Methoxsalen Inhibits the Acquisition of Nicotine Self-Administration: Attenuation by Cotinine Replacement in Male Rats.

INTRODUCTION: Cigarette smoking remains the leading preventable cause of disease and death. Nicotine is the primary reinforcing ingredient in cigarettes sustaining addiction. Cotinine is the major metabolite of nicotine that produces a myriad of neurobehavioral effects. Previous studies showed that cotinine-supported self-administration in rats and rats with a history of cotinine self-administration exhibited relapse-like drug-seeking behavior, suggesting that cotinine may also be reinforcing. To date, whether cotinine may contribute to nicotine reinforcement remains unknown. Nicotine metabolism is mainly catalyzed by hepatic CYP2B1/2 enzymes in rats and methoxsalen is a potent CYP2B1/2 inhibitor. AIMS AND METHODS: The study examined nicotine metabolism, self-administration, and locomotor activity. The hypothesis is that methoxsalen inhibits nicotine self-administration and cotinine replacement attenuates the inhibitory effects of methoxsalen in male rats. RESULTS: Methoxsalen decreased plasma cotinine levels following a subcutaneous nicotine injection. Repeated daily methoxsalen treatments reduced the acquisition of nicotine self-administration, leading to fewer nicotine infusions, lower nicotine intake, and lower plasma cotinine levels. However, methoxsalen did not alter the maintenance of nicotine self-administration despite a significant reduction of plasma cotinine levels. Cotinine replacement by mixing cotinine with nicotine for self-administration dose-dependently increased plasma cotinine levels and enhanced the acquisition of self-administration. Neither basal nor nicotine-induced locomotor activity was altered by methoxsalen. CONCLUSIONS: These results indicate that methoxsalen inhibition of cotinine formation impaired the acquisition of nicotine self-administration, and cotinine replacement attenuated the inhibitory effects of methoxsalen on the acquisition of self-administration, suggesting that cotinine may contribute to the initial development of nicotine reinforcement. IMPLICATIONS: Smoking cessation medications targeting nicotine's effects are only moderately effective, making it imperative to better understand the mechanisms of nicotine misuse. Methoxsalen inhibited nicotine metabolism to cotinine and impaired the acquisition of nicotine self-administration. Cotinine replacement restored plasma cotinine and attenuated the methoxsalen inhibition of nicotine self-administration in rats. These results suggest that (1) the inhibition of nicotine metabolism may be a viable strategy in reducing the development of nicotine reinforcement, (2) methoxsalen may be translationally valuable, and (3) cotinine may be a potential pharmacological target for therapeutic development given its important role in the initial development of nicotine reinforcement.

Characterization of Proteoform Post-Translational Modifications by Top-Down and Bottom-Up Mass Spectrometry in Conjunction with Annotations.

Many proteoforms can be produced from a gene due to genetic mutations, alternative splicing, post-translational modifications (PTMs), and other variations. PTMs in proteoforms play critical roles in cell signaling, protein degradation, and other biological processes. Mass spectrometry (MS) is the primary technique for investigating PTMs in proteoforms, and two alternative MS approaches, top-down and bottom-up, have complementary strengths. The combination of the two approaches has the potential to increase the sensitivity and accuracy in PTM identification and characterization. In addition, protein and PTM knowledge bases, such as UniProt, provide valuable information for PTM characterization and verification. Here, we present a software pipeline PTM-TBA (PTM characterization by Top-down and Bottom-up MS and Annotations) for identifying and localizing PTMs in proteoforms by integrating top-down and bottom-up MS as well as PTM annotations. We assessed PTM-TBA using a technical triplicate of bottom-up and top-down MS data of SW480 cells. On average, database search of the top-down MS data identified 2000 mass shifts, 814.5 (40.7%) of which were matched to 11 common PTMs and 423 of which were localized. Of the mass shifts identified by top-down MS, PTM-TBA verified 435 mass shifts using the bottom-up MS data and UniProt annotations.

Relapse to cotinine seeking in rats: differential effects of sex.

