bluuebunny/crossref_metadata_2025 huggingface dataset:
Communication Between Health Workers and Ethnic Minorities in Vietnam
Authors: McKinn Shannon , Linh Duong Thuy , Foster Kirsty , McCaffery Kirsten
Published: October 2017

Background:

Vietnam has made notable progress in reducing maternal mortality rates during the past 2 decades, but this overall improvement conceals regional and ethnic inequalities. Ethnic minorities in Vietnam experience high rates of poverty and mortality, and they face communication and cultural barriers when accessing health services. Poor communication with health professionals combined with limited health literacy is concerning, particularly in the maternal health context, and may exacerbate existing inequalities.

Objective:

This study explores primary health care professionals' perceptions of the quality of their communication with ethnic minority women during and after pregnancy.

Methods:

Semi-structured interviews were conducted with 22 primary health care professionals in Dien Bien province. A thematic analysis was performed using a framework analysis method.

Key Results:

Health professionals had mostly positive perceptions about their communication with ethnic minority women. However, they generally perceived the effectiveness of their communication as being based on women's individual capacities to understand health information (both the language used and the content) and factors such as ethnic and cultural differences, rather than reflecting on the suitability of information and materials or on their own communication skills. This placed much of the burden of communication and understanding health information on ethnic minority women and their families.

Conclusions:

Health professionals perceived of communication as being mainly a one-way street for the provision of health information, and rarely acknowledged the interactive nature of communication. Patient-professional communication and health literacy in Dien Bien province may be improved through the introduction of patient-centered communication skills training that applies health literacy approaches at the health professional level.

[
Health Literacy Research and Practice
. 2017;1(4):e163–e172.]

Plain Language Summary:

We looked at how primary health care professionals in Vietnam perceive their communication with ethnic minority women, particularly about pregnancy. Health professionals generally perceived the quality and effectiveness of their communication as being based on ethnic minority women's individual capacities and limitations. Applying a health literacy approach to communication skills training could improve patient-professional communication and health literacy.
Health Literacy Is an Independent Predictor of Cancer Patients' Hospitalizations
Authors: Cartwright Laura A. , Dumenci Levent , Cassel J. Brian , Thomson Maria D. , Matsuyama Robin K.
Published: October 2017

Background:

Lower levels of health literacy predict higher hospitalization rates. Hospitalization is expensive and many admissions are potentially avoidable. Research examining the relationship between health literacy and health outcomes for cancer patients is limited, and no published studies to date have focused on hospitalization and health literacy in cancer patients.

Objective:

This study examined the ability of health literacy to predict hospitalization of cancer patients.

Methods:

This secondary data analysis investigated the relationship between health literacy and hospitalization rates of adult cancer patients in the first 5 years after their diagnosis. The sample included a diverse selection of cancer diagnoses and all stages of cancer were represented. Health literacy was assessed using the Cancer Health Literacy Test 30 (CHLT-30), a psychometrically sound measure of health literacy validated in a cancer patient population. Health literacy's ability to predict the number of times hospitalized, total days spent hospitalized, and number of 30-day readmissions was examined using multivariate negative binomial multiple regression to control for the outcomes and potentially confounding variables.

Key Results:

When controlling for potentially confounding variables, a negative relationship was found between health literacy and the number of inpatient hospitalizations (β = −0.041,
p
= .009) and the total number of days spent hospitalized (β = −0.028,
p
= .023) independently, whereas the relationship between health literacy and the number of 30-day readmissions failed to reach significance (β = −0.002,
p
= .903).

Conclusions:

Patients with lower health literacy need additional assistance to avoid unplanned hospitalizations.

[
Health Literacy Research and Practice
. 2017;1(4):e153–e162.]

Plain Language Summary:

Results from this study found that as cancer patients' health literacy scores decreased, the number of times hospitalized and the total number of days spent in the hospital increased. This relationship was significant when controlling for diagnosis, stage at diagnosis, receipt of chemotherapy, number of comorbidities, death, education, and race. Cancer patients with low health literacy are at a higher risk of being hospitalized.
Use of Recommended Communication Techniques by Diabetes Educators
Authors: Howe Carol J. , Walker Danielle , Watts Jordan
Published: October 2017

Background:

Diabetes educators are challenged to teach diabetes self-management to patients, ensuring comprehension. Effectiveness with patients may be dependent on the communication skills of the diabetes educator.

Objective:

This study sought to determine diabetes educators' use of and perceived effectiveness of recommended communication techniques with patients to teach diabetes self-management and to determine differences in communication by educator characteristics.

Methods:

In this cross-sectional study, a convenience sample of 522 diabetes educators, comprised mostly of nurses, dieticians, and pharmacists, completed the American Medical Association (AMA) Communication Techniques Survey at a national conference. The AMA survey assessed diabetes educators' self-reported use of and perceived effectiveness of 14 communication techniques. Internal consistency for items reporting frequency of communication techniques was α = 0.83 and for items reporting perceived effectiveness was α = 0.87.

