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Air pollution is a leading environmental health risk factor, and in situ toxicity assessment is urgently needed. Bacteria-based bioassays offer cost-effective and rapid toxicity assessments. However, the application of these bioassays for air toxicity assessment has been challenging, due to the instability of bacterial survival and functionality when directly exposed to air pollutants. Here, we developed an approach employing self-assembly passive colonization hydrogel (SAPCH) for in situ air toxicity assessment. The SAPCH features a core–shell structure, enabling the quantitatively immobilization of bacteria on its shell while continuously provides nutrients from its core. An antimicrobial polyelectrolyte layer between the core and shell confines bacteria to the air–liquid interface, synchronizing bacterial survival with exposure to air pollutants. The SAPCH immobilized a battery of natural and recombinant luminescent bacteria, enabling simultaneous detection of various toxicological endpoints (cytotoxicity, genotoxicity and oxidative stress) of air pollutants within 2 h. Its sensitivity was 3–5 orders of magnitude greater than that of traditional liquid-phase toxicity testing, and successfully evaluating the toxicity of volatile organic compounds and combustion smoke. This study presents a method for in situ, rapid, and economical toxicity assessment of air pollution, making a significant contribution to future air quality monitoring and control.

The generation of waste printed circuit boards (WPCBs) is increasing due to the global consumption of electronic devices. In this study, a novel approach has been proposed by employing pyrolysis oil derived from the pyrolysis of non-metallic particles (NMPs) as self-generated collector for WPCBs flotation, thereby improves the flotation effect of WPCBs, which not only circumventing the consumption of non-regenerative collectors but also offering a viable means for the clean utilization of non-metallic components (NMCs). The surface morphology and phase composition of WPCBs and NMPs were analyzed respectively. The SEM and XRD results indicate that the dissociation degree of NMPs is limited, thereby exacerbating the challenges associated with flotation of WPCBs. Additionally, FTIR analysis was conducted to investigate the adsorption mechanism between pyrolysis oil and NMPs. The results show that both NMPs and pyrolysis oil contain -CH3, CC diffraction peaks, which is conducive to the adsorption of pyrolysis oil. The floatability of NMPs before and after conditioned by pyrolysis oil, as well as the mechanism of particle-bubble interaction, were investigated through contact angle and wrap angle measurements. The results demonstrate a substantial increase of over 45° in the contact angle of NMPs. Additionally, the results show that the wrap angles of NMPs conditioned by pyrolysis oil also significantly increase. Flotation tests with pyrolysis oil were conducted on WPCBs and NMPs to validate the effectiveness of pyrolysis oil on flotation. The flotation results of WPCBs show that when 8 kg/t pyrolysis oil is added, the Cu grade for concentrate of 1–0.5, 0.5–0.25, 0.25–0.125, 0.125–0.074, −0.074 mm increases by 4.24 %, 10.26 %, 17.86 %, 17.20 %, and 22.21 %, respectively. This study presents a viable approach to address the challenges of achieving higher separation accuracy for WPCBs and optimizing resource utilization of NMPs.

In order to reduce the loss of energy in the process of conversion and utilization, and to improve the energy utilization effect and economic benefits of urban integrated energy system, a thermoelectricity optimization method of urban integrated energy system based on multi-objective double dynamic genetic algorithm is proposed. The method analyzes the operation characteristics of the urban integrated energy system through its operation model, constructs a multi-objective optimization model for the thermoelectricity of the urban integrated energy system, determines the optimization multi-objective function that minimizes the operation cost, minimizes the carbon emission, minimizes the technical dissatisfaction and the related constraints, and then solves the optimization model by using the dual dynamic genetic algorithm to output the results of the optimization of the thermoelectricity of the urban integrated energy system.The test results show that the algorithm has a crowding distance of 0.75 or above when solving, and the overall unit energy consumption cost reduction ratio is about 22.97 %. The state of charge results of the four energy storage power stations are all below 10 %, and the lowest convergence speed is only 7 seconds, effectively improving energy utilization efficiency.

Fungal corneal ulcer is one of the leading causes of corneal blindness in developing countries. Corneal scars such as leukoplakia are formed due to inflammation, oxidative stress and non-directed repair, which seriously affect the patients' subsequent visual and life quality. In this study, drawing inspiration from the oriented structure of collagen fibers within the corneal stroma, we first proposed the directional arrangement of CuTA-CMHT hydrogel system at micro and macro scales based on the 3D printing extrusion method combined with secondary patterning. It played an antifungal role and induced oriented repair in therapy of fungal corneal ulcer. The results showed that it effectively inhibited Candida albicans, Aspergillus Niger, Fusarium sapropelum, which mainly affects TNF, NF-kappa B, and HIF-1 signaling pathways, achieving effective antifungal functions. More importantly, the fibroblasts interacted with extracellular matrix (ECM) of corneal stroma through formation of focal adhesions, promoted the proliferation and directional migration of cells in vitro,induced the directional alignment of collagen fibers and corneal stromal orthogonally oriented repair in vivo. This process is mainly associated with MYLK, MYL9, and ITGA3 molecules. Furthermore, the downregulation the growth factors TGF-β and PDGF-β inhibits myofibroblast development and reduces scar-type ECM production, thereby reducing corneal leukoplakia. It also activates the PI3K-AKT signaling pathway, promoting corneal healing. In conclusion, the oriented CuTA-CMHT hydrogel system mimics the orthogonal arrangement of collagen fibers, inhibits inflammation, eliminates reactive oxygen species, and reduces corneal leukoplakia, which is of great significance in the treatment of fungal corneal ulcer and is expected to write a new chapter in corneal tissue engineering.

