The 1st International Workshop on Context-Aware Recommendation Systems with Big Data Analytics (CARS-BDA)

Motivation and Goals. With the explosive growth of online service platforms, increasing number of people and enterprises are doing everything online. In order for organizations, governments, and individuals to understand their users, and promote their products or services, it is necessary for them to analyse big data and recommend the media or online services in real time. Effective recommendation of items of interest to consumers has become critical for enterprises in domains such as retail, e-commerce, and online media. Driven by the business successes, academic research in this field has also been active for many years. Though many scientific breakthroughs have been achieved, there are still tremendous challenges in developing effective and scalable recommendation systems for real-world industrial applications. Existing solutions focus on recommending items based on pre-set contexts, such as time, location, weather etc. The big data sizes and complex contextual information add further challenges to the deployment of advanced recommender systems. This workshop aims to bring together researchers with wide-ranging backgrounds to identify important research questions, to exchange ideas from different research disciplines, and, more generally, to facilitate discussion and innovation in the area of context-aware recommender systems and big data analytics. In a broad sense, the objective of such a workshop is to present results of the research undertaken in the area of data driven context-aware recommender systems, as a fishow and tellfi occasion. To some extent, the workshop is an exercise in showcasing research activities and findings, rather than in and not of fiworkshoppingfi or holding group discussions on research. This orientation, and the large number of presentations which are being made, means that tight timelines have to be followed. An intensive series of presentations is made, the downside of which is that the time available for group discussion is limited

Tumor necrosis factor–Α contributes to below-level neuropathic pain after spinal cord injury

Objective Our objective was to elucidate the mechanisms responsible for below-level pain after partial spinal cord injury (SCI). Methods We used lateral hemisection to model central neuropathic pain and herpes simplex viral (HSV) vector–mediated transfer of the cleaved soluble receptor for tumor necrosis factor–Α (TNF-Α) to evaluate the role of TNF-Α in the pathogenesis of below-level pain. Results We found activation of microglia and increased expression of TNF-Α below the level of the lesion in the lumbar spinal cord after T13 lateral hemisection that correlated with emergence of mechanical allodynia in the hind limbs of rats. Lumbar TNF-Α had an apparent molecular weight of 27kDa, consistent with the full-length transmembrane form of the protein (mTNF-Α). Expression of the p55 TNF soluble receptor (sTNFRs) by HSV-mediated gene transfer resulted in reduced pain behavior and a decreased number of ED1-positive cells, as well as decreased phosphorylation of the p38 MAP kinase (p-p38) and diminished expression of mTNF-Α in the dorsal horn. Interpretation These results suggest that expression of mTNF-Α after injury is related to development of pain, and that reverse signaling through mTNF-Α by sTNFR at that level reduces cellular markers of inflammatory response and pain-related behavior. Ann Neurol 2006;59:843–851Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/50655/1/20855_ftp.pd

Social event detection with retweeting behavior correlation

Event detection over microblogs has attracted great research interest due to its wide application in crisis management and decision making etc. In natural disasters, complex events are reported in real time on social media sites, but these reports are invisible to crisis coordinators. Detecting these crisis events helps watchers to make right decisions rapidly, reducing injuries, deaths and economic loss. In sporting activities, detecting events helps audiences make better and more timely game viewing plans. However, existing event detection techniques are not effective at handling complex social events that evolve over time. In this paper, we propose an event detection method that takes advantage of retweeting behavior for handling the events evolution. Specifically, we first propose a topic model called RL-LDA to capture the social media information over hashtag, location, textual and retweeting behavior. Using RL-LDA, a complex event can be well handled by exploring the correlation between retweeting behavior and the event. Then to maintain the RL-LDA in a dynamic environment, we propose a dynamic update algorithm, which incrementally updates events over real time streams. Experiments over real-world datasets show that RL-LDA detects the temporal evolution of complex events effectively and efficiently

CA1-projecting subiculum neurons facilitate object-place learning.

