Assistive diagnostic technology for congenital heart disease based on fusion features and deep learning

Introduction: Congenital heart disease (CHD) is a cardiovascular disorder caused by structural defects in the heart. Early screening holds significant importance for the effective treatment of this condition. Heart sound analysis is commonly employed to assist in the diagnosis of CHD. However, there is currently a lack of an efficient automated model for heart sound classification, which could potentially replace the manual process of auscultation.Methods: This study introduces an innovative and efficient screening and classification model, combining a locally concatenated fusion approach with a convolutional neural network based on coordinate attention (LCACNN). In this model, Mel-frequency spectral coefficients (MFSC) and envelope features are locally fused and employed as input to the LCACNN network. This model automatically analyzes feature map energy information, eliminating the need for denoising processes.Discussion: The proposed classification model in this study demonstrates a robust capability for identifying congenital heart disease, potentially substituting manual auscultation to facilitate the detection of patients in remote areas.Results: This study introduces an innovative and efficient screening and classification model, combining a locally concatenated fusion approach with a convolutional neural network based on coordinate attention (LCACNN). In this model, Mel-frequency spectral coefficients (MFSC) and envelope features are locally fused and employed as input to the LCACNN network. This model automatically analyzes feature map energy information, eliminating the need for denoising processes. To assess the performance of the classification model, comparative ablation experiments were conducted, achieving classification accuracies of 91.78% and 94.79% on the PhysioNet and HS databases, respectively. These results significantly outperformed alternative classification models

Textural effect of Pt catalyst layers with different carbon supports on internal oxygen diffusion during oxygen reduction reaction

One way to address the cost issue of polymer electrolyte membrane fuel cells (PEMFCs) is to reduce the amount of platinum used in the cathode catalyst layers (CLs). The oxygen mass transfer resistance of the cathode CLs is the main bottleneck limiting the polarization performance of low Pt-loading membrane electrodes at high current densities. Pt nanoparticles, ionomers, carbon supports, and water in cathode CLs can all affect their oxygen mass transfer resistance. From the perspective of carbon supports, this paper changed the texture of CLs by adding carbon nanotubes (CNTs) or graphene oxide (GO) into carbon black (XC72) and studied its impact on the oxygen mass transfer resistance. A mathematical model was adopted to correlate total mass transfer resistance and internal diffusion efficiency factor with CL structure parameters in order to determine the dominant textural effect of a CL. The results show that adding 30%CNT or 20GO to carbon black of XC72 improved the electrocatalytic performance and mass transfer capability of the composite carbon-supported Pt catalyst layers during oxygen reduction reaction. The study further reveals that the smaller particle-sized carbon material with tiny Pt nanoparticles deposition can minimize the internal oxygen diffusion resistance. A less dense CL structure can reduce the oxygen transfer resistance through the secondary pores. The conclusion obtained can provide guidance for the rational design of optimal cathode CLs of PEMFCs

Controllable Synthesis of Tunable Microstructures of Self-Supporting Graphene Films from Opened Bubble to Cube via in Situ Template-Modulating

Three-dimensional (3D) microstructured building units have replaced layer-to-layer stacked designs in transparent graphene films to fully exploit the advantages of two-dimensional graphene. However, it is still challenging to precisely control the size and microstructures of these building blocks to develop multifunctional graphene-based materials that satisfy the performance requirements of diverse applications. In this study, we propose a controllable method to regulate the microstructures of building units to form structures ranging from opened bubbles and cubes, while the size decreased from 20 to 3 μm, via an in situ template-modulating technology. NaCl was used as either a liquid or solid template by changing the dc bias. The reduced size and dense arrangement of the building units not only provide an improved mass loading for the transparent films but also build multiple pathways for fast ion/electron transmission, enhancing their promise for various practical applications. Generally, we provide a convenient protocol for finely regulating the microstructure and size of these building units, resulting in multifunctional films with a controllable transmittance, which enables the use of these graphene-based architectures as transparent electrodes in various applications and extends the family of multifunctional materials that will present new possibilities for electronics and other devices

AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head

Large language models (LLMs) have exhibited remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. Despite the recent success, current LLMs are not capable of processing complex audio information or conducting spoken conversations (like Siri or Alexa). In this work, we propose a multi-modal AI system named AudioGPT, which complements LLMs (i.e., ChatGPT) with 1) foundation models to process complex audio information and solve numerous understanding and generation tasks; and 2) the input/output interface (ASR, TTS) to support spoken dialogue. With an increasing demand to evaluate multi-modal LLMs of human intention understanding and cooperation with foundation models, we outline the principles and processes and test AudioGPT in terms of consistency, capability, and robustness. Experimental results demonstrate the capabilities of AudioGPT in solving 16 AI tasks with speech, music, sound, and talking head understanding and generation in multi-round dialogues, which empower humans to create rich and diverse audio content with unprecedented ease. Code can be found in https://github.com/AIGC-Audio/AudioGP

Characterization of large-scale genomic differences in the first complete human genome

Abstract Background The first telomere-to-telomere (T2T) human genome assembly (T2T-CHM13) release is a milestone in human genomics. The T2T-CHM13 genome assembly extends our understanding of telomeres, centromeres, segmental duplication, and other complex regions. The current human genome reference (GRCh38) has been widely used in various human genomic studies. However, the large-scale genomic differences between these two important genome assemblies are not characterized in detail yet. Results Here, in addition to the previously reported “non-syntenic” regions, we find 67 additional large-scale discrepant regions and precisely categorize them into four structural types with a newly developed website tool called SynPlotter. The discrepant regions (~ 21.6 Mbp) excluding telomeric and centromeric regions are highly structurally polymorphic in humans, where the deletions or duplications are likely associated with various human diseases, such as immune and neurodevelopmental disorders. The analyses of a newly identified discrepant region—the KLRC gene cluster—show that the depletion of KLRC2 by a single-deletion event is associated with natural killer cell differentiation in ~ 20% of humans. Meanwhile, the rapid amino acid replacements observed within KLRC3 are probably a result of natural selection in primate evolution. Conclusion Our study provides a foundation for understanding the large-scale structural genomic differences between the two crucial human reference genomes, and is thereby important for future human genomics studies