Dietary levels of pure flavonoids improve spatial memory performance and increase hippocampal brain-derived neurotrophic factor.

Evidence suggests that flavonoid-rich foods are capable of inducing improvements in memory and cognition in animals and humans. However, there is a lack of clarity concerning whether flavonoids are the causal agents in inducing such behavioral responses. Here we show that supplementation with pure anthocyanins or pure flavanols for 6 weeks, at levels similar to that found in blueberry (2% w/w), results in an enhancement of spatial memory in 18 month old rats. Pure flavanols and pure anthocyanins were observed to induce significant improvements in spatial working memory (p = 0.002 and p = 0.006 respectively), to a similar extent to that following blueberry supplementation (p = 0.002). These behavioral changes were paralleled by increases in hippocampal brain-derived neurotrophic factor (R = 0.46, p<0.01), suggesting a common mechanism for the enhancement of memory. However, unlike protein levels of BDNF, the regional enhancement of BDNF mRNA expression in the hippocampus appeared to be predominantly enhanced by anthocyanins. Our data support the claim that flavonoids are likely causal agents in mediating the cognitive effects of flavonoid-rich foods

Open data from the third observing run of LIGO, Virgo, KAGRA and GEO

The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in April of 2019 and lasting six months, O3b starting in November of 2019 and lasting five months, and O3GK starting in April of 2020 and lasting 2 weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main dataset, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages.Comment: 27 pages, 3 figure

Open data from the third observing run of LIGO, Virgo, KAGRA, and GEO

The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in 2019 April and lasting six months, O3b starting in 2019 November and lasting five months, and O3GK starting in 2020 April and lasting two weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main data set, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages

Remote estimation of leaf area index and biomass in corn and soybean

The importance of studying vegetation dynamics has been recognized for decades. A key driver has been the interest in understanding the patterns of terrestrial vegetation productivity and its relationships with global biogeochemical cycles. Since the first Landsat satellite, launched in 1972, the study of terrestrial vegetation dynamics has been one of the most important applications of remote sensing. Remote sensing is the technology by which the electromagnetic energy emitted or reflected by the earth\u27s surface, is recorded by sensors on the ground, aircrafts and spacecrafts. Data recorded by such sensors can be used to infer the nature and state of the earth\u27s surface, their patterns of change through time and space, as well as to measure vegetation productivity. These capabilities, in combination with the synoptic view provided by imaging sensors, made remote sensing an attractive and powerful way of analyzing vegetation, at scales ranging from local to global. A multitude of algorithms have been developed for the remote estimation of canopy biophysical characteristics, in terms of combinations of spectral bands, derivatives of reflectance spectra, neural networks, inversion of radiative transfer models, and several multi-spectral statistical approaches. But by far, the most widespread type of algorithm is the mathematical combination of red and near-infrared reflectance bands, in the form of spectral vegetation indices. Applications of such vegetation indices have ranged from leaves to the entire globe, but in many instances their applicability is specific to species, vegetation types or local conditions. The general objective of this study was to devise a new approach for the remote estimation of green leaf area index and green leaf biomass in crop canopies, that is robust across different species, with different canopy architectures and leaf structures. The model was based on radiative transfer in the canopy through the application of the Kubelka-Munk theory, and was designed for estimating the amount of chlorophyll present in the crop canopy per unit of ground area. This characteristic is a proxy of crop canopy photosynthetic rates, and thus above-ground net primary productivity. The model described in this study proved to be robust for the remote estimation of green leaf area index and green leaf biomass in crops with different canopy architectures (e.g. corn and soybean). Thus, it can be used to assess seasonal dynamics, inter-annual variability, stresses, phenology and primary productivity of agro-ecosystems, by means of the synoptic view provided by remote sensing techniques

Code for Creating and Modeling Virtual Species

The R code for simulating and then modeling a virtual species across landscape in Scandinavia. This is the code for the habitat generalist in the heterogeneous landscape - the same analysis was used for other created species (except for parameters changed described in paper)