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The voltage source converter (VSC) based HVDC (high voltage direct current system) offers the possibility to integrate other renewable energy sources (RES) into the electrical grid, and allows power flow reversal capability. These appealing features of VSC technology led to the further development of multi-terminal direct current (MTDC) systems. MTDC grids provide the possibility of interconnection between conventional power systems and other large-scale offshore sources like wind and solar systems. The modular multilevel converter (MMC) has become a popular technology in the development of the VSC-MTDC system due to its salient features such as modularity and scalability. Although, the employment of MMC converter in the MTDC system improves the overall system performance. However, there are some technical challenges related to its operation, control, modeling and protection that need to be addressed. This paper mainly provides a comprehensive review and investigation of the control and protection of the MMC-based MTDC system. In addition, the issues and challenges associated with the development of the MMC-MTDC system have been discussed in this paper. It majorly covers the control schemes that provide the AC system support and state-of-the-art relaying algorithm/ dc fault detection and location algorithms. Different types of dc fault detection and location algorithms presented in the literature have been reviewed, such as local measurement-based, communication-based, traveling wave-based and artificial intelligence-based. Characteristics of the protection techniques are compared and analyzed in terms of various scenarios such as implementation in CBs, system configuration, selectivity, and robustness. Finally, future challenges and issues regarding the development of the MTDC system have been discussed in detail.

Background Sleep disorders are commonly experienced by community caregivers for persons with cancer, with at least 72% reporting moderate to severe disorders. A consequence of this condition, which is associated with the presence of overload in the caregiver, is the increased risk of clinical depression. The aim of this study is to evaluate the effects of music on the sleep quality achieved by informal caregivers for cancer patients receiving home palliative care. In addition, we will assess the influence of specific variables that could modify these effects, analyse the correlates related to nocturnal wakefulness and consider the diurnal consequences according to the sleep characteristics identified. Methods This single-blind, multicentre, randomised clinical trial will focus on informal providers of care for cancer patients. Two samples of 40 caregivers will be recruited. The first, intervention, group will receive seven music-based sessions. The control group will be masked with seven sessions of therapeutic education (reinforcing previous sessions). Outcomes will be evaluated using the Pittsburgh Sleep Quality Index, a triaxial accelerometer, EuroQol-5D-5L, the Caregiver Strain Index, the Epworth Sleepiness Scale and the Client Satisfaction Questionnaire. The caregivers’ satisfaction with the intervention performed will also be examined. Discussion This study is expected to extend our understanding of the efficacy of music therapy in enhancing the sleep quality of caregivers for patients receiving home palliative care. To our knowledge, no reliable scientific investigations of this subject have previously been undertaken. Music is believed to benefit certain aspects of sleep, but this has yet to be proven and, according to a Cochrane review, high-quality research in this field is necessary. One of the main strengths of our study, which heightens the quality of the randomised clinical trial design, is the objective assessment of physical activity by accelerometry and the use of both objective and subjective measures of sleep in caregivers. Music therapy for the caregivers addressed in this study is complementary, readily applicable, provokes no harmful side effects and may produce significant benefits. Trial registration The IMECA study is registered at Clinical Trials.gov, ClinicalTrials.gov Identifier: NCT04491110. Registered 29 July, 2020.

Transitioning into higher education (HE) impacts health behaviours. Poor dietary and lifestyle behaviours may correlate and increase risk of co-morbidities. The introduction of the Okanagan Charter detailed the important role of health promotion within a HE setting. The aim of this study was to assess the relationship between dietary quality and lifestyle behaviours of students attending HE.

Vitamin K1 (VitK1) and Vitamin K2 (VitK2), two important naturally occurring micronutrients in the VitK family, found, respectively, in green leafy plants and algae (VitK1) and animal and fermented foods (VitK2). The present review explores the multiple biological functions of VitK2 from recently published in vitro and in vivo studies, including promotion of osteogenesis, prevention of calcification, relief of menopausal symptoms, enhancement of mitochondrial energy release, hepato-and neuro-protective effects, and possible use in treatment of coronavirus disease. The mechanisms of action associated with these biological effects are also explored. Overall, the findings presented here suggest that VitK, especially VitK2, is an important nutrient family for the normal functioning of human health. It acts on almost all major body systems and directly or indirectly participates in and regulates hundreds of physiological or pathological processes. However, as biological and clinical data are still inconsistent and conflicting, more in-depth investigations are warranted to elucidate its potential as a therapeutic strategy to prevent and treat a range of disease conditions.

