bluuebunny/crossref_metadata_2025 huggingface dataset:
Fine-Tuning Nonhomogeneous Regression for Probabilistic Precipitation Forecasts: Unanimous Predictions, Heavy Tails, and Link Functions
Authors: Gebetsberger Manuel , Messner Jakob W. , Mayr Georg J. , Zeileis Achim
Published: October 2017

Raw ensemble forecasts of precipitation amounts and their forecast uncertainty have large errors, especially in mountainous regions where the modeled topography in the numerical weather prediction model and real topography differ most. Therefore, statistical postprocessing is typically applied to obtain automatically corrected weather forecasts. This study applies the nonhomogenous regression framework as a state-of-the-art ensemble postprocessing technique to predict a full forecast distribution and improves its forecast performance with three statistical refinements. First of all, a novel split-type approach effectively accounts for unanimous zero precipitation predictions of the global ensemble model of the ECMWF. Additionally, the statistical model uses a censored logistic distribution to deal with the heavy tails of precipitation amounts. Finally, it is investigated which are the most suitable link functions for the optimization of regression coefficients for the scale parameter. These three refinements are tested for 10 stations in a small area of the European Alps for lead times from +24 to +144 h and accumulation periods of 24 and 6 h. Together, they improve probabilistic forecasts for precipitation amounts as well as the probability of precipitation events over the default postprocessing method. The improvements are largest for the shorter accumulation periods and shorter lead times, where the information of unanimous ensemble predictions is more important.
Thou Shalt Recycle: How Social Norms of Environmental Protection Narrow the Scope of the Low-Cost Hypothesis
Authors: Keuschnigg Marc , Kratz Fabian
Published: October 2017

According to the “low-cost hypothesis” (LCH), attitudes explain behavior only if complying with personal convictions requires little effort. Environmental research has seized this argument to explain moderate participation in pro-environmental action against a backdrop of rising environmental awareness. However, evidence for the LCH remains ambiguous, and recent studies have reported contradictory results. Here, we reconcile prior findings on household waste recycling and argue that many environmental behaviors evolved into every day, “normal” practices increasingly encouraged by social norms, and thus slip out of the LCH’s scope. We combine a natural experiment exploiting households’ variation in geocoded walking distances to drop-off recycling sites in Munich, Germany ( N = 754) with an independent online survey ( N = 640) measuring local intensities of recycling norms for two distinct waste categories, plastics and glass. Our results suggest that normative change narrows the LCH’s scope to include only environmental action for which normative expectations are weak.
Abrasive wear of polypropylene/Cloisite 30B/Elvaloy AC 3427 nanocomposites
Authors: Daniel D Jafrey , Panneerselvam K
Published: October 2017

Polymer nanocomposite may be defined as a structure that is formed by infusing at least one dimension of the dispersed particles in the nanoscale range into a thermoplastic polymer matrix. Polypropylene/Cloisite-30B/Elvaloy-AC3427 nanocomposites were manufactured by melt intercalation method using a twin screw extruder. X-ray diffraction shows an intercalated structure of nanocomposite category. The investigation of tribological properties of polypropylene/Cloisite-30B/Elvaloy-AC3427 nanocomposite material has enormous practical importance to ascertain the friction and wear values. In the tribological study, the two-body abrasive wear was conducted with input parameters (Cloisite-30B, load and sliding distance) and output parameters (wear loss, specific wear rate and coefficient of friction) at 0.5 m/s sliding velocity. The detailed validation of the abrasive wear volume with mechanical properties (tensile strength, elongation at break and shore d hardness), and the dominant factors of abrasive wear by analysis of variance and worn surface morphology by Scanning Electron Microscope were reported.
Gender fluidity and child abuse: A personal view
Authors: Lewis Charles
Published: October 2017

Gender fluidity and a failure to respect biological norms may have potentially horrific implications for children and adolescents who express doubt about their bodies. Are transgender activists driving an agenda that will result in inappropriate interventions that block normal development in children and adolescents from which there can be no return? Can the Law protect children and adolescents from harm committed with the intention of helping them?
Truth Commissions after Economic Crises: Political Learning or Blame Game?
Authors: Kovras Iosif , McDaid Shaun , Hjalmarsson Ragnar
Published: October 2017

This article addresses an important but understudied aspect of the recent Great Recession in Europe: the institutional strategies political elites deployed to learn from past policy failures and address accountability, more specifically, truth commissions. We raise two overlapping puzzles. The first concerns the timing of the decision to adopt an economic truth commission: while Iceland established a truth commission at an early stage of the crisis, Greece and Ireland did so much later. What accounts for ‘early’ versus ‘delayed’ truth seekers? The second concerns variations in learning outcomes. Iceland’s commission paved the way for learning institutional lessons, but truth commissions in Greece and Ireland became overtly politicised. What accounts for these divergences? This article compares truth commissions in Iceland, Greece and Ireland and identifies two types of political learning – institutional and instrumental – related to the establishment of a truth commission. It argues that political elites in countries with higher pre-crisis levels of trust in institutions and public transparency are more likely to establish economic truth commissions quickly; this is the ‘institutional logic’ of learning. The ‘instrumental logic’ of learning, in contrast, leads governments interested in apportioning blame to their predecessors to establish commissions at a later date, usually proximal to critical elections.
A meta-modeling framework in public health emergency management
Authors: Chen Bin , Zhang Peng
Published: October 2017

