Advances In Experimental Design Research Articles

Metabolomics: Challenges and Opportunities in Systems Biology Studies.

Metabolomics can provide diagnostic, prognostic, and therapeutic biomarker profiles of individual patients because a large number of metabolites can be simultaneously measured in biological samples in an unbiased manner. Minor stimuli can result in substantial alterations, making it a valuable target for analysis. Due to the complexity and sensitivity of the metabolome, studies must be devised to maintain consistency, minimize subject-to-subject variation, and maximize information recovery. This effort has been aided by technological advances in experimental design, rodent models, and instrumentation. Proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy of biofluids, such as plasma, urine, and faeces provide the opportunity to identify biomarker change patterns that reflect the physiological or pathological status of an individual patient. Metabolomics has the ultimate potential to be useful in a clinical context, where it could be used to predict treatment response and survival and for early disease diagnosis. During drug treatment, an individual's metabolic status could be monitored and used to predict deleterious effects. Therefore, metabolomics has the potential to improve disease diagnosis, treatment, and follow-up care. In this chapter, we demonstrate how a metabolomics study can be used to diagnose a disease by classifying patients as either healthy or pathological, while accounting for individual variation.

Parameterization of the durations of phases of foot-and-mouth disease in pigs

The global interconnectedness of the pig-production industry and the diversity of foot-and-mouth disease (FMD) viruses (FMDVs) currently circulating, makes modeling disease spread and control in FMD-free areas challenging. However, advances in experimental design and transmission studies create opportunities to strengthen our understanding and ability to model FMD transmission. In the current study, we estimated the duration of defined phases of FMDV infection in pigs by using data from a large collection of controlled in vivo experiments. Because the detection of low-levels of viral RNA does not correspond to infectiousness, an experimentally defined minimum threshold of FMDV RNA shedding in oropharyngeal fluids was used to estimate the onset of infectiousness in experiments in which transmission was not evaluated. Animal-level data were used in Accelerated Failure Time models to assess the effect of experimental design factors in the duration of defined phases of FMDV infection: latent, incubation, pre-clinical infectious, clinical infectious, and total infectious periods. The estimated means of the phases were latent: 25 h (95%CI 21, 29), incubation: 70 h (95%CI 64, 76), pre-clinical infectious: 36 h (95%CI 32, 41), clinical infectious: 265 h (95%CI 258, 272) and total infectious: 282 h (95%CI 273, 290). Virus strains and exposure methods had no significant influence on the duration of latency, incubation, or clinical infectious phases. By contrast, the estimated means of the duration of the pre-clinical infectious and total infectious phases were significantly influenced by virus strains, and the duration of the pre-clinical infectious phase was significantly influenced by exposure methods. This study provides disease parameters based on an estimated threshold of the onset of infectiousness and a probability distribution representing the end of infectiousness. Disease parameters that incorporate experimentally-based quantitative proxies to define phases of FMDV infection may improve planning and preparedness for FMD.

Understanding coralline algal responses to ocean acidification: Meta-analysis and synthesis.

Ocean acidification (OA) is a major threat to the persistence of biogenic reefs throughout the world's ocean. Coralline algae are comprised of high magnesium calcite and have long been considered one of the most susceptible taxa to the negative impacts of OA. We summarize these impacts and explore the causes of variability in coralline algal responses using a review/qualitative assessment of all relevant literature, meta-analysis, quantitative assessment of critical responses, and a discussion of physiological mechanisms and directions for future research. We find that most coralline algae experienced reduced abundance, calcification rates, recruitment rates, and declines in pH within the site of calcification in laboratory experiments simulating OA or at naturally elevated CO2 sites. There were no other consistent physiological responses of coralline algae to simulated OA (e.g., photo-physiology, mineralogy, and survival). Calcification/growth was the most frequently measured parameters in coralline algal OA research, and our meta-analyses revealed greater declines in seawater pH were associated with significant decreases in calcification in adults and similar but nonsignificant trends for juveniles. Adults from the family Mesophyllumaceae also tended to be more robust to OA, though there was insufficient data to test similar trends for juveniles. OA was the dominant driver in the majority of laboratory experiments where other local or global drivers were assessed. The interaction between OA and any other single driver was often additive, though factors that changed pH at the surface of coralline algae (light, water motion, epiphytes) acted antagonistically or synergistically with OA more than any other drivers. With advances in experimental design and methodological techniques, we now understand that the physiology of coralline algal calcification largely dictates their responses to OA. However, significant challenges still remain, including improving the geographic and life-history spread of research effort and a need for holistic assessments of physiology.

