RESUMO
The basidiomycetous fungus Rhizoctonia solani Kühn (teleomorph Thanatephorus cucumeris [Frank] Donk) is a fungal pathogen that causes various diseases on economically important crops, such as foxtail millet, maize, and rice. Using the PacBio Sequel platform, we assembled a draft genome of an R. solani strain AG4-JY that was isolated from foxtail millet with sheath blight at the stem. The genome was approximately 43.43 Mb on 53 scaffolds, with a scaffold N50 length of 2.10 Mb. In all, 10,545 genes and 179 noncoding RNAs were predicted, and 10,488 genes had at least one database annotation. In addition, the proteins encoded by 709 genes were predicted as secretory proteins. The AG4-JY genome sequence provides a valuable resource for understanding the interactions between R. solani and foxtail millet and controls sheath blight in the world.
Assuntos
Setaria (Planta) , Setaria (Planta)/genética , Rhizoctonia/genéticaRESUMO
Cancer immunotherapy is a promising therapeutic approach, but the prognostic value of immune-related genes in osteosarcoma (OS) is unknown. Here, Target-OS RNA-seq data were analyzed to detect differentially expressed genes (DEGs) between OS subgroups, followed by functional enrichment analysis. Cox proportional risk regression was performed for each immune-related gene, and a risk score model to predict the prognosis of patients with OS was constructed. The risk scores were calculated using the risk signature to divide the training set into high-risk and low-risk groups, and validation was performed with GSE21257. We identified two immune-associated clusters, C1 and C2. C1 was closely related to immunity, and the immune score was significantly higher in C1 than in C2. Furthermore, we validated 6 immune cell hub genes related to the prognosis of OS: CD8A, KIR2DL1, CD79A, APBB1IP, GAL, and PLD3. Survival analysis revealed that the prognosis of the high-risk group was significantly worse than that of the low-risk group. We also explored whether the 6-gene prognostic risk model was effective for survival prediction. In conclusion, the constructed a risk score model based on immune-related genes and the survival of patients with OS could be a potential tool for targeted therapy.
Assuntos
Biomarcadores Tumorais/genética , Neoplasias Ósseas/genética , Neoplasias Ósseas/imunologia , Osteossarcoma/genética , Osteossarcoma/imunologia , Proteínas Adaptadoras de Transdução de Sinal , Biomarcadores Tumorais/metabolismo , Neoplasias Ósseas/diagnóstico , Neoplasias Ósseas/mortalidade , Antígenos CD79 , Antígenos CD8 , Complemento C1 , Complemento C2 , Exodesoxirribonucleases , Feminino , Galanina , Humanos , Masculino , Proteínas de Membrana , Terapia de Alvo Molecular , Osteossarcoma/diagnóstico , Osteossarcoma/mortalidade , Fosfolipase D , Prognóstico , Receptores KIR2DL1 , Fatores de Risco , Taxa de SobrevidaRESUMO
This brief is concerned with the robust stability problem for a class of discrete-time uncertain Markovian jumping neural networks with defective statistics of modes transitions. The parameter uncertainties are considered to be norm-bounded, and the stochastic perturbations are described in terms of Brownian motion. Defective statistics means that the transition probabilities of the multimode neural networks are not exactly known, as assumed usually. The scenario is more practical, and such defective transition probabilities comprise three types: known, uncertain, and unknown. By invoking the property of the transition probability matrix and the convexity of uncertain domains, a sufficient stability criterion for the underlying system is derived. Furthermore, a monotonicity is observed concerning the maximum value of a given scalar, which bounds the stochastic perturbation that the system can tolerate as the level of the defectiveness varies. Numerical examples are given to verify the effectiveness of the developed results.