Search

Face anti-spoofing (FAS) and face forgery detection play vital roles in securing face biometric systems from presentation attacks (PAs) and vicious digital manipulation (e.g., deepfakes). Despite satisfactory performance upon large-scale data and powerful deep models, recent advances in face spoofing and forgery detection approaches usually focus on 1) unimodal visual appearance or physiological (i.e., remote photoplethysmography (rPPG)) cues; and 2) separated feature representation for FAS or face forgery detection. On one side, unimodal appearance and rPPG features are respectively vulnerable to high-fidelity face 3D mask and video replay attacks, inspiring us to design reliable multi-modal fusion mechanisms for generalized FAS. On the other side, there are rich common features across FAS and face forgery detection tasks (e.g., periodic rPPG rhythms and vanilla appearance for bonafides), providing solid evidence to design a joint FAS and face forgery detection system in a multi-task learning fashion. In this paper, we establish the first joint face spoofing and forgery detection benchmark using both visual appearance and physiological rPPG cues. To enhance the rPPG periodicity discrimination, we design a two-branch physiological network using both facial spatio-temporal rPPG signal map and its continuous wavelet transformed counterpart as inputs. To mitigate the modality bias and improve the fusion efficacy, we conduct a weighted batch and layer normalization for both appearance and rPPG features before multi-modal fusion. We also investigate prevalent deep models, feature fusion strategies and multi-task learning configurations for joint face spoofing and forgery detection. We find that the generalization capacities of both unimodal (appearance or rPPG) and multi-modal (appearance+rPPG) models can be obviously improved via joint training on these two tasks. We hope this new benchmark will facilitate the future research of both FAS and deepfake detection communities.

Measuring vital signs (VS) contained in the echoes is crucial to the analyses of breathing and heartbeat signals using medical radar. Although many advanced signal processing algorithms have been developed for radar-based VS measurement and make some improved progress, existing schemes cannot achieve a good estimation of echo phases modulated by the respiratory and cardiac activities with high accuracy or low computation, and thus resulting in serious performance degradation on the subsequent separation of breathing and heartbeat patterns as well as the assessment of breathing rate (BR), heart rate (HR), and heart rate variability (HRV). In this paper, we propose a simple yet effective method to measure VS for medical radar, named 3M method. Specifically, our method firstly introduces the Markov-Gauss model to obtain the recursive expression of the echo phases carrying VS, and secondly derive a simple observation equation (SOE) to reflect the relationship between the observed signal and VS of radar measurement. Thirdly, the aforementioned Markov-Gauss model and SOE are fused by Kalman filter to measure VS with accurate estimation. The 3M method demonstrates an elegant structure, low complexity and excellent features introduced by Kalman filter. Simulation results show the superiority of 3M over other methods. Then, we conduct extensive experiments with insightful visualizations to validate the effectiveness of the 3M method. Comparative results on different scenarios illustrate that the 3M method not only achieves state-of-the-art VS measurement performance but also expresses robust properties to HRV analysis.

Membrane-bound styrene oxide isomerase (SOI) catalyses the Meinwald rearrangement—a Lewis-acid-catalysed isomerization of an epoxide to a carbonyl compound—and has been used in single and cascade reactions. However, the structural information that explains its reaction mechanism has remained elusive. Here we determine cryo-electron microscopy (cryo-EM) structures of SOI bound to a single-domain antibody with and without the competitive inhibitor benzylamine, and elucidate the catalytic mechanism using electron paramagnetic resonance spectroscopy, functional assays, biophysical methods and docking experiments. We find ferric haem b bound at the subunit interface of the trimeric enzyme through H58, where Fe(III) acts as the Lewis acid by binding to the epoxide oxygen. Y103 and N64 and a hydrophobic pocket binding the oxygen of the epoxide and the aryl group, respectively, position substrates in a manner that explains the high regio-selectivity and stereo-specificity of SOI. Our findings can support extending the range of epoxide substrates and be used to potentially repurpose SOI for the catalysis of new-to-nature Fe-based chemical reactions.

Evenness of filament yarn is a crucial indicator that significantly impacts the quality of downstream textile products. Therefore, accurate real-time prediction and classification of the coefficient of variation (CV) value, which serves as an indicator of evenness, are of utmost importance. However, current detection methods predominantly rely on offline evenness testing devices, compromising the real-time capability and accuracy of evenness detection. To address this challenge, a semisupervised sequence Gaussian mixture variational autoencoder (VAE) model is developed for predicting and classifying the CV value. This model combines a mix VAE and a sequence-to-sequence structure, integrating a classifier to achieve semisupervised classification of time-series data. To validate the effectiveness of the proposed method, both software and hardware enhancements were implemented on the existing capacitance-based yarn evenness testing device, enabling uninterrupted measurement of yarn evenness and length. The collected data were then used to train the model. Experimental results demonstrate that the proposed model achieves an accuracy rate of 85% in classifying the CV value of the filament yarn.

