IMOVNN: incomplete multi-omics data integration variational neural networks for gut microbiome disease prediction and biomarker identification.

The gut microbiome has been regarded as one of the fundamental determinants regulating human health, and multi-omics data profiling has been increasingly utilized to bolster the deep understanding of this complex system. However, stemming from cost or other constraints, the integration of multi-omics often suffers from incomplete views, which poses a great challenge for the comprehensive analysis. In this work, a novel deep model named Incomplete Multi-Omics Variational Neural Networks (IMOVNN) is proposed for incomplete data integration, disease prediction application and biomarker identification. Benefiting from the information bottleneck and the marginal-to-joint distribution integration mechanism, the IMOVNN can learn the marginal latent representation of each individual omics and the joint latent representation for better disease prediction. Moreover, owing to the feature-selective layer predicated upon the concrete distribution, the model is interpretable and can identify the most relevant features. Experiments on inflammatory bowel disease multi-omics datasets demonstrate that our method outperforms several state-of-the-art methods for disease prediction. In addition, IMOVNN has identified significant biomarkers from multi-omics data sources.

Progress on network modeling and analysis of gut microecology: a review.

The gut microecological network is a complex microbial community within the human body that plays a key role in linking dietary nutrition and host physiology. To understand the complex relationships among microbes and their functions within this community, network analysis has emerged as a powerful tool. By representing the interactions between microbes and their associated omics data as a network, we can gain a comprehensive understanding of the ecological mechanisms that drive the human gut microbiota. In addition, the network-based approach provides a more intuitive analysis of the gut microbiota, simplifying the study of its complex dynamics and interdependencies. This review provides a comprehensive overview of the methods used to construct and analyze networks in the context of gut microecological background. We discuss various types of network modeling approaches, including co-occurrence networks, causal networks, dynamic networks, and multi-omics networks, and describe the analytical techniques used to identify important network properties. We also highlight the challenges and limitations of network modeling in this area, such as data scarcity and heterogeneity, and provide future research directions to overcome these limitations. By exploring these network-based methods, researchers can gain valuable insights into the intricate relationships and functional roles of microbial communities within the gut, ultimately advancing our understanding of the gut microbiota's impact on human health.

The therapeutic potential of dietary intervention: based on the mechanism of a tryptophan derivative-indole propionic acid on metabolic disorders.

Tryptophan (TRP) contributes to individual immune homeostasis and good condition via three complex metabolism pathways (5-hydroxytryptamine (5-HT), kynurenine (KP), and gut microbiota pathway). Indole propionic acid (IPA), one of the TRP derivatives of the microbiota pathway, has raised more attention because of its impact on metabolic disorders. Here, we retrospect increasing evidence that TRP metabolites/IPA derived from its proteolysis impact host health and disease. IPA can activate the immune system through aryl hydrocarbon receptor (AHR) and/or Pregnane X receptor (PXR) as a vital mediator among diet-caused host and microbe cross-talk. Different levels of IPA in systemic circulation can predict the risk of NAFLD, T2DM, and CVD. IPA is suggested to alleviate cognitive impairment from oxidative damage, reduce gut inflammation, inhibit lipid accumulation and attenuate the symptoms of NAFLD, putatively enhance the intestinal epithelial barrier, and maintain intestinal homeostasis. Now, we provide a general description of the relationships between IPA and various physiological and pathological processes, which support an opportunity for diet intervention for metabolic diseases.

Collaborative Multiple Players to Address Label Sparsity in Quality Prediction of Batch Processes.

