Search

The β-1,6-glucan is the key linker between mannoproteins in the outermost part of the cell wall and β-1,3-glucan/chitin polysaccharide to maintain the rigid structure of the cell wall. The β-1,6-glucanase GluM, which was purified from the fermentation supernatant of Corallococcus sp. EGB, was able to inhibit the germination of Fusarium oxysporum f. sp. cucumerinum conidia at a minimum concentration of 2.0 U/mL (0.08 μg/mL). The survival rates of GluM-treated conidia and monohyphae were 10.4% and 30.7%, respectively, which were significantly lower than that of β-1,3-glucanase treatment (Zymolyase, 20.0 U/mL; equate to 1.0 mg/mL) (72.9% and 73.9%). In contrast to β-1,3-glucanase treatment, the high-osmolarity glycerol (HOG) pathway of F. oxysporum f. sp. cucumerinum cells was activated after GluM treatment, and the intracellular glycerol content was increased by 2.6-fold. Moreover, the accumulation of reactive oxygen species (ROS) in F. oxysporum f. sp. cucumerinum cells after GluM treatment induced apoptosis, but it was not associated with the increased intracellular glycerol content. Together, the results indicate that β-1,6-glucan is a promising target for the development of novel broad-spectrum antifungal agents. [ABSTRACT FROM AUTHOR]

The spatter forming mechanism of underwater flux-cored wire wet welding is characterized by using a selfdeveloped X-ray transmission system. Three representative spatter modes, droplet repelled spatter, explosive spatter and molten pool shock spatter, forming during underwaterwet welding have been observed. The generation of droplet repelled spatter is due to the excessive repulsive forces for metal transfer. The diameter of this type of spatter is largest relatively, which can reach about 4.5 mm. In addition, explosive spatter, with size about 2.5 mm, is changed from the droplet with high horizontal speed caused by a slight explosion occurs at the touching location of the droplet and molten surface. Moreover, the dimension of molten pool shock spatter is smallest potentially among the spatter types during underwater wet welding, and it is only about 0.8 mm. It is generated from ridgy liquid column on the molten pool surface because of the relatively severe concussion of themolten pool. The size, macroscopic pattern aswell as chemical composition of these three types of spatters are different. [ABSTRACT FROM AUTHOR]

This study examines an automatic broadcast soccer video composition system. The research is important as the ability to automatically compose broadcast sports video will not only improve broadcast video generation efficiency, but also provides the possibility to customize sports video broadcasting. We present a novel approach to the two major issues required in the system’s implementation, specifically the camera view selection/switching module and the automatic replay generation module. In our implementation, we use multi-modal framework to perform video content analysis, event and event boundary detection from the raw unedited main/sub-camera captures. This framework explores the possible cues using mid-level representations to bridge the gap between low-level features and high-level semantics. The video content analysis results are utilized for camera view selection/switching in the generated video composition, and the event detection results and mid-level representations are used to generate replays which are automatically inserted into the broadcast soccer video. Our experimental results are promising and found to be comparable to those generated by broadcast professionals. [ABSTRACT FROM AUTHOR]

Recently, user cold-start recommendations have attracted a lot of attention from industry and academia. In user cold-start recommendation systems, the user attribute information is often used by existing approaches to learn user preferences due to the unavailability of user action data. However, most existing recommendation methods often ignore the sparsity of user attributes in cold-start recommendation systems. To tackle this limitation, this article proposes a novel Inductive Heterogeneous Graph Neural Network (IHGNN) model, which utilizes the relational information in user cold-start recommendation systems to alleviate the sparsity of user attributes. Our model converts new users, items, and associated multimodal information into a Modalityaware Heterogeneous Graph (M-HG) that preserves the rich and heterogeneous relationship information among them. Specifically, to utilize rich and heterogeneous relational information in an M-HG for enriching the sparse attribute information of new users, we design a strategy based on random walk operations to collect associated neighbors of new users by multiple times sampling operation. Then, a well-designed multiple hierarchical attention aggregationmodel consisting of the intra- and inter-type attention aggregating module is proposed, focusing on useful connected neighbors and neglecting meaningless and noisy connected neighbors to generate high-quality representations for user cold-start recommendations. Experimental results on three real datasets demonstrate that the IHGNN outperforms the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Medicine recommendation systems target to recommend a set of medicines given a set of symptoms which play a crucial role in assisting doctors in their daily clinics. Existing approaches are either rule-based or supervised. However, the former heavily relies on expert labeling, which is time-consuming and costly to collect, and the latter suffers from the data sparse problem. To automate medicine recommendation on sparse data, we propose MedRec, which introduces two graphs in modeling: (1) a knowledge graph connecting diseases, medicines, symptoms, and examinations; (2) an attribute graph connecting medicines via shared attributes and molecular structures. These two graphs enhance the connectivity between symptoms and medicines, which thus alleviate the data sparse problem. By learning the interrelationship between diseases, medicines, symptoms and examinations and the inner relationship within medicine, we can acquire unified embedding representations of symptoms and medicines which can be used in medicine recommendation. The experimental results show that the proposed model outperforms state-of-the-art methods. In addition, we find that these two tasks: learning graph representation and medical recommendation can benefit each other. [ABSTRACT FROM AUTHOR]

