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Bacterial wound infections are a growing challenge in healthcare, posing severe risks like systemic infection, organ failure, and sepsis, with projections predicting over 10 million deaths annually by 2050. Antibacterial hydrogels, with adaptable extracellular matrix-like features, are emerging as promising solutions for treating infectious wounds. However, the antibacterial properties of most of these hydrogels are largely attributed to extrinsic agents, and their mechanisms of action remain poorly understood. Herein we introduce for the first time, modified imidazolidinyl urea (IU) as the polymeric backbone for developing tissue-like antibacterial hydrogels. As-designed hydrogels behave tissue-like mechanical features, outstanding antifreeze behavior, and rapid self-healing capabilities. Molecular dynamics (MD) simulation and density functional theory (DFT) calculation were employed to well-understand the extent of H-bonding and metal-ligand coordination to finetune hydrogels’ properties. In vitro studies suggest good biocompatibility of hydrogels against mouse fibroblasts & human skin, lung, and red blood cells, with potential wound healing capacity. Additionally, the hydrogels exhibit good 3D printability and remarkable antibacterial activity, attributed to concentration dependent ROS generation, oxidative stress induction, and subsequent disruption of bacterial membrane. On top of that, in vitro biofilm studies confirmed that developed hydrogels are effective in preventing biofilm formation. Therefore, these tissue-mimetic hydrogels present a promising and effective platform for accelerating wound healing while simultaneously controlling bacterial infections, offering hope for the future of wound care.

Abstract Objective In clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially when 3D consolidation-to-tumor ratio (CTR) ≥ 50%, as malignant ones are more invasive. This study aims to develop and validate an AI-driven radiomics prediction model for such nodules to enhance diagnostic accuracy. Methods Data of 2,591 pulmonary nodules from five medical centers (Zhengzhou People’s Hospital, etc.) were collected. Applying exclusion criteria, 370 nodules (78 benign, 292 malignant) with 3D CTR ≥ 50% were selected and randomly split 7:3 into training and validation cohorts. Using R programming, Lasso regression with 10-fold cross-validation filtered features, followed by univariate and multivariate logistic regression to construct the model. Its efficacy was evaluated by ROC, DCA curves and calibration plots. Results Lasso regression picked 18 non-zero coefficients from 108 features. Three significant factors—patient age, solid component volume and mean CT value—were identified. The logistic regression equation was formulated. In the training set, the ROC AUC was 0.721 (95%CI: 0.642–0.801); in the validation set, AUC was 0.757 (95%CI: 0.632–0.881), showing the model’s stability and predictive ability. Conclusion The model has moderate accuracy in differentiating benign from malignant 3D CTR ≥ 50% nodules, holding clinical potential. Future efforts could explore more to improve its precision and value. Clinical trial number Not applicable.

Tamping squeeze is essential for maintaining ballasted tracks. Previous studies have simulated squeezing behavior as a linear motion with a constant squeezing distance, which is unsuitable for various ballast beds in the field. This study aims to develop realistic formulas for squeezing behavior in tamping maintenance. First, a universal expression for the squeezing behavior of tamping operation was proposed through theoretical derivation. Subsequently, a custom testing method for squeezing distance with high accuracy was designed. Finally, the parameters of the universal expression were obtained from testing results using the response surface method and novel formulas for squeezing behavior were innovatively developed. This study can lead to greatly improved simulation accuracy and effective maintenance of tamping squeeze.

Abstract Since the discovery of the turbulence drag reduction phenomenon over 70 years ago, it has been recognized that the addition of small quantities of drag-reducing agents to fluids can significantly decrease wall shear stress, thereby enhancing fluid pumpability. In many applications, the fluids often contain salts, such as those used in fracturing processes within the petroleum sector. The aim of this study is to experimentally investigate the effects of salinity, flow rate, and polymer concentration on the drag reduction performance of sodium alginate in circular pipes. The experimental process was designed using Response Surface Methodology (RSM), and a predictive regression model for the drag reduction rate (DR) was developed. By comparing the predicted results with the experimental outcomes, we found that the accuracy of the predictive model is high, with the error controlled within ± 20%. To provide a more intuitive understanding of the effect of salinity on the drag reduction performance of sodium alginate, this paper introduces the innovative concept of Drag Reduction Inhibition Rate (ε). The results indicate that the drag reduction rate in saline solutions is generally lower than that in salt-free solutions. However, during the initial stage of turbulence, a small amount of salt facilitates the unfolding of sodium alginate molecules, thereby enhancing the drag reduction effect (ε > 100%). Following this initial turbulent phase, the inhibitory effect of salinity on the polymer becomes pronounced, causing the drag reduction inhibition rate to decrease from 115 to 20%. We propose that high concentrations of salt may restrict the extent of polymer unfolding, resulting in concentrated solutions degrading into dilute solutions.

Abstract Background With the rising incidence of pulmonary nodules (PNs), lung adenocarcinoma in situ (AIS) is a critical early stage of lung cancer, necessitating accurate diagnosis for early intervention. This study applies artificial intelligence (AI) for quantitative imaging analysis to differentiate AIS from atypical adenomatous hyperplasia (AAH) and minimally invasive adenocarcinoma (MIA), aiming to enhance clinical diagnosis and prevent misdiagnosis. Methods The study analyzed 1215 PNs with confirmed AAH, AIS, and MIA from six centers using the Shukun AI diagnostic module. Parameters evaluated included demographic data and various CT imaging metrics to identify indicators for clinical application, focusing on the mean CT value's predictive value. Results Significant differences were found in several parameters between AAH and AIS, with nodule mass showing the highest predictive value. When comparing AIS to MIA, total nodule volume was the best predictor, followed by the maximum CT value. Conclusion The mean CT value has limited discriminative power for AIS diagnosis. Instead, the maximum CT value and maximum 3D diameter are recommended for clinical differentiation. Nodule mass and volume of solid components are strong indicators for differentiating AIS from AAH and MIA, respectively.

Abstract Single-pixel detectors are popular devices in optical sciences because of their fast temporal response, high sensitivity, and low cost. However, when being used for imaging, they face a fundamental challenge in acquiring high-dimensional information of an optical field because they are essentially zero-dimensional sensors and measure only the light intensity. To address this problem, we developed a cascaded compressed-sensing single-pixel camera, which decomposes the measurement into multiple stages, sequentially reducing the dimensionality of the data from a high-dimensional space to zero dimension. This measurement scheme allows us to exploit the compressibility of a natural scene in multiple domains, leading to highly efficient data acquisition. We demonstrated our method in several demanding applications, including enabling tunable single-pixel full-waveform hyperspectral light detection and ranging (LIDAR) for the first time.

Abstract While Alzheimer’s disease and other neurodegenerative diseases have traditionally been viewed as brain disorders, there is growing evidence indicating their manifestation in the eyes as well. The retina, being a developmental extension of the brain, represents the only part of the central nervous system that can be noninvasively imaged at a high spatial resolution. The discovery of the specific pathological hallmarks of Alzheimer’s disease in the retina of patients holds great promise for disease diagnosis and monitoring, particularly in the early stages where disease progression can potentially be slowed. Among various retinal imaging methods, hyperspectral imaging has garnered significant attention in this field. It offers a label-free approach to detect disease biomarkers, making it especially valuable for large-scale population screening efforts. In this review, we discuss recent advances in the field and outline the current bottlenecks and enabling technologies that could propel this field toward clinical translation.

