Search

In this study, oral supplementation of stachyose significantly improved HFD-induced MetS symptom, including overweight, hyperlipidemia, hepatic steatosis and system-wide inflammation. The subsequent analysis revealed that stachyose supplement significantly enhanced the integrity of the intestinal epithelial barrier and effectively reversed the gut microbiota dysbiosis, as evidenced by improvements in gut microbial gene richness, microbiota composition, and functional characteristics. The abundance of butyrate-producing strains, such as Bacteroides faecis, Butyomonas faecalis, Parabacteroides distasonis and Phocaeicola coprophilus is significantly increased, concurrently with the activation of the PPAR-γ signaling pathway. The findings of our study suggest that stachyose exhibits potential as a prebiotic agent for the prevention of gut microbiota dysbiosis and the intestinal epithelial barrier disruption in obese. The results of our study suggest that stachyose demonstrates potential as a prebiotic agent for mitigating gut microbiota dysbiosis and preserving the integrity of the intestinal epithelial barrier in individuals with MetS.

The dielectric barrier discharge (DBD) actuator has the advantages of being lightweight, having no moving parts, ease of use, and fast response, and has received widespread attention in flow control applications. Turbulence boundary layer drag reduction is one of many applications of DBD flow control, but the mechanism of DBD actuator turbulence drag reduction needs further investigation. The effect of DBD excitation on the skin-friction drag of a turbulent boundary layer on a flat plate at different flow speeds was investigated experimentally. The change in skin-friction drag was measured using oil film interferometry, and the velocity distribution within the boundary layer was obtained using a particle image velocimetry system. The results showed that under the action of the plasma actuator, the local skin-friction coefficient was measured to decrease by 49%. Through dynamic mode decomposition, plasma actuators can increase the thickness of the low-velocity region in the boundary layer, reduce the intensity of Q2 and Q4 events, and inhibit the development of coherent structures, thereby achieving drag reduction.

Developmental dysplasia of the hip (DDH) is the most common hip deformity in pediatric orthopedics. One of the common pathological changes in DDH is the thickening and hypertrophy of the ligamentum teres. However, the underlying pathogenic mechanism responsible for these changes remains unclear. This study represents the first time that the heterogeneity of cell subsets in the abnormal ligamentum teres of patients with DDH has been resolved at the single-cell and spatial levels by snRNA-Seq and MiP-Seq. Through gene set enrichment and intercellular communication network analyses, we found that receptor-like cells and ligament stem cells may play an essential role in the pathological changes resulting in ligamentum teres thickening and hypertrophy. Eight ligand–receptor pairs related to the ECM–receptor pathway were observed to be closely associated with DDH. Further, using the Monocle R package, we predicted a differentiation trajectory of pericytes into two branches, leading to junctional ligament stem cells or fibroblasts. The expression of extracellular matrix-related genes along pseudotemporal trajectories was also investigated. Using MiP-Seq, we determined the expression distribution of marker genes specific to different cell types within the ligamentum teres, as well as differentially expressed DDH-associated genes at the spatial level.

Unilateral biportal endoscopic spinal surgery (UBE) is a rapidly growing surgical method and has attracted much interest recently. The most common complication of this technique is cerebrospinal fluid (CSF) leakage due to intraoperative dural tears. There have been no reports of bacterial meningitis due to dural tears in UBE surgery and its treatment and prevention. We reported a 47 year-old man with CSF due to an intraoperative dural tear. A drainage tube was routinely placed and removed on the fourth day after surgery, resulting in fever and headache on the fifith postoperative day. Blood and CSF cultures showed Klebsiella pneumoniae infection, and with lumbar drainage and appropriate antibiotics based on sensitivity tests, the patient's fever and headache were effectively relieved. This case report suggests the importance of prolonged drainage tube placement, adequate drainage, careful intraoperative separation to avoid dural tears, and effective sensitive antibiotic therapy.

Heat is almost everywhere. Unlike electricity, which can be easily manipulated, the current ability to control heat is still highly limited owing to spontaneous thermal dissipation imposed by the second law of thermodynamics. Optical illumination and pressure have been used to switch endothermic/exothermic responses of materials via phase transitions; however, these strategies are less cost-effective and unscalable. Here, we spectroscopically demonstrate the glassy crystal state of 2-amino-2-methyl-1,3-propanediol (AMP) to realize an affordable, easily manageable approach for thermal energy recycling. The supercooled state of AMP is so sensitive to pressure that even several megapascals can induce crystallization to the ordered crystal, resulting in a substantial temperature increase of 48 K within 20 s. Furthermore, we demonstrate a proof-of-concept device capable of programable heating with an extremely high work-to-heat conversion efficiency of ∼383. Such delicate and efficient tuning of heat may remarkably facilitate rational utilization of waste heat.

Abstract Objectives Abdominal palpation is an essential examination to diagnose various digestive system diseases. This study aimed to develop an objective and standardized test based on abdominal palpation simulators, and establish a credible pass/fail standard of basic competency. Methods Two tests were designed using the newly developed Jucheng abdominal palpation simulator (test 1) and the AbSim simulator (test 2), respectively. Validity evidence for both tests was gathered according to Messick’s contemporary framework by using experts to define test content and then administering the tests in a highly standardized way to participants of different experience. Different simulator setups modified by the built-in software were selected from hepatomegaly, splenomegaly, positive McBurney’s sign plus rebound tenderness, gallbladder tenderness (Murphy’s sign), pancreas tenderness, and a normal setup without pathologies, with six sets used in test 1 and five sets used in test 2. Different novices and experienced were included in the tests, and test 1 was also administered to an intermediate group. Scores and test time were collected and analyzed statistically. Results The internal consistency reliability of test 1 and test 2 showed low Cronbach’s alphas of 0.35 and -0.41, respectively. Cronbach’s alpha for palpation time across cases were 0.65 for test 1 and 0.76 for test 2. There was no statistical difference in total time spent and total scores among the three groups in test 1 (P-values (ANOVA) were 0.53 and 0.35 respectively), nor between novices and experienced groups in test 2 (P-values (t-test) were 0.13 and 1.0 respectively). It was not relevant to try to establish pass/fail standards due to the low reliability and lack of discriminatory ability of the tests. Conclusions It was not possible to measure abdominal palpation skills in a valid way using either of the two standardized, simulation-based tests in our study. Assessment of the patient’s abdomen using palpation is a challenging clinical skill that is difficult to simulate as it highly relies on tactile sensations and adequate responsiveness from the patients.

