Impact of increasing fruit and vegetables and flavonoid intake on the human gut microbiota

Epidemiological studies have shown protective effects of fruits and vegetables (F&V) in lowering the risk of developing cardiovascular diseases (CVD) and cancers. Plant-derived dietary fibre (non-digestible polysaccharides) and/or flavonoids may mediate the observed protective effects particularly through their interaction with the gut microbiota. The aim of this study was to assess the impact of fruit and vegetable (F&V) intake on gut microbiota, with an emphasis on the role of flavonoids, and further to explore relationships between microbiota and factors associated with CVD risk. In the study, a parallel design with 3 study groups, participants in the two intervention groups representing high-flavonoid (HF) and low flavonoid (LF) intakes were asked to increase their daily F&V intake by 2, 4 and 6 portions for a duration of 6 weeks each, while a third (control) group continued with their habitual diet. Faecal samples were collected at baseline and after each dose from 122 subjects. Faecal bacteria enumeration was performed by fluorescence in situ hybridisation (FISH). Correlations of dietary components, flavonoid intake and markers of CVD with bacterial numbers were also performed. A significant dose X treatment interaction was only found for Clostidium leptum-Ruminococcus bromii/flavefaciens with a significant increase after intake of 6 additional portions in the LF group. Correlation analysis of the data from all 122 subjects independent from dietary intervention indicated an inhibitory role of F&V intake, flavonoid content and sugars against the growth of potentially pathogenic clostridia. Additionally, we observed associations between certain bacterial populations and CVD risk factors including plasma TNF-α, plasma lipids and BMI/waist circumference.

CHP toolkit: case study of LAIe sensitivity to discontinuity of canopy cover in fruit plantations

This paper presents an open-source canopy height profile (CHP) toolkit designed for processing small-footprint full-waveform LiDAR data to obtain the estimates of effective leaf area index (LAIe) and CHPs. The use of the toolkit is presented with a case study of LAIe estimation in discontinuous-canopy fruit plantations. The experiments are carried out in two study areas, namely, orange and almond plantations, with different percentages of canopy cover (48% and 40%, respectively). For comparison, two commonly used discrete-point LAIe estimation methods are also tested. The LiDAR LAIe values are first computed for each of the sites and each method as a whole, providing “apparent” site-level LAIe, which disregards the discontinuity of the plantations’ canopies. Since the toolkit allows for the calculation of the study area LAIe at different spatial scales, between-tree-level clumpingcan be easily accounted for and is then used to illustrate the impact of the discontinuity of canopy cover on LAIe retrieval. The LiDAR LAIe estimates are therefore computed at smaller scales as a mean of LAIe in various grid-cell sizes, providing estimates of “actual” site-level LAIe. Subsequently, the LiDAR LAIe results are compared with theoretical models of “apparent” LAIe versus “actual” LAIe, based on known percent canopy cover in each site. The comparison of those models to LiDAR LAIe derived from the smallest grid-cell sizes against the estimates of LAIe for the whole site has shown that the LAIe estimates obtained from the CHP toolkit provided values that are closest to those of theoretical models.