We examined 20,648 prokaryotic unique taxids with respect to the annotation of the 3’ end of the 16S rRNA, which contains the anti-Shine-Dalgarno sequence. We used the sequence of highly conserved helix 45 of the 16S rRNA as a guide. By this criterion, 8,153 annotated 3’ ends correctly included the anti-Shine-Dalgarno sequence, but 12,495 were foreshortened or otherwise mis-annotated, missing part or all of the anti-Shine-Dalgarno sequence, which immediately follows helix 45. We re-annotated, giving a total of 20,648 16S rRNA 3’ ends. The vast majority indeed contained a consensus anti-Shine-Dalgarno sequence, embedded in a highly conserved 13 base “tail”. However, 128 exceptional organisms had either a variant anti-Shine-Dalgarno, or no recognizable anti-Shine-Dalgarno, in their 16S rRNA(s). For organisms both with and without an anti-Shine-Dalgarno, we identified the Shine-Dalgarno motifs actually enriched in front of each organism’s open reading frames. This showed to what extent the Shine-Dalgarno motifs correlated with anti-Shine Dalgarno motifs. In general, organisms whose rRNAs lacked a perfect anti-Shine-Dalgarno motif also lacked a recognizable Shine-Dalgarno. For organisms whose 16S rRNAs contained a perfect anti-Shine-Dalgarno motif, a variety of results were obtained. We found one genus, Alteromonas, where several taxids apparently maintain two different types of 16S rRNA genes, with different, but conserved, antiSDs. The fact that some organisms do not seem to have or use Shine-Dalgarno motifs supports the idea that prokaryotes have other robust mechanisms for recognizing start codons for translation.

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.

In this study, we present a Bangla multi-domain sentiment analysis dataset, named as SentiGOLD, developed using 70,000 samples, which was compiled from a variety of sources and annotated by a gender-balanced team of linguists. This dataset was created in accordance with a standard set of linguistic conventions that were established after multiple meetings between the Government of Bangladesh and a nationally recognized Bangla linguistics committee. Although there are standard sentiment analysis datasets available for English and other rich languages, there are not any such datasets in Bangla, especially because, there was no standard linguistics framework agreed upon by national stakeholders. Senti-GOLD derives its raw data from online video comments, social media posts and comments, blog posts and comments, news and numerous other sources. Throughout the development of this dataset, domain distribution and class distribution were rigorously maintained. SentiGOLD was created using data from a total of 30 domains (e.g. politics, entertainment, sports, etc.) and was labeled using 5 classes (e.g. strongly negative, weakly negative, neutral, weakly positive, and strongly positive). In order to maintain annotation quality, the national linguistics committee approved an annotation scheme to ensure a rigorous Inter Annotator Agreement (IAA) in a multi-annotator annotation scenario. This procedure yielded an IAA score of 0.88 using Fleiss' kappa method, which is elaborated upon in the paper. A protocol for intra- and cross-dataset evaluation was utilized in our efforts to develop a classification system as a standard. The cross-dataset evaluation was performed on the SentNoB dataset, which contains noisy Bangla text samples, thereby establishing a demanding test scenario. We also performed cross-dataset testing by employing zero-shot experiments, and our best model produced competitive performance, which exemplify our dataset's generalizability. Our top model attained a macro f1 of 0.62 (intra-dataset) for 5 classes establishing the benchmark for SentiGOLD, and 0.61 (cross-dataset from SentNoB) for 3 classes which stands comparable to the current state-of-the-art. Our fine-tuned sentiment analysis model\footnotehttps://sentiment.bangla.gov.bd can be accessed online.

Most of the HCI work on mental health is based on the Western metaphysical definition of mind that is less applicable outside the West. This article focuses on this issue and critically examines “ Kaan Pete Roi ” (KPR), a suicide prevention and emotional support helpline in Bangladesh, through an interview study with 20 participants. We find that KPR’s service, grounded in the “befriending” model—originating from the UK and emphasizing non-judgmental active listening without offering direct advice—often struggles to ensure callers’ safety, provide long-term support, and protect volunteers from harassment and distress. We argue that such failures are often rooted in some foundational ideas of the UK-born “befriending” model that underpins the service. Building on Enrique Dussel’s decolonial philosophy, we argue that “befriending” model and its underpinning Western metaphysical ideation of mind carry a colonial impulse, and we discuss how community-based approaches may better address the mental health problems in the Global South.

Data annotation is a process of meaning-making and is inherently political. The literature on ethics in data-driven technologies explores these political aspects, primarily focusing on questions of bias and power. This paper argues that the politics of annotation often overemphasize secular and modern values and overlooks faith-based, religious, and spiritual aspects (FRS) in data annotation. This oversight particularly affects the postcolonial regions of the Global South, where FRS are intertwined with people’s everyday experiences and ethics. We conducted a focus group discussion and contextual inquiries with six annotators who annotated a faith-related “violence” dataset from South Asian YouTube content. Our analysis reveals that FRS blindness in data annotation manifests through the politics of achieving objectivity and the “scientific” process of meaning-making. Due to these goals, which are predominantly shaped by Western values, FRS sensitivities are overlooked from the initial stages of data curation through annotation, ultimately leading to a context collapse within the annotation process. Finally, we advocate for the adaptation of FRS sensitivities into the annotation process and data infrastructure, particularly when the dataset clearly pertains to FRS, to promote greater cultural and contextual inclusivity in annotation practices.