Quantitative quality control in microarray image processing and data acquisition

A new integrated image analysis package with quantitative quality control schemes is described for cDNA microarray technology. The package employs an iterative algorithm that utilizes both intensity characteristics and spatial information of the spots on a microarray image for signal–background segmentation and defines five quality scores for each spot to record irregularities in spot intensity, size and background noise levels. A composite score q_com is defined based on these individual scores to give an overall assessment of spot quality. Using q_com we demonstrate that the inherent variability in intensity ratio measurements is closely correlated with spot quality, namely spots with higher quality give less variable measurements and vice versa. In addition, gauging data by q_com can improve data reliability dramatically and efficiently. We further show that the variability in ratio measurements drops exponentially with increasing q_com and, for the majority of spots at the high quality end, this improvement is mainly due to an improvement in correlation between the two dyes. Based on these studies, we discuss the potential of quantitative quality control for microarray data and the possibility of filtering and normalizing microarray data using a quality metrics-dependent scheme.     

Profile of mood states and stress-related biochemical indices in long-term yoga practitioners

Background

Both insufficient sleep and oversleeping have been reported as precipitating and aggravating factors of tension-type headache (TTH). However, previous studies relied on recalled self-reports, and the relationship has not been confirmed prospectively and objectively in a daily life situation. Recently, ecological momentary assessment (EMA) using electronic diaries, i.e., computerized EMA, is used to record subjective symptoms with the advantages of avoiding recall bias and faked compliance in daily settings. In addition, actigraphy has become an established method to assess sleep outside laboratories. Therefore, the aim of this study was to investigate the within-individual effect of sleep on the following momentary headache intensity in TTH patients during their daily lives utilizing EMA and actigraphy.

Methods

Twenty-seven patients with TTH wore watch-type computers as electronic diaries for seven consecutive days and recorded their momentary headache intensity using a visual analog scale of 0-100 approximately every six hours, on waking up, when going to bed, and at the time of headache exacerbations. They also recorded their self-report of sleep quality, hours of sleep and number of awakenings with the computers when they woke up. Physical activity was continuously recorded by an actigraph inside the watch-type computers. Activity data were analyzed by Cole's algorithm to obtain total sleep time, sleep efficiency, sleep latency, wake time after sleep onset and number of awakenings for each night. Multilevel modeling was used to test the effect of each subjective and objective sleep-related variable on momentary headache intensity on the following day.

Results

Objectively measured total sleep time was significantly positively associated with momentary headache intensity on the following day, while self-reported sleep quality was significantly negatively associated with momentary headache intensity on the following day.

Conclusions

Using computerized EMA and actigraphy, longer sleep and worse sleep quality were shown to be related to more intense headache intensity on within-individual basis and they may be precipitating or aggravating factors of TTH.     

Evaluation of Two Influenza Surveillance Systems in South Africa

Background

The World Health Organisation recommends outpatient influenza-like illness (ILI) and inpatient severe acute respiratory illness (SARI) surveillance. We evaluated two influenza surveillance systems in South Africa: one for ILI and another for SARI.

Methodology

The Viral Watch (VW) programme has collected virological influenza surveillance data voluntarily from patients with ILI since 1984 in private and public clinics in all 9 South African provinces. The SARI surveillance programme has collected epidemiological and virological influenza surveillance data since 2009 in public hospitals in 4 provinces by dedicated personnel. We compared nine surveillance system attributes from 2009–2012.

Results

We analysed data from 18,293 SARI patients and 9,104 ILI patients. The annual proportion of samples testing positive for influenza was higher for VW (mean 41%) than SARI (mean 8%) and generally exceeded the seasonal threshold from May to September (VW: weeks 21–40; SARI: weeks 23–39). Data quality was a major strength of SARI (most data completion measures >90%; adherence to definitions: 88–89%) and a relative weakness of the VW programme (62% of forms complete, with limited epidemiologic data collected; adherence to definitions: 65–82%). Timeliness was a relative strength of both systems (e.g. both collected >93% of all respiratory specimens within 7 days of symptom onset). ILI surveillance was more nationally representative, financially sustainable and expandable than the SARI system. Though the SARI programme is not nationally representative, the high quality and detail of SARI data collection sheds light on the local burden and epidemiology of severe influenza-associated disease.

