Effects of Berry Anthocyanins on Cognitive Performance,Vascular Function and Cardiometabolic Risk Markers: A Systematic Review of Randomized Placebo-Controlled Intervention Studies in Humans

Supplementation with anthocyanins, which are a type of flavonoids mainly found in various berries, is hypothesized to be a promising approach to lower the risk of developing cognitive decline. The aim of this systematic review was to provide a comprehensive overview of dietary intervention trials describing effects of berry anthocyanins on cognitive performance in humans, while also addressing potential underlying mechanisms. A total of 1197 articles were identified through a systematic search, and 49 studies reporting effects on cognitive performance (n = 18), vascular function (n = 22), or cardiometabolic risk markers (n = 32) were included. Significant improvements were observed on memory, while some of the studies also reported effects on attention and psychomotor speed or executive function. Vascular function markers such as brachial artery flow-mediated vasodilation were also affected and consistent evidence was provided for the beneficial effects of berry anthocyanins on endothelial function. Finally, studies reported improvements in blood pressure, but effects on metabolic risk markers (e.g. carbohydrate and lipid metabolism) were less consistent. In conclusion, this review provides evidence for the beneficial effects of berry anthocyanins on cognitive performance as memory improved. Whether observed anthocyanin-induced improvements in vascular function and blood pressure underlie beneficial effects on cognitive performance warrants further study.     

The mitigation hierarchy upside down – a study of nature protection measures in Danish infrastructure projects

This paper presents a Danish study of mitigation directed at nature protection in environmental impact assessment (EIA) of infrastructure projects. The study is based on a document analysis of EIA reports, a workshop held with EIA professionals, and a study of two cases. The paper takes a point of departure in the mitigation hierarchy as a central conceptual framework, identifies which mitigation measures have been suggested in the EIA reports, and compares this to the mitigation hierarchy. Further, the paper explores the dynamics behind which mitigation measures are chosen and later implemented. The findings point to a discrepancy between the prevention principle embedded in the mitigation hierarchy and the actual EIA practice with increased use of nature compensation. Further, the research reveals significant variation in the design of mitigation measures, e.g. in the level of detail used in describing them and the level of clarity as to aims and actions.     

Discrete choice in commuter space: Small area analysis of diurnal population change in the Tokyo Metropolitan Region

Considerations of metropolitan commuting have often related to urban space as a continuum, and to commuting itself as a process. This approach has led to escalating complexity in the measurement, modeling and analysis of commuting, often with no commensurate results. The present study extends an alternative approach that considers commuter space of a metropolis as a discrete set of small areas. Each small area throughout the metropolis is a nighttime location of some households and, as a possible discrete destination choice of individuals within households, each subarea is also a daytime location of some persons. Each subarea, therefore, may be thought of as attaining two values, corresponding to its daytime (e.g., noontime) and nighttime (e.g., midnight) populations. The diurnal demographic shift of subareas, as a set of binary events, or toggles, between daytime and nighttime populations, is associated with commuter access throughout the metropolis. Diurnal shift offers structural simplicity aimed at estimating small-area daytime populations across a metropolitan region that might suitably precede and complement conventional continuum considerations of commuting. Using data for the Tokyo Metropolitan Region, we identify average household size as the nighttime indicator of small-areas, and daytime population density as their daytime indicator. The potential for the estimation of daytime populations across metropolitan regions where daytime population data are unavailable, is underscored by the clear tendency towards inverse relation of these two indicators of diurnal shift.     

Temperature-insensitive polarimetric vibration sensor based on HiBi microstructured optical fiber

A new type of highly birefringent microstructured optical fiber has been tested for vibration measurements using a polarimetric technique. This technique takes advantage of the stress-induced phase shift between the two orthogonally polarized fiber eigenmodes. Comparison of three different fiber types shows that standard single-mode fibers do not provide stable measurements and that conventional polarization-maintaining fibers lead to a significant cross-sensitivity to temperature. However, for highly birefringent microstructured fibers specifically designed to provide a temperature-independent birefringence, our experiments show repeatable vibration measurements over a frequency range extending from 50?Hz to 1?kHz that are unaffected by temperature variations (up to 120?°C).     

