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A synthetic pathway to a novel 4-aryl-3,4-dihydro-2H-1,4-benzoxazine scaffold was developed and a series of compounds based on the scaffold were synthesised as potential anticancer agents. The 4-aryl-substituted compounds were prepared via Buchwald–Hartwig cross-coupling between substituted bromobenzenes and various 1,4-benzoxazines, which in turn were generated from a cascade hydrogenation and reductive amination one-pot reaction. These analogues exhibited moderate to good potency against various cancer cell lines. Structure–activity relationship analysis indicated that the inclusion of hydroxyl groups on ring A and ring B was beneficial to biological activity, while having a para-amino group on ring C significantly enhanced potency. Molecule 14f displayed the most potent anticancer activity (IC50 = 7.84–16.2 µM against PC-3, NHDF, MDA-MB-231, MIA PaCa-2, and U-87 MG cancer cell lines), indicating its potential as a lead compound for further structural optimisation. All the synthesised compounds were fully characterised with NMR, HMRS, and IR. The novel benzoxazine scaffold described in this study holds promise and deserves further in-depth studies. [ABSTRACT FROM AUTHOR]

Living organisms are capable of dynamically changing their structures for adaptive functions through sophisticated reaction‐diffusion processes. Here we show how active supramolecular hydrogels with programmable lifetimes and macroscopic structures can be created by relying on a simple reaction‐diffusion strategy. Two hydrogel precursors (poly(acrylic acid) PAA/CaCl2 and Na2CO3) diffuse from different locations and generate amorphous calcium carbonate (ACC) nanoparticles at the diffusional fronts, leading to the formation of hydrogel structures driven by electrostatic interactions between PAA and ACC nanoparticles. Interestingly, the formed hydrogels are capable of autonomously disintegrating over time because of a delayed influx of electrostatic‐interaction inhibitors (NaCl). The hydrogel growth process is well explained by a reaction‐diffusion model which offers a theoretical means to program the dynamic growth of structured hydrogels. Furthermore, we demonstrate a conceptual access to dynamic information storage in soft materials using the developed reaction‐diffusion strategy. This work may serve as a starting point for the development of life‐like materials with adaptive structures and functionalities. [ABSTRACT FROM AUTHOR]

Living organisms are capable of dynamically changing their structures for adaptive functions through sophisticated reaction‐diffusion processes. Here we show how active supramolecular hydrogels with programmable lifetimes and macroscopic structures can be created by relying on a simple reaction‐diffusion strategy. Two hydrogel precursors (poly(acrylic acid) PAA/CaCl2 and Na2CO3) diffuse from different locations and generate amorphous calcium carbonate (ACC) nanoparticles at the diffusional fronts, leading to the formation of hydrogel structures driven by electrostatic interactions between PAA and ACC nanoparticles. Interestingly, the formed hydrogels are capable of autonomously disintegrating over time because of a delayed influx of electrostatic‐interaction inhibitors (NaCl). The hydrogel growth process is well explained by a reaction‐diffusion model which offers a theoretical means to program the dynamic growth of structured hydrogels. Furthermore, we demonstrate a conceptual access to dynamic information storage in soft materials using the developed reaction‐diffusion strategy. This work may serve as a starting point for the development of life‐like materials with adaptive structures and functionalities. [ABSTRACT FROM AUTHOR]

Pod phenotypic traits are closely related to grain yield and quality. Pod phenotype detection in soybean populations in natural environments is important to soybean breeding, cultivation, and field management. For an accurate pod phenotype description, a dynamic detection method is proposed based on an improved YOLO-v5 network. First, two varieties were taken as research objects. A self-developed field soybean three-dimensional color image acquisition vehicle was used to obtain RGB and depth images of soybean pods in the field. Second, the red–green–blue (RGB) and depth images were registered using an edge feature point alignment metric to accurately distinguish complex environmental backgrounds and establish a red–green–blue-depth (RGB-D) dataset for model training. Third, an improved feature pyramid network and path aggregation network (FPN+PAN) structure and a channel attention atrous spatial pyramid pooling (CA-ASPP) module were introduced to improve the dim and small pod target detection. Finally, a soybean pod quantity compensation model was established by analyzing the influence of the number of individual plants in the soybean population on the detection precision to statistically correct the predicted pod quantity. In the experimental phase, we analyzed the impact of different datasets on the model and the performance of different models on the same dataset under the same test conditions. The test results showed that compared with network models trained on the RGB dataset, the recall and precision of models trained on the RGB-D dataset increased by approximately 32% and 25%, respectively. Compared with YOLO-v5s, the precision of the improved YOLO-v5 increased by approximately 6%, reaching 88.14% precision for pod quantity detection with 200 plants in the soybean population. After model compensation, the mean relative errors between the predicted and actual pod quantities were 2% to 3% for the two soybean varieties. Thus, the proposed method can provide rapid and massive detection for pod phenotyping in soybean populations and a theoretical basis and technical knowledge for soybean breeding, scientific cultivation, and field management. [ABSTRACT FROM AUTHOR]

In this paper, we consider a nonlocal advection model for two populations on a bounded domain. The first part of the paper is devoted to the existence and uniqueness of solutions and the associated semi-flow properties. By employing the notion of solution integrated along the characteristics, we rigorously prove the segregation property of solutions. Furthermore, we construct an energy functional to investigate the asymptotic behavior of solutions. To resolve the lack of compactness of the positive orbits, we obtain a description of the asymptotic behavior of solutions by using the narrow convergence in the space of Young measures. The last section of the paper is devoted to numerical simulations, which confirm and complement our theoretical results. [ABSTRACT FROM AUTHOR]

Summary DelaySSAToolkit.jl is a Julia package for modelling reaction systems with non-Markovian dynamics, specifically those with time delays. These delays implicitly capture multiple intermediate reaction steps and hence serve as an effective model reduction technique for complex systems in biology, chemistry, ecology and genetics. The package implements a variety of exact formulations of the delay stochastic simulation algorithm. Availability and implementation The source code and documentation of DelaySSAToolkit.jl are available at https://github.com/palmtree2013/DelaySSAToolkit.jl. [ABSTRACT FROM AUTHOR]

In this work, we describe a hyperbolic model with cell–cell repulsion with a dynamics in the population of cells. More precisely, we consider a population of cells producing a field (which we call "pressure") which induces a motion of the cells following the opposite of the gradient. The field indicates the local density of population and we assume that cells try to avoid crowded areas and prefer locally empty spaces which are far away from the carrying capacity. We analyze the well-posedness property of the associated Cauchy problem on the real line. We start from bounded initial conditions and we consider some invariant properties of the initial conditions such as the continuity, smoothness and monotony. We also describe in detail the behavior of the level sets near the propagating boundary of the solution and we find that an asymptotic jump is formed on the solution for a natural class of initial conditions. Finally, we prove the existence of sharp traveling waves for this model, which are particular solutions traveling at a constant speed, and argue that sharp traveling waves are necessarily discontinuous. This analysis is confirmed by numerical simulations of the PDE problem. [ABSTRACT FROM AUTHOR]

