A protectin DX (PDX) analog with in vitro activity against influenza A(H1N1) viruses.

Antiviral therapy based on neuraminidase (oseltamivir) or polymerase (baloxavir marboxil) inhibitors plays an important role in the management of influenza infections. However, the emergence of drug resistance and the uncontrolled inflammatory response are major limitations in the treatment of severe influenza disease. Protectins D1 (PD1) and DX (PDX), part of a family of pro-resolving mediators, have previously demonstrated anti-influenza activity as well as anti-inflammatory properties in various clinical contexts. Herein, we synthetized a series of simplified PDX analogs and assessed their in vitro antiviral activity against influenza A(H1N1) viruses, including oseltamivir- and baloxavir-resistant variants. In ST6GalI-MDCK cells, the PDX analog AN-137B reduced viral replication in a dose-dependent manner with IC50 values of 23.8 for A/Puerto Rico/8/1934 (H1N1) and between 32.6 and 36.7 µM for susceptible and resistant A(H1N1)pdm09 viruses. In MTS-based cell viability experiments, AN-137B showed a 50% cellular cytotoxicity (CC50 ) of 638.7 µM with a resulting selectivity index of 26.8. Of greater importance, the combination of AN-137B with oseltamivir or baloxavir resulted in synergistic and additive in vitro effects, respectively. Treatment of lipopolysaccharide (LPS)-stimulated macrophages with AN-137B resulted in a decrease of iNOS activity as shown by the reduction of nitrite production, suggesting an anti-inflammatory effect. In conclusion, our results indicate that the protectin analog AN-137B constitutes an interesting therapeutic modality against influenza A virus, warranting further evaluation in animal models.

NoiseCapture smartphone application as pedagogical support for education and public awareness.

Teaching science subjects such as acoustics to youth or the general public can be facilitated by illustrating physical phenomena or scientific issues using fun experiences. A few years ago, our team developed a smartphone application named NoiseCapture with the aim of offering to anyone the opportunity to measure their sound environment and to share their geolocated measurements with the community in order to build a collective noise map. Since then, NoiseCapture team members have experimented with numerous interventions in schools or scientific events for the general public based on the app to explain not only societal and environmental issues related to noise but also to teach acoustic notions and to address technical and scientific topics associated with sound measurement. This paper describes some of the interventions implemented, in particular, in a school context through training courses given to middle school and university students, as well as teachers of secondary school, that focused on basic knowledge of buildings and environmental acoustics, on the practice of acoustic measurement, and on noise mapping. Some examples of interventions with the general public are also presented that were mostly integrated into scientific events.

A Smartphone-Based Crowd-Sourced Database for Environmental Noise Assessment.

Noise is a major source of pollution with a strong impact on health. Noise assessment is therefore a very important issue to reduce its impact on humans. To overcome the limitations of the classical method of noise assessment (such as simulation tools or noise observatories), alternative approaches have been developed, among which is collaborative noise measurement via a smartphone. Following this approach, the NoiseCapture application was proposed, in an open science framework, providing free access to a considerable amount of information and offering interesting perspectives of spatial and temporal noise analysis for the scientific community. After more than 3 years of operation, the amount of collected data is considerable. Its exploitation for a sound environment analysis, however, requires one to consider the intrinsic limits of each collected information, defined, for example, by the very nature of the data, the measurement protocol, the technical performance of the smartphone, the absence of calibration, the presence of anomalies in the collected data, etc. The purpose of this article is thus to provide enough information, in terms of quality, consistency, and completeness of the data, so that everyone can exploit the database, in full control.

Polyphonic training set synthesis improves self-supervised urban sound classification.

Machine listening systems for environmental acoustic monitoring face a shortage of expert annotations to be used as training data. To circumvent this issue, the emerging paradigm of self-supervised learning proposes to pre-train audio classifiers on a task whose ground truth is trivially available. Alternatively, training set synthesis consists in annotating a small corpus of acoustic events of interest, which are then automatically mixed at random to form a larger corpus of polyphonic scenes. Prior studies have considered these two paradigms in isolation but rarely ever in conjunction. Furthermore, the impact of data curation in training set synthesis remains unclear. To fill this gap in research, this article proposes a two-stage approach. In the self-supervised stage, we formulate a pretext task (Audio2Vec skip-gram inpainting) on unlabeled spectrograms from an acoustic sensor network. Then, in the supervised stage, we formulate a downstream task of multilabel urban sound classification on synthetic scenes. We find that training set synthesis benefits overall performance more than self-supervised learning. Interestingly, the geographical origin of the acoustic events in training set synthesis appears to have a decisive impact.

Low-Cost Sensors for Urban Noise Monitoring Networks-A Literature Review.