Traditional treatments against advanced non-small cell lung cancer (NSCLC) with high morbidity and mortality continue to be dissatisfactory. Given this situation, there is an urgent requirement for alternative modalities that provide lower invasiveness, superior clinical effectiveness, and minimal adverse effects. The combination of photodynamic therapy (PDT) and immunotherapy gradually become a promising approach for high-grade malignant NSCLC. Nevertheless, owing to the absence of precise drug delivery techniques as well as the hypoxic and immunosuppressive characteristics of the tumor microenvironment (TME), the efficacy of this combination therapy approach is less than ideal. In this study, we construct a novel nanoplatform that indocyanine green (ICG), a photosensitizer, loads into hollow manganese dioxide (MnO2) nanospheres (NPs) (ICG@MnO2), and then encapsulated in PD-L1 monoclonal antibodies (anti-PD-L1) reprogrammed exosomes (named ICG@MnO2@Exo-anti-PD-L1), to effectively modulate the TME to oppose NSCLC by the synergy of PDT and immunotherapy modalities. The ICG@MnO2@Exo-anti-PD-L1 NPs are precisely delivered to the tumor sites by targeting specially PD-L1 highly expressed cancer cells to controllably release anti-PD-L1 in the acidic TME, thereby activating T cell response. Subsequently, upon endocytic uptake by cancer cells, MnO2catalyzes the conversion of H2O2to O2, thereby alleviating tumor hypoxia. Meanwhile, ICG further utilizes O2to produce singlet oxygen (1O2) to kill tumor cells under 808 nm near-infrared (NIR) irradiation. Furthermore, a high level of intratumoral H2O2reduces MnO2to Mn2+, which remodels the immune microenvironment by polarizing macrophages from M2 to M1, further driving T cells. Taken together, the current study suggests that the ICG@MnO2@Exo-anti-PD-L1 NPs could act as a novel drug delivery platform for achieving multimodal therapy in treating NSCLC.

To establish a reliable ultrasound (US) method of evaluating dynamic extrusion of lateral meniscus in healthy population, and to investigate the pattern of dynamic meniscus extrusion (ME) in lateral meniscus under loading conditions. The lateral ME was examined via US method in unloaded, double‐leg standing, and single‐leg standing positions. Two different US measurement methods were compared to the magnetic resonance imaging (MRI) results to determine the optimal measurement methods. The US results obtained by different researchers were tested for interobserver consistency and the results obtained by the same researcher on two separate days were tested for intraobserver consistency. The patterns of dynamic extrusion were compared between medial and lateral sides. A total of healthy 44 volunteers were included in the study, with 86 knees assessed by US, and 25 knees evaluated by MRI. The US evaluation of dynamic lateral ME demonstrated excellent interobserver and intraobserver reliability. The US measurements using method A were consistent with the MRI results with no significant difference (P= .861, intraclass correlation coefficient [ICC] = 0.868), while method B underestimated the lateral ME compared to MRI (P= .001, ICC = 0.649). Lateral ME decreased slightly from unloaded (1.0 ± 0.8 mm) to single‐leg standing position (0.8 ± 0.8 mm), whereas medial ME increased significantly in both double‐leg and single‐leg standing positions (2.4 ± 0.7 mm, 2.6 ± 0.7 mm). A novel US evaluation method of lateral ME was established with reliable and accurate results compared to the MRI. Lateral ME in healthy populations decreased slightly as the loadings increased, which was different from the pattern of dynamic extrusion in medial meniscus.

For industrial hydrogen purification in the methanol-reforming process, it is crucial to develop catalysts that exhibit excellent water tolerance for preferential CO oxidation (CO-PROX) in the presence of a high amount of H2O. Herein, the hydrophobic strategy was proposed by bridging stearic acid molecules on the surface of the Pt/Al2O3catalyst. The stearic acid molecules significantly enhanced the hydrophobicity of the Pt/Al2O3catalyst, increasing the water contact angle from 20.1° to 150.2°, avoiding the water coverage of active sites, and extending the lifetime of the catalyst. By modulating the stearic acid treatment, the formation of the R–O–Pt structure has been successfully avoided to a certain extent, maintaining the ability of CO linear adsorption on the Pt sites with the high Pt electron density. Compared with the Pt/Al2O3catalyst with poor stability, the hydrophobic Pt/Al2O3catalyst achieved a 100% CO conversion in the presence of CO2and H2O within the temperature range of 140–200 °C and maintained catalytic stability over 100 h. This work gives a novel approach to mitigate the adverse effects of water on the catalyst in realistic industrial CO preferential oxidation.

We report a visible-light-promoted cyclization/sulfonylation cascade of N-heterocycles with thianthrenium salts using DABSO as the SO2surrogate. This method features excellent functional group tolerance, wide substrate scope, and late-stage elaboration of bioactive relevant molecules. Mechanistic investigations reveal that the photoactive electron donor–acceptor (EDA) complexes between thianthrenium salts and DABCO are capable of the generation of aryl radicals, which induce the following SO2insertion by attacking DABSO, thus triggering the key radical cyclization step.

The ability to recognize and differentiate between conspecifics and heterospecifics as well as their signals is critical for the coexistence of closely related species. In the genus Rattus, species are morphologically similar and multiple species often coexist. Here, we investigated the interspecific recognition and signal differentiation of two sympatric rat species, the brown rat (Rattus norvegicus, RN) and the Asian house rat (Rattus tanezumi, RT). In a two-way choice test, both RN and RT females showed a preference for conspecific male rats to heterospecific ones. RT females showed a significant preference for accessible urine of males of same species to those of other species, but not for the inaccessible urine. On the other hand, there were significant differences in the structural characteristics of the ultrasonic vocalization emitted by males of these two rat species. Sodium dodecyl sulphate‒polyacrylamide gel electrophoresis (SDS‒PAGE) and isoelectric focusing electrophoresis unveiled that major urinary proteins (MUPs) in voided urine were more highly expressed in RN males versus RT males. The interspecific differences of urinary volatile compounds were also discussed. In conclusion, female rats had the ability to distinguish between males of either species.

