Relatively little is known about how the diet of chronically undernourished children may impact cardiometabolic biomarkers. The objective of this exploratory study was to characterise relationships between dietary patterns and the cardiometabolic profile of 153 3–5-year-old Peruvian children with a high prevalence of chronic undernutrition. We collected monthly dietary recalls from children when they were 9–24 months old. At 3–5 years, additional dietary recalls were collected, and blood pressure, height, weight, subscapular skinfolds and fasting plasma glucose, insulin and lipid profiles were assessed. Nutrient intakes were expressed as average density per 100 kcals (i) from 9 to 24 months and (ii) at follow-up. The treelet transform and sparse reduced rank regress'ion (RRR) were used to summarize nutrient intake data. Linear regression models were then used to compare these factors to cardiometabolic outcomes and anthropometry. Linear regression models adjusting for subscapular skinfold-for-age Z-scores (SSFZ) were then used to test whether observed relationships were mediated by body composition. 26 % of children were stunted at 3–5 years old. Both treelet transform and sparse RRR-derived child dietary factors are related to protein intake and associated with total cholesterol and SSFZ. Associations between dietary factors and insulin were attenuated after adjusting for SSFZ, suggesting that body composition mediated these relationships. Dietary factors in early childhood, influenced by protein intake, are associated with cholesterol profiles, fasting glucose and body fat in a chronically undernourished population.

Reading difficulties are prevalent worldwide, including in economically developed countries, and are associated with low academic achievement and unemployment. Longitudinal studies have identified several early childhood predictors of reading ability, but studies frequently lack genotype data that would enable testing of predictors with heritable influences. The National Child Development Study (NCDS) is a UK birth cohort study containing direct reading skill variables at every data collection wave from age 7 years through to adulthood with a subsample (final n = 6431) for whom modern genotype data are available. It is one of the longest running UK cohort studies for which genotyped data are currently available and is a rich dataset with excellent potential for future phenotypic and gene-by-environment interaction studies in reading. Here, we carry out imputation of the genotype data to the Haplotype Reference Panel, an updated reference panel that offers greater imputation quality. Guiding phenotype choice, we report a principal components analysis of nine reading variables, yielding a composite measure of reading ability in the genotyped sample. We include recommendations for use of composite scores and the most reliable variables for use during childhood when conducting longitudinal, genetically sensitive analyses of reading ability.

Dietary pattern analysis is typically based on dimension reduction and summarises the diet with a small number of scores. We assess ‘joint and individual variance explained’ (JIVE) as a method for extracting dietary patterns from longitudinal data that highlights elements of the diet that are associated over time. The Auckland Birthweight Collaborative Study, in which participants completed an FFQ at ages 3·5 (n 549), 7 (n 591) and 11 (n 617), is used as an example. Data from each time point are projected onto the directions of shared variability produced by JIVE to yield dietary patterns and scores. We assess the ability of the scores to predict future BMI and blood pressure measurements of the participants and make a comparison with principal component analysis (PCA) performed separately at each time point. The diet could be summarised with three JIVE patterns. The patterns were interpretable, with the same interpretation across age groups: a vegetable and whole grain pattern, a sweets and meats pattern and a cereal v. sweet drinks pattern. The first two PCA-derived patterns were similar across age groups and similar to the first two JIVE patterns. The interpretation of the third PCA pattern changed across age groups. Scores produced by the two techniques were similarly effective in predicting future BMI and blood pressure. We conclude that when data from the same participants at multiple ages are available, JIVE provides an advantage over PCA by extracting patterns with a common interpretation across age groups.

Data on the combination of foods consumed simultaneously at specific eating occasions are scarce, primarily due to a lack of assessment tools. We applied a recently developed meal coding system to multiple-day dietary intake data for assessing its ability to estimate food and nutrient intakes and characterise meal-based dietary patterns in the Japanese context. A total of 242 Japanese adults completed sixteen non-consecutive-day weighed dietary records, including 14 734 eating occasions (3788 breakfasts, 3823 lunches, 3856 dinners and 3267 snacks). Common food group combinations were identified by meal type to identify a range of generic meals. Dietary intake was calculated on the basis of not only the standard food composition database but also the substituted generic meal database. In total, eighty generic meals (twenty-three breakfasts, twenty-one lunches, twenty-four dinners and twelve snacks) were identified. The Spearman correlation coefficients between food group intakes calculated based on the standard food composition database and the substituted generic meal database ranged from 0·26 to 0·85 (median 0·69). The corresponding correlations for nutrient intakes ranged from 0·17 to 0·82 (median 0·61). A total of eleven meal patterns were established using principal components analysis, and these accounted for 39·1 % of total meal variance. Considerable variation in patterns was seen in meal type inclusion and choice of staple foods (bread, rice and noodles) and drinks, and also in meal constituents. In conclusion, this study demonstrated the usefulness of a meal coding system for assessing habitual diet, providing a scientific basis towards the development of simple meal-based dietary assessment tools.