Abstract Polyploidization is a major driver of genome diversification and environmental adaptation. However, the merger of different genomes may result in genomic conflicts, raising a major question regarding how genetic diversity is interpreted and regulated to enable environmental plasticity. By analyzing the genome-wide binding of 191 trans-factors in allopolyploid wheat, we identified like heterochromatin protein 1 (LHP1) as a master regulator of subgenome-diversified genes. Transcriptomic and epigenomic analyses of LHP1 mutants reveal its role in buffering the expression of subgenome-diversified defense genes by controlling H3K27me3 homeostasis. Stripe rust infection releases latent subgenomic variations by eliminating H3K27me3-related repression. The simultaneous inactivation of LHP1 homoeologs by CRISPR–Cas9 confers robust stripe rust resistance in wheat seedlings. The conditional repression of subgenome-diversified defenses ensures developmental plasticity to external changes, while also promoting neutral-to-non-neutral selection transitions and adaptive evolution. These findings establish an LHP1-mediated buffering system at the intersection of genotypes, environments, and phenotypes in polyploid wheat. Manipulating the epigenetic buffering capacity offers a tool to harness cryptic subgenomic variations for crop improvement.

Abstract Transposable elements (TEs) comprise ~85% of the common wheat genome, which are highly diverse among subgenomes, possibly contribute to polyploid plasticity, but the causality is only assumed. Here, by integrating data from gene expression cap analysis and epigenome profiling via hidden Markov model in common wheat, we detect a large proportion of enhancer-like elements (ELEs) derived from TEs producing nascent noncoding transcripts, namely ELE-RNAs, which are well indicative of the regulatory activity of ELEs. Quantifying ELE-RNA transcriptome across typical developmental stages reveals that TE-initiated ELE-RNAs are mainly from RLG_famc7.3 specifically expanded in subgenome A. Acquisition of spike-specific transcription factor binding likely confers spike-specific expression of RLG_famc7.3-initiated ELE-RNAs. Knockdown of RLG_famc7.3-initiated ELE-RNAs resulted in global downregulation of spike-specific genes and abnormal spike development. These findings link TE expansion to regulatory specificity and polyploid developmental plasticity, highlighting the functional impact of TE-driven regulatory innovation on polyploid evolution.

Abstract This paper proposes a wheeled mobile robot (WMR) robust control scheme. The feasible strategy is developed to achieve an efficient and robust autonomous mobile robot motion. To realize kinematic autonomous planning and control of the WMR, a novel controller is designed based on control Lyapunov function. This part can be divided into the following two aspects: 1) considering the nonholonomic constraints in the autonomous mobile robot trajectory tracking, a dynamic feedback‐linearization is adopted by utilizing differential flatness‐based integrated control framework to achieve full‐state controllability; 2) to compensate the structured uncertainties and slippage disturbances related to the robot kinematic model, a robust controller is designed based on control Lyapunov function with quadratic programming. Such a strategy can achieve autonomous motion even with unknown slippage disturbances subject to various constraints. Moreover, the sufficient condition is also analyzed to ensure the WMR system exponential stability. The effectiveness and performance of the proposed method are verified by numerical simulation.

Abstract Recently, radiotherapy (RT) has entered a new realm of precision cancer therapy with the introduction of magnetic resonance (MR) imaging guided radiotherapy systems into the clinic. Nonetheless, identifying an optimized radiotherapy time window (ORTW) is still critical for the best therapeutic efficacy of RT. Here we describe pH and O2 dual-sensitive, perfluorooctylbromide (PFOB)-based and glycerol-weighted chemical exchange saturation transfer (CEST) nano-molecular imaging probes (Gly-PFOBs) with dual fluorine and hydrogen proton based CEST MR imaging properties (19F/1H-CEST). Oxygenated Gly-PFOBs ameliorate tumor hypoxia and improve O2-dependent radiotherapy. Moreover, the pH and O2 dual-sensitive properties of Gly-PFOBs could be quantitatively, spatially, and temporally monitored by 19F/1H-CEST imaging to optimize ORTW. In this study, we describe the CEST signal characteristics exhibited by the glycerol components of Gly-PFOBs. The pH and O2 dual-sensitive Gly-PFOBs with19F/1H-CEST MR dual-modality imaging properties, with superior therapeutic efficacy and biosafety, are employed for sensitive imaging-guided lung cancer RT, illustrating the potential of multi-functional imaging to noninvasively monitor and enhance RT-integrated effectiveness.

Abstract Thermal management is of great significance for human activities. Attaining thermal comfort not only requires thermal control of building's macroenvironment, but also additionally personal thermal regulation. Radiative cooling technologies are anticipated to effectively lower building energy utilization and provide optimal thermal comfort for individuals in hot weather. Nevertheless, these static and single‐purpose characteristics lack the ability to adjust to rapidly changing weather conditions, often leading to excessive cooling. To overcome this challenge, the emergence of dual‐mode smart flipping technologies has provided a pathway for dynamically adjusting the heating or cooling of objects in response to fluctuations in ambient temperature. First, the underlying principles of dual‐mode smart flipping are shown. Then the evolving materials and approaches of smart flipping are given an introduction to adapt to different environments under external stimuli, such as mechanical flipping, temperature, humidity, and so forth. Afterward, we present the recent applications of dual‐mode smart flipping materials and devices in personal thermal management, thermoelectric generation, energy‐saving buildings, and smart windows. Finally, the challenges and outlook of dual‐mode smart flipping are presented and future directions are identified.

Aiming at the problem of high line loss caused by the access station of distributed generation (DG), a two-stage coordinated line loss reduction model based on elephant herding optimization (EHO) and second-order cone programming (SOCP) was put forward by using static var generators (SVG) and energy storage system (ESS) to reduce the line loss from reactive and active power aspects, respectively. Firstly, based on the elephant herding optimization, the location model of distributed generation, SVG and ESS was constructed and determined. On this basis, a cooperative line loss reduction model is proposed, which takes into account multiple line loss reduction methods, such as SVG reactive power compensation device and energy storage. By properly dealing with power flow constraints of distributed generation, the influence of operation control mode of distributed generation on optimal distribution and line loss reduction is precisely explained. The constraint is transformed into a second-order cone programming problem by using second-order cone relaxation. In this way, the coordinated reduction of line loss is realized, and the goal of line loss minimization and voltage stability maximization is achieved. Finally, a practical station is taken as an example to verify the correctness of the proposed model and algorithm and the effectiveness of the proposed method.

In order to find the anti-freezing durability differences between concrete in the frequent natural freeze–thaw conditions in the northwest of Sichuan Province, China, and concrete in the rapid freeze–thaw conditions of the standard rapid method, the typical temperature and humidity of the northwest of Sichuan Province were simulated. The results showed that the average number of freeze–thaw cycles in the northwest of this province can reach up to 150 per year. The relative dynamic modulus of C30 ordinary concrete, which is 100% pre-saturated, still remained above 90% after 450 cycles in simulated environments. However, during the rapid freeze–thaw test, even the C30 air-entrained concrete failed after 425 cycles. Compared to the saturation degree of concrete itself, the continuous replenishment of external moisture during freeze–thaw cycles is a key factor affecting the frost resistance of concrete. Rapid freeze–thaw reduces the number of the most probable pore sizes in ordinary concrete, and the pore size distribution curve tends to flatten. The reduction rate of the surface porosity of air-entrained concrete before and after rapid freeze–thaw is only about one third of that of ordinary concrete.

