Plant leaf diseases are closely related to people's daily life. Due to the wide variety of diseases, it is not only time-consuming and labor-intensive to identify and classify diseases by artificial ...eyes, but also easy to be misidentified with having a high error rate. Therefore, we proposed a deep learning-based method to identify and classify plant leaf diseases. The proposed method can take the advantages of the neural network to extract the characteristics of diseased parts, and thus to classify target disease areas. To address the issues of long training convergence time and too-large model parameters, the traditional convolutional neural network was improved by combining a structure of inception module, a squeeze-and-excitation (SE) module and a global pooling layer to identify diseases. Through the Inception structure, the feature data of the convolutional layer were fused in multi-scales to improve the accuracy on the leaf disease dataset. Finally, the global average pooling layer was used instead of the fully connected layer to reduce the number of model parameters. Compared with some traditional convolutional neural networks, our model yielded better performance and achieved an accuracy of 91.7% on the test data set. At the same time, the number of model parameters and training time have also been greatly reduced. The experimental classification on plant leaf diseases indicated that our method is feasible and effective.
The gut microbiota has an important role in animal health and performance, but its contribution is difficult to determine, in particular given the effects of host genetic factors. Here, whole-genome ...sequencing of the hosts and 16S rRNA gene sequencing of the microbiota were performed to separate the effects between host genetics and the microbiota in the duodenum, jejunum, ileum, caecum and faeces on fat deposition in 206 yellow broilers reared under identical conditions. Despite the notable spatial variation in the diversity, composition and potential function of the gut microbiota, host genetics exerted limited effects on the gut microbial community. The duodenal and caecal microbiota made greater contributions to fat deposition and could separately account for 24% and 21% of the variance in the abdominal fat mass after correcting for host genetic effects. We further identified two caecal microbial taxa, Methanobrevibacter and Mucispirillum schaedleri, which were significantly correlated with fat deposition. Chickens with a lower Methanobrevibacter abundance had significantly lower abdominal fat content than those with a higher abundance of Methanobrevibacter (35.51 vs. 55.59 g), and the body weights of these chickens did not notably differ. Chickens with a higher M. schaedleri abundance exhibited lower abdominal fat accumulation (39.88 vs. 55.06 g) and body weight (2.23 vs. 2.41 kg) than those with a lower abundance of this species. These findings may aid the development of strategies for altering the gut microbiota to control fat deposition during broiler production.
3D point cloud is one of the most common and basic 3D object representation model that is widely used in virtual/augmented reality applications, e.g., immersive communication. Compression of 3D point ...cloud is a big challenge because of its huge data volume and irregular data structure. In this paper, we propose a sampling-based compression algorithm for 3D point clouds. First, a 3D point cloud was resampled by a graph filter to obtain a subset of representative 3D points. Then, the representative points were compressed by the G-PCC (geometry-based point cloud compression) encoder software that was released by MPEG. Finally, the decoded representative points were used to reconstruct the original 3D point clouds by a CNN-based up-sampling approach. Experimental results demonstrate that a significant (73.15%) bit rate reduction can be achieved by the proposed 3D point cloud compression algorithm with minimal quality degradation of the reconstructed 3D point clouds.
•Hydrothermal treatment is divided into three stages with the temperature increasing.•Hydrothermal treatment promotes the cleavage of weak bonds by hydrolysis.•Hydrogen transfer takes place between ...water and lignite.•Pyrolysis tar yields increase 22% and pyrolysis water decreases after hydrothermal treatment at 513K.•The char yields increase and the gas yields decrease for the treated lignite pyrolysis.
Hydrothermal treatment of Xiaolongtan lignite (XLT) was carried out in a lab autoclave and the pyrolysis was studied in a tube reactor at 873K for 15min. The structure of the lignite was characterized by solid state 13C nuclear magnetic resonance (NMR) and Fourier transform infrared spectroscopy (FTIR). The results show that the lignite structure and pyrolysis product distribution have a significant change after hydrothermal treatment. Hydrothermal treatment is divided into three stages with the temperature increasing. The oxygen functional groups decompose and substituted by hydrogen in the first stage (<493K). The pyrolysis tar yields increase from 7.97wt.% of XLT to 9.60wt.% of XLT-493 and the pyrolysis water decreases. In the second stage (493–533K), hydrothermal treatment disrupts the weak covalent bond such as ether, ester and side-chain substituent by hydrolysis. Meanwhile, hydrogen transfer takes place between water and lignite or among the intra-molecular structure lead to increment of the CH2/CH3 ratio. The pyrolysis tar yields increase to 9.76wt.% of XLT-513 and the pyrolysis water decreases sequentially. Then, the pyrolysis water increases and pyrolysis tar decreases because of the obvious increment of the oxygen-linked aromatic carbon and little increase of the ratio of CH2/CH3. The pyrolysis water and tar decrease subsequently in the third stage (>533K) as the result of the cleavage of covalent bonds.
Background
Information about orthopedics diseases on the Internet has not been extensively assessed. Our purpose was to evaluate the quality of online information of osteosarcoma on current ...video-sharing platforms in mainland China.
Method
TikTok and Bilibili were independently queried from June to July 2023 by four independent researchers using the Microsoft Edge web browser. Information about the videos and creators was recorded, and descriptive analyses were conducted.