Key Results:

Simple language, written patient education materials, and using Teach-Back were the most frequently reported techniques. Follow-up phone calls and drawing pictures were the least reported. Educators of Hispanic ethnicity used significantly more communication techniques than Caucasian respondents (
p
< .05). Educators with more than 16 years in practice and those who provided more than 16 hours of diabetes education per week used significantly more techniques than those with less experience (
p
< .01). Nurses used significantly more techniques than dieticians (
p
< .01) and those who had health literacy or communication as part of their schooling used more techniques than those who did not (
p
< .05). No significant difference on the routine use of communication techniques was found by education level or diabetes educator certification status.

Conclusions:

The findings provide a baseline assessment of diabetes educator communication practice. Inclusion of health literacy and communication in health professional school curricula appears to support competency, suggesting opportunities for inter-professional health literacy and communication education and research.

[
Health Literacy Research and Practice
. 2017;1(4):e145–e152.]

Plain Language Summary:

We did a survey asking diabetes educators how they communicate with their patients with diabetes. They mostly used simple language, gave out pamphlets, and used Teach-Back. They did not draw pictures to help explain things. Diabetes educators who were Hispanic, nurses, and had health literacy training in school used more communication techniques. Health literacy and communication should be included in training of health providers.
Fostering Innovation and Creativity Through Systemic Design
Authors: Coelho Denis A. , Carrola Tuiago E. P. , Couvinhas Ana F.
Published: October 2017

Based on empirical studies carrying a common thread of design creativity as well as systemic analysis, interdisciplinarity's fostering of innovation is exemplified. The studies illustrate contexts for systems perspectives in design. A lot of transformation in socio-economic systems is taking place, individuals have a choice to either react to it as it happens or attempt to take the lead on change and position themselves in the frontline or ahead of the fundamental changes that are bound to occur. Designers are well versed in the knowledge and skills necessary to lead this transformational process, requiring forward looking, focusing on problems, working across disciplines, participating in teams and leading by example and inspiration, while adopting a systems perspective and focusing on people. Systemic analysis triggers design work, through the development of solutions, as illustrated in the two cases. One concerns sustainable solutions for water management in a community. The other focuses on the production system for a Portuguese semi-artisanal certified cheese.
Adapting to New Labor Market Characteristics
Authors: Buchanan William
Published: October 2017

In this chapter, the author describes the education for the professions in the nineteenth century, with particular emphasis on the extent to which professional education relied on the apprenticeship model rather than on formal, university-based education. The author describes how such non-standardized education was eventually brought under control after the establishment of professional associations which sought to standardize such education. With the establishment of such standards, the education of professions was eventually brought into a higher education setting where disciplinary education programs co-existed within a standardized education environment. This coexistence or colocation enabled professional education efforts to begin interacting with each other and gave rise to interdisciplinary, multidisciplinary, and transdisciplinary efforts in the preparation of professionals for the workforce. The author reports on the variety of interdisciplinary educational programs that have emerged in higher education and demonstrates the availability of professional positions that exist in the work force for new graduates with interdisciplinary and multidisciplinary educational preparation. Routes to attaining an interdisciplinary education are discussed, including student-designed interdisciplinary educational programs as well as predesigned interdisciplinary programs in which students may enroll. The role of higher education program directors and student advisors is considered as well as their importance in enabling students to transition into interdisciplinary careers. The concepts of problem passing and problem solution are described as a means of enabling various disciplines to work together (particularly in localized settings) to generate new interdisciplinary solutions to problems, particularly in the area of the applied sciences. Suggestions for further reading are provided.
Is Interdisciplinary Collaboration in Academia an Elusive Dream?
Authors: Lillard Linda L.
Published: October 2017

A description of how interdisciplinary collaboration can take place is presented to frame this chapter on the risks and barriers to interdisciplinary collaboration. Beginning with a working definition of interdisciplinary collaboration, defined as jointly co-authoring a paper, academic project, or grant with somebody from another discipline or jointly creating a program that contains courses from the joint collaborators' disciplines, this chapter reviews recommendations for creating and supporting successful interdisciplinary collaborations. Included are ten simple rules for successful cross-disciplinary collaborations put together by a group of researchers in the sciences, who more often participate in interdisciplinary collaborations than do their counterparts in the humanities and social sciences. The chapter closes with the case of issues with interdisciplinary collaboration in library and information science, an area that is truly interdisciplinary, yet often becomes the object of turf battles with other academic areas. This case provides a true-life look at just how attempts at interdisciplinary collaboration that should work, can easily backfire.
Historical Development of the Interdisciplinary Studies
Authors: AI-Saleem Naifa Eid
Published: October 2017