Changes in human behaviors, such as reductions of physical contacts and the adoption of preventive measures, impact the transmission of infectious diseases considerably. Behavioral adaptations may be the result of individuals aiming to protect themselves or mere responses to public containment measures, or a combination of both. What drives autonomous and policy-induced adaptation, how they are related and change over time is insufficiently understood. Here, we develop a framework for more precise analysis of behavioral adaptation, focusing on confluence, interactions and time variance of autonomous and policy-induced adaptation. We carry out an empirical analysis of Germany during the fall of 2020 and beyond. Subsequently, we discuss how behavioral adaptation processes can be better represented in behavioral-epidemiological models. We find that our framework is useful to understand the interplay of autonomous and policy-induced adaptation as a "moving target". Our empirical analysis suggests that mobility patterns in Germany changed significantly due to both autonomous and policy-induced adaption, with potentially weaker effects over time due to decreasing risk signals, diminishing risk perceptions and an erosion of trust in the government. We find that while a number of simulation and prediction models have made great efforts to represent behavioral adaptation, the interplay of autonomous and policy-induced adaption needs to be better understood to construct convincing counterfactual scenarios for policy analysis. The insights presented here are of interest to modelers and policy makers aiming to understand and account for behaviors during a pandemic response more accurately. [ABSTRACT FROM AUTHOR]

Recently, the advancement of machine learning (ML) techniques, especially deep learning, reinforcement learning, and federated learning, has led to remarkable breakthroughs in a variety of application domains. The success of ML benefits from the advancement of the Internet, mobile networks, data center networks, and the Internet of Things (IoT) that facilitate data creation and sharing. On the other hand, we have also witnessed a fast growing trend in the networking community toward using ML to tackle challenging problems in network design, management, and optimization, which are traditionally addressed using mathematical optimization theory or human-generated heuristics. ML is also an essential ingredient in the realization of autonomous or self-driving networks. [ABSTRACT FROM AUTHOR]

Load balancing in datacenter networks (DCNs) is an important and challenging task for datacenter managers. A number of sophisticated technologies have been proposed to improve load balancing performance in a complicated circumstance, i.e., with various traffic characteristics. Many approaches need a high cost to implement, such as changing switch hardware. The efficiency problem has not been well addressed. MPTCP was proposed as a low-cost approach to improve data transmission in DCNs, which uses subflows to balance workloads across multiple paths. However, current MPTCP is not satisfying, especially when there are rack-local flows or many-to-one short flows. In this article, we propose DCMPTCP to improve the efficacy of MPTCP. We gradually develop three mechanisms. First, DCMPTCP identifies rack-local traffic and eliminates unnecessary subflows to reduce the overhead. Second, DCMPTCP estimates flow length and establishes subflows in a smarter way. Third, DCMPTCP strengthens explicit congestion notification to improve the congestion control performance on inter-rack many-to-one short flows. We have implemented DCMPTCP in both the Linux kernel and ns-3 simulator. Our comprehensive testbed experiments and simulations show that DCMPTCP outperforms MPTCP in both 1 Gbps testbed, and 10 Gbps large-scale simulation network. [ABSTRACT FROM AUTHOR]

In this work, we discuss a cell–cell repulsion model based on a hyperbolic Keller–Segel equation with two populations, which aims at describing the cell growth and dispersion in the co-culture experiment from the work of Pasquier et al. (Biol Direct 6(1):5, 2011). We introduce the notion of solution integrated along the characteristics, which allows us to prove the existence and uniqueness of solutions and the segregation property for the two species. From a numerical perspective, we also observe that our model admits a competitive exclusion principle which is different from the classical competitive exclusion principle for the corresponding ODE model. More importantly, our model shows the complexity of the short term (6 days) co-cultured cell distribution depending on the initial distribution of each species. Through numerical simulations, we show that the impact of the initial distribution on the proportion of each species in the final population lies in the initial number of cell clusters and that the final proportion of each species is not influenced by the precise distribution of the initial distribution. We also find that a fast dispersion rate gives a short-term advantage while the vital dynamics contributes to a long-term population advantage. When the initial condition for the two species is not segregated, the numerical simulations suggest that asymptotic segregation occurs when the dispersion coefficients are not equal for two populations. [ABSTRACT FROM AUTHOR]

With the popularity of smart mobile terminals and advances in wireless communication and positioning technologies, Geo-Social Networks (GSNs), which combine location awareness and social service functions, have become increasingly prevalent. The increasing amount of user and location information in GSNs makes the information overload phenomenon more and more serious. Although massive user-generated data brings convenience to users' social and travel activities, it also causes certain trouble for their daily life. In this context, users are expecting smarter mobile applications, so that the location information can be employed to perceive their surrounding environment intelligently and further mine their behavior patterns in GSNs, which ultimately provides personalized location-based services for users. Therefore, research on user location prediction comes into existence and has received extensive and in-depth attention from researchers. Through systematically analyzing the location data carried by user check-ins and comments, user location prediction can mine various user behavior patterns and personal preferences, thus determining the visiting location of users in the future. Research on user location prediction is still in the ascendant and it has become an important topic of common concern in both academia and industry. This survey takes Geo-social networking data as the focal point to elaborate the recent progress in user location prediction from multiple aspects such as problem categories, data sources, feature extraction, mathematical models and evaluation metrics. Besides, the difficulties to be studied and the future developmental trends of user location prediction are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we consider a stochastic epidemic model with time delay and general incidence rate. We first prove the existence and uniqueness of the global positive solution. By using the Krylov–Bogoliubov method, we obtain the existence of invariant measures. Furthermore, we study a special case where the incidence rate is bilinear with distributed time delay. When the basic reproduction number R 0 < 1 , the analysis of the asymptotic behavior around the disease-free equilibrium E 0 is provided while when R 0 > 1 , we prove that the invariant measure is unique and ergodic. The numerical simulations also validate our analytical results. • The well-posedness of a stochastic delayed SIRS model with general incidence rate is studied. • Establish a general condition on the drift and diffusion coefficients for the existence of invariant measures. • The asymptotic behavior around the disease-free equilibrium is studied. • Obtain a sufficient condition for the uniqueness and the ergodicity of the invariant measure. [ABSTRACT FROM AUTHOR]

