Identification and Genome Characterization of a Novel Nege-like Virus Isolated from Aphids (Aphis gossypii) in Yunnan Province.

Negeviruses are insect-specific enveloped RNA viruses that exhibit a wide geographic distribution. A novel nege-like virus, tentatively named Aphis gossypii nege-like virus (AGNLV, GenBank: OR880429.1), was isolated from aphids (Aphis gossypii) in Lijiang City, Yunnan, China. AGNLV has a genome sequence of 9258 nt (excluding the polyA tail) encoding three open reading frames (ORFs). ORF1 (7149 nt) encodes a viral methyltransferase, a viral RNA helicase, and an RNA-dependent RNA polymerase. ORF2 (1422 nt) encodes a DiSB-ORF2_chro domain and ORF3 encodes an SP24 domain. The genome sequence of AGNLV shares the highest nucleotide identity of 60.0% and 59.5% with Wuhan house centipede virus 1 (WHCV1) and Astegopteryx formosana nege-like virus (AFNLV), respectively. Phylogenetic analysis based on the RNA-dependent RNA polymerase shows that AGNLV is clustered with other negeviruses and nege-like viruses discovered in aphids, forming a distinct "unclassified clade". Interestingly, AGNLV only encodes three ORFs, whereas AFNLV and WHCV1 have four ORFs. Structure and transmembrane domain predictions show the presence of eight alpha helices and five transmembrane helices in the AGNLV ORF3. Translational enhancement of the AGNLV 5' UTR was similar to that of the 5' UTR of plant viruses. Our findings provide evidence of the diversity and structure of nege-like viruses and are the first record of such a virus from a member of the genus Aphis.

Open-Set Recognition of Individual Cows Based on Spatial Feature Transformation and Metric Learning.

The automated recognition of individual cows is foundational for implementing intelligent farming. Traditional methods of individual cow recognition from an overhead perspective primarily rely on singular back features and perform poorly for cows with diverse orientation distributions and partial body visibility in the frame. This study proposes an open-set method for individual cow recognition based on spatial feature transformation and metric learning to address these issues. Initially, a spatial transformation deep feature extraction module, ResSTN, which incorporates preprocessing techniques, was designed to effectively address the low recognition rate caused by the diverse orientation distribution of individual cows. Subsequently, by constructing an open-set recognition framework that integrates three attention mechanisms, four loss functions, and four distance metric methods and exploring the impact of each component on recognition performance, this study achieves refined and optimized model configurations. Lastly, introducing moderate cropping and random occlusion strategies during the data-loading phase enhances the model's ability to recognize partially visible individuals. The method proposed in this study achieves a recognition accuracy of 94.58% in open-set scenarios for individual cows in overhead images, with an average accuracy improvement of 2.98 percentage points for cows with diverse orientation distributions, and also demonstrates an improved recognition performance for partially visible and randomly occluded individual cows. This validates the effectiveness of the proposed method in open-set recognition, showing significant potential for application in precision cattle farming management.

Prediction of lake chlorophyll concentration using the BP neural network and Sentinel-2 images based on time features.

One of the most important indicators of lake eutrophication is chlorophyll-a (Chl-a) concentration, which is also an essential component of lake water quality monitoring. It is an efficient, economical and convenient method to monitor the Chl-a concentration through remote sensing images. Taking the Wuliangsuhai Lake as an example, the relevant bands of Sentinel-2 images were used as the input and the Chl-a concentration as the output to build neural network models. In the process of building the model, we mainly studied and tested the impact of adding time features to the model input on the model accuracy. Through the experiment, it was found that the month and day difference features of remote sensing images and Chl-a measurement could significantly improve the prediction accuracy of Chl-a concentration in varying degrees. Finally, it was determined that the neural network prediction model with 12 bands of Sentinel-2 images combined month features as inputs and one hidden layer, eight neurons and Chl-a concentration as outputs was the best. Then, the accuracy of the model was validated when the test set accounts for 20 and 30%, and good results were obtained.

Nanopore/Illumina Hybrid Whole-Genome Sequence Resource of Plenodomus lindquistii Strain US01 Infecting Sunflower.