Self-supervised learning has made significant progress in point cloud processing. Currently, the primary tasks of self-supervised learning, which include point cloud reconstruction and representation learning, are trained separately due to their structural differences. This separation inevitably leads to increased training costs and neglects the potential for mutual assistance between tasks. In this paper, a self-supervised method named PointUR-RL is introduced, which integrates point cloud reconstruction and representation learning. The method features two key components: a variable masked autoencoder (VMAE) and contrastive learning (CL). The VMAE is capable of processing input point cloud blocks with varying masking ratios, ensuring seamless adaptation to both tasks. Furthermore, CL is utilized to enhance the representation learning capabilities and improve the separability of the learned representations. Experimental results confirm the effectiveness of the method in training and its strong generalization ability for downstream tasks. Notably, high-accuracy classification and high-quality reconstruction have been achieved with the public datasets ModelNet and ShapeNet, with competitive results also obtained with the ScanObjectNN real-world dataset.

The intricate composition of wastewater impedes the recycling of agricultural and industrial effluents. This study aims to investigate the potential of sisal leaf wastewater (SLW), both water-treated (WTSLW) and alkali-treated (ATSLW), as a substitute for the alkali activator (NaOH solution) in the production of slag-powder- and fly-ash-based composites, with a focus on the effects of WTSLW substitution ratios and sisal leaf soaking durations. Initially, the fresh properties were assessed including electrical conductivity and fluidity. A further analysis was conducted on the influence of both WTSLW and ATSLW on drying shrinkage, density, and mechanical strength, including flexural and compressive measures. Microstructural features were characterized using SEM and CT imaging, while XRD patterns and FTIR spectra were employed to dissect the influence of WTSLW substitution on the composite’s products. The results show that incorporating 14 wt% WTSLW into the composite enhances 90-day flexural and compressive strengths by 34.8% and 13.2%, respectively, while WTSLW curtails drying shrinkage. Conversely, ATSLW increases porosity and decreases density. Organic constituents in both WTSLW and ATSLW encapsulated in the alkaline matrix fail to modify the composites’ chemical composition. These outcomes underscore the potential for sustainable construction materials through the integrated recycling of plant wastewater and solid by-products.

The mechanical response of the marine reef sand–geogrid (RG) interface can be influenced by a high-temperature climate, grain size, and variable stress environments. These factors are critical to the effectiveness of geogrid reinforcement in reef sand engineering. However, there are few studies on the influences of grain size, temperature, and stress history on the mechanical characteristics of RG interfaces, with most studies centering on the influence of single factors on the mechanical characteristics of RG interfaces. In this paper, based on self-developed temperature-controlled large interface shear equipment, a series of before/post-cyclic shear tests were carried out on RG interfaces in the temperature range of 5–80 °C. The impact of different reef sand grain sizes on the RG interface was explored (S1: 1–2 mm; S2: 2–4 mm). It was shown that temperature and grain size had significant influences on the mechanical characteristics of the RS interface. Compared with the S1 RG interfaces, the S2 RG interfaces had higher sensitivity to temperature changes with respect to the before/post-cyclic maximum shear strength. Moreover, in comparison to the before-cyclic shear strength, the post-cyclic maximum shear strength is more responsive to temperature changes. The before/post-cyclic maximum shear strength of the S2 RG interfaces was greater than the maximum shear strength of the S1 RG interfaces as the temperature changed. Based on the results of physical tests, a machine learning model containing 450 datasets was constructed, which can accurately predict the shear strength of the RG interface.

Soil visible and near–infrared reflectance spectroscopy is an effective tool for the rapid estimation of soil organic carbon (SOC). The development of spectroscopic technology has increased the application of spectral libraries for SOC research. However, the direct application of spectral libraries for SOC prediction remains challenging due to the high variability in soil types and soil–forming factors. This study aims to address this challenge by improving SOC prediction accuracy through spectral classification. We utilized the European Land Use and Cover Area frame Survey (LUCAS) large–scale spectral library and employed a geographically weighted principal component analysis (GWPCA) combined with a fuzzy c–means (FCM) clustering algorithm to classify the spectra. Subsequently, we used partial least squares regression (PLSR) and the Cubist model for SOC prediction. Additionally, we classified the soil data by land cover types and compared the classification prediction results with those obtained from spectral classification. The results showed that (1) the GWPCA–FCM–Cubist model yielded the best predictions, with an average accuracy of R2 = 0.83 and RPIQ = 2.95, representing improvements of 10.33% and 18.00% in R2 and RPIQ, respectively, compared to unclassified full sample modeling. (2) The accuracy of spectral classification modeling based on GWPCA–FCM was significantly superior to that of land cover type classification modeling. Specifically, there was a 7.64% and 14.22% improvement in R2 and RPIQ, respectively, under PLSR, and a 13.36% and 29.10% improvement in R2 and RPIQ, respectively, under Cubist. (3) Overall, the prediction accuracy of Cubist models was better than that of PLSR models. These findings indicate that the application of GWPCA and FCM clustering in conjunction with the Cubist modeling technique can significantly enhance the prediction accuracy of SOC from large–scale spectral libraries.

The species of the adaptive male–female sex ratio has different effects on the bio-community. This paper is aimed at figuring out these effects through mechanism analysis and Ordinary Differential Equation (ODE). Hence, the ODE environmental model is created by combining the Lotka–Volterra model, the interspecific model, and other external factors. The stability is used to characterize these effects. According to this model, effects on bio-community stability under different male–female sex ratios are roughly observed. By innovatively considering different living environments during the species’ lifecycle, the ODE environmental model is optimized, and the effects of different male–female sex ratios on the bio-community are further analyzed by phase-track maps and relative standard deviation. It is found that there are different findings and features in resource-rich and resource-scarce living environments during the lifecycle. Meanwhile, bio-communities in these two types of environments are in a stable state based on different male–female sex ratios. Based on these findings, directive opinions can be used to manage and help relevant bio-communities.

Hepatitis B surface antigen (HBsAg) is not only the biomarker of hepatitis B virus (HBV) infection and expression activity in hepatocytes, but it also contributes to viral specific T cell exhaustion and HBV persistent infection. Therefore, anti-HBV therapies targeting HBsAg to achieve HBsAg loss are key approaches for an HBV functional cure. In this study, we found that YZH-106, a rupestonic acid derivative, inhibited HBsAg secretion and viral replication. Further investigation demonstrated that YZH-106 promoted the lysosomal degradation of viral L- and M-HBs proteins. A mechanistic study using Biacore and docking analysis revealed that YZH-106 bound directly to the PreS2 domain of L- and M-HBsAg, thereby blocking their entry into the endoplasmic reticulum (ER) and promoting their degradation in cytoplasm. Our work thereby provides the basis for the design of a novel compound therapy to target HBsAg against HBV infection.