Random forest model is the mainstream research method used to accurately describe the distribution law and impact mechanism of regional population. We took Shijiazhuang as the research area, with comprehensive zoning based on endowments as the modeling unit, conducted stratified sampling on a hectare grid cell, and systematically carried out incremental selection experiments of population density impact factors, optimizing the population density random forest model throughout the process (zonal modeling, stratified sampling, factor selection, weighted output). The results are as follows: (1) Zonal modeling addresses the issue of confusion in population distribution laws caused by a single model. Sampling on a grid cell not only ensures the quality of training data by avoiding the modifiable areal unit problem (MAUP) but also attempts to mitigate the adverse effects of the ecological fallacy. Stratified sampling ensures the stability of population density label values (target variable) in the training sample. (2) Zonal selection experiments on population density impact factors help identify suitable combinations of factors, leading to a significant improvement in the goodness of fit (R2) of the zonal models. (3) Weighted combination output of the population density prediction dataset substantially enhances the model’s robustness. (4) The population density dataset exhibits multi-scale superposition characteristics. On a large scale, the population density in plains is higher than that in mountainous areas, while on a small scale, urban areas have higher density compared to rural areas. The optimization scheme for the population density random forest model that we propose offers a unified technical framework for uncovering local population distribution law and the impact mechanisms.

Ophthalmic diseases such as central serous chorioretinopathy (CSC) significantly impair the vision of millions of people globally. Precise segmentation of choroid and macular edema is critical for diagnosing and treating these conditions. However, existing 3D medical image segmentation methods often fall short due to the heterogeneous nature and blurry features of these conditions, compounded by medical image clarity issues and noise interference arising from equipment and environmental limitations. To address these challenges, we propose the Spectrum Analysis Synergy Axial-Spatial Network (SASAN), an approach that innovatively integrates spectrum features using the Fast Fourier Transform (FFT). SASAN incorporates two key modules: the Frequency Integrated Neural Enhancer (FINE), which mitigates noise interference, and the Axial-Spatial Elementum Multiplier (ASEM), which enhances feature extraction. Additionally, we introduce the Self-Adaptive Multi-Aspect Loss ( $\mathcal {L}_{\textit {SM}}$ ), which balances image regions, distribution, and boundaries, adaptively updating weights during training. We compiled and meticulously annotated the Choroid and Macular Edema OCT Mega Dataset (CMED-18k), currently the world’s largest dataset of its kind. Comparative analysis against 13 baselines shows our method surpasses these benchmarks, achieving the highest Dice scores and lowest HD95 in the CMED and OIMHS datasets. Our code is publicly available at https://github.com/IMOP-lab/SASAN-Pytorch.

Traditional computing systems necessitate frequent data transfers and communication between physically separated chips for acquisition, storage, and computation. This challenge is further intensified during the complex training of recurrent neural networks (RNNs) for edge device applications. Neuromorphic computing systems based on artificial synapses (AS) demonstrate considerable potential in the field of brain-inspired information processing. In this work, we report an intelligent visual perception system utilizing IGZO/InHfOx nanowires (NWs)/IGZO-based neuromorphic phototransistors. We further exploited the device’s ability to process temporal visual information by leveraging its synaptic plasticity. By incorporating dynamic reservoir computing (RC) and a dedicated readout function, we achieved high-accuracy recognition of various standard digit categories. This work demonstrates a multiclassification hardware circuit built upon the intrinsic nonlinear sustained light response of the neuromorphic photodetector. The ZYNQ7020 development board, integrating the software programmability of a processor with the hardware programmability of a field-programmable gate array (FPGA), provides a more efficient and flexible hardware platform for neuromorphic computation based on these synaptic devices.

Current trackers only rely on a fixed target template to localize the target in each frame, which is however prone to fail in case of fast appearance changes or the presence of distractor objects. Having some historical knowledge about the tracked targets as well as their surrounding scenes can be highly beneficial for robust tracking. This historical information can be propagated through the sequence and used to timely perceive the change in target appearance and explicitly avoid distractor objects. In this work, we propose a Spatial-Temporal Context Attention (STCA) model which utilizes the appearance and state information of previously tracked targets as well as their surrounding scenes to more accurately localize the real target in the current frame. We embed an improved position encoder into the STCA, which enables the target template, context template and search patch to perform extensive interactional fusion through simultaneously self-attention and cross-attention calculation. By embedding the STCA module into Transformer, we construct a target-aware based online tracking network (named TATrack) that has a backbone to extract features better suited to the tracking task, a neck to further suppress distractors and highlight target, and a classification-regression head to make the tracking scores consistently reflect the quality of the bounding boxes. In addition, we also design a simple yet effective online updating approach to select high-quality context templates. Our tracker reaches the latest level on several benchmarks, including LaSOT, TrackingNet, GOT10k, OTB100 and UAV123. The code and trained models are available at https://github.com/hekaijie123/TATrack.

It has been well established that there is a connection between polycystic ovary syndrome pathology and gut microbiome dysbiosis. A marine-derived oligosaccharide, GV-971, has been reported to alter gut microbiota and alleviate Aβ amyloidosis. In this study, the effects of GV-971 on polycystic ovary syndrome–like mice were explored. Mice were randomly assigned into four groups: control, letrozole, letrozole + GV-971, and control + GV-971. Glucose metabolism in polycystic ovary syndrome–like mice was ameliorated by GV-971, while the reproductive endocrine disorder of polycystic ovary syndrome–like mice was partially reversed. The messenger ribonucleic acid levels of steroidogenic enzymes in ovaries of polycystic ovary syndrome–like mice were improved. GV-971 restored the fertility of polycystic ovary syndrome–like mice and significantly increase the number of litters. Furthermore, GV-971 treatment effectively mitigated abnormal bile acid metabolism. Notably, after GV-971 intervention, gut microbiota alpha-diversity was considerably raised and the relative abundance of Firmicuteswas reduced. In conclusion, the hyperinsulinemia and hyperandrogenemia of polycystic ovary syndrome–like mice were alleviated by GV-971 intervention, which was associated with mitigating bile acid metabolism and modulating gut microbiota.Sodium oligomannate (GV-971) treatment ameliorated metabolic disorders and reproductive dysfunction through remodeling gut microbiota in a letrozole-induced PCOS-like mice model.Graphical Abstract

•A heterogeneous neuron network is constructed with Rulkov and HR neurons.•The coupling strength dependant symmetric bifurcation scenario emerges.•Hidden homogeneous extreme multistability is observed.•A feedback control scheme is designed to select desired firing patterns.

