Search

In addition to the filter coefficients, the location of the microphone array is a crucial factor in improving the overall performance of a beamformer. The optimal microphone array placement can considerably enhance speech quality. However, the optimization problem with microphone configuration variables is non-convex and highly non-linear. Heuristic algorithms that are frequently employed take a long time and have a chance of missing the optimal microphone array placement design. We extend the Bayesian optimization method to solve the microphone array configuration design problem. The proposed Bayesian optimization method does not depend on gradient and Hessian approximations and makes use of all the information available from prior evaluations. Furthermore, Gaussian process regression and acquisition functions make up the Bayesian optimization method. The objective function is given a prior probabilistic model through Gaussian process regression, which exploits this model while integrating out uncertainty. The acquisition function is adopted to decide the next placement point based upon the incumbent optimum with the posterior distribution. Numerical experiments have demonstrated that the Bayesian optimization method could find a similar or better microphone array placement compared with the hybrid descent method and computational time is significantly reduced. Our proposed method is at least four times faster than the hybrid descent method to find the optimal microphone array configuration from the numerical results. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is prevalent in the remote sensing field but is difficult to interpret by human visual perception. Recently, SAR-to-optical (S2O) image conversion methods have provided a prospective solution. However, since there is a substantial domain difference between optical and SAR images, they suffer from low image quality and geometric distortion in the produced optical images. Motivated by the analogy between pixels during the S2O image translation and molecules in a heat field, a thermodynamics-inspired network for SAR-to-optical image translation (S2O-TDN) is proposed in this paper. Specifically, we design a third-order finite difference (TFD) residual structure in light of the TFD equation of thermodynamics, which allows us to efficiently extract inter-domain invariant features and facilitate the learning of nonlinear translation mapping. In addition, we exploit the first law of thermodynamics (FLT) to devise an FLT-guided branch that promotes the state transition of the feature values from an unstable diffusion state to a stable one, aiming to regularize the feature diffusion and preserve image structures during S2O image translation. S2O-TDN follows an explicit design principle derived from thermodynamic theory and enjoys the advantage of explainability. Experiments on the public SEN1-2 dataset show the advantages of the proposed S2O-TDN over the current methods with more delicate textures and higher quantitative results. [ABSTRACT FROM AUTHOR]

Dendritic cells (DCs) are antigen-presenting cells that play a crucial role in initiating immune responses by cross-presenting relevant antigens to initial T cells. The activation of DCs is a crucial step in inducing anti-tumor immunity. Upon recognition and uptake of tumor antigens, activated DCs present these antigens to naive T cells, thereby stimulating T cell-mediated immune responses and enhancing their ability to attack tumors. It is particularly noted that DCs are able to cross-present foreign antigens to major histocompatibility complex class I (MHC-I) molecules, prompting CD8+ T cells to proliferate and differentiate into cytotoxic T cells. In the malignant progression of hepatocellular carcinoma (HCC), the inactivation of DCs plays an important role, and the activation of DCs is particularly important in anti-HCC immunotherapy. In this review, we summarize the mechanisms of DCs activation in HCC, the involved regulatory factors and strategies to activate DCs in HCC immunotherapy. It provides a basis for the study of HCC immunotherapy through DCs activation. [ABSTRACT FROM AUTHOR]

The early monitoring and inactivation of bacteria are of crucial importance in preventing the further spread of foodborne pathogens. Staphylococcus aureus (S. aureus), a prototypical foodborne pathogen, is widely present in the natural environment and has the capability to trigger a range of diseases at low concentrations. In this work, we designed Fe3O4@SiO2-Au core–shell–satellite nanocomposites (NCs) modified with aptamer for efficient capture, high-sensitivity surface-enhanced Raman scattering (SERS) detection, and photothermal therapy (PTT) against S. aureus. Fe3O4@SiO2-Au NCs with tunable Au nanocrystal nanogaps were prepared. By combining the finite-difference time-domain (FDTD) method and experimental results, we studied the electric field distribution of Fe3O4@SiO2-Au under different Au nanogaps and ultimately obtained the optimal SERS substrate FSA-60. The modification of aptamer on the surfaces of FSA-60 could be used for the specific capture and selective detection of S. aureus, achieving a detection limit of as low as 50 cfu/mL. Furthermore, Apt-FSA-60 possessed excellent photothermal properties, demonstrating the strong photothermal killing ability against S. aureus. Therefore, Apt-FSA-60 is a promising high-sensitivity SERS substrate and efficient photothermal agent and is expected to be widely applied and promoted in future disease prevention and treatment. [ABSTRACT FROM AUTHOR]

Assessing Sustainable Development Goal (SDG) indicator 15.3.1, which refers to the proportion of degraded land to total land area, and analysing its status and drivers is essential for the development of policies to promote the early achievement of SDG target 15.3 of Land Degradation Neutrality (LDN). In this study, Northeast China was selected as the study area, and the progress of indicator 15.3.1 was assessed based on the perspective of Net Primary Productivity (NPP) calculated by the CASA model. WorldPop population spatial distribution data were used as a proxy for human activities, combined with climate data to calculate the effects of changes in temperature, precipitation and population spatial distribution on vegetation NPP based on the partial correlation coefficient method and the Geodetector method. The results showed that 92.81% of the areas that passed the test of significance showed an increasing trend in vegetation NPP from 2000 to 2020. The vegetation NPP was affected by a combination of temperature, precipitation and population. The effects of temperature and precipitation on spatial differences in NPP for various vegetation types were significantly greater than those of population, but in high-density population zones, the effects of population on spatial differences in NPP were generally greater than those of temperature and precipitation. Precipitation was the main driver for spatial variation in NPP in deciduous broad-leaved forests, cultivated vegetation and thickets, while temperature was the main driver for spatial variation in NPP in evergreen coniferous forests. Generally, the warming and wetting trend in Northeast China contributed to the accumulation of NPP in cultivated vegetation, thickets, steppes and grasslands. The sensitivity of NPP to temperature and precipitation in deciduous broad-leaved and deciduous coniferous forests varied according to geographical location. [ABSTRACT FROM AUTHOR]

One of the common illnesses that affect women's physical and mental health is urinary tract infection (UTI). The disappointing results of empirical anti-infective treatment and the lengthy time required for urine bacterial culture are two issues. Antibiotic misuse is common, especially in females who experience recurrent UTI (rUTI). This leads to a higher prevalence of antibiotic resistance in the microorganisms that cause the infection. Antibiotic therapy will face major challenges in the future, prompting clinicians to update their practices. New testing techniques are making the potential association between the urogenital microbiota and UTIs increasingly apparent. Monitoring changes in female urinary tract (UT) microbiota, as well as metabolites, may be useful in exploring newer preventive treatments for UTIs. This review focuses on advances in urogenital microbiology and organismal metabolites relevant to the identification and handling of UTIs in an attempt to provide novel methods for the identification and management of infections of the UT. Particular attention is paid to the microbiota and metabolites in the patient's urine in relation to their role in supporting host health. [ABSTRACT FROM AUTHOR]