Infrared images are susceptible to interference from a variety of factors during acquisition and transmission, resulting in the inclusion of mixed noise, which seriously affects the accuracy of subsequent vision tasks. To solve this problem, we designed a mixed noise removal algorithm for infrared images based on improved CycleGAN. First, we proposed a ResNet-E Block that incorporates the EMA (Efficient Multi-Scale Attention Module) and build a generator based on it using the skip-connection structure to improve the network’s ability to remove mixed noise of different strengths. Second, we added the PSNR (Peak Signal-to-Noise Ratio) as an extra calculation item of cycle consistency loss, so that the network can effectively retain the detailed information of infrared images while denoising. Finally, we conducted experimental validation on both synthetic noisy images and real noisy images, which proved that our algorithm can effectively remove the mixed noise in infrared images and the denoising effect is better than other similar methods.

Natural bone is a complex material that has been carefully designed. To prepare a successful bone substitute, two challenging conditions need to be met: biocompatible and bioactive materials for cell proliferation and differentiation, and appropriate mechanical stability after implantation. Therefore, a hybrid Poly ε-caprolactone/Poly(lactic-co-glycolide)/β-tricalcium phosphate (PCL/PLGA/β-TCP) scaffold has been introduced as a suitable composition that satisfies the above two conditions. The blended PCL and PLGA can improve the scaffold’s mechanical properties and biocompatibility compared to single PCL or PLGA scaffolds. In addition, the incorporated β-TCP increases the mechanical strength and osteogenic potential of PCL/PLGA scaffolds, while the polymer improves the mechanical stability of ceramic scaffolds. The PCL/PLGA/β-TCP scaffold is designed using spiral structures to provide a much better transport system through the gaps between spiral walls than conventional cylindrical scaffolds. Human fetal osteoblasts (hFOBs) were cultured on spiral PCL/PLGA/β-TCP (PPBS), cylindrical PCL/PLGA/β-TCP (PPBC), and cylindrical PCL scaffolds for a total of 28 days. The cell proliferation, viability, and osteogenic differentiation capabilities were analyzed. Compared with PCL and PPBC scaffolds, the PPBS scaffold exhibits great biocompatibility and potential to stimulate cell proliferation and differentiation and, therefore, can serve as a bone substitute for bone tissue regeneration.

With the rapid development of urban air traffic, Unmanned Aerial Vehicles (UAVs) are gradually being widely used in cities. Since UAVs are prohibited over important places in Urban Air Mobility (UAM), such as government and airports, it is important to develop air–ground non-cooperative UAV surveillance for air security all day and night. In the paper, an all-time UAV detection algorithm based on visible images during the day and infrared images at night is proposed by our team. We construct a UAV dataset used in urban visible backgrounds (UAV–visible) and a UAV dataset used in urban infrared backgrounds (UAV–infrared). In the daytime, the visible images are less accurate for UAV detection in foggy environments; therefore, we incorporate a defogging algorithm with the detection network that can ensure the undistorted output of images for UAV detection based on the realization of defogging. At night, infrared images have the characteristics of a low-resolution, unclear object contour, and complex image background. We integrate the attention and the transformation of space feature maps into depth feature maps to detect small UAVs in images. The all-time detection algorithm is trained separately on these two datasets, which can achieve 96.3% and 94.7% mAP50 on the UAV–visible and UAV–infrared datasets and perform real-time object detection with an inference speed of 40.16 FPS and 28.57 FPS, respectively.

Multimode interference (MMI) couplers based on silicon slot-waveguide structures have received widespread attention in recent years. The key issues that need to be addressed are the size and loss of such devices. This study introduces a 1 × 3 silicon-based slot-waveguide multimode interference power splitter. The device uses a gallium-nitride slot-waveguide structure to reduce the length of the coupling region and decrease additional losses. To reduce the width of the coupling region, the multimode interference coupling area is designed with a parabolic-shaped structure. The introduction of a tapered structure between the input/output waveguides and the coupling region improves additional losses and non-uniformity. Furthermore, we conducted an analysis of the fabrication tolerances of the coupling region. In this paper, we use mode solution to simulate the design of the device in the 1550 nm optical wavelength range. The eigenmode expansion method is used to simulate and optimize the parameters of the device. The device is simulated using the eigenmode expansion solver. The simulation results show that the total length of the coupling region for the device is only 4 μm. The normalized transmission of the device is 0.992, and its excess loss and imbalance are 0.036 dB and 0.003 dB, respectively. The proposed power splitter can be applied to integrated optical circuit design, optical sensing, and optical power measurement.

Potential inconsistencies between the goals of unsupervised representation learning and clustering within multi-stage deep clustering can diminish the effectiveness of these techniques. However, because the goal of unsupervised representation learning is inherently flexible and can be tailored to clustering, we introduce PointStaClu, a novel single-stage point cloud clustering method. This method employs stable cluster discrimination (StaClu) to tackle the inherent instability present in single-stage deep clustering training. It achieves this by constraining the gradient descent updates for negative instances within the cross-entropy loss function, and by updating the cluster centers using the same loss function. Furthermore, we integrate entropy constraints to regulate the distribution entropy of the dataset, thereby enhancing the cluster allocation. Our framework simplifies the process, employing a single loss function and an encoder for deep point cloud clustering. Extensive experiments on the ModelNet40 and ShapeNet dataset demonstrate that PointStaClu significantly narrows the performance gap between unsupervised point cloud clustering and supervised point cloud classification, presenting a novel approach to point cloud classification tasks.

Fiber-reinforced polymer (FRP) laminates are popular in the strengthening of concrete structures, but the durability of the strengthened structures is of great concern. Due to the susceptibility of the epoxy resin used for bonding and the deterioration of materials, the bond performance of the FRP–concrete interface could be degraded due to environmental exposure. This paper aimed to establish a data-driven method for bond strength prediction using existing test results. Therefore, a method composed of a Back Prorogation Net (BPNN) and Meta-learning Net was proposed, which can be used to solve the implicit regression problems in few-shot learning and can obtain the deteriorated bond strength and the impact weight of each parameter. First, the pretraining database Meta1, a database of material strength degradation, was established from the existing results and used in the meta-learning network. Then, the database Meta2 was built and used in the meta-learning network for model fine-tuning. Finally, combining all prior knowledge, not only the degradation of the FRP–concrete bond’s strength was predicted, but the respective weights of the environment parameters were also obtained. This method can accurately predict the degradation of the bond performance of FRP–concrete interfaces in complex environments, thus facilitating the further assessment of the remaining service life of FRP-reinforced structures.

