Conventional and multi-omics assessments of subacute inhalation toxicity due to propylene glycol and vegetable glycerin aerosol produced by electronic cigarettes.

Propylene glycol (PG) and vegetable glycerin (VG) are the most common solvents used in electronic cigarette liquids. No long-term inhalation toxicity assessments have been performed combining conventional and multi-omics approaches on the potential respiratory effects of the solvents in vivo. In this study, the systemic toxicity of aerosol generated from a ceramic heating coil-based e-cigarette was evaluated. First, the aerosol properties were characterized, including carbonyl emissions, the particle size distribution, and aerosol temperatures. To determine toxicological effects, rats were exposed, through their nose only, to filtered air or a propylene glycol (PG)/ glycerin (VG) (50:50, %W/W) aerosol mixture at the target concentration of 3 mg/L for six hours daily over a continuous 28-day period. Compared with the air group, female rats in the PG/VG group exhibited significantly lower body weights during both the exposure period and recovery period, and this was linked to a reduced food intake. Male rats in the PG/VG group also experienced a significant decline in body weight during the exposure period. Importantly, rats exposed to the PG/VG aerosol showed only minimal biological effects compared to those with only air exposure, with no signs of toxicity. Moreover, the transcriptomic, proteomic, and metabolomic analyses of the rat lung tissues following aerosol exposure revealed a series of candidate pathways linking aerosol inhalation to altered lung functions, especially the inflammatory response and disease. Dysregulated pathways of arachidonic acids, the neuroactive ligand-receptor interaction, and the hematopoietic cell lineage were revealed through integrated multi-omics analysis. Therefore, our integrated multi-omics approach offers novel systemic insights and early evidence of environmental-related health hazards associated with an e-cigarette aerosol using two carrier solvents in a rat model.

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 .

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.

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.

A joint space-angle regularization approach for single 4D diffusion image super-resolution.

PURPOSE: Low signal-to-noise-ratio and limited scan time of diffusion magnetic resonance imaging (dMRI) in current clinical settings impede obtaining images with high spatial and angular resolution (HSAR) for a reliable fiber reconstruction with fine anatomical details. To overcome this problem, we propose a joint space-angle regularization approach to reconstruct HSAR diffusion signals from a single 4D low resolution (LR) dMRI, which is down-sampled in both 3D-space and q-space. METHODS: Different from the existing works which combine multiple 4D LR diffusion images acquired using specific acquisition protocols, the proposed method reconstructs HSAR dMRI from only a single 4D dMRI by exploring and integrating two key priors, that is, the nonlocal self-similarity in the spatial domain as a prior to increase spatial resolution and ridgelet approximations in the diffusion domain as another prior to increase the angular resolution of dMRI. To more effectively capture nonlocal self-similarity in the spatial domain, a novel 3D block-based nonlocal means filter is imposed as the 3D image space regularization term which is accurate in measuring the similarity and fast for 3D reconstruction. To reduce computational complexity, we use the L2 -norm instead of sparsity constraint on the representation coefficients. RESULTS: Experimental results demonstrate that the proposed method can obtain the HSAR dMRI efficiently with approximately 2% per-voxel root-mean-square error between the actual and reconstructed HSAR dMRI. CONCLUSION: The proposed approach can effectively increase the spatial and angular resolution of the dMRI which is independent of the acquisition protocol, thus overcomes the inherent resolution limitation of imaging systems.

Chronic Co-species Housing Mice and Rats Increased the Competitiveness of Male Mice.

Rats are predators of mice in nature. Nevertheless, it is a common practice to house mice and rats in a same room in some laboratories. In this study, we investigated the behavioral and physiological responsively of mice in long-term co-species housing conditions. Twenty-four male mice were randomly assigned to their original raising room (control) or a rat room (co-species-housed) for more than 6 weeks. In the open-field and light-dark box tests, the behaviors of the co-species-housed mice and controls were not different. In a 2-choice test of paired urine odors [rabbit urine (as a novel odor) vs. rat urine, cat urine (as a natural predator-scent) vs. rabbit urine, and cat urine vs. rat urine], the co-species-housed mice were more ready to investigate the rat urine odor compared with the controls and may have adapted to it. In an encounter test, the rat-room-exposed mice exhibited increased aggression levels, and their urines were more attractive to females. Correspondingly, the levels of major urinary proteins were increased in the co-species-housed mouse urine, along with some volatile pheromones. The serum testosterone levels were also enhanced in the co-species-housed mice, whereas the corticosterone levels were not different. The norepinephrine, dopamine, and 5-HT levels in the right hippocampus and striatum were not different between the 2. Our findings indicate that chronic co-species housing results in adaptation in male mice; furthermore, it appears that long-term rat-odor stimuli enhance the competitiveness of mice, which suggests that appropriate predator-odor stimuli may be important to the fitness of prey animals.

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.

Distinct correlation network of clinical characteristics in suicide attempters having adolescent major depressive disorder with non-suicidal self-injury.