The design of networked switched systems with event-based communication is attractive due to its potential to save bandwidth and energy. However, ensuring the stability and performance of networked systems with event-triggered communication and asynchronous switching is challenging due to their time-varying nature. This paper presents a novel sampled-data approach to design event-triggered asynchronous H∞ filters for networked switched systems. Unlike most existing event-based filtering results, which either design the event-triggering scheme only or co-design the event-triggering condition and the filter, we consider that the event-triggering policy is predefined and synthesize the filter. We model the estimation error system as an event-triggered switched system with time delay and non-uniform sampling. By implementing a delay-dependent multiple Lyapunov method, we derive sufficient conditions to ensure the global asymptotic stability of the filtering error system and an H∞ performance level. The efficacy of the proposed design technique and the superiority of the filter performance is illustrated by numerical examples and by comparing the performance with a recent result.

International audience; In tribute to the bicentenary of the birth of Louis Pasteur, this report focuses on cyanotoxins, other natural products and bioactive compounds of cyanobacteria, a phylum of Gram-negative bacteria capable of carrying out oxygenic photosynthesis. These microbes have contributed to changes in the geochemistry and the biology of Earth as we know it today. Furthermore, some bloom-forming cyanobacterial species are also well known for their capacity to produce cyanotoxins. This phylum is preserved in live cultures of pure, monoclonal strains in the Pasteur Cultures of Cyanobacteria (PCC) collection. The collection has been used to classify organisms within the Cyanobacteria of the bacterial kingdom and to investigate several characteristics of these bacteria, such as their ultrastructure, gas vacuoles and complementary chromatic adaptation. Thanks to the ease of obtaining genetic and further genomic sequences, the diversity of the PCC strains has made it possible to reveal some main cyanotoxins and to highlight several genetic loci dedicated to completely unknown natural products. It is the multidisciplinary collaboration of microbiologists, biochemists and chemists and the use of the pure strains of this collection that has allowed the study of several biosynthetic pathways from genetic origins to the structures of natural products and, eventually, their bioactivity.

International audience; This paper proposes a novel code for optical code division multiple access (OCDMA) systems, called the three-dimensional (3D) spectral/temporal/spatial single weight zero cross-correlation (3D-SWZCC) code. The proposed code could potentially be used in the next generation of passive optical networks (NG-PONs) to provide a 3D-SWZCC-OCDMA-NG-PON system. The developed code has a high capacity and a zero cross-correlation property that completely suppresses the multiple access interference (MAI) effects that are a main drawback for OCDMA systems. Previously, a two-dimensional (2D) SWZCC code was proposed for two-dimensional OCDMA (2D-OCDMA) systems. It works by devoting the first and second components to spectral and spatial encodings, respectively. However, the proposed code aims to carry out encoding domains in spectral, time, and spatial aspects for the first, second, and third components, respectively. One-dimensional, 2D, and 3D systems can support up to 68, 157, and 454 active users with total code lengths equal to 68, 171, and 273, respectively. Numerical results reveal that the 3D-SWZCC code outperforms codes from previous studies, including 3D codes such as perfect difference (PD), PD/multi-diagonal (PD/MD), dynamic cyclic shift/MD (DCS/MD), and Pascal’s triangle zero cross-correlation (PTZCC), according to various metrics. The system function is provided by exhibiting the architecture of the transmitter and receiver in the PON context, where the proposed code demonstrates its effectiveness in meeting optical communication requirements based on 3D-OCDMA-PON by producing a high quality factor (Q) of 18.8 and low bit error rate (BER) of 3.48 × 10−29 over a long distance that can reach 30 Km for a data rate of 0.622