Plenodomus lindquistii (basionym: Leptosphaeria lindquistii) is the most prevalent fungus pathogen causing the black stem disease in sunflower. To date, only one whole-genome sequence is reported in NCBI, but no complete gene annotation has been submitted. Herein, we sequenced a Leptosphaeria lindquistii isolate from sunflower seeds imported from the United States using Oxford Nanopore long-read sequencing and Illumina short-read sequencing. We generated a 38.6-Mb genome assembly consisting of 29 contigs with an N50 of 1.689 Mb and a maximum length of 5.316 Mb. In the genome assembly, we identified 13,481 protein-coding genes, of which 12,531 were annotated. The high-quality assembly and annotation of the Leptosphaeria lindquistii genome will allow us to conduct further studies for the specific identification of pathogens causing sunflower black stem disease and provide a better understanding of the infection mechanisms of Leptosphaeria lindquistii.

Applying meta-data of soybean grain in origin trace and quarantine inspection.

Soybean and derived products are among the most important food for both humans and animals. China is the world's largest importer of soybeans, with more than 100 million tons of annual imports, mainly from the United States of America (US), Brazil, and Argentina. However, there have been limited studies on the microbiota associated with imported soybean grains. Here, we reveal the soybean microbiota using amplicon sequencing based on samples from four countries on three continents of North America (US), South America (Argentina, Brazil), and Asia (China). Our results showed that the soybean-associated microbiota from different continents significantly separated, presenting strong geographic variations. The core microbial taxa and geographically specified taxa were defined, with Alternaria, Enterobacter, Plectosphaerella, Stenotrophomanas, and Xeromyces defined as the core microbiota for soybean from Asia; Amanita, Aspergillus, Fusarium, Nigrospora, Herbiconiux, Pseudomonas, Saccharopolyspora, and Schumannella from North America; and Bradyrhizobium, Colletotrichum, Filobasidium, Phialosimplex, Mycosphaerella, Septoria, Sphingomonas, and Weissalla, from South America. In addition, we build the Random Forest (RF) model to predict the source of imported soybean grains. We could accurately predict the original countries of imported soybean grains within the RF prediction models, with accuracies greater than 95 %. We constructed a database of soybean-related quarantine pathogens using full-length sequences of fungal ITS region and bacterial 16S rDNA region. Two phytopathogenic fungi, Diaporthe caulivora and Cladosporium cucumerinum, listed in the Chinese quarantine catalog, were intercepted through metabarcoding sequencing. The former was further confirmed using an available national standard protocol of qPCR diagnosis. In summary, our NGS-based approach revealed the microbiota associated with soybeans. It could provide comprehensive information and valuable method on the trace the origin of soybean and detection of quarantine pathogens at Customs and departments of inspection and quarantine.

Cadophora meredithiae and C. interclivum, new species from roots of sedge and spruce in a western Canada subalpine forest.

Two new species of Cadophora are described based on multigene phylogenetic analyses and phenotypic and ecological characters. The species delimitation was based on concordance of gene genealogies. The cultures of the Cadophora species were isolated from the roots of long-beaked sedge and white spruce from a subalpine forest in western Canada; however, they probably have a broader distribution because their internal transcribed spacer (ITS) sequences have high similarity with a number of GenBank sequences from ecological studies of plant roots. The taxonomy of Cadophora in Leotiomycetes is discussed based on the phylogeny generated in this study. Results from this work will facilitate ecological and evolutionary studies on root-associated fungi.

Diaporthe is paraphyletic.

Previous studies have shown that our understanding of species diversity within Diaporthe (syn. Phomopsis) is limited. In this study, 49 strains obtained from different countries were subjected to DNA sequence analysis. Based on these results, eight new species names are introduced for lineages represented by multiple strains and distinct morphology. Twelve Phomopsis species previously described from China were subjected to DNA sequence analysis, and confirmed to belong to Diaporthe. The genus Diaporthe is shown to be paraphyletic based on multi-locus (LSU, ITS and TEF1) phylogenetic analysis. Several morphologically distinct genera, namely Mazzantia, Ophiodiaporthe, Pustulomyces, Phaeocytostroma, and Stenocarpella, are embedded within Diaporthe s. lat., indicating divergent morphological evolution. However, splitting Diaporthe into many smaller genera to achieve monophyly is still premature, and further collections and phylogenetic datasets need to be obtained to address this situation.