Profiling the ionome of rice and its use in discriminating geographical origins at the regional scale, China

Element profile was investigated for their use to trace the geographical origin of rice (Oryza sativa L.) samples. The concentrations of 13 elements (calcium (Ca), potassium (K), magnesium (Mg), phosphorus (P), boron (B), manganese (Mn), iron (Fe), nickel (Ni), copper (Cu), arsenic (As), selenium (Se), molybdenum (Mo), and cadmium (Cd)) were determined in the rice samples by inductively coupled plasma optical emission and mass spectrometry. Most of the essential elements for human health in rice were within normal ranges except for Mo and Se. Mo concentrations were twice as high as those in rice from Vietnam and Spain. Meanwhile, Se concentrations were three times lower in the whole province compared to the Chinese average level of 0.088 mg/kg. About 12% of the rice samples failed the Chinese national food safety standard of 0.2 mg/kg for Cd. Combined with the multi-elemental profile in rice, the principal component analysis (PCA), discriminant function analysis (DFA) and Fibonacci index analysis (FIA) were applied to discriminate geographical origins of the samples. Results indicated that the FIA method could achieve a more effective geographical origin classification compared with PCA and DFA, due to its efficiency in making the grouping even when the elemental variability was so high that PCA and DFA showed little discriminatory power. Furthermore, some elements were identified as the most powerful indicators of geographical origin: Ca, Ni, Fe and Cd. This suggests that the newly established methodology of FIA based on the ionome profile can be applied to determine the geographical origin of rice.

Choice of commuting mode among employees: Do home neighborhood environment, worksite neighborhood environment, and worksite policy and supports matter?

BACKGROUND: Promoting the use of public transit and active transport (walking and cycling) instead of car driving is an appealing strategy to increase overall physical activity.

PURPOSE: To quantify the combined associations between self-reported home and worksite neighborhood environments, worksite support and policies, and employees' commuting modes.

METHOD: Between 2012 and 2013, participants residing in four Missouri metropolitan areas were interviewed via telephone (n = 1,338) and provided information on socio-demographic characteristics, home and worksite neighborhoods, and worksite support and policies. Commuting mode was self-reported and categorized into car driving, public transit, and active commuting. Commuting distance was calculated using geographic information systems. Commuters providing completed data were included in the analysis. Multivariate logistic regression models were used to examine the correlates of using public transit and active commuting.

RESULT: The majority of participants reported commuting by driving (88.9%); only 4.9% used public transit and 6.2% used active modes. After multivariate adjustment, having transit stops within 10-15 minutes walking distance from home (p=0.05) and using worksite incentive for public transit (p<0.001) were associated with commuting by public transit. Commuting distance (p<0.001) was negatively associated with active commuting. Having free or low cost recreation facilities around the worksite (p=0.04) and using bike facilities to lock bikes at the worksite (p<0.001) were associated with active commuting.

CONCLUSION: Both environment features and worksite supports and policies are associated with the choice of commuting mode. Future studies should use longitudinal designs to investigate the potential of promoting alternative commuting modes through worksite efforts that support sustainable commuting behaviors as well as the potential of built environment improvements.

Fruit and vegetable intake and risk of incident of type 2 diabetes: results from the consortium on health and ageing network of cohorts in Europe and the United States (CHANCES)

BACKGROUND/OBJECTIVES: There is limited information to support definitive recommendations concerning the role of diet in the development of type 2 Diabetes mellitus (T2DM). The results of the latest meta-analyses suggest that an increased consumption of green leafy vegetables may reduce the incidence of diabetes, with either no association or weak associations demonstrated for total fruit and vegetable intake. Few studies have, however, focused on older subjects.

SUBJECTS/METHODS: The relationship between T2DM and fruit and vegetable intake was investigated using data from the NIH-AARP study and the EPIC Elderly study. All participants below the age of 50 and/or with a history of cancer, diabetes or coronary heart disease were excluded from the analysis. Multivariate logistic regression analysis was used to calculate the odds ratio of T2DM comparing the highest with the lowest estimated portions of fruit, vegetable, green leafy vegetables and cabbage intake.

RESULTS: Comparing people with the highest and lowest estimated portions of fruit, vegetable or green leafy vegetable intake indicated no association with the risk of T2DM. However, although the pooled OR across all studies showed no effect overall, there was significant heterogeneity across cohorts and independent results from the NIH-AARP study showed that fruit and green leafy vegetable intake was associated with a reduced risk of T2DM OR 0.95 (95% CI 0.91,0.99) and OR 0.87 (95% CI 0.87,0.90) respectively.

CONCLUSIONS: Fruit and vegetable intake was not shown to be related to incident T2DM in older subjects. Summary analysis also found no associations between green leafy vegetable and cabbage intake and the onset of T2DM. Future dietary pattern studies may shed light on the origin of the heterogeneity across populations.European Journal of Clinical Nutrition advance online publication, 17 August 2016; 

Probabilistic program analysis for parallelizing compilers

Parallelizing compilers have difficulty analysing and optimising complex code. To address this, some analysis may be delayed until run-time, and techniques such as speculative execution used. Furthermore, to enhance performance, a feedback loop may be setup between the compile time and run-time analysis systems, as in iterative compilation. To extend this, it is proposed that the run-time analysis collects information about the values of variables not already determined, and estimates a probability measure for the sampled values. These measures may be used to guide optimisations in further analyses of the program. To address the problem of variables with measures as values, this paper also presents an outline of a novel combination of previous probabilistic denotational semantics models, applied to a simple imperative language.