Relapse is a defining feature of smoking and a significant challenge in cessation management. Elucidation of novel factors underlying relapse may inform future treatments. Cotinine, the major metabolite of nicotine, has been shown to support intravenous self-administration in rats, implicating it as one potential factor contributing to nicotine reinforcement. However, it remains unknown whether cotinine would induce relapse-like behaviors. The current study investigated relapse to cotinine seeking in two relapse models, the reinstatement of drug seeking and incubation of drug craving models. In the reinstatement model, rats were trained to self-administer cotinine, underwent extinction of cotinine-associated responses, and were tested for cue-, drug-, or stress-induced reinstatement. Conditioned cues associated with cotinine self-administration, cotinine (1-2 mg/kg), or the pharmacological stressor yohimbine (1.25-2.5 mg/kg) induced reinstatement of cotinine seeking. Female rats displayed more pronounced cue-induced, but not drug- or stress-induced reinstatement than male rats. In the incubation of the craving model, rats were trained to self-administer cotinine and underwent forced withdrawal in home cages. Rats were tested for cue-induced cotinine-seeking on both withdrawal day 1 and withdrawal day 18. Rats exhibited greater cue-induced cotinine-seeking on withdrawal day 18 compared to withdrawal day 1, with no difference between male and female rats. These findings indicate that cotinine induces sex-specific relapse to drug seeking in rats, suggesting that cotinine may contribute to relapse.

Self-Administration of Cotinine in Wistar Rats: Comparisons to Nicotine.

Nicotine is the major addictive component in tobacco. Cotinine is the major metabolite of nicotine and a weak agonist for nicotinic acetylcholine receptors (nAChRs). Nicotine supports self-administration in rodents. However, it remains undetermined whether cotinine can be self-administered. This study aimed to characterize cotinine self-administration in rats, to compare effects of cotinine to those of nicotine, and to determine potential involvement of nAChRs in cotinine's effects. Adult Wistar rats were trained to self-administer cotinine or nicotine (0.0075, 0.015, 0.03, or 0.06 mg/kg per infusion) under fixed-ratio (FR) and progressive-ratio (PR) schedules. Blood nicotine and cotinine levels were determined after the last FR session. Effects of mecamylamine, a nonselective nAChR antagonist, and varenicline, a partial agonist for α4ß2* nAChRs, on cotinine and nicotine self-administration were determined. Rats readily acquired cotinine self-administration, responded more on active lever, and increased motivation to self-administer cotinine when the reinforcement requirement increased. Blood cotinine levels ranged from 77 to 792 ng/ml. Nicotine induced more infusions at lower doses during FR schedules and greater breakpoints at higher doses during the PR schedule than cotinine. There was no difference in cotinine self-administration between male and female rats. Mecamylamine and varenicline attenuated nicotine but not cotinine self-administration. These results indicate that cotinine was self-administered by rats. These effects of cotinine were less robust than nicotine and exhibited no sex difference. nAChRs appeared to be differentially involved in self-administration of nicotine and cotinine. These results suggest cotinine may play a role in the development of nicotine use and misuse. SIGNIFICANCE STATEMENT: Nicotine addiction is a serious public health problem. Cotinine is the major metabolite of nicotine, but its involvement in nicotine reinforcement remains elusive. Our findings indicate that cotinine, at doses producing clinically relevant blood cotinine levels, supported intravenous self-administration in rats. Cotinine self-administration was less robust than nicotine. Mecamylamine and varenicline attenuated nicotine but not cotinine self-administration. These results suggest cotinine may play a role in the development of nicotine use and misuse.

Reduced Levels of mGlu2 Receptors within the Prelimbic Cortex Are Not Associated with Elevated Glutamate Transmission or High Alcohol Drinking.

