Growth performance, in vitro antioxidant properties and chemical composition of the halophyte Limonium algarvense Erben are strongly influenced by the irrigation salinity

Limonium algarvense Erben (sea lavender) is a halophyte species with potential to provide natural ingredients with in vitro antioxidant, anti-inflammatory, neuroprotective and antidiabetic properties. This study reports for the first time the 1) cultivation of sea lavender in greenhouse conditions under irrigation with freshwater (approx. 0 mM NaCl) and saline aquaculture wastewater (300 and 600 mM NaCl), and 2) the influence of the irrigation salinity on the plant performance (e.g growth, number of produced leaves and flowers), in vitro antioxidant properties [radical scavenging activity (DPPH and ABTS), ferric reducing antioxidant power (FRAP), metal chelating properties on copper (CCA) and iron (ICA)], toxicity (in vitro on three mammalian cell lines) and chemical composition (determined by LC-ESI-HRMS/MS). The freshwater-irrigated plants had better growth performance than those irrigated with saltwater. Extracts from wild plants, had the highest antioxidant activity, but those from cultivated ones kept high in vitro antioxidant properties and interesting chemical profile. The flowers' extracts of plants irrigated with 300 mM NaCl had the highest antioxidant activities against DPPH, whereas those from freshwater-irrigated plants were more active on ABTS, CCA and FRAP. Most of the extracts showed nil toxicity. The flowers' extracts displayed the highest diversity of compounds, mainly quercetin, apigenin, luteolin, naringenin and their glycoside derivatives. Moreover, their abundance varied with the irrigation salinity. These data indicate that sea lavender plants can be successfully cultivated in greenhouse conditions under fresh- and saltwater irrigation, maintaining interesting biological and chemical properties.Funding Agency Portuguese Foundation for Science and Technology Portuguese National Budget CCMAR/Multi/04326/2019 GreenVet project ALG-01-0145-FEDER-028876 XtrerneAquaCrops FA-05-2017-028 Lisboa-01-0145-FEDER-022125-RNEM-IST ID/QUI/00100/201 Portuguese Foundation for Science and Technology SFRH/BD/116604/2016 CEECIND/00425/2017info:eu-repo/semantics/publishedVersio

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Multilingual character recognition dataset for Moroccan official documents

This article focuses on the construction of a dataset for multilingual character recognition in Moroccan official documents. The dataset covers languages such as Arabic, French, and Tamazight and are built programmatically to ensure data diversity. It consists of sub-datasets such as Uppercase alphabet (26 classes), Lowercase alphabet (26 classes), Digits (9 classes), Arabic (28 classes), Tifinagh letters (33 classes), Symbols (14 classes), and French special characters (16 classes). The dataset construction process involves collecting representative fonts and generating multiple character images using a Python script, presenting a comprehensive variety essential for robust recognition models. Moreover, this dataset contributes to the digitization of these diverse official documents and archival papers, essential for preserving cultural heritage and enabling advanced text recognition technologies. The need for this work arises from the advancements in character recognition techniques and the significance of large-scale annotated datasets. The proposed dataset contributes to the development of robust character recognition models for practical applications

VOLT Valve: Applying the Frugal Innovation in a Green Touchless Low-Tech Solution

In their innovative development strategy, the third world countries should escape the sheep-like behaviour by following the same pathway as the developed western countries. Because they don’t have the same historical, geopolitical and social factors. There's a lot of truth behind Bruce Lee's well-known quote: “Using no way as way; having no limitation as limitation” by using practical technique with flexibility and reliability instead of theoretical and rigid method. The Frugal Innovation is a good demonstration of this practical way with the ability to “do more with less” [1-2] by creating more business and social value while minimizing the use of diminishing resources such as water, energy and funding. Within this context, the aim of the VOLT valve, where VOLT is the abbreviation of “Valve Opening with Low Tech”, is to propose an autonomous, low tech, eco-efficiency and compact product. This innovation have five distinct aspects that can be resumed in two words, WLoG NoTS (Water, Low, Green, No Touch and Social Technology) which is reflecting a homophony with “ Have nots “ who need to meet their energy and water needs in a quick and smart way

Character Recognition Using Pre-Trained Models and Performance Variants Based on Datasets Size: A Survey

The most efficient and beneficial mechanism to the feature of extracting data from an image, has been the Convolutional Neural Network (CNN) and it is used in many fields (Optical character recognition, image classification, object recognition and Facial recognition etc.). In this papier, we studied the character classification problems, using pre-trained models based on Convolutional Neural Network (CNN), and how the performance can change the outcome of dataset that is given. For that, we have used five pre-trained models’ such as VGG16/19, ResNet, Xception et MobileNet. The experiment shows that Xception had the best performance rate compared to other models for all datasets, VGG16/19 performance rate are variants depend on dataset. However, Experiments shows that ResNet achieve the worst accuracy rate compared to other methods

Synthesis and Antimicrobial Activity of 4-S-Methyl-1,3,4-Oxadiazole Derivatives of Some Natural Amino Acids Bearing Secondary Quaternary Ammonium Salt Moieties

Secondary quaternary ammonium salts (QAS) derived from three natural amino acids (L-Leucine, L-Phenylalanine and L-Methionine) bearing 1,3,4-oxadiazole and acetic acid moieties have been synthesized and characterized by IR, 1H and 13C NMR. All the synthesized compounds were evaluated for their preliminary in vitro antibacterial and antifungal activity against different bacterial and fungal strains. All the synthesized compounds showed varying degrees of inhibition against the tested microorganisms. DOI: http://dx.doi.org/10.17807/orbital.v10i7.990</p

Service for fault tolerance in the Ad Hoc Networks based on Multi Agent Systems

The Ad hoc networks are distributed networks, self-organized and does not require infrastructure. In such network, mobile infrastructures are subject of disconnections. This situation may concern a voluntary or involuntary disconnection of nodes caused by the high mobility in the Ad hoc network. In these problems we are trying through this work to contribute to solving these problems in order to ensure continuous service by proposing our service for faults tolerance based on Multi Agent Systems (MAS), which predict a problem and decision making in relation to critical nodes. Our work contributes to study the prediction of voluntary and involuntary disconnections in the Ad hoc network; therefore we propose our service for faults tolerance that allows for effective distribution of information in the Network by selecting some objects of the network to be duplicates of information