For decades, soft sensors have been extensively renowned for their efficiency in real-time tracking of expensive variables for advanced process control. However, despite the diverse efforts lavished on enhancing their models, the issue of label sparsity when modeling the soft sensors has always posed challenges across various processes. In this paper, a fledgling technique, called co-training, is studied for leveraging only a small ratio of labeled data, to hone and formulate a more advantageous framework in soft sensor modeling. Dissimilar to the conventional routine where only two players are employed, we investigate the efficient number of players in batch processes, making a multiple-player learning scheme to assuage the sparsity issue. Meanwhile, a sliding window spanning across both time and batch direction is used to aggregate the samples for prediction, and account for the unique 2D correlations among the general batch process data. Altogether, the forged framework can outperform the other prevalent methods, especially when the ratio of unlabeled data is climbing up, and two case studies are showcased to demonstrate its effectiveness.

Thorectidiol A Isolated from the Marine Sponge Dactylospongia elegans Disrupts Interactions of the SARS-CoV-2 Spike Receptor Binding Domain with the Host ACE2 Receptor.

Thorectidiols isolated from the marine sponge Dactylospongia elegans (family Thorectidae, order Dictyoceratida) collected in Papua New Guinea are a family of symmetrical and unsymmetrical dimeric biphenyl meroterpenoid stereoisomers presumed to be products of oxidative phenol coupling of a co-occurring racemic monomer, thorectidol (3). One member of the family, thorectidiol A (1), has been isolated in its natural form, and its structure has been elucidated by analysis of NMR, MS, and ECD data. Acetylation of the sponge extract facilitated isolation of additional thorectidiol diacetate stereoisomers and the isolation of the racemic monomer thorectidol acetate (6). Racemic thorectidiol A (1) showed selective inhibition of the SARS-CoV-2 spike receptor binding domain (RBD) interaction with the host ACE2 receptor with an IC50 = 1.0 ± 0.7 µM.

Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks.

With the rapid development of the intelligent transportation system (ITS), routing in vehicular ad hoc networks (VANETs) has become a popular research topic. The high mobility of vehicles in urban streets poses serious challenges to routing protocols and has a significant impact on network performance. Existing topology-based routing is not suitable for highly dynamic VANETs, thereby making location-based routing protocols the preferred choice due to their scalability. However, the working environment of VANETs is complex and interference-prone. In wireless-network communication, the channel contention introduced by the high density of vehicles, coupled with urban structures, significantly increases the difficulty of designing high-quality communication protocols. In this context, compared to topology-based routing protocols, location-based geographic routing is widely employed in VANETs due to its avoidance of the route construction and maintenance phases. Considering the characteristics of VANETs, this paper proposes a novel environment-aware adaptive reinforcement routing (EARR) protocol aimed at establishing reliable connections between source and destination nodes. The protocol adopts periodic beacons to perceive and explore the surrounding environment, thereby constructing a local topology. By applying reinforcement learning to the vehicle network's route selection, it adaptively adjusts the Q table through the perception of multiple metrics from beacons, including vehicle speed, available bandwidth, signal-reception strength, etc., thereby assisting the selection of relay vehicles and alleviating the challenges posed by the high dynamics, shadow fading, and limited bandwidth in VANETs. The combination of reinforcement learning and beacons accelerates the establishment of end-to-end routes, thereby guiding each vehicle to choose the optimal next hop and forming suboptimal routes throughout the entire communication process. The adaptive adjustment feature of the protocol enables it to address sudden link interruptions, thereby enhancing communication reliability. In experiments, the EARR protocol demonstrates significant improvements across various performance metrics compared to existing routing protocols. Throughout the simulation process, the EARR protocol maintains a consistently high packet-delivery rate and throughput compared to other protocols, as well as demonstrates stable performance across various scenarios. Finally, the proposed protocol demonstrates relatively consistent standardized latency and low overhead in all experiments.

Machine learning framework for gut microbiome biomarkers discovery and modulation analysis in large-scale obese population.