With the explosive growth of scholarly information, researchers spend much time and effort copiously quoting authoritative works to support their ideas or motivations. We aim to alleviate this situation by proposing a citation recommendation strategy that recalls related papers for a rough idea (a piece of text, i.e., abstract, manuscript). However, the perspective of existing citation recommendations can not be well applied to our task for two defects. First, these methods neglect the reasoning of research topics, which makes the recommendation mechanism not meticulous enough and lacks explainability. For instance, they are not able to mine the hidden citing logic for the candidate paper while recommending. We fill the research gap by constructing structural topics consisting of knowledge concepts from the textual content, where reasoning paths between topics are extracted from an external knowledge graph. Second, the citation network is viewed as a crucial structural context to enhance the recommendation performance, but the new target idea does not have links to the citation network as published papers do. To simulate the prospective topological structure, our model, meanwhile, incorporates a contrastive-learning-based alignment paradigm to encourage the consistency of content embeddings and structure-oriented embeddings. We evaluate our proposed model on three real-world datasets and demonstrate that it significantly improves recommendation accuracy while providing high-quality knowledge-aware reasoning. And an interesting visual example illustrates the reasoning process when our model actually judges samples, which supports the feasibility of our topic-view learning paradigm.

Connected and autonomous vehicles offer the possibility to carry out control strategies, thus having great potential to improve traffic efficiency and road safety. The efficient passage of an emergency vehicle calls for the collaborative driving decision-making among multiple vehicles in a dynamically changing local area. However, existing work fails to efficiently adapt to dynamic and complex traffic conditions, thus cannot well solve the task. For better solution, we propose a flexible cooperative multi-agent reinforcement learning approach based on value function factorization, called Q-LSTM. Since the traffic environment is partially observable, the centralized training and decentralized execution paradigm is adopted to learn effective cooperative strategies for individual agents. To flexibly adapt to the changing neighborhood condition around the emergency vehicle, we introduce a long short-term memory network to decompose the learned global value function into local value function of each agent within the neighborhood, whose quantity and entities vary over time. To address the credit assignment problem and realize different roles of the emergency and regular vehicles, reward mechanism and the way agent-wise Q-networks update are well-designed. Extensive experiments are conducted on the Simulation of Urban MObility platform. Results show that our Q-LSTM outperforms state-of-the-art value-based MARL methods. Moreover, the robustness and adaptability of the Q-LSTM are verified in the cases of increased traffic density.

This survey study examined 164 in-service special education teachers’ perceptions of training strategies in their cross categorical teacher preparation program in the United Sates for developing knowledge and skills in systematic instruction, an evidence-based practice for students with extensive support needs. Both classroom-based and field-based training strategies were evaluated along with teachers’ perceptions of the contribution and importance of the various training strategies. Results from Chi-square tests, Pearson correlations, multivariate analysis of covariance, and repeated measures of analysis of covariance indicated that teachers felt prepared to implement systematic instruction after exiting their program and after teaching students with disabilities, and the perceived effectiveness of training strategies was related to teacher experience. Teachers perceived modeling and receiving performance feedback in both university classrooms and field-based settings to contribute to their knowledge and skill development in systematic instruction. We present the results in terms of implications for practice and future research.

Polycystic ovary syndrome (PCOS) is the leading cause of anovulatory infertility. Inadequate understanding of the ovulation drivers hinders PCOS intervention. Herein, we report that follicle stimulating hormone (FSH) controls follicular fluid (FF) glutamine levels to determine ovulation. Murine ovulation starts from FF-exposing granulosa cell (GC) apoptosis. FF glutamine, which decreases in pre-ovulation porcine FF, elevates in PCOS patients FF. High-glutamine chow to elevate FF glutamine inhibits mouse GC apoptosis and induces hormonal, metabolic, and morphologic PCOS traits. Mechanistically, follicle-development-driving FSH promotes GC glutamine synthesis to elevate FF glutamine, which maintain follicle wall integrity by inhibiting GC apoptosis through inactivating ASK1-JNK apoptotic pathway. FSH and glutamine inhibit the rapture of cultured murine follicles. Glutamine removal or ASK1-JNK pathway activation with metformin or AT-101 reversed PCOS traits in PCOS models that are induced with either glutamine or EsR1-KO. These suggest that glutamine, FSH, and ASK1-JNK pathway are targetable to alleviate PCOS.

An experimental study of single carbon dioxide (CO2) bubbles ascending in aqueous solutions of carboxymethyl cellulose (CMC) is performed as a means to obtain further understanding of the rising behavior and mass transfer of soluble bubbles in shear-thinning non-Newtonian fluids. High-speed photography and digital image processing technique are employed to quantify the characteristic parameters of the bubble rising process. The rising behavior of single CO2bubbles in a Newtonian fluid (tap water) is measured as a reference. It has been found that the rheology of the liquid phase significantly influenced the rising behavior of the bubbles. In CMC aqueous solutions, CO2bubbles mostly followed a straight rising path characterized by increasing aspect ratio and decreasing vertical velocity. Increase in initial bubble size led to bubble deformation in less concentrated CMC aqueous solutions which resulted in an irregular rising path. The evolution of bubble equivalent diameter can be mainly divided into two stages: a fast reduction stage followed by a slow one. Meanwhile, the variation of liquid-side mass transfer coefficient kLof CO2bubbles in tap water and CMC aqueous solution with different weight percentages is discussed. The mobility of gas-liquid interface is considered to have significant impact on gas-liquid mass transfer. A dimensionless correlation considering the rheology of the liquid phase is proposed to describe the evolution of the motion and deformation of single soluble CO2bubbles in infinite stagnant shear-thinning non-Newtonian liquids.

