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Objectives: This study was aimed to quantitatively assess hyperperfusion using arterial spin labeling (ASL) to predict hemorrhagic transformation (HT) in acute ischemic stroke (AIS) patients. Methods: This study enrolled 98 AIS patients with anterior circulation large vessel occlusion within 24 h of symptom onset. ASL was performed before mechanical endovascular therapy. On pre-treatment ASL maps, a region with relative cerebral blood flow (CBF) ≥ 1.4 was defined as an area of hyperperfusion. The maximum CBF (CBFmax) of hyperperfusion was calculated for each patient. A non-contrast CT scan was performed during the subacute phase for the evaluation of HT. Good clinical outcome was defined as a 90-day modified Rankin scale score of 0–2. Results: The CBFmax of hyperperfusion (odds ratio, 1.023; 95% confidence interval [CI], 1.005–1.042; p = 0.012) was an independent risk factor for the status of HT. The CBFmax of hyperperfusion for HT showed an area under the curve of 0.735 (95% CI, 0.588–0.882) with optimal cutoff value, sensitivity, and specificity being 146.5 mL/100 g/min, 76.9%, and 69.6%, respectively. There was a statistically significant relationship between HT grades (from no HT to PH2) and CBFmax of hyperperfusion with a Spearman rank correlation of 0.446 (p = 0.001). In addition, low CBFmax of hyperperfusion were associated with good functional outcome (95% CI, 17.130–73.910; p = 0.002). Conclusions: High CBFmax of hyperperfusion was independently associated with subsequent HT and low CBFmax of hyperperfusion linked to good functional outcome. There was a positive correlation between HT grade and CBFmax. Clinical relevance statement: Arterial spin labeling is a noninvasive and contrast agent–independent technique, which is sensitive in detecting hyperperfusion. This study shows that the cerebral blood flow of hyperperfusion is associated with clinical prognosis, which will benefit more patients. Key Points: • Quantitative assessment of hyperperfusion using pre-treatment arterial spin labeling to predict hemorrhagic transformation and prognosis in acute ischemic stroke patients. • The maximum cerebral blood flow of hyperperfusion was associated with hemorrhagic transformation and clinical prognosis and higher maximum cerebral blood flow of hyperperfusion was associated with higher grade hemorrhagic transformation. • The maximum cerebral blood flow of hyperperfusion can predict hemorrhagic transformation which enables timely intervention to prevent parenchymal hematoma. [ABSTRACT FROM AUTHOR]

Cyclodextrins (CDs) are widely used macrocycles in supramolecular assembly due to their easy availability, versatile functionality and excellent biocompatibility. Although they are well-known for forming host–guest complexes with a wide range of guests and this host–guest chemistry has long been utilized in industry and academia, new opportunities have arisen in recent years, particularly in supramolecular assembly. In the present review, we will first provide a basic introduction to CDs and then summarize their emerging roles in the fields of supramolecular chemistry and materials. This includes their involvement in hybrid frameworks with inorganic components such as metal ions and polyoxometalates, crystalline self-assembly with amphiphilic molecules, and their new possibility of "catassembly" and induced chiral supramolecular structures that have previously been overlooked. Finally, we will comment on the future perspectives of CDs to inspire more ideas and efforts, with the aim of promoting diverse applications of CDs in supramolecular materials. [ABSTRACT FROM AUTHOR]

The detection of anomalies in spatiotemporal traffic data is not only critical for intelligent transportation systems and public safety but also very challenging. Anomalies in traffic data often exhibit complex forms in two aspects, (i) spatiotemporal complexity (i.e. we need to associate individual locations and time intervals formulating a panoramic view of an anomaly) and (ii) multi-source complexity (i.e. we need an algorithm that can model the anomaly degree of the multiple data sources of different densities, distributions and scales). To tackle these challenges, we proposed a three-step method that uses factor analysis to extract features, then uses the goodness-of-fit test to obtain the anomaly score of a single data point and then uses one class support vector machine to synthesize the anomaly score. Finally, we conduct extensive experiments on real-world trip data include taxi and bike data. And these extensive experiments demonstrate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Most existing heterogeneous information network (HIN) embedding methods focus on static environments while neglecting the evolving characteristic of real-world networks. Although several dynamic embedding methods have been proposed, they are merely designed for homogeneous networks and cannot be directly applied in heterogeneous environments. To tackle above challenges, we propose a novel framework for incorporating temporal information into HIN embedding, named multi-view dynamic HIN embedding (MDHNE), which can efficiently preserve evolution patterns of implicit relationships from different views in updating node vectors over time. We first transform HIN to a series of homogeneous networks corresponding to different views. Then our proposed MDHNE applies recurrent neural network (RNN) to incorporate evolving pattern of complex network structure and semantic relationships between nodes into latent embedding spaces, and thus the node vectors from multiple views can be learned and updated when HIN evolves over time. Moreover, we come up with an attention-based fusion mechanism, which can automatically infer weights of latent vectors corresponding to different views by minimizing the objective function specific for different mining tasks. Extensive experiments clearly demonstrate that our MDHNE model outperforms state-of-the-art baselines on three real-world dynamic datasets for different network mining tasks. [ABSTRACT FROM AUTHOR]

Dynamic pricing plays an important role in solving the problems such as traffic load reduction, congestion control, and revenue improvement. Efficient dynamic pricing strategies can increase capacity utilization, total revenue of service providers, and the satisfaction of both passengers and drivers. Many proposed dynamic pricing technologies focus on short-term optimization and face poor scalability in modeling long-term goals for the limitations of solution optimality and prohibitive computation. In this article, a deep reinforcement learning framework is proposed to tackle the dynamic pricing problem for ride-hailing platforms. A soft actor-critic (SAC) algorithm is adopted in the reinforcement learning framework. First, the dynamic pricing problem is translated into a Markov Decision Process (MDP) and is set up in continuous action spaces, which is no need for the discretization of action space. Then, a new reward function is obtained by the order response rate and the KL-divergence between supply distribution and demand distribution. Experiments and case studies demonstrate that the proposed method outperforms the baselines in terms of order response rate and total revenue. [ABSTRACT FROM AUTHOR]

