Multiple Experimental Design Research Articles

Stakeholder-specific data acquisition and urban freight policy evaluation: evidence, implications and new suggestions

ABSTRACTCities, characterised by scarce resources and facing increasing citizens’ requests for more liveable and attractive environments, need to define and implement more efficient urban freight transport policy interventions. It is strategically relevant to perform an ex-ante in-depth policy evaluation. The paper aims at investigating the relevance, the needs and the implications of the data acquisition process by combining a focused review with an original research study. It critically compares five categories of methodological approaches adopted for policy evaluation purposes with an explicit focus on the stated preference method given its behavioural peculiarity and robustness. The review, following a systematic procedure where scientific quality is complemented by relevance and coverage, underlines a high variability in the level of sophistication for data acquisition. While a stakeholder-specific approach seems natural in all those cases where local authorities want to adopt distinct policy instruments for each stakeholder, this is also needed when homogeneous policies impacting the various stakeholders are tested. Stakeholder-specific data acquisition translates into the creation of multiple experimental designs, explicitly conceived for each stakeholder considered. A case study, intended to compare two alternative strategies for data acquisition, provides a quantitative measure of potential distortions in policy evaluation due to the adoption of a stakeholder-generic approach. Results, in terms of willingness to pay measures, show that a stakeholder-specific approach is needed not only when modelling but also in the data acquisition process.

Bioenergy and Breast Cancer: A Report on Tumor Growth and Metastasis.

As many as 80% of the 296,000 women and 2,240 men diagnosed with breast cancer in the United States will seek out complementary and alternative medicine (CAM) treatments. One such therapy is Healing Touch (HT), recognized by the National Center for Complementary and Integrative Health (NCCIH) as a treatment modality. Using a multiple experimental groups design, fifty-six six- to eight-week-old Balb/c mice were injected with 4T1 breast cancer tumor cells and randomly divided into intervention and positive control groups. Five days after tumor cell injection, mice in the intervention groups received HT either daily or every other day for 10 minutes by one HT practitioner. At 15 days after tumor cell injection, tumor size was measured, and metastasis was evaluated by a medical pathologist after necropsy. Tumor size did not differ significantly among the groups (F(3,52) = 0.75, p value = 0.53). The presence of metastasis did not differ across groups (chi-square(3) = 3.902, p = 0.272) or when compared within an organ (liver: chi-square(3) = 2.507, p = 0.474; lungs: chi-square(3) = 3.804, p = 0.283; spleen: chi-square(3) = 0.595, p = 0.898). However, these results did indicate a moderate, though insignificant, positive impact of HT and highlight the need for continued research into dose, length of treatment, and measurable outcomes (tumor size, metastasis) to provide evidence to suggest application for nursing care.

Effects of virtual reality training with modified constraint-induced movement therapy on upper extremity function in acute stage stroke: a preliminary study.

[Purpose] The purpose of this study was to investigate the effects of virtual reality training combined with modified constraint-induced movement therapy on upper extremity motor function recovery in acute stage stroke patients. [Subjects and Methods] Four acute stage stroke patients participated in the study. A multiple baseline single subject experimental design was utilized. Modified constraint-induced movement therapy was used according to the EXplaining PLastICITy after stroke protocol during baseline sessions. Virtual reality training with modified constraint-induced movement therapy was applied during treatment sessions. The Manual Function Test and the Box and Block Test were used to measure upper extremity function before every session. [Results] The subjects’ upper extremity function improved during the intervention period. [Conclusion] Virtual reality training combined with modified constraint-induced movement is effective for upper extremity function recovery in acute stroke patients.

