KS-Net: Multi-band joint speech restoration and enhancement network for 2024 ICASSP SSI Challenge

This paper presents the speech restoration and enhancement system created by the 1024K team for the ICASSP 2024 Speech Signal Improvement (SSI) Challenge. Our system consists of a generative adversarial network (GAN) in complex-domain for speech restoration and a fine-grained multi-band fusion module for speech enhancement. In the blind test set of SSI, the proposed system achieves an overall mean opinion score (MOS) of 3.49 based on ITU-T P.804 and a Word Accuracy Rate (WAcc) of 0.78 for the real-time track, as well as an overall P.804 MOS of 3.43 and a WAcc of 0.78 for the non-real-time track, ranking 1st in both tracks.Comment: Accepted to ICASSP 2024; Rank 1st in ICASSP 2024 Speech Signal Improvement (SSI) Challeng

In Vivo RNA Interference Screens Identify Regulators of Antiviral CD4+ and CD8+ T Cell Differentiation

SummaryClassical genetic approaches to examine the requirements of genes for T cell differentiation during infection are time consuming. Here we developed a pooled approach to screen 30–100+ genes individually in separate antigen-specific T cells during infection using short hairpin RNAs in a microRNA context (shRNAmir). Independent screens using T cell receptor (TCR)-transgenic CD4+ and CD8+ T cells responding to lymphocytic choriomeningitis virus (LCMV) identified multiple genes that regulated development of follicular helper (Tfh) and T helper 1 (Th1) cells, and short-lived effector and memory precursor cytotoxic T lymphocytes (CTLs). Both screens revealed roles for the positive transcription elongation factor (P-TEFb) component Cyclin T1 (Ccnt1). Inhibiting expression of Cyclin T1, or its catalytic partner Cdk9, impaired development of Th1 cells and protective short-lived effector CTL and enhanced Tfh cell and memory precursor CTL formation in vivo. This pooled shRNA screening approach should have utility in numerous immunological studies

CT-based deep learning radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer

ObjectivesAlthough the preoperative assessment of whether a bladder cancer (BCa) indicates muscular invasion is crucial for adequate treatment, there currently exist some challenges involved in preoperative diagnosis of BCa with muscular invasion. The aim of this study was to construct deep learning radiomic signature (DLRS) for preoperative predicting the muscle invasion status of BCa.MethodsA retrospective review covering 173 patients revealed 43 with pathologically proven muscle-invasive bladder cancer (MIBC) and 130 with non–muscle–invasive bladder cancer (non- MIBC). A total of 129 patients were randomly assigned to the training cohort and 44 to the test cohort. The Pearson correlation coefficient combined with the least absolute shrinkage and selection operator (LASSO) was utilized to reduce radiomic redundancy. To decrease the dimension of deep learning features, Principal Component Analysis (PCA) was adopted. Six machine learning classifiers were finally constructed based on deep learning radiomics features, which were adopted to predict the muscle invasion status of bladder cancer. The area under the curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of the model.ResultsAccording to the comparison, DLRS-based models performed the best in predicting muscle violation status, with MLP (Train AUC: 0.973260 (95% CI 0.9488-0.9978) and Test AUC: 0.884298 (95% CI 0.7831-0.9855)) outperforming the other models. In the test cohort, the sensitivity, specificity and accuracy of the MLP model were 0.91 (95% CI 0.551-0.873), 0.78 (95% CI 0.594-0.863) and 0.58 (95% CI 0.729-0.827), respectively. DCA indicated that the MLP model showed better clinical utility than Radiomics-only model, which was demonstrated by the decision curve analysis.ConclusionsA deep radiomics model constructed with CT images can accurately predict the muscle invasion status of bladder cancer

Influence of Degree of Compaction on Unsaturated Hydraulic Properties of a Compacted Completely Decomposed Granite

It is crucial to understand hydraulic properties, i.e., soil-water characteristic curve (SWCC) and unsaturated permeability function (UPF), of completely decomposed granite (CDG) for relevant engineering projects in southeastern China. Previous studies mainly focused on SWCCs of CDG, whereas UPFs of CDG have not yet been well understood. In this study, the effects of the degree of compaction (DOC) on SWCCs and UPFs of CDG were investigated based on experiments where suction range was from 0 to 500 kPa. The microstructure of soil specimens was then analyzed by mercury intrusion porosimetry (MIP). Furthermore, the UPFs of CDG under different values of DOC were calculated using four prediction models and compared with experimental data. Results showed that the pore volume of specimens at higher DOC was smaller than that at lower DOC, and there were more macropores observed in specimens at lower DOC. Meanwhile, it was found that increasing compaction effort produced negligible influence on the volume of micropores. When the suction was less than 100 kPa, the permeability was reduced with the increase in DOC, due to the decrease of macropore volume. However, the influence of DOC on SWCCs and UPFs became marginal when the suction exceeded 100 kPa. The Fredlund and Xing model provided the best prediction of UPF among the four models when suction was smaller than air entry value (AEV). It is suggested that these models could be improved to capture UPFs at higher suctions than AEV by considering suction-induced volume contraction