In this paper, we study the stability and the bifurcation properties of the positive interior equilibrium for a reaction-diffusion equation with nonlocal advection. Under rather general assumption on the nonlocal kernel, we first study the local well posedness of the problem in suitable fractional spaces and we obtain stability results for the homogeneous steady state. As a special case, we obtain that “standard” kernels such as Gaussian, Cauchy, Laplace and triangle, will lead to stability. Next we specify the model with a given step function kernel and investigate two types of bifurcations, namely Turing bifurcation and Turing-Hopf bifurcation. In fact, we prove that a single scalar equation may display these two types of bifurcations with the dominant wave number as large as we want. Moreover, similar instabilities can also be observed by using a bimodal kernel. The resulting complex spatiotemporal dynamics are illustrated by numerical simulations. [ABSTRACT FROM AUTHOR]

Compared to binary PbSe or PbS nanocrystals (NCs), ternary PbSe1−xSx alloyed NCs have unique electronic and optical properties such as high quantum efficiency, narrow band edge, and fast response time, and are widely used in infrared optoelectronic devices. In this work, high-quality composition-tunable monodisperse ternary PbSe1−xSx alloyed NCs were synthesized by employing cation exchange from host CdSe1−xSx alloyed NCs, which were pre-fabricated by using cadmium oxide, myristic acid and 1-octadecene (ODE) solutions of selenium/sulphur powders (Se/S-ODE), excluding toxic phosphine-containing solvents. The alloyed nature of PbSe1−xSx NCs was confirmed using powder X-ray diffraction (XRD). XRD patterns and field emission transmission electron microscopy (FETEM) images demonstrated that the average diameters of the resulting PbSe1−xSx alloyed NCs were not more than 5 nm, and ultraviolet-visible-near infrared (UV-vis-NIR) absorption spectra indicated that the NC size, hence the band gap, bared a linear relationship with the first absorption peak position. The temporal evolution of the absorption spectra revealed that the PbSe1−xSx alloyed NCs were air-stable due to the hybrid surface passivation of Cl− and Cd2+. X-ray fluorescence results indicated that slight stoichiometric deviations of PbSe1−xSx NCs occurred but never exceeded 15% of the expected composition, based on the amount of introduced hosts. [ABSTRACT FROM AUTHOR]

Objective: To explore a new technique for reconstructing and measuring three-dimensional (3D) models of orthodontic plaster casts using multi-baseline digital close-range photogrammetry (MBDCRP) with a single-lens reflex camera. Study design: Thirty sets of orthodontic plaster casts that do not exhibit severe horizontal overlap (>2 mm) between any two teeth were recorded by a single-lens reflex camera with 72 pictures taken in different directions. The 3D models of these casts were reconstructed and measured using the open source software MeshLab. These parameters, including mesio-distal crown diameter, arch width, and arch perimeter, were recorded six times on both the 3D digital models and on plaster casts by two examiners. Statistical analysis was carried out using the Bland–Altman method by calculating the differences between mean values. Results: The average differences between the measurements of the photogrammetric 3D models and the plaster casts were 0.011–0.402mm. The mean differences between measurements obtained by the photogrammetric 3D models and the dental casts were not significant except for the lower arch perimeter (P>0.05), and all the differences were regarded as clinically acceptable (<0.5 mm). Conclusions: Measurements obtained by MBDCRP are compared well with those obtained from plaster casts, indicating that MBDCRP is an alternate way to store and measure dental plaster casts without severe horizontal overlap between any two teeth. [ABSTRACT FROM AUTHOR]

A novel DNA-modified nanocomposite of three-dimensional reduced graphene oxide and chitosan particles (CS@3D-rGO@DNA) was prepared by one-step method and used as a new electrochemical biosensing strategy to detect Hg 2+ in drinking water. This biosensor was fabricated by the chemical coordination of T-Hg 2+ -T between the T-rich oligonucleotide strands contained in the as-prepared CS@3D-rGO@DNA nanocomposite and Hg 2+ , thereby varying the electrochemical signals. The developed electrochemical biosensor exhibited high sensitivity toward the determination of Hg 2+ with a low detection limit of 0.016 nM, ranging from 0.1 nM to 10 nM. This novel biosensor also showed high selectivity, good stability, and repeatability. Hence, this proposed CS@3D-rGO@DNA-based electrochemical strategy is feasible and may offer a simple, rapid, cost-effective, highly selective, and sensitive method for Hg 2+ quantification. [ABSTRACT FROM AUTHOR]

Abstract: In order to study the irradiation performance of fuel spheres for the 10MW high-temperature gas-cooled reactor (HTR-10), two in-pile irradiation tests were performed. The first irradiation test was performed in the Russian reactor IVV-2M from July 2000 to February 2003. The four irradiated fuel spheres were randomly taken from the first product batches. The central temperature of irradiated fuel spheres during this irradiation was kept between 950 and 1050°C. The maximum burnup of the irradiated spheres was 107,000MWd/tU and the maximum fast neutron fluence (E >0.1MeV) was 1.31×1025 m−2. The release-to-birth rate ratios (R/B) of Kr and Xe fission products were 10−7 to 10−5. The second irradiation test was performed in HFR Petten (HFR-EU1) from September 29, 2006 to February 19, 2010. Experiment HFR-EU1 consisted of 3 German AVR pebbles and 2 INET pebbles. The two INET pebbles were randomly taken from the latest production batch. The surface temperature of pebble INET 2 during irradiation was approx. 940°C. The experiment was performed in two campaigns from 29 September 2006 to 24 February 2008 (12 reactor cycles of 28 days) and continued from 19 October 2009 to 19 February 2010 (4 reactor cycles). Up to end of irradiation, the experiment had accumulated 16 reactor cycles totaling 445 efpd and calculated burnup and maximum fast neutron fluence (E >0.1MeV) were 9.3% (pebble INET 1) and 11.6%FIMA (pebble INET 2) and about 4.95×1025 m−2, respectively. At end of irradiation, based on 85mKr, R/B of approx. 8×10−8 was measured. The second irradiation test results are much better than the first. The irradiated sample manufacture technology and characterization, irradiation conditions and results of two irradiation tests are compared in detail in this paper. [Copyright &y& Elsevier]

Crystalline WO nanorods of less than 100 nm in diameter have been successfully synthesized at 240 °C for 48 h at pH 1.5 by the hydrothermal method with sodium tungstate as a tungsten source and potassium sulphate as a subsidiary salt. The morphologies and structures of WO rods were characterized by X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM) and selected-area electron diffraction (SAED). SEM analysis confirms that the slenderness ratio of the WO rods is enlarged with the increase of the reaction temperature at pH 1.5. SAED analysis shows that the synthesized WO nanorods are crystalline. Ultraviolet-visible (UV-VIS) absorption spectroscopy shows that absorbent power of UV light for WO nanorods enhances with an increase of the slenderness ratio. [ABSTRACT FROM AUTHOR]