This paper addresses the anomaly detection problem in feature-evolving systems such as server machines, cyber security, financial markets and so forth where in every millisecond, |$N$| -dimensional feature-evolving heterogeneous time series are generated. However, due to stochasticity and uncertainty in evolving heterogeneous time series coupled with temporal dependencies, their anomaly detection are extremely challenging. Furthermore, it is practically impossible to train an anomaly detection model per single time series across millions of metrics, leave alone memory space required to maintain the model and evolving data points in memory for timely processing in feature-evolving data streams. Thus, this paper proposes |$\underline{o}$| ne sketch |$\underline{f}$| its all |$\underline{a}$| lgorithm (OFA), which is a real-time stochastic recurrent deep neural network anomaly detector built on assumption-free probabilistic conditional quantile regression with well-calibrated predictive uncertainty estimates. The proposed framework is capable of detecting anomalies robustly, accurately and efficiently in real time while handling randomness and variabilities in feature-evolving heterogeneous time series. Extensive experiments and rigorous evaluation on large-scale real-world data sets showcase that OFA outperforms other competitive state-of-the-art anomaly detector methods. [ABSTRACT FROM AUTHOR]

Predicting the bike demand can help rebalance the bikes and improve the service quality of a bike-sharing system. A lot of works focus on predicting the bike demand for all the stations, which is unnecessary as the travel cost of rebalance operations increases sharply as the number of stations increases. In this paper, we propose a framework for predicting the hourly bike demand based on the central stations we define. Firstly, we propose Two-Stage Station Clustering Algorithm to assign central stations and common stations into each cluster. Secondly, we propose a hierarchical prediction model to predict the hourly bike demand for every cluster and each central station progressively. Thirdly, we use a well-studied queuing model to determine the target initial inventory for each central station. The most innovative contribution of this paper is proposing the concept of central station, the use of a novel algorithm to cluster the central stations and present a hierarchical model, containing the Time and Weather Similarity Weighted K-Nearest Neighbor Algorithm and a linear model to predict the bike demand for central stations. The experimental results on the New York citi bike system demonstrate that our proposed method is more accurate than other methods in solving existing problems. [ABSTRACT FROM AUTHOR]

Spatial temporal imputation is an important part of intelligent transportation system, and spatial temporal data missing also largely affects the performance of its downstream tasks. Spatial temporal data are affected by spatial, temporal, and unexpected conditions, and has a complex change pattern. Existing researches mostly focus on time series and often ignore the modeling of spatial dependency. Moreover, the imputation performance is poor under high missing rate or block missing scenarios, needing to enhance the model robustness. Therefore, we proposed a spatio-temporal generative adversarial imputation network (named STGAIN) to handle with the complex spatial temporal dependency and the diversity of missing conditions on spatio-temporal data. It includes a spatio-temporal generator and a spatio-temporal discriminator, which uses a graph convolutional neural network as a spatial aggregator and a one-dimensional deformable convolutional neural network as a temporal extractor. The generator observes the unmissing part data and the spatial relationships to impute the missing parts. The discriminator then takes the completed data as input and attempts to determine which components were actually observed and which were imputed. The spatio-temporal modeling ability was improved in the competition with each other. In order to make the generator in GAN distinguish the observed part from the missing part, a fusion loss function (including reconstruction loss and BCE loss) was designed for the generator training. Finally, experiments were conducted on two real highway datasets for different missing rates. And we test the model robustness under different size of dataset and noise interference. The results show that STGAIN significantly outperforms the existing methods. [ABSTRACT FROM AUTHOR]

Guangzhou Tongcao paintings in Qing Dynasty of China witnessed the exchange of economy, culture, art and technology between China and foreign countries, which were an important export product in historical China. In this paper, by using Optical Stereo Microscope, Polarized Light Microscope, Scanning Electron Microscope attached Energy Dispersive Spectroscopy and micro confocal laser Raman Spectroscope, a Qing Dynasty Tongcao painting with the subject of family life of an official's female relative was analyzed. Our study revealed that: (1) the hexagonal cell morphology which was easily observed in the Tongcao pith could contain more pigments and increase the stereo sense of painting, and the cell of historical Tongcao pith had started to degrade, which indicated an urgent requirement on conservation of Tongcao paintings; (2) alum was needed as the fixing agent in the preparation of pigments and the painting process; (3) both the China domestic pigments including red lead, calcite, lead white, carbon black and gamboge, and the synthetic pigments imported from Europe (synthetic ultramarine, prussian blue and emerald green) were applied in this painting, which reflected the bi-directional communication of culture and technology between China and western world. Meanwhile, the existence of synthetic ultramarine, prussian blue and emerald green was helpful to further identify the production date of this painting was late Qing Dynasty, mainly the mid-late 19th century. [ABSTRACT FROM AUTHOR]

Due to the positive impact of ride sharing on urban traffic and environment, it has attracted a lot of research attention recently. However, most existing researches focused on the profit maximization or the itinerary minimization of drivers, only rare work has covered on adjustable price function and matching algorithm for the batch requests. In this paper, we propose a request matching algorithm and an adjustable price function that benefits drivers as well as passengers. Our request-matching algorithm consists of an exact search algorithm and a group search algorithm. The exact search algorithm consists of three steps. The first step is to prune some invalid groups according to the total number of passengers and the capacity of vehicles. The second step is to filter out all candidate groups according to the compatibility of requests in same group. The third step is to obtain the most profitable group by the adjustable price function, and recommend the most profitable group to drivers. In order to enhance the efficiency of the exact search algorithm, we further design an improved group search algorithm based on the idea of original simulated annealing. Extensive experimental results show that our method can improve the income of drivers, and reduce the expense of passengers. Meanwhile, ride sharing can also keep the utilization rate of seats 80%, driving distance is reduced by 30%. [ABSTRACT FROM AUTHOR]

Realistically representing the Arctic amplification in global climate models (GCMs) represents a key to accurately predict the climate system's response to increasing anthropogenic forcings. We examined the amplified Arctic warming over the past century simulated by 36 state‐of‐the‐art GCMs against observation. We found a clear difference between the simulations and the observation in terms of the evolution of the secular warming rates. The observed rates of the secular Arctic warming increase from 0.14 °C/10a in the early 1890s to 0.21 °C/10a in the mid‐2010s, while the GCMs show a negligible trend to 0.35 °C/10a at the corresponding times. The overestimation of the secular warming rate in the GCMs starts from the mid‐twentieth century and aggravates with time. Further analysis indicates that the overestimation mainly comes from the exaggerated heating contribution from the Arctic sea ice melting. This result implies that the future secular Arctic warming may have been over‐projected. Key Points: The anthropogenic emissions most likely determined the nonlinear, secular trend of Arctic warming during the past centuryState‐of‐the‐art climate models overestimate the secular Arctic warming rate since the mid‐twentieth centuryAggravating secular warming rate with time may lead to over-projection of the future Arctic warming [ABSTRACT FROM AUTHOR]

