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In direction of arrival (DOA) estimation exploiting array motions, conventional designs consider sparse arrays moving along or perpendicular to the array direction, and only the second-order statistics are exploited. As the estimation performance is highly related to the synthetic virtual structure and source distribution, moving direction will definitely influence the performance. In this paper, we propose a generalized array synthesis method with a sparse linear array moving towards an arbitrary direction based on high-order difference co-arrays, where the physical array and moving sampling motions are specifically designed with improved degrees of freedom (DOFs). Unambiguity property of this arbitrary moving model is analyzed, followed by an affine transformation based unitary ESPRIT method for 2-D DOA estimation. The Cramér-Rao bound is derived and an iterative moving direction optimization method is then proposed for further performance improvement. Simulation results are provided to verify the superior performance of the synthetic model, showing the impact of moving direction on the performance, and a better performance is achieved by optimizing the moving direction iteratively.

Immunotherapy combined with chemotherapy regimen has been shown to be effective in recurrent or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC). However, due to the small number of patients, its efficacy remains controversial in Asian populations, particularly in mainland China. Here a randomized, double-blind phase 3 trial evaluated the efficacy and safety of finotonlimab (SCT-I10A), a programmed cell death 1 (PD-1) monoclonal antibody, combined with cisplatin plus 5-fluorouracil (C5F) for the first-line treatment of R/M HNSCC. Eligible patients (n= 370) were randomly 2:1 assigned to receive finotonlimab plus C5F (n= 247) or placebo plus C5F (n= 123). The primary endpoint was overall survival (OS). In the finotonlimab plus C5F group, OS was 14.1 months (95% confidence interval (CI) 11.1–16.4), compared with 10.5 months (95% CI 8.1–11.8) in the placebo plus C5F group. The hazard ratio was 0.73 (95% CI 0.57–0.95, P= 0.0165), meeting the predefined superiority criteria for the primary endpoint. Finotonlimab plus C5F showed significant OS superiority compared with C5F alone and acceptable safety profile with R/M HNSCC, supporting its use as a first-line treatment option for R/M HNSCC. These results validate the efficacy and safety of the combination of finotonlimab and C5F in Asian patients with R/M HNSCC. ClinicalTrials.gov identifier: NCT04146402.

Equilibrium optimizer (EO) is a novel metaheuristic algorithm that exhibits superior performance in solving global optimization problems, but it may encounter drawbacks such as imbalance between exploration and exploitation capabilities, and tendency to fall into local optimization in tricky multimodal problems. In order to address these problems, this study proposes a novel ensemble algorithm called hybrid moth equilibrium optimizer (HMEO), leveraging both the moth flame optimization (MFO) and EO. The proposed approach first integrates the exploitation potential of EO and then introduces the exploration capability of MFO to help enhance global search, local fine-tuning, and an appropriate balance during the search process. To verify the performance of the proposed hybrid algorithm, the suggested HMEO is applied on 29 test functions of the CEC 2017 benchmark test suite. The test results of the developed method are compared with several well-known metaheuristics, including the basic EO, the basic MFO, and some popular EO and MFO variants. Friedman rank test is employed to measure the performance of the newly proposed algorithm statistically. Moreover, the introduced method has been applied to address the mobile robot path planning (MRPP) problem to investigate its problem-solving ability of real-world problems. The experimental results show that the reported HMEO algorithm is superior to the comparative approaches.

The advanced development of soft yet tough hydrogels with high crack resistance significantly extends their application scope from biomedical applications for tissue engineering to emerging electronics applications for soft flexible devices. We investigated the fracture toughness of agar/PAAm hybrid double-network (DN) hydrogels at various strain rates. In large deformations without precut, the loading–unloading curves of agar/PAAm hybrid DN hydrogels exhibit rate-independent hysteresis. However, the fracture toughness of the precut agar/PAAm hybrid DN hydrogel exhibits a transition region around strain rate (ε̇) as 0.12 s–1, and rate-independent toughness is observed at ε̇ < 0.12 s–1, while the fracture toughness decreases significantly if ε̇ > 0.12 s–1. We also discover that the fracture toughness scales with strain rate as Γ ∼ (dλ/dt)−0.58at a high strain rate. A possible mechanism is proposed to interpret this phenomenon. It is believed this work provides new insight into the fracture behavior of hybrid DN gels.

