This study was conducted with the aim of exploring the general parsing mechanisms involved in processing different kinds of dependency relations, namely verb agreement with subjects versus objects in Punjabi, an SOV Indo-Aryan language. Event related brain potentials (ERPs) were recorded as twenty-five native Punjabi speakers read transitive sentences. Critical stimuli were either fully acceptable as regards verb agreement, or alternatively violated gender agreement with the subject or object. A linear mixed-models analysis confirmed a P600 effect at the position of the verb for all violations, regardless of whether subject or object agreement was violated. These results thus suggest that an identical mechanism is involved in gender agreement computation in Punjabi regardless of whether the agreement is with the subject or the object argument.

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.

BACKGROUND: Delay in the diagnosis of neurodevelopmental disorders (NDDs) in toddlers and postnatal depression (PND) is a major public health issue. In both cases, early intervention is crucial but too rarely implemented in practice.
OBJECTIVE: Our goal was to determine if a dedicated mobile app can improve screening of 5 NDDs (autism spectrum disorder [ASD], language delay, dyspraxia, dyslexia, and attention-deficit/hyperactivity disorder [ADHD]) and reduce PND incidence.
METHODS: We performed an observational, cross-sectional, data-based study in a population of young parents in France with at least 1 child aged <10 years at the time of inclusion and regularly using Malo, an \"all-in-one\" multidomain digital health record electronic patient-reported outcome (PRO) app for smartphones. We included the first 50,000 users matching the criteria and agreeing to participate between May 1, 2022, and February 8, 2024. Parents received periodic questionnaires assessing skills in neurodevelopment domains via the app. Mothers accessed a support program to prevent PND and were requested to answer regular PND questionnaires. When any PROs matched predefined criteria, an in-app recommendation was sent to book an appointment with a family physician or pediatrician. The main outcomes were the median age of the infant at the time of notification for possible NDD and the incidence of PND detection after childbirth. One secondary outcome was the relevance of the NDD notification by consultation as assessed by health professionals.
RESULTS: Among 55,618 children median age 4 months (IQR 9), 439 (0.8%) had at least 1 disorder for which consultation was critically necessary. The median ages of notification for probable ASD, language delay, dyspraxia, dyslexia, and ADHD were 32.5 (IQR 12.8), 16 (IQR 13), 36 (IQR 22.5), 80 (IQR 5), and 61 (IQR 15.5) months, respectively. The rate of probable ADHD, ASD, dyslexia, language delay, and dyspraxia in the population of children of the age included between the detection limits of each alert was 1.48%, 0.21%, 1.52%, 0.91%, and 0.37%, respectively. Sensitivity of alert notifications for suspected NDDs as assessed by the physicians was 78.6% and specificity was 98.2%. Among 8243 mothers who completed a PND questionnaire, highly probable PND was detected in 938 (11.4%), corresponding to a reduction of -31% versus our previous study without a support program. Suspected PND was detected a median 96 days (IQR 86) after childbirth. Among 130 users who filled in the satisfaction survey, 99.2% (129/130) found the app easy to use and 70% (91/130) reported that the app improved follow-up of their child. The app was rated 4.8/5 on Apple\'s App Store.
CONCLUSIONS: Algorithm-based early alerts suggesting NDDs were highly specific with good sensitivity as assessed by real-life practitioners. Early detection of 5 NDDs and PNDs was efficient and led to a possible 31% reduction in PND incidence.
BACKGROUND: ClinicalTrials.gov NCT06301087; https://www.clinicaltrials.gov/study/NCT06301087.

UNASSIGNED: Little is known about how the brains of autistic children process language during real-world \"social contexts,\" despite the fact that challenges with language, communication, and social interaction are core features of Autism Spectrum Disorder (ASD).
UNASSIGNED: We investigated the neural bases of language processing during social and non-social contexts in a sample of N=20 autistic and N=20 neurotypical (NT) preschool-aged children, 3 to 6 years old. Functional near-infrared spectroscopy (fNIRS) was used to measure children\'s brain response to \"live language\" spoken by a live experimenter during an in-person social context (i.e., book reading), and \"recorded language\" played via an audio recording during a non-social context (i.e., screen time). We examined within-group and between-group differences in the strength and localization of brain response to live language and recorded language, as well as correlations between children\'s brain response and language skills measured by the Preschool Language Scales.
UNASSIGNED: In the NT group, brain response to live language was greater than brain response to recorded language in the right temporal parietal junction (TPJ). In the ASD group, the strength of brain response did not differ between conditions. The ASD group showed greater brain response to recorded language than the NT group in the right inferior and middle frontal gyrus (IMFG). Across groups, children\'s language skills were negatively associated with brain response to recorded language in the right IMFG, suggesting that processing recorded language required more cognitive effort for children with lower language skills. Children\'s language skills were also positively associated with the difference in brain response between conditions in the right TPJ, demonstrating that children who showed a greater difference in brain response to live language versus recorded language had higher language skills.
UNASSIGNED: Findings should be considered preliminary until they are replicated in a larger sample.
UNASSIGNED: Findings suggest that the brains of NT children, but not autistic children, process language differently during social and non-social contexts. Individual differences in how the brain processes language during social and non-social contexts may help to explain why language skills are so variable across children with and without autism.