Recent anatomical evidence suggests a functionally significant back-projection pathway from the subiculum to the CA1. Here we show that the afferent circuitry of CA1-projecting subicular neurons is biased by inputs from CA1 inhibitory neurons and the visual cortex, but lacks input from the entorhinal cortex. Efferents of the CA1-projecting subiculum neurons also target the perirhinal cortex, an area strongly implicated in object-place learning. We identify a critical role for CA1-projecting subicular neurons in object-location learning and memory, and show that this projection modulates place-specific activity of CA1 neurons and their responses to displaced objects. Together, these experiments reveal a novel pathway by which cortical inputs, particularly those from the visual cortex, reach the hippocampal output region CA1. Our findings also implicate this circuitry in the formation of complex spatial representations and learning of object-place associations

A Forecast Model Based on The BP Neural Network Used in Refinery's Steel Equipment's Corrosion

The forecasting of the corrosion of refinery's steel equipments shows great importance in preventing the accident. Considering the numerous factors affecting the corroding of refinery's steel equipments, which are uneasily predictable and with complex relationships, this paper proposed a new technology based on the BP neural network technology used in forecasting of the corrosion of refinery's steel equipments. A new model is also built and implemented in this paper. Finally, the experimental results prove the feasibility of the new model and the forecasted results by this new model fixes well with the sample data set

Human-centered design and evaluation of AI-empowered clinical decision support systems: a systematic review

IntroductionArtificial intelligence (AI) technologies are increasingly applied to empower clinical decision support systems (CDSS), providing patient-specific recommendations to improve clinical work. Equally important to technical advancement is human, social, and contextual factors that impact the successful implementation and user adoption of AI-empowered CDSS (AI-CDSS). With the growing interest in human-centered design and evaluation of such tools, it is critical to synthesize the knowledge and experiences reported in prior work and shed light on future work.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a systematic review to gain an in-depth understanding of how AI-empowered CDSS was used, designed, and evaluated, and how clinician users perceived such systems. We performed literature search in five databases for articles published between the years 2011 and 2022. A total of 19874 articles were retrieved and screened, with 20 articles included for in-depth analysis.ResultsThe reviewed studies assessed different aspects of AI-CDSS, including effectiveness (e.g., improved patient evaluation and work efficiency), user needs (e.g., informational and technological needs), user experience (e.g., satisfaction, trust, usability, workload, and understandability), and other dimensions (e.g., the impact of AI-CDSS on workflow and patient-provider relationship). Despite the promising nature of AI-CDSS, our findings highlighted six major challenges of implementing such systems, including technical limitation, workflow misalignment, attitudinal barriers, informational barriers, usability issues, and environmental barriers. These sociotechnical challenges prevent the effective use of AI-based CDSS interventions in clinical settings.DiscussionOur study highlights the paucity of studies examining the user needs, perceptions, and experiences of AI-CDSS. Based on the findings, we discuss design implications and future research directions

Avian Influenza (H5N1) Virus in Waterfowl and Chickens, Central China

In 2004, 3 and 4 strains of avian influenza virus (subtype H5N1) were isolated from waterfowl and chickens, respectively, in central People’s Republic of China. Viral replication and pathogenicity were evaluated in chickens, quails, pigeons, and mice. We analyzed the sequences of the hemagglutinin and neuraminidase genes of the isolates and found broad diversity among them

Acetylome profiling of Vibrio alginolyticus reveals its role in bacterial virulence

It is well known that lysine acetylation (Kace) modification is a common post-translational modification (PTM) that plays an important role in multiple biological and pathological functions in bacteria. However, few studies have focused on lysine acetylation modification in aquatic pathogens to date. In this study, the acetylome profiling of fish pathogen, Vibrio alginolyticus was investigated by combining affinity enrichment with LC MS/MS. A total of 2883 acetylation modification sites on 1178 proteins in this pathogen were identified. The Kace modification of several selected proteins were further validated by Co-immunocoprecipitation combined with Western blotting. Bioinformatics analysis showed that seven conserved motifs can be enriched among Kace peptides, and many of them were significantly enriched in metabolic processes such as biosynthesis of secondary metabolites, microbial metabolism in diverse environments, and biosynthesis of amino acids, which was similar to data previously published for V. parahaemolyticus. Moreover, we found at least 102 acetylation modified proteins predicted as virulence factors, which indicate the important role of PTM on bacterial virulence. In general, our results provide a promising starting point for further investigations of the biological role of lysine acetylation on bacterial virulence in V. alginolyticus