bluuebunny/crossref_metadata_2025 huggingface dataset:
The Need for Review and Understanding of SELDI/MALDI Mass Spectroscopy Data Prior to Analysis
Authors: Grizzle William E. , Semmes O. John , Bigbee William , Zhu Liu , Malik Gunjan , Oelschlager Denise K , Manne Barkha , Manne Upender
Published: October 2017

Multiple studies have reported that surface enhanced laser desorption/ionization time of flight mass spectroscopy (SELDI-TOF-MS) is useful in the early detection of disease based on the analysis of bodily fluids. Use of any multiplex mass spectroscopy based approach as in the analysis of bodily fluids to detect disease must be analyzed with great care due to the susceptibility of multiplex and mass spectroscopy methods to biases introduced via experimental design, patient samples, and/or methodology. Specific biases include those related to experimental design, patients, samples, protein chips, chip reader and spectral analysis. Contributions to biases based on patients include demographics (e.g., age, race, ethnicity, sex), homeostasis (e.g., fasting, medications, stress, time of sampling), and site of analysis (hospital, clinic, other). Biases in samples include conditions of sampling (type of sample container, time of processing, time to storage), conditions of storage, (time and temperature of storage), and prior sample manipulation (freeze thaw cycles). Also, there are many potential biases in methodology which can be avoided by careful experimental design including ensuring that cases and controls are analyzed randomly. All the above forms of biases affect any system based on analyzing multiple analytes and especially all mass spectroscopy based methods, not just SELDI-TOF-MS. Also, all current mass spectroscopy systems have relatively low sensitivity compared with immunoassays (e.g., ELISA). There are several problems which may be unique to the SELDI-TOF-MS system marketed by Ciphergen®. Of these, the most important is a relatively low resolution (±0.2%) of the bundled mass spectrometer which may cause problems with analysis of data. Foremost, this low resolution results in difficulties in determining what constitutes a “peak” if a peak matching approach is used in analysis. Also, once peaks are selected, the peaks may represent multiple proteins. In addition, because peaks may vary slightly in location due to instrumental drift, long term identification of the same peaks may prove to be a challenge. Finally, the Ciphergen® system has some “noise” of the baseline which results from the accumulation of charge in the detector system. Thus, we must be very aware of the factors that may affect the use of proteomics in the early detection of disease, in determining aggressive subsets of cancers, in risk assessment and in monitoring the effectiveness of novel therapies.
Assessing the Statistical Significance of the Achieved Classification Error of Classifiers Constructed using Serum Peptide Profiles, and a Prescription for Random Sampling Repeated Studies for Massive High-Throughput Genomic and Proteomic Studies
Authors: Lyons-Weiler James , Pelikan Richard , Herbert J Zeh , Whitcomb David C , Malehorn David E , Bigbee William L , Hauskrecht Milos
Published: October 2017

Peptide profiles generated using SELDI/MALDI time of flight mass spectrometry provide a promising source of patient-specific information with high potential impact on the early detection and classification of cancer and other diseases. The new profiling technology comes, however, with numerous challenges and concerns. Particularly important are concerns of reproducibility of classification results and their significance. In this work we describe a computational validation framework, called PACE (Permutation-Achieved Classification Error), that lets us assess, for a given classification model, the significance of the Achieved Classification Error (ACE) on the profile data. The framework compares the performance statistic of the classifier on true data samples and checks if these are consistent with the behavior of the classifier on the same data with randomly reassigned class labels. A statistically significant ACE increases our belief that a discriminative signal was found in the data. The advantage of PACE analysis is that it can be easily combined with any classification model and is relatively easy to interpret. PACE analysis does not protect researchers against confounding in the experimental design, or other sources of systematic or random error. We use PACE analysis to assess significance of classification results we have achieved on a number of published data sets. The results show that many of these datasets indeed possess a signal that leads to a statistically significant ACE.
Design of Early Validation Trials of Biomarkers
Authors: Normolle Daniel , Ruffin Mack T. , Brenner Dean
Published: October 2017

The design of early-phase studies of putative screening markers in clinical populations is discussed. Biological, epidemiological, statistical and computational issues all affect the design of early-phase studies of these markers, but there are frequently little or no data in hand to facilitate the design. Early-phase studies must be designed as part of a development program, considering the final use of the marker, directly informing the decision to made at the study's conclusion. Therefore, they should test for sensitivity and specificity that would be minimally acceptable to proceed to the next stage of development. Designing such trials requires explicit assumptions about prevalence and false positive and negative costs in the ultimate target population. Early discussion of these issues strengthens the development process, since enthusiasm for developing technologies is balanced by realism about the requirements of a valid population screen. Receiver operating characteristic (ROC) curves, which are useful descriptive tools, may be misleading when evaluating tests in low-prevalence populations, because they emphasize the relationship between specificity and sensitivity in the range of specificity likely to be too low to be useful in mass screening applications.
Applying a 4D Multiscale In Vivo Tumor Growth Model to the Exploration of Radiotherapy Scheduling: The Effects of Weekend Treatment Gaps and P53 Gene Status on the Response of Fast Growing Solid Tumors
Authors: Dionysiou Dimitra D. , Stamatakos Georgios S.
Published: October 2017