While on the go, people are using their phones as a personal concierge discovering what is around and deciding what to do. Mobile phone has become a recommendation terminal customized for individuals--capable of recommending activities and simplifying the accomplishment of related tasks. In this article, we conduct usage mining on the check-in data, with summarized statistics identifying the local recommendation challenges of huge solution space, sparse available data, and complicated user intent, and discovered observations to motivate the hierarchical, contextual, and sequential solution. We present a point-of-interest (POI) category-transition--based approach, with a goal of estimating the visiting probability of a series of successive POIs conditioned on current user context and sensor context. A mobile local recommendation demo application is deployed. The objective and subjective evaluations validate the effectiveness in providing mobile users both accurate recommendation and favorable user experience. [ABSTRACT FROM AUTHOR]

The cold-start recommendation is an urgent problem in contemporary online applications. It aims to provide users whose behaviors are literally sparse with as accurate recommendations as possible. Many data-driven algorithms, such as the widely used matrix factorization, underperform because of data sparseness. This work adopts the idea of meta-learning to solve the user's cold-start recommendation problem. We propose a meta-learning-based cold-start sequential recommendation framework called metaCSR, including three main components:Diffusion Representer for learning better user/item embedding through information diffusion on the interaction graph; Sequential Recommender for capturing temporal dependencies of behavior sequences; and Meta Learner for extracting and propagating transferable knowledge of prior users and learning a good initialization for new users. metaCSR holds the ability to learn the common patterns from regular users' behaviors and optimize the initialization so that the model can quickly adapt to new users after one or a few gradient updates to achieve optimal performance. The extensive quantitative experiments on three widely used datasets show the remarkable performance of metaCSR in dealing with the user cold-start problem. Meanwhile, a series of qualitative analysis demonstrates that the proposed metaCSR has good generalization. [ABSTRACT FROM AUTHOR]

Diabetic cardiac fibrosis increases ventricular stiffness and facilitates the occurrence of diastolic dysfunction. Retinoid X receptor (RXR) plays an important role in cardiac development and has been implicated in cardiovascular diseases. In the present study, we investigated the effects of RXR agonist treatment on streptozotocin (STZ)-induced diabetic cardiomyopathy (DCM) and the underlying mechanism. Sprague-Dawley (SD) rats induced by STZ injection were treated with either RXR agonist bexarotene (Bex) or vehicle alone. Echocardiography was performed to determine cardiac structure and function. Cardiac fibroblasts (CFs) were treated with high glucose (HG) with or without the indicated concentration of Bex or the RXR ligand 9-cis-retinoic acid (9-cis-RA). The protein abundance levels were measured along with collagen, body weight (BW), blood biochemical indexes and transforming growth factor-β (TGF-β) levels. The effects of RXRα down-regulation by RXRα small interfering RNA (siRNA) were examined. The results showed that bexarotene treatment resulted in amelioration of left ventricular dysfunction by inhibiting cardiomyocyte apoptosis and myocardial fibrosis. Immunoblot with heart tissue homogenates from diabetic rats revealed that bexarotene activated liver kinase B1 (LKB1) signaling and inhibited p70 ribosomal protein S6 kinase (p70S6K). The increased collagen levels in the heart tissues of DCM rats were reduced by bexarotene treatment. Treatment of CFs with HG resulted in significantly reduced LKB1 activity and increased p70S6K activity. RXRα mediated the antagonism of 9-cis-RA on HG-induced LKB1/p70S6K activation changes in vitro. Our findings suggest that RXR agonist ameliorates STZ-induced DCM by inhibiting myocardial fibrosis via modulation of the LKB1/p70S6K signaling pathway. RXR agonists may serve as novel therapeutic agents for the treatment of DCM. [ABSTRACT FROM AUTHOR]

The aim of the present study was to examine the effects of Shenqi Fuzheng injection (SFI) on Fas and FasL protein expression levels in the cardiomyocytes of mice with viral myocarditis (VMC) and to explore the underlying anti-apoptotic mechanisms. A total of 120 male BALB/c mice were randomly divided into five groups as follows: Blank control group, model group, ribavirin group, low-dose SFI group and high-dose SFI group. The VMC model was established by the injection of coxsackievirus group B type 3 and saline, ribavirin or SFI was administered 30 min later. Cardiac samples were harvested from mice in each group on days 3, 10 and 30. Apoptosis of cardiac cells was examined using terminal deoxynucleotidyl transferase dUTP nick-end labeling, and Fas and FasL protein expression levels were detected using immunohistochemistry. Myocardial apoptosis and Fas/FasL protein expression levels were significantly increased in the model group, as compared with the blank group (P<0.01), whereas the apoptotic index (AI) and Fas/FasL protein expression levels of cardiac cells in the high-dose SFI group were significantly decreased compared with those in the model group on day 10 (acute phase; P<0.01). The AI and Fas/FasL protein expression levels of cardiac cells in the lowand high-dose SFI groups were also significantly decreased on day 30 (chronic phase; P<0.01); however, no differences between the high- and low-dose groups were detected. In conclusion, SFI relieves VMC via the downregulation of Fas and FasL protein expression and the inhibition of cell apoptosis. [ABSTRACT FROM AUTHOR]