MEASURING GROWTH CONDITIONS OF SALAD PLANTS USING SENSORS: A HIGH SCHOOL PROJECT

Geoinformatics (GI) education is widely used as a spatial visualization-interdisciplinary tools for its ability to understand the geographical phenomenon around us in the past and model the future scenario. Its global importance and usage have made the need of disseminating the education with public and school students. The MSc. Students of different backgrounds at Institute for Geoinformatics in the University of Munster were involved in one of such works through the seminar cum project on ‘Transdisciplinary education in Geoinformatics’ through GI@School Lab with the aim of engaging high school students on applying GI knowledge on Agriculture. The grade 12 students were presented with the ongoing GI empowered research projects at first such that the school students developed the project ideas of their interests to use GI on agricultural sectors based on which MSc students developed 4 different projects and Growth Condition (Sensors) is one of them. This project aims to determine the best suited condition for Salad plants growth based on the size of the Salad leaves measured after monitoring the growth of the plants by planting them on 4 plastic boxes filled with same soil type but in different lighting conditions and water conditions to be measured by the concerned sensors to after the 8 weeks of indoor growth. The project execution week took place as the 5-day workshop and feedbacks were taken as questionnaire surveys from the participated students and concerned teachers for the project evaluation. The sensors-collected data could even serve as the ground truth data of a citizen observatory projects for Copernicus in-situ component. The whole project aims at reducing generational gaps between the students by bringing them the opportunity for knowledge co-creation through transdisciplinary projects on agricultural sector using GI technologies

MOTIVATING ENVIRONMENTAL CITIZEN SCIENTISTS AND OPEN DATA ACQUISITION ON OPENSENSEMAP WITH OPEN BADGES

Citizen science projects making the public part of scientific research are a growing trend and often have a strong geospatial focus with mapping and observation activities on biodiversity or environmental topics. To keep participants engaged, gamification is often used, adding elements of competition and rewards. Digital badges are a common gamification component that can increase participant motivation. Open Badges, an open standard for digital micro-credentials, can be used in citizen science projects to incentivize participants and showcase their achievements. They can also be adapted to open education, where learners can build a portfolio of evidence to demonstrate their achievements and credentials. Open Badges can enhance the learning experience and increase motivation, leading to improved educational outcomes. The use of Open Badges in citizen science and open education aligns with the spirit of collaboration and transparency in science and technology. In this paper we propose a solution linking the openSenseMap, as an open environmental citizen science platform, to myBadges, an Open Badges infrastructure, to allow an automatic issuing of badges for achievements made. A short study reveals first impressions of the proposed solution, its motivational aspects to contribute and improve open data on the platform, and the potential for future work

An NMR-based scoring function improves the accuracy of binding pose predictions by docking by two orders of magnitude

Low-affinity ligands can be efficiently optimized into high-affinity drug leads by structure based drug design when atomic-resolution structural information on the protein/ligand complexes is available. In this work we show that the use of a few, easily obtainable, experimental restraints improves the accuracy of the docking experiments by two orders of magnitude. The experimental data are measured in nuclear magnetic resonance spectra and consist of protein-mediated NOEs between two competitively binding ligands. The methodology can be widely applied as the data are readily obtained for low-affinity ligands in the presence of non-labelled receptor at low concentration. The experimental inter-ligand NOEs are efficiently used to filter and rank complex model structures that have been pre-selected by docking protocols. This approach dramatically reduces the degeneracy and inaccuracy of the chosen model in docking experiments, is robust with respect to inaccuracy of the structural model used to represent the free receptor and is suitable for high-throughput docking campaigns

The exchange activities of [Fe] hydrogenase (iron–sulfur-cluster-free hydrogenase) from methanogenic archaea in comparison with the exchange activities of [FeFe] and [NiFe] hydrogenases