Repurposing existing drugs for cancer therapy has become an important strategy because of its advantages, such as cost reduction, effect and safety. The present study was designed to investigate the antimelanoma effect and possible mechanisms of action of nebivolol, which is an approved and widely prescribed antihypertensive agent. In this study, we explored the effect of nebivolol on cell proliferation and cell activity in melanoma in vitroand the potential antimelanoma mechanism of nebivolol through a series of experiments, including the analysis of the effects with regard to cell apoptosis and metastasis. Furthermore, we evaluated the antimelanoma effect on xenograft tumor models and inspected the antimelanoma mechanism of nebivolol in vivousing immunohistochemical and immunofluorescence staining assays. As results in this work, in vitro, nebivolol possessed a strong activity for suppression proliferation and cell cycle arrest on melanoma. Moreover, nebivolol significantly induced cell apoptosis in melanoma through a mitochondrial-mediated endogenous apoptosis pathway. Additionally, nebivolol inhibited melanoma cell metastasis. More importantly, nebivolol exhibited significantly effective melanoma xenograft models in vivo, which related to the mechanism of apoptosis induction, proliferation inhibition, metastasis blocking and angiogenesis arrest. Overall, the data of the present study recommend that nebivolol holds great potential in application as a novel agent for the treatment of melanoma.

Al coating was fabricated on the surface of Mg−Zn−Al−Sn−Mn alloy by cold spraying process to enhance its corrosion resistance. The effects of temperature and pressure of working gas on the microstructure and corrosion behaviors of Al coating were investigated. The results show that as gas temperature and pressure increase, the densification and thickness of Al coating obviously increase. High gas temperature declines the critical velocity of powder deposition and increases the deposition efficiency. High gas pressure not only improves the quality of mechanical bonding, but also provides strong tamping effect on the deposited coating. Due to the reduction of porosity, the corrosion resistance of Al coating is improved with the increase of gas temperature and pressure. The corrosion products of Al(OH)3and Al2O3can block the pores in Al coating and act as physical barriers to corrosion, which effectively protects Mg alloy from being corroded.

This study supports the association between apolipoproteins and cardiac health by conducting an analysis of the impact of ApoA1 and ApoB and the ratio of ApoB/A1 on cardiovascular mortality and all-cause mortality. These findings may provide some ideas for promoting public health.

To investigate the incidence, clinical and genetic characteristics of pediatric lymphoma patients of China with inborn errors of immunity (IEI)-related gene mutations, which have not been fully studied.

To get rid of the dilemma between the speed and the resolution of optical fiber Fabry–Perot (F-P) magnetic field sensor (MFS) demodulation algorithm and to address the problem of difficult detection in the low magnetic field area, the compact parallel dual neural network (PDNN) with wavelet decomposition (WD) was proposed for the first time to demodulate the magnetic field. PDNN consists of the prediction neural network (PNN) and the error compensation neural network (ECNN). PNN is used to predict the true magnet field and ECNN is designed to predict the error between the true value and predictive value. With the combination of the dual networks, PDNN achieves a higher accuracy and resolution compared with traditional series single networks. In addition, to reduce the computational cost of PDNN, a dimensionality reduction method based on WD is developed. It is demonstrated according to simulation and experiments that PDNN obtains stronger anti-interference capability, less computational cost, and higher demodulation resolution after the process of WD. Three-layer WD is used to extract the features of F-P MFS spectral signal, which can keep 13.8% signal energy and reduce the spectral data points by about eight times. PDNN with WD achieves 0.004 Gs demodulation resolution with 1 kHz in low magnetic field, which is applicable to all fields requiring high speed and resolution.

• Prominent behaviors such as EPSC, PPF, and SFPD are successfully simulated with power consumption as low as 22 pJ. • Owning to its good short-term (STP) and tunable amplitude-frequency characteristics, the as-constructed device can function as a biomimetic high-pass filter for picture edge detection. • After photoelectric-synergistic modulation, the high synaptic weights enable the device to simulate complex neural learning rules for neuromorphic applications, including gesture recognition, image perception in the visual system and classically conditioned reflexes. The development of high-performance neuromorphic phototransistors is of paramount importance for image perception and depth memory learning. Here, based on metal-oxide heterojunction architecture, artificial synaptic phototransistors with synaptic plasticity have been achieved, demonstrating an artificial synapse that integrates central and optic nerve functions. Thanks to the sensitive light-detection properties, the optical power consumption of such photonic artificial synapses can be as low as 22 pico-joules, which is extremely competitive compared with other pure metal oxide photoelectric synapses ever reported. What is more, owing to its good short-term (STP) and tunable amplitude-frequency characteristics, the as-constructed device can function as a biomimetic high-pass filter for picture edge detection. Dual-mode synaptic modulation has been performed, combining photonic pulse with gate voltage stimulus. After photoelectric-synergistic modulation, the high synaptic weights enable the device to simulate complex neural learning rules for neuromorphic applications, including gesture recognition, image perception in the visual system, and classically conditioned reflexes. These results suggest that the current oxide-based heterojunction architecture displays potential application in future multifunction neuromorphic devices and systems. [Display omitted] [ABSTRACT FROM AUTHOR]

Anti-PD-1 therapy and chemotherapy is a recommended first-line treatment for recurrent or metastatic nasopharyngeal carcinoma, but the role of PD-1 blockade remains unknown in patients with locoregionally advanced nasopharyngeal carcinoma. We assessed the addition of sintilimab, a PD-1 inhibitor, to standard chemoradiotherapy in this patient population.