Conclusions

To best monitor influenza in South Africa, we propose that both ILI and SARI should be under surveillance. Improving ILI surveillance will require better quality and more systematic data collection, and SARI surveillance should be expanded to be more nationally representative, even if this requires scaling back on information gathered.     

Inconsistencies in Quality of Life Data Collection in Clinical Trials: A Potential Source of Bias? Interviews with Research Nurses and Trialists

Background

Patient-reported outcomes (PROs), such as health-related quality of life (HRQL) are increasingly used to evaluate treatment effectiveness in clinical trials, are valued by patients, and may inform important decisions in the clinical setting. It is of concern, therefore, that preliminary evidence, gained from group discussions at UK-wide Medical Research Council (MRC) quality of life training days, suggests there are inconsistent standards of HRQL data collection in trials and appropriate training and education is often lacking. Our objective was to investigate these reports, to determine if they represented isolated experiences, or were indicative of a potentially wider problem.

Methods And Findings

We undertook a qualitative study, conducting 26 semi-structured interviews with research nurses, data managers, trial coordinators and research facilitators involved in the collection and entry of HRQL data in clinical trials, across one primary care NHS trust, two secondary care NHS trusts and two clinical trials units in the UK. We used conventional content analysis to analyze and interpret our data. Our study participants reported (1) inconsistent standards in HRQL measurement, both between, and within, trials, which appeared to risk the introduction of bias; (2), difficulties in dealing with HRQL data that raised concern for the well-being of the trial participant, which in some instances led to the delivery of non-protocol driven co-interventions, (3), a frequent lack of HRQL protocol content and appropriate training and education of trial staff, and (4) that HRQL data collection could be associated with emotional and/or ethical burden.

Conclusions

Our findings suggest there are inconsistencies in the standards of HRQL data collection in some trials resulting from a general lack of HRQL-specific protocol content, training and education. These inconsistencies could lead to biased HRQL trial results. Future research should aim to develop HRQL guidelines and training programmes aimed at supporting researchers to carry out high quality data collection.     

Molecular Techniques in Ecohealth Research Toolkit: Facilitating Estimation of Aggregate Gastroenteritis Burden in an Irrigated Periurban Landscape

Assessment of microbial hazards associated with certain environmental matrices, livelihood strategies, and food handling practices are constrained by time-consuming conventional microbiological techniques that lead to health risk assessments of narrow geographic or time scope, often targeting very few pathogens. Health risk assessment based on one or few indicator organisms underestimates true disease burden due a number of coexisting causative pathogens. Here, we employed molecular techniques in a survey of Cryptosporidium parvum, Giardia lamblia, Campylobacter jejuni, Escherichia coli O157:H7, Listeria monocytogenes, Salmonella spp., Shigella spp., Vibrio cholera, and Rotavirus A densities in canal water with respect to seasonality and spatial distribution of point–nonpoint pollution sources. Three irrigational canals stretching across nearly a 150-km² periurban landscape, traditionally used for agricultural irrigation but function as vital part of municipal wastewater stabilization in recent years, were investigated. Compiled stochastic data (pathogen concentration, susceptible populations) and literature-obtained deterministic data (pathogen dose–response model parameter values) were used in estimating waterborne gastroenteritis burden. Exposure scenarios include swimming or fishing, consuming canal water-irrigated vegetables, and ingesting or inhaling water aerosols while working in canal water-irrigated fields. Estimated annual gastroenteritis burden due individual pathogens among the sampling points was −10.6log₁₀ to −2.2log₁₀ DALYs. Aggregated annual gastroenteritis burden due all the target pathogens per sampling point was −3.1log₁₀ to −1.9log₁₀ DALYs, far exceeding WHO acceptable limit of −6.0log₁₀ DALYs. The present approach will facilitate the comprehensive collection of surface water microbiological baseline data and setting of benchmarks for interventions aimed at reducing microbial hazards in similar landscapes worldwide.     

