the Chinese REST-meta-MDD project, consisting of 839 patients with MDD and 788 normal controls (NCs). All data was preprocessed using a standardized protocol. Then, we performed a granger causality analysis to calculate the effectivity connectivity (EC) within and between brain networks for each participant, and compared the differences between the groups.ResultsOur findings revealed that R-MDD exhibited increased EC in the fronto-parietal network (FPN) and decreased EC in the cerebellum network, while FEDN-MDD demonstrated increased EC from the sensorimotor network (SMN) to the FPN compared with the NCs. Importantly, the two MDD subgroups displayed significant differences in EC within the FPN and between the SMN and visual network. Moreover, the EC from the cingulo-opercular network to the SMN showed a significant negative correlation with the Hamilton Rating Scale for Depression (HAMD) score in the FEDN-MDD group.ConclusionThese findings suggest that first-episode and recurrent MDD have distinct effects on the effective connectivity in large-scale brain networks, which could be potential neural mechanisms underlying their different clinical manifestations.

Little attention has been paid to the place attachment and homeland construction for refugees and their descendants in China. This study investigates the process by which the place attachment of Young Overseas Chinese Relatives is shaped in the context of resettlement sites. This qualitative research employed ethnographic fieldwork, and the author collected local literature and materials from February to December 2019 through participatory observation, in-depth interviews, and questionnaires. It is believed that the construction of a new homeland in the community, the emotional experience of the Young in childhood, and the cultural logic of place attachment shape place attachment. The process by which place attachment is shaped is interwoven with homeland construction, which indicates that the living state and mentality of the Young are becoming increasingly stable. The Young developed different mentalities on the basis of traditional Confucian culture in responding to the socio-cultural environments. The resettlement site has become a homeland to which young persons are solidly attached, people give this site meanings and experience certain emotions regarding it, which generates place identity and begins the process of homeland construction.

Introduction: Attention deficit and hyperactivity disorder (ADHD) is a common inherited disease of the nervous system whose cause(s) and pathogenesis remain unclear. Currently, the diagnosis of ADHD is mainly based on clinical experience and guidelines that have laid out some diagnostic standards. Our study aimed to apply a learning-based classification method to assist the ADHD diagnosis based on high-dimensional resting-state fMRI. Methods: Our study selected the ADHD-200 Peking dataset of resting-state fMRI, which has an ADHD patient (n = 142) group and a typically developing control (TDC) healthy control (n = 102) group. We first used Pearson and partial correlation coefficients to perform functional connectivity (FC) analysis between ROIs. Then, the Pearson and partial correlation coefficient matrices were concatenated into a dual-channel feature to build a dual data channel as input to the transfer learning neural network (TLNN) architecture. Finally, we transferred the pretrained model from the auxiliary domain to our target domain and fine-tuned it. Results: Based on the Pearson correlation coefficient, FC between ROIs was detected in 22 brain regions, including the fusiform gyrus, superior frontal gyrus, posterior superior temporal sulcus, inferior parietal lobule, anterior cingulate cortex, and parahippocampal gyrus. Based on the partial correlation coefficient, we found FC in the salient network, default network, sensory-motor network, dorsal attention network, and cerebellum network. With the TLNN architecture, we solved the problem of insufficient training data and improved the sensitivity of the classification method. When the VGG model (fine-tuned transfer strategy, 1,024 fully connected layers) was applied, the accuracy of TLNN classification ultimately reached 82%. Conclusion: Our study suggests that completing the training of the target domain by transferring the prior knowledge of the auxiliary domain is effective in solving the classification problem of small sample datasets. Based on prior knowledge of FC analysis, TLNN classification may assist ADHD diagnosis in a new way.

