Multilevel Contrastive Graph Masked Autoencoders for Unsupervised Graph-Structure Learning.

Unsupervised graph-structure learning (GSL) which aims to learn an effective graph structure applied to arbitrary downstream tasks by data itself without any labels' guidance, has recently received increasing attention in various real applications. Although several existing unsupervised GSL has achieved superior performance in different graph analytical tasks, how to utilize the popular graph masked autoencoder to sufficiently acquire effective supervision information from the data itself for improving the effectiveness of learned graph structure has been not effectively explored so far. To tackle the above issue, we present a multilevel contrastive graph masked autoencoder (MCGMAE) for unsupervised GSL. Specifically, we first introduce a graph masked autoencoder with the dual feature masking strategy to reconstruct the same input graph-structured data under the original structure generated by the data itself and learned graph-structure scenarios, respectively. And then, the inter-and intra-class contrastive loss is introduced to maximize the mutual information in feature and graph-structure reconstruction levels simultaneously. More importantly, the above inter-and intra-class contrastive loss is also applied to the graph encoder module for further strengthening their agreement at the feature-encoder level. In comparison to the existing unsupervised GSL, our proposed MCGMAE can effectively improve the training robustness of the unsupervised GSL via different-level supervision information from the data itself. Extensive experiments on three graph analytical tasks and eight datasets validate the effectiveness of the proposed MCGMAE.

A novel two-way rebalancing strategy for identifying carbonylation sites.

BACKGROUND: As an irreversible post-translational modification, protein carbonylation is closely related to many diseases and aging. Protein carbonylation prediction for related patients is significant, which can help clinicians make appropriate therapeutic schemes. Because carbonylation sites can be used to indicate change or loss of protein function, integrating these protein carbonylation site data has been a promising method in prediction. Based on these protein carbonylation site data, some protein carbonylation prediction methods have been proposed. However, most data is highly class imbalanced, and the number of un-carbonylation sites greatly exceeds that of carbonylation sites. Unfortunately, existing methods have not addressed this issue adequately. RESULTS: In this work, we propose a novel two-way rebalancing strategy based on the attention technique and generative adversarial network (Carsite_AGan) for identifying protein carbonylation sites. Specifically, Carsite_AGan proposes a novel undersampling method based on attention technology that allows sites with high importance value to be selected from un-carbonylation sites. The attention technique can obtain the value of each sample's importance. In the meanwhile, Carsite_AGan designs a generative adversarial network-based oversampling method to generate high-feasibility carbonylation sites. The generative adversarial network can generate high-feasibility samples through its generator and discriminator. Finally, we use a classifier like a nonlinear support vector machine to identify protein carbonylation sites. CONCLUSIONS: Experimental results demonstrate that our approach significantly outperforms other resampling methods. Using our approach to resampling carbonylation data can significantly improve the effect of identifying protein carbonylation sites.

Cancer survival prediction by learning comprehensive deep feature representation for multiple types of genetic data.

BACKGROUND: Cancer is one of the leading death causes around the world. Accurate prediction of its survival time is significant, which can help clinicians make appropriate therapeutic schemes. Cancer data can be characterized by varied molecular features, clinical behaviors and morphological appearances. However, the cancer heterogeneity problem usually makes patient samples with different risks (i.e., short and long survival time) inseparable, thereby causing unsatisfactory prediction results. Clinical studies have shown that genetic data tends to contain more molecular biomarkers associated with cancer, and hence integrating multi-type genetic data may be a feasible way to deal with cancer heterogeneity. Although multi-type gene data have been used in the existing work, how to learn more effective features for cancer survival prediction has not been well studied. RESULTS: To this end, we propose a deep learning approach to reduce the negative impact of cancer heterogeneity and improve the cancer survival prediction effect. It represents each type of genetic data as the shared and specific features, which can capture the consensus and complementary information among all types of data. We collect mRNA expression, DNA methylation and microRNA expression data for four cancers to conduct experiments. CONCLUSIONS: Experimental results demonstrate that our approach substantially outperforms established integrative methods and is effective for cancer survival prediction. AVAILABILITY AND IMPLEMENTATION: https://github.com/githyr/ComprehensiveSurvival .