Integrated energy systems (IESs) seek to minimize power generating costs in future power grids through the coupling of different energy technologies. To accommodate fluctuations in load demand due to the penetration of renewable energy sources, flexible operation capabilities must be fully exploited, and even power plants that are traditionally considered as base-load units need to be operated according to unconventional paradigms. Thermomechanical loads induced by frequent power adjustments can accelerate the wear and tear. If a unit is flexibly operated without respecting limits on materials, the risk of failures of expensive components will eventually increase, nullifying the additional profits ensured by flexible operation. In addition to the bounds on power variations (explicit constraints),the solution of the unit dispatch problem needs to meet the limits on the variation of key process variables, including temperature, pressure and flow rate (implicit constraints).The FARM (Feasible Actuator Range Modifier) module was developed to enable existing optimization algorithms to identify solutions to the unit dispatch problem that are both economically favorable and technologically sustainable. Thanks to the iterative dispatcher–validator scheme, FARM permits addressing all the imposed constraints without excessively increasing the computational costs. In this work, the algorithms constituting the module are described, and the performance was assessed by solving the unit dispatch problem for an IES composed of three units, i.e., balance of plant, gas turbine, and high-temperature steam electrolysis. Finally, the FARM module provides dedicated tools for visualizing the response of the constrained variables of interest during operational transients and a tool aiding the operator at making decisions. These techniques might represent the first step towards the deployment of an ecological interface design (EID) for IES units.

In the design of offshore engineering foundations, a critical consideration involves determining the peak shear strength of marine soft clay sediment. To enhance the accuracy of estimating this value, a database containing 729 direct shear tests on marine soft clay sediment was established. Employing a machine learning approach, the Particle Swarm Optimization algorithm (PSO) was integrated with the Adaptive Boosting Algorithm (ADA) and Back Propagation Artificial Neural Network (BPANN). This novel methodology represents the initial effort to employ such a model for predicting the peak shear strength of the soil. To validate the proposed approach, four conventional machine learning algorithms were also developed as references, including PSO-optimized BPANN, Support Vector Machine (SVM), BPANN, and ADA-BPANN. The study results show that the PSO-BPANN model, which has undergone optimization via Particle Swarm Optimization (PSO), has prediction accuracy and efficiency in determining the peak shear performance of marine soft clay sediments that surpass that offered by traditional machine learning models. Additionally, a sensitivity analysis conducted with this innovative model highlights the notable impact of factors such as normal stress, initial soil density, the number of drying–wetting cycles, and average soil particle size on the peak shear strength of this type of sediment, while the impact of initial soil moisture content and temperature is comparatively minor. Finally, an analytical formula derived from the novel algorithm allows for precise estimation of the peak shear strength of marine soft clay sediment, catering to individuals lacking a background in machine learning.

Accurately modeling and predicting epidemic diseases is crucial to prevent disease transmission and reduce mortality. Due to various unpredictable factors, including population migration, vaccination, control efforts, and seasonal fluctuations, traditional epidemic models that rely on prior knowledge of virus transmission mechanisms may not be sufficient to forecast complex epidemics like coronavirus disease 2019(COVID-19). The application of traditional epidemiological models such as susceptible-exposed-infectious-recovered (SEIR) may face difficulties in accurately predicting such complex epidemics. Data-driven prediction approaches lack the ability to generalize and exhibit low accuracy on small datasets due to their reliance on large amounts of data without incorporating prior knowledge. To overcome this limitation, we introduce a flexible ensemble data-driven framework (Neural-SEIR) that "neuralizes" the SEIR model by approximating the core parameters through neural networks while preserving the propagation structure of SEIR. Neural-SEIR employs long short-term memory (LSTM) neural network to capture complex correlation features, exponential smoothing (ES) to model seasonal information, and prior knowledge from SEIR. By incorporating SEIR parameters into the neural network structure, Neural-SEIR leverages prior knowledge while updating parameters with real-world data. Our experimental results demonstrate that Neural-SEIR outperforms traditional machine learning and epidemiological models, achieving high prediction accuracy and efficiency in forecasting epidemic diseases.

Abstract Since its discovery in 2019, coronavirus disease 2019 (COVID-2019) spans a wide clinical spectrum from the asymptomatic stage, mild infection, to severe pneumonia. In patients with COVID-2019, factors such as advanced age, diabetes, or hypertension are associated with a significantly increased risk of severe diseases and death. Of note, the mechanisms underlying differences in the risk and symptoms of COVID-2019 among different populations are still poorly characterized. Accordingly, it is imperative to elucidate potential pathophysiological mechanisms and develop targeted therapeutic approaches for COVID-2019 infection. N6-methyladenosine (m6A) is one of the most common modifications in mammalian RNA transcripts and is widely found in messenger RNAs and some non-coding RNAs. It has been reported that m6A methylation modifications are present in viral RNA transcripts, which are of great significance for the regulation of the viral life cycle. Furthermore, m6A methylation has recently been found to be strongly associated with COVID-2019 infection. Therefore, this article reviews recent advances in studies related to the role of m6A methylation in COVID-2019 infection.

Abstract. Background:. Previous studies have revealed that the number of cancer survivors developing a second primary malignancy is increasing, especially among thyroid cancer patients, and lung cancer is still the main cause of cancer death. Therefore, we aimed to investigate the risk of second primary lung cancer (SPLC) in patients with thyroid cancer. Methods:. We searched the PubMed, Web of Science, Embase, and Scopus databases up to November 24, 2021, for relevant research and merged the standardized incidence ratios (SIRs) and 95% confidence intervals (95% CIs) to evaluate the risk of developing SPLC in patients with thyroid cancer. Results:. Fourteen studies involving 1,480,816 cases were included in our meta-analysis. The pooled result demonstrated that thyroid cancer patients may have a higher risk of SPLC than the general population (SIR = 1.21, 95% CI: 1.07–1.36, P

Abstract The incidence of bladder cancer (BLCA) in men is higher than that in women. Differences in androgen levels between men and women are considered the main causes of incidence rate differences. In this study, dihydrotestosterone (DHT) significantly increased the proliferation and invasion of BLCA cells. In addition, BLCA formation and metastatic rates were higher in N-butyl-N-(4-hydroxybutyl) nitrosamine (BBN)-treated male mice than in female and castrated male mice in vivo. However, immunohistochemistry showed that androgen receptor (AR) was expressed at low levels in normal and BLCA tissues of men and women. The classical AR pathway considers that DHT binds to AR and induces it to enter the nucleus, where it functions as a transcription factor. Here, a non-AR combination pathway of androgen that promoted BLCA development was investigated. The EPPK1 protein was bombarded with DHT, as determined by biotinylated DHT-binding pull-down experiments. EPPK1 was highly expressed in BLCA tissues, and EPPK1 knockdown significantly inhibited BLCA cell proliferation and invasion promoted by DHT. Moreover, JUP expression was elevated in DHT-treated high-EPPK1 expressing cells, and JUP knockdown inhibited cell proliferation and invasion. EPPK1 overexpression increased tumour growth and JUP expression in nude mice. Furthermore, DHT increased the expression of the MAPK signals p38, p-p38, and c-Jun, and c-Jun could bind to the JUP promoter. However, the promotion of p38, p-p38, and c-Jun expression by DHT was not observed in EPPK1 knockdown cells, and a p38 inhibitor suppressed the DHT-induced effects, indicating that p38 MAPK may be involved in the regulation of DHT-dependent EPPK1-JUP-promoted BLCA cell proliferation and invasion. The growth of bladder tumours in BBN-treated mice was inhibited by the addition of the hormone inhibitor goserelin. Our findings indicated the potential oncogenic role and mechanism of DHT in BLCA pathogenesis through a non-AR pathway, which may serve as a novel therapeutic target for BLCA.