Measuring local brain volume is clinically important in neuroimaging studies. Voxel preserved warping (VPW) and Jacobian determinant are effective methods for studying local brain volume changes and variations (LBVCV) across multiple brains. However, these LBVCV methods typically depend on the local deformation without using the global deformation, while both deformations are needed in co-registering the brains under examination so that the brains can be compared on a common and fair basis. However, instead of employing a uniformed strategy, different co-registration methods have developed their own unique strategy in performing global and local transformation of the co-registration of the brains, and how the global and local transformations may combine to achieve the final goal of co-registration is not their concern, as long as the final registration may accomplish the co-registering job satisfactorily. The aforementioned inconsistency thus makes the LBVCV measurement that relies on the registration methods for studying local brain volumes totally unstable and actually unreliable. To address the uncertainty in measuring local brain volume variability caused by the un-uniqueness of performing global and local deformations during co-registration, the present study proposes new VPW approaches (VPWα and VPWβ), which no longer require the separation of the global and local transformation components but employ only the general deformation concatenating both components, as long as the general registration may achieve the task of co-registering brain images. The new VPW methods are validated in theory and in practice, using both simulated and real-world imaging data, respectively, based on two registration methods popularly in use by the neuroimaging research community, i.e., the Automatic Registration Toolbox (ART) and Symmetric Image Normalization Method (SyN) registration methods. Experiments using simulated data demonstrated that the proposed new VPW methods may reliably measure local brain volume changes and variability. In contrast, traditional methods typically may result in LBVCV maps containing significantly inconsistent even false findings. In the experiments using real neuroimaging datasets from a schizophrenia study, the results based on the proposed new VPW methods were highly consistent, no matter which registration method was employed. Otherwise, the LBVCV results based on traditional approaches would show significant difference, depending on the individual registration method that the analysis employed. LBVCV assessments based on traditional methods appear to be unreliable. The proposed new VPW methods for measuring local volume changes is independent of registration methods, and therefore can serve as alternative approaches for assessing LBVCV reliably.

偏头痛是一种严重危害人类健康的脑疾病,其中无先兆偏头痛在临床中占比最多且诊断困难.当前无先兆偏头痛辅助诊断算法研究中,基于机器学习的脑影像功能连接分析方法是最主要的研究方向.由于此类方法多依赖于预定义的脑图谱模板,受模板选择主观因素及分类器性能影响,现有方法的智能化程度和准确率较低,难以满足临床及研究需求.基于设计的新型3D-CNN技术,提出了一种无先兆偏头痛智能辅助诊断算法MwoA3D-Net(3D convolutional neural network based diagnosis of migraine without aura).该算法采用组信息指导的独立成分分析方法,生成被试的静息态脑网络,并以此作为输入训练MwoA3D-Net,实现对无先兆偏头痛患者与健康对照的自动诊断,可避免因先验模板不同导致的结果差异.在算法设计中引入3D数据增强、L1和L2正则化等一系列优化策略,可有效防止过拟合现象的发生.在60名无先兆偏头痛和65名健康被试数据集上的实验结果表明,MwoA3D-Net的平均诊断准确率为98.40％,鲁棒性较高,且所选静息态脑功能网络均具有较强的辨识性,可作为无先兆偏头痛的潜在生物标志物用于个体化诊断.

膀胱癌MRI图像存在肿瘤边界不清晰、肿瘤区域较小、肿瘤分布不连续等问题,现有的分割算法参数量庞大,计算复杂,且分割精度有待提高.因此,设计了一种多尺度特征融合的轻量化膀胱癌分割算法(pyramidal convolution lightweight network,PylNet),该算法在编码阶段设计的多尺度语义特征提取模块可提取不同尺度的肿瘤区域信息,确保对微小肿瘤信息提取的可靠性和全面性;在解码阶段设计的融合模块可以在保证分割精度的同时,极大地减少算法参数量和复杂度.实验结果表明,相较于FCN8s、DeepLabV3+、U-Net等算法,PylNet算法分割精度有一定的提高,Dice系数达88.40％,参数量是FCN8s的1/13,可实现对膀胱MRI的快速分割.

Attribute-based access control is an effective cryptographic mechanism that allows a data owner to perform attribute-level access control over users’ access capabilities. In the application background of blockchain, it is urgent to use attribute-based access control to solve a series of security problems. Although there exist several similar schemes, they do not consider attribute revocation. By adopting the attribute revocation technique based on binary trees, we extended Waters’ ciphertext policy attribute-based encryption (CP-ABE) scheme into a revocable CP-ABE (RCP-ABE) scheme. By combining our RCP-ABE with blockchain, we constructed a revocable attribute-based access control system. The new system supports expressive access control policies and allows the attribute authority to revoke users’ attributes or part of their attributes. The security analysis shows that the new system satisfies several ideal properties, i.e., forward security, backward security, confidentiality, and integrity.

