Cyclohexyl-Substituted Anthracene Derivatives for High Thermal Stability Organic Semiconductors

A novel p-type organic semiconductor with high thermal stability is developed by simply incorporating cyclohexyl substituted aryl groups into the 2,6-position of anthracene, namely 2,6-di(4-cyclohexylphenyl)anthracene (DcHPA), and a similar compound with linear alkyl chain, 2,6-di(4-n-hexylphenyl)anthracene (DnHPA), is also studied for comparison. DcHPA shows sublimation temperature around 360°C, and thin film field-effect transistors of DcHPA could maintain half of the original mobility value when heated up to 150°C. Corresponding DnHPA has sublimation temperature of 310°C and the performance of its thin film devices decreases by about 50% when heated to 80°C. The impressing thermal stability of the cyclohexyl substitution compounds might provide guidelines for developing organic electronic materials with high thermal stability

Dynamic Neural Patterns of Human Emotions in Virtual Reality: Insights from EEG Microstate Analysis

Emotions play a crucial role in human life and affect mental health. Understanding the neural patterns associated with emotions is essential. Previous studies carried out some exploration of the neural features of emotions, but most have designed experiments in two-dimensional (2D) environments, which differs from real-life scenarios. To create a more real environment, this study investigated emotion-related brain activity using electroencephalography (EEG) microstate analysis in a virtual reality (VR) environment. We recruited 42 healthy volunteers to participate in our study. We explored the dynamic features of different emotions, and four characteristic microstates were analyzed. In the alpha band, microstate A exhibited a higher occurrence in both negative and positive emotions than in neutral emotions. Microstate C exhibited a prolonged duration of negative emotions compared to positive emotions, and a higher occurrence was observed in both microstates C and D during positive emotions. Notably, a unique transition pair was observed between microstates B and C during positive emotions, whereas a unique transition pair was observed between microstates A and D during negative emotions. This study emphasizes the potential of integrating virtual reality (VR) and EEG to facilitate experimental design. Furthermore, this study enhances our comprehension of neural activities during various emotional states

Effect of Quercetin on PC12 Alzheimer’s Disease Cell Model Induced by Aβ25-35 and Its Mechanism Based on Sirtuin1/Nrf2/HO-1 Pathway

Objective. This study is aimed at studying the effect of quercetin on the Alzheimer disease cell model induced by Aβ25-35 in PC12 cells and its mechanism of action. Methods. The AD cell model was established by Aβ25-35. Quercetin was used at different concentrations (0, 10, 20, 40, and 80 μmol/L). The morphology of cells was observed, and the effect on cell survival rate was detected by the MTT method. Cell proliferation was detected by the SRB method. The contents of LDH, SOD, MDA, GSH-Px, AChE, CAT, and T-AOC were detected by kits. The expression of sirtuin1/Nrf2/HO-1 was detected by RT-qPCR and Western blot. Results. PC12 cells in the control group grew quickly and adhered well to the wall, most of which had extended long axons and easily grew into clusters. In the model group, cells were significantly damaged and the number of cells was significantly reduced. It was found that PC12 cells were swollen, rounded, protruding, and retracting, with reduced adherent function and floating phenomenon. Quercetin could increase the survival rate and proliferation rate of PC12 cells; reduce the levels of LDH, AChE, MDA, and HO-1 protein; and increase the levels of SOD, GSH-Px, CAT, T-AOC, sirtuin1, and Nrf2 protein. Conclusion. Quercetin can increase the survival rate of PC12 injured by Aβ25-35, promote cell proliferation, and antagonize the toxicity of Aβ; it also has certain neuroprotective effects. Therefore, quercetin is expected to become a drug for the treatment of AD

