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Previous studies have been indicated that tumor necrosis factor receptor-associated factor 6 (TRAF6)-induced inflammation leads to acute kidney injury (AKI). How microRNA (miR) contributes to this process is poorly defined. The aim of this study was to investigate whether miR-590-3p regulated lipopolysaccharide (LPS)-induced inflammatory response by inhibiting TRAF6. LPS-induced septic mice were treated with adenovirus expressing miR-590-3p (ad-miR-590-3p) via tail-vein injection. AKI was evaluated by examining serum cystatin C (CysC), serum β2-microglobulin (β2-MG), and blood urea nitrogen (BUN). The mRNA and protein levels were assayed by RT-qPCR and western blotting, respectively. The proliferation of podocytes was monitored using the MTT assay. Cell apoptosis was analyzed by flow cytometry. Survival outcomes in ad-miR-590-3p-transfected septic mice were markedly improved compared with mice with LPS-induced sepsis. Ad-miR-590-3p transfection significantly attenuated LPS-induced AKI, which was reflected by an improved glomerular filtration rate (GFR) as determined by measuring CysC, β2-MG, and BUN. Moreover, we observed that miR-590-3p was a novel regulator of TRAF6, binding to its 3′-untranslated regions (3′-UTRs). In vitro, a miR-590-3p gain-of-function mutation blocked LPS-induced podocyte growth inhibition and apoptosis, as well as overactivation of the inflammatory response. miR-590-3p has the ability to suppress LPS-induced AKI and podocyte apoptosis by targeting TRAF6. This might provide a novel strategy for the treatment of LPS-induced renal injuries.

Human rabies is a serious public health problem that can't be ignored. Rabies immune globulin (RIG) is an indispensable component of rabies post-exposure prophylaxis (PEP). However, current PEP relies on RIG purified from pooled human or equine plasma, which are either in chronic shortage or associated with safety concerns. Monoclonal antibodies have become widely accepted as safer and more cost-effective alternatives to RIG products in recent years. Here, we assessed the neutralization breadth of human monoclonal antibody ormutivimab and its protective efficacy in PEP models. Ormutivimab was able to neutralize a broad panel of Chinese prevalent street RABVs with neutralizing potency form 198–1487.6 IU/mL. Furthermore, ormutivimab offered comparable protection to that with HRIG both at standard doses (20 IU/kg) and higher doses (100 IU/kg and 200 IU/kg). The interference of ormutivimab on vaccine potency was also analyzed and found slightly reduced neutralizing antibody titers similar to HRIG. The broad-spectrum neutralization activities, highly protective potency, and rapid onset of action make ormutivimab an effective candidate for human rabies PEP.

Preference tuning is a crucial process for aligning deep generative models with human preferences. This survey offers a thorough overview of recent advancements in preference tuning and the integration of human feedback. The paper is organized into three main sections: 1) introduction and preliminaries: an introduction to reinforcement learning frameworks, preference tuning tasks, models, and datasets across various modalities: language, speech, and vision, as well as different policy approaches, 2) in-depth exploration of each preference tuning approach: a detailed analysis of the methods used in preference tuning, and 3) applications, discussion, and future directions: an exploration of the applications of preference tuning in downstream tasks, including evaluation methods for different modalities, and an outlook on future research directions. Our objective is to present the latest methodologies in preference tuning and model alignment, enhancing the understanding of this field for researchers and practitioners. We hope to encourage further engagement and innovation in this area. Additionally, we provide a GitHub link https://github.com/hanyang1999/Preference-Tuning-with-Human-Feedback.

A PMSM based servo system VSI switching frequency optimization strategy is proposed in this paper. In servo systems, high switching frequency is favored in order to improve the performance. However, switching frequency is limited by power electronic inherent characteristic. The ideal switching frequency is related to motor rotating speed in systems using SVPWM technique. Therefore, it is a better trade-off way to modify the switching frequency with rotating speed. The switching frequency regulation strategy in this paper is that, switching frequency is determined by power electronics under high speed condition while under low speed condition it's determined by the system performance. The constraint conditions are analyzed and the simulation platform and experiment platform are established. Simulation and experiment results show that the switching frequency optimization strategy proposed in this paper is feasible and effective. [PUBLICATION ABSTRACT]

Material degradation is usually preceded by nonlinear ultrasonic, and higher harmonics will be generated. The project studied the method to improve the second harmonic signal to noise ratio with pulse-inversion technique. A finite element method model of nonlinear was established by a special element which account for a nonlinear stress-strain relation. Calculation was performed for the influence of the pulse-inversion technique to ultrasonic nonlinearity parameters. The simulation results show that the second harmonic signal to noise ratio is obviously improved. Measurement method of nonlinearity parameters and signal processing algorithms with pulse-inversion technique were established, and a robust experimental procedure was developed. Using this method, ultrasonic nonlinearity parameters of a group of LY12 aluminum samples stretched were measured. The experimental results show that the pulse-inversion technique is very efficient in extracting this second-harmonic amplitude by canceling out the odd harmonics which are mainly due to the instrumentation. Introduction

