Hand, foot, and mouth disease (HFMD) shows spatiotemporal heterogeneity in China. A spatiotemporal filtering model was constructed and applied to HFMD data to explore the underlying spatiotemporal structure of the disease and determine the impact of different spatiotemporal weight matrices on the results. HFMD cases and covariate data in East China were collected between 2009 and 2015. The different spatiotemporal weight matrices formed by Rook, K-nearest neighbour (KNN; K = 1), distance, and second-order spatial weight matrices (SO-SWM) with first-order temporal weight matrices in contemporaneous and lagged forms were decomposed, and spatiotemporal filtering model was constructed by selecting eigenvectors according to MC and the AIC. We used MI, standard deviation of the regression coefficients, and five indices (AIC, BIC, DIC, R2, and MSE) to compare the spatiotemporal filtering model with a Bayesian spatiotemporal model. The eigenvectors effectively removed spatial correlation in the model residuals (Moran’s I < 0.2, p > 0.05). The Bayesian spatiotemporal model’s Rook weight matrix outperformed others. The spatiotemporal filtering model with SO-SWM was superior, as shown by lower AIC (92,029.60), BIC (92,681.20), and MSE (418,022.7) values, and higher R2 (0.56) value. All spatiotemporal contemporaneous structures outperformed the lagged structures. Additionally, eigenvector maps from the Rook and SO-SWM closely resembled incidence patterns of HFMD.

Dietary intervention is a key strategy for preventing and managing chronic kidney disease (CKD). However, evidence on specific foods’ effects on CKD is limited. This study aims to clarify the impact of various foods on CKD risk. We used two-sample Mendelian randomization (TSMR) to analyze the causal relationships between the intake of 18 foods (e.g., cheese, processed meat, poultry, beef, non-oily fish) and CKD risk, as well as eGFRcr and eGFRcys levels. The inverse variance weighting (IVW) method, weighted median method, MR-Egger regression, simple mode and weighted mode were employed. Sensitivity analysis included Cochran’s Q test and the Egger intercept test. Frequent alcohol intake was linked to higher CKD risk (P=0.007, 0.048). Protective factors included cheese (OR=0.71, [95%CI: 0.53, 0.94], P=0.017), tea (OR=0.66, [95%CI: 0.43, 1.00], P=0.048) and dried fruit (OR=0.78, [95%CI: 0.63, 0.98], P=0.033). Oily fish (β=0.051, [95%CI: 0.001, 0.102], p=0.046) and dried fruit (β=0.082, [95%CI: 0.016, 0.149], p=0.014) were associated with elevated eGFRcys. Salad/raw vegetables (β=0.024, [95%CI: 0.003, 0.045], p=0.028) and dried fruit (β=0.013, [95%CI: 0.001, 0.031], p=0.014) were linked to higher eGFRcr, while cereal intake (β=-0.021, [95%CI: -0.033, -0.010], p<0.001) was associated with lower eGFRcr. These findings provide insights for optimizing dietary strategies for CKD patients.

Major depressive disorder (MDD) and coronary heart disease (CHD) can both cause significant morbidity and mortality. The association of MDD and CHD has long been identified, but the mechanisms still require further investigation. Seven mRNA microarray datasets containing samples from patients with MDD and CHD were downloaded from Gene Expression Omnibus. Combined matrixes of MDD and CAD were constructed for subsequent analysis. Differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on shared DEGs were conducted to identify pivotal pathways. A protein-protein network was also applied to further investigate the functional interaction. Results showed that 24 overlapping genes were identified. Enrichment analysis indicated that the shared genes are mainly associated with immune function and ribosome biogenesis. The functional interactions of shared genes were also demonstrated by PPI network analysis. In addition, three hub genes including MMP9, S100A8, and RETN were identified. Our results indicate that MDD and CHD have a genetic association. Genes relevant to immune function, especially IL-17 signalling pathway may be involved in the pathogenesis of MDD and CHD.