Measuring Pharmacogene Variant Function at Scale Using Multiplexed Assays.

As costs of next-generation sequencing decrease, identification of genetic variants has far outpaced our ability to understand their functional consequences. This lack of understanding is a central challenge to a key promise of pharmacogenomics: using genetic information to guide drug selection and dosing. Recently developed multiplexed assays of variant effect enable experimental measurement of the function of thousands of variants simultaneously. Here, we describe multiplexed assays that have been performed on nearly 25,000 variants in eight key pharmacogenes (ADRB2, CYP2C9, CYP2C19, NUDT15, SLCO1B1, TMPT, VKORC1, and the LDLR promoter), discuss advances in experimental design, and explore key challenges that must be overcome to maximize the utility of multiplexed functional data.

Analytical methods matter too: Establishing a framework for estimating maximum metabolic rate for fishes.

Advances in experimental design and equipment have simplified the collection of maximum metabolic rate (MMR) data for a more diverse array of water‐breathing animals. However, little attention has been given to the consequences of analytical choices in the estimation of MMR. Using different analytical methods can reduce the comparability of MMR estimates across species and studies and has consequences for the burgeoning number of macroecological meta‐analyses using metabolic rate data. Two key analytical choices that require standardization are the time interval, or regression window width, over which MMR is estimated, and the method used to locate that regression window within the raw oxygen depletion trace. Here, we consider the effect of both choices by estimating MMR for two shark and two salmonid species of different activity levels using multiple regression window widths and three analytical methods: rolling regression, sequential regression, and segmented regression. Shorter regression windows yielded higher metabolic rate estimates, with a risk that the shortest windows (<1‐min) reflect more system noise than MMR signal. Rolling regression was the best candidate model and produced the highest MMR estimates. Sequential regression models consistently produced lower relative estimates than rolling regression models, while the segmented regression model was unable to produce consistent MMR estimates across individuals. The time‐point of the MMR regression window along the oxygen consumption trace varied considerably across individuals but not across models. We show that choice of analytical method, in addition to more widely understood experimental choices, profoundly affect the resultant estimates of MMR. We recommend that researchers (1) employ a rolling regression model with a reliable regression window tailored to their experimental system and (2) explicitly report their analytical methods, including publishing raw data and code.

Usability Evaluation—Advances in Experimental Design in the Context of Automated Driving Human–Machine Interfaces

The projected introduction of conditional automated driving systems to the market has sparked multifaceted research on human–machine interfaces (HMIs) for such systems. By moderating the roles of the human driver and the driving automation system, the HMI is indispensable in avoiding side effects of automation such as mode confusion, misuse, and disuse. In addition to safety aspects, the usability of HMIs plays a vital role in improving the trust and acceptance of the automated driving system. This paper aggregates common research methods and findings based on an extensive literature review. Empirical studies, frameworks, and review articles are included. Findings and conclusions are presented with a focus on study characteristics such as test cases, dependent variables, testing environments, or participant samples. These methods and findings are discussed critically, taking into consideration requirements for usability assessments of HMIs in the context of conditional automated driving. The paper concludes with a derivation of recommended study characteristics framing best practice advice for the design of experiments. The advised selection of scenarios and metrics will be applied in a future validation study series comprising a driving simulator experiment and three real driving experiments on test tracks in Germany, the USA, and Japan.

