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Existing layout-to-image generation methods based on generative adversarial networks (GANs) have made great progress. However, a common problem that has not been effectively solved is region missing; that is, as the number of target objects in an image increases, multiple overlapping objects cannot be accurately generated at the boundary. The generation of the overlapped object parts directly determines the quality difference between the generated image and the real image. To solve this problem, we propose the Boundary-Aware GAN model (BAGAN). We adopt a strategy of separately generating the foreground and background. In the foreground generation process, BAGAN first uses attention regularization to accurately locate the boundaries of overlapping objects and then generates a clear foreground image by sharing the parameters of two transfer networks. During background generation, BAGAN uses conditional normalization to improve the quality of the generated background image. To better judge the overlap generation quality, we design the BoundaryFID evaluation metric. We validate and test our model on the COCO-Stuff and Visual Genome datasets. The experimental results show that according to the general evaluation metrics and BoundaryFID, our model can achieve the best results compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Trichoderma, widely distributed all over the world, is commonly found in soil and root ecosystems. It is a group comprising beneficial fungi that improve plant disease resistance and promote plant growth. Studies have shown that Trichoderma cellulases can also improve plant disease resistance. Based on previous studies, we reported that a C6 zinc finger protein (Thc6) regulates two cellulase genes, thph1 and thph2, to induce ISR responses in plants. Therefore, in this study, we focused on the role of thph2 in the colonization of maize roots by T. harzianum and the induction of systemic resistance against southern leaf blight. The results showed that thph2 had a positive regulatory effect on the Trichoderma colonization of maize roots. After the root was treated with Trichoderma, the leaf defense genes AOS, LOX5, HPL, and OPR1 were expressed to resist the attack of Cochliobolus heterostrophus. The pure Thph2 protein also resulted in a similar induction activity of the AOS, LOX5, HPL, and OPR1 expression in maize roots, further demonstrating that thph2 can induce plant defense responses and that signal transduction occurs mainly through the JA signaling pathway. [ABSTRACT FROM AUTHOR]

Objects in a real scene often occlude each other and inferring a complete appearance from the visible part is an important and challenging task. In this paper, the authors propose a self‐supervised generative adversarial network GIGAN (GAN for generating the invisible), which can generate the complete appearance of objects without labelled invisible part information. The authors build two cycle transformation networks CycleIncomplete (CycleI) and CycleComplete (CycleC) that share parameters to improve the accuracy of mask completion. This design does not require well‐matched training images and can make better use of the limited labelled samples. In addition, the authors propose a conditional normalization module and combine it with the inferred complete mask output. The combination not only enhances the content recovery ability and obtains more realistic outputs, but also improves the efficiency of the generation process. Experimental results show that compared with existing self‐supervised learning models, our method achieves l1 error, mean intersection‐over‐union (mIOU), and Fréchet inception distance (FID) improvements on the COCOA and KINS datasets. [ABSTRACT FROM AUTHOR]

Most existing representation learning models for heterogeneous graphs depend on meta-paths, which requires domain-specific prior knowledge and reduces model practicality. In addition, real-world graphs usually conform to power-law distributions, and conventional graph models defined in Euclidean space lead to high distortion for such data. In this paper, we propose a Multi-curvature Hyperbolic Heterogeneous Graph Convolutional Network (McH-HGCN) based on the graph's inherent type triplets. By selecting triplets as data units for message passing and defining the model in hyperbolic space, our model caters to the power-law properties of heterogeneous graphs while avoiding meta-paths dependence. To model the heterogeneity of the graph, we set distinct hyperbolic curvatures as learnable parameters for different types of nodes to obtain the optimal parameterized space mapping after training. Additionally, we introduce a dynamic heterogeneous attention mechanism to compute the attention weights for heterogeneous neighbor aggregation. Node classification and recommendation experiments with several heterogeneous graph datasets show that our model outperforms state-of-the-art methods on multiple datasets, achieving excellent performance without relying on meta-paths. [ABSTRACT FROM AUTHOR]

