Gut brain interaction theory reveals gut microbiota mediated neurogenesis and traditional Chinese medicine research strategies

Adult neurogenesis is the process of differentiation of neural stem cells (NSCs) into neurons and glial cells in certain areas of the adult brain. Defects in neurogenesis can lead to neurodegenerative diseases, mental disorders, and other maladies. This process is directionally regulated by transcription factors, the Wnt and Notch pathway, the extracellular matrix, and various growth factors. External factors like stress, physical exercise, diet, medications, etc., affect neurogenesis and the gut microbiota. The gut microbiota may affect NSCs through vagal, immune and chemical pathways, and other pathways. Traditional Chinese medicine (TCM) has been proven to affect NSCs proliferation and differentiation and can regulate the abundance and metabolites produced by intestinal microorganisms. However, the underlying mechanisms by which these factors regulate neurogenesis through the gut microbiota are not fully understood. In this review, we describe the recent evidence on the role of the gut microbiota in neurogenesis. Moreover, we hypothesize on the characteristics of the microbiota-gut-brain axis based on bacterial phyla, including microbiota’s metabolites, and neuronal and immune pathways while providing an outlook on TCM’s potential effects on adult neurogenesis by regulating gut microbiota

Evolution and Association Analysis of Ghd7 in Rice

Plant height, heading date, and yield are the main targets for rice genetic improvement. Ghd7 is a pleiotropic gene that controls the aforementioned traits simultaneously. In this study, a rice germplasm collection of 104 accessions (Oryza sativa) and 3 wild rice varieties (O.rufipogon) was used to analyze the evolution and association of Ghd7 with plant height, heading date, and yield. Among the 104 accessions, 76 single nucleotide polymorphisms (SNPs) and six insertions and deletions were found within a 3932-bp DNA fragment of Ghd7. A higher pairwise π and θ in the promoter indicated a highly diversified promoter of Ghd7. Sixteen haplotypes and 8 types of Ghd7 protein were detected. SNP changes between haplotypes indicated that Ghd7 evolved from two distinct ancestral gene pools, and independent domestication processes were detected in indica and japonica varietals respectively. In addition to the previously reported premature stop mutation in the first exon of Ghd7, which caused phenotypic changes of multiple traits, we found another functional C/T mutation (SNP S_555) by structure-based association analysis. SNP S_555 is located in the promoter and was related to plant height probably by altering gene expression. Moreover, another seven SNP mutations in complete linkage were found to be associated with the number of spikelets per panicle, regardless of the photoperiod. These associations provide the potential for flexibility of Ghd7 application in rice breeding programs

A Survey on Hand Pose Estimation with Wearable Sensors and Computer-Vision-Based Methods

Real-time sensing and modeling of the human body, especially the hands, is an important research endeavor for various applicative purposes such as in natural human computer interactions. Hand pose estimation is a big academic and technical challenge due to the complex structure and dexterous movement of human hands. Boosted by advancements from both hardware and artificial intelligence, various prototypes of data gloves and computer-vision-based methods have been proposed for accurate and rapid hand pose estimation in recent years. However, existing reviews either focused on data gloves or on vision methods or were even based on a particular type of camera, such as the depth camera. The purpose of this survey is to conduct a comprehensive and timely review of recent research advances in sensor-based hand pose estimation, including wearable and vision-based solutions. Hand kinematic models are firstly discussed. An in-depth review is conducted on data gloves and vision-based sensor systems with corresponding modeling methods. Particularly, this review also discusses deep-learning-based methods, which are very promising in hand pose estimation. Moreover, the advantages and drawbacks of the current hand gesture estimation methods, the applicative scope, and related challenges are also discussed

Comparison of means between different haplotypes in the three traits.

<p>Hap, haplotype; SD, standard deviation; N, number of cultivars tested. Within an environment, means followed by different letters are significantly different at <i>P</i> = 0.05.</p

Haplotype analysis of the <i>Ghd7</i> gene region in the 104 cultivars.

<p>(a) The <i>Ghd7</i> containing two exons (indicated in gray) and the entire length of the 3923-bp genome is shown in graphics on the top. The position of every SNP is shown in the first row (SNP frequency>5%). Twelve haplotypes (H0–H11) were detected in the 104 cultivars of <i>O. sativa</i>, which can be divided into an <i>indica</i> group (<i>ind-G</i>) and a <i>japonica</i> group (<i>jap-G</i>) based on the population structure analysis. The number of cultivars (cvs) in every subpopulation is shown in the right columns: Q1 indicates the <i>indica</i> population and Q2 and Q3 indicate the <i>japonica</i> population. Yellow represents polymorphisms characteristic of the <i>indica</i> haplogroup, light blue shows the <i>japonica</i> haplogroup polymorphisms. Red indicates the new mutation. WR1–3 indicates the three wild rice varieties of <i>O.rufipogon.</i> (b) Phylogenetic tree of the twelve haplotypes (H0–H11).</p

Comparison of means of three traits among the major 4 protein types.

<p>The first line indicates the main 4 protein types. SD, standard deviation; N, number of cultivars tested. Means followed by different letters each row are significantly different at <i>P</i> = 0.05 within one environment. <i>F</i> ratio and probability based on one-way analysis of variance.</p

Results of GLM association of SNP traits.

<p>Result of structure-based association mapping (<i>P</i><0.05) of haplotypes H1–H5, by GLM analysis of TASSEL. <i>R²</i>, the total variation explained by the SNP.</p>a<p>7 SNPs in LD:S_194, S_278, S_968, S_1804, S_1808, S_3207, and S_3635. They were in complete linkage disequilibrium and gathered in haplotype H4.</p>b<p>10 SNP in LD:S_30, S_58, S_207, S_392, S_857, S_876, S_2652, S_3252, S_3346, and S_3815. They were in complete linkage disequilibrium and they were the introgressed SNPs transferred from <i>japonica</i> to <i>indica</i>.</p