Community detection plays a significant role in understanding the essence of a network. A recently proposed algorithm Attractor, which is based on distance dynamics, can spot communities effectively, but it depends on a cohesion parameter. Moreover, no efficient way is provided to find an optimal cohesion parameter setting. In this paper, we propose a parameter-free community detection algorithm by synchronizing distances iteratively. In each iteration, the distance of each edge will change dynamically according to the effect generated by its related neighbours. Several iterations later, distances between vertices belonging to the same community will synchronize to 0, while distances between vertices not in the same community will synchronize to 1. Besides, merging and division strategies are built up in the process of community detection. Experiments on both real-world and synthetic networks demonstrate benefits of our method compared to the baseline methods. [ABSTRACT FROM AUTHOR]

Spatio-temporal data are critical for intelligent systems, such as smart transportation and smart cities. However, due to sensor failure or power failure, the spatiotemporal data missing tends to have a big impact on downstream tasks. Meanwhile, if sensors are scarce, some spatial positions without sensors need data enhancement through intelligent methods. Existing workarounds focus on modeling temporal information (such as time series), often ignoring spatial dependency, or modeling the spatial and temporal domain separately for imputation. In this paper, we propose a Long-term Multi-dimensional Spatial–Temporal Graph Convolution Network (LMSTGCN), which not only inductively estimates some missing data, but also achieves data augmentation of target locations. It contains a periodic temporal encoding mechanism, a gated temporal capture module, and a multi-dimensional spatial–temporal GCN module. The long-term temporal dependencies are captured by the periodic temporal encoding mechanism. The spatial and extra-short-term temporal dependencies are simultaneously modeled by the multi-dimensional GCN module, which can achieve exponential growth in the range of receptive fields. Corresponding to this module, we designed a spatiotemporal adjacency matrix construction method. It generates spatiotemporal adjacency matrices of corresponding time length as needed. The short-term dependencies in sequences are captured by the gated temporal capture module. In experimental analysis, results demonstrate that the proposed model outperforms the state-of-the-art baselines on real-world data sets. [ABSTRACT FROM AUTHOR]

Long term dependency capture is essentially important for time series prediction and spatial–temporal forecasting. In recent years, many deep learning-based forecasting methods have been proposed, leading to rapid development in this area. We systematically reviewed long-term dependency capture methods, including temporal dependency in sequence (named intra-sequence temporal dependency), temporal dependency out of sequence (named inter-sequence temporal dependency, in this scenario the long-term dependencies are split by many subsequences). Because the batch technique is widely adopted in machine learning and deep learning, the range of temporal capturing ability for many proposed methods is intra-sequence temporal dependency, which limits the capacity of long-term dependency capture. Aiming at the above problems, we designed three type memory mechanisms (i.e., a temporal encoding memory mechanism, a cross-sequence memory mechanism and a query-key based memory mechanism) to solve those long term dependency problems. Moreover, based on the cross-sequence memory mechanism and query-key architecture, an Attention-based Long-Term Dependency Capture model (ALTDC) is proposed for long-term dependency modeling and further solves the temporal dependency coherence problem. ALTDC includes temporal Transformer and spatial Transformer. The temporal Transformer adopts multi-head attention mechanism in temporal dimension and takes into consideration the relative position encoding. The spatial Transformer leverages attention mechanism in spatial dimension, and utilizes learnable position encoding and graph convolution to capture spatial relationships. Experiments demonstrate that the proposed model outperforms the state-of-art baselines on real world time-series datasets and spatial–temporal datasets. [ABSTRACT FROM AUTHOR]

Extend lifetime of near-earth orbit satellites by on-orbit refueling has significant economic benefits, while the orbit deployment strategy of the on-orbit servicing (OOS) system which implements the refueling task directly affects the efficiency of on-orbit refueling mission. Three orbit deployment modes were proposed, including ground deployment, low earth orbit (LEO) deployment and the same orbit deployment. For the client satellites to be refueled on near-earth orbit including LEO, medium earth orbit (MEO) and geosynchronous orbit (GEO), the three deployment modes were compared quantitatively based on two evaluation indicators: the refueling response time and the economic benefits. The comparing results showed that for MEO and GEO client satellites, it is appropriate to adopt the same orbit deployment mode, while for LEO client satellites, ground deployment mode is more suitable. As the maximum economic benefits can be earned to refuel GEO client satellites, the orbit deployment scheme of OOS system for refueling GEO client satellites was further studied. A new architecture of OOS system called "1+N" consist of 1 fuel storage station and N refueling vehicles was proposed. The fuel storage station carries a great deal of fuel, running on orbit steadily. The refueling vehicles maneuver on GEO and implement the refueling operations for GEO client satellites. If the vehicles run out of fuel, they arrive at the station to be refueled. The weight and orbital altitude of fuel storage station, the number, weight, orbital altitude and orbital phase of refueling vehicles are the key parameters of the orbit deployment scheme. The mathematical model for optimizing the scheme was constructed, which takes shorten the refueling response time and lower the cost of OOS system as multi-objective. The calculation process of the optimization was explained. By analyzing the optimization results, the optimal orbit deployment scheme of OOS system containing 1 fuel storage station and 4 to 6 refueling vehicles was proposed. • Orbit deployment strategy of on-orbit service system is studied. • Appropriate orbit deployment mode of refueling vehicle is compared and selected. • A new architecture of on-orbit service system called "1+N" is proposed. • A multi-objective optimization model for orbit deployment scheme is proposed. • A set of multi-objective optimal solutions are derived and analyzed. [ABSTRACT FROM AUTHOR]

The fluorescence of a coordinative molecule DCM displaying an intramolecular charge transfer (ICT) effect is regulated by several metal ions. These DCM–metal complexes were adopted to recognize different chemicals, including the recognition of triethylenetetramine, thiol-containing amino acids, and H2S upon binding DCM with Zn2+, Ag+, and Pb2+, respectively. This is in analogy to the general mode of human trichromatic color vision. [ABSTRACT FROM AUTHOR]

The last decade has witnessed rapid developments in aggregation‐induced emission (AIE). In contrast to traditional aggregation, which causes luminescence quenching (ACQ), AIE is a reverse phenomenon that allows robust luminescence to be retained in aggregated and solid states. This makes it possible to fabricate various highly efficient luminescent materials, which opens new paradigms in a number of fields, such as imaging, sensing, medical therapy, light harvesting, light‐emitting devices, and organic electronic devices. Of the various important features of AIE molecules, their self‐assembly behavior is very attractive because the formation of a well‐defined emissive nanostructure may lead to advanced applications in diverse fields. However, due to the nonplanar topology of AIEgens, it is not easy for them to self‐assemble into well‐defined structures. To date, some strategies have been proposed to achieve the self‐assembly of AIEgens. Herein, we summarize the most recent approaches for the self‐assembly of AIE molecules. These approaches can be sorted into two classes: 1) covalent molecular design and 2) noncovalent supramolecular interactions. We hope this will inspire more excellent work in the field of AIE. AIEgens assemble! The nonplanar topology of AIEgens makes it difficult for them to self‐assemble into well‐defined structures. However, by using appropriate covalent molecular design and noncovalent supramolecular interactions, the elegant self‐assembly of AIEgens can be achieved too. [ABSTRACT FROM AUTHOR]