Human Collective Intelligence Inspired Multi-View Representation Learning - Enabling View Communication by Simulating Human Communication Mechanism.

In real-world applications, we often encounter multi-view learning tasks where we need to learn from multiple sources of data or use multiple sources of data to make decisions. Multi-view representation learning, which can learn a unified representation from multiple data sources, is a key pre-task of multi-view learning and plays a significant role in real-world applications. Accordingly, how to improve the performance of multi-view representation learning is an important issue. In this work, inspired by human collective intelligence shown in group decision making, we introduce the concept of view communication into multi-view representation learning. Furthermore, by simulating human communication mechanism, we propose a novel multi-view representation learning approach that can fulfill multi-round view communication. Thus, each view of our approach can exploit the complementary information from other views to help with modeling its own representation, and mutual help between views is achieved. Extensive experiment results on six datasets from three significant fields indicate that our approach substantially improves the average classification accuracy by 4.536% in medicine and bioinformatics fields as well as 4.115% in machine learning field.

Joint learning sample similarity and correlation representation for cancer survival prediction.

BACKGROUND: As a highly aggressive disease, cancer has been becoming the leading death cause around the world. Accurate prediction of the survival expectancy for cancer patients is significant, which can help clinicians make appropriate therapeutic schemes. With the high-throughput sequencing technology becoming more and more cost-effective, integrating multi-type genome-wide data has been a promising method in cancer survival prediction. Based on these genomic data, some data-integration methods for cancer survival prediction have been proposed. However, existing methods fail to simultaneously utilize feature information and structure information of multi-type genome-wide data. RESULTS: We propose a Multi-type Data Joint Learning (MDJL) approach based on multi-type genome-wide data, which comprehensively exploits feature information and structure information. Specifically, MDJL exploits correlation representations between any two data types by cross-correlation calculation for learning discriminant features. Moreover, based on the learned multiple correlation representations, MDJL constructs sample similarity matrices for capturing global and local structures across different data types. With the learned discriminant representation matrix and fused similarity matrix, MDJL constructs graph convolutional network with Cox loss for survival prediction. CONCLUSIONS: Experimental results demonstrate that our approach substantially outperforms established integrative methods and is effective for cancer survival prediction.

Corrigendum: Menthol Flavor in E-Cigarette Vapor Modulates Social Behavior Correlated With Central and Peripheral Changes of Immunometabolic Signalings.

[This corrects the article DOI: 10.3389/fnmol.2022.800406.].

Menthol Flavor in E-Cigarette Vapor Modulates Social Behavior Correlated With Central and Peripheral Changes of Immunometabolic Signalings.

The use of electronic cigarette (e-cigarette) has been increasing dramatically worldwide. More than 8,000 flavors of e-cigarettes are currently marketed and menthol is one of the most popular flavor additives in the electronic nicotine delivery systems (ENDS). There is a controversy over the roles of e-cigarettes in social behavior, and little is known about the potential impacts of flavorings in the ENDS. In our study, we aimed to investigate the effects of menthol flavor in ENDS on the social behavior of long-term vapor-exposed mice with a daily intake limit, and the underlying immunometabolic changes in the central and peripheral systems. We found that the addition of menthol flavor in nicotine vapor enhanced the social activity compared with the nicotine alone. The dramatically reduced activation of cellular energy measured by adenosine 5' monophosphate-activated protein kinase (AMPK) signaling in the hippocampus were observed after the chronic exposure of menthol-flavored ENDS. Multiple sera cytokines including C5, TIMP-1, and CXCL13 were decreased accordingly as per their peripheral immunometabolic responses to menthol flavor in the nicotine vapor. The serum level of C5 was positively correlated with the alteration activity of the AMPK-ERK signaling in the hippocampus. Our current findings provide evidence for the enhancement of menthol flavor in ENDS on social functioning, which is correlated with the central and peripheral immunometabolic disruptions; this raises the vigilance of the cautious addition of various flavorings in e-cigarettes and the urgency of further investigations on the complex interplay and health effects of flavoring additives with nicotine in e-cigarettes.

