Multi-View Integrative Approach For Imputing Short-Chain Fatty Acids and Identifying Key factors predicting Blood SCFA.

Short-chain fatty acids (SCFAs) are the main metabolites produced by bacterial fermentation of dietary fiber within gastrointestinal tract. SCFAs produced by gut microbiotas (GMs) are absorbed by host, reach bloodstream, and are distributed to different organs, thus influencing host physiology. However, due to the limited budget or the poor sensitivity of instruments, most studies on GMs have incomplete blood SCFA data, limiting our understanding of the metabolic processes within the host. To address this gap, we developed an innovative multi-task multi-view integrative approach (M2AE, Multi-task Multi-View Attentive Encoders), to impute blood SCFA levels using gut metagenomic sequencing (MGS) data, while taking into account the intricate interplay among the gut microbiome, dietary features, and host characteristics, as well as the nuanced nature of SCFA dynamics within the body. Here, each view represents a distinct type of data input (i.e., gut microbiome compositions, dietary features, or host characteristics). Our method jointly explores both view-specific representations and cross-view correlations for effective predictions of SCFAs. We applied M2AE to two in-house datasets, which both include MGS and blood SCFAs profiles, host characteristics, and dietary features from 964 subjects and 171 subjects, respectively. Results from both of two datasets demonstrated that M2AE outperforms traditional regression-based and neural-network based approaches in imputing blood SCFAs. Furthermore, a series of gut bacterial species (e.g., Bacteroides thetaiotaomicron and Clostridium asparagiforme), host characteristics (e.g., race, gender), as well as dietary features (e.g., intake of fruits, pickles) were shown to contribute greatly to imputation of blood SCFAs. These findings demonstrated that GMs, dietary features and host characteristics might contribute to the complex biological processes involved in blood SCFA productions. These might pave the way for a deeper and more nuanced comprehension of how these factors impact human health.

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.

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.

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.

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.

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.

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.

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.

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.

Deep learning for AI-based diagnosis of skin-related neglected tropical diseases: A pilot study.

BACKGROUND: Deep learning, which is a part of a broader concept of artificial intelligence (AI) and/or machine learning has achieved remarkable success in vision tasks. While there is growing interest in the use of this technology in diagnostic support for skin-related neglected tropical diseases (skin NTDs), there have been limited studies in this area and fewer focused on dark skin. In this study, we aimed to develop deep learning based AI models with clinical images we collected for five skin NTDs, namely, Buruli ulcer, leprosy, mycetoma, scabies, and yaws, to understand how diagnostic accuracy can or cannot be improved using different models and training patterns. METHODOLOGY: This study used photographs collected prospectively in Côte d'Ivoire and Ghana through our ongoing studies with use of digital health tools for clinical data documentation and for teledermatology. Our dataset included a total of 1,709 images from 506 patients. Two convolutional neural networks, ResNet-50 and VGG-16 models were adopted to examine the performance of different deep learning architectures and validate their feasibility in diagnosis of the targeted skin NTDs. PRINCIPAL FINDINGS: The two models were able to correctly predict over 70% of the diagnoses, and there was a consistent performance improvement with more training samples. The ResNet-50 model performed better than the VGG-16 model. A model trained with PCR confirmed cases of Buruli ulcer yielded 1-3% increase in prediction accuracy across all diseases, except, for mycetoma, over a model which training sets included unconfirmed cases. CONCLUSIONS: Our approach was to have the deep learning model distinguish between multiple pathologies simultaneously-which is close to real-world practice. The more images used for training, the more accurate the diagnosis became. The percentages of correct diagnosis increased with PCR-positive cases of Buruli ulcer. This demonstrated that it may be better to input images from the more accurately diagnosed cases in the training models also for achieving better accuracy in the generated AI models. However, the increase was marginal which may be an indication that the accuracy of clinical diagnosis alone is reliable to an extent for Buruli ulcer. Diagnostic tests also have their flaws, and they are not always reliable. One hope for AI is that it will objectively resolve this gap between diagnostic tests and clinical diagnoses with the addition of another tool. While there are still challenges to be overcome, there is a potential for AI to address the unmet needs where access to medical care is limited, like for those affected by skin NTDs.

Methoxsalen inhibited the acquisition of nicotine self-administration: attenuation by cotinine replacement in rats.

