Using 3D facial information to predict malnutrition and consequent complications.

BACKGROUND: Phase angle (PhA) correlates with body composition and could predict the nutrition status of patients and disease prognosis. We aimed to explore the feasibility of predicting PhA-diagnosed malnutrition using facial image information based on deep learning (DL). METHODS: From August 2021 to April 2022, inpatients were enrolled from surgery, gastroenterology, and oncology departments in a tertiary hospital. Subjective global assessment was used as the gold standard of malnutrition diagnosis. The highest Youden index value was selected as the PhA cutoff point. We developed a multimodal DL framework to automatically analyze the three-dimensional (3D) facial data and accurately determine patients' PhA categories. The framework was trained and validated using a cross-validation approach and tested on an independent dataset. RESULTS: Four hundred eighty-two patients were included in the final dataset, including 176 with malnourishment. In male patients, the PhA value with the highest Youden index was 5.55°, and the area under the receiver operating characteristic curve (AUC) = 0.68; in female patients, the PhA value with the highest Youden index was 4.88°, and AUC = 0.69. Inpatients with low PhA had higher incidence of infectious complications during the hospital stay (P = 0.003). The DL model trained with 4096 points extracted from 3D facial data had the best performance. The algorithm showed fair performance in predicting PhA, with an AUC of 0.77 and an accuracy of 0.74. CONCLUSION: Predicting the PhA of inpatients from facial images is feasible and can be used for malnutrition assessment and prognostic prediction.

Looking into the Market Behaviors through the Lens of Correlations and Eigenvalues: An Investigation on the Chinese and US Markets Using RMT.

This research systematically analyzes the behaviors of correlations among stock prices and the eigenvalues for correlation matrices by utilizing random matrix theory (RMT) for Chinese and US stock markets. Results suggest that most eigenvalues of both markets fall within the predicted distribution intervals by RMT, whereas some larger eigenvalues fall beyond the noises and carry market information. The largest eigenvalue represents the market and is a good indicator for averaged correlations. Further, the average largest eigenvalue shows similar movement with the index for both markets. The analysis demonstrates the fraction of eigenvalues falling beyond the predicted interval, pinpointing major market switching points. It has identified that the average of eigenvector components corresponds to the largest eigenvalue switch with the market itself. The investigation on the second largest eigenvalue and its eigenvector suggests that the Chinese market is dominated by four industries whereas the US market contains three leading industries. The study later investigates how it changes before and after a market crash, revealing that the two markets behave differently, and a major market structure change is observed in the Chinese market but not in the US market. The results shed new light on mining hidden information from stock market data.

Exploring the Core Bacteria and Functional Traits in Pecan (Carya illinoinensis) Rhizosphere.

Pecan (Carya illinoinensis) and Chinese hickory (Carya cathayensis) are important commercially cultivated nut trees. They are phylogenetically closely related plants; however, they exhibit significantly different phenotypes in response to abiotic stress and development. The rhizosphere selects core microorganisms from bulk soil, playing a pivotal role in the plant's resistance to abiotic stress and growth. In this study, we used metagenomic sequencing to compare the selection capabilities of seedling pecan and seedling hickory at taxonomic and functional levels in bulk soil and the rhizosphere. We observed that pecan has a stronger capacity to enrich rhizosphere plant-beneficial microbe bacteria (e.g., Rhizobium, Novosphingobium, Variovorax, Sphingobium, and Sphingomonas) and their associated functional traits than hickory. We also noted that the ABC transporters (e.g., monosaccharide transporter) and bacterial secretion systems (e.g., type IV secretion system) are the core functional traits of pecan rhizosphere bacteria. Rhizobium and Novosphingobium are the main contributors to the core functional traits. These results suggest that monosaccharides may help Rhizobium to efficiently enrich this niche. Novosphingobium may use a type IV secretion system to interact with other bacteria and thereby influence the assembly of pecan rhizosphere microbiomes. Our data provide valuable information to guide core microbial isolation and expand our knowledge of the assembly mechanisms of plant rhizosphere microbes. IMPORTANCE The rhizosphere microbiome has been identified as a fundamental factor in maintaining plant health, helping plants to fight the deleterious effects of diseases and abiotic stresses. However, to date, studies on the nut tree microbiome have been scarce. Here, we observed a significant "rhizosphere effect" on the seedling pecan. We furthermore demonstrated the core rhizosphere microbiome and function in the seedling pecan. Moreover, we deduced possible factors that help the core bacteria, such as Rhizobium, to efficiently enrich the pecan rhizosphere and the importance of the type IV system for the assembly of pecan rhizosphere bacterial communities. Our findings provide information for understanding the mechanism of the rhizosphere microbial community enrichment process.

GROP: A genomic information repository for oilplants.

Biomass energy is an essential component of the agriculture economy and represents an important and particularly significant renewable energy source in the fight against fossil fuel depletion and global warming. The recognition that many plants naturally synthesize hydrocarbons makes these oil plants indispensable resources for biomass energy, and the advancement of next-generation sequencing technology in recent years has now made available mountains of data on plants that synthesize oil. We have utilized a combination of bioinformatic protocols to acquire key information from this massive amount of genomic data and to assemble it into an oil plant genomic information repository, built through website technology, including Django, Bootstrap, and echarts, to create the Genomic Information Repository for Oil Plants (GROP) portal (http://grop.site/) for genomics research on oil plants. The current version of GROP integrates the coding sequences, protein sequences, genome structure, functional annotation information, and other information from 18 species, 22 genome assemblies, and 46 transcriptomes. GROP also provides BLAST, genome browser, functional enrichment, and search tools. The integration of the massive amounts of oil plant genomic data with key bioinformatics tools in a database with a user-friendly interface allows GROP to serve as a central information repository to facilitate studies on oil plants by researchers worldwide.