Abstract The sensor that simultaneously perceives bending strain and magnetic field has the potential to detect the finger bending state and hand position of the human and robot. Based on unique magneto-mechanical coupling effect of magnetostrictive materials, the proposed a bi-perceptive flexible sensor, consisting of the Co–Fe film and magnetic sensing plane coils, can realize dual information perception of strain/magnetic field through the change of magnetization state. The sensor structure and interface circuit of the sensing system are designed to provide high sensitivity and fast response, based on the input–output characteristics of the simulation model. An asynchronous multi-task deep learning method is proposed, which takes the output of the position task as the partial input of the bending state task to analyze the output information of the sensor quickly and accurately. The sensing system, integrating with the proposed model, can better predict the bending state and approach distance of human or robot hand.

Abstract Blowouts are a common type of wind-eroded landform found in sandy and desertified areas. They also represent a major degradative surface process affecting grassland ecosystems. Blowouts exacerbate changes in surface morphology through their effects on other surface phenomena including vegetation. In this paper, Xilingol League sandy grassland blowouts are taken as the research object, and the U.S. Keyhole satellite data and China’s Gaofen-1 satellite data are used as the data source, and the blowouts are extracted based on the 3 S technology for a total of six periods of high-resolution remote sensing image data in the study area from 1962 to 2023. The Landscape Pattern Index method and Fuzzy Land Use Simulation (FLUS) modelling applied to changes over the last six decades provided spatial evolution parameters for predicting future blowout distributions. Results showed that blowouts affecting the Xilingol grassland area increased by 16.81% over the past 60 years. The patch density (PD) increased by 0.9 per hectare. The mean proximity index (PROX_MN) and mean Euclidean nearest neighbour distance (ENN_MN) showed a tendency to decrease and then increase indicating initial expansion and then merging of adjacent blowouts to create the present landscape. The FLUS model used ten factors to predict changes in blowout distributions from 2023 to 2033. Factors included digital elevation model (DEM), slope, aspect, normalized difference vegetation index (NDVI), mean annual temperature, mean annual precipitation, population density, real GDP, distance to water, and distance to impervious surfaces. It was found that grassland area decreased by 6217.12 hm2 and blowout area decreased by 102.91 hm2. Results of this study can expand understanding of blowout morphodynamics in ecologically sensitive areas.

Abstract Compound flood‐heatwave extremes (CFHWs) have threatened the sustainable development of human society and ecosystems. However, the disproportionate risks in regions with different economic development under a warming climate have not been quantified. This study carries out a global investigation on the future CFHWs under three scenarios based on 11 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Results reveal a 7.5‐fold increase in global annual CFHW days by 2100 under the intermediate greenhouse‐gas‐emission scenario SSP2‐4.5 compared to that in 1980. Under SSP2‐4.5, population exposure in low‐income countries in the late future (2071–2090) will be about 9‐fold higher than in high‐income countries compared to baseline period (1995–2014). Moreover, exposure of the poor groups living on less than $6.85/day will increase by nearly 28.1‐fold. Eastern Africa and South Asia are identified as particularly high‐risk regions, where large populations living in poverty face rapidly increasing CFHWs. These findings indicate that climate inequality will become more pronounced if climate warming continues without immediate effective measures. Our study also underscores the urgent need for mitigation and adaptation strategies against the future increasing CFHWs, especially for the vast low‐income and high‐risk regions.

Abstract Numerous files, such as records and logs, are generated in the process of equipment diagnosis and maintenance (D&M). These files contain lots of unstructured plain text. Knowledge in these files could be reused for similar equipment faults. In practice, knowledge presented in plain text is hard to acquire. Thus, automated named entity recognition (NER) and relation extraction (RE) methods based on pretrained encoders could be used to extract entities and relations and develop a structured knowledge graph (KG), thus facilitating intelligent manufacturing. However, equipment fault NER exhibits suboptimal performance with existing encoders pretrained on general‐domain corpus. In this paper, domain‐adaptation‐based NER with information enrichment is proposed for developing an equipment fault KG. A domain‐adapted encoder is tailored for equipment fault NER through domain‐adaptive pretraining (DAPT). Update of word segmentation dictionary and adjustment of masking approach are implemented during DAPT for information enrichment, which helps make the most of the limited domain‐specific pretraining corpus. Experimental results show that the F1 score of NER is improved by 1.22% using the domain‐adapted encoder compared to its counterpart using the encoder pretrained on general‐domain corpus. Furthermore, a reliable and robust question answering (QA) application of the developed equipment fault KG is also shown.

Abstract Miniaturized computational spectrometers have emerged as a promising strategy for miniaturized spectrometers, which breaks the compromise between footprint and performance in traditional miniaturized spectrometers by introducing computational resources. They have attracted widespread attention and a variety of materials, optical structures, and photodetectors are adopted to fabricate computational spectrometers with the cooperation of reconstruction algorithms. Here, a comprehensive review of miniaturized computational spectrometers, focusing on two crucial components: spectral encoding and reconstruction algorithms are provided. Principles, features, and recent progress of spectral encoding strategies are summarized in detail, including space‐modulated, time‐modulated, and light‐source spectral encoding. The reconstruction algorithms are classified into traditional and deep learning algorithms, and they are carefully analyzed based on the mathematical models required for spectral reconstruction. Drawing from the analysis of the two components, cooperations between them are considered, figures of merits for miniaturized computational spectrometers are highlighted, optimization strategies for improving their performance are outlined, and considerations in operating these systems are provided. The application of miniaturized computational spectrometers to achieve hyperspectral imaging is also discussed. Finally, the insights into the potential future applications and developments of computational spectrometers are provided.

Extensive power shortages and airstrikes have plunged Gaza into a severe humanitarian crisis. Here, we introduce a rapid and efficient framework to evaluate the impacts of armed conflict and monitor potential humanitarian crises in the Gaza Strip based on multi-source satellite imageries and cutting-edge artificial intelligence (AI) techniques. Since October 9th, 2023, the complete blockade had led to widespread power outages across the entire Gaza Strip, particularly in the southern region. Airstrikes destroyed more than 20% of buildings, including nearly 60% of shops and hospitals, as well as 80% of water infrastructure. The affected vulnerable population exposure is further quantified at the block level, which further helps determine high-risk humanitarian crisis locations and aid in the precise allocation of humanitarian resources. In general, the proposed framework performs well in applying to complex environments during an armed conflict. It can also offer rapid and continuous monitoring of the Gaza Strip and can be broadly applied in armed conflicts or natural disasters. Our research is one of the early studies to apply AI foundation models to improve the efficiency of rapid post-disaster investigation.

