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Catalytic combustion provides a feasible technology to effectively avoid NOx formation due to the high temperature in the process of hydrogen combustion. Recently, the development of efficient and affordable catalysts has attracted extensive attention as the cost of noble metals remained high. In this study, a list of cordierite monolithic catalysts with low noble metal loading are prepared by introducing the appropriate amount of transition metals (Mn, Cu, Fe, Co, and Ce), and their catalytic performance are further investigated. On this basis, the study on catalytic stability and kinetic is carried out, and the synergistic interaction between the bimetals is revealed with comprehensive characterization methods. The results show that Cu could promote the reaction rate of the Pt-based catalyst which show the best catalytic performance with the Cu loading of 1 g/L. The ignition temperature T10 is 58.5 °C, and the burnout temperature T100 is 100 °C, with activation energy as low as 44.00 kJ/mol. The enhancement of the catalytic performance is attributed to the interaction between the bimetallic elements. On the one hand, the valence state of Cu and Pt is changed, and on the other hand the dispersion of Pt get improved, which contributed to the catalytic reaction rate.

In 2019, the world experienced the rapid outbreak of the Covid-19 pandemic creating an alarming situation worldwide. The virus targets the respiratory system causing pneumonia with other symptoms such as fatigue, dry cough, and fever which can be mistakenly diagnosed as pneumonia, lung cancer, or TB. Thus, the early diagnosis of COVID-19 is critical since the disease can provoke patients’ mortality. Chest X-ray (CXR) is commonly employed in healthcare sector where both quick and precise diagnosis can be supplied. Deep learning algorithms have proved extraordinary capabilities in terms of lung diseases detection and classification. They facilitate and expedite the diagnosis process and save time for the medical practitioners. In this paper, a deep learning (DL) architecture for multi-class classification of Pneumonia, Lung Cancer, tuberculosis (TB), Lung Opacity, and most recently COVID-19 is proposed. Tremendous CXR images of 3615 COVID-19, 6012 Lung opacity, 5870 Pneumonia, 20,000 lung cancer, 1400 tuberculosis, and 10,192 normal images were resized, normalized, and randomly split to fit the DL requirements. In terms of classification, we utilized a pre-trained model, VGG19 followed by three blocks of convolutional neural network (CNN) as a feature extraction and fully connected network at the classification stage. The experimental results revealed that our proposed VGG19 + CNN outperformed other existing work with 96.48 % accuracy, 93.75 % recall, 97.56 % precision, 95.62 % F1 score, and 99.82 % area under the curve (AUC). The proposed model delivered superior performance allowing healthcare practitioners to diagnose and treat patients more quickly and efficiently.

Real-time monitoring in smart grids requires efficient handling of massive amount of data. Fog cloud nodes can be strategically located within the smart grid to: pull readings from smart meters, implement local processing and control, and make all data available to the smart grid control center with minimum overall latency. Unlike existing studies in literature, we propose a novel Fog node allocation strategy that is tightly coupled with the power grid structure, and hence, accounts for the spatial distribution of data traffic sources (e.g., smart meters) within the power grid. Furthermore, the allocation strategy considers the diverse latency requirements of fixed scheduling and event driven data services within the power grid. The proposed allocation strategy first implements an unsupervised machine learning approach to determine initial number and locations of Fog nodes that can serve the data traffic with minimum overall latency. Then, a reinforcement-based mechanism is applied to minimize the required number of Fog nodes, and hence capital cost, through efficient mapping between Fog nodes and smart meters while still complying with the latency requirements. Our simulation studies demonstrate that a 50% reduction in required number of Fog nodes can be achieved while minimizing overall latency when the proposed allocation strategy is adopted.

The majority of cancer-related deaths globally are due to lung cancer, which also has the second-highest mortality rate. The segmentation of lung tumours, treatment evaluation, and tumour stage classification have become significantly more accessible with the advent of PET/CT scans. With the advent of PET/CT scans, it is possible to obtain both functioning and anatomic data during a single examination. However, integrating images from different modalities can indeed be time-consuming for medical professionals and remains a challenging task. This challenge arises from several factors, including differences in image acquisition techniques, image resolutions, and the inherent variations in the spectral and temporal data captured by different imaging modalities. Artificial Intelligence (AI) methodologies have shown potential in the automation of image integration and segmentation. To address these challenges, multimodal fusion approach-based U-Net architecture (early fusion, late fusion, dense fusion, hyper-dense fusion, and hyper-dense VGG16 U-Net) are proposed for lung tumour segmentation. Dice scores of 73% show that hyper-dense VGG16 U-Net is superior to the other four proposed models. The proposed method can potentially aid medical professionals in detecting lung cancer at an early stage.

Abstract Real‐time facial expression recognition is the basis for computers to understand human emotions and detect abnormalities in time. To effectively solve the problems of server overload and privacy information leakage, a real‐time facial expression recognition method based on iterative transfer learning and efficient attention network (EAN) for edge resource‐constrained scenes is proposed in this paper. Firstly, an EAN is designed with its parameter number and computation amount strictly limited by depth separable convolution and local channel attention mechanism. Then, the soft labels of facial expression data were obtained by EAN based on the idea of knowledge distillation, so as to provide more supervision information for the training process. Finally, an iterative transfer learning method of teacher‐student (T‐S) network was proposed; it refines the soft labels of the teacher network and further improves the recognition accuracy of the student network. The tests on the public datasets, FER2013 and RAF‐DB, show that this method can significantly reduce the model complexity and achieve high recognition accuracy. Compared with other advanced methods, the proposed method strikes a good balance between complexity and accuracy, and well meets the real‐time deployment requirements of facial expression recognition technology for edge resource‐constrained scenes.

In the near future, intelligent vehicles will be part of the Internet of Things (IoT) and will offer valuable services and opportunities that could revolutionise human life in smart cities. The Vehicular Ad-hoc Network (VANET) is the core structure of intelligent vehicles. It ensures the accuracy and security of communication in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) modes to enhance road safety and decrease traffic congestion. However, VANET is subject to security vulnerabilities such as denial-of-service (DoS), replay attacks and Sybil attacks that may undermine the security and privacy of the network. Such issues may lead to the transmission of incorrect information from a malicious node to other nodes in the network. In this paper, we present a biometrics blockchain (BBC) framework to secure data sharing among vehicles in VANET and to retain statuary data in a conventional and trusted system. In the proposed framework, we take advantage of biometric information to keep a record of the genuine identity of the message sender, thus preserving privacy. Therefore, the proposed BBC scheme establishes security and trust between vehicles in VANET alongside the capacity to trace identities whenever required. Simulations in OMNeT++, veins and SUMO were carried out to demonstrate the viability of the proposed framework using the urban mobility model. The performance of the framework is evaluated in terms of packet delivery rate, packet loss rate and computational cost. The results show that our novel model is superior to existing approaches.

