Blockage of L2HGDH-mediated S-2HG catabolism orchestrates macrophage polarization to elicit antitumor immunity.

The high infiltration of tumor-associated macrophages (TAMs) in the immunosuppressive tumor microenvironment prominently attenuates the efficacy of immune checkpoint blockade (ICB) therapies, yet the underlying mechanisms are not fully understood. Here, we investigate the metabolic profile of TAMs and identify S-2-hydroxyglutarate (S-2HG) as a potential immunometabolite that shapes macrophages into an antitumoral phenotype. Blockage of L-2-hydroxyglutarate dehydrogenase (L2HGDH)-mediated S-2HG catabolism in macrophages promotes tumor regression.

The protective mechanism of Tao Hong Si Wu decoction against breast cancer through regulation of EGFR/ERK1/2 signaling.

Ethnopharmacological Relevance: Tao Hong Si Wu Decoction (THSWD), a traditional Chinese herbal medicine, is widely utilized in clinical settings, either alone or in combination with other medications, for the treatment of breast cancer.

Aim Of The Study: The specific targeting molecule(s) of THSWD and its associated molecular mechanisms remain unclear. This research aims to elucidate the underlying molecular mechanisms of THSWD in the treatment of breast cancer.

A Data Augmentation Method for Motor Imagery EEG Signals Based on DCGAN-GP Network.

Motor imagery electroencephalography (EEG) signals have garnered attention in brain-computer interface (BCI) research due to their potential in promoting motor rehabilitation and control. However, the limited availability of labeled data poses challenges for training robust classifiers. In this study, we propose a novel data augmentation method utilizing an improved Deep Convolutional Generative Adversarial Network with Gradient Penalty (DCGAN-GP) to address this issue.

Electroencephalographic Signal Data Augmentation Based on Improved Generative Adversarial Network.

EEG signals combined with deep learning play an important role in the study of human-computer interaction. However, the limited dataset makes it challenging to study EEG signals using deep learning methods. Inspired by the GAN network in image generation, this paper presents an improved generative adversarial network model L-C-WGAN-GP to generate artificial EEG data to augment training sets and improve the application of BCI in various fields.

Fast reconstruction of EEG signal compression sensing based on deep learning.

When traditional EEG signals are collected based on the Nyquist theorem, long-time recordings of EEG signals will produce a large amount of data. At the same time, limited bandwidth, end-to-end delay, and memory space will bring great pressure on the effective transmission of data. The birth of compressed sensing alleviates this transmission pressure.

Shrinkage behavior of compacted bentonite considering physicochemical effects.

Understanding the shrinkage behavior of bentonite considering physicochemical effects is important to assess the efficiency of buffer barriers in environmental geotechnical engineering. In this paper, shrinkage experiments were conducted on Na-bentonite specimens prepared with salt solutions at various concentrations. NMR and SEM tests were conducted to study the moisture distribution and structural evolution of specimens during the evaporation of water.

EEG Emotion Recognition by Fusion of Multi-Scale Features.

Electroencephalogram (EEG) signals exhibit low amplitude, complex background noise, randomness, and significant inter-individual differences, which pose challenges in extracting sufficient features and can lead to information loss during the mapping process from low-dimensional feature matrices to high-dimensional ones in emotion recognition algorithms. In this paper, we propose a Multi-scale Deformable Convolutional Interacting Attention Network based on Residual Network (MDCNAResnet) for EEG-based emotion recognition. Firstly, we extract differential entropy features from different channels of EEG signals and construct a three-dimensional feature matrix based on the relative positions of electrode channels.

Network Security Situation Prediction Based on Optimized Clock-Cycle Recurrent Neural Network for Sensor-Enabled Networks.

We propose an optimized Clockwork Recurrent Neural Network (CW-RNN) based approach to address temporal dynamics and nonlinearity in network security situations, improving prediction accuracy and real-time performance. By leveraging the clock-cycle RNN, we enable the model to capture both short-term and long-term temporal features of network security situations. Additionally, we utilize the Grey Wolf Optimization (GWO) algorithm to optimize the hyperparameters of the network, thus constructing an enhanced network security situation prediction model.

