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Two novel derivatives of 2-methyl-1-(2,3,4,6-tetrahydroxyphenyl)propan-1-one and (S)-2-methyl-1-(2,3,4,6-tetrahydroxyphenyl)butan-1-one (1 and 2), four novel six-membered lactone phenols (3-6), and a nor-ursane type triterpenoid (7) named Achroacid, were isolated from the aerial part of Achyrocline satureioides. The absolute configurations of 1-7 are presented by spectroscopic data and X-ray crystallographic analysis. A DP4plus evaluation was applied to determine the final stereochemistry for 1 and 2. The biosynthesis pathway of 1 and 2 was proposed. 1 has potential on anti-Gram-negative bacteria. Both 1 and 2 exhibited a significant impact on anti-H1299 cells. Compounds 3-7 showed moderate cancer cell lethality and significant anti-inflammatory activities.

Distributed Denial-of-Service (DDoS) attacks have become a critical threat to the Internet. Due to the increasing number of vulnerable Internet of Things (IoT) devices, attackers can easily compromise a large set of nodes and launch high-volume DDoS attacks from the botnets. State-of-the-art DDoS defenses, however, have not caught up with the fast development of the attacks. Middlebox-based defenses can achieve high performance with specialized hardware; however, these defenses incur a high cost, and deploying new defenses typically requires a device upgrade. On the other hand, software-based defenses are highly flexible, but software-based packet processing leads to high performance overheads. In this article, we propose Poseidon, a system that addresses these limitations in today’s DDoS defenses. It leverages emerging programmable switches, which can be reconfigured in the field without additional hardware upgrades. Users of Poseidon can specify their defense strategies in a modular fashion in the form of a set of defense primitives; this can be further customized easily for each network and extended to include new defenses. Poseidon then maps the defense primitives to run on programmable switches—and when necessary, on server software—for effective defense. When attacks change, Poseidon can reconfigure the underlying defense primitives to respond to the new attack patterns. Evaluations using our prototype demonstrate that Poseidon can effectively defend against high-volume attacks, easily support customization of defense strategies, and adapt to dynamic attacks with low overheads.

The preparation of Ti coatings on mild steel can both effectively improve the corrosion resistance of the substrate and reduce the application cost of Ti, which is an effective measure to improve the service performance of mild steel in the marine environment. Plasma cladding technology is an efficient method for preparing metal coatings, and the type of powder is a key process parameter for coating preparation. In this work, high-performance Ti coatings are prepared on the surface of mild steel by plasma cladding technology, and the effects of different particle sizes and shapes of Ti powders on the surface morphology, microstructure and properties of the coatings are studied. The results show that powder particle size and sphericity are the key factors affecting the morphology, structure and service performance of Ti coatings. After 1000 h of salt spray test, the spherical powder cladding coatings only suffer slight corrosion, while the irregular shape powder coating is more severely corroded. Powder cladding with moderate powder particle size and good sphericity have a smoother coating and fewer defects. Ti powders with different particle sizes and shapes all have the diffusion of Fe element during the cladding process. The surface of Ti coating prepared by spherical powder are dominated by α-Ti and Fe0.2Ti0.8 phases, while the surface of Ti coating prepared by irregular shape powder is dominated by FeTi and Ti2Fe. The interface between the coating and the substrate shows metallurgical bonding, and the increase in Ti-Fe brittle phase will deteriorate the mechanical properties and corrosion resistance of the coating. The shear strength of coatings prepared from spherical Ti powders of 75–150 μm can reach 105.18 MPa, the corrosion potential is the most positive (−0.2206 V), and the self-corrosion current density is the lowest (6.220 × 10−8 A/cm2).