Simultaneous optimization of heat transfer and reciprocation intensity for thermal processing of liquid particulate mixtures undergoing reciprocating agitation

Abstract Simultaneous optimization of heat transfer and reciprocation intensity (RI) was carried out for reciprocating agitation thermal processing (RA-TP) of liquid-particulate foods. Heat transfer data under various processing conditions (temperature, frequency, and headspace) and product properties (liquid viscosity, particle concentration, and particle density) during RA-TP were utilized to develop a composite model using quadratic stepwise regression analysis. Heat transfer coefficients were maximized individually and simultaneously with minimal RI using multiple-variable optimization. High RI (37–45 ms − 2 ) was recommended to maximize heat transfer alone, whereas lower RI (16–19 ms − 2 ) was found optimal for simultaneous optimization of heat transfer & RI. Product formulations containing low viscosity liquids filled with 23–27% volume of particles with density of 1130–1350 kg/m 3 were found most desirable for maintaining good quality under RA-TP. Optimal conditions were also reported for applying RA-TP under different operating temperatures, liquid viscosities, particle concentrations and particle densities. Industrial significance Reciprocating agitation of containers is receiving interest from thermal processing industry for enhancing product quality. The aim of this study was to optimize the use of reciprocating agitation thermal processing for processing particulate liquid foods. This study attempts to predict optimal conditions of processing conditions and product compositions for reciprocating agitation thermal processing. Multi-variable optimization based on data from multiple experimental designs is conducted in this study, which will be directly relevant to be used by industry seeking to employ reciprocating agitation thermal processing.

Visual Attention and Key Word Sign in Children with Autism Spectrum Disorder

The aim of this study was to examine the relationship between children’s visual attention to, and acquisition of, Key Word Sign (KWS). Our hypothesis was that children’s visual attention to the clinician’s modelling would be associated with their acquisition and production of KWS. A multiple baseline single case experimental design with additional exploratory analyses was used to examine the effect of visual attention on sign acquisition among three preschool children with ASD. This paper extends from the previous intervention study (see Tan et al. 2014) by examining visual attention as a potential factor underpinning individual differences in sign acquisition documented in the initial study. All three children visually attended to the clinician’s modelling of KWS, and acquired signs to varying degrees following the introduction of KWS intervention. A weak, non-significant correlation was found between the children’s amount of visual attention and their production of signs. The results provide preliminary evidence for a lack of association between children’s amount of ‘looking’ and ‘doing’ during KWS intervention. Replication and examination of other factors impacting on KWS intervention outcomes is required.

Decreased Cortisol and Pain in Breast Cancer: Biofield Therapy Potential.

Breast cancer is one of the leading causes of cancer death among women of all races. Pain is a common symptom associated with cancer; 75–90% of cancer patients experience pain during their illness and up to 50% of that pain is undertreated. Unrelieved pain leads to increased levels of the stress hormone cortisol. The purpose of this study was to examine the impact of bioenergy on fecal cortisol levels for mice injected with murine mammary carcinoma 4T1 in two separate pilot studies. Using a multiple experimental group design, six to eight week old female BALB/c mice were injected with tumor and randomly assigned, in groups of 10, to daily treatment, every other day treatment, and no treatment groups. Five days after tumor cell injection, bioenergy interventions were begun for a period of ten consecutive days. Fecal samples were collected for each study and ELISA analysis was conducted at the end of both studies. For both studies, cortisol levels were decreased in the every other day treatment groups but remained high in the no treatment groups. Future studies utilizing bioenergy therapies on cortisol levels in a murine breast cancer model can begin to describe pain outcomes and therapeutic dose.

PrimerDesign-M: a multiple-alignment based multiple-primer design tool for walking across variable genomes.

Analyses of entire viral genomes or mtDNA requires comprehensive design of many primers across their genomes. Furthermore, simultaneous optimization of several DNA primer design criteria may improve overall experimental efficiency and downstream bioinformatic processing. To achieve these goals, we developed PrimerDesign-M. It includes several options for multiple-primer design, allowing researchers to efficiently design walking primers that cover long DNA targets, such as entire HIV-1 genomes, and that optimizes primers simultaneously informed by genetic diversity in multiple alignments and experimental design constraints given by the user. PrimerDesign-M can also design primers that include DNA barcodes and minimize primer dimerization. PrimerDesign-M finds optimal primers for highly variable DNA targets and facilitates design flexibility by suggesting alternative designs to adapt to experimental conditions. PrimerDesign-M is available as a webtool at http://www.hiv.lanl.gov/content/sequence/PRIMER_DESIGN/primer_design.html tkl@lanl.gov or seq-info@lanl.gov.

