Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Extractive single document summarization using binary differential evolution: Optimization of different sentence quality measures.

With the increase in the amount of text information in different real-life applications, automatic text-summarization systems become more predominant in extracting relevant information. In the current study, we formulated the problem of extractive text-summarization as a binary optimization problem, and multi-objective binary differential evolution (DE) based optimization strategy is employed to solve this. The solutions of DE encode a possible subset of sentences to be present in the summary which is then evaluated based on some statistical features (objective functions) namely, the position of the sentence in the document, the similarity of a sentence with the title, length of the sentence, cohesion, readability, and coverage. These objective functions, measuring different aspects of summary, are optimized simultaneously using the search capability of DE. Some newly designed self-organizing map (SOM) based genetic operators are incorporated in the optimization process to improve the convergence. SOM generates a mating pool containing solutions and their neighborhoods. This mating pool takes part in the genetic operation (crossover and mutation) to create new solutions. To measure the similarity or dissimilarity between sentences, different existing measures like normalized Google distance, word mover distance, and cosine similarity are explored. For the purpose of evaluation, two standard summarization datasets namely, DUC2001, and DUC2002 are utilized, and the obtained results are compared with various supervised, unsupervised and optimization strategy based existing summarization techniques using ROUGE measures. Results illustrate the superiority of our approach in terms of convergence rate and ROUGE scores as compared to state-of-the-art methods. We have obtained 45% and 5% improvements over two recent state-of-the-art methods considering ROUGE-2 and ROUGE-1 scores, respectively, for the DUC2001 dataset. While for the DUC2002 dataset, improvements obtained by our approach are 20% and 5%, considering ROUGE-2 and ROUGE-1 scores, respectively. In addition to these standard datasets, CNN news dataset is also utilized to evaluate the efficacy of our proposed approach. It was also shown that the best performance not only depends on the objective functions used but also on the correct choice of similarity/dissimilarity measure between sentences

A novel memristor-based hardware security primitive

Memristor is an exciting new addition to the repertoire of fundamental circuit elements. Alternatives to many security protocols originally employing traditional mathematical cryptography involve novel hardware security primitives, such as Physically Unclonable Functions (PUFs). In this article, we propose a novel hybrid memristor-CMOS PUF circuit and demonstrate its suitability through extensive simulations of environmental and process variation effects. The proposed PUF circuit has substantially less hardware overhead than previously proposed memristor-based PUF circuits while being inherently resistant to machine learning-based modeling attacks because of challenge-dependent delays of the memristor stages. The proposed PUF can be conveniently used in many security applications and protocols based on hardware-intrinsic security.</jats:p

Functional characterization of stromal osteopontin in melanoma progression and metastasis.

BACKGROUND: Recent studies demonstrated that not only tumor derived- but stroma derived factors play crucial role in cancer development. Osteopontin (OPN) is a secreted non-collagenous, sialic acid rich, chemokine-like phosphoglycoprotein that facilitates cell-matrix interactions and promotes tumor progression. Elevated level of OPN has been shown in melanoma patient and predicted as a prognostic marker. Recent reports have indicated that stroma-derived OPN are involved in regulating stem cell microenvironment and pre-neoplastic cell growth. However, the function of stroma derived OPN in regulation of side population (SP) enrichment leading to melanoma growth, angiogenesis and metastasis is not well studied and yet to be the focus of intense investigation. METHODOLOGY/PRINCIPAL FINDINGS: In this study, using melanoma model, in wild type and OPN knockout mice, we have demonstrated that absence of host OPN effectively curbs melanoma growth, angiogenesis and metastasis. Melanoma cells isolated from tumor of OPN wild type (OPN(+/+)) mice exhibited more tumorigenic feature as compared to the parental cell line or cells isolated from the tumors of OPN KO (OPN(-/-)) mice. Furthermore, host OPN induces VEGF, ABCG2 and ERK1/2 expression and activation in B16-WT cells. We report for the first time that stroma derived OPN regulates SP phenotype in murine melanoma cells. Moreover, loss in and gain of function studies demonstrated that stroma-derived OPN regulates SP phenotype specifically through ERK2 activation. CONCLUSIONS: This study establish at least in part, the molecular mechanism underlying the role of host OPN in melanoma growth and angiogenesis, and better understanding of host OPN-tumor interaction may assist the advancement of novel therapeutic strategy for the management of malignant melanoma

Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment

The aim of this paper was to develop a computer assisted tissue classification (granulation, necrotic, and slough) scheme for chronic wound (CW) evaluation using medical image processing and statistical machine learning techniques. The red-green-blue (RGB) wound images grabbed by normal digital camera were first transformed into HSI (hue, saturation, and intensity) color space and subsequently the “S” component of HSI color channels was selected as it provided higher contrast. Wound areas from 6 different types of CW were segmented from whole images using fuzzy divergence based thresholding by minimizing edge ambiguity. A set of color and textural features describing granulation, necrotic, and slough tissues in the segmented wound area were extracted using various mathematical techniques. Finally, statistical learning algorithms, namely, Bayesian classification and support vector machine (SVM), were trained and tested for wound tissue classification in different CW images. The performance of the wound area segmentation protocol was further validated by ground truth images labeled by clinical experts. It was observed that SVM with 3rd order polynomial kernel provided the highest accuracies, that is, 86.94%, 90.47%, and 75.53%, for classifying granulation, slough, and necrotic tissues, respectively. The proposed automated tissue classification technique achieved the highest overall accuracy, that is, 87.61%, with highest kappa statistic value (0.793)

ERK2 but not ERK1 regulates SP phenotype in B16F10 cells in response to stromal OPN.

<p>(<b>A</b>) Western blot analysis of ABCG2 expression in the lysates of B16F10, B16-KO and B16-WT cells treated with either wortmannin or U0126. (<b>B</b>) <i>Panels I and II</i>, B16F10 cells, stably transfected with either ERK1-dn or ERK2-dn were treated with conditioned media of B16-WT cells, stained with Hoechst and SP phenotype was analyzed using flow cytometer. (<b>C</b>) <i>Panels I and II</i>, B16F10 cells stably transfected with either ERK1-wt or ERK2-wt were treated with CM collected from B16-WT cells, stained with Hoechst and SP phenotype was analyzed. <i>Inset:</i> control setup for SP analysis treated with reserpine for respective treatment group. (<b>D</b>) Analysis of ABCG2 expression from lysates of B16F10 cells stably transfected with ERK1-dn, ERK2-dn, ERK1-wt or ERK2-wt in presence of CM of B16-WT. Actin was used as loading control. All the figures are representative of three independent experiments exhibiting similar results.</p

Stromal OPN regulates SP phenotype in B16F10 through ERK pathway.

<p>(<b><i>A</i></b>) B16-WT cells were stained with Hoechst and SP analysis was performed with flow cytometer. (<b><i>B</i></b>) B16-WT cells were treated with wortmannin for 24 h, stained with Hoechst and analyzed for SP phenotype. (<b><i>C</i></b>) B16-WT cells were treated with U0126 and SP analysis was performed. (<b><i>D</i></b>) Flow cytometeric analysis of SP phenotype in B16F10 cells. (<b><i>E</i></b>) B16F10 cells were treated with conditioned media of B16-WT cells for 24 h and SP analysis was performed. (<b><i>F</i></b>) B16F10 cells were pre-treated with conditioned media of B16-WT cells and then treated with wortmannin for 24 h and SP analysis were performed. (<b><i>G</i></b>) B16F10 cells were pre-treated with conditioned media of B16-WT cells and then treated with U0126 for 24 h and SP analysis was performed. <b><i>Inset:</i></b> control setup for SP analysis treated with reserpine for respective panels. All the panels are representative of three independent repeats exhibiting similar results.</p