Dietary supplementation of Allium mongolicum modulates rumen-hindgut microbial community structure in Simmental calves

Compared to traditional herbage, functional native herbage is playing more important role in ruminant agriculture through improving digestion, metabolism and health of livestock; however, their effects on rumen microbial communities and hindgut fermentation are still not well understood. The objective of present study was to evaluate the effects of dietary addition of Allium mongolicum on bacterial communities in rumen and feces of claves. Sixteen 7-month-old male calves were randomly divided into four groups (n = 4). All calves were fed a basal ration containing roughage (alfalfa and oats) and mixed concentrate in a ratio of 60:40 on dry matter basis. In each group, the basal ration was supplemented with Allium mongolicum 0 (SL0), 200 (SL200), 400 (SL400), and 800 (SL800) mg/kg BW. The experiment lasted for 58 days. Rumen fluid and feces in rectum were collected, Rumen fluid and hindgut fecal were collected for analyzing bacterial community. In the rumen, Compared with SL0, there was a greater relative abundance of phylum Proteobacteria (p < 0.05) and genera Rikenellaceae_RC9_gut_group (p < 0.01) in SL800 treatment. In hindgut, compared with SL0, supplementation of A. mongolicum (SL200, SL400, or SL800) decreased in the relative abundances of Ruminococcaceae_UCG-014 (p < 0.01), Ruminiclostridium_5 (p < 0.01), Eubacterium_coprostanoligenes_group (p < 0.05), and Alistipes (p < 0.05) in feces; Whereas, the relative abundances of Christensenellaceae_R-7_group (p < 0.05), and Prevotella_1 (p < 0.01) in SL800 were higher in feces, to maintain hindgut stability. This study provided evidence that A. mongolicum affects the gastrointestinal of calves, by influencing microbiota in their rumen and feces

Application Research of Bridge Damage Detection Based on the Improved Lightweight Convolutional Neural Network Model

To ensure the safety and rational use of bridge traffic lines, the existing bridge structural damage detection models are not perfect for feature extraction and have difficulty meeting the practicability of detection equipment. Based on the YOLO (You Only Look Once) algorithm, this paper proposes a lightweight target detection algorithm with enhanced feature extraction of bridge structural damage. The BIFPN (Bidirectional Feature Pyramid Network) network structure is used for multi-scale feature fusion, which enhances the ability to extract damage features of bridge structures, and uses EFL (Equalized Focal Loss) to optimize the sample imbalance processing mechanism, which improves the accuracy of bridge structure damage target detection. The evaluation test of the model has been carried out in the constructed BDD (Bridge Damage Dataset) dataset. Compared with the YOLOv3-tiny, YOLOv5S, and B-YOLOv5S models, the [email protected] of the BE-YOLOv5S model increased by 45.1%, 2%, and 1.6% respectively. The analysis and comparison of the experimental results prove that the BE-YOLOv5S network model proposed in this paper has a better performance and a more reliable performance in the detection of bridge structural damage. It can meet the needs of bridge structure damage detection engineering with high requirements for real-time and flexibility

Application Research of Bridge Damage Detection Based on the Improved Lightweight Convolutional Neural Network Model

To ensure the safety and rational use of bridge traffic lines, the existing bridge structural damage detection models are not perfect for feature extraction and have difficulty meeting the practicability of detection equipment. Based on the YOLO (You Only Look Once) algorithm, this paper proposes a lightweight target detection algorithm with enhanced feature extraction of bridge structural damage. The BIFPN (Bidirectional Feature Pyramid Network) network structure is used for multi-scale feature fusion, which enhances the ability to extract damage features of bridge structures, and uses EFL (Equalized Focal Loss) to optimize the sample imbalance processing mechanism, which improves the accuracy of bridge structure damage target detection. The evaluation test of the model has been carried out in the constructed BDD (Bridge Damage Dataset) dataset. Compared with the YOLOv3-tiny, YOLOv5S, and B-YOLOv5S models, the [email protected] of the BE-YOLOv5S model increased by 45.1%, 2%, and 1.6% respectively. The analysis and comparison of the experimental results prove that the BE-YOLOv5S network model proposed in this paper has a better performance and a more reliable performance in the detection of bridge structural damage. It can meet the needs of bridge structure damage detection engineering with high requirements for real-time and flexibility

Genomic analysis of Colletotrichum camelliae responsible for tea brown blight disease