Epidemic transmission is a common type of public health emergency that is difficult to forecast and often causes substantial harm. Artificial societal models provide a novel approach to the study of public health problems. However, public health emergency management (PHEM) always involves multi-disciplinary and multi-hierarchical models that complicate the work of modeling. Models are also made more complex by the consideration of new requirements and interactions. Therefore, we propose a domain-specific methodology to guide the modeling process in PHEM. By analyzing domain characteristics and modeling requirements, a meta-modeling framework can be constructed, containing the basic elements with which to construct an artificial society to study epidemic transmission. In this paper, the designs of meta-models are discussed in detail, and domain models are implemented by code generation, which enables the support of large-scale, agent-based computational experiments on the KD-ACP platform. Case studies of Ebola are outlined, emergency scenarios are reconstructed based on pre-designed meta-models, and “scenario-response” experiments are presented. This study provides a valuable framework and methodology with which to study complex social problems in PHEM. The proposed method has been verified effectively and efficiently.
Development of a new knowledge-based fabric recommendation system by integrating the collaborative design process and multi-criteria decision support
Authors: Hong Yan , Zeng Xianyi , Bruniaux Pascal , Chen Yan , Zhang Xujing
Published: October 2017

In this paper, a perception-based fabric recommendation system is proposed to help fashion designers to select the most appropriate fabric in the design process, meeting the perception of the target consumer. The proposed methodology is based on the development of an interactive hierarchical structure, decomposing the decision problem into five levels: goal, criteria (consumers' requirements), sub evaluation criteria (fabric properties), rating scale, and alternatives, which ensures the analysis of requirements of consumers and knowledge sharing among designers. The proposed knowledge-based recommendation system includes a collaborative design process, a commonly used sensory evaluation procedure, and a computational model using the Fuzzy AHP (Analytic Hierarchy Process) and Fuzzy TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) algorithms. The Fuzzy AHP method is used to structure the fabric selection problem and determine the relevant weights of the concerned criteria and corresponding components, while the Fuzzy TOPSIS method is used to evaluate the alternative fabrics based on the criteria obtained from the AHP process and to give the total final ranking of the involved fabric alternatives. Experimental results indicate that, using the proposed interactive hierarchical structure, professional knowledge of the designers can be fully extracted to ensure a high level of consumer satisfaction.
Fuzzy Multiple Attribute Conditions in fsQCA: Problems and Solutions
Authors: Veri Francesco
Published: October 2017

This article aims to clarify the fundamental aspects of aggregating fuzzy scores of conditions with multiple attributes in fuzzy set qualitative comparative analysis (fsQCA). Fuzzy multiple attribute conditions (FMACs) are conditions that are built using different types of concepts. FMACs are flexible conditions that express the ontological nature of the concept and deals with the causal heterogeneity. In fact, researchers can add a new attribute to a concept in order to consider the concept’s meaning vis-à-vis to the outcome of interest instead of only considering the theory. In relation to FMAC fuzzy scores, we have individuated one problematic issue which is the aggregation strategy of attributes that are already calibrated that should be able to capture conceptual properties of membership and similarity. In this article, we will employ an empirical example in order to deal with causal heterogeneity and aggregation strategies. After discussing the disadvantages of the aggregation techniques used by QCA scholars, we individuate an axiomatic framework for defining logical conjunction operators that allows one to aggregate parts of concepts in accordance with membership and similarity. Then, we propose a technique to assign fuzzy scores to FMAC using the arithmetic mean–based compensatory fuzzy logic. This technique indirectly affects the solution formula(s) following the QCA and allows one to better locate cases in the XY plot during the post-QCA analysis.
Designing Research With Qualitative Comparative Analysis (QCA): Approaches, Challenges, and Tools
Authors: Thomann Eva , Maggetti Martino
Published: October 2017

Recent years have witnessed a host of innovations for conducting research with qualitative comparative analysis (QCA). Concurrently, important issues surrounding its uses have been highlighted. In this article, we seek to help users design QCA studies. We argue that establishing inference with QCA involves three intertwined design components: first, clarifying the question of external validity; second, ensuring internal validity; and third, explicitly adopting a specific mode of reasoning. We identify several emerging approaches to QCA rather than just one. Some approaches emphasize case knowledge, while others are condition oriented. Approaches emphasize either substantively interpretable or redundancy-free explanations, and some designs apply an inductive/explorative mode of reasoning, while others integrate deductive elements. Based on extant literature, we discuss issues surrounding inference with QCA and the tools available under different approaches to address these issues. We specify trade-offs and the importance of doing justice to the nature and goals of QCA in a specific research context.
Risk society, anxiety and exit: A case study of South Korean migration decision-making
Authors: Chang Ik Young , Jackson Steven J , Sam Michael P
Published: October 2017