Team formation, which aims to form a team to complete a given task by covering its required skills, furnishes a natural way to help organizers complete projects effectively. In this work, we propose a new team hiring problem. Given a set of projects with required skills, and a pool of experts , each of which has his own skillset, compensation demand and participation constraint (i.e., the maximum number of projects the expert can participate in simultaneously), we seek to hire a team of participation-constrained experts to complete all the projects so that the overall compensation is minimized. We refer to this as the participation constrained team hire problem. To the best of our knowledge, this is the first work to investigate the problem. We also study a special case of the problem, where the number of projects is within the participation constraint of each expert and design an exact algorithm for it. Since participation constrained team hire problem is proven to be NP-hard, we design three novel efficient approximate algorithms as its solution, each of which focuses on a particular perspective of the problem. We perform extensive experimental studies, on both synthetic and real datasets, to evaluate the performance of our algorithms. Experimental results show that our exact algorithm far surpasses the brute-force solutions and works well in practice. Besides, the three algorithms behave differently when distinct facets of the problem are involved. [ABSTRACT FROM AUTHOR]

Most of the existing similarity metrics in heterogeneous information networks depend on the pre-specified meta-path or meta-structure. This dependency may cause them to be sensitive to different meta-paths or meta-structures. In this paper, we propose a stratified meta-structure-based similarity measure named SMSS in heterogeneous information networks. The stratified meta-structure can be constructed automatically and capture rich semantics.Then, we define the commuting matrix of the stratified meta-structure by virtue of the commuting matrices of meta-paths and meta-structures. As a result, the SMSS is defined by virtue of this commuting matrix. Experimental evaluations show that the existing metrics are sensitive to different meta-paths or meta-structures and that the proposed SMSS outperforms the state-of-the-art metrics in terms of ranking and clustering. [ABSTRACT FROM AUTHOR]

Catalytic conversion of glycerol to 1,2-propanediol is an important issue for sustainable production of fine chemicals and biodiesel. Here it is found that pure silica Beta zeolite supported copper species via hydrothermal treatment (Cu/Si-Beta-HT) are highly efficient for hydrogenolysis of glycerol to 1,2-propanediol, achieving 96.0% conversion and more than 99.9% selectivity after reaction at 240 °C for 3 h. TEM images and N 2 O titration experiments confirm the high dispersion of Cu nanoparticles in the catalyst. In addition, pyridine-adsorption FT-IR spectra and NH 3 -TPD profiles suggest the presence of copper species as Lewis acidic sites in the catalyst. The hydrogenolysis of glycerol to 1,2-propanediol mainly includes the dehydration of glycerol to acetol and hydrogenation of acetol into 1,2-propanediol, which are catalyzed by copper cations and Cu nanoparticles, respectively. In situ FT-IR spectra and kinetic studies suggest that the dehydration rate is slower than the hydrogenation rate, and the dehydration is a key step for the hydrogenolysis of glycerol to 1,2-propanediol. [Display omitted] • Cu nanoparticles with rich copper cation on pure silica Beta zeolite were prepared. • The copper cation sites are resulted from Cu–O–Si–O (cupric silicate) species. • This catalyst is highly active for hydrogenolysis of glycerol to 1,2-propanediol. • Kinetic studies confirm that the glycerol dehydration is a key step. [ABSTRACT FROM AUTHOR]

Much uncertainty exists in reproducing Arctic temperature using different general circulation models (GCMs). Therefore, evaluating the performance of GCMs in reproducing Arctic temperature is critically important. In our study, 32 GCMs in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) during the period 1900-2005 are used, and several metrics, i.e., bias, correlation coefficient (R), and root mean square error (RMSE), are applied. The Cowtan data set is adopted as the reference data. The results suggest that the GCMs used can reasonably reproduce the Arctic warming trend during the period 1900-2005, as observed in the observational data, whereas a large variation of inter-model differences exists in modeling the Arctic warming magnitude. With respect to the reference data, most GCMs have large cold biases, whereas others have weak warm biases. Additionally, based on statistical thresholds, the models MIROC-ESM, CSIRO-Mk3-6-0, HadGEM2-AO, and MIROC-ESM-CHEM (bias ≤ ±0.10 °C, R ≥ 0.50, and RMSE ≤ 0.60 °C) are identified as well-performing GCMs. The ensemble of the four best-performing GCMs (ES4), with bias, R, and RMSE values of −0.03 °C, 0.72, and 0.39 °C, respectively, performs better than the ensemble with all 32 members, with bias, R, and RMSE values of −0.04 °C, 0.64, and 0.43 °C, respectively. Finally, ES4 is used to produce projections for the next century under the scenarios of RCP2.6, RCP4.5, and RCP8.0. The uncertainty in the projected temperature is greater in the higher emissions scenarios. Additionally, the projected temperature in the cold half year has larger variations than that in the warm half year. [ABSTRACT FROM AUTHOR]

Vesicles displaying aggregation induced emission are very promising in fields related to biology. In this review, we summarized recent progresses in the creation of such vesicles and their applications. The following contents are included: 1) the general background of AIE vesicles; 2) creation of AIE vesicles with synthetic covalent compounds; 3) creation of AIE vesicles with supramolecular chemistry; 4) the application of AIE vesicles in real-time imaging, visualized drug delivery, cell communication and fission-fusion process, and light-harvesting. Finally, we envision AIE vesicles may have profound impact on medical science, biological science and material science. [Display omitted] [ABSTRACT FROM AUTHOR]

Simulation and projection of the characteristics of heat waves over China were investigated using 12 CMIP5 global climate models and the CN05.1 observational gridded dataset. Four heat wave indices (heat wave frequency, longest heat wave duration, heat wave days, and high temperature days) were adopted in the analysis. Evaluations of the 12 CMIP5 models and their ensemble indicated that the multi-model ensemble could capture the spatiotemporal characteristics of heat wave variation over China. The inter-decadal variations of heat waves during 1961-2005 can be well simulated by multi-model ensemble. Based on model projections, the features of heat waves over China for eight different global warming targets (1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0 °C) were explored. The results showed that the frequency and intensity of heat waves would increase more dramatically as the global mean temperature rise attained higher warming targets. Under the RCP8.5 scenario, the four China-averaged heat wave indices would increase from about 1.0 times/year, 2.5, 5.4, and 13.8 days/year to about 3.2 times/year, 14.0, 32.0, and 31.9 days/year for 1.5 and 5.0 °C warming targets, respectively. Those regions that suffer severe heat waves in the base climate would experience the heat waves with greater frequency and severity following global temperature rise. It is also noteworthy that the areas in which a greater number of severe heat waves occur displayed considerable expansion. Moreover, the model uncertainties exhibit a gradual enhancement with projected time extending from 2006 to 2099. [ABSTRACT FROM AUTHOR]