Background: Trichoderma spp. are important agricultural biocontrol microorganisms that are often used as effective components of microbial fungicides and microbial biofertilizers. However, most of these products are prepared by a single strain in monoculture, which significantly limits the biocontrol efficiency and stability of Trichoderma products. Therefore, the establishment of a design and screening approach for consortia with multi-Trichoderma strains for co-culture is of great importance to overcome the shortage of traditional Trichoderma biocontrol products. Results: First, 15 Trichoderma strains were screened in terms of mycelium growth rate, antagonistic activity to a variety of pathogens, stress tolerance to high temperature and salt stress, and cucumber seedling growth promotion level. Then, the combinations of Trichoderma asperellum GDSF1009 (CGMCC NO. 9512), Trichoderma asperelloides Z4-1 (CGMCC NO. 40245), Trichoderma harzianum 10569 (CGMCC NO. 40246), and T. asperellum 10264 (CGMCC NO. 22404) were finally screened as an optimal consortium for co-culture underlying the levels of plant growth-promoting and antagonistic activity to Fusarium oxysporum and seed germination promotion relative to the monoculture of a single strain. Consortia with multiple co-cultured strains were found to generate larger amounts of free amino acids than those from the monoculture of a single strain, and a pot assay also indicated that metabolites of co-cultures were able to promote cucumber seedling growth superior to that with monoculture of a single strain, even though the promotion was better than from simply mixed cultures from each of the four Trichoderma strains. Taken together, the co-culture consortia composed of the four compatible interactive Trichoderma strains was a potential novel multiple strain biocontrol agent based on the combination of synthetic consortia design and co-culture. In the field experiment, we found that the growth-promoting effect of the co-culture fermentation filtrate was better than that of the single culture fermentation filtrate. Compared with T-Z4-1, T-1009, T-10264 and T-10569, the plant height of cucumber was increased by 22.99%, 42.06%, 24.18% and 30.09%, respectively, and the stem diameter was increased by 16.59%, 18.83%, 13.65% and 14.70%, respectively. Conclusion: An approach to designing and screening Trichoderma consortia for co-culture was established. The consortia co-culture presented a better performance in antagonistic activity and cucumber growth compared with a monoculture of a single strain. Thus, it is of great significance to lay the foundation for the creation of a novel Trichoderma biofungicide or biomanure to resist cucumber Fusarium wilt and promote cucumber growth. [ABSTRACT FROM AUTHOR]

Accordingly, the mass unbalance of the rotors is usually the major cause of excessive vibration. The information extracted at the fundamental frequency is often employed to fix the unbalance issue. However, other rotor faults like rotor bending and bearing-failure effect also generate additional components to the characteristics. Thus, it is necessary to isolate the corresponding features and obtain the intrinsic causes of the multiple failures. In this paper, a productive hybrid method is successfully developed to deal with the root mass unbalance problem with additional force interference by integrating the superiority of different methods, including Ensemble Empirical Mode Decomposition (EEMD) and Nonnegative Matrix Factorization (NMF), where EEMD is used to obtain sensitive IMFs and NMF is employed to acquire the inherent source signal, respectively. Meanwhile, a root dynamic balancing and implemental framework is also developed to accomplish the task of vibration reduction. For verification, a serial of simulations and experimental investigations have been analysed to demonstrate the preferable potentialities of the proposed method. In particular, a standard Bently Nevada rotor rig with a specifically designed device was employed to simulate appended faults by adjusting the additional forces during the experimental steps. The analysis results show that the proposed method can isolate and extract the unbalance faults from the raw vibration signals and achieve accurate correction balancers, where a nearly identical correction angle has been achieved, which indicates that the optimal installation position has been successfully figured out. [ABSTRACT FROM AUTHOR]

In knowledge distillation, the knowledge from the teacher model is often too complex for the student model to thoroughly process. However, good teachers in real life always simplify complex material before teaching it to students. Inspired by this fact, we propose student-friendly knowledge distillation (SKD) to simplify teacher output into new knowledge representations, which makes the learning of the student model easier and more effective. SKD involves a softening processing and a learning simplifier. First, the softening processing uses the temperature hyperparameter to soften the output logits of the teacher model, which simplifies the output to some extent and makes it easier for the learning simplifier to process. The learning simplifier utilizes the attention mechanism to further simplify the knowledge of the teacher model and is jointly trained with the student model using the distillation loss, which means that the process of simplification is correlated with the training objective of the student model and ensures that the simplified new teacher knowledge representation is more suitable for the specific student model. Furthermore, since SKD does not change the form of the distillation loss, it can be easily combined with other distillation methods that are based on the logits or features of intermediate layers to enhance its effectiveness. Therefore, SKD has wide applicability. The experimental results on the CIFAR-100 and ImageNet datasets show that our method achieves state-of-the-art performance while maintaining high training efficiency. • Simplify the knowledge difficulty for the student model in knowledge distillation. • Use softening and attention mechanism to simplify the knowledge. • Achieve state-of-the-art performance while maintaining high training efficiency. • Easy to blend with other knowledge distillation models. [ABSTRACT FROM AUTHOR]