In cloud computing environment, while preventing users from illegally accessing resources, various cloud service systems must provide protection for authorized users of their personal sensitive information. Attribute-based authentication (ABA) makes it possible to solve the above problems. An efficient ABA system was put forward by extending the direct anonymous attestation (DAA) scheme of Brickell et al. During the construction of the new system, the technique of lightweight ciphertext policy attribute-based encryption was adopted, and the online computing task of users was optimized. Compared with other similar systems, the new system is characterized by the use of trusted computing technology to improve the level of privacy protection of users, and the computational complexity of users in the authentication stage is independent of the size of access policy.

为了改进传统教学方式的不足,将BOPPPS模式引入《数据结构实验》的教学过程.以"抽象数据类型三元组"为例,提出基于BOPPPS模式的教学设计方案.在参与式学习阶段,为了帮助学生增强信心和激发学习兴趣,将实验内容划分为"由易到难"的三个阶段.通过精心设置"陷阱",使学生在动手实践过程中深入体会函数参数的"传值"调用与"传地址"调用的差别,达到体验式教学的效果.此外,在教学过程中综合运用"雨课堂"工具、类比教学法和图解教学法等辅助手段,确保了教学目标的顺利达成.

Our recent study reported that adolescent-onset schizophrenia showed an uncoupling between intraventricular brain temperature (iBT) and local spontaneous brain activity (SBA). While auditory verbal hallucinations (AVH) are common in schizophrenia, the role of AVH in the iBT-SBA relationship is unclear. The current study recruited 24 drug-naïve schizophrenia patients with AVH, 20 patients without AVH and 30 matched healthy controls (HC). We used a diffusion-weighted imaging (DWI) based thermometry method to calculate the iBT for each participant and used both regional homogeneity and amplitude of low-frequency fluctuation methods to assess the SBA. One-way ANOVA was used to detect group differences in iBT, and a partial correlation analysis controlling for lateral ventricles volume, sex and age was applied to detect the relationships between iBT and SBA across the three groups. The results demonstrated that the AVH group showed a significant coupling between iBT and SBA in the bilateral lingual gyrus, left superior occipital gyrus and caudate compared with the other two groups, and no uncoupling was found in the two patients groups relative to HCs. These findings suggest that AVH may modulate the relationship between iBT and SBA in schizophrenia-related regions.

Abstract Currently, attribute-based authentication provides a feasible solution for fine-grained access control in cloud environment. However, the existing schemes can not solve the following problems at the same time, that is, how to ensure that the computation cost of the client does not depend on the size of underlying access structure, and how to introduce distributed authorities to manage and maintain the attribute universe. To solve the above problems, an efficient multi-authority attribute-based authentication scheme is proposed. The new scheme uses the technique of distributed attribute-based encryption to realize the access control of anonymous users, and reduces users’ computation burden by optimizing the standard implementation zero-knowledge proof and outsourcing users’ computing tasks in the authentication stage. Under the new definition of security, it can be proved that the new scheme is secure and satisfies many attractive properties, such as introducing distributed authorities, supporting outsourcing computation, satisfying attribute anonymity.

Attribute-based authentication is an effective cryptography mechanism, which makes it possible for service providers to implement fine-grained access control on cloud resources. Although many attribute-based authentication schemes have been proposed, most of them only support single attribute authority and users have to perform a large amount of computation in the authentication phase. By extending the ciphertext policy attribute-based encryption scheme of Rouselakis et al, a distributed attribute-based authentication scheme was designed. The feature of the new scheme is to optimize the online computation efficiency of both service providers and users, i.e., by introducing the technique of online/offline attribute-based encryption, the online computational burden of semi trusted servers is greatly reduced. On the other hand, by introducing outsourcing decryption, users’ computation in the authentication stage is independent of the size of underlying access structures. Compared with previous schemes, the new scheme satisfies several ideal properties, that is, introducing distributed authorities, supporting outsourcing computation, satisfying anonymity and unlinkability, and so on.