A Si3N4-based antimicrobial coating was fabricated from suspensions with long-time stability. The stability of Si3N4suspension and the antimicrobial performance of coating were investigated. The viscosity of Si3N4suspension was found to be the key factor affecting both stability and antimicrobial performance. The Si3N4suspensions were modified with HASE acrylic copolymer, PAA-NH4, and Polyurethane, resulting in an improvement of the stability of Si3N4suspension from a few hours to six months. The coating produced using the modified Si3N4suspension exhibited an antibacterial activity of 99.9%. This study highlights the crucial role of modifying the Si3N4suspension in improving its long-term stability and coating antimicrobial activity.

Recently, bioinspired fluorescent materials have drawn ever-increasing attention due to their ecofriendliness and easy accessibility. Herein, we demonstrate that anthraquinone/metal ion coordination complexes can form well-defined crystals and possess obvious fluorescence enhancement properties. The fluorescence quantum yields of anthraquinone/metal ion assemblies are more than 2 orders of magnitude compared to those of anthraquinone assemblies. The electronic structures of the first excited singlet states of anthraquinone/metal ion molecules are obtained, and the mechanism of the fluorescence enhancement is elucidated. Such photoluminescent anthraquinone/metal ion crystals can be considered as efficient phosphors in fabricating light-emitting diodes. This work provides a simple route for the development of highly efficient natural fluorescent materials.

WiFi-based smart human sensing technology enabled by Channel State Information (CSI) has received great attention in recent years. However, CSI-based sensing systems suffer from performance degradation when deployed in different environments. Existing works solve this problem by domain adaptation using massive unlabeled high-quality data from the new environment, which is usually unavailable in practice. In this article, we propose a novel augmented environment-invariant robust WiFi gesture recognition system named AirFi that deals with the issue of environment dependency from a new perspective. The AirFi is a novel domain generalization framework that learns the critical part of CSI regardless of different environments and generalizes the model to unseen scenarios, which does not require collecting any data for adaptation to the new environment. AirFi extracts the common features from several training environment settings and minimizes the distribution differences among them. The feature is further augmented to be more robust to environments. Moreover, the system can be further improved by few-shot learning techniques. Compared to state-of-the-art methods, AirFi is able to work in different environment settings without acquiring any CSI data from the new environment. The experimental results demonstrate that our system remains robust in the new environment and outperforms the compared systems.

Developing novel porous adsorbents for efficient wastewater treatment is significant to the environment protection. Herein, three porous polycalix[n]arenes (n= 4, 6, and 8) which had varying cavity sizes of the macrocycle (Azo-CX4P, Azo-CX6P, and Azo-CX8P) were prepared under mild conditions and tested for their potential application in water purification. Azo-CX8P with a larger cavity size of the macrocycle outperformed Azo-CX4P and Azo-CX6P in screening studies involving a range of organic micropollutants. It was proved that Azo-CX8P was especially efficient in the removal of cationic dyes because of its high negative surface charge. In terms of the adsorption of Rhodamine B with Azo-CX8P, the pseudo-second-order rate constant reaches 5.025 g·mg–1·min–1with the maximum adsorption capacity being 1345 mg·g–1. These values are significantly higher compared with those recorded for most adsorbents. In addition, the easily prepared Azo-CX8P can be reused at least six times without a loss of the adsorption efficiency, demonstrating its potential use in water purification.

For the analysis of spinal and disc diseases, automated tissue segmentation of the lumbar spine is vital. Due to the continuous and concentrated location of the target, the abundance of edge features, and individual differences, conventional automatic segmentation methods perform poorly. Since the success of deep learning in the segmentation of medical images has been shown in the past few years, it has been applied to this task in a number of ways. The multi-scale and multi-modal features of lumbar tissues, however, are rarely explored by methodologies of deep learning. Because of the inadequacies in medical images availability, it is crucial to effectively fuse various modes of data collection for model training to alleviate the problem of insufficient samples. In this paper, we propose a novel multi-modality hierarchical fusion network (MHFN) for improving lumbar spine segmentation by learning robust feature representations from multi-modality magnetic resonance images. An adaptive group fusion module (AGFM) is introduced in this paper to fuse features from various modes to extract cross-modality features that could be valuable. Furthermore, to combine features from low to high levels of cross-modality, we design a hierarchical fusion structure based on AGFM. Compared to the other feature fusion methods, AGFM is more effective based on experimental results on multi-modality MR images of the lumbar spine. To further enhance segmentation accuracy, we compare our network with baseline fusion structures. Compared to the baseline fusion structures (input-level: 76.27%, layer-level: 78.10%, decision-level: 79.14%), our network was able to segment fractured vertebrae more accurately (85.05%).