Assessing the genetic diversity of Panicum coloratum var. makarikariense using agro‐morphological traits and microsatellite‐based markers

Panicum coloratum var. makarikariense is a perennial C₄ grass native to South Africa with relatively good forage production under limited‐resource conditions. Genetic characterisation and breeding efforts have been scant, thus limiting its use in cattle raising systems. The goal of the present study was to assess the genetic diversity of a collection of P. coloratum var. makarikariense using agro‐morphological traits and molecular markers, in comparison with one accession of var. coloratum and one population of Panicum bergii. Agro‐morphological variability between and within accessions of var. makarikariense in a common garden setting was observed, showing that there is still opportunity for selection. Some accessions performed better than the commercialised material in relation to potential forage production. A total of 117 ISSR bands and 48 SSR alleles allowed the detection of genetic variability between and within accessions. The presence of accession‐specific bands suggested distinctness and limited gene flow. The genetic variability encountered in the commercialised material suggested that it is a stabilised population which has not undergone a strong selection process. Low correlation between agro‐morphologic and molecular variability was observed indicating that both approaches provide complementary information. Both morphological and molecular markers reveal genetic differentiation between varieties and species. This study provides a set of new SSR markers available for diversity assessment and valuable information that can be applied directly in collection management for breeding and conservation programmes.     

Photodynamic Antimicrobial Polymers for Infection Control

Hospital-acquired infections pose both a major risk to patient wellbeing and an economic burden on global healthcare systems, with the problem compounded by the emergence of multidrug resistant and biocide tolerant bacterial pathogens. Many inanimate surfaces can act as a reservoir for infection, and adequate disinfection is difficult to achieve and requires direct intervention. In this study we demonstrate the preparation and performance of materials with inherent photodynamic, surface-active, persistent antimicrobial properties through the incorporation of photosensitizers into high density poly(ethylene) (HDPE) using hot-melt extrusion, which require no external intervention except a source of visible light. Our aim is to prevent bacterial adherence to these surfaces and eliminate them as reservoirs of nosocomial pathogens, thus presenting a valuable advance in infection control. A two-layer system with one layer comprising photosensitizer-incorporated HDPE, and one layer comprising HDPE alone is also described to demonstrate the versatility of our approach. The photosensitizer-incorporated materials are capable of reducing the adherence of viable bacteria by up to 3.62 Log colony forming units (CFU) per square centimeter of material surface for methicillin resistant Staphylococcus aureus (MRSA), and by up to 1.51 Log CFU/cm² for Escherichia coli. Potential applications for the technology are in antimicrobial coatings for, or materials comprising objects, such as tubing, collection bags, handrails, finger-plates on hospital doors, or medical equipment found in the healthcare setting.     

Semiparametric count data regression for self-reported mental health

‘‘For how many days during the past 30 days was your mental health not good?” The responses to this question measure self-reported mental health and can be linked to important covariates in the National Health and Nutrition Examination Survey (NHANES). However, these count variables present major distributional challenges: The data are overdispersed, zero-inflated, bounded by 30, and heaped in 5- and 7-day increments. To address these challenges—which are especially common for health questionnaire data—we design a semiparametric estimation and inference framework for count data regression. The data-generating process is defined by simultaneously transforming and rounding (star ) a latent Gaussian regression model. The transformation is estimated nonparametrically and the rounding operator ensures the correct support for the discrete and bounded data. Maximum likelihood estimators are computed using an expectation-maximization (EM) algorithm that is compatible with any continuous data model estimable by least squares. star regression includes asymptotic hypothesis testing and confidence intervals, variable selection via information criteria, and customized diagnostics. Simulation studies validate the utility of this framework. Using star regression, we identify key factors associated with self-reported mental health and demonstrate substantial improvements in goodness-of-fit compared to existing count data regression models.     