Currently, Android malware detection methods always focus on one kind of app feature, such as structural, semantic, or other statistical features. This paper proposes a novel Android malware detection method that integrates multiple features of Android applications. First, to effectively extract the structural and semantic features, we propose a new type of call graph named the class-set call graph (CSCG) that uses the sets of Java classes as nodes and the call relationships between class sets as edges, and we design a dynamic adaptive CSCG construction method that can automatically determine the node size for applications with different scales. The topic model is used to mine the source code semantics from the class sets as the node features. Then, we use a graph attention network (GAT) with max pooling to extract the CSCG feature that encompass both the semantic and structural features of the Android application. Furthermore, we construct a deep multimodal feature fusion network to fuse the CSCG features with permission features. Experimental results show that our method achieves a detection accuracy of 97.28%–99.54% on the three constructed datasets, which is better than the existing methods. • Proposing multimodal features deep fusion method for Android malware detection. • Designing class-set call graph to integrates structural and semantic features. • Adaptive class merging method for class-set call graph construction. • Introducing GAT and max pooling to extract salient graph features. • Proposing fusion network for graph features and permission features. [ABSTRACT FROM AUTHOR]

Esophageal candidiasis is a common opportunistic infection in immunocompromised hosts. Here, we report an aged man receiving long-term treatment with glucocorticoids, who is diagnosed with esophageal candidiasis. [ABSTRACT FROM AUTHOR]

[Display omitted] • Anisotropic hydrogels are obtained based on a magnetic field pre-induction strategy. • Oriented nanohybrids impart hydrogels with anisotropic electromechanical properties. • Anisotropic hydrogels exhibit highly accurate multidimensional sensing capability. • It contributes to the integration of strain sensing and magnetothermal physiotherapy. In this study, we present the development of wearable electronic devices with integrated sensing and physiotherapy functionalities, aiming to advance flexible multidimensional strain sensors. The as-developed electronic devices are based on a composite hydrogel with unique anisotropic electromechanical properties and exceptional magnetothermal capabilities. Specifically, two-dimensional nanohybrids possessing both conductive and magnetic properties are induced to align orientationally within the polyvinyl alcohol (PVA) network through a magnetic field pre-induction strategy, resulting in the conductive hydrogels with anisotropic structures. The as-optimized composite hydrogel demonstrates distinct conductivities in the parallel and vertical directions, enabling the creation of a multidimensional strain sensor. This sensor is capable of accurately sensing strains in orthogonal directions, with distinguishable gauge factors (GF ∥ = 3.20, while GF ⊥ = 1.78) within the tensile strain range of 0 ∼ 60%. Additionally, the strain sensor exhibits a relatively fast response time (t ∥ = 74.47 ms, t ⊥ = 157.63 ms). Moreover, the composite hydrogel exhibits excellent magnetothermal physiotherapy capabilities, making it suitable for therapeutic applications. Furthermore, the conductive hydrogel can be assembled into a 3D distribution array, enabling the detection of both the magnitude and spatial distribution of stress. In summary, the anisotropic conductive hydrogel shows promise as a next-generation, multidimensional flexible strain and stress sensors with rehabilitation assistant functions. [ABSTRACT FROM AUTHOR]

Abstract A novel and facile approach to 3-allyl-2-aminoindoles via the copper-catalyzed reaction of 3-diazoindolin-2-imines with allyltrimethylsilane is described. The domino reaction proceeds efficiently in one-pot and shows broad substrate scope with respect to the diazo substrates. Furthermore, the synthesized 3-allyl-2-aminoindoles can be conveniently transformed into 3-allyl-3-hydroxylindolin-2-imines and 3-allyl-3-fluoroindolin-2-imines. Graphical abstract Image 1 [ABSTRACT FROM AUTHOR]

Abstract In recent years, machine learning has been widely applied to problems in detecting network attacks, particularly novel attacks. However, traditional machine learning methods depend heavily on feature engineering, and extracting features is often time-consuming and complex. Thus, it is impractical to detect attacks with traditional machine learning methods in real-time applications. To discover network attacks efficiently, we propose an end-to-end detection approach. We implement deep learning models to analyze payloads and propose a convolutional neural network-based payload classification approach (PL-CNN) and a recurrent neural network-based payload classification approach (PL-RNN) for use in attack detection. Our two approaches learn feature representations from original payloads without feature engineering and support end-to-end detection. These approaches achieve accuracies of 99.36% and 99.98% when applied to the DARPA1998 dataset, respectively; these accuracies are comparable to or better than those of state-of-the-art methods. In addition, our methods are efficient and practical. [ABSTRACT FROM AUTHOR]