BACKGROUND: Systematically screening published literature to determine the relevant publications to synthesize in a review is a time-consuming and difficult task. Large language models (LLMs) are an emerging technology with promising capabilities for the automation of language-related tasks that may be useful for such a purpose.
METHODS: LLMs were used as part of an automated system to evaluate the relevance of publications to a certain topic based on defined criteria and based on the title and abstract of each publication. A Python script was created to generate structured prompts consisting of text strings for instruction, title, abstract, and relevant criteria to be provided to an LLM. The relevance of a publication was evaluated by the LLM on a Likert scale (low relevance to high relevance). By specifying a threshold, different classifiers for inclusion/exclusion of publications could then be defined. The approach was used with four different openly available LLMs on ten published data sets of biomedical literature reviews and on a newly human-created data set for a hypothetical new systematic literature review.
RESULTS: The performance of the classifiers varied depending on the LLM being used and on the data set analyzed. Regarding sensitivity/specificity, the classifiers yielded 94.48%/31.78% for the FlanT5 model, 97.58%/19.12% for the OpenHermes-NeuralChat model, 81.93%/75.19% for the Mixtral model and 97.58%/38.34% for the Platypus 2 model on the ten published data sets. The same classifiers yielded 100% sensitivity at a specificity of 12.58%, 4.54%, 62.47%, and 24.74% on the newly created data set. Changing the standard settings of the approach (minor adaption of instruction prompt and/or changing the range of the Likert scale from 1-5 to 1-10) had a considerable impact on the performance.
CONCLUSIONS: LLMs can be used to evaluate the relevance of scientific publications to a certain review topic and classifiers based on such an approach show some promising results. To date, little is known about how well such systems would perform if used prospectively when conducting systematic literature reviews and what further implications this might have. However, it is likely that in the future researchers will increasingly use LLMs for evaluating and classifying scientific publications.

BACKGROUND: Large language models (LLMs) have demonstrated impressive performances in various medical domains, prompting an exploration of their potential utility within the high-demand setting of emergency department (ED) triage. This study evaluated the triage proficiency of different LLMs and ChatGPT, an LLM-based chatbot, compared to professionally trained ED staff and untrained personnel. We further explored whether LLM responses could guide untrained staff in effective triage.
OBJECTIVE: This study aimed to assess the efficacy of LLMs and the associated product ChatGPT in ED triage compared to personnel of varying training status and to investigate if the models\' responses can enhance the triage proficiency of untrained personnel.
METHODS: A total of 124 anonymized case vignettes were triaged by untrained doctors; different versions of currently available LLMs; ChatGPT; and professionally trained raters, who subsequently agreed on a consensus set according to the Manchester Triage System (MTS). The prototypical vignettes were adapted from cases at a tertiary ED in Germany. The main outcome was the level of agreement between raters\' MTS level assignments, measured via quadratic-weighted Cohen κ. The extent of over- and undertriage was also determined. Notably, instances of ChatGPT were prompted using zero-shot approaches without extensive background information on the MTS. The tested LLMs included raw GPT-4, Llama 3 70B, Gemini 1.5, and Mixtral 8x7b.
RESULTS: GPT-4-based ChatGPT and untrained doctors showed substantial agreement with the consensus triage of professional raters (κ=mean 0.67, SD 0.037 and κ=mean 0.68, SD 0.056, respectively), significantly exceeding the performance of GPT-3.5-based ChatGPT (κ=mean 0.54, SD 0.024; P<.001). When untrained doctors used this LLM for second-opinion triage, there was a slight but statistically insignificant performance increase (κ=mean 0.70, SD 0.047; P=.97). Other tested LLMs performed similar to or worse than GPT-4-based ChatGPT or showed odd triaging behavior with the used parameters. LLMs and ChatGPT models tended toward overtriage, whereas untrained doctors undertriaged.
CONCLUSIONS: While LLMs and the LLM-based product ChatGPT do not yet match professionally trained raters, their best models\' triage proficiency equals that of untrained ED doctors. In its current form, LLMs or ChatGPT thus did not demonstrate gold-standard performance in ED triage and, in the setting of this study, failed to significantly improve untrained doctors\' triage when used as decision support. Notable performance enhancements in newer LLM versions over older ones hint at future improvements with further technological development and specific training.