Multivariate Statistical Analysis Applied to an IL6 Signal Transduction Model in Hepatocytes

This paper introduces the application of linear multivariate statistical techniques, including partial least squares (PLS), canonical correlation analysis (CCA) and reduced rank regression (RRR), into the area of Systems Biology. This new approach aims to extract the important proteins embedded in complex signal transduction pathway models.The analysis is performed on a model of intracellular signalling along the janus-associated kinases/signal transducers and transcription factors (JAK/STAT) and mitogen activated protein kinases (MAPK) signal transduction pathways in interleukin-6 (IL6) stimulated hepatocytes, which produce signal transducer and activator of transcription factor 3 (STAT3).A region of redundancy within the MAPK pathway that does not affect the STAT3 transcription was identified using CCA. This is the core finding of this analysis and cannot be obtained by inspecting the model by eye. In addition, RRR was found to isolate terms that do not significantly contribute to changes in protein concentrations, while the application of PLS does not provide such a detailed picture by virtue of its construction.This analysis has a similar objective to conventional model reduction techniques with the advantage of maintaining the meaning of the states prior to and after the reduction process. A significant model reduction is performed, with a marginal loss in accuracy, offering a more concise model while maintaining the main influencing factors on the STAT3 transcription.The findings offer a deeper understanding of the reaction terms involved, confirm the relevance of several proteins to the production of Acute Phase Proteins and complement existing findings regarding cross-talk between the two signalling pathways.

Peat multi-proxy data from Mannikjarve bog as indicators of late Holocene climate changes in Estonia

As part of a wider project on European climate change over the past 4500 years, a 4.5-m peat core was taken from a lawn microform on Mannikjarve bog, Estonia. Several methods were used to yield proxy-climate data: (i) a quadrat and leaf-count method for plant macrofossil data, (ii) testate amoebae analysis, and (iii) colorimetric determination of peat humification. These data are provided with an exceptionally high resolution and precise chronology. Changes in bog surface wetness were inferred using Detrended Correspondence Analysis (DCA) and zonation of macrofossil data, particularly concerning the occurrence of Sphagnum balticum, and a transfer function for water-table depth for testate amoebae data. Based on the results, periods of high bog surface wetness appear to have occurred at c. 3100, 3010-2990, 2300, 1750-1610, 1510, 14 10, 1110, 540 and 3 10 cal. yr BP, during four longer periods between c. 3170 and 2850 cal. yr BP, 2450 and 2000 cal. yr BP, 1770 and 1530 cal. yr BP and in the period from 880 cal. yr BP until the present. In the period between 1770 and 1530 cal. yr BP. the extension or initiation of a hollow microtope occurred, which corresponds with other research results from Mannikjarve bog. This and other changes towards increasing bog surface wetness may be the responses to colder temperatures and the predominance of a more continental climate in the region, which favoured the development of bog microdepressions and a complex bog microtopography. Located in the border zone of oceanic and continental climatic sectors, in an area almost without land uplift, this study site may provide valuable information about changes in palaeohydrological and palaeoclimatological conditions in the northern parts of the eastern Baltic Sea region.

Globally synchronous climate change 2800 years ago: Proxy data from peat in South America

Initial findings from high-latitude ice-cores implied a relatively unvarying Holocene climate, in contrast to the major climate swings in the preceding late-Pleistocene. However, several climate archives from low latitudes imply a less than equable Holocene climate, as do recent studies on peat bogs in mainland north-west Europe, which indicate an abrupt climate cooling 2800 years ago, with parallels claimed in a range of climate archives elsewhere. A hypothesis that this claimed climate shift was global, and caused by reduced solar activity, has recently been disputed. Until now, no directly comparable data were available from the southern hemisphere to help resolve the dispute. Building on investigations of the vegetation history of an extensive mire in the Valle de Andorra, Tierra del Fuego, we took a further peat core from the bog to generate a high-resolution climate history through the use of determination of peat hurnification and quantitative leaf-count plant macrofossil analysis. Here, we present the new proxy-climate data from the bog in South America. The data are directly comparable with those in Europe, as they were produced using identical laboratory methods. They show that there was a major climate perturbation at the same time as in northwest European bogs. Its timinia, nature and apparent global synchronicity lend support to the notion of solar forcing of past climate change, amplified by oceanic circulation. This finding of a similar response simultaneously in both hemispheres may help validate and improve global climate models. That reduced solar activity might cause a global climatic change suggests that attention be paid also to consideration of any global climate response to increases in solar activity. This has implications for interpreting the relative contribution of climate drivers of recent 'global warming'. (c) 2006 Elsevier B.V. All rights reserved.

Effect of signal intensity normalization on the multivariate analysis of spectral data in complex 'real-world' datasets

Spectral signal intensities, especially in 'real-world' applications with nonstandardized sample presentation due to uncontrolled variables/factors, commonly require additional spectral processing to normalize signal intensity in an effective way. In this study, we have demonstrated the complexity of choosing a normalization routine in the presence of multiple spectrally distinct constituents by probing a dataset of Raman spectra. Variation in absolute signal intensity (90.1% of total variance) of the Raman spectra of these complex biological samples swamps the variation in useful signals (9.4% of total variance), degrading its diagnostic and evaluative potential.