The present paper aims at demonstrating clinically oriented applications of the multiscale four dimensional in vivo tumor growth simulation model previously developed by our research group. To this end the effect of weekend radiotherapy treatment gaps and p53 gene status on two virtual glioblastoma tumors differing only in p53 gene status is investigated in silico. Tumor response predictions concerning two rather extreme dose fractionation schedules (daily dose of 4.5 Gy administered in 3 equal fractions) namely HART (Hyperfractionated Accelerated Radiotherapy weekend less) 54 Gy and CHART (Continuous HART) 54 Gy are presented and compared. The model predictions suggest that, for the same p53 status, HART 54 Gy and CHART 54 Gy have almost the same long term effects on locoregional tumor control. However, no data have been located in the literature concerning a comparison of HART and CHART radiotherapy schedules for glioblastoma. As non small cell lung carcinoma (NSCLC) may also be a fast growing and radiosensitive tumor, a comparison of the model predictions with the outcome of clinical studies concerning the response of NSCLC to HART 54 Gy and CHART 54 Gy is made. The model predictions are in accordance with corresponding clinical observations, thus strengthening the potential of the model.
Challenges in the Analysis of Mass-Throughput Data: A Technical Commentary from the Statistical Machine Learning Perspective
Authors: Aliferis Constantin F. , Statnikov Alexander , Tsamardinos Ioannis
Published: October 2017

Sound data analysis is critical to the success of modern molecular medicine research that involves collection and interpretation of mass-throughput data. The novel nature and high-dimensionality in such datasets pose a series of non-trivial data analysis problems. This technical commentary discusses the problems of over-fitting, error estimation, curse of dimensionality, causal versus predictive modeling, integration of heterogeneous types of data, and lack of standard protocols for data analysis. We attempt to shed light on the nature and causes of these problems and to outline viable methodological approaches to overcome them.
Data Perturbation Independent Diagnosis and Validation of Breast Cancer Subtypes Using Clustering and Patterns
Authors: Alexe G. , Dalgin G.S. , Ramaswamy R. , Delisi C. , Bhanot G.
Published: October 2017

Molecular stratification of disease based on expression levels of sets of genes can help guide therapeutic decisions if such classifications can be shown to be stable against variations in sample source and data perturbation. Classifications inferred from one set of samples in one lab should be able to consistently stratify a different set of samples in another lab. We present a method for assessing such stability and apply it to the breast cancer (BCA) datasets of Sorlie et al. 2003 and Ma et al. 2003. We find that within the now commonly accepted BCA categories identified by Sorlie et al. Luminal A and Basal are robust, but Luminal B and ERBB2+ are not. In particular, 36% of the samples identified as Luminal B and 55% identified as ERBB2+ cannot be assigned an accurate category because the classification is sensitive to data perturbation. We identify a “core cluster” of samples for each category, and from these we determine “patterns” of gene expression that distinguish the core clusters from each other. We find that the best markers for Luminal A and Basal are (ESR1, LIV1, GATA-3) and (CCNE1, LAD1, KRT5), respectively. Pathways enriched in the patterns regulate apoptosis, tissue remodeling and the immune response. We use a different dataset (Ma et al. 2003) to test the accuracy with which samples can be allocated to the four disease subtypes. We find, as expected, that the classification of samples identified as Luminal A and Basal is robust but classification into the other two subtypes is not.
PubMed QUEST: The PubMed Query Search Tool. An informatics tool to aid cancer centers and cancer investigators in searching the PubMed databases
Authors: Hanauer David A. , Chinnaiyan Arul M.
Published: October 2017

Searching PubMed for citations related to a specific cancer center or group of authors can be labor-intensive. We have created a tool, PubMed QUEST, to aid in the rapid searching of PubMed for publications of interest. It was designed by taking into account the needs of entire cancer centers as well as individual investigators. The experience of using the tool by our institution's cancer center administration and investigators has been favorable and we believe it could easily be adapted to other institutions. Use of the tool has identified limitations of automated searches for publications based on an author's name, especially for common names. These limitations could likely be solved if the PubMed database assigned a unique identifier to each author.
Integrative Analysis of Gene Expression Data Including an Assessment of Pathway Enrichment for Predicting Prostate Cancer
Authors: Hu Pingzhao , Greenwood Celia M.T. , Beyene Joseph
Published: October 2017

Background

Microarray technology has been previously used to identify genes that are differentially expressed between tumour and normal samples in a single study, as well as in syntheses involving multiple studies. When integrating results from several Affymetrix microarray datasets, previous studies summarized probeset-level data, which may potentially lead to a loss of information available at the probe-level. In this paper, we present an approach for integrating results across studies while taking probe-level data into account. Additionally, we follow a new direction in the analysis of microarray expression data, namely to focus on the variation of expression phenotypes in predefined gene sets, such as pathways. This targeted approach can be helpful for revealing information that is not easily visible from the changes in the individual genes.

