Standard Analysis Of Variance Research Articles

The impact of planting dates and hybrid selection on sunflower seed yield and oil content

Suitable planting dates and hybrid selection are crucial in optimising yield, component traits and oil production in sunflower (Helianthus annuus L.). In South Africa, there is limited information on the effects of planting dates on currently available sunflower hybrids in the marketplace. This study aimed to investigate the impact of planting dates on sunflower seed yield, oil content and oil yield, and to discern optimal planting dates for selected sunflower hybrids. Nineteen commercial sunflower hybrids were evaluated at three planting dates and three consecutive seasons in Potchefstroom, South Africa. The study used a complete block design with three replicates, and the standard analysis of variance was conducted. Significant differences were detected among seasons, planting dates, hybrids, and their interactions. The mean seed yield varied from 1.86 (LG 5678 CLP) to 2.50 tonnes ha−1 (PAN 7160 CLP). The oil content varied from 37.23 (P 65LP 54) to 53.16% (SY 3970 CLP), and the oil yield ranged from 0.74 (AGSUN 5101 CLP) to 1.25 tonnes ha−1 (LG 5710). Planting sunflowers in November and December resulted in the highest seed yield, oil content and oil yield, while planting in January and early February showed linear declines. January planting reduced 1 to 20%, 2 to 10%, and 3 to 26% in seed yield, oil content and oil yield, respectively. February planting significantly reduced seed yield by 72%, oil content by 20% and oil yield by 77%. The study recommended November and December as optimal sunflower planting dates in South Africa.

A full second-order statistical analysis of strictly linear and widely linear estimators with MSE and Gaussian entropy criteria

Novel physical insights are provided into second-order weight error bounds for both strictly linear (SL) and widely linear (WL) estimators for noncircular Gaussian data, under both mean square error (MSE) and Gaussian entropy criteria. This is achieved by first defining complementary weight error variances of these estimators and by further exploiting the so obtained additional degrees of freedom, related to complex noncircularity. Next, the strong uncorrelating transform (SUT) is employed for a joint diagonalization of both the input covariance matrix and the complementary covariance matrix, to allow for the boundedness of complementary weight error variances for different estimators to be addressed. Furthermore, a joint consideration of both the standard and complementary weight error variance analyses is shown to make it possible to measure weight error power distribution of these estimators over both real and imaginary data channels, an important finding which is not possible to obtain through the standard variance analysis only. Simulations in both system identification and channel estimation settings support the analysis.

A comparative study on steady and fluctuating semi-occluded vocal tract exercises between phonation of/a/and/E/

Context: Semi-occluded vocal tract exercises (SOVTEs) helps to change the acoustic vocal tract impedance in relation to the glottal impedance, improving voice quality. SOVTE has been divided into two groups: steady and fluctuating SOVTEs based on their source of vibration and acoustic characteristics. Aims: The aim of the study was to examine and compare the difference between steady and fluctuating SOVTE with phonation of/a/and/E/on electroglottography and F1–F0 analysis. Settings and Design: Survey design. Study Type: Consort type of study. Materials and Methods: Hundred male participants within the age range of 18–25 years were selected for the study. All the participants were asked to phonate (a) and (E) vowels at a constant pitch for at least 3 s, then all samples were recorded. Assessment of contact quotient percentage value in electroglottography and PRAAT software were used for F1–F0 analysis. Data processing was done on Excel spreadsheet and scoring was processed by SPSS software. Statistical analysis used: One-way analysis of variance (ANOVA) in SPSS Statistics was used for statistical analysis. Results: Mean ± standard deviation and one-way ANOVA revealed that the significant difference was obtained at P

Statistical Evaluation of Barkhausen Noise Testing (BNT) for Ground Samples.