A visible-light-enabled photoredox radical cascade cyclization of 2-vinyl benzimidazole derivatives is developed. This chemistry is applicable to a wide range of N-aroyl 2-vinyl benzimidazoles as acceptors, and halo compounds, including alkyl halides, acyl chlorides and sulfonyl chlorides, as radical precursors. The Langlois reagent also serves as an effective partner in this photocatalytic oxidative cascade process. This protocol provides a robust alternative for rendering highly functionalized benzo[4,5]imidazo[1,2-b]isoquinolin-11(6H)-ones

For the past several decades, climate change has been driving vegetation dynamics in arid regions worldwide. This study investigates vegetation dynamics and their links to climate from 1990 to 2020 in Xinjiang, China, using data from 30-m resolution land use and cover change, remote sensing, and climate reanalysis. Our approach encompasses a range of analytical techniques, including transfer matrix analysis, modeling, correlation, regression, and trend analysis. During the study period, there were major vegetation conversions from grassland to forestland in the mountains, and from cropland to grassland in the plains. Climate change emerged as an important trigger of these changes, as evidenced by the increase in net primary productivity in most vegetation types, except for cropland-grassland and grassland-cropland conversions. Precipitation and soil moisture were the most influential climatic factors, contributing 15.1% and 15.2%, respectively, to natural vegetation changes. The study also found that evapotranspiration serves as a key mechanism for moisture dissipation in the hydrological cycle of vegetation dynamics. The interplay between precipitation, soil moisture, and evapotranspiration is a critical pattern of climatic influence that shapes vegetation dynamics across zones of intersection, increase, decrease, and change. These insights are invaluable for informing vegetation conservation and development strategies in Xinjiang and other similar environments facing climate-driven ecological transitions.

• Flower-like superstructure of semiconducting boron carbon nitride was successfully synthesized. • FS-BCNNSs with optimized band structure promoted efficient photocatalytic H 2 O 2 production. • The catalytic role of carbon sites with controlled chemical environments was investigated. The self-assembly of two-dimensional (2D) semiconductor nanosheets into three-dimensional (3D) ordered superstructures represents an ingenious way to avoid aggregation, expose massive available active sites and benefit the mass transfer, which maximizes the advantages of the 2D nanostructures in photocatalysis. Herein, a flower-like superstructure of ternary semiconducting boron carbon nitride nanosheets (FS-BCNNSs) was synthesized through the morphology-preserved thermal transformation of a flower-like superstructure of boron-containing metal-organic framework nanosheets (FS-MOFNSs). Taking advantage of this functional superstructure, FS-BCNNSs was employed for the pioneering application in the field of photocatalytic hydrogen peroxide (H 2 O 2) production and exhibited excellent photocatalytic performance, yielding an impressive rate of 1415.9 μmol g−1 h−1 for the production of H 2 O 2. The results of this work offer not just a promising catalyst for photocatalytic H 2 O 2 production but also a facile strategy to fabricate unique superstructures constructed from 2D nanosheets for catalysis, energy conversion, and other related fields. [Display omitted] [ABSTRACT FROM AUTHOR]

Ni-rich layered ternary cathodes are promising candidates thanks to their low toxic Co-content and high energy density (∼800 Wh/kg). However, a critical challenge in developing Ni-rich cathodes is to improve cyclic stability, especially under high voltage (>4.3 V), which directly affects the performance and lifespan of the battery. In this study, niobium-doped strontium titanate (Nb-STO) is successfully synthesized via a facile solvothermal method and used as a surface modification layer onto the LiNi0.8Co0.1Mn0.1O2(NCM811) cathode. The results exhibited that the Nb-STO modification significantly improved the cycling stability of the cathode material even under high-voltage (4.5 V) operational conditions. In particular, the best sample in our work could provide a high discharge capacity of ∼190 mAh/g after 100 cycles under 1 C with capacity retention over 84% in the voltage range of 3.0–4.5 V, superior to the pristine NCM811 (∼61%) and pure STO modified STO-811-600 (∼76%) samples under the same conditions. The improved electrochemical performance and stability of NCM811 under high voltage should be attributed to not only preventing the dissolution of the transition metals, further reducing the electrolyte’s degradation by the end of charge, but also alleviating the internal resistance growth from uncontrollable cathode–electrolyte interface (CEI) evolution. These findings suggest that the as-synthesized STO with an optimized Nb-doping ratio could be a promising candidate for stabilizing Ni-rich cathode materials to facilitate the widespread commercialization of Ni-rich cathodes in modern LIBs.

We propose an effective strategy to significantly enhance the thermoelectric power factor (PF) of a series of 2D semimetals and semiconductors by driving them toward a topological phase transition (TPT). Employing first-principles calculations with an explicit consideration of electron–phonon interactions, we analyze the electronic transport properties of germanene across the TPT by applying hydrogenation and biaxial strain. We reveal that the nontrivial semimetal phase, hydrogenated germanene with 8% biaxial strain, achieves a considerable 4-fold PF enhancement, attributed to the highly asymmetric electronic structure and semimetallic nature of the nontrivial phase. We extend the strategy to another two representative 2D materials (stanene and HgSe) and observe a similar trend, with a marked 7-fold and 5-fold increase in PF, respectively. The wide selection of functional groups, universal applicability of biaxial strain, and broad spectrum of 2D semimetals and semiconductors render our approach highly promising for designing novel 2D materials with superior thermoelectric performance.

The synthesis of complex alkanes by the tetrafunctionalization of alkynes is limited and challenging. Herein, an unprecedented efficient geminal diazidation and dibromination of terminal alkynes is developed, which provides novel access to structurally diverse organic azides. The approach has exclusive chemo- and regioselectivity and features mild reaction conditions, good tolerance of various functional groups, and more crucially, no metal involved in the reaction, thereby benefiting the late-stage decoration of medicinal molecules. A mechanistic study showed that the current geminal diazidation and dibromination proceeds via a radical pathway.

