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.

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.

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.

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.

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.

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.

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.

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.

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 [...].

Multi-omics analysis reveals genes and metabolites involved in Bifidobacterium pseudocatenulatum biofilm formation.

Bacterial biofilm is an emerging form of life that involves cell populations living embedded in a self-produced matrix of extracellular polymeric substances (EPS). Currently, little is known about the molecular mechanisms of Bifidobacterium biofilm formation. We used the Bifidobacterium biofilm fermentation system to preparation of biofilms on wheat fibers, and multi-omics analysis of both B. pseudocatenulatum biofilms and planktonic cells were performed to identify genes and metabolites involved in biofilm formation. The average diameter of wheat fibers was around 50 µm, while the diameter of particle in wheat fibers culture of B. pseudocatenulatum was over 260 µm at 22 h with 78.96% biofilm formation rate (BR), and the field emission scanning electron microscopy (FESEM) results showed that biofilm cells on the surface of wheat fibers secreted EPS. Transcriptomic analysis indicated that genes associated with stress response (groS, mntH, nth, pdtaR, pstA, pstC, radA, rbpA, whiB, ybjG), quorum sensing (dppC, livM, luxS, sapF), polysaccharide metabolic process (rfbX, galE, zwf, opcA, glgC, glgP, gtfA) may be involved in biofilm formation. In addition, 17 weighted gene co-expression network analysis (WGCNA) modules were identified and two of them positively correlated to BR. Metabolomic analysis indicated that amino acids and amides; organic acids, alcohols and esters; and sugar (trehalose-6-phosphate, uridine diphosphategalactose, uridine diphosphate-N-acetylglucosamine) were main metabolites during biofilm formation. These results indicate that stress response, quorum sensing (QS), and EPS production are essential during B. pseudocatenulatum biofilm formation.

Uncovering Predictive Factors and Interventions for Restoring Microecological Diversity after Antibiotic Disturbance.

Antibiotic treatment can lead to a loss of diversity of gut microbiota and may adversely affect gut microbiota composition and host health. Previous studies have indicated that the recovery of gut microbes from antibiotic-induced disruption may be guided by specific microbial species. We expect to predict recovery or non-recovery using these crucial species or other indices after antibiotic treatment only when the gut microbiota changes. This study focused on this prediction problem using a novel ensemble learning framework to identify a set of common and reasonably predictive recovery-associated bacterial species (p-RABs), enabling us to predict the host microbiome recovery status under broad-spectrum antibiotic treatment. Our findings also propose other predictive indicators, suggesting that higher taxonomic and functional diversity may correlate with an increased likelihood of successful recovery. Furthermore, to explore the validity of p-RABs, we performed a metabolic support analysis and identified Akkermansia muciniphila and Bacteroides uniformis as potential key supporting species for reconstruction interventions. Experimental results from a C57BL/6J male mouse model demonstrated the effectiveness of p-RABs in facilitating intestinal microbial reconstitution. Thus, we proved the reliability of the new p-RABs and validated a practical intervention scheme for gut microbiota reconstruction under antibiotic disturbance.

Statistical modeling of gut microbiota for personalized health status monitoring.

BACKGROUND: The gut microbiome is closely associated with health status, and any microbiota dysbiosis could considerably impact the host's health. In addition, many active consortium projects have generated many reference datasets available for large-scale retrospective research. However, a comprehensive monitoring framework that analyzes health status and quantitatively present bacteria-to-health contribution has not been thoroughly investigated. METHODS: We systematically developed a statistical monitoring diagram for personalized health status prediction and analysis. Our framework comprises three elements: (1) a statistical monitoring model was established, the health index was constructed, and the health boundary was defined; (2) healthy patterns were identified among healthy people and analyzed using contrast learning; (3) the contribution of each bacterium to the health index of the diseased population was analyzed. Furthermore, we investigated disease proximity using the contribution spectrum and discovered multiple multi-disease-related targets. RESULTS: We demonstrated and evaluated the effectiveness of the proposed monitoring framework for tracking personalized health status through comprehensive real-data analysis using the multi-study cohort and another validation cohort. A statistical monitoring model was developed based on 92 microbial taxa. In both the discovery and validation sets, our approach achieved balanced accuracies of 0.7132 and 0.7026, and AUC of 0.80 and 0.76, respectively. Four health patterns were identified in healthy populations, highlighting variations in species composition and metabolic function across these patterns. Furthermore, a reasonable correlation was found between the proposed health index and host physiological indicators, diversity, and functional redundancy. The health index significantly correlated with Shannon diversity ([Formula: see text]) and species richness ([Formula: see text]) in the healthy samples. However, in samples from individuals with diseases, the health index significantly correlated with age ([Formula: see text]), species richness ([Formula: see text]), and functional redundancy ([Formula: see text]). Personalized diagnosis is achieved by analyzing the contribution of each bacterium to the health index. We identified high-contribution species shared across multiple diseases by analyzing the contribution spectrum of these diseases. CONCLUSIONS: Our research revealed that the proposed monitoring framework could promote a deep understanding of healthy microbiomes and unhealthy variations and served as a bridge toward individualized therapy target discovery and precise modulation. Video Abstract.