Gut Microbiome and Atherosclerosis: A Mendelian Randomization Study

Background: According to recent studies, atherosclerosis and gut microbiota are related. Nevertheless, it has been discovered that the gut microbiota varies across studies, with its function still being debated, and such relationships not proven to be causal. Thus, our study aimed to identify the key gut microbiota taxa (GM taxa) at different taxonomic levels, namely, the phylum, class, order, family, and genus, to investigate any potential causal links to atherosclerosis. Methods: We employed summary data from the MiBioGen consortium on the gut microbiota to conduct a sophisticated two-sample Mendelian randomization (MR) analysis. Pertinent information regarding atherosclerosis statistics was acquired from the FinnGen Consortium R8 publication. To assess causality, the utilized principal analytical technique was the inverse variance-weighted (IVW) method. Supplementary to IVW, additional MR methodologies were employed, including weighted median, MR-Egger, weighted methods, and simple mode. Sensitivity analyses involved the application of Cochrane’s Q-test, MR-Egger intercept test, MR-PRESSO global test, and leave-one-out analysis. Results: Finally, after performing an MR study on the risk of 211 GM taxa on atherosclerosis, we discovered 20 nominal links and one strong causal link. Firmicutes (phylum ID: 1672) (odds ratio (OR) = 0.852 (0.763, 0.950), p = 0.004) continued to be connected with a lower incidence of coronary atherosclerosis, even after Bonferroni correction. Conclusions: Based on the discovered data, it was established that the phylum Firmicutes exhibits a causal relationship with a reduced occurrence of coronary atherosclerosis. This investigation could potentially provide novel insights into therapeutic objectives for atherosclerosis by focusing on the gut microbiota

Improved SSD network for fast concealed object detection and recognition in passive terahertz security images

Abstract With the strengthening of global anti-terrorist measures, it is increasingly important to conduct security checks in public places to detect concealed objects carried by the human body. Research in recent years has shown that deep learning is helpful for detecting concealed objects in passive terahertz images. However, previous studies have failed to achieve superior accuracy and performance for real-time labeling. Our research aims to propose a novel method for accurate and real-time detection of concealed objects in terahertz images. To reach this goal we trained and tested a promising detector based on deep residual networks using human image data collected by passive terahertz devices. Specifically, we replaced the backbone network of the SSD (Single Shot MultiBox Detector) algorithm with a more representative residual network to reduce the difficulty of network training. Aiming at the problems of repeated detection and missed detection of small targets, a feature fusion-based terahertz image target detection algorithm was proposed. Furthermore, we introduced a hybrid attention mechanism in SSD to improve the algorithm’s ability to acquire object details and location information. Finally, the Focal Loss function was introduced to improve the robustness of the model. Experimental results show that the accuracy of the SSD algorithm is improved from 95.04 to 99.92%. Compared with other current mainstream models, such as Faster RCNN, YOLO, and RetinaNet, the proposed method can maintain high detection accuracy at a faster speed. This proposed method based on SSD achieves a mean average precision of 99.92%, an F1 score of 0.98, and a prediction speed of 17 FPS on the validation subset. This proposed method based on SSD-ResNet-50 can provide a technical reference for the application and development of deep learning technology in terahertz smart security systems. In the future, it can be widely used in some public scenarios with real-time security inspection requirements

Ultra-Wideband MIMO Array for Penetrating Lunar Regolith Structures on the Chang’e-5 Lander

The Chang’e-5 lunar exploration mission of China is equipped with a Lunar Regolith Penetrating Radar (LRPR) for measuring the thickness and structures of the lunar regolith in the landing area. Since the LRPR is stationary, an ultra-wideband multiple-input multiple-output (MIMO) array is designed as a replacement for conventional mobile subsurface probing systems. The MIMO array, with 12 antenna elements and a switch matrix, operates in the frequency band from 1.0 to 4.75 GHz. In this work, the design and layout of the antenna elements were optimized with respect to the lander. To this end, the antenna elements were designed as miniaturized Vivaldi antennas with quarter elliptical slots (i.e., quarter elliptical slotted antenna, or QESA). QESAs are significantly small while being able to mitigate the impact of the lander on antenna electrical performances. QESAs also have a wide operating bandwidth, flat gain, and excellent time domain characteristics. In addition, a high-temperature resistant ultra-light radome with high transmissivity is designed to protect the external antenna array. After calibration, the MIMO array is used to detect targets embedded in volcanic ash. The detection depth reaches 2.5 m, and the detection effect is good