High-entropy semiconductors are now an important class of materials widely investigated for thermoelectric applications. Understanding the impact of chemical and structural heterogeneity on transport properties in these compositionally complex systems is essential for thermoelectric design. In this work, we uncover the polar domain structures in the high-entropy PbGeSnSe1.5Te1.5system and assess their impact on thermoelectric properties. We found that polar domains induced by crystal symmetry breaking give rise to well-structured alternating strain fields. These fields effectively disrupt phonon propagation and suppress the thermal conductivity. We demonstrate that the polar domain structures can be modulated by tuning crystal symmetry through entropy engineering in PbGeSnAgxSbxSe1.5+xTe1.5+x. Incremental increases in the entropy enhance the crystal symmetry of the system, which suppresses domain formation and loses its efficacy in suppressing phonon propagation. As a result, the room-temperature lattice thermal conductivity increases from ?L= 0.63 Wm–1K–1(x= 0) to 0.79 Wm–1K–1(x= 0.10). In the meantime, the increase in crystal symmetry, however, leads to enhanced valley degeneracy and improves the weighted mobility from µw= 29.6 cm2V–1s–1(x= 0) to 35.8 cm2V–1s–1(x= 0.10). As such, optimal thermoelectric performance can be achieved through entropy engineering by balancing weighted mobility and lattice thermal conductivity. This work, for the first time, studies the impact of polar domain structures on thermoelectric properties, and the developed understanding of the intricate interplay between crystal symmetry, polar domains, and transport properties, along with the impact of entropy control, provides valuable insights into designing GeTe-based high-entropy thermoelectrics.

Programmed cell death protein 1 (PD-1) inhibitor sintilimab is effective in relapsed and refractory extranodal natural killer/T cell lymphoma (ENKTL), nasal type. We aimed to assess the safety and activity of sintilimab plus P-GEMOX (pegaspargase, gemcitabine, and oxaliplatin) in the first-line setting for advanced ENKTL.

Pure organic boron/nitrogen (B/N)-containing multiple resonance (MR) materials are very attractive emitters for the fabrication of narrowband organic light-emitting diodes (OLEDs). Recently, v-DABNA, having a meta-positioned double boron framework, has gained prominent recognition for its unprecedented narrowband blue emission at 468 nm. Therefore, creating narrowband green emitters by modification this particular framework is regarded as a promising endeavor. Herein, we showcase two narrowband emitters named BN-NAP and BN-ANAP, emitting green light at 511 and 518 nm with small full-width at half-maximum (FWHM) of 26 and 20 nm, respectively. Their synthesis by the fusion of the meta-positioned double boron framework with two naphthalene moieties is proven to be a simple yet effective tactic to red-shift emissions while keeping small FWHM. Utilizing BN-ANAP as the emitter, high-performance narrowband pure-green OLEDs have been assembled, achieving a maximum external quantum efficiency (EQEmax) as high as 21.0% with small efficiency roll-off and Commission International de l’Éclairage (CIE) coordinates of [026, 067].

Purpose: The purpose of this study is to propose a precise and standardized strategy for numerically simulating vehicle aerodynamics. Design/methodology/approach: Error sources in computational fluid dynamics were analyzed. Additionally, controllable experiential and discretization errors, which significantly influence the calculated results, are expounded upon. Considering the airflow mechanism around a vehicle, the computational efficiency and accuracy of each solution strategy were compared and analyzed through numerous computational cases. Finally, the most suitable numerical strategy, including the turbulence model, simplified vehicle model, calculation domain, boundary conditions, grids and discretization scheme, was identified. Two simplified vehicle models were introduced, and relevant wind tunnel tests were performed to validate the selected strategy. Findings: Errors in vehicle computational aerodynamics mainly stem from the unreasonable simplification of the vehicle model, calculation domain, definite solution conditions, grid strategy and discretization schemes. Using the proposed standardized numerical strategy, the simulated steady and transient aerodynamic characteristics agreed well with the experimental results. Originality/value: Building upon the modified Low-Reynolds Number k-e model and Scale Adaptive Simulation model, to the best of the authors' knowledge, a precise and standardized numerical simulation strategy for vehicle aerodynamics is proposed for the first time, which can be integrated into vehicle research and design. [ABSTRACT FROM AUTHOR]

Cross-coupling reactions represent an indispensable tool in chemical synthesis. An intriguing challenge in this field is to achieve selective cross-coupling between two precursors with similar reactivity or, to the limit, the identical molecules. Here we report an unexpected dehydrobrominative cross-coupling between 1,3,5-tris(2-bromophenyl)benzene molecules on silver surfaces. Using scanning tunneling microscopy, we examine the reaction process at the single-molecular level, quantify the selectivity of the dehydrobrominative cross-coupling, and reveal the modulation of selectivity by substrate lattice-related catalytic activity or molecular assembly effect. Theoretical calculations indicate that the dehydrobrominative cross-coupling proceeds via regioselective C–H bond activation of debrominated TBPB and subsequent highly selective C–C coupling of the radical-based intermediates. The reaction kinetics plays an important role in the selectivity for the cross-coupling. This work not only expands the toolbox for chemical synthesis but also provides important mechanistic insights into the selectivity of coupling reactions on the surface.

An unprecedented palladium-catalyzed and visible-light-driven relay reaction of allenylphosphine oxide with in situ generated nitrile imines is presented for the direct synthesis of highly valuable polyarylbipyrazole skeletons. This one-pot strategy involves double 1,3-dipolar cycloaddition and C(sp3)–P(V) bond cleavage under photocatalyst-free and mild reaction conditions. The approach features simple operation, a high step economy, and a broad substrate scope, affording the corresponding products in moderate to excellent yields.