In licensed shared access (LSA) systems, the protection of the incumbent in the shared spectrum may degrade the spectrum and energy efficiency of the licensee. In this paper, we examine the optimization of these two performance metrics in a LSA vertical sharing scenario between an airport incumbent, and a mobile network operator licensee. Considering a restriction zone of a pre-defined radius, we derive the probability of the incumbent's interference threshold and then formulate a power allocation scheme as a multi-objective optimization of both energy and spectrum efficiency. We then adopt the weighted sum method to convert this multi-objective optimization into a single objective optimization and convert that into a quasi concave optimization problem. The optimum power allocation is then obtained using fractional programming. We further investigate the impact of various critical operational parameters in conjunction with the two performance metrics. Simulation results indicate a significantly improved energy efficiency in the licensee network as well as the spectrum efficiency comparable to even when the LSA spectrum utilization is unrestricted by the incumbent's maximum interference threshold. Furthermore, we show that with careful selection of the licensee eNodeB coverage radius, transmit power, and users number per eNodeB coverage area, one can engineer the best possible trade-off between the spectrum and energy efficiency.

Due to the tremendous volume of data generated by urban surveillance systems, big data oriented low-complexity automatic background subtraction techniques are in great demand. In this paper, we propose a novel automatic background subtraction algorithm for urban surveillance systems in which the computer can automatically renew an image as the new background image when no object is detected. This method is both simple and robust with respect to changes in light conditions.

Cognitive radio (CR) enables unlicensed users to sense for and access underutilized licensed channels (or white spaces) owned by the licensed users in an opportunistic manner. Clustering segregates nodes in a network into logical groups called clusters. In CR networks (CRNs), larger cluster size improves network scalability thereby contributing to reduced routing overhead; however, it reduces cluster stability as the number of available common channels in a cluster reduces resulting in increased number of re-clusterings and clustering overhead. This paper presents our proposed first-of-its-kind cluster size adjustment scheme based on an artificial intelligence approach called reinforcement learning. The proposed scheme adapts the cluster size with the amount of white spaces as time goes by in order to improve network scalability and cluster stability in CRNs. Due to the lack of progress in the investigation of cluster size adjustment schemes in the literature, this paper also analyzes their attributes, and then presents such schemes investigated in various kinds of distributed wireless networks. Simulation results show that our proposed scheme improves network scalability by creating larger clusters, and improves cluster stability by reducing the number of re-clusterings (i.e., the number of cluster splits) and clustering overhead, while reducing interference between licensed and unlicensed users in CRNs.

Hybrid networks consisting of both millimeter wave (mmWave) and microwave $(\mu W)$ capabilities are strongly contested for next-generation cellular communications. A similar avenue of current research is device-to-device (D2D) communications, where users establish direct links with each other rather than using central base stations. However, a hybrid network, where D2D transmissions coexist, requires special attention in terms of efficient resource allocation. This paper investigates dynamic resource sharing between network entities in a downlink transmission scheme to maximize energy efficiency (EE) of the cellular users (CUs) served by either $(\mu W)$ macrocells or mmWave small cells while maintaining a minimum quality-of-service (QoS) for the D2D users. To address this problem, first, a self-adaptive power control mechanism for the D2D pairs is formulated, subject to an interference threshold for the CUs while satisfying their minimum QoS level. Subsequently, an EE optimization problem, which is aimed at maximizing the EE for both CUs and D2D pairs, has been solved. Simulation results demonstrate the effectiveness of our proposed algorithm, which studies the inherent tradeoffs between system EE, system sum rate, and outage probability for various QoS levels and varying densities of D2D pairs and CUs.

In this paper, we investigate the resource allocation problem for achieving spectral efficiency (SE) and energy efficiency (EE) tradeoff with users' minimum rate requirements in hybrid multi-carrier non-orthogonal multiple access (MC-NOMA) systems which incorporate both NOMA and orthogonal multiple access (OMA) modes into one unified framework. All the degrees of freedom involved in resource allocation, including the choice of multiple access (MA) modes, user clustering, subcarrier assignment, and power allocation, are jointly considered. We first formulate the SE-EE tradeoff as a multiobjective optimization (MOO) problem with minimum rate requirement constraints. Then, considering the non-convexity of the MOO problem, it is converted into a single-objective optimization (SOO) problem by utilizing weighted Tchebycheff method. Lagrangian dual decomposition and sequential convex programming are applied to solve the SOO problem. We propose a joint resource allocation algorithm which is applicable to the general case, where an arbitrary number of users can be multiplexed on the same subcarrier. Simulation results demonstrate that users' minimum rate requirements and channel conditions have great impact on the selection of MA modes. The proposed hybrid MC-NOMA mode significantly outperforms MC-NOMA and OMA in terms of SE-EE tradeoff, and the performance gain brought by four or more users sharing the same subcarrier is minimal. Meanwhile, the hybrid MC-NOMA also shows great potential to improve the tradeoff between fairness and system efficiency.

Non-orthogonal multiple access (NOMA) has recently received much attention as a candidate technique for the fifth generation networks. In this paper, considering both the direct and relay-aid paths, we investigate the performance of a downlink NOMA-based cooperative system and further analyze two different user ordering schemes. The outage probability, diversity gain, and ergodic rate are studied as three benchmarks to evaluate the system performance. For different user ordering schemes, the exact outage probabilities of users are first solved in the closed form. Then, the outage behavior in the high signal-to-noise ratio (SNR) region is discussed to obtain the diversity gain. In addition, the closed-form expression of ergodic rate for the strongest user and upper bounds for the rest users at high SNR is provided. Finally, numerical results verify the accuracy of our analysis and demonstrate that sorting users based on the relay-aided path can provide a larger ergodic sum rate in some cases. By contrast, sorting users based on the direct path can provide a better diversity gain, and the corresponding performance is less sensitive to relay's location.

This paper considers a line-of-sight underwater acoustic (UWA) sensor network consisting of M underwater sensor nodes randomly deployed according to uniform distribution within a vertical half-disc (the so-called trusted zone). The sensor nodes report their sensed data to a sink node on water surface on a shared UWA reporting channel in a time-division multiple-access fashion, while an active-yet-invisible adversary (so-called Eve) is present in the close vicinity who aims to inject malicious data into the system by impersonating some Alice node. To this end, this paper first considers an additive white Gaussian noise (AWGN) UWA channel, and proposes a novel, multiple-feature-based, two-step method at the sink node to thwart the potential impersonation attack by Eve. Specifically, the sink node exploits the noisy estimates of the distance, the angle of arrival, and the location of the transmit node as device fingerprints to carry out a number of binary hypothesis tests (for impersonation detection) as well as a number of maximum-likelihood (ML) hypothesis tests (for transmitter identification when no impersonation is detected). We provide closed-form expressions for the error probabilities (i.e., the performance) of most of the hypothesis tests. We then consider the case of a UWA with colored noise and frequency-dependent pathloss, and derive a ML distance estimator as well as the corresponding Cramer-Rao bound. We then invoke the proposed two-step, impersonation detection framework by utilizing distance as the sole feature. Finally, we provide detailed simulation results for both AWGN UWA channel and the UWA channel with colored noise. Simulation results verify that the proposed scheme is indeed effective for a UWA channel with the colored noise and frequency-dependent pathloss.