In a fast-changing world, which depends on science and technology, it is very hard for disciplines to remain isolated and hide away from each other. When disciplines are isolated, students' outcomes and achievements are affected negatively. A graduate will be equipped with multi skills, when disciplines interact with each other instead of just one skill when a particular discipline exists in isolation. To address this problem of graduates, many universities offer “interdisciplinary studies”. This chapter discusses the “interdisciplinary studies” and their history. To discover the history of the “interdisciplinary studies”, the current study aims to explore the definition of the “interdisciplinary studies”, as well as their historical development. This study is a theoretical study designed to trace the history of “interdisciplinary studies”. The study adopted a qualitative research methodology, as the essential purpose of the qualitative methodology is to understand reality. The findings revealed that the literature is full of studies that define “interdisciplinary studies”, and all the studies that related to the “interdisciplinary studies” agreed that the “interdisciplinary studies” involve researchers from different institutes and disciplines to solve a problem that is hard to solve by a single discipline. With respect to historical development of “interdisciplinary studies”, the study found that, as a term, it appeared in the 20th century, but as a practice, it is imbedded in the work of old societies, where several millennia ago, the Mesopotamians, the Egyptians, and the Greeks put interdisciplinary into practice.
Soft Systems Methodology (SSM) as an Interdisciplinary Approach
Authors: Al-Kharousi Rahma , Al-Harrasi Nabhan Harith , Jabur Naeema H. , Bouazza Abdelmajed
Published: October 2017

Soft Systems Methodology (SSM) provides an inquiry process for taking a systemic view of a problem situation, incorporating elements of intervention, social and political analysis, and then understanding of the real-world problem situation. The holistic view embodied by SSM facilitated capture of the relationships, procedures, attitudes, culture and structure of each participating organizations through thematic analysis and developing rich pictures. Furthermore, the use of SSM enabled creation of homogeneous groups of actors and system owners to go through different intervention processes. This chapter aims mainly to introduce SSM as interdisciplinary approach that can be applied in complex situation and deal effectively with different viewpoints about the definition of the problem. In the current research, reflection on the use of SSM in adoption of Web 2.0 applications in Omani academic libraries is reported. It focuses on contributions of SSM in enhancing knowledge and practice of participants and researchers through different stages of SSM. This study approves that SSM is a methodology rather than a method. A set of tools and techniques can be adapted to investigate the problematical situation and deal with complexity and different perspectives of organizational people. In this research, SSM is described by participants as a learning process that not only define the problem but also improve the situation.
Evaluating Maintainability of Open Source Software
Authors: Hanandeh Feras , Saifan Ahmad A. , Akour Mohammed , Al-Hussein Noor Khamis , Shatnawi Khadijah Zayed
Published: October 2017

Maintainability is one of the most important quality attribute that affect the quality of software. There are four factors that affect the maintainability of software which are: analyzability, changeability, stability, and testability. Open source software (OSS) developed by collaborative work done by volunteers through around the world with different management styles. Open source code is updated and modified all the time from the first release. Therefore, there is a need to measure the quality and specifically the maintainability of such code. This paper discusses the maintainability for the three domains of the open source software. The domains are: education, business and game. Moreover, to observe the most effective metrics that directly affects the maintainability of software. Analysis of the results demonstrates that OSS in the education domain is the most maintainable code and cl_stat (number of executable statements) metric has the highest degree of influence on the calculation of maintenance in all three domains.
Time-Varying Dynamic Topic Model
Authors: Han Jun , Huang Yu , Kumar Kuldeep , Bhattacharya Sukanto
Published: October 2017

In this paper the authors build on prior literature to develop an adaptive and time-varying metadata-enabled dynamic topic model (mDTM) and apply it to a large Weibo dataset using an online Gibbs sampler for parameter estimation. Their approach simultaneously captures the maximum number of inherent dynamic features of microblogs thereby setting it apart from other online document mining methods in the extant literature. In summary, the authors' results show a better performance of mDTM in terms of the quality of the mined information compared to prior research and showcases mDTM as a promising tool for the effective mining of microblogs in a rapidly changing global information space.
Using DEMATEL-Based ANP Model to Measure the Successful Factors of E-Commerce
Authors: Wu Chia-Huei , Tsai Sang-Bing
Published: October 2017

Factors affecting online businesses are complicated and intertwined. To stimulate online sales, understanding of key influential factors and causal relationships among the factors is important. This paper introduces a hybrid method, called the DANP method, to expose the driving factors that most influence the online businesses. A hierarchy of fourteen criteria in six dimensions is formed to denote the influence levels and weights of factors. The causal graph shows that “Uncertainty” dimension has the highest degree of direct impact on other dimensions; thus, strong efforts should be made to eliminate the influence of this dimension throughout the online shopping process. In addition, the results also show “perceived price” to be the greatest important criterion among all; thus, it should be treated with top priority to boost the online businesses.
Cervical Fistula Caused by Submandibular Sialolithiasis
Authors: Kusunoki Takeshi , Homma Hirotomo , Kidokoro Yoshinobu , Yanai Aya , Hara Satoshi , Kobayashi Yuko , To Miri , Wada Ryo , Ikeda Katsuhisa
Published: October 2017