Leaf cuticle micromorphology has been cited as an important set of taxonomic characters in gymnosperms, but previous studies have largely been based on small sample sizes. The premise of this study was to understand whether external factors affect cuticular micromorphology of Podocarpaceae. Two example species, Prumnopitys andina and Podocarpus salignus, were studied. Of 21 sampled characters, nine (c.43% of the total) were visually assessed as being moderately reliable or highly reliable for taxonomic discrimination for both species, with an additional six (c.29%) being moderately reliable or highly reliable for only one or other of the example species, and six characters (c.29%) unreliable for both. Seven of the most variable stomatal characters were selected for further analysis to establish whether environmental factors affect them. The relationship between these seven stomatal characters, the environment and climate was analysed using the R ‘vegan’ package and climate data gathered from WorldClim. Our results showed that both species had larger stomata in moist and shady conditions, and a higher density of (smaller) stomata in sunny and drier conditions. An additional novel finding was the presence of stomata on the adaxial leaf surface in 46% of samples of Prumnopitys andina: the first record of adaxial stomata in this species, highlighting the necessity of studying multiple samples of a given species. In conclusion, these results indicate that larger sample sizes than have hitherto been employed in cuticle micromorphological studies are necessary to fully document the amount of phenotypic variation that exists.

Knowledge of the influence of environmental factors on weed populations is important in developing sustainable turfgrass management practices. Studies were conducted to evaluate the relationship of green and false-green kyllinga population densities with elevation and edaphic factors in turfgrass systems. Studies were conducted on five different golf courses in North Carolina, three affected by green kyllinga, and two affected by false-green kyllinga. According to Spearman correlation coefficients, both green and false-green kyllinga were correlated with increasing soil volumetric water content, whereas correlation of other edaphic variables varied among sites and species. Stepwise logistic regression confirmed the correlation of volumetric water with green kyllinga presence, but model components varied among sites for false-green kyllinga. Increasing green kyllinga populations correlated with increasing soil sodium; however, sodium did not reach a level believed to be detrimental to turfgrass growth. No other variables correlated with green or false-green kyllinga across all sites. We hypothesized that the lack of significant correlations was due to the overall influence of relative elevation on edaphic variables. According to principal components analysis (PCA), relative elevation had a profound impact on the measured edaphic variables at all sites. However, results of PCA at one site differed sharply from other sites. Results from that site demonstrate the potentially strong effects of management practices to alter edaphic trends normally observed with topography.

Field pea seed from bin cleaning operations stored overwinter on nearby cropland was observed to correlate with weed and crop growth suppression for up to three subsequent years. To explore the phenomenon more explicitly, plant growth suppression trials were undertaken with soil sampled 18 mo apart from two locations that had contained field pea seed residues. Test plant species grown in the residue-affected and nearby residue-free soils were compared in greenhouse experiments. Germination was either fully inhibited or emergence was delayed by more than one week. Dry matter accumulation of test species grown in residue-affected soil was significantly reduced compared to dry matter of these test species grown in residue-free soil (P < 0.0001). Canola and field pea were inhibited more than wheat and green foxtail over both years. Greenhouse trials also revealed that germination of wild oat was inhibited in the residue-affected soils, although wheat and grassy weeds were less suppressed than dicots overall. Significant reductions of weed species diversity and abundance were correlated to residue-affected soils (P < 0.0001) when compared to residue-free soils using multi-response permutations procedures. Germination of wheat and canola seed was inhibited, using aqueous extracts of weathered pea seeds or extracts of the residue-affected soil in bioassays in sterile media. An allelopathic response was proposed to explain the above results, indicating a need for further research on this system. Weed management strategies could be developed with field pea seed residues to provide innovative weed control techniques.