Barkhausen noise testing (BNT) is a nondestructive method for investigating many properties of ferromagnetic materials. The most common application is the monitoring of grinding burns caused by introducing locally high temperatures while grinding. Other features, such as microstructure, residual stress changes, hardening depth, and so forth, can be monitored as well. Nevertheless, because BNT is a method based on a complex magnetoelectric phenomenon, it is not yet standardized. Therefore, there is a need to study the traceability and stability of the measurement method. This study aimed to carry out a statistical analysis of ferromagnetic samples after grinding processes by the use of BNT. The first part of the experiment was to grind samples in different facilities (Sweden and Finland) with similar grinding parameters, different grinding wheels, and different hardness values. The second part was to evaluate measured BNT parameters to determine significant factors affecting BNT signal value. The measurement data from the samples were divided into two different batches according to where they were manufactured. Both grinding batches contained measurement data from three different participants. The main feature for calculation was the root-mean-square (RMS) value. The first processing step was to normalize the RMS values for all the measurements. A standard analysis of variance (ANOVA) was applied for the normalized dataset. The ANOVA showed that the grinding parameters had a significant impact on the BNT signal value, while the other investigated factors (e.g., participant) were negligible. The reasons for this are discussed at the end of the paper.

Structural and phase analysis of multi-ion doped hydroxyapatite for biomedical applications

Multi-ion doping in synthetic HA was carried out using high energy planetary ball milling followed by calcination at 1250 °C for 2 h. The influence of Sr+2, Zn+2, Ag+, and F- ion doping on crystallinity and crystallite size was analyzed using Taguchi design of experiments (DOE) and optimal concentration of different dopants has been identified to achieve desired crystallinity and crystallite size. The doped HA samples have been characterized using X-ray diffraction and Fourier transform infrared spectroscopy to determine their phase purity, degree of crystallinity, crystallite size and functional groups. Standard Analysis of variance (ANOVA) showed relatively high contribution of Sr+2 and Zn+2 doping in changing the crystallinity and crystal size of HA compared to the effect of Ag+ and F- doping. Our analysis demonstrated strong interaction between dopants at binary level doping, while ternary and quaternary doping of elements did not exhibit any interaction in influencing the crystallinity and crystallite size of HA. In general, multi-ion doping in HA found to decrease its crystallinity from 92% to 72% (max.), but enhance the hardness, depending on the type and concentration of doping element. Similarly, a minimum crystallite size of 31 nm was achieved with some binary compositions and other combinations resulted in crystallite sizes up to 59 nm. The compositions that ensure desired crystallinity and crystallite size can also provide high hardness. Our results can be used to tailor the composition of HA in achieving desired functional properties, dependent on crystallinity and crystallite size, such as strength, bioactivity and degradation to suit variety of implant applications.

A Normalized Complex LMS Based Blind I/Q Imbalance Compensator for GFDM Receivers and Its Full Second-Order Performance Analysis

Generalized frequency division multiplexing (GFDM) has become one of the most important waveform candidates for air interface in 5G communications. However, like the standard orthogonal frequency division multiplexing (OFDM), its direct-conversion receivers are vulnerable to radio frequency impairments, due to their cost and size constraints. In this paper, the performance deterioration resulting from inphase/quadrature (I/Q) imbalance is first analyzed in terms of signal-to-interference plus noise ratio (SINR) for typical GFDM receivers. Next, a blind adaptive I/Q imbalance compensator based on normalized complex least mean square (NCLMS), originally designed for OFDM receivers, is extended for GFDM ones. In order to provide more physical insight into its compensation capability, a full second-order performance assessment is established, via a joint consideration of the weight error variance and its complementary variance of the proposed compensator in both the transient and steady-state stages. Apart from an accurate evaluation on its overall self-image attenuation performance, the proposed full second-order analysis is also able to quantify the individual contributions from both the I and Q channels of the compensator, an important finding missing in the literature and not possible to discover by using the standard variance analysis only. This analysis also facilitates theoretical quantifications of SINR improvements by NCLMS for GFDM receivers. Simulations on GFDM waveforms support the analysis.

Leucocytes telomere length and breast cancer risk/ susceptibility: A case-control study.

