BACKGROUND: Wikipedia is a vital open educational resource in computational biology. The quality of computational biology coverage in English-language Wikipedia has improved steadily in recent years. However, there is an increasingly large \'knowledge gap\' between computational biology resources in English-language Wikipedia, and Wikipedias in non-English languages. Reducing this knowledge gap by providing educational resources in non-English languages would reduce language barriers which disadvantage non-native English speaking learners across multiple dimensions in computational biology.
RESULTS: Here, we provide a comprehensive assessment of computational biology coverage in Spanish-language Wikipedia, the second most accessed Wikipedia worldwide. Using Spanish-language Wikipedia as a case study, we generate quantitative and qualitative data before and after a targeted educational event, specifically, a Spanish-focused student editing competition. Our data demonstrates how such events and activities can narrow the knowledge gap between English and non-English educational resources, by improving existing articles and creating new articles. Finally, based on our analysis, we suggest ways to prioritize future initiatives to improve open educational resources in other languages.
METHODS: Scripts for data analysis are available at: https://github.com/ISCBWikiTeam/spanish.

The present paper examines how English native speakers produce scopally ambiguous sentences and how they make use of gestures and prosody for disambiguation. As a case in point, the participants in the present study produced the English negative quantifiers. They appear in two different positions as (1) The election of no candidate was a surprise (a: \'for those elected, none of them was a surprise\'; b: \'no candidate was elected, and that was a surprise\') and (2) no candidate\'s election was a surprise (a: \'for those elected, none of them was a surprise\'; b: # \'no candidate was elected, and that was a surprise.\' We were able to investigate the gesture production and the prosodic patterns of the positional effects (i.e., a-interpretation is available at two different positions in 1 and 2) and the interpretation effects (i.e., two different interpretations are available in the same position in 1). We discovered that the participants tended to launch more head shakes in the (a) interpretation despites the different positions, but more head nod/beat in the (b) interpretation. While there is not a difference in prosody of no in (a) and (b) interpretation in (1), there are pitch and durational differences between (a) interpretations in (1) and (2). This study points out the abstract similarities across languages such as Catalan and Spanish (Prieto et al. in Lingua 131:136-150, 2013. 10.1016/j.lingua.2013.02.008; Tubau et al. in Linguist Rev 32(1):115-142, 2015. 10.1515/tlr-2014-0016) in the gestural movements, and the meaning is crucial for gesture patterns. We emphasize that gesture patterns disambiguate ambiguous interpretation when prosody cannot do so.

THIS ARTICLE USES WORDS OR LANGUAGE THAT IS CONSIDERED PROFANE, VULGAR, OR OFFENSIVE BY SOME READERS. Different types of abusive content such as offensive language, hate speech, aggression, etc. have become prevalent in social media and many efforts have been dedicated to automatically detect this phenomenon in different resource-rich languages such as English. This is mainly due to the comparative lack of annotated data related to offensive language in low-resource languages, especially the ones spoken in Asian countries. To reduce the vulnerability among social media users from these regions, it is crucial to address the problem of offensive language in such low-resource languages. Hence, we present a new corpus of Persian offensive language consisting of 6,000 out of 520,000 randomly sampled micro-blog posts from X (Twitter) to deal with offensive language detection in Persian as a low-resource language in this area. We introduce a method for creating the corpus and annotating it according to the annotation practices of recent efforts for some benchmark datasets in other languages which results in categorizing offensive language and the target of offense as well. We perform extensive experiments with three classifiers in different levels of annotation with a number of classical Machine Learning (ML), Deep learning (DL), and transformer-based neural networks including monolingual and multilingual pre-trained language models. Furthermore, we propose an ensemble model integrating the aforementioned models to boost the performance of our offensive language detection task. Initial results on single models indicate that SVM trained on character or word n-grams are the best performing models accompanying monolingual transformer-based pre-trained language model ParsBERT in identifying offensive vs non-offensive content, targeted vs untargeted offense, and offensive towards individual or group. In addition, the stacking ensemble model outperforms the single models by a substantial margin, obtaining 5% respective macro F1-score improvement for three levels of annotation.