To date there has been little explicit theorization concerning the role of risk in global migration studies. Drawing upon Beck’s concept of the ‘risk society,’ this paper presents an investigation of the interplay between societal risk and micro-level migration decision-making. Thick historical contextualization and interview data are used to examine the process of decision-making of South Koreans’ migration to New Zealand. Four risk factors were found to contribute to their ‘exit’ decisions: South Korea’s highly competitive, work-oriented society brought about by compressed modernity; North Korea’s threat of war and the South’s consequent political and military culture; the home nation’s obsession with education and academic performance; and the difficulties of reconciling traditional collectivist values with upward mobility. It is argued that while elements of risk may be universal, they need to be understood within specific cultural contexts and in relation to how they influence peoples’ lived experiences.
The Effectiveness of the Nurse Care Coordinator Role on Patient-Reported and Health Service Outcomes: A Systematic Review
Authors: Conway Aaron , O’Donnell Chris , Yates Patsy
Published: October 2017

This systematic review examined the effectiveness of nurse care coordinator (CC) roles on patient-reported and health service outcomes. Multiple electronic databases (Medline, CINAHL, and EMBASE) were searched and the Cochrane Risk of Bias Assessment Tool was applied by two independent reviewers. The Grades of Recommendation, Assessment, Development, and Evaluation system was used to assess the quality of evidence. A total of 45 articles (reporting on 36 studies) were included. The majority of studies ( n = 28, 78%) were conducted in the United States and published after 2009 ( n = 24, 67%). Thirteen studies (36%) used a randomized controlled trial design. A total of 17 studies evaluated patient-reported outcomes and 29 studies reported health service outcomes. The individual components of nurse CC roles that were evaluated ranged considerably. The impact of nurse care coordination on patient-reported and health service outcomes was inconsistent. There was an indication from higher quality studies that nurse care coordination roles were more likely to result in improved patient and health service outcomes where they involved frequent, in-person interactions, had ongoing follow-up with monitoring of disease status, and involved transition care and the application of behavior change principles.
Clinical application of intra-aortic balloon counterpulsation in high-risk patients undergoing cardiac surgery
Authors: Hou Dengbang , Yang Feng , Hou Xiaotong
Published: October 2017

The intra-aortic balloon pump (IABP) has been the most commonly used mechanical circulatory support device for nearly five decades. In theory, the IABP can increase the blood and oxygen supply of the coronary artery by increasing the diastolic pressure in the aortic root when the balloon is inflated and reduce left ventricular afterload by rapidly deflating the balloon during the systolic phase. Therefore, some researchers put forward the idea of preoperative prophylactic use of an IABP, which has been frequently performed in high-risk patients undergoing elective percutaneous coronary intervention (PCI) or coronary artery bypass graft surgery (CABG). Previous studies have suggested preoperative IABP has a controversial effect on patients undergoing revascularization; the role of preoperative IABP insertion in those patients undergoing CABG alone remains uncertain. This review will give further insight into routine IABP use by presenting the basic principles and discussing current evidence.
Rejection evaluation after renal transplantation using MR diffusion tensor imaging
Authors: Deger Emin , Celik Azim , Dheir Hamad , Turunc Volkan , Yardimci Ahmet , Torun Mert , Cihangiroglu Mutlu
Published: October 2017

Background

Renal allograft dysfunction monitoring is mainly performed using the serum creatinine (SC) level, Doppler ultrasound (US), or renal biopsy. Recently proposed diffusion-based magnetic resonance imaging (MRI) methods have been explored as new, non-invasive tools for assessing renal function after transplantation.

Purpose

To investigate the value of fractional anisotropy (FA) measurements in the evaluation of acute rejection cases after renal transplant.

Material and Methods

Doppler US and MRI diffusion tensor imaging (DTI) were performed in 21 patients with graft dysfunction requiring graft biopsy after renal transplantation and in 21 patients with normal graft function. The MR examinations were performed on a 1.5-T MRI using two b-values (0 and 800 s/mm2). FA values were measured from the cortex and medulla of the transplanted kidney at the upper, middle, and lower poles.

Results

Twenty-one transplant patients diagnosed with acute rejection (Group 1) were compared to the control group of 21 transplant patients with normal graft function (Group 2). The measured FA values of the medulla were 0.19 ± 0.02 and 0.22 ± 0.05 ( P = 0.017) for Groups 1 and 2, respectively. On the other hand, the measured FA values of the renal cortex were 0.18 ± 0.04 and 0.18 ± 0.04 ( P = 0.97) for Groups 1 and 2, respectively.