In this paper, we investigate the resource optimization problem for device-to-device (D2D) communications in the fifth-generation cellular networks, where multiple D2D links and cellular links share the same spectrum. A general framework is proposed to jointly optimize the energy efficiency (EE), spectral efficiency (SE), and queuing delay. Specifically, we formulate the problem as a stochastic optimization model aiming at maximizing the EE and SE concurrently under the network stability constraint, where subchannel allocation and power control are jointly optimized. Afterwards, with the help of Lyapunov techniques and weighted sum method, it is then transformed into a single-objective optimization problem, which is a mixed-integer and non-convex problem. Therefore, to solve this challenging subchannel allocation and power control problem with low computational complexity, we separate it into two levels of problems, and a two-stage iterative algorithm is proposed, which only requires polynomial computational complexity. Through theoretical analysis and numerical results, the effectiveness, convergence, and optimality of the proposed algorithm are validated.

In this paper, we present a novel power allocation scheme for multicarrier cognitive radio networks. The proposed scheme performs subchannel power allocation by incorporating primary users activity in adjacent cells. Therefore, we first define the aggregated subchannel activity index (ASAI) as an average indicator which characterizes the collective networkwide primary users’ communication activity level. The optimal transmit power allocation is then obtained with the objective of maximizing a total utility function at the secondary base station (SBS), subject to the maximum SBS transmit power, and collision probability constraint at the primary receivers. Utilizing ASAI, we further obtain an energy efficient power allocation for the secondary system. Optimal energy efficiency (EE) and spectral efficiency (SE) are contradicting objectives, and thus, there is a tradeoff between these two performance metrics. We also propose a design approach to handle this tradeoff as a function of the ASAI, which provides quantitative insights into efficient system design. In addition to a lower signaling overhead, the simulation results confirm that the proposed scheme achieves a significantly higher achievable rate. Simulation results further indicate that using ASAI enables obtaining an optimal operating point based on the tradeoff between EE and SE. The optimal operating point can be further adjusted by relaxing/restricting the sensing parameters depending on the system requirements.

The advent of deep-learning technology promises major leaps forward in addressing the ever-enduring problems of wireless resource control and optimization, and improving key network performances, such as energy efficiency, spectral efficiency, transmission latency, etc. Therefore, a common understanding for quantum deep-learning algorithms is that they exploit advantages of quantum hardware, enabling massive optimization speed ups, which cannot be achieved by using classical computer hardware. In this respect, this paper investigates the possibility of resolving the energy efficiency problem in wireless communications by developing a quantum neural network (QNN) algorithm of deep-learning that can be tested on a classical computer setting by using any popular numerical simulation tool, such as Python. The computed results show that our QNN algorithm can be indeed trainable and that it can lead to solution convergence during the training phase. We also show that the proposed QNN algorithm exhibits slightly faster convergence speed than its classical ANN counterpart, which was considered in our previous work. Finally, we conclude that our solution can accurately resolve the energy efficiency problem and that it can be extended to optimize other communications problems, such as the global optimal power control problem, with promising trainability and generalization ability.

Current resource allocation techniques in cellular networks are largely based on single-slope path loss model, which falls short in accurately capturing the effect of physical environment. The phenomenon of densification makes cell patterns more irregular; therefore, the multi-slope path loss model is more realistic to approximate the increased variations in the links and interferences. In this paper, we investigate the impacts of multi-slope path loss models, where different link distances are characterized by different path loss exponents. We propose a framework for joint user association, power and subcarrier allocation on the downlink of a heterogeneous network (HetNet). The proposed scheme is formulated as a weighted sum rate maximization problem, ensuring the users' quality-of-service requirements, namely users' minimum rate, and the base stations' (BSs) maximum transmission power. We then compare the performance of the proposed approach under different path loss models with demonstrate the effectiveness of dual-slope path loss model in comparison to the single-slope path loss model. Simulation results show that the dual-slope model leads to significant improvement in network's performance in comparison to the standard single-slope model by accurately approximating the path loss exponent dependence on the link distance. Moreover, it improves the user offloading from macrocell BS to small cells by connecting the users to nearby BSs with minimal attenuation. It has been shown that the path loss exponents significantly influence the user association lying across the critical radius in the case of the dual-slope path loss model.

The quality of experience (QoE) perceived by users is a critical performance measure for Web browsing. “Above-The-Fold” (ATF) time has been recently recognized and widely used as a direct measure of user-end QoE by a number of studies. To reduce the ATF time, the existing works mainly focus on reducing the delay of networking. However, we observe that the webpage structures and content orders can also significantly affect theWeb QoE. In this paper, we propose a novel optimization framework that reorders the webpage objects to minimize the user-end ATF time. Our core idea is to first identify the webpage objects that consume the ATF time but have no impact on the page experience and then change the positions of these objects to achieve the minimum ATF time. We implement this framework and evaluate its performance with popular websites. The results show that the ATF time is greatly reduced compared with the existing works, especially for complex webpages.

This work was supported in part by the China MOST Program of International S&T Cooperation under Grant 2016YFE0133000 and Grant 2016YFE0119000, in part by the National Natural Science Foundation of China (NSFC) under Grant 61461136004, in part by NSFC Major International Joint Research Project under Grant 61210002, in part by the Fundamental Research Funds for the Central Universities under Grant 2015XJGH011, in part by EU Horizon 2020 Program EXCITING project under Grant 723227, in part by EU FP7 CROWN project under Grant PIRSES-GA-2013-610524, and in part by the China International Joint Research Center of Green Communications and Networking under Grant 2015B01008.

In this paper, we investigate the resource allocation problem for multi-user full-duplex deviceto-device (D2D) underlay communication, considering both perfect channel state information (CSI) and statistical CSI scenarios. In perfect CSI scenario, the weighted sum-rate maximization problem under cellular users' minimum rate constraints is formulated as a mixed integer programming problem. To solve the challenging problem, we decouple it into two subproblems as power allocation and channel assignment. Then we proposed a power allocation algorithm based on difference of two convex functions programming and a channel assignment algorithm based on Kuhn-Munkres algorithm, respectively. In statistical CSI scenario, we formulate the resource allocation problem as an outage probability constrained weighted ergodic sumrate maximization problem. To solve the problem, the closed-form expressions of outage probability and weighted ergodic sum-rate are derived first. Then we decouple resource allocation problem into power allocation and channel assignment. An optimization solution that consists of a 2-D global searching and Kuhn-Munkres algorithm is then developed. Simulation results demonstrate that the proposed algorithms can improve the weighted sum-rate of full-duplex D2D communications significantly both in perfect CSI and statistical CSI scenarios and confirm the accuracy of our derived closed-form expressions.

