Distinct neural activation patterns of age in subcomponents of inhibitory control: A fMRI meta-analysis

Inhibitory control (IC) is a fundamental cognitive function showing age-related change across the healthy lifespan. Since different cognitive resources are needed in the two subcomponents of IC (cognitive inhibition and response inhibition), regions of the brain are differentially activated. In this study, we aimed to determine whether there is a distinct age-related activation pattern in these two subcomponents. A total of 278 fMRI articles were included in the current analysis. Multilevel kernel density analysis was used to provide data on brain activation under each subcomponent of IC. Contrast analyses were conducted to capture the distinct activated brain regions for the two subcomponents, whereas meta-regression analyses were performed to identify brain regions with distinct age-related activation patterns in the two subcomponents of IC. The results showed that the right inferior frontal gyrus and the bilateral insula were activated during the two IC subcomponents. Contrast analyses revealed stronger activation in the superior parietal lobule during cognitive inhibition, whereas stronger activation during response inhibition was observed primarily in the right inferior frontal gyrus, bilateral insula, and angular gyrus. Furthermore, regression analyses showed that activation of the left anterior cingulate cortex, left inferior frontal gyrus, bilateral insula, and left superior parietal lobule increased and decreased with age during cognitive inhibition and response inhibition, respectively. The results showed distinct activation patterns of aging for the two subcomponents of IC, which may be related to the differential cognitive resources recruited. These findings may help to enhance knowledge of age-related changes in the activation patterns of IC

Research for Inertia Response and Primary Frequency Regulation Ability of Wind Turbine

[Introduction] Large-scale connection of wind power to the power grid poses great challenges to the stability (especially frequency stability) of grid operation.In order to solve the problem of inadequate frequency regulation capability caused by large-scale connection of wind power to the power grid and improve the frequency adaptability of wind power grid connection, wind turbines need to have frequency regulation function and response timeliness. [Method] This paper adopted a frequency regulation system scheme based on rotor kinetic energy and pitch angle reserve, which could provide active support for the power grid quickly and accurately during the power grid frequency change. Firstly, the main control algorithm was designed based on the theoretical analysis of inertia response and primary frequency regulation algorithm logic. Then, the functional verification was carried out on the co-simulation platform. Finally, the actual test was carried out in a project.[Result] The simulation and test results showed that the frequency regulation system scheme based on rotor kinetic energy and pitch angle reserve could cope with a variety of grid frequency changes and quickly provided active support. [Conclusion] The frequency regulation system scheme of wind turbines can perform a fast inertia response (with the response time less than 500 ms) and primary frequency regulation response (with the response time less than 5 s) under various frequency change conditions and provide active support for the power grid, which can help recover the grid frequency and effectively improve the frequency adaptability of wind turbines

Chinese Open Instruction Generalist: A Preliminary Release

Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release of InstructGPT~\citep{ouyang2022training} and ChatGPT\footnote{\url{https://chat.openai.com/}}. Despite impressive progress in English-oriented large-scale language models (LLMs), it is still under-explored whether English-based foundation LLMs can perform similarly on multilingual tasks compared to English tasks with well-designed instruction tuning and how we can construct the corpora needed for the tuning. To remedy this gap, we propose the project as an attempt to create a Chinese instruction dataset by various methods adapted to the intrinsic characteristics of 4 sub-tasks. We collect around 200k Chinese instruction tuning samples, which have been manually checked to guarantee high quality. We also summarize the existing English and Chinese instruction corpora and briefly describe some potential applications of the newly constructed Chinese instruction corpora. The resulting \textbf{C}hinese \textbf{O}pen \textbf{I}nstruction \textbf{G}eneralist (\textbf{COIG}) corpora are available in Huggingface\footnote{\url{https://huggingface.co/datasets/BAAI/COIG}} and Github\footnote{\url{https://github.com/FlagOpen/FlagInstruct}}, and will be continuously updated

Circular RNA Expression in the Brain of a Neonatal Rat Model of Periventricular White Matter Damage