Computing k -edge connected components can be used to capture closely related vertices in a graph. It is an important problem with many applications. Due to the high time complexities of traditional algorithms for computing k -edge connected components, it is difficult for them to be applied to efficiently process large scale graphs. In this paper, an early merging and splitting based maximal k -edge connected subgraph detection algorithm, named MSK, is proposed. It computes the k -edge connected components of a graph with an order list of vertices which is decided according to the connectivity of vertices in the graph. During the processing of the vertex list, if two vertices are k -edge connected, they are merged into a super-vertex. On the contrary , if the vertex pair does not satisfy the condition of k -edge connectivity, the related vertices and edges are removed from the graph, and the graph is decomposed into several subgraphs. The above steps are performed iteratively in the obtained subgraphs until each subgraph become a k -edge connected component. In order to further improve the time efficiency, an approximate algorithm PMSK is also proposed. The experimental results on a large number of real and synthetic graphs show that the proposed algorithms are accurate and more efficient than the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Supramolecular self-assembly into various nano- or microscopic structures based on non-covalent interactions between molecules has been recognized as a very efficient approach that leads to functional materials. Since most non-covalent interactions are relatively weak and form and break without significant activation barriers, the thermodynamic equilibrium of many supramolecular systems can be easily influenced by processing pathways that allow the system to stay in a kinetically trapped state. Thus far, kinetic traps have been found to be very important in producing more elaborate structural and functional diversity of self-assembled systems. In this review, we try to summarize the approaches that can produce kinetically trapped self-assemblies based on examples made by us. We focus on the following subjects: (1) supramolecular pathway dependent self-assembly, including kinetically trapped self-assemblies facilitated by host–guest chemistry, coordination chemistry, and electrostatic interactions; (2) physical processing pathway dependent self-assembly, including solvent quality controlled self-assembly, evaporation induced self-assembly and crystallization induced self-assembly. [ABSTRACT FROM AUTHOR]

This study was conducted to investigate the effects of partial replacement of fish meal (FM) with porcine meat meal (PMM) on growth performance, plasma antioxidant parameters, intestine morphological parameters, gut microflora and expression of intestinal Nrf2 and Nf-κB signaling pathway of juvenile golden pompano. Fish were fed with six experimental diets (0 (PC), 10% (P10), 20% (P20), 30% (P30), 40% (P40) and 50% (P50) replacement levels) for 8-weeks. The percent weight gain and specific growth rate in fish fed with P40 and P50 diets were significantly lower than those of the control group (P < 0.05). Compared with the control group, the plasma antioxidant indexes including the activities of superoxide dismutase, catalase, glutathione peroxidase, total antioxidant capacity and malondialdehyde content in fish fed with P30, P40 and P50 diets were significantly affected (P < 0.05). With dietary replacement of FM with PMM increasing 0 to 30%, the relative expressions of IL-10, TGF-β, Nrf2, SOD, Cu/Zn-SOD and GSH-Px were significantly down-regulated, while the relative expressions of NF-κB, TNF-α, IL-1β and IL-8 were significantly up-regulated (P < 0.05). Villus heights and crypt depths of proximal and mid intestine were significantly affected with dietary replacement of FM with PMM increasing 0 to 40% (P < 0.05). The structure of the intestinal microbial flora showed that the relative abundances of beneficial bacteria decreased, and the relative abundances of pathogenic bacteria increased in fish fed with P50 diet compared with the control group. In conclusion, replacing FM with PMM up to 30% had no significant impact on growth performance and feed utilization of golden pompano. However, 30% replacements of FM with PMM exerted adverse effects on antioxidant capacity and immune response of fish. In addition, replacement of a high proportion of FM with PMM negatively affect the gut microflora of fish. • Replacing 30% FM with PMM had no significant impact on growth of golden pompano. • 30% replacement negatively affected antioxidant capacity and intestinal health. • 50% replacement increased the proportion of Photobacterium in gut. [ABSTRACT FROM AUTHOR]

Regional climate models often lack detailed description of ice sheet surface and, as a result, are limited in their capability to provide useful information for Antarctic climate research and field campaigns. In this study, an upgraded scheme of surface physics for Antarctic ice sheet (IST) is developed to improve the surface temperature simulations in Antarctica. Through stand-alone simulations, IST shows advantages over the Noah glacial module, a commonly utilized scheme in the widely used Weather Research and Forecasting (WRF) model. These improvements are mainly attributed to the incorporation of detailed snow physics and optimized surface layer parameterization, which results in better simulations of both the surface albedo in summer and the turbulent sensible heat flux in winter. When coupled with IST instead of Noah, WRF models show improved simulation of surface temperatures throughout the year. The bias and root-mean-square-error of annual mean surface temperatures are reduced from 5.7 and 6.0 to 0.2 and 2.7 K. [ABSTRACT FROM AUTHOR]

Since real-world networks evolve over time, detecting communities in dynamic networks is a challenging research problem with wide applications. In this paper, we first improve our previous method and propose a more stable algorithm which is label-propagation-based for the discovery of communities in complex networks. Then, we present a novel evolutionary clustering approach DLPAE for dynamic networks based on the stable algorithm. According to DLPAE, community labels of nodes are determined by their neighbors, and a confidence (i.e., the importance of its neighbor to the node) is attached to each neighbor. During clustering, the confidences of nodes are calculated in terms of the structures of the current network and the network at last timestamp. We compute confidences’ variance of each node and update nodes’ labels in a descending order according to the values. In our setting, each node can keep one or more labels with belonging coefficients no less than a threshold, which renders DLPAE suitable for detecting overlapping and non-overlapping communities in dynamic networks. Experimental results on both real and synthetic datasets show the ability of DLPAE to detect overlapping and non-overlapping communities in dynamic networks, and demonstrate its higher accuracy compared to other related methods. [ABSTRACT FROM AUTHOR]