ABSTRACTMedical education is primarily based on practical schooling and the accumulation of experience and skills, which is important for the growth and development of young ophthalmic surgeons. However, present learning and refresher methods are constrained by several factors. Nevertheless, virtual reality (VR) technology has considerably contributed to medical training worldwide, providing convenient and practical auxiliary value for the selection of students’ sub-majors. Moreover, it offers previously inaccessible surgical step training, scenario simulations, and immersive evaluation exams. This paper outlines the current applications of VR immersive teaching methods for ophthalmic surgery interns.

Suitable biomaterials with seed cells have promising potential to repair bone defects. However, bone marrow mesenchymal stem cells (BMSCs), one of the most common seed cells used in tissue engineering, cannot differentiate efficiently and accurately into functional osteoblasts. In view of this, a new tissue engineering technique combined with BMSCs and scaffolds is a major task for bone defect repair. Lentiviruses interfering with miR‐136‐5p or Smurf1 expression were transfected into BMSCs. The effects of miR‐136‐5p or Smurf1 on the osteogenic differentiation (OD) of BMSCs were evaluated by measuring alkaline phosphatase activity and calcium deposition. Then, the targeting relationship between miR‐136‐5p and Smurf1 was verified by bioinformatics website analysis and dual luciferase reporter assay. Then, a rabbit femoral condyle bone defect model was established. miR‐136‐5p/BMSCs/β‐TCP scaffold was implanted into the defect, and the repair of the bone defect was detected by Micro‐CT and HE staining. Elevating miR‐136‐5p‐3p or suppressing Smurf1 could stimulate OD of BMSCs. miR‐136‐5p negatively regulated Smurf1 expression. Overexpressing Smurf1 reduced the promoting effect of miR‐136‐5p on the OD of BMSCs. miR‐136‐5p/BMSCs/β‐TCP could strengthen bone density in the defected area and accelerate bone repair. SmurF1‐targeting miR‐136‐5p‐modified BMSCs combined with 3D‐printed β‐TCP scaffolds can strengthen osteogenic activity and alleviate bone defects.

The introduction of NCIL simultaneously achieves a rapid Zn2+desolvation, homogeneous Zn2+spatial distribution and the construction of ion-screening interface, thus contributing to a kinetically enhanced and dendrites-free galvanization process.

Infectious diseases are a global public health problem, with emerging and re-emerging infectious diseases on the rise worldwide. Therefore, their prevention and treatment are still major challenges. Bile acids are common metabolites in both hosts and microorganisms that play a significant role in controlling the metabolism of lipids, glucose, and energy. Bile acids have historically been utilized as first-line, valuable therapeutic agents for related metabolic and hepatobiliary diseases. Notably, bile acids are the major active ingredients of cow bezoar and bear bile, which are commonly used traditional Chinese medicines (TCMs) with the therapeutic effects of clearing heat, detoxification, and relieving wind and spasm. In recent years, the promising performance of bile acids against infectious diseases has attracted attention from the scientific community. This paper reviews for the first time the biological activities, possible mechanisms, production routes, and potential applications of bile acids in the treatment and prevention of infectious diseases. Compared with previous reviews, we comprehensively summarize existing studies on the use of bile acids against infectious diseases caused by pathogenic microorganisms that are leading causes of global morbidity and mortality. In addition, to ensure a stable supply of bile acids at affordable prices, it is necessary to clarify the biosynthesis of bile acids in vivo, which will assist scientists in elucidating the accumulation of bile acids and discovering how to engineer various bile acids by means of chemosynthesis, biosynthesis, and chemoenzymatic synthesis. Finally, we explore the current challenges in the field and recommend a development strategy for bile-acid-based drugs and the sustainable production of bile acids. The presented studies suggest that bile acids are potential novel therapeutic agents for managing infectious diseases and can be artificially synthesized in a sustainable way.

Quinone-based electrodes using carbonyl redox reactions are promising candidates for aqueous energy storage due to their high theoretical specific capacity and high-rate performance. However, the proton storage manners and their influences on the electrochemical performance of quinone are still not clear. Herein, we reveal that proton storage could determine the products of the enol conversion and the electrochemical stability of the organic electrode. Specifically, the protons preferentially coordinated with the prototypical pyrene-4,5,9,10-tetraone (PTO) cathode, and increasing the proton concentration in the electrolyte can improve its working potentials and cycling stability by tailoring the enol conversion reaction. We also found that exploiting Al2(SO4)3as a pH buffer can increase the energy density of the Zn||PTO batteries from 242.8 to 284.6 Wh kg–1. Our research has a guiding significance for emphasizing proton storage of organic electrodes based on enol conversion reactions and improving their electrochemical performance.

The production of bioactive compounds from traditional Chinese medicine and medicinal plants mainly depends on the extraction and separation from medicinal materials, which is time-consuming, laborious, and requires large amounts of medicinal resources. As the market demand for bioactive compounds increases, the shortage of medicinal resources tops the list. For a stable and sustainable supply of affordable bioactive compounds, it is necessary to optimize chemosynthetic and biosynthetic pathways. Although some progress has been made in chemocatalysis and biosynthesis, there are drawbacks and bottlenecks in current approaches. We hold the opinion that the combination of chemosynthesis and biosynthesis will be the key direction to efficiently produce bioactive compounds. Chemo-enzymatic synthesis, a strategy that combines biosynthesis and chemosynthesis, is an alternative approach for the heterologous production of bioactive compounds. This paper reviews the recent advances in the chemo-enzymatic synthesis of bioactive compounds derived from traditional Chinese medicine and medicinal plants, highlights the potential application, and presents our perspectives for future research.

