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Two-dimensional (2D) high-entropy transition metal dichalcogenides (HETMDs) have gained significant interest due to their structural properties and correlated possibilities for high-end devices. However, the controlled synthesis of 2D HETMDs presents substantial challenges owing to the distinction in the inherent characteristics among diverse metal elements in the synthesis, such as saturated vapor pressure of precursors and formation energy of products. Here, we present the synthesis of a 2D HETMD single crystal with 0.92 nm thickness through a liquid-phase reaction system, where the metal elements are fed uniformly and simultaneously. The rapid codeposition of different precursors facilitates the formation of high-entropy products, thereby preventing phase separation. The method can be expanded to produce a variety of 2D HETMDs, such as quinary (MoNbTaV)S2, hexahydroxy (MoWNbTaV)S2, and multichalcogenide (MoWNb)SSe. The as-prepared 2D HETMD is an excellent catalyst for the hydrogen evolution reaction (HER), demonstrating the overpotential of 84 mV at 10 mA cm–2of an individual crystal, which is much better than that of pristine MoS2(260 mV at 10 mA cm–2). The strategy offers the flexibility to artificially design the element selectivity and properties of HETMD single crystals in the 2D limit, enabling applications across a wide range of advanced fields.

The position and configuration of the C═C bond have a significant impact on the spatial conformation of unsaturated lipids, which subsequently affects their biological functions. Double bond isomerization of lipids is an important mechanism of bacterial stress response, but its in-depth mechanistic study still lacks effective analytical tools. Here, we developed a visible-light-activated dual-pathway reaction system that enables simultaneous [2 + 2] cycloaddition and catalytic cis–transisomerization of the C═C bond of unsaturated lipids via directly excited anthraquinone radicals. Density functional theory calculations revealed the oxygen radical addition transition state and the addition–elimination isomerization mechanism of the reaction. A full-dimensional resolution method for C═C bond position and configuration was developed based on the bifunctional reaction and liquid chromatography–mass spectrometry. This method was then applied to the study of bacterial environmental stress response mechanisms. The C═C bond cis–transand positional isomerization patterns of Pseudomonasmembrane lipids under temperature stress were discovered, and the effect of temperature stress on fatty acid biosynthesis was also revealed. This study not only provides an effective tool and key information for the study of bacterial stress response mechanisms, but also enriches the toolbox of visible light chemical reactions.

Efficient adsorption/separation of radioactive Xe/Kr is very important and challenging for the rapid development of nuclear energy. Recently, a novel approach for electric field-controlled gas capture/release has been proposed, which provides important advantages such as controllable kinetics and reversibility compared to membrane separation, cryogenic distillation and traditional solid adsorption method. Herein, we for the first time used C3N for efficient separation of Xe/Kr in the presence of an electric field studied by density functional theory (DFT) method. DFT calculations reveal that a negative electric field can better modulate the surface characteristics of a C3N nanosheet than a positive electric field, resulting in a transition from physisorption to chemisorption of Xe/Kr on C3N, and can realize efficient and switchable capture/release of Xe/Kr on C3N by turning on/off the introduced electric field. The interaction between Xe and the C3N substrate is more sensitive to an external electric field than that between Kr and C3N. Under an external electric field of −0.012 au, both the difference in the adsorption energies of Xe and Kr and the corresponding difference rate are large enough, meaning that efficient separation of Xe and Kr on C3N can be realized. The microscopic mechanism of the interaction between Xe/Kr and C3N was revealed in terms of adsorption energies, interatomic distances, charge transfers, frontier orbital interactions and projected density of states (PDOSs), providing useful information for the study of adsorption/separation of radioactive Xe/Kr.

Genetic loss of the 2-oxoglutarate oxygenase factor inhibiting hypoxia-inducible factor (FIH) enhances both glycolysis and aerobic metabolism. FIH is thus a potential target for adiposity control and improving hepatic steatosis. We describe development of a series of novel, potent, and selective FIH inhibitors that occupy both the FIH catalytic site and a recently defined tyrosine conformational-flip pocket. ZG-2305, with a Kiof 79.6 nM for FIH, manifests 38-fold selectivity over the hypoxia-inducible factor (HIF) prolyl hydroxylase PHD2. Oral administration of ZG-2305in the western-diet induced obesity mouse model results in improved lipid accumulation and recovery from abnormal body weight/hepatic steatosis. Amelioration of nonalcoholic steatohepatitis (NASH) related pathological phenotypes in the HF-CDAA-diet induced NASH mouse model was observed. Preliminary preclinical studies indicate ZG-2305has good pharmacokinetic properties and an acceptable safety profile. The results imply ZG-2305is a promising candidate for treatment of obesity and fatty liver disease.

