OSlms: A Web Server to Evaluate the Prognostic Value of Genes in Leiomyosarcoma

The availability of transcriptome data and clinical annotation offers the opportunity to identify prognosis biomarkers in cancer. However, efficient online prognosis analysis tools are still lacking. Herein, we developed a user-friendly web server, namely Online consensus Survival analysis of leiomyosarcoma (OSlms), to centralize published gene expression data and clinical datasets of leiomyosarcoma (LMS) patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). OSlms comprises of a total of 268 samples from three independent datasets, and employs the Kaplan Meier survival plot with hazard ratio (HR) and log rank test to estimate the prognostic potency of genes of interests for LMS patients. Using OSlms, clinicians and basic researchers could determine the prognostic significance of genes of interests and get opportunities to identify novel potential important molecules for LMS. OSlms is free and publicly accessible at http://bioinfo.henu.edu.cn/LMS/LMSList.jsp

Highly Sensitive Dual-Phase Nanoglass-Ceramics Self-Calibrated Optical Thermometer

A strategy to achieve high sensitivity of noncontact optical thermometer via the structure design of nanoglass-ceramic and the usage of Ln³⁺ (Ln = Eu, Tb, Dy) luminescence as reference signal and Cr³⁺ emission as temperature signal was provided. Specifically, the synthesized dual-phase glass-ceramics were evidenced to enable spatially confined doping of Ln³⁺ in the hexagonal GdF₃ nanocrystals and Cr³⁺ in the cubic Ga₂O₃ nanoparticles, being beneficial to suppressing detrimental energy transfer between Ln³⁺ and Cr³⁺ and thus significantly enhancing their luminescence. As a consequence, completely different temperature-sensitive luminescence of Ln³⁺4f → 4f transition and Cr³⁺ 3d → 3d transition in the present glass-ceramic resulted in obvious variation of Cr³⁺/Ln³⁺ fluorescence intensity ratio with temperature and strikingly high detecting temperature sensitivity of 15–22% per K. We believe that this preliminary study will provide an important advance in exploring other innovative optical thermometry

Mine Microseismic Signal Denoising Based on a Deep Convolutional Autoencoder

Mine microseismic signal denoising is a basic and crucial link in microseismic data processing, which influences the accuracy and reliability of the monitoring system, and is of great significance with regard to safety during mining. Therefore, this study introduces a deep learning method to improve the mapping function and sparsity of signals in the time-frequency domain and constructs a denoising framework based on a deep convolutional autoencoder to address the denoising problem of mine microseismic signals. First, all noisy microseismic signals are normalized to ensure the nonlinear expression ability of the constructed denoising framework. Then, the normalized signals are transformed into the time-frequency domain using the short-time Fourier transform (STFT), and the real and imaginary parts of time-frequency coefficients serve as the input of the deep convolutional autoencoder to output the masks of the effective and noise signals. Next, these masks are applied to the time-frequency coefficients of the noisy microseismic signals, and the time-frequency coefficients of the potentially effective and noise signals are estimated. Finally, inverse STFT is used to transform these time-frequency coefficients to the time domain to obtain the final denoised effective and noise signals. The constructed framework automatically learns rich features from synthetic data to separate the effective and noise signals, thereby achieving the purpose of fast and automatic denoising. The experimental results show that compared with the wavelet threshold and ensemble empirical mode decomposition, the denoising framework considerably improves the signal-to-noise ratio of mine microseismic signals with less waveform distortion. Moreover, it can achieve a better denoising effect efficiently even in the case of a low SNR, which has obvious advantages. The constructed denoising framework is suitable for microseismic monitoring signals of various mine dynamic disasters and provides strong technical support for intelligent monitoring and early warning concerning production risks in mines

Calculation of Blade-profile Icing and Analysis of Flow Field

The icing of the blade will change its original aerodynamic shape and affect its aerodynamic characteristics. The FENSAP-ICE software is used to compare and verify the numerical calculation results of icing on NACA0012 airfoil with the experimental results. The two-dimensional icing calculation of the compressor inlet guide vane profile is carried out, and the numerical calculation results of flow field are analyzed. The results show that the aerodynamic effect of glaze ice on the blade profile is greater than that of rime ice, the aerodynamic characteristics of the blade profile are mainly affected by the upper separation vortex in the separation zone, the upturned glaze ice on the leading edge of blade profile causes the tailing separation area to expand, and the strong separation vortex can increase the pressure loss of the icing blade profile

Full-Spectral Fine-Tuning Visible Emissions from Cation Hybrid Cs_1–<i>m</i>FA<i>_m</i>PbX₃ (X = Cl, Br, and I, 0 ≤ <i>m</i> ≤ 1) Quantum Dots

