Design of validation studies for estimating the odds ratio of exposure-disease relationships when exposure is misclassified

We compared several validation study designs for estimating the odds ratio of disease with misclassified exposure. We assumed that the outcome and misclassified binary covariate are available and that the error-free binary covariate is measured in a subsample, the validation sample. We considered designs in which the total size of the validation sample is fixed and the probability of selection into the validation sample may depend on outcome and misclassified covariate values. Design comparisons were conducted for rare and common disease scenarios, where the optimal design is the one that minimizes the variance of the maximum likelihood estimator of the true log odds ratio relating the outcome to the exposure of interest. Misclassification rates were assumed to be independent of the outcome. We used a sensitivity analysis to assess the effect of misspecifying the misclassification rates. Under the scenarios considered, our results suggested that a balanced design, which allocates equal numbers of validation subjects into each of the four outcome/mismeasured covariate categories, is preferable for its simplicity and good performance. A user-friendly Fortran program is available from the second author, which calculates the optimal sampling fractions for all designs considered and the efficiencies of these designs relative to the optimal hybrid design for any scenario of interest.     

Affinity precipitation of proteins: design criteria for an efficient polymer

Affinity precipitation is fast emerging as a successful technique for the purification of proteins which can be introduced at an early stage of downstream processing. The technique applies the use of reversibly soluble-insoluble polymers which have either natural or synthetic origin. Apart from the successful use of some natural polymers, such as chitosan and alginate, the vast application of the technique depends upon the design of efficient synthetic polymers. In this laboratory, N-isopropylacrylamide (NIPAM) copolymers have been developed for metal chelate affinity precipitation of proteins. The copolymers of 1-vinylimidazole (VI) and iminodiacetic acid (IDA) with NIPAM were synthesized. The copolymers were thoroughly characterized with a view to designing an efficient soluble-insoluble polymer for metal chelate affinity precipitation of proteins.     

An efficient Z-score algorithm for assessing sequence alignments.

We describe an alternative method for scoring of the pairwise alignment of two biological sequences. Designed to overcome the bias due to the composition of the alignment, it measures the distance (in standard deviations) between the given alignment and the mean value of all other alignments that can be obtained by a permutation of either sequence. We demonstrate that the standard deviation can be calculated efficiently. By concentrating upon the ungapped case, the mean and standard deviation can be calculated exactly and in two steps, the first being O(N) time, where N is the length of the sequence, the second in a fixed number of calculations, i.e., in O(1) time. We argue that this statistic is a more consistent measure than a similarity score based upon a standard scoring matrix. Even in the ungapped case, the statistic proves in many cases to be more accurate than the commonly used (FASTA) (Pearson and Lipman, 1988) gapped Z-score in which the sequence is matched against a random sample of the database. We demonstrate the use of the POZ-score as a secondary filter which screens out several well-known types of false positive, reducing the amount of manual screening to be done by the biologist.     

An efficient paradigm for genetic epidemiology cohort creation

Development of novel methodologies to efficiently create large genetic epidemiology cohorts is needed. Here we describe a rapid, precise and cost-efficient method for collection of DNA from cases previously experiencing an osteoporotic fracture by identifying cases using and administrative health-care databases. Over the course of 14 months we collected DNA from 1,130 women experiencing an osteoporotic fracture, at a cost of $54 per sample. This cohort is among the larger DNA osteoporotic fracture collections in the world. The novel method described addresses a major unmet health care research need and is widely applicable to any disease that can be identified accurately through administrative data.     

A case-cohort design for assessing covariate effects in longitudinal studies

The case-cohort design for longitudinal data consists of a subcohort sampled at the beginning of the study that is followed repeatedly over time, and a case sample that is ascertained through the course of the study. Although some members in the subcohort may experience events over the study period, we refer to it as the "control-cohort." The case sample is a random sample of subjects not in the control-cohort, who have experienced at least one event during the study period. Different correlations among repeated observations on the same individual are accommodated by a two-level random-effects model. This design allows consistent estimation of all parameters estimable in a cohort design and is a cost-effective way to study the effects of covariates on repeated observations of relatively rare binary outcomes when exposure assessment is expensive. It is an extension of the case-cohort design (Prentice, 1986, Biometrika73, 1-11) and the bidirectional case-crossover design (Navidi, 1998, Biometrics54, 596-605). A simulation study compares the efficiency of the longitudinal case-cohort design to a full cohort analysis, and we find that in certain situations up to 90% efficiency can be obtained with half the sample size required for a full cohort analysis. A bootstrap method is presented that permits testing for intra-subject homogeneity in the presence of unidentifiable nuisance parameters in the two-level random-effects model. As an illustration we apply the design to data from an ongoing study of childhood asthma.     

