A feedback-driven bubble G24.136+00.436: a possible site of triggered star formation

We present a multi-wavelength study of the IR bubble G24.136+00.436. The J=1-0 observations of $^{12}$CO, $^{13}$CO and C$^{18}$O were carried out with the Purple Mountain Observatory 13.7 m telescope. Molecular gas with a velocity of 94.8 km s$^{-1}$ is found prominently in the southeast of the bubble, shaping as a shell with a total mass of $\sim2\times10^{4}$ $M_{\odot}$. It is likely assembled during the expansion of the bubble. The expanding shell consists of six dense cores. Their dense (a few of $10^{3}$ cm$^{-3}$) and massive (a few of $10^{3}$ $M_{\odot}$) characteristics coupled with the broad linewidths ($>$ 2.5 km s$^{-1}$) suggest they are promising sites of forming high-mass stars or clusters. This could be further consolidated by the detection of compact HII regions in Cores A and E. We tentatively identified and classified 63 candidate YSOs based on the \emph{Spitzer} and UKIDSS data. They are found to be dominantly distributed in regions with strong emission of molecular gas, indicative of active star formation especially in the shell. The HII region inside the bubble is mainly ionized by a $\sim$O8V star(s), of the dynamical age $\sim$1.6 Myr. The enhanced number of candidate YSOs and secondary star formation in the shell as well as time scales involved, indicate a possible scenario of triggering star formation, signified by the "collect and collapse" process.Comment: 13 pages, 10 figures, 4 tables, accepted by Ap

Neural differences between the processing of musical meaning conveyed by direction of pitch change and natural music in congenital amusia

Music is a unique communication system for human beings. Iconic musical meaning is one dimension of musical meaning, which emerges from musical information resembling sounds of objects, qualities of objects, or qualities of abstract concepts. The present study investigated whether congenital amusia, a disorder of musical pitch perception, impacts processing of iconic musical meaning. With a cross-modal semantic priming paradigm, target images were primed by semantically congruent or incongruent musical excerpts, which were characterized by direction (upward or downward) of pitch change (Experiment 1), or were selected from natural music (Experiment 2). Twelve Mandarin-speaking amusics and 12 controls performed a recognition (implicit) and a semantic congruency judgment (explicit) task while their EEG waveforms were recorded. Unlike controls, amusics failed to elicit an N400 effect when musical meaning was represented by direction of pitch change, regardless of the nature of the tasks (implicit versus explicit). However, the N400 effect in response to musical meaning in natural musical excerpts was observed for both groups in both types of tasks. These results indicate that amusics are able to process iconic musical meaning through multiple acoustic cues in natural musical excerpts, but not through direction of pitch change. This is the first study to investigate the processing of musical meaning in congenital amusia, providing evidence in support of the “melodic contour deafness hypothesis” with regard to iconic musical meaning processing in this disorder

Research Directions and Projects In an Institute of Developmental Psychology in China

We are a team who maintained to focus on 3 fields in people’s mental health recent years: marriage and family research and therapy, mental health of middle and primary school students, and internet addiction in youth. In every field, we focus on both fundamental research and clinical practice. We aim to explore mechanisms using survey, observation and cognitive neuroscience methods (fMRI), and develop prediction and intervention projects based on research, to improve people’s life and policies

DeepFake detection based on high-frequency enhancement network for highly compressed content

The DeepFake, which generates synthetic content, has sparked a revolution in the fight against deception and forgery. However, most existing DeepFake detection methods mainly focus on improving detection performance with high-quality data while ignoring low-quality synthetic content that suffers from high compression. To address this issue, we propose a novel High-Frequency Enhancement framework, which leverages a learnable adaptive high-frequency enhancement network to enrich weak high-frequency information in compressed content without uncompressed data supervision. The framework consists of three branches, i.e., the Basic branch with RGB domain, the Local High-Frequency Enhancement branch with Block-wise Discrete Cosine Transform, and the Global High-Frequency Enhancement branch with Multi-level Discrete Wavelet Transform. Among them, the local branch utilizes the Discrete Cosine Transform coefficient and channel attention mechanism to indirectly achieve adaptive frequency-aware multi-spatial attention, while the global branch supplements the high-frequency information by extracting coarse-to-fine multi-scale high-frequency cues and cascade-residual-based multi-level fusion by Discrete Wavelet Transform coefficients. In addition, we design a Two-Stage Cross-Fusion module to effectively integrate all information, thereby greatly enhancing weak high-frequency information in low-quality data. Experimental results on FaceForensics++, Celeb-DF, and OpenForensics datasets show that the proposed method outperforms the existing state-of-the-art methods and can effectively improve the detection performance of DeepFakes, especially on low-quality data. The code is available here

Identification and validation of critical genes with prognostic value in gastric cancer

Background: Gastric cancer (GC) is a digestive system tumor with high morbidity and mortality rates. Molecular targeted therapies, including those targeting human epidermal factor receptor 2 (HER2), have proven to be effective in clinical treatment. However, better identification and description of tumor-promoting genes in GC is still necessary for antitumor therapy.Methods: Gene expression and clinical data of GC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Last absolute shrinkage and selection operator (LASSO) Cox regression were applied to build a prognostic model, the Prognosis Score. Functional enrichment and single-sample gene set enrichment analysis (ssGSEA) were used to explore potential mechanisms. Western blotting, RNA interference, cell migration, and wound healing assays were used to detect the expression and function of myosin light chain 9 (MYL9) in GC.Results: A four-gene prognostic model was constructed and GC patients from TCGA and meta-GEO cohorts were stratified into high-prognosis score groups or low-prognosis score groups. GC patients in the high-prognosis score group had significantly poorer overall survival (OS) than those in the low-prognosis score groups. The GC prognostic model was formulated as PrognosisScore = (0.06 × expression of BGN) - (0.008 × expression of ATP4A) + (0.12 × expression of MYL9) - (0.01 × expression of ALDH3A1). The prognosis score was identified as an independent predictor of OS. High expression of MYL9, the highest weighted gene in the prognosis score, was correlated with worse clinical outcomes. Functional analysis revealed that MYL9 is mainly associated with the biological function of epithelial-mesenchymal transition (EMT). Knockdown of MYL9 expression inhibits migration of GC cells in vitro.Conclusion: We found that PrognosisScore is potential reliable prognostic marker and verified that MYL9 promotes the migration and metastasis of GC cells

Black-Box watermarking and blockchain for IP protection of voiceprint recognition model

Deep neural networks are widely used for voiceprint recognition, whilst voiceprint recognition models are vulnerable to attacks. Existing protection schemes for voiceprint recognition models are insufficient to withstand various robustness attacks and cannot prevent model theft. This paper proposes a black-box voiceprint recognition model protection framework that combines active and passive protection. It embeds key information into the Mel spectrogram to generate trigger samples that are difficult to detect and remove and injects them into the host model as watermark W, thereby enhancing the copyright protection performance of the voiceprint recognition model. To restrict the use of the model by unauthorized users, the index number corresponding to the model and the encrypted model information are stored on the blockchain, and then, an exclusive smart contract is designed to restrict access to the model. Experimental results show that this framework effectively protects voiceprint recognition model copyrights and restricts unauthorized access