BACKGROUND: A Grm2 cys407* stop codon mutation, which results in a loss of the metabotropic glutamate 2 (mGlu2) receptor protein, was identified as being associated with high alcohol drinking by alcohol-preferring (P) rats. The objectives of the current study were to characterize the effects of reduced levels of mGlu2 receptors on glutamate transmission and alcohol drinking. METHODS: Quantitative no-net-flux microdialysis was used to test the hypothesis that basal extracellular glutamate levels in the prelimbic (PL) cortex and nucleus accumbens shell (NACsh) will be higher in P than Wistar rats. A lentiviral-delivered short-hairpin RNA (shRNA)-mediated knockdown was used to test the hypothesis that reduced levels of mGlu2 receptors within the PL cortex will increase voluntary alcohol drinking by Wistar rats. A linear regression analysis was used to test the hypothesis that there will be a significant correlation between the Grm2 cys407* mutation and level of alcohol intake. RESULTS: Extracellular glutamate concentrations within the PL cortex (3.6 ± 0.6 vs. 6.4 ± 0.6 µM) and NACsh (3.2 ± 0.4 vs. 6.6 ± 0.6 µM) were significantly lower in female P than female Wistar rats. Western blot detected the presence of mGlu2 receptors in these regions of female Wistar rats, but not female P rats. Micro-infusion of shRNAs into the PL cortex significantly reduced local mGlu2 receptor levels (by 40%), but did not alter voluntary alcohol drinking in male Wistar rats. In addition, there was no significant correlation between the Grm2 mutation and alcohol intake in 36 rodent lines (r = 0.29, p > 0.05). CONCLUSIONS: Collectively, these results suggest a lack of association between the loss of mGlu2 receptors and glutamate transmission in the NACsh and PL cortex of female P rats, and between the level of mGlu2 receptors in the PL cortex and alcohol drinking of male Wistar rats.

Gene Expression Changes in Glutamate and GABA-A Receptors, Neuropeptides, Ion Channels, and Cholesterol Synthesis in the Periaqueductal Gray Following Binge-Like Alcohol Drinking by Adolescent Alcohol-Preferring (P) Rats.

BACKGROUND: Binge drinking of alcohol during adolescence is a serious public health concern with long-term consequences, including increased pain, fear, and anxiety. The periaqueductal gray (PAG) is involved in processing pain, fear, and anxiety. The effects of adolescent binge drinking on gene expression in this region have yet to be studied. METHODS: Male adolescent alcohol-preferring (P) rats were exposed to repeated binge drinking (three 1-hour sessions/d during the dark/cycle, 5 days/wk for 3 weeks starting at 28 days of age; ethanol intakes of 2.5 to 3 g/kg/session). We used RNA sequencing to assess the effects of ethanol intake on gene expression. RESULTS: Ethanol significantly altered the expression of 1,670 of the 12,123 detected genes: 877 (53%) decreased. In the glutamate system, 23 genes were found to be altered, including reduction in 7 of 10 genes for metabotropic and NMDA receptors. Subunit changes in the NMDA receptor may make it less sensitive to ethanol. Changes in GABAA genes would most likely increase the ability of the PAG to produce tonic inhibition. Five serotonin receptor genes, 6 acetylcholine receptor genes, and 4 glycine receptor genes showed decreased expression in the alcohol-drinking rats. Opioid genes (e.g., Oprk1, Oprm1) and genes for neuropeptides linked to anxiety and panic behaviors (e.g., Npy1r) had mostly decreased expression. Genes for 27 potassium, 10 sodium, and 5 calcium ion channels were found to be differentially expressed. Nine genes in the cholesterol synthesis pathway had decreased expression, including Hmgcr, encoding the rate-limiting enzyme. Genes involved in the production of myelin also had decreased expression. CONCLUSIONS: The results demonstrate that binge alcohol drinking during adolescence produces developmental changes in the expression of key genes within the PAG; many of these changes point to increased susceptibility to pain, fear, and anxiety, which could contribute to excessive drinking to relieve these negative effects.

The reinforcing effects of ethanol within the posterior ventral tegmental area depend on dopamine neurotransmission to forebrain cortico-limbic systems.

