Extraction and Quantification of Chlorophylls, Carotenoids, Phenolic Compounds, and Vitamins from Halophyte Biomasses

Halophytes are salt-tolerant plants, and they have been utilised as healthy, nutritious vegetables and medicinal herbs. Various studies have shown halophytes to be rich in health-beneficial compounds with antioxidant activity, anti-inflammatory and antimicrobial effects, and cytotoxic properties. Despite their potential, these plants are still underutilised in agriculture and industrial applications. This review includes the state-of-the-art literature concerning the contents of proanthocyanidins (also known as condensed tannins), total phenolic compounds, photosynthetic pigments (chlorophyll and carotenoids), and vitamins in various halophyte biomasses. Various extraction and analytical methods are also considered. The study shows that various species have exhibited potential for use not only as novel food products but also in the production of nutraceuticals and as ingredients for cosmetics and pharmaceuticals

Harnessing the Value of Tripolium pannonicum and Crithmum maritimum Halophyte Biomass through Integrated Green Biorefinery

Bioactive extracts are often the target fractions in bioprospecting, and halophyte plants could provide a potential source of feedstock for high-value applications as a part of integrated biorefineries. Tripolium pannonicum (Jacq.) Dobrocz. (sea aster) and Crithmum maritimum L. (sea fennel) are edible plants suggested for biosaline halophyte-based agriculture. After food production and harvesting of fresh leaves for food, the inedible plant fractions could be utilized to produce extracts rich in bioactive phytochemicals to maximize feedstock application and increase the economic feasibility of biomass processing to bioenergy. This study analyzed fresh juice and extracts from screw-pressed sea aster and sea fennel for their different phenolic compounds and pigment concentrations. Antioxidant and enzyme inhibition activities were also tested in vitro. Extracts from sea aster and sea fennel had phenolic contents up to 45.2 mgGAE/gDM and 64.7 mgGAE/gDM, respectively, and exhibited >70% antioxidant activity in several assays. Ethanol extracts also showed >70% inhibition activity against acetylcholinesterase and >50% inhibition of tyrosinase and α-glucosidase. Therefore, these species can be seen as potential feedstocks for further investigations.LA/P/0101/2020info:eu-repo/semantics/publishedVersio

European Research Agenda for Career Guidance and Counselling

In a changing world, there is a need to reflect about the research basis of career guidance and counselling (CGC) as a professional practice, considering the contributions of various disciplines and research traditions. This paper outlines a possible European research agenda (ERA) to further enhance the knowledge foundation of the CGC practice. The proposed lines of research, which are pronounced in the ERA, are based on a literature review involving 45 researchers concerned with the CGC practice. At three events, approximately 150 researchers from across Europe were engaged in the discussion, what kind of research is needed to enhance the knowledge foundation of the CGC practice. The paper provides a systematic overview of the relevant research fields, and links key research questions to current research endeavours. Due to the necessary involvement of diverse types of practitioners, policy makers, and researchers from different disciplines to share the CGC practice and contribute to the development of its knowledge basis, the paper calls for open, cooperative and integrative research approaches, including the combination of different research paradigms and methods. The development of the European Research Agenda was co-funded by the European Union through the Lifelong Learning Programme

“There is no such thing as waste”: Redoing concepts of materials and values in the Shrub

No abstrac

Deep Learning from Label Proportions for Emphysema Quantification

We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts using a standard grading system, in which grades correspond to intervals (label example: 1-5% of diseased tissue). The proposed architecture encodes the knowledge that the labels represent a volumetric proportion. A custom loss is designed to learn with intervals. Thus, during training, our network learns to segment the diseased tissue such that its proportions fit the ground truth intervals. Our architecture and loss combined improve the performance substantially (8% ICC) compared to a more conventional regression network. We outperform traditional lung densitometry and two recently published methods for emphysema quantification by a large margin (at least 7% AUC and 15% ICC), and achieve near-human-level performance. Moreover, our method generates emphysema segmentations that predict the spatial distribution of emphysema at human level.Comment: Accepted to MICCAI 201