Recent studies suggest that highly activated, polyfunctional CD4

In this article, two cooperative guidance schemes with impact angle and time constraints are proposed for multiple missiles cooperatively intercepting a maneuvering target in the presence or absence of a leader missile. First, cooperative interception with impact angle constraint is formulated based on the relative motion equation between the interceptors. Subsequently, the design of the guidance law is divided into two stages. In the first stage, based on finite-time consensus theory and the super-twisting control algorithm, the cooperative guidance law in the direction of the line of sight (LOS) is derived to control the impact time so that all missiles can simultaneously intercept the target in finite time. Then, based on finite-time sliding mode control and the super-twisting control algorithm, a finite-time convergence cooperative guidance law in the normal direction to the LOS is developed to control the intercept angle and the LOS angle rate so that each missile can accurately hit the target at a desired angle. Finally, by controlling the consensus error of flight time in the presence of a leader missile, a finite-time cooperative guidance method in the “leader–follower” scenario is derived for simultaneous arrival at the target. The proposed methods can effectively suppress chattering and ensure fast convergence in finite time. Simulation experiments are conducted to verify the effectiveness of the proposed cooperative guidance schemes.

As direct prospecting data, geochemical data play an important role in modelling prospect potential. Geochemical element assemblage anomalies are usually reflected by the correlation between elements. Correlation coefficients are computed from the values of two elements, which reflect only the correlation at a global level. Thus, the spatial details of the correlation structure are ignored. In fact, an element combination anomaly often exists in geological backgrounds, such as on a fault zone or within a lithological unit. This anomaly may cause some combination of anomalies that are submerged inside the overall area and thus cannot be effectively extracted. To address this problem, we propose a local correlation coefficient based on spatial neighbourhoods to reflect the global distribution of elements. In this method, the sampling area is first divided into a set of uniform grid cells. A moving window with a size of 3×3 is defined with an integer of 3 to represent the sampling unit. The local correlation in each unit is expressed by the Pearson correlation coefficient. The whole area is scanned by the moving window, which produces a correlation coefficient matrix, and the result is portrayed with a thermal diagram. The local correlation approach was tested on two selected geochemical soil survey sites in Xiao Mountain, Henan Province. The results show that the areas of high correlation are mainly distributed in the fault zone or the known mineral spots. Therefore, the local correlation method is effective in extracting geochemical element combination anomalies.

The estimation of geolocation for aerial images is significant for tasks like map creating, or automatic navigation for unmanned aerial vehicles (UAVs). We propose a novel geolocalization method for the UAVs using only aerial images and reference road map. The corresponding road maps of the aerial images are firstly merged into a whole mosaic image using our newly-designed aerial image mosaicking algorithm, where the relative homography transformations between road images are firstly estimated using keypoints tracking in RGB aerial images, and then further refined with registration between detected roads. The geolocalization of the aerial mosaic image is then taken as the problem of registering observed roads in the aerial images to the reference road map under the homography transformation. The registration problem is solved with our fast search algorithm based on a novel projective-invariant feature, which consists of two road intersections augmented with their tangents. Experiments demonstrate that the proposed method can localize the aerial image sequence over an area larger than 1000 km $$^2$$ within a few seconds.

According to the characteristics of the road features, an Encoder-Decoder deep semantic segmentation network is designed for the road extraction of remote sensing images. Firstly, as the features of the road target are rich in local details and simple in semantic features, an Encoder-Decoder network with shallow layers and high resolution is designed to improve the ability to represent detail information. Secondly, as the road area is a small proportion in remote sensing images, the cross-entropy loss function is improved, which solves the imbalance between positive and negative samples in the training process. Experiments on large road extraction datasets show that the proposed method gets the recall rate 83.9%, precision 82.5% and F1-score 82.9%, which can extract the road targets in remote sensing images completely and accurately. The Encoder-Decoder network designed in this paper performs well in the road extraction task and needs less artificial participation, so it has a good application prospect.

This study discusses the finite-time boundedness (FTB) of the state of linear systems with a finite-time H 2-gain performance constraint. With time-varying exogenous disturbances, the sufficient conditions for linear time-invariant and linear time-varying systems to satisfy both the FTB constraint and the finite-time H 2-gain performance constraint are developed. On the basis of the matrix inequality theory, a method for designing a controller by using state feedback is derived for each of the two classes of linear systems. These methods can effectively keep the state from reaching an unacceptable value and modulate the output performance within a specified finite-time interval. Two application examples of the terminal guidance scenario verify the effectiveness of the proposed methods.