Peanut (Arachis hypogaea L.) is an allotetraploid oilseed crop worldwide due to its abundant high-quality oil production. Peanut oil stability and quality are determined by the relative proportions of saturated fatty acids (SFAs) and unsaturated fatty acids (UFAs). The principle approach to minimize the content of SFAs in peanut is to reduce the content of palmitic acid, which is linked to cardiovascular disease. Acyl-acyl carrier protein thioesterases (FATs) determine the types and levels of fatty acids that are exported them from the plastids. Two different classes of FAT have been classified into two families in plants, FatA and FatB. Among them, AhFatB has become the primary objective to genetically reduce the content of palmitic acid in peanut. Here, we identified 18 AhFatB genes in A. hypogaea genome and grouped into four major subfamilies through gene structures and phylogenetic relationships. Expression profiling of AhFatB genes was assessed using the publicly available RNA-seq data and qRT-PCR in 22 tissues. Using the CRISPR/Cas9 system, we designed two sgRNAs to edit the homologs AhFatB genes Arahy.4E7QKU and Arahy.L4EP3N , and identified different types of mutations. Additionally, we discovered mutations at Arahy.4E7QKU exhibited low palmitic acid and high oleic acid phenotypes. The obtained peanut mutants with altered SFAs content have great potential for improving peanut oil quality for human health. • A total of 18 AhFatB members were characterized in Arachis hypogaea L. • Mutations of AhFatB showed low palmitic and high oleic acid using CRISPR/Cas9 system. • AhFatB determined the oil profile and improved oil quality in peanut. [ABSTRACT FROM AUTHOR]

Summer temperature extremes over the global land area were investigated by comparing 26 models of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) with observations from the Goddard Institute for Space Studies (GISS) and the Climate Research Unit (CRU). Monthly data of the observations and models were averaged for each season, and statistics were calculated for individual models before averaging them to obtain ensemble means. The summers with temperature anomalies (relative to 1951–1980) exceeding 3σ (σ is based on the local internal variability) are defined as “extremely hot”. The models well reproduced the statistical characteristics evolution, and partly captured the spatial distributions of historical summer temperature extremes. If the global mean temperature increases 2°C relative to the pre-industrial level, “extremely hot” summers are projected to occur over nearly 40% of the land area (multi-model ensemble mean projection). Summers that exceed 5σ warming are projected to occur over approximately 10% of the global land area, which were rarely observed during the reference period. Scenarios reaching warming levels of 3°C to 5°C were also analyzed. After exceeding the 5°C warming target, “extremely hot” summers are projected to occur throughout the entire global land area, and summers that exceed 5σ warming would become common over 70% of the land area. In addition, the areas affected by “extremely hot” summers are expected to rapidly expand by more than 25%/°C as the global mean temperature increases by up to 3°C before slowing to less than 16%/°C as the temperature continues to increase by more than 3°C. The area that experiences summers with warming of 5σ or more above the warming target of 2°C is likely to maintain rapid expansion of greater than 17%/°C. To reduce the impacts and damage from severely hot summers, the global mean temperature increase should remain low. [ABSTRACT FROM AUTHOR]

The RANKL-induced NF-κB signaling pathway is required for osteoclast formation and function. By screening for compounds that inhibit RANKL-induced NF-κB activation using a luciferase reporter gene assay in RAW264.7 cells, we identified triptolide (PG490), as a candidate compound targeting osteoclast differentiation and osteoclast-mediated osteolysis. Triptolide (PG490) is an active compound of the medicinal herb Tripterygium wilfordii Hook F (TWHF) or Lei Gong Teng with known anti-inflammatory properties. We found that triptolide inhibited osteoclastogenesis and bone resorption, as well as RANKL-induced NF-қB activities as monitored by luciferase reporter gene assays and the nuclear translocation of p65. In vivo studies showed that triptolide attenuates titanium-induced osteolysis and osteoclast formation in a mouse calvarial model. Considering that drugs which protect against localized bone loss are critically needed for the effective treatment of particle-induced osteolysis, our data suggest that triptolide might have therapeutic potential for the treatment of bone lytic diseases caused by prosthetic wear particles. [ABSTRACT FROM AUTHOR]

The problem of mobile sequential recommendation is to suggest a route connecting a set of pick-up points for a taxi driver so that he/she is more likely to get passengers with less travel cost. Essentially, a key challenge of this problem is its high computational complexity. In this paper, we propose a novel dynamic programming based method to solve the mobile sequential recommendation problem consisting of two separate stages: an offline pre-processing stage and an online search stage. The offline stage pre-computes potential candidate sequences from a set of pick-up points. A backward incremental sequence generation algorithm is proposed based on the identified iterative property of the cost function. Simultaneously, an incremental pruning policy is adopted in the process of sequence generation to reduce the search space of the potential sequences effectively. In addition, a batch pruning algorithm is further applied to the generated potential sequences to remove some non-optimal sequences of a given length. Since the pruning effectiveness keeps growing with the increase of the sequence length, at the online stage, our method can efficiently find the optimal driving route for an unloaded taxi in the remaining candidate sequences. Moreover, our method can handle the problem of optimal route search with a maximum cruising distance or a destination constraint. Experimental results on real and synthetic data sets show that both the pruning ability and the efficiency of our method surpass the state-of-the-art methods. Our techniques can therefore be effectively employed to address the problem of mobile sequential recommendation with many pick-up points in real-world applications. [ABSTRACT FROM PUBLISHER]

In this paper, an incremental density-based clustering algorithm IncOrder is proposed for detecting communities in dynamic networks. It consists of two separate stages: an online stage and an offline stage. The online stage maintains the traversal sequence of a network and the offline stage extracts communities from the sequence. Based on a symmetric measure core-connectivity-similarity between pairs of adjacent nodes, the online stage builds an index structure, called core-connected chain, for dynamic networks. Since the slight change of a network has a very limited impact on its cluster chain, the chain of a dynamic network can be efficiently preserved. The offline stage extracts all possible density-based clustering results for all similarity thresholds from the chain. By maximizing a modularity function, the proposed method can automatically select the parameter of similarity threshold. Experimental results on a large number of real-world and synthetic networks show that the proposed method achieves high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Embedding knowledge graphs into continuous vector spaces has recently attracted increasing interest in knowledge base completion. However, in most existing embedding methods, only fact triplets are utilized, and logical rules have not been thoroughly studied for the knowledge base completion task. To overcome the problem, we propose an association rules enhanced knowledge graph attention network (AR-KGAN). In this paper, triplets and logical rules are jointly modeled in the proposed unified framework to achieve more predictive entity and relation embeddings. Association rules and corresponding correlation degrees between them can be automatically obtained according to our designed mining algorithm. The major component of AR-KGAN is an encoder of an effective neighborhood aggregator, which addresses the problems by aggregating neighbors with both association rules based and graph based attention weights. The decoder enables AR-KGAN to be translational between entities and relations while keeping the superior link prediction performance. Then, the global loss is minimized over both atomic and complex formulas to achieve the embedding task. In this manner, we learn embeddings compatible with triplets and association rules, which are certainly more predictive for knowledge acquisition and inference. The results show that the proposed AR-KGAN model achieves significant and consistent improvements over state-of-the-art methods on three benchmark datasets. [ABSTRACT FROM AUTHOR]