BACKGROUND: The gut microbiome has proven to be an important factor affecting obesity; however, it remains a challenge to identify consistent biomarkers across geographic locations and perform precisely targeted modulation for obese individuals. RESULTS: This study proposed a systematic machine learning framework and applied it to 870 human stool metagenomes across five countries to obtain comprehensive regional shared biomarkers and conduct a personalized modulation analysis. In our pipeline, a heterogeneous ensemble feature selection diagram is first developed to determine an optimal subset of biomarkers through the aggregation of multiple techniques. Subsequently, a deep reinforcement learning method was established to alter the targeted composition to the desired healthy target. In this manner, we can realize personalized modulation by counterfactual inference. Consequently, a total of 42 species were identified as regional shared biomarkers, and they showed good performance in distinguishing obese people from the healthy group (area under curve (AUC) =0.85) when demonstrated on validation datasets. In addition, by pooling all counterfactual explanations, we found that Akkermansia muciniphila, Faecalibacterium prausnitzii, Prevotella copri, Bacteroides dorei, Bacteroides eggerthii, Alistipes finegoldii, Alistipes shahii, Eubacterium sp. _CAG_180, and Roseburia hominis may be potential broad-spectrum targets with consistent modulation in the multi-regional obese population. CONCLUSIONS: This article shows that based on our proposed machine-learning framework, we can obtain more comprehensive and accurate biomarkers and provide modulation analysis for the obese population. Moreover, our machine-learning framework will also be very useful for other researchers to further obtain biomarkers and perform counterfactual modulation analysis in different diseases.

Biomolecular Regulation of Zinc Deposition to Achieve Ultra-Long Life and High-Rate Zn Metal Anodes.

Aqueous zinc-ion batteries (ZIBs) have been extensively studied due to their inherent safety and high energy density for large-scale energy storage. However, the practical application is significantly limited by the growing Zn dendrites on metallic Zn anode during cycling. Herein, an environmental biomolecular electrolyte additive, fibroin (FI), is proposed to guide the homogeneous Zn deposition and stabilize Zn anode. This work demonstrates that the FI molecules with abundant electron-rich groups (NH, OH, and CO) can anchor on Zn anode surface to provide more nucleation sites and suppress the side reactions, and the strong interaction with water molecules can simultaneously regulate the Zn2+ coordination environment facilitating the uniform deposition of Zn. As a consequence, only 0.5 wt% FI additive enables a highly reversible Zn plating/stripping over 4000 h at 1 mA cm-2 , indicating a sufficient advance in performance over state-of-the-art Zn anodes. Furthermore, when applied to a full battery (NaVO/Zn), the cell exhibits excellent capacity retention of 98.4% after 1000 cycles as well as high Coulombic efficiency of 99%, whereas the cell only operates for 68 cycles without FI additive. This work offers a non-toxic, low-cost, effective additive strategy to solve dendrites problems and achieve long-life and high-performance rechargeable aqueous ZIBs.

Limosilactobacillus reuteri Attenuates Atopic Dermatitis via Changes in Gut Bacteria and Indole Derivatives from Tryptophan Metabolism.

Gut bacteria are closely associated with the development of atopic dermatitis (AD) due to their immunoregulatory function. Indole derivatives, produced by gut bacteria metabolizing tryptophan, are ligands to activate the aryl hydrocarbon receptor (AHR), which plays a critical role in attenuating AD symptoms. Limosilactobacillus reuteri, a producer of indole derivatives, regulates mucosal immunity via activating the AHR signaling pathway. However, the effective substance and mechanism of L. reuteri in the amelioration of AD remain to be elucidated. In this research, we found that L. reuteri DYNDL22M62 significantly improved AD-like symptoms in mice by suppressing IgE levels and the expressions of thymic stromal lymphopoietin (TSLP), IL-4, and IL-5. L. reuteri DYNDL22M62 induced an increase in the production of indole lactic acid (ILA) and indole propionic acid (IPA) via targeted tryptophan metabolic analysis and the expression of AHR in mice. Furthermore, L. reuteri DYNDL22M62 increased the proportions of Romboutsia and Ruminococcaceae NK4A214 group, which were positively related to ILA, but decreased Dubosiella, which was negatively related to IPA. Collectively, L. reuteri DYNDL22M62 with the role of modulating gut bacteria and the production of indole derivatives may attenuate AD via activating AHR in mice.

Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study.

This work considers industrial process monitoring using a variational autoencoder (VAE). As a powerful deep generative model, the variational autoencoder and its variants have become popular for process monitoring. However, its monitoring ability, especially its fault diagnosis ability, has not been well investigated. In this paper, the process modeling and monitoring capabilities of several VAE variants are comprehensively studied. First, fault detection schemes are defined in three distinct ways, considering latent, residual, and the combined domains. Afterwards, to conduct the fault diagnosis, we first define the deep contribution plot, and then a deep reconstruction-based contribution diagram is proposed for deep domains under the fault propagation mechanism. In a case study, the performance of the process monitoring capability of four deep VAE models, namely, the static VAE model, the dynamic VAE model, and the recurrent VAE models (LSTM-VAE and GRU-VAE), has been comparatively evaluated on the industrial benchmark Tennessee Eastman process. Results show that recurrent VAEs with a deep reconstruction-based diagnosis mechanism are recommended for industrial process monitoring tasks.

A Glass-Ceramic with Accelerated Surface Reconstruction toward the Efficient Oxygen Evolution Reaction.

The effective non-precious metal catalysts toward the oxygen evolution reaction (OER) are highly desirable for electrochemical water splitting. Herein, we prepare a novel glass-ceramic (Ni1.5 Sn@triMPO4 ) by embedding crystalline Ni1.5 Sn nanoparticles into amorphous trimetallic phosphate (triMPO4 ) matrix. This unique crystalline-amorphous nanostructure synergistically accelerates the surface reconstruction to active Ni(Fe)OOH, due to the low vacancy formation energy of Sn in glass-ceramic and high adsorption energy of PO4 3- at the VO sites. Compared to the control samples, this dual-phase glass-ceramic exhibits a remarkably lowered overpotential and boosted OER kinetics after surface reconstruction, rivaling most of state-of-the-art electrocatalysts. The residual PO4 3- and intrinsic VO sites induce redistribution of electron states, thus optimizing the adsorption of OH* and OOH* intermediates on metal oxyhydroxides and promoting the OER activity.

[Risk factors for congenital anal atresia].

OBJECTIVE: To investigate the risk factors for the development of congenital anal atresia in neonates. METHODS: A total of 70 neonates who were admitted to 17 hospitals in Foshan, China from January 2011 to December 2014 were enrolled as case group, and another 70 neonates who were hospitalized during the same period and had no anal atresia or other severe deformities were enrolled as control group. Univariate and multivariate logistic regression analyses were used to investigate the risk factors for the development of congenital anal atresia. RESULTS: The univariate analysis revealed that the age of mothers, presence of oral administration of folic acid, infection during early pregnancy, and polyhydramnios, and sex of neonates showed significant differences between the case and control groups (P<0.05). The multivariate logistic regression analysis revealed that infection during early pregnancy (OR=18.776) and male neonates (OR=9.304) were risk factors for congenital anal atresia, and oral administration of folic acid during early pregnancy was the protective factor (OR=0.086). CONCLUSIONS: Infection during early pregnancy is the risk factor for congenital anal atresia, and male neonates are more likely to develop congenital anal atresia than female neonates. Supplementation of folic acid during early pregnancy can reduce the risk of congenital anal atresia.

Effects of Varied Sulfamethazine Dosage and Exposure Durations on Offspring Mice.