Abstract Background Due to regional and cultural differences, the current status of extremely preterm infants(EPIs) treatment across different areas of mainland China remains unclear. This study investigated the survival rate and incidence of major diseases among EPIs in the southwest area of Fujian province. Method This retrospective and multicenter study collected perinatal data from EPIs with gestational ages between 22–27+ 6w and born in the southwest area of Fujian province. The study population was divided into 6 groups based on gestational age at delivery. The primary outcome was the survival status at ordered hospital discharge or correct gestational age of 40 weeks, and the secondary outcome was the incidence of major diseases. The study analyzed the actual survival status of EPIs in the area. Result A total of 2004 preterm infants with gestational ages of 22–27+ 6 weeks were enrolled in this study. Among them, 1535 cases (76.6%) were born in the delivery room but did not survive, 469 cases (23.4%) were transferred to the neonatal department for treatment, 101 cases (5.0%) received partial treatment, and 368 cases (18.4%) received complete treatment. The overall all-cause mortality rate was 84.4% (1691/2004). The survival rate and survival rate without major serious disease for EPIs who received complete treatment were 85.1% (313/368) and 31.5% (116/318), respectively. The survival rates for gestational ages 22–22+ 6w, 23–23+ 6w, 24–24+ 6w, 25–25+ 6w, 26–26+ 6w, and 27–27+ 6w were 0%, 0%, 59.1% (13/22), 83% (39/47), 88.8% (87/98), and 89.7% (174/198), respectively. The survival rates without major serious disease were 0%, 0%, 9.1% (2/22), 19.1% (9/47), 27.6% (27/98), and 40.2% (78/194), respectively. Conclusion The all-cause mortality of EPIs in the southwest area of Fujian Province remains high, with a significant number of infants were given up after birth in the delivery room being the main influencing factor. The survival rate of EPIs who received complete treatment at 25–27 weeks in the NICU was similar to that in developed countries. However, the survival rate without major serious disease was significantly lower compared to high-income countries.

Background: Numerous approaches have been utilized to optimize mesenchymal stem cells (MSCs) performance in treating osteoarthritis (OA), however, the constrained diminished activity and chondrogenic differentiation capacity impede their therapeutic efficacy. Previous investigations have successfully shown that pretreatment with nanosecond pulsed electric fields (nsPEFs) significantly enhances the chondrogenic differentiation of MSCs. Therefore, this study aims to explore nsPEFs as a strategy to improve OA therapy by enhancing MSCs' activity and chondrogenic differentiation and also investigate its potential mechanism. Methods: In this study, a million MSCs were carefully suspended within a 0.4-cm gap cuvette and subjected to five pulses of nsPEFs (100 ns at 10 kV/cm, 1 Hz), with a 1-s interval between each pulse. A control group of MSCs was maintained without nsPEFs treatment for comparative analysis. nsPEFs were applied to regulate the MSCs performance and hinder OA progresses. In order to further explore the corresponding mechanism, we examined the changes of MSCs transcriptome after nsPEF pretreatment. Finally, we studied the properties of extracellular vesicles (EVs) secreted by MSCs affected by nsPEF and the therapeutic effect on OA. Results: We found that nsPEFs pretreatment promoted MSCs migration and viability, particularly enhancing their viability temporarily in vivo, which is also confirmed by mRNA sequencing analysis. It also significantly inhibited the development of OA-like chondrocytes in vitro and prevented OA progression in rat models. Additionally, we discovered that nsPEFs pretreatment reprogrammed MSC performance by enhancing EVs production (5.77 ± 0.92 folds), and consequently optimizing their therapeutic potential. Conclusions: In conclusion, nsPEFs pretreatment provides a simple and effective strategy for improving the MSCs performance and the therapeutic effects of MSCs for OA. EVs-nsPEFs may serve as a potent therapeutic material for OA and hold promise for future clinical applications. The translational potential of this article: This study indicates that MSCs pretreated by nsPEFs greatly inhibited the development of OA. nsPEFs pretreatment will be a promising and effective method to optimize the therapeutic effect of MSCs in the future.

Abstract Advanced 3D high-resolution imaging techniques are essential for investigating biological challenges, such as neural circuit analysis and tumor microenvironment in intact tissues. However, the fluorescence signal emitted by endogenous fluorescent proteins in cleared or expanded biological samples gradually diminishes with repeated irradiation and prolonged imaging, compromising its ability to accurately depict the underlying scientific problem. We have developed a strategy to preserve fluorescence in cleared and expanded tissue samples during prolonged high-resolution three-dimensional imaging. We evaluated various compounds at different concentrations to determine their ability to enhance fluorescence intensity and resistance to photobleaching while maintaining the structural integrity of the tissue. Specifically, we investigated the impact of EDTP utilization on GFP, as it has been observed to significantly improve fluorescence intensity, resistance to photobleaching, and maintain fluorescence during extended room temperature storage. This breakthrough will facilitate extended hydrophilic and hydrogel-based clearing and expansion methods for achieving long-term high-resolution 3D imaging of cleared biological tissues by effectively safeguarding fluorescent proteins within the tissue.

Abstract The utilization of drag-reducing polymers has long been hindered by their irritancy, corrosiveness, and toxicity across various domains. In this investigation, we explored sodium alginate, a natural drag reducer, for its efficacy in reducing drag and its resilience to shear in millimeter-scale pipelines. Initially, an experimental setup was devised to assess the drag reduction capabilities of sodium alginate at varying concentrations and flow rates using Response Surface Methodology (RSM). The relationship between drag reduction (DR), concentration (C), and flow rate (Q) was established by analyzing the experimental data. Subsequently, variance analysis was employed to validate the data accuracy, with a comparison between predicted and experimental DR values revealing an error margin within ± 20%. Analysis of cyclic shear testing of sodium alginate solution in tubes demonstrated its effectiveness as a shear flow drag reducer. Furthermore, results from laser particle size analysis indicated minimal molecular breakage of sodium alginate during cyclic shear.

Abstract Wearable devices, such as data gloves and electronic skins, can perceive human instructions, behaviors and even emotions by tracking a hand's motion, with the help of knowledge learning. The shape or position single-mode sensor in such devices often lacks comprehensive information to perceive interactive gestures. Meanwhile, the limited computing power of wearable applications restricts the multimode fusion of different sensing data and the deployment of deep learning networks. We propose a perceptive fusion electronic skin (PFES) with a bioinspired hierarchical structure that utilizes the magnetization state of a magnetostrictive alloy film to be sensitive to external strain or magnetic field. Installed at the joints of a hand, the PFES realizes perception of curvature (joint shape) and magnetism (joint position) information by mapping corresponding signals to the two-directional continuous distribution such that the two edges represent the contributions of curvature radius and magnetic field, respectively. By autonomously selecting knowledge closer to the user's hand movement characteristics, the reinforced knowledge distillation method is developed to learn and compress a teacher model for rapid deployment on wearable devices. The PFES integrating the autonomous learning algorithm can fuse curvature-magnetism dual information, ultimately achieving human machine interaction with gesture recognition and haptic feedback for cross-space perception and manipulation.