Toward predicting community‐level effects of climate: relative temperature scaling of metabolic and ingestion rates

Predicting the effects of climate change on ecological communities requires an understanding of how environmental factors influence both physiological processes and species interactions. Specifically, the net impact of temperature on community structure depends on the relative response of physiological energetic costs (metabolism) and energetic gains (ingestion of resources) that mediate the flow of energy throughout a food web. However, the relative temperature scaling of metabolic and ingestion rates have rarely been measured for multiple species within an ecological assemblage and it is not known how, and to what extent, these relative scaling differences vary among species. To investigate the relative influence of these processes, I measured the temperature scaling of metabolic and ingestion rates for a suite of rocky intertidal species using a multiple regression experimental design. I compared oxygen consumption rates (as a proxy for metabolic rate) and ingestion rates by estimating the temperature scaling parameter of the universal temperature dependence (UTD) model, a theoretical model derived from first principles of biochemical kinetics and allometry. The results show that consumer metabolic rates (energetic costs) tend to be more sensitive to temperature than ingestion rates (energetic gains). Thus, as temperature increases, metabolic rates tend to increase faster relative to ingestion rates, causing the overall energetic efficiencies of these rocky intertidal invertebrates to decline. Metabolic and ingestion rates largely scaled in accordance with the UTD model; however, nonlinearity was evident in several cases, particularly at higher temperatures, in which alternative models were more appropriate. There are few studies where multiple rate dependencies are measured on multiple species from the same ecological community. These results indicate that there may be general patterns across species in the temperature scaling of biological rates, with important implications for forecasting temperature effects on ecological communities.

Effect of Endurance Exercise among Filipino Body Builders With Chronic, Non-specific Neck Pain and Disability

Neck pain can hamper a bodybuilder’s quest for a bigger body. The study evaluated the effect of endurance exercises on the chronic nonspecific neck pain and disability of bodybuilders. The study used a quantitative multiple time series experimental research design. The subjects were chosen purposively and randomly assigned to either a 4-week neck endurance (experimental group, EG) or usual training (control group, CG). A demographic questionnaire was used in collecting the person and task variables; analog visual scale for the environmental factors, and the Neck Disability Index (NDI) was used in evaluating neck pain and disability. Multiple linear regressions were used in identifying the strongest predictor of NDI while t- test was used to test the hypothesis. Age was the strongest predictor of NDI (r=-0.571, p=0.342). There was a reduction in the NDI score of both the EG and CG, which was statistically significant on the fourth week. A t-test revealed a statistically insignificant result (p=0.124) on the CG while a statistically significant improvement (p=0.000) was found in the EG. A modification of general workout of younger bodybuilders and inclusion of endurance exercises can potentially decrease their risk of neck pain. Keywords - Physical therapy, endurance exercises, neck pain and disability, experimentaltime series, Antipolo City, Philippines

The Effects of a Supplementary Computerized Fluency Intervention on the Generalization of the Oral Reading Fluency and Comprehension of First-Grade Students

The current study investigated the effects of a repeated reading intervention on the oral reading fluency (ORF) and comprehension on generalization passages for eight, first-grade students with reading risk. The intervention involved a commercial computerized program (Read Naturally Software Edition [RNSE], 2009) and a generalization principle requiring a “greater magnitude” of responding. A multiple probe experimental design with two treatment phases was used to determine the effects of the intervention. Phase I used a standard end-of-year benchmark score (i.e., 40 CWMP) as fluency criteria for all participants. During Phase II, fluency criteria were changed and individualized for each participant based on performance during Phase I. Data were collected on ORF and word retell fluency (WRF) across treatment and generalization probes. Results showed ORF and comprehension increases in both phases; however, satisfactory generalization did not occur for most of the participants until the second phase was implemented. These results are discussed relative to classroom implications and directions for future research.

