Antithrombotic properties of Spirulina extracts against platelet-activating factor and thrombin

Spirulina (Arthrospira maxima) components have shown several health-benefits, including immunomodulatory and anti-inflammatory properties. Even though a few studies have examined the effects of some Spirulina-derived ingredients on platelets, none studied the effects on platelet aggregation induced by inflammatory and thrombotic mediators, namely platelet-activating actor (PAF) and thrombin. In the present study, the antithrombotic properties of compounds extracted from Spirulina, namely phycocyanobilin (PCB), phycocyanin-protein, polysaccharides (PS) and lipid extracts, but also of bioactive HPLC-separated lipid-fractions, were assessed in washed rabbit platelets (WRP) activated by PAF and thrombin. All extracts showed strong anti-PAF and anti-thrombin activities in WRP. PCB showed the strongest inhibitory effects on PAF/thrombin-induced platelet aggregation, with a stronger anti-thrombin effect, followed by the relative strong anti-PAF inhibitory effects of the total lipid extracts. HPLC-separation of Spirulina lipid extracts into lipid subclasses’ fractions showed that specific polar lipid fractions of glyco-lipids and PAF-like phosphatidylcholine moieties exhibited the strongest anti-PAF effects. Some of these lipid-fractions and PCB, when assessed at higher concentrations on WRP showed also an anti-PAF agonistic effect, with the PS extract exhibiting the strongest platelet-aggregatory effect. In conclusion, the results suggested that Spirulina seems to be a sustainable source of bioactive compounds with strong anti-PAF and anti-thrombin properties, and thus a potential candidate for developing food supplements and nutraceuticals against inflammation, thrombosis and related disorders. However, more studies are needed to explore the potential and safety of further use of Spirulina. © 2020 Elsevier Lt

The schemata of the stars: Byzantine astronomy from 1300 A.D.

Most of the knowledge of ancient Greek science survived through Byzantine codices. A short Byzantine article, extant in three manuscripts, contains advanced astronomical ideas and pre-Copernican diagrams; it presents improvements on ancient and medieval astronomy. This important book includes the edited version and translation of the text and analyzes its content. It surveys the development of astronomical models from Ptolemy to Byzantium and compares them mathematically with several works of Arab astronomers, as well as with the heliocentric system of Copernicus and Newton

A Domain of the Alpha-Subunit of Rabbit Phosphorylase-Kinase Shows Homologies with Regions of Rabbit Alpha-Tropomyosin, Human Egf Receptor, and the Alpha-Chain of Bovine S-100 Protein

Journal URL: http://www.springerlink.com/content/105341

A low-molecular weight acid phosphatase present in crystalline preparations of rabbit skeletal muscle glycogen phosphorylase b

AbstractCrystalline preparations of glycogen phosphorylase b contain traces of acid phosphatase activity. Non-denaturing gel electrophoresis of phosphorylase b reveals a single band of 1-naphthyl phosphate phosphohydrolase activity which co-migrates with phosphorylase. The two enzymes can be separated by Sephadex G-200 column chromatography, where the phosphatase exhibits an apparent Mr, of 17000. The contaminant enzyme hydrolyzes effectively the phenolic ester of monoorthophosphate with optimal activity for p-nitrophenyl phosphate and L-phosphotyrosine between pH 5.5 and 6.0. The phosphatase is insensitive to inhibition by L(+)-tartrate but strongly inhibited by μM vanadate and Zn2+