Results

We used a recently developed method to integrate Affymetrix expression data across studies. The idea is based on a probe-level based test statistic developed for testing for differentially expressed genes in individual studies. We incorporated this test statistic into a classic random-effects model for integrating data across studies. Subsequently, we used a gene set enrichment test to evaluate the significance of enriched biological pathways in the differentially expressed genes identified from the integrative analysis. We compared statistical and biological significance of the prognostic gene expression signatures and pathways identified in the probe-level model (PLM) with those in the probeset-level model (PSLM). Our integrative analysis of Affymetrix microarray data from 110 prostate cancer samples obtained from three studies reveals thousands of genes significantly correlated with tumour cell differentiation. The bioinformatics analysis, mapping these genes to the publicly available KEGG database, reveals evidence that tumour cell differentiation is significantly associated with many biological pathways. In particular, we observed that by integrating information from the insulin signalling pathway into our prediction model, we achieved better prediction of prostate cancer.

Conclusions

Our data integration methodology provides an efficient way to identify biologically sound and statistically significant pathways from gene expression data. The significant gene expression phenotypes identified in our study have the potential to characterize complex genetic alterations in prostate cancer.
Magellan: A Web Based System for the Integrated Analysis of Heterogeneous Biological Data and Annotations; Application to DNA Copy Number and Expression Data in Ovarian Cancer
Authors: Kingsley Chris B. , Kuo Wen-Lin , Polikoff Daniel , Berchuck Andy , Gray Joe W. , Jain Ajay N.
Published: October 2017

Recent advances in high throughput biological methods allow researchers to generate enormous amounts of data from a single experiment. In order to extract meaningful conclusions from this tidal wave of data, it will be necessary to develop analytical methods of sufficient power and utility. It is particularly important that biologists themselves be able to perform many of these analyses, such that their background knowledge of the experimental system under study can be used to interpret results and direct further inquiries. We have developed a web-based system, Magellan, which allows the upload, storage, and analysis of multivariate data and textual or numerical annotations. Data and annotations are treated as abstract entities, to maximize the different types of information the system can store and analyze. Annotations can be used in analyses/visualizations, as a means of subsetting data to reduce dimensionality, or as a means of projecting variables from one data type or data set to another. Analytical methods are deployed within Magellan such that new functionalities can be added in a straightforward fashion. Using Magellan, we performed an integrated analysis of genome-wide comparative genomic hybridization (CGH), mRNA expression, and clinical data from ovarian tumors. Analyses included the use of permutation-based methods to identify genes whose mRNA expression levels correlated with patient survival, a nearest neighbor classifier to predict patient survival from CGH data, and curated annotations such as genomic position and derived annotations such as statistical computations to explore the quantitative relationship between CGH and mRNA expression data.
aCGHViewer: A Generic Visualization Tool for aCGH Data
Authors: Shankar Ganesh , Rossi Michael R. , Mcquaid Devin E. , Conroy Jeffrey M. , Gaile Daniel G. , Cowell John K. , Nowak Norma J. , Liang Ping
Published: October 2017

Array-Comparative Genomic Hybridization (aCGH) is a powerful high throughput technology for detecting chromosomal copy number aberrations (CNAs) in cancer, aiming at identifying related critical genes from the affected genomic regions. However, advancing from a dataset with thousands of tabular lines to a few candidate genes can be an onerous and time-consuming process. To expedite the aCGH data analysis process, we have developed a user-friendly aCGH data viewer (aCGHViewer) as a conduit between the aCGH data tables and a genome browser. The data from a given aCGH analysis are displayed in a genomic view comprised of individual chromosome panels which can be rapidly scanned for interesting features. A chromosome panel containing a feature of interest can be selected to launch a detail window for that single chromosome. Selecting a data point of interest in the detail window launches a query to the UCSC or NCBI genome browser to allow the user to explore the gene content in the chromosomal region. Additionally, aCGHViewer can display aCGH and expression array data concurrently to visually correlate the two. aCGHViewer is a stand alone Java visualization application that should be used in conjunction with separate statistical programs. It operates on all major computer platforms and is freely available at http://falcon.roswellpark.org/aCGHview/ .
Germinal Center B Cell-Like (GCB) and Activated B Cell-Like (ABC) Type of Diffuse Large B Cell Lymphoma (DLBCL): Analysis of Molecular Predictors, Signatures, Cell Cycle State and Patient Survival
Authors: Blenk S. , Engelmann J. , Weniger M. , Schultz J. , Dittrich M. , Rosenwald A. , Müller-Hermelink H.K. , Müller T. , Dandekar T.
Published: October 2017

Aiming to find key genes and events, we analyze a large data set on diffuse large B-cell lymphoma (DLBCL) gene-expression (248 patients, 12196 spots). Applying the loess normalization method on these raw data yields improved survival predictions, in particular for the clinical important group of patients with medium survival time. Furthermore, we identify a simplified prognosis predictor, which stratifies different risk groups similarly well as complex signatures.