BackgroundTelomere length in peripheral blood leukocytes (PBL-TL) was proposed as a biomarker of cancer risk. Recent scientific evidence suggested PBL-TL plays a diverse role in different cancers. Inconsistent results were obtained on PBL-TL in relation to breast cancer risk and specifically to the presence of BRCA1 and BRCA2 mutations. The aim of the present case-control study was to analyse the correlation between family history of breast cancer or presence of a BRCA mutation and PBL-TL in the hypothesis that TL is a modifier of cancer risk.MethodsPBL-TL was measured using the real-time quantitative PCR method in DNA for 142 cases and 239 controls. All the women enrolled were characterized for cancer family history. A subgroup of 48 women were classified for the presence of a BRCA mutation. PBL-TL were summarized as means and standard deviations, and compared by standard analysis of variance. A multivariable Generalised Linear Model was fitted to the data with PBL-TL as the dependent variable, case/control status and presence of a BRCA/VUS mutation as factors, and age in 4 strata as a covariate.ResultsAge was significantly associated with decreasing PBL-TL in controls (p = 0.01), but not in BC cases. The telomere length is shorter in cases than in controls after adjusting for age. No effect on PBL-TL of BMI, smoke nor of the most common risk factors for breast cancer was observed. No association between PBL-TL and family history was detected both in BC cases and controls. In the multivariate model, no association was observed between BRCA mutation and decreased PBL-TL. A statistically significant interaction (p = 0.031) between case-control status and a BRCA-mutation/VUS was observed, but no effect was detected for the interaction of cancer status and BRCA or VUS.ConclusionOur study fails to provide support to the hypothesis that PBL-TL is associated with the risk of hereditary BC, or that is a marker of inherited mutations in BRCA genes.

Pregabalin versus placebo in targeting pro-nociceptive mechanisms to prevent chronic pain after whiplash injury in at-risk individuals \u2013 a feasibility study for a randomised controlled trial

BackgroundWhiplash-associated disorders (WAD) are an enormous and costly burden to Australian society. Up to 50% of people who experience a whiplash injury will never fully recover. Whiplash is resistant to treatment and no early management approach has yet been shown to prevent chronic pain. The early presence of central sensitization is associated with poor recovery. Pregabalin’s effects on central sensitization indicate the potential to prevent or modulate these processes after whiplash injury and to improve health outcomes, but this has not been investigated. This paper describes the protocol for a feasibility study for a randomised controlled trial of pregabalin plus evidence-based advice compared to placebo plus evidence-based advice for individuals with acute whiplash injury who are at risk of poor recovery.MethodsThis double blind, placebo-controlled randomised feasibility study will examine the feasibility and potential effectiveness of pregabalin and evidence-based advice (intervention) compared to placebo and evidence-based advice (control) for individuals with acute whiplash injury at risk of poor recovery. Thirty participants (15 per group) aged 18–65 years with Grade II WAD, within 48 hours of injury and currently experiencing at least moderate pain (NRS: ≥ 5/10) will be recruited from Emergency Departments of public hospitals in Queensland, Australia. Pregabalin will be commenced at 75 mg bd and titrated up to 300 mg bd as tolerated for 4 weeks followed by 1 week of weaning.ResultsThe feasibility of trial procedures will be tested, as well as the potential effect of the intervention on the outcomes. The primary outcome of neck pain intensity at 3 months from randomisation will be compared between the treatment groups using standard analysis of variance techniques.DiscussionFeasibility and potential effectiveness data will inform an appropriately powered full trial, which if successful, will provide an effective and cost-effective intervention for a costly and treatment resistant condition. It will also have implications for the early management of other traumatic conditions beyond whiplash.Trial registrationClinical Trials Primary Registry: Australian and New Zealand Clinical Trials Registry.Clinical Trial Registration Number: ACTRN12617000059369.Date of Registration: 11/01/2017.Primary Trial Sponsor: The University of Queensland, Brisbane QLD 4072 Australia.