Abstract Rare earth ions with d-f transitions (Ce3+, Eu2+) have emerged as promising candidates for electroluminescence applications due to their abundant emission spectra, high light conversion efficiency, and excellent stability. However, directly injecting charge into 4f orbitals remains a significant challenge, resulting in unsatisfied external quantum efficiency and high operating voltage in rare earth light-emitting diodes. Herein, we propose a scheme to solve the difficulty by utilizing the energy transfer process. X-ray photoelectron spectroscopy and transient absorption spectra suggest that the Cs3CeI6 luminescence process is primarily driven by the energy transfer from the I2-based self-trapped exciton to the Ce-based Frenkel exciton. Furthermore, energy transfer efficiency is largely improved by enhancing the spectra overlap between the self-trapped exciton emission and the Ce-based Frenkel exciton excitation. When implemented as an active layer in light-emitting diodes, they show the maximum brightness and external quantum efficiency of 1073 cd m−2 and 7.9%, respectively.

Abstract In recent studies, erianin, a natural product isolated from Dendrobium chrysotoxum Lindl, has exhibited notable anticancer properties. Ferroptosis, a novel form of programmed cell death, holds potential as a strategy to overcome Temozolomide (TMZ) resistance in glioma by inducing ferroptosis in TMZ-resistant glioma cells. Here, utilizing various phenotyping experiments, including cell counting kit-8 (CCK-8) assays, EdU assays, transwell assays, neurosphere formation assays and extreme limiting dilution (ELDA) assays, we demonstrated that erianin exerts its anticancer activity on both TMZ sensitive and TMZ-resistant glioma stem cells (GSCs). Furthermore, we made an exciting discovery that erianin enhances TMZ sensitivity in TMZ-resistant GSCs. Subsequently, we demonstrated that erianin induced ferroptosis in TMZ-resistant GSCs and enhances TMZ sensitivity through inducing ferroptosis, which was confirmed by intracellular measurements of ROS, GSH, and MDA, as well as through the use of BODIPY (581/591) C11 and transmission electron microscopy. Conversely, the ferroptosis inhibitor ferrostatin-1 (Fer-1) blocked the effects of erianin. The underlying mechanism of ferroptosis induced by erianin was further explored through co-immunoprecipitation (Co-IP) assays, ubiquitination assays, protein stability assessments, chromatin immunoprecipitation (ChIP) assays and luciferase reporter gene assays. We found that erianin specifically targets REST, inhibiting its transcriptional repression function without altering its expression levels. Consequently, this suppression of REST’s role leads to an upregulation of LRSAM1 expression. In turn, LRSAM1 ubiquitinates and degrades SLC40A1, a protein that inhibits ferroptosis by exporting ferrous ions. By downregulating SLC40A1, erianin ultimately induces ferroptosis in TMZ-resistant GSCs. Taken together, our research demonstrates that the natural product erianin inhibits the malignant phenotype of GSCs and increases the sensitivity of TMZ in TMZ-resistant GSCs by inducing ferroptosis. These findings suggest erianin as a prospective compound for the treatment of TMZ-resistant glioma.

Abstract The aim of this study was to develop a real-time risk prediction model for extrauterine growth retardation (EUGR). A total of 2514 very preterm infants were allocated into a training set and an external validation set. The most appropriate independent variables were screened using univariate analysis and Lasso regression with tenfold cross-validation, while the prediction model was designed using binary multivariate logistic regression. A visualization of the risk variables was created using a nomogram, while the calibration plot and receiver operating characteristic (ROC) curves were used to calibrate the prediction model. Clinical efficacy was assessed using the decision curve analysis (DCA) curves. Eight optimal predictors that namely birth weight, small for gestation age (SGA), hypertensive disease complicating pregnancy (HDCP), gestational diabetes mellitus (GDM), multiple births, cumulative duration of fasting, growth velocity and postnatal corticosteroids were introduced into the logistic regression equation to construct the EUGR prediction model. The area under the ROC curve of the training set and the external verification set was 83.1% and 84.6%, respectively. The calibration curve indicate that the model fits well. The DCA curve shows that the risk threshold for clinical application is 0–95% in both set. Introducing Birth weight, SGA, HDCP, GDM, Multiple births, Cumulative duration of fasting, Growth velocity and Postnatal corticosteroids into the nomogram increased its usefulness for predicting EUGR risk in very preterm infants.

Abstract To date, earlier diagnosis of Alzheimer’s disease (AD) is still challenging. Recent studies revealed the elevated expression of connective tissue growth factor (CTGF) in AD brain is an upstream regulator of amyloid-beta (Aβ) plaque, thus CTGF could be an earlier diagnostic biomarker of AD than Aβ plaque. Herein, we develop a peptide-coated gold nanocluster that specifically targets CTGF with high affinity (KD ~ 21.9 nM). The probe can well penetrate the blood-brain-barrier (BBB) of APP/PS1 transgenic mice at early-stage (earlier than 3-month-old) in vivo, allowing non-invasive NIR-II imaging of CTGF when there is no appearance of Aβ plaque deposition. Notably, this probe can also be applied to measuring CTGF on postmortem brain sections by multimodal analysis, including fluorescence imaging, peroxidase-like chromogenic imaging, and ICP-MS quantitation, which enables distinguishment between the brains of AD patients and healthy people. This probe possesses great potential for precise diagnosis of earlier AD before Aβ plaque formation.

ObjectivesBattery energy storage system is one of the effective means to ensure the reliability of photovoltaic (PV) power generation system and improve the utilization rate of PV power generation. However, there are some problems in the PV-energy storage power station, such as the difficulty of power fluctuation suppression and the unreasonable configuration of energy storage capacity. In order to solve these problems, relevant research was carried out.MethodsFor energy-based battery energy storage, the characteristics of scheduling mode and autonomous mode were analyzed, and a power-limited suppression strategy of PV-energy storage system based on energy storage operation in scheduling mode was proposed to realize power suppression and reduce the frequency of energy storage charge/discharge switching. The optimization objective of minimizing abandoned power losses in the PV-energy storage system was established, with constraints such as the probability of power fluctuation exceeding the limit. An algorithm was used to solve and optimize the energy storage configuration. Taking the 50 MW Sangzhuzi PV-energy storage power station in Langming, Tibet as an example, the effectiveness of the proposed grid-connected power suppression strategy was validated.ResultsThe proposed grid-connected power suppression strategy can reduce the probability of power fluctuation exceeding the limit from 25.64% to 6.41% without increasing the frequency of energy storage charge/discharge switching. When the probability of grid-connected power fluctuation exceeding the limit is 5%, the optimal configuration for the energy storage system of the power station is determined to be 14.5 MW/94 MW⋅h.ConclusionsThe proposed power fluctuation suppression strategy and energy storage optimization configuration method can provide technical reference for the optimal design and operation of grid-connected PV-energy storage system.

Enzymatic reaction-mediated microbial transformation has emerged as a promising technology with significant potential in various industries. These technologies offer the ability to produce enzymes on a large scale, optimize their functionality, and enable sustainable production processes. By utilizing microbial hosts and manipulating their genetic makeup, enzymes can be synthesized efficiently and tailored to meet specific industrial requirements. This leads to enhanced enzyme performance and selectivity, facilitating the development of novel processes and the production of valuable compounds. Moreover, microbial transformation and biosynthesis offer sustainable alternatives to traditional chemical methods, reducing environmental impact and promoting greener production practices. Microbial transformations enrich drug candidate diversity and enhance active ingredient potency, benefiting the pharmaceutical industry. Continued advancements in genetic engineering and bioprocess optimization drive further innovation and application development in Enzymatic reaction-mediated microbial transformation. The integration of AI for predicting enzymatic reactions and optimizing pathways marks a promising direction for future research. In summary, these technologies have the potential to revolutionize several industries by providing cost-effective, sustainable solutions.