We identify specific, activated B cell-like (ABC) and germinal center B cell-like (GCB) distinguishing genes. These include early (e.g. CDKN3) and late (e.g. CDKN2C) cell cycle genes.

Independently from previous classification by marker genes we confirm a clear binary class distinction between the ABC and GCB subgroups. An earlier suggested third entity is not supported. A key regulatory network, distinguishing marked over-expression in ABC from that in GCB, is built by: ASB13, BCL2, BCL6, BCL7A, CCND2, COL3A1, CTGF, FN1, FOXP1, IGHM, IRF4, LMO2, LRMP, MAPK10, MME, MYBL1, NEIL1 and SH3BP5. It predicts and supports the aggressive behaviour of the ABC subgroup. These results help to understand target interactions, improve subgroup diagnosis, risk prognosis as well as therapy in the ABC and GCB DLBCL subgroups.
Asterias: A Parallelized Web-based Suite for the Analysis of Expression and aCGH Data
Authors: Alibés Andreu , Morrissey Edward R. , Cañada Andrés , Rueda Oscar M. , Casado David , Yankilevich Patricio , Díaz-Uriarte Ramón
Published: October 2017

The analysis of expression and CGH arrays plays a central role in the study of complex diseases, especially cancer, including finding markers for early diagnosis and prognosis, choosing an optimal therapy, or increasing our understanding of cancer development and metastasis. Asterias ( http://www.asterias.info ) is an integrated collection of freely-accessible web tools for the analysis of gene expression and aCGH data. Most of the tools use parallel computing (via MPI) and run on a server with 60 CPUs for computation; compared to a desktop or server-based but not parallelized application, parallelization provides speed ups of factors up to 50. Most of our applications allow the user to obtain additional information for user-selected genes (chromosomal location, PubMed ids, Gene Ontology terms, etc.) by using clickable links in tables and/or figures. Our tools include: normalization of expression and aCGH data (DNMAD); converting between different types of gene/clone and protein identifiers (IDconverter/IDClight); filtering and imputation (preP); finding differentially expressed genes related to patient class and survival data (Pomelo II); searching for models of class prediction (Tnasas); using random forests to search for minimal models for class prediction or for large subsets of genes with predictive capacity (GeneSrF); searching for molecular signatures and predictive genes with survival data (SignS); detecting regions of genomic DNA gain or loss (ADaCGH). The capability to send results between different applications, access to additional functional information, and parallelized computation make our suite unique and exploit features only available to web-based applications.
Statistical Issues and Analyses of in vivo and in vitro Genomic Data in order to Identify Clinically Relevant Profiles
Authors: Poisson Laila M. , Ghosh Debashis
Published: October 2017

In vitro experimentation provides a convenient controlled environment for testing biological hypotheses of functional genomics in cancer induction and progression. However, it is necessary to validate resulting gene signatures from these in vitro experiments in human tumor samples (i.e. in vivo). We discuss the several methods for integrating data from these two sources paying particular attention to formulating statistical tests and corresponding null hypotheses. We propose a classification null hypothesis that can be simply modeled via permutation testing. A classification method is proposed based upon the Tissue Similarity Index of Sandberg and Ernberg (PNAS, 2005) that uses the classification null hypothesis. This method is demonstrated using the in vitro signature of Core Serum Response developed by Chang et al. (PLoS Biology, 2004).
Investigation of miRNA Biology by Bioinformatic Tools and Impact of miRNAs in Colorectal Cancer—Regulatory Relationship of c-Myc and p53 with miRNAs
Authors: Xi Yaguang , Edwards John R. , Ju Jingfang
Published: October 2017

MicroRNAs (miRNAs) are a class of small non-coding RNAs that mediate gene expression at the post-transcriptional and translational levels and have been demonstrated to be involved in diverse biological functions. Mounting evidence in recent years has shown that miRNAs play key roles in tumorigenesis due to abnormal expression of and mutations in miRNAs. High throughput miRNA expression profiling of several major tumor types has identified miRNAs associated with clinical diagnosis and prognosis of cancer treatment. Previously our group has discovered a novel regulatory relationship between tumor suppressor gene p53 with miRNAs expression and a number of miRNA promoters contain putative p53 binding sites. In addition, others have reported that c-myc can mediate a large number of miRNAs expression. In this review, we will emphasize algorithms to identify mRNA targets of miRNAs and the roles of miRNAs in colorectal cancer. In particular, we will discuss a novel regulatory relationship of miRNAs with tumor suppressor p53 and c-myc. miRNAs are becoming promising novel targets and biomarkers for future cancer therapeutic development and clinical molecular diagnosis.
Combining Clinical and Genomic Covariates via Cov-TGDR
Authors: Ma Shuangge , Huang Jian
Published: October 2017