Evidence that the Transcription Elongation Function of Rpb9 Is Involved in Transcription-Coupled DNA Repair in Saccharomyces cerevisiae

Rpb9, a small nonessential subunit of RNA polymerase II, has been shown to have multiple transcription-related functions in Saccharomyces cerevisiae. These functions include promoting transcription elongation and mediating a subpathway of transcription-coupled repair (TCR) that is independent of Rad26, the homologue of human Cockayne syndrome complementation group B protein. Rpb9 is composed of three distinct domains: the N-terminal Zn1, the C-terminal Zn2, and the central linker. Here we show that the Zn1 and linker domains are essential, whereas the Zn2 domain is almost dispensable, for both transcription elongation and TCR functions. Impairment of transcription elongation, which does not dramatically compromise Rad26-mediated TCR, completely abolishes Rpb9-mediated TCR. Furthermore, Rpb9 appears to be dispensable for TCR if its transcription elongation function is compensated for by removing a transcription repression/elongation factor. Our data suggest that the transcription elongation function of Rpb9 is involved in TCR

Synthesis of Cellulose-2,3-bis(3,5-dimethylphenylcarbamate) in an Ionic Liquid and Its Chiral Separation Efficiency as Stationary Phase

A chiral selector of cellulose-2,3-bis(3,5-dimethylphenylcarbamate) (CBDMPC) was synthesized by reacting 3,5-dimethylphenyl isocyanate with microcrystalline cellulose dissolved in an ionic liquid of 1-allyl-3-methyl-imidazolium chloride (AMIMCl). The obtained chiral selector was effectively characterized by infrared spectroscopy, elemental analysis and 1H NMR. The selector was reacted with 3-aminopropylsilanized silica gel and the CBDMPC bonded chiral stationary phase (CSP) was obtained. Chromatographic evaluation of the prepared CSPs was conducted by high performance liquid chromatographic (HPLC) and baseline separation of three typical fungicides including hexaconazole, metalaxyl and myclobutanil was achieved using n-hexane/isopropanol as the mobile phase with a flow rate 1.0 mL/min. Experimental results also showed that AMIMCl could be recycled easily and reused in the preparation of CSPs as an effective reaction media

Evaluation of the Effects of Forest on Slope Stability and Its Implications for Forest Management: A Case Study of Bailong River Basin, China

Previous studies have shown that the mechanical effects of vegetation roots on slope stability can be classified as additional cohesion effects and anchorage effects. The present study investigated the combined mechanical effects (additional cohesion effects and anchorage effects) of vegetation on a slope with coarse-grained soil in the mountainous region (significantly prone to slope failure) of Gansu Province, China. A detailed survey of tree density, root system morphology and slope profiles was conducted, and we also assessed the soil cohesion provided by the root systems of monospecific stands of Robinia pseudoacacia growing in different locations on the slope. The measured data were incorporated into a numerical slope model to calculate the stability of the slope under the influence of trees. The results indicated that it was necessary to consider the anchoring effect of coarse roots when estimating the mechanical effects of trees on slope stability. In particular, the FoS (factor of safety) of the slope was increased by the presence of trees. The results also demonstrated that vegetation increased slope stability. The reinforcing effects were most significant when the trees were planted along the entire slope. Although the reinforcing effects contributed by trees were limited (only 4–11%), they were essential for making optimal use of vegetation for enhancing slope stability. Overall, vegetation development can make a major contribution to ecosystem restoration in the study region

Derivation of L-system models from measurements of biological branching structures using genetic algorithms

L-systems are widely used in the modelling of branching structures and the growth process of biological objects such as plants, nerves and airways in lungs. The derivation of such L-system models involves a lot of hard mental work and time-consuming manual procedures. A method based on genetic algorithms for automating the derivation of L-systems is presented here. The method involves representation of branching structure, translation of L-systems to axial tree architectures, comparison of branching structure and the application of genetic algorithms. Branching structures are represented as axial trees and positional information is considered as an important attribute along with length and angle in the database configuration of branches. An algorithm is proposed for automatic L-system translation that compares randomly generated branching structures with the target structure. Edit distance, which is proposed as a measure of dissimilarity between rooted trees, is extended for the comparison of structures represented in axial trees and positional information is involved in the local cost function. Conventional genetic algorithms and repair mechanics are employed in the search for L-system models having the best fit to observational data