Chagas disease vaccine design: the search for an efficient Trypanosoma cruzi immune-mediated control

Chagas disease is currently endemic to 21 Latin-American countries and has also become a global concern because of globalization and mass migration of chronically infected individuals. Prophylactic and therapeutic vaccination might contribute to control the infection and the pathology, as complement of other strategies such as vector control and chemotherapy. Ideal prophylactic vaccine would produce sterilizing immunity; however, a reduction of the parasite burden would prevent progression from Trypanosoma cruzi infection to Chagas disease. A therapeutic vaccine for Chagas disease may improve or even replace the treatment with current drugs which have several side effects and require long term treatment that frequently leads to therapeutic withdrawal.Here, we will review some aspects about sub-unit vaccines, the rationale behind the selection of the immunogen, the role of adjuvants, the advantages and limitations of DNA-based vaccines and the idea of therapeutic vaccines. One of the main limitations to advance vaccine development against Chagas disease is the high number of variables that must be considered and the lack of uniform criteria among research laboratories. To make possible comparisons, much of this review will be focused on experiments that kept many variables constant including antigen mass/doses, type of eukaryotic plasmid, DNA-delivery system, mice strain and sex, lethal and sublethal model of infection, and similar immunogenicity and efficacy assessments.     

An efficient resampling method for assessing genome-wide statistical significance in mapping quantitative trait Loci

Assessing genome-wide statistical significance is an important and difficult problem in multipoint linkage analysis. Due to multiple tests on the same genome, the usual pointwise significance level based on the chi-square approximation is inappropriate. Permutation is widely used to determine genome-wide significance. Theoretical approximations are available for simple experimental crosses. In this article, we propose a resampling procedure to assess the significance of genome-wide QTL mapping for experimental crosses. The proposed method is computationally much less intensive than the permutation procedure (in the order of 10(2) or higher) and is applicable to complex breeding designs and sophisticated genetic models that cannot be handled by the permutation and theoretical methods. The usefulness of the proposed method is demonstrated through simulation studies and an application to a Drosophila backcross.     

Rational design and construction of an efficient E. coli for production of diapolycopendioic acid

We applied elementary mode analysis to a recombinant metabolic network of carotenoid-producing E. coli in order to identify multiple-gene knockouts for an enhanced synthesis of the carotenoids diapolycopendial (DPL) and diapolycopendioic acid (DPA). Based on the model, all inefficient carotenoid biosynthesis pathways were eliminated in a strain containing a combination of eight gene deletions. To validate the model prediction, the designed strain was constructed and tested for its performance. The designed mutant produces the carotenoids at significantly increased yields and rates as compared to the wild-type. The consistency between model prediction and experimental results demonstrates that elementary mode analysis is useful as a guiding tool also for the rational strain design of more complex pathways for secondary metabolite production.     

Bioprocess-centered molecular design (BMD) for the efficient production of an interfacially active peptide

The efficient expression and purification of an interfacially active peptide (mLac21) was achieved by using bioprocess-centered molecular design (BMD), wherein key bioprocess considerations are addressed during the initial molecular biology work. The 21 amino acid mLac21 peptide sequence is derived from the lac repressor protein and is shown to have high affinity for the oil-water interface, causing a substantial reduction in interfacial tension following adsorption. The DNA coding for the peptide sequence was cloned into a modified pET-31(b) vector to permit the expression of mLac21 as a fusion to ketosteroid isomerase (KSI). Rational iterative molecular design, taking into account the need for a scaleable bioprocess flowsheet, led to a simple and efficient bioprocess yielding mLac21 at 86% purity following ion exchange chromatography (and >98% following chromatographic polishing). This case study demonstrates that it is possible to produce acceptably pure peptide for potential commodity applications using common scaleable bioprocess unit operations. Moreover, it is shown that BMD is a powerful strategy that can be deployed to reduce bioseparation complexity.     

RNA-binding proteins: modular design for efficient function

Many RNA-binding proteins have modular structures and are composed of multiple repeats of just a few basic domains that are arranged in various ways to satisfy their diverse functional requirements. Recent studies have investigated how different modules cooperate in regulating the RNA-binding specificity and the biological activity of these proteins. They have also investigated how multiple modules cooperate with enzymatic domains to regulate the catalytic activity of enzymes that act on RNA. These studies have shown how, for many RNA-binding proteins, multiple modules define the fundamental structural unit that is responsible for biological function.     