Clinical covariates such as age, gender, tumor grade, and smoking history have been extensively used in prediction of disease occurrence and progression. On the other hand, genomic biomarkers selected from microarray measurements may provide an alternative, satisfactory way of disease prediction. Recent studies show that better prediction can be achieved by using both clinical and genomic biomarkers. However, due to different characteristics of clinical and genomic measurements, combining those covariates in disease prediction is very challenging. We propose a new regularization method, Covariate-Adjusted Threshold Gradient Directed Regularization (Cov-TGDR), for combining different type of covariates in disease prediction. The proposed approach is capable of simultaneous biomarker selection and predictive model building. It allows different degrees of regularization for different type of covariates. We consider biomedical studies with binary outcomes and right censored survival outcomes as examples. Logistic model and Cox model are assumed, respectively. Analysis of the Breast Cancer data and the Follicular lymphoma data show that the proposed approach can have better prediction performance than using clinical or genomic covariates alone.
Parametric Power Spectral Density Analysis of Noise from Instrumentation in MALDI TOF Mass Spectrometry
Authors: Shin Hyunjin , Mutlu Miray , Koomen John M. , Markey Mia K.
Published: October 2017

Noise in mass spectrometry can interfere with identification of the biochemical substances in the sample. For example, the electric motors and circuits inside the mass spectrometer or in nearby equipment generate random noise that may distort the true shape of mass spectra. This paper presents a stochastic signal processing approach to analyzing noise from electrical noise sources (i.e., noise from instrumentation) in MALDI TOF mass spectrometry. Noise from instrumentation was hypothesized to be a mixture of thermal noise, 1/f noise, and electric or magnetic interference in the instrument. Parametric power spectral density estimation was conducted to derive the power distribution of noise from instrumentation with respect to frequencies. As expected, the experimental results show that noise from instrumentation contains 1/f noise and prominent periodic components in addition to thermal noise. These periodic components imply that the mass spectrometers used in this study may not be completely shielded from the internal or external electrical noise sources. However, according to a simulation study of human plasma mass spectra, noise from instrumentation does not seem to affect mass spectra significantly. In conclusion, analysis of noise from instrumentation using stochastic signal processing here provides an intuitive perspective on how to quantify noise in mass spectrometry through spectral modeling.
Comparison of Supervised Classification Methods for Protein Profiling in Cancer Diagnosis
Authors: Dossat Nadège , Mangé Alain , Solassol Jérôme , Jacot William , Lhermitte Ludovic , Maudelonde Thierry , Daurès Jean-Pierre , Molinari Nicolas
Published: October 2017

A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The first two steps are the selection of the most discriminating biomarkers with a construction of different classifiers. Finally, we compare and validate their performance and robustness using different supervised classification methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classification Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested.
Identifying Biomarkers from Mass Spectrometry Data with Ordinal Outcome
Authors: Kwon Deukwoo , Tadesse Mahlet G. , Sha Naijun , Pfeiffer Ruth M. , Vannucci Marina
Published: October 2017

In recent years, there has been an increased interest in using protein mass spectroscopy to identify molecular markers that discriminate diseased from healthy individuals. Existing methods are tailored towards classifying observations into nominal categories. Sometimes, however, the outcome of interest may be measured on an ordered scale. Ignoring this natural ordering results in some loss of information. In this paper, we propose a Bayesian model for the analysis of mass spectrometry data with ordered outcome. The method provides a unified approach for identifying relevant markers and predicting class membership. This is accomplished by building a stochastic search variable selection method within an ordinal outcome model. We apply the methodology to mass spectrometry data on ovarian cancer cases and healthy individuals. We also utilize wavelet-based techniques to remove noise from the mass spectra prior to analysis. We identify protein markers associated with being healthy, having low grade ovarian cancer, or being a high grade case. For comparison, we repeated the analysis using conventional classification procedures and found improved predictive accuracy with our method.
Public Databases and Software for the Pathway Analysis of Cancer Genomes
Authors: Tsui Ivy F.L. , Chari Raj , Buys Timon P.H. , Lam Wan L.
Published: October 2017

The study of pathway disruption is key to understanding cancer biology. Advances in high throughput technologies have led to the rapid accumulation of genomic data. The explosion in available data has generated opportunities for investigation of concerted changes that disrupt biological functions, this in turns created a need for computational tools for pathway analysis. In this review, we discuss approaches to the analysis of genomic data and describe the publicly available resources for studying biological pathways.
Normalized, Segmented or Called aCGH Data?
Authors: Van Wieringen Wessel N. , Van De Wiel Mark A. , Ylstra Bauke
Published: October 2017