The genetic variation of quackgrass as a species and the array of environments in which it is found indicate that selection in these different environments could lead to differentiation among quackgrass populations. Yet, a highly diverse environment might not promote the genetic divergence of quackgrass if it poses contradictory selection pressures. To assess the extent of divergence among quackgrass populations, this study compared the morphology of populations of quackgrass for 1 yr in Rosemount, MN, in a “common garden” study. The quackgrass was initially collected from three different farming systems in southeast Minnesota: corn–soybean (CS), oats–hay–corn (OHC), and permanent pasture (PP). The systems represent pasture or arable land and differ in cropping rotations and levels of disturbance. Although no differences among farming systems were detected in multivariate or univariate comparisons, a significant farming system effect was detected between CS and PP systems when the most diversified system, OHC, was excluded from the analysis. Consistent with this result, a principal components analysis suggested that plants from two of the three farming systems exemplified contrasting modes of perennial plant growth. Relative to each other, the CS plants showed more features of the “guerrilla” growth mode (longer intra-ramet distances, sparse, large patches), whereas PP plants showed more “phalanx” mode features (short intra-ramet distances, dense, smaller patches). Plants from the most diversified system, OHC, did not fit into either growth form, and for this farming system, the variation among populations was the highest. The results suggest that the CS and PP systems selected for distinct growth forms, whereas the diversified OHC system did not. This is consistent with the hypothesis that diversification of a farming system and weed management decreases the risk of evolution of a weed population highly adapted to control measures used in that farming system.

Microtubule analysis is of significant value for a better understanding of normal and pathological cellular processes. Although immunofluorescence microscopic techniques have proven useful in the study of microtubules, comparative results commonly rely on a descriptive and subjective visual analysis. We developed an objective and quantitative method based on image processing and analysis of fluorescently labeled microtubular patterns in cultured cells. We used a multi-parameter approach by analyzing four quantifiable characteristics to compose our quantitative feature set. Then we interpreted specific changes in the parameters and revealed the contribution of each feature set using principal component analysis. In addition, we verified that different treatment groups could be clearly discriminated using principal components of the multi-parameter model. High predictive accuracy of four commonly used multi-classification methods confirmed our method. These results demonstrated the effectiveness and efficiency of our method in the analysis of microtubules in fluorescence images. Application of the analytical methods presented here provides information concerning the organization and modification of microtubules, and could aid in the further understanding of structural and functional aspects of microtubules under normal and pathological conditions.

A liquid chromatography–MS (LC-MS) metabolomics analysis of plasma from portal–arterial catheterised pigs fed breads prepared with whole-grain rye or wheat flour with added concentrated arabinoxylan (AX) or β-glucan (BG) was conducted. Comparison of the effects of concentrated fibres with whole grains has received little attention. Six female catheterised pigs were given two white wheat breads with wheat AX or oat BG, two rye breads with ground rye (GR) or intact rye kernels (RK), and a control white wheat bread (WF) on separate occasions in a randomised cross-over design. The amount of available carbohydrate was similar for the five breads but varied in the content of protein. Plasma was collected continuously for 4 h after feeding. Glucose levels in the portal vein were reduced postprandially in response to the AX, GR and RK breads that had high contents of AX compared with WF bread (P < 0·03). AX and RK breads further tended to decrease plasma levels of some lysophosphatidylcholine species (P ≤ 0·10). The abundance of amino acids in plasma correlated with the protein contents in the breads and leucine uptake significantly affected insulin secretion in the mesenteric artery. In conclusion, the present study revealed that concentrated AX in wheat bread had similar positive effects as whole-grain rye bread on glucose and lipid metabolism.

Dietary patterns derived empirically using principal components analysis (PCA) are widely employed for investigating diet–disease relationships. In the present study, we investigated whether PCA performed better at identifying such associations than an analysis of each food on a FFQ separately, referred to here as an exhaustive single food analysis (ESFA). Data on diet and disease were simulated using real FFQ data and by assuming a number of food intakes in combination that were associated with the risk of disease. In each simulation, ESFA and PCA were employed to identify the combinations of foods that are associated with the risk of disease using logistic regression, allowing for multiple testing and adjusting for energy intake. ESFA was also separately adjusted for principal components of diet, foods that were significant in the unadjusted ESFA and propensity scores. For each method, we investigated the power with which an association between diet and disease could be identified, and the power and false discovery rate (FDR) for identifying the specific combination of food intakes. In some scenarios, ESFA had greater power to detect a diet–disease association than PCA. ESFA also typically had a greater power and a lower FDR for identifying the combinations of food intakes that are associated with the risk of disease. The FDR of both methods increased with increasing sample size, but when ESFA was adjusted for foods that were significant in the unadjusted ESFA, FDR were controlled at the desired level. These results question the widespread use of PCA in nutritional epidemiology. The adjusted ESFA identifies the combinations of foods that are causally linked to the risk of disease with low FDR and surprisingly good power.