Ontogenetic variation in the mineral, phytochemical and yield attributes of brassicaceous microgreens

Microgreens constitute novel gastronomic ingredients that combine visual, kinesthetic and bioactive qualities. The definition of the optimal developmental stage for harvesting microgreens remains fluid. Their superior phytochemical content against mature leaves underpins the current hypothesis of significant changes in compositional profile during the brief interval of ontogeny from the appearance of the first (S1) to the second true leaf (S2). Microgreens of four brassicaceous genotypes (Komatsuna, Mibuna, Mizuna and Pak Choi) grown under controlled conditions and harvested at S1 and S2 were appraised for fresh and dry yield traits. They were further analyzed for macro-and micromineral content using inductively coupled plasma optical emission spectrometry (ICP-OES), carotenoid content using high-performance liquid chromatography with a diode-array detector (HPLC-DAD), volatile organic compounds using solid-phase microextraction followed by gas chromatography-mass spectrometry (SPME-GC/MS), anthocyanins and polyphenols using liquid chromatography-high resolution-tandem mass spectrometry (LC-MS/MS) with Orbitrap technology and for chlorophyll and ascorbate concentrations, well as antioxidant capacity by spectrophotometry. Analysis of compositional profiles revealed genotype as the principal source of variation for all constituents. The response of mineral and phytochemical composition and of antioxidant capacity to the growth stage was limited and largely genotype-dependent. It is, therefore, questionable whether delaying harvest from S1 to S2 would significantly improve the bioactive value of microgreens while the cost-benefit analysis for this decision must be genotype-specific. Finally, the lower-yielding genotypes (Mizuna and Pak Choi) registered higher relative increase in fresh yield between S1 and S2, compared to the faster-growing and higher-yielding genotypes. Although the optimal harvest stage for specific genotypes must be determined considering the increase in yield against reduction in crop turnover, harvesting at S2 seems advisable for the lower-yielding genotypes

A common approach to the conservation of threatened island vascular plants: First results in the mediterranean basin

The Mediterranean islands represent a center of vascular plant diversity featuring a high rate of endemic richness. Such richness is highly threatened, however, with many plants facing the risk of extinction and in need of urgent protection measures. The CARE-MEDIFLORA project promoted the use of ex situ collections to experiment with in situ active actions for threatened plants. Based on common criteria, a priority list of target plant species was elaborated, and germplasm conservation, curation and storage in seed banks was carried out. Accessions were duplicated in the seed banks of the partners or other institutions. Germination experiments were carried out on a selected group of threatened species. A total of 740 accessions from 429 vascular plants were stored in seed banks, and 410 seed germination experiments for 283 plants species were completed; a total of 63 in situ conservation actions were implemented, adopting different methodological protocols. For each conservation program, a specific monitoring protocol was implemented in collaboration with local and regional authorities. This project represents the first attempt to develop common strategies and an opportunity to join methods and methodologies focused on the conservation of threatened plants in unique natural laboratories such as the Mediterranean islands

An early evaluation of translocation actions for endangered plant species on Mediterranean islands

In situ conservation is widely considered a primary conservation strategy. Plant translocation, specifically, represents an important tool for reducing the extinction risk of threatened species. However, thus far, few documented translocations have been carried out in the Mediterranean islands. The Care-Mediflora project, carried out on six Mediterranean islands, tackles both short- and long-term needs for the insular endangered plants through in situ and ex situ conservation actions. The project approach is based on using ex situ activities as a tool to improve in situ conservation of threatened plant species. Fifty island plants (representing 45 taxa)were selected for translocations using common criteria. During the translocations, several approaches were used, which differed in site selection method, origin of genetic material, type of propagative material, planting method, and more. Although only preliminary data are available, some general lessons can be learned from the experience of the Care-Mediflora project. Among the factors restricting the implementation of translocations, limited financial resources appear to be the most important. Specific preliminary management actions, sometimes to be reiterated after translocation, increase the overall cost, but often are necessary for translocation success. Translocation using juvenile/reproductive plants produces better results over the short term, although seeds may provide good results over the long run (to be assessed in the future). Regardless, plant translocation success can only be detected over long periods; therefore, proper evaluation of plant translocations requires a long-term monitoring protocol. Care-Mediflora project represents the first attempt to combine the existing approaches in a common plant conservation strategy specifically focusing on the Mediterranean islands