Abstract Light‐sheet fluorescence microscopy (LSFM) introduces fast scanning of biological phenomena with deep photon penetration and minimal phototoxicity. This advancement represents a significant shift in 3‐D imaging of large‐scale biological tissues and 4‐D (space + time) imaging of small live animals. The large data associated with LSFM require efficient imaging acquisition and analysis with the use of artificial intelligence (AI)/machine learning (ML) algorithms. To this end, AI/ML‐directed LSFM is an emerging area for multiorgan imaging and tumor diagnostics. This review will present the development of LSFM and highlight various LSFM configurations and designs for multiscale imaging. Optical clearance techniques will be compared for effective reduction in light scattering and optimal deep‐tissue imaging. This review will further depict a diverse range of research and translational applications, from small live organisms to multiorgan imaging to tumor diagnosis. In addition, this review will address AI/ML‐directed imaging reconstruction, including the application of convolutional neural networks (CNNs) and generative adversarial networks (GANs). In summary, the advancements of LSFM have enabled effective and efficient post‐imaging reconstruction and data analyses, underscoring LSFM's contribution to advancing fundamental and translational research.

Abstract Thermal metamaterials are typically achieved by mixing different natural materials to realize effective thermal conductivities (ETCs) that conventional materials do not possess. However, the necessity for multifunctional design of metamaterials, encompassing both thermal and mechanical functionalities, is somewhat overlooked, resulting in the fixation of mechanical properties in thermal metamaterials designed within current research endeavors. Thus far, conventional methods have faced challenges in designing thermal metamaterials with configurable mechanical properties because of intricate inherent relationships among the structural configuration, thermal and mechanical properties in metamaterials. Here, a data‐driven approach is proposed to design a thermal metamaterial capable of seamlessly achieving thermal functionalities and harnessing the advantages of microstructural diversity to configure its mechanical properties. The designed metamaterial possesses thermal cloaking functionality while exhibiting exceptional mechanical properties, such as load‐bearing capacity, shearing strength, and tensile resistance, thereby affording mechanical protection for the thermal metadevice. The proposed approach can generate numerous distinct inverse design candidate topological functional cells (TFCs), designing thermal metamaterials with dramatic improvements in mechanical properties compared to traditional ones, which sets up a novel paradigm for discovering thermal metamaterials with extraordinary mechanical structures. Furthermore, this approach also paves the way for investigating thermal metamaterials with additional physical properties.

Objective Previous studies have shown the anti-inflammatory effect of 25-hydroxyvitamin D (25(OH)D) and the crucial roles of high-sensitive C reactive protein (hsCRP) and novel inflammatory markers (red blood cell distribution width–platelet count ratio (RDWPCR), mean platelet volume–platelet count ratio (MPVPCR), neutrophil–lymphocyte ratio (NLR) and white blood cell–neutrophil ratios (WBCNR)) in several diseases, but scarce data explored the associations of 25(OH)D with hsCRP and novel inflammatory markers. This study aimed to investigate these associations in children.Design Cross-sectional study.Setting Children in China.Participants 10141 children (mean age 14.6 months) were included.Primary and secondary outcome measures HsCRP, red blood cell distribution width, platelet count, mean platelet volume, neutrophil, lymphocyte and white blood cell were measured.Results Overall, serum 25(OH)D was inversely associated with hsCRP and novel inflammatory biomarkers. In multivariable analysis, serum 25(OH)D was inversely associated with hsCRP and novel inflammatory biomarkers (Q quartile (Q) 4 vs Q1: 1129.75 vs 2090.99 for hsCRP; 4246.94 vs 6829.89 for RDWPCR; 4863.57 vs 5545.66 for MPVPCR; 4345.76 vs 6507.46 for NLR; 2418.84 vs 2868.39 for WBCNR). Similar results also were observed in stratified analyses by sex (boys and girls). Moreover, serum 25(OH)D was inversely associated with elevated inflammation levels. After adjustment for other potential covariates, inverse associations between serum 25(OH)D and elevated inflammation levels were still observed. The corresponding ORs (95% CI) were 0.05 (0.04, 0.06) for hsCRP, 0.13 (0.11, 0.15) for RDWPCR, 0.74 (0.64, 0.85) for MPVPCR, 0.11 (0.09, 0.13) for NLR and 0.57 (0.49, 0.66) for WBCNR in the fourth quartile compared with the first quartile, respectively.Conclusions Generally, the graded and inverse associations of serum 25(OH)D with hsCRP and four novel inflammatory markers (RDWPCR, MPVPCR, NLR and WBCNR) were observed. The present study provided further support for the anti-inflammatory effects of 25(OH)D.

Abstract Naphthalene diimides (NDI) are widely serving as the skeleton to construct electron transport materials (ETMs) for optoelectronic devices. However, most of the reported NDI‐based ETMs suffer from poor interfaces with the perovskite which deteriorates the carrier extraction and device stability. Here, a representative design concept for editing the peripheral groups of NDI molecules to achieve multifunctional properties is introduced. The resulting molecule 2,7‐bis(2,2,3,3,4,4,4‐heptafluorobutyl)benzo[lmn][3,8]phenanthroline‐1,3,6,8(2H,7H)‐tetraone (NDI‐C4F) incorporated with hydrophobic fluorine units contributes to the prevention of excessive molecular aggregation, the improvement of surface wettability and the formation of strong chemical coordination with perovskite precursors. All these features favor retarding the perovskite crystallization and achieving superior buried interfaces, which subsequently promote charge collection and improve the structural compatibility between perovskite and ETMs. The corresponding PSCs based on low‐temperature processed NDI‐C4F yield a record efficiency of 23.21%, which is the highest reported value for organic ETMs in n‐i‐p PSCs. More encouragingly, the unencapsulated devices with NDI‐C4F demonstrate extraordinary stability by retaining over 90% of their initial PCEs after 2600 h in air. This work provides an alternative molecular strategy to engineer the buried interfaces and can trigger further development of organic ETMs toward reliable PSCs.

Abstract Visual defect recognition techniques based on deep learning models are crucial for modern industrial quality inspection. The backbone, serving as the primary feature extraction component of the defect recognition model, has not been thoroughly exploited. High‐performance vision transformer (ViT) is less adopted due to high computational complexity and limitations of computational resources and storage hardware in industrial scenarios. This paper presents LSA‐Former, a lightweight transformer architectural backbone that integrates the benefits of convolution and ViT. LSA‐Former proposes a novel self‐attention with linear computational complexity, enabling it to capture local and global semantic features with fewer parameters. LSA‐Former is pre‐trained on ImageNet‐1K and surpasses state‐of‐the‐art methods. LSA‐Former is employed as the backbone for various detectors, evaluated specifically on the PCB defect detection task. The proposed method reduces at least 18M parameters and exceeds the baseline by more than 2.2 mAP.