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. Cotinine supported self-administration and rats with a history of intravenous self-administration of cotinine exhibited relapse-like drug-seeking behavior, suggesting cotinine may also be reinforcing. To date, a potential contribution of cotinine to nicotine reinforcement remains unknown. Nicotine metabolism is mainly catalyzed by hepatic CYP2B1 enzyme in the rat and methoxsalen is a potent CYP2B1 inhibitor. The study tested the hypothesis that methoxsalen inbibits nicotine metabolism and self-administration, and that cotinine replacement attenuates the inhibitory effects of methoxsalen. Acute methoxsalen decreased plasma cotinine levels and increased nicotine levels following subcutaneous nicotine injection. Repeated methoxsalen reduced the acquisition of nicotine self-administration, leading to fewer nicotine infusions, disruption of lever differentiation, smaller total nicotine intake, and lower plasma cotinine levels. On the other hand, methoxsalen did not alter nicotine self-administration during the maintenance phase despite great reduction of plasma cotinine levels. Cotinine replacement by mixing cotinine with nicotine for self-administration dose-dependently increased plasma cotinine levels, counteracted effects of methoxsalen, and enhanced the acquisition of self-administration. Neither basal nor nicotine-induced locomotor activity was altered by methoxsalen. These results indicate that methoxsalen depressed cotinine formation from nicotine and the acquisition of nicotine self-administration, and that replacement of plasma cotinine attenuated the inhibitory effects of methoxsalen, suggesting that cotinine may contribute to the development of nicotine reinforcement.

Angle Basis: a Generative Model and Decomposition for Functional Connectivity.

Functional connectivity (FC) is one of the most common inputs to fMRI-based predictive models, due to a combination of its simplicity and robustness. However, there may be a lack of theoretical models for the generation of FC. In this work, we present a straightforward decomposition of FC into a set of basis states of sine waves with an additional jitter component. We show that the decomposition matches the predictive ability of FC after including 5-10 bases. We also find that both the decomposition and its residual have approximately equal predictive value, and when combined into an ensemble, exceed the AUC of FC-based prediction by up to 5%. Additionally, we find the residual can be used for subject fingerprinting, with 97.3% same-subject, different-scan identifiability, compared to 62.5% for FC. Unlike PCA or Factor Analysis methods, our method does not require knowledge of a population to perform its decomposition; a single subject is enough. Our decomposition of FC into two equally-predictive components may lead to a novel appreciation of group differences in patient populations. Additionally, we generate synthetic patient FC based on user-specified characteristics such as age, sex, and disease diagnosis. By creating synthetic datasets or augmentations we may reduce the high financial burden associated with fMRI data acquisition.

CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data.

Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data. 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, which is used to maximize the mutual information between different types of omics, is employed before latent feature concatenation. 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. Extensive experiments were conducted on four public multi-omics datasets. The experimental results indicated that the proposed CLCLSA outperformed the state-of-the-art approaches for multi-omics data classification using incomplete multiomics data.

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.

Marginalized Augmented Few-Shot Domain Adaptation.

Domain adaptation (DA) has recently drawn a lot of attention, as it facilitates unlabeled target learning by borrowing knowledge from an external source domain. Most existing DA solutions seek to align feature representations between the labeled source and unlabeled target data. However, the scarcity of target data easily results in negative transfer, as it misleads the cross DA to the dominance of the source. To address the challenging few-shot domain adaptation (FSDA) problem, in this article, we propose a novel marginalized augmented FSDA (MAF) approach to address the cross-domain distribution disparity and insufficiency of target data simultaneously. On the one hand, cross-domain continuity augmentation (CCA) synthesizes abundant intermediate patterns across domains leading to a continuous domain-invariant latent space. On the other hand, sufficient source-supervised semantic augmentation (SSA) is explored to progressively diversify the conditional distribution within and across domains. Moreover, the proposed augmentation strategies are implemented efficiently via an expected transferable cross-entropy (CE) loss over the augmented distribution instead of explicit data synthesis, and minimizing the upper bound of the expected loss introduces negligible extra computing cost. Experimentally, our method outperforms the state of the art in various FSDA benchmarks, which demonstrates the effectiveness and contribution of our work. Our source code is provided at https://github.com/scottjingtt/MAF.git.

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.

Inhibitory and excitatory alcohol-seeking cues distinct roles in behavior, neurochemistry, and mesolimbic pathway in alcohol preferring (P) rats.