In oto-rhino-laryngology, cases of submandibuillar sialolithiasis are common. Submandibular sialoadentis with sialolith may cause severe complications such as deep neck abscess and sepsis. We introduce a rare case of a cervical fistula with abscess caused by submandibular sialolith. The patient had diabetes. We performed drainage of the left submandibular gland that included a Wharton duct stone and abscess by an external skin incision approach. Submandibular sialoadentis due to sialolith would likely progress to neck abscess and the formation of a neck skin fistula; moreover, the condition can be worsen by the coexistence of diabetes. This neck abscess with skin fistula could have caused potentially fatal complications such a carotid artery rupture or sepsis. In such cases the infected source should be carefully removed as soon as possible.
Identification and Investigation of Drosophila Postsynaptic Density Homologs
Authors: Liebl Faith L.W. , Featherstone David E.
Published: October 2017

AMPA receptors are responsible for fast excitatory transmission in the CNS and the trafficking of these receptors has been implicated in LTP and learning and memory. These receptors reside in the postsynaptic density, a network of proteins that links the receptors to downstream signaling components and to the neuronal cytoskeleton. To determine whether the fruit fly, Drosophila melanogaster, possesses a similar array of proteins as are found at the mammalian PSD, we identified Drosophila homologs of 95.8% of mammalian PSD proteins. We investigated, for the first time, the role of one of these PSD proteins, Pod1 in GluR cluster formation at the Drosophila neuromuscular junction and found that mutations in pod1 resulted in a specific loss of A-type receptors at the synapse.
Utility of Computational Methods to Identify the Apoptosis Machinery in Unicellular Eukaryotes
Authors: Durand Pierre Marcel , Coetzer Theresa Louise
Published: October 2017

Apoptosis is the phenotypic result of an active, regulated process of self-destruction. Following various cellular insults, apoptosis has been demonstrated in numerous unicellular eukaryotes, but very little is known about the genes and proteins that initiate and execute this process in this group of organisms. A bioinformatic approach presents an array of powerful methods to direct investigators in the identification of the apoptosis machinery in protozoans. In this review, we discuss some of the available computational methods and illustrate how they may be applied using the identification of a Plasmodium falciparum metacaspase gene as an example.
Effects of Feeding Periods of High Cholesterol and Saturated Fat Diet on Blood Biochemistry and Hydroxyproline Fractions in Rabbits
Authors: Abdelhalim Mohamed A.K. , Siiddiqi N.J. , Alhomida A.S. , Al-Ayed Mohammed S.
Published: October 2017

Hypercholesterolemia and hypertriglyceridemia are considered as important risk factors during the atherosclerotic process. The aim of the present investigation was to study the total cholesterol (TC), low-density lipoprotein cholesterol (LDLC), high density lipoprotein (HDL), triglyceride (TG), platelet levels and hydroxyproline fractions during the pathogenesis of atherosclerosis. For this purpose, twenty five 12-weeks, New Zealand white male rabbits, were purchased, individually caged, and divided into either control group or cholesterol-fed group. The control group (n = 10) was fed 100 g/day of normal diet, ORC-4 (Oriental Yeast Co. Ltd., Tokyo, Japan) for a period of 15 weeks. The cholesterol-fed group (n = 15) was fed a high cholesterol and saturated fat diet of ORC-4 containing 1% cholesterol plus 1% olive oil (100 g/day) for periods of 5 (group 1), 10 (group 2) and 15 (group 3) weeks. Blood sample from each animal was taken at the end of the experimental period for the biochemical analysis. The results of the present study showed that TC, LDLC, TG, HDLC and platelets were significantly (P < 0.01) increased in cholesterol-fed rabbits as compared with control rabbits. The serum hydroxyproline (Hyp) in rabbits belonging to group 1 showed no significant alteration when compared to control group. Group 2 rabbits showed a significant increase of 103% (P < 0.01) and 100% (P < 0.001) in free and protein—bound hydroxyproline fractions respectively when compared to control rabbits. However, there was no significant change in peptide—bound and total serum hydroxyproline levels as compared to the control group (P > 0.05). There was no significant (P > 0.05) decrease of free serum hydroxyproline in group 3 rabbits when compared to control rabbits. On the other hand, group 3 rabbits showed a significant increase in peptide-bound and protein- bound Hyp by 517% (P < 0.05) and 100% (P < 0.01) respectively when compared to control rabbits. However, total serum Hyp in group 3 rabbits showed no significant (P > 0.05) change when compared to control rabbits. These results suggest that feeding rabbits high cholesterol and saturated fat diet for feeding periods of 5, 10 and 15 weeks induced significant change in TC, LDLC, HDL, TG, platelet levels and various Hyp fractions in serum without any significant change in the total Hyp content.
Coarse-Grained Models Reveal Functional Dynamics - I. Elastic Network Models – Theories, Comparisons and Perspectives
Authors: Yang Lee-Wei , Chng Choon-Peng
Published: October 2017