Wavelet threshold based TDL and TDR algorithms for real-time ECG signal compression

In this paper, two novel and simple, target distortion level (TDL) and target data rate (TDR), Wavelet threshold based ECG compression algorithms are proposed for real-time applications. The issues on the use of objective error measures, such as percentage root mean square difference (PRD) and root mean square error (RMSE) as a quality measures, in quality controlled/guranteed algorithm are investigated with different sets of experiments. For the proposed TDL and TDR algorithm, data rate variability and reconstructed signal quality is evaluated under different ECG signal test conditions. Experimental results show that the TDR algorithm achieves the required compression data rate to meet the demands of wire/wireless link while the TDL algorithm does not. The compression performance is assessed in terms of number of iterations required to achieve convergence and accuracy, reconstructed signal quality and coding delay. The reconstructed signal quality is evaluated by correct diagnosis (CD) test through visual inspection. Three sets of ECG data from three different databases, the MIT-BIH Arrhythmia (mita) (F_s=360 Hz, 11 b/sample), the Creighton University Ventricular Tachyarrhythmia (cuvt) (F_s=250 Hz, 12 b/sample) and the MIT-BIH Supraventricular Arrhythmia (mitsva) (F_s=128 Hz, 10 b/sample), are used for this work. For each set of ECG data, the compression ratio (CR) range is defined. The CD value of 100% is achieved for CR ≤12, CR ≤ 8 and CR ≤ 4 for data from mita, cuvt and mitsva databases, respectively. The experimental results demonstrate that the proposed TDR algorithm is suitable for real-time applications.     

Alcoholic fixation over formalin fixation: A new,safer option for morphologic and molecular analysis of tissues

Formalin is a widely used fixative but there is potential public health risks to exposure. Besides, alcoholic fixation is advantageous over formalin fixation because of faster fixation, optimal preservation and safer workplace environment. Following fixation by EMA and 10% neutral buffered formalin (NBF), we analyzed the tissue morphology, antigenic stability, DNA and RNA quantity with quality (OD value). The findings of EMA fixing on both the tissue morphology and molecular characterization, were satisfactory. Specially, EMA was faster in penetration of tissues than NBF, fixed ideally as early as 8 h of fixation whereas improper fixation was evident for NBF. In Hematoxylin and Eosin (H & E) staining, better cellular details with stronger affinity for staining were observed. In immunohistochemistry, better antigenic stability was reported for EMA-fixed tissues. The nucleic acid analysis revealed that total genomic DNA and RNA yield from EMA fixed tissues were significantly higher (P < 0.05) with superior quality than NBF fixed tissues. Our results suggest that EMA could be a potential alternative to NBF for fixation and preservation of tissues. These data provide new insights into an option for a safer working environment to support study and research.     

Adherence to combination prophylaxis for prevention of mother-to-child-transmission of HIV in Tanzania

Background

Since 2008, Tanzanian guidelines for prevention of mother-to-child-transmission of HIV (PMTCT) recommend combination regimen for mother and infant starting in gestational week 28. Combination prophylaxis is assumed to be more effective and less prone to resistance formation compared to single-drug interventions, but the required continuous collection and intake of drugs might pose a challenge on adherence especially in peripheral resource-limited settings. This study aimed at analyzing adherence to combination prophylaxis under field conditions in a rural health facility in Kyela, Tanzania.

Methods and Findings

A cohort of 122 pregnant women willing to start combination prophylaxis in Kyela District Hospital was enrolled in an observational study. Risk factors for decline of prophylaxis were determined, and adherence levels before, during and after delivery were calculated. In multivariate analysis, identified risk factors for declining pre-delivery prophylaxis included maternal age below 24 years, no income-generating activity, and enrolment before 24.5 gestational weeks, with odds ratios of 5.8 (P = 0.002), 4.4 (P = 0.015) and 7.8 (P = 0.001), respectively. Women who stated to have disclosed their HIV status were significantly more adherent in the pre-delivery period than women who did not (P = 0.004). In the intra- and postpartum period, rather low drug adherence rates during hospitalization indicated unsatisfactory staff performance. Only ten mother-child pairs were at least 80% adherent during all intervention phases; one single mother-child pair met a 95% adherence threshold.

