Crosby Mejia (tiedesign2)

The consensus sequences obtained after polishing exhibit strikingly high identity to references derived by Illumina and Sanger methods (99.83%-100%). This study suggests that ONT-based amplicon sequencing is a promising platform to deploy in regional aquatic animal health diagnostic laboratories in low- and medium-income countries, for fast identification and genotyping of emerging infectious pathogens from field samples within a single day. Although large datasets are available, to learn a robust dose prediction model from a limited dataset still remains challenging. This work employed cascaded deep-learning models and advanced training strategies with a limited dataset to precisely predict three-dimensional (3D) dose distribution. A Cascade 3D (C3D) model is developed based on the cascade mechanism and 3D U-Net network units. During model training, data augmentations are used to improve the generalization ability of the prediction model. A knowledge distillation technique is employed to further improve the capability of model learning. The C3D network was evaluated using the OpenKBP challenge dataset and competed with those models proposed by more than 40 teams globally. Additionally, it was compared with five existing cutting-edge dose prediction models. The performance of these prediction models were evaluated by voxel-based mean absolute error (MAE) and clinical-related dosimetric metrics. The code and models are publicly available online . The MAE of a single C3D model without test-time augmentation is 2.50Gy (3.57% related to prescription dose) for non-zero dose area, which outperforms the other five dose prediction models by about 0.1Gy-1.7Gy. The C3D model won both Dose and DVH streams of AAPM 2020 OpenKBP challenge with dose score of 2.31 and DVH score of 1.55. The Cascading U-Nets is an ideal solution for 3D dose prediction from a limited dataset. The proper data pre-processing, data augmentation and optimization procedure are more important than architectural modifications of deep learning network. The Cascading U-Nets is an ideal solution for 3D dose prediction from a limited dataset. The proper data pre-processing, data augmentation and optimization procedure are more important than architectural modifications of deep learning network.The newborn coronaivus disease 2019 (COVID-19) pandemic has become the foremost concern of health system worldwide. Interferon typeI (IFN-I) are among the well-known antiviruses. find more Hence IFN-α have gained much attention as a treatment for COVID-19 recently. To sum up the efficiency of IFN-α against COVID-19, we searched PubMed, SCOPUS, and EMBASE, from the date of genesis to the 1st of October 2020. Discharge from hospital and virus clearance considered as primary and secondary outcomes, respectively. We compared the aforementioned outcomes of patients treated with standard care protocol and the patients treated with IFN-α in addition to standard care protocol. Out of 356 identified records, 14 studies were subjected for full-text screening. Finally, a systematic review was performed with inclusion of five studies. Majority of the participants were males (ranged from 43.50% to 90.0%). We found that time of viral clearance and polymerase chain reaction negative (days) in most studies were decreased in the INF-α + standard care group. The mean days of virus's clearance in INF-α group and standard group reported 27.3 and 32.43. Likewise, the average days of hospitalization was found also lower in INF-α group (18.55 vs. 24.36). This study provides a stand to conclude that early administration of INF-α may be accounted as a promising treatment of COVID-19. Inhibition of pancreatic ATP-sensitive K (K ) channels is the intended effect of oral sulphonylureas to increase insulin release in diabetes. However, pertinent to off-target effects of sulphonylurea medication, sex differences in cardiac K channel