Multiomics reveals the mechanism of B. longum in promoting the formation of mixed-species biofilms.

It has been found previously that Bifidobacterium longum, Bacteroides ovatus, Enterococcus faecalis, and Lactobacillus gasseri can form a biofilm better when co-cultured in vitro and B. longum is the core biofilm-formation-promoting strain in this community. B. longum is part of the core microbiota in the gut and is widely recognized as a probiotic. Therefore, it is necessary to explore its role in mixed-species biofilms through transcriptomics and metabolomics. Metabolomics showed that the increase in amino acid and purine content could promote biofilm formation. In transcriptomic analysis, many genes related to carbohydrate metabolism, amino acid metabolism, and environmental tolerance of B. longum were up-regulated. Combined with the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) analysis, the differentially expressed genes (DEGs) of B. longum in mixed-species biofilms were mainly correlated to "quorum sensing (QS)", "ABC transporters", "biosynthesis of amino acids", "microbial metabolism in different environments", "carbohydrate metabolism" and "two-component system". In addition, the rpl and rps gene families, which function in the metabolism of organic substances and the biosynthesis of amino acids, were the core DEGs according to the analysis of the protein-protein interaction (PPI) network. Finally, by combining metabolomics and quorum sensing mechanisms, it was found that the metabolism of autoinducer peptides (proliylglycine and glycylleucine), N-acyl homoserine lactone (N-(3-oxo hydroxy) homoserine lactone), and AI-2 can promote the formation of biofilms, both mono- and mixed-species biofilms composed of B. longum. Our research enabled us to understand the critical role of B. longum in mixed-species biofilms and the interactions between biofilm metabolism and gut health. In addition, the generated knowledge will be of great significance for us to develop biofilm products with beneficial functions in future.

Achieving high performance in aqueous iron-ion batteries using tunnel-like VO2 as a cathode material.

The research on aqueous iron-ion batteries (AIIBs) is still in its early stages and highly limited by the lack of suitable cathode materials. In this study, we propose using tunnel-like VO2 as a cathode material, which delivers a high capacity of 198 mA h g-1 at 0.2 A g-1. Besides, the AIIB exhibits appreciable cycling performance, retaining 78.9% of its initial capacity after 200 cycles. The unique structure of VO2 and the multiple valence states of vanadium in VO2 enable the reversible storage of Fe2+ during cycling. This work presents a new choice for the cathode and considerable development prospects in AIIBs.

Clostridium butyricum Reduces Obesity in a Butyrate-Independent Way.

Accumulating evidence from recent studies links the gut microbiota to obesity, and microbiome therapy has been examined as a treatment. Clostridium butyricum (C. butyricum), an intestinal symbiont, protects the host from a range of diseases. Studies have shown a negative correlation between the relative abundance of C. butyricum and a predisposition for obesity. However, the physiological function and material basis of C. butyricum for obesity are unclear. Here, five C. butyricum isolates were administered to mice on a high-fat diet (HFD) to determine their anti-obesity effects. All isolates suppressed the formation and inflammation of subcutaneous fat, and the two effective strains considerably reduced weight gain and ameliorated dyslipidemia, hepatic steatosis, and inflammation. These positive effects were not achieved by increasing the concentration of intestinal butyrate, and the effective strains could not be replaced by sodium butyrate (NaB). We also discovered that oral supplementation with the two most effective strains changed the metabolism of tryptophan and purine and altered the composition of the gut microbiota. In summary, C. butyricum improved the metabolic phenotypes under the HFD by controlling the composition of the gut microbiota and modulating intestinal metabolites, thereby demonstrating its ability to fight obesity and providing a theoretical foundation for microbial preparations production.

An Ultrafast, Energy-Efficient Electrochromic and Thermochromic Device for Smart Windows.

