Search

The byssal threads of the fan shell Atrina pectinata are non-living functional materials intimately associated with living tissue, which provide an intriguing paradigm of bionic interface for robust load-bearing device. An interfacial load-bearing protein (A. pectinata foot protein-1, apfp-1) with L-3,4-dihydroxyphenylalanine (DOPA)-containing and mannose-binding domains has been characterized from Atrina's foot. apfp-1 was localized at the interface between stiff byssus and the soft tissue by immunochemical staining and confocal Raman imaging, implying that apfp-1 is an interfacial linker between the byssus and soft tissue, that is, the DOPA-containing domain interacts with itself and other byssal proteins via Fe3(+)-DOPA complexes, and the mannose-binding domain interacts with the soft tissue and cell membranes. Both DOPA- and sugar-mediated bindings are reversible and robust under wet conditions. This work shows the combination of DOPA and sugar chemistry at asymmetric interfaces is unprecedented and highly relevant to bionic interface design for tissue engineering and bionic devices.

PurposeTo detect and quantify eye blinks during 24-hour intraocular pressure (IOP) monitoring with a contact lens sensor (CLS).MethodsA total of 249 recordings of 24-hour IOP patterns from 202 participants using a CLS were included. Software was developed to automatically detect eye blinks, and wake and sleep periods. The blink detection method was based on detection of CLS signal peaks greater than a threshold proportional to the signal amplitude. Three methods for automated detection of the sleep and wake periods were evaluated. These relied on blink detection and subsequent comparison of the local signal amplitude with a threshold proportional to the mean signal amplitude. These methods were compared to manual sleep/wake verification. In a pilot, simultaneous video recording of 10 subjects was performed to compare the software to observer-measured blink rates.ResultsMean (SD) age of participants was 57.4 ± 16.5 years (males, 49.5%). There was excellent agreement between software-detected number of blinks and visually measured blinks for both observers (intraclass correlation coefficient [ICC], 0.97 for observer 1; ICC, 0.98 for observer 2). The CLS measured a mean blink frequency of 29.8 ± 15.4 blinks/min, a blink duration of 0.26 ± 0.21 seconds and an interblink interval of 1.91 ± 2.03 seconds. The best method for identifying sleep periods had an accuracy of 95.2 ± 0.5%.ConclusionsAutomated analysis of CLS 24-hour IOP recordings can accurately quantify eye blinks, and identify sleep and wake periods.Translational relevanceThis study sheds new light on the potential importance of eye blinks in glaucoma and may contribute to improved understanding of circadian IOP characteristics.

Sea stars adhere firmly but temporarily to various substrata as a result of underwater efficient adhesive secretions released by their tube feet. Previous studies showed that this material is mainly made up of proteins, which play a key role in its adhesiveness and cohesiveness. Recently, we solubilized the majority of these proteins and obtained 43 de novo-generated peptide sequences by tandem MS. Here, one of these sequences served to recover the full-length sequence of Sea star footprint protein 1 (Sfp1), by RT-PCR and tube foot transcriptome analysis. Sfp1, a large protein of 3,853 aa, is the second most abundant constituent of the secreted adhesive. By using MS and Western blot analyses, we showed that Sfp1 is translated from a single mRNA and then cleaved into four subunits linked together by disulphide bridges in tube foot adhesive cells. The four subunits display specific protein-, carbohydrate-, and metal-binding domains. Immunohistochemistry and immunocytochemistry located Sfp1 in granules stockpiled by one of the two types of adhesive cells responsible for the secretion of the adhesive material. We also demonstrated that Sfp1 makes up the structural scaffold of the adhesive footprint that remains on the substratum after tube foot detachment. Taken together, the results suggest that Sfp1 is a major structural protein involved in footprint cohesion and possibly in adhesive interactions with the tube foot surface. In recombinant form, it could be used for the design of novel sea star-inspired biomaterials.