Cues associated with alcohol use can readily enhance self-reported cravings for alcohol, which increases the likelihood of reusing alcohol. Understanding the neuronal mechanisms involved in alcohol-seeking behavior is important for developing strategies to treat alcohol use disorder. In all experiments, adult female alcohol-preferring (P) rats were exposed to three conditioned odor cues; CS+ associated with EtOH self-administration, CS- associated with the absence of EtOH (extinction training), and a CS0, a neutral stimulus. The data indicated that presentation of an excitatory conditioned cue (CS+) can enhance EtOH- seeking while the CS- can inhibit EtOH-seeking under multiple test conditions. Presentation of the CS+ activates a subpopulation of dopamine neurons within the interfascicular nucleus of the posterior ventral tegmental area (posterior VTA) and basolateral amygdala (BLA). Pharmacological inactivation of the BLA with GABA agonists inhibits the ability of the CS+ to enhance EtOH-seeking but does not alter context-induced EtOH-seeking or the ability of the CS- to inhibit EtOH-seeking. Presentation of the conditioned odor cues in a non-drug-paired environment indicated that presentation of the CS+ increased dopamine levels in the BLA. In contrast, presentation of the CS- decreased both glutamate and dopamine levels in the BLA. Further analysis revealed that presentation of a CS+ EtOH-associated conditioned cue activates GABA interneurons but not glutamate projection neurons. Overall, the data indicate that excitatory and inhibitory conditioned cues can contrarily alter EtOH-seeking behaviors and that different neurocircuitries are mediating these distinct cues in critical brain regions. Pharmacotherapeutics for craving should inhibit the CS+ and enhance the CS- neurocircuits.

CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data.

Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data. 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, which is used to maximize the mutual information between different types of omics, is employed before latent feature concatenation. 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. Extensive experiments were conducted on four public multi-omics datasets. The experimental results indicated that the proposed CLCLSA outperformed the state-of-the-art approaches for multi-omics data classification using incomplete multi-omics data.

Deep learning for AI-based diagnosis of skin-related neglected tropical diseases: a pilot study.

Background: Deep learning, which is a part of a broader concept of artificial intelligence (AI) and/or machine learning has achieved remarkable success in vision tasks. While there is growing interest in the use of this technology in diagnostic support for skin-related neglected tropical diseases (skin NTDs), there have been limited studies in this area and fewer focused on dark skin. In this study, we aimed to develop deep learning based AI models with clinical images we collected for five skin NTDs, namely, Buruli ulcer, leprosy, mycetoma, scabies, and yaws, to understand how diagnostic accuracy can or cannot be improved using different models and training patterns. Methodology: This study used photographs collected prospectively in Côte d'Ivoire and Ghana through our ongoing studies with use of digital health tools for clinical data documentation and for teledermatology. Our dataset included a total of 1,709 images from 506 patients. Two convolutional neural networks, ResNet-50 and VGG-16 models were adopted to examine the performance of different deep learning architectures and validate their feasibility in diagnosis of the targeted skin NTDs. Principal findings: The two models were able to correctly predict over 70% of the diagnoses, and there was a consistent performance improvement with more training samples. The ResNet-50 model performed better than the VGG-16 model. Model trained with PCR confirmed cases of Buruli ulcer yielded 1-3% increase in prediction accuracy over training sets including unconfirmed cases. Conclusions: Our approach was to have the deep learning model distinguish between multiple pathologies simultaneously - which is close to real-world practice. The more images used for training, the more accurate the diagnosis became. The percentages of correct diagnosis increased with PCR-positive cases of Buruli ulcer. This demonstrated that it may be better to input images from the more accurately diagnosed cases in the training models also for achieving better accuracy in the generated AI models. However, the increase was marginal which may be an indication that the accuracy of clinical diagnosis alone is reliable to an extent for Buruli ulcer. Diagnostic tests also have its flaws, and they are not always reliable. One hope for AI is that it will objectively resolve this gap between diagnostic tests and clinical diagnoses with addition of another tool. While there are still challenges to be overcome, there is a potential for AI to address the unmet needs where access to medical care is limited, like for those affected by skin NTDs. AUTHOR SUMMARY: The diagnosis of skin diseases depends in large part, though not exclusively on visual inspection. The diagnosis and management of these diseases is thus particularly amenable to teledermatology approaches. The widespread availability of cell phone technology and electronic information transfer provides new potential for access to health care in low-income countries, yet there are limited efforts targeting these neglected populations with dark skin and consequently limited availability of tools. In this study, we leveraged a collection of skin images gathered through a system of teledermatology in the West African countries of Côte d'Ivoire and Ghana, and applied deep learning, a form of artificial intelligence (AI) - to see if deep learning models can distinguish between different diseases and support their diagnosis. Skin-related neglected tropical diseases, or skin NTDs, prevail in these regions and were our target conditions: Buruli ulcer, leprosy, mycetoma, scabies, and yaws. The accuracy of prediction depended on the number of images that were fed into the model for training with marginal improvement using laboratory confirmed cases in training. Using more images and greater efforts in this area, it is possible that AI can help address the unmet needs where access to medical care is limited.