Visible light positioning : a machine learning approach

Visible light positioning (VLP) systems have experienced substantial revolutionary progress over the past year because they can offer great positioning accuracy without needing any additional infrastructure, as conventional radio-frequency (RF)-based systems. Received signal strength (RSS)-based VLP systems are a promising approach to many indoor positioning estimation problems, but still suffer from difficulty in providing high accuracy and reliability. A potential solution to these challenges is to combine VLP systems, and machine learning (ML) approaches to enhance the position prediction accuracy in two-dimensional (2-D) spaces, or more complex problems. In this paper, we propose a ML approach to accurately predict the 2-D indoor position of a mobile receiver (eg. an automated guided vehicles-AGV), based on the measured RSS values of 4 photodiodes (PDs) forming a star architecture. We examine and evaluate the performance of different ML learners applied to the above-described problem. The proposed ML and Neural Network (NN) methods exhibit great accuracy results in predicting the 2-D coordinates of a PD-based receiver

Optimization of Ultrasound- and Microwave-Assisted Extraction for the Determination of Phenolic Compounds in Peach Byproducts Using Experimental Design and Liquid Chromatography–Tandem Mass Spectrometry

The conversion of plant byproducts, which are phenolic-rich substrates, to valuable co-products by implementing non-conventional extraction techniques is the need of the hour. In the current study, ultrasound- (UAE) and microwave-assisted extraction (MAE) were applied for the recovery of polyphenols from peach byproducts. Two-level screening and Box–Behnken design were adopted to optimize extraction efficiency in terms of total phenolic content (TPC). Methanol:water 4:1% v/v was the extraction solvent. The optimal conditions of UAE were 15 min, 8 s ON-5 s OFF, and 35 mL g−1, while MAE was maximized at 20 min, 58 °C, and 16 mL g−1. Regarding the extracts’ TPC and antioxidant activity, MAE emerged as the method of choice, whilst their antiradical activity was similar in both techniques. Furthermore, a liquid chromatography–tandem mass spectrometry (LC-MS/MS) method was developed and validated to determine chlorogenic acid and naringenin in byproducts’ extracts. 4-Chloro-4′-hydroxybenzophenone is proposed as a new internal standard in LC-MS/MS analysis in foods and byproducts. Chlorogenic acid was extracted in higher yields when UAE was used, while MAE favored the extraction of the flavonoid compound, naringenin. To conclude, non-conventional extraction could be considered as an efficient and fast alternative for the recovery of bioactive compounds from plant matrices

Artificial intelligence in visible light positioning for indoor IoT : a methodological review

Indoor communication and positioning are significant fields of applications for indoor Internet of Things (IoT) given the rapid growth of IoT in smart cities, smart grids, and smart industries. Visible light positioning (VLP) has become more and more attractive for researchers to provide indoor location-aware IoT services. Additionally, artificial intelligence (AI) has attracted considerable research effort to address the challenges in visible-light communication (VLC) systems. This is an emerging technology in next-generation wireless networks. However, despite the rapid progress, the use of AI in localization, navigation, and position estimation is still underexplored in VLC systems, and various research challenges are still open. This methodological review summarizes the research efforts regarding the use of AI in the field of VLP, to improve the position estimation accuracy in both two-dimensional (2D) and three-dimensional (3D) indoor IoT applications. This treatise also presents open issues and potential future directions for motivating further research in the field. Various databases were utilized in this paper: Scopus, Google Scholar, and IEEE Xplore; obtained 88 papers from 2017 to early 2023. Most (68%) of the AI articles in VLP systems are machine learning (ML) methods applied for localization and position estimation in VLC systems, while the other 32% of the research articles focussed on evolutionary algorithms. ML and evolutionary models may present limitations in terms of complexity and time-consuming nature but offer highly accurate, robust, reliable, and cost-effective results in terms of position estimation over conventional approaches

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

The discipline of Deep Learning has been recognized for its strong computational tools, which have been extensively used in data and signal processing, with innumerable promising results. Among the many commercial applications of Deep Learning, Music Signal Processing has received an increasing amount of attention over the last decade. This work reviews the most recent developments of Deep Learning in Music signal processing. Two main applications that are discussed are Music Information Retrieval, which spans a plethora of applications, and Music Generation, which can fit a range of musical styles. After a review of both topics, several emerging directions are identified for future research