Speaker verification using attentive multi-scale convolutional recurrent network

In this paper, we propose a speaker verification method by an Attentive Multi-scale Convolutional Recurrent Network (AMCRN). The proposed AMCRN can acquire both local spatial information and global sequential information from the input speech recordings. In the proposed method, logarithm Mel spectrum is extracted from each speech recording and then fed to the proposed AMCRN for learning speaker embedding. Afterwards, the learned speaker embedding is fed to the back-end classifier (such as cosine similarity metric) for scoring in the testing stage. The proposed method is compared with state-of-the-art methods for speaker verification. Experimental data are three public datasets that are selected from two large-scale speech corpora (VoxCeleb1 and VoxCeleb2). Experimental results show that our method exceeds baseline methods in terms of equal error rate and minimal detection cost function, and has advantages over most of baseline methods in terms of computational complexity and memory requirement. In addition, our method generalizes well across truncated speech segments with different durations, and the speaker embedding learned by the proposed AMCRN has stronger generalization ability across two back-end classifiers.Comment: 21 pages, 6 figures, 8 tables. Accepted for publication in Applied Soft Computin

Molecular Dynamic Simulation to Explore the Molecular Basis of Btk-PH Domain Interaction with Ins(1,3,4,5)P4

Bruton’s tyrosine kinase contains a pleckstrin homology domain, and it specifically binds inositol 1,3,4,5-tetrakisphosphate (Ins(1,3,4,5)P4), which is involved in the maturation of B cells. In this paper, we studied 12 systems including the wild type and 11 mutants, K12R, S14F, K19E, R28C/H, E41K, L11P, F25S, Y40N, and K12R-R28C/H, to investigate any change in the ligand binding site of each mutant. Molecular dynamics simulations combined with the method of molecular mechanics/Poisson-Boltzmann solvent-accessible surface area have been applied to the twelve systems, and reasonable mutant structures and their binding free energies have been obtained as criteria in the final classification. As a result, five structures, K12R, K19E, R28C/H, and E41K mutants, were classified as “functional mutations,” whereas L11P, S14F, F25S, and Y40N were grouped into “folding mutations.” This rigorous study of the binding affinity of each of the mutants and their classification provides some new insights into the biological function of the Btk-PH domain and related mutation-causing diseases

Lightweight tomato ripeness detection algorithm based on the improved RT-DETR

Tomatoes, widely cherished for their high nutritional value, necessitate precise ripeness identification and selective harvesting of mature fruits to significantly enhance the efficiency and economic benefits of tomato harvesting management. Previous studies on intelligent harvesting often focused solely on identifying tomatoes as the target, lacking fine-grained detection of tomato ripeness. This deficiency leads to the inadvertent harvesting of immature and rotten fruits, resulting in economic losses. Moreover, in natural settings, uneven illumination, occlusion by leaves, and fruit overlap hinder the precise assessment of tomato ripeness by robotic systems. Simultaneously, the demand for high accuracy and rapid response in tomato ripeness detection is compounded by the need for making the model lightweight to mitigate hardware costs. This study proposes a lightweight model named PDSI-RTDETR to address these challenges. Initially, the PConv_Block module, integrating partial convolution with residual blocks, replaces the Basic_Block structure in the legacy backbone to alleviate computing load and enhance feature extraction efficiency. Subsequently, a deformable attention module is amalgamated with intra-scale feature interaction structure, bolstering the capability to extract detailed features for fine-grained classification. Additionally, the proposed slimneck-SSFF feature fusion structure, merging the Scale Sequence Feature Fusion framework with a slim-neck design utilizing GSConv and VoVGSCSP modules, aims to reduce volume of computation and inference latency. Lastly, by amalgamating Inner-IoU with EIoU to formulate Inner-EIoU, replacing the original GIoU to expedite convergence while utilizing auxiliary frames enhances small object detection capabilities. Comprehensive assessments validate that the PDSI-RTDETR model achieves an average precision mAP50 of 86.8%, marking a 3.9% enhancement over the original RT-DETR model, and a 38.7% increase in FPS. Furthermore, the GFLOPs of PDSI-RTDETR have been diminished by 17.6%. Surpassing the baseline RT-DETR and other prevalent methods regarding precision and speed, it unveils its considerable potential for detecting tomato ripeness. When applied to intelligent harvesting robots in the future, this approach can improve the quality of tomato harvesting by reducing the collection of immature and spoiled fruits

