Explore the vast range of titles available.

Soft magnetic materials with flake geometry can provide shape anisotropy for breaking the Snoek limit, which is promising for achieving high-frequency ferromagnetic resonances and microwave absorption properties. Here, two-dimensional (2D) Fe 3 C microflakes with crystal orientation are obtained by solid-state phase transformation assisted by electrochemical dealloying. The shape anisotropy can be further regulated by manipulating the thickness of 2D Fe 3 C microflakes under different isothermally quenching temperatures. Thus, the resonant frequency is adjusted effectively from 9.47 and 11.56 GHz under isothermal quenching from 700 °C to 550 °C. The imaginary part of the complex permeability can reach 0.9 at 11.56 GHz, and the minimum reflection loss ( RL min ) is −52.09 dB (15.85 GHz, 2.90 mm) with an effective absorption bandwidth (EAB ≤−10 dB ) of 2.55 GHz. This study provides insight into the preparation of high-frequency magnetic loss materials for obtaining high-performance microwave absorbers and achieves the preparation of functional materials from traditional structural materials. Fe 3 C microflakes with high magnetic anisotropy are prepared through solid-state phase transformation and electrochemical dealloying. The magnetic anisotropy can be tuned by adjusting the morphology, resulting in optimized ferromagnetic resonance behavior for microwave absorption

This exploratory metabolomics pilot study employed non-targeted liquid chromatography-tandem mass spectrometry (LC‒MS/MS) to characterize serum metabolic profiles in elite male water polo athletes, thereby assessing physiological adaptation to high-intensity training. We aimed to provide a scientific basis for evaluating physical fitness and optimizing performance capacity in elite athletes. Sixteen male water polo athletes of the Chinese national team were recruited. All athletes underwent a one-week complete break following the end of the previous competitive season to mitigate accumulated fatigue and establish a true resting metabolic baseline. Fasting venous blood samples (5 mL) were collected at 7:00 AM on two time points: the first sample (E1) was collected before commencement of the official training week, and the second sample (E2) was collected immediately after the completion of that week of training. The data were analyzed via XCMS, MetaboAnalyst 6.0, SPSS 21.0, and GraphPad Prism. (1) Metabolomic analysis identified 363 metabolites in total, 33 of which were differentially expressed between pre- and post-training time points. After one week of routine training, 11 metabolites were significantly up-regulated (  0.01), and 22 were significantly down-regulated (  0.01). (2) KEGG pathway analysis identified the top eight metabolic pathways, with MetPA further highlighting lysine degradation (  0.01) and vitamin B6 metabolism (  0.05) as key altered pathways. (3) Three metabolites were identified as potential markers associated with the training week changes in water polo athletes on the basis of significant alterations post-training. N6, N6, N6-trimethyl-L-lysine (  0.01) and 2-aminoadipic acid (  0.01) were significantly decreased, whereas 4-pyridoxic acid (  0.01) was significantly increased. Non-targeted LC‒MS/MS provides a valuable tool for monitoring metabolic adaptations at the molecular level in aquatic athletes. In this exploratory study, we observed associated changes in the serum metabolome following intensive training, pointing to adjustments in amino acid and lipid metabolism. These findings offer preliminary insights for guiding fitness and performance optimization.

The early detection and accurate histopathological diagnosis of gastric cancer increase the chances of successful treatment. The worldwide shortage of pathologists offers a unique opportunity for the use of artificial intelligence assistance systems to alleviate the workload and increase diagnostic accuracy. Here, we report a clinically applicable system developed at the Chinese PLA General Hospital, China, using a deep convolutional neural network trained with 2,123 pixel-level annotated H&E-stained whole slide images. The model achieves a sensitivity near 100% and an average specificity of 80.6% on a real-world test dataset with 3,212 whole slide images digitalized by three scanners. We show that the system could aid pathologists in improving diagnostic accuracy and preventing misdiagnoses. Moreover, we demonstrate that our system performs robustly with 1,582 whole slide images from two other medical centres. Our study suggests the feasibility and benefits of using histopathological artificial intelligence assistance systems in routine practice scenarios. The early detection and accurate histopathological diagnosis of gastric cancer are essential factors that can help increase the chances of successful treatment. Here, the authors report on a digital pathology tool achieving high performance on a real world test dataset and show that the system can aid pathologists in improving diagnostic accuracy.