The space effects of oval-shoped furrow pit slopes were analyzed by the elastic mechanics principle. The interaction of limit equilibrium slope angle, friction coefficient, cohesion and horizontal radius of oval-shaped furrow, pits has been derived. The oral trumpet-like rock mass is homogeneous and elastic while only loaded by its dead weight. The interaction indicates that the deeper an oral-shaped furrow pit is excavated, the greater the limit equilibrium slope angle. Both the theory base for reducing stripping waste rock in an oval- shaped furrow pit and the basic way to determine the configuration of a stable slope were developed from the mentioned interaction.The theory includes the preceding principles of stability analysis of slopes. Compared with the configuration determined by traditional theory of slope stability, a great quantity of stripping waste rock can be reduced by that determined in this paper trader stable conditions.

To date, mainstream target speech separation (TSS) approaches are formulated to estimate the complex ratio mask (cRM) of the target speech in time-frequency domain under supervised deep learning framework. However, the existing deep models for estimating cRM are designed in the way that the real and imaginary parts of the cRM are separately modeled using real-valued training data pairs. The research motivation of this study is to design a deep model that fully exploits the temporal-spectral-spatial information of multi-channel signals for estimating cRM directly and efficiently in complex domain. As a result, a novel TSS network is designed consisting of two modules, a complex neural spatial filter (cNSF) and an MVDR. Essentially, cNSF is a cRM estimation model and an MVDR module is cascaded to the cNSF module to reduce the nonlinear speech distortions introduced by neural network. Specifically, to fit the cRM target, all input features of cNSF are reformulated into complex-valued representations following the supervised learning paradigm. Then, to achieve good hierarchical feature abstraction, a complex deep neural network (cDNN) is delicately designed with U-Net structure. Experiments conducted on simulated multi-channel speech data demonstrate the proposed cNSF outperforms the baseline NSF by 12.1% scale-invariant signal-to-distortion ratio and 33.1% word error rate.

In recent years there has been increasing interest concerning the synthesis of hollow materials in nanometer to micrometer dimensions. Hollow hydroxyapatite(HAP) microspheres with pores on their surfaces were prepared by a Li-Ca-B glass in situ immersion conversion process in an aqueous phosphate solution at 37°C. The chemical reaction ability of the Li-Ca-B glass was studied by immersing the glass in SBF solution, and the phases of the reaction product were identified by the XRD method. The morphology, composition, phases and thermal property of the hollow HAP microspheres were investigated using by chemical analysis, SEM ,and XRD . The microphere wall consisted of calcium deficient hydroxyapatite (CDHAP) crystals only. Furthermore, the formation mechanism of the hollow HAP microsphere was also discussed.

Target speech separation refers to extracting a target speaker's voice from an overlapped audio of simultaneous talkers. Previously the use of visual modality for target speech separation has demonstrated great potentials. This work proposes a general multi-modal framework for target speech separation by utilizing all the available information of the target speaker, including his/her spatial location, voice characteristics and lip movements. Also, under this framework, we investigate on the fusion methods for multi-modal joint modeling. A factorized attention-based fusion method is proposed to aggregate the high-level semantic information of multi-modalities at embedding level. This method firstly factorizes the mixture audio into a set of acoustic subspaces, then leverages the target's information from other modalities to enhance these subspace acoustic embeddings with a learnable attention scheme. To validate the robustness of proposed multi-modal separation model in practical scenarios, the system was evaluated under the condition that one of the modalities is temporarily missing, invalid or corrupted. Experiments are conducted on a large-scale audio-visual dataset collected from YouTube (to be released) that spatialized by simulated room impulse responses (RIRs). Experiment results illustrate that our proposed multi-modal framework significantly outperforms single-modal and bi-modal speech separation approaches, while can still support real-time processing.

Background noise, interfering speech and room reverberation frequently distort target speech in real listening environments. In this study, we address joint speech separation and dereverberation, which aims to separate target speech from background noise, interfering speech and room reverberation. In order to tackle this fundamentally difficult problem, we propose a novel multimodal network that exploits both audio and visual signals. The proposed network architecture adopts a two-stage strategy, where a separation module is employed to attenuate background noise and interfering speech in the first stage and a dereverberation module to suppress room reverberation in the second stage. The two modules are first trained separately, and then integrated for joint training, which is based on a new multi-objective loss function. Our experimental results show that the proposed multimodal network yields consistently better objective intelligibility and perceptual quality than several one-stage and two-stage baselines. We find that our network achieves a 21.10% improvement in ESTOI and a 0.79 improvement in PESQ over the unprocessed mixtures. Moreover, our network architecture does not require the knowledge of the number of speakers.