Exploring Web-based Visual Interfaces for Searching Research Articles on Digital Library Systems

Previous studies that present information archived in digital libraries have used either document meta-data or document content. The current search mechanisms commonly return text-based results that were compiled from the meta-data without reflecting the underlying content. Visual analytics is a possible solution for improving searches by presenting a large amount of information, including document content alongside meta-data, in a limited screen space. This paper introduces a multi-tiered visual interface for searching research articles stored in Digital Library systems. The goals of this system are to allow users to find research papers about their interests in a large work space, to see how document content relates to a search terms, and to refine their search queries using document content. The current, under development pilot system successfully presents graphical illustrations of search results produced from both meta-data and underlying content in an intuitive visual interface that will assist user’s search activities. With minor modification, the proposed system can be applied to a variety of other text-based data repositories

YacG from Escherichia coli is a specific endogenous inhibitor of DNA gyrase

We assign a function for a small protein, YacG encoded by Escherichia coli genome. The NMR structure of YacG shows the presence of an unusual zinc-finger motif. YacG was predicted to be a part of DNA gyrase interactome based on protein–protein interaction network. We demonstrate that YacG inhibits all the catalytic activities of DNA gyrase by preventing its DNA binding. Topoisomerase I and IV activities remain unaltered in the presence of YacG and its action appears to be restricted only to DNA gyrase. The inhibition of the enzyme activity is due to the binding of YacG to carboxyl terminal domain of GyrB. Overexpression of YacG results in growth inhibition and alteration in DNA topology due to uncontrolled inhibition of gyrase

YacG from Escherichia coli is a specific endogenous inhibitor of DNA gyrase

We assign a function for a small protein, YacG encoded by Escherichia coli genome. The NMR structure of YacG shows the presence of an unusual zinc-finger motif. YacG was predicted to be a part of DNA gyrase interactome based on protein–protein interaction network. We demonstrate that YacG inhibits all the catalytic activities of DNA gyrase by preventing its DNA binding. Topoisomerase I and IV activities remain unaltered in the presence of YacG and its action appears to be restricted only to DNA gyrase. The inhibition of the enzyme activity is due to the binding of YacG to carboxyl terminal domain of GyrB. Overexpression of YacG results in growth inhibition and alteration in DNA topology due to uncontrolled inhibition of gyrase

Multi-year school-based implementation and student outcomes of an evidence-based risk reduction intervention

Background Intervention effects observed in efficacy trials are rarely replicated when the interventions are broadly disseminated, underscoring the need for more information about factors influencing real-life implementation and program impact. Using data from the ongoing national implementation of an evidence-based HIV prevention program [Focus on Youth in The Caribbean (FOYC)] in The Bahamas, this study examines factors influencing teachers’ patterns of implementation, the impact of teachers’ initial implementation of FOYC, and subsequent delivery of the booster sessions on students’ outcomes. Methods Data were collected from the 80 government elementary and 34 middle schools between 2011 and 2014, involving 208 grade 6, 75 grade 7, and 58 grade 8 teachers and 4411 students initially in grade 6 and followed for 3 years. Student outcomes include HIV/AIDS knowledge, reproductive health skills, self-efficacy, and intention to use protection. Data from teachers includes implementation and modification of the curriculum, attitudes towards the prevention program, comfort level with the curriculum, and attendance at training workshops. Structural equation modeling and mixed-effect modeling analyses were applied to examine the impact of teachers’ implementation. Results Teachers’ attitudes towards and comfort with the intervention curriculum, and attendance at the curriculum training workshop had a direct effect on teachers’ patterns of implementation, which had a direct effect on student outcomes. Teachers’ attitudes had a direct positive effect on student outcomes. Teachers’ training in interactive teaching methods and longer duration as teachers were positively associated with teachers’ comfort with the curriculum. High-quality implementation in grade 6 was significantly related to student outcomes in grades 6 and 7 post-implementation. Level of implementation of the booster sessions in grades 7 and 8 were likewise significantly related to subsequent student outcomes in both grades. Conclusions High-quality initial implementation of a prevention program is significantly related to better program outcomes. Poor subsequent delivery of booster sessions can undermine the positive effects from the initial implementation while strong subsequent delivery of booster sessions can partially overcome poor initial implementation

Potential prognostic marker ubiquitin carboxyl-terminal hydrolase-L1 does not predict patient survival in non-small cell lung carcinoma