This paper introduces a novel active dual-coupler ring based compound-cavity (ADCR-CC) filter, specifically designed for use in single-longitudinal-mode (SLM) fiber lasers, which features adjustable full-width at half-maximum (FWHM) and transmittance. A comprehensive methodology for the design, fabrication, and characterization of the ADCR-CC filter is proposed as an innovative approach to fabricating actively controlled compound-cavity filters for SLM operations in fiber lasers. Both theoretical analysis and experimental results demonstrate that adjusting the pump power of the ADCR-CC filter enables control over the filter's FWHM and transmittance. Optimizing the pump power allows the ADCR-CC filter to simultaneously achieve a FWHM below 7 MHz and transmittance exceeding 0.99. To illustrate the longitudinal-mode selectivity afforded by the ADCR-CC filter, we constructed a dual-wavelength-switchable erbium-doped fiber laser (DWS-EDFL) leveraging the ADCR-CC filter and a high-birefringence fiber Bragg grating (HB-FBG). The DWS-EDFL can be switched among three lasing states, including two single-wavelength operations and one dual-wavelength operation, all with high stability. In single-wavelength mode, each output exhibits an optical signal-to-noise ratio of >78 dB, a linewidth of <720 Hz, a relative intensity noise of ≤−155.1 dB/Hz@>3 MHz, an output power fluctuation of ≤0.32 dB, and superior polarization characteristics.

The production of ultra-low sulfur clean fuels holds global significance for improving energy efficiency and reducing environmental pollution. Photocatalytic aerobic oxidative desulfurization (PODS) offers an attractive approach to efficiently remove refractory heterocyclic aromatic sulfur compounds under mild conditions. However, the sluggish charge dynamics, insufficient molecule activation, and slow mass transfer within photocatalysts restrict the overall desulfurization efficiency, hindering the widespread application of this technology. Herein, we developed a defective g-C3N4(M1U3CN) photocatalyst with bridged N3Cvacancies and hierarchical porous structures to address these limitations. This catalyst not only demonstrates outstanding dibenzothiophene (DBT) removal efficiency of 99.6% for model oil with high conversion and selectivity but also achieves complete sulfur removal from gasoline as well as 96.7% sulfur removal for distilled diesel at room temperature, successfully achieving ultradeep aerobic oxidative desulfurization. The introduction of rich N3Cvacancies and abundant micro-meso-macroporous distribution in M1U3CN enhances kinetic efficiencies by promoting charge carrier separation, mass transfer, and molecular activation simultaneously; resulting in exceptional PODS performance. This work provides a collaborative strategy to overcome kinetic challenges in aerobic oxidative desulfurization viadefect and structural engineering of the catalyst.

Star identification is the most important part of satellite attitude determination. Existing star image identification algorithms show lower robustness with an increase in the number of stars. This study proposes a method for star identification based on local magnitude fitting. First, the similarity of neighboring star images is used for denoising. Then, the Gaussian distribution is used to determine the star point range and calculate the real grayscale cumulative value (RGCV). Finally, the star magnitude fitting range is obtained using the star tracker parameters and the fitting parameters between the RGCV and the star magnitude are determined. This method is used to optimize the rotation invariant additive vector sequence algorithm in this article. The results show that this method can reduce the storage capacity by 96%, enhance the efficiency of the algorithm and achieve a recognition rate of above 98% in real-situations. Furthermore, this method can also be applied to other star identification algorithms.

The next generation of wireless network 6G is envisioned to enable seamless global connectivity for the Internet of Everything (IoE) on the Earth. To this end, implementing high-speed wireless communication from the deep ocean to the sea surface leveraging multiple autonomous underwater vehicles (AUVs) with underwater wireless optical communication (UWOC) is an emerging and promising technology that enables real-time data collection for accurate underwater exploration and monitoring, for example, coordinated moving target monitoring. However, multihop UWOC is more susceptible to beam misalignment and positional uncertainty caused by external interference in the harsh environment. To address these challenges, we design a cooperative movement scheme for multiple AUVs based on a deep reinforcement learning (DRL) approach to perform robust optical communication. We first model the optical channel and then analyze the link performance to satisfy the bit error rate (BER) requirements. Afterward, we map the cooperative optical communication problem into a Markov decision process (MDP) and then we propose a deep deterministic policy gradient (DDPG)-based cooperative movement strategy, which is integrated with the extended Kalman filter (EKF) technique. Finally, we design a multi-AUV adaptive adjustment scheme for enhanced optical link adaptation, including an optical link distance adjustment algorithm, and an adaptive transmit power adjustment algorithm based on twin-delayed deep deterministic policy gradient (TD3). Through extensive simulations, it is demonstrated that the proposed algorithms are effective in achieving cooperative and adaptive underwater optical communication via multi-AUV under mobile scenarios.