Complete allocation sampling: an efficient and easily implemented adaptive sampling design

Adaptive sampling designs are becoming increasingly popular in environmental science, particularly for surveying rare and aggregated populations. An adaptive sample is one in which the survey design is modified, or adapted, in some way on the basis of information gained during the survey. There are many different adaptive survey designs that can be used to estimate animal and plant abundance. In adaptive cluster sampling, additional sample effort is allocated during the survey to the immediate neighborhood in which the species is found. In adaptive stratified sampling, additional sample effort is allocated during the survey to strata of high abundance. The appealing feature of these adaptive designs is that the field biologist gets to do what innately seems sensible when working with rare and aggregated populations—field effort is targeted around where the species is observed in the first wave of the survey. However, there are logistical challenges of applying this principle of targeted field effort while remaining in the framework of probability-based sampling. We propose a simplified adaptive survey design that incorporates both targeting field effort and being logistically feasible. We show with a case study population of rockfish that complete allocation stratified sampling is a very efficient design.     

Quantifying and better assessing an engineering design challenge: Going beyond the egg drop

The NGSS MS-ETS1 Engineering Design (1–4) is the focus of the article. Development of a challenging problem-based activity that is an improvement over the traditional egg drop competition is emphasized. Quantification of data collected and real-world relevance are two activity components that are viewed as improvements over the egg drop. The article describes a set of lessons that prepares pharmaceutical companies to drop medicine into the Amazon Rain Forest. Lessons 1–2 involve planning and parachute variable testing (e.g., weight, string length, number of strings, parachute size/shape, etc.) to measure changes in descent rates. Lesson 3 involves building a prototype using small dosage cups, parachute making materials and other random materials (e.g., larger cups, popsicle sticks, etc.). And, Lesson 4 has groups build a final parachute to drop an open container of liquid medication into a remote village. The challenge involves having to use an open container for the medication to remain effective. Due to expense, medication spillage is not permitted. Amount that spills provides a data point to assess performance.     

An efficient Monte Carlo approach to assessing statistical significance in genomic studies

MOTIVATION: Multiple hypothesis testing is a common problem in genome research, particularly in microarray experiments and genomewide association studies. Failure to account for the effects of multiple comparisons would result in an abundance of false positive results. The Bonferroni correction and Holm's step-down procedure are overly conservative, whereas the permutation test is time-consuming and is restricted to simple problems. RESULTS: We developed an efficient Monte Carlo approach to approximating the joint distribution of the test statistics along the genome. We then used the Monte Carlo distribution to evaluate the commonly used criteria for error control, such as familywise error rates and positive false discovery rates. This approach is applicable to any data structures and test statistics. Applications to simulated and real data demonstrate that the proposed approach provides accurate error control, and can be substantially more powerful than the Bonferroni and Holm methods, especially when the test statistics are highly correlated.     

Selecting an ornamental pepper banker plant for Amblyseius swirskii in floriculture crops

Preference of phytoseiid mite, Amblyseius swirskii (Athias-Henriot) was assessed on four cultivars of ornamental pepper banker plant candidates; Red Missile (RM), Masquerade (MA), Explosive Ember (EE) and Black Pearl (BP) for potential control of pestiferous insects in floriculture. Significant differences in cultivar preference by A. swirskii was observed in choice experiments whether the test was pre- (with pollen) or during bloom. Overall, female mites laid more eggs when pollen was provided as a food source. The number of tuft domatia per cultivar leaf appeared to positively influence host preference in the choice plant tests pre-bloom. In addition, cultivar RM had the highest mean number ± SEM of tuft domatia per leaf (5.1 ± 0.3) and motiles per plant (4.0 ± 1.2), followed by MA, EE and BP. In choice tests on blooming plants, A. swirskii showed preference for both cultivars RM and MA compared to EE. These experiments indicated that the number of tuft domatia and availability of pollen can influence the host preference of A. swirskii for an ornamental pepper banker plant cultivar. Results from this study will help growers, researchers, educators and extension personnel in understanding the plant phenology promoting adoption of suitable banker plants for managing greenhouse and landscape insect pests.     

Selecting dissimilar genes for multi-class classification,an application in cancer subtyping

Background  

Gene expression microarray is a powerful technology for genetic profiling diseases and their associated treatments. Such a process involves a key step of biomarker identification, which are expected to be closely related to the disease. A most important task of these identified genes is that they can be used to construct a classifier which can effectively diagnose disease and even recognize the disease subtypes. Binary classification, for example, diseased or healthy, in microarray data analysis has been successful, while multi-class classification, such as cancer subtyping, remains challenging.