Dr Saiful Islam

Twitter has become a great source of user-generated information about events. Very often people report causal relationships between events in their tweets. Automatic detection of causality information in these events might play an important role in prescriptive event analytics. Existing approaches include both rule-based and data-driven supervised methods. However, it is challenging to identify event causality accurately using linguistic rules due to the unstructured nature and grammatical incorrectness of social media short text such as tweets. Also, it is difficult to develop a data-driven supervised method for event causality detection in tweets due to insufficient contextual information. This paper proposes a novel event context word extension technique based on background knowledge. To demonstrate the effectiveness of our event context word extension technique, we develop a feed-forward neural network based approach to detect event causality from tweets. Extensive experiments demonstrate the superiority of our approach.

EGR1 (early growth response 1) is dysregulated in many cancers and exhibits both tumor suppressor and promoter activities, making it an appealing target for cancer therapy. Here, we used a systematic multi-omics analysis to review the expression of EGR1 and its role in regulating clinical outcomes in breast cancer (BC). EGR1 expression, its promoter methylation, and protein expression pattern were assessed using various publicly available tools. COSMIC-based somatic mutations and cBioPortal-based copy number alterations were analyzed, and the prognostic roles of EGR1 in BC were determined using Prognoscan and Kaplan-Meier Plotter. We also used bc-GenEx-Miner to investigate the EGR1 co-expression profile. EGR1 was more often downregulated in BC tissues than in normal breast tissue, and its knockdown was positively correlated with poor survival. Low EGR1 expression levels were also associated with increased risk of ER+, PR+, and HER2-BCs. High positive correlations were observed among EGR1, DUSP1, FOS, FOSB, CYR61, and JUN mRNA expression in BC tissue. This systematic review suggested that EGR1 expression may serve as a prognostic marker for BC patients and that clinicopathological parameters influence its prognostic utility. In addition to EGR1, DUSP1, FOS, FOSB, CYR61, and JUN can jointly be considered prognostic indicators for BC. [BMB Reports 2021; 54(10): 497-504]

In today's dynamic ICT environments, the ability to control users' access to information resources and services has become ever important. On the one hand, it should provide flexibility to adapt to the users' changing needs, while on the other hand, it should not be compromised. The user is often faced with different contexts and environments that may change the user's information needs. To allow for this, it is essential to incorporate the dynamically changing context information into the access control policies to reflect different contexts and environments through the use of a new context-aware access control (CAAC) approach with both dynamic associations of user-role and role-permission capabilities. Our proposed CAAC framework differs from the existing access control frameworks in that it supports context-sensitive access control to information resources and dynamically re-evaluates the access control decisions when there are dynamic changes to the context. It uses the dynamic context information to specify the user-role and role-permission assignment policies. We first present a formal policy model for our framework, specifying CAAC policies. Using this model, we then introduce a policy ontology for modeling CAAC policies and a policy enforcement architecture which supports access to resources according to the dynamically changing context information. In addition, we demonstrate the feasibility of our framework by considering (i) the completeness, correctness and consistency of the ontology concepts through application to healthcare scenarios and (ii) the performance and usability testing of the framework when using desktop and mobile-based prototypes.

Recent development and advancement of information and communication technologies facilitate people in different dimensions of life. Most importantly, in the healthcare industry, this has become more and more involved with the information and communication technology-based services. One of the most important services is monitoring of remote patients, that enables the healthcare providers to observe, diagnose and prescribe the patients without being physically present. The advantage of miniaturization of sensor technologies gives the flexibility of installing in, on or off the body of patients, which is capable of forwarding physiological data wirelessly to remote servers. Such technology is named as Wireless Body Area Network (WBAN). In this paper, WBAN architecture, communication technologies for WBAN, challenges and different aspects of WBAN are illustrated. This paper also describes the architectural limitations of existing WBAN communication frameworks. blueFurthermore, implementation requirements are presented based on IEEE 802.15.6 standard. Finally, as a source of motivation towards future development of research incorporating Software Defined Networking (SDN), Energy Harvesting (EH) and Blockchain technology into WBAN are also provided.

Fast reachability detection is one of the key problems in graph applications. Most of the existing works focus on creating an index and answering reachability based on that index. For these approaches, the index construction time and index size can become a concern for large graphs. More recently query-preserving graph compression has been proposed, and searching reachability over the compressed graph has been shown to be able to significantly improve query performance as well as reducing the index size. In this paper, we introduce a multilevel compression scheme for DAGs, which builds on existing compression schemes, but can further reduce the graph size for many real-world graphs. We propose an algorithm to answer reachability queries using the compressed graph. Extensive experiments with four existing state-of-the-art reachability algorithms and 12 real-world datasets demonstrate that our approach outperforms the existing methods. Experiments with synthetic datasets ensure the scalability of this approach. We also provide a discussion on possible compression for k-reachability.