Objective: This study aims to evaluate the clinical utility and effectiveness of a nomogram model in predicting outcomes for patients with benign esophagogastric anastomotic stenosis (BES) undergoing fluoroscopic balloon dilation (FBD). Methods: The clinical data of 428 patients with BES who received FBD treatment at our hospital between January 2013 and June 2023 were retrospectively analyzed. The patients were divided into training and validation cohorts in a 7:3 ratio. Relevant risk factors influencing patient prognosis were identified, and a nomogram model was developed to predict stenosis-free survival rates at 3, 6, 12, and 24 months. The model’s accuracy was assessed by calculating the area under the receiver operating characteristic curve (AUC), and its predictive performance was validated in the test group. Results: The baseline data comparison between the training and validation groups revealed no significant differences, ensuring the comparability of the groups. Cox regression analysis identified that age, history of fistula or not, stenosis severity, and balloon diameter were independent risk factors influencing stenosis-free survival in patients with BES. The area under AUC for the nomogram prediction model of stenosis-free survival at 3, 6, 12, and 24 months was 0.77, 0.81, 0.85, and 0.83, respectively, in the training group, and 0.74, 0.80, 0.84, and 0.83, respectively, in the validation group. Conclusions: Age, history of fistula, stenosis severity, and balloon diameter were identified as independent risk factors influencing the prognosis of BES. The nomogram model developed in this study demonstrates strong discriminatory power and holds significant clinical value for prognostic assessment.

One of the basic questions in the aging field is whether there is a fundamental difference between the aging of lower invertebrates and mammals. A major difference between the lower invertebrates and mammals is the abundancy of noncoding RNAs, most of which are not conserved. We have previously identified a noncoding RNA Terc-53that is derived from the RNA component of telomerase Terc. To study its physiological functions, we generated two transgenic mouse models overexpressing the RNA in wild-type and early-aging Terc−/−backgrounds. Terc-53mice showed age-related cognition decline and shortened life span, even though no developmental defects or physiological abnormality at an early age was observed, indicating its involvement in normal aging of mammals. Subsequent mechanistic study identified hyaluronan-mediated motility receptor (Hmmr) as the main effector of Terc-53. Terc-53mediates the degradation of Hmmr, leading to an increase of inflammation in the affected tissues, accelerating organismal aging. adeno-associated virus delivered supplementation of Hmmr in the hippocampus reversed the cognition decline in Terc-53transgenic mice. Neither Terc-53nor Hmmr has homologs in C. elegans. Neither do arthropods express hyaluronan. These findings demonstrate the complexity of aging in mammals and open new paths for exploring noncoding RNA and Hmmr as means of treating age-related physical debilities and improving healthspan.

Seismic inversion methods for PP waves in transversely isotropic media with a vertical axis of symmetry (VTI) have been extensively studied. In comparison, seismic shear waves (SV-SV and SH-SH waves) exhibit much higher sensitivity to anisotropy. Among them, the SV-SV wave in VTI media displays nonelliptical anisotropy, which is more complex than that of the SH-SH wave. Incorporating anisotropic effects in the inversion of SV-SV wave data is, therefore, crucial for accurate subsurface characterization. In this study, we propose a seismic amplitude inversion method for SV-SV waves in VTI media based on a modified approximate equation for the SV-wave reflection coefficient. This method facilitates the inversion of three key parameters: (A) vertical shear wave impedance, (B) shear modulus related to the anellipticity anisotropy parameter, and (C) vertical shear wave velocity. Additionally, the effective parameter $\sigma $ , which governs the influence of anisotropy on the SV-wave, can be derived from these parameters. Synthetic tests demonstrate that the proposed inversion method maintains strong robustness against varying noise levels. Application to field data from a shale oil exploration area validates the method’s efficacy in recovering both elastic parameters and the anisotropic characteristics of fine layers. This work provides a reliable tool for the characterization of anisotropic reservoir formations.

Introduction: The Steinberg classification system is commonly used by orthopedic surgeons to stage the severity of patients with osteonecrosis of the femoral head (ONFH), and it includes mild, moderate, and severe grading of each stage based on the area of the femoral head affected. However, clinicians mostly grade approximately by visual assessment or not at all. To accurately distinguish the mild, moderate, or severe grade of early stage ONFH, we propose a convolutional neural network (CNN) based on magnetic resonance imaging (MRI) of the hip joint of patients to accurately grade and aid diagnosis of ONFH. Materials and Methods: T1-MRI images of patients diagnosed with early stage ONFH were collected. Three orthopedic surgeons selected 261 slices containing images of the femoral head and labeled each case with the femoral head necrosis classification. Our CNN model learned, trained, and segmented the regions of femoral head necrosis in all the data. Results: The accuracy of the proposed CNN for femoral head segmentation is 97.73%, sensitivity is 91.17%, specificity is 99.40%, and positive predictive value is 96.98%. The diagnostic accuracy of the overall framework is 90.80%. Conclusions: Our proposed CNN model can effectively segment the region where the femoral head is in MRI and can identify the region of early stage femoral head necrosis for the purpose of aiding diagnosis.

Synapses are the key component in neuromorphic computing due to their abilities of signal memory, preprocessing and transmission. Synapses with nonvolatile and reconfigurable logic functions show high flexibility and strong calculating capacity when facing complex practical problems. We propose a nonvolatile all-fiber synapse (AFS) with reconfigurable logic functions. The functions are realized in an optical device where nonvolatile Ge2Sb2Te5 (GST)-integrated microfiber is placed on top of one arm of the fiber Mach-Zehnder interferometer. By using laser pulses, GST complex refractive index can be modulated through strong light-matter interactions and thermo-optic effects. This modifies the device transmission and subsequent the weight of AFS, enabling 10 dB modulation contrast. In addition, important synaptic functionalities, such as excitatory/inhibitory postsynaptic current (EPSC/IPSC), long-term potentiation/depression (LTP/LTD), spike amplitude-dependent plasticity (SADP), and spike number-dependent plasticity (SNDP) can be mimicked in such device. Remarkably, the reconfigurable logic functions such as AND, OR, NAND, NOR, and XOR can be also achieved in the single fiber-based device by controlling the phase of one arm of AFS and applying SADP, to obtain various on-demand responses to input optical signals. Overall, this work shows promising prospects for information processing in fiber communication networks.