Background Schisandra chinensis (S. chinensis)is a drug commonly used in the clinical treatment of cardiovascular diseases in Traditional Chinese Medicine. However, the specific components and mechanisms of its action are still unclear. We screened six kinds of lignans from S. chinensiswith high content and found that schisandrol A and schisantherin A had a strong vasorelaxant effect. The purpose of this study was to investigate the relaxation and underlying mechanism of schisandrol A in the isolated thoracic aorta of rats.Materials and Methods Isolated rat endothelium-intact and endothelium-removed thoracic aorta strips were pre-constricted with phenylephrine (PE), and the relaxation of schisandrol A on the strips was observed. Then, the mechanism was explored by pre-incubating the strips with nitric oxide synthetase inhibitor Nɷ-nitro-l-arginine methyl ester (L-NAME), cyclooxygenase inhibitor (indomethacin), potassium channel blockers 4-aminopyridine (4-AP), barium chloride (BaCl2), tetraethylamine (TEA), and glibenclamide, respectively, and changing the calcium concentration in the bath. In addition, expressions of endothelial nitric oxide synthetase (eNOS) mRNA and protein in rat thoracic aorta were detected.Results Schisandrol A induced both endothelium-dependent and endothelium-independent relaxation of isolated thoracic aorta strips of rats, and the mechanism might be related to promoting the synthesis of NO, inhibiting Ca2+release from the sarcoplasmic reticulum, and blocking the Ca2+channels.Conclusion These discoveries may provide a theoretical basis for the traditional application of S. chinensis to treat cardiovascular disease.

Glycidic esters represent pivotal constituents in synthetic chemistry, offering enhanced versatility for tailoring toward a diverse array of molecular targets in comparison with simple epoxides. While considerable progress has been made in the asymmetric synthesis of trans- and trisubstituted glycidic esters, achieving enantioselective preparation of cis-glycidic esters has remained a long-standing challenge. Here, we demonstrate a selectivity-predictable modular platform for the asymmetric synthesis of cis-glycidic esters via a novel dinuclear (salen)titanium(III)-catalyzed radical-type kinetic resolution (KR) approach. This radical KR protocol operates under mild conditions and demonstrates a wide substrate scope, facilitating the synthesis of alkyl- and aryl-substituted cis-glycidic esters with high levels of regioselectivity and enantioselectivity, along with hydroxy ester byproducts representing synthetically valuable motifs as well. This study presents a unique exploration of radical-type KR applied to epoxides, effectively overcoming the steric challenges inherent in conventional nucleophilic-type methodologies typically employed in epoxide chemistry.

Chlorine-based electrochemical energy storage is a promising candidate for sustainable battery technology. The anionic redox reaction of Cl0/−1is of interest due to its superior redox potential (1.36 V vs.standard hydrogen electrode [SHE]), capacity (756 mAh g−1), high power, and low cost. Although Cl chemistry has been used in aqueous batteries for a long time, its deployment in organic lithium batteries has been significantly impeded due to the insolubility of Cl−ions (<0.1 M). Scarce oxidizable Cl−blocks redox reactions and the inevitable lithium chloride (LiCl) film passivates electrodes on discharge. We report a eutectic effect to improve the Cl−solubility in organic electrolytes (2 M or higher) by mixing a series of N-/P-centered chloride salts with lithium bis(trifluoromethanesulfonyl)imide at specific ratios. Based on an optimized Cl−concentration, a Li-Cl2cell using I as a chemical fixation can achieve a three-electron transfer with a specific capacity of 702 mAh g−1and an energy density of 1,116 Wh kg−1.

• The high scanning speeds lead to equiaxed and short columnar grains with high dislocation density, while low scanning speeds result in elongated columnar grains with low dislocation density. • The high laser scanning speed gives rise to numerous lamellar and large lack-of-fusion pores, and the excessively low laser scanning speed produces relatively small keyhole pores with high sphericity. • The growth and coalescence of the large lack-of-fusion pores cause the brittle fracture mode and consequent inferior mechanical properties of LPBF-processed Invar 36 alloy. • The decrease in scanning speeds leads to large grain sizes and low dislocation density, slightly reducing the yield strength of LPBF-proessed Invar 36 alloy while enhancing its ductility. Laser powder bed fusion (LPBF) is a potential additive manufacturing process to manufacture Invar 36 alloy components with complicated geometry. Whereas it inevitably introduces specific microstructures and pore defects, which will further influence the mechanical properties. Hence, aiming at exploring the LPBF process-related microstructures and pore defects, and especially their influences on the damage mechanism and mechanical properties, Invar 36 alloy was manufactured by LPBF under designed different laser scanning speeds. The microstructure observations reveal that higher scanning speeds lead to equiaxed and short columnar grains with higher dislocation density, while lower scanning speeds result in elongated columnar grains with lower dislocation density. The pore defects analyzed by X-ray computed tomography (XCT) suggest that the high laser scanning speed gives rise to numerous lamellar and large lack-of-fusion (LOF) pores, and the excessively low laser scanning speed produces relatively small keyhole pores with high sphericity. Moreover, the in-situ XCT tensile tests were originally performed to evaluate the damage evolution and failure mechanism. Specifically, high laser scanning speed causes brittle fracture due to the rapid growth and coalescence of initial lamellar LOF pores along the scanning direction. Low laser scanning speed induces ductile fracture originating from unstable depressions in the surfaces, while metallurgical and keyhole pores have little impact on damage evolution. Eventually, the process-structure-property correlation is established. The presence of high volume fraction of lamellar LOF pores, resulting from high scanning speed, leads to inferior yield strength and ductility. Besides, specimens without LOF pores exhibit larger grain sizes and lower dislocation density at decreased scanning speeds, slightly reducing yield strength while slightly enhancing ductility. This understanding lays the foundation for widespread applications of LPBF-processed Invar 36 alloy. [Display omitted] [ABSTRACT FROM AUTHOR]