We consider the scenario where two pursuers are required to intercept an enemy evader that launches a decoy. Under the assumption that all four aircrafts have first-order linear dynamic characteristics, a cooperative guidance law is designed in two stages: the unidentified decoy stage and the discriminated decoy stage. In the unidentified decoy stage, the predictive guidance law is designed based on the method of the highest probability interval (HPI). This guidance law can maximize the probability of intercepting the real target despite the decoy's presence, thus providing an interception maneuver advantage for later cooperative guidance to the real target. In the discriminated decoy stage, based on the optimal control theory, an optimal guidance law considering the detection configuration is designed, which reduces the estimation error in the detection process and improves the interception performance. Simulation results verify the feasibility of the phased cooperative guidance law.

Detecting interested targets on aerial robotic systems is a challenging task. Due to the long view distance of air-to-ground observation, the target size is small and the number is large in the scene. In addition, the target only occupies part of the image, and the complex background environment can easily cover the feature information of the target. In this paper, a novel target detection method based on spatial attention model is designed, which changes the existing methods to enhance the features of target areas by enhancing global semantic information. By learning the feature weights of different spatial locations in feature space, the method proposed can focus attention on the target regions of interest in an image, and suppress the background interference features, which enhances the feature information of the target regions, and deals with the class imbalance problem in detection. The experimental results show that the algorithm improves the detection accuracy of small air-to-ground targets and has a good detection effect for dense target areas. Compared with RefineDet, the state-of-art small target detector, our method can achieve better performance at a lower cost.

The measurement noise of the standard Kalman filter (SKF) and related filtering algorithms are usually required to obey Gaussian distribution. However, it is hard to be satisfied in practice and the reliability of the filtering results is reduced when the measurement noise is not normally distributed. The robust Kalman filter (RKF) has the ability to bound the abnormal observations by weighing the influence of the updated parameters in accordance with the magnitude of discrepancy between the updated parameters and the robust estimates obtained from measurements at each time step. However, there is a limitation that it wastes the useful information of the state model sometimes. In order to solve the problem of magnetic interference compensation under noise uncertainty, this paper addresses a new method based on the robust M estimation to make the uncertain noise suppressed. The double-model adaptive estimation (DMAE) approach is proposed, which consists of a SKF and a RKF. Two filters are selected adaptively by a conditional probability according to the innovation covariance at each time step. More specifically, the DMAE has ability to resist the measurement noise and make full use of effective information at the same time. Compared with the SKF and the RKF method, the DMAE achieves a better performance in terms of compensation accuracy and robustness for parameter estimation. After compensation, errors of components are reduced from 502, 613, 493 to 3.9, 2.1, and 23.9 nT, respectively, with the proposed method, the ideal accuracy of total field is achieved as well.

Description, Spatial transcriptome technique was applied to decipher the spatial architecture and TME characteristics of liver cancers., Heterogeneity is the major challenge for cancer prevention and therapy. Here, we first constructed high-resolution spatial transcriptomes of primary liver cancers (PLCs) containing 84,823 spots within 21 tissues from seven patients. The progressive comparison of spatial tumor microenvironment (TME) characteristics from nontumor to leading-edge to tumor regions revealed that the tumor capsule potentially affects intratumor spatial cluster continuity, transcriptome diversity, and immune cell infiltration. Locally, we found that the bidirectional ligand-receptor interactions at the 100-μm-wide cluster-cluster boundary contribute to maintaining intratumor architecture and the PROM1+ and CD47+ cancer stem cell niches are related to TME remodeling and tumor metastasis. Last, we proposed a TLS-50 signature to accurately locate tertiary lymphoid structures (TLSs) spatially and unveiled that the distinct composition of TLSs is shaped by their distance to tumor cells. Our study provides previous unknown insights into the diverse tumor ecosystem of PLCs and has potential benefits for cancer intervention.