3D spinal structures segmentation is crucial to reduce the time-consumption issue and provide quantitative parameters for disease treatment and surgical operation. However, the most related studies of spinal structures segmentation are based on 2D or 3D single structure segmentation. Due to the high complexity of spinal structures, the segmentation of 3D multiple spinal structures with consistently reliable and high accuracy is still a significant challenge. We developed and validated a relatively complete solution for the simultaneous 3D semantic segmentation of multiple spinal structures at the voxel level named as the S 3 egANet. Firstly, S 3 egANet explicitly solved the high variety and variability of complex 3D spinal structures through a multi-modality autoencoder module that was capable of extracting fine-grained structural information. Secondly, S 3 egANet adopted a cross-modality voxel fusion module to incorporate comprehensive spatial information from multi-modality MRI images. Thirdly, we presented a multi-stage adversarial learning strategy to achieve high accuracy and reliability segmentation of multiple spinal structures simultaneously. Extensive experiments on MRI images of 90 patients demonstrated that S 3 egANet achieved mean Dice coefficient of 88.3% and mean Sensitivity of 91.45%, which revealed its effectiveness and potential as a clinical tool.

Migraine is a brain disease that seriously endangers human health in which migraine without aura accounts for the largest proportion in the clinic and is challenging to diagnose. Currently, the auxiliary diagnosis methods based on functional connectivity analysis combined with machine learning algorithms is an important research domain for migraine without aura. Although a few earlier studies have made significant progress, it is still hard to meet the clinical and research needs. The main reason is that the functional connectivity analysis methods mostly rely on the prior template, which is easily affected by subjective factors and the performance of the classifier, the intelligence and accuracy are still at a low level. In this paper, we propose an intelligent auxiliary diagnosis algorithm for migraine without aura based on improved 3D convolutional neural network dubbed MwoA3D-Net. To avoid the difference results caused by varying prior templates, a group information guided independent component analysis method is employed to obtain the resting state network for training the MwoA3D-Net algorithm. Subsequently, the MwoA3D-Net algorithm is applied to diagnose migraine without aura patients and healthy controls automatically. Several optimization strategies, such as 3D data augmentation and L2 regularization, are introduced to prevent overfitting effectively. Experimental results on a data set of 65 migraine without aura patients and 60 healthy subjects show that MwoA3D-Net has a highly robust performance, with an average diagnostic accuracy of 98.40%. Furthermore, the selected resting-state brain function network has robust identification and can be adopted as potential biomarkers of migraine without aura toward individualized diagnosis.

PURPOSE:A recent study has reported that schizophrenia patients show an uncoupled association between intraventricular brain temperature (BT) and cerebral blood flow (CBF). CBF has been found to be closely coupled with spontaneous brain activities (SBAs) derived from resting-state BOLD fMRI metrics. Yet, it is unclear so far whether the relationship between the intraventricular BT and the SBAs may change in patients with adolescent-onset schizophrenia (AOS) compared with that in healthy controls (HCs).METHODS:The present study recruited 28 first-episode, drug-naïve AOS patients and 22 matched HCs. We measured the temperature of the lateral ventricles (LV) using diffusion-weighted imaging thermometry and measured SBAs using both regional homogeneity and amplitude of low-frequency fluctuation methods. A nonparametric Wilcoxon rank sum test was used to detect the difference in intraventricular BT between AOS patients and HCs with LV volume, age, and sex as covariates. We also evaluated the relationship between the intraventricular BT and the SBAs using partial correlation analysis controlling for LV volume, age, and sex.RESULTS:We found that HCs showed a significant negative correlation between the intraventricular BT and the local SBAs in the bilateral putamina and left superior temporal gyrus, while such a correlation was absent in AOS patients. Additionally, no significant difference between the two groups was found in the intraventricular BT.CONCLUSION:These findings suggest that AOS patients may experience an uncoupling between intraventricular BT and SBAs in several schizophrenia-related brain areas, which may be associated with the altered relationships among intraventricular BT, CBF, and metabolism.