• A multidimensional data secure aggregation scheme in smart grid is proposed. • The scheme achieves fine-grained access control at dimension-level. • Access permission can be updated flexibly and securely. • The proposed scheme is implemented efficiently. In smart grid, for saving communication bandwidth and performing fine-grained analysis, the power data need to be collected and aggregated in multi-dimensions. As each dimension contains sensitive information of users and may be used by different analytic organizations, privacy preserving and access control for multidimensional power data are obviously important. Existing schemes either cannot support dimensional access control or require transmitting attribute sets of data together with ciphertexts, which leaks the relevant information of power data and increases the security risk. Moreover, to disseminate and implement access control of the multidimensional data, control center (CC) must firstly decrypt the data of all dimensions, which may result in the data leakage. In this paper, we propose a Multidimensional-data Tight Aggregation scheme which supports Privacy preserving and fine-grained Access control (MTA–PA). In MTA–PA, we import the extended Boneh–Goh–Nissim (EBGN) homomorphic encryption from wireless sensor networks to aggregate multidimensional power data into one ciphertext and simultaneously use key-policy attribute-based encryption (KP–ABE) to enforce fine-grained access control at the dimension level. By using MTA–PA, the gateway need not to distinguish different ciphertexts before aggregating them, and organizations only can analyze the authorized dimensions of power data. MTA–PA further achieves secure access-permission update via updating related parameters of EBGN and KP–ABE. And then, we also present the implementation methods of the algorithms. The security analysis and experimental results show that MTA–PA is a secure and efficient data protection scheme in smart grid. [ABSTRACT FROM AUTHOR]

Transformer-based graph neural networks have accomplished notable achievements by utilizing the self-attention mechanism for message passing in various domains. However, traditional methods overlook the diverse significance of intra-node representations, focusing solely on internode interactions. To overcome this limitation, we propose a DAG (Dual Attention Graph), a novel approach that integrates both intra-node and internode dynamics for node classification tasks. By considering the information exchange process between nodes from dual branches, DAG provides a holistic understanding of information propagation within graphs, enhancing the interpretability of graph-based machine learning applications. The experimental evaluations demonstrate that DAG excels in node classification tasks, outperforming current benchmark models across ten datasets. [ABSTRACT FROM AUTHOR]

Sm1 and Chit42 of Trichoderma have been universally confirmed as crucial biocontrol factors against pathogen infection through induced resistance and mycoparasitism, respectively. However, not enough work has been conducted to understand the novel function of fused expression of these two proteins in Trichoderma. The results of this study demonstrated that Sm1-Chit42 protein (SCf) engineered T. afroharzianum strain OE:SCf exerted synergistic inhibition to Botrytis cinerea growth at multiple stages of mycoparasitic interaction of T. afroharzianum and B. cinerea including chemotropism sensing, hyphal coiling, hydrophobicity modulation, cell wall adhesion, virulence reduction and pathogen killing by ROS. These results highlight a novel mycoparasitic system in Trichoderma strains engineered with Sm1-Chit42 chimeric protein to combat B. cinerea growth and reproduction, which would lay a strong foundation for exploring a new engineered Trichoderma biofungicide created with chimeric proteins in the future. [ABSTRACT FROM AUTHOR]

Although text-to-image synthesis has shown remarkable success in generating high-resolution photorealistic images and semantic consistency, it still faces challenges in generating images with complex backgrounds. In this paper, we address this problem by proposing a novel generative adversarial text-to-image synthesis framework based on attention regularization modules and region proposal networks (ARRPNGAN). ARRPNGAN can precisely locate the keywords in text by exploiting attention model advantages and improving the accuracy in locating the subimage of target objects with the help of an RPN. Leveraging both attention regularization and the RPN a generative adversarial network (GAN) can obtain the most text description semantics and reduce the interference of complex background information. The results of extensive experiments on the Caltech-UCSD Birds and MS COCO datasets demonstrate that the proposed ARRPNGAN significantly outperforms other state-of-the-art text-to-image methods, especially in generating photorealistic images with complex backgrounds. Codes are available at: https://github.com/quanFN/ARRPNGAN. • Text-to-image synthesis combines textual and image features. • Generative adversarial network shows promising results in image synthesis. • Attention mechanism is effective at locating target objects and features. • Region proposal network can eliminate background interferences. • Proposing networks for synthesizing real-world images. [ABSTRACT FROM AUTHOR]

The circulation of rice from contaminated areas could escalate exposure risk from a local problem to a national issue and affect a wider population beyond the region of origin, as confirmed by the 'Poison Rice Incident' in May 2013 in Guangzhou, China. In the present study, the authors established a food chain model based on the aquivalence method to identify major sources of methylmercury (MeHg), estimate the levels of MeHg, and quantify exposure to MeHg via rice and aquatic food consumption. Different types of organism samples from the Haihe River also were collected to verify the calculated values. The MeHg intake in pregnant women was 1529.1 ng/d from the aquatic food chain and as high as 2804.0 ng/d from rice, although the intake varied among scenarios. The maximum possible MeHg concentration in the blood of pregnant women was 5.21 µg/L, higher than the threshold value of MeHg recommended by the US Environmental Protection Agency (4.4 µg/L), which indicated that pregnant women could face risk from MeHg exposure. The authors also assessed the risk of MeHg exposure in pregnant women and their breastfed infants using a new index, HQEquivalent. In 4 scenarios, the HQEquivalent indices ranged from 0.42 to 1.18 for pregnant women and from 0.29 to 0.83 for breastfed infants. Environ Toxicol Chem 2015;34:1161-1168. © 2014 SETAC [ABSTRACT FROM AUTHOR]

