A Case of Disseminated Melioidosis With Cerebritis.

Melioidosis is caused by Burkholderia pseudomallei, a Gram-negative, facultative intracellular bacterium. Because melioidosis can mimic many diseases, it requires more advanced laboratory facilities with the necessary expertise and can become an underdiagnosed yet serious infection with high mortality and morbidity. Our patient is a middle-aged male with new-onset uncontrolled type 2 diabetes mellitus who presented with high-grade fever, productive cough and altered mental status. CT thorax showed diffuse middle and lower zone consolidation while MRI brain noted meningitis with cerebritis. Blood culture grew Burkholderia pseudomallei. The patient was started on meropenem for melioidosis, however, no adequate improvement was seen. In view of this inadequate response, parenteral cotrimoxazole was added. Significant improvement was noted and cotrimoxazole was continued for six months.

An ethical visualization of the NorthCOVID-19 model.

When modelling epidemics, the outputs and techniques used may be hard for the general public to understand. This can cause fear mongering and confusion on how to interpret the predictions provided by these models. This article proposes a solution for such a model that was created by a Canadian institute for COVID-19 in their region; namely, the NorthCOVID-19 model. In taking these ethical concerns into consideration, first the web interface of this model is analyzed to see how it may be difficult for a user without a strong mathematical background to understand how to use it. Second, a system is developed that takes this model's outputs as an input and produces a video summarization with an auto-generated audio to address the complexity of the interface, while ensuring that the end user is able to understand the important information produced by this model. A survey conducted on this proposed output asked participants, on a scale of 1 to 5, whether they strongly disagreed (1) or strongly agreed (5) with statements regarding the output of the proposed method. The results showed that the audio in the output was helpful in understanding the results (80% responded with 4 or 5) and that it helped improve overallcomprehension of the model (85% responded with 4 or 5). For the analysis of the NorthCOVID-19 interface, a System Usability Scale (SUS) survey was performed where itreceived a scoring of 70.94 which is slightly above the average of 68.

AdCOFE: Advanced Contextual Feature Extraction in conversations for emotion classification.

Emotion recognition in conversations is an important step in various virtual chatbots which require opinion-based feedback, like in social media threads, online support, and many more applications. Current emotion recognition in conversations models face issues like: (a) loss of contextual information in between two dialogues of a conversation, (b) failure to give appropriate importance to significant tokens in each utterance, (c) inability to pass on the emotional information from previous utterances. The proposed model of Advanced Contextual Feature Extraction (AdCOFE) addresses these issues by performing unique feature extraction using knowledge graphs, sentiment lexicons and phrases of natural language at all levels (word and position embedding) of the utterances. Experiments on emotion recognition in conversations datasets show that AdCOFE is beneficial in capturing emotions in conversations.

Draft Genome Sequence of Pasteurella multocida subsp. multocida B:2 Strain VTCCBAA264 Isolated from Bubalus bubalis in North India.

The Pasteurella multocida subsp. multocida B:2 serotype causes hemorrhagic septicemia in bubalines with high morbidity and mortality in the Indian subcontinent. We report the draft genome sequence of Pasteurella multocida strain VTCCBAA264 isolated from the small-intestine of a buffalo calf that died of high fever.