Neural Speaker Diarization Using Memory-Aware Multi-Speaker Embedding with Sequence-to-Sequence Architecture

We propose a novel neural speaker diarization system using memory-aware multi-speaker embedding with sequence-to-sequence architecture (NSD-MS2S), which integrates the strengths of memory-aware multi-speaker embedding (MA-MSE) and sequence-to-sequence (Seq2Seq) architecture, leading to improvement in both efficiency and performance. Next, we further decrease the memory occupation of decoding by incorporating input features fusion and then employ a multi-head attention mechanism to capture features at different levels. NSD-MS2S achieved a macro diarization error rate (DER) of 15.9% on the CHiME-7 EVAL set, which signifies a relative improvement of 49% over the official baseline system, and is the key technique for us to achieve the best performance for the main track of CHiME-7 DASR Challenge. Additionally, we introduce a deep interactive module (DIM) in MA-MSE module to better retrieve a cleaner and more discriminative multi-speaker embedding, enabling the current model to outperform the system we used in the CHiME-7 DASR Challenge. Our code will be available at https://github.com/liyunlongaaa/NSD-MS2S.Comment: Submitted to ICASSP 202

Case study of a rhizosphere microbiome assay on a bamboo rhizome with excessive shoots.

Peer reviewe

Exploring retinal ganglion cells encoding to multi-modal stimulation using 3D microelectrodes arrays

Microelectrode arrays (MEA) are extensively utilized in encoding studies of retinal ganglion cells (RGCs) due to their capacity for simultaneous recording of neural activity across multiple channels. However, conventional planar MEAs face limitations in studying RGCs due to poor coupling between electrodes and RGCs, resulting in low signal-to-noise ratio (SNR) and limited recording sensitivity. To overcome these challenges, we employed photolithography, electroplating, and other processes to fabricate a 3D MEA based on the planar MEA platform. The 3D MEA exhibited several improvements compared to planar MEA, including lower impedance (8.73 ± 1.66 kΩ) and phase delay (−15.11° ± 1.27°), as well as higher charge storage capacity (CSC = 10.16 ± 0.81 mC/cm2), cathodic charge storage capacity (CSCc = 7.10 ± 0.55 mC/cm2), and SNR (SNR = 8.91 ± 0.57). Leveraging the advanced 3D MEA, we investigated the encoding characteristics of RGCs under multi-modal stimulation. Optical, electrical, and chemical stimulation were applied as sensory inputs, and distinct response patterns and response times of RGCs were detected, as well as variations in rate encoding and temporal encoding. Specifically, electrical stimulation elicited more effective RGC firing, while optical stimulation enhanced RGC synchrony. These findings hold promise for advancing the field of neural encoding

CCL4 participates in the reprogramming of glucose metabolism induced by ALV-J infection in chicken macrophages

Interferon and chemokine-mediated immune responses are two general antiviral programs of the innate immune system in response to viral infections and have recently emerged as important players in systemic metabolism. This study found that the chemokine CCL4 is negatively regulated by glucose metabolism and avian leukosis virus subgroup J (ALV-J) infection in chicken macrophages. Low expression levels of CCL4 define this immune response to high glucose treatment or ALV-J infection. Moreover, the ALV-J envelope protein is responsible for CCL4 inhibition. We confirmed that CCL4 could inhibit glucose metabolism and ALV-J replication in chicken macrophages. The present study provides novel insights into the antiviral defense mechanism and metabolic regulation of the chemokine CCL4 in chicken macrophages

Structural and biochemical studies of human lysine methyltransferase Smyd3 reveal the important functional roles of its post-SET and TPR domains and the regulation of its activity by DNA binding

The SET- and MYND-domain containing (Smyd) proteins constitute a special subfamily of the SET-containing lysine methyltransferases. Here we present the structure of full-length human Smyd3 in complex with S-adenosyl-l-homocysteine at 2.8 Å resolution. Smyd3 affords the first example that other region(s) besides the SET domain and its flanking regions participate in the formation of the active site. Structural analysis shows that the previously uncharacterized C-terminal domain of Smyd3 contains a tetratrico-peptide repeat (TPR) domain which together with the SET and post-SET domains forms a deep, narrow substrate binding pocket. Our data demonstrate the important roles of both TPR and post-SET domains in the histone lysine methyltransferase (HKMT) activity of Smyd3, and show that the hydroxyl group of Tyr239 is critical for the enzymatic activity. The characteristic MYND domain is located nearby to the substrate binding pocket and exhibits a largely positively charged surface. Further biochemical assays show that DNA binding of Smyd3 can stimulate its HKMT activity and the process may be mediated via the MYND domain through direct DNA binding

