The Differential Effects of Two Critical Osteoclastogenesis Stimulating Factors on Bone Biomechanics

Many skeletal diseases, such as osteoporosis and malignant bone metastases, are generally osteolytic and associated with increased bone resorption and decreased bone strength. Within a complex cytokine environment, the proteins RANKL and M-CSF are critical for osteoclast differentiation and activation, and thus fundamental effectors of osteolytic disorders. Previous studies showed that M-CSF stimulates the proliferation and early differentiation of osteoclast progenitors to osteoclast lineage, while RANKL targets the later stages of fusion and activation, and stimulates the formation of functional active osteoclasts. However, impacts of artificially elevated levels of these proteins on the skeleton system have not been fully characterized. In this project, we amplified the circulating levels of RANKL and M-CSF by injections or continuous administrations and examined the effects on bone volume and quality. We hypothesized that while M-CSF and RANKL can both stimulate osteoclastogenesis, the differences in activation stages targeted by these two cytokines would result in distinct responses on bone biomechanics. RANKL would directly stimulate osteoclast activity and increase bone resorption, while M-CSF would act anabolically through coupling between osteoblast development and the promoted osteoclastogenesis at the early stage, and promote bone formation indirectly. Data obtained in this project demonstrated that in vivo administration of RANKL and M-CSF induced general opposing effects on bone volume, architecture, mineralization and strength. RANKL directly stimulated bone resorption and reduceed bone biomechanical properties. The destructive skeleton induced by RANKL could serve as a novel animal model that exhibits a series of skeletal complications similar to those observed in osteolytic skeletal diseases, such as osteoporosis. Alternately, administrations of M-CSF markedly stimulated trabecular bone formation and had less of an influence on cortical bone. These changes demonstrated the potential of M-CSF as an anabolic agent for osteoporosis. This project has further examined the in vivo characteristics and functional effects of RANKL and M-CSF on the skeleton system. Findings in this project, such as the creation of RANKL induced bone loss model and characterization of the anabolic potential of M-CSF on the skeleton, could provide useful information and tools for further explorations on human skeletal diseases

Contextual Attention Recurrent Architecture for Context-aware Venue Recommendation

Venue recommendation systems aim to effectively rank a list of interesting venues users should visit based on their historical feedback (e.g. checkins). Such systems are increasingly deployed by Location-based Social Networks (LBSNs) such as Foursquare and Yelp to enhance their usefulness to users. Recently, various RNN architectures have been proposed to incorporate contextual information associated with the users' sequence of checkins (e.g. time of the day, location of venues) to effectively capture the users' dynamic preferences. However, these architectures assume that different types of contexts have an identical impact on the users' preferences, which may not hold in practice. For example, an ordinary context such as the time of the day reflects the user's current contextual preferences, whereas a transition context - such as a time interval from their last visited venue - indicates a transition effect from past behaviour to future behaviour. To address these challenges, we propose a novel Contextual Attention Recurrent Architecture (CARA) that leverages both sequences of feedback and contextual information associated with the sequences to capture the users' dynamic preferences. Our proposed recurrent architecture consists of two types of gating mechanisms, namely 1) a contextual attention gate that controls the influence of the ordinary context on the users' contextual preferences and 2) a time- and geo-based gate that controls the influence of the hidden state from the previous checkin based on the transition context. Thorough experiments on three large checkin and rating datasets from commercial LBSNs demonstrate the effectiveness of our proposed CARA architecture by significantly outperforming many state-of-the-art RNN architectures and factorisation approaches

A Strategy Optimization Approach for Mission Deployment in Distributed Systems

In order to increase operational efficiency, reduce delays, and/or maximize profit, almost all the organizations have split their mission into several tasks which are deployed in distributed system. However, due to distributivity, the mission is prone to be vulnerable to kinds of cyberattacks. In this paper, we propose a mission deployment scheme to optimize mission payoff in the face of different attack strategies. Using this scheme, defenders can achieve “appropriate security” and force attackers to jointly safeguard the mission situation

Generation of Biotechnology-Derived Flavobacterium columnare Ghosts by PhiX174 Gene E-Mediated Inactivation and the Potential as Vaccine Candidates against Infection in Grass Carp

Flavobacterium columnare is a bacterial pathogen causing high mortality rates for many freshwater fish species. Fish vaccination with a safe and effective vaccine is a potential approach for prevention and control of fish disease. Here, in order to produce bacterial ghost vaccine, a specific Flavobacterium lysis plasmid pBV-E-cat was constructed by cloning PhiX174 lysis gene E and the cat gene with the promoter of F. columnare into the prokaryotic expression vector pBV220. The plasmid was successfully electroporated into the strain F. columnare G4cpN22 after curing of its endogenous plasmid. F. columnare G4cpN22 ghosts (FCGs) were generated for the first time by gene E-mediated lysis, and the vaccine potential of FCG was investigated in grass carp (Ctenopharyngodon idellus) by intraperitoneal route. Fish immunized with FCG showed significantly higher serum agglutination titers and bactericidal activity than fish immunized with FKC or PBS. Most importantly, after challenge with the parent strain G4, the relative percent survival (RPS) of fish in FCG group (70.9%) was significantly higher than FKC group (41.9%). These results showed that FCG could confer immune protection against F. columnare infection. As a nonliving whole cell envelope preparation, FCG may provide an ideal alternative to pathogen-based vaccines against columnaris in aquaculture