Array comparative genomic hybridization (aCGH) is a high-throughput lab technique to measure genome-wide chromosomal copy numbers. Data from aCGH experiments require extensive pre-processing, which consists of three steps: normalization, segmentation and calling. Each of these pre-processing steps yields a different data set: normalized data, segmented data, and called data. Publications using aCGH base their findings on data from all stages of the pre-processing. Hence, there is no consensus on which should be used for further down-stream analysis. This consensus is however important for correct reporting of findings, and comparison of results from different studies. We discuss several issues that should be taken into account when deciding on which data are to be used. We express the believe that called data are best used, but would welcome opposing views.
CGHregions: Dimension Reduction for Array CGH Data with Minimal Information Loss
Authors: Van De Wiel Mark A. , Van Wieringen Wessel N.
Published: October 2017

An algorithm to reduce multi-sample array CGH data from thousands of clones to tens or hundreds of clone regions is introduced. This reduction of the data is performed such that little information is lost, which is possible due to the high dependencies between neighboring clones. The algorithm is explained using a small example. The potential beneficial effects of the algorithm for downstream analysis are illustrated by re-analysis of previously published colorectal cancer data. Using multiple testing corrections suitable for these data, we provide statistical evidence for genomic differences on several clone regions between MSI+ and CIN+ tumors. The algorithm, named CGHregions, is available as an easy-to-use script in R.
An Architecture for Computer-Aided Detection and Radiologic Measurement of Lung Nodules in Clinical Trials
Authors: Brown Matthew S. , Pais Richard , Qing Peiyuan , Shah Sumit , McNitt-Gray Michael F. , Goldin Jonathan G. , Petkovska Iva , Tran Lien , Aberle Denise R.
Published: October 2017

Computer tomography (CT) imaging plays an important role in cancer detection and quantitative assessment in clinical trials. High-resolution imaging studies on large cohorts of patients generate vast data sets, which are infeasible to analyze through manual interpretation.

In this article we describe a comprehensive architecture for computer-aided detection (CAD) and surveillance on lung nodules in CT images. Central to this architecture are the analytic components: an automated nodule detection system, nodule tracking capabilities and volume measurement, which are integrated within a data management system that includes mechanisms for receiving and archiving images, a database for storing quantitative nodule measurements and visualization, and reporting tools.

We describe two studies to evaluate CAD technology within this architecture, and the potential application in large clinical trials. The first study involves performance assessment of an automated nodule detection system and its ability to increase radiologist sensitivity when used to provide a second opinion. The second study investigates nodule volume measurements on CT made using a semi-automated technique and shows that volumetric analysis yields significantly different tumor response classifications than a 2D diameter approach. These studies demonstrate the potential of automated CAD tools to assist in quantitative image analysis for clinical trials.
Perturbation of Interaction Networks for Application to Cancer Therapy
Authors: Quayle Adrian P. , Siddiqui Asim S. , Jones Steven J. M.
Published: October 2017

We present a computational approach for studying the effect of potential drug combinations on the protein networks associated with tumor cells. The majority of therapeutics are designed to target single proteins, yet most diseased states are characterized by a combination of many interacting genes and proteins. Using the topology of protein-protein interaction networks, our methods can explicitly model the possible synergistic effect of targeting multiple proteins using drug combinations in different cancer types.

The methodology can be conceptually split into two distinct stages. Firstly, we integrate protein interaction and gene expression data to develop network representations of different tissue types and cancer types. Secondly, we model network perturbations to search for target combinations which cause significant damage to a relevant cancer network but only minimal damage to an equivalent normal network. We have developed sets of predicted target and drug combinations for multiple cancer types, which are validated using known cancer and drug associations, and are currently in experimental testing for prostate cancer. Our methods also revealed significant bias in curated interaction data sources towards targets with associations compared with high-throughput data sources from model organisms. The approach developed can potentially be applied to many other diseased cell types.
Rarity of Somatic Mutation and Frequency of Normal Sequence Variation Detected in Sporadic Colon Adenocarcinoma Using High-Throughput cDNA Sequencing
Authors: Kan Takatsugu , Paun Bogdan C. , Mori Yuriko , Sato Fumiaki , Jin Zhe , Hamilton James P. , Ito Tetsuo , Cheng Yulan , David Stefan , Olaru Alexandru V. , Yang Jian , Agarwal Rachana , Abraham John M. , Meltzer Stephen J.
Published: October 2017