Catalytic Mechanism Investigation of Lysine-Specific Demethylase 1 (LSD1): A Computational Study

Lysine-specific demethylase 1 (LSD1), the first identified histone demethylase, is a flavin-dependent amine oxidase which specifically demethylates mono- or dimethylated H3K4 and H3K9 via a redox process. It participates in a broad spectrum of biological processes and is of high importance in cell proliferation, adipogenesis, spermatogenesis, chromosome segregation and embryonic development. To date, as a potential drug target for discovering anti-tumor drugs, the medical significance of LSD1 has been greatly appreciated. However, the catalytic mechanism for the rate-limiting reductive half-reaction in demethylation remains controversial. By employing a combined computational approach including molecular modeling, molecular dynamics (MD) simulations and quantum mechanics/molecular mechanics (QM/MM) calculations, the catalytic mechanism of dimethylated H3K4 demethylation by LSD1 was characterized in details. The three-dimensional (3D) model of the complex was composed of LSD1, CoREST, and histone substrate. A 30-ns MD simulation of the model highlights the pivotal role of the conserved Tyr761 and lysine-water-flavin motif in properly orienting flavin adenine dinucleotide (FAD) with respect to substrate. The synergy of the two factors effectively stabilizes the catalytic environment and facilitated the demethylation reaction. On the basis of the reasonable consistence between simulation results and available mutagenesis data, QM/MM strategy was further employed to probe the catalytic mechanism of the reductive half-reaction in demethylation. The characteristics of the demethylation pathway determined by the potential energy surface and charge distribution analysis indicates that this reaction belongs to the direct hydride transfer mechanism. Our study provides insights into the LSD1 mechanism of reductive half-reaction in demethylation and has important implications for the discovery of regulators against LSD1 enzymes

Network modelling reveals the mechanism underlying colitis-associated colon cancer and identifies novel combinatorial anti-cancer targets

The connection between inflammation and tumourigenesis has been well established. However, the detailed molecular mechanism underlying inflammation-associated tumourigenesis remains unknown because this process involves a complex interplay between immune microenvironments and epithelial cells. To obtain a more systematic understanding of inflammation-associated tumourigenesis as well as to identify novel therapeutic approaches, we constructed a knowledge-based network describing the development of colitis-associated colon cancer (CAC) by integrating the extracellular microenvironment and intracellular signalling pathways. Dynamic simulations of the CAC network revealed a core network module, including P53, MDM2, and AKT, that may govern the malignant transformation of colon epithelial cells in a pro-tumor inflammatory microenvironment. Furthermore, in silico mutation studies and experimental validations led to a novel finding that concurrently targeting ceramide and PI3K/AKT pathway by chemical probes or marketed drugs achieves synergistic anti-cancer effects. Overall, our network model can guide further mechanistic studies on CAC and provide new insights into the design of combinatorial cancer therapies in a rational manner

From metabolism to malignancy: the multifaceted role of PGC1α in cancer

PGC1α, a central player in mitochondrial biology, holds a complex role in the metabolic shifts seen in cancer cells. While its dysregulation is common across major cancers, its impact varies. In some cases, downregulation promotes aerobic glycolysis and progression, whereas in others, overexpression escalates respiration and aggression. PGC1α’s interactions with distinct signaling pathways and transcription factors further diversify its roles, often in a tissue-specific manner. Understanding these multifaceted functions could unlock innovative therapeutic strategies. However, challenges exist in managing the metabolic adaptability of cancer cells and refining PGC1α-targeted approaches. This review aims to collate and present the current knowledge on the expression patterns, regulators, binding partners, and roles of PGC1α in diverse cancers. We examined PGC1α’s tissue-specific functions and elucidated its dual nature as both a potential tumor suppressor and an oncogenic collaborator. In cancers where PGC1α is tumor-suppressive, reinstating its levels could halt cell proliferation and invasion, and make the cells more receptive to chemotherapy. In cancers where the opposite is true, halting PGC1α’s upregulation can be beneficial as it promotes oxidative phosphorylation, allows cancer cells to adapt to stress, and promotes a more aggressive cancer phenotype. Thus, to target PGC1α effectively, understanding its nuanced role in each cancer subtype is indispensable. This can pave the way for significant strides in the field of oncology

Serum Protein KNG1, APOC3, and PON1 as Potential Biomarkers for Yin-Deficiency-Heat Syndrome

