Llam-Mdcnet for Detecting Remote Sensing Images of Dead Tree Clusters

Clusters of dead trees are forest fires-prone. To maintain ecological balance and realize its protection, timely detection of dead trees in forest remote sensing images using existing computer vision methods is of great significance. Remote sensing images captured by Unmanned aerial vehicles (UAVs) typically have several issues, e.g., mixed distribution of adjacent but different tree classes, interference of redundant information, and high differences in scales of dead tree clusters, making the detection of dead tree clusters much more challenging. Therefore, based on the Multipath dense composite network (MDCN), an object detection method called LLAM-MDCNet is proposed in this paper. First, a feature extraction network called Multipath dense composite network is designed. The network\u27s multipath structure can substantially increase the extraction of underlying and semantic features to enhance its extraction capability for rich-information regions. Following that, in the row, column, and diagonal directions, the Longitude Latitude Attention Mechanism (LLAM) is presented and incorporated into the feature extraction network. The multi-directional LLAM facilitates the suppression of irrelevant and redundant information and improves the representation of high-level semantic feature information. Lastly, an AugFPN is employed for down-sampling, yielding a more comprehensive representation of image features with the combination of low-level texture features and high-level semantic information. Consequently, the network\u27s detection effect for dead tree cluster targets with high-scale differences is improved. Furthermore, we make the collected high-quality aerial dead tree cluster dataset containing 19,517 images shot by drones publicly available for other researchers to improve the work in this paper. Our proposed method achieved 87.25% mAP with an FPS of 66 on our dataset, demonstrating the effectiveness of the LLAM-MDCNet for detecting dead tree cluster targets in forest remote sensing images

Akkermansia muciniphila Enhances Egg Quality and the Lipid Profile of Egg Yolk by Improving Lipid Metabolism

Akkermansia muciniphila (A. muciniphila) has shown potential as a probiotic for the prevention and treatment of non-alcoholic fatty liver disease in both humans and mice. However, relatively little is known about the effects of A. muciniphila on lipid metabolism, productivity, and product quality in laying hens. In this study, we explored whether A. muciniphila supplementation could improve lipid metabolism and egg quality in laying hens and sought to identify the underlying mechanism. In the first experiment, 80 Hy-Line Brown laying hens were divided into four groups, one of which was fed a normal diet (control group), while the other three groups were administered a high-energy, low-protein diet to induce fatty liver hemorrhagic syndrome (FLHS). Among the three FLHS groups, one was treated with phosphate-buffered saline, one with live A. muciniphila, and one with pasteurized A. muciniphila. In the second experiment, 140 Hy-Line Brown laying hens were divided into two groups and respectively fed a basal diet supplemented or not with A. muciniphila lyophilized powder. The results showed that, in laying hens with FLHS, treatment with either live or pasteurized A. muciniphila efficiently decreased body weight, abdominal fat deposition, and lipid content in both serum and the liver; downregulated the mRNA expression of lipid synthesis-related genes and upregulated that of lipid transport-related genes in the liver; promoted the growth of short-chain fatty acids (SCFAs)-producing microorganisms and increased the cecal SCFAs content; and improved the yolk lipid profile. Additionally, the supplementation of lyophilized powder of A. muciniphila to aged laying hens reduced abdominal fat deposition and total cholesterol (TC) levels in both serum and the liver, suppressed the mRNA expression of cholesterol synthesis-related genes in the liver, reduced TC content in the yolk, increased eggshell thickness, and reshaped the composition of the gut microbiota. Collectively, our findings demonstrated that A. muciniphila can modulate lipid metabolism, thereby, promoting laying hen health as well as egg quality and nutritive value. Live, pasteurized, and lyophilized A. muciniphila preparations all have the potential for use as additives for improving laying hen production

The Gut Microbiome Signatures Discriminate Healthy From Pulmonary Tuberculosis Patients

Cross talk occurs between the human gut and the lung through a gut-lung axis involving the gut microbiota. However, the signatures of the human gut microbiota after active Mycobacterium tuberculosis infection have not been fully understood. Here, we investigated changes in the gut microbiota in tuberculosis (TB) patients by shotgun sequencing the gut microbiomes of 31 healthy controls and 46 patients. We observed a dramatic changes in gut microbiota in tuberculosis patients as reflected by significant decreases in species number and microbial diversity. The gut microbiota of TB patients were mostly featured by the striking decrease of short-chain fatty acids (SCFAs)-producingbacteria as well as associated metabolic pathways. A classification model based on the abundance of three species, Haemophilus parainfluenzae, Roseburia inulinivorans, and Roseburia hominis, performed well for discriminating between healthy and diseased patients. Additionally, the healthy and diseased states can be distinguished by SNPs in the species of B. vulgatus. We present a comprehensive profile of changes in the microbiota in clinical TB patients. Our findings will shed light on the design of future diagnoses and treatments for M. tuberculosis infections

The GAAS Metagenomic Tool and Its Estimations of Viral and Microbial Average Genome Size in Four Major Biomes

Metagenomic studies characterize both the composition and diversity of uncultured viral and microbial communities. BLAST-based comparisons have typically been used for such analyses; however, sampling biases, high percentages of unknown sequences, and the use of arbitrary thresholds to find significant similarities can decrease the accuracy and validity of estimates. Here, we present Genome relative Abundance and Average Size (GAAS), a complete software package that provides improved estimates of community composition and average genome length for metagenomes in both textual and graphical formats. GAAS implements a novel methodology to control for sampling bias via length normalization, to adjust for multiple BLAST similarities by similarity weighting, and to select significant similarities using relative alignment lengths. In benchmark tests, the GAAS method was robust to both high percentages of unknown sequences and to variations in metagenomic sequence read lengths. Re-analysis of the Sargasso Sea virome using GAAS indicated that standard methodologies for metagenomic analysis may dramatically underestimate the abundance and importance of organisms with small genomes in environmental systems. Using GAAS, we conducted a meta-analysis of microbial and viral average genome lengths in over 150 metagenomes from four biomes to determine whether genome lengths vary consistently between and within biomes, and between microbial and viral communities from the same environment. Significant differences between biomes and within aquatic sub-biomes (oceans, hypersaline systems, freshwater, and microbialites) suggested that average genome length is a fundamental property of environments driven by factors at the sub-biome level. The behavior of paired viral and microbial metagenomes from the same environment indicated that microbial and viral average genome sizes are independent of each other, but indicative of community responses to stressors and environmental conditions

Two ultraviolet radiation datasets that cover China

Ultraviolet (UV) radiation has significant effects on ecosystems, environments, and human health, as well as atmospheric processes and climate change. Two ultraviolet radiation datasets are described in this paper. One contains hourly observations of UV radiation measured at 40 Chinese Ecosystem Research Network stations from 2005 to 2015. CUV3 broadband radiometers were used to observe the UV radiation, with an accuracy of 5%, which meets the World Meteorology Organization's measurement standards. The extremum method was used to control the quality of the measured datasets. The other dataset contains daily cumulative UV radiation estimates that were calculated using an all-sky estimation model combined with a hybrid model. The reconstructed daily UV radiation data span from 1961 to 2014. The mean absolute bias error and root-mean-square error are smaller than 30% at most stations, and most of the mean bias error values are negative, which indicates underestimation of the UV radiation intensity. These datasets can improve our basic knowledge of the spatial and temporal variations in UV radiation. Additionally, these datasets can be used in studies of potential ozone formation and atmospheric oxidation, as well as simulations of ecological processes