AbstractNano-Silica (NS) modification is a way to enhance the performance of the cement stone. However, the mechanism of NS-modified cement is not clear at low (80 °C) and high (140 °C) temperatures. Therefore, this study focused on tricalcium silicate (C3S) and the porosity, types and polymerization degree of hydration products with different NS contents with C3S at 80 and 140 °C were analyzed using 1H NMR, XRD, SEM, TGA, XPS, and FTIR methods. According to the following results, temperature and initial Ca/Si are the key factors affecting the hydration products of the silicate phase: NS-induced decalcification led to an increase in C-S-H polymerization degree and chain length, but an excessive amount of NS increased the porosity of the hydration product. NS reacted with Ca(OH)2(CH) and high Ca/Si hydration products, resulting in the formation of structurally dense hydration products with low Ca/Si. However, excessive NS rendered the hydration product unstable and caused high Ca/Si at 80 °C, while it deteriorated Xonotlite with excellent performance at 140 °C, thus affecting the polymerization degree and strength of products.

Carbon peak and carbon neutrality (dual-carbon) are important targets for the international response to climate change. The Silk Road Economic Belt is a strategic resource region and is important for future ecological environment and tourism development. Based on the “dual-carbon” targets, the Single index quantification, Multiple index synthesis, and Poly-criteria integration evaluation model were used in this study to measure the coordinated development index of the ecological environment, public service, and tourism economy along the Silk Road Economic Belt and to analyze its spatial and temporal evolution. Further, it explores the dynamic evolution and development trend of the three systems using the Kernel Density and Grey Markov Prediction Model. The results show that the coordinated development index along this region needs to be improved during the study period. Furthermore, the coordinated development index of the Southwest region is relatively higher than that of the Northwest region. From the development trend of the three systems, all of them develop in a stable manner; however, the tourism economy system is easily affected by external disturbances. The coordinated development index of the three systems changes dynamically and tends to be in a good state of coordination. There is a certain spatial and temporal heterogeneity. The gravity center of the coordinated development index has been in the Southwest region. During the forecast period, the coordinated development index along this region will improve significantly, while insufficient and unbalanced development will continue.

Memory CD8+T cells play a crucial role in infection and cancer and mount rapid responses to repeat antigen exposure. Although memory cell transcriptional programmes have been previously identified, the regulatory mechanisms that control the formation of CD8+T cells have not been resolved. Here we report ECSIT as an essential mediator of memory CD8+T cell differentiation. Ablation of ECSIT in T cells resulted in loss of fumarate synthesis and abrogated TCF-1 expression via demethylation of the TCF-1 promoter by the histone demethylase KDM5, thereby impairing memory CD8+T cell development in a cell-intrinsic manner. In addition, ECSIT expression correlated positively with stem-like memory progenitor exhausted CD8+T cells and the survival of patients with cancer. Our study demonstrates that ECSIT-mediated fumarate synthesis stimulates TCF-1 activity and memory CD8+T cell development during viral infection and tumorigenesis and highlights the utility of therapeutic fumarate analogues and PD-L1 inhibition for tumour immunotherapy.

Mandatory lane-change scenarios are often challenging for autonomous vehicles in complex environments. In this paper, a human-knowledge-enhanced reinforcement learning (RL) method for lane-change decision making is proposed, where the human intelligence is integrated with RL algorithm in a multiple manner. First, this paper constructs a complex ramp-off scenario with congested traffic flow to help agents master lane-change skills. On the basis of the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm, the human prior experience is encoded into reward function and safety constraints offline, and the online guidance of experts is also introduced into the framework, which can limit the unsafe exploration during the training process and provide demonstration in complex scenarios. The experimental results indicate that our method can effectively improve the training efficiency and outperform typical RL method and expert drivers, without specific requirements on the expertise. The proposed method can enhance the learning ability of RL based driving strategies.

Objective: Biliary interventional procedures require physicians to track the interventional instrument tip (Tip) precisely with X-ray image. However, Tip positioning relies heavily on the physicians' experience due to the limitations of X-ray imaging and the respiratory interference, which leads to biliary damage, prolonged operation time, and increased X-ray radiation. Methods: We construct an augmented reality (AR) navigation system for biliary interventional procedures. It includes system calibration, respiratory motion correction and fusion navigation. Firstly, the magnetic and 3D computed tomography (CT) coordinates are aligned through system calibration. Secondly, a respiratory motion correction method based on manifold regularization is proposed to correct the misalignment of the two coordinates caused by respiratory motion. Thirdly, the virtual biliary, liver and Tip from CT are overlapped to the corresponding position of the patient for dynamic virtual-real fusion. Results: Our system is respectively evaluated and achieved an average alignment error of 0.75 $\pm$ 0.17 mm and 2.79 $\pm$ 0.46 mm on phantoms and patients. The navigation experiments conducted on phantoms achieve an average Tip positioning error of 0.98 $\pm$ 0.15 mm and an average fusion error of 1.67 $\pm$ 0.34 mm after correction. Conclusion: Our system can automatically register the Tip to the corresponding location in CT, and dynamically overlap the 3D virtual model onto patients to provide accurate and intuitive AR navigation. Significance: This study demonstrates the clinical potential of our system by assisting physicians during biliary interventional procedures. Our system enables dynamic visualization of virtual model on patients, reducing the reliance on contrast agents and X-ray usage.