There is a lack of effective treatments to overcome resistance to EGFR-TKIs in EGFR mutant tumors. A deeper understanding of resistance mechanisms can provide insights into reducing or eliminating resistance, and can potentially deliver targeted treatment measures to overcome resistance. Here, we identified that the dynamic changes of the tumor immune environment were important extrinsic factors driving tumor resistance to EGFR-TKIs in EGFR mutant cell lines and syngeneic tumor-bearing mice. Our results demonstrate that the acquired resistance to EGFR-TKIs is accompanied by aberrant expression of PD-L2, leading a dynamic shift from an initially favorable tumor immune environment to an immunosuppressive phenotype. PD-L2 expression significantly affected EGFR mutant cell apoptosis that depended on the proportion and function of CD8+T cells in the tumor immune environment. Combined with single-cell sequencing and experimental results, we demonstrated that PD-L2 specifically inhibited the proliferation of CD8+T cells and the secretion of granzyme B and perforin, leading to reduced apoptosis mediated by CD8+T cells and enhanced immune escape of tumor cells, which drives EGFR-TKIs resistance. Importantly, we have identified a potent natural small-molecule inhibitor of PD-L2, zinc undecylenate. In vitro, it selectively and potently blocks the PD-L2/PD-1 interaction. In vivo, it abolishes the suppressive effect of the PD-L2-overexpressing tumor immune microenvironment by blocking PD-L2/PD-1 signaling. Moreover, the combination of zinc undecylenate and EGFR-TKIs can synergistically reverse tumor resistance, which is dependent on CD8+T cells mediating apoptosis. Our study uncovers the PD-L2/PD-1 signaling pathway as a driving factor to mediate EGFR-TKIs resistance, and identifies a new naturally-derived agent to reverse EGFR-TKIs resistance.

Background and Objective: The study aimed to investigate the clinical efficacy of CT-guided microwave ablation (MWA) combined with 125I seed implantation or bronchial arterial infusion (BAI) chemotherapy in the treatment of malignant pulmonary tumors.Methods: A total of 56 patients who underwent MWA, MWA combined with 125I particle implantation, or MWA combined with BAI chemotherapy for advanced lung cancer or metastatic lung cancer from January 2015 to June 2021 in Guangdong Provincial People’s Hospital were enrolled. Among them, 21 patients were treated with MWA (MWA), 18 with MWA combined with 125I seed implantation (MWA+125I), and 17 with MWA combined with BAI chemotherapy (MWA+BAI). The short-term outcomes, complications, Eastern Cooperative Oncology Group (ECOG) performance score (Zubrod-ECOG-WHO, ZPS), survival, and factors related to survival were compared between the three groups.Results: The response rate of the MWA group (9.52%) was significantly lower than that of the MWA+125I group (50.00%) and MWA+BAI chemotherapy group (47.06%), and the differences were statistically significant (p < 0.05). The incidence of complications in the MWA, MWA+125I, and MWA+BAI chemotherapy groups was 47.62%, 55.56%, and 52.94%, respectively, with no significant difference (p > 0.05). Three months after the treatment, the ZPS of the MWA+125I and MWA+BAI chemotherapy groups was significantly lower than before treatment and significantly lower than that of the MWA group in the same period; the differences were statistically significant (p < 0.05). The median survival time of the MWA+125I group was 18 (9.983, 26.017) months and that of the MWA+BAI chemotherapy group was 21 (0.465, 41.535) months, both of which were higher than that of the MWA group [11 (6.686, 15.314) months]; the differences were statistically significant (p < 0.05). Cox regression analysis was performed on the factors related to survival and revealed treatment mode as a protective factor [HR = 0.433, 95% CI = (0.191, 0.984), p = 0.046]. Other factors, such as gender, age, and tumor size, did not independently affect survival.Conclusion: CT-guided MWA combined with 125I seed implantation and MWA combined with BAI chemotherapy are safe and effective for the treatment of advanced lung cancer and metastatic lung cancer, and can control tumor progression and prolong survival time.