This article deals with the assessment of the environmental impact of shredded scrap tires mixed with sand to build embankments. Pollution of water as well as consequences of fire ignition were considered. Percolation, lixiviation, and lysimeter tests were performed on the raw material as well as on residue resulting from the fire. The collected liquids were analysed considering 200 physicochemical parameters. Their toxicity on a small shellfish and a bacterium was assessed. Measurements were compared with existing regulations and recommendations. Fire propagation risk in the core of the embankment and on the surface was also investigated. Measurements confirmed the limited consequences on the environment except in the case of fire, where residue must be considered hazardous waste. Key words: used tires, leaching, percolation, toxicity, fire. Cet article traite de l'evaluation de l'impact environnemental des pneus dechiquetes melanges avec du sable utilises pour la construction de remblais. La pollution de l'eau ainsi que les consequences d'un incendie ont ete prises en compte. Des essais de percolation, de lixiviation et en lysimetre ont ete realises sur le materiau brut ainsi que sur le residu d'incendie. Les liquides collectes ont ete analyses considerant plus de 200 parametres physico-chimiques. Leur toxicite sur un micro-crustace et une bacterie a ete mesuree. Les mesures ont ete comparees a des limites reglementaires et a des recommandations existantes. Le risque de propagation d'un incendie en surface et en profondeur du melange a ete evalue. Selon les mesures, on a confirme que l'impact sur l'environnement est limite, sauf pour les residus d'incendie a considerer comme dechets dangereux. Mots-cles: pneus recycles, lixiviation, percolation, toxicite, feu., Introduction Since the end of the 1980s, used tires have been widely used in civil engineering applications, including recycling either as whole tires, halved or shredded tires, alone or mixed [...]

Brominated flame retardants (BFRs) are used in different types of products such as plastic, textiles and electronic circuits with the aim to increase fire resistance and to avoid or to delay the propagation of accidental combustion. However, high concentrations of bromine in waste from electrical and electronic equipment (WEEE) hinder the possibility of their use as secondary raw materials. Furthermore, the possibility that BFRs can be released during mechanical recycling processes, imposes the need for ad hoc separation processes through an appropriate selection system. In order to adopt solutions based on the circular economy, it is necessary to develop an efficient strategy for selecting plastic waste containing BFRs from waste flow addressed to recycling. In this perspective, the use of a rapid and non-destructive technique, such as hyperspectral imaging (HSI), represents a powerful solution for the identification and to perform quality control of plastics waste. The study aims to develop a new methodology based on HSI in the short-wave infrared range (SWIR: 1000-2500 nm) coupled with chemometrics and X-ray fluorescence analysis in order to discriminate plastic waste with BFRs into two classes (low Br <2000 mg/kg and high Br> 2000 mg/kg) in agreement with the limits recommended by technical specification for WEEE. The promising results achieved suggest that this methodology may be applicable for the separation of plastics containing BFRs in recycling plants. [ABSTRACT FROM AUTHOR]

Sea stars adhere to various underwater substrata using an efficient protein-based adhesive secretion. The protein Sfp1 is a major component of this secretion. In the natural glue, it is cleaved into four subunits (Sfp1 Alpha, Beta, Delta and Gamma) displaying specific domains which mediate protein-protein or protein-carbohydrate interactions. In this study, we used the bacterium E. coli to produce recombinantly two fragments of Sfp1 comprising most of its functional domains: the C-terminal part of the Beta subunit (rSfp1 Beta C-term) and the Delta subunit (rSfp1 Delta). Using native polyacrylamide gel electrophoresis and size exclusion chromatography, we show that the proteins self-assemble and form oligomers and aggregates in the presence of NaCl. Moreover, they adsorb onto glass and polystyrene upon addition of Na+ and/or Ca2+ ions, forming homogeneous coatings or irregular meshworks, depending on the cation species and concentration. We show that coatings made of each of the two proteins have no cytotoxic effects on HeLa cells and even increase their proliferation. We propose that the Sfp1 recombinant protein coatings are valuable new materials with potential for cell culture or biomedical applications. Biological adhesives offer impressive performance in their natural context and, therewith, the potential to inspire the development of advanced biomaterials for an increasing variety of applications in medicine or in material sciences. To date, most marine adhesive proteins that have been produced recombinantly in order to develop bio-inspired adhesives are small proteins from mussels and barnacles. Here, we produced two multi-modular proteins based on the sequence of Sfp1, a major protein from sea star adhesive secretion. These two proteins comprise most of Sfp1 functional domains which mediate protein-protein and protein-carbohydrate interactions. We characterized the two recombinant proteins with an emphasis on functional characteristics such as self-assembly, adsorption and cytocompatibility. We discuss their potential as biomaterials. Image, graphical abstract [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, an emerging trend is to increase the reliability of industrial process by using machine learning (ML) to detect anomalies of production machines. The main advantages of ML are in the ability to (1) capture non-linear phenomena, (2) adapt to many different processes without human intervention and (3) learn incrementally and improve over time. In this paper, we take the perspective of IT system architects and analyse the implications of the inclusion of ML components into a traditional anomaly detection systems. Through a prototype that we deployed for chemical reactors, our findings are that such ML components are impacting drastically the architecture of classical alarm systems. First, there is a need for long-term storage of the data that are used to train the models. Second, the training and usage of ML models can be CPU intensive and may request using specific resources. Third, there is no single algorithm that can detect machine errors. Fourth, human crafted alarm rules can now also include a learning process to improve these rules, for example by using active learning with a human-in-the-loop approach. These reasons are the motivations behind a microservice-based architecture for an alarm system in industrial machinery. [ABSTRACT FROM AUTHOR]