Suicidal behavior and non-suicidal self-injury (NSSI) are common in adolescent patients with major depressive disorder (MDD). Thus, delineating the unique characteristics of suicide attempters having adolescent MDD with NSSI is important for suicide prediction in the clinical setting. Here, we performed psychological and biochemical assessments of 130 youths having MDD with NSSI. Participants were divided into two groups according to the presence/absence of suicide attempts (SAs). Our results demonstrated that the age of suicide attempters is lower than that of non-attempters in participants having adolescent MDD with NSSI; suicide attempters had higher Barratt Impulsiveness Scale (BIS-11) impulsivity scores and lower serum CRP and cortisol levels than those having MDD with NSSI alone, suggesting levels of cortisol and CRP were inversely correlated with SAs in patients with adolescent MDD with NSSI. Furthermore, multivariate regression analysis revealed that NSSI frequency in the last month and CRP levels were suicidal ideation predictors in adolescent MDD with NSSI, which may indicate that the increased frequency of NSSI behavior is a potential risk factor for suicide. Additionally, we explored the correlation between psychological and blood biochemical indicators to distinguish suicide attempters among participants having adolescent MDD with NSSI and identified a unique correlation network that could serve as a marker for suicide attempters. Our research data further suggested a complex correlation between the psychological and behavioral indicators of impulsivity and anger. Therefore, our study findings may provide clues to identify good clinical warning signs for SA in patients with adolescent MDD with NSSI.

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.

Dose-dependent action of cordycepin on the microbiome-gut-brain-adipose axis in mice exposed to stress.

The high risk for anxiety and depression among individuals with stress has become a growing concern globally. Stress-related mental disorders are often accompanied by symptoms of metabolic dysfunction. Cordycepin is a Chinese herbal medicine commonly used for its metabolism-enhancing effects. We aimed to investigate the dose-dependent effects of cordycepin on psycho-metabolic disorders induced by stress. Our behavioral tests revealed that 12.5 mg/kg cordycepin by oral gavage significantly attenuated the anxiety- and depression-like behaviors induced by stress in mice. At 25 mg/kg, cordycepin restored the reduced weight and cell size of adipose tissues caused by stress. Besides ameliorating the metabolic dysbiosis of gut microbiota due to stress, cordycepin significantly reduced the elevated contents of 5-hydroxyindoleacetic acid in the serum and prefrontal cortex at 12.5 mg/kg and reversed the decrease in adipose induced by stress at 25 mg/kg. Correlation analyses further revealed that 12.5 mg/kg cordycepin reversed stress-induced changes in the intestinal microbiome of NK4A214_group and decreased serum Myristic acid and PC(15:0/18:1(11Z)) and cytokines, such as IFN-γ and IL-1ß. 25 mg/kg cordycepin reversed stress-induced changes in the abundances of Prevoteaceae_UCG-001 and Desulfovibrio, increased serum L-alanine level, and decreased serum Inosine-5'-monophosphate level. Cordycepin thereby ameliorated the anxiety- and depression-like behaviors as well as disturbances in the adipose metabolism of mice exposed to stress. Overall, these findings offer evidence indicating that the prominent effects of cordycepin in the brain and adipose tissues are dose dependent, thus highlight the importance of evaluating the precise therapeutic effects of different cordycepin doses on psycho-metabolic diseases.

Pharmacokinetic differences in nicotine and nicotine salts mediate reinforcement-related behavior: an animal model study.

Since their introduction in the United States and Europe in 2007, electronic cigarettes (E-Cigs) have become increasingly popular among smokers. Nicotine, a key component in both tobacco and e-cigarettes, can exist in two forms: nicotine-freebase (FBN) and nicotine salts (NS). While nicotine salt is becoming more popular in e-cigarettes, the effect of nicotine salts on reinforcement-related behaviors remains poorly understood. This study aimed to compare the reinforcing effects of nicotine and nicotine salts in animal models of drug self-administration and explore potential mechanisms that may contribute to these differences. The results demonstrated that three nicotine salts (nicotine benzoate, nicotine lactate, and nicotine tartrate) resulted in greater reinforcement-related behaviors in rats compared to nicotine-freebase. Moreover, withdrawal-induced anxiety symptoms were lower in the three nicotine salt groups than in the nicotine-freebase group. The study suggested that differences in the pharmacokinetics of nicotine-freebase and nicotine salts in vivo may explain the observed behavioral differences. Overall, this study provides valuable insights into the reinforcing effects of nicotine as well as potential differences between nicotine-freebase and nicotine salts.

Palmprint and face multi-modal biometric recognition based on SDA-GSVD and its kernelization.

