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Bio-integrated devices need power sources to operate 1 , 2 . Despite widely used technologies that can provide power to large-scale targets, such as wired energy supplies from batteries or wireless energy transduction 3 , a need to efficiently stimulate cells and tissues on the microscale is still pressing. The ideal miniaturized power source should be biocompatible, mechanically flexible and able to generate an ionic current for biological stimulation, instead of using electron flow as in conventional electronic devices 4 – 6 . One approach is to use soft power sources inspired by the electrical eel 7 , 8 ; however, power sources that combine the required capabilities have not yet been produced, because it is challenging to obtain miniaturized units that both conserve contained energy before usage and are easily triggered to produce an energy output. Here we develop a miniaturized soft power source by depositing lipid-supported networks of nanolitre hydrogel droplets that use internal ion gradients to generate energy. Compared to the original eel-inspired design 7 , our approach can shrink the volume of a power unit by more than 10 5 -fold and it can store energy for longer than 24 h, enabling operation on-demand with a 680-fold greater power density of about 1,300 W m −3 . Our droplet device can serve as a biocompatible and biological ionic current source to modulate neuronal network activity in three-dimensional neural microtissues and in ex vivo mouse brain slices. Ultimately, our soft microscale ionotronic device might be integrated into living organisms. A study describes the development of a miniaturized hydrogel-based soft power source capable of modulating the activity of networks of neuronal cells without the need for metal electrodes.

Background Hypertension is a significant global public health issue and its pathogenesis is strongly associated with visceral obesity. The Chinese Visceral Adiposity Index (CVAI), a well-established indicator based on the metabolic profile of Asian populations, has been linked to an increased risk of hypertension. However, there is a lack of comprehensive evidence regarding the differential relationship between the CVAI and the incidence of hypertension in participants with normal vs. elevated blood pressure (BP). This study aimed to utilize longitudinal data to examine the association between the CVAI and the incidence of hypertension across populations with different baseline BP levels. Methods Data from the China Health and Retirement Longitudinal Study (CHARLS) database were used for this retrospective cohort analysis. Participants aged ≥45 years without hypertension at baseline (2011) were included and followed up until 2020 to assess the incidence of hypertension. The CVAI was calculated using a sex-specific formula incorporating age, waist circumference (WC), body mass index (BMI), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C). Multivariate logistic regression and restricted cubic spline (RCS) models were used to evaluate the nonlinear association between CVAI and hypertension risk. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive ability of CVAI. For the sensitivity analyses, univariate RCS models were employed to assess the robustness of the findings. Results Among 5311 participants, 1,819 (34.25%) developed hypertension during the 9-year follow-up period. CVAI emerged as an independent risk factor for incident hypertension. Comparing the highest to the lowest quartile of CVAI, the adjusted odds ratio (OR) was 1.77 (95% CI: 1.32–2.38) in the normal BP group and 3.21 (95% CI: 2.26–4.61) in the elevated BP group. Conclusions A linear dose-response relationship was observed between CVAI and hypertension risk in both groups. These associations remained robust and linear in sensitivity analyses after excluding participants with diabetes, heart disease, or stroke. Clinically, CVAI offers a simple, low-cost tool to improve hypertension risk stratification and enable earlier targeted prevention, particularly among individuals with elevated BP. Graphical Abstract

Engineering human tissue with diverse cell types and architectures remains challenging. The cerebral cortex, which has a layered cellular architecture composed of layer-specific neurons organised into vertical columns, delivers higher cognition through intricately wired neural circuits. However, current tissue engineering approaches cannot produce such structures. Here, we use a droplet printing technique to fabricate tissues comprising simplified cerebral cortical columns. Human induced pluripotent stem cells are differentiated into upper- and deep-layer neural progenitors, which are then printed to form cerebral cortical tissues with a two-layer organization. The tissues show layer-specific biomarker expression and develop a structurally integrated network of processes. Implantation of the printed cortical tissues into ex vivo mouse brain explants results in substantial structural implant-host integration across the tissue boundaries as demonstrated by the projection of processes and the migration of neurons, and leads to the appearance of correlated Ca 2+ oscillations across the interface. The presented approach might be used for the evaluation of drugs and nutrients that promote tissue integration. Importantly, our methodology offers a technical reservoir for future personalized implantation treatments that use 3D tissues derived from a patient’s own induced pluripotent stem cells. Brain injuries can result in significant damage to the cerebral cortex, and restoring the cellular architecture of the tissue remains challenging. Here, the authors use a droplet printing technique to fabricate a simplified human cerebral cortical column and demonstrate its functionality and potential for future personalized therapy approaches.