Biological vision systems have become highly optimized over millions of years of evolution, developing complex neural structures to represent and process stimuli. Moreover, biological systems of vision are typically far more efficient than current human-made machine vision systems. The present report describes a non-task-dependent image representation schema that simulates the early phase of a biological neural vision mechanism. We designed a neural model involving multiple types of computational units to simulate ganglion cells and their non-classical receptive fields, local feedback control circuits and receptive field dynamic self-adjustment mechanisms in the retina. We found that, beyond the pixel level, our model was able to represent images self-adaptively and rapidly. A series of statistical analyses revealed that this model not only produces compact and abstract approximations of images, but also retains their primary visual features. In addition, the improved representation was found to substantially facilitate contour detection and image segmentation. We propose that this improvement arose because ganglion cells can resize their receptive fields, enabling multi-scale analysis functionality, a neighborhood referring function and a localized synthesis function. The ganglion cell layer is the starting point of subsequent diverse visual processing. The universality of this cell type and its functional mechanisms suggests that it will be useful for designing image processing algorithms in future. [ABSTRACT FROM AUTHOR]

Abstract: The broad region outside the classical receptive field (CRF) of a vision neuron, known as the non-classical receptive field (nCRF), exerts a robust modulatory effect on the responses to visual stimuli presented within the CRF, and plays an important role in visual information processing. One possible role for the nCRF is the extract object contours from disorderly background textures. In this study, a multi-scale integration based contour extraction model, inspired by the inhibitory and disinhibitory interactions between the CRF and the nCRF is presented. Unlike previous models, our model not only includes both the simple and complex cell mechanisms but also introduces pre-processing of the external information by the retinal ganglion cells at an early stage. The multi-scale representation of a physical scene acquired through such pre-processing was filtered through Gabor filters, and then inhibited or disinhibited at different spatial locations on different scales until a final response was obtained. Our results show that by introducing this kind of mechanism into the model, numbers of non-meaningful texture elements can be removed significantly, while at the same time, the object contours can be detected effectively. In addition to the superior contour detection performance in comparison to other contour detection models, our model provides a better understanding of the role of the nCRF and a novel approach for computer vision and pattern recognition. [Copyright &y& Elsevier]

Abstract: More powerful mobile devices stimulate mobile visual search to become a popular and unique image retrieval application. A number of challenges come up with such application, resulting from appearance variations in mobile images. Performance of state-of-the-art image retrieval systems is improved using bag-of-words approaches. However, for visual search by mobile images with large variations, there are at least two critical issues unsolved: (1) the loss of features discriminative power due to quantization; and (2) the underuse of spatial relationships among visual words. To address both issues, this paper presents a novel visual search method based on feature grouping and local soft match, which considers properties of mobile images and couples visual and spatial information consistently. First features of the query image are grouped using both matched visual features and their spatial relationships; and then grouped features are softly matched to alleviate quantization loss. An efficient score scheme is devised to utilize inverted file index and compared with vocabulary-guided pyramid kernels. Finally experiments on Stanford mobile visual search database and a collected database with more than one million images show that the proposed method achieves promising improvement over the approach with a vocabulary tree, especially when large variations exist in query images. [Copyright &y& Elsevier]

Trichoderma spp. are widely used beneficial microbes in agricultural production; however, the improper carrier choice for Trichoderma agent preparation can alter the effectiveness of Trichoderma fungicides. In this study, the co-culture of four Trichoderma strains produced a large amount of free amino acids, with a content of 392.8414 ug/mL, and significantly improved the production level of γ-aminobutyric acid. A greenhouse experiment further showed that the co-culture of Trichoderma synergistically improved the female flower development and bacterial angular leaf spot resistance. The effects of ten kinds of carriers were compared in terms of water absorption and heat generation, as well as their effects on the seedling emergence rate and the plant growth promotion of maize, cucumber, and pakchoi cabbage. Each carrier was screened to mix with four strains of co-culture metabolites to prepare highly soluble and quality powders. The results showed that there were different effects of the carriers themselves and Trichoderma strain co-culture metabolite powder prepared with the carriers on seedling emergence rate and seedling growth. Β-cyclodextrin performed best in high solubility and low heat generation upon absorbing water and in easy drying in processing operations. Trichoderma strains co-culture metabolite powder with β-cyclodextrin as a carrier provided the most obvious promotion effects on seedling emergence rate and seedling growth. Therefore, β-cyclodextrin was determined to be an ideal carrier to prepare a highly water-soluble Trichoderma agent. Taken together, the study successfully developed a new type of highly soluble powder containing Trichoderma co-culture metabolites that is expected to benefit farming drip irrigation and spraying systems for the promotion of crop growth and disease control. [ABSTRACT FROM AUTHOR]