The development of antibiotics was a turning point in the history of medicine; however, their misuse and overuse have contributed to the current global epidemic of antibiotic resistance. According to epidemiological studies, early antibiotic exposure increases the risk of immunological and metabolic disorders. This study investigated the effects of exposure to different doses of sulfamethazine (SMZ) on offspring mice and compared the effects of exposure to SMZ on offspring mice in prenatal and early postnatal periods and continuous periods. Furthermore, the effects of SMZ exposure on the gut microbiota of offspring mice were analyzed using metagenome. According to the results, continuous exposure to high-dose SMZ caused weight gain in mice. IL-6, IL-17A, and IL-10 levels in the female offspring significantly increased after high-dose SMZ exposure. In addition, there was a significant gender difference in the impact of SMZ exposure on the gut microbiota of offspring: Continuous high-dose SMZ exposure significantly decreased the relative abundance of Ligilactobacillus murinus, Limosilactobacillus reuteri, Lactobacillus johnsonii, and Bifidobacterium pseudolongum (p < 0.05) in female offspring mice; however, these significant changes were not observed in male offspring mice.

Integrative analysis with microbial modelling and machine learning uncovers potential alleviators for ulcerative colitis.

Ulcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To identify the potential alleviators of UC, we employed an integrative analysis combining microbial community modeling with advanced machine learning techniques. Using metagenomics data sourced from the Integrated Human Microbiome Project, we constructed individualized microbiome community models for each participant. Our analysis highlighted a significant decline in both α and ß-diversity of strain-level microbial populations in UC subjects compared to controls. Distinct differences were also observed in the predicted fecal metabolite profiles and strain-to-metabolite contributions between the two groups. Using tree-based machine learning models, we successfully identified specific microbial strains and their associated metabolites as potential alleviators of UC. Notably, our experimental validation using a dextran sulfate sodium-induced UC mouse model demonstrated that the administration of Parabacteroides merdae ATCC 43,184 and N-acetyl-D-mannosamine provided notable relief from colitis symptoms. In summary, our study underscores the potential of an integrative approach to identify novel therapeutic avenues for UC, paving the way for future targeted interventions.

Mannan-oligosaccharides promote gut microecological recovery after antibiotic disturbance.

Antibiotic treatment often causes collateral damage to the gut microbiota, including changes in its diversity and composition. Dietary fiber helps maintain intestinal health, regulate short-chain fatty acids, and promote the recovery of the intestinal microbiome. However, it is currently unknown which specific plant-based dietary fiber is optimal as a dietary supplement for restoring the intestinal microbiota after antibiotic disturbance. Previously, we proposed predictive recovery-associated bacterial species (p-RABs) and identified the most important interventions. This study aimed to identify an optimal form of dietary fiber to recover the gut microbiome after antibiotic treatment. Therefore, we examined the types of dietary fibers associated with p-RABs through a p-RAB-metabolite bilayer network constructed from prior knowledge; we searched for dietary fiber that could provide nutritional support for Akkermansia muciniphila and Bacteroides uniformis. C57BL/6J mice were fed with 500 mg kg-1 of different types of dietary fibers daily for one week after being treated with ampicillin. The results showed that mannan-oligosaccharides could better promote the diversity of intestinal microbial growth, enhance the recovery of most genera, including Akkermansia and Bacteroides, and inhibit certain pathogenic bacteria, such as Proteus, compared to the other fiber types. Furthermore, mannan-oligosaccharides could regulate the levels of short-chain fatty acids, especially butyric acid. Functional predictions showed that starch metabolism, galactose metabolism, and the metabolism of other carbohydrates played key roles in the early recovery process. In conclusion, mannan-oligosaccharides could enhance the recovery of the intestinal microbiome after antibiotic treatment, offering valuable insights for targeted dietary strategies.

A gut aging clock using microbiome multi-view profiles is associated with health and frail risk.