In this review, we summarize the progress on coarse-grained elastic network models (CG-ENMs) in the past decade. Theories were formulated to allow study of conformational dynamics in time/space frames of biological interest. Several highlighted models and their underlined hypotheses are introduced in physical depth. Important ENM offshoots, motivated to reproduce experimental data as well as to address the slow-mode-encoded configurational transitions, are also introduced. With the theoretical developments, computational cost is significantly reduced due to simplified potentials and coarse-grained schemes. Accumulating wealth of data suggest that ENMs agree equally well with experiment in describing equilibrium dynamics despite their distinct potentials and levels of coarse-graining. They however do differ in the slowest motional components that are essential to address large conformational changes of functional significance. The difference stems from the dissimilar curvatures of the harmonic energy wells described for each model. We also provide our views on the predictability of ‘open to close’ (open→close) transitions of biomolecules on the basis of conformational selection theory. Lastly, we address the limitations of the ENM formalism which are partially alleviated by the complementary CG-MD approach, to be introduced in the second paper of this two-part series.
Topological Properties of Co-Occurrence Networks in Published Gene Expression Signatures
Authors: Muller Heiko , Acquati Francesco
Published: October 2017

Meta-analysis of high-throughput gene expression data is often used for the interpretation of proprietary gene expression data sets. We have recently shown that co-occurrence patterns of gene expression in published cancer-related gene expression signatures are reminiscent of several cancer signaling pathways. Indeed, significant co-occurrence of up to ten genes in published gene expression signatures can be exploited to build a co-occurrence network from the sets of co-occurring genes (“co-occurrence modules”). Such co-occurrence network is represented by an undirected graph, where single genes are assigned to vertices and edges indicate that two genes are significantly co-occurring. Thus, graph-cut methods can be used to identify groups of highly interconnected vertices (“network communities”) that correspond to sets of genes that are significantly co-regulated in human cancer. Here, we investigate the topological properties of co-occurrence networks derived from published gene expression signatures and show that co-occurrence networks are characterized by scale-free topology and hierarchical modularity. Furthermore, we report that genes with a “promiscuous” or a “faithful” co-occurrence pattern can be distinguished. This behavior is reminiscent of date and party hubs that have been identified in protein-protein interaction networks.
Article Commentary: Neuroinformatics: From Bioinformatics to Databasing the Brain
Authors: Morse Thomas M.
Published: October 2017

Neuroinformatics seeks to create and maintain web-accessible databases of experimental and computational data, together with innovative software tools, essential for understanding the nervous system in its normal function and in neurological disorders. Neuroinformatics includes traditional bioinformatics of gene and protein sequences in the brain; atlases of brain anatomy and localization of genes and proteins; imaging of brain cells; brain imaging by positron emission tomography (PET), functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magnetoencephalography (MEG) and other methods; many electrophysiological recording methods; and clinical neurological data, among others. Building neuroinformatics databases and tools presents difficult challenges because they span a wide range of spatial scales and types of data stored and analyzed. Traditional bioinformatics, by comparison, focuses primarily on genomic and proteomic data (which of course also presents difficult challenges). Much of bioinformatics analysis focus on sequences (DNA, RNA, and protein molecules), as the type of data that are stored, compared, and sometimes modeled. Bioinformatics is undergoing explosive growth with the addition, for example, of databases that catalog interactions between proteins, of databases that track the evolution of genes, and of systems biology databases which contain models of all aspects of organisms. This commentary briefly reviews neuroinformatics with clarification of its relationship to traditional and modern bioinformatics.
Gene Copy Number Analysis for Family Data Using Semiparametric Copula Model
Authors: Yuan Ao , Chen Guanjie , Zhou Zhong-Cheng , Bonney George , Rotimi Charles
Published: October 2017

Gene copy number changes are common characteristics of many genetic disorders. A new technology, array comparative genomic hybridization (a-CGH), is widely used today to screen for gains and losses in cancers and other genetic diseases with high resolution at the genome level or for specific chromosomal region. Statistical methods for analyzing such a-CGH data have been developed. However, most of the existing methods are for unrelated individual data and the results from them provide explanation for horizontal variations in copy number changes. It is potentially meaningful to develop a statistical method that will allow for the analysis of family data to investigate the vertical kinship effects as well. Here we consider a semiparametric model based on clustering method in which the marginal distributions are estimated nonparametrically, and the familial dependence structure is modeled by copula. The model is illustrated and evaluated using simulated data. Our results show that the proposed method is more robust than the commonly used multivariate normal model. Finally, we demonstrated the utility of our method using a real dataset.
Evidence of Highly Regulated Genes (in-Hubs) in Gene Networks of Saccharomyces Cerevisiae
Authors: Lundström Jesper , Björkegren Johan , Tegnér Jesper
Published: October 2017