Conclusions

Achieving adherence to combination prophylaxis has shown to be challenging in this rural study setting. Our findings underline the need for additional supervision for PMTCT staff as well as for clients, especially by encouraging them to seek social support through status disclosure. Prophylaxis uptake might be improved by preponing drug intake to an earlier gestational age. Limited structural conditions of a healthcare setting should be taken into serious account when implementing PMTCT combination prophylaxis.     

Type D Personality Predicts Poor Medication Adherence in Chinese Patients with Type 2 Diabetes Mellitus: A Six-Month Follow-Up Study

Background

Type D personality and medication nonadherence have been shown to be associated with poor health outcomes. Type D personality is associated with poor medication adherence in patients with coronary artery disease, myocardial infarction, and heart failure. However, the relationship between type D personality and medication adherence in patients with Type 2 Diabetes Mellitus (T2DM) remains unknown. This study aims to examine whether type D personality was associated with medication adherence in patients with T2DM.

Design and Settings

A follow-up study was conducted in general hospital of the People''s Liberation Army in Beijing.

Methods

412 T2DM patients (205 females), who were recruited by circular systematic random sampling, provided demographic and baseline data about medical information and completed measures of Type D personality. Then, 330 patients went on to complete a self-report measure of medication adherence at the sixth month after baseline data collection. Chi-square test, t tests, and hierarchical multiple regression analyses were conducted, as needed.

Results

Patients with type D personality were significantly more likely to have poor medication adherence (p<0.001). Type D personality predicts poor medication adherence before and after controlling for covariates when it was analyzed as a categorical variable. However, the dimensional construct of type D personality was not associated with medication adherence when analyzed as a continuous variable.

Conclusion

Although, as a dimensional construct, type D personality may not reflect the components of the personality associated with poor medication adherence in patients with T2DM, screening for type D personality may help to identify those who are at higher risk of poor medication adherence. Interventions, aiming to improve medication adherence, should be launched for these high-risk patients.     

Medication adherence in type 2 diabetes: the ENTRED study 2007, a French Population-Based Study

Background

Adherence to prescribed medications is a key dimension of healthcare quality. The aim of this large population-based study was to evaluate self-reported medication adherence and to identify factors linked with poor adherence in patients with type 2 diabetes in France.

Methodology

The ENTRED study 2007, a French national survey of people treated for diabetes, was based on a representative sample of patients who claimed reimbursement for oral hypoglycaemic agents and/or insulin at least three times between August 2006 and July 2007, and who were randomly selected from the database of the two main National Health Insurance Systems. Medication adherence was determined using a six-item self-administered questionnaire. A multinomial polychotomous logistic regression model was used to identify factors associated with medication adherence in the 3,637 persons with type 2 diabetes.

Principal Findings

Thirty nine percent of patients reported good medication adherence, 49% medium adherence and 12% poor adherence. The factors significantly associated with poor adherence in multivariate analysis were socio-demographic factors: age <45 years, non-European geographical origin, financial difficulties and being professionally active; disease and therapy-related factors: HbA_1c>8% and existing diabetes complications; and health care-related factors: difficulties for taking medication alone, decision making by the patient only, poor acceptability of medical recommendations, lack of family or social support, need for information on treatment, reporting no confidence in the future, need for medical support and follow-up by a specialist physician.

Conclusions

In a country with a high level of access to healthcare, our study demonstrated a substantial low level of medication adherence in type 2 diabetic patients. Better identification of those with poor adherence and individualised suitable recommendations remain essential for better healthcare management.     

Automation of random conical tilt and orthogonal tilt data collection using feature-based correlation

Visualization by electron microscopy has provided many insights into the composition, quaternary structure, and mechanism of macromolecular assemblies. By preserving samples in stain or vitreous ice it is possible to image them as discrete particles, and from these images generate three-dimensional structures. This ‘single-particle’ approach suffers from two major shortcomings; it requires an initial model to reconstitute 2D data into a 3D volume, and it often fails when faced with conformational variability. Random conical tilt (RCT) and orthogonal tilt (OTR) are methods developed to overcome these problems, but the data collection required, particularly for vitreous ice specimens, is difficult and tedious. In this paper, we present an automated approach to RCT/OTR data collection that removes the burden of manual collection and offers higher quality and throughput than is otherwise possible. We show example datasets collected under stain and cryo conditions and provide statistics related to the efficiency and robustness of the process. Furthermore, we describe the new algorithms that make this method possible, which include new calibrations, improved targeting and feature-based tracking.     