Visual-Depth Matching Network: Deep RGB-D Domain Adaptation With Unequal Categories.

Existing domain adaptation (DA) methods generally assume that different domains have identical label space, and the training data are only sampled from a single domain. This unrealistic assumption is quite restricted for real-world applications, since it neglects the more practical scenario, where the source domain can contain the categories that are not shared by the target domain, and the training data can be collected from multiple modalities. In this article, we address a more difficult but practical problem, which recognizes RGB images through training on RGB-D data under the label space inequality scenario. There are three challenges in this task: 1) source and target domains are affected by the domain mismatch issue, which results in that the trained models perform imperfectly on the test data; 2) depth images are absent in the target domain (e.g., target images are captured by smartphones), when the source domain contains both the RGB and depth data. It makes the ordinary visual recognition approaches hardly applied to this task; and 3) in the real world, the source and target domains always have different numbers of categories, which would result in a negative transfer bottleneck being more prominent. Toward tackling the above challenges, we formulate a deep model, called visual-depth matching network (VDMN), where two new modules and a matching component can be trained in an end-to-end fashion jointly to identify the common and outlier categories effectively. The significance of VDMN is that it can take advantage of depth information and handle the domain distribution mismatch under label inequality simultaneously. The experimental results reveal that VDMN exceeds the state-of-the-art performance on various DA datasets, especially under the label inequality scenario.

mGlu2/3 receptor in the prelimbic cortex is implicated in stress resilience and vulnerability in mice.

Resilience, referring to "achieving a positive outcome in the face of adversity", is a common phenomenon in daily life. Elucidating the mechanisms of stress resilience is instrumental to developing more effective treatments for stress-related psychiatric disorders such as depression. Metabotropic glutamate receptors (mGlu2/3 and mGlu5) within the medial prefrontal cortex (mPFC) have been recently recognized as promising therapeutic targets for rapid-acting antidepressant treatment. In this study, we assessed the functional roles of the mGlu2/3 and mGlu5 within different subregions of the mPFC in modulating stress resilience and vulnerability by using chronic social defeat stress (CSDS) paradigms in mice. Our results showed that approximately 51.6% of the subjects exhibited depression- or anxiety-like behaviors after exposure to CSDS. When a susceptible mouse was confronted with an attacker, c-Fos expression in the prelimbic cortex (PrL) subregion of the mPFC substantially increased. Compared with the resilient and control groups, the expression of mGlu2/3 was elevated in the PrL of the susceptible group. The expression of mGlu5 showed no significant difference among the three groups in the whole mPFC. Finally, we found that the social avoidance symptoms of the susceptible mice were rapidly relieved by intra-PrL administration of LY341495-an mGluR2/3 antagonists. The above results indicate that mGluR2/3 within the PrL may play an important regulatory role in stress-related psychiatric disorders. Our results are meaningful, as they expand our understanding of stress resilience and vulnerability which may open an avenue to develop novel, personalized approaches to mitigate depression and promote stress resilience.

The GABA(B1) receptor within the infralimbic cortex is implicated in stress resilience and vulnerability in mice.

Resilience is the capacity to maintain normal psychological and physical functions in the face of stress and adversity. Understanding how one can develop and enhance resilience is of great relevance to not only promoting coping mechanisms but also mitigating maladaptive stress responses in psychiatric illnesses such as depression. Preclinical studies suggest that GABA(B) receptors (GABA(B1) and GABA(B2)) are potential targets for the treatment of major depression. In this study, we assessed the functional role of GABA(B) receptors in stress resilience and vulnerability by using a chronic unpredictable stress (CUS) model in mice. As the medial prefrontal cortex (mPFC) plays a key role in the top-down modulation of stress responses, we focused our study on this brain structure. Our results showed that only approximately 41.9% of subjects exhibited anxiety- or despair-like behaviors after exposure to CUS. The vulnerable mice showed higher c-Fos expression in the infralimbic cortex (IL) subregion of the mPFC when exposed to a social stressor. Moreover, the expression of GABA(B1) but not GABA(B2) receptors was significantly downregulated in IL subregion of susceptible mice. Finally, we found that intra-IL administration of baclofen, a GABA(B) receptor agonist, rapidly relieved the social avoidance symptoms of the "stress-susceptible" mice. Taken together, our results show that the GABA(B1) receptor within the IL may play an important role in stress resilience and vulnerability, and thus open an avenue to develop novel, personalized approaches to promote stress resilience and treat stress-related psychiatric disorders.