Abstract: An irradiation test of four spherical fuel elements (SFE) had been performed in the Russian reactor IVV-2M. The elements were sampled randomly from the first and second product batches which were manufactured for the 10MW high-temperature gas-cooled test reactor (HTR-10). The maximum burnup of the irradiated fuel elements reached 107,000MWd/tU and the maximum fast neutron fluence was 1.31×1025 m−2. The release-to-birth rate ratio (R/B) did not increase significantly during irradiation. However, an in-pile heating-up test of element SFE 7 in Capsule 5 led to a failure of approximately 6% of the coated particles. After the test it was estimated that the fuel temperature had very likely been much higher than the intended 1600°C. [Copyright &y& Elsevier]

Despite increased attention to the aquaculture environment, there is still a lack of understanding regarding the significance of water quality. To address this knowledge gap, this study utilized high-throughput sequencing of 16S rRNA and 18S rRNA to examine microbial communities (bacteria and eukaryotes) in coastal water over different months through long-term observations. The goal was to explore interaction patterns in the microbial community and identify potential pathogenic bacteria and red tide organisms. The results revealed significant differences in composition, diversity, and richness of bacterial and eukaryotic operational taxonomic units (OTUs) across various months. Principal coordinate analysis (PCoA) demonstrated distinct temporal variations in bacterial and eukaryotic communities, with significant differences (P = 0.001) among four groups: F (January-April), M (May), S (June–September), and T (October-December). Moreover, a strong association was observed between microbial communities and months, with most OTUs showing a distinct temporal preference. The Kruskal–Wallis test (P < 0.05) indicated significant differences in dominant bacterial and eukaryotic taxa among months, with each group exhibiting unique dominant taxa, including potential pathogenic bacteria and red tide organisms. These findings emphasize the importance of monitoring changes in potentially harmful microorganisms in aquaculture. Network analysis highlighted positive correlations between bacteria and eukaryotes, with bacteria playing a key role in network interactions. The key bacterial genera associated with other microorganisms varied significantly (P < 0.05) across different groups. In summary, this study deepens the understanding of aquaculture water quality and offers valuable insights for maintaining healthy aquaculture practices. Key points: • Bacterial and eukaryotic communities displayed distinct temporal variations. • Different months exhibited unique potential pathogenic bacteria and red tide organisms. • Bacteria are key taxonomic taxa involved in microbial network interactions. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the authors discusses various reports within the issue on topics including data networking, data encryption, and security monitoring for named data networking.

Mobile Cloud Computing refers to an infrastructure where both the data storage and the data processing occur outside of the mobile device. Mobile cloud applications move the computing power and data storage away from mobile devices and into the cloud, bringing applications and mobile computing not only to smartphone users but also to a much broader range of mobile subscribers. [ABSTRACT FROM PUBLISHER]

Computation offloading is a proven successful paradigm for enabling resource-intensive applications on mobile devices. Moreover, in view of emerging mobile collaborative application, the offloaded tasks can be duplicated when multiple users are in the same proximity. This motivates us to design a collaborative offloading scheme and cache the popular computation results that are likely to be reused by other mobile users. In this paper, we consider the scenario where multiple mobile users offload duplicated computation tasks to the network edge, and share the computation results among them. Our goal is to develop the optimal fine-grained collaborative offloading strategies with caching enhancements to minimize the overall execution delay at the mobile terminal side. To this end, we propose an optimal offloading with caching-enhancement scheme (OOCS) for femto-cloud scenario and mobile edge computing scenario, respectively. Simulation results show that compared to six alternative solutions in literature, our single-user OOCS can reduce execution delay up to $42.83$ % and $33.28$ % for single-user femto-cloud and single-user mobile edge computing, respectively. Our multi-user OOCS can further reduce $11.71$ % delay compared to single-user OOCS through users’ cooperation. [ABSTRACT FROM AUTHOR]

Quantum entanglement brings high-speed and inherently privacy-preserving transmission for information communication in quantum networks. The qubit scarcity is an important issue that cannot be ignored in quantum networks due to the limited storage capacity of quantum devices, the short lifespan of qubits, and so on. In this article, we first formulate the qubit competition problem as the Cooperative-Qubit-Allocation-Problem (CQAP) by taking into account both the waiting time and the fidelity of end-to-end entanglement with the given transmission link set. We then model the CQAP as a Markov Decision Process (MDP) and adopt a Reinforcement Learning (RL) algorithm to self-adaptively and cooperatively allocate qubits among quantum repeaters. Further, we introduce an Active Learning (AL) algorithm to improve the efficiency of the RL algorithm by reducing its trial-error times. Simulation results demonstrate that our proposed algorithm outperforms the benchmark algorithms, with 23.5 ms reduction on the average waiting time and 19.2 improvement on the average path maturity degree, respectively. [ABSTRACT FROM AUTHOR]

The research on the relationship between mechanization level in planting industry and labor demand was carried out based on the present literatures. The former estimation model of labor demand in planting industry was established without analyzing the effects of planting structure on labor demand in planting industry. The purpose of the research is to develop and perfect the theory of estimating both labor demand and rural surplus labor in planting industry, then to provide some theoretical references for scientific estimation. The model established in this research can be used to calculate not only the amount of current labor demand, but also the demand in the various moment of future according to forecasted mechanization level and cultivated areas. Furthermore, it was explored how to obtain the indexes of cultivated areas, mechanization level and the average cultivated area that each labor can burden when the mechanization level is 0 and 100%. According to statistics principle, the methods of inspection to eliminate abnormal data and data processing were given in order to make the data more credible. Finally, an example was presented for demonstration purposes. [ABSTRACT FROM AUTHOR]

Disruption-tolerant networks (DTNs) are sparse mobile ad hoc networks where nodes connect with each other intermittently and end-to-end communication paths do not exist. Data routing in DTNs is challenging and has drawn much attention from research communities recently. Although many DTN routing strategies have been proposed in the past, they confront problems such as blind spots and dead ends and lack efficient implementation in a decentralized, large-scale, mobile, and dynamic environment. To overcome these difficulties, we introduce a new solution for DTNs that leverages the social properties and mobility characteristics of users. Our observation of the mobile trajectories of three data sets collected from real DTNs reveals that user movements appear locally and they tend to form communities correlated to geographic locations. Based on these findings, we propose a social- and mobile-aware routing strategy (SMART). It exploits a distributed community partitioning algorithm to divide the DTN into smaller communities regarding user locations and interaction routines. For intracommunity communications, a decayed routing metric convoluting social similarity and social centrality is calculated, which is used to decide forwarding node efficiently while avoiding the blind-spot and dead-end problems. To enable efficient intercommunity communications, we choose the fringe nodes that travel remotely as relays, and we propose the node-to-community utilities for routing decision across communities. We present empirical analysis to show that SMART reduces the occurrence of blind spots and dead ends to a level below 1%. The efficiency of SMART is evaluated by extensive trace-driven experiments, which illustrate that it outperforms other routing strategies in various real DTN traces. [ABSTRACT FROM PUBLISHER]

