Decoupling ion concentrations from effluent conductivity profiles in capacitive and battery electrode deionizations using an artificial intelligence model.

Owing to its simplicity of measurement, effluent conductivity is one of the most studied factors in evaluations of desalination performance based on the ion concentrations in various ion adsorption processes such as capacitive deionization (CDI) or battery electrode deionization (BDI). However, this simple conversion from effluent conductivity to ion concentration is often incorrect, thereby necessitating a more congruent method for performing real-time measurements of effluent ion concentrations. In this study, a random forest (RF)-based artificial intelligence (AI) model was developed to address this shortcoming. The proposed RF model showed an excellent prediction accuracy when it was first validated in predicting the effluent conductivity for both CDI (R2 = 0.86) and BDI (R2 = 0.95) data. Moreover, the RF model successfully predicted the concentration of each ion (Na⁺, K⁺, Ca2⁺, and Cl⁻) from the conductivity values. The accuracy of the ion concentration prediction was even higher than that of the effluent conductivity prediction, likely owing to the linear correlation between the input and output variables of the dataset. The effect of the sampling interval was also evaluated for conductivity and ion concentrations, and there was no significant difference up to sampling intervals of <80 s based on the error value of the model. These findings suggest that an RF model can be used to predict ion concentrations in CDI/BDI, which may be used as core indicators in evaluating desalination performance.

Psychobiotics and fecal microbial transplantation for autism and attention-deficit/hyperactivity disorder: microbiome modulation and therapeutic mechanisms.

Dysbiosis of the gut microbiome is thought to be the developmental origins of the host's health and disease through the microbiota-gut-brain (MGB) axis: such as immune-mediated, metabolic, neurodegenerative, and neurodevelopmental diseases. Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are common neurodevelopmental disorders, and growing evidence indicates the contribution of the gut microbiome changes and imbalances to these conditions, pointing to the importance of considering the MGB axis in their treatment. This review summarizes the general knowledge of gut microbial colonization and development in early life and its role in the pathogenesis of ASD/ADHD, highlighting a promising therapeutic approach for ASD/ADHD through modulation of the gut microbiome using psychobiotics (probiotics that positively affect neurological function and can be applied for the treatment of psychiatric diseases) and fecal microbial transplantation (FMT).

Gut microbiome profiling of neonates using Nanopore MinION and Illumina MiSeq sequencing.

This study aimed to evaluate the difference in gut microbiomes between preterm and term infants using third-generation long-read sequencing (Oxford Nanopore Technologies, ONT) compared with an established gold standard, Illumina (second-generation short-read sequencing). A total of 69 fecal samples from 51 term (T) and preterm (P) infants were collected at 7 and 28 days of life. Gut colonization profiling was performed by 16S rRNA gene sequencing using ONT. We used Illumina to validate and compare the patterns in 13 neonates. Using bioinformatic analysis, we identified features that differed between P and T. Both T1 and P1 microbiomes were dominated by Firmicutes (Staphylococcus and Enterococcus), whereas sequentially showed dominant transitions to Lactobacillus (p < 0.001) and Streptococcus in T2 (p = 0.001), and pathogenic bacteria (Klebsiella) in P2 (p = 0.001). The abundance of beneficial bacteria (Bifidobacterium and Lactobacillus) increased in T2 (p = 0.026 and p < 0.001, respectively). These assignments were correlated with the abundance at the species-level. Bacterial α-diversity increased in T (p = 0.005) but not in P (p = 0.156), and P2 showed distinct ß-diversity clustering than T2 (p = 0.001). The ONT reliably identified pathogenic bacteria at the genus level, and taxonomic profiles were comparable to those identified by Illumina at the genus level. This study shows that ONT and Illumina are highly correlated. P and T had different microbiome profiles, and the α- and ß-diversity varied. ONT sequencing has potential for pathogen detection in neonates in clinical settings.

Integrative biohydrogen- and biomethane-producing bioprocesses for comprehensive production of biohythane.

The production of biohythane, a combination of energy-dense hydrogen and methane, from the anaerobic digestion of low-cost organic wastes has attracted attention as a potential candidate for the transition to a sustainable circular economy. Substantial research has been initiated to upscale the process engineering to establish a hythane-based economy by addressing major challenges associated with the process and product upgrading. This review provides an overview of the feasibility of biohythane production in various anaerobic digestion systems (single-stage, dual-stage) and possible technologies to upgrade biohythane to hydrogen-enriched renewable natural gas. The main goal of this review is to promote research in biohythane production technology by outlining critical needs, including meta-omics and metabolic engineering approaches for the advancements in biohythane production technology.