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment. This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data- and model-driven method. First, a data-driven decision-making module based on deep reinforcement learning (DRL) is developed to pursue a rational driving performance as much as possible. Then, model predictive control (MPC) is employed to execute both longitudinal and lateral motion planning tasks. Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements. Finally, two principles of safety and rationality for the self-evolution of autonomous driving are proposed. A motion envelope is established and embedded into a rational exploration and exploitation scheme, which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent. Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted, and the results show that the proposed online-evolution framework is able to generate safer, more rational, and more efficient driving action in a real-world environment.

Hydrogels are widely used as quasi-solid-state electrolytes in aqueous batteries. However, they are not applicable in high-voltage batteries because the hydrogen evolution reaction cannot be effectively suppressed even when water is incorporated into the polymer network. Herein, by profoundly investigating the states of water molecules in hydrogels, we designed supramolecular hydrogel electrolytes featuring much more nonfreezable bound water and much less free water than that found in conventional hydrogels. Specifically, two strategies are developed to achieve this goal. One strategy is adopting monomers with a variety of hydrophilic groups to enhance the hydrophilicity of polymer chains. The other strategy is incorporating zwitterionic polymers or polymers with counterions as superhydrophilic units. In particular, the nonfreezable bound water content increased from 0.129 in the conventional hydrogel to >0.4 mg mg–1in the fabricated hydrogels, while the free water content decreased from 1.232 to ∼0.15 mg mg–1. As a result, a wide electrochemical stability window of up to 3.25 V was obtained with the fabricated hydrogels with low concentrations of incorporated salts and enhanced hydrophilic groups or superhydrophilic groups. The ionic conductivities achieved with our developed hydrogel electrolytes were much higher than those in the conventional highly concentrated salt electrolytes, and their cost is also much lower. The designed supramolecular hydrogel electrolytes endowed an aqueous K-ion battery (AKIB) system with a high voltage plateau of 1.9 V and contributed to steady cycling of the AKIB for over 3000 cycles. The developed supramolecular hydrogel electrolytes are also applicable to other batteries, such as aqueous lithium-ion batteries, hybrid sodium-ion batteries, and multivalent-ion aqueous batteries, and can achieve high voltage output.

Impaired macroautophagy/autophagy flux has been implicated in the treatment of prostate cancer (PCa). However, the mechanism underlying autophagy dysregulation in PCa remains unknown. In the current study, we investigated the role of diacylglycerol acyltransferases 1 (DGAT1) and its potential effects on cellular energy homeostasis and autophagy flux in PCa. The results of immunohistochemical staining suggested that DGAT1 expression was positively corrected with tumor stage and node metastasis, indicating DGAT1 is an important factor involved in the development and progression of PCa. Furthermore, targeting DGAT1 remarkably inhibited cell proliferation in vitro and suppressed PCa growth in xenograft models by triggering severe oxidative stress and subsequently autophagy flux blockage. Mechanically, DGAT1 promoted PCa progression by maintaining cellular energy homeostasis, preserving mitochondrial function, protecting against reactive oxygen species, and subsequently promoting autophagy flux via regulating lipid droplet formation. Moreover, we found that fenofibrate exhibits as an upstream regulator of DGAT1. Fenofibrate performed its anti-PCa effect involved the aforementioned mechanisms, and partially dependent on the regulation of DGAT1. Collectively. These findings indicate that DGAT1 regulates PCa lipid droplets formation and is essential for PCa progression. Targeting DGAT1 might be a promising method to control the development and progression of PCa.

Accurate rainfall nowcasting is crucial for protecting lives and property. Multisource meteorological data can reflect rainfall evolution from different perspectives. Making full use of multisource meteorological data is an effective method to improve the performance of rainfall nowcasting. For the rainfall nowcasting task, we propose a cross-modal spatiotemporal joint predictive network, referred to as CM-STJointNet. This is a nonlinear rainfall nowcasting model with radar and satellite data as inputs. A multitask learning (MTL) strategy is used to implement radar echo extrapolation and satellite imagery prediction, and this constraint realizes that the model forecasting follows the motion of clouds. We experiments conducted on the publicly available meteorological dataset [Storm EVent ImageRy (SEVIR)], CM-STJointNet uses radar and infrared bright temperature observations (IRBTs) as input data and realizes 2 h of rainfall nowcasting. Compared with four unimodal methods (ConvLSTM, PredRNN, Rainformer, and Earthformer), four multimodal methods (Rain-F, RN-Net, MetNet-2, and S2DIR) demonstrated superior prediction capabilities. The experimental results affirm the high efficiency of CM-STJointNet and provide more insights for future advances in rainfall nowcasting.

Studying the stability of sit-to-stand (STS) could provide a foundation for clinicians when guiding rehabilitation training. The influence of the seat height on STS stability is unclear. The primary goal of this paper was to identify the position of the center of pressure (COP) and the lower limb joint angles during STS movement from different seat heights in order to analyze the relationship between joint angle and COP and understand the influence of seat height on stability during the STS movement. Thirty young volunteers participated in this experiment. The COP offset in the anterior–posterior direction (COP [ap]) and the change in distance of the COP and the center of gravity (COP–COG) were analyzed and compared. The results indicated that seat height has a significant effect on COP at Extension phase (Phase III [p= 0.045]), and COP–COG at the time when the buttocks are about to leave the seat (T1 [p= 0.01]). The stability of STS from the low seat was better than from the high seat. The lower limb joint angles at the time when the hip joint angle reaches its maximum value (T3) and COP (ap) during Phase III were negatively correlated, while lower limb joint angles at the time when the buttocks are about to leave the seat (T1) and COP–COG at the time when the buttocks are about to leave the seat (T1) were positively correlated. The ankle joint angle had more influence than other lower limb joints on STS stability at the time when the buttocks are about to leave the seat (T1).