To explore the clinical efficacy of scalp electroacupuncture combined with rehabilitation training for aphasia after head injury, and analyze its effect on patients\' language function and quality of life. Our hospital randomly enrolled 100 aphasia patients caused by head injury treated from March 2020 to March 2022 as the experimental object and divided them into the control group and experimental group, with 50 cases in each group. The general rehabilitation training was performed to the control group and the scalp electroacupuncture combined with rehabilitation training was performed to the experimental group to compare their mini-mental state examination scores, communicative activities in daily living scores, aphasia battery of Chinese scores, quality of life scores, mental status scale in nonpsychiatric settings scores, National Institutes of Health Stroke Scale scores, effective rates, satisfaction of patients, and adverse reaction rates. In the between-group comparison, the patients in the experimental group had significantly higher mini-mental state examination scores, communicative activities in daily living scores, aphasia battery of Chinese scores, quality of life scores, effective rates, and satisfaction, and significantly lower mental status scale in nonpsychiatric settings scores, National Institutes of Health Stroke Scale scores and adverse reaction rates, which was statistically significant (P < .05 in all cases). The combination treatment of scalp electroacupuncture and rehabilitation training can effectively improve the language function and quality of life of patients with aphasia after head injury and remarkably enhance the treatment effect.

BACKGROUND: Preterm born infants are at risk for brain injury and subsequent developmental delay. Treatment options are limited, but optimizing postnatal nutrition may improve brain- and neurodevelopment in these infants. In pre-clinical animal models, combined supplementation of docosahexaenoic acid (DHA), choline, and uridine-5-monophosphate (UMP) have shown to support neuronal membrane formation. In two randomized controlled pilot trials, supplementation with the investigational product was associated with clinically meaningful improvements in cognitive, attention, and language scores. The present study aims to assess the effect of a similar nutritional intervention on brain development and subsequent neurodevelopmental outcome in infants born very and extremely preterm.
METHODS: This is a randomized, placebo-controlled, double-blinded, parallel-group, multi-center trial. A total of 130 infants, born at less than 30 weeks of gestation, will be randomized to receive a test or control product between term-equivalent age and 12 months corrected age (CA). The test product is a nutrient blend containing DHA, choline, and UMP amongst others. The control product contains only fractions of the active components. Both products are isocaloric powder supplements which can be added to milk and solid feeds. The primary outcome parameter is white matter integrity at three months CA, assessed using diffusion-tensor imaging (DTI) on MRI scanning. Secondary outcome parameters include volumetric brain development, cortical thickness, cortical folding, the metabolic and biochemical status of the brain, and product safety. Additionally, language, cognitive, motor, and behavioral development will be assessed at 12 and 24 months CA, using the Bayley Scales of Infant Development III and digital questionnaires (Dutch version of the Communicative Development Inventories (N-CDI), Ages and Stages Questionnaire 4 (ASQ-4), and Parent Report of Children\'s Abilities - Revised (PARCA-R)).
CONCLUSIONS: The investigated nutritional intervention is hypothesized to promote brain development and subsequent neurodevelopmental outcome in preterm born infants who have an inherent risk of developmental delay. Moreover, this innovative study may give rise to new treatment possibilities and improvements in routine clinical care.
BACKGROUND: WHO International Clinical Trials Registry: NL-OMON56181 (registration assigned October 28, 2021).

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.

Researchers tend to use oral- and silent-reading fluency measures interchangeably and to generalize research findings across reading modes, especially from oral to silent reading. In this study, we sought to examine if oral and silent word-reading fluency rely on the same cognitive-linguistic skills. Three hundred and forty-five Greek children (80 from Grade 2, 85 from Grade 4, 91 from Grade 6, and 89 from Grade 10) were assessed on measures of general cognitive ability, speed of processing, phonological awareness, rapid automatized naming, orthographic knowledge, articulation rate, and word-reading fluency (oral and silent). Results of hierarchical regression analyses revealed that phonological awareness was a unique predictor of both reading outcomes in Grade 2 and orthographic knowledge was a unique predictor of both reading outcomes in Grades 4, 6, and 10. However, rapid automatized naming predicted only oral word-reading fluency. These findings suggest that silent and oral word-reading fluency do not necessarily rely on the same cognitive-linguistic skills at the same grade level and we need to exercise some caution when we generalize the findings across reading modes.