Morphological,Secondary Metabolite and ITS (rDNA) Variability within Usnic Acid‐Containing Lichen Thalli of Xanthoparmelia Explored at the Local Scale of Rock Outcrop in W‐Alps

Lichen secondary metabolites (LSMs) are regarded with interest for valuable biological properties, but chemical variability among/within lichen taxa has been only fragmentarily characterized by advanced analytical techniques. Knowledge of variability at a local geographic scale has been particularly neglected, while it should address the collection of chemically homogeneous materials to test and exploit LSMs. Here we evaluated the chemical variability of 48 Xanthoparmelia specimens from two rock outcrops in Western Italian Alps, representative of nine morphotypes and sixteen rDNA ITS haplotypes. Qualitative and quantitative analyses were performed by HPLC‐DAD‐ESI‐MS₂ and UPLC‐HDR‐DAD, respectively, and revealed the occurrence of 18 LSMs. Chemical partition allowed distinguishing six chemical groups, only partially overlapping with distinct morphotypes and three divergent haplotype groups, which, overall, accounted for the co‐occurrence of different taxa only in part identifiable with species described for Europe. Some morphotypes were variable in presence and concentration of LSMs, and chemical divergences also characterized single ITS haplotypes. Accordingly, the collection of chemically homogeneous materials, even at a local scale, may be not properly addressed by morphological features and ITS barcoding, and should be confirmed by a specimen‐level chemical characterization.     

Trauma History and Depression Predict Incomplete Adherence to Antiretroviral Therapies in a Low Income Country

Background

As antiretroviral therapy (ART) for HIV becomes increasingly available in low and middle income countries (LMICs), understanding reasons for lack of adherence is critical to stemming the tide of infections and improving health. Understanding the effect of psychosocial experiences and mental health symptomatology on ART adherence can help maximize the benefit of expanded ART programs by indicating types of services, which could be offered in combination with HIV care.

Methodology

The Coping with HIV/AIDS in Tanzania (CHAT) study is a longitudinal cohort study in the Kilimanjaro Region that included randomly selected HIV-infected (HIV+) participants from two local hospital-based HIV clinics and four free-standing voluntary HIV counselling and testing sites. Baseline data were collected in 2008 and 2009; this paper used data from 36 month follow-up interviews (N = 468). Regression analyses were used to predict factors associated with incomplete self-reported adherence to ART.

Results

Incomplete ART adherence was significantly more likely to be reported amongst participants who experienced a greater number of childhood traumatic events: sexual abuse prior to puberty and the death in childhood of an immediate family member not from suicide or homicide were significantly more likely in the non-adherent group and other negative childhood events trended toward being more likely. Those with incomplete adherence had higher depressive symptom severity and post-traumatic stress disorder (PTSD). In multivariable analyses, childhood trauma, depression, and financial sacrifice remained associated with incomplete adherence.

Discussion

This is the first study to examine the effect of childhood trauma, depression and PTSD on HIV medication adherence in a low income country facing a significant burden of HIV. Allocating spending on HIV/AIDS toward integrating mental health services with HIV care is essential to the creation of systems that enhance medication adherence and maximize the potential of expanded antiretroviral access to improve health and reduce new infections.     

Quality control for system count rate performance with scatter in gamma cameras

We aimed to analyze the optimal conditions to carry out the periodical statistical control tests of the gamma camera count rate performance. First we focused in reproducing the actual R₂₀ value of the gamma camera response. Second we studied the variability of this parameter in the statistical control test. We performed a reference measurement, which consisted of the determination of the complete curve relating observed and incident count rates, the counting model describing it and the reference R₂₀. This reference determined the conditions for the statistical control tests and the way to analyze the results obtained. Results from three different gamma cameras were studied. Each gamma camera showed a different behavior and required specific data analysis. The optimal conditions to perform the statistical control test were determined in each case. Our procedure provides the information necessary to correlate the average value of R₂₀ obtained in the quality control test with the reference one. The critical requirement to perform any statistical control test, that is to have a reduced variability of the control variable, can be fulfilled in this case only for relatively high activities.     