This paper provides an overview of the current status of ultrafast and ultra-intense lasers with peak powers exceeding 100 TW and examines the research activities in high-energy-density physics within China. Currently, 10 high-intensity lasers with powers over 100 TW are operational, and about 10 additional lasers are being constructed at various institutes and universities. These facilities operate either independently or are combined with one another, thereby offering substantial support for both Chinese and international research and development efforts in high-energy-density physics.

The multi-robot path planning problem is an NP-hard problem. The coati optimization algorithm (COA) is a novel metaheuristic algorithm and has been successfully applied in many fields. To solve multi-robot path planning optimization problems, we embed two differential evolution (DE) strategies into COA, a self-adaptive differential evolution-based coati optimization algorithm (SDECOA) is proposed. Among these strategies, the proposed algorithm adaptively selects more suitable strategies for different problems, effectively balancing global and local search capabilities. To validate the algorithm’s effectiveness, we tested it on CEC2020 benchmark functions and 48 CEC2020 real-world constrained optimization problems. In the latter’s experiments, the algorithm proposed in this paper achieved the best overall results compared to the top five algorithms that won in the CEC2020 competition. Finally, we applied SDECOA to optimization multi-robot online path planning problem. Facing extreme environments with multiple static and dynamic obstacles of varying sizes, the SDECOA algorithm consistently outperformed some classical and state-of-the-art algorithms. Compared to DE and COA, the proposed algorithm achieved an average improvement of 46% and 50%, respectively. Through extensive experimental testing, it was confirmed that our proposed algorithm is highly competitive. The source code of the algorithm is accessible at: https://ww2.mathworks.cn/matlabcentral/fileexchange/164876-HDECOA.

This study aimed to demonstrate the utilization value of 1PN embryos. The 1PN zygotes collected from December 2021 to September 2022 were included in this study. The embryo development, the pronuclear characteristics, and the genetic constitutions were investigated. The overall blastocyst formation and good-quality blastocyst rates in 1PN zygotes were 22.94 and 16.24%, significantly lower than those of 2PN zygotes (63.25 and 50.23%, respectively, P = 0.000). The pronuclear characteristics were found to be correlated with the developmental potential. When comparing 1PN zygotes that developed into blastocysts to those that arrested, the former exhibited a significantly larger area (749.49 ± 142.77 vs. 634.00 ± 119.05, P = 0.000), a longer diameter of pronuclear (29.81 ± 3.08 vs. 27.30 ± 3.00, P = 0.000), and a greater number of nucleolar precursor body (NPB) (11.56 ± 3.84 vs. 7.19 ± 2.73, P = 0.000). Among the tested embryos, the diploidy euploidy rate was significantly higher in blastocysts in comparison with the arrested embryos (66.67 vs. 11.76%, P = 0.000), which was also significantly higher in IVF-1PN blastocysts than in ICSI-1PN blastocysts (75.44 vs. 25.00%, P = 0.001). However, the pronuclear characteristics were not found to be linked to the chromosomal ploidy once they formed blastocysts.

In summary, while the developmental potential of 1PN zygotes is reduced, our study shows that, in addition to the reported pronuclear area and diameter, the number of NPB is also associated with their developmental potential. The 1PN blastocysts exhibit a high diploidy euploidy rate, are recommend to be clinically used post genetic testing, especially for patients who do not have other 2PN embryos available.

This paper presents a three-stage E-band low-noise amplifier (LNA) fabricated in a 28-nm Complementary Metal Oxide Semiconductor High-Performance Compact Plus process. The proposed E-band LNA achieves a peak gain of 16.8 dB, exhibiting a gain variation of less than ±0.5 dB across the frequency range of 67.8–90.4 GHz. The measured 3-dB gain bandwidth spans from 64 to 93.8 GHz, and the minimum measured noise figure (NF) is 3.8 dB. By employing a one-stage common-source with a two-stage cascode topology, the proposed E-band LNA demonstrates competitiveness in terms of gain flatness and NF when compared to recently published E-band CMOS LNAs.

Introduction: Late-life depression (LLD) is associated with cognitive deficit with risk of future dementia. By examining the entropy of the spontaneous brain activity, we aimed to understand the neural mechanism pertaining to cognitive decline in LLD.