Ion-sensing hydrogels exhibit electrical conductivity, softness, and mechanical and sensory properties akin to human tissue, rendering them an ideal material for mimicking human skin. In the realm of fabricating sensors for detecting human physiological activities, they present an ideal alternative to traditional rigid metal conductors. Nevertheless, achieving ionic hydrogels with outstanding tensile properties, toughness, ionic conductivity, and transport stability poses a significant challenge. This paper describes a simple method of forming a basic network by free radical polymerization of acrylamide, and then bacterial cellulose (BC) and 1-ethyl-3-methylimidazolium chloride ([EMIM]Cl) were introduced into the basic network. The polyhydrogen bonds and electrostatic interactions in the system gave the hydrogel notable tensile properties (3271 ± 37%), toughness (7.39 ± 0.13 MJ m−3), and high ultimate tensile stress (385.1 ± 7.2 kPa). In addition, the combination of BC and [EMIM]Cl collaboratively enhanced the mechanical properties and electrical conductivity. Ion sensing hydrogels have a wide operating strain range (1000%) and high sensitivity (gage factor (GF) = 11.85), and are therefore considered promising candidates for next-generation gel-based strain sensor platforms.

A novel air bubble lubrication method using WAIP (Winged Air Induction Pipe) device was introduced to reduce frictional drag of the hull of ship and hence increase efficiency of the propulsion system. This bubble lubrication technique utilizes the negative pressure region above the upper surface of the hydrofoilas ship moves forward to drive air to the skin of the hull. In the presentstudy, reduction rate of dragby applying WAIP device is numerically investigated with open source toolbox OpenFOAM. The generated air layer and bubblesareobserved. The numerical results indicate that reduction rate of drag significantly depends on the depth of submergence of the hydrofoil, angle of attack of the hydrofoil, and the pressure in the air inlet. It is also proportional to the air flow rate. The underlying physics of the fluid dynamics is explored.

In this article, a coupling-path reconfigurable quadrature coupler is proposed with a wide tuning range of frequencies and power division ratios, which consists of three pairs of varactors and eight transmission lines. By analyzing the admittance matrices of the proposed coupler, the constraint conditions for both forward coupling (FWCP) and backward coupling (BWCP) can be established. Then, through selecting suitable circuit parameters (characteristic impedances, lumped elements, varactors, and so on), a wide range of tunable frequencies and power division ratios can be realized for both FWCP and BWCP paths, respectively. Finally, a prototype is fabricated and measured for verification. The measured results show that the proposed coupler can realize a wide tuning range of frequencies from 0.8 to 1.5 GHz (0.9–1.5 GHz), and a large tuning range of the power division ratios from −10.2 to −0.7 dB (−20.3 to 19.3 dB) in the FWCP (BWCP) path. Excellent agreement is obtained among the results of circuit simulation, electromagnetic (EM) simulation, and measurement, it not only demonstrates that the proposed quadrature coupler features a simple topology, reconfigurable coupling path, and wide tuning range of frequencies and power division ratios, but also confirms the effectiveness of the applied constraint conditions.

Many studies suggest that tumor microbiome closely relates to the oncogenesis and anti-tumor responses in multiple cancer types (e.g., colorectal cancer (CRC), breast cancer, lung cancer and pancreatic cancer), thereby raising an emerging research area of bacteria-related tumor therapy. Nanomaterials have long been used for both cancer and bacterial infection treatment, holding great potential for bacteria-related tumor therapy. In this review, we summarized recent progress in nanomaterials for bacteria-related tumor therapy. We focus on the types and mechanisms of pathogenic bacteria in the development and promotion of cancers and emphasize how nanomaterials work. We also briefly discuss the design principles and challenges of nanomaterials for bacteria-related tumor therapy. We hope this review can provide some insights into this emerging and rapidly growing research area.

Recently, thanks to the introduction of human feedback, Chat Generative Pre-trained Transformer (ChatGPT) has achieved remarkable success in the language processing field. Analogically, human drivers are expected to have great potential in improving the performance of autonomous driving under real-world traffic. Therefore, this study proposes a novel framework for evolutionary decision-making and planning by developing a hybrid augmented intelligence (HAI) method to introduce human feedback into the learning process. In the framework, a decision-making scheme based on interactive reinforcement learning (Int-RL) is first developed. Specifically, a human driver evaluates the learning level of the ego vehicle in real-time and intervenes to assist the learning of the vehicle with a conditional sampling mechanism, which encourages the vehicle to pursue human preferences and punishes the bad experience of conflicts with the human. Then, the longitudinal and lateral motion planning tasks are performed utilizing model predictive control (MPC), respectively. The multiple constraints from the vehicle’s physical limitation and driving task requirements are elaborated. Finally, a safety guarantee mechanism is proposed to ensure the safety of the HAI system. Specifically, a safe driving envelope is established, and a safe exploration/exploitation logic based on the trial-and-error on the desired decision is designed. Simulation with a high-fidelity vehicle model is conducted, and results show the proposed framework can realize an efficient, reliable, and safe evolution to pursue higher traffic efficiency of the ego vehicle in both multi-lane and congested ramp scenarios.