Abstract Background A greater emphasis has been placed on the part of cell cycle progression (CCP) in cancer in recent years. Nevertheless, the precise connection between CCP-related genes and bladder cancer (BCa) has remained elusive. This study endeavors to establish and validate a reliable risk model incorporating CCP-related factors, aiming to predict both the prognosis and immune landscape of BCa. Methods Clinical information and RNA sequencing data were collected from the GEO and TCGA databases. Univariate and multivariate Cox regression analyses were conducted to construct a risk model associated with CCP. The performance of the model was assessed using ROC and Kaplan–Meier survival analyses. Functional enrichment analysis was employed to investigate potential cellular functions and signaling pathways. The immune landscape was characterized using CIBERSORT algorithms. Integration of the risk model with various clinical variables led to the development of a nomogram. Results To build the risk model, three CCP-related genes (RAD54B, KPNA2, and TPM1) were carefully chosen. ROC and Kaplan–Meier survival analysis confirm that our model has good performance. About immunological infiltration, the high-risk group showed decreased levels of regulatory T cells and dendritic cells coupled with increased levels of activated CD4 + memory T cells, M2 macrophages, and neutrophils. Furthermore, the nomogram showed impressive predictive power for OS at 1, 3, and 5 years. Conclusion This study provides new insights into the association between the CCP-related risk model and the prognosis of BCa, as well as its impact on the immune landscape.

Abstract Chiral inorganic semiconductors with high dissymmetric factor are highly desirable, but it is generally difficult to induce chiral structure in inorganic semiconductors because of their structure rigidity and symmetry. In this study, we introduced chiral ZnO film as hard template to transfer chirality to CsPbBr3 film and PbS quantum dots (QDs) for circularly polarized light (CPL) emission and detection, respectively. The prepared CsPbBr3/ZnO thin film exhibited CPL emission at 520 nm and the PbS QDs/ZnO film realized CPL detection at 780 nm, featuring high dissymmetric factor up to around 0.4. The electron transition based mechanism is responsible for chirality transfer. Graphical Abstract

Abstract Continuous-flow biocatalysis utilizing immobilized enzymes emerged as a sustainable route for chemical synthesis. However, inadequate biocatalytic efficiency from current flow reactors, caused by non-productive enzyme immobilization or enzyme-carrier mismatches in size, hampers its widespread application. Here, we demonstrate a general-applicable and robust approach for the fabrication of a high-performance enzymatic continuous-flow reactor via integrating well-designed scalable isoporous block copolymer (BCP) membranes as carriers with an oriented and productive immobilization employing material binding peptides (MBP). Densely packed uniform enzyme-matched nanochannels of well-designed BCP membranes endow the desired nanoconfined environments towards a productive immobilized phytase. Tuning nanochannel properties can further regulate the complex reaction process and fortify the catalytic performance. The synergistic design of enzyme-matched carriers and efficient enzyme immobilization empowers an excellent catalytic performance with >1 month operational stability, superior productivity, and a high space-time yield (1.05 × 105 g L−1 d−1) via a single-pass continuous-flow process. The obtained performance makes the designed nano- and isoporous block copolymer membrane reactor highly attractive for industrial applications.

The weathered bedrock aquifer in the Jurassic coalfield of northern Shaanxi Province is a direct water-bearing aquifer, and accurately predicting its water-bearing properties is essential for preventing and controlling water hazards in mining operations. Traditional Bayes discriminant methods have limitations in predicting water-bearing properties, particularly because not all primary factors influencing water-bearing properties meet the criteria for multivariate normal distribution. In this paper, the southern flank of the Ningtiaota Minefield is taken as an example, with the weathered bedrock aquifer as the research object. Six main controlling factors are selected: weathered bedrock thickness, core recovery rate, degree of weathering, lithological combination, elevation of the weathered bedrock surface, and sand-to-base ratio. A kernel density estimator–Bayes (KDE–Bayes) discriminant method for predicting water-bearing properties is presented. The kernel density estimation was carried out on the three main controlling factors that do not conform to a normal distribution—weathered bedrock thickness, core recovery rate, and sand-to-base ratio—and, in conjunction with other primary factors, a KDE–Bayes model was constructed for predicting the water-bearing properties in the southern flank of the Ningtiaota Minefield, based on which a detailed prediction of the water-bearing properties of the south flank of the Ningtiaota Minefield was conducted. By analyzing the actual dewatering data from the S1231 working face and past water inrush (or outburst) incidents, the feasibility and accuracy of this prediction method are demonstrated, providing valuable insights for predicting the water-bearing properties of weathered bedrock aquifers in the Ningtiaota Coal Mine and similar mining conditions.

In nature, gliding birds frequently execute intricate flight maneuvers such as aerial somersaults, perched landings, and swift descents, enabling them to navigate obstacles or hunt prey. It is evident that birds rely on different wing–tail configurations to accomplish a wide range of aerial maneuvers. For traditional fixed‐wing unmanned aerial vehicles (UAVs), pitch control primarily comes from the tail's elevators, while adjusting flight lift and drag involves deploying wing flaps. Although these designs ensure reliable flight, they compromise the drones’ maneuverability to maintain longitudinal stability. Therefore, the study introduces a biomimetic morphing wing UAV, and presents a pitch control strategy that simultaneously engages morphing wings, ailerons, and tail elevators. The pull‐up maneuver tests indicate that the proposed control method results in a pitch rate that is approximately 2.5 times greater than when using only the elevator control. A closed‐loop control system for the drone is also established. The closed‐loop flight experiment, which tracks a 45° pitch angle, demonstrates the effectiveness of the proposed coupled control method in adjusting the flight attitude. In addition, during cruising, the UAV employs three configurations, straight wing, forward‐swept wing, and back‐swept wing, to cater to different mission objectives and augment its flight capabilities.

Osteoarthritis is a chronic degenerative disease based on the degeneration and loss of articular cartilage. Inflammation and aging play an important role in the destruction of the extracellular matrix, in which microRNA (miRNA) is a key point, such as miRNA-34a-5p. Upregulation of miRNA-34a-5p was previously reported in a rat OA model, and its inhibition significantly suppressed interleukin (IL)-1β-induced apoptosis in rat chondrocytes. However, Oxidative stress caused by reactive oxygen species (ROS) can exacerbate the progression of miRNA regulated OA by mediating inflammatory processes. Thus, oxidative stress effects induced via tert-butyl hydroperoxide (tBHP) in human chondrocytes were assessed in the current research by evaluating mitochondrial ROS production, mitochondrial cyclooxygenase (COX) activity, and cell apoptosis. We also analyzed the activities of antioxidant enzymes including glutathione peroxidase (GSH-Px), catalase (CAT), and superoxide dismutase (SOD). Additionally, inflammatory factors, such as tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6, IL-8, and IL-24, which contribute to OA development, were detected by enzyme-linked immunosorbent assay (ELISA). The results of this study indicated that miR-34a-5p/silent information regulator 1 (SIRT1)/p53 axis was involved in the ROS-induced injury of human chondrocytes. Moreover, dual-luciferase assay revealed that SIRT1 expression was directly regulated by miR-34a-5p, indicating the presence of a positive feedback loop in the miR-34a-5p/SIRT1/p53 axis that plays an important role in cell survival. However, ROS disrupted the miR-34a-5p/SIRT1/p53 axis, leading to the development of OA, and articular injection of SIRT1 agonist, SRT1720, in a rat model of OA effectively ameliorated OA progression in a dose-dependent manner. Our study confirms that miRNA-34a-5p could participate in oxidative stress responses caused by ROS and further regulate the inflammatory process via the SIRT1/p53 signaling axis, ultimately affecting the onset of OA, thus providing a new treatment strategy for clinical treatment of OA.