Conclusion

The good correlation between the renal medulla FA values and allograft function shows that MR DTI has potential for non-invasive functional assessment of transplanted kidneys. On the other hand, the renal cortex FA values had no correlation with the allograft function.
Le marketing du Neuromarketing : Enjeux académiques d’un domaine de recherche controversé
Authors: Wannyn William
Published: October 2017

Since the 1990s, a growing number of social science researchers collaborate in the creation of areas of research such as neurolaw, neuroeducation, neuroeconomy or neuromarketing. Sometimes referred to as ‘neurodisciplines’, these areas of research share a common postulate: the measure and analysis of the nervous system’s activity offers the possibility of discovering new ways of explaining human behaviour. Neuromarketing first appeared in the early 2000s and has developped in both university laboratories and private ones. Neuromarketers aim to understand consumer behaviour by applying neuroscientific theories and methods of measuring neurobiological activity to marketing questions. As a controversial topic, neuromarketing is critized in both the public space and academia. Some members of the media, some consumer associations and some neuromarketers see neuromarketing as having a more or less realistic power of persuasion (Lindstrom, 2009) while most neuroscientists qualify it as a scam or publicity stunt (Nature, 2004). Starting from bibliometric analysis of neuromarketing publications, we define the shifting boundaries of this area of research whose subject itself is still opened to debate. Building on Pierre Bourdieu’s work on the scientific field, we highlight the forces that shape this speciality both in and out of the academic field. Based on semidirective interviews, we demonstrate that neuromarketers have to develop discursive strategies to distance themselves from the controversial image of neuromarketing and adopt publication strategies in order to disseminate the results of their research in the scientific field.
Echodynamics: Interpretation, Limitations, and Clinical Integration!
Authors: Roshdy Ashraf
Published: October 2017

Echodynamics refers to the use of echocardiography as hemodynamic tool mostly in intensive and acute care settings. It implies a smooth drift from the classic cardiology use to a more critical care adjusted use. A more personalized approach is advocated in critical care, and echo is one way to reach such goal. Correct application necessitates optimum understanding, interpretation, and finally integration into patients’ clinical management. As more critical care doctors are advancing from basic focused echo examinations to a more advanced one, this article is trying to underlie many pitfalls of critical care echocardiography in order to guide better practice.
Exploring Indian Surrogates’ Perceptions of the Ban on International Surrogacy
Authors: Huber Sarah , Karandikar Sharvari , Gezinski Lindsay
Published: October 2017

This study examined surrogate women’s perceptions on the Indian ban on international surrogacy. In-depth interviews were conducted with 25 surrogate women at a fertility clinic in Gujarat, India. Three themes emerged: (1) perceptions of the ban; (2) impact of the ban on surrogates, international couples, and fertility clinics; and (3) long-term economic results of surrogacy. Women stated that international surrogacy should not be banned, because it provides substantial economic benefits. They also noted that the ban affects childless couples and clinics by cutting off a source of income. This research provides recommendations for social work action.
Consumption of Raw Materials by Crafters
Authors: Stannard Casey R. , Mullet Kathy
Published: October 2017

Crafters are reliant upon raw materials in order to create craft projects; however, there is little research focused on understanding how the characteristics of raw materials or the retail environment affect the consumption of crafters. The purpose of this research was to describe why crafters choose to consume from certain craft businesses and what characteristics of yarn affected the consumption of crafters. The levels of design as described by Donald Norman and principles of the slow fashion movement provided the theoretical framework for the study. Data were gathered using an online survey. Both quantitative and qualitative data were gathered. A total of 657 usable surveys were collected. Four themes emerged related to the characteristics of retail environments and raw materials, which influenced crafters to consume yarn: business factors, utilitarian requirements, visual/tactile requirements, and intangible requirements.
Effect of extrusion and compression moulding on the thermal properties of nylon-6/silica aerogel composites
Authors: Krishnaswamy Suryanarayanan , Tinsley Lawrence , Marchante Veronica , Addepalli Sri , Huang Zhaorong , Abhyankar Hrushikesh
Published: October 2017

The article presents the effect of a lower extrusion speed and compression moulding processes on the thermal properties of polyamide 6 (PA-6)/aerogel composite. Scanning electron and optical microscope images showed that although most of the aerogel was destroyed during extrusion at 65 r/min, extrusion at 5 r/min showed a better retention of the aerogel structure. However, when subjected to moulding in a compression press, both composites extruded at different speeds suffered significant damage. Nevertheless, the extruded samples did show a lower thermal conductivity compared to the virgin polymer. Further, it was observed that the sample extruded at 5 r/min had a lower damage coefficient value with an overall loss of around 33% to the aerogel structure when compared to the material extruded at 65 r/min, which endured a structural loss of 41% to the aerogel in it.
Coordinated control of the steering system and the distributed motors for comprehensive optimization of the dynamics performance and the energy consumption of an electric vehicle
Authors: Li Yutong , Zhang Junzhi , Lv Chen , Yuan Ye
Published: October 2017

