Attention Is All You Need For Blind Room Volume Estimation

In recent years, dynamic parameterization of acoustic environments has raised increasing attention in the field of audio processing. One of the key parameters that characterize the local room acoustics in isolation from orientation and directivity of sources and receivers is the geometric room volume. Convolutional neural networks (CNNs) have been widely selected as the main models for conducting blind room acoustic parameter estimation, which aims to learn a direct mapping from audio spectrograms to corresponding labels. With the recent trend of self-attention mechanisms, this paper introduces a purely attention-based model to blindly estimate room volumes based on single-channel noisy speech signals. We demonstrate the feasibility of eliminating the reliance on CNN for this task and the proposed Transformer architecture takes Gammatone magnitude spectral coefficients and phase spectrograms as inputs. To enhance the model performance given the task-specific dataset, cross-modality transfer learning is also applied. Experimental results demonstrate that the proposed model outperforms traditional CNN models across a wide range of real-world acoustics spaces, especially with the help of the dedicated pretraining and data augmentation schemes.Comment: 5 pages, 4 figures, submitted ICASSP 202

Identification of the target genes of AhTWRKY24 and AhTWRKY106 transcription factors reveals their regulatory network in Arachis hypogaea cv. Tifrunner using DAP-seq

WRKY transcription factors (TFs) have been identified as important core regulators in the responses of plants to biotic and abiotic stresses. Cultivated peanut (Arachis hypogaea) is an important oil and protein crop. Previous studies have identified hundreds of WRKY TFs in peanut. However, their functions and regulatory networks remain unclear. Simultaneously, the AdWRKY40 TF is involved in drought tolerance in Arachis duranensis and has an orthologous relationship with the AhTWRKY24 TF, which has a homoeologous relationship with AhTWRKY106 TF in A. hypogaea cv. Tifrunner. To reveal how the homoeologous AhTWRKY24 and AhTWRKY106 TFs regulate the downstream genes, DNA affinity purification sequencing (DAP-seq) was performed to detect the binding sites of TFs at the genome-wide level. A total of 3486 downstream genes were identified that were collectively regulated by the AhTWRKY24 and AhTWRKY106 TFs. The results revealed that W-box elements were the binding sites for regulation of the downstream genes by AhTWRKY24 and AhTWRKY106 TFs. A gene ontology enrichment analysis indicated that these downstream genes were enriched in protein modification and reproduction in the biological process. In addition, RNA-seq data showed that the AhTWRKY24 and AhTWRKY106 TFs regulate differentially expressed genes involved in the response to drought stress. The AhTWRKY24 and AhTWRKY106 TFs can specifically regulate downstream genes, and they nearly equal the numbers of downstream genes from the two A. hypogaea cv. Tifrunner subgenomes. These results provide a theoretical basis to study the functions and regulatory networks of AhTWRKY24 and AhTWRKY106 TFs

Disulfiram/Copper Induce Ferroptosis in Triple-Negative Breast Cancer Cell Line MDA-MB-231

Background: The complex formed by disulfiram (DSF) and copper (Cu) is safe and effective for the prevention and treatment of triple-negative breast cancer (TNBC). Although previous studies have shown that DSF/Cu induces ferroptosis, the mechanism remains unclear. Methods: The mitochondrial morphology of TNBC treated with DSF/Cu was observed by transmission microscopy, and intracellular levels of iron, lipid reactive oxygen species (ROS), malondialdehyde, and glutathione were evaluated to detect the presence of ferroptosis. Target genes for the DSF/Cu-activated ferroptosis signaling pathway were examined by transcriptome sequencing analysis. Expression of the target gene, HOMX1, was detected by qRT-PCR, immunofluorescence and western blot. Results: The mitochondria of TNBC cells were significantly atrophied following treatment with DSF/Cu for 24 h. Addition of DSF/Cu supplement resulted in significant up-regulation of intracellular iron, lipid ROS and malondialdehyde levels, and significant down-regulation of glutathione levels, all of which are important markers of ferroptosis. Transcriptome analysis confirmed that DSF/Cu activated the ferroptosis signaling pathway and up-regulated several ferroptosis target genes associated with redox regulation, especially heme oxygenase-1 (HMOX-1). Inhibition of ferroptosis by addition of the ROS scavenger N-acetyl-L-cysteine (NAC) significantly increased the viability of DSF/Cu-treated TNBC cells. Conclusions: These results show that DSF/Cu increases lipid peroxidation and causes a sharp increase in HMOX1 activity, thereby inducing TNBC cell death through ferroptosis. DSF/Cu is a promising therapeutic drug for TNBC and could lead to ferroptosis-mediated therapeutic strategies for human cancer