Ethanol can be self-infused directly into the posterior ventral tegmental area (pVTA) and these effects involve activation of local dopamine neurons. However, the neuro-circuitry beyond the pVTA involved in these reinforcing effects has not been explored. Intra-pVTA microinjection of ethanol increases dopamine release in the nucleus accumbens (NAC), medial prefrontal cortex (mPFC) and ventral pallidum (VP). The present study tested the hypothesis that the reinforcing effects of ethanol within the pVTA involve the activation of dopamine projections from the pVTA to the NAC, VP and mPFC. Following the acquisition of self-infusions of 200 mg% ethanol into the pVTA, either the dopamine D2 receptor antagonist sulpiride (0, 10 or 100 µM) or the D1 receptor antagonist SCH-23390 (0, 10 or 100 µM) was microinjected into the ipsilateral NAC shell (NACsh), NAC core (NACcr), VP or mPFC immediately prior to the self-infusion sessions to assess the involvement of the different dopamine projections in the reinforcing effects of ethanol. Microinjection of each compound at higher concentration into the NACsh, VP or mPFC, but not the NACcr, significantly reduced the responses on the active lever (from 40-50 to approximately 20 responses). These results indicate that activation of dopamine receptors in the NACsh, VP or mPFC, but not the NACcr, is involved in mediating the reinforcing effects of ethanol in the pVTA, suggesting that the 'alcohol reward' neuro-circuitry consist of, at least in part, activation of the dopamine projections from the pVTA to the NACsh, VP and mPFC.

Selective breeding for high alcohol preference increases the sensitivity of the posterior VTA to the reinforcing effects of nicotine.

The rate of codependency for alcohol and nicotine is extremely high. Numerous studies have indicated that there is a common genetic association for alcoholism and nicotine dependency. The current experiments examined whether selective breeding for high alcohol preference in rats may be associated with increased sensitivity of the posterior ventral tegmental area (pVTA) to the reinforcing properties of nicotine. In addition, nicotine can directly bind to the serotonin-3 (5-HT3 ) receptor, which has been shown to mediate the reinforcing properties of other drugs of abuse within the pVTA Wistar rats were assigned to groups that were allowed to self-infuse 0, 10, 50, 100, 200, 400 or 800 µM nicotine in two-lever (active and inactive) operant chambers. P rats were allowed to self-infuse 0, 1, 10, 50 or 100 µM nicotine. Co-infusion of 5-HT3 receptor antagonists with nicotine into the pVTA was also determined. P rats self-infused nicotine at lower concentrations than required to support self-administration in Wistar rats. In addition, P rats received more self-infusions of 50 and 100 µM nicotine than Wistar rats; including a 5HT3 receptor antagonist (LY-278,584 or zacopride) with nicotine reduced responding on the active lever. Overall, the data support an association between selective breeding for high alcohol preference and increased sensitivity of the pVTA to the reinforcing properties of nicotine. In addition, the data suggest that activation of 5HT3 receptors may be required to maintain the local reinforcing actions of nicotine within the pVTA.

Decouple Ego-View Motions for Predicting Pedestrian Trajectory and Intention.

Pedestrian trajectory prediction is a critical component of autonomous driving in urban environments, allowing vehicles to anticipate pedestrian movements and facilitate safer interactions. While egocentric-view-based algorithms can reduce the sensing and computation burdens of 3D scene reconstruction, accurately predicting pedestrian trajectories and interpreting their intentions from this perspective requires a better understanding of the coupled vehicle (camera) and pedestrian motions, which has not been adequately addressed by existing models. In this paper, we present a novel egocentric pedestrian trajectory prediction approach that uses a two-tower structure and multi-modal inputs. One tower, the vehicle module, receives only the initial pedestrian position and ego-vehicle actions and speed, while the other, the pedestrian module, receives additional prior pedestrian trajectory and visual features. Our proposed action-aware loss function allows the two-tower model to decompose pedestrian trajectory predictions into two parts, caused by ego-vehicle movement and pedestrian movement, respectively, even when only trained on combined ego-view motions. This decomposition increases model flexibility and provides a better estimation of pedestrian actions and intentions, enhancing overall performance. Experiments on three publicly available benchmark datasets show that our proposed model outperforms all existing algorithms in ego-view pedestrian trajectory prediction accuracy.