Effects of an Individualized Narrative Intervention on Children’s Storytelling and Comprehension Skills

Narrative skills that are important for preschoolers include retelling stories, telling personal stories, and answering questions about stories. Narrative abilities form the foundation of reading comprehension. The purpose of this study was to investigate the effect of an individually delivered language intervention on the narrative skills of preschoolers with developmental disabilities. Five preschoolers attending Head Start and participating in special education received 24 sessions of intervention each lasting 10 to 15 min. A multiple baseline, multiple probe experimental design was used to investigate the effects of the intervention, which incorporated visual supports (e.g., icons and pictures) and retell and personal storytelling practice, on story retells, personal stories, and story comprehension. Improvements on all three measures were related to the intervention. Parents and teachers reported that the storytelling activities were engaging, enjoyable, and produced improvements in the children’s language skills.

FMRI reliability: Influences of task and experimental design

As scientists, it is imperative that we understand not only the power of our research tools to yield results, but also their ability to obtain similar results over time. This study is an investigation into how common decisions made during the design and analysis of a functional magnetic resonance imaging (fMRI) study can influence the reliability of the statistical results. To that end, we gathered back-to-back test-retest fMRI data during an experiment involving multiple cognitive tasks (episodic recognition and two-back working memory) and multiple fMRI experimental designs (block, event-related genetic sequence, and event-related m-sequence). Using these data, we were able to investigate the relative influences of task, design, statistical contrast (task vs. rest, target vs. nontarget), and statistical thresholding (unthresholded, thresholded) on fMRI reliability, as measured by the intraclass correlation (ICC) coefficient. We also utilized data from a second study to investigate test-retest reliability after an extended, six-month interval. We found that all of the factors above were statistically significant, but that they had varying levels of influence on the observed ICC values. We also found that these factors could interact, increasing or decreasing the relative reliability of certain Task × Design combinations. The results suggest that fMRI reliability is a complex construct whose value may be increased or decreased by specific combinations of factors.

Error estimation properties of Gaussian process models in stochastic simulations

The theoretical relationship between the prediction variance of a Gaussian process model (GPM) and its mean square prediction error is well known. This relationship has been studied for the case when deterministic simulations are used in GPM, with application to design of computer experiments and metamodeling optimization. This article analyzes the error estimation of Gaussian process models when the simulated data observations contain measurement noise. In particular, this work focuses on the correlation between the GPM prediction variance and the distribution of prediction errors over multiple experimental designs, as a function of location in the input space. The results show that the error estimation properties of a Gaussian process model using stochastic simulations are preserved when the signal-to-noise ratio in the data is larger than 10, regardless of the number of training points used in the metamodel. Also, this article concludes that the distribution of prediction errors approaches a normal distribution with a variance equal to the GPM prediction variance, even in the presence of significant bias in the GPM predictions.

Micro–Single-Photon Emission Computed Tomography Image Acquisition and Quantification of Sodium-Iodide Symporter–Mediated Radionuclide Accumulation in Mouse Thyroid and Salivary Glands

Micro-single-photon emission computed tomography (SPECT) provides a noninvasive way to evaluate the effects of genetic and/or pharmacological modulation on sodium-iodide symporter (NIS)-mediated radionuclide accumulation in mouse thyroid and salivary glands. However, parameters affecting image acquisition and analysis of mouse thyroids and salivary glands have not been thoroughly investigated. In this study, we investigated the effects of region-of-interest (ROI) selection, collimation, scan time, and imaging orbit on image acquisition and quantification of thyroidal and salivary radionuclide accumulation in mice. The effects of data window minima and maxima on thyroidal and salivary ROI selection using a visual boundary method were examined in SPECT images acquired from mice injected with (123)I NaI. The effects of collimation, scan time, and imaging orbit on counting linearity and signal intensity were investigated using phantoms filled with various activities of (123)I NaI or Tc-99m pertechnetate. Spatial resolution of target organs in whole-animal images was compared between circular orbit with parallel-hole collimation and spiral orbit with five-pinhole collimation. Lastly, the inter-experimental variability of the same mouse scanned multiple times was compared with the intra-experimental variability among different mice scanned at the same time. Thyroid ROI was separated from salivary glands by empirically increasing the data window maxima. Counting linearity within the range of 0.5-14.2 μCi was validated by phantom imaging using single- or multiple-pinhole collimators with circular or spiral imaging orbit. Scanning time could be shortened to 15 minutes per mouse without compromising counting linearity despite proportionally decreased signal intensity. Whole-animal imaging using a spiral orbit with five-pinhole collimators achieved a high spatial resolution and counting linearity. Finally, the extent of inter-experimental variability of NIS-mediated radionuclide accumulation in the thyroid and salivary glands by SPECT imaging in the same mouse was less than the magnitude of variability among the littermates. The impacts of multiple variables and experimental designs on micro-SPECT imaging and quantification of radionuclide accumulation in mouse thyroid and salivary glands can be minimized. This platform will serve as an invaluable tool to screen for pharmacologic reagents that differentially modulate thyroidal and salivary radioiodine accumulation in preclinical mouse models.