The recognition of complex geographic scenes requires to simultaneously describe different kinds of relations such as hyponymy and spatial relations among multiple-scale categories. The former is hierarchical structure, and the latter is called eigen structure. Current researches focus on representing these relations in Euclidean space. However, the key characteristic of the Euclidean spaces is that it expands polynomially with respect to the radius, which makes it difficult to describe both the hierarchical and eigen structure within finite computable dimensions simultaneously. To address the above issues, a hyperbolic embedding model for representing complex geographic scenes (HEMR) is first proposed. First, the visual features of remote sensing objects are extracted through a deep neural network in Euclidean space and mapped onto the n-dimensional Poincare ball in hyperbolic space by an exponential projection. Second, a two-stage contrastive learning mechanism based on Möbius transformation is designed, which uses the property of exponential expansion to obtain the ability to represent different kinds of relations. Finally, a new quaternion loss is designed to describe the relations between feature categories in remote sensing scenarios by hyperbolic distance. The experiments indicate that our model can leverage the characteristic of spatial exponential growth with the radius in hyperbolic space, simultaneously describing the hierarchical and eigen structures of complex remote sensing scenes in finite dimensions. This provides support for downstream tasks such as semantic segmentation of remote sensing, knowledge graph representation, and link prediction.

Current spectral compressed sensing methods via Hankel matrix completion employ symmetric factorization to demonstrate the low-rank property of the Hankel matrix. However, previous non-convex gradient methods only utilize asymmetric factorization to achieve spectral compressed sensing. In this paper, we propose a novel nonconvex projected gradient descent method for spectral compressed sensing via symmetric factorization named Symmetric Hankel Projected Gradient Descent (SHGD), which updates only one matrix and avoids a balancing regularization term. SHGD reduces about half of the computation and storage costs compared to the prior gradient method based on asymmetric factorization. Besides, the symmetric factorization employed in our work is completely novel to the prior low-rank factorization model, introducing a new factorization ambiguity under complex orthogonal transformation. Novel distance metrics are designed for our factorization method and a linear convergence guarantee to the desired signal is established with $O(r^{2}\log(n))$ observations. Numerical simulations demonstrate the superior performance of the proposed SHGD method in phase transitions and computation efficiency compared to state-of-the-art methods.

A unified group-sparsity-based framework for wideband sparse spectrum fitting is proposed for wideband direction-of-arrival (DOA) estimation, which is capable of handling both uncorrelated and correlated sources. Then, by making four different assumptions on a priori knowledge about the sources, four variants under the proposed framework are formulated as solutions to the underdetermined DOA estimation problem without the need of employing sparse arrays. As verified by simulations, improved estimation performance can be achieved by the wideband methods compared with narrowband ones, and adopting more a priori information leads to better performance in terms of resolution capacity and estimation accuracy.

The blockchain has been the main computing scenario for smart contracts, and the decentralized infrastructure of the blockchain is effectively implemented in a de-trusted and executable environment. However, vulnerabilities in smart contracts are particularly vulnerable to exploitation by malicious attackers and have always been a key issue in blockchain security. Existing traditional tools are inefficient in detecting vulnerabilities and have a high rate of false positives when detecting contracts. Some neural network methods have improved the detection efficiency, but they are not competent for fine-grained (code line level) vulnerability detection. We propose the ContractCheck model for detecting contract vulnerabilities based on neural network methods. ContractCheck extracts fine-grained segments from the abstract syntax tree (AST) and function call graph of smart contract source code. Furthermore, the segments are parsed into token flow retaining semantic information as uint, which are used to generate numerical vector sequences that can be trained using neural network methods. We conduct multiple rounds of experiments using a dataset constructed from 36,885 smart contracts and identified the optimal ContractCheck model structure by employing the Fasttext embedding vector algorithm and constructing a composite model using CNN and BiGRU for training the network. Evaluation on other datasets demonstrates that ContractCheck exhibits significant improvement in contract-level detection performance compared to other methods, with an increase of 23.60% in F1 score over the best existing method. Particularly, it achieves fine-grained detection based on neural network methods. The cases provide indicate that ContractCheck can effectively assist developers in accurately locating the presence of vulnerabilities, thereby enhancing the security of Ethereum smart contracts.