When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person's overall biometric data should be regarded as one class in the input space, and his different biometric data can form different Gaussians distributions, i.e., different subclasses. Hence, we propose a novel multimodal feature extraction and recognition approach based on subclass discriminant analysis (SDA). Specifically, one person's different bio-data are treated as different subclasses of one class, and a transformed space is calculated, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, the obtained multimodal features are used for classification. Two solutions are presented to overcome the singularity problem encountered in calculation, which are using PCA preprocessing, and employing the generalized singular value decomposition (GSVD) technique, respectively. Further, we provide nonlinear extensions of SDA based multimodal feature extraction, that is, the feature fusion based on KPCA-SDA and KSDA-GSVD. In KPCA-SDA, we first apply Kernel PCA on each single modal before performing SDA. While in KSDA-GSVD, we directly perform Kernel SDA to fuse multimodal data by applying GSVD to avoid the singular problem. For simplicity two typical types of biometric data are considered in this paper, i.e., palmprint data and face data. Compared with several representative multimodal biometrics recognition methods, experimental results show that our approaches outperform related multimodal recognition methods and KSDA-GSVD achieves the best recognition performance.

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.

In vitro and in vivo Activity of Phibilin Against Candida albicans.

The increase in the occurrence of antifungal-resistant Candida albicans infections necessitates more research to explore alternative effective and safe agents against this fungus. In this work, Phibilin, a new antimicrobial peptide obtained from Philomycus bilineatus and used in traditional Chinese medicine, effectively inhibits the growth and activities of C. albicans, including the clinical resistant strains. Phibilin is a fungicidal antimicrobial peptide that exhibited its antimicrobial effect against C. albicans mainly by disrupting the membrane and interacting with the DNA of the fungi. In particular, Phibilin induces the necrosis of C. albicans via the ROS-related pathway. Moreover, this antifungal compound inhibited the biofilm formation of C. albicans by preventing the development of hyphae in a dose-dependent manner. Furthermore, Phibilin and clotrimazole displayed a synergistic effect in inhibiting the growth of the fungi. In the mouse cutaneous infection model, Phibilin significantly inhibited the formation of skin abscesses and decreased the counts of C. albicans cells in the infected area. Overall, Phibilin is potentially an effective agent against skin infections caused by C. albicans.

In vitro and in vivo antibacterial activity of a lysine-rich scorpion peptide derivative.

Antimicrobial peptides are widely acknowledged as an alternative class of antimicrobial agents. In this study, a lysine-rich scorpion peptide derivative Pacavin-5K was designed, which showed an improved antibacterial spectrum, significantly higher antibacterial activity, and lower toxicity compared to the native peptide. It also showed an improved thermal and serum stability. Notably, Pacavin-5K significantly decreased the bacterial counts in the wounded region in the mouse cutaneous infection model caused by Staphylococcus aureus and Pseudomonas aeruginosa. Moreover, Pacavin-5K did not induce bacterial resistance associated with its antibacterial mechanism disrupting the membrane. Furthermore, Pacavin-5K could kill the S. aureus cells at the biofilm state. Overall, Pacavin-5K could be a potential alternative antibacterial agent against skin infection caused by S. aureus and P. aeruginosa.

Mitigation Effects of Selenium Nanoparticles on Depression-Like Behavior Induced by Fluoride in Mice via the JAK2-STAT3 Pathway.

Depression is a mental health problem with typically high levels of distress and dysfunction, and 150 mg/L fluoride (F) can induce depression-like behavior. The development of depression is correlated with neuronal atrophy, insufficient secretion of monoamine neurotransmitters, extreme deviations from the normal microglial activation status, and immune-inflammatory response. Studies found that Se supplementation was related to the improvement of depression. In this study, we applied selenium nanoparticles (SeNPs) for F-induced depression disease mitigation by regulating the histopathology, metabolic index, genes, and protein expression related to the JAK2-STAT3 signaling pathway in vivo. Results showed that F and 2 mg Se/kg BW/day SeNPs lowered the dopamine (DA) content (P < 0.05), altered the microglial morphology, ramification index as well as solidity, and triggered the microglial neuroinflammatory response by increasing the p-STAT3 nuclear translocation (P < 0.01). Furthermore, F reduced the cortical Se content and the number of surviving neurons (P < 0.05), increasing the protein expressions of p-JAK2/JAK2 and p-STAT3/STAT3 of the cortex (P < 0.01), accompanied by the depression-like behavior. Importantly, 1 mg Se/kg BW/day SeNPs alleviated the microglial ramification index as well as solidity changes and decreased the interleukin-1ß secretion induced by F by suppressing the p-STAT3 nuclear translocation (P < 0.01). Likewise, 1 mg Se/kg BW/day SeNPs restored the F-disturbed dopamine and noradrenaline secretion, increased the number of cortical surviving neurons, and reduced the vacuolation area, ultimately suppressing the occurrence of depression-like behavior through inhibiting the JAK2-STAT3 pathway activation. In conclusion, 1 mg Se/kg BW/day SeNPs have mitigation effects on the F-induced depression-like behavior. The mechanism of how SeNPs repair neural functions will benefit depression mitigation. This study also indicates that inhibiting the JAK/STAT pathway can be a promising novel treatment for depressive disorders.

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.

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.