Remote sensing is the main technical means for urban researchers and planners to effectively observe targeted urban areas. Generally, it is difficult for only one image to cover a whole urban area and one image cannot support the demands of urban planning tasks for spatial statistical analysis of a whole city. Therefore, people often artificially find multiple images with complementary regions in an urban area on the premise of meeting the basic requirements for resolution, cloudiness, and timeliness. However, with the rapid increase of remote sensing satellites and data in recent years, time-consuming and low performance manual filter results have become more and more unacceptable. Therefore, the issue of efficiently and automatically selecting an optimal image collection from massive image data to meet individual demands of whole urban observation has become an urgent problem. To solve this problem, this paper proposes a large-area full-coverage remote sensing image collection filtering algorithm for individual demands (LFCF-ID). This algorithm achieves a new image filtering mode and solves the difficult problem of selecting a full-coverage remote sensing image collection from a vast amount of data. Additionally, this is the first study to achieve full-coverage image filtering that considers user preferences concerning spatial resolution, timeliness, and cloud percentage. The algorithm first quantitatively models demand indicators, such as cloudiness, timeliness, resolution, and coverage, and then coarsely filters the image collection according to the ranking of model scores to meet the different needs of different users for images. Then, relying on map gridding, the image collection is genetically optimized for individuals using a genetic algorithm (GA), which can quickly remove redundant images from the image collection to produce the final filtering result according to the fitness score. The proposed method is compared with manual filtering and greedy retrieval to verify its computing speed and filtering effect. The experiments show that the proposed method has great speed advantages over traditional methods and exceeds the results of manual filtering in terms of filtering effect.

PurposeGastrointestinal neuroendocrine carcinoma (NEC) with bone metastasis is rarely reported. The purpose of this study is to explore the prognosis and risk factors of such patients.Patients and MethodsWe retrospectively reviewed patients diagnosed as gastrointestinal NECs with bone metastasis at diagnosis from 2010 to 2016 by using the Surveillance, Epidemiology and End Results (SEER) database. Predictors of overall survival (OS) and cancer-specific survival (CSS) were analyzed by univariable and multivariable Cox analyses. Kaplan–Meier plots were constructed to show the correlation between independent predictors and survival.ResultsA total of 330 gastrointestinal NEC patients with bone metastasis at diagnosis were included for analysis. Over half of patients were male and older than 60 years old. The most common primary site of gastrointestinal NEC with bone metastasis was the pancreas. The prognosis of gastrointestinal NEC with bone metastasis (3-year OS and CSS rates: 16.7 and 17.0%) was very poor. On Cox multivariable analysis, age over 60 years old, no surgery, and lung metastasis were independent predictors of decreased OS and CSS.ConclusionsWe identified three independent factors associated with prognosis among gastrointestinal NEC patients with bone metastasis, namely age, surgery, and lung metastasis. For younger gastrointestinal NEC patients with bone metastasis, surgical resection of primary tumors as well as actively treating lung metastasis might be useful for prolonging survival.

BackgroundMyeloid‐derived suppressor cells (MDSCs) can potently inhibit T-cell activity, promote growth and metastasis of tumor and contribute to resistance to immunotherapy. Targeting MDSCs to alleviate their protumor functions and immunosuppressive activities is intimately associated with cancer immunotherapy. Natural killer (NK) cells can engage in crosstalk with multiple myeloid cells to alter adaptive immune responses, triggering T-cell immunity. However, whether the NK-cell-MDSC interaction can modulate the T-cell immune response requires further study. Cryo-thermal therapy could induce the maturation of MDSCs by creating an acute inflammatory environment to elicit a CD4+Th1-dominant immune response, but the mechanism regulating this process remains unclear.MethodsNK cells were depleted and NKG2D was blocked with monoclonal antibodies in vivo. MDSCs, NK cells and T cells were assessed by flow cytometry and isolated by magnetic-activated cell sorting (MACS). MDSCs and NK cells were cocultured with T cells to determine their immunological function. The transcriptional profiles of MDSCs were measured by qRT-PCR and RNA-sequencing. Isolated NK cells and MDSCs by MACS were cocultured to study the viability and maturation of MDSCs regulated by NK cells. TIMER was used to comprehensively examine the immunological, clinical, and genomic features of tumors.ResultsNK-cell activation after cryo-thermal therapy decreased MDSC accumulation and reprogrammed immunosuppressive MDSCs toward a mature phenotype to promote T cell antitumor immunity. Furthermore, we discovered that NK cells could kill MDSCs via the NKG2D-NKG2DL axis and promote MDSC maturation by interferon gamma (IFN-γ) in response to NKG2D. In addition, CD4+Th1-dominant antitumor immune response was dependent on NKG2D, which promoted the major histocompatibility complex Ⅱ pathway of MDSCs. High activated NK-cell infiltration and NKG2D level in tumors were positively correlated with better clinical outcomes.ConclusionsCryo-thermal therapy induces effective CD4+Th1-dominant antitumor immunity by activating NK cells to reprogram MDSCs, providing a promising therapeutic strategy for cancer immunotherapy.