Abstract Background Colletotrichum camelliae, one of the most important phytopathogenic fungi infecting tea plants (Camellia sinensis), causes brown blight disease resulting in significant economic losses in yield of some sensitive cultivated tea varieties. To better understand its phytopathogenic mechanism, the genetic information is worth being resolved. Results Here, a high-quality genomic sequence of C. camelliae (strain LT-3-1) was sequenced using PacBio RSII sequencing platform, one of the most advanced Three-generation sequencing platforms and assembled. The result showed that the fungal genomic sequence is 67.74 Mb in size (with the N50 contig 5.6 Mb in size) containing 14,849 putative genes, of which about 95.27% were annotated. The data revealed a large class of genomic clusters potentially related to fungal pathogenicity. Based on the Pathogen Host Interactions database, a total of 1698 genes (11.44% of the total ones) were annotated, containing 541 genes related to plant cell wall hydrolases which is remarkably higher than those of most species of Colletotrichum and others considered to be hemibiotrophic and necrotrophic fungi. It’s likely that the increase in cell wall-degrading enzymes reflects a crucial adaptive characteristic for infecting tea plants. Conclusion Considering that C. camelliae has a specific host range and unique morphological and biological traits that distinguish it from other species of the genus Colletotrichum, characterization of the fungal genome will improve our understanding of the fungus and its phytopathogenic mechanism as well

Molecular identification of Physalis species (Solanaceae) using a candidate DNA barcode: the chloroplast psbA–trnH intergenic region

Physalis L., an important genus of the family Solanaceae, includes many commercially important edible and medicinal species. Traditionally, species identification is based on morphological traits; however, the highly similar morphological traits among Physalis species make this approach difficult. In this study, we evaluated the feasibility of using a popular DNA barcode, the chloroplast psbA–trnH intergenic region, in the identification of Physalis species. Thirty-six psbA–trnH regions of Physalis species and of the closely related plant Nicandra physalodes were analyzed. The success rates of PCR amplification and sequencing of the psbA–trnH region were 100%. MEGA V6.0 was utilized to align the psbA–trnH sequences and to compute genetic distances. The results show an apparent barcoding gap between intra- and inter-specific variations. Results of both BLAST1 and nearest-distance methods prove that the psbA–trnH regions can be used to identify all species examined in the present study. In addition, phylogenetic analysis using psbA–trnH data revealed a distinct boundary between species. It also confirmed the relationship between Physalis species and closely related species, as established by previous studies. In conclusion, the psbA–trnH intergenic region can be used as an efficient DNA barcode for the identification of Physalis species.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

The role of plasma cortisol in dementia, epilepsy, and multiple sclerosis: A Mendelian randomization study

BackgroundMany clinical studies have shown a correlation between plasma cortisol and neurological disorders. This study explored the causal relationship between plasma cortisol and dementia, epilepsy and multiple sclerosis based on Mendelian randomization (MR) method.MethodsData were taken from the summary statistics of a genome-wide association study, FinnGen consortium and United Kingdom Biobank. Dementia, epilepsy, and multiple sclerosis were used as outcomes, and genetic variants associated with plasma cortisol were used as instrumental variables. The main analysis was performed using the inverse variance weighted method, and the results were assessed according to the odds ratio (OR) and 95% confidence interval. Heterogeneity tests, pleiotropy tests, and leave-one-out method were conducted to evaluate the stability and accuracy of the results.ResultsIn two-sample MR analysis, the inverse variance weighted method showed that plasma cortisol was associated with Alzheimer’s disease (AD) [odds ratio (95% confidence interval) = 0.99 (0.98-1.00), P = 0.025], vascular dementia (VaD) [odds ratio (95% confidence interval) = 2.02 (1.00-4.05), P = 0.049)], Parkinson’s disease with dementia (PDD) [odds ratio (95% confidence interval) = 0.24 (0.07-0.82), P = 0.023] and epilepsy [odds ratio (95% confidence interval) = 2.00 (1.03-3.91), P = 0.042]. There were no statistically significant associations between plasma cortisol and dementia with Lewy bodies (DLB), frontotemporal dementia (FTD) and multiple sclerosis.ConclusionThis study demonstrates that plasma cortisol increase the incidence rates of epilepsy and VaD and decrease the incidence rates of AD and PDD. Monitoring plasma cortisol concentrations in clinical practice can help prevent diseases, such as AD, PDD, VaD and epilepsy

Remotely Sensed Prediction of Rice Yield at Different Growth Durations Using UAV Multispectral Imagery