This paper proposes a joint energy efficiency (EE) and spectrum efficiency (SE) tradeoff analysis as a multi-objective optimization problem (MOP) in the uplink of multi-user multi-carrier two-tier orthogonal frequency division multiplexing access heterogeneous networks subject to users' maximum transmission power and minimum rate constraints. The proposed MOP is modeled such that the network providers can dynamically tune the tradeoff parameters to switch between different communication scenarios with diverse design requirements. In order to find its Pareto optimal solution, the MOP is transformed, using a weighted sum method, into a single-objective optimization problem (SOP), which itself can further be transformed from a fractional form, by exploiting fractional programming, into a subtractive form. Since the formulated SOP is hard to solve due to the combinatorial channel allocation indicators, we reformulate the SOP into a better tractable problem by relaxing the combinatorial indicators using the idea of time-sharing. We then prove that this reformulated SOP is strictly quasi-concave with respect to the transmission power and the subcarrier allocation indicator. We then propose an iterative two-layer distributed framework to achieve an upper bound Pareto optimal solution of the original proposed MOP. The numerical simulations demonstrate the effectiveness of our proposed two-layer framework achieving an upper bound Pareto optimal solution, which is very close to an optimal solution, with fast convergence, lower and acceptable polynomial complexity, and balanced EE-SE tradeoff.

With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS).

To understand the possibility of monitoring the crustal stress and tectonic activities via satellite remote sensing technology, an experimental study focused on the thermal infrared variation was performed for cyclic loaded rock in the outdoor condition with two types of strong interference background. The stress-induced infrared radiation was extracted using wavelet analysis. The results showed that due to the significant effect of the ambient temperature, the weak stress-induced infrared signal was indistinguishable from the original infrared radiation. However, after wavelet decomposition, the infrared radiation concurrent with the change in stress became clear, and the correlation coefficient with the stress increased significantly with the value of 0.91 after decomposition. Additionally, the amplitude of the extracted stress-induced infrared signal was close to the theoretical result, indicating that the wavelet analysis method can extract the weak infrared signals induced by cyclic loading in the background of strong interference to some degree. The results provide an experimental basis and ideas for monitoring crustal stress and tectonic activities using thermal infrared remote sensing.

Previous studies have shown that thermal infrared radiation (TIR) changes with stress for loaded rocks. TIR changes were mainly attributed to temperature change without considering the change in surface emissivity. And it remains unclear whether there was a change in emissivity during the rock loading process. Therefore, based on the spectral radiance observations in this paper, an experimental study involving the emissivity variation in the 8.0–13.0 μm range for elastic loaded quartz sandstone under outdoor conditions was conducted. The experiments yield the following results. First, a variation in the stress condition led to the emissivity change in addition to the temperature change. The spectral radiance change was the combined result of the temperature changes and emissivity changes. Second, the emissivity changes linearly with the stress change, and the amplitude is relatively large in the 8.0–10.0 μm range. The waveband features of emissivity variation are the main factor leading to the waveband features of stress-induced radiance change. Third, the explanations for the changes in temperature and emissivity during loading process are analyzed. And the significance and difficulty for further satellite remote sensing purpose is discussed. The experimental results provide an experimental foundation for crustal stress field monitoring.

High quality photovoltaic (PV) power prediction intervals (PIs) are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance.

The landscape of human phosphorylation networks has not been systematically explored, representing vast, unchartered territories within cellular signaling networks. Although a large number of in vivo phosphorylated residues have been identified by mass spectrometry (MS)‐based approaches, assigning the upstream kinases to these residues requires biochemical analysis of kinase‐substrate relationships (KSRs). Here, we developed a new strategy, called CEASAR, based on functional protein microarrays and bioinformatics to experimentally identify substrates for 289 unique kinases, resulting in 3656 high‐quality KSRs. We then generated consensus phosphorylation motifs for each of the kinases and integrated this information, along with information about in vivo phosphorylation sites determined by MS, to construct a high‐resolution map of phosphorylation networks that connects 230 kinases to 2591 in vivo phosphorylation sites in 652 substrates. The value of this data set is demonstrated through the discovery of a new role for PKA downstream of Btk (Bruton's tyrosine kinase) during B‐cell receptor signaling. Overall, these studies provide global insights into kinase‐mediated signaling pathways and promise to advance our understanding of cellular signaling processes in humans.

Bacterial infection hampers wound repair by impeding the healing process. Concurrently, inflammation at the wound site triggers the production of reactive oxygen species (ROS), causing oxidative stress and damage to proteins and cells. This can lead to chronic wounds, posing severe risks. Therefore, eliminating bacterial infection and reducing ROS levels are crucial for effective wound healing. Nanozymes, possessing enzyme-like catalytic activity, can convert endogenous substances into highly toxic substances, such as ROS, to combat bacteria and biofilms without inducing drug resistance. However, the current nanozyme model with single enzyme activity falls short of meeting the complex requirements of antimicrobial therapy. Thus, developing nanozymes with multiple enzymatic activities is essential. Herein, we engineered a novel metalloenzyme called Ru-procyanidin nanoparticles (Ru-PC NPs) with diverse enzymatic activities to aid wound healing and combat bacterial infections. Under acidic conditions, due to their glutathione (GSH) depletion and peroxidase (POD)-like activity, Ru-PC NPs combined with H2O2 exhibit excellent antibacterial effects. However, in a neutral environment, the Ru-PC NPs, with catalase (CAT) activity, decompose H2O2 to O2, alleviating hypoxia and ensuring a sufficient oxygen supply. Furthermore, Ru-PC NPs possess exceptional antioxidant capacity through their superior superoxide dismutase (SOD) enzyme activity, effectively scavenging excess ROS and reactive nitrogen species (RNS) in a neutral environment. This maintains the balance of the antioxidant system and prevents inflammation. Ru-PC NPs also promote the polarization of macrophages from M1 to M2, facilitating wound healing. More importantly, Ru-PC NPs show good biosafety with negligible toxicity. In vivo wound infection models have confirmed the efficacy of Ru-PC NPs in inhibiting bacterial infection and promoting wound healing. The focus of this work highlights the quadruple enzymatic activity of Ru-PC NPs and its potential to reduce inflammation and promote bacteria-infected wound healing.

Abstract Understanding the burden associated with occupational asbestos exposure on a global and regional scale is necessary to implement coordinated prevention and control strategies. By the GBD Study 2019, we conducted a comprehensive assessment of the non-communicable diseases burden attributable to occupational asbestos exposure. In 2019, 239,330 deaths and 4,189,000 disability-adjusted life years (DALYs) worldwide due to occupational asbestos exposure occurred. 1990–2019, deaths and DALYs attributed to occupational asbestos exposure increased by 65.65% and 43.66%, respectively. Age-standardized mortality rate (ASMR) and age-standardized DALYs rate (ASDR) decreased, with the most rapid declines in high Socio-Demographic Index (SDI) regions, with average annual percent change (AAPC) of − 1.05(95%CI: −1.2, −0.89) and −1.53(95%CI: −1.71, −1.36), respectively. Lung cancer, mesothelioma and ovarian cancer were the top three contributors to the increase in deaths and DALYs, accounting for more than 96%. AAPCs of ASMR and ASDR were positively associated with SDI. Global deaths from occupational asbestos exposure were predicted to increase and ASMR to decrease by 2035, mostly in males. Due consideration should be given to the susceptibility of the elderly, the lag of asbestos onset, and the regional differences, and constantly improve the prevention and control measures of occupational asbestos exposure and related diseases.