Models of Individual Dietary Behavior Based on Smartphone Data: The Influence of Routine,Physical Activity,Emotion, and Food Environment

IntroductionSmartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline patterns and for monitoring progress in interventions aimed at promoting healthier lifestyles. Individual-based models can be used to examine whether behavior, such as diet, corresponds to certain typological patterns. The objectives of this paper are to demonstrate individual-based modeling methods relevant to a person’s eating behavior, and the value of such approach compared to typical regression models.MethodUsing a mobile app, 2 weeks of physical activity and ecological momentary assessment (EMA) data, and 6 days of diet data were collected from 12 university students recruited from a university in Kunming, a rapidly developing city in southwest China. Phone GPS data were collected for the entire 2-week period, from which exposure to various food environments along each subject’s activity space was determined. Physical activity was measured using phone accelerometry. Mobile phone EMA was used to assess self-reported emotion/feelings. The portion size of meals and food groups was determined from voice-annotated videos of meals. Individual-based regression models were used to characterize subjects as following one of 4 diet typologies: those with a routine portion sizes determined by time of day, those with portion sizes that balance physical activity (energy balance), those with portion sizes influenced by emotion, and those with portion sizes associated with food environments.ResultsAmple compliance with the phone-based behavioral assessment was observed for all participants. Across all individuals, 868 consumed food items were recorded, with fruits, grains and dairy foods dominating the portion sizes. On average, 218 hours of accelerometry and 35 EMA responses were recorded for each participant. For some subjects, the routine model was able to explain up to 47% of the variation in portion sizes, and the energy balance model was able to explain over 88% of the variation in portion sizes. Across all our subjects, the food environment was an important predictor of eating patterns. Generally, grouping all subjects into a pooled model performed worse than modeling each individual separately.ConclusionA typological modeling approach was useful in understanding individual dietary behaviors in our cohort. This approach may be applicable to the study of other human behaviors, particularly those that collect repeated measures on individuals, and those involving smartphone-based behavioral measurement.     

Comparative Analysis and Limitations of Ethidium Monoazide and Propidium Monoazide Treatments for the Differentiation of Viable and Nonviable Campylobacter Cells

The lack of differentiation between viable and nonviable bacterial cells limits the implementation of PCR-based methods for routine diagnostic approaches. Recently, the combination of a quantitative real-time PCR (qPCR) and ethidium monoazide (EMA) or propidium monoazide (PMA) pretreatment has been described to circumvent this disadvantage. In regard to the suitability of this approach for Campylobacter spp., conflicting results have been reported. Thus, we compared the suitabilities of EMA and PMA in various concentrations for a Campylobacter viability qPCR method. The presence of either intercalating dye, EMA or PMA, leads to concentration-dependent shifts toward higher threshold cycle (C_T) values, especially after EMA treatment. However, regression analysis resulted in high correlation coefficient (R²) values of 0.99 (EMA) and 0.98 (PMA) between Campylobacter counts determined by qPCR and culture-based enumeration. EMA (10 μg/ml) and PMA (51.10 μg/ml) removed DNA selectively from nonviable cells in mixed samples at viable/nonviable ratios of up to 1:1,000. The optimized EMA protocol was successfully applied to 16 Campylobacter jejuni and Campylobacter coli field isolates from poultry and indicated the applicability for field isolates as well. EMA-qPCR and culture-based enumeration of Campylobacter spiked chicken leg quarters resulted in comparable bacterial cell counts. The correlation coefficient between the two analytical methods was 0.95. Nevertheless, larger amounts of nonviable cells (>10⁴) resulted in an incomplete qPCR signal reduction, representing a serious methodological limitation, but double staining with EMA considerably improved the signal inhibition. Hence, the proposed Campylobacter viability EMA-qPCR provides a promising rapid method for diagnostic applications, but further research is needed to circumvent the limitation.