Question Directed Graph Attention Network for Numerical Reasoning over Text

Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation. To address this challenge, we propose a heterogeneous graph representation for the context of the passage and question needed for such reasoning, and design a question directed graph attention network to drive multi-step numerical reasoning over this context graph.Comment: Accepted at EMNLP 202

Modeling Mobile Cellular Networks Based on Social Characteristics

Social characteristics have become an important aspect of cellular systems, particularly in next generation networks where cells are miniaturised and social effects can have considerable impacts on network operations. Traffic load demonstrates strong spatial and temporal fluctuations caused by users social activities. In this article, we introduce a new modelling method which integrates the social aspects of individual cells in modelling cellular networks. In the new method, entropy based social characteristics and time sequences of traffic fluctuations are defined as key measures, and jointly evaluated. Spectral clustering techniques can be extended and applied to categorise cells based on these key parameters. Based on the social characteristics respectively, we implement multi-dimensional clustering technologies, and categorize the base stations. Experimental studies are carried out to validate our proposed model, and the effectiveness of the model is confirmed through the consistency between measurements and model. In practice, our modelling method can be used for network planning and parameter dimensioning to facilitate cellular network design, deployments and operations

Evaluation of Bletilla striata Polysaccharide Deproteinized System Based on Entropy Weighted TOPSIS Model

The entropy TOPSIS model was used to compare the effects of Sevage method， acetonitrile method and trichloroacetic acid (TCA) method for removaling crude Bletilla striata polysaccharide (BSP) protein, and to explore the rationality of entropy TOPSIS for BSP deproteinization system evaluation. Based on the comprehensive score of BSP retention rate and protein removal rate, the optimal treatment conditions were screened out. Nine evaluation indicators including monosaccharide components, oxidative radical scavenging ability (ORAC), and half scavenging concentration of DPPH radicals (IC50) were constructed. Supplemented by UV and FTIR, the entropy TOPSIS was used to evaluate the results of three BSP deproteinization programs. After comprehensive score, the best extraction times of sevage method was 1 time. At same time, the protein removal rate was 22.9%, and the polysaccharide retention rate was 99.11%. The optimal mass concentration of the TCA method was 10%, when the protein removal rate was 70.64%, and the polysaccharide retention rate was 70.03%. Compared with the ORAC values and IC50 of the three polysaccharide, it was found that the value of polysaccharide ORAC treated by the acetonitrile method was higher than that of the positive control group (P<0.05), and the polysaccharides treated by the Sevage method had the strongest antioxidant activity. The BSP deproteinization evaluation system was analyzed by the entropy TOPSIS model, and the sevage method deproteinization effect was the best and the expected result. The results showed that the entropy TOPSIS model could be used in the evaluation of BSP deproteinization system

A Deep Recurrent Collaborative Filtering Framework for Venue Recommendation

Venue recommendation is an important application for Location-Based Social Networks (LBSNs), such as Yelp, and has been extensively studied in recent years. Matrix Factorisation (MF) is a popular Collaborative Filtering (CF) technique that can suggest relevant venues to users based on an assumption that similar users are likely to visit similar venues. In recent years, deep neural networks have been successfully applied to tasks such as speech recognition, computer vision and natural language processing. Building upon this momentum, various approaches for recommendation have been proposed in the literature to enhance the effectiveness of MF-based approaches by exploiting neural network models such as: word embeddings to incorporate auxiliary information (e.g. textual content of comments); and Recurrent Neural Networks (RNN) to capture sequential properties of observed user-venue interactions. However, such approaches rely on the traditional inner product of the latent factors of users and venues to capture the concept of collaborative filtering, which may not be sufficient to capture the complex structure of user-venue interactions. In this paper, we propose a Deep Recurrent Collaborative Filtering framework (DRCF) with a pairwise ranking function that aims to capture user-venue interactions in a CF manner from sequences of observed feedback by leveraging Multi-Layer Perception and Recurrent Neural Network architectures. Our proposed framework consists of two components: namely Generalised Recurrent Matrix Factorisation (GRMF) and Multi-Level Recurrent Perceptron (MLRP) models. In particular, GRMF and MLRP learn to model complex structures of user-venue interactions using element-wise and dot products as well as the concatenation of latent factors. In addition, we propose a novel sequence-based negative sampling approach that accounts for the sequential properties of observed feedback and geographical location of venues to enhance the quality of venue suggestions, as well as alleviate the cold-start users problem. Experiments on three large checkin and rating datasets show the effectiveness of our proposed framework by outperforming various state-of-the-art approaches