Yin-deficiency-heat (YDH) syndrome is a concept in Traditional Chinese Medicine (TCM) for describing subhealth status. However, there are few efficient diagnostic methods available for confirming YDH syndrome. To explore the novel method for diagnosing YDH syndrome, we applied iTRAQ to observe the serum protein profiles in YDH syndrome rats and confirmed protein levels by ELISA. A total of 92 differentially expressed proteins (63 upregulated proteins and 29 downregulated proteins), which were mainly involved in complement and coagulation cascades and glucose metabolism pathway, were identified by the proteomic experiments. Kininogen 1 (KNG1) was significantly increased (p<0.0001), while apolipoprotein C-III (APOC3, p<0.005) and paraoxonase 1 (PON1, p<0.001) were significantly decreased in the serum of YDH syndrome rats. The combination of KNG1, APOC3, and PON1 constituted a diagnostic model with 100.0% sensitivity and 85.0% specificity. The results indicated that KNG1, APOC3, and PON1 may act as potential biomarkers for diagnosing YDH syndrome. KNG1 may regulate cytokines and chemokines release in YDH syndrome, and the low levels of PON1 and APOC3 may increase oxidative stress and lipolysis in YDH syndrome, respectively. Our work provides a novel method for YDH syndrome diagnosis and also provides valuable experimental basis to understand the molecular mechanism of YDH syndrome

Suicide rates among patients with first and second primary cancer

Abstract Aims With advancements in cancer treatments, the survival rates of patients with their first primary cancer (FPC) have increased, resulting in a rise in the number of patients with second primary cancer (SPC). However, there has been no assessment on the incidence of suicide among patients with SPC. This study assessed the occurrence of suicide among patients with SPC and compared them with that in patients with FPC. Methods This was a retrospective, population-based cohort study that followed patients with FPC and SPC diagnosed from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) 17 registries database between 1 January 2000 and 31 December 2019. Results For patients with SPC, an age of 85+ years at diagnosis was associated with a higher incidence of suicide death (HR, 1.727; 95% CI, 1.075–2.774), while the suicide death was not considerably different in the chemotherapy group (P > 0.05). Female genital system cancers (HR, 3.042; 95% CI, 1.819–6.361) accounted for the highest suicide death among patients with SPC. The suicide death distribution of patients with SPC over time indicated that suicide events mainly occurred within 5 to 15 years of diagnosis. Compared with patients with FPC, patients with SPC in general had a lower risk of suicide, but increased year by year. Conclusion The risk of suicide was reduced in patients with SPC compared with patients with FPC, but increased year by year. Therefore, oncologists and related health professionals need to provide continuous psychological support to reduce the incidence of suicide. The highest suicide death was found among patients with female genital system cancer

Serum HBsAg and HBcrAg is associated with inflammation in HBeAg-positive chronic hepatitis B patients

Backgrounds &amp; aimsLiver inflammation is the main risk factor for developing liver fibrosis, cirrhosis, and even hepatocellular carcinoma in chronic hepatitis B (CHB) patients. To replace biopsy, additional non-invasive biomarkers to diagnose and grade liver necroinflammation are urgently required in clinical practice.MethodNinety-four CHB patients, including 74 HBeAg-positive and 20 HBeAg-negative patients, were enrolled and started entecavir or adefovir therapy. Serum HBV RNA, HBV DNA, HBsAg, hepatitis B core-related antigen (HBcrAg), ALT and AST levels, as well as intrahepatic HBV DNA and cccDNA were measured at baseline and during treatment. Liver inflammation was assessed at baseline and month 60 by liver biopsy. Inflammation regression was defined as a ≥1-grade decrease according to the Scheuer scoring system.ResultsIn HBeAg-positive CHB patients, at baseline, serum HBsAg and HBcrAg levels negatively correlated with inflammation grade, while ALT and AST levels positively correlated with inflammation grade. AST plus HBsAg exhibited excellent diagnostic ability for significant inflammation with an AUROC of 0.896. After 60 months of antiviral treatment, almost all the patients’ liver inflammation ameliorated to G1, and no patients had inflammation progression.ConclusionBesides ALT and AST, serum HBsAg and HBcrAg correlated with inflammation grade in HBeAg-positive CHB patients before NAs treatment. Moreover, the combination of HBsAg and AST exhibited excellent diagnostic ability for significant inflammation