Recently, WiFi-based human activity recognition (HAR) has emerged as a promising technique for human–computer interactions, owing to its widespread availability and noninvasiveness. However, deploying WiFi-based HAR systems in new environments often results in performance degradation. Existing WiFi-based HAR systems across different environments typically assume identical category spaces between the source and target, an assumption challenged by practical scenarios. In this article, we present WiCAU, a comprehensive adaptation with uncertainty awareness for WiFi-based HAR across environments, designed to tackle the challenges of environments with unequal category spaces—a scenario known as partial domain adaptation (PDA). Different from conventional PDA methods that usually focus on training the feature extractor to align feature distributions or implement separate reweighting models to adjust source domain feature weights, WiCAU integrates feature alignment and source data reweighting to mitigate the risk of negative transfer. It also introduces an uncertain complement entropy to effectively handle uncertainty within the source environment. Moreover, WiCAU uses a hybrid network that combines wavelet analysis with deep neural networks to capture both the temporal and spatial dynamics present in WiFi channel state information (CSI) data. WiCAU’s superior performance in PDA scenarios for HAR is demonstrated through comprehensive experiments with both self-built and publicly available datasets.

Human activity recognition (HAR) is an important task due to its far-reaching applications, such as surveillance, healthcare systems, and human-computer interaction. Recently, Channel State Information (CSI)-based HAR has attracted increasing attention in the research community due to its ubiquitous availability, good user privacy, and fewer constraints on working conditions. Most of the existing methods for CSI-based HAR use various deep learning models, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Transformers, to distinguish activities based on their temporal patterns. Despite their remarkable effectiveness, these methods solely focus on temporal patterns while ignoring the correlations among sub-carriers. This limitation prevents them from achieving further performance improvement. Moreover, recent works often involve advanced yet massive and inefficient neural architectures, like Transformers, to obtain satisfactory recognition accuracy. The performance gain is traded off with a steep increase in model complexity, which leads to low efficacy and high training/inference costs outsides the small time window. To address these issues, we propose a lightweight CSI-based HAR model. Our model makes the first effort to explore the graphical correlations of CSI sub-carriers, working in conjunction with a temporal causal convolution module. The high efficacy design enables our model to be highly effective without requiring excessive model complexity. Extensive experiments conducted on four real-world datasets demonstrate that our model outperforms state-of-the-art methods, including a strong Transformer-based baseline. It achieves an average improvement of 8 percentage points in recognition accuracy, with only 10% of the parameters compared to the Transformer-based method (4.95M vs. 49.24M). Additionally, our model is significantly faster, with empirical training and execution times at least 2.07 times faster than the baseline.

The traditional simultaneous localization and mapping (SLAM) systems rely on the assumption of a static environment and fail to accurately estimate the system’s location when dynamic objects are present in the background. While learning-based dynamic SLAM systems have difficulties in handling unknown moving objects, geometry-based methods have limited success in addressing the residual effects of unidentified dynamic objects on location estimation. To address these issues, we propose an anti-dynamics two-stage RGB-D SLAM approach. In the first stage, we identify potential motion regions for both known and unknown dynamic objects and rapidly generate pose estimates through optical flow tracking and model generation techniques. In the second stage, dynamic features within each frame are eliminated through dynamic assessment. For unidentified dynamic objects, we propose an approach involving superpixel extraction and geometric clustering to delineate potential motion regions based on color and geometric cues within the image. We conducted extensive experiments using public datasets and real-world scenarios, which demonstrated that our method surpasses current state-of-the-art (SOTA) dynamic SLAM techniques on public datasets. Our method’s robustness was also confirmed through experiments in real scenes featuring objects moving at varying speeds.

A tandem Wheatstone bridge low-pressure (TWBLP) sensor with high sensitivity which can be widely used in medical and industrial fields has been designed and fabricated. Compared with the conventional low-pressure (CLP) sensor, the TWBLP sensor membrane is arranged with eight piezoresistors, which are formed into two Wheatstone bridges. They achieved the detection of membrane stress with different positions and different directions. The output ports of the two Wheatstone bridges are connected in tandem to realize the non-interference superposition of the output electric potential, thus improving the sensitivity of the sensor. The structure of the sensor membrane and the position of the piezoresistors are optimized using the finite element analysis (FEA). The TWBLP sensor is simple in structure design and easy to produce in large quantities, with the advantage of high sensitivity, which can provide tiny pressure measurement results for pressure measurement in related fields. In the laboratory environment, the local atmospheric pressure is used as the reference zero point to calibrate the sensor. The test results show that the sensitivity of the TWBLP sensor is 26.20% higher than that of the CLP sensor. For the measurement range from −10 to 10 kPa, the fabricated sensor achieves a high sensitivity of 23.07 mV/kPa and an accuracy of 0.19%F.S.

