Megaselia scalaris (Diptera: Phoridae), commonly known as scuttle fly, is widely distributed all over the world. It is easily cultured in the laboratory condition making it a potential model organism. Besides, it has forensic importance. However, no report from Bangladesh could be retrieved about this fly. So, in the present study, identification of this species was attempted using both morphological and molecular approaches. Characteristics of male hypopygium and legs played key roles in morphological identification. To strengthen identification, mitochondrial COI and 16S rRNA gene fragments were amplified and sequenced. Blast search at NCBI provided highest hits to available COI and 16S rRNA sequences of M. scalaris. A neighbor joining phylogenetic tree was built using sequences of respective COI gene region to show its relationship among other closely related dipteran flies. Dhaka Univ. J. Biol. Sci. 25(2): 149-159, 2016 (July)

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Background With the global spread of COVID-19, the world has seen many patients, including many severe cases. The rapid development of machine learning (ML) has made significant disease diagnosis and prediction achievements. Current studies have confirmed that omics data at the host level can reflect the development process and prognosis of the disease. Since early diagnosis and effective treatment of severe COVID-19 patients remains challenging, this research aims to use omics data in different ML models for COVID-19 diagnosis and prognosis. We used several ML models on omics data of a large number of individuals to first predict whether patients are COVID-19 positive or negative, followed by the severity of the disease. Results On the COVID-19 diagnosis task, we got the best AUC of 0.99 with our multilayer perceptron model and the highest F1-score of 0.95 with our logistic regression (LR) model. For the severity prediction task, we achieved the highest accuracy of 0.76 with an LR model. Beyond classification and predictive modeling, our study founds ML models performed better on integrated multi-omics data, rather than single omics. By comparing top features from different omics dataset, we also found the robustness of our model, with a wider range of applicability in diverse dataset related to COVID-19. Additionally, we have found that omics-based models performed better than image or physiological feature-based models, proving the importance of the omics-based dataset for future model development. Conclusions This study diagnoses COVID-19 positive cases and predicts accurate severity levels. It lowers the dependence on clinical data and professional judgment, by leveraging the utilization of state-of-the-art models. our model showed wider applicability across different omics dataset, which is highly transferable in other respiratory or similar diseases. Hospital and public health care mechanisms can optimize the distribution of medical resources and improve the robustness of the medical system.

Optoelectric biosensors measure the conformational changes of biomolecules and their molecular interactions, allowing researchers to use them in different biomedical diagnostics and analysis activities. Among different biosensors, surface plasmon resonance (SPR)-based biosensors utilize label-free and gold-based plasmonic principles with high precision and accuracy, allowing these gold-based biosensors as one of the preferred methods. The dataset generated from these biosensors are being used in different machine learning (ML) models for disease diagnosis and prognosis, but there is a scarcity of models to develop or assess the accuracy of SPR-based biosensors and ensure a reliable dataset for downstream model development. Current study proposed innovative ML-based DNA detection and classification models from the reflective light angles on different gold surfaces of biosensors and associated properties. We have conducted several statistical analyses and different visualization techniques to evaluate the SPR-based dataset and applied t-SNE feature extraction and min-max normalization to differentiate classifiers of low-variances. We experimented with several ML classifiers, namely support vector machine (SVM), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbors (KNN), logistic regression (LR) and random forest (RF) and evaluated our findings in terms of different evaluation metrics. Our analysis showed the best accuracy of 0.94 by RF, DT and KNN for DNA classification and 0.96 by RF and KNN for DNA detection tasks. Considering area under the receiver operating characteristic curve (AUC) (0.97), precision (0.96) and F1-score (0.97), we found RF performed best for both tasks. Our research shows the potentiality of ML models in the field of biosensor development, which can be expanded to develop novel disease diagnosis and prognosis tools in the future.