The roadway space of coal mine is narrow, and the illumination is low and uneven. Dynamic mining is accompanied by dust and water mist. The accuracy and reliability of roadway data collected by vision and laser sensors are poor. Based on this, a digital modeling method of coal mine roadway based on millimeter-wave radar array is proposed. Firstly, aiming at the problem of complex environmental interference, combined with the characteristics of small amount of single frame data of millimeter-wave point cloud, a multi-layer filtering noise reduction processing and dynamic subgraph registration method of millimeter-wave point cloud is proposed to filter out interference points and realize single radar point cloud registration. Secondly, aiming at the problem that a single millimeter-wave radar cannot scan the complete roadway information at one time, combined with the characteristics of small elevation field of view of millimeter-wave radar, a millimeter-wave radar array acquisition system is built, and an improved iterative closest point (ICP) registration algorithm based on point cloud features is established to construct the roadway point cloud fusion model. Finally, aiming at the problem of uneven and sparse point cloud after array fusion, a Poisson surface reconstruction method based on point cloud density weighted interpolation is proposed to refine the roadway structure and realize the accurate reconstruction of digital roadway model. The experimental results show that the digital modeling method of millimeter-wave radar array can accurately obtain the information of roadway surrounding rock, the density of roadway point cloud is increased by 22.4%, and the average absolute error percentage of the width and height of the reconstructed roadway model is only 0.82% and 0.72%, which provides a new research method for the reconstruction of underground roadway and an important basis for the digital modeling method of coal mine roadway.

Digital twin technology puts forward higher requirements on the real-time performance of simulations. In order to realize the online simulation of the heat treatment process, a transient temperature field-microstructure field coupling calculation method based on the reduced-order model was constructed. This mathematical model was applied to the online simulation of the end-quenching treatment of 42CrMo steel with complex time-varying water spray. The results show that the utilization of a 10th-order reduced-order model diminishes the total computation time from 1 h to 3.4 s, with a reduction in storage requirements by a factor of 610. The calculation accuracy of the reduced-order model is 99.2%, which satisfies the requirements of real-time online simulation. A framework for the heat treatment digital twin system has been proposed and an online simulation platform for end-quenching treatment was developed. The single-step calculation time for the proposed platform is 0.4 s. The online simulation temperature is basically consistent with the measured temperature results. This work provides novel avenues for fast calculation and real-time control of the heat treatment process.

Submergence stress creates a hypoxic environment. Roots are the first plant organ to face these low-oxygen conditions, which causes damage and affects the plant growth and yield. Orchardgrass ( L.) is one of the most important cold-season forage grasses globally. However, their submergence stress-induced gene expression and the underlying molecular mechanisms of orchardgrass roots are still unknown. Using the submergence-tolerant 'Dianbei' and submergence-sensitive 'Anba', the transcriptomic analysis of orchardgrass roots at different time points of submergence stress (0 h, 8 h, and 24 h) was performed. We obtained 118.82Gb clean data by RNA-Seq. As compared with the control, a total of 6663 and 9857 differentially expressed genes (DEGs) were detected in Dianbei, while 7894 and 11215 DEGs were detected in Anba at 8 h and 24 h post-submergence-stress, respectively. Gene Ontology (GO) enrichment analysis obtained 986 terms, while Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis obtained 123 pathways. Among them, the DEGs in plant hormones, mitogen-activated protein kinase (MAPK) and Ca signal transduction were significantly differentially expressed in Dianbei, but not in Anba. This study was the first to molecularly elucidate the submergence stress tolerance in the roots of two orchardgrass cultivars. These findings not only enhanced our understanding of the orchardgrass submergence tolerance, but also provided a theoretical basis 36 for the cultivation of submergence-tolerant forage varieties.

