Automated segmentation of brain tumour from multimodal MR images is pivotal for the analysis and monitoring of disease progression. As gliomas are malignant and heterogeneous, efficient and accurate segmentation techniques are used for the successful delineation of tumours into intra-tumoural classes. Deep learning algorithms outperform on tasks of semantic segmentation as opposed to the more conventional, context-based computer vision approaches. Extensively used for biomedical image segmentation, Convolutional Neural Networks have significantly improved the state-of-the-art accuracy on the task of brain tumour segmentation. In this paper, we propose an ensemble of two segmentation networks: a 3D CNN and a U-Net, in a significant yet straightforward combinative technique that results in better and accurate predictions. Both models were trained separately on the BraTS-19 challenge dataset and evaluated to yield segmentation maps which considerably differed from each other in terms of segmented tumour sub-regions and were ensembled variably to achieve the final prediction. The suggested ensemble achieved dice scores of 0.750, 0.906 and 0.846 for enhancing tumour, whole tumour, and tumour core, respectively, on the validation set, performing favourably in comparison to the state-of-the-art architectures currently available.

Pure and cobalt-doped nanocrystalline TiO 2 nanostructures, with different concentrations of cobalt (1, 2.5, 5, 10 mol%), were synthesized by a simple hydrothermal route and their antibacterial activity was tested. The corresponding structural and chemical properties of the as-synthesized nanoparticles clearly suggested the occurrence of lattice defects in TiO 2 structure. The crystalline phases and particle-size study of the pure and doped TiO 2 nanoparticles showed the formation of highly pure anatase phase with grain sizes ranging between 7 and 14 nm, where the functional groups and bond lengths were observed through Fourier transform infrared (FTIR) analysis. Raman spectroscopy analysis displayed the broadening of Raman peaks and a systematic frequency shifts with increase in cobalt concentration because of the reduced particle size. Moreover, UV-Vis spectroscopy indicated the visible light absorption of TiO 2 with increase in the concentration of cobalt, which suggests decrease in the energy bandgap. Furthermore, these visible light-activated photocatalysts showed enhanced antibacterial activity in particular against notorious foodborne Gram negative pathogen i.e., Campylobacter jejuni. Moreover, these high energy photocatalysts were able to effectively kill waterborne Gram negative pathogen i.e., Vibrio cholerae as well as foodborne Gram positive pathogen i.e., Staphylococcus aureus. Therefore, we believe that these visible light-activated photocatalysts may be used as broad spectrum antimicrobial agents against groups of such pathogens impacting significantly human health and economy.

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Background and ObjectivesOff-campus online learning methods abruptly increased and gained popularity during the COVID-19 pandemic. Previous studies have highlighted the limitations of online learning mode; however, further studies on the experiences of medical students are needed. This study aimed to investigate the preclinical medical students and faculty members' experiences with online education and learning.Subjects and MethodsIn this cross-sectional study, data were collected using convenience sampling. Two hundred nine students and 13 faculty members who participated in the online courses offered during the spring semester of 2019–2020 completed an online questionnaire. A 30-item questionnaire for the students and a 25-item questionnaire for the faculty were used in this cross-sectional study.ResultsOverall, 30% of the student sample was satisfied; importantly, high-achieving students (GPA > 3.5) were less satisfied (25 vs. 32%; p = 0.006). Satisfaction was also low (35%) for student-faculty interaction opportunities. About half of the student sample agreed that small-group interactive sessions would improve learning (53%). The most favored course format was the blended mode (43%), followed by traditional (40%) and online modes (17%). Six out of 13 (46%) faculty members were satisfied with their online experiences. Most of them found virtual teaching applications convenient (77%). Conversely, few faculty members agreed to interact effectively (54%), while 69% favored a blended format.ConclusionsThe level of satisfaction in fully online courses offered during the COVID-19 pandemic remained low, especially among high-achieving students. Both students and faculty favored the blended format for future purposes. Small group active-learning strategies and web-based interactive tools may facilitate engagement and student-faculty interactions.