This paper presents a coordinated control algorithm for comprehensive optimization of the vehicle dynamics performance and the energy consumption for a full drive-by-wire electric vehicle, which is driven by a four-in-wheel-motor actuated system and steered by a steer-by-wire system. In order to coordinate the four-in-wheel-motor actuated system and the steer-by-wire system, first, the mechanisms influencing the vehicle dynamics control performance and the energy consumption of the two systems are derived on the basis of quantitative analyses of a typical vehicle motion control process and, then, the control algorithms for each subsystem are developed. For the steer-by-wire system, a triple-step control technique is implemented to decouple the yaw rate and the side-slip angle controls, which makes it easier to tune the control parameters. The control algorithm of the four-in-wheel-motor actuated system is designed with a hierarchical control scheme, which is able not only to satisfy the yaw rate and side-slip angle tracking demands but also to deal with the actuation redundancy and the constraints. Finally, based on the mechanisms influencing the control performances of the two subsystems, coordinated control is proposed to obtain comprehensive optimization of the vehicle dynamics control performance and the energy consumption. The coordinated control developed is convenient for implementation as the structures and the control algorithms of the subsystems remain unchanged. In this way, the control algorithms of the subsystems can be developed independently. Simulations are carried out with a CarSim software full-vehicle model for three typical driving scenarios and with different road conditions. The results show the effectiveness of the coordinated control algorithm developed.
Path-tracking of an autonomous vehicle via model predictive control and nonlinear filtering
Authors: Cui Qingjia , Ding Rongjun , Zhou Bing , Wu Xiaojian
Published: October 2017

To improve the stability of the autonomous vehicle for high speed tracking, a vehicle estimator scheme integrated into a path-tracking system has been proposed in this paper. Vehicle stability is related to road condition (low road adhesion, high road adhesion, and changing road adhesion) and vehicle state, thus a state observer has been preferred in this paper to estimate vehicle state and tire-road friction as a means of judging vehicle stabilization. For the approach to the estimation, an unscented Kalman filter (UKF) employing a three degrees-of-freedom vehicle model combined with a Magic Formula (MF) tire model was designed. As a widely used model control method, the multi-constraints model predictive control (MMPC) was proposed and that was then used to calculate the desired front steering angle for tracking the planned path. The performance of the MMPC controller, with the estimator, was evaluated by the vehicle simulation software CARSIM and Matlab/Simulink. The simulation results show that the designed MMPC controller with the estimator successfully performs path-tracking at high speed for the intelligent vehicle.
Vehicle lateral control in the presence of uncertainty for lane change maneuver using adaptive sliding mode control with fuzzy boundary layer
Authors: Norouzi Armin , Kazemi Reza , Azadi Shahram
Published: October 2017

Recent researches on advanced driver-assistance systems indicate great advances in terms of safety and comfort in automated driving. Advanced driver-assistance systems use control systems to perform most of the maneuvers as performed by the driver in the past. One of the useful advanced driver-assistance systems is automatic lane change system in order to avoid accidents. This study designs the controller of an automatic lane change system for an autonomous vehicle. The control law in this study is adaptive sliding mode control. To avoid chattering in adaptive sliding mode control, fuzzy boundary layer is used. Also, adaptive law is used for sliding-based switching gain. This adaptive controlling law is used to avoid the calculation of upper bound of system uncertainties. In this study, based on the boundary conditions, the vehicle lane change path planning and different maneuver periods are evaluated. To simulate the designed controller, CarSim–Simulink joint simulation model is used. This linkage leads to a full non-linear vehicle model. The results of simulation show excellent tracking for dry road conditions and acceptable tracking in icy and wet roads in some maneuvers of above 4-s long.
Development of a nonlinear adaptive absorber based on magnetorheological elastomer
Authors: Sun Shuaishuai , Yang Jian , Yildirim Tanju , Du Haiping , Alici Gursel , Zhang Shiwu , Li Weihua
Published: October 2017

The resonance shift property of magnetorheological elastomer is important for developing adaptive absorbers. However, it is well-known that passive nonlinear absorbers have wider effective frequency bandwidths. This article combines these two characteristics in order to develop a hybrid magnetorheological elastomer absorber which can shift its natural frequency and has a wider absorption bandwidth under each constant current. The adaptability and nonlinearity were fully verified experimentally. Afterwards, the absorption ability of the hybrid magnetorheological elastomer absorber was investigated and analyzed. The results show that the effective bandwidth of this absorber is broadened under certain levels of current than linear absorber, and this is caused by the presence of nonlinearity; and the adaptability induced by the magnetorheological elastomer undoubtedly empowers the absorber possible to trace the excitation frequency changing in real time. A short-time Fourier transform was finally used to control the magnetorheological elastomer absorber to verify its controllability, showing that an optimal absorption transmissibility was achieved by the controlled hybrid absorber.
One-step synthesis of core/shell-like structured polypyrrole/sodium alginate nanocomposites and their electro-responsive performances
Authors: Zhang Tongjin , Zhang Wenling , Wang Baoxiang , Liu Jingquan
Published: October 2017