We performed high-throughput cDNA sequencing in colorectal adenocarcinoma and matching normal colorectal epithelium. All six hundred three genes in the UCSC database that were expressed in colon cancers and contained open reading frames of 1000 nucleotides or less were selected for study (total basepairs/bp, 366,686). 304,350 of these 366,686 bp (83.0%) were amplified and sequenced successfully. Seventy-eight sequence variants present in germline (i.e. normal) as well as matching somatic (i.e. tumor) DNA were discovered, yielding a frequency of 1 variant per 3,902 bp. Fifty-one of these sequence variants were homozygous (26 synonymous, 25 non-synonymous), while 27 were heterozygous (11 synonymous, 16 non-synonymous). Cancer tissue contained only one sequence-altered allele of the gene ATP50, which was present heterozygously alongside the wild-type allele in matching normal epithelium. Despite this relatively large number of bp and genes sequenced, no somatic mutations unique to tumor were found. High-throughput cDNA sequencing is a practical approach for detecting novel sequence variations and alterations in human tumors, such as those of the colon.
The Penna Model of Biological Aging
Authors: Stauffer D.
Published: October 2017

This review deals with computer simulation of biological aging, particularly with the Penna model of 1995. They are based on the mutation accumulation theory of half a century ago. The results agree well with demographical reality, and also with the seemingly contradictory influence of predators on the aging of prey.
Finding the Sources of Missing Heritability within Rare Variants Through Simulation
Authors: Bandyopadhyay Baishali , Chanda Veda , Wang Yupeng
Published: October 2017

Thousands of genome-wide association studies (GWAS) have been conducted to identify the genetic variants associated with complex disorders. However, only a small proportion of phenotypic variances can be explained by the reported variants. Moreover, many GWAS failed to identify genetic variants associated with disorders displaying hereditary features. The “missing heritability” problem can be partly explained by rare variants. We simulated a causality scenario that gestational ages, a quantitative trait that can distinguish preterm (<37 weeks) and term births, were significantly correlated with the rare variant aggregations at 1000 single-nucleotide polymorphism loci. These 1000 simulated causal rare variants were embedded into randomly selected subsets of 9642 promoter regions from the 1000 Genomes Project genotypic data according to different proportions of causal rare variants within the embedded promoters. Through analysis of the correlations between rare variant aggregations and gestational ages, we found that the embedded promoters as a whole showed weaker genetic association when the proportion of causal rare variants decreased, and no individual embedded promoters showed genetic association when the proportion of causal rare variants was smaller than 0.4. Our analyses indicate that association signals can be greatly diluted when causal rare variants are dispersedly and sparsely distributed in the genome, accounting for an important source of missing heritability.
Comparing the Effects of Negative and Mixed Emotional Messages on Predicted Occasional Excessive Drinking
Authors: Carrera Pilar , Caballero Amparo , Muñoz Dolores
Published: October 2017

In this work we present two types of emotional message, negative (sadness) versus mixed (joy and sadness), with the aim of studying their differential effect on attitude change and the probability estimated by participants of repeating the behavior of occasional excessive drinking in the near future. The results show that for the group of participants with moderate experience in this behavior the negative message, compared to the mixed one, is associated with higher probability of repeating the risk behavior and a less negative attitude toward it. These results suggest that mixed emotional messages (e.g. joy and sadness messages) could be more effective in campaigns for the prevention of this risk behavior.
Optimal Calibration and Uncertainty Analysis of SWAT for an Arid Climate
Authors: Jajarmizadeh Milad , Sidek Lariyah Mohd , Harun Sobri , Salarpour Mohsen
Published: October 2017

One of the major issues for semidistributed models is calibration of sensitive parameters. This study compared 3 scenarios for Soil and Water Assessment Tool (SWAT) model for calibration and uncertainty. Roodan watershed has been selected for simulation of daily flow in southern part of Iran with an area of 10 570 km2. After preparation of required data and implementation of the SWAT model, sensitivity analysis has been performed by Latin Hypercube One-factor-At-a-Time method on those parameters which are effective for flow simulation. Then, SWAT Calibration and Uncertainty Program (SWAT-CUP) has been used for calibration and uncertainty analysis. Three schemes for calibration were followed for the Roodan watershed modeling in calibration analysis as evolution. These include the following: the global method (scheme 1), this is a method that takes in all globally adjusted sensitive parameters for the whole watershed; the discretization method (scheme 2), this method considered the dominant features in calibration such as land use and soil type; the optimum parameters method (scheme 3), this method only adjusted those sensitive parameters by considering the effectiveness of their features. The results show that scheme 3 has better performance criteria for calibration and uncertainty analysis. Nash-Sutcliffe (NS) coefficient has been obtained 0.75 for scheme 3. However, schemes 1 and 2 resulted in NS 0.71 and 0.74, respectively, between predicted and observed daily flows. Moreover, percentage bias (P-bias) obtained was 6.7, 5.2, and 1.5 for schemes 1, 2, and 3, respectively. The result also shows that condition of parameters (parameter set) during calibration in SWAT-CUP program model has an important role to increase the performance of the model.
Student leadership in the middle years: A matter of concern
Authors: Coffey Anne , Lavery Shane
Published: October 2017