<p>Abstract</p> <p>Background</p> <p>Ubiquitin Carboxyl-Terminal Hydrolase-L1 (UCH-L1) is a deubiquitinating enzyme that is highly expressed throughout the central and peripheral nervous system and in cells of the diffuse neuroendocrine system. Aberrant function of UCH-L1 has been associated with neurological disorders such as Parkinson's disease and Alzheimer's disease. Moreover, UCH-L1 exhibits a variable expression pattern in cancer, acting either as a tumour suppressor or promoter, depending on the type of cancer. In non-small cell lung carcinoma primary tumour samples, UCH-L1 is highly expressed and is associated with an advanced tumour stage. This suggests UCH-L1 may be involved in oncogenic transformation and tumour invasion in NSCLC. However, the functional significance of UCH-L1 in the progression of NSCLC is unclear. The aim of this study was to investigate the role of UCH-L1 using NSCLC cell line models and to determine if it is clinically relevant as a prognostic marker for advanced stage disease.</p> <p>Methods</p> <p>UCH-L1 expression in NSCLC cell lines H838 and H157 was modulated by siRNA-knockdown, and the phenotypic changes were assessed by flow cytometry, haematoxylin & eosin (H&E) staining and poly (ADP-ribose) polymerase (PARP) cleavage. Metastatic potential was measured by the presence of phosphorylated myosin light chain (MLC2). Tumour microarrays were examined immunohistochemically for UCH-L1 expression. Kaplan-Meier curves were generated using UCH-L1 expression levels and patient survival data extracted from Gene Expression Omnibus data files.</p> <p>Results</p> <p>Expression of UCH-L1 was decreased by siRNA in both cell lines, resulting in increased cell death in H838 adenocarcinoma cells but not in the H157 squamous cell line. However, metastatic potential was reduced in H157 cells. Immunohistochemical staining of UCH-L1 in patient tumours confirmed it was preferentially expressed in squamous cell carcinoma rather than adenocarcinoma. However the Kaplan-Meier curves generated showed no correlation between UCH-L1 expression levels and patient outcome.</p> <p>Conclusions</p> <p>Although UCH-L1 appears to be involved in carcinogenic processes in NSCLC cell lines, the absence of correlation with patient survival indicates that caution is required in the use of UCH-L1 as a potential prognostic marker for advanced stage and metastasis in lung carcinoma.</p

Informal dissemination scenarios and the effectiveness of evacuation warning dissemination of households - A simulation study

Timely warning of the public during large scale emergencies is essential to ensure safety and save lives. This ongoing study proposes an agent-based simulation model to simulate the warning message dissemination among the public considering both official channels and unofficial channels The proposed model was developed in NetLogo software for a hypothetical area, and requires input parameters such as effectiveness of each official source (%), estimated time to begin informing others, estimated time to inform others and estimated percentage of people (who do not relay the message). This paper demonstrates a means of factoring the behaviour of the public as informants into estimating the effectiveness of warning dissemination during large scale emergencies. The model provides a tool for the practitioner to test the potential impact of the informal channels on the overall warning time and sensitivity of the modelling parameters. The tool would help the practitioners to persuade evacuees to disseminate the warning message informing others similar to the ’Run to thy neighbour campaign conducted by the Red cross

A means of assessing deep learning-based detection of ICOS protein expression in colon cancer.

Biomarkers identify patient response to therapy. The potential immune‐checkpoint bi-omarker, Inducible T‐cell COStimulator (ICOS), expressed on regulating T‐cell activation and involved in adaptive immune responses, is of great interest. We have previously shown that open-source software for digital pathology image analysis can be used to detect and quantify ICOS using cell detection algorithms based on traditional image processing techniques. Currently, artificial intelligence (AI) based on deep learning methods is significantly impacting the domain of digital pa-thology, including the quantification of biomarkers. In this study, we propose a general AI‐based workflow for applying deep learning to the problem of cell segmentation/detection in IHC slides as a basis for quantifying nuclear staining biomarkers, such as ICOS. It consists of two main parts: a simplified but robust annotation process, and cell segmentation/detection models. This results in an optimised annotation process with a new user‐friendly tool that can interact with1 other open‐source software and assists pathologists and scientists in creating and exporting data for deep learning. We present a set of architectures for cell‐based segmentation/detection to quantify and analyse the trade‐offs between them, proving to be more accurate and less time consuming than traditional methods. This approach can identify the best tool to deliver the prognostic significance of ICOS protein expression

ICOSeg: real-time ICOS protein expression segmentation from immunohistochemistry slides using a lightweight conv-transformer network.

In this article, we propose ICOSeg, a lightweight deep learning model that accurately segments the immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS) protein in colon cancer from immunohistochemistry (IHC) slide patches. The proposed model relies on the MobileViT network that includes two main components: convolutional neural network (CNN) layers for extracting spatial features; and a transformer block for capturing a global feature representation from IHC patch images. The ICOSeg uses an encoder and decoder sub-network. The encoder extracts the positive cell's salient features (i.e., shape, texture, intensity, and margin), and the decoder reconstructs important features into segmentation maps. To improve the model generalization capabilities, we adopted a channel attention mechanism that added to the bottleneck of the encoder layer. This approach highlighted the most relevant cell structures by discriminating between the targeted cell and background tissues. We performed extensive experiments on our in-house dataset. The experimental results confirm that the proposed model achieves more significant results against state-of-the-art methods, together with an 8× reduction in parameters