Abstract Objective To investigate the effect of sequential distalization on increasing gaps in the maxillary anterior teeth, focusing on the control of torque and three-dimensional teeth movement during anterior retraction with clear aligners in extraction cases. Methods We recruited 24 patients who were undergoing extraction bilateral maxillary first premolars with clear aligners. According to a predetermined increment in the spaces between the maxillary anterior teeth, the patients were divided into three groups: those with no gap (9 cases), a 0.5 mm gap (6 cases) and a 1.0 mm gap (9 cases). In each group, a 2.0 mm en-mass retraction was applied on the anterior teeth. Plaster casts of the upper full dentition were obtained both before and after a 2 mm retraction. The palatal folds were used to overlap each pair of models. The three-dimensional movement of the teeth and the change of torque for the anterior teeth were subsequently analyzed using Geomagic Studio 2014 software. Results The change in torque in the groups with added gaps was significantly smaller than that in the group with no gaps (P 0.05). In the labial-lingual and vertical directions, the displacements of the central and lateral incisors were smaller in the groups with additional gaps compared to those in the groups without gaps (P 0.05). Then, a comparison was made between the displacement of the second premolar to the second molar in the mesial-distal direction across all groups. The study revealed that the anchorage molars in the group without gaps demonstrated significantly smaller displacement compared to those in the group with additional gaps (P

Fluoride is known to induce nephrotoxicity; however, the underlying mechanisms remain incompletely understood. Therefore, this study aims to explore the roles and mechanisms of lysosomal membrane permeabilization (LMP) and the GSDME/HMGB1 axis in fluoride-induced nephrotoxicity and the protective effects of rutin. Rutin, a naturally occurring flavonoid compound known for its antioxidative and anti-inflammatory properties, is primarily mediated by inhibiting oxidative stress and reducing proinflammatory markers. To that end, we established in vivo and in vitro models. In the in vivo study, rats were exposed to sodium fluoride (NaF) throughout pregnancy and up until 2 months after birth. In parallel, we employed in vitro models using HK-2 cells treated with NaF, n-acetyl-L-cysteine (NAC), or rutin. We assessed lysosomal permeability through immunofluorescence and analyzed relevant protein expression via western blotting. Our findings showed that NaF exposure increased ROS levels, resulting in enhanced LMP and increased cathepsin B (CTSB) and D (CTSD) expression. Furthermore, the exposure to NaF resulted in the upregulation of cleaved PARP1, cleaved caspase-3, GSDME-N, and HMGB1 expressions, indicating cell death and inflammation-induced renal damage. Rutin mitigates fluoride-induced nephrotoxicity by suppressing ROS-mediated LMP and the GSDME/HMGB1 axis, ultimately preventing fluoride-induced renal toxicity occurrence and development. In conclusion, our findings suggest that NaF induces renal damage through ROS-mediated activation of LMP and the GSDME/HMGB1 axis, leading to pyroptosis and inflammation. Rutin, a natural antioxidative and anti-inflammatory dietary supplement, offers a novel approach to prevent and treat fluoride-induced nephrotoxicity.

Abstract This prospective multicenter phase II study evaluated the clinical efficacy of neoadjuvant nivolumab-exclusive (N) and nivolumab–chemotherapy (N/C) combinations based on PD-L1 expression. Eligible patients exhibited resectable clinical stage IIA–IIIB (AJCC 8th edition) NSCLC without EGFR/ALK alterations. Patients received either mono-nivolumab (N) or nivolumab + nab-paclitaxel+ carboplatin (N/C) for three cycles based on PD-L1 expression. The primary endpoint was the major pathological response (MPR). Key secondary endpoints included the pathologic complete response (pCR), objective response rate (ORR), and event-free survival (EFS). Baseline PD-L1 expression and perioperative circulating tumor DNA (ctDNA) status were correlated with pCR and EFS. Fifty-two patients were enrolled, with 46 undergoing surgeries. The MPR was 50.0% (26/52), with 25.0% (13/52) achieving pCR, and 16.7% and 66.7% for patients with PD-L1 ≥ 50% in N and N/C groups, respectively. Thirteen (25.0%) patients experienced grade 3 or higher immune-related adverse events during neoadjuvant treatment. Patients with post-neoadjuvant ctDNA negativity was more likely to have pCR (39.1%) compared with those remained positive (6.7%, odds ratio = 6.14, 95% CI 0.84-Inf, p = 0.077). With a median follow-up of 25.1 months, the 18-month EFS rate was 64.8% (95% CI 51.9–81.0%). For patients with ctDNA– vs. ctDNA + , the 18m-EFS rate was 93.8% vs 47.3% (HR, 0.15; 95% CI 0.04, 0.94; p = 0.005). Immunochemotherapy may serve as an optimal neoadjuvant treatment even for patients with PD-L1 expression ≥ 50%. ctDNA negativity following neoadjuvant treatment and surgery could help identify superior pathological and survival benefits, which requires further confirmation in a prospective clinical trial (NCT04015778).

The COVID-19 pandemic, a significant public health emergency, has underscored the criticality of jobs–housing proximity. Static statistical research, however, struggles to uncover the mechanisms underlying jobs–housing balance, providing limited guidance for urban management. This paper adopts the concept of jobs–housing dynamic balance, analyzing the trends in jobs–housing balance in the metropolitan development area of Wuhan in the early and later period of the pandemic from the perspective of individual jobs–housing migration. Using mobile phone signaling data, we identified a stable population of 161,698 residents in June 2019, June 2021, and June 2023, and calculated jobs–housing synchronization and migration impact indices across seven regions. The study finds the following: (1) there is a pronounced misalignment of jobs–housing in the new cities of Wuhan’s suburbs, with clear asynchronous in-migration and out-migration; (2) COVID-19 initially led to a unidirectional exodus of the local population for job purposes, significantly contributing to regional jobs–housing imbalance, followed by a partial rebound in the later stages; and (3) the stability of jobs–housing balance in suburban new cities lacking policy support and comprehensive urban functions is worse, primarily due to insufficient employment resilience and the out-migration of the employed population. This paper puts forward a set of recommendations for the sustainable development of suburban new cities. It offers insights into the theoretical advancement of jobs–housing balance and the dynamic, refined transformation of urban studies, enhancing urban managers’ understanding of human–place interactions and new city construction.

Background: There is growing evidence indicating that environmental endocrine disruptors may influence the development of prostate cancer. Despite this, the connection between BPA and PSA levels is still not fully understood and appears intricate. In this study, we aimed to assess the link between BPA exposure and PSA levels using data from the NHANES database. Methods: We conducted a weighted linear regression, logistic regression analysis, natural cubic spline (NCS), subgroup analysis, and interaction analysis on 2768 participants. Urinary BPA was considered the independent variable, while PSA was the dependent variable. Results: In the study, the average age of the participants selected was 62.70 years (±12.93). Age was negatively correlated with BPA, while PSA and BMI were positively correlated with BPA concentration (all of the p-value < 0.05). In the fully adjusted model, the weighted linear and logistic regression results showed that BPA was positively correlated with PSA and prostate cancer. NCS analysis results show that BPA and PSA have a non-linear relationship. Sensitivity and subgroup analyses showed similar results. In addition, there were interactions between BPA and age, PIR, education, HbA1c, high-density lipoprotein, smoking status, and Diabetes. Conclusions: There was a positive correlation between urinary BPA and PSA in older American males, especially when the BPA concentration was higher than 4.46 ng/mL. In future practical applications of prostate cancer screening, it is crucial to focus on individuals aged 75 years and older, as well as those with a PIR between 0 and 1, non-Hispanic black, and other risk groups to provide reference values for the primary and secondary prevention of prostate cancer.