INTRODUCTION:Previously in a three-generation study of families at high risk for depression, we found that belief in the importance of religion/spirituality (R/S) was associated with thicker cortex in bilateral parietal and occipital regions. In the same sample using functional magnetic resonance imaging and electroencephalograph (EEG), we found that offspring at high familial risk had thinner cortices, increased default mode network connectivity, and reduced EEG power. These group differences were significantly diminished in offspring at high risk who reported high importance of R/S beliefs, suggesting a protective effect.METHODS:This study extends previous work examining brain microstructural differences associated with risk for major depressive disorder (MDD) and tests whether these are normalized in at-risk offspring who report high importance of R/S beliefs. Diffusion tensor imaging (DTI) data were selected from 99 2nd and 3rd generation offspring of 1st generation depressed (high-risk, HR) or nondepressed (low-risk, LR) parents. Whole-brain and region-of-interest analyses were performed, using ellipsoidal area ratio (EAR, an alternative diffusion anisotropy index comparable to fractional anisotropy). We examined microstructural differences associated with familial risk for depression within the groups of high and low importance of R/S beliefs (HI, LI).RESULTS:In the LI group, HR individuals showed significantly decreased EAR in white matter regions neighboring the precuneus, superior parietal lobe, superior and middle frontal gyrus, and bilateral insula, supplementary motor area, and postcentral gyrus. In the HI group, HR individuals showed reduced EAR in white matter surrounding the left superior, and middle frontal gyrus, left superior parietal lobule, and right supplementary motor area. Microstructural differences associated with familial risk for depression in precuneus, frontal lobe, and temporal lobe were nonsignificant or less significant in the HI group.CONCLUSION:R/S beliefs may affect microstructure in brain regions associated with R/S, potentially conferring resilience to depression among HR individuals.

Although the default mode network (DMN) is known to be abnormal in schizophrenia (SZ) patients with auditory verbal hallucinations (AVHs), it is still unclear whether AVHs that occur in SZ are associated with certain information flow in the DMN. This study collected resting-state functional magnetic resonance imaging data from 28 first-episode, drug-naïve SZ patients with AVHs, 20 SZ patients without AVHs, and 38 healthy controls. We used Granger causality analysis (GCA) to examine effective connectivity (EC) of two hub regions [posterior cingulate cortex (PCC) and anteromedial prefrontal cortex (aMPFC)] within the DMN. We used two-sample t-tests to compare the difference in EC between the two patient groups, and used Spearman correlation analysis to characterize the relationship between imaging findings and clinical assessments. The GCA revealed that, compared with the non-AVHs group, EC decreased from aMPFC to left inferior temporal gyrus (ITG) and from PCC to left cerebellum posterior lobe, ITG, and right middle frontal gyrus in SZ patients with AVHs. We also found significant correlations between clinical assessments and mean strengths of connectivity from aMPFC to left ITG and from PCC to left ITG. Moreover, receiver operating characteristic analysis revealed that the above-mentioned effective connectivities had a diagnostic value for distinguishing SZ patients with AVHs from non-AVHs patients. These findings suggest that AVHs in SZ patients may be associated with the aberrant information flows of the DMN, and the left ITG may probably serve as a potential biomarker for the neural mechanisms underlying AVHs in SZ patients.

Intergroup relationships can impact on a third party's willingness to punish a violator, but few researchers have explored how intergroup relationships affect third-party compensation tendencies. We recruited 163 participants to observe a dictator game, and then choose either to punish the dictator or compensate the recipient, each of whom could be from the participant's in-group or out-group. Third parties often chose not to punish in-group dictators and to compensate both in-group victims and out-group victims. When out-group members transgressed against the in-group, participants punished these out-group members just as often as they compensated the in-group recipients, although they punished out-group dictators more harshly than others overall. However, when both proposer and recipient came from the out-group, participants often did not intervene. We also found that third-party punishment and compensation were related to individual differences in participants' trait empathy and Machiavellianism. Our findings shed light on the modulating effect of intergroup relationships on third-party altruistic decisions.

PURPOSE:Because individual variance always exists, using the same set of predetermined parameters for magnetic resonance imaging (MRI) may not be exactly suitable for each participant. We propose a knowledge-based method that can repair MRI data of undesired contrast as if a new scan were acquired using imaging parameters that had been individually optimized.METHODS:The method employed a strategy called analogical reasoning to deduce voxel-wise relaxation properties using morphological and biological similarity. The proposed framework involves steps of intensity normalization, tissue segmentation, relaxation time deducing, and image deducing.RESULTS:This approach has been preliminarily validated using conventional MRI data at 3T from several examples, including 5 normal and 9 clinical datasets. It can effectively improve the contrast of real MRI data by deducing imaging data using optimized imaging parameters based on deduced relaxation properties. The statistics of deduced images shows a high correlation with real data that were actually collected using the same set of imaging parameters.CONCLUSION:The proposed method of deducing MRI data using knowledge of relaxation times alternatively provides a way of repairing MRI data of less optimal contrast. The method is also capable of optimizing an MRI protocol for individual participants, thereby realizing personalized MR imaging.