Methods: We collected MRI scans in older adults with LLD (n = 32), mild cognitive impairment [MCI (n = 25)] and normal cognitive function [NC, (n = 47)]. Multiscale entropy analysis (MSE) was applied to resting-state fMRI data. Under the scale factor (tau) 1 and 2, reliable separation of fMRI data and noise was achieved. We calculated the brain entropy in 90 brain regions based on automated anatomical atlas (AAL). Due to exploratory nature of this study, we presented data of group-wise comparison in brain entropy between LLD vs. NC, MCI vs. NC, and LLD and MCD with a p-value below 0.001.

Results: The mean Mini-Mental State Examination (MMSE) score of LLD and MCI was 27.9 and 25.6. Under tau 2, we found higher brain entropy of LLD in left globus pallidus than MCI (p = 0.002) and NC (p = 0,009). Higher brain entropy of LLD than NC was also found in left frontal superior gyrus, left middle superior gyrus, left amygdala and left inferior parietal gyrus. The only brain region with higher brain entropy in MCI than control was left posterior cingulum (p-value = 0.015). Under tau 1, higher brain entropy was also found in LLD than in MCI in right orbital part of medial frontal gyrus and left globus pallidus (p-value = 0.007 and 0.005).

Conclusions: Our result is consistent with prior hypothesis where higher brain entropy was found during early aging process as compensation. We found such phenomenon particular in left globus pallidus in LLD, which could be served as a discriminative brain region. Being a key region in reward system, we hypothesis such region may be associated with apathy and with unique pathway of cognitive decline in LLD. We will undertake subsequent analysis longitudinally in this cohort

Key words: resting-state fMRI, Late-life depression, Brain entropy, globus pallidus, cognitive decline

Nano-silicon has been regarded as the most promising anode material for next-generation lithium-ion batteries (LIBs). However, the preparation of nano-silicon suffers from high cost, complex procedures, and low yield, which hinders its commercial application. In this study, porous nano-silicon with particle sizes in the range of 50–100 nm was prepared through molten salt-assisted magnesiothermic reduction using porous nano-silica derived from clay minerals as the precursor. Through combining ball milling and acid activation, the synthesised nano-silica derived from montmorillonite exhibited smaller particle sizes (below 50 nm), higher specific surface area (647 m2 g–1), and total pore volume (0.71 cm3 g–1). This unique structure greatly facilitated the conversion efficiency of silica into nano-silicon by maximising the contact area between silica and magnesium powder and optimising the diffusion kinetics of magnesium atoms. When used as anodes in LIBs, the synthesised nano-silicon materials demonstrated a high specific capacity of up to 1222 mAh g–1 and an excellent capacity retention rate of 79% after 150 cycles at a current density of 0.5 A g–1. This method provides a novel approach for the cost-effective and large-scale production of nano-silicon materials for high-performance anodes.

This study aimed to understand the potassium voltage-gated channel KQT-like subfamily, member 1 gene polymorphism in a rural elderly population in a county in Guangxi and to explore the possible relationship between its gene polymorphism and blood sugar. The 6 SNP loci of blood DNA samples from 4355 individuals were typed using the imLDRTM Multiple SNP Typing Kit from Shanghai Tianhao Biotechnology Co. The data combining epidemiological information (baseline questionnaire and physical examination results) and genotyping results were statistically analyzed using GMDR0.9 software and SPSS22.0 software. A total of 4355 elderly people aged 60 years and above were surveyed in this survey, and the total abnormal rate of glucose metabolism was 16·11 % (699/4355). Among them, male:female ratio was 1:1·48; the age group of 60–69 years old accounted for the highest proportion, with 2337 people, accounting for 53·66 % (2337/4355). The results of multivariate analysis showed that usually not doing farm work (OR 1·26; 95 % CI 1·06, 1·50), TAG ≥ 1·70 mmol/l (OR 1·19; 95 % CI 1·11, 1·27), hyperuricaemia (OR 1·034; 95 % CI 1·01, 1·66) and BMI ≥ 24 kg/m2 (OR 1·06; 95 % CI 1·03, 1·09) may be risk factors for abnormal glucose metabolism. Among all participants, rs151290 locus AA genotype, A allele carriers (AA+AC) were 0.70 times more likely (0.54 to 0.91) and 0.82 times more likely (0.70 to 0.97) to develop abnormal glucose metabolism than CC genotype carriers, respectively. Carriers of the T allele at the rs2237892 locus (CT+TT) were 0.85 times more likely to have abnormal glucose metabolism than carriers of the CC genotype (0.72 to 0.99); rs2237897 locus CT gene. The possibility of abnormal glucose metabolism in the carriers of CC genotype, TT genotype and T allele (CT + TT) is 0·79 times (0·67–0·94), 0·74 times (0·55–0·99) and 0·78 times (0·66, 0·92). The results of multifactor dimensionality reduction showed that the optimal interaction model was a three-factor model consisting of farm work, TAG and rs2237897. The best model dendrogram found that the interaction between TAG and rs2237897 had the strongest effect on fasting blood glucose in the elderly in rural areas, and they were mutually antagonistic. Environment–gene interaction is an important factor affecting abnormal glucose metabolism in the elderly of a county in Hechi City, Guangxi.