ResSAT: Enhancing Spatial Transcriptomics Prediction from H&E- Stained Histology Images with Interactive Spot Transformer.

Spatial transcriptomics (ST) revolutionizes RNA quantification with high spatial resolution. Hematoxylin and eosin (H&E) images, the gold standard in medical diagnosis, offer insights into tissue structure, correlating with gene expression patterns. Current methods for predicting spatial gene expression from H&E images often overlook spatial relationships. We introduce ResSAT (Residual networks - Self-Attention Transformer), a framework generating spatially resolved gene expression profiles from H&E images by capturing tissue structures and using a self-attention transformer to enhance prediction.Benchmarking on 10× Visium datasets, ResSAT significantly outperformed existing methods, promising reduced ST profiling costs and rapid acquisition of numerous profiles.

Fluorocitrate inhibition of astrocytes reduces nicotine self-administration and alters extracellular levels of glutamate and dopamine within the nucleus accumbens in male wistar rats.

Emerging evidence suggests an important role of astrocytes in mediating behavioral and molecular effects of commonly misused drugs. Passive exposure to nicotine alters molecular, morphological, and functional properties of astrocytes. However, a potential involvement of astrocytes in nicotine reinforcement remains largely unexplored. The overall hypothesis tested in the current study is that astrocytes play a critical role in nicotine reinforcement. Protein levels of the astrocyte marker glial fibrillary acidic protein (GFAP) were examined in key mesocorticolimbic regions following chronic nicotine intravenous self-administration. Fluorocitrate, a metabolic inhibitor of astrocytes, was tested for its effects on behaviors related to nicotine reinforcement and relapse. Effects of fluorocitrate on extracellular neurotransmitter levels, including glutamate, GABA, and dopamine, were determined with microdialysis. Chronic nicotine intravenous self-administration increased GFAP expression in the nucleus accumbens core (NACcr), but not other key mesocorticolimbic regions, compared to saline intravenous self-administration. Both intra-ventricular and intra-NACcr microinjection of fluorocitrate decreased nicotine self-administration. Intra-NACcr fluorocitrate microinjection also inhibited cue-induced reinstatement of nicotine seeking. Local perfusion of fluorocitrate decreased extracellular glutamate levels, elevated extracellular dopamine levels, but did not alter extracellular GABA levels in the NACcr. Fluorocitrate did not alter basal locomotor activity. These results indicate that nicotine reinforcement upregulates the astrocyte marker GFAP expression in the NACcr, metabolic inhibition of astrocytes attenuates nicotine reinforcement and relapse, and metabolic inhibition of astrocytes disrupts extracellular dopamine and glutamate transmission. Overall, these findings suggest that astrocytes play an important role in nicotine reinforcement and relapse, potentially through regulation of extracellular glutamate and dopamine neurotransmission.

Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies.

Functional connectivity (FC) as derived from fMRI has emerged as a pivotal tool in elucidating the intricacies of various psychiatric disorders and delineating the neural pathways that underpin cognitive and behavioral dynamics inherent to the human brain. While Graph Neural Networks (GNNs) offer a structured approach to represent neuroimaging data, they are limited by their need for a predefined graph structure to depict associations between brain regions, a detail not solely provided by FCs. To bridge this gap, we introduce the Gated Graph Transformer (GGT) framework, designed to predict cognitive metrics based on FCs. Empirical validation on the Philadelphia Neurodevelopmental Cohort (PNC) underscores the superior predictive prowess of our model, further accentuating its potential in identifying pivotal neural connectivities that correlate with human cognitive processes.

CLCLSA: Cross-omics linked embedding with contrastive learning and self attention for integration with incomplete multi-omics data.

Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding etiology of complex genetic diseases. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phenotypes. However, one obstacle faced when performing multi-omics data integration is the existence of unpaired multi-omics data due to instrument sensitivity and cost. Studies may fail if certain aspects of the subjects are missing or incomplete. In this paper, we propose a deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention (CLCLSA). Utilizing complete multi-omics data as supervision, the model employs cross-omics autoencoders to learn the feature representation across different types of biological data. The multi-omics contrastive learning is employed, which maximizes the mutual information between different types of omics. In addition, the feature-level self-attention and omics-level self-attention are employed to dynamically identify the most informative features for multi-omics data integration. Finally, a Softmax classifier is employed to perform multi-omics data classification. Extensive experiments were conducted on four public multi-omics datasets. The experimental results indicate that our proposed CLCLSA produces promising results in multi-omics data classification using both complete and incomplete multi-omics data.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevel-opmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

Microinjections of acetaldehyde or salsolinol into the posterior ventral tegmental area increase dopamine release in the nucleus accumbens shell.

BACKGROUND: Published findings indicate that acetaldehyde (ACD; the first metabolite of ethanol [EtOH]) and salsolinol (SAL; formed through the nonenzymatic condensation of ACD and dopamine [DA]) can be formed following EtOH consumption. Both ACD and SAL exhibit reinforcing properties within the posterior ventral tegmental area (pVTA) and both exhibit an inverted "U-shaped" dose-response curve. The current study was undertaken to examine the dose-response effects of microinjections of ACD or SAL into the pVTA on DA efflux in the nucleus accumbens shell (AcbSh). METHODS: For the first experiment, separate groups of male Wistar rats received pulse microinjections of artificial cerebrospinal fluid (aCSF) or 12-, 23-, or 90-µM ACD into the pVTA, while extracellular DA levels were concurrently measured in the AcbSh. The second experiment was similarly conducted, except rats were given microinjections of aCSF or 0.03-, 0.3-, 1.0-, or 3.0-µM SAL, while extracellular levels of DA were measured in the AcbSh. RESULTS: Both ACD and SAL produced a dose-dependent inverted "U-shaped" response on DA release in the AcbSh, with 23-µM ACD (200% baseline) and 0.3-µM SAL (300% baseline) producing maximal peak responses with higher concentrations of ACD (90 µM) and SAL (3.0 µM) producing significantly lower DA efflux. CONCLUSIONS: The findings from the current study indicate that local application of intermediate concentrations of ACD and SAL stimulated DA neurons in the pVTA, whereas higher concentrations may be having secondary effects within the pVTA that inhibit DA neuronal activity. The present results parallel the studies on the reinforcing effects of ACD and SAL in the pVTA and support the idea that the reinforcing effects of ACD and SAL within the pVTA are mediated by activating DA neurons.

Alcohol drinking and deprivation alter basal extracellular glutamate concentrations and clearance in the mesolimbic system of alcohol-preferring (P) rats.

The present study determined the effects of voluntary ethanol drinking and deprivation on basal extracellular glutamate concentrations and clearance in the mesolimbic system and tested the hypothesis that chronic ethanol drinking would persistently increase basal glutamate neurotransmission. Three groups of alcohol-preferring (P) rats were used: 'water group (WG),' 'ethanol maintenance group (MG; 24-hour free choice water versus 15% ethanol)' and 'ethanol deprivation group (DG; 2 weeks of deprivation).' Quantitative microdialysis and Western blots were conducted to measure basal extracellular glutamate concentrations, clearance and proteins associated with glutamate clearance. Chronic alcohol drinking produced a 70-100% increase of basal extracellular glutamate concentrations in the posterior ventral tegmental area (4.0 versus 7.0 µM) and nucleus accumbens shell (3.0 versus 6.0 µM). Glutamate clearances were reduced by 30-40% in both regions of MG rats compared with WG rats. In addition, Western blots revealed a 40-45% decrease of excitatory amino transporter 1 (EAAT1) protein, but no significant changes in the levels of EAAT2 or cystine-glutamate antiporter in these regions of MG versus WG rats. The enhanced glutamate concentrations returned to control levels, accompanied by a recovery of glutamate clearance following deprivation. These results indicated that chronic alcohol drinking enhanced extracellular glutamate concentrations in the mesolimbic system, as a result, in part, of reduced clearance, suggesting that enhanced glutamate neurotransmission may contribute to the maintenance of alcohol drinking. However, because the increased glutamate levels returned to normal after deprivation, elevated glutamate neurotransmission may not contribute to the initiation of relapse drinking.