Special Issue: Advancing Statistical Methods for Testing and Evaluating Defense Systems

The DoD acquires highly complex systems that must be tested and evaluated before they are fielded to military users and used in actual operations. The statistical challenges are vast. Analysis challenges include non-normal data, messy and missing data, functional outcomes, limited data, and data from various types of activities that needs to be combined into a single evaluation. Additionally, these tests are often expensive and are conducted on ranges with limited availability. The systems are operated by military users and their time is valuable and hard to acquire. For all of these reasons, every data point that is requested from a test is scrutinized and evaluated. The tools and methods of experimental design have proven very useful in this regard. The articles in this journal highlight a collection of recent research from the consortium. They show the breadth of research necessary. The articles included showcase advances in experimental design for multiple response variables and space filling designs for simulation experiments that allow for the estimation of second-order effects. Bayesian methods highlighted addressing challenges for multiphase reliability assessments, accelerated reliability growth testing, and profile monitoring for nonlinear profiles. Model validation is addressed through a case study showing the usefulness of wavelets. Unique analysis challenges and their links to experimental designs are addressed in articles on split-plot reliability experiments and D-optimal designs for logistic regression with separation. Finally, one article address practical sample size challenges for testers and answers the canonical question of “How much testing is enough?” A unique aspect of this special issue of the journal is that we included reviewers from both the statistical academic community and those practicing statistics in DoD. I am extremely grateful for their suggestions and reviews, which further improved this special issue. The result is a diversity of research that showcases the depth and breadth of statistical challenges faced in the Defense context. However, they will also provide useful for anyone who works with complex systems or emerging technologies.

A two-stage experimental design for dilution assays.

Dilution assays to determine solute concentration have found wide use in biomedical research. Many dilution assays return imprecise concentration estimates because they are only done to orders of magnitude. Previous statistical work has focused on how to design efficient experiments that can return more precise estimates, however this work has not considered the practical difficulties of implementing these designs in the laboratory. We developed a two-stage experiment with a first stage that obtains an order of magnitude estimate and a second stage that concentrates effort on the most informative dilution to increase estimator precision. We show using simulations and an empirical example that the best two-stage experimental designs yield estimates that are remarkably more accurate than standard methods with equivalent effort. This work demonstrates how to utilize previous advances in experimental design in a manner consistent with current laboratory practice. We expect that multi-stage designs will prove to be useful for obtaining precise estimates with minimal experimental effort.

1913-1914: the First Truly International Year of Crystallography

It is appropriate that 2014 has been chosen as the International Year of Crystallography since it was a century ago that crystallography as we know it emerged as a truly international science. The pattern becomes clearer if one considers academic years starting on September 1. Thus 1911-1912, when the first successful diffraction experiments were carried out, becomes the German year of crystallography. With the statement of Bragg's Law and the first structure determinations, 1912-1913 can be called the British year of crystallography. By late 1913 our science had achieved truly international scope, demonstrated by publications describing advances in experimental design, theory and structure determination emanating from France, Japan, the Netherlands, Russia and Switzerland as well as Germany and Britain. While many countries worked on the first two areas, structure determination was done mainly in Britain (dominated by the Braggs) and Japan (to a limited extent). In the belligerent countries this magnificent flowering was sadly curtailed by the outbreak of World War I. However, at about this time the United States entered the field. The international spread followed by curtailment is readily visualised when presented in the form of a timeline.

Lactate: valuable for physical performance and maintenance of brain function during exercise

Lactate accumulation has long been associated with impaired sports performance, with many supporting the lactate acidosis hypothesis. However, due to advances in experimental design and research, numerous beneficial roles of lactate have been established that may impact upon sports performance. Recent studies highlight lactate as a biomarker of fatigue rather than as a direct cause. The lactate-shuttle mechanism facilitates the utilization of lactate as an energy substrate in both type I and type II skeletal muscle fibres, promoting energy sufficiency during exercise. Recent literature also supports a role for lactate in enhancing human oxidative capacity by up-regulating skeletal muscle mitochondrial biogenesis. In addition, lactate-neuron and lactate-astrocyte shuttles enable lactate to supply energy to support cognitive function, during periods of low blood glucose such as prolonged aerobic exercise. This review aims to clarify the role of lactate in modulating aerobic performance and critically investigates the mechanisms responsible.