Core/shell-like structured nanocomposites of polypyrrole and sodium alginate were synthesized through an in situ oxidative polymerization. The morphology of the samples was studied by scanning electron microscopy and transmission electron microscopy. The structure and chemical components were investigated by X-ray diffraction, Fourier-transform infrared spectroscopy, and Raman spectra. These polypyrrole and sodium alginate nanocomposites exhibited better thermal stability than that of polypyrrole as confirmed by thermogravimetric analyzer. Afterward, the electrorheological characteristics of these as-obtained samples dispersed in silicone oil were examined by a rotational rheometer. The typical electro-responsive properties were observed under an external electric field. Furthermore, compared with polypyrrole, the core/shell-like structured polypyrrole and sodium alginate nanocomposites exhibited enhanced electrorheological behaviors, providing potential application in smart automatic control systems.
Understanding the Characteristics of Mass Spectrometry Data through the use of Simulation
Authors: Coombes Kevin R. , Koomen John M. , Baggerly Keith A. , Morris Jeffrey S. , Kobayashi Ryuji
Published: October 2017

Background

Mass spectrometry is actively being used to discover disease-related proteomic patterns in complex mixtures of proteins derived from tissue samples or from easily obtained biological fluids. The potential importance of these clinical applications has made the development of better methods for processing and analyzing the data an active area of research. It is, however, difficult to determine which methods are better without knowing the true biochemical composition of the samples used in the experiments.

Methods

We developed a mathematical model based on the physics of a simple MALDI-TOF mass spectrometer with time-lag focusing. Using this model, we implemented a statistical simulation of mass spectra. We used the simulation to explore some of the basic operating characteristics of MALDI or SELDI instruments.

Results

The simulation reproduced several characteristics of actual instruments. We found that the relative mass error is affected by the time discretization of the detector (about 0.01%) and the spread of initial velocities (about 0.1%). The accuracy of calibration based on external standards decays rapidly outside the range spanned by the calibrants. Natural isotope distributions play a major role in broadening peaks associated with individual proteins. The area of a peak is a more accurate measure of its size than the height.

Conclusions

The model described here is capable of simulating realistic mass spectra. The simulation should become a useful tool for generating spectra where the true inputs are known, allowing researchers to evaluate the performance of new methods for processing and analyzing mass spectra.

Availability

http://bioinformatics.mdanderson.org/cromwell.html
Construction and Clarification of Dynamic Gene Regulatory Network of Cancer Cell Cycle via Microarray Data
Authors: Li Cheng-Wei , Chu Yung-Hsiang , Chen Bor-Sen
Published: October 2017

Background

Cell cycle is an important clue to unravel the mechanism of cancer cells. Recently, expression profiles of cDNA microarray data of Cancer cell cycle are available for the information of dynamic interactions among Cancer cell cycle related genes. Therefore, it is more appealing to construct a dynamic model for gene regulatory network of Cancer cell cycle to gain more insight into the infrastructure of gene regulatory mechanism of cancer cell via microarray data.

Results

Based on the gene regulatory dynamic model and microarray data, we construct the whole dynamic gene regulatory network of Cancer cell cycle. In this study, we trace back upstream regulatory genes of a target gene to infer the regulatory pathways of the gene network by maximum likelihood estimation method. Finally, based on the dynamic regulatory network, we analyze the regulatory abilities and sensitivities of regulatory genes to clarify their roles in the mechanism of Cancer cell cycle.

Conclusions

Our study presents a systematically iterative approach to discern and characterize the transcriptional regulatory network in Hela cell cycle from the raw expression profiles. The transcription regulatory network in Hela cell cycle can also be confirmed by some experimental reviews. Based on our study and some literature reviews, we can predict and clarify the E2F target genes in G1/S phase, which are crucial for regulating cell cycle progression and tumorigenesis. From the results of the network construction and literature confirmation, we infer that MCM4, MCM5, CDC6, CDC25A, UNG and E2F2 are E2F target genes in Hela cell cycle.
Spatial Turing-type Pattern Formation in a Model of Signal Transduction Involving Membrane-based Receptors Coupled by G Proteins
Authors: Rattanakul Chontita , Lenbury Yongwimon , Bell Jonathan , Chatsudthipong Varanuj , Triampo Wannapong , Crooke Philip S.
Published: October 2017

