PM-CNN: microbiome status recognition and disease detection model based on phylogeny and multi-path neural network.

Motivation: The human microbiome, found throughout various body parts, plays a crucial role in health dynamics and disease development. Recent research has highlighted microbiome disparities between patients with different diseases and healthy individuals, suggesting the microbiome's potential in recognizing health states. Traditionally, microbiome-based status classification relies on pre-trained machine learning (ML) models. However, most ML methods overlook microbial relationships, limiting model performance. Results: To address this gap, we propose PM-CNN (Phylogenetic Multi-path Convolutional Neural Network), a novel phylogeny-based neural network model for multi-status classification and disease detection using microbiome data. PM-CNN organizes microbes based on their phylogenetic relationships and extracts features using a multi-path convolutional neural network. An ensemble learning method then fuses these features to make accurate classification decisions. We applied PM-CNN to human microbiome data for status and disease detection, demonstrating its significant superiority over existing ML models. These results provide a robust foundation for microbiome-based state recognition and disease prediction in future research and applications. Availability and implementation: PM-CNN software is available at https://github.com/qdu-bioinfo/PM_CNN.

Gut microbiome predicts selenium supplementation efficiency across different Chinese adult cohorts using hybrid modeling and feature refining.

Selenium (Se) is an essential trace element that plays a vital role in various physiological functions of the human body, despite its small proportion. Due to the inability of the human body to synthesize selenium, there has been increasing concern regarding its nutritional value and adequate intake as a micronutrient. The efficiency of selenium absorption varies depending on individual biochemical characteristics and living environments, underscoring the importance of accurately estimating absorption efficiency to prevent excessive or inadequate intake. As a crucial digestive organ in the human body, gut harbors a complex and diverse microbiome, which has been found to have a significant correlation with the host's overall health status. To investigate the relationship between the gut microbiome and selenium absorption, a two-month intervention experiment was conducted among Chinese adult cohorts. Results indicated that selenium supplementation had minimal impact on the overall diversity of the gut microbiome but was associated with specific subsets of microorganisms. More importantly, these dynamics exhibited variations across regions and sequencing batches, which complicated the interpretation and utilization of gut microbiome data. To address these challenges, we proposed a hybrid predictive modeling method, utilizing refined gut microbiome features and host variable encoding. This approach accurately predicts individual selenium absorption efficiency by revealing hidden microbial patterns while minimizing differences in sequencing data across batches and regions. These efforts provide new insights into the interaction between micronutrients and the gut microbiome, as well as a promising direction for precise nutrition in the future.

Comprehensive Assessment of 16S rRNA Gene Amplicon Sequencing for Microbiome Profiling across Multiple Habitats.

The 16S rRNA gene works as a rapid and effective marker for the identification of microorganisms in complex communities; hence, a huge number of microbiomes have been surveyed by 16S amplicon-based sequencing. The resolution of the 16S rRNA gene is always considered only at the genus level; however, it has not been verified on a wide range of microbes yet. To fully explore the ability and potential of the 16S rRNA gene in microbial profiling, here, we propose Qscore, a comprehensive method to evaluate the performance of amplicons by integrating the amplification rate, multitier taxonomic annotation, sequence type, and length. Our in silico assessment by a "global view" of 35,889 microbe species across multiple reference databases summarizes the optimal sequencing strategy for 16S short reads. On the other hand, since microbes are unevenly distributed according to their habitats, we also provide the recommended configuration for 16 typical ecosystems based on the Qscores of 157,390 microbiomes in the Microbiome Search Engine (MSE). Detailed data simulation further proves that the 16S amplicons produced with Qscore-suggested parameters exhibit high precision in microbiome profiling, which is close to that of shotgun metagenomes under CAMI metrics. Therefore, by reconsidering the precision of 16S-based microbiome profiling, our work not only enables the high-quality reusability of massive sequence legacy that has already been produced but is also significant for guiding microbiome studies in the future. We have implemented the Qscore as an online service at http://qscore.single-cell.cn to parse the recommended sequencing strategy for specific habitats or expected microbial structures. IMPORTANCE 16S rRNA has long been used as a biomarker to identify distinct microbes from complex communities. However, due to the influence of the amplification region, sequencing type, sequence processing, and reference database, the accuracy of 16S rRNA has not been fully verified on a global range. More importantly, the microbial composition of different habitats varies greatly, and it is necessary to adopt different strategies according to the corresponding target microbes to achieve optimal analytical performance. Here, we developed Qscore, which evaluates the comprehensive performance of 16S amplicons from multiple perspectives, thus providing the best sequencing strategies for common ecological environments by using big data.

Effects of Dietary Vitamin C on the Growth Performance, Biochemical Parameters, and Antioxidant Activity of Coho Salmon Oncorhynchus kisutch (Walbaum, 1792) Postsmolts.