As Nature Chemical Biologyapproaches its third decade we asked a collection of chemical biologists, “What do you think are the most exciting frontiers or the most needed developments in your main field of research?” — here is what they said.

SiC particle-reinforced titanium (Ti) composites fabricated through Selective Laser Melting (SLM) demonstrate significant improvements in mechanical and tribological properties. However, the influence of SLM processing on microstructure and property evolution remains underexplored. This study investigates the microstructural development of 1 wt % SiC-reinforced Ti composites processed at varying laser scanning power and their resulting mechanical and tribological performance. Results indicate that mechanical properties initially improve with increased scanning power but deteriorate beyond a threshold. Optimal laser power was determined at 140 W where the composites achieved a relative density of 99.6 %, hardness of 344 HV, and tensile strength of 1097 MPa representing enhancements of 66.7 % in hardness and 74.5 % in tensile strength over unreinforced Ti. Tribological performance was also optimized at this power. Reactions between SiC and Ti during SLM enhanced hardness and strength. These findings highlight how scanning power affects microstructural evolution, providing critical insights for optimizing the performance of SiC/Ti composites in advanced engineering applications.

Liver fibrosis is an urgent clinical problem without effective therapies. Here we conducted a high-content screening on a natural Euphorbiaceae diterpenoid library to identify a potent anti-liver fibrosis lead, 12-deoxyphorbol 13-palmitate (DP). Leveraging a photo-affinity labeling approach, apolipoprotein L2 (APOL2), an endoplasmic reticulum (ER)-rich protein, was identified as the direct target of DP. Mechanistically, APOL2 is induced in activated hepatic stellate cells upon transforming growth factor-β1 (TGF-β1) stimulation, which then binds to sarcoplasmic/ER calcium ATPase 2 (SERCA2) to trigger ER stress and elevate its downstream protein kinase R-like ER kinase (PERK)–hairy and enhancer of split 1 (HES1) axis, ultimately promoting liver fibrosis. As a result, targeting APOL2 by DP or ablation of APOL2significantly impairs APOL2–SERCA2–PERK–HES1 signaling and mitigates fibrosis progression. Our findings not only define APOL2 as a novel therapeutic target for liver fibrosis but also highlight DP as a promising lead for treatment of this symptom.

Sonochemotherapy (SCT) has emerged as a powerful modality for cancer treatment by triggering excessive production of reactive oxygen species (ROS) and controlled release of chemotherapeutic agents under ultrasound. However, achieving spatiotemporally controlled release of chemotherapeutic agents during ROS generation is still an enormous challenge. In this work, we developed a cascade-activated nanoprodrug (CAN) system that utilizes a reversible covalent Schiff base mixed with a hypoxia-activatable camptothecin (CPT) prodrug. Briefly, the designed fluorinated CANsystem is self-assembled into nanoparticles under aqueous conditions, which could penetrate deep tumors to offer sufficient oxygen for ultrasound-triggered ROS production. Consequently, the nanoparticles substantially exacerbated the hypoxia of the tumor microenvironment (TME) by elevating oxygen consumption. The aggravated hypoxia in turn served as a positive amplifier to boost the tumor-specific CPT release of Azo-CPT prodrug, which made up for the insufficient treatment efficacy of sonodynamic therapy (SDT). On this basis, we observed a substantial reduction, approximately 3.5-fold, in the half-maximal inhibitory concentration (IC50) of the CANsystem compared to that of free CPT in bladder cancer cell lines (T24). Furthermore, the CANsystem demonstrated potent antitumor efficacy with reduced side effects, resulting in regression and eradication of T24 tumors in various mouse models. In summary, the CANsystem can be easily extended by incorporating different chemotherapeutic agents, showing great potential to revolutionize the clinical management paradigm of bladder cancer.

Two-dimensional 1T-TaS2is renowned for its exotic physical properties including superconductivity, Mott physics, flat-band electronics, and charge density wave (CDW) orders. In particular, the CDW phase transitions (PTs) in 1T-TaS2attracted extensive research interest, showing prominent potential in electronic devices. However, mechanisms underlying electrically driven PTs remain elusive. Here, we systematically studied the evolution of multistep PTs during the I–Vsweep in 1T-TaS2. Comprehensive investigations, covering variations in temperature, pulsed voltage duration, and light illumination, reveal that the underlying PT mechanism shifts from current-driven to thermally driven with increasing current. Initially, the current-driven PT step occurs at a constant current density, independent of the temperature. Subsequently, thermally driven PT steps manifest at a constant conductivity highly sensitive to the thermal effect. These transitions are strongly associated with the metastable CDW electronic structures and their response to carrier injection and thermal variations. Our findings reconcile long-standing debates regarding the electrically driven CDW PTs in 1T-TaS2.