Online social networks such as Facebook, Twitter and Gowalla allow people to communicate and interact across borders. In past years online social networks have become increasingly important for studying the behavior of individuals, group formation, and the emergence of online societies. Here we focus on the characterization of the average growth of online social networks and try to understand which are possible processes behind seemingly long-range temporal correlated collective behavior. In agreement with recent findings, but in contrast to Gibrat's law of proportionate growth, we find scaling in the average growth rate and its standard deviation. In contrast, Renren and Twitter deviate, however, in certain important aspects significantly from those found in many social and economic systems. Whereas independent methods suggest no significance for temporally long-range correlated behavior for Renren and Twitter, a scaling analysis of the standard deviation does suggest long-range temporal correlated growth in Gowalla. However, we demonstrate that seemingly long-range temporal correlations in the growth of online social networks, such as in Gowalla, can be explained by a decomposition into temporally and spatially independent growth processes with a large variety of entry rates. Our analysis thus suggests that temporally or spatially correlated behavior does not play a major role in the growth of online social networks. [ABSTRACT FROM AUTHOR]

• Enzyme-like catalyst S/CuMgAL-41 with confined space was successfully constructed by coprecipitation and thiourea modification. • The S/CuMgAl-41 is a bifunctional catalyst with both acidic and alkaline active site. • NO 3 2− and SO 4 2− come from calcination of CuMgAL-41 modified by thiourea not only enhance the electrophilicity of aldehydes but also anchor of p -NBD with NO 3 2− and SO 4 2− by hydrogen, thereby increase the selectivity of the entantiomers and ee value. • The asymmetric aldol addition of p -NBD with acetone catalyzed S/CuMgAL-41 is the synergistic result of diverse factors such as the acid-base active center on the S/CuMgAL-41, hydrogen bonding, confined effect, anchoring effect. In this study, a series catalysts of CuMgAl-LDHs were successfully prepared by co-precipitation method. Subsequently, the catalyst S/CuMgAl-41 was prepared by modifying CuMgAl-41 with thiourea. The physical and chemical properties of the catalyst were characterized by XRD, N 2 adsorption-desorption, SEM, ICP, FT-IR, TG-DSC and XPS. The catalytic performance of the catalyst was investigated. It showed that S/CuMgAl-41 exhibited synergistic catalytic activity with better ee value due to both acidic and alkaline active sites and confined spaces with proper interlayer spacing. When isopropyl alcohol was chosen as solvent, the S/CuMgAl-41 has the best catalytic effect for asymmetric aldol addition reaction between p -nitrobenzaldehyde (p -NBD) and acetone. Additionally, the process conditions for optimizing the asymmetric addition reaction have been thoroughly studied. It was found that S/CuMgAl-41 exhibited good reusability and stability. Moreover, the asymmetric aldol addition of acetone and p -NBD catalyzed by S/CuMgAL-41 can occur due to the result of synergistic effects of various factors, including acid-base active center, hydrogen bond, confined effect and anchoring effects between hydrotalcite laminate of S/CuMgAL-41. This novel catalytic design provides a new idea for asymmetric synthesis of p -NBD and acetone. In this study, a series catalysts of CuMgAl-LDHs were successfully prepared by co-precipitation method. Subsequently, the catalyst S/CuMgAl-41 was prepared by modifying CuMgAl-41 with thiourea. The physical and chemical properties of the catalyst were characterized by XRD, N 2 adsorption-desorption, SEM, ICP, FT-IR, TG-DSC and XPS. The catalytic performance of the catalyst was investigated. It showed that S/CuMgAl-41 exhibited synergistic catalytic activity with better ee value due to both acidic and alkaline active sites and confined spaces with proper interlayer spacing. When isopropyl alcohol was chosen as solvent, the S/CuMgAl-41 has the best catalytic effect for asymmetric aldol addition reaction between p -nitrobenzaldehyde (p -NBD) and acetone. Additionally, the process conditions for optimizing the asymmetric addition reaction have been thoroughly studied. It was found that S/CuMgAl-41 exhibited good reusability and stability. Moreover, the asymmetric aldol addition of acetone and p -NBD catalyzed by S/CuMgAL-41 can occur due to the result of synergistic effects of various factors, including acid-base active center, hydrogen bond, confined effect and anchoring effects between hydrotalcite laminate of S/CuMgAL-41. This novel catalytic design provides a new idea for asymmetric synthesis of p -NBD and acetone. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: By applying the duplicate form of Carlitz inversions to three special cases of the -Saalschütz theorem respectively, we establish three classes of interesting summation formulae. [Copyright &y& Elsevier]

Background: Giant vascular eccrine spiradenoma is a rare variant of eccrine spiradenoma. It is different from the eccrine spiradenoma in its larger size and greater degree of vascularity. It is often clinically confusedwith a vascular or malignant tumor. Case presentation: Here, we report a case of a 67-year-old man who presented with a tumor in the scrotum scrotal region for 6 years. The tumor had begun as a small nodule and had grown slowly with over time. Surgical excision and pathological examination revealed that the lesion was the a giant vascular eccrine spiradenoma. Conclusion: This study reports the first case of giant vascular eccrine spiradenoma in the scrotum. [ABSTRACT FROM AUTHOR]

Coreness is an important index to reflect the cohesiveness of a graph. The problems of core computation in static graphs and core update in dynamic graphs, known as the core decomposition and core maintenance problems respectively, have been extensively studied in previous work. However, most of these work focus on unweighted graphs. Considering that graphs are weighted in a lot of realistic applications, it is indispensable to extend the coreness to weighted graphs and devise efficient algorithms for weighted core decomposition and weighted core maintenance. In this work, we present a new definition of weighted coreness for vertices in a weighted graph, by taking into account the weights of vertices, which makes the coreness in unweighted graph be a special case. We propose efficient algorithms for both weighted core decomposition and weighted core maintenance problems. The coreness of vertices can be computed in linear time by the proposed decomposition algorithm, while the proposed core maintenance algorithm can process multiple-edge insertions/deletions simultaneously, which greatly reduces the core update time. Comprehensive experiments on both realistic networks and temporal graphs exhibit our algorithms are efficient and scalable. [ABSTRACT FROM AUTHOR]