Highlights • We present a graph convolutional neural network (g-CNN) model that can perform feature learning on graph data directly. • We present the disordered graph convolutional layer that can change the discrete parameter learning into the sampling. • The experimental results show that the proposed method outperforms the existing g-CNN methods and other types of methods. Abstract Convolutional neural networks (CNNs) can be applied to graph similarity matching, in which case they are called graph CNNs. Graph CNNs are attracting increasing attention due to their effectiveness and efficiency. However, the existing convolution approaches focus only on regular data forms and require the transfer of the graph or key node neighborhoods of the graph into the same fixed form. During this transfer process, structural information of the graph can be lost, and some redundant information can be incorporated. To overcome this problem, we propose the disordered graph convolutional neural network (DGCNN) based on the mixed Gaussian model, which extends the CNN by adding a preprocessing layer called the disordered graph convolutional layer (DGCL). The DGCL uses a mixed Gaussian function to realize the mapping between the convolution kernel and the nodes in the neighborhood of the graph. The output of the DGCL is the input of the CNN. We further implement a backward-propagation optimization process of the convolutional layer by which we incorporate the feature-learning model of the irregular node neighborhood structure into the network. Thereafter, the optimization of the convolution kernel becomes part of the neural network learning process. The DGCNN can accept arbitrary scaled and disordered neighborhood graph structures as the receptive fields of CNNs, which reduces information loss during graph transformation. Finally, we perform experiments on multiple standard graph datasets. The results show that the proposed method outperforms the state-of-the-art methods in graph classification and retrieval. [ABSTRACT FROM AUTHOR]

Since the mechanisms of methane-mudstone interactions are important for estimating shale gas reserves, methane adsorption under supercritical conditions of 30 MPa pressure and 303.15, 333.15, 363.15 K temperatures was studied to measure the excess methane adsorption in two mudstone samples from Yanchang Formation, Ordos Basin. Excess adsorption features inflection points where the amount of adsorbed gas changes from increasing to decreasing concentrations. Three methods (fixed, slope, and freely fitted density) were applied to calculate the adsorbed-phase density (rad), which was then used to fit the measured excess adsorption. Two criteria, the goodness-of-fit and whether the fitting can obtain reasonable absolute adsorption, were applied to determine the most accurate model. Results indicated that the supercritical Dubinin-Radushkevich (SDR) model with freely fitted rad was the most reasonable model. The volume of adsorbed methane at 363.15 K is close to the micropore (d < 2 nm) volume of the corresponding mudstone. Considering the actual geological conditions, the adsorbed gas should be predominantly stored in micropores. Thermodynamic parameters reveal that the methane adsorption on mudstone is a physisorption process that is jointly controlled by the heterogeneity of, and interaction forces between the methane molecule and, the rock surface. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the rapid development of car-hailing services, which provide a convenient approach for connecting passengers and local drivers using their personal vehicles. At the same time, the concern on passenger safety has gradually emerged and attracted more and more attention. While car-hailing service providers have made considerable efforts on developing real-time trajectory tracking systems and alarm mechanisms, most of them only focus on providing rescue-supporting information rather than preventing potential crimes. Recently, the newly available large-scale car-hailing order data have provided an unparalleled chance for researchers to explore the risky travel area and behavior of car-hailing services, which can be used for building an intelligent crime early warning system. To this end, in this article, we propose a Risky Area and Risky Behavior Evaluation System (RARBEs) based on the real-world car-hailing order data. In RARBEs, we first mine massive multi-source urban data and train an effective area risk prediction model, which estimates area risk at the urban block level. Then, we propose a transverse and longitudinal double detection method, which estimates behavior risk based on two aspects, including fraud trajectory recognition and fraud patterns mining. In particular, we creatively propose a bipartite graph-based algorithm to model the implicit relationship between areas and behaviors, which collaboratively adjusts area risk and behavior risk estimation based on random walk regularization. Finally, extensive experiments on multi-source real-world urban data clearly validate the effectiveness and efficiency of our system. [ABSTRACT FROM AUTHOR]

Abstract: Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. [Copyright &y& Elsevier]