Uncovering interactions between genes, gene networks, is important to increase our understanding of intrinsic cellular processes and responses to external stimuli such as drugs. Gene networks can be computationally inferred from repeated measurements of gene expression, using algorithms, which assume that each gene is controlled by only a small number of other proteins. Here, by extending the transcription network with cofactors (defined from protein-protein binding data) as active regulators, we identified the effective gene network, providing evidence of in-hubs in the gene regulatory networks of yeast. Then, using the notion that in-hub genes will be differentially expressed over several experimental conditions, we designed an algorithm, the HubDetector, enabling identification of in-hubs directly from gene expression data. Applying the HubDetector to 488 genome-wide expression profiles from two independent datasets, we identified putative in-hubs overlapping significantly with in-hubs in the effective gene network.
Modeling and Analyzing Gene Co-Expression in Hepatocellular Carcinoma Using Actor-Semiotic Networks and Centrality Signatures
Authors: Fung David C.Y.
Published: October 2017

Primary hepatocellular carcinoma (HCC) is currently the fifth most common malignancy and the third most common cause of cancer mortality worldwide. Because of its high prevalence in developing nations, there have been numerous efforts made in the molecular characterization of primary HCC. However, a better understanding into the pathology of HCC required software-assisted network modeling and analysis. In this paper, the author presented his first attempt in exploring the biological implication of gene co-expression in HCC using actor-semiotic network modeling and analysis. The network was first constructed by integrating inter-actor relationships, e.g. gene co-expression, microRNA-to-gene, and protein interactions, with semiotic relationships, e.g. gene-to-Gene Ontology Process. Topological features that are highly discriminative of the HCC phenotype were identified by visual inspection. Finally, the author devised a graph signature-based analysis method to supplement the network exploration.
Quantitative Proteomic Approach for MicroRNA Target Prediction Based on 18O/16O Labeling
Authors: Ma Xuepo , Zhu Ying , Huang Yufei , Tegeler Tony , Gao Shou-Jiang , Zhang Jianqiu
Published: October 2017

Motivation

Among many large-scale proteomic quantification methods, 18O/16O labeling requires neither specific amino acid in peptides nor label incorporation through several cell cycles, as in metabolic labeling; it does not cause significant elution time shifts between heavy- and light-labeled peptides, and its dynamic range of quantification is larger than that of tandem mass spectrometry-based quantification methods. These properties offer 18O/16O labeling the maximum flexibility in application. However, 18O/16O labeling introduces large quantification variations due to varying labeling efficiency. There lacks a processing pipeline that warrants the reliable identification of differentially expressed proteins (DEPs). This motivates us to develop a quantitative proteomic approach based on 18O/16O labeling and apply it on Kaposi sarcoma-associated herpesvirus (KSHV) microRNA (miR) target prediction. KSHV is a human pathogenic y-herpesvirus strongly associated with the development of B-cell proliferative disorders, including primary effusion lymphoma. Recent studies suggest that miRs have evolved a highly complex network of interactions with the cellular and viral transcriptomes, and relatively few KSHV miR targets have been characterized at the functional level. While the new miR target prediction method, photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP), allows the identification of thousands of miR targets, the link between miRs and their targets still cannot be determined. We propose to apply the developed proteomic approach to establish such links.

Method

We integrate several 18O/16O data processing algorithms that we published recently and identify the messenger RNAs of downregulated proteins as potential targets in KSHV miR-transfected human embryonic kidney 293T cells. Various statistical tests are employed for picking DEPs, and we select the best test by examining the enrichment of PAR-CLIP-reported targets with seed match to the miRs of interest among top ranked DEPs returned by statistical tests. Subsequently, the list of DEPs picked by the selected statistical test is filtered with the criteria that they must have downregulated gene expressions, must have reported as targets by an miR target prediction algorithm SVMcrio, and must have reported as targets by PAR-CLIP.

Result

We test the developed approach in the problem of finding targets of KSHV miR-K1. The RNAs of three DEPs are identified as miR-K1 targets, among which RAB23 and HNRNPU are novel. Results from both Western blotting and Luciferase reporter assays confirm the novel targets. These results show that the developed quantitative approach based on 18O/16O labeling can be combined with genomic, PAR-CLIP, and target prediction algorithms for the confident identification of KSHV miR targets. The developed approach could also be applied in other applications.
Bayesian ABC-MCMC Classification of Liquid Chromatography–Mass Spectrometry Data
Authors: Banerjee Upamanyu , Braga-Neto Ulisses M.
Published: October 2017