Brominated flame retardants (BFRs), commonly found in consumer products, have been identified as potential hazards to liver function. While the individual effects of specific BFRs are somewhat understood, there is limited evidence on how mixtures of these chemicals, especially when influenced by demographic factors, interact to affect liver function. This study utilized data from 10,828 participants aged 12 and above from the National Health and Nutrition Examination Survey (2005–2016) to investigate the associations between BFRs (both individually and in combinations) and biomarkers of liver injury. The study focused on how age, gender, and body mass index (BMI) modify modulate these effects. Multivariate linear regression, restricted cubic spline function, weighted quantile sum (WQS) regression, and quantile g-computation (qgcomp) models were used to analyze the linear, non-linear, and joint associations between BFR levels and liver function parameters. We found positive associations between the mixed BFRs index and AST, ALT, GGT, ALP, and TBIL levels and a negative association with ALB levels. PBDE28, PBDE47, and PBB153 consistently contributed to the top weight in both the WQS and qgcomp models. Most critically, the study demonstrated that the relationship between co-exposure to BFRs and liver function parameters was modified by age, gender, and BMI. Therefore, our study highlights the importance of considering demographic diversity in assessing the risk of BFR-induced liver damage and supports the implementation of tailored preventive and intervention strategies.

With the rapid development of the logistics industry, the demand for logistics activities is increasing significantly. Concurrently, growing urbanization is causing the space for logistics and warehousing to become limited. Thus, more and more attention is being paid to the planning and construction of logistics facilities. However, due to spatiotemporal trajectory data (such as truck GPS data) being used less often in planning, the method of quantitative analysis for freight spatiotemporal activity is limited. Thus, the spatial layout of logistics and warehousing land does not match the current demand very well. In addition, it is necessary to consider the interactive relationship with the urban built environment in the process of optimizing layout, in order to comprehensively balance the spatial coupling with the functions of housing, transportation, industry, and so on. Therefore, the layout of logistics and warehouse land could be treated as a multi-objective optimization problem. This study aims to establish a model for logistics and warehouse land layout optimization to achieve a supply–demand matching. The proposed model comprehensively considers economic benefits, time benefits, cost benefits, environmental benefits, and other factors with freight GPS data, land-use data, transportation network data, and other multi-source data. A genetic algorithm is built to solve the model. Finally, this study takes the Wuhan urban development area as an example to practice the proposed method in three scenarios in order to verify its effectiveness. The results show that the optimization model solves the problem of mismatch between the supply and demand of logistics spaces to a certain extent, demonstrating the efficiency and scientificity of the optimization solutions. Based on the results of the three scenarios, it is proven that freight activities could effectively enhance the scientific validity of the optimization solution and the proposed model could optimize layouts under different scenario requirements. In summary, this study provides a practical and effective tool for logistics- and warehouse-land layout evaluation and optimization for urban planners and administrators.

Background and aimsMaternal malnutrition is a major global public health problem that can lead to serious maternal diseases. This study aimed to analyze and predict the spatio-temporal trends in the burden of maternal disorders attributable to malnutrition, and to provide a basis for scientific improvement of maternal malnutrition and targeted prevention of maternal disorders.MethodsData on maternal disorders attributable to malnutrition, including number of deaths, disability-adjusted life years (DALYs), population attributable fractions (PAFs), age-standardized mortality rates (ASMRs), and age-standardized DALY rates (ASDRs) were obtained from the Global Burden of Disease Study 2019 to describe their epidemiological characteristics by age, region, year, and type of disease. A log-linear regression model was used to calculate the annual percentage change (AAPC) of ASMR or ASDR to reflect their temporal trends. Bayesian age-period-cohort model was used to predict the number of deaths and mortality rates to 2035.ResultsGlobal number of deaths and DALYs for maternal disorders attributable to malnutrition declined by 42.35 and 41.61% from 1990 to 2019, with an AAPC of –3.09 (95% CI: −3.31, −2.88) and –2.98 (95% CI: −3.20, −2.77) for ASMR and ASDR, respectively. The burden was higher among younger pregnant women (20–29 years) in low and low-middle socio-demographic index (SDI) regions, whereas it was higher among older pregnant women (30–39 years) in high SDI region. Both ASMR and ASDR showed a significant decreasing trend with increasing SDI. Maternal hemorrhage had the highest burden of all diseases. Global deaths are predicted to decline from 42,350 in 2019 to 38,461 in 2035, with the ASMR declining from 1.08 (95% UI: 0.38, 1.79) to 0.89 (95% UI: 0.47, 1.31).ConclusionMaternal malnutrition is improving globally, but in the context of the global food crisis, attention needs to be paid to malnutrition in low SDI regions, especially among young pregnant women, and corresponding measures need to be taken to effectively reduce the burden of disease.

Water-based or waterborne polyurethane matting resins find extensive application in surface coating to diminish gloss, offering a pleasant tactile experience and a matte aesthetic. This review represents the inaugural effort to consolidate the recent advancements in waterborne polyurethane matting resins, encompassing both physical and chemical matting types. The exploration commences with an introduction to a range of innovative matting agents tailored for the formulation of physical matting waterborne polyurethane resins. Subsequently, a thorough analysis and discussion unfold, delving into the synthesis, characterization, and matting mechanisms of chemical matting waterborne polyurethane resins. This comprehensive discussion draws upon a decade of dedicated research work by our group, contributing fresh perspectives to the evolution of chemical matting techniques. In conclusion, the review not only addresses the current state but also outlines potential challenges and future trends. This forward-looking perspective is intended to offer guidance for the design and synthesis of innovative waterborne polyurethane matting resins.

Urban agglomerations are important spatial carriers for regional economic development, but sustained urban expansion has triggered a series of issues such as conflicts between “people and land” and “built-up areas and green spaces”. However, many studies have rarely incorporated spatiotemporal patterns and driving mechanism into scenario simulation of urban growth, and has also overlooked subsequent discussions on the specific integration of simulation results with spatial planning. Considering the case of Min Delta Region, this study analyzes the spatiotemporal evolution of urban expansion based on multi-source data and explore the influential driving mechanism using Random forest algorithm. A combination of Markov and FLUS models was used to dynamically simulate future urban growth patterns. The results showed that 1) From 1995 to 2015, the urban expansion increased by 2.26 times, showing a trend of first accelerating and then slowing down. It was also discovered that there is an anisotropy toward expansion in the trajectory of urban growth. 2) With marginal increase and enclave increase, general urban expansion was observed to be changing from diffusion to agglomeration, and the urban expansion hotspots mainly located in the southeast coastal area. 3) Humanistic links, geographic position, social and economic considerations all played a role in the development of the Min Delta region, and can be summarized as the “four forces” driving mechanism model. 4) By 2035, the trend of continuous development with the Min Delta built-up area as the main body will be basically formed, and incorporating ecological restrictions can effectively restrain urban growth's encroachment into ecological spaces. Based on the simulation results, Min Delta region gradually showing a spatial structure evolution trend from the single core to the dual core mode, and then to the multi-center and networked mode. This study would serve as a multi-angle decision-making reference for regional spatial and ecological protection planning and urban growth management, and as a scientific foundation for high-quality development planning in the region.