Abstract The objective of this study was to explore the positive influence and potential mechanism of Erianin on the recovery of brain cells following a traumatic brain injury (TBI). TBI rat models were prepared and treated with Erianin injection via tail vein. The assessment included evaluating the rats' levels of oxidative stress, inflammation, neuronal damage, mitochondrial damage, neuronal regeneration, transformation of pro-inflammatory microglial cells, activation status of the ERK signal pathway, and the functionality of their learning and memory. After administering Erianin, there was a suppression of oxidative stress, inflammation, nerve cell damage, and mitochondrial damage in the TBI rats. Additionally, there was an increase in neuronal regeneration in the cortex and hippocampus, inhibition of pro-inflammatory microglial cell transformation in the cortex, improvement in learning and memory function in TBI rats, and simultaneous inhibition of the activation of the ERK1/c-Jun signal pathway. The findings suggest that Erianin has the potential to reduce oxidative stress and inflammatory reaction in rats with TBI, safeguard nerve cells against apoptosis, stimulate the growth of new neural cells, ultimately enhancing the cognitive abilities and memory function of the rats. The inhibition of the ERK signaling pathway could be closely associated with these effects.

Bridges, as vital infrastructure, require ongoing monitoring to maintain safety and functionality. This study introduces an innovative algorithm that refines bridge component performance assessment through the integration of modified K-means clustering, silhouette coefficient optimization, and cloud model theory. The purpose is to provide a reliable method for monitoring the safety and serviceability of critical infrastructure, particularly double-layer truss arch bridges. The algorithm processes large datasets to identify patterns and manage uncertainties in structural health monitoring (SHM). It includes field monitoring techniques and a model-driven approach for establishing assessment thresholds. The main findings, validated by case studies, show the algorithm’s effectiveness in enhancing clustering quality and accurately evaluating bridge performance using multiple indicators, such as statistical significance, cluster centroids, average silhouette coefficient, Davies–Bouldin index, average deviation, and Sign-Rank test p-values. The conclusions highlight the algorithm’s utility in assessing structural integrity and aiding data-driven maintenance decisions, offering scientific support for bridge preservation efforts.

Glioblastoma multiforme (GBM) is the most aggressive brain tumor. There is a pressing need to develop novel treatment strategies due to the poor targeting effect of current therapeutics. Here, a gold cluster coated with optimized GBM-targeting peptide is engineered, namely NA. NA can efficiently target GBM both in vitro and in vivo. Interestingly, the uptake of NA significantly sensitizes GBM cells to ferroptosis, a form of programmed cell death that can bypass the tumor resistance to apoptosis. This effect is exerted through regulating the HO-1-dependent iron ion metabolism, which is the non-canonical pathway of ferroptosis. The combined treatment of a ferroptosis inducer and NA profoundly inhibited tumor growth in both the GBM spheroid model and a syngeneic mouse model with enhanced ferroptosis levels and excellent biosafety. Importantly, the infiltration of tumoricidal lymphocytes is also significantly increased within tumor. Therefore, NA presents a potential novel nanomaterial-based strategy for GBM treatment.

Abstract Polymeric materials exhibiting room temperature phosphorescence (RTP) show a promising application potential. However, the conventional ways of preparing such materials are mainly focused on doping, which may suffer from phase separation, poor compatibility, and lack of effective methods to promote intersystem crossing and suppress the nonradiative deactivation rates. Herein, we present an intrinsically polymeric RTP system producing long-lived phosphorescence, high quantum yields and multiple colors by stepwise structural confinement to tame triplet excitons. In this strategy, the performance of the materials is improved in two aspects simultaneously: the phosphorescence lifetime of one polymer (9VA-B) increased more than 4 orders of magnitude, and the maximum phosphorescence quantum yield reached 16.04% in halogen-free polymers. Moreover, crack detection is realized by penetrating steam through the materials exposed to humid surroundings as a special quenching effect, and the information storage is carried out by employing the Morse code and the variations in lifetimes. This study provides a different strategy for constructing intrinsically polymeric RTP materials toward targeted applications.

Abstract Combining information from multispectral images into a fused image is informative and beneficial for human or machine perception. Currently, multiple photodetectors with different response bands are used, which require complicated algorithms and systems to solve the pixel and position mismatch problem. An ideal solution would be pixel-level multispectral image fusion, which involves multispectral image using the same photodetector and circumventing the mismatch problem. Here we presented the potential of pixel-level multispectral image fusion utilizing colloidal quantum dots photodiode array, with a broadband response range from X-ray to near infrared and excellent tolerance for bending and X-ray irradiation. The colloidal quantum dots photodiode array showed a specific detectivity exceeding 1012 Jones in visible and near infrared range and a favorable volume sensitivity of approximately 2 × 105 μC Gy−1 cm−3 for X-ray irradiation. To showcase the advantages of pixel-level multispectral image fusion, we imaged a capsule enfolding an iron wire and soft plastic, successfully revealing internal information through an X-ray to near infrared fused image.

Abstract Background Glioma exhibit heterogeneous susceptibility for targeted ferroptosis. How circRNAs alterations in glioma promote iron metabolism and ferroptosis defense remains unclarified. Methods The highly enriched circRNAs in glioblastoma (GBM) were obtained through analysis of sequencing datasets. Quantitative real-time PCR (qRT–PCR) was used to determine the expression of circRNF10 in glioma and normal brain tissue. Both gain-of-function and loss-of-function studies were used to assess the effects of circRNF10 on ferroptosis using in vitro and in vivo assays. The hypothesis that ZBTB48 promotes ferroptosis defense was established using bioinformatics analysis and functional assays. RNA pull-down and RNA immunoprecipitation (RIP) assays were performed to examine the interaction between circRNF10 and target proteins including ZBTB48, MKRN3 and IGF2BP3. The posttranslational modification mechanism of ZBTB48 was verified using coimmunoprecipitation (co-IP) and ubiquitination assays. The transcription activation of HSPB1 and IGF2BP3 by ZBTB48 was confirmed through luciferase reporter gene and chromatin immunoprecipitation (ChIP) assays. The stabilizing effect of IGF2BP3 on circRNF10 was explored by actinomycin D assay. Finally, a series of in vivo experiments were performed to explore the influences of circRNF10 on the glioma progression. Results A novel circular RNA, hsa_circ_0028912 (named circRNF10), which is significantly upregulated in glioblastoma tissues and correlated with patients’ poor prognosis. Through integrated analysis of the circRNA-proteins interaction datasets and sequencing results, we reveal ZBTB48 as a transcriptional factor binding with circRNF10, notably promoting upregulation of HSPB1 and IGF2BP3 expression to remodel iron metabolism and facilitates the launch of a circRNF10/ZBTB48/IGF2BP3 positive feedback loop in GSCs. Additionally, circRNF10 can competitively bind to MKRN3 and block E3 ubiquitin ligase activity to enhance ZBTB48 expression. Consequently, circRNF10-overexpressed glioma stem cells (GSCs) display lower Fe2+ accumulation, selectively priming tumors for ferroptosis evading. Conclusion Our research presents abnormal circRNAs expression causing a molecular and metabolic change of glioma, which we leverage to discover a therapeutically exploitable vulnerability to target ferroptosis.