Proteomics promises to revolutionize cancer treatment and prevention by facilitating the discovery of molecular biomarkers. Progress has been impeded, however, by the small-sample, high-dimensional nature of proteomic data. We propose the application of a Bayesian approach to address this issue in classification of proteomic profiles generated by liquid chromatography-mass spectrometry (LC-MS). Our approach relies on a previously proposed model of the LC-MS experiment, as well as on the theory of the optimal Bayesian classifier (OBC). Computation of the OBC requires the combination of a likelihood-free methodology called approximate Bayesian computation (ABC) as well as Markov chain Monte Carlo (MCMC) sampling. Numerical experiments using synthetic LC-MS data based on an actual human proteome indicate that the proposed ABC-MCMC classification rule outperforms classical methods such as support vector machines, linear discriminant analysis, and 3-nearest neighbor classification rules in the case when sample size is small or the number of selected proteins used to classify is large.
MCAM: A Database to Accelerate the Identification of Functional Cell Adhesion Molecules
Authors: Sadanandam Anguraj , Pal Sudipendra Nath , Ziskovsky Joe , Hegde Prathibha , Singh Rakesh K.
Published: October 2017

In the post-genomic era, computational identification of cell adhesion molecules (CAMs) becomes important in defining new targets for diagnosis and treatment of various diseases including cancer. Lack of a comprehensive CAM-specific database restricts our ability to identify and characterize novel CAMs. Therefore, we developed a comprehensive mammalian cell adhesion molecule (MCAM) database. The current version is an interactive Web-based database, which provides the resources needed to search mouse, human and rat-specific CAMs and their sequence information and characteristics such as gene functions and virtual gene expression patterns in normal and tumor tissues as well as cell lines. Moreover, the MCAM database can be used for various bioinformatics and biological analyses including identifying CAMs involved in cell-cell interactions and homing of lymphocytes, hematopoietic stem cells and malignant cells to specific organs using data from high-throughput experiments. Furthermore, the database can also be used for training and testing existing transmembrane (TM) topology prediction methods specifically for CAM sequences. The database is freely available online at http://www.app1.unmc.edu/mcam .
Global Associations between Copy Number and Transcript mRNA Microarray Data: An Empirical Study
Authors: Gu Wenjuan , Choi Hyungwon , Ghosh Debashis
Published: October 2017

With an increasing number of cancer profiling studies assaying both transcript mRNA and copy number expression levels, a natural question then involves the potential to combine information across the two types of genomic data. In this article, we perform a study to assess the nature of association between the two types of data across several experiments. We report on several interesting findings: 1) global correlation between gene expression and copy number is relatively weak but consistent across studies; 2) there is strong evidence for a cis-dosage effect of copy number on gene expression; 3) segmenting the copy number levels helps to improve correlations.
Comparisons of Robustness and Sensitivity between Cancer and Normal Cells by Microarray Data
Authors: Chu Liang-Hui , Chen Bor-Sen
Published: October 2017

Robustness is defined as the ability to uphold performance in face of perturbations and uncertainties, and sensitivity is a measure of the system deviations generated by perturbations to the system. While cancer appears as a robust but fragile system, few computational and quantitative evidences demonstrate robustness tradeoffs in cancer. Microarrays have been widely applied to decipher gene expression signatures in human cancer research, and quantification of global gene expression profiles facilitates precise prediction and modeling of cancer in systems biology. We provide several efficient computational methods based on system and control theory to compare robustness and sensitivity between cancer and normal cells by microarray data. Measurement of robustness and sensitivity by linear stochastic model is introduced in this study, which shows oscillations in feedback loops of p53 and demonstrates robustness tradeoffs that cancer is a robust system with some extreme fragilities. In addition, we measure sensitivity of gene expression to perturbations in other gene expression and kinetic parameters, discuss nonlinear effects in feedback loops of p53 and extend our method to robustness-based cancer drug design.
Detecting Role Errors in the Gene Hierarchy of the NCI Thesaurus
Authors: Min Hua , Cohen Barry , Halper Michael , Oren Marc , Perl Yehoshua
Published: October 2017

Gene terminologies are playing an increasingly important role in the ever-growing field of genomic research. While errors in large, complex terminologies are inevitable, gene terminologies are even more susceptible to them due to the rapid growth of genomic knowledge and the nature of its discovery. It is therefore very important to establish quality-assurance protocols for such genomic-knowledge repositories. Different kinds of terminologies oftentimes require auditing methodologies adapted to their particular structures. In light of this, an auditing methodology tailored to the characteristics of the NCI Thesaurus's (NCIT's) Gene hierarchy is presented. The Gene hierarchy is of particular interest to the NCIT's designers due to the primary role of genomics in current cancer research. This multiphase methodology focuses on detecting role-errors, such as missing roles or roles with incorrect or incomplete target structures, occurring within that hierarchy. The methodology is based on two kinds of abstraction networks, called taxonomies, that highlight the role distribution among concepts within the IS-A (subsumption) hierarchy. These abstract views tend to highlight portions of the hierarchy having a higher concentration of errors. The errors found during an application of the methodology are reported. Hypotheses pertaining to the efficacy of our methodology are investigated.
BRB-ArrayTools Data Archive for Human Cancer Gene Expression: A Unique and Efficient Data Sharing Resource
Authors: Zhao Yingdong , Simon Richard
Published: October 2017