Abstract Background Hepatocellular carcinoma still has a high incidence and mortality rate worldwide, and further research is needed to investigate its occurrence and development mechanisms in depth in order to identify new therapeutic targets. Ferritinophagy is a type of autophagy and a key factor in ferroptosis that could influence tumor onset and progression. Although, the potential role of ferritinophagy-related genes (FRGs) in liver hepatocellular carcinoma (LIHC) is unknown. Methods Single-cell RNA sequencing (scRNA-seq) data of LIHC were obtained from the Gene Expression Omnibus (GEO) dataset. In addition, transcriptome and clinical follow-up outcome data of individuals with LIHC were extracted from the The Cancer Genome Atlas (TCGA) dataset. FRGs were collected through the GeneCards database. Differential cell subpopulations were distinguished, and differentially expressed FRGs (DEFRGs) were obtained. Differential expression of FRGs and prognosis were observed according to the TCGA database. An FRG-related risk model was constructed to predict patient prognosis by absolute shrinkage and selection operator (LASSO) and COX regression analyses, and its prognosis predictive power was validated. Ultimately, the association between risk score and tumor microenvironment (TME), immune cell infiltration, immune checkpoints, drug sensitivity, and tumor mutation burden (TMB) was analyzed. We also used quantitative reverse transcription polymerase chain reaction (qRT-PCR) to validate the expression of key genes in normal liver cells and liver cancer cells. Results We ultimately identified 8 cell types, and 7 differentially expressed FRGs genes (ZFP36, NCOA4, FTH1, FTL, TNF, PCBP1, CYB561A3) were found among immune cells, and we found that Monocytes and Macrophages were closely related to FRGs genes. Subsequently, COX regression analysis showed that patients with high expression of FTH1, FTL, and PCBP1 had significantly worse prognosis than those with low expression, and our survival prediction model, constructed based on age, stage, and risk score, showed better prognostic prediction ability. Our risk model based on 3 FRGs genes ultimately revealed significant differences between high-risk and low-risk groups in terms of immune infiltration and immune checkpoint correlation, drug sensitivity, and somatic mutation risk. Finally, we validated the key prognostic genes FTH1, FTL, using qRT-PCR, and found that the expression of FTH1 and FTL was significantly higher in various liver cancer cells than in normal liver cells. At the same time, immunohistochemistry showed that the expression of FTH1, FTL in tumor tissues was significantly higher than that in para-tumor tissues. Conclusion This study identifies a considerable impact of FRGs on immunity and prognosis in individuals with LIHC. The collective findings of this research provide new ideas for personalized treatment of LIHC and a more targeted therapy approach for individuals with LIHC to improve their prognosis.

Since sheet metal exhibits significant anisotropy in processing and forming, which has a significant impact on its performance during processing, forming, and use, we explore the anisotropic behavior of materials in the forming process of sheet metal. The ability of the Yld2000-2d criterion to describe anisotropic behavior is analyzed, and its accuracy for characterization of the anisotropic behavior of metal plates is improved, based on which anisotropic behavior is predicted in three-dimensional space. Theoretical and experimental results on the anisotropy of sheet metal are compared, and two materials, 5754O aluminum alloy and DP980 steel plate, are tested and analyzed, and the anisotropic behaviors, such as three-point bending and cylindrical deep-drawing, are well predicted.

ABSTRACT Methicillin-resistant Staphylococcus aureus (MRSA) is a well-recognized cause of bacterial pneumonia in general. The gut microbiota and their metabolic byproducts act as important modulators of the gut-lung axis. Our investigation indicates a significant reduction in the abundance of butyrate producer unclassified_f__Lachnospiraceae within the lung and gut microbiota of MRSA-infected mice, as well as a significant decrease in the levels of butyrate in gut and serum. Additionally, supplementary sodium butyrate (NaB) significantly reduces bacteria colonization in the lung, suppresses pro-inflammatory cytokines expression, and enhances lung tissue morphology in MRSA-treated mice. The results of high-throughput 16S rDNA sequencing demonstrate that NaB reshapes the gut and lung microbiota by drastically reducing the abundance of potential pathogenic bacteria in the gut and cell motility-related bacteria in the lung, which are induced by MRSA. Moreover, NaB treatment augments the gut and circulating butyrate levels. Mechanistically, NaB promotes signal transducer and activator of transcription 1 (STAT1) acetylation and inhibits dimer STAT1 phosphorylation by reducing the binding of histone deacetylase 3 to STAT1, thereby altering alveolar macrophage polarization toward the M2 phenotype. Collectively, our findings suggest that NaB exerts a preventative effect against MRSA-induced pneumonia by enhancing the gut-lung microbiota and promoting macrophage polarization toward an anti-inflammatory M2 phenotype. The prophylactic administration of NaB emerges as a promising strategy for combating MRSA pneumonia. IMPORTANCE Pneumonia caused by methicillin-resistant Staphylococcus aureus (MRSA) continues to carry a high burden in terms of mortality. With the roles of gut microbiota in mediating lung diseases being gradually uncovered, the details of the molecular mechanism of the “gut-lung axis” mediated by beneficial microorganisms and small-molecule metabolites have gradually attracted the attention of researchers. However, further studies are still necessary to determine the efficacy of microbial-based interventions. Our findings indicate that sodium butyrate (NaB) alleviates MRSA-induced pulmonary inflammation by improving gut-lung microbiota and promoting M2 polarization of alveolar macrophages. Therefore, the preventive administration of NaB might be explored as an effective strategy to control MRSA pneumonia.

Abstract Background Anatomical variations often pose challenges to pulmonary surgery. Previous studies have mainly described the frequencies of bronchovascular anatomical variations in pulmonary segments, but did not determine the differences between pulmonary segments and the regularity behind these anatomical variations. Here, we attempted to investigate the regularity of bronchovascular anatomical variations in different pulmonary segments. Methods Thin‐slice enhanced computed tomography data of 800 cases from our center were included in this study. Digitalized three‐dimensional virtual lung segmentation was done, the dominant and inferior lung segments of the right upper lobe were defined, and the regularity of anatomical variations was explored. Results The mean volume ratio of the anterior segment of the right upper lobe (39.6 ± 8.6%) was highest, and that of the posterior segment (28.6 ± 7.9%) was lowest. Therefore, the dominant‐type segment (DS + SDS) was dominant in the anterior segment, accounting for 74.6% (597/800), and the inferior‐type segment (SIS + IS) was dominant in the posterior segment of the right upper lobe, accounting for 71.5% of cases (573/800). During the transformation of dominant and inferior lung segments, the corresponding regularity of anatomical variations could be displayed. For example, with an increase in the volume of the anterior segment of the right upper lobe, the occurrence rate of the bifurcated type of bronchus (B1 + 2, B3), the “central vein type” and the involvement of the trunk inferior and ascending artery in the blood supply of anterior segment gradually increased. Conclusions The existence of dominant segments will increase the diversity of anatomical variations and the complexity of pulmonary segmentectomy.