In this paper, a model of signaling pathways involving G proteins is investigated. The model incorporates reaction-diffusion mechanisms in which various reactants participate inside and on the extra-cellular surface membrane. The messenger molecules may diffuse over the surface of the cell membrane and signal transduction across the cell membrane is mediated by membrane receptor bound proteins which connect the genetically controlled biochemical intra-cellular reactions to the production of the second messenger, leading to desired functional responses. Dynamic and steady-state properties of the model are then investigated through weakly nonlinear stability analysis. Turing-type patterns are shown to form robustly under different delineating conditions on the system parameters. The theoretical predictions are then discussed in the context of some recently reported experimental evidence.
On the Number of Close-to-Optimal Feature Sets
Authors: Dougherty Edward R. , Brun Marcel
Published: October 2017

The issue of wide feature-set variability has recently been raised in the context of expression-based classification using microarray data. This paper addresses this concern by demonstrating the natural manner in which many feature sets of a certain size chosen from a large collection of potential features can be so close to being optimal that they are statistically indistinguishable. Feature-set optimality is inherently related to sample size because it only arises on account of the tendency for diminished classifier accuracy as the number of features grows too large for satisfactory design from the sample data. The paper considers optimal feature sets in the framework of a model in which the features are grouped in such a way that intra-group correlation is substantial whereas inter-group correlation is minimal, the intent being to model the situation in which there are groups of highly correlated co-regulated genes and there is little correlation between the co-regulated groups. This is accomplished by using a block model for the covariance matrix that reflects these conditions. Focusing on linear discriminant analysis, we demonstrate how these assumptions can lead to very large numbers of close-to-optimal feature sets.
Detecting Outlier Microarray Arrays by Correlation and Percentage of Outliers Spots
Authors: Yang Song , Guo Xiang , Yang Yaw-Ching , Papcunik Denise , Heckman Caroline , Hooke Jeffrey , Shriver Craig D. , Liebman Michael N. , Hu Hai
Published: October 2017

We developed a quality assurance (QA) tool, namely microarray outlier filter (MOF), and have applied it to our microarray datasets for the identification of problematic arrays. Our approach is based on the comparison of the arrays using the correlation coefficient and the number of outlier spots generated on each array to reveal outlier arrays. For a human universal reference (HUR) dataset, which is used as a technical control in our standard hybridization procedure, 3 outlier arrays were identified out of 35 experiments. For a human blood dataset, 12 outlier arrays were identified from 185 experiments. In general, arrays from human blood samples displayed greater variation in their gene expression profiles than arrays from HUR samples. As a result, MOF identified two distinct patterns in the occurrence of outlier arrays. These results demonstrate that this methodology is a valuable QA practice to identify questionable microarray data prior to downstream analysis.
Bioinformatics Resources for Cancer Research with an Emphasis on Gene Function and Structure Prediction Tools
Authors: Kihara Daisuke , Yang Yifeng David , Hawkins Troy
Published: October 2017

The immensely popular fields of cancer research and bioinformatics overlap in many different areas, e.g. large data repositories that allow for users to analyze data from many experiments (data handling, databases), pattern mining, microarray data analysis, and interpretation of proteomics data. There are many newly available resources in these areas that may be unfamiliar to most cancer researchers wanting to incorporate bioinformatics tools and analyses into their work, and also to bioinformaticians looking for real data to develop and test algorithms. This review reveals the interdependence of cancer research and bioinformatics, and highlight the most appropriate and useful resources available to cancer researchers. These include not only public databases, but general and specific bioinformatics tools which can be useful to the cancer researcher. The primary foci are function and structure prediction tools of protein genes. The result is a useful reference to cancer researchers and bioinformaticians studying cancer alike.
Cross-platform Analysis of Cancer Biomarkers: A Bayesian Network Approach to Incorporating Mass Spectrometry and Microarray Data
Authors: Deng Xutao , Geng Huimin , Ali Hesham H.
Published: October 2017

Many studies showed inconsistent cancer biomarkers due to bioinformatics artifacts. In this paper we use multiple data sets from microarrays, mass spectrometry, protein sequences, and other biological knowledge in order to improve the reliability of cancer biomarkers. We present a novel Bayesian network (BN) model which integrates and cross-annotates multiple data sets related to prostate cancer. The main contribution of this study is that we provide a method that is designed to find cancer biomarkers whose presence is supported by multiple data sources and biological knowledge. Relevant biological knowledge is explicitly encoded into the model parameters, and the biomarker finding problem is formulated as a Bayesian inference problem. Besides diagnostic accuracy, we introduce reliability as another quality measurement of the biological relevance of biomarkers. Based on the proposed BN model, we develop an empirical scoring scheme and a simulation algorithm for inferring biomarkers. Fourteen genes/proteins including prostate specific antigen (PSA) are identified as reliable serum biomarkers which are insensitive to the model assumptions. The computational results show that our method is able to find biologically relevant biomarkers with highest reliability while maintaining competitive predictive power. In addition, by combining biological knowledge and data from multiple platforms, the number of putative biomarkers is greatly reduced to allow more-focused clinical studies.
Estimating the False Discovery Rate Using Mixed Normal Distribution for Identifying Differentially Expressed Genes in Microarray Data Analysis
Authors: Hirakawa Akihiro , Sato Yasunori , Sozu Takashi , Hamada Chikuma , Yoshimura Isao
Published: October 2017