Owing to the high H2O activity, the aqueous electrolyte in the Zn battery exhibits a narrow electrochemical window and inevitable hydrogen evolution reaction, limiting the anode utilization ratio and performance at high voltage. Carbonate ester, the well-developed electrolyte solvent in Li-ion batteries, exhibits aprotic properties and high anodic stability. However, its use in Zn metal batteries is limited due to the low solubility of Zn salts in carbonate esters. Herein, we propose a carbonate ester-based electrolyte (EC:DMC:EMC = 1:1:1 wt %), which contains a new Zn salt (Zn(BHFip)2) characterized by low cost, easy synthesis, and excellent aprotic solvent solubility. The BHFip–anion assists in forming Zn2+conductive SEI on the anode and decomposes at high voltage to generate a protective CEI layer on the cathode. The Zn//Zn symmetric cell using such electrolyte achieves a remarkable Zn utilization ratio of 91% for 125 h, which has rarely been reported before. Furthermore, the Zn//LiMn2O4full cell with an average operation voltage of 1.7 V demonstrates reliable cycling for 135 cycles with an N/P ratio of 1:1. In addition, the Zn//LiNi0.5Mn1.5O4full cell exhibits a high discharge median voltage exceeding 2.2 V for 280 cycles, with the high voltage plateau (above 2 V) constituting 82% of the total capacity.

Recent investigations have shown that the necroptosis of tissue cells in joints is important in the development of osteoarthritis (OA). This study aimed to investigate the potential effects of exogenous skeletal stem cells (SSCs) on the necroptosis of subchondral osteoblasts in OA. Human SSCs and subchondral osteoblasts isolated from human tibia plateaus were used for Western blotting, real-time PCR, RNA sequencing, gene editing, and necroptosis detection assays. In addition, the rat anterior cruciate ligament transection OA model was used to evaluate the effects of SSCs on osteoblast necroptosis in vivo. The micro-CT and pathological data showed that intra-articular injections of SSCs significantly improved the microarchitecture of subchondral trabecular bones in OA rats. Additionally, SSCs inhibited the necroptosis of subchondral osteoblasts in OA rats and necroptotic cell models. The results of bulk RNA sequencing of SSCs stimulated or not by tumor necrosis factor α suggested a correlation of SSCs-derived tumor necrosis factor α-induced protein 3 (TNFAIP3) and cell necroptosis. Furthermore, TNFAIP3-derived from SSCs contributed to the inhibition of the subchondral osteoblast necroptosis in vivo and in vitro. Moreover, the intra-articular injections of TNFAIP3-overexpressing SSCs further improved the subchondral trabecular bone remodeling of OA rats. Thus, we report that TNFAIP3 from SSCs contributed to the suppression of the subchondral osteoblast necroptosis, which suggests that necroptotic subchondral osteoblasts in joints may be possible targets to treat OA by stem cell therapy.Graphical Abstract

Background/objectives: Relationship between dietary intake and sensory sensitivity and intelligence in autism spectrum disorder (ASD) is not well described. This study aims to investigate nutrition status in children with ASD and ID and its association with sensory sensitivity and intelligence. Subjects/methods: 84 students (34 ASD and 48 ID) in a special education school were recruited. Dietary intakes were evaluated with 3-day food dairy. Sensory sensitivity was measured using short sensory profile (SSP). Wechsler IQ test were performed. Results: Two out of twenty-four investigated nutrients had an adequate intake rate of over 50%. Four out of ten investigated vitamins had an inadequate intake rate of over 90% and another two over 80%. 75.6% of participants had inadequate energy intake. After adjusting for age, ASD participants had a higher intake of all surveyed nutrients but only the intake of vitamin E and folate were statistically different (p< 0.05) along with a smaller number of inadequate nutrients (p< 0.001). Taste/smell sensitivity, movement sensitivity, underresponsive/seeks sensation, and total sensory sensitivity symptoms severity were slightly higher in ASD group than in ID group (all p< 0.05). After adjusting for IQ and sensory sensitivity score, difference of number of inadequate nutrients between ASD and ID group were not statistically significant (p= 0.193). Conclusions: Individuals with ASD had better dietary intake but worse sensory sensitivity compared to those with ID. Sensory sensitivity and intelligence are correlated with nutritional status in individuals with neurodevelopmental disorder.

Skeletal stem cells (SSC) have gained attentions as candidates for the treatment of osteoarthritis due to their osteochondrogenic capacity. However, the immunomodulatory properties of SSC, especially under delivery operations, have been largely ignored. In the study, we found that Pdpn+and Grem1+SSC subpopulations owned immunoregulatory potential, and the single-cell RNA sequencing (scRNA-seq) data suggested that the mechanical activation of microgel carriers on SSC induced the generation of Pdpn+Grem1+Ptgs2+SSC subpopulation, which was potent at suppressing macrophage inflammation. The microgel carriers promoted the YAP nuclear translocation, and the activated YAP protein was necessary for the increased expression of Ptgs2 and PGE2in microgels-delivered SSC, which further suppressed the expression of TNF-ɑ, IL-1β and promoted the expression of IL-10 in macrophages. SSC delivered with microgels yielded better preventive effects on articular lesions and macrophage activation in osteoarthritic rats than SSC without microgels. Chemically blocking the YAP and Ptgs2 in microgels-delivered SSC partially abolished the enhanced protection on articular tissues and suppression on osteoarthritic macrophages. Moreover, microgel carriers significantly prolonged SSC retention time in vivowithout increasing SSC implanting into osteoarthritic joints. Together, our study demonstrated that microgel carriers enhanced SSC reprogramming towards immunomodulatory phenotype to regulate macrophage phenotype transformation for effectively osteoarthritic therapy by promoting YAP protein translocation into nucleus. The study not only complement and perfect the immunological mechanisms of SSC-based therapy at the single-cell level, but also provide new insight for microgel carriers in stem cell-based therapy.