Fluorescence turn-on sensing of thiamine based on Arginine – functionalized graphene quantum dots (Arg-GQDs): Central composite design for process optimization

A low-cost, sensitive and convenient fluorescent probe of highly fluorescent Arginine – functionalized graphene quantum dots (Arg-GQDs) was designed and synthesized by a facile one–pot hydrothermal treatment of citric acid in the presence of Arginine. The fabricated Arg-GQDs with oxygen-rich functional groups exhibit a strong blue emission with 28.3% quantum yield (QY). It is found that such Arg-GQDs with high QY can be used as efficient fluorescent probe for thiamine determination. In the detection, photoluminescence (PL) intensity of the Arg-GQDs was reversibly quenched and recovered by Ag+ and thiamine, respectively. The effect of different parameters were optimized by central composite design (CCD) combined with response surface methodology (RSM). Furthermore, results of CCD were evaluated by standard analysis of variance (ANOVA) to assess the accuracy and suitability of the quadratic model. Under optimal conditions, the fluorescence “Off-On” process showed a sensitive response to thiamine in the concentration range of 0.1–8.0μM with a detection limit of 53nM. Finally, the Arg-GQDs based fluorescence method was applied to the analysis of thiamine in pharmaceuticals and foodstuff samples with satisfying recoveries for the spiked samples.

Retrospective Evaluation of the Impact of a Geriatric Trauma Institute on Fragility Hip Fracture Patient Outcomes.

Fragility hip fractures occur in the older than 65-year population at an alarming rate. It is estimated that 260,000 hip fractures occur annually. Patient outcomes following hip fractures are devastating. One of every 5 patients dies within 1 year of injury, and 1 of 3 remains in a nursing home for years after the injury. Published literature recommends an interdisciplinary approach to caring for hip-fractured patients and expediting surgery to improve outcomes. The purpose of this study was to retrospectively evaluate the impact of the Geriatric Trauma Institute (GTI) on fragility hip fracture patient outcomes. Specific outcomes included length of stay (LOS), length of time from emergency department (ED) arrival to operating room (OR), complication rate, and discharge destination. This study is a single-center pre- and post-retrospective chart review. Data were collected using database queries within the hospital system. Pre-GTI (n = 326) patients older than 65 years with International Classification of Disease, Ninth Revision (ICD-9) codes 820.0-820.9 (hip fractures) admitted to either a primary care physician or orthopaedic surgeon service between April 1, 2011, and April 1, 2013, were compared with post-GTI (n = 245) patients older than 65 years with ICD-9 codes 820.0-820.9 (hip fractures) admitted to trauma services (GTI) between May 1, 2013, and May 1, 2015. Descriptive statistics including demographic data (age, sex) and comparison of outcomes (LOS, ED to OR time, complications, and disposition) across the groups using standard analysis of variance (ANOVA) and correlation techniques. No statistical difference was found between groups for age, sex, or time from ED to OR pre- versus post-time period using one-way ANOVA, F(1,569) = 1.08, p = .30. The complication rate was calculated pre- and post-GTI and compared using the 2-proportion z-test. The difference between the pre-GTI group (16.6%; 54 of 326 patients) and the post-GTI group (9.4%; 23 of 245 patients) was statistically significant, p = .013. Mean LOS was statistically significantly higher in the pre-GTI group (M = 5) than in the post-GTI group (M = 5.2), U = 33,55, z = -3.32, p = .001. No statistical significance was found between pre- and postdischarge destination, χ(4) = .4.82, p = .307; likelihood ratio test, χ(4) = .5.19, p = .269. This retrospective pre- and post-GTI chart review demonstrates the effectiveness of a multidisciplinary team approach in decreasing complications and LOS for fragility hip-fractured patients. A team approach to the care of these patients improves outcomes and quality of life.

Optimization of critical process parameters for amylase production by Bacillus sp. using statistical approach (RSM)

The aim of this work was to optimize the process parameters through the statistical approach for the production of alpha amylase by Bacillus sp. in submerged fermentation (SMF). Initially critical physical and chemical process parameters influencing the enzyme production were identified by Plackett-Burman method (eleven variables, seven nutritional, two physical and two dummies) were analyzed. Then optimum levels of most influencing parameters affecting amylase production were obtained by CCD and results were analyzed by standard analysis of variance (ANOVA). The effect of interaction of physiochemical parameters on the amylase production (z axis) was studied by plotting three dimensional response surface curves against any two independent variables. A high similarity was observed between the predicted and experimental results, which reflected the accuracy and applicability of RSM to optimize the process for enzyme production. As a result of RSM the optimum values for starch concentration -0.65%, (NH4)2SO4 -0.55% and pH- 8.33. As a result of media optimization, a titre of 1, 22,000 U/L-1 was achieved. A total of 2.4 fold increase in enzyme activity was observed.