The recent development of DNA microarray technology allows us to measure simultaneously the expression levels of thousands of genes and to identify truly correlated genes with anticancer drug response (differentially expressed genes) from many candidate genes. Significance Analysis of Microarray (SAM) is often used to estimate the false discovery rate (FDR), which is an index for optimizing the identifiability of differentially expressed genes, while the accuracy of the estimated FDR by SAM is not necessarily confirmed. We propose a new method for estimating the FDR assuming a mixed normal distribution on the test statistic and examine the performance of the proposed method and SAM using simulated data. The simulation results indicate that the accuracy of the estimated FDR by the proposed method and SAM, varied depending on the experimental conditions. We applied both methods to actual data comprised of expression levels of 12,625 genes of 10 responders and 14 non-responders to docetaxel for breast cancer. The proposed method identified 280 differentially expressed genes correlated with docetaxel response using a cut-off value for achieving FDR <0.01 to prevent false-positive genes, although 92 genes were previously thought to be correlated with docetaxel response ones.
Development of Query Strategies to Identify a Histologic Lymphoma Subtype in a Large Linked Database System
Authors: Graiser Michael , Moore Susan G. , Victor Rochelle , Hilliard Ashley , Hill Leroy , Keehan Michael S. , Flowers Christopher R.
Published: October 2017

Background

Large linked databases (LLDB) represent a novel resource for cancer outcomes research. However, accurate means of identifying a patient population of interest within these LLDBs can be challenging. Our research group developed a fully integrated platform that provides a means of combining independent legacy databases into a single cancer-focused LLDB system. We compared the sensitivity and specificity of several SQL-based query strategies for identifying a histologic lymphoma subtype in this LLDB to determine the most accurate legacy data source for identifying a specific cancer patient population.

Methods

Query strategies were developed to identify patients with follicular lymphoma from a LLDB of cancer registry data, electronic medical records (EMR), laboratory, administrative, pharmacy, and other clinical data. Queries were performed using common diagnostic codes (ICD-9), cancer registry histology codes (ICD-O), and text searches of EMRs. We reviewed medical records and pathology reports to confirm each diagnosis and calculated the sensitivity and specificity for each query strategy.

Results

Together the queries identified 1538 potential cases of follicular lymphoma. Review of pathology and other medical reports confirmed 415 cases of follicular lymphoma, 300 pathology-verified and 115 verified from other medical reports. The query using ICD-O codes was highly specific (96%). Queries using text strings varied in sensitivity (range 7–92%) and specificity (range 86–99%). Queries using ICD-9 codes were both less sensitive (34–44%) and specific (35–87%).

Conclusions

Queries of linked-cancer databases that include cancer registry data should utilize ICD-O codes or employ structured free-text searches to identify patient populations with a precise histologic diagnosis.
Comparison of Predicted Probabilities of Proportional Hazards Regression and Linear Discriminant Analysis Methods Using a Colorectal Cancer Molecular Biomarker Database
Authors: Meleth Sreelatha , Chatla Chakrapani , Katkoori Venkat R. , Anderson Billie , Hardin James M. , Jhala Nirag C. , Bartolucci Al , Grizzle William E. , Manne Upender
Published: October 2017

Background

Although a majority of studies in cancer biomarker discovery claim to use proportional hazards regression (PHREG) to the study the ability of a biomarker to predict survival, few studies use the predicted probabilities obtained from the model to test the quality of the model. In this paper, we compared the quality of predictions by a PHREG model to that of a linear discriminant analysis (LDA) in both training and test set settings.

Methods

The PHREG and LDA models were built on a 491 colorectal cancer (CRC) patient dataset comprised of demographic and clinicopathologic variables, and phenotypic expression of p53 and Bcl-2. Two variable selection methods, stepwise discriminant analysis and the backward selection, were used to identify the final models. The endpoint of prediction in these models was five-year post-surgery survival. We also used linear regression model to examine the effect of bin size in the training set on the accuracy of prediction in the test set.

Results

The two variable selection techniques resulted in different models when stage was included in the list of variables available for selection. However, the proportion of survivors and non-survivors correctly identified was identical in both of these models. When stage was excluded from the variable list, the error rate for the LDA model was 42% as compared to an error rate of 34% for the PHREG model.

Conclusions

This study suggests that a PHREG model can perform as well or better than a traditional classifier such as LDA to classify patients into prognostic classes. Also, this study suggests that in the absence of the tumor stage as a variable, Bcl-2 expression is a strong prognostic molecular marker of CRC.