Maximum Structural Generation Discrepancy for Unsupervised Domain Adaptation.

Unsupervised domain adaptation (UDA) has recently become an appealing research topic in visual recognition, since it exploits all accessible well-labeled source data to train a model with high generalization on target domain without any annotations. However, due to the significant domain discrepancy, the bottleneck for UDA is to learn effective domain-invariant feature representations. To fight off such an obstacle, we propose a novel cross-domain learning framework named Maximum Structural Generation Discrepancy (MSGD) to accurately estimate and mitigate domain shift via introducing an intermediate domain. First, the cross-domain topological structure is explored to propagate target samples to generate a novel intermediate domain paired with the specific source instances. The intermediate domain plays as the bridge to gradually reduce distribution divergence across source and target domains. Concretely, the similar category semantic across source and intermediate features tends to naturally conduct the class-level alignment on eliminating their domain shift. In terms of no target annotation, the domain-level alignment manner is suitable to narrow down the distance between intermediate and target domains. Moreover, to produce high-quality generative instances, we develop the class-driven collaborative translation (CDCT) module to generate class-consistent cross-domain samples in each mini-batch with the assistance of pseudo-labels. Extensive experimental analyses on five domain adaptation benchmarks demonstrate the effectiveness of our MSGD on solving UDA problem.

Generative Inference Network for Imbalanced Domain Generalization.

Domain generalization (DG) aims to learn transferable knowledge from multiple source domains and generalize it to the unseen target domain. To achieve such expectation, the intuitive solution is to seek domain-invariant representations via generative adversarial mechanism or minimization of cross-domain discrepancy. However, the widespread imbalanced data scale problem across source domains and category in real-world applications becomes the key bottleneck of improving generalization ability of model due to its negative effect on learning the robust classification model. Motivated by this observation, we first formulate a practical and challenging imbalance domain generalization (IDG) scenario, and then propose a straightforward but effective novel method generative inference network (GINet), which augments reliable samples for minority domain/category to promote discriminative ability of the learned model. Concretely, GINet utilizes the available cross-domain images from the identical category and estimates their common latent variable, which derives to discover domain-invariant knowledge for unseen target domain. According to these latent variables, our GINet further generates more novel samples with optimal transport constraint and deploys them to enhance the desired model with more robustness and generalization ability. Considerable empirical analysis and ablation studies on three popular benchmarks under normal DG and IDG setups suggests the advantage of our method over other DG methods on elevating model generalization. The source code is available in GitHub https://github.com/HaifengXia/IDG.

Characterization of proteoform post-translational modifications by top-down and bottom-up mass spectrometry in conjunction with UniProt annotations.

Many proteoforms can be produced from a gene due to genetic mutations, alternative splicing, post-translational modifications (PTMs), and other variations. PTMs in proteoforms play critical roles in cell signaling, protein degradation, and other biological processes. Mass spectrometry (MS) is the primary technique for investigating PTMs in proteoforms, and two alternative MS approaches, top-down and bottom-up, have complementary strengths. The combination of the two approaches has the potential to increase the sensitivity and accuracy in PTM identification and characterization. In addition, protein and PTM knowledgebases, such as UniProt, provide valuable information for PTM characterization and validation. Here, we present a software pipeline called PTM-TBA (PTM characterization by Top-down, Bottom-up MS and Annotations) for identifying and localizing PTMs in proteoforms by integrating top-down and bottom-up MS as well as UniProt annotations. We identified 1,662 mass shifts from a top-down MS data set of SW480 cells, 545 (33%) of which were matched to 12 common PTMs, and 351 of which were localized. PTM-TBA validated 346 of the 1,662 mass shifts using UniProt annotations or a bottom-up MS data set of SW480 cells.