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Alzheimer's disease (AD) is a highly heritable disease. The morphological changes of cortical cortex (such as, cortical thickness and surface area) in AD always accompany by the change of the functional connectivity to other brain regions and influence the short‐ and long‐range brain network connections, causing functional deficits of AD. In this study, the first hypothesis is that genetic variations might affect morphology‐based brain networks, leading to functional deficits; the second hypothesis is that protein–protein interaction (PPI) between the candidate proteins and known interacting proteins to AD might exist and influence AD. 600 470 variants and structural magnetic resonance imaging scans from 175 AD patients and 214 healthy controls were obtained from the Alzheimer's Disease Neuroimaging Initiative‐1 database. A co‐sparse reduced‐rank regression model was fit to study the relationship between non‐synonymous mutations and morphology‐based brain networks. After that, PPIs between selected genes and BACE1, an enzyme that was known to be related to AD, are explored by using molecular dynamics (MD) simulation and co‐immunoprecipitation (Co‐IP) experiments. Eight genes affecting morphology‐based brain networks were identified. The results of MD simulation showed that the PPI between TGM4 and BACE1 was the strongest among them and its interaction was verified by Co‐IP. Hence, gene variations influence morphology‐based brain networks in AD, leading to functional deficits. This finding, validated by MD simulation and Co‐IP, suggests that the effect is robust. [ABSTRACT FROM AUTHOR]

Powder metallurgy is widely used to fabricate high-nitrogen, nickel-free austenitic stainless steel. However, after sintering and nitriding, additional solution treatment is typically required to achieve uniform nitrogen distribution and a homogeneous austenite phase. This work proposes a novel method to eliminate the need for lengthy and high-temperature solution treatment by switching the nitrogen atmosphere to argon during the cooling process. The effects of different N2-Ar atmosphere-switching temperatures (750–1320 °C) on the phase composition, element distribution, microstructure, mechanical properties, and corrosion resistance of the studied steels were systematically investigated. Results show that cooling in the N2 atmosphere initially transforms the matrix to a fully austenitic structure enriched with nitrogen. Excessive nitrogen infiltration leads to Cr2N precipitation, inducing partial austenite decomposition and forming a multiphase structure comprising austenite, α-Fe, and Cr2N. Strategic switching from N2 to Ar reverses this reaction, yielding a high-nitrogen, chemically uniform austenitic structure. Specifically, switching at 1150 °C, the steel exhibits excellent mechanical properties and corrosion resistance, with a yield strength of 749 MPa, an ultimate tensile strength of 1030 MPa, an elongation of 38.7%, and a corrosion current of 0.06 mA/cm2, outperforming the steels cooled solely in N2 and subsequently solution-treated. This novel method offers advantages in cost reduction, energy saving, and operational effectiveness, highlighting its potential for broad industrial application. [ABSTRACT FROM AUTHOR]

In this study, a terahertz dual-band–high-gain, multi-input–multi-output (MIMO) antenna with two and four input ports was realized, with gain values of up to 7.16 dBi and 6.44 dBi, respectively. This represents an increase in gain of 102.4% and 81.8%, respectively, compared to single-input port antennas. By calculating and analyzing the voltage standing wave ratio (VSWR), gain, envelope correlation coefficient (ECC), and diversity gain, the two MIMO antennas achieved perfect impedance matching at their resonance points. Moreover, the interference between the elements of the MIMO antenna was small, and an isolation effect between the units was realized. These MIMO antennas can achieve multiple input signals to regulate the antenna radiation performance and improve signal strength and quality. In addition, they possess good development prospects for achieving high-speed data transmission in the terahertz band and in mobile communication. [ABSTRACT FROM AUTHOR]

With the rapid development of apparel e-commerce, the variety of apparel is increasing, and it becomes more and more important to classify the apparel according to its collar design. Traditional image processing methods have been difficult to cope with the increasingly complex image backgrounds. To solve this problem, an EMRes-50 classification algorithm is proposed to solve the problem of garment collar image classification, which is designed based on the ECA-ResNet50 model combined with the MC-Loss loss function method. Applying the improved algorithm to the Coller-6 dataset, and the classification accuracy obtained was 73.6%. To further verify the effectiveness of the algorithm, it was applied to the DeepFashion-6 dataset, and the classification accuracy obtained was 86.09%. The experimental results show that the improved model has higher accuracy than the existing CNN model, and the model has better feature extraction ability, which is helpful to solve the problem of the difficulty of fine-grained collar classification and promote the further development of clothing product image classification.