Abstract: The objective of this study was to prepare amorphous fenofibrate (FB) solid dispersions using thin film freezing (TFF) and to incorporate the solid dispersions into pharmaceutically acceptable dosage forms. FB solid dispersions prepared with optimized drug/polymer ratios were characterized by modulated differential scanning calorimetry (MDSC), powder X-ray diffraction (XRD), scanning electron microscopy (SEM), Brunauer–Emmett–Teller (BET) specific surface area measurements, Fourier-transform infrared spectroscopy-attenuated total reflectance (FTIR-ATR), and supersaturation dissolution testing. Furthermore, a dry granulation technique was used to encapsulate the TFF compositions for in vitro dissolution and in vivo animal pharmacokinetic studies. The results showed that the TFF process produced amorphous, porous, microstructured, and stable solid dispersions with high surface areas. Development of solid oral dosage forms revealed that the performance of the FB containing solid dispersions was not affected by the formulation process, which was confirmed by DSC and XRD. Moreover, an in vivo pharmacokinetic study in rats revealed a significant increase in FB absorption compared to bulk FB. We confirmed that amorphous solid dispersions with large surface areas produced by the TFF process displayed superior dissolution rates and corresponding enhanced bioavailability of the poorly water-soluble drug, FB. [Copyright &y& Elsevier]

Abstract: This study aims to make a 2-hydroxypropyl-β-cyclodextrin (HPβCD) solubilized itraconazole (ITZ) solution (i.e., HPβCD-ITZ) suitable for pulmonary delivery by nebulization, and compare pharmacokinetics of inhaled nebulized aerosols of HPβCD-ITZ versus a colloidal dispersion of ITZ nanoparticulate formulation (i.e., URF-ITZ). Solid state characterizations of lyophilized HPβCD-ITZ by differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS) indicated the formation of dynamic inclusion complexes between ITZ and HPβCD. Nebulized aerosols of both HPβCD-ITZ and colloidal dispersion of URF-ITZ were confirmed suitable for deep lung delivery. Single doses of the nebulized aerosols (equivalent to 5.3mg ITZ/mL in 5mL) in mice produced similar ITZ lung depositions and pharmacokinetic profiles, with ITZ lung levels of approximately 4μg/g wet lung weight upon completion of nebulization and remained above 0.5μg/g at 24h. HPβCD-ITZ demonstrated faster systemic absorption of ITZ across lung epithelium than URF-ITZ, with t max values of 1.5 and 3.0h, and AUC0–∞ of 2513 and 3717ngh/mL, respectively. The fast absorption of solubilized ITZ across lung mucosal surface may be due in part to the elimination of the phase-to-phase transition. [Copyright &y& Elsevier]

Abstract: HRG-β1 stimulation of breast cancer cell line SKBR3 resulted in not only increased cell migration and invasion, upregulation of some mesenchymal markers, and downregulation of epithelial marker, but also upregulation of transcription factor Snail and its nuclear translocation. Similar results were acquired for cells transfected with Snail cDNA. Furthermore, downregulation of Snail by siRNA attenuated HRG-β1 induced EMT-like phenotype. Inhibition of Akt kinase activation by a PI3K inhibitor LY294002, or exogenous expression of a kinase-dead mutant of Akt abrogated the increase of Snail expression induced by HRG-β1. Conversely, expression of a constitutively active Akt resulted in increase of Snail expression. These results indicated that Snail upregulation by HRG-β1 is mediated via the PI3K/Akt signaling pathway and that Snail plays a key role in HRG-β1 induced breast cancer cell metastasis through induction of EMT. [Copyright &y& Elsevier]

In this work, a population balance model has been developed and calibrated with experimental data to be used as a predictive tool to replace or reinforce experimental studies of high shear wet granulation. The system studied also involves dry binder addition necessitating the incorporation of dissolution calculations, viscosity-binder correlations and drop penetration calculations to address some of the heterogeneities arising from the dry addition of binder. To address inhomogeneous liquid distribution in the powder bed, the granulator has been divided into two compartments, one with more liquid, less solid and the other one with the opposite. 27 wet granulation experiments were performed by varying liquid addition amount, liquid addition rate, impeller speed and wet massing time. Geometric mean diameter (GMD) in one case and d 10 along with d 50 in the other case for the 9 of these experiments were used to estimate the tuning parameters in the model. With the estimated parameters, GMD and d 10 , d 50 were predicted for the remaining 18 experiments using the model and compared with experimental results. The model was able to predict the GMD of 8 experiments with a deviation of <10 μm and 4 more with <20 μm deviation. The model was also able to predict the d 10 of 6 batches within 10 μm and 9 more batches within 20 μm. For d 50 prediction, the deviation was <10 μm for 12 batches and <20 μm for 4 more batches. The model predicted particle size distribution (PSD) was also compared with the experimental PSD for two of the predicted sets as an example. It was seen that the model was able to replicate the PSD fairly well with the exception of the presence of some big granules seen in the experiments. [ABSTRACT FROM AUTHOR]