Digital light processing 3D printing is a powerful manufacturing technology for shaping materials into complex geometries with high resolution. However, the rheological and chemical requirements for printing limit the use of materials to photoactive resins. Here, we propose a versatile manufacturing platform for constructing versatile materials using DLP-printed water-soluble granular polyacrylamide as sacrificial molds. The polymerization-induced phase separation during printing results in a close packed granular geometry with intrinsic micropores, which greatly accelerates the dissolution rate of polyacrylamide. Combined with precise control over the molecular weight, this salt-like sacrificial mold can be fully dissolved in neutral water at room temperature within 30 min. Furthermore, significant surface oxygen inhibition promotes the leveling and spreading of liquid resin on the cured part surfaces, achieving a printing speed of 375 mm/h in a top-down printer. Due to the mild conditions for mold removal, complex-shaped architectures can be created from a variety of compositions, including temperature-sensitive low-melting alloys, alkaline-degradable polyesters, as well as widely used materials such as silicone rubber, polyurethane, polyolefin elastomer, and epoxy. Considering the fast mold dissolution rate and mild dissolution conditions, the present platform represents a potential low-cost, and universal indirect 3D printing method for shaping versatile materials. Digital light processing 3D printing can be used for shaping materials into complex geometries, but the useable materials are limited to photoactive resins. Here, the authors report a method using DLP-printed water-soluble granular polyacrylamide as a sacrificial mould to allow the preparation of complex microstructures.

Skin aging, autonomic mobility, memory function and physical deterioration are important features of aging, and effective anti-aging treatments are important in slowing down these processes. The objective of this research was to evaluate the protective effect of Colla Corii Asini (Ejiao) Collagen Peptides (CCACPs) on D-galactose (D-gal) injection combined with UV irradiation-induced senescence in mice. BY-HEALTH collagen oral solution (Bcos) was used as a positive control. Behavioural experiments showed that CCACPs significantly improved voluntary activity, learning memory and exercise endurance in aging mice. Elisa results showed that CCACPs reduced the levels of matrix metalloproteinase-1 (MMP-1) and MMP-3 in the skin, acetylcholinesterase (AChE) in the brain, and alanine aminotransferase (ALT) and azelaic aminotransferase (AST) in the liver of mice, while increasing the levels of collagen I in the skin and SOD in the brain. RT-qPCR revealed that CCACPs reduced the expression of p16, p19 and p21 genes in the liver and hippocampus, as well as the expression of IL-6 in the skin. Histological analysis of brain hippocampus, liver and skin confirmed the protective effects of CCACPs. The findings indicated that CCACPs may potentially slow the aging effects caused by D-galactose and UVB exposure in mice by reducing cellular senescence and oxidative stress levels. The results of this research provide the scientific basis for continuing to advance the extraction of collagen peptides from Colla Corii Asini as a potential anti-aging therapy.

Tensor decomposition is widely used to exploit the internal correlation in multi-way data analysis and process for communications and radar systems. As one of the main tensor decomposition methods, CANDECOMP/PARAFAC decomposition has advantages of uniqueness and interpretation properties which are significant in practical applications. However, traditional decomposition method is sensitive to both predefined rank and noise that results in inaccurate tensor decomposition. In this paper, we propose a improved algorithm called the Element-wise Average Alternating Direction Method of Multipliers by minimizing the sum of all factors’ trace norm and the noise variance. Our algorithm could overcome the dependence on predefined rank in traditional decomposition algorithms and alleviate the impact of noise. Moreover, this algorithm can be transferred to solve the problem of tensor completion conveniently. The simulation results show that our proposed algorithm could decompose the noisy tensor to the factors with above 90% similarity in various SNR and also interpolate the incomplete tensor with higher similar coefficient and lower relative reconstruction error when the missing rate is less than 0.5.

Recent advances in networked control systems have resulted in linear time-invariant (LTI) networks of increasing scale and complexity, thereby presenting significant challenges for controllability optimization. This study proposes a novel optimization framework to enhance control efficiency and reduce network complexity in large-scale complex networks, leveraging exact controllability theory and matrix similarity transformations for both directed and undirected topologies. Firstly, a method is introduced for identifying node correspondences before and after similarity transformations of the network’s adjacency matrix. Subsequently, a minimal-edge connection framework is proposed to link Jordan blocks associated with distinct eigenvalues in the Jordan canonical form. Finally, building upon this method and framework, a specific optimization strategy is developed to minimize the number of inter-node connections while preserving network controllability. The proposed approach effectively simplifies the network structure and improves control efficiency.