Traditionally, student leadership has been seen as the prerogative of senior students. Very little research has been conducted on how schools nurture and develop leadership skills in students in the middle years of schooling. This article provides an overview of student leadership in six secondary schools with a particular focus on student leadership opportunities in the middle years. These schools were drawn from the Government, Catholic and Independent sectors in Western Australia. Specifically, the opinions and experiences of either principals or their delegates were sought in order to develop a sense of the importance placed on student leadership in the middle years and the types of leadership opportunities available to students. Initially, the literature is reviewed on student leadership per se and student leadership in the middle years. This review is followed by an outline of the purpose, research question and significance of the research. The research methodology is then explained, providing a summary of participants, the school contexts and methods of data collection and analysis. The subsequent section on results and discussion highlights three themes: the role of teacher leaders, student leadership structures in middle years and the holistic development of middle year students. The article concludes by providing a number of recommendations, in particular, the need to gain a ‘student voice’ in any understanding of student leadership at the middle school.
The inclusion of children’s rights and responsibilities in the South African school curriculum
Authors: Munongi Lucia , Pillay Jace
Published: October 2017

This study aimed to explore Grade 9 learners’ perceptions on the extent to which rights and responsibilities are taught in the school curriculum. The sample consisted of 577 learners from 13 public, independent and independent-subsidised schools, randomly sampled from four Johannesburg education districts. Data were collected through a quantitative questionnaire that was self-administered. Results showed that rights and responsibilities were being taught to a low or moderate extent in various learning areas. The findings suggest a gap in the teaching of children’s rights and responsibilities in the school curriculum. Based on the findings, we make several recommendations for the inclusion of children’s rights in the school curriculum in South African schools.
Moral economy: What’s in a name
Authors: Carrier James G
Published: October 2017

The idea of moral economy has been increasingly popular in the social sciences over the past decade, given a confusing variety of meanings and sometimes invoked as an empty symbol. This paper begins by describing this state of affairs and some of its undesirable corollaries, which include unthinking invocations of the moral and simplistic views of some sorts of economic activity. Then, referring especially to the work of EP Thompson and James C Scott, this paper proposes a more precise definition of moral economy that roots moral economic activity in the mutual obligations that arise when people transact with each other over the course of time. It thus distinguishes between the moral values that are the context of economic activity and those that arise from the activity itself. The solution that the paper proposes to the confused state of ‘moral economy’ can, therefore, be seen as terminological, as the sub-title suggests, but it is intended to have the substantive benefits of a better approach to economic activity and circulation and a more explicit and thoughtful attention to moral value.
Book Review: Cleaver, Frances. 2012: Development Through Bricolage. Rethinking Institutions for Natural Resource Management
Authors: Alfranca Oscar
Published: October 2017

Cleaver, Frances. 2012: Development Through Bricolage. Rethinking Institutions for Natural Resource Management. London: Routledge. 240 pp. £31.99 (Paperback). ISBN: 1844078698.
Dapagliflozin attenuates human vascular endothelial cell activation and induces vasorelaxation: A potential mechanism for inhibition of atherogenesis
Authors: Gaspari Tracey , Spizzo Iressa , Liu HongBin , Hu Yunshan , Simpson Richard W , Widdop Robert E , Dear Anthony E
Published: October 2017

Background:

Sodium glucose transporter type 2 inhibitors may reduce cardiovascular events in type 2 diabetes. Our study aimed to determine the effect of the sodium glucose transporter type 2 inhibitor dapagliflozin on endothelial cell activation, vasoreactivity and atherogenesis using in vitro and in vivo models and identify associated molecular mechanisms.

Methods:

In vitro studies utilised human vascular endothelial cells stimulated with tumour necrosis factor α or hyperglycaemic conditions. In vivo studies were performed in C57Bl/6J mice to evaluate direct vasorelaxation responses evoked by acute dapagliflozin administration and acute vaso-protective effects of dapagliflozin on hyperglycaemia-induced endothelial dysfunction. Adult and aged Apolipoprotein E–deficient mice maintained on a high-fat diet were used to investigate endothelial-dependent vascular reactivity and atherogenesis. Dapagliflozin treatment (1.0 mg/kg/day) was administered for 4 weeks.

Results:

In vitro studies demonstrated dapagliflozin-mediated attenuation of tumour necrosis factor α- and hyperglycaemia-induced increases in intercellular adhesion molecule-1, vascular cell adhesion molecule-1, plasminogen activator inhibitor type 1 and NFκB expression. Acute dapagliflozin administration dose-dependently induced endothelium-independent vasorelaxation. Chronic dapagliflozin treatment improved endothelial function and significantly reduced in vivo vascular adhesion molecule and phospho-IκB expression together with macrophage vessel wall infiltration.

Conclusion:

These observations identify a potential role for dapagliflozin in the attenuation of atherogenesis and identify anti-inflammatory molecular mechanisms associated with these effects.