All-hydrocarbon stapled peptides, with their covalent side-chain constraints, provide enhanced proteolytic stability and membrane permeability, making them superior to linear peptides. However, tools for extracting structural and physicochemical descriptors to predict the properties of hydrocarbon-stapled peptides are lacking. To address this, we present StaPep, a Python-based toolkit for generating 3D structures and calculating 21 features for hydrocarbon-stapled peptides. StaPep supports peptides containing two non-standard amino acids (norleucine and 2-aminoisobutyric acid) and six non-natural anchoring residues (S3, S5, S8, R3, R5, and R8), with customization options for other non-standard amino acids. We showcase StaPep’s utility through three case studies. The first generates 3D structures of these peptides with a mean RMSD of 1.62 ± 0.86, offering essential structural insights for drug design and biological activity prediction. The second develops machine learning models based on calculated molecular features to differentiate between membrane-permeable and non-permeable stapled peptides, achieving an AUC of 0.93. The third constructs regression models to predict the antimicrobial activity of stapled peptides against Escherichia coli, with a Pearson correlation of 0.84. StaPep’s pipeline spans data retrieval, structure generation, feature calculation, and machine learning modeling for hydrocarbon-stapled peptides. The source codes and data set are freely available on Github: https://github.com/dahuilangda/stapep_package.

BiVO4is a stable photocatalytic material but has poor photocatalytic activity in visible light. Herein, the surfactants were investigated to enhance the photocatalytic degradation of tetracycline (TC) of BiVO4through a hydrothermal method. The different molecular structures and properties of surfactant-modified BiVO4show clustered small spheres and stacked plate-like microcrystals. The N-hexadecyltrimethylammonium chloride (CTAC)-modified BiVO4(BiVO4-CTAC) with stacked plate-like morphology increases the light absorption range and decreases the energy band gap. Surfactants with different hydrophilic groups and molecular structures affect the formation process of BiVO4, regulating the morphology, crystal structure, and crystalline surface exposure of BiVO4. BiVO4-CTAC has demonstrated superior TC degradation efficiency compared with the original BiVO4(BiVO4-Blank). This enhancement is attributed to the observation in the Nyquist plot, where BiVO4-CTAC exhibits the smallest arc radius, indicative of reduced charge transfer resistance and improved charge separation. Furthermore, holes (h+) and superoxide radicals (•O2–) reactive species are the main active radicals for TC photocatalytic degradation. This study develops a novel method to synthesize monoclinic phase lamellar BiVO4materials by simply changing the surfactant type. This study holds potential implications for advancing surfactant-assisted synthesis of high-efficiency photocatalysts.

Arsenic in the flue gas has been widely reported as a common poison for SCR catalysts; however, an appropriate coping strategy is still lacking to improve the arsenic resistance performance. Herein, a unique AsOx/CeO2interface is constructed to transform arsenic from poison into active site with balanced acid-redox property, successfully achieving efficient NOxremoval. The optimized AsOx/CeO2exhibits high NOxremoval efficiency, four times that of the As-poisoned V2O5/TiO2catalyst, and even comparable to the state-of-the-art SCR catalysts. It was found that the As–O–Ce interfacial sites in oxygen-bridged As dimers on CeO2can provide both Lewis acid sites and active lattice oxygen species, enhancing the adsorption and activation of NH3to form key –NH2intermediates, thereby facilitating the NH3–SCR reaction. More surprisingly, a thin CeO2layer on the top of V2O5/TiO2can capture arsenic to protect catalysts from arsenic attacking, which improves the catalytic activity to 2.8 × 10–7mol g–1s–1, even higher than that of fresh V2O5/TiO2(2.0 × 10–7mol g–1s–1). Therefore, this strategy provides new ideas not only for designing antipoisoning SCR catalysts but also a feasible solution for the stable operation of commercial SCR catalysts in arsenic-containing flue gas.

Glutarimidedioxime is a cyclic amidoxime moiety formed during the synthesis of amidoxime-functionalized fibers and apparently facilitates the extraction of uranium from seawater. Herein, we comprehensively explore differences between molybdenum and vanadium coordinated by glutarimidedioxime. The high adsorption of vanadium is explained by the formation of rare nonoxido vanadium(V) complexes, where each bare V5+is coordinated with two tridentate glutarimidedioxime ligands. By contrast, molybdenum is coordinated by only one glutarimidedioxime ligand, and the oxido Mo═O bonds in molybdate cannot be displaced by the ligand. Under seawater conditions, vanadium is fully complexed. Meanwhile, approximately 25% of molybdenum ions are in the form of free molybdate even if the concentration of glutarimidedioxime is 100000 times that of molybdenum. Glutarimidedioxime was expected to be more stable in the presence of metal ions than without them. However, complexation with molybdenum accelerated the degradation of the glutarimidedioxime ligand to release hydroxylamine. Molybdenum(VI) was then reduced by hydroxylamine, which itself was oxidized into nitrosyl. Vanadium heavily outcompetes adsorption of uranium, while molybdenum causes the degradation of glutarimidedioxime; the latter issue has previously been neglected and was first reported here.