Vitamin C (VC) plays an essential role in fish physiological function and normal growth. However, its effects and requirement of coho salmon Oncorhynchus kisutch (Walbaum, 1792) are still unknown. Based on the influences on growth, serum biochemical parameters, and antioxidative ability, an assessment of dietary VC requirement for coho salmon postsmolts (183.19 ± 1.91 g) was conducted with a ten-week feeding trial. Seven isonitrogenous (45.66% protein) and isolipidic (10.76% lipid) diets were formulated to include graded VC concentrations of 1.8, 10.9, 50.8, 100.5, 197.3, 293.8, and 586.7 mg/kg, respectively. Results showed that VC markedly improved the growth performance indexes and liver VC concentration, enhanced the hepatic and serum antioxidant activities, and increased the contents of serum alkaline phosphatase (AKP) activity, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and total cholesterol (TC) whereas decreased the serum aspartate aminotransferase (AST), alanine aminotransferase (ALT) activities, and triglyceride (TG) level. Polynomial analysis showed that the optimal VC levels in the diet of coho salmon postsmolts were 188.10, 190.68, 224.68, 132.83, 156.57, 170.12, 171.00, 185.50, 142.77, and 93.08 mg/kg on the basis of specific growth rate (SGR), feed conversion ratio (FCR), liver VC concentration, catalase (CAT), hepatic superoxide dismutase (SOD) activities, malondialdehyde (MDA) content, and serum total antioxidative capacity (T-AOC), AKP, AST, and ALT activities, respectively. The dietary VC requirement was in the range of 93.08-224.68 mg/kg for optimum growth performance, serum enzyme activities, and antioxidant capacity of coho salmon postsmolts.

Identification of biomarkers associated with clinical severity of chronic obstructive pulmonary disease.

We sought to identify the biomarkers related to the clinical severity of stage I to stage IV chronic obstructive pulmonary disease (COPD). Gene expression profiles from the blood samples of COPD patients at each of the four stages were acquired from the Gene Expression Omnibus Database (GEO, accession number: GSE54837). Genes showing expression changes among the different stages were sorted by soft clustering. We performed functional enrichment, protein-protein interaction (PPI), and miRNA regulatory network analyses for the differentially expressed genes. The biomarkers associated with the clinical classification of COPD were selected from logistic regression models and the relationships between TLR2 and inflammatory factors were verified in clinical blood samples by qPCR and ELISA. Gene clusters demonstrating continuously rising or falling changes in expression (clusters 1, 2, and 7 and clusters 5, 6, and 8, respectively) from stage I to IV were defined as upregulated and downregulated genes, respectively, and further analyzed. The upregulated genes were enriched in functions associated with defense, inflammatory, or immune responses. The downregulated genes were associated with lymphocyte activation and cell activation. TLR2, HMOX1, and CD79A were hub proteins in the integrated network of PPI and miRNA regulatory networks. TLR2 and CD79A were significantly correlated with clinical classifications. TLR2 was closely associated with inflammatory responses during COPD progression. Functions associated with inflammatory and immune responses as well as lymphocyte activation may play important roles in the progression of COPD from stage I to IV. TLR2 and CD79A may serve as potential biomarkers for the clinical severity of COPD. TLR2 and CD79A may also serve as independent biomarkers in the clinical classification in COPD. TLR2 may play an important role in the inflammatory responses of COPD.

Optimal Design of Hierarchical Cloud-Fog&Edge Computing Networks with Caching.

This paper investigates the optimal design of a hierarchical cloud-fog&edge computing (FEC) network, which consists of three tiers, i.e., the cloud tier, the fog&edge tier, and the device tier. The device in the device tier processes its task via three computing modes, i.e., cache-assisted computing mode, cloud-assisted computing mode, and joint device-fog&edge computing mode. Specifically, the task corresponds to being completed via the content caching in the FEC tier, the computation offloading to the cloud tier, and the joint computing in the fog&edge and device tier, respectively. For such a system, an energy minimization problem is formulated by jointly optimizing the computing mode selection, the local computing ratio, the computation frequency, and the transmit power, while guaranteeing multiple system constraints, including the task completion deadline time, the achievable computation capability, and the achievable transmit power threshold. Since the problem is a mixed integer nonlinear programming problem, which is hard to solve with known standard methods, it is decomposed into three subproblems, and the optimal solution to each subproblem is derived. Then, an efficient optimal caching, cloud, and joint computing (CCJ) algorithm to solve the primary problem is proposed. Simulation results show that the system performance achieved by our proposed optimal design outperforms that achieved by the benchmark schemes. Moreover, the smaller the achievable transmit power threshold of the device, the more energy is saved. Besides, with the increment of the data size of the task, the lesser is the local computing ratio.

Dewatering and granulation of sewage sludge by biophysical drying and thermo-degradation performance of prepared sludge particles during succedent fast pyrolysis.

A novel two-step technology, fast biophysical drying (BPD) coupling with fast pyrolysis (FP), was investigated for moisture removal and energy recovery from sewage sludge. For BPD, combined operations of extreme thermophilic amendment (with accelerated increasing and controllable maintenance of substrate temperature) and enhanced convective evaporation were conducted, both beneficial for moisture removal (moisture content reaching 23.1% for 7d) and organic preservation. Biophysical-dried sludge (BPDS) was characterized by homogeneous fine-particle morphology and well-developed porous microstructure. The synthesized BPDS particle preserved most organic components (92% volatile matters and 79% HHV of traditional thermal-dried sludge [TTDS]) attributable to the inhibitory effect of BPD adjustment, presenting considerable capacity for subsequent residue-derived energy. For FP, the distribution of products from BPDS pyrolysis indicated that syngas and char yields were higher than those of TTDS. The syngas from BPDS is a type of hydrogen-rich gas composed of 42.6 vol.% H(2) at 900°C.