Genotype × Environmental Interaction on Seed Cotton Yield and Yield Components

Cotton yield and fibre quality parameters are dependent on the environment in which the crop is grown. A major challenge in crop genotype recommendations is genotype × environment interaction. The identification of cultivars with high adaptability and stability is one of the best ways to address this challenge. Genotype × Environmental interaction on Gosypium hirsutum was investigated. Ten genotypes were planted in a randomised complete block design with three replications. Collected data were subjected to Analysis of Variance (ANOVA) with genotype and location as factors. Additive Main Effect and Multiplicative Interaction model which combines standard analysis of variance with principal component analysis was used to investigate the genotype main effects, environment main effects and the GE interactions. Significant genotype × environment interactions existed for lint yield, boll weights, staple length, and fuzzy seed grade. There were no significant cultivar × site interactions on total seed cotton yield. Cotton cultivars respond differently to different growing conditions implying that, correct choice of varieties by growers for specific production conditions, must be a must to avoid losses due to genotype × environment interactions.

Bayesian alternatives to null-hypothesis significance testing for repeated-measures designs

We present a mathematical derivation that establishes the validity of a proposed adaptation to repeated-measures designs of Wagenmakers’ (2007) Bayesian information criterion (BIC) method for estimating Bayes factors. We also introduce an improved definition of the penalty in this BIC approximation that accommodates the repeated-measures correlation through an effective sample size based on the Fisher Information. Monte Carlo simulations of repeated-measures data were used to compare the BIC method to two Bayesian procedures for analysis of variance (ANOVA) designs and to the standard null-hypothesis significance testing (NHST) approach. When no effects of the independent variable were present in the populations and a reasonable sample size was used, the Bayesian methods consistently yielded posterior probabilities clearly favoring the null model. We discuss two different approaches to comparing the outcome of the Bayesian analyses with NHST results when an effect is present. In general, a direct comparison between NHST p values and Bayesian posterior probabilities indicates that the latter is somewhat conservative when effect size is small. We also derive a closed-form expression for approximating the posterior probability distributions for condition means in one-factor repeated-measures designs and present an R routine for computing these distributions and the posterior probability of H0 that requires as input nothing more than values from a standard ANOVA.

Development of a Surgical Skills Curriculum for the Training and Assessment of Manual Skills in Orthopedic Surgical Residents

To develop and conduct a pilot study of a curriculum of 4 surrogate bone training modules to assess and track progress in basic orthopedic manual skills outside the operating room. Four training modules were developed with faculty and resident input. The modules include (1) cortical drilling, (2) drill trajectory, (3) oscillating saw, and (4) pedicle probing. Orthopedic resident's performance was evaluated. Validity and reliability results were calculated using standard analysis of variance and multivariate regression analysis accounting for postgraduate year (PGY) level, number of attempts, and specific outcome target results specific to the simulation module. St. Mary's Medical Center in San Francisco, CA. These modules were tested on 15 orthopedic surgery residents ranging from PGY 1 to PGY 5 experience. The cortical drilling module had a mean success rate of 56% ± 5%. There was a statistically significant difference in performance according to the diameter of the drill used from 33% ± 7% with large diameter to 70% ± 6% with small diameter. The drill trajectory module had a success rate of 85% ± 3% with a trend toward improvement across PGY level. The oscillating saw module had a mean success rate of 25% ± 5% (trajectory) and 84% ± 6% (depth). We observed a significant improvement in trajectory performance during the second attempt. The pedicle probing module had a success rate of 46% ± 10%. The results of this pilot study on a small number of residents are promising. The modules were inexpensive and easy to administer. Conclusions of statistical significance include (1) residents who could easily detect changes in surrogate bone thickness with a smaller diameter drill than with a larger diameter drill and (2) residents who significantly improved saw trajectory with an additional attempt at the module.

Bias and discriminability during emotional signal detection in melancholic depression.