The COVID-19 pandemic has a devastating impact on the health and well-being of global population. Cough audio signals classification showed potential as a screening approach for diagnosing people, infected with COVID-19. Recent approaches need costly deep learning algorithms or sophisticated methods to extract informative features from cough audio signals. In this paper, we propose a low-cost envelope approach, called CovidEnvelope, which can classify COVID-19 positive and negative cases from raw data by avoiding above disadvantages. This automated approach can pre-process cough audio signals by filter-out background noises, generate an envelope around the audio signal, and finally provide outcomes by computing area enclosed by the envelope. It has been seen that reliable datasets are also important for achieving high performance. Our approach proves that human verbal confirmation is not a reliable source of information. Finally, the approach reaches highest sensitivity, specificity, accuracy, and AUC of 0.92, 0.87, 0.89, and 0.89 respectively. The automatic approach only takes 1.8 to 3.9 minutes to compute these performances. Overall, this approach is fast and sensitive to diagnose the people living with COVID-19, regardless of having COVID-19 related symptoms or not, and thus have vast applicability in human well-being by designing HCI devices incorporating this approach.

Females of many insect species are unreceptive to remating for a period following their first mating. This inhibitory effect may be mediated by either the female or her first mate, or both, and often reflects the complex interplay of reproductive strategies between the sexes. Natural variation in remating inhibition and how this phenotype responds to captive breeding are largely unexplored in insects, including many pest species. We investigated genetic variation in remating propensity in the Queensland fruit fly, Bactrocera tryoni, using strains differing in source locality and degree of domestication. We found up to threefold inherited variation between strains from different localities in the level of intra-strain remating inhibition. The level of inhibition also declined significantly during domestication, which implied the existence of genetic variation for this trait within the starting populations as well. Inter-strain mating and remating trials showed that the strain differences were mainly due to the genotypes of the female and, to a lesser extent, the second male, with little effect of the initial male genotype. Implications for our understanding of fruit fly reproductive biology and population genetics and the design of Sterile Insect Technique pest management programs are discussed.

Mosquitoes are vectors of numerous deadly diseases, and mosquito classification task is vital for their control programs. To ease manual labor and time-consuming classification tasks, numerous image-based machine-learning (ML) models have been developed to classify different mosquito species. Mosquito wing-beating sounds can serve as a unique classifier for mosquito classification tasks, which can be adopted easily in field applications. The current study aims to develop a deep neural network model to identify six mosquito species of three different genera, based on their wing-beating sounds. While existing models focused on raw audios, we developed a comprehensive pre-processing step to convert raw audios into more informative Mel-spectrograms, resulting in more robust and noise-free extracted features. Our model, namely ’Wing-beating Network’ or ’WbNet’, combines the state-of-art residual neural network (ResNet) model as a baseline, with self-attention mechanism and data-augmentation technique, and outperformed other existing models. The WbNet achieved the highest performance of 89.9% and 98.9% for WINGBEATS and ABUZZ data respectively. For species of Aedes and Culex genera, our model achieved 100% precision, recall and F1-scores, whereas, for Anopheles, the WbNet reached above 95%. We also compared two existing wing-beating datasets, namely WINGBEATS and ABUZZ, and found our model does not need sophisticated audio devices, hence performed better on ABUZZ audios, captured on usual mobile devices. Overall, our model has potential to serve in mosquito monitoring and prevalence studies in mosquito eradication programs, along with potential implementation in classification tasks of insect pests or other sound-based classifications.

Autism spectrum disorder (ASD) is a neuro-developmental disorder that results in behavioural retardation in verbal communications and social interactions. Traditional ASD detection methods involve assessing patients' behavioural patterns by medical practitioners, which often lack credibility and precision. The contribution of the current study involves a 3D-CNN (convolutional neural network) model to diagnose ASD patients from healthy individuals using functional magnetic resonance imaging (fMRI) of the brain. We utilised a publicly available dataset, Autism Brain Imaging Data Exchange (ABIDE I), and tested different CNN-based models in individual and combined brain parcellations. Our model showed a better outcome (74.53% accuracy, 69.98% sensitivity, and 76.00% specificity) for combined parcellations than individuals. Further, we compared our model with several state-ofthe-art models and discussed the suitability of our model for future prospects. The current model would be a predecessor of future prognosis models or behavioural patterns-based multi-modal models for early detection of ASD.