GABA(B) receptors within the lateral habenula modulate stress resilience and vulnerability in mice.

Resilience means "the ability to withstand or recover quickly in the face of adversity". Elucidating the neural and molecular mechanisms underlying stress resilience will facilitate the development of more effective treatments for stress-induced psychiatric disorders such as depression. The habenular nuclei, which consist of the medial and lateral sub-regions (MHb and LHb, respectively), have been described as a critical node in emotional regulations. GABA(B) receptors play an important regulatory role in habenular activity. In this study, we assessed the functional role of GABA(B) receptors within the habenula in stress resilience and vulnerability by using chronic social defeat stress (CSDS) model in C57BL/6 male mice. Approximately 47.1% of mice exhibited depression- or anxiety-like behaviors after exposure to CSDS. The vulnerable mice presented elevated c-Fos expression in the LHb when confronted with an attacker. On the other hand, the expression of GABA(B) receptors, including both GABA(B1) and GABA(B2) subunits, was significantly down-regulated in the LHb of the susceptible mice. Finally, we found the stress-induced social withdrawal symptoms could be rapidly relieved by intra-LHb injection of both baclofen and CGP36216 (a GABA(B) receptor agonist and antagonist respectively). The above results indicated that GABA(B) receptors in the LHb may play an important role in stress resilience and vulnerability, and thus, may be an important therapeutic target for treatments of stress-induced psychiatric disorders.

Multiset Feature Learning for Highly Imbalanced Data Classification.

With the expansion of data, increasing imbalanced data has emerged. When the imbalance ratio (IR) of data is high, most existing imbalanced learning methods decline seriously in classification performance. In this paper, we systematically investigate the highly imbalanced data classification problem, and propose an uncorrelated cost-sensitive multiset learning (UCML) approach for it. Specifically, UCML first constructs multiple balanced subsets through random partition, and then employs the multiset feature learning (MFL) to learn discriminant features from the constructed multiset. To enhance the usability of each subset and deal with the non-linearity issue existed in each subset, we further propose a deep metric based UCML (DM-UCML) approach. DM-UCML introduces the generative adversarial network technique into the multiset constructing process, such that each subset can own similar distribution with the original dataset. To cope with the non-linearity issue, DM-UCML integrates deep metric learning with MFL, such that more favorable performance can be achieved. In addition, DM-UCML designs a new discriminant term to enhance the discriminability of learned metrics. Experiments on eight traditional highly class-imbalanced datasets and two large-scale datasets indicate that: the proposed approaches outperform state-of-the-art highly imbalanced learning methods and are more robust to high IR.

Modal-Regression-Based Structured Low-Rank Matrix Recovery for Multiview Learning.

Low-rank Multiview Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL-based methods are incapable of handling well view discrepancy and discriminancy simultaneously, which, thus, leads to performance degradation when there is a large discrepancy among multiview data. To circumvent this drawback, motivated by the block-diagonal representation learning, we propose structured low-rank matrix recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of the structured low-rank matrix. Furthermore, recent low-rank modeling provides a satisfactory solution to address the data contaminated by the predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution. However, these models are not practical, since complicated noise in practice may violate those assumptions and the distribution is generally unknown in advance. To alleviate such a limitation, modal regression is elegantly incorporated into the framework of SLMR (termed MR-SLMR). Different from previous LMvSL-based methods, our MR-SLMR can handle any zero-mode noise variable that contains a wide range of noise, such as Gaussian noise, random noise, and outliers. The alternating direction method of multipliers (ADMM) framework and half-quadratic theory are used to optimize efficiently MR-SLMR. Experimental results on four public databases demonstrate the superiority of MR-SLMR and its robustness to complicated noise.