With the rapid development of autonomous driving, collision avoidance has attracted attention from both academia and industry. Many collision avoidance strategies have emerged in recent years, but the dynamic and complex nature of driving environment poses a challenge to develop robust collision avoidance algorithms. Therefore, in this paper, we propose a decentralized framework named RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE. Leveraging a hierarchical architecture we develop an algorithm named Co-DDPG to efficiently train autonomous vehicles. Through a security abiding channel, the autonomous vehicles distribute their driving policies. We use the relative distances obtained by the opponent sensors to build the VANET instead of locations, which ensures the vehicle's location privacy. With a leader-follower architecture and parameter distribution, RACE accelerates the learning of optimal policies and efficiently utilizes the remaining resources. We implement the RACE framework in the widely used TORCS simulator and conduct various experiments to measure the performance of RACE. Evaluations show that RACE quickly learns optimal driving policies and effectively avoids collisions. Moreover, RACE also scales smoothly with varying number of participating vehicles. We further compared RACE with existing autonomous driving systems and show that RACE outperforms them by experiencing 65% less collisions in the training process and exhibits improved performance under varying vehicle density. [ABSTRACT FROM AUTHOR]

Recently, the issue of offloading cellular data while reducing the duplicated cellular transmission has gained more and more attention. Several studies have shown that sharing contents through Device-to-Device (D2D) to offload traffic to local connections nearby can offer better performance for mobile users. Nevertheless, most existing proposals are somewhat confined to small-scale data sets or limited feature dimensions, relied on unconsolidated hypotheses and measurements of data sets. This paper presents a prior work of large-scale measurements on 3.56 TBytes of real-world data sets, which contain D2D content sharing activities from a popular D2D sharing application (APP). We conduct a comprehensive analysis of multi-dimensional features, including time series, structural properties, meeting dynamics, location relationship, and propagation tree. Our analysis reveals that (i) D2D sharing makes the hops between users shorter (in 3 or 4 degrees), (ii) the maximum spreading distance of content dissemination is 27 hops, (iii) we provide a new evidence of log-normal distribution of all user encounters (named meeting dynamics in this paper) based the fit of inter-TX time, Inter-Content Time (ICT) and Contact Time, (iv) online factor (O) and social factor (S) demonstrate the largest positive correlation and indicate that the two factors have high linear correlation. Finally, we analyze the correlations among all the impact factors by Pearson coefficient, principal component analysis, and latent semantic analysis, respectively. Results reveal that online factor (O) and social factor (S) have a high correlation, especially both of them have a great effect on D2D sharing activities. [ABSTRACT FROM AUTHOR]

Polyetheretherketone (PEEK) is considered a potential orthopedic/dental material because of its excellent mechanical and chemical properties (e.g., similar elastic modulus to that of human bone). However, the poor bacteriostasis and anti-inflammatory and osseointegrative properties of bioinert PEEK impede its clinical application. We previously developed a facile and versatile surface modification method using dexamethasone plus minocycline-loaded liposomes (Dex/Mino liposomes) bonded by a mussel-inspired polydopamine coating, which effectively modulated cell inflammatory response and discouraged bacterial colonization in vitro. Herein, we report the application of this multifunctional surface modification method to improve bioinert PEEK, aimed at further studying the in vitro osteogenesis and in vivo properties of Dex/Mino liposome-modified PEEK to prevent bacterial contamination, attenuate the inflammatory response, and enhance ossification for physiologic osseointegration. Our study established that the Dex/Mino liposome-modified PEEK surface presented favorable stability and cytocompatibility. Compared with bare PEEK, improved osteogenic differentiation of human mesenchymal stem cells under both osteoinductive and osteoconductive conditions was found on the functionalized surface due to the liposomal Dex releasing. In vivo bacteriostasis assay confirmed that Mino released from the functionalized surface provided an effective antibacterial effect. Moreover, the subcutaneous foreign body reaction and beagle femur implantation models corroborated the enhanced anti-inflammatory and osteointegrative properties of the functionalized PEEK. Our findings indicate that the developed Dex/Mino liposome-modified PEEK with enhanced antibacterial, anti-inflammatory, and osseointegrative capacity has great potential as an orthopedic/dental implant material for clinical application. Image 1 [ABSTRACT FROM AUTHOR]

Abstract Live Virtual Machine (VM) migration within a data center is an important technology for cloud management, and has brought many benefits to both cloud providers and users. With the development of cloud computing, across-data-center VM migration is also desired. Normally, there is no shared storage system between data centers, hence the storage data (disk image) of a VM will be migrated to the destination data center as well. However, the slow network speed of the Internet and the comparatively large size of VM disk image make VM storage data migration become a bottleneck for live VM migration across data centers. In this paper, based on a detailed analysis of VM deployment models and the nature of VM image data, we design and implement a new migration system, called CBase. The key concept of CBase is a newly introduced central base image repository for reliable and efficient data sharing between VMs and data centers. With this central repository, further performance optimizations to VM storage data migration are made possible. Two migration mechanisms (data deduplication and Peer-to-Peer (P2P) file sharing) are utilized to accelerate base image migration, and a strategy is designed to elevate the synchronization of newly-written disk blocks. The results from an extensive experiment show that CBase significantly outperforms existing migration mechanisms under different conditions regarding total migration time and total network traffic. In particular, CBase with data deduplication is better than P2P file sharing for base image migration in our experimental environment. Highlights • A central repository is introduced to deploy and store base images for data centers. • Data deduplication and Peer-to-Peer (P2P) file sharing are used to accelerate the migration of a base image. • A mechanism is proposed to lower the overhead of migrating the writable layer of VM storage data. • An orchestration strategy is designed to improve the robustness of the migration system. • Experimental results validate the efficiency of our new approaches. [ABSTRACT FROM AUTHOR]

Our daily lives have been immersed in widespread location-based social networks (LBSNs). As an open platform, LBSNs typically allow all kinds of users to register accounts. Malicious attackers can easily join and post misleading information, often with the intention of influencing users' decisions in urban computing environments. To provide reliable information and improve the experience for legitimate users, we design and implement DeepScan, a malicious account detection system for LBSNs. Different from existing approaches, DeepScan leverages emerging deep learning technologies to learn users' dynamic behavior. In particular, we introduce the long short-term memory (LSTM) neural network to conduct time series analysis of user activities. DeepScan combines newly introduced time series features and a set of conventional features extracted from user activities, and exploits a supervised machine-learning-based model for detection. Using real traces collected from Dianping, a representative LBSN, we demonstrate that DeepScan can achieve excellent prediction performance with an F1-score of 0.964. We also find that the time series features play a critical role in the detection system. [ABSTRACT FROM AUTHOR]