Crystals-Dilithium is a post-quantum cryptography digital signature scheme with quantum attack resistance. In this brief, a high speed processor towards Dilithium is represented. An architecture based on multiple instruction parallel execution is proposed to reduce operation cycles. The pseudo random number extension generator is designed with byte absorption precision to adapt to practical application scenarios. In addition, a three-stage pipelined modular multiplier is optimized based on Karatsuba algorithm, increasing the maximum operating frequency of the system. On the Zynq UltraScale+ FPGA, our processor is implemented consuming 32035 LUTs and 9707 FFs at 300MHz. Compared with the related works, it has the highest operating frequency and the shortest execution time.

Online 3D Bin Packing Problem (3D-BPP) has a wide range of industrial applications and there is an emerging research interest in learning optimal bin packing policy and deploying it for real logistics applications. From the heuristic methods to the deep reinforcement learning (DRL) methods, the previous works have proposed many solutions to solve the online 3D-BPP. However, none of them have studied what and how heuristics can be modelled into DRL to build a more effective and practical bin packing pipeline. In this work, we thoroughly investigate what heuristics can be used in online 3D-BPP and how to effectively integrate the heuristics with the DRL. First, we design 3 different heuristics based on the physical rules of the real world and the experiences of the human packers, including the Physics-Heuristics, the Packing-Heuristics and the Unpacking-Heuristics. Second, we model the 3 types of heuristics into the DRL framework and propose a novel heuristic DRL method to solve the online 3D-BPP. Extensive experimental results show that our method achieves state-of-the-art bin packing performance and the resulting real-world system is able to reliably finish the bin packing task in real logistics scenarios. Supplementary video is available at https://www.youtube.com/watch?v=x8GpmEELq18. Note to Practitioners—The rapid growth of e-commerce has significantly increased the burden of human packers in logistic warehouses, where the workers need to pick the products from a conveyor and pack them into bins (i.e. the online 3D bin packing). Thus it is of great importance to develop intelligent robotic systems to replace human labor, which is a long-standing topic in the field of control and automation science. This paper makes a substantial contribution to the related field by studying the online 3D bin packing in terms of both the theory and practice. On the one hand, the simulated experiments suggest that the presented algorithm significantly improves the space utilization of bin packing. On the other hand, the robotic system developed based on the proposed method can favourably finish the bin packing task in real logistics scenarios, demonstrating the practical use of our approach. Consequently, the approach proposed in this paper is totally applicable in logistic warehouses and is promising to drastically improve the working efficiency of the product packing in real warehouses. In the future, we will extend the presented approach to pack irregular-shaped objects and then facilitate more logistics applications.

In this article, we present a general approach for controlling robotic systems that make and break contact with their environments. Contact-implicit model predictive control (CI-MPC) generalizes linear MPC to contact-rich settings by utilizing a bilevel planning formulation with lower level contact dynamics formulated as time-varying linear complementarity problems (LCPs) computed using strategic Taylor approximations about a reference trajectory. These dynamics enable the upper level planning problem to reason about contact timing and forces, and generate entirely new contact-mode sequences online. To achieve reliable and fast numerical convergence, we devise a structure-exploiting interior-point solver for these LCP contact dynamics and a custom trajectory optimizer for the tracking problem. We demonstrate real-time solution rates for CI-MPC and the ability to generate and track nonperiodic behaviors in hardware experiments on a quadrupedal robot. We also show that the controller is robust to model mismatch and can respond to disturbances by discovering and exploiting new contact modes across a variety of robotic systems in simulation, including a pushbot, planar hopper, planar quadruped, and planar biped.

Autonomous vehicles are expected to achieve self-evolution in the real-world environment to gradually cover more complex and changing scenarios. Reinforcement learning focuses on how agents act in the environment to maximize the cumulative reward, with a great potential to achieve self-evolution ability. However, most of reinforcement learning algorithms suffer from a low sample efficiency, which greatly limits their application in autonomous driving. This paper presents an efficient self-evolution method for any given algorithm based on the combination of Soft Actor Critic (SAC) and Behavioral Cloning(BC). First, the states of the sample trajectory in the replay buffer are separated and input into the given algorithm (algorithm with fundamental performance) to get the output label of actions such that the SAC algorithm can be guided using BC to achieve fast iteration in the direction of optimization with existing basic performance. Then, the value iteration algorithm is combined to achieve the proportion allocation of mixed gradient feedback, in order to trade off exploitation and exploration. In addition, the proposed methodology is evaluated in simulation environment taking automated speed control as an example. Experiment results show that compared with SAC algorithm, the proposed method can realize more than three times of convergence efficiency improvement, while without destroying the exploration enhancement advantage of reinforcement learning algorithm, that is, the performance is improved by 20% compared with the given algorithm (Intelligent Driver Model, IDM). The proposed method can easily extended to improve any given model no matter it is model-based or learning-based algorithm.

