Ultrasensitive in-vitro monitoring of monoamine neurotransmitters from dopaminergic cells

The design of biosensing assay of monoamine neurotransmitters (MANTs) such as epinephrine (Ep), norepinephrine (NE), and dopamine (DA), as well as the monitoring of these MANTs released from dopaminergic cells, are of particular interest. Electrochemical sensors based on the novel construction of nickel oxides (NiO) were fabricated and employed for electrochemical screening of MANTs. A novel NiO-lacy flower-like (NLF) geometrical structure with semi-spherical head surfaces connected with a trunk as an arm was achieved. The designed semi-spherical head associated with abundant and the well-dispersed tubular branches with needle-like open ends might lead to the creation of vascular vessels for facile diffusion and suitable accommodation of the released MANTs throughout active and wide-surface-area coverage, multi-diffusive pores, and caves with connective open macro-/meso-windows along the entire top-view nanoneedles of lacy flower head and trunk. These electrode surfaces possess high-index catalytic site facets associated with the formation of ridges/defects on {110}-top-cover surface dominants for strong binding, fast response, and signaling of MANTs. The NLF- modified electrode enabled high sensitivity for MANTs and a low limit of detection of 6 nM. Ultrasensitive in-vitro monitoring of DA released from dopaminergic cells (such as PC12) was realized. The NLF electrode was used to detect MANTs from its sources (PC12), and it could be used for clinical diagnosis

The European Industrial Data Space (EIDS)

This research work has been performed in the framework of the Boost 4.0 Big Data lighthouse initiative, a project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 780732. This datadriven digital transformation research is also endorsed by the Digital Factory Alliance (DFA)The path that the European Commission foresees to leverage data in the best possible way for the sake of European citizens and the digital single market clearly addresses the need for a European Data Space. This data space must follow the rules, derived from European values. The European Data Strategy rests on four pillars: (1) Governance framework for access and use; (2) Investments in Europe’s data capabilities and infrastructures; (3) Competences and skills of individuals and SMEs; (4) Common European Data Spaces in nine strategic areas such as industrial manufacturing, mobility, health, and energy. The project BOOST 4.0 developed a prototype for the industrial manufacturing sector, called European Industrial Data Space (EIDS), an endeavour of 53 companies. The publication will show the developed architectural pattern as well as the developed components and introduce the required infrastructure that was developed for the EIDS. Additionally, the population of such a data space with Big Data enabled services and platforms is described and will be enriched with the perspective of the pilots that have been build based on EIDS.publishersversionpublishe

Ecological Distribution Modeling of Two Malaria Mosquito Vectors Using Geographical Information System in Al-Baha Province, Kingdom of Saudi Arabia

Abstract.-Malaria is considered as an endemic mosquito borne disease in the Kingdom of Saudi Arabia (KSA). Previous investigations addressed the diseases incidences in KSA, however few studies highlighted the mosquito vectors habitats characterization in regards to ecological variables. Ecological models of mosquito vectors will help in defining potential suitable habitats for their spatial distribution and understanding how much the ecological variables contribute in delineating these suitable habitats. This information will help in developing targeted surveillance and control strategies. Ecological niche modeling was carried out using the evolutionary algorithms implemented in maximum entropy (MaxEnt) to predict the suitable larval habitats of two malaria vectors, Anopheles gambiae s.l. and An. sergentii (Theobald) in Al-Baha Province, KSA. Climatic and topographical data layers from Worldclim databases and larval occurrence records were used to model the two malaria vectors. Six topographical and four bioclimatic variables were significantly predict An. gambiae larval suitable habitat. Both streams covered with vegetation and algae and elevation above sea level were strong predictors of distribution of this mosquito vector. However, for An. sergentii, four topographical and ten bioclimatic variables were found to be significant predictors of suitable habitat distribution. Soil and altitude were strong predictors of An. sergentii distribution. Also, the linear regression statistical analysis (LM) indicates non linear correlation between TDS/pH and abundance of these two mosquito species