Noise pollution reduction in the environment is a major challenge from a societal and health point of view. To implement strategies to improve sound environments, experts need information on existing noise. The first source of information is based on the elaboration of noise maps using software, but with limitations on the realism of the maps obtained, due to numerous calculation assumptions. The second is based on the use of measured data, in particular through professional measurement observatories, but in limited numbers for practical and financial reasons. More recently, numerous technical developments, such as the miniaturization of electronic components, the accessibility of low-cost computing processors and the improved performance of electric batteries, have opened up new prospects for the deployment of low-cost sensor networks for the assessment of sound environments. Over the past fifteen years, the literature has presented numerous experiments in this field, ranging from proof of concept to operational implementation. The purpose of this article is firstly to review the literature, and secondly, to identify the expected technical characteristics of the sensors to address the problem of noise pollution assessment. Lastly, the article will also put forward the challenges that are needed to respond to a massive deployment of low-cost noise sensors.

Collaborative noise data collected from smartphones.

Noise stands for an important human health and environmental issue. Indeed, noise causes annoyance and fatigue, interferes with communication and sleep, damages hearing and entails cardiovascular problems (WHO, 2011) [1]. From an environmental point of view, noise implies a lessening of both the richness and abundance of the animal species, an alteration of the communication, which can threaten the reproduction and predation, etc. (Newport et al., 2014; Shannon et al., 2014) [2], [3]. Consequently, effects related to environmental noise result in a huge cost for society, with 2.2 billion euros in France, for example, for the year 2013 (Bourges and Diel, 2015) [4]. In this context, the reduction of noise in the environment is a burning issue, which requires, firstly, carrying out an evaluation of noise in the environment, and secondly, to establish action plans to reduce noise annoyance. With the development of the concept of participatory measurement, and considering the extremely large number of people equipped with a smartphone while being "in mobility", the use of smartphones is potentially a relevant solution to realize a large-scale environmental noise evaluation. The data presented hereinafter are collected from the Android NoiseCapture application and shared from the OnoMap Spatial Data Infrastructure (SDI). The NoiseCapture approach consists in measuring noise along a path, and then to share data with the community. This approach has been developed within the framework of the European ENERGIC-OD project, which aims at deploying a set of Virtual Hubs (VH) to share heterogeneous data with third parties, in respect with the European INSPIRE, and at developing new and original services that can be useful for the community. The noise data that are acquired by volunteers around the world (citizen observations), are organized in three files, containing the path of measures (a set of points), standardized noise indicators, noise description and other useful variables (GPS accuracy, speed…). These data can be very relevant later to propose an environmental noise evaluation, through simple or complex treatments.

Glucose monitoring using a polymer brush modified polypropylene hollow fiber-based hydraulic flow sensor.

Tight regulation of blood glucose levels of diabetic patients requires durable and robust continuous glucose sensing schemes. This manuscript reports the fabrication of ultrathin, phenylboronic acid (PBA) functionalized polymer brushes that swell upon glucose binding and which were integrated as the sensing interface in a new polypropylene hollow fiber (PPHF)-based hydraulic flow glucose sensor prototype. The polymer brushes were prepared via surface-initiated atom transfer radical polymerization of sodium methacrylate followed by postpolymerization modification with 3-aminophenyl boronic acid. In a first series of experiments, the glucose-response of PBA-functionalized poly(methacrylic acid) (PMAA) brushes grafted from planar silicon surfaces was investigated by quartz crystal microbalance with dissipation (QCM-D) and atomic force microscopy (AFM) experiments. The QCM-D experiments revealed a more or less linear change of the frequency shift for glucose concentrations up to ∼10 mM and demonstrated that glucose binding was completely reversible for up to seven switching cycles. The AFM experiments indicated that glucose binding was accompanied by an increase in the film thickness of the PBA functionalized PMAA brushes. The PBA functionalized PMAA brushes were subsequently grafted from the surface of PPHF membranes. The hydraulic permeability of these porous fibers depends on the thickness and swelling of the PMAA brush coating. PBA functionalized brush-coated PPHFs showed a decrease in flux upon exposure to glucose, which is consistent with swelling of the brush coating. Because they avoid the use of enzymes and do not rely on an electrochemical transduction scheme, these PPHF-based hydraulic flow sensors could represent an interesting alternative class of continuous glucose sensors.

Biodegradable polycarbonate-b-polypeptide and polyester-b-polypeptide block copolymers: synthesis and nanoparticle formation towards biomaterials.

The amino poly(trimethylene carbonate)-NHt-Boc (PTMC-NHt-Boc) and poly(epsilon-caprolactone)-NH -Boc (PCL-NHt-Boc) were synthesized by ring-opening polymerization (ROP) of TMC or CL and subsequently deprotected into the corresponding PTMC-NH2 and PCL-NH2. These functional homopolymers were used as macroinitiators for the ROP of gamma-benzyl-L-glutamate N-carboxyanhydride (BLG), consequently, giving the respective diblock copolymers PTMC-b-PBLG and PCL-b-PBLG in almost quantitative yields. The (co)polymers have been characterized by NMR and SEC analyses. DSC and IR studies confirmed the block structure of the copolymers and highlighted a phase separation between the rigid peptide (alpha-helix conformation) and the more flexible polyester segments. The self-assembly and the degradation behaviors of the copolymers depended on the nature of the polyester block and on the copolymer composition. Nanoparticles obtained from PBLG block copolymers were twice smaller ( RH < 100 nm) than those formed from PTMC and PCL homopolymers. Finally, their enzymatic degradation revealed that PTMC nanoparticles degraded faster than those made of PCL.