The rhodium-catalyzed reactions of 3-diazoindolin-2-imines with β-enamino esters furnished 2,3-diaminoindoles in excellent yields with wide functional group tolerance. The synthesized 2,3-diaminoindoles could be converted into 5 H -pyrazino [2,3- b ]indoles via a sequential dehydrogenation/6π-ERC/aromatization process. [ABSTRACT FROM AUTHOR]

Hash-based nearest neighbor search has become attractive in many applications. However, the quantization in hashing usually degenerates the discriminative power when using Hamming distance ranking. Besides, for large-scale visual search, existing hashing methods cannot directly support the efficient search over the data with multiple sources, and while the literature has shown that adaptively incorporating complementary information from diverse sources or views can significantly boost the search performance. To address the problems, this paper proposes a novel and generic approach to building multiple hash tables with multiple views and generating fine-grained ranking results at bitwise and tablewise levels. For each hash table, a query-adaptive bitwise weighting is introduced to alleviate the quantization loss by simultaneously exploiting the quality of hash functions and their complement for nearest neighbor search. From the tablewise aspect, multiple hash tables are built for different data views as a joint index, over which a query-specific rank fusion is proposed to rerank all results from the bitwise ranking by diffusing in a graph. Comprehensive experiments on image search over three well-known benchmarks show that the proposed method achieves up to 17.11% and 20.28% performance gains on single and multiple table search over the state-of-the-art methods. [ABSTRACT FROM PUBLISHER]

The title substrates are transformed into α-imino rhodiumcarbene intermediates which react with furans or tetrahydrofurans to give pyridoindoles and tetrahydrofuropyrroloindoles, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Hough-based object shape representation model called Pair Hough Model (PHM) and its corresponding object detection framework. PHM constructs the voting models implicitly with automatically detected interest points and their local descriptors for unseen object categories. In addition, by casting votes according to key point pairs instead of individual key points and taking the orientations of objects as well as their sizes into consideration, PHM can recognize and localize objects after their scaling and/or rotation, which makes it suitable for processing images with major rotations such as pictures taken by mobile devices. Evaluation experiments proved that PHM does not need to be trained on rotated images to recognize rotated objects, and PHM achieved comparable results to the state-of-the-art methods on several widely used public data sets. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel label propagation algorithm named Proxy-based Local Consistency Propagation (PLCP), in which the label information is first propagated from labeled examples to the unlabeled ones, and then spreads only among unlabeled ones mutually until a steady state is reached. To meet the requirements of efficiency in many real-world image annotation applications, we propose an online semi-supervised annotation framework where the new examples can be predicted and used to update the model. Specifically, we extend PLCP to work under an inductive setting and propose an incremental model updating method that can incorporate the new examples including labeled and unlabeled examples. The comprehensive experiments on MNIST, CIFAR-10 and PIE datasets show that our proposed PLCP achieves superior performance compared with the baselines, and our proposed incremental model updating method can achieve significant promotion in efficiency, with the nearly identical accuracy compared to re-training. [ABSTRACT FROM AUTHOR]

Abstract: MIAPS (medical image access and presentation system) is a web-based system designed for remotely accessing and presenting DICOM image. MIAPS is accessed with web browser through the Internet. MIAPS provides four features: DICOM image retrieval, maintenance, presentation and output. MIAPS does not intent to replace sophisticated commercial and open source packages, but it provides a web-based solution for teleradiology and medical image sharing. The system has been evaluated by 39 hospitals in China for 10 months. [Copyright &y& Elsevier]

HRG-beta1 stimulation of breast cancer cell line SKBR3 resulted in not only increased cell migration and invasion, upregulation of some mesenchymal markers, and downregulation of epithelial marker, but also upregulation of transcription factor Snail and its nuclear translocation. Similar results were acquired for cells transfected with Snail cDNA. Furthermore, downregulation of Snail by siRNA attenuated HRG-beta1 induced EMT-like phenotype. Inhibition of Akt kinase activation by a PI3K inhibitor LY294002, or exogenous expression of a kinase-dead mutant of Akt abrogated the increase of Snail expression induced by HRG-beta1. Conversely, expression of a constitutively active Akt resulted in increase of Snail expression. These results indicated that Snail upregulation by HRG-beta1 is mediated via the PI3K/Akt signaling pathway and that Snail plays a key role in HRG-beta1 induced breast cancer cell metastasis through induction of EMT. [ABSTRACT FROM AUTHOR]

The reaction can be scaled up. [ABSTRACT FROM AUTHOR]