In label-noise learning, estimating the transition matrix is a hot topic as the matrix plays an important role in building statistically consistent classifiers. Traditionally, the transition from clean labels to noisy labels (i.e., clean-label transition matrix (CLTM)) has been widely exploited on class-dependent label-noise (wherein all samples in a clean class share the same label transition matrix). However, the CLTM cannot handle the more common instance-dependent label-noise well (wherein the clean-to-noisy label transition matrix needs to be estimated at the instance level by considering the input quality). Motivated by the fact that classifiers mostly output Bayes optimal labels for prediction, in this paper, we study to directly model the transition from Bayes optimal labels to noisy labels (i.e., Bayes-Label Transition Matrix (BLTM)) and learn a classifier to predict Bayes optimal labels. Note that given only noisy data, it is ill-posed to estimate either the CLTM or the BLTM. But favorably, Bayes optimal labels have no uncertainty compared with the clean labels, i.e., the class posteriors of Bayes optimal labels are one-hot vectors while those of clean labels are not. This enables two advantages to estimate the BLTM, i.e., (a) a set of examples with theoretically guaranteed Bayes optimal labels can be collected out of noisy data; (b) the feasible solution space is much smaller. By exploiting the advantages, this work proposes a parametrical model for estimating the instance-dependent label-noise transition matrix by employing a deep neural network, leading to better generalization and superior classification performance.

Nowadays, Content Delivery Network (CDN) is widely used for its convenience in providing services. However, the increasing demand for bandwidth puts tremendous pressure on CDN. Inspired by the great potential of periodic broadcasting to save bandwidth, we suggest an Efficient and Adaptive Content Delivery System (EACDS) to decrease traffic and shorten video content delivery delays. Specifically, we propose the Peak cutting and Valley filling for VBR (PVV) and the Exhaustive Periodic Broadcasting algorithm (EPB) to process videos and arrange slices with low bandwidth and delay. Furthermore, we introduce an enhanced version of EPB that supports Fast-Forwarding, namely FFB. To effectively deal with the constantly changing Internet, we also demonstrate an adaptive decision model that switches distribution schemes according to the online user scale. Extensive experiments show that the EACDS is excellent in many aspects. The PVV and decision model can save up to 57% of bandwidth and 54.7% of traffic respectively. Compared with some existing schemes, EPB allows the most negligible delay with the same bandwidth, and FFB supports fast-forwarding in periodic broadcasting.

Background: Rising from a sitting position, or “sit-to-stand” (STS), is one of the basic activities in daily life. Therefore, studying the kinematics and kinetics of STS could help optimize STS movement strategy. This study analyzes the comprehensive influence of the handrail and foot position under the knee joint support on STS kinematics and kinetics. Methods: A total of 30 young people were recruited to participate in this study. Their average age, height, weight, and BIM were 24.21 years old, 168.52 cm, 60.92 kg, and 21.46 kg/m2, respectively. The experiment was conducted under six experimental conditions. Total movement time, percent of movement time of each phase, trunk tilt angle, and peak joint moment were analyzed and compared. In addition, a correlation analysis was carried out between the range of motion of the joint angle and the peak joint moment. Results: The results showed that the far handrail had the largest trunk tilt angle, and the smallest total moment impulse of the lower limb joints. The closer the foot was to the seat, the smaller the peak hip joint moment, and the larger the peak moment of ankle and knee joints, and a shorter total movement time. Conclusions: The position of the handrail had a significant influence on the trunk tilt angle, percent of movement time of certain phase, and the total moment impulse of the lower limb joints. And the position of the foot had a significant influence on the peak moments of the ankle, hip, and knee joints.

Although the tobacco industry is a significant contributor to energy consumption and carbon emissions its negative environmental impact has received inadequate attention globally. Cigarette factories are a key link in the tobacco industry’s production chain, and using data provided by a cigarette factory in China we conduct a life cycle assessment to account for the carbon footprint of cigar production in cigarette factories. The results of the assessment show that factory air conditioning is the most important contributor to the environmental load of the cigar manufacturing process, while electricity is the key factor that contributes the greatest environmental load across all of the processes in the product life cycle. In addition, packaging, including small boxes and cigarette cartons, have a significant impact on the industry’s environmental footprint due to its use of raw materials. We find the carbon footprint of the entire production process for cigar products to be 383.59 kg CO2eq. Based on our findings, we suggest ways to optimize cigar/cigarette factory processes to reduce carbon emissions that can help to promote sustainable development in related industries.

A multifunctional additive EDTMPS with multiple anchor points is introduced into the precursor of perovskite, which can tune perovskite crystallization and passivate non-stoichiometric vacancy defects of perovskite film, and realize the in-situ absorption of leaked lead ions.

Herein, a series of polyimide (PI)/titanium dioxide (TiO2) organic–inorganic flexible composite microfibers with high photocatalytic performance and good reusability were prepared by combining electrospinning technology and a hydrothermal method. Under simulated sunlight, the photocatalytic characteristics of the as-prepared PI nanofibers, TiO2nanorods, and PI/TiO2microfibers were evaluated with photocatalytic degradation of Rhodamine B (RhB) solution. Among the tested samples, PI/TiO2-3 mL hydrochloric acid-160 °C-14 h (PI/TiO-3-160-14) (100%) exhibited a superior photocatalytic degradation rate compared to pure PI (84.0%) and TiO2(62.2%). The enhancement of the photocatalytic performance was attributed to the Z-scheme heterojunction mechanism. When the interface was irradiated by simulated sunlight, the band edge bending, built-in electric field, and Coulomb interaction synergistically facilitated the separation and transport of electron–hole pairs in the heterojunction. This enhanced the oxidation and reduction abilities of the valence and conduction bands of PI/TiO2. These results were adequately verified by X-ray photoelectron spectroscopy (XPS) analyses and radical trapping experiments. Additionally, PI/TiO2microfibers also demonstrated excellent photocatalytic activity toward methylene blue (MB, 81.4%), methyl orange (MO, 95.9%), and malachite green (KG, 98.9%), underscoring the versatile applicability of PI/TiO2. Further supplementary investigations illustrated that PI/TiO2microfibers also possess excellent photostability during our extensive recycling photocatalytic experiments.