A Threshold Switching Selector Based on Highly Ordered Ag Nanodots for X‐Point Memory Applications

Abstract Leakage interference between memory cells is the primary obstacle for enlarging X‐point memory arrays. Metal‐filament threshold switches, possessing excellent selectivity and low leakage current, are developed in series with memory cells to reduce sneak path current and lower power consumption. However, these selectors typically have limited on‐state currents (≤10 µA), which are insufficient for memory RESET operations. Here, a strategy is proposed to achieve sufficiently large RESET current (≈2.3 mA) by introducing highly ordered Ag nanodots to the threshold switch. Compared to the Ag thin film case, Ag nanodots as active electrode could avoid excessive Ag atoms migration into solid electrolyte during operations, which causes stable conductive filament growth. Furthermore, Ag nanodots with rapid thermal processing contribute to forming multiple weak Ag filaments at a lower voltage and then spontaneous rupture as the applied voltage reduced, according to quantized conductance and simulation analysis. Impressively, the Ag nanodots based threshold switch, which is bidirectional and truly electroforming‐free, demonstrates extremely high selectivity >109, ultralow leakage current <1 pA, very steep slope of 0.65 mV dec−1, and good thermal stability up to 200 °C, and further represents significant suppression of leakage currents and excellent performances for SET/RESET operations in the one‐selector‐one‐resistor configuration

Multiresponsive Supramolecular Gel Based on Pillararene-Containing Polymers

A multiresponsive supramolecular gel was constructed based on a bis­(pyridinium) dication guest and a copolymer with pillararenes as the pendant groups, which was synthesized by free radical copolymerization of methacrylate-functionalized pillararenes and methyl methacrylate. The mechanism of gel formation was explored by the intensive study. Upon addition of competitive host or guest molecules, pillararene-based gel could be transferred into sol due to the competition of host–guest complexation. Surprisingly, the ordered stacking of pillararenes was indispensable to obtain the supramolecular gel, which endowed the system with response to temperature change

Application of mathematical morphology operation with memristor-based computation-in-memory architecture for detecting manufacturing defects

Mathematical morphology operations are widely used in image processing such as defect analysis in semiconductor manufacturing and medical image analysis. These data-intensive applications have high requirements during hardware implementation that are challenging for conventional hardware platforms such as central processing units (CPUs) and graphics processing units (GPUs). Computation-in-memory (CIM) provides a possible solution for highly efficient morphology operations. In this study, we demonstrate the application of morphology operation with a novel memristor-based auto-detection architecture and demonstrate non-neuromorphic computation on a multi-array-based memristor system. Pixel-by-pixel logic computations with low parallelism are converted to parallel operations using memristors. Moreover, hardware-implemented computer-integrated manufacturing was used to experimentally demonstrate typical defect detection tasks in integrated circuit (IC) manufacturing and medical image analysis. In addition, we developed a new implementation scheme employing a four-layer network to realize small-object detection with high parallelism. The system benchmark based on the hardware measurement results showed significant improvement in the energy efficiency by approximately 358 times and 32 times more than when a CPU and GPU were employed, respectively, exhibiting the advantage of the proposed memristor-based morphology operation