ABSTRACTOne of the most formative aspects of college involves increasing independence from parents. Emerging adults may be forming ideas of what independence entails or how to discuss it, especially considering many parents still provide support in some form. Participants included 117 emerging adult college students who completed an online questionnaire. We categorized topics of recalled student-initiated conversations, responses to parents’ talk about independence, and potential reasons students had not initiated a conversation. Analyses revealed that emerging adults most frequently initiated conversations about setting expectations. In responding to parent-initiated conversations, they most frequently reported agreeing with or understanding parents, which may highlight opportunities for perspective sharing and empathic communication. If emerging adults had not initiated a conversation, it was most often because they did not see a need to do so, claiming they had already had a discussion with parents, did not want to alter the status quo, did not view themselves as independent, or because they were already independent. The theories of emerging adulthood (Arnett, 1998, 2000) and relational dialectics (Baxter, 1993, 2011) shed light on our findings. Results should provide families, universities, and family practitioners with areas for discussion in order to equip parents and emerging adults to more successfully navigate the college years when discussing independence.

In direct sequence spread spectrum systems, the multipath effect (MPE) is known to be one of the most important factors to degrade measurement accuracy. To tackle this problem, we propose a 2-D fast adaptive filtering-based multipath estimation (2D-FAFME) algorithm. A preprocessing is first introduced to compensate the waveform distortion caused by the signal path dynamic. Based on the preprocessing outputs, an optimized adaptive filter is reiteratively designed for each individual range-Doppler cell in the minimum mean-square error sense. Accurate estimates of signal path parameters can be obtained by suppressing the cross-interferences among different paths, and thus the measurement error induced by MPE can be mitigated. An improved 2D-FAFME method is further introduced to reduce the computational complexity and storage memory based on a waveform approximation. The proposed methods have lower computational burden, less storage memory, and better signal-to-noise ratio performance compared with several representative methods. The proposed methods are applicable to complex multipath scenarios, which is verified by simulation results.

Dl-3-n-butylphthalide (NBP) is a small-molecule drug used in the treatment of ischemic stroke in China, which is proven to ameliorate the symptoms of ischemic stroke and improve the prognosis of patients. Previous studies have shown that NBP accelerates recovery after stroke by promoting angiogenesis. In this study, we investigated the mechanisms underlying the angiogenesis-promoting effects of NBP in ischemic stroke models in vitro and in vivo. OGD/R model was established in human umbilical vein endothelial cells (HUVECs) and human brain microvascular endothelial cells (HBMECs), while the tMCAO model was established in mice. The cells were pretreated with NBP (10, 50, 100 µM); the mice were administered NBP (4, 8 mg/kg, i.v.) twice after tMCAO. We showed that NBP treatment significantly stimulated angiogenesis by inducing massive production of angiogenic growth factors VEGFA and CD31 in both in vitro and in vivo models of ischemic stroke. NBP also increased the tubule formation rate and migration capability of HUVECs in vitro. By conducting the weighted gene co-expression network analysis, we found that these effects were achieved by upregulating the expression of a hedgehog signaling pathway. We demonstrated that NBP treatment not only changed the levels of regulators of the hedgehog signaling pathway but also activated the transcription factor Gli1. The pro-angiogenesis effect of NBP was abolished when the hedgehog signaling pathway was inhibited by GDC-0449 in HUVECs, by Sonic Hedgehog(Shh) knockdown in HUVECs, or by intracerebroventricular injection of AAV-shRNA(shh)-CMV in tMCAO mice. Furthermore, we found that HUVECs produced a pro-angiogenic response not only to autocrine Shh, but also to paracrine Shh secreted by astrocytes. Together, we demonstrate that NBP promotes angiogenesis via upregulating the hedgehog signaling pathway. Our results provide an experimental basis for the clinical use of NBP.

The control of DNA assembly systems on cells has increasingly shown great importance for precisely targeted therapies. Here, we report a controllable DNA self-assembly system based on the regulation of G-quadruplex DNA topology by a reduction-sensitive azobenzene ligand. Specifically, three azobenzene multiamines are developed, and AzoDiTren is identified as the best G4 binder, which displays high affinity and specificity for G4 DNA. Moreover, the reduction-sensitive nature of the azobenzene scaffold allows AzoDiTren to induce a complete change of the G4 topology in a tissue-specific manner, even at high metal cation concentrations. On this basis, the AzoDiTren-induced G4 conformational switch achieves control of the self-assembly of G4-functionalized DNAs on cells. This strategy enables the regulation of G4 and DNA self-assembly by the bioreductant-responsive ligand.