BackgroundCognitive disturbances in depression are pernicious and so contribute strongly to the burden of the disorder. Cognitive function has been traditionally studied by challenging subjects with modality-specific psychometric tasks and analysing performance using standard analysis of variance. Whilst informative, such an approach may miss deeper perceptual and inferential mechanisms that potentially unify apparently divergent emotional and cognitive deficits. Here, we sought to elucidate basic psychophysical processes underlying the detection of emotionally salient signals across individuals with melancholic and non-melancholic depression.MethodsSixty participants completed an Affective Go/No-Go (AGN) task across negative, positive and neutral target stimuli blocks. We employed hierarchical Bayesian signal detection theory (SDT) to model psychometric performance across three equal groups of those with melancholic depression, those with a non-melancholic depression and healthy controls. This approach estimated likely response profiles (bias) and perceptual sensitivity (discriminability). Differences in the means of these measures speak to differences in the emotional signal detection between individuals across the groups, while differences in the variance reflect the heterogeneity of the groups themselves.ResultsMelancholic participants showed significantly decreased sensitivity to positive emotional stimuli compared to those in the non-melancholic group, and also had a significantly lower discriminability than healthy controls during the detection of neutral signals. The melancholic group also showed significantly higher variability in bias to both positive and negative emotionally salient material.ConclusionsDisturbances of emotional signal detection in melancholic depression appear dependent on emotional context, being biased during the detection of positive stimuli, consistent with a noisier representation of neutral stimuli. The greater heterogeneity of the bias across the melancholic group is consistent with a more labile disorder (i.e. variable across the day). Future work will aim to understand how these findings reflect specific individual differences (e.g. prior cognitive biases) and clarify whether such biases change dynamically during cognitive tasks as internal models of the sensorium are refined and updated in response to experience.

Reading aloud: Does previous trial history modulate the joint effects of stimulus quality and word frequency?

No one would argue with the proposition that how we process events in the world is strongly affected by our experience. Nonetheless, recent experience (e.g., from the previous trial) is typically not considered in the analysis of timed cognitive performance in the laboratory. Masson and Kliegl (2013) reported that, in the context of the lexical decision task, the nature of the previous trial strongly modulates the joint effects of word frequency and stimulus quality-a joint effect that is widely reported to be additive when averaged over trial history. In particular, their analysis suggests there may be no genuine additivity of these factors. Here we extended this line of investigation by reanalyzing data reported by O'Malley and Besner (2008) in which subjects read words and nonwords aloud, with word frequency and stimulus quality as manipulated factors. These factors are additive on reaction time in the standard analysis of variance. Contrary to Masson and Kliegl's finding for lexical decision, when previous trial history is taken into consideration, these 2 factors still do not interact. This suggests that, at least in the context of reading aloud, previous trial does not modulate how the effects of these 2 factors combine. Some implications are briefly noted.

Use of lisdexamfetamine dimesylate in treatment of executive functioning deficits and chronic fatigue syndrome: A double blind, placebo-controlled study

The purpose of this study was to assess the efficacy of lisdexamfetamine dimesylate (LDX) for the treatment of executive functioning deficits in adults (ages 18–60) with chronic fatigue syndrome (CFS). The study's primary outcome measure was the Behavior Rating Inventory of Executive Function—Adult (BRIEF—A). Secondary outcome measures were standardized assessments of fatigue, pain and global functioning. Twenty-six adults who met criteria for CFS and had clinically significant executive functioning deficits were randomly assigned to a flexible morning dose (30, 50, 70mg/day) of either placebo or LDX for a 6-week trial. The data were analyzed with standard analysis of variance (ANOVA) procedures. Participants in the LDX group showed significantly more positive change in BRIEF—A scores (Mchange=21.38, SD=15.85) than those in the placebo group (Mchange=3.36, SD=7.26). Participants in the active group also reported significantly less fatigue and generalized pain relative to the placebo group. Although future studies with LDX should examine whether these benefits generalize to larger, more diverse samples of patients, these results suggest that LDX could be a safe and efficacious treatment for the executive functioning deficits often associated with CFS. The possibility that dopaminergic medications could play an important role addressing the symptoms of CFS is also discussed.