For the purpose of this study, A statistical test of Biblical books was conducted using the recently discovered probability models for text homogeneity and text change point detection. Accordingly, translations of Biblical books of Tigrigna and Amharic (major languages spoken in Eritrea and Ethiopia) and English were studied. A Zipf-Mandelbrot distribution with a parameter range of 0.55 to 0.88 was obtained in these three Bibles. According to the statistical analysis of the texts\' homogeneity, the translation of Bible in each of these three languages was a heterogeneous concatenation of different books or genres. Furthermore, an in-depth examination of the text segmentation of prat of a single genre-the English Bible letters revealed that the Pauline letters are heterogeneous concatenations of two homogeneous segments.

UNASSIGNED: Pediatric cerebral palsy (CP) is a non-progressive brain injury syndrome characterized by central motor dysfunction and insufficient brain coordination ability. The etiology of CP is complex and often accompanied by diverse complications such as intellectual disability and language disorders, making clinical treatment difficult. Despite the availability of pharmacological interventions, rehabilitation programs, and spasticity relief surgery as treatment options for CP, their effectiveness is still constrained. Electroacupuncture (EA) stimulation has demonstrated great improvements in motor function, but its comprehensive, objective therapeutic effects on pediatric CP remain to be clarified.
UNASSIGNED: We present a case of a 5-year-old Chinese female child who was diagnosed with CP at the age of 4. The patient exhibited severe impairments in motor, language, social, and cognitive functions. We performed a 3-month period of EA rehabilitation, obtaining resting state functional magnetic resonance imaging (rs-fMRI) of the patient at 0 month, 3 months and 5 months since treatment started, then characterized brain functional connectivity patterns in each phase for comparison.
UNASSIGNED: After a 12-month follow-up, notable advancements were observed in the patient\'s language and social symptoms. Changes of functional connectivity patterns confirmed this therapeutic effect and showed specific benefits for different recovery phase: starting from language functions then modulating social participation and other developmental behaviors.
UNASSIGNED: This is a pioneering report demonstrating the longitudinal effect of EA stimulation on functional brain connectivity in CP patients, suggesting EA an effective intervention for developmental disabilities (especially language and social dysfunctions) associated with pediatric CP.

OBJECTIVE: Evaluate Artificial Intelligence (AI) language models (ChatGPT-4, BARD, Microsoft Copilot) in simplifying radiology reports, assessing readability, understandability, actionability, and urgency classification.
METHODS: This study evaluated the effectiveness of these AI models in translating radiology reports into patient-friendly language and providing understandable and actionable suggestions and urgency classifications. Thirty radiology reports were processed using AI tools, and their outputs were assessed for readability (Flesch Reading Ease, Flesch-Kincaid Grade Level), understandability (PEMAT), and the accuracy of urgency classification. ANOVA and Chi-Square tests were performed to compare the models\' performances.
RESULTS: All three AI models successfully transformed medical jargon into more accessible language, with BARD showing superior readability scores. In terms of understandability, all models achieved scores above 70%, with ChatGPT-4 and BARD leading (p < 0.001, both). However, the AI models varied in accuracy of urgency recommendations, with no significant statistical difference (p = 0.284).
CONCLUSIONS: AI language models have proven effective in simplifying radiology reports, thereby potentially improving patient comprehension and engagement in their health decisions. However, their accuracy in assessing the urgency of medical conditions based on radiology reports suggests a need for further refinement.
CONCLUSIONS: Incorporating AI in radiology communication can empower patients, but further development is crucial for comprehensive and actionable patient support.

Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient. Previous results demonstrated that these methods can even improve performance on some classification tasks. This paper complements existing research by investigating how these techniques influence classification performance and computation costs compared to full fine-tuning. We focus specifically on multilingual text classification tasks (genre, framing, and persuasion techniques detection; with different input lengths, number of predicted classes and classification difficulty), some of which have limited training data. In addition, we conduct in-depth analyses of their efficacy across different training scenarios (training on the original multilingual data; on the translations into English; and on a subset of English-only data) and different languages. Our findings provide valuable insights into the applicability of parameter-efficient fine-tuning techniques, particularly for multilabel classification and non-parallel multilingual tasks which are aimed at analysing input texts of varying length.