Full-color visible emissions are particularly crucial for applications in displays and lightings. In this work, we developed a facile room-temperature ligand-assisted supersaturated recrystallization synthesis of monodisperse, cubic structure Cs_1–<i>m</i>FA<i>_m</i>PbX₃ (X = Cl, Br, and I or their mixtures Cl/Br and Br/I, 0 ≤ <i>m</i> ≤ 1) hybrid perovskite quantum dots (QDs). Impressively, cation substitution of Cs⁺ by FA⁺ was beneficial in finely tuning the band gap and in exciton recombination kinetics, improving the structural stability, and raising the absolute quantum yields up to 85%. With further assistance of anion replacement, full-spectral visible emissions in the wavelength range of 450–750 nm; narrow full width at half-maxima, and a wide color gamut, encompassing 130% of National Television System Committee television color standard, were achieved. Finally, Cs_1–<i>m</i>FA<i>_m</i>PbX₃-polymer films retaining multicolor luminescence are prepared and a prototype white light-emitting diode device was constructed using green Cs_0.1FA_0.9PbBr₃ and red Cs_0.1FA_0.9Br_1.5I_1.5 QDs as color converters, certainly suggesting their potential applications in the optoelectronics field

EuF₃/Ga₂O₃ Dual-Phase Nanostructural Glass Ceramics with Eu²⁺/Cr³⁺ Dual-Activator Luminescence for Self-Calibrated Optical Thermometry

To circumvent the requirement of small energy gap between thermally coupled levels of lanthanide probes in optical thermometry, a strategy using dual-activator fluorescence intensity ratio as temperature signal in dual-phase nanostructural glass ceramics was reported. Specifically, oxyfluoride glass with specially designed composition of SiO₂–Al₂O₃–LiF–EuF₃–Ga₂O₃–Cr₂O₃ was fabricated, and subsequently glass crystallization was used to induce homogeneous precipitation of hexagonal EuF₃ and cubic Ga₂O₃ nanocrystals among the glass matrix. Impressively, Eu²⁺ activators were produced after glass crystallization in an air atmosphere, and the Cr³⁺ emitting center was evidenced to incorporate into Ga₂O₃ crystalline lattice. As a result, temperature determination with high sensitivity of 0.8% K^–1, large energy gap of 8500 cm^–1, and superior thermal stability were realized by taking advantage of the fluorescence intensity ratio between Eu²⁺ and Cr³⁺ as detecting parameter, which exhibited a linear dependence on temperature. We believe that this preliminary investigation will provide a practical approach for developing a high-performance self-calibrated optical thermometer

Freezing characters study of the sessile seawater drop on a cold substrate

Sea spray icing poses risks to vessels and offshore structures in cold ocean regions. Compared to many research works dealing with the freezing of fresh water, the freezing process of sessile seawater drop was less discussed. The coupled level set and volume of fluid method combined with the enthalpy–porosity method is used to solve the Stefan problem. A two-dimensional (2D) axis-symmetric model is adopted to describe the freezing and temperature variation process. Numerical results were verified by our experiment results. The initial geometric profile of sessile drops was characterized by the Young–Laplace equation. Various salinities within the oceanographic range (10–40 g/kg) were adopted, and results showed that the freezing time increases dramatically with increasing salinity. The influence of the contact angle and substrate temperature in the freezing process was also concentrated. All these results contributed to a better understanding of the icing mechanism on marine surfaces

Discriminability and Transferability Estimation: A Bayesian Source Importance Estimation Approach for Multi-Source-Free Domain Adaptation

Source free domain adaptation (SFDA) transfers a single-source model to the unlabeled target domain without accessing the source data. With the intelligence development of various fields, a zoo of source models is more commonly available, arising in a new setting called multi-source-free domain adaptation (MSFDA). We find that the critical inborn challenge of MSFDA is how to estimate the importance (contribution) of each source model. In this paper, we shed new Bayesian light on the fact that the posterior probability of source importance connects to discriminability and transferability. We propose Discriminability And Transferability Estimation (DATE), a universal solution for source importance estimation. Specifically, a proxy discriminability perception module equips with habitat uncertainty and density to evaluate each sample's surrounding environment. A source-similarity transferability perception module quantifies the data distribution similarity and encourages the transferability to be reasonably distributed with a domain diversity loss. Extensive experiments show that DATE can precisely and objectively estimate the source importance and outperform prior arts by non-trivial margins. Moreover, experiments demonstrate that DATE can take the most popular SFDA networks as backbones and make them become advanced MSFDA solutions

Exploring Channel-Aware Typical Features for Out-of-Distribution Detection

Detecting out-of-distribution (OOD) data is essential to ensure the reliability of machine learning models when deployed in real-world scenarios. Different from most previous test-time OOD detection methods that focus on designing OOD scores, we delve into the challenges in OOD detection from the perspective of typicality and regard the feature’s high-probability region as the feature’s typical set. However, the existing typical-feature-based OOD detection method implies an assumption: the proportion of typical feature sets for each channel is fixed. According to our experimental analysis, each channel contributes differently to OOD detection. Adopting a fixed proportion for all channels results in several channels losing too many typical features or incorporating too many abnormal features, resulting in low performance. Therefore, exploring the channel-aware typical features is crucial to better-separating ID and OOD data. Driven by this insight, we propose expLoring channel-Aware tyPical featureS (LAPS). Firstly, LAPS obtains the channel-aware typical set by calibrating the channel-level typical set with the global typical set from the mean and standard deviation. Then, LAPS rectifies the features into channel-aware typical sets to obtain channel-aware typical features. Finally, LAPS leverages the channel-aware typical features to calculate the energy score for OOD detection. Theoretical and visual analyses verify that LAPS achieves a better bias-variance trade-off. Experiments verify the effectiveness and generalization of LAPS under different architectures and OOD scores