Tantalum is widely used in capacitor field in modern times. The tantalum shell of capacitor is usually obtained by deep drawing process, and adhesive wear of the shell is the main hidden danger during its deep drawing process. In this paper, ultrasonic vibration–assisted deep drawing technology is proposed to manufacture tantalum capacitor shell, in order to effectively control the adhesive wear of tantalum capacitor with the assistance of ultrasonic vibration energy. Influence of ultrasonic vibration on deep drawing force and surface forming accuracy of tantalum cup deep drawing is discussed by applying different amplitudes of ultrasonic vibration signals at different time periods. The results reveal that in the amplitude range of 3–7 μm, the amplitude of 5 μm has the best inhibition effect on adhesive wear during the deep drawing of tantalum cup, and the effect is especially obvious in the initial stage of the deep drawing process; the reduction of wearing can reach more than 22% comparing with the results without ultrasonic amplitude. [ABSTRACT FROM AUTHOR]

In order to research the forming mechanism of zonal disintegration, the stress distribution of surrounding rock in deep roadway was studied through three dimensional photoelastic test with stress freezing. In this work, the loads of 5, 30, 50, 70 and 90 kN were respectively applied to the axes of the roadway based on radial constraint in photoelastic test models, also the stress fields of surrounding rock were transparently presented via observing photoelastic slice. The results show that when the axial stress reaches a certain level, the stress distribution of surrounding rock of roadway is presented as concentric circles of roadway, and the stress shows its maximum near roadway. In addition, the number of stress circles increases and the level of stress improves with increasing axial stress of roadway, and the increased distance between the area of maximum stress and roadway surface can be obtained. Studies show that the range of layered fractures is determined by the magnitude of the axial stress, which is a critical influence on zonal disintegration. Furthermore, high stress 'bypass' roadway as the axial loads increases. The method of three dimensional photoelastic test brings methodological progress for the study of zonal disintegration. The research results can provide new ideas for the stability control of surrounding rock, and give a theoretical guidance for the roadway excavation and supporting design. [ABSTRACT FROM AUTHOR]

Clustering is an important technique for mining the intrinsic community structures in networks. The density-based network clustering method is able to not only detect communities of arbitrary size and shape, but also identify hubs and outliers. However, it requires manual parameter specification to define clusters, and is sensitive to the parameter of density threshold which is difficult to determine. Furthermore, many real-world networks exhibit a hierarchical structure with communities embedded within other communities. Therefore, the clustering result of a global parameter setting cannot always describe the intrinsic clustering structure accurately. In this paper, we introduce a novel density-based network clustering method, called graph-skeleton-based clustering (gSkeletonClu). By projecting an undirected network to its core-connected maximal spanning tree, the clustering problem can be converted to detect core connectivity components on the tree. The density-based clustering of a specific parameter setting and the hierarchical clustering structure both can be efficiently extracted from the tree. Moreover, it provides a convenient way to automatically select the parameter and to achieve the meaningful cluster tree in a network. Extensive experiments on both real-world and synthetic networks demonstrate the superior performance of gSkeletonClu for effective and efficient density-based clustering. [ABSTRACT FROM AUTHOR]

Abstract: Clustering is an essential approach for detecting the intrinsic groups in data. An efficient clustering algorithm based on a generalized local synchronization model is proposed. It uses a novel stopping criterion of data synchronization to detect clusters prior to the perfect synchronization. Moreover, a density-biased sampling method is adopted to extract samples from the original data set. The clustering structure can be effectively revealed on the samples. As a result, the clustering efficiency is significantly improved. By using a cluster validity criterion, the proposed algorithm can find clusters of arbitrary number, shape, size and density as well as isolate noises in the vector data without any data distribution assumption. Extensive experiments on several synthetic and real-world data sets show that the proposed algorithm possesses high accuracy and it is more efficient than the state-of-the-art synchronization-based clustering method. [Copyright &y& Elsevier]

Coacervation plays an important role in the molecular assembly towards soft materials with a diversity of function (e. g., underwater adhesives of mussels and membraneless organelles). Coacervation is observed when one homogenous solution spontaneously separates into two immiscible liquid phases of low and high solute concentration, also known as liquid‐liquid phase separation (LLPS), which enables spatiotemporally local concentration of specific molecules. LLPS is a common physical phenomenon in aqueous solutions of polyelectrolytes, surfactants and biomolecules, which has been extensively explored for applications in the fields of environmental remediation, cosmetic formulation, protein purification, extractive fermentation and pharmaceutical microencapsulation. This review summarizes the development of LLPS with low molecular weight amphiphiles to construct simple and complex coacervates using conventional surfactants and novel amphiphiles such as azobenzene‐derivatives and peptides. We also highlight the applications of these amphiphile coacervates in the extraction of biomolecules, construction of protocell models and drug delivery. [ABSTRACT FROM AUTHOR]

The investigation of community structure in networks has aroused great interest in multiple disciplines. One of the challenges is to find local communities from a starting vertex in a network without global information about the entire network. Many existing methods tend to be accurate depending on a priori assumptions of network properties and predefined parameters. In this paper, we introduce a new quality function of local community and present a fast local expansion algorithm for uncovering communities in large-scale networks. The proposed algorithm can detect multiresolution community from a source vertex or communities covering the whole network. Experimental results show that the proposed algorithm is efficient and well-behaved in both real-world and synthetic networks. [ABSTRACT FROM AUTHOR]

Abstract: The investigation of community structure in networks is an important issue in many disciplines, which still remains a challenging task. First, complex networks often show a hierarchical structure with communities embedded within other communities. Moreover, communities in the network may overlap and have noise, e.g., some nodes belonging to multiple communities and some nodes marginally connected with the communities, which are called hub and outlier, respectively. Therefore, a good algorithm is desirable to be able to not only detect hierarchical communities, but also to identify hubs and outliers. In this paper, we propose a parameter-free hierarchical network clustering algorithm DenShrink. By combining the advantages of density-based clustering and modularity optimization methods, our algorithm can reveal the embedded hierarchical community structure efficiently in large-scale weighted undirected networks, and identify hubs and outliers as well. Moreover, it overcomes the resolution limit possessed by other modularity-based methods. Our experiments on the real-world and synthetic datasets show that DenShrink generates more accurate results than the baseline methods. [Copyright &y& Elsevier]