Direct spun aligned carbon nanotube web-reinforced proton exchange membranes for fuel cells

A composite membrane prepared by electrospinning SPEEK and direct spinning of CNTs is more robust than SPEEK alone and outperforms SPEEK and Nafion 212 membranes.</p

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Observations of Forbush Decreases of Cosmic-Ray Electrons and Positrons with the Dark Matter Particle Explorer

The Forbush decrease (FD) represents the rapid decrease of the intensities of charged particles accompanied with the coronal mass ejections or high-speed streams from coronal holes. It has been mainly explored with the ground-based neutron monitor network, which indirectly measures the integrated intensities of all species of cosmic rays by counting secondary neutrons produced from interaction between atmospheric atoms and cosmic rays. The space-based experiments can resolve the species of particles but the energy ranges are limited by the relatively small acceptances except for the most abundant particles like protons and helium. Therefore, the FD of cosmic-ray electrons and positrons have just been investigated by the PAMELA experiment in the low-energy range (&lt;5 GeV) with limited statistics. In this paper, we study the FD event that occurred in 2017 September with the electron and positron data recorded by the Dark Matter Particle Explorer. The evolution of the FDs from 2 GeV to 20 GeV with a time resolution of 6 hr are given. We observe two solar energetic particle events in the time profile of the intensity of cosmic rays, the earlier, and weaker, one has not been shown in the neutron monitor data. Furthermore, both the amplitude and recovery time of fluxes of electrons and positrons show clear energy dependence, which is important in probing the disturbances of the interplanetary environment by the coronal mass ejections

Measurement of the light component (p+He) energy spectrum with the DAMPE space mission

The DArk Matter Particle Explorer (DAMPE) is a space-based particle detector launched in a Sun- synchronous orbit on December 17th, 2015 from the Jiuquan Satellite Launch Center, in China. It has been taking data very smoothly for more than 5 years. Science goals of the DAMPE mission include the study of the electron-positron energy spectrum, the study of galactic cosmic-rays, gamma-ray astronomy, and indirect dark matter search. Performing precise measurements of light elements in space, the most abundant components of cosmic radiation, is necessary to address major problems in galactic cosmic ray acceleration and propagation mechanisms. Selecting a combined proton and helium sample (instead of proton or helium alone) allows larger efficiency and purity, also minimizing systematic effects in the reconstruction of the energy spectrum, due to possible cross-contaminations. The use of looser analysis cuts allows collecting larger statistics thus extending the covered energy range and providing a link between direct and indirect cosmic- ray measurements. The measurement of the p+He energy spectrum up to ∼ 150 TeV will be presented, along with a discussion on the features of the spectrum and a comparison with other experimental results

Machine learning methods for helium flux analysis with DAMPE experiment

DAMPE is a space-borne experiment for the measurement of the cosmic-ray fluxes at energies up to around 100 TeV per nucleon. At energies above several tens of TeV, the electronics of DAMPE calorimeter would saturate, leaving certain bars with no energy recorded. It is also observed that at high energies the tracker and the scintillator detector that serve for the charge identification become heavily populated with back-splash tracks. Both effects interfere in precise measurements of the helium flux at highest energies. In the present contribution we discuss the application of machine learning techniques for the treatment of DAMPE data, to compensate the calorimeter energy lost by saturation and to identify helium events

Search for gamma-ray lines in the Galaxy with DAMPE

DArk Matter Particle Explorer (DAMPE) has a great potential in the search of monochromatic and sharp gamma-ray structures in GeV-TeV range thanks to its good energy resolution. In this work, we search for gamma-ray line structures using 5.0 years of DAMPE data. To improve the sensitivity, we develop two types of data sets and adopt the signal-to-noise ratio optimized regions of interest (ROIs) for different DM density profiles. No line signals or candidates, including those located at 133 GeV and 43 GeV, are found between 10 GeV and 300 GeV in the Galaxy. Therefore we calculate the 95% confidence level constraints on the velocity-averaged cross section for χχ → γγ and the decay lifetime for χ → γν with systematic uncertainties included. Our constraints on DM parameters are mostly comparable to the Fermi-LAT 5.8-yr results. The lower limit for DM decay lifetime below 100 GeV are better than that of Fermi-LAT