Background Mosquitoes play an absolute role in the spread of epidemic arbovirus diseases. Worldwide, Aedes aegypti and Aedes albopictus are the main vectors responsible for the spread of these mosquito-borne diseases. Aedes vexans , a mosquito species native to China, also carries mosquito-borne viruses, such as dengue fever virus and Japanese encephalitis virus, but research on this mosquito has been inadequate. Mapping the potential distribution range of and monthly change in the distribution of these three Aedes species is of particular importance for mosquito surveillance, eradication and disease control. Methods Monitoring data were collected for the three Aedes species in China. Long-term temperature and precipitation data (2001–2021) and land cover data were used to represent various climate and environmental conditions. An ecological niche model was developed using a maximum entropy modeling method to predict the current optimum habitat areas for the three Aedes species and to obtain important variables influencing their monthly distribution. Results The distribution model for the three Aedes species performed well, with an area under the receiver operating characteristic curve value of 0.991 for Ae. aegypti , 0.928 for Ae. albopictus and 0.940 for Ae. vexans . Analysis of the distribution change and mapping of the optimum habitat range for each Aedes species for each month demonstrated that temperature, precipitation and construction land were important factors influencing the distribution of these three Aedes species. Conclusions In China, Aedes aegypti is mainly concentrated in a few tropical regions and along the Yunnan border; Aedes albopictus is widely distributed throughout most of the country, except for the arid and semi-arid regions of northwest China; and Aedes vexans is mainly found in the northern regions. Our results provide a basis for the timing and location of surveillance efforts for high-priority mosquitoes. Graphical abstract

Capacitive deionization (CDI) is an effective desalination technique offering an appropriate route to obtain clean water. In order to obtain excellent CDI performance, a rationally designed structure of electrode materials has been an urgent need for CDI application. In this work, a novel graphene sponge (GS) was proposed as CDI electrode for the first time. The GS was fabricated via directly freeze-drying graphene oxide solution followed by annealing in nitrogen atmosphere. The morphology, structure and electrochemical performance of GS were characterized by scanning electron microscopy, Raman spectroscopy, nitrogen adsorption-desorption, X-ray photoelectron spectroscopy, cyclic voltammetry and electrochemical impedance spectroscopy. The electrosorption performance of GS in NaCl solution was studied and compared with pristine graphene (PG). The results show that due to the unique 3D interconnected porous structure, large accessible surface area and low charge transfer resistance, GS electrode exhibits an ultrahigh electrosorption capacity of 14.9 mg g −1 when the initial NaCl concentration is ~500 mg L −1 , which is about 3.2 times of that of PG (4.64 mg g −1 ) and to our knowledge, it should be the highest value reported for graphene electrodes in similar experimental conditions by now. These results indicate that GS should be a promising candidate for CDI electrode.

A novel variant of rabbit haemorrhagic disease virus, designated RHDV2/b/GI.2, was first discovered in France in 2010. Subsequently, RHDV2 rapidly spread to Africa, North America, Australia, and Asia. RHDV2 outbreaks have resulted in significant economic losses in the global rabbit industry and disrupted the balance of natural ecosystems. Our study investigated the seasonal characteristics of RHDV2 outbreaks using seasonal indices. RHDV2 is prone to causing significant outbreaks within domestic and wild rabbit populations during the spring season and is more likely to induce outbreaks within wild rabbit populations during late autumn in the Southern Hemisphere. Furthermore, based on outbreak data for domestic and wild rabbits and environmental variables, our study established two MaxEnt models to explore the relationship between RHDV2 outbreaks and the environmental factors and conducted outbreak risk predictions for RHDV2 in global domestic and wild rabbit populations. Both models demonstrated good predictive performance, with AUC values of 0.960 and 0.974, respectively. Road density, isothermality, and population density were identified as important variables in the outbreak of RHDV2 in domestic rabbits, while road density, normalized difference vegetation index, and mean annual solar radiation were considered key variables in the outbreak of RHDV2 in wild rabbits. The environmental factors associated with RHDV2 outbreaks identified in our study and the outbreak risk prediction maps generated in our study will aid in the formulation of appropriate RHDV2 control measures to reduce the risk of morbidity in domestic and wild rabbits.