Copyright of Archives of Cardiovascular Diseases is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

7β -hydroxy-epiandrosterone (7β-OH-EpiA) is an endogenous androgen metabolite that has been shown to exert neuroprotective, anti-inflammatory and anti-estrogenic effects. However, to the best of our knowledge no information is available about this androgen steroid in relation to sperm quality. We analyzed 7β-OH-EpiA in plasma and seminal plasma using a newly developed isotope dilution ultra-high performance liquid chromatography - mass spectrometry method. Validation met the requirements of FDA guidelines. Levels of 7β-OH-EpiA were measured in 191 men with different degrees of infertility. One-way analysis of variance followed by multiple comparison and correlation analysis adjusted for age, BMI and abstinence time were performed to evaluate the relationships between this steroid and sperm quality. Concentrations of 7β-OH-EpiA in seminal plasma were significantly higher in severely infertile men in comparison with healthy men and slightly infertile men. The same trend was found when blood plasma was evaluated. Furthermore, plasma 7β-OH-EpiA negatively correlated with sperm concentration (-0.215; p<0.01) and total count (-0.15; p<0.05). Seminal 7β-OH-EpiA was negatively associated with motility (-0.26; p<0.01), progressively motile spermatozoa (-0.233; p<0.01) and nonprogressively motile spermatozoa (-0.188; p<0.05). 7β-OH-EpiA is associated with lower sperm quality and deserves more research in that respect. [ABSTRACT FROM AUTHOR]

Electricity load monitoring in residential buildings has become an important task allowing for energy consumption understanding, indirect human activity recognition and occupancy modelling. In this context, Non Intrusive Load Monitoring (NILM) is an approach based on the analysis of the global electricity consumption signal of the habitation. Current NILM solutions are reaching good precision for the identification of electrical devices but at the cost of difficult setups with expensive equipments typically working at high frequency. In this work we propose to use a low-cost and easy to install low frequency sensor for which we improve the performances with an active machine learning strategy. At setup, the system is able to identify some appliances with typical signatures such as a fridge. During usage, the system detects unknown signatures and provides a user-friendly procedure to include new appliances and to improve the identification precision over time. [ABSTRACT FROM AUTHOR]

Future buildings will more and more rely on advanced Building Management Systems (BMS) connected to a variety of sensors, actuators and dedicated networks. Their objectives are to observe the state of rooms and apply automated rules to preserve or increase comfort while economizing energy. In this work, we advocate for the inclusion of a dedicated system for sensors data storage and processing, based on Big Data technologies. This choice enables new potentials in terms of data analytics and applications development, the most obvious one being the ability to scale up seamlessly from one smart building to several, in the direction of smart areas and smart cities. We report in this paper on our system architecture and on several challenges we met in its elaboration, attempting to meet requirements of scalability, data processing, flexibility, interoperability and privacy. We also describe current and future end-user services that our platform will support, including historical data retrieval, visualisation, processing and alarms. The platform, called BBData - Big Building Data , is currently in production at the Smart Living Lab of Fribourg and is offered to several research teams to ease their work, to foster the sharing of historical data and to avoid that each project develops its own data gathering and processing pipeline. [ABSTRACT FROM AUTHOR]

Copyright of Sociologias is the property of Universidade Federal do Rio Grande do Sul and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)