Rapid earth modelling for appraisal and development studies of deep-water clastic reservoirs and the concept of ‘procycling’

Creating earth models for deep-water appraisal and development studies is perhaps the most challenging task confronting the petroleum geologist today. Data are limited (few wells, limited core, untested seismic quality), time is limited and drilling, testing and facilities costs are very high. Uncertainty in geological characterization of the reservoir can have the greatest potential impact on project value. How can a thorough characterization of reservoir uncertainty be made based on limited data and in a timely fashion? A workflow for creation of suites of models for appraisal and development studies of deep-water reservoirs is described. The goal of the workflow is to rapidly construct suites of earth models based on limited data that capture the full range of uncertainty in reservoir characteristics and properties. After characterizing possible distributions for individual parameters, suites of earth models are built in a single step using an experimental design framework, aided by a powerful workflow manager which automates earth model construction. Earth models created using the experimental design framework are seamlessly linked to flow simulation software. Plackett–Burman, folded Plackett–Burman and Full Factorial experimental designs were used in different appraisal and development cases. Multiple experimental designs were produced by adding and modifying uncertainty parameters as additional data arrived, and ideas about the possible character of the reservoir evolved. In the appraisal case described here, 6 experimental designs were made, 388 earth models were created and studied, and 79 of those models were dynamically simulated. The process of quickly building and re-building suites of earth models using experimental designs to address changing perceptions and concerns about uncertainty in reservoir character is termed here ‘procycling’. Procycling is complementary to experimental design studies, in that multiple experimental designs are employed over time: procycling focuses on changes in predictions made by individual experimental design studies. The results of procycling are not necessarily to change the perception of uncertainty (for example the range of possible outcomes), but to anchor what the limits of uncertainty are and what the most important uncertainties are with the given data.

Wanted dead or alive? The tradeoff between in-vivo versus ex-vivo MR brain imaging in the mouse

High-resolution MRI of the mouse brain is gaining prominence in estimating changes in neuroanatomy over time to understand both normal developmental as well as disease processes and mechanisms. These types of experiments, where a change in time is to be captured as accurately as possible using imaging, face multiple experimental design choices. Chief amongst these choices is whether to image ex-vivo, where superior resolution and contrast are available, or in-vivo, where resolution and contrast are lower but the animal can be followed longitudinally. Here we explore this tradeoff by first estimating the sources of variability in anatomical mouse MRI and then, using statistical simulations, provide power analyses of these experiment design choices.

Triple Combination Antiviral Drug (TCAD) Composed of Amantadine, Oseltamivir, and Ribavirin Impedes the Selection of Drug-Resistant Influenza A Virus