Background/Aims: Periventricular white matter damage (PWMD) is the predominant neurologic lesion in preterm infants who survive brain injury. In this study, we assessed the global changes in and characteristics of the transcriptome of circular RNAs (circRNAs) in the brain tissues of rats with PWMD. Methods: We compared the expression profiles of circRNAs in brain samples from three rats with PWMD and three paired control tissues using deep RNA sequencing. Bioinformatics analysis was applied to investigate these differentially expressed circRNAs, and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) analysis was performed to confirm the results. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to predict associated cell signaling pathways and functions. Network analysis was performed to predict circRNAs-microRNAs, and target genes related to PWMD. Results: A total of 2151 more reliable circRNAs were dysregulated in the brain tissues of rats with PWMD, indicating a potential role in the condition. Of the 98 circRNAs significantly differentially expressed in rat brains with PWMD (P< 0.05), 52 were significantly over-expressed and 46 were significantly under-expressed. The expression profiles of seven of 10 randomly selected circRNAs were confirmed by qRT-PCR analysis. The glutamatergic synapse pathway and the VEGF signaling pathway, both associated with hypoxia/ischemia induced brain damage, were inriched. Relationship between miRNA (rno-miR-433-3p and rno-miR-206-3p) and HIF-1α were evident and potential associations between chr6: 48820833|48857932 and their target genes (rno-miR-433-3p and rno-miR-206-3p) were identified. Conclusion: The distinct expression patterns of circRNAs in the brain tissues of rats with PWMD suggest that circRNAs actively respond to hypoxia-ischemia. These findings could assist the development of novel diagnostic and therapeutic targets for PWMD therapy

Prenatal genetic diagnosis associated with fetal ventricular septal defect: an assessment based on chromosomal microarray analysis and exome sequencing

Objective: In the study, we investigated the genetic etiology of the ventricular septal defect (VSD) and comprehensively evaluated the diagnosis rate of prenatal chromosomal microarray analysis (CMA) and exome sequencing (ES) for VSD to provide evidence for genetic counseling.Methods: We carried out chromosomal microarray analysis (CMA) on 468 fetuses with VSD and exome sequencing (ES) on 51 fetuses.Results: In our cohort, 68 (14.5%) VSD fetuses received a genetic diagnosis, including 61 (13.03%, 61/468) cases with chromosomal abnormalities and seven (13.7%, 7/51) cases with gene sequence variants. The detection rate of total pathogenic and likely pathogenic gene variations in the non-isolated VSD group (61/335, 18.2%, 55 by QF-PCR/karyotype/CMA + 6 by ES) was significantly higher than that in the isolated VSD group (7/133, 5.3%, 6 by QF-PCR/karyotype/CMA + 1 by ES, p = 0.000). The most common copy number variation (CNV) was 22q11.2 microdeletion syndrome. Additionally, we found six previously unreported variants, which expanded the variation spectrum of VSD-related genes.Conclusion: In this study, CNVs and sequence variants were found in 13.03% and 13.7% of cases, respectively. ES can be recommended for fetuses with VSD without chromosome abnormalities and pathogenic CNVs, especially those that are combined with other ultrasound abnormalities

Kinetic energy and spatial distribution of ions in high irradiance laser ionization source

Characteristics of laser ionization in vacuum and low pressure background gas (He) have been investigated through the measurement of kinetic energy and spatial distributions of copper and tungsten ions. A Q-switched Nd:YAG laser with 532 nm wavelength was utilized and the laser irradiance was fixed at 9 x 10(9) W cm(-2). A plume splitting was observed in the low pressure regime investigated (from 100 Pa to 2000 Pa). The plume propagation translates from a free expansion in vacuum to shockwave-like expansion at relative low pressure and finally diffusion into background gas at relative high pressure environment. A measurement of ion spatial distribution in the ion source has also been carried out for characterizing ions at different pressures and the behaviors of doubly charged, singly charged, and polyatomic ions to reveal the effect of plume-background gas interaction.Natural Science Foundation of China Financial[20775063, 21027011]; National 863 program[2009AA06Z109]; NFFTBS[J1030415

MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training

Self-supervised learning (SSL) has recently emerged as a promising paradigm for training generalisable models on large-scale data in the fields of vision, text, and speech. Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored. This is primarily due to the distinctive challenges associated with modelling musical knowledge, particularly its tonal and pitched characteristics of music. To address this research gap, we propose an acoustic Music undERstanding model with large-scale self-supervised Training (MERT), which incorporates teacher models to provide pseudo labels in the masked language modelling (MLM) style acoustic pre-training. In our exploration, we identified a superior combination of teacher models, which outperforms conventional speech and audio approaches in terms of performance. This combination includes an acoustic teacher based on Residual Vector Quantization - Variational AutoEncoder (RVQ-VAE) and a musical teacher based on the Constant-Q Transform (CQT). These teachers effectively guide our student model, a BERT-style transformer encoder, to better model music audio. In addition, we introduce an in-batch noise mixture augmentation to enhance the representation robustness. Furthermore, we explore a wide range of settings to overcome the instability in acoustic language model pre-training, which allows our designed paradigm to scale from 95M to 330M parameters. Experimental results indicate that our model can generalise and perform well on 14 music understanding tasks and attains state-of-the-art (SOTA) overall scores. The code and models are online: https://github.com/yizhilll/MERT