Ethnic differences in realising desires to leave urban neighbourhoods

Selective mobility into and out of urban neighbourhoods is one of the main driving forces of segregation. Earlier research has found group differences in who wants to leave or who leaves certain types of neighbourhoods. A factor that has received little attention so far is that some residents will have a desire to leave their neighbourhood, but are unable to do so. If there are differences between population groups in the realisation of desires to leave the neighbourhood, this might lead to involuntary segregation. This paper uses a unique combination of register data and survey data. We combine data from a large housing survey in the Netherlands (WoON) with longitudinal register data from the Netherlands (SSD) which contains individual-level information on residential mobility histories. This allows us to study whether households with a desire to leave their neighbourhood do realise this desire and which households are successful in leaving which neighbourhoods. A more thorough insight in who wants to leave which neighbourhoods but is unable to do so will contribute to a better understanding of selective mobility and segregation. We find that ethnic minorities and low-income households are less likely to realise a desire to leave their neighbourhood. We expected that ethnic minorities would be especially unsuccessful in realising desires to leave minority concentration neighbourhoods; however, for none of the ethnic groups we found an effect of neighbourhood ethnic composition on the realisation of desires to leave.     

Commercial farmers’ strategies to control water resources in South Africa: an empirical view of reform

This article shows how large-scale commercial farmers, individually and collectively, are responding to land and water reform processes in the Thukela River basin, KwaZulu-Natal, South Africa. With a high degree of innovative agency, commercial farmers have effectively executed four strategies, enabling them to adapt and use their access to resources to neutralize multiple water reform efforts that once promised to be catalysts for inclusive change in the post-apartheid era. It is likely that policy alone will not facilitate the envisioned transformation, if local practices are not sufficiently understood and anticipated by the governmental officials charged with the implementation of water reform processes.     

Selecting informative data for developing peptide-MHC binding predictors using a query by committee approach

Strategies for selecting informative data points for training prediction algorithms are important, particularly when data points are difficult and costly to obtain. A Query by Committee (QBC) training strategy for selecting new data points uses the disagreement between a committee of different algorithms to suggest new data points, which most rationally complement existing data, that is, they are the most informative data points. In order to evaluate this QBC approach on a real-world problem, we compared strategies for selecting new data points. We trained neural network algorithms to obtain methods to predict the binding affinity of peptides binding to the MHC class I molecule, HLA-A2. We show that the QBC strategy leads to a higher performance than a baseline strategy where new data points are selected at random from a pool of available data. Most peptides bind HLA-A2 with a low affinity, and as expected using a strategy of selecting peptides that are predicted to have high binding affinities also lead to more accurate predictors than the base line strategy. The QBC value is shown to correlate with the measured binding affinity. This demonstrates that the different predictors can easily learn if a peptide will fail to bind, but often conflict in predicting if a peptide binds. Using a carefully constructed computational setup, we demonstrate that selecting peptides with a high QBC performs better than low QBC peptides independently from binding affinity. When predictors are trained on a very limited set of data they cannot be expected to disagree in a meaningful way and we find a data limit below which the QBC strategy fails. Finally, it should be noted that data selection strategies similar to those used here might be of use in other settings in which generation of more data is a costly process.     

Fast cross-validation of high-breakdown resampling methods for PCA

Cross-validation (CV) is a very popular technique for model selection and model validation. The general procedure of leave-one-out CV (LOO-CV) is to exclude one observation from the data set, to construct the fit of the remaining observations and to evaluate that fit on the item that was left out. In classical procedures such as least-squares regression or kernel density estimation, easy formulas can be derived to compute this CV fit or the residuals of the removed observations. However, when high-breakdown resampling algorithms are used, it is no longer possible to derive such closed-form expressions. High-breakdown methods are developed to obtain estimates that can withstand the effects of outlying observations. Fast algorithms are presented for LOO-CV when using a high-breakdown method based on resampling, in the context of robust covariance estimation by means of the MCD estimator and robust principal component analysis. A robust PRESS curve is introduced as an exploratory tool to select the number of principal components. Simulation results and applications on real data show the accuracy and the gain in computation time of these fast CV algorithms.