Results
After data extraction, a total of 95 videos were included, in which 43 videos were uploaded by certified doctors (45.3%), with 35 videos (36.8%) uploaded by certified orthopedic surgeons. Of the content of these videos, 78.9% were introduction (n = 75), 64.2% were on professional knowledge (n = 61), 28.4% were on treatment (n = 27), while 5.3% were on surgical techniques (n = 5). The mean DISCERN total score was 43.8 ± 13.4, and the mean JAMA score was 3.8 ± 0.3.
Conclusions
Videos about osteosarcoma on current video-sharing platforms were extensive, but were not comprehensive and professional. Although current online videos have the potential to improve public awareness on osteosarcoma, due to their quality and content, were not assessed to be good sources for medical education.
Despite the convenience and non-invasiveness of fecal sampling, the fecal microbiota does not fully represent that of the gastrointestinal (GI) tract, and the efficacy of fecal sampling to accurately ...represent the gut microbiota in birds is poorly understood. In this study, we aim to identify the efficacy of feces as a gut proxy in birds using chickens as a model. We collected 1,026 samples from 206 chickens, including duodenum, jejunum, ileum, cecum, and feces samples, for 16S rRNA amplicon sequencing analyses. In this study, the efficacy of feces as a gut proxy was partitioned to microbial community membership and community structure. Most taxa in the small intestine (84.11-87.28%) and ceca (99.39%) could be identified in feces. Microbial community membership was reflected with a gut anatomic feature, but community structure was not. Excluding shared microbes, the small intestine and ceca contributed 34.12 and 5.83% of the total fecal members, respectively. The composition of Firmicutes members in the small intestine and that of Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria members in the ceca could be well mirrored by the observations in fecal samples (ρ = 0.54-0.71 and 0.71-0.78, respectively, P < 0.001). However, there were few significant correlations for each genus between feces and each of the four gut segments, and these correlations were not high (ρ = -0.2-0.4, P < 0.05) for most genera. Our results suggest that fecal microbial community has a good potential to identify most taxa in the chicken gut and could moderately mirror the microbial structure in the intestine at the microbial population level with phylum specificity. However, it should be interpreted with caution by using feces as a proxy to study associations for microbial structure at individual microorganism level.
Carcass traits in broiler chickens are complex traits that are influenced by multiple genes. To gain deeper insights into the genetic mechanisms underlying carcass traits, here we conducted a ...weighted single-step genome-wide association study (wssGWAS) in a population of Chinese yellow-feathered chicken. The objective was to identify genomic regions and candidate genes associated with carcass weight (CW), eviscerated weight with giblets (EWG), eviscerated weight (EW), breast muscle weight (BMW), drumstick weight (DW), abdominal fat weight (AFW), abdominal fat percentage (AFP), gizzard weight (GW), and intestine length (IL). A total of 1,338 broiler chickens with phenotypic and pedigree information were included in this study. Of these, 435 chickens were genotyped using a 600K single nucleotide polymorphism chip for association analysis. The results indicate that the most significant regions for 9 traits explained 2.38% to 5.09% of the phenotypic variation, from which the region of 194.53 to 194.63Mb on chromosome 1 with the gene RELT and FAM168A identified on it was significantly associated with CW, EWG, EW, BMW, and DW. Meanwhile, the 5 traits have a strong genetic correlation, indicating that the region and the genes can be used for further research. In addition, some candidate genes associated with skeletal muscle development, fat deposition regulation, intestinal repair, and protection were identified. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses suggested that the genes are involved in processes such as vascular development (CD34, FGF7, FGFR3, ITGB1BP1, SEMA5A, LOXL2), bone formation (FGFR3, MATN1, MEF2D, DHRS3, SKI, STC1, HOXB1, HOXB3, TIPARP), and anatomical size regulation (ADD2, AKT1, CFTR, EDN3, FLII, HCLS1, ITGB1BP1, SEMA5A, SHC1, ULK1, DSTN, GSK3B, BORCS8, GRIP2). In conclusion, the integration of phenotype, genotype, and pedigree information without creating pseudo-phenotype will facilitate the genetic improvement of carcass traits in chickens, providing valuable insights into the genetic architecture and potential candidate genes underlying carcass traits, enriching our understanding and contributing to the breeding of high-quality broiler chickens.
Yellow-feather broilers take a large portion of poultry industry in China due to its meat characteristics. Improving the growth traits of yellow-feathered broilers will have great significance for ...the Chinese poultry market. The current study was designed to investigate the potential genetic factors using the weighted single-step genome-wide association study (wssGWAS) method, which takes consideration of more factors including pedigree, sex, environment and has more accuracy than traditional GWAS. The yellow-feather dwarf chickens from Wens Nanfang Poultry Breeding Co. Ltd. were revolved to recode 9 growth traits: Average daily gain (ADG), body weight (BW) at 45 d, 49 d, 56 d, 63 d, 70 d, 77 d, 84 d, 91 d for analysis. For the results, the region 4.63 to 5.03 Mb on chromosome 15, which was the QTL overlapped in BW45, BW49, BW56, BW63, BW84, might be the crucial genetic region for growth traits. Seven GO terms and 3 KEGG pathways, GO:0005200, GO:0005882, GO:0045111, GO:0099513, GO:0099081, GO:0099512, GO:0099080, KEGG:gga04020, KEGG:gga04540, KEGG:gga04210, were detected to relevant with growth traits. The genes enriched in these biological processes (NRAS, TUBB1, ADORA2B, NTRK3, NGF, TNNC2, F-KER, LOC429492, LOC431325, LOC431324, LOC396480) might have the function in growth of yellow-feather broilers.
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