The influence of outer large-scale motions (LSMs) on near-wall structures in compressible turbulent channel flows is investigated. To separate the compressibility effects, velocity fluctuations are decomposed into solenoidal and dilatational components using the Helmholtz decomposition method. Solenoidal velocity fluctuations manifest as near-wall streaks and outer large-scale structures. The spanwise drifting of near-wall solenoidal streaks is found to be driven by the outer LSMs, while LSMs have a trivial influence on the spanwise density of solenoidal streaks, consistent with the outer LSM impacts found in incompressible flows (Zhou et al., J. Fluid Mech., vol. 940, 2022, p. A23). Dilatational motions are characterized by the near-wall small-scale travelling-wave packets and the large-scale parts in the outer region. The streamwise advection velocity of the near-wall structures remains at $16 \sim 18u_{\tau }$, hardly influenced by Mach numbers, Reynolds numbers and wall temperatures. The spanwise drifting of near-wall dilatational structures, quantified by the particle image velocimetry method, follows a mechanism distinct from solenoidal streaks. This drifting velocity is notably larger than those of the solenoidal streaks, and the influence of outer LSMs is not the primary trigger for this drifting.

The practical implementation of machine learning in flow control is limited due to its significant training expenses. In the present study the convolutional neural network (CNN) trained with the data of the restricted nonlinear (RNL) model is used to predict the normal velocity on a detection plane at $y^+=10$ in a turbulent channel flow, and the predicted velocity is used as wall blowing and suction for drag reduction. An active control test is carried out by using the well-trained CNN in direct numerical simulation (DNS). Substantial drag reduction rates up to 19 % and 16 % are obtained based on the spanwise and streamwise wall shear stresses, respectively. Furthermore, we explore the online control of wall turbulence by combining the RNL model with reinforcement learning (RL). The RL is constructed to determine the optimal wall blowing and suction based on its observation of the wall shear stresses without using the label data on the detection plane for training. The controlling and training processes are conducted synchronously in a RNL flow field. The control strategy discovered by RL has similar drag reduction rates with those obtained previously by the established method. Also, the training cost decreases by over thirty times at $Re_{\tau }=950$ compared with the DNS-RL model. The present results provide a perspective that combining the RNL model with machine learning control for drag reduction in wall turbulence can be effective and computationally economical. Also, this approach can be easily extended to flows at higher Reynolds numbers.

The measurement of X-ray continuous emission from laser-driven plasma was achieved through multiple monochromatic imaging utilizing a multilayer mirror array. This methodology was exemplified by the development of an eight-channel X-ray imaging system, capable of operating in the energy range of several keV with a spatial resolution of approximately 3 μm. By integrating this system with a streak camera, the temperature and trajectory of imploding capsules were successfully measured at the kJ-class Shenguang III prototype laser facility. This approach provides a synchronous diagnostic method for the spatial, temporal and spectral analysis of laser-driven plasma, characterized by its high efficiency and resolution.