Point cloud object detection is a pivotal technology in autonomous driving and robotics. Currently, the majority of cutting-edge point cloud detectors utilize Bird’s Eye View (BEV) for detection, as it allows them to take advantage of well-explored 2D detection techniques. Nevertheless, dimensionality reduction of features from 3D space to BEV space unavoidably leads to information loss, and there is a lack of research on this issue. Existing methods typically obtain BEV features by collapsing voxel or point features along the height dimension via a pooling operation or convolution, resulting in a significant decrease in geometric information. To tackle this problem, we present a new point cloud backbone network for grid-based object detection, MDRNet, which is based on adaptive dimensionality reduction and multi-level spatial residual strategies. In MDRNet, the Spatial-aware Dimensionality Reduction (SDR) is designed to dynamically concentrate on the essential components of the object during 3D-to-BEV transformation. Moreover, the Multi-level Spatial Residuals (MSR) strategy is proposed to effectively fuse multi-level spatial information in BEV feature maps. Our MDRNet can be employed on any existing grid-based object detector, resulting in a remarkable improvement in performance. Numerous experiments conducted on nuScenes, KITTI and DAIR-V have shown that MDRNet surpasses existing SOTA approaches. In particular, on the nuScenes dataset, we attained an impressive 7.2% mAP and 5.0% NDS enhancement compared with CenterPoint.

Uncontrolled and persistent inflammation is closely related to numerous acute and chronic diseases. However, effective targeting delivery systems remain to be developed for precision therapy of inflammatory diseases. Herein we report a novel strategy for engineering inflammation-accumulation nanoparticles via phenolic functionalization. Different phenol-functionalized nanoparticles were first developed, which can undergo in situ aggregation upon triggering by the inflammatory/oxidative microenvironment. Phenolic compound-decorated poly (lactide-co-glycolide) nanoparticles, in particular tyramine (Tyr)-coated nanoparticles, showed significantly enhanced accumulation at inflammatory sites in mouse models of colitis, acute liver injury, and acute lung injury, mainly resulting from in situ cross-linking and tissue anchoring of nanoparticles triggered by local myeloperoxidase and reactive oxygen species. By combining a cyclodextrin-derived bioactive material with Tyr decoration, a multifunctional nanotherapy (TTN) was further developed, which displayed enhanced cellular uptake, anti-inflammatory activities, and inflammatory tissue accumulation, thereby affording amplified therapeutic effects in mice with colitis or acute liver injury. Moreover, TTN can serve as a bioactive and inflammation-targeting nanoplatform for site-specifically delivering a therapeutic peptide to the inflamed colon post oral administration, leading to considerably potentiated in vivo efficacies. Preliminary studies also revealed good safety of orally delivered TTN. Consequently, Tyr-based functionalization is promising for inflammation targeting amplification and therapeutic potentiation of nanotherapies.

A novel zero-reference low-light image-enhancement approach based on noise estimation (ZLEN) is proposed to mitigate noise interference in image-enhancement processes, while the tenets of zero-reference and lightweight network architecture are maintained. ZLEN improves the high-order curve expression governing the mapping of low-light images to their enhanced counterparts, addressing image noise through a meticulously designed noise-estimation module and a zero-reference noise loss function. First, the higher-order curve expression with a noise term is defined, and then the noise map undergoes feature extraction through the semantic-aware attention module; following this, the resulting features are integrated with the low-light image. Ultimately, a lightweight convolutional neural network is adjusted to estimate higher-order curve parameters that link the low-light image to its enhanced version. Notably, ZLEN achieves luminance enhancement and noise reduction without paired or unpaired training data. Rigorous qualitative and quantitative evaluations were conducted on diverse benchmark datasets, demonstrating that ZLEN attained state-of-the-art (SOAT) status among existing zero-reference and unpaired-reference image-enhancement methodologies, while it exhibited comparable performance to full-reference image-enhancement methods. To confirm the practicality and robustness of ZLEN, the luminance enhancement was applied to mine images, which yielded satisfactory results.

Pyroptosis provides a new window for relieving the tumor immunosuppressive microenvironment (TIM) and promoting systemic immune responses for tumor treatments. However, gasdermin D (GSDMD), a key protein in the pyroptosis process mediated by caspase-1, is low expressed in the majority of tumor cells and small-molecule inhibitors of DNA methylation suffer from nonspecific or single-function defects. To address these issues, hexahistidine (His6)-metal assembly (HmA) was employed as the drug delivery vector to load nigericin (Nig) and decitabine (DAC) affording a dual-drug delivery system (Nig + DAC)@HmA. The (Nig + DAC)@HmA nanoparticles are efficiently internalized by cells through endocytosis, easily escape from the lysosome, and are highly distributed in the tumor sites. DAC up-regulates the expression of GSDMD which is then cleaved by the nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome and caspase-1 protein activated by Nig. Effective cancer cell pyroptosis is thus achieved and induces a significant systemic antitumor immunity for impressive tumor suppression with negligible side effects in vivo. Our results suggest that such an easy-to-manipulate self-assembled nano-system (Nig + DAC)@HmA provides a new anticancer path by enhancing pyroptosis through reinforced inflammation.

Current studies of maize kernel development mostly focus on endosperm and embryo development, and little is known about the control of the nucellus tissue. Here, the authors report a NAC-EXPB15 pathway that regulate maize kernel size by modulating nucellus elimination and early endosperm development.

Abstract Objective In recent years, the lung cancer incidence has grown and the population is younger. We intend to find out the true detection rate of pulmonary nodules and the incidence of lung cancer in the population and search for the risk factors. Method Hospital employees ≥40 years old who underwent low‐dose computed tomography (CT) lung cancer screening from January 2019 to March 2022 were selected to record CT‐imaging characteristics, pathology, staging, and questionnaires to investigate past history, smoking history, diet, mental health, etc. PM2.5 and radiation intake in radiation‐related occupation received monitoring in hospital. Result The detection rate of suspicious pulmonary nodules was 9.1% (233/2552), and the incidence rate of lung cancer (including adenocarcinoma in situ) was 4.0% (103/2552). Morbidity among doctors, nurses, technicians, administers, and logistics was no difference (p = 0.184), but higher in women than in men (4.7% vs 2.4% p = 0.002). The invasiveness increased with age and CT density of nodules (p = 0.018). The relationship between lung cancer morbidity and PM2.5 was not clear (p = 0.543); and no lung cancer has been found in employees related ionizing radiation. Conclusion The high screening rate has brought about a high incidence of lung cancer. At present, the risk factor analysis of lung cancer based on small samples cannot find the direct cause. Most of the ground glass opacity (GGO)s detected by LDCT screening are indolent, but there are also rapidly progressive lung cancer. A predictive model to identify active and indolent GGO is necessary.