Within the Natural Language Processing (NLP) framework, Named Entity Recognition (NER) is regarded as the basis for extracting key information to understand texts in any language. As Bangla is a highly inflectional, morphologically rich, and resource-scarce language, building a balanced NER corpus with large and diverse entities is a demanding task. However, previously developed Bangla NER systems are limited to recognizing only three familiar entities: person, location, and organization. To address this significant limitation, we introduce a novel Bangla NER dataset B-NER, which was created using 22,144 manually annotated Bangla sentences collected from Bangla newspapers and Bangla Wikipedia. This dataset includes a total of 9,895 unique words which were manually categorized into eight different entity types, such as a person, organization, event, artifact, time indicator, natural phenomenon, geopolitical entity, and geographical location. Inter-annotator agreement experiments were conducted to validate the quality of annotations performed by three annotators, resulting in a Kappa score of 0.82. In this paper, we provide an outline of the annotation guideline illustrated with examples, discuss the B-NER dataset properties, and present benchmark evaluations of the dataset. To establish that B-NER is more comprehensive and balanced in comparison to other publicly accessible datasets, we conducted cross-dataset modeling and validation, i.e. trained NER model on one dataset while tested on another, and found that the model trained on B-NER performed the best in that settings. Furthermore, we performed exhaustive benchmark evaluations based on Bidirectional LSTM with fastText embeddings and sentence transformer models. Among these models, fine-tuned IndicBERT achieved noticeable results with a Macro-F1 of 86%. This dataset and baseline results will be publicly available under a CC-BY 4.0 license in the CoNLL-2002 format to facilitate further research on Bangla NER.

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Histopathological work in clinical labs relying on immunostaining of proteins represents a bottleneck in processing medical tissue samples. Multiplexed spatial proteomics imaging can increase interpretive power but cannot cost-effectively sample the entire proteomic retinue important to diagnostic medicine or drug development. Here we present a structural similarity index measure (SSIM) guided conditional Generative Adversarial Network (cGAN) that generatively performs image-to-image (i2i) synthesis to generate photo-accurate protein channels in multiplexed spatial proteomics images. This approach can be utilized to accurately generate missing spatial proteomics channels that were not included during experimental data collection either at the bench or the clinic. Experimental spatial proteomic data from the Human BioMolecular Atlas Program (HuBMAP) was used to generate spatial representations of missing proteins through a U-Net based image synthesis pipeline. HuBMAP channels were hierarchically clustered by the (SSIM) as a heuristic to obtain the minimal set needed to recapitulate the underlying biology represented by the spatial landscape of proteins. We subsequently prove that our SSIM based architecture allows for scaling of generative image synthesis to slides with up to 100 channels, which is better than current state of the art algorithms which are limited to data with 11 channels. We validate these claims by generating a new experimental spatial proteomics data set from human lung adenocarcinoma tissue sections and show that a model trained on HuBMAP can accurately synthesize channels from our new data set. The ability to recapitulate experimental data from sparsely stained multiplexed histological slides containing spatial proteomic will have tremendous impact on medical diagnostics and drug development, and also raises important questions on the medical ethics of utilizing data produced by generative image synthesis in the clinical setting. The algorithm that we present in this paper will allow researchers and clinicians to save time and Preprint. Under review.

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Dengue is one of the emerging diseases of this century, which established itself as both endemic and epidemic - particularly in the tropical and subtropical-regions. Because of its high morbidity and mortality rates, Dengue is a significant economic and health burden for middle to lower-income countries. The lack of a stable, cost-effective, and suitable surveillance system has made the identification of dengue zones and designing potential control programs very challenging. As a result, it is not feasible to assess the effect of the intervention actions properly. Therefore, most of the prevention and mitigation efforts by the associated health officials are failing. In this work, we chose Bangladesh, a developing country from the South-East Asia region with its occasional history of dengue outbreaks and with a high out-of-pocket medical expenditure, as a use case. We use some well known data-mining techniques on the local newspapers, written in Bengali, to unearth valuable insights and develop a dengue news surveillance system. We categorize dengue-news and detect the spatio-temporal trends among crucial variables. Our technique provides an f-score of 91.45\% and very closely follows the ground truth of reported cases. Additionally, we identify the under-reported regions of the country effectively while establishing a meaningful relationship between complex socio-economic factors and reporting of dengue.