Widespread resistance among circulating influenza A strains to at least one of the anti-influenza drugs is a major public health concern. A triple combination antiviral drug (TCAD) regimen comprised of amantadine, oseltamivir, and ribavirin has been shown to have synergistic and broad spectrum activity against influenza A strains, including drug resistant strains. Here, we used mathematical modeling along with three different experimental approaches to understand the effects of single agents, double combinations, and the TCAD regimen on resistance in influenza in vitro, including: 1) serial passage at constant drug concentrations, 2) serial passage at escalating drug concentrations, and 3) evaluation of the contribution of each component of the TCAD regimen to the suppression of resistance. Consistent with the modeling which demonstrated that three drugs were required to suppress the emergence of resistance in influenza A, treatment with the TCAD regimen resulted in the sustained suppression of drug resistant viruses, whereas treatment with amantadine alone or the amantadine-oseltamivir double combination led to the rapid selection of resistant variants which comprised ∼100% of the population. Furthermore, the TCAD regimen imposed a high genetic barrier to resistance, requiring multiple mutations in order to escape the effects of all the drugs in the regimen. Finally, we demonstrate that each drug in the TCAD regimen made a significant contribution to the suppression of virus breakthrough and resistance at clinically achievable concentrations. Taken together, these data demonstrate that the TCAD regimen was superior to double combinations and single agents at suppressing resistance, and that three drugs at a minimum were required to impede the selection of drug resistant variants in influenza A virus. The use of mathematical modeling with multiple experimental designs and molecular readouts to evaluate and optimize combination drug regimens for the suppression of resistance may be broadly applicable to other infectious diseases.

The role of chemometrics in single and sequential extraction assays: A Review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques

Single and sequential extraction procedures are used for studying element mobility and availability in solid matrices, like soils, sediments, sludge, and airborne particulate matter. In the first part of this review we reported an overview on these procedures and described the applications of chemometric uni- and bivariate techniques and of multivariate pattern recognition techniques based on variable reduction to the experimental results obtained. The second part of the review deals with the use of chemometrics not only for the visualization and interpretation of data, but also for the investigation of the effects of experimental conditions on the response, the optimization of their values and the calculation of element fractionation. We will describe the principles of the multivariate chemometric techniques considered, the aims for which they were applied and the key findings obtained. The following topics will be critically addressed: pattern recognition by cluster analysis (CA), linear discriminant analysis (LDA) and other less common techniques; modelling by multiple linear regression (MLR); investigation of spatial distribution of variables by geostatistics; calculation of fractionation patterns by a mixture resolution method (Chemometric Identification of Substrates and Element Distributions, CISED); optimization and characterization of extraction procedures by experimental design; other multivariate techniques less commonly applied.

Research of reliability for measuring high-g micro array acceleration sensor

High-g is an acceleration value larger than 105 g. For the requirements of detecting high-g, people pay more and more attention to the reliability of an accelerometer. In this paper, the design and manufacture of high-g micro array acceleration sensor are researched to improve the reliability of the accelerometer. Owing to the superiority of MEMS manufacturing, in one processing, multiple micro-acceleration sensors with the same function can be integrated in one chip. According to the method of the array layout of multiple micro-acceleration sensors on a two-dimensional plane, the layout is ascertained by using multiple non-linear regression and orthogonal experimental design. Multiple micro-acceleration sensors lay reasonably to improve the reliability of the high-g micro array acceleration sensor. The linked line of sensor system, which is manufactured according to these parameters, remains intact by the impact of 1.22 × 105 g. Owing to the optimised design and manufacture, the reliability of the acceleration is increased to 0.9906, which meets the requirements of the application.

The filamentous fungal gene expression database (FFGED)

Filamentous fungal gene expression assays provide essential information for understanding systemic cellular regulation. To aid research on fungal gene expression, we constructed a novel, comprehensive, free database, the filamentous fungal gene expression database (FFGED), available at http://bioinfo.townsend.yale.edu. FFGED features user-friendly management of gene expression data, which are assorted into experimental metadata, experimental design, raw data, normalized details, and analysis results. Data may be submitted in the process of an experiment, and any user can submit multiple experiments, thus classifying the FFGED as an “active experiment” database. Most importantly, FFGED functions as a collective and collaborative platform, by connecting each experiment with similar related experiments made public by other users, maximizing data sharing among different users, and correlating diverse gene expression levels under multiple experimental designs within different experiments. A clear and efficient web interface is provided with enhancement by AJAX (Asynchronous JavaScript and XML) and through a collection of tools to effectively facilitate data submission, sharing, retrieval and visualization.