Recently, immunotherapy has redefined cancer treatment by promoting the rapid killing of tumor cells through the immune system. Herbal medicines have been increasingly used as adjunct therapies to complement cancer treatment along with chemotherapy and radiotherapy to delay tumor development, reduce pain, and prolong patient survival. However, the potential immunotherapeutic effects of these herbal derivatives are limited by their structural instability, poor membrane permeability, and low bioavailability. To address this issue, nanotechnology has been used to enhance the activity of active compounds. Therefore, this review focuses on the effectiveness of the active ingredients of herbal medicines in suppressing tumor progression by modulating both the innate and adaptive immune systems, challenges in their delivery, and the application of nanocarriers for the effective delivery of these herbal components.

Our study aimed to analyse the relationship between the time from waking to the first cigarette and incidence of atrial fibrillation (AF), and the modification role of genetic susceptibility.Shorter time from waking to the first cigarette was related to the elevated risk of incident AF.Genetic susceptibility to AF strengthened the gradient inverse association of time from waking to the first cigarette with the incidence of AF.Graphical Abstract

Detecting targets in dynamically changing clutter environments has always been challenging for radar techniques. The innovative matrix information geometry-based (MIG) detector, which equips with different geometric distances to discriminate the target and clutter on the Hermitian positive-definite manifold, treats the detection issue from a geometric point of view, however, the current geometric distances do not always perform satisfactory under the changing environment. To address this problem, this article proposes an adaptive log-Euclidean (LE) metric on the manifold to properly match the actual clutter environment and achieve satisfactory performance. Specifically, the adaptive LE metric is developed on a dimension-reduced manifold by deriving a discriminative manifold transformation, and the determination of the transformation is formulated as a joint optimization problem between the cell under test and the clutter centroid. In this study, manifold-based methods and an eigen-decomposition-based method are proposed to solve the optimization problem. Moreover, an adaptive LE metric-based MIG detector is designed, and its bounded constant false alarm rate property is analyzed. At last, both simulated data and real radar data are utilized to validate the performance of the proposed method. Particularly, in the unmanned aerial vehicle target detection experiment, the results show that the strong clutter is effectively suppressed and the target is simultaneously highlighted without any prior knowledge.

As a promising urban shared transport mode, the Customized Bus (CB) system has the potential to improve diversity and service quality in urban transportation. This paper is driven by the objective of minimizing costs while fulfilling all service requests. A mixed integer nonlinear programming model is developed for the CB service design problem that jointly optimizes routes, timetables (including the arrival and holding time of each vehicle at each stop), and request-route assignment schemes, with particular consideration for a heterogeneous fleet. The model is subsequently linearized and solved using a Column Generation (CG) based decomposition algorithm, which produces precise solutions for small and medium-scale cases. To address the challenge of solving large-scale cases, we hybridize an Improved Genetic Algorithm (IGA) into the CG framework (CG-IGA) to enhance efficiency in solving the pricing subproblem. Finally, two sets of numerical experiments, involving the Sioux Falls network and a real-world road network in Beijing, are conducted. Computational results show that: (1) the optimality can be achieved for small and medium-scale cases when applying the CG algorithm; (2) the CG-IGA exhibits an exceptional performance compared to other solving methods for large-scale cases in terms of optimality and time-efficiency; (3) the holding control strategy allows for trade-offs between timeout costs and operating costs while improving the flexibility of CB services; and (4) the application of heterogeneous fleets bring at least 17.28% reduction of operating costs and ensures high utilization of transport resources.

With the rise of artificial intelligence and big data applications, the general-purpose Von Neumann architecture is no longer capable of fulfilling the requirements of these application scenarios. The large amount of parallelizable and repeatable multiply-and-accumulate (MAC) operations in deep neural networks provide the possibility for the emergence of storage-computing integrated architectures. Current-based computation and quantization are employed to circumvent signal margin limitations on the power supply voltage of the computing unit, thereby facilitating low-power design. The proposed design is a computing-in-memory (CIM) circuit based on current sampling accumulation and applies a current-sensing analog-to-digital converter design that exhibits reduced sensitivity to parasitic capacitance compared to voltage-based analog-to-digital converters. Its power consumption is proportional to the input current, achieving higher area efficiency and energy efficiency gains. The design of the CIM circuit based on the current sampling in the 22-nm FDSOI process is fabricated with an area efficiency of 264 GOPS/mm2. The peak energy efficiency is 20.81 TOPS/W, and the inference accuracy reaches 92.11% when employed to VGG-16 under CIFAR-10 dataset.

