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SUBESCO is an audio-only emotional speech corpus for Bangla language. The total duration of the corpus is in excess of 7 hours containing 7000 utterances, and it is the largest emotional speech corpus available for this language. Twenty native speakers participated in the gender-balanced set, each recording of 10 sentences simulating seven targeted emotions. Fifty university students participated in the evaluation of this corpus. Each audio clip of this corpus, except those of Disgust emotion, was validated four times by male and female raters. Raw hit rates and unbiased rates were calculated producing scores above chance level of responses. Overall recognition rate was reported to be above 70% for human perception tests. Kappa statistics and intra-class correlation coefficient scores indicated high-level of inter-rater reliability and consistency of this corpus evaluation. SUBESCO is an Open Access database, licensed under Creative Common Attribution 4.0 International, and can be downloaded free of charge from the web link: https://doi.org/10.5281/zenodo.4526477.

In this study, we have presented a deep learning-based implementation for speech emotion recognition (SER). The system combines a deep convolutional neural network (DCNN) and a bidirectional long-short term memory (BLSTM) network with a time-distributed flatten (TDF) layer. The proposed model has been applied for the recently built audio-only Bangla emotional speech corpus SUBESCO. A series of experiments were carried out to analyze all the models discussed in this paper for baseline, cross-lingual, and multilingual training-testing setups. The experimental results reveal that the model with a TDF layer achieves better performance compared with other state-of-the-art CNN-based SER models which can work on both temporal and sequential representation of emotions. For the cross-lingual experiments, cross-corpus training, multi-corpus training, and transfer learning were employed for the Bangla and English languages using the SUBESCO and RAVDESS datasets. The proposed model has attained a state-of-the-art perceptual efficiency achieving weighted accuracies (WAs) of 86.9%, and 82.7% for the SUBESCO and RAVDESS datasets, respectively.

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Loa loa is a nematode that is highly endemic in the tropical rainforests of Western and Central Africa. It is also known as “African eye worm”. Occasionally the adult parasite is seen in the subcutaneous tissue space of humans and occasionally into the subconjunctival space. Our case is a 29-year-old male presented to the outpatient department with history of swelling, redness and foreign body sensation in the inferior bulbar conjunctiva of his right eye. Slit lamp examination shows, a nodular swelling in the inferior conjunctival space and diagnosed as subconjunctival granulomatous lesion. In the operation theater, the lesion was explored and a live worm was removed from the subconjunctival space. The worm was measured about 3.5 cm in length. The worm was confirmed to be a Loa loa adult specimen. The patient was treated with 400 mg oral albendazole for 3 weeks and 60 mg prednisone. Ophthalmologists should be aware of the typical manifestations and possible unusual presentations. An increasing number of subconjunctival Loa loa cases are reported from non-endemic areas are due to increased travel and migration. This report illustrates an unusual ocular disease, which is usually not found outside of Africa, but easily diagnosed and treated.

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Boron is directly or indirectly involved in the enhancement of quality and quantity of crops especially vegetables and fruits so this study was planned to assess the boron uptake, yield and quality of carrot in response to boron application at