Multinomial Logistic Factor Regression for Multi-source Functional Block-wise Missing Data.

Multi-source functional block-wise missing data arise more commonly in medical care recently with the rapid development of big data and medical technology, hence there is an urgent need to develop efficient dimension reduction to extract important information for classification under such data. However, most existing methods for classification problems consider high-dimensional data as covariates. In the paper, we propose a novel multinomial imputed-factor Logistic regression model with multi-source functional block-wise missing data as covariates.

Membrane behavior of clay considering the effect of fixed charges.

Membrane efficiency coefficient of clay is evaluated with considering the effect of fixed charges adsorbed on clay mineral surfaces. By virtue of the concept of chemical potential, the ionic concentration of pore water is calculated. An equation is first proposed to calculate the Donnan osmotic pressure based on the activity of water (HO), and then a new method is developed to determine the membrane efficiency coefficient, based on the theoretical chemo-osmotic pressure difference.

The Dynamic Relationship among Bank Credit, House Prices and Carbon Dioxide Emissions in China.

This paper explores the dynamic relationship among bank credit, house prices and carbon dioxide emissions in China by systematically analyzing related data from January 2000 to December 2019 with the help of the time-varying parameter vector autoregression with stochastic volatility (TVP-SV-VAR) model and the Bayesian DCC-GARCH model. Empirical results show the expansion of bank credit significantly drives up house prices and increases carbon dioxide emissions in mosttimes. The rise in house prices inhibits the expansion of bank credit but increases carbon dioxide emissions and aggravates environment pollution, and that the increase in carbon dioxide is helpful to stimulate bank credit expansion and house price rise.

Structural Diversity in Oxadiazole-Containing Silver Complexes Dependent on the Anions.

Two coordination polymers, namely [Ag2(L)(SO3CF3)(H2O)](SO3CF3)•CH2Cl2 (1) and [Ag5(L)4(H2O)2](SbF6)5•5THF (2), were obtained by reacting oxadiazole-containing tri-armed ligand 1,3,5-tri(2-methylthio-1,3,4-oxadiazole-5yl) ben-zene (L) and silver salts in CH2Cl2/THF medium. The two complexes crystallized in the tetragonal space group I41/a and orthorhombic space group Fdd2, respectively. The Single-crystal X-ray diffraction revealed that the two complexes ex-hibit strikingly different 3D polymeric structures, which can be ascribed to the different counter anions.

Synthesis and in vivo Anti-inflammatory Evaluation of Piperazine Derivatives Containing 1,4-Benzodioxan Moiety.

Six piperazine derivatives 6a-f containing 1,4-benzodioxan moiety have been synthesized and characterized by 1H NMR, ESI-MS and elemental analysis. The structure of 6d was further confirmed by single crystal X-ray diffraction. All these novel compounds were screened for their in vivo anti-inflammatory activity employing classical para-xylene-induced mice ear-swelling model.

Seismic stability of subsea tunnels subjected to seepage.

Strength reduction method and ADINA software are adopted to study the stability of submarine tunnel structures subjected to seepage and earthquake under different seawater depths and overlying rock strata thicknesses. First, the excess pore water pressure in the rock mass is eliminated through consolidation calculation. Second, dynamic time-history analysis is performed by inputting the seismic wave to obtain the maximum horizontal displacement at the model top.

[Finite element analysis of total hip replacement].

Total hip replacement is a very effective method to cure many kinds of hip joint illnesses. About a century ago, it was first used in clinic. Since then, total hip replacement has been well developed.

Highly selective biotransformation of ginsenoside Rb1 to Rd by the phytopathogenic fungus Cladosporium fulvum (syn. Fulvia fulva).

Fourteen phytopathogenic fungi were tested for their ability to transform the major ginsenosides to the active minor ginsenoside Rd. The transformation products were identified by TLC and HPLC, and their structures were assigned by NMR analysis. Cladosporium fulvum, a tomato pathogen, was found to transform major ginsenoside Rb(1) to Rd as the sole product.