Semi-Supervised Multi-View Deep Discriminant Representation Learning.

Learning an expressive representation from multi-view data is a key step in various real-world applications. In this paper, we propose a semi-supervised multi-view deep discriminant representation learning (SMDDRL) approach. Unlike existing joint or alignment multi-view representation learning methods that cannot simultaneously utilize the consensus and complementary properties of multi-view data to learn inter-view shared and intra-view specific representations, SMDDRL comprehensively exploits the consensus and complementary properties as well as learns both shared and specific representations by employing the shared and specific representation learning network. Unlike existing shared and specific multi-view representation learning methods that ignore the redundancy problem in representation learning, SMDDRL incorporates the orthogonality and adversarial similarity constraints to reduce the redundancy of learned representations. Moreover, to exploit the information contained in unlabeled data, we design a semi-supervised learning framework by combining deep metric learning and density clustering. Experimental results on three typical multi-view learning tasks, i.e., webpage classification, image classification, and document classification demonstrate the effectiveness of the proposed approach.

Dual-regression model for visual tracking.

Existing regression based tracking methods built on correlation filter model or convolution model do not take both accuracy and robustness into account at the same time. In this paper, we propose a dual-regression framework comprising a discriminative fully convolutional module and a fine-grained correlation filter component for visual tracking. The convolutional module trained in a classification manner with hard negative mining ensures the discriminative ability of the proposed tracker, which facilitates the handling of several challenging problems, such as drastic deformation, distractors, and complicated backgrounds. The correlation filter component built on the shallow features with fine-grained features enables accurate localization. By fusing these two branches in a coarse-to-fine manner, the proposed dual-regression tracking framework achieves a robust and accurate tracking performance. Extensive experiments on the OTB2013, OTB2015, and VOT2015 datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.

Multiview Hybrid Embedding: A Divide-and-Conquer Approach.

We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multiview subspace learning (MvSL) that aims to learn a latent subspace shared by multiview data. Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multiview data is sampled from nonlinear manifolds or suffers from heavy outliers. To circumvent this drawback, motivated by the Divide-and-Conquer strategy, we propose multiview hybrid embedding (MvHE), a unique method of dividing the problem of cross-view classification into three subproblems and building one model for each subproblem. Specifically, the first model is designed to remove view discrepancy, whereas the second and third models attempt to discover the intrinsic nonlinear structure and to increase the discriminability in intraview and interview samples, respectively. The kernel extension is conducted to further boost the representation power of MvHE. Extensive experiments are conducted on four benchmark datasets. Our methods demonstrate the overwhelming advantages against the state-of-the-art MvSL-based cross-view classification approaches in terms of classification accuracy and robustness.

Intraspectrum Discrimination and Interspectrum Correlation Analysis Deep Network for Multispectral Face Recognition.

Multispectral images contain rich recognition information since the multispectral camera can reveal information that is not visible to the human eye or to the conventional RGB camera. Due to this characteristic of multispectral images, multispectral face recognition has attracted lots of research interest. Although some multispectral face recognition methods have been presented in the last decade, how to fully and effectively explore the intraspectrum discriminant information and the useful interspectrum correlation information in multispectral face images for recognition has not been well studied. To boost the performance of multispectral face recognition, we propose an intraspectrum discrimination and interspectrum correlation analysis deep network (IDICN) approach. Multiple spectra are divided into several spectrum-sets, with each containing a group of spectra within a small spectral range. The IDICN network contains a set of spectrum-set-specific deep convolutional neural networks attempting to extract spectrum-set-specific features, followed by a spectrum pooling layer, whose target is to select a group of spectra with favorable discriminative abilities adaptively. IDICN jointly learns the nonlinear representations of the selected spectra, such that the intraspectrum Fisher loss and the interspectrum discriminant correlation are minimized. Experiments on the well-known Hong Kong Polytechnic University, Carnegie Mellon University, and the University of Western Australia multispectral face datasets demonstrate the superior performance of the proposed approach over several state-of-the-art methods.

