Speech Enhancement with Multi-granularity Vector Quantization

With advances in deep learning, neural network based speech enhancement (SE) has developed rapidly in the last decade. Meanwhile, the self-supervised pre-trained model and vector quantization (VQ) have achieved excellent performance on many speech-related tasks, while they are less explored on SE. As it was shown in our previous work that utilizing a VQ module to discretize noisy speech representations is beneficial for speech denoising, in this work we therefore study the impact of using VQ at different layers with different number of codebooks. Different VQ modules indeed enable to extract multiple-granularity speech features. Following an attention mechanism, the contextual features extracted by a pre-trained model are fused with the local features extracted by the encoder, such that both global and local information are preserved to reconstruct the enhanced speech. Experimental results on the Valentini dataset show that the proposed model can improve the SE performance, where the impact of choosing pre-trained models is also revealed

Identification of autophagy-associated genes and prognostic implications in adults with acute myeloid leukemia by integrated bioinformatics analysis

Acute myeloid leukemia (AML) is one of the most common malignant blood neoplasma in adults. The prominent disease heterogeneity makes it challenging to foresee patient survival. Autophagy, a highly conserved degradative process, played indispensable and context-dependent roles in AML. However, it remains elusive whether autophagy-associated stratification could accurately predict prognosis of AML patients. Here, we developed a prognostic model based on autophagy-associated genes, and constructed scoring systems that help to predicte the survival of AML patients in both TCGA data and independent AML cohorts. The Nomogram model also confirmed the autophagy-associated model by showing the high concordance between observed and predicted survivals. Additionally, pathway enrichment analysis and protein-protein interaction network unveiled functional signaling pathways that were associated with autophagy. Altogether, we constructed the autophagy-associated prognostic model that might be likely to predict outcome for AML patients, providing insights into the biological risk stratification strategies and potential therapeutic targets

Optimal Routing for Safe Construction and Demolition Waste Transportation: A CVaR Criterion and Big Data Analytics Approach

Rapid urbanisation worldwide, especially in developing countries and areas, has led to the generation of large amounts of construction and demolition waste (C&DW). The resultant transportation demands pose severe threats to safe transportation and secure city operation. By considering the low-probability–high-consequence nature of C&DW traffic accidents and the effectiveness of route optimisation in transportation risk control, a risk-averse project was implemented. Furthermore, an optimal routing model based on the conditional value at risk (CVaR) criterion is proposed. The model considered various risk-averse attitudes of decision-makers. For practicality and for strongly supporting policy-making, big data technology, including the construction of multistructure databases and in-depth analysis, was applied to achieve the proposed CVaR routing model. Therefore, the present study extended the CVaR method to optimal routing design in the field of safe urban C&DW transportation and integrated the optimal model with big data technology

5-[(2-Chloro-4-nitro­anilino)methyl­idene]-2,2-dimethyl-1,3-dioxane-4,6-dione

In the title compound, C13H11ClN2O6, the dihedral angles between the benzene ring and the amino­methyl­ene unit and between the amino­methyl­ene group and the dioxane ring are 8.19 (14) and 1.39 (17)°, respectively. The dioxane ring has a half-boat conformation, in which the C atom between the dioxane O atoms is 0.662 (4)Å out of the plane through the remaining ring atoms. Intra­molecular N—H⋯O and N—H⋯Cl inter­actions occur

Circ-CCS regulates oxaliplatin resistance via targeting miR-874-3p/HK2 axis in colorectal cancer

Background. Colorectal cancer (CRC) is a malignancy that threatens the patient’s life. Previous reports showed that circular RNAs (circRNAs) can affect CRC development. Herein, we demonstrated the characters of circular RNA copper chaperone for superoxide dismutase (circ-CCS) in CRC tissues and cells. Methods. Circ-CCS, CCS mRNA, microRNA-8743p (miR-874-3p) and hexokinase 2 (HK2) were indicated by qRT-PCR and western blot in CRC. The cell roles were examined. Additionally, the interaction between miR-874-3p and circ-CCS or HK2 was forecasted by the bioinformatics method and assessed by dual-luciferase reporter assay. Finally, the mouse test was implemented to demonstrate the effect of circ-CCS in vivo. Results. Circ-CCS and HK2 were increased, whereas miR-874-3p was diminished in CRC. Circ-CCS lack subdued the IC50 value of oxaliplatin, cell proliferation, migration, invasion and glycolysis metabolism in CRC cells, while it endorsed cell apoptosis. Furthermore, miR-874-3p was validated as having a tumor repressive effect in CRC cells by restraining HK2. The results also showed that HK2 could regulate the development of CRC. In mechanism, circ-CCS targeted miR-874-3p to control HK2. In addition, circ-CCS knock-down also attenuated tumor growth in mice. Conclusion. Circ-CCS expedited CRC through miR874-3p/HK