Designs with a priori information for nonmarket valuation with choice experiments: A Monte Carlo study

Good practice in experimental design is essential for choice experiments used in nonmarket valuation. We review the practice of experimental design for choice experiments in environmental economics and we compare it with advances in experimental design. We then evaluate the statistical efficiency of four different designs by means of Monte Carlo experiments. Correct and incorrect specifications are investigated with gradually more precise information on the true parameter values. The data generating process (DGP) is based on estimates from data of a real study. Results indicate that D-efficient designs are promising, especially when based on Bayesian algorithms with informative prior. However, if good quality a priori information is lacking, and if there is strong uncertainty about the real DGP—conditions which are quite common in environmental valuation—then practitioners might be better off with shifted designs built from conventional fractional factorial designs for linear models.

Mental health moves up the agenda

The sensitivity of single-cell proteomics (SCP) has increased dramatically in recent years due to advances in experimental design, sample preparation, separations and mass spectrometry instrumentation. Further increasing the sensitivity of SCP methods and instrumentation will enable the study of proteins within single cells that are expressed at copy numbers too small to be measured by current methods. Here we combine efficient nanoPOTS sample preparation and ultra-low-flow liquid chromatography with a newly developed data acquisition and analysis scheme termed wide window acquisition (WWA) to quantify &gt;3,000 proteins from single cells in fast label-free analyses. WWA is based on data-dependent acquisition (DDA) but employs larger precursor isolation windows to intentionally co-isolate and co-fragment additional precursors along with the selected precursor. The resulting chimeric MS2 spectra are then resolved using the CHIMERYS search engine within Proteome Discoverer 3.0. Compared to standard DDA workflows, WWA employing isolation windows of 8-12 Th increases peptide and proteome coverage by ~28% and ~39%, respectively. For a 40-min LC gradient operated at ~15 nL/min, we identified an average of 2,150 proteins per single-cell-sized aliquots of protein digest directly from MS2 spectra, which increased to an average of 3,524 proteins including proteins identified with MS1-level feature matching. Reducing the active gradient to 20 min resulted in a modest 10% decrease in proteome coverage. We also compared the performance of WWA with DIA. DIA underperformed WWA in terms of proteome coverage, especially with faster separations. Average proteome coverage for single HeLa and K562 cells was respectively 1,758 and 1,642 based on MS2 identifications with 1% false discovery rate and 3042 and 2891 with MS1 feature matching. As such, WWA combined with efficient sample preparation and rapid separations extends the depths of the proteome that can be studied at the single-cell level.

Reply to Discussion

Scandinavian Journal of StatisticsVolume 31, Issue 2 p. 196-201 Reply to Discussion Donald B. Rubin, Donald B. RubinSearch for more papers by this authorElja Arjas, Elja ArjasSearch for more papers by this author Donald B. Rubin, Donald B. RubinSearch for more papers by this authorElja Arjas, Elja ArjasSearch for more papers by this author First published: 19 May 2004 https://doi.org/10.1111/j.1467-9469.2004.03-200C.x Donald B. Rubin and Elja Arjas E-mail: rubin@stat.harvard.eduE-mail: elja.arjas@rni.helsinki.fi AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume31, Issue2June 2004Pages 196-201 RelatedInformation

Light as an ecological factor and its measurement

Refinement in knowledge and new scientific discoveries follow closely behind the introduction of new scientific instruments and new scientific techniques. And in no phase of botany has this been more evident than in the general field of plants in relation to light. Of outstanding importance has been the general availability of cheap reliable light-measuring devices that use as their sensitive element the photoelectric cell. Equally important have been the advances made in growing plants in controlled environment through the use of newly developed artificial sources of light, particularly the neon and sodium vapor lamps and the filters that transmit definite ranges of wave lengths. As a result recent workers have been able to surpass their predecessors in the accuracy of light measurement and in the precision of light control. And through advances in experimental design and statistical analysis developed by Professor R. A. Fisher and his American disciples (55, 156) they have learned how to analyze the separate and combined effects of varying several factors simultaneously in the same experiment. All three of these developments appeared before 1935 when the original paper under this title was published (148), but had not yet been put to widespread use in botanical research. Just as the trends in research might have been predicted ten years ago on the basis of new light sources, new light measurement devices and new experimental design and analyses, so today we may predict that advances in understanding the relationship of light to plants during the coming ten years will result from a more widespread use of fluorescent light sources and the introduction of heavy carbon as an indicator element in photosynthesis. We may expect further refinement of the mathematical relationships between total available light and plant growth as a result of the increased general availability of light recorders.