Adaptive Weighted Sparse Principal Component Analysis for Robust Unsupervised Feature Selection.

Current unsupervised feature selection methods cannot well select the effective features from the corrupted data. To this end, we propose a robust unsupervised feature selection method under the robust principal component analysis (PCA) reconstruction criterion, which is named the adaptive weighted sparse PCA (AW-SPCA). In the proposed method, both the regularization term and the reconstruction error term are constrained by the l2,1 -norm: the l2,1 -norm regularization term plays a role in the feature selection, while the l2,1 -norm reconstruction error term plays a role in the robust reconstruction. The proposed method is in a convex formulation, and the selected features by it can be used for robust reconstruction and clustering. Experimental results demonstrate that the proposed method can obtain better reconstruction and clustering performance, especially for the corrupted data.

Involvement of oxytocin and GABA in consolation behavior elicited by socially defeated individuals in mandarin voles.

Consolation, which entails comforting contact directed toward a distressed party, is a common empathetic response in humans and other species with advanced cognition. Here, using the social defeat paradigm, we provide empirical evidence that highly social and monogamous mandarin voles (Microtus mandarinus) increased grooming toward a socially defeated partner but not toward a partner who underwent only separation. This selective behavioral response existed in both males and females. Accompanied with these behavioral changes, c-Fos expression was elevated in many of the brain regions relevant for emotional processing, such as the anterior cingulate cortex (ACC), bed nucleus of the stria terminalis, paraventricular nucleus (PVN), basal/basolateral and central nucleus of the amygdala, and lateral habenular nucleus in both sexes; in the medial preoptic area, the increase in c-Fos expression was found only in females, whereas in the medial nucleus of the amygdala, this increase was found only in males. In particular, the GAD67/c-Fos and oxytocin (OT)/c-Fos colocalization rates were elevated in the ACC and PVN, indicating selective activation of GABA and OT neurons in these regions. The "stressed" pairs matched their anxiety-like behaviors in the open-field test, and their plasma corticosterone levels correlated well with each other, suggesting an empathy-based mechanism. This partner-directed grooming was blocked by pretreatment with an OT receptor antagonist or a GABAA receptor antagonist in the ACC but not by a V1a subtype vasopressin receptor antagonist. We conclude that consolation behavior can be elicited by the social defeat paradigm in mandarin voles, and this behavior may be involved in a coordinated network of emotion-related brain structures, which differs slightly between the sexes. We also found that the endogenous OT and the GABA systems within the ACC are essential for consolation behavior in mandarin voles.

Single Sample Face Recognition under Varying Illumination via QRCP Decomposition.

In this paper, we present a novel high-frequency facial feature and a high-frequency based sparse representation classification to tackle single sample face recognition (SSFR) under varying illumination. Firstly, we propose the assumption that QRCP bases can represent intrinsic face surface features with different frequencies, and their corresponding energy coefficients describe illumination intensities. Based on this assumption, we take QRCP bases with corresponding weighting coefficients (i.e. the major components of energy coefficients) to develop the high-frequency facial feature of the face image, which is named as QRCP-face. The normalized QRCP-face (NQRCPface) is constructed to further constraint illumination effects by normalizing the weighting coefficients of QRCP-face. Moreover, we propose the adaptive QRCP-face (AQRCP-face) that assigns a special parameter to NQRCP-face via the illumination level estimated by the weighting coefficients. Secondly, we consider that the differences of pixel images cannot model the intraclass variations of generic faces with illumination variations, and the specific identification information of the generic face is redundant for the current SSFR with generic learning. To tackle above two issues, we develop a general high-frequency based sparse representation (GHSP) model. Two practical approaches separated high-frequency based sparse representation (SHSP) and unified high-frequency based sparse representation (UHSP) are developed. Finally, the performances of the proposed methods are verified on the Extended Yale B, CMU PIE, AR, LFW and our self-built Driver face databases. The experimental results indicate that the proposed methods outperform previous approaches for SSFR under varying illumination.