The current spatial atmospheric forcing data cannot accurately depict the actual conditions of the Qinghai–Tibet Plateau (QTP), where monitoring stations are scarce and unevenly distributed. This deficiency in atmospheric data hinders accurate simulation of plateau permafrost changes on the plateau. In this study, we develop a new approach to evaluate regional permafrost changes, which does not rely on spatially distributed meteorological data but instead uses the regional climate change processes or temperature change rates. Centred on a transient heat conduction permafrost model, this approach was applied to the Qinghai Hoh Xil National Nature Reserve (referred to as Hoh Xil) within the QTP from 1960 to 2015, using the rate of air temperature change provided by the Wudaoliang Meteorological Station, the only national station in Hoh Xil. Simulation results showed that the difference between the simulated and observed change rates of mean annual ground temperature (MAGT) was less than 0.04 °C per decade from 2001 to 2015 at five long-term monitoring sites. The simulated ground temperature profiles in four boreholes from various permafrost zones revealed an error of less than 0.7 °C below 5 m in depth. Model validation demonstrates the reliability of this approach for predicting long-term permafrost changes. Future regional permafrost changes were further simulated based on the latest warming scenarios (BCC-CSM2-MR) from the Coupled Model Intercomparison Project Phase 6. Predictions revealed significant differences in the regional permafrost degradation rate under different climate warming scenarios. Under the most severe warming scenario (SSP5-8.5), permafrost in the study area is projected to still cover 72.2% of the total area by 2100, with most of the Hoh Xil's permafrost becoming warm (MAGT > −1 °C) permafrost. This approach not only facilitates the simulation of frozen ground changes in areas with few meteorological monitoring stations but also provides a new perspective for using coarse-resolution palaeoclimate data to investigate permafrost formation and evolution over long time scales.

Graph neural networks (GNNs) have recently received significant attention. Learning node-wise message propagation in GNNs aims to set personalized propagation steps for different nodes in the graph. Despite the success, existing methods ignore node priority that can be reflected by node influence and heterophily. In this paper, we propose a versatile framework PriPro, which can be integrated with most existing GNN models and aim to learn prioritized node-wise message propagation in GNNs. Specifically, the framework consists of three components: a backbone GNN model, a propagation controller to determine the optimal propagation steps for nodes, and a weight controller to compute the priority scores for nodes. We design a mutually enhanced mechanism to compute node priority, optimal propagation step and label prediction. We also propose an alternative optimization strategy to learn the parameters in the backbone GNN model and two parametric controllers. We conduct extensive experiments to compare our framework with other 12 state-of-the-art competitors on 10 benchmark datasets. Experimental results show that our framework can lead to superior performance in terms of propagation strategies and node representations.

Learning based approaches have witnessed great successes in blind single image super-resolution (SISR) tasks, however, handcrafted kernel priors and learning based kernel priors are typically required. In this paper, we propose a meta-learning and Markov Chain Monte Carlo (MCMC) based SISR approach to learn kernel priors from organized randomness. In concrete, a lightweight network is adopted as kernel generator, and is optimized via learning from the MCMC simulation on random Gaussian distributions. This procedure provides an approximation for the rational blur kernel, and introduces a network-level Langevin dynamics into SISR optimization processes, which contributes to preventing bad local optimal solutions for kernel estimation. Meanwhile, a meta-learning based alternating optimization procedure is proposed to optimize the kernel generator and image restorer, respectively. In contrast to the conventional alternating minimization strategy, a meta-learning based framework is applied to learn an adaptive optimization strategy, which is less-greedy and results in better convergence performance. These two procedures are iteratively processed in a plug-and-play fashion, for the first time, realizing a learning-based but plug-and-play blind SISR solution in unsupervised inference. Extensive simulations demonstrate the superior performance and generalization ability of the proposed approach when compared with the Start-of-the-Art solutions on synthesis and real-world datasets.

A fundamental limitation of object detectors is that they suffer from “spatial bias”, and in particular perform less satisfactorily when detecting objects near image borders. For a long time, there has been a lack of effective ways to measure and identify spatial bias, and little is known about where it comes from and what degree it is. To this end, we present a new zone evaluation protocol, extending from the traditional evaluation to a more generalized one, which measures the detection performance over zones, yielding a series of Zone Precisions (ZPs). For the first time, we provide numerical results, showing that the object detectors perform quite unevenly across the zones. Surprisingly, the detector's performance in the 96% border zone of the image does not reach the AP value (Average Precision, commonly regarded as the average detection performance in the entire image zone). To better understand spatial bias, a series of heuristic experiments are conducted. Our investigation excludes two intuitive conjectures about spatial bias that the object scale and the absolute positions of objects barely influence the spatial bias. We find that the key lies in the human-imperceptible divergence in data patterns between objects in different zones, thus eventually forming a visible performance gap between the zones. With these findings, we finally discuss a future direction for object detection, namely, spatial disequilibrium problem, aiming at pursuing a balanced detection ability over the entire image zone. By broadly evaluating 10 popular object detectors and 5 detection datasets, we shed light on the spatial bias of object detectors. We hope this work could raise a focus on detection robustness.