[Fe] hydrogenase (iron–sulfur-cluster-free hydrogenase) catalyzes the reversible reduction of methenyltetrahydromethanopterin (methenyl-H4MPT+) with H2 to methylene-H4MPT, a reaction involved in methanogenesis from H2 and CO2 in many methanogenic archaea. The enzyme harbors an iron-containing cofactor, in which a low-spin iron is complexed by a pyridone, two CO and a cysteine sulfur. [Fe] hydrogenase is thus similar to [NiFe] and [FeFe] hydrogenases, in which a low-spin iron carbonyl complex, albeit in a dinuclear metal center, is also involved in H2 activation. Like the [NiFe] and [FeFe] hydrogenases, [Fe] hydrogenase catalyzes an active exchange of H2 with protons of water; however, this activity is dependent on the presence of the hydride-accepting methenyl-H4MPT+. In its absence the exchange activity is only 0.01% of that in its presence. The residual activity has been attributed to the presence of traces of methenyl-H4MPT+ in the enzyme preparations, but it could also reflect a weak binding of H2 to the iron in the absence of methenyl-H4MPT+. To test this we reinvestigated the exchange activity with [Fe] hydrogenase reconstituted from apoprotein heterologously produced in Escherichia coli and highly purified iron-containing cofactor and found that in the absence of added methenyl-H4MPT+ the exchange activity was below the detection limit of the tritium method employed (0.1 nmol min−1 mg−1). The finding reiterates that for H2 activation by [Fe] hydrogenase the presence of the hydride-accepting methenyl-H4MPT+ is essentially required. This differentiates [Fe] hydrogenase from [FeFe] and [NiFe] hydrogenases, which actively catalyze H2/H2O exchange in the absence of exogenous electron acceptors

Comprehensive Fragment Screening of the SARS-CoV-2 Proteome Explores Novel Chemical Space for Drug Development

12 pags., 4 figs., 3 tabs.SARS-CoV-2 (SCoV2) and its variants of concern pose serious challenges to the public health. The variants increased challenges to vaccines, thus necessitating for development of new intervention strategies including anti-virals. Within the international Covid19-NMR consortium, we have identified binders targeting the RNA genome of SCoV2. We established protocols for the production and NMR characterization of more than 80 % of all SCoV2 proteins. Here, we performed an NMR screening using a fragment library for binding to 25 SCoV2 proteins and identified hits also against previously unexplored SCoV2 proteins. Computational mapping was used to predict binding sites and identify functional moieties (chemotypes) of the ligands occupying these pockets. Striking consensus was observed between NMR-detected binding sites of the main protease and the computational procedure. Our investigation provides novel structural and chemical space for structure-based drug design against the SCoV2 proteome.Work at BMRZ is supported by the state of Hesse. Work in Covid19-NMR was supported by the Goethe Corona Funds, by the IWBEFRE-program 20007375 of state of Hesse, the DFG through CRC902: “Molecular Principles of RNA-based regulation.” and through infrastructure funds (project numbers: 277478796, 277479031, 392682309, 452632086, 70653611) and by European Union’s Horizon 2020 research and innovation program iNEXT-discovery under grant agreement No 871037. BY-COVID receives funding from the European Union’s Horizon Europe Research and Innovation Programme under grant agreement number 101046203. “INSPIRED” (MIS 5002550) project, implemented under the Action “Reinforcement of the Research and Innovation Infrastructure,” funded by the Operational Program “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020) and co-financed by Greece and the EU (European Regional Development Fund) and the FP7 REGPOT CT-2011-285950—“SEE-DRUG” project (purchase of UPAT’s 700 MHz NMR equipment). The support of the CERM/CIRMMP center of Instruct-ERIC is gratefully acknowledged. This work has been funded in part by a grant of the Italian Ministry of University and Research (FISR2020IP_02112, ID-COVID) and by Fondazione CR Firenze. A.S. is supported by the Deutsche Forschungsgemeinschaft [SFB902/B16, SCHL2062/2-1] and the Johanna Quandt Young Academy at Goethe [2019/AS01]. M.H. and C.F. thank SFB902 and the Stiftung Polytechnische Gesellschaft for the Scholarship. L.L. work was supported by the French National Research Agency (ANR, NMR-SCoV2-ORF8), the Fondation de la Recherche Médicale (FRM, NMR-SCoV2-ORF8), FINOVI and the IR-RMN-THC Fr3050 CNRS. Work at UConn Health was supported by grants from the US National Institutes of Health (R01 GM135592 to B.H., P41 GM111135 and R01 GM123249 to J.C.H.) and the US National Science Foundation (DBI 2030601 to J.C.H.). Latvian Council of Science Grant No. VPP-COVID-2020/1-0014. National Science Foundation EAGER MCB-2031269. This work was supported by the grant Krebsliga KFS-4903-08-2019 and SNF-311030_192646 to J.O. P.G. (ITMP) The EOSC Future project is co-funded by the European Union Horizon Programme call INFRAEOSC-03-2020—Grant Agreement Number 101017536. Open Access funding enabled and organized by Projekt DEALPeer reviewe