Age-related changes in the microbiome have been reported in previous studies; however, direct evidence for their association with frailty is lacking. Here, we introduce biological age based on gut microbiota (gAge), an integrated prediction model that integrates gut microbiota data from different perspectives with potential background factors for aging assessment. Simulation results show that, compared with a single model, the ensemble model can not only significantly improve the prediction accuracy, but also make full use of the data in unpaired samples. From this, we identified markers associated with age development and grouped markers into accelerated aging and mitigated aging according to their effect on the prediction. Importantly, the application of gAge to an elderly cohort with different frailty levels confirmed that gAge and its predictive residuals are closely related to the individual's health status and frailty stage, and age-related markers overlap significantly with disease and frailty characteristics. Furthermore, we applied the gAge prediction model to another independent cohort of the elderly population for aging assessment and found that gAge could effectively represent the aging population. Overall, our study explains the association between the gut microbiota and frailty, providing potential targets for the development of gut microbiota-based targeted intervention strategies for aging.

Engineering Interfacial Fast Ion Channels toward Highly Stable Zn Metal Batteries.

The development of aqueous zinc-ion batteries (AZIBs) is hindered by dendrites and side reactions, such as interfacial byproducts, corrosion, and hydrogen evolution. The construction of an artificial interface protective layer on the surface of the zinc anode has been extensively researched due to its strong operability and potential for large-scale application. In this study, we have designed an organic hydrophobic hybrid inorganic intercalation composite coating to achieve stable Zn2+ plating/stripping. The hydrophobic poly(vinylidene fluoride) (PVDF) effectively prevents direct contact between free water and the zinc anode, thereby mitigating the risk of dendrite formation. Simultaneously, the inorganic layer of vanadium phosphate (VOPO4·2H2O) after the insertion of polyaniline (PA) establishes a robust ion channel for facilitating rapid transport of Zn2+, thus promoting uniform electric field distribution and reducing concentration polarization. As a result, the performance of the modified composite PVDF/PA-VOP@Zn anode exhibited significant enhancement compared with that of the bare zinc anode. The assembled symmetric cell exhibits an exceptionally prolonged lifespan of 3070 h at a current density of 1 mA cm-2, while the full battery employing KVO as the cathode demonstrates a remarkable capability to undergo 2000 cycles at 5 A g-1 with a capacity retention rate of 78.2%. This study offers valuable insights into the anodic modification strategy for AZIBs.

Styryl lactone derivatives and aristolactam alkaloids from Goniothalamus tapis Miq. and their α-glucosidase inhibitory activity.

Two new styryl lactone derivatives, goniothapic acids A (1) and B (2), and 18 known compounds, were isolated from the twig and leaf extracts of Goniothalamus tapis Miq. The structures of new compounds were characterised by spectroscopic methods and HRESITOFMS. Their absolute configuration was established by comparing the experimental and calculated ECD spectra. Eleven compounds were evaluated for their α-glucosidase inhibitory activity. Of these, (-)-goniothalamin (5) and oldhamactam (16) showed the best α-glucosidase inhibitory activity with IC50 values of 54.8 and 57.9 µM, respectively.

Artificial Intelligence Technology for Food Nutrition.

Food nutrition is generally defined as the heat energy and nutrients obtained from food by the human body, such as protein, fat, carbohydrates and so on [...].

EnsDeepDP: An Ensemble Deep Learning Approach for Disease Prediction Through Metagenomics.

A growing number of studies show that the human microbiome plays a vital role in human health and can be a crucial factor in predicting certain human diseases. However, microbiome data are often characterized by the limited samples and high-dimensional features, which pose a great challenge for machine learning methods. Therefore, this paper proposes a novel ensemble deep learning disease prediction method that combines unsupervised and supervised learning paradigms. First, unsupervised deep learning methods are used to learn the potential representation of the sample. Afterwards, the disease scoring strategy is developed based on the deep representations as the informative features for ensemble analysis. To ensure the optimal ensemble, a score selection mechanism is constructed, and performance boosting features are engaged with the original sample. Finally, the composite features are trained with gradient boosting classifier for health status decision. For case study, the ensemble deep learning flowchart has been demonstrated on six public datasets extracted from the human microbiome profiling. The results show that compared with the existing algorithms, our framework achieves better performance on disease prediction.