The explosion of available microarray data on human cancer increases the urgency for developing methods for effectively sharing this data among clinical cancer investigators. Lack of a smooth interface between the databases and statistical analysis tools limits the potential benefits of sharing the publicly available microarray data. To facilitate the efficient sharing and use of publicly available microarray data among cancer investigators, we have built a BRB-ArrayTools Data Archive including over one hundred human cancer microarray projects for 28 cancer types. Expression array data and clinical descriptors have been imported into BRB-ArrayTools and are stored as BRB-ArrayTools project folders on the archive. The data archive can be accessed from: http://www.linus.nci.nih.gov/~brb/DataArchive.html Our BRB-ArrayTools data archive and GEO importer represent ongoing efforts to provide effective tools for efficiently sharing and utilizing human cancer microarray data.
An Analysis of Risk Factors for Developing Hepatocellular Carcinoma in a Group of Hepatitis C Patients with Stage 3 Fibrosis following Interferon Therapy
Authors: Mahmood Sabina , Togawa Kazumi , Kawanaka Miwa , Niiyama Gouichi , Yamada Gotaro
Published: October 2017

The risk of Hepatocellular carcinoma (HCC) is high in HCV-infected patients who have biochemically and histologically active chronic hepatitis. To observe the long prognosis of Chronic Hepatitis C (CHC) patients with stage 3 fibrosis (F3), 55 CHC patients after initial Interferon (IFN) therapy were followed up for up to 12 years (average 9.8 ± 2.3 years). According to the annual average alanine aminotransferase (ALT) levels, patients were grouped into, low (ALT ≤g 30 IU/l); moderate (ALT >30 <80 IU/l) and high (ALT ≥ 80 IU/l) ALT groups. Eleven patients were re-treated with IFN. During the follow-up period of 12 years, HCC developed in 26 patients with an average annual incidence of 3.9%. Biochemical responders to initial IFN therapy (n = 8) and those re-treated with IFN (n = 10), except 1, did not develop HCC. Cox regression analysis to evaluate risk factors for HCC occurrence, found development of Liver Cirrhosis within 3 years of initial IFN therapy( P = 0.05) and the 3 year annual average ALT post initial IFN therapy ( P = 0.033) to be significant. The 12 year annual average ALT was also found to be significantly related to HCC occurrence ( P = 0.016), on univariate analysis. Patients belonging to the continuously low ALT group (ALT ≤ 30 IU/l for ≥3 years), did not develop HCC or receive IFN re-treatment. In CHC patients with F3, after initial IFN therapy, keeping ALT continuously low, below 30 IU/l for 3 years or more seems important. Continuing treatment with anti-inflammatory drugs along with subsequent IFN re-treatment may prevent or delay HCC even in elderly patients.
Efficiency Analysis of Competing Tests for Finding Differentially Expressed Genes in Lung Adenocarcinoma
Authors: Jordan Rick , Patel Satish , Hu Hai , Lyons-Weiler James
Published: October 2017

In this study, we introduce and use Efficiency Analysis to compare differences in the apparent internal and external consistency of competing normalization methods and tests for identifying differentially expressed genes. Using publicly available data, two lung adenocarcinoma datasets were analyzed using caGEDA ( http://www.bioinformatics2.pitt.edu/GE2/GEDA.html ) to measure the degree of differential expression of genes existing between two populations. The datasets were randomly split into at least two subsets, each analyzed for differentially expressed genes between the two sample groups, and the gene lists compared for overlapping genes. Efficiency Analysis is an intuitive method that compares the differences in the percentage of overlap of genes from two or more data subsets, found by the same test over a range of testing methods. Tests that yield consistent gene lists across independently analyzed splits are preferred to those that yield less consistent inferences. For example, a method that exhibits 50% overlap in the 100 top genes from two studies should be preferred to a method that exhibits 5% overlap in the top 100 genes. The same procedure was performed using all available normalization and transformation methods that are available through caGEDA. The ‘best’ test was then further evaluated using internal cross-validation to estimate generalizable sample classification errors using a Naïve Bayes classification algorithm. A novel test, termed D1 (a derivative of the J5 test) was found to be the most consistent, and to exhibit the lowest overall classification error, and highest sensitivity and specificity. The D1 test relaxes the assumption that few genes are differentially expressed. Efficiency Analysis can be misleading if the tests exhibit a bias in any particular dimension (e.g. expression intensity); we therefore explored intensity-scaled and segmented J5 tests using data in which all genes are scaled to share the same intensity distribution range. Efficiency Analysis correctly predicted the ‘best’ test and normalization method using the Beer dataset and also performed well with the Bhattacharjee dataset based on both efficiency and classification accuracy criteria.