Surrogate variable analysis using partial least squares (SVA-PLS) in gene expression studies

In a typical gene expression profiling study, our prime objective is to identify the genes that are differentially expressed between the samples from two different tissue types. Commonly, standard analysis of variance (ANOVA)/regression is implemented to identify the relative effects of these genes over the two types of samples from their respective arrays of expression levels. But, this technique becomes fundamentally flawed when there are unaccounted sources of variability in these arrays (latent variables attributable to different biological, environmental or other factors relevant in the context). These factors distort the true picture of differential gene expression between the two tissue types and introduce spurious signals of expression heterogeneity. As a result, many genes which are actually differentially expressed are not detected, whereas many others are falsely identified as positives. Moreover, these distortions can be different for different genes. Thus, it is also not possible to get rid of these variations by simple array normalizations. This both-way error can lead to a serious loss in sensitivity and specificity, thereby causing a severe inefficiency in the underlying multiple testing problem. In this work, we attempt to identify the hidden effects of the underlying latent factors in a gene expression profiling study by partial least squares (PLS) and apply ANCOVA technique with the PLS-identified signatures of these hidden effects as covariates, in order to identify the genes that are truly differentially expressed between the two concerned tissue types. We compare the performance of our method SVA-PLS with standard ANOVA and a relatively recent technique of surrogate variable analysis (SVA), on a wide variety of simulation settings (incorporating different effects of the hidden variable, under situations with varying signal intensities and gene groupings). In all settings, our method yields the highest sensitivity while maintaining relatively reasonable values for the specificity, false discovery rate and false non-discovery rate. Application of our method to gene expression profiling for acute megakaryoblastic leukemia shows that our method detects an additional six genes, that are missed by both the standard ANOVA method as well as SVA, but may be relevant to this disease, as can be seen from mining the existing literature.

Chlorine Treatment To Inactivate Norovirus on Food Contact Surfaces

This study was conducted to determine the concentration and optimal treatment time of chlorine for reducing feline calicivirus (FCV) and murine norovirus (MNV) as surrogates of norovirus (NoV) on stainless steel surfaces and to develop a predictive inactivation method using a response surface methodology. The reduction levels of FCV VR-782 and MNV on stainless steel surfaces after treatment with various concentrations of chlorine (0 to 5,000 ppm) for various times (0 to 5 min) were measured. The reduction values of both FCV and MNV on stainless steel surfaces after 5,000 ppm of chlorine treatment for 5 min were 5.20 TCID(50) per coupon. The predictive results obtained by central composite design were analyzed by standard analysis of variance. The application of multiple regression analysis was related to the following polynomial equations: (i) FCV (log TCID(50) per coupon) = -0.3714 + 0.8362x(1) + 0.0011x(2) + 0.0001x(1)x(2) - 0.1143x(2)(1) -0.0001x(2)(2) (x(1), time; x(2), concentration) and (ii) MNV (log TCID(50) per coupon) = + 0.0471 + 0.0807x(1) + 0.0011x(2) + 0.0001x(1)x(2) -0.0910x(2)(1) -0.0001x(2)(2) (x(1), time; x(2), concentration). It was concluded that these polynomial equation models of reduction of FCV and MNV could be used to determine the minimum concentration of chlorine and exposure times to control human NoV on food contact surfaces.

Error Estimates for the ANOVA Method with Polynomial Chaos Interpolation: Tensor Product Functions

We focus on the analysis of variance (ANOVA) method for high dimensional function approximation using Jacobi polynomial chaos to represent the terms of the expansion. First, we develop a weight theory inspired by quasi-Monte Carlo theory to identify which functions have low effective dimension using the ANOVA expansion in different norms. We then present estimates for the truncation error in the ANOVA expansion and for the interpolation error using multielement polynomial chaos in the weighted Korobov spaces over the unit hypercube. We consider both the standard ANOVA expansion using the Lebesgue measure and the anchored ANOVA expansion using the Dirac measure. The optimality of different sets of anchor points is also examined through numerical examples.