Smart windows nowadays undertake the esteemed obligation of reducing energy consumption as well as upgrading living experience. This project aims to devise a smart window that responds to both electricity and heat, with the intention of achieving energy efficiency, privacy preservation, and enhanced decorative attributes. Through the implementation of a novel electrochromic material design, coupled with the optimization of electrochromic devices (ECDs), a high-performance ECD is obtained, demonstrating coloring/bleaching time of 0.53/0.16 s, a transmittance modulation of 78% (from 99% to 21%), and superior performance in six dimensions. Furthermore, temperature-responsive units and an ionic liquid are incorporated into the electrolyte system to create a novel thermochromic gel electrolyte with transmittance modulation from 80% to 0%, and excellent thermal insulation (6.4 °C reduction). Ultimately, an electro- and thermochromic device is developed, featuring an ultrafast color-switching speed of 0.82/0.60 s and multiple working modes. Overall, this work showcases a prospective design pathway for the development of next-generation ultrafast-switching, and energy-efficient intelligent windows.

L-Fucose increases the fucosylation of colorectal cancer cells via promoting the accumulation of serine.

Fucosylation, a kind of posttranslational modification, has been identified as a key regulator of health, with alterations in this process serving as an indicator of diseases such as colorectal cancer. L-Fucose, an essential substrate of fucosylation, was reported to possess anticancer potential and increase fucosylation. However, the association between its tumour-inhibitory effect and its ability to regulate fucosylation was not fully understood. Herein, we demonstrate that the simultaneous inhibitory effect of L-fucose on cancer cell growth and enhanced fucosylation occurred only in certain colorectal cancer cells (HCT-116 cells) but not in normal cells (HCoEpic cells), which may be related to the induction of pro-apoptotic fucosylated proteins by L-fucose in HCT-116 cells. RNA-seq analysis showed that upregulation of the transcription levels of serine biosynthesis genes (e.g. PSAT1) and decreased levels of genes involved in serine consumption with supplemental L-fucose were also unique to HCT-116 cells. Increased serine concentrations only in HCT-116 cells and increased α1,3/6-fucosylation in CRC cells induced by exogenous serine also verified that L-fucose enhanced fucosylation via promoting intracellular serine accumulation. Additionally, the knockdown of PSAT1 and serine-deficiency impaired fucosylation. Notably, PSAT1 knockdown weakened the inhibitory effect of L-fucose on cell proliferation and migration. Interestingly, simultaneous increased levels of α1,3/6-fucosylation and PSAT1 transcription were also identified in colorectal tumor tissues of CRC patients. Together, these results uncover a novel role of serine synthesis and PSAT1 in the regulation of fucosylation and provide insights into the potential application of L-fucose in CRC therapy.

Uncovering the specificity and predictability of tryptophan metabolism in lactic acid bacteria with genomics and metabolomics.

Tryptophan is metabolized by microorganisms into various indole derivatives that have been proven to alleviate diseases and promote human health. Lactic acid bacteria (LAB) are a broad microbial concept, some of which have been developed as probiotics. However, the capacity of most LAB to metabolize tryptophan is unknown. In this study, the aim is to reveal the rule of tryptophan metabolism in LAB by multi-omics. The findings showed that LAB were rich in genes for tryptophan catabolism and that multiple genes were shared among LAB species. Although the number of their homologous sequences was different, they could still form the same metabolic enzyme system. The metabolomic analysis revealed that LAB were capable of producing a variety of metabolites. Strains belonging to the same species can produce the same metabolites and have similar yields. A few strains showed strain-specificity in the production of indole-3-lactic acid (ILA), indole-3-acetic acid, and 3-indolealdehyde (IAld). In the genotype-phenotype association analysis, the metabolites of LAB were found to be highly consistent with the outcomes of gene prediction, particularly ILA, indole-3-propionic acid, and indole-3-pyruvic acid. The overall prediction accuracy was more than 87% on average, which indicated the predictability of tryptophan metabolites of LAB. Additionally, genes influenced the concentration of metabolites. The levels of ILA and IAld were significantly correlated with the numbers of aromatic amino acid aminotransferase and amidase, respectively. The unique indolelactate dehydrogenase in Ligilactobacillus salivarius was the primary factor contributing to its large production of ILA. In summary, we demonstrated the gene distribution and production level of tryptophan metabolism in LAB and explored the correlation between genes and phenotypes. The predictability and specificity of the tryptophan metabolites in LAB were proven. These results provide a novel genomic method for the discovery of LAB with tryptophan metabolism potential and offer experimental data for probiotics that produce specific tryptophan metabolites.