A precise forecast of rice yields at the plot scale is essential for both food security and precision agriculture. In this work, we developed a novel technique to integrate UAV-based vegetation indices (VIs) with brightness, greenness, and moisture information obtained via tasseled cap transformation (TCT) to improve the precision of rice-yield estimates and eliminate saturation. Eight nitrogen gradients of rice were cultivated to acquire measurements on the ground, as well as six-band UAV images during the booting and heading periods. Several plot-level VIs were then computed based on the canopy reflectance derived from the UAV images. Meanwhile, the TCT-based retrieval of the plot brightness (B), greenness (G), and a third component (T) indicating the state of the rice growing and environmental information, was performed. The findings indicate that ground measurements are solely applicable to estimating rice yields at the booting stage. Furthermore, the VIs in conjunction with the TCT parameters exhibited a greater ability to predict the rice yields than the VIs alone. The final simulation models showed the highest accuracy at the booting stage, but with varying degrees of saturation. The yield-prediction models at the heading stage satisfied the requirement of high precision, without any obvious saturation phenomenon. The product of the VIs and the difference between the T and G (T − G) and the quotient of the T and B (T/B) was the optimum parameter for predicting the rice yield at the heading stage, with an estimation error below 7%. This study offers a guide and reference for rice-yield estimation and precision agriculture

Application of the ribosomal DNA ITS2 region of Physalis (Solanaceae): DNA barcoding and phylogenetic study

Recently, commercial interest in Physalis species has grown worldwide due to their high nutritional value, edible fruit and potential medicinal properties. However, many Physalis species have similar shapes and are easily confused, and consequently the phylogenetic relationships between Physalis species are poorly understood. This hinders their safe utilization and genetic resource conservation. In this study, the nuclear ribosomal ITS2 region was used to identify species and phylogenetically examine Physalis. Eighty-six ITS2 regions from 45 Physalis species were analyzed. The ITS2 sequences were aligned using Clustal W and genetic distances were calculated using MEGA V6.0. The results showed that ITS2 regions have significant intra- and inter-specific divergences, obvious barcoding gaps, and higher species discrimination rates (82.2% for both the BLASTA1 and nearest distance methods). In addition, the secondary structure of ITS2 provided another way to differentiate species. Cluster analysis based on ITS2 regions largely concurred with the relationships among Physalis species established by many previous molecular analyses, and showed that most sections of Physalis appear to be polyphyletic. Our results demonstrated that ITS2 can be used as an efficient and powerful marker in the identification and phylogenetic study of Physalis species. The technique provides a scientific basis for the conservation of Physalis plants and for utilization of resources

Development of Chloroplast Microsatellite Markers and Evaluation of Genetic Diversity and Population Structure of Cutleaf Groundcherry (<i>Physalis angulata</i> L.) in China

Cutleaf groundcherry (Physalis angulata L.), an annual plant containing a variety of active ingredients, has great medicinal value. However, studies on the genetic diversity and population structure of P. angulata are limited. In this study, we developed chloroplast microsatellite (cpSSR) markers and applied them to evaluate the genetic diversity and population structure of P. angulata. A total of 57 cpSSRs were identified from the chloroplast genome of P. angulata. Among all cpSSR loci, mononucleotide markers were the most abundant (68.24%), followed by tetranucleotide (12.28%), dinucleotide (10.53%), and trinucleotide (8.77%) markers. In total, 30 newly developed cpSSR markers with rich polymorphism and good stability were selected for further genetic diversity and population structure analyses. These cpSSRs amplified a total of 156 alleles, 132 (84.62%) of which were polymorphic. The percentage of polymorphic alleles and the average polymorphic information content (PIC) value of the cpSSRs were 81.29% and 0.830, respectively. Population genetic diversity analysis indicated that the average observed number of alleles (Na), number of effective alleles (He), Nei’s gene diversity (h), and Shannon information indices (I) of 16 P. angulata populations were 1.3161, 1.1754, 0.1023, and 0.1538, respectively. Moreover, unweighted group arithmetic mean, neighbor-joining, principal coordinate, and STRUCTURE analyses indicated that 203 P. angulata individuals from 16 populations were grouped into four clusters. A molecular variance analysis (AMOVA) illustrated the considerable genetic variation among populations, while the gene flow (Nm) value (0.2324) indicated a low level of gene flow among populations. Our study not only provided a batch of efficient genetic markers for research on P. angulata but also laid an important foundation for the protection and genetic breeding of P. angulata resources