As earable devices have become indispensable smart devices in people's lives, earable-based user authentication has gradually attracted widespread attention. In our work, we explore novel in-ear breathing biometrics and design an earable-based authentication approach, named BreathSign, which takes advantage of inward-facing microphones on commercial earphones to capture in-ear breathing sounds for passive authentication. To expand the differences among individuals, we model the process of breathing sound generation, transmission, and reception. Based on that, we derive hard-to-forge physical-level features from in-ear breathing sounds as biometrics. Furthermore, to eliminate the impact of breathing behavioral patterns (e.g., duration and intensity), we design a triple network model to extract breathing behavior-independent features and design an online user template update mechanism for long-term authentication. Extensive experiments with 35 healthy subjects have been conducted to evaluate the performance of BreathSign. The results show that our system achieves the average authentication accuracy of 93.15%, 98.06%, and 99.74% via one, five, and nine breathing cycles, respectively. Regarding the resistance of spoofing attacks, BreathSign could achieve an average EER of approximately 3.5%. Compared with other behavior-based authentication schemes, BreathSign does not require users to perform complex movements or postures but only effortless breathing for authentication and can be easily implemented on commercial earphones with high usability and enhanced security.

The continuous innovation of automation technology is expanding its application in the workplace, with wide-ranging implications for the economy and society. However, it is not yet clear how workplace automation changes people’s consumption behavior. This paper conducts an empirical analysis in this regard based on the Chinese General Social Survey. The main results are shown as follows: (1) One standard deviation increase in automation contributes to an average reduction of 7.073 % in family consumption. This finding is validated by conducting several robustness and endogeneity checks using various measures of automation and consumption, instrumental variable approach, placebo analysis, etc. (2) The mechanism is that automation decreases family income and work-related social capital, resulting in a decline in families’ socioeconomic status and increased financial precarities. In addition, financial uncertainties brought about by automation decrease people’s subjective well-being, expectations for future life and risk appetite, thus prompting them to lower consumption as a precautionary measure to prepare for potential risks caused by the technological change. (3) Automation has greater negative effects on hedonic and developmental consumption, which are about three times the impact on non-hedonic and basic living expenses respectively, thus leading to a downgrade in families’ consumption structure. In addition, its effect is more pronounced for families with lower economic status, having no houses and living in urban areas. This study also highlights the importance of demand-side policies in the application of automation technology by finding that better labor protection is needed to mitigate automation’s adverse consequences for family consumption. In the context of automation’s increasingly profound impact on the society, this research has important policy implications.

Multichips paralleled insulated gate bipolar transistor (IGBT) power modules are widely employed in industrial and automotive power conversion systems. The asymmetry of the circuit topology and the differences in chip characteristics are the main reasons for the unbalanced current distribution among parallel chips, leading to excessive electrical stress in power modules, which in turn forces the power modules to operate at a reduced rated current. Additionally, the unbalanced current distribution inevitably results in different chip losses, which are further amplified under high current loading conditions, causing some chips to be overheated, and significantly reducing the reliability. Optimizing the circuit topology is a common method to improve current distribution, but global changes often entail higher costs and longer development cycles. Moreover, the electrical parasitic of the fast recovery diodes (FRD) chip branches are not given sufficient attention in circuit topology research. Under rectification and blocked conditions in electric vehicles, FRDs often become the bottleneck for lifespan due to excessively high junction temperatures. Therefore, this article conducts a comparative analysis of the circuit parasitic in IGBT and FRD loops with several typical layouts, without changing the substrate and chip dimensions, and proposes a current sharing slot structure design to balance the parasitic parameters. Compared with the direct bonded copper (DBC) layout of the typical EconoDUAL power module, the optimized module reduces the current imbalance of FRD from 45.5% to 11.6% and the switching loss can be reduced by 8.6%. By adopting the current sharing groove, the parasitic parameter distribution can be further improved while maintaining a fixed DBC layout, considering the IGBT and FRD branches in concert. Finally, the steady-state and transient current sharing characteristics were verified under constant current and inverter conditions. Under constant current conditions, the temperature difference of FRD in the upper and lower parallel layout modules was reduced by 16.7 °C, and the junction temperature of IGBT was significantly lowered. Under inverter conditions, the highest junction temperature of the columnar layout modules could be reduced by 10 °C, and the temperature difference of FRD decreased by 62%. This article significantly optimizes the current imbalance issue through layout design, reduces the thermal equilibrium difference of the module under application conditions, and is conducive to improving the output capacity and fatigue life of the module, providing a solution for the design of power modules with high junction temperature operation capability and high-reliability.

This article proposes a novel demagnetization fault diagnosis scheme for the interior permanent magnet (IPM) motor without parameter estimation. First, a feature enhancement-based disturbance observer is proposed, which incorporates the estimation error and the sliding mode surface as reinforcement factors for fault characteristics. Then, a sliding mode assessment-based demagnetization fault diagnosis method is developed, which takes the sliding mode variable structure signal of the disturbance observer as the carrier of demagnetization fault. Finally, the proposed method is validated by the hardware-in-the-loop-based platform and the real hardware platform. The test results show that the proposed method can achieve millisecond-level demagnetization fault diagnosis without being affected by mismatch in stator resistance of the IPM motor.