Serpin peptidase inhibitor, clade E, member 2 in physiology and pathology: recent advancements

Serine protease inhibitors (serpins) are the most numerous and widespread multifunctional protease inhibitor superfamily and are expressed by all eukaryotes. Serpin E2 (serpin peptidase inhibitor, clade E, member 2), a member of the serine protease inhibitor superfamily is a potent endogenous thrombin inhibitor, mainly found in the extracellular matrix and platelets, and expressed in numerous organs and secreted by many cell types. The multiple functions of serpin E2 are mainly mediated through regulating urokinase-type plasminogen activator (uPA, also known as PLAU), tissue-type plasminogen activator (tPA, also known as PLAT), and matrix metalloproteinase activity, and include hemostasis, cell adhesion, and promotion of tumor metastasis. The importance serpin E2 is clear from its involvement in numerous physiological and pathological processes. In this review, we summarize the structural characteristics of the Serpin E2 gene and protein, as well as its roles physiology and disease

One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks

Unlearnable examples (ULEs) aim to protect data from unauthorized usage for training DNNs. Error-minimizing noise, which is injected to clean data, is one of the most successful methods for preventing DNNs from giving correct predictions on incoming new data. Nonetheless, under specific training strategies such as adversarial training, the unlearnability of error-minimizing noise will severely degrade. In addition, the transferability of error-minimizing noise is inherently limited by the mismatch between the generator model and the targeted learner model. In this paper, we investigate the mechanism of unlearnable examples and propose a novel model-free method, named \emph{One-Pixel Shortcut}, which only perturbs a single pixel of each image and makes the dataset unlearnable. Our method needs much less computational cost and obtains stronger transferability and thus can protect data from a wide range of different models. Based on this, we further introduce the first unlearnable dataset called CIFAR-10-S, which is indistinguishable from normal CIFAR-10 by human observers and can serve as a benchmark for different models or training strategies to evaluate their abilities to extract critical features from the disturbance of non-semantic representations. The original error-minimizing ULEs will lose efficiency under adversarial training, where the model can get over 83\% clean test accuracy. Meanwhile, even if adversarial training and strong data augmentation like RandAugment are applied together, the model trained on CIFAR-10-S cannot get over 50\% clean test accuracy

The population structural transition effect on rising per capita CO₂ emissions:evidence from China

The per capita CO2 emissions (PCCE) of many developing countries like China have been rising faster than total CO2 emissions, and display spatial divergence. Such temporal growth and spatial divergence will have a significant influence on efforts to mitigate CO2 emissions. Given the research gap on the impact of the structural transition in population on PCCE, we constructed an econometric model using the dynamic panel method. The results reveal that the population structural transition has a significant nonlinear impact on PCCE, as the rate of population growth in China decelerates. Both demographic ageing and urban-rural migration have a stronger impact on PCCE than other factors. This effect, however, decreases beyond a certain threshold. An increase in the number of households due to urbanization and family downsizing has resulted in a positive effect on PCCE, without a threshold turning point. The research also finds that an increased share of the service sector in employment can reduce PCCE only if the sector employs more than 31.56% of the total employed population. Overall, these findings indicate that policymakers should pay attention to the prominence of the demographic structural transition for effective climate policy. Key policy insights Policymakers should address rising per capita carbon emissions (PCCE) and their spatial divergence in future climate policies, not just total CO2 emissions. The transitioning demographics of ageing and urbanization in China show a nonlinear, inverted U-shaped effect on PCCE instead of a continuously positive effect. Based on the nonlinear effect of employment structure on PCCE, policymakers should focus on the relationship between the structural transition of the economy and PCCE in future climate mitigation policies