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Background: Accurate determination of infant mortality causes and understanding sociocultural factors influencing care-seeking behaviors are crucial for targeted interventions in resource-limited settings. This mixed-methods study aimed to assess the accuracy of verbal autopsy (VA) in ascertaining infant death causes and explore sociocultural determinants of infant mortality in Gujarat, India.

Materials And Methods: It was a mixed-method study with a retrospective cohort component for which data from 661 infant records were extracted from the main health office's database, with a subset of 328 infant deaths selected for verbal autopsy analysis.

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Introduction: , a protein kinase located on human chromosome 21, plays a role in postembryonic neuronal development and degeneration. Alterations to have been consistently associated with cognitive functioning and neurodevelopmental disorders (e.g.

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Studies of perception have long shown that the brain adds information to its sensory analysis of the physical environment. A touchstone example for humans is language use: to comprehend a physical signal like speech, the brain must add linguistic knowledge, including syntax. Yet, syntactic rules and representations are widely assumed to be atemporal (i.

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Speech comprehension involves the dynamic interplay of multiple cognitive processes, from basic sound perception, to linguistic encoding, and finally to complex semantic-conceptual interpretations. How the brain handles the diverse streams of information processing remains poorly understood. Applying Hidden Markov Modeling to fMRI data obtained during spoken narrative comprehension, we reveal that the whole brain networks predominantly oscillate within a tripartite latent state space.

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Tibetan-Chinese speech-to-speech translation based on discrete units.

Sci Rep

January 2025

Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China.

Speech-to-speech translation (S2ST) has evolved from cascade systems which integrate Automatic Speech Recognition (ASR), Machine Translation (MT), and Text-to-Speech (TTS), to end-to-end models. This evolution has been driven by advancements in model performance and the expansion of cross-lingual speech datasets. Despite the paucity of research on Tibetan speech translation, this paper endeavors to tackle the challenge of Tibetan-to-Chinese direct speech-to-speech translation within the multi-task learning framework, employing self-supervised learning (SSL) and sequence-to-sequence model training.

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