Abstract Descending tracts carry motor signals from the brain to spinal cord. However, few previous studies show the full view of the long tracts from a 3D perspective. In this study, we have followed five less well-known tracts that project from midbrain, hindbrain, and cerebellum to the mouse spinal cord, using the tissue clearing method in combination with tiling light sheet microscopy. By tracing axons in spinal cord, we found several notable features: among the five tracts the collateral "sister" branches occurred only in the axons originating from the cerebellospinal tracts; the axons from the spinal trigeminal nucleus crossed the midline of spinal cord to the contralateral side; those arising in the medullary reticular formation ventral part gave many branches in both cervical and lumbar segments; the axons from superior colliculus terminated only at upper cervical but with abundant branches in the hindbrain. Furthermore, we investigated the monosynaptic connections between the tracts and motor neurons in the spinal cord through hydrogel-based tissue expansion, and found several monosynaptic connections between the medullary reticular formation ventral part axons and spinal motor neurons. We believe that this is the first study to show the full 3D scope of the projection patterns and axonal morphologies of these five descending tracts to the mouse spinal cord. In addition, we have developed a new method for future study of descending tracts by three-dimensional imaging.

At present, BIM platforms rely on foreign software. Homemade software and industry applications are mostly secondary developments, which present stranglehold problems caused by interruptions to the software supply. To solve the problem, key technical research on the 3D integrated design of railway engineering was stuedied based on homemade graphics engines to propose an innovative railway BIM platform framework. The entire process was completed from the top-level design to the engineering verification of the platform. The co-designed mechanism of a ''center model and link'' hybrid mode was constructed, which solved the difficulties of data management and increment synchronization at a large scale, achieving teamwork among surveying and mapping, alignments, and bridges. The results of this study could provide strong support for the development of BIM software for a whole railway and all majors.

Accurate extraction of three-dimensional (3D) vegetation is essential for monitoring urban ecological environments and carbon sinks. Two-dimensional vegetation data in cities has been widely researched. However, large-scale urban vegetation height inventories are lacking. This study proposes a novel framework for 3D extraction of urban vegetation, which can be widely applied based on remote sensing approaches. A multi-task convolutional neural network is established to extract the urban vegetation cover and estimate the vegetation height at the pixel level. The results indicate that this method can derive the complete urban vegetation cover and height from stereo satellite data. Compared with the traditional stereo-photogrammetry method, this method enables rapid inference of vegetation height in urban areas with a root mean square error (RMSE) of 3.16 m. This model is capable of accurately separating vegetation in complex urban environments and performs well despite shadow effects. Furthermore, in this study, the first vegetation height map with 1-m spatial resolution has been produced, covering six urban districts in Beijing (approximately 1,378 km2). It only takes 2–3 min to process the imagery of the whole study area. The high-resolution map can display more urban vegetation details over the existing 10 m/30 m resolution vegetation height maps. Furthermore, the established framework and benchmark for urban vegetation 3D information offer unique insights and provide a basis for further research.

There are numerous ecological challenges that are of considerable concern caused by urbanization and natural calamities. The spatial pattern of the landscape is of great significance in maintaining species diversity and regulating material cycles and individual ecophysiological processes as a medium for ecosystem service functions. Resolving the conflicting relationships between people and land necessitates the precise identification of essential ecological sources. Therefore, there is an urgent need to investigate the relationship between ecological sources and ecosystem service values (ESVs) and to optimize the matter and energy.connectivity network. The study optimizes ecological network simulation using a penetration threshold, population density inverse distance formula, and geodetector weight analysis, constructs ecological spatial networks and evaluates their structure using the minimum cumulative resistance (MCR) model and complex network analysis, and discusses the relationship between network topological characteristics (NTCs) and the spatial pattern of the various ESVs. The results show that the major sources and corridors are primarily along the banks of the Wei River and the Fen River. The clustering coefficient has a significant negative correlation with both water supply and water regulation service values. It is found that the sources with higher ecological service values related to water resources have better network connectivity. This result plays a major reference role for the identification of several crucial ecological components, such as sources, nodes and stepping stones, in the middle Yellow River basin (MYRB). Ultimately, we propose strengthening the ecological protection of the eastern part of the Qinling Mountains and increasing the ecological communication capacity of the forestland on both sides of the river valley to create an optimized ecological pattern.

Abstract Researches had proven that the occurrence of bladder cancer (BC) is much higher in men than those in women, which induced us to explore whether androgen plays a role in BC. A total of 147 patients who were diagnosed with primary BC by histopathological biopsy were included. Meanwhile 154 non-tumor patients were matched as the control group. The continuous variables were expressed as median (interquartile range, IQR) and compared by Mann–Whitney U test, for the reason that the data were not matched the requirementsthe of normal test. A Chi-square test was used to compare the categorical variables, which were expressed as frequency (percentage). Meanwhile univariate and multivariate logistic regression was done to further evaluating the potential independent factor of BC. P

Abstract Tyro3, Axl, and Mertk (abbreviated TAMs) comprise a family of homologous type 1 receptor tyrosine kinases (RTKs) that have been implicated as inhibitory receptors that dampen inflammation, but their roles in the pathogenesis of rheumatoid arthritis remains understudied. Here, to investigate TAMs in an inflammatory arthritis model, antibody-induced arthritis in single TAM-deficient mice (Tyro3- KO, Axl-KO, Mertk-KO) was induced by K/BxN serum injection. Subsequently, joint inflammation and cytokine levels, as well as the expression of Fcγ Rs and complement receptors were assessed in WT and TAM-deficient mice. Compared with littermate control mice, Axl−/− and Mertk−/− mice developed more severe antibody-induced arthritis, while in contrast, Tyro3−/− mice showed diminished joint inflammation. Concomitantly, the levels of cytokines in joints of Axl−/− and Mertk−/− mice were also significantly increased, while cytokines in the Tyro3−/− joint tissues were decreased. At the molecular and cellular level, TAMs showed distinct expression patterns, whereby monocytes expressed Axl and Mertk, but no Tyro3, while neutrophils expressed Axl and Tyro3 but little Mertk. Moreover, expression of Fcγ receptors and C5aR showed different patterns with TAMs expression, whereby FcγRIV was higher in monocytes of Axl−/− and Mertk−/− mice compared to wild-type mice, while Tyro3−/− neutrophils showed lower expression levels of FcγRI, FcγRIII and FcγRIV. Finally, expression of C5aR was increased in Mertk−/− monocytes, and was decreased in Tyro3−/− neutrophils. These data indicate that Axl, Mertk and Tyro3 have distinct functions in antibody-induced arthritis, due in part to the differential regulation of cytokines production, as well as expression of FcγRs and C5aR. Video Abstract