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. Aiming at provisioning flexible on-demand mobile-edge cloud service, in this paper we propose a comprehensive framework consisting of a resource-efficient computation offloading mechanism for users and a joint communication and computation (JCC) resource allocation mechanism for network operator. Specifically, we first study the resource-efficient computation offloading problem for a user, in order to reduce user's resource occupation by determining its optimal communication and computation resource profile with minimum resource occupation and meanwhile satisfying the QoS constraint. We then tackle the critical problem of user admission control for JCC resource allocation, in order to properly select the set of users for resource demand satisfaction. We show the admission control problem is NP-hard, and hence develop an efficient approximation solution of a low complexity by carefully designing the user ranking criteria and rigourously derive its performance guarantee. To prevent the manipulation that some users may untruthfully report their valuations in acquiring mobile-edge cloud service, we further resort to the powerful tool of critical value approach to design truthful pricing scheme for JCC resource allocation. Extensive performance evaluation demonstrates that the proposed schemes can achieve superior performance for on-demand mobile-edge cloud computing. [ABSTRACT FROM AUTHOR]

MoSe2 represents important layered transition metal dichalcogenides (TMDs) that have high electrocatalytic activity in the hydrogen evolution reaction (HER). A key issue to achieve excellent electrochemical properties of MoSe2 is to synthesize nanostructures that are composed of few-layered MoSe2 with abundant exposure of active edge sites. Nanoflowers of layered materials are promising building blocks for photocatalysis due to their large specific surface area and a wealth of exposed edge sites. In this work, free-standing colloidal MoSe2 nanoflowers, with a size of 250 nm, were synthesized by employing a quick and nontoxic phosphine-free solution-processing approach. Oleic acid (OA), 1-octadecene (ODE) and 1-octylamine (OLA) were primarily used as solvents to control the morphology of MoSe2 nanostructures. Experimental results revealed that the shape evolution of MoSe2 nanostructures started with a first fast precipitation of amorphous materials followed by crystallization of a few layers of nanosheets. The prepared MoSe2 nanoflowers demonstrated excellent HER activities with low overpotentials and small Tafel slopes, and the chronopotentiometry responses revealed good stability, making them promising materials for the electrocatalytic HER. [ABSTRACT FROM AUTHOR]

In current cloud computing systems, when leveraging virtualization technology, the customer’s requested data computing or storing service is accommodated by a set of communicated virtual machines (VM) in a scalable and elastic manner. These VMs are placed in one or more server nodes according to the node capacities or failure probabilities. The VM placement availability refers to the probability that at least one set of all customer’s requested VMs operates during the requested lifetime. In this paper, we first study the problem of placing at most $H$ groups of $k$ requested VMs on a minimum number of nodes, such that the VM placement availability is no less than $\delta$ , and that the specified communication delay and connection availability for each VM pair under the same placement group are not violated. We consider this problem with and without Shared-Risk Node Group (SRNG) failures, and prove this problem is NP-hard in both cases. We subsequently propose an exact Integer Nonlinear Program (INLP) and an efficient heuristic to solve this problem. We conduct simulations to compare the proposed algorithms with two existing heuristics in terms of performance. Finally, we study the related reliable routing problem of establishing a connection over at most $w$ link-disjoint paths from a source to a destination, such that the connection availability requirement is satisfied and each path delay is no more than a given value. We devise an exact algorithm and two heuristics to solve this NP-hard problem, and evaluate them via simulations. [ABSTRACT FROM PUBLISHER]

Droplet drift wastes pesticide, pollutes the environment, and has become one of the focus issues of agricultural crop protection. Electrostatic spray technology reduces drift to a certain degree. In order to investigate the droplet drift pattern of a conical electrostatic nozzle, the droplet drift mass center distance was defined as an experimental index and used to conduct experimental wind tunnel studies on droplet drift. A mathematical model of the droplet drift mass center distance versus electrostatic voltage and wind speed was created via the regression method. The test results showed that the electrostatic voltage had an insignificant effect on droplet drift, the wind speed and its interaction with the electrostatic voltage had significant effects on droplet drift. When the wind speed was less than 3 m/s and stable, the crop adsorbability of a droplet had a dominant effect on the droplet drift; the droplet drift decreased with the increase of electrostatic voltage. When the wind speed exceeded 3 m/s and was stable, the reduced droplet particle size had a dominant effect on droplet drift, where droplet drift increased as the electrostatic voltage increased. When the wind speed was 0 m/s and the electrostatic voltage was 12 kV, the minimum droplet drift mass center distance was 35.5 mm, which was 56 mm less than that of conventional nozzle droplet drift. Therefore, a conical electrostatic nozzle is inapplicable for operation in an environment where wind speeds exceed 3 m/s. This study provides a reference for optimizing operational parameters of conical electrostatic nozzles and improving the anti-drift capability of droplets. [ABSTRACT FROM AUTHOR]

Recent studies reveal 2-aminoadipic acid (2-AAA) is both elevated in subjects at risk for diabetes and mechanistically linked to glucose homeostasis. Prior studies also suggest enrichment of protein-bound 2-AAA as an oxidative post-translational modification of lysyl residues in tissues associated with degenerative diseases of aging. While in vitro studies suggest redox active transition metals or myeloperoxidase (MPO) generated hypochlorous acid (HOCl) may produce protein-bound 2-AAA, the mechanism(s) responsible for generation of 2-AAA during inflammatory diseases are unknown. In initial studies we observed that traditional acid- or base-catalyzed protein hydrolysis methods previously employed to measure tissue 2-AAA can artificially generate protein-bound 2-AAA from an alternative potential lysine oxidative product, lysine nitrile (LysCN). Using a validated protease-based digestion method coupled with stable isotope dilution LC/MS/MS, we now report protein bound 2-AAA and LysCN are both formed by hypochlorous acid (HOCl) and the MPO/H 2 O 2 /Cl − system of leukocytes. At low molar ratio of oxidant to target protein N ε -lysine moiety, 2-AAA is formed via an initial N ε -monochloramine intermediate, which ultimately produces the more stable 2-AAA end-product via sequential generation of transient imine and semialdehyde intermediates. At higher oxidant to target protein N ε -lysine amine ratios, protein-bound LysCN is formed via initial generation of a lysine N ε -dichloramine intermediate. In studies employing MPO knockout mice and an acute inflammation model, we show that both free and protein-bound 2-AAA, and in lower yield, protein-bound LysCN, are formed by MPO in vivo during inflammation. Finally, both 2-AAA and to lesser extent LysCN are shown to be enriched in human aortic atherosclerotic plaque, a tissue known to harbor multiple MPO-catalyzed protein oxidation products. Collectively, these results show that MPO-mediated oxidation of protein lysyl residues serves as a mechanism for producing 2-AAA and LysCN in vivo . These studies further support involvement of MPO-catalyzed oxidative processes in both the development of atherosclerosis and diabetes risk. [ABSTRACT FROM AUTHOR]