UNASSIGNED: Nonword repetition (NWR) has been described as a clinical marker of developmental language disorder (DLD), as NWR tasks consistently discriminate between DLD and typical development (TD) cross-linguistically, with Cantonese as the only reported exception. This study reexamines whether NWR is able to generate TD/DLD group differences in Cantonese-speaking children by reporting on a novel set of NWR stimuli that take into account factors known to affect NWR performance and group differentiation, including lexicality, sublexicality, length, and syllable complexity.
UNASSIGNED: Sixteen Cantonese-speaking children with DLD and 16 age-matched children with TD repeated two sets of high-lexicality nonwords, where all constituent syllables are morphemic in Cantonese but meaningless when combined, and one set of low-lexicality nonwords, where all constituent syllables are nonmorphemic. Low-lexicality nonwords were further classified on sublexicality in terms of consonant-vowel (CV) combination attestedness (whether or not CV combinations in nonword syllables occur in real Cantonese words).
UNASSIGNED: Children with DLD scored significantly below their peers with TD. Effect sizes showed that high-lexicality nonwords and nonword syllables with attested CV combinations offered the greatest TD/DLD group differentiation. Nonword length and syllable complexity did not affect TD/DLD group differentiation.
UNASSIGNED: NWR can capture TD/DLD group differences in Cantonese-speaking children. Lexicality and sublexicality effects must be considered in designing NWR stimuli for TD/DLD group differentiation. Future studies should replicate the present study on a larger sample size and a younger population as well as examine the diagnostic accuracy of this NWR test.
UNASSIGNED: https://doi.org/10.23641/asha.25529371.

Automated stance detection and related machine learning methods can provide useful insights for media monitoring and academic research. Many of these approaches require annotated training datasets, which limits their applicability for languages where these may not be readily available. This paper explores the applicability of large language models for automated stance detection in a challenging scenario, involving a morphologically complex, lower-resource language, and a socio-culturally complex topic, immigration. If the approach works in this case, it can be expected to perform as well or better in less demanding scenarios. We annotate a large set of pro- and anti-immigration examples to train and compare the performance of multiple language models. We also probe the usability of GPT-3.5 (that powers ChatGPT) as an instructable zero-shot classifier for the same task. The supervised models achieve acceptable performance, but GPT-3.5 yields similar accuracy. As the latter does not require tuning with annotated data, it constitutes a potentially simpler and cheaper alternative for text classification tasks, including in lower-resource languages. We further use the best-performing supervised model to investigate diachronic trends over seven years in two corpora of Estonian mainstream and right-wing populist news sources, demonstrating the applicability of automated stance detection for news analytics and media monitoring settings even in lower-resource scenarios, and discuss correspondences between stance changes and real-world events.

OBJECTIVE: Participants\' comprehension of research process affects the quality of research output, which is the reason why translation of research instruments into local languages is standard practice. Literature has consistently reported that in Africa, knowledge about cervical cancer is low but paradoxically, expressed, and actual uptake of human papillomavirus vaccine for its prevention is high. This study explored the Yoruba names of cervical cancer among Yoruba people in Ibadan, Nigeria to guide the translation of cervical cancer research instruments to Yoruba language.
METHODS: Exploratory case study design was used and data were obtained with 10 in-depth interviews and four focused group discussions. Data were analysed using content analysis.
METHODS: The study took place in Ibadan North local government area, Southwest Nigeria.
METHODS: These were 4 traditional healers, 3 Yoruba linguists, 3 public health educators and 38 parents of adolescents.
METHODS: These were Yoruba names for cervical cancer and their meanings.
RESULTS: Participants were aware of cervical cancer but only the traditional healers and public health educators had names for it. These names were highly varied. The public health educators gave names that were linked with different parts of the female reproductive system and external genital which were actually different medical conditions. Each traditional healer also had different names for cervical cancer, which either described the female body parts, or symptoms of female genital infections. These various names can lead to unnecessary misconceptions and misinformation about cervical cancer, its prevention, management, and research.
CONCLUSIONS: There was no consensus Yoruba name for cervical cancer among the study participants. Efforts to educate the Yoruba speaking populace about cervical cancer, its prevention, management and participation in its research can be frustrated if a generally accepted Yoruba name is not provided for this cancer. Stakeholders\' collaboration is required to get an appropriate Yoruba name for cervical cancer.