Time series analysis is essential to many far-reaching applications of data science and statistics including economic and financial forecasting, surveillance, and automated business processing. Though being greatly successful of Transformer in computer vision and natural language processing, the potential of employing it as the general backbone in analyzing the ubiquitous times series data has not been fully released yet. Prior Transformer variants on time series highly rely on task-dependent designs and pre-assumed “pattern biases”, revealing its insufficiency in representing nuanced seasonal, cyclic, and outlier patterns which are highly prevalent in time series. As a consequence, they can not generalize well to different time series analysis tasks. To tackle the challenges, we propose DifFormer, an effective and efficient Transformer architecture that can serve as a workhorse for a variety of time-series analysis tasks. DifFormer incorporates a novel multi-resolutional differencing mechanism, which is able to progressively and adaptively make nuanced yet meaningful changes prominent, meanwhile, the periodic or cyclic patterns can be dynamically captured with flexible lagging and dynamic ranging operations. Extensive experiments demonstrate DifFormer significantly outperforms state-of-the-art models on three essential time-series analysis tasks, including classification, regression, and forecasting. In addition to its superior performances, DifFormer also excels in efficiency – a linear time/memory complexity with empirically lower time consumption.

This paper proposes a paradigm of uncertainty injection for training deep learning model to solve robust optimization problems. The majority of existing studies on deep learning focus on the model learning capability, while assuming the quality and accuracy of the inputs data can be guaranteed. However, in realistic applications of deep learning for solving optimization problems, the accuracy of inputs, which are the problem parameters in this case, plays a large role. This is because, in many situations, it is often costly or sometime impossible to obtain the problem parameters accurately, and correspondingly, it is highly desirable to develop learning algorithms that can account for the uncertainties in the input and produce solutions that are robust against these uncertainties. This paper presents a novel uncertainty injection scheme for training machine learning models that are capable of implicitly accounting for the uncertainties and producing statistically robust solutions. We further identify the wireless communications as an application field where uncertainties are prevalent in problem parameters such as the channel coefficients. We show the effectiveness of the proposed training scheme in two applications: the robust power loading for multiuser multiple-input-multiple-output (MIMO) downlink transmissions; and the robust power control for device-to-device (D2D) networks.

Several inorganic coatings, such as ZrO2, SiO2, Al2O3, and AlPO4, have been utilized in the TiO2pigment industry in order to reduce the natural photoactivity of TiO2nanoparticles. However, the different role of each material in impacting the TiO2photoactivity lacks comparative studies, and this is critical to provide a composite coating strategy for TiO2pigments to achieve optimal weatherability. Our work has shown that each of these coating materials plays a unique role in suppressing the TiO2photoactivity. For instance, ZrO2and AlPO4increase the rate of charge recombination, while SiO2reduces it. By comparing and analyzing their effects on the charge carrier processes, we have designed and created a composite-coated TiO2pigment that features a sequential deposition of ZrO2, SiO2, and AlPO4. This product has been proved to exhibit excellent weatherability.

Railway track settlements are extremely important for the safe operation of trains. This study utilizes an in situ test and a three-dimensional finite element method (3D FEM) to investigate the settlement of underpasses and railway tracks caused by shield tunneling. First, the 3D FEM results are compared with field monitoring data. Second, the settlement variation of tracks during shield construction is analyzed. Finally, the effect of the tunnel–underpass distance is observed. A comparative study establishes the reliability of the achieved 3D FEM results, which showed the consistency of railway track settlement with that of the underpass top plate. Based on the cutter head location, the underpass and railway track settlements were divided into three stages. With an increase in the tunnel–underpass distance, the underpass and railway track settlements increased, and the speed of increase was significantly higher when the tunnel was beyond the scope of the underpass. When the tunnel axis was within the scope of the underpass, the settlement of the bottom plate > top plate > railway track, and the differences between them were relatively small. Contrarily, the top plate settlement was larger than the bottom plate, while the railway track settlement increased. In both cases, the difference between the top and bottom plate settlements gradually decreased as the tunnel–underpass distance increased. This study shows that the underpass may have a settlement control effect on the deformation of railway tracks of high stiffness.