A low cost and large scale synthetic approach was developed for colloidal PbS quantum dots (QDs). The heat up strategy was adapted for large scale production, thioacetamide in oleylamine (OLA) was used as the sulphur precursor for low cost and safe synthesis, and PbCl2 in OLA was used as the lead precursor for a well passivated surface. Due to the balance between the diffusion coefficient and monomer oversaturation generated by the dissolution of small particles, an optimum growth temperature exists in the new synthesis. The synthesis was easily scaled up to produce 18 g of PbS QDs from one reaction batch, with a high chemical yield of ∼80% and the as synthesized QDs exhibit a high photoluminescence quantum yield of ＞40% and excellent air-stability of at least 60 days, due to the Cl− and oleate co-passivation. [ABSTRACT FROM AUTHOR]

Summary Normal platelet function is critical to blood hemostasis and maintenance of a closed circulatory system. Heightened platelet reactivity, however, is associated with cardiometabolic diseases and enhanced potential for thrombotic events. We now show gut microbes, through generation of trimethylamine N-oxide (TMAO), directly contribute to platelet hyperreactivity and enhanced thrombosis potential. Plasma TMAO levels in subjects (n > 4,000) independently predicted incident (3 years) thrombosis (heart attack, stroke) risk. Direct exposure of platelets to TMAO enhanced sub-maximal stimulus-dependent platelet activation from multiple agonists through augmented Ca 2+ release from intracellular stores. Animal model studies employing dietary choline or TMAO, germ-free mice, and microbial transplantation collectively confirm a role for gut microbiota and TMAO in modulating platelet hyperresponsiveness and thrombosis potential and identify microbial taxa associated with plasma TMAO and thrombosis potential. Collectively, the present results reveal a previously unrecognized mechanistic link between specific dietary nutrients, gut microbes, platelet function, and thrombosis risk. [ABSTRACT FROM AUTHOR]

Summary Trimethylamine (TMA) N-oxide (TMAO), a gut-microbiota-dependent metabolite, both enhances atherosclerosis in animal models and is associated with cardiovascular risks in clinical studies. Here, we investigate the impact of targeted inhibition of the first step in TMAO generation, commensal microbial TMA production, on diet-induced atherosclerosis. A structural analog of choline, 3,3-dimethyl-1-butanol (DMB), is shown to non-lethally inhibit TMA formation from cultured microbes, to inhibit distinct microbial TMA lyases, and to both inhibit TMA production from physiologic polymicrobial cultures (e.g., intestinal contents, human feces) and reduce TMAO levels in mice fed a high-choline or L-carnitine diet. DMB inhibited choline diet-enhanced endogenous macrophage foam cell formation and atherosclerotic lesion development in apolipoprotein e −/− mice without alterations in circulating cholesterol levels. The present studies suggest that targeting gut microbial production of TMA specifically and non-lethal microbial inhibitors in general may serve as a potential therapeutic approach for the treatment of cardiometabolic diseases. [ABSTRACT FROM AUTHOR]

We study the problem of group formation in online social networks. In particular, we focus on one of the most important human groups—the triad—and try to understand how closed triads are formed in dynamic networks, by employing data from a large microblogging network as the basis of our study. We formally define the problem of triadic closure prediction and conduct a systematic investigation. The study reveals how user demographics, network characteristics, and social properties influence the formation of triadic closure. We also present a probabilistic graphical model to predict whether three persons will form a closed triad in a dynamic network. Different kernel functions are incorporated into the proposed graphical model to quantify the similarity between triads. Our experimental results with the large microblogging dataset demonstrate the effectiveness (+10 percent over alternative methods in terms of F1-Score) of the proposed model for the prediction of triadic closure formation. [ABSTRACT FROM PUBLISHER]

Predicting individual socioeconomic status (SES) from social media content benefits various applications in economic and social fields. Most previous works adopt machine learning methods with predefined features to infer SES. Nevertheless, they ignore some important information of social media content, such as order, structure and relation information, which leads to limited performance. In this paper, we propose a COupled social media content REpresentation model (CORE) for individual SES prediction, which efficiently exploits latent complex couplings of social media content. CORE devises a structure-aware social media text representation method to incorporate the order and the hierarchy of social media text, and leverages a coupled attribute representation method to take into account intra-coupled and inter-coupled interaction relationships among user level attributes. Our experiments on a real data set of a Chinese microblogging platform demonstrate that our approach significantly outperforms benchmark methods, which validates its efficiency and robustness. The proposed model could be applied to improve the SES prediction and other user profiling tasks. • A coupled social media content representation model for predicting individual SES is proposed. • A structure-aware social media text representation method is presented. • A coupled attribute representation method exploring intrinsic relationships is devised. • Experiments prove that the proposed model greatly outperforms state-of-the-art models. [ABSTRACT FROM AUTHOR]

Recent studies have indicated that high-density lipoproteins (HDLs) and their major structural protein, apolipoprotein A1 (apoA1), recovered from human atheroma are dysfunctional and are extensively oxidized by myeloperoxidase (MPO). In vitro oxidation of either apoA1 or HDL particles by MPO impairs their cholesterol acceptor function. Here, using phage display affinity maturation, we developed a high-affinity monoclonal antibody that specifically recognizes both apoA1 and HDL that have been modified by the MPO-H2O2-Cl− system. An oxindolyl alanine (2-OH-Trp) moiety at Trp72 of apoA1 is the immunogenic epitope. Mutagenesis studies confirmed a critical role for apoA1 Trp72 in MPO-mediated inhibition of the ATP-binding cassette transporter A1 (ABCA1)-dependent cholesterol acceptor activity of apoA1 in vitro and in vivo. ApoA1 containing a 2-OH-Trp72 group (oxTrp72-apoA1) is in low abundance within the circulation but accounts for 20% of the apoA1 in atherosclerosis-laden arteries. OxTrp72-apoA1 recovered from human atheroma or plasma is lipid poor, virtually devoid of cholesterol acceptor activity and demonstrated both a potent proinflammatory activity on endothelial cells and an impaired HDL biogenesis activity in vivo. Elevated oxTrp72-apoA1 levels in subjects presenting to a cardiology clinic (n = 627) were associated with increased cardiovascular disease risk. Circulating oxTrp72-apoA1 levels may serve as a way to monitor a proatherogenic process in the artery wall. [ABSTRACT FROM AUTHOR]