Given two closed oriented manifolds $M,N$ of the same dimension, we denote the set of degrees of maps from $M$ to $N$ by $D(M,N)$. The set $D(M,N)$ always contains zero. We show the following (non-)realisability results: (i) There exists an infinite subset $A$ of $\mathbb Z$ containing $0$ which cannot be realised as $D(M,N)$, for any closed oriented $n$-manifolds $M,N$. (ii) Every finite arithmetic progression of integers containing $0$ can be realised as $D(M,N)$, for some closed oriented $3$-manifolds $M,N$. (iii) Together with $0$, every finite geometric progression of positive integers starting from $1$ can be realised as $D(M,N)$, for some closed oriented manifolds $M,N$., 17 pages; v2: final version, to appear in Bulletin of the London Mathematical Society

Two novel derivatives of 2-methyl-1-(2,3,4,6-tetrahydroxyphenyl)propan-1-one and (

Aiming at the problems of low recognition accuracy and low real-time in the existing human motion recognition in the complex background, a registration model recognition algorithm of human posture limit learning machine(ELM) is proposed, which uses Kinect measurement to calculate the joint association angle. Through Kinect, the spatial coordinates of human joints are obtained. Then, three-point method is used to calculate the correlation angle of human joints, and the elm registration model corresponding to the correlation angle of the target attitude joints is defined. Finally, the movement recognition test of the elm registration model of human body posture is carried out with the joint correlation angle calculated based on Kinect real-time measurement. The experimental results show that this method has high real-time and accuracy, strong robustness and generalization ability, and can identify more pose by defining more joint angles of target pose to establish elm registration model of human pose.

Macrophages play critical roles in both innate and adaptive immunity and are known for their high plasticity in response to various external signals. Macrophages are involved in regulating systematic iron homeostasis and they sequester iron by phagocytotic activity, which triggers M1 macrophage polarization and typically exerts antitumor effects. We previously developed a novel cryo-thermal therapy that can induce the mass release of tumor antigens and damage-associated molecular patterns (DAMPs), promoting M1 macrophage polarization. However, that study did not examine whether iron released after cryo-thermal therapy induced M1 macrophage polarization, this question still needed to be addressed. We hypothesized that cryo-thermal therapy would cause the release of a large quantity of iron to augment M1 macrophage polarization due to the disruption of tumor cells and blood vessels, which would further enhance antitumor immunity. In this study, we investigated iron released in primary tumors, the level of iron in splenic macrophages after cryo-thermal therapy and the effect of iron on macrophage polarization and CD4+ T cell differentiation in metastatic 4T1 murine mammary carcinoma. We found that a large amount of iron was released after cryo-thermal therapy and could be taken up by splenic macrophages, which further promoted M1 macrophage polarization by inhibiting ERK phosphorylation. Moreover, iron promoted DC maturation, which was possibly mediated by iron-induced M1 macrophages. In addition, iron-induced M1 macrophages and mature DCs promoted the differentiation of CD4+ T cells into the CD4 cytolytic T lymphocytes (CTL) subset and inhibited differentiation into Th2 and Th17 cells. This study explains the role of iron in cryo-thermal therapy-induced antitumor immunity from a new perspective.