The variations of global atmospheric oscillations over the last millennium, including the North Atlantic Oscillation (NAO), the North Pacific Oscillation (NPO) highly associated with the Pacific Decadal Oscillation (PDO), the Southern Oscillation (SO) and the Antarctic Oscillation (AAO), are studied and compared in this paper based on observations and reconstructed data. The cross correlation analysis of AAO, NAO and NPO shows that there is no significant relationship on interannual variation among them. However, the consistency on decadal variability is prominent. During A.D.1920--1940 and A.D.1980--2000, the positive (strong) phase was dominant and the negative (weak) one noticeable during A.D.1940--1980. In addition, the reconstructed atmospheric oscillations series demonstrate that the positive phase existed in the early of the last millennium for NAO and in the late of the last millennium for AAO, respectively; while it occurred in the mid-late of the last millennium for PDO and ENSO. [ABSTRACT FROM AUTHOR]

Abstract: Hierarchical assemblies of coordination supramolecules are reviewed. These assemblies are self-organized by coordination supramolecules themselves or co-assemblies with other modules. By utilizing electrostatic interaction, the coordination supramolecules can be incorporated into films, liquid crystals, micelles, and hydrogels, etc. These coordination supramolecules-containing novel materials exhibit many switchabilities and other desired properties. The following contents are covered in this review: (1) coordination supramolecules of different architecture; (2) hierarchical molecular devices containing these coordination supramolecules; (3) short perspectives and conclusions. [Copyright &y& Elsevier]

Abstract: Studies on the climate of humid period and the impacts of changing precession in the early-mid Holocene are reviewed in this paper. High-resolution proxy data indicated that the African Humid Period, strong summer monsoon from the Arabian Sea to South Asia, northward migration of ITCZ (Intertropical Convergence Zone) over the northern South America, and the humid period of China appeared in 10.5–5.5kaBP, 10.0–6.0kaBP, 10.5–5.4kaBP, and 11.0–8.0kaBP, respectively. Modeling studies proved that summer insolation over the Northern Hemisphere increased following the changes of precession in the early Holocene, which increased the land–sea temperature contrasts, intensified the summer monsoon circulation in the Northern Hemisphere, and finally induced a humid climate over the area under the influence of summer monsoon. However, modeling results underestimated the increase of precipitation and the degree of northward extension of monsoon rain belt compared with palaeo-environmental data. These discrepancies between the modeling results and the palaeo-environmental data may be associated with the changes of North Atlantic circulation, sea ice and vegetation covers. Moreover, climate of the humid period was not stable, in which several droughts were inlaid on centennial scale. In this review, perspectives for further studies of the climate change of the humid period in the early-mid Holocene are also proposed. [Copyright &y& Elsevier]

Four periods with predominated droughts are identified in 4-2 ka BP according to documentary data, namely 3.6-3.5, 3.1-3.0, 2.8-2.7, and 2.45-2.35 ka BP. Palaeo-environmental data indicated that droughts were predominated in 4-2 ka BP in the south of Northeast China, Inner-Mongolia, east of Qinghai-Tibetan Plateau, and South China. Modeling study shows that precession may be responsible for the occurrence of droughts in 4-2 ka BP, integrating the GCM with forcing of insolation. [ABSTRACT FROM AUTHOR]

Abstract: In the present work, the assembly morphology changes during the solubilization process of the sonicated unilamellar vesicles from dimyristoyl phosphocholine (DMPC) by a cationic surfactant, n-dodecyl triethylammonium bromide (DTEAB) were well characterized with DSC, FF-TEM and DLS and fluorescence probes technique. Based on an analysis on the above results, a primary multi-stage model was brought forward to sketch the assembly morphology changes during the DMPC vesicle solubilization by DTEAB. In comparison with classical models, vesicles division, tubule-like structure formation and fission to vesicle were found in the middle stages of this model. Additionally, it is the first time that the transversally-cut profiles of tubule-like structures were observed during vesicle solubilization process. [Copyright &y& Elsevier]

Summary: Coordination between cell proliferation and cell death is essential to maintain homeostasis in multicellular organisms. In Drosophila, these two processes are regulated by a pathway involving the Ste20-like kinase Hippo (Hpo) and the NDR family kinase Warts (Wts; also called Lats). Hpo phosphorylates and activates Wts, which in turn, through unknown mechanisms, negatively regulates the transcription of cell-cycle and cell-death regulators such as cycE and diap1. Here we identify Yorkie (Yki), the Drosophila ortholog of the mammalian transcriptional coactivator yes-associated protein (YAP), as a missing link between Wts and transcriptional regulation. Yki is required for normal tissue growth and diap1 transcription and is phosphorylated and inactivated by Wts. Overexpression of yki phenocopies loss-of-function mutations of hpo or wts, including elevated transcription of cycE and diap1, increased proliferation, defective apoptosis, and tissue overgrowth. Thus, Yki is a critical target of the Wts/Lats protein kinase and a potential oncogene. [Copyright &y& Elsevier]

The effect of ethanol on the surface properties of bolaamphiphiles and their mixed systems with oppositely charged conventional surfactants is studied and compared with the corresponding conventional surfactant systems. It is found that both the bolaamphiphile and the conventional surfactant have the similar variation tendency of γcmc and Γ∞ in the single surfactant system. However, in the cationic and anionic mixed surfactants system the γcmc decreases with the ethanol addition in the bolaamphiphile systems. This tendency is reverse to that in conventional surfactant system. Surface activity of the asymmetric bolaamphiphiles SHMC decreases greatly when ethanol is added into the system due to the existence of the hydroxyl group in the SHMC molecule. Adding opposite charged surfactant may greatly increase surface activity in the SHMC/C12NEt3Br/ethanol systems. [Copyright &y& Elsevier]

The coordination between cell proliferation and cell death is essential to maintain homeostasis within multicellular organisms. The mechanisms underlying this regulation are yet to be completely understood. Here, we report the identification of hippo (hpo) as a gene that regulates both cell proliferation and cell death in Drosophila. hpo encodes a Ste-20 family protein kinase that binds to and phosphorylates the tumor suppressor protein Salvador (Sav), which is known to interact with the Warts (Wts) protein kinase. Loss of hpo results in elevated transcription of the cell cycle regulator cyclin E and the cell-death inhibitor diap1, leading to increased proliferation and reduced apoptosis. Further, we show that hpo, sav, and wts define a pathway that regulates diap1 at the transcriptional level. A human homolog of hpo completely rescues the overgrowth phenotype of Drosophila hpo mutants, suggesting that hpo might play a conserved role for growth control in mammals. [Copyright &y& Elsevier]