A drill-anchor robot is an essential means of efficient drilling and anchoring in coal-mine roadways. It is significant to calculate the position of the drill-anchor robot based on the positioning information of the supported anchor rod to improve tunneling efficiency. Therefore, identifying and positioning the supported anchor rod has become a critical problem that needs to be solved urgently. Aiming at the problem that the target in the image is blurred and cannot be accurately identified due to the low and uneven illumination environment, we proposed an improved YOLOv7 (the seventh version of the You Only Look Once) model based on the fusion of image enhancement and multiattention mechanism, and the self-made dataset is used for testing and training. Aiming at the problem that the traditional positioning method cannot guarantee accuracy and efficiency simultaneously, an anchor-rod positioning method using depth image and RGB image alignment combined with least squares linear fitting is proposed, and the positioning accuracy is improved by processing the depth map. The results show that the improved model improves the mAP by 5.7% compared with YOLOv7 and can accurately identify the target. Through the positioning method proposed in this paper, the error between the positioning coordinate and the measurement coordinate of the target point on each axis does not exceed 11 mm, which has high positioning accuracy and improves the positioning accuracy and robustness of the anchor rod in the coal-mine roadway.

To determine the pollution characteristics, chemical compositions, and population health risks of PM2.5 at different pollution levels, PM2.5 samples were intensively collected during the long-lasting winter haze episode from 13–23 January 2018 in Xiantao in Jianghan Plain (JHP), central China. The higher PM2.5 levels during the severe pollution period were dominated by the WNW-NNE air-masses, whereas the lower PM2.5 concentrations during other pollution periods were mainly affected by the NE, S, and NW air-masses. The NO3−/SO42− and OC/EC ratios indicated a mixed contribution of intensive vehicle exhaust and secondary formation. The enrichment factor and geo-accumulation index for assessing the PM2.5-bound metal(loid)s contamination levels were positively correlated. Ingestion is the dominant exposure pathway of PM2.5-bound metal(loid)s for children and adults, followed by inhalation and dermal contact. As, Cr, and Pb may pose carcinogenic and non-carcinogenic risks, whereas Sb and V may only pose non-carcinogenic risks for children and adults. The population health risks may not depend on the pollution levels but depend on the PM2.5-bound metal(loid)s concentrations. PM2.5-bound metal(loid)s may pose much higher population health risks for adults compared to children. More attentions should be paid to the population health risks of PM2.5-bound metal(loid)s during a long-lasting winter haze episode in JHP.

Laser melting deposition (LMD) is an advanced repairing and remanufacturing technology that involves complex thermodynamic and kinetic characteristics in a molten pool. A thorough understanding of the thermal-fluid dynamic behavior of molten metal is crucial for investigating the distribution of reinforced particles and the characterization of solidification. This study proposes a three-dimensional transient thermal-fluid coupling model for describing thermal-fluid dynamic behavior and particle migration behavior within a molten pool during LMD. The simulated morphology of the deposition layer and temperature agree well with the experiments. The simulation results indicate that Marangoni convection induced by temperature gradient dominates the convection mode. The fluid velocity distribution along the scanning direction of the molten metal is showing M shape. Meanwhile, the dimension of molten pool increases slightly, and the Marangoni convection becomes more vigorous with higher heat input. The Marangoni motion tends to drive the TiC particle migration from the center to both lateral sides, resulting in a more uniformly distributed of TiC particle in deposition layer. Furthermore, the solidification parameters at solid–liquid interface are calculated, which used to assess the solidification microstructure in deposition layer.

Natural polysaccharides are attractive and promising biomacromolecules for the green synthesis of silver nanoparticles (Ag NPs) with a broad spectrum of useful functions. This study aims to evaluate the synthetic conditions and physical properties of Ag NPs using three fractions of exopolysaccharide (EPS), namely EPS-1, EPS-2, and EPS-3, produced by a medicinal fungus known as Cs-HK1, with variations in their chemical composition and molecular weight. Each of the EPS fractions had a unique set of optimal synthetic conditions (reaction time course, temperature, and reagent concentration), resulting in a specific range of Ag NP size distributions. The Ag NPs synthesized with the EPS-1 fraction had the smallest particle size (~160 nm) and the most significant antibacterial activities against Escherichia coli (Gram−) and Staphylococcus aureus (Gram+), with a minimal inhibitory concentration (MIC) of 0.2 mg/mL on E. coli and 0.075 mg/mL on S. aureus. The results proved the success of the scheme of this green synthesis scheme with all three EPS fractions and the potential antibacterial application of EPS-coated Ag NPs.