The imbalance of the lateral shoulder is reflected by the clavicle angle (CA) in radiology. It remains unclear how to achieve postoperative lateral shoulder balance (LSB) after spinal deformity correction surgery. A retrospective analysis was conducted on AIS patients who underwent surgery by the same spine surgeon at our hospital from 2016 to 2020. A total of 110 patients with spinal deformity were included in the study to verify the correlation between the T1–T5 tilt angle and CA before and after surgery, as well as the relation-ship between the change in T1–T5 tilt angle before and after surgery and the change in CA before and after surgery. By comparing the correlation coefficients, it was found that there may not be a direct relationship between the pre- and postoperative tilt angles of T1–5 and CA, but their changes were closely related to the changes in CA. The change in T1 tilt angle after orthopaedic surgery was significantly correlated with the change in CA, with a correlation coefficient of 0.976, indicating a close relationship between T1 and the clavicle. As the vertebrae moved down, the correlation gradually decreased. In summary, this study suggests that there is a close relationship between T1–T5 and the clavicle and that the change in T1 tilt angle after spinal scoliosis correction surgery is significantly correlated with CA, which decreases as the vertebra moves down.

Summary: In the mammalian cochlea, moderate acoustic overexposure leads to loss of ribbon-type synapse between the inner hair cell (IHC) and its postsynaptic spiral ganglion neuron (SGN), causing a reduced dynamic range of hearing but not a permanent threshold elevation. A prevailing view is that such ribbon loss (known as synaptopathy) selectively impacts the low-spontaneous-rate and high-threshold SGN fibers contacting predominantly the modiolar IHC face. However, the spatial pattern of synaptopathy remains scarcely characterized in the most sensitive mid-cochlear region, where two morphological subtypes of IHC with distinct ribbon size gradients coexist. Here, we used volume electron microscopy to investigate noise exposure-related changes in the mouse IHCs with and without ribbon loss. Our quantifications reveal that IHC subtypes differ in the worst-hit area of synaptopathy. Moreover, we show relative enrichment of mitochondria in the surviving SGN terminals, providing key experimental evidence for the long-proposed role of SGN-terminal mitochondria in synaptic vulnerability.

Layered Li-rich oxides have attracted much attention because of higher capacity than that of traditional layered oxides (more than 250 mAh g−1). However, the intrinsic issues of Li-rich cathode materials suffer from lattice oxygen loss, poor rate capability, voltage fade, and limited cycle life. To tackle these problems, the Li-rich cathode containing intergrown layer and spinel phases was proposed, and this heterostructure material meets the requirements of high energy and stable surface with a fast Li+ diffusion channel. Herein, we review the recent progress and in-depth understanding about heterostructure including microstructure and morphology, performance of advancement and degradation mechanisms, and modification strategies. Special attention is given to the high-performance energy mechanism as follows: (a) spinel phase and oxygen vacancy jointly enhance the lattice structure and prevent the irreversible oxygen release, (b) higher capacity is achieved by promotion of activation of Li2MnO3 phase and control of the activation rate to realize stable long-term cyclability, and (c) spinel phase provides the 3D interconnected Li+ diffusion channels and protects the surface region from side reactions. The other issue that aroused interest is the undesirable changes of phase transition and degradation mechanisms as follows: (a) the key reconstruction process is to produce a “good” spinel to maintain the surface and interior structure stability. (b) It is significant to figure out the structure degradation and phase transition mechanism in the cycled heterostructure. This review aims to provide inspiration and opportunities for the design of high-energy-density cathode materials, thereby bridging the gap between laboratory research and practical battery applications.

Originally designed for medical segmentation, the U-Net model excels in ocean remote sensing for segmentation, forecasting, and image enhancement. We propose enhancements like attention mechanisms, knowledge-data integration, and diffusion models to improve small target detection, ocean phenomena forecasting, and image super-resolution, expanding U-Net’s application and support in oceanographic research.

Background. Coronary artery disease (CAD) is a type of cardiovascular disease which is one of the leading causes of death around the world. The presence of coronary stenosis is considered a pivotal indicator in the diagnosis of various CADs. The main purpose of this paper was to investigate the feasibility of an anchor-free deep learning (DL) method, fully convolutional one-stage object detection (FCOS), in coronary artery stenosis automatic detection. Methods. First, 2786 invasive coronary angiography (ICA) images from 130 patients were randomly divided into training, validation, and testing datasets using the 10-fold cross-validation approach. Then, FCOS was compared with other three widely used anchor-based DL models: single shot multibox detector (SSD), faster region-based convolutional network (Faster R-CNN), and you only look once (YOLOv3), in terms of precision, recall, F1 score, average precision (AP), and average recall (AR). Finally, the performances of different models in the detection of stenosis were compared in either single or multiple lesion scenarios using statistical tests. Results. FCOS achieved significantly superior precision (96.14% ± 0.53%), recall (94.36% ± 0.79%), F1 score (95.22% ± 0.56%), AP0.50 (93.36% ± 0.93%), AR0.50:0.95 (64.73% ± 1.46%), APsmall (55.04 ± 0.96%), APmedium (59.97 ± 1.13%), and APlarge (68.09 ± 5.18%) compared to Faster R-CNN and YOLOv3. Moreover, FCOS demonstrated significantly higher AR0.50:0.95 and APsmall compared to SSD. Regardless of the presence of single or multiple coronary stenoses in ICA images, FCOS also outperformed Faster R-CNN and YOLOv3. Furthermore, it showed significantly higher AR0.50:0.95 compared to SSD when in the multiple stenosis scenario. Conclusions. It is feasible to use the anchor-free DL model FCOS in detecting coronary stenosis based on ICA images.

The short-term prediction of single well production can provide direct data support for timely guiding the optimization and adjustment of oil well production parameters and studying and judging oil well production conditions. In view of the coupling effect of complex factors on the daily output of a single well, a short-term prediction method based on a multi-agent hybrid model is proposed, and a short-term prediction process of single well output is constructed. First, CEEMDAN method is used to decompose and reconstruct the original data set, and the sliding window method is used to compose the data set with the obtained components. Features of components by decomposition are described as feature vectors based on values of fuzzy entropy and autocorrelation coefficient, through which those components are divided into two groups using cluster algorithm for prediction with two sub models. Optimized online sequential extreme learning machine and the deep learning model based on encoder-decoder structure using self-attention are developed as sub models to predict the grouped data, and the final predicted production comes from the sum of prediction values by sub models. The validity of this method for short-term production prediction of single well daily oil production is verified. The statistical value of data deviation and statistical test methods are introduced as the basis for comparative evaluation, and comparative models are used as the reference model to evaluate the prediction effect of the above multi-agent hybrid model. Results indicated that the proposed hybrid model has performed better with MAE value of 0.0935, 0.0694 and 0.0593 in three cases, respectively. By comparison, the short-term prediction method of single well production based on multi-agent hybrid model has considerably improved the statistical value of prediction deviation of selected oil well data in different periods. Through statistical test, the multi-agent hybrid model is superior to the comparative models. Therefore, the short-term prediction method of single well production based on a multi-agent hybrid model can effectively optimize oilfield production parameters and study and judge oil well production conditions.

It is crucial to assess the impact of climate change on crop productivity and sustainability for the development of effective adaptation measures. Crop models are essential for quantifying this impact on crop yields. To better express crops’ intrinsic growth and development patterns and their plasticity under different environmental conditions, the functional–structural plant model (FSPM) “GreenLab” has been developed. GreenLab is an organ-level model that can describe the intrinsic growth and development patterns of plants based on mathematical expressions without considering the influence of environmental factors, and then simulate the growth and development of plants in expressing plant plasticity under different environmental conditions. Moreover, the distinctive feature of GreenLab lies in its ability to compute model source–sink parameters affecting biomass production and allocation based on measured plant data. Over the past two decades, the GreenLab model has undergone continuous development, incorporating novel modeling methods and techniques, including the dual-scale automaton, substructure methods, the inverse of source–sink parameters, crown analysis, organic series, potential structure, and parameter optimization techniques. This paper reviews the development history, the basic concepts, main theories, characteristics, and applications of the GreenLab model. Additionally, we introduce the software tools that implement the GreenLab model. Last, we discuss the perspectives and directions for the GreenLab model’s future development.

Intussusception refers to the invagination of a proximal loop of the bowel into an adjacent distal segment. This condition is rare in adults, especially when it involves a complete folding of the ileocecal area out of the body cavity. Meanwhile, enterogenous cysts are congenital malformations that are largely identified in childhood following symptoms of bowel obstruction. While surgical treatment is ultimately required for both diseases, deciding on the type of surgery and the right time to operate can be a challenge for clinicians. It is especially difficult to decide on treatment for an adult with the coincidental occurrence of both conditions and no definitive pathologic diagnosis prior to surgery. Here, we present the case study of a 19-year-old female patient who presented with a prolapsed anus due to intussusception caused by a large ileocecal mass. The patient was admitted to the emergency department with a “massive anal mass.” She remained symptomatic after receiving conventional conservative treatment and had to undergo emergency surgery after developing an intestinal obstruction. While the patient’s intraoperative condition also confirmed the preoperative CT findings, the situation became more complicated during surgery. The postoperative pathological report indicated the presence of an enterogenous cyst. After recovery from surgery, the patient was successfully discharged. Intussusception or intestinal obstruction caused by an intestinal mass is a surgical indication, and removal is the only way to cure the condition. This case study provides a helpful reference for general surgeons, especially anorectal surgeons, imaging physicians, and pathologists, and informs the diagnosis and treatment of this patient population.

Aircraft anti-icing and de-icing, aimed at reducing energy consumption, is a compelling technology that enables a plethora of eco-friendly energy applications to overcome long-standing crisis challenges. In this study, we present a tri-conversion strategy, inspired by superhydrophobicity, photothermal conversion, and electrothermal conversion, to develop an all-weather, high-efficiency, and low-energy rotor leading edge anti-icing and de-icing patch. The experimental samples not only possess stable superhydrophobic photothermal conversion capabilities but also achieve a 610-second delay in droplet icing, a 1-second nitrogen blow for frost, and a low ice adhesion strength of 11.6 KPa. Under one sun illumination, it can achieve a stable 54-second static melt and 37-second dynamic melt. With a connection to a 24 V DC stabilized power supply, it showcases an excellent 146-second de-icing capability. The synergistic action of multiple barriers enhances the anti-icing and de-icing performance. This work not only provides rational design principles for high-energy consumption de-icing but also offers insights into harnessing the power of nature for photothermal conversion de-icing.

Selective withdrawal from reservoirs is an effective strategy for balancing economic goals and environmental demands. Multilevel intake operation (MIO) is one of the main options for improving the thermal regime of deep reservoirs. In this study, a combination of measured data and a two-dimensional hydrodynamic model (CE-QUAL-W2) was utilized to analyze the impact of MIO processes on the thermal stability of a reservoir and the improvement effect of the withdrawal water temperature (WWT). The purpose of this study was to identify an optimal selective withdrawal strategy to meet the water temperature needs of fish downstream during their spawning and breeding activities. The results showed that the MIOs raised the withdrawal water intake position (8.3–11.2 m) for the WWT. Meanwhile, MIOs resulted in statistically significant changes in the water temperature structure of the reservoir (p

Abstract Full‐color 3D printing technology has become more and more popular for industrial manufacturing applications. The voxelization of color 3D models and the expression of appearance colors become more and more important. To illustrate the complex appearance and internal characteristics of 3D models, we present a voxelization algorithm of the color 3D models for color 3D printing in this paper. Specifically, we use the 3D model of 3D Manufacturing Format (3MF) with color appearance features for surface voxelization first. Then, we propose an improved scan line filling algorithm to realize the voxelization of the interior of the model and obtain the color voxel model. Finally, to improve the high quality of color printing, we propose a surface color inward diffusion algorithm, which can make the surface color of the voxel model diffuse inward and further express the surface color of the voxel model. The experimental results show our proposed method can obtain an effective color voxel model and accurately express the color of the voxel model surface, which can enhance the color effect of the color model and realize the control of voxels.

Aims/hypothesis: The association between gastroesophageal reflux disease (GERD) and rheumatoid arthritis (RA) has been reported by many observational studies in the Asian population. This study aimed to examine the bidirectional causal effects between GERD and RA by two-sample Mendelian randomization (MR) analyses using genetic evidence.Methods: Two-sample Mendelian randomization analyses were performed to determine the causal effect of GERD (129,080 cases vs. 602,604 control participants) on RA (6,236 cases vs. 147,221 control participants) and RA on GERD, respectively. The inverse-variance weighted (IVW) method was used as the primary analysis. Weighted median and MR-Egger regression were taken as supplementary analyses. Cochran’s Q test evaluated the heterogeneity. Horizontal pleiotropy was detected by estimating the intercept term of MR-Egger regression. Furthermore, multivariable MR analyses were performed to exclude the influence of confounding factors, including the years of schooling, BMI, and time spent watching television, between GERD and RA.Result: Both univariate MR (UVMR) and multivariable MR (MVMR) provided valid evidence that RA was causally and positively influenced by GERD (UVMR: OR = 1.49, 95% CI = 1.25–1.76, p = 6.18*10−6; MVMR: OR = 1.69, 95% CI = 1.24–2.31, p = 8.62*10−4), whereas GERD was not influenced by RA (UVMR: OR = 1.03, 95% CI = 1.00–1.06, p = 0.042; MVMR: OR = 1.04, 95% CI = 1.00–1.07, p = 0.0271).Conclusion: Our comprehensive bidirectional MR analysis found that for the European population, GERD can induce the occurrence of RA (OR = 1.69, p < 0.00125), whereas RA only has no significant influence on GERD. In particular, patients with GERD are suffering a 69% increased risk of RA occurrence, which means GERD is a substantial risk factor for RA.

Abstract Surveillance is an intricate challenge worldwide especially in those complicated environments such as nuclear plants, banks, crowded areas, barns, etc. Deploying self‐powered wireless sensor nodes can increase the system's event detection capabilities by collecting environmental changes, while the incompatibility among components (energy harvesters, sensors, and wireless modules) limits their application. Here, a broadly compatible self‐powered visualized platform (SPVP) is reported to construct a passive internet of events (IoE) network for surveillance systems. By encoding electric signals into reference and working LEDs, SPVP can visualize resistance change generated by commercial resistive sensors with a broad working range (

Accurately spatiotemporal distribution of water bodies is of great importance in the fields of ecology and environment. Recently, convolutional neural networks (CNN) have been widely used for this purpose due to their powerful features extraction ability. However, the CNN methods have two limitations in extracting water bodies. First, the large variations in both the spatial and spectral characteristics of water bodies require that the CNN-based methods have the ability of extracting multi-scale features and using multi-layer features. Second, collecting enough samples is a difficult problem in the training phase of CNN. Therefore, this paper proposed a multi-scale features extraction network (MSFENet) for water extraction, and its advantages are contributed to two distinct features: (1) scale features extractor (MSFE) is designed to extract multi-layer multi-scale features of water bodies; (2) contrastive learning (CL) is adopted to reduce the sample size requirement. Experimental results show that MSFE can effectively improve the small water body extraction performance, and the CL can significantly improve the extraction accuracy when the training sample size is small. Compared with other methods, MSFENet achieves the highest F1-score and kappa coefficient in two datasets. Furthermore, spectral variability analysis shows that MSFENet is more robust than other neural networks in a spectrum variation scenario.

A large number of farms in developing countries are smallholder farms. In China, this trend is expected to persist for the foreseeable future. However, smallholder farmers in China face several challenges, including excessive fertilizer use for higher yields, low levels of education, aging and limited credit information. Moreover, they are struggling with a digital divide, which hampers their agricultural productivity and limits their economic and social integration. Agricultural services based on information and communication technology (ICT) can bridge the digital gap, but are hindered by complexity and cost. Aiming at serving the numerous smallholders and building a sustainable digital ecosystem, this paper presents a distributed agriculture service system that provides decision support to farmers by considering both social and physical information. The system is lightweight, low cost and intelligent, managing personnel, plots, crops and equipment using technologies such as wireless sensor networks, cloud computing and distributed systems architecture. Services include crop planning, production guidance, equipment control, etc. User terminals include computers, mobile applications and Applets. A case study on fruit production in a typical solar greenhouse illustrates the system's functionality. The distributed agricultural services system can well support smallholder farmers and promote sustainable agriculture.

ABSTRACTHaemophilus parasuis (H. parasuis) causes exudative inflammation, implying endothelial dysfunction during pathogen infection. However, so far, the molecular mechanism of endothelial dysfunction caused by H. parasuis has not been clarified. By using the transwell-based cell co-culture system, we demonstrate that knocking out resistin in porcine alveolar macrophages (PAMs) dramatically attenuated endothelial monolayer damage caused by H. parasuis. The resistin secreted by PAMs inhibited the expression of the tight junction proteins claudin-5 and occludin rather than the adherens junction protein VE-cadherin in co-cultured porcine aortic endothelial cells (PAECs). Furthermore, we demonstrate that resistin regulated claudin-5 and occludin expression and monolayer PAEC permeability in an LKB1/AMPK/mTOR pathway-dependent manner. Additionally, we reveal that the outer membrane lipoprotein gene lppA in H. parasuis induced resistin expression in PAMs, as deleting lppA reduced resistin expression in H. parasuis-infected PAMs, causing a significant change in LKB1/AMPK/mTOR pathway activity in co-cultured PAECs, thereby restoring tight junction protein levels and endothelial monolayer permeability. Thus, we postulate that the H. parasuis lppA gene enhances resistin production in PAMs, disrupting tight junctions in PAECs and causing endothelial barrier dysfunction. These findings elucidate the pathogenic mechanism of exudative inflammation caused by H. parasuis for the first time and provide a more profound angle of acute exudative inflammation caused by bacteria.

Abstract Background Spermatogenesis depends on the supporting of the Sertoli cells and their communications with germ cells. However, the regulation of crosstalk between the Sertoli cells and germ cells remains unclear. Results In this report, we used conditional knockout technology to generate the Sertoli cells-specific knockout of Rnf20 in mice. The Amh-Rnf20 −/− male mice were infertile owing to spermatogenic failure that mimic the Sertoli cell-only syndrome (SCOS) in humans. Knockout of Rnf20 resulted in the H2BK120ub loss in the Sertoli cells and impaired the transcription elongation of the Cldn11, a gene encoding a component of tight junction. Notably, RNF20 deficiency disrupted the cell adhesion, caused disorganization of the seminiferous tubules, and led to the apoptotic cell death of both spermatogonia and spermatocytes in the seminiferous tubules. Conclusions This study describes a Rnf20 knockout mouse model that recapitulates the Sertoli cell-only syndrome in humans and demonstrates that RNF20 is required for male fertility through regulation of H2B ubiquitination in the Sertoli cells.

The effect of the mixing method (normal and ethanol-assisted) on selected properties of natu-ral rubber with butadiene rubber blends (NR/BR 80/20) was investigated. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA) were used to evaluate the properties. In addition, the mechanical tensile properties, tear resistance and ther-mal aging, as well as vulcanization characteristic were determined. It was found that ethanol-assisted mixing can significantly improve the mechanical properties and thermal stability of the blends as are-sult of better dispersion of additives in the rubber matrix.

Abstract Background Prediabetes is common and associated with poor prognosis in patients with acute coronary syndrome and those undergoing revascularization. However, the impact of prediabetes on prognosis in patients with coronary intermediate lesions remains unclear. The objective of the current study is to explore the impact of prediabetes and compare the prognostic value of the different definitions of prediabetes in patients with coronary intermediate lesions. Methods A total of 1532 patients attending Fuwai hospital (Beijing, China), with intermediate angiographic coronary lesions, not undergoing revascularization, were followed-up from 2013 to 2021. Patients were classified as normal glucose tolerance (NGT), prediabetes and diabetes according to various definitions based on HbA1c or admission fasting plasma glucose (FPG). The primary endpoint was defined as major adverse cardiovascular events (MACE), the composite endpoint of all-cause death, non-fatal myocardial infarction and repeated revascularization therapy. Multivariate cox regression model was used to explore the association between categories of abnormal glucose category and MACE risk. Results The proportion of patients defined as prediabetes ranged from 3.92% to 47.06% depending on the definition used. A total of 197 MACE occurred during a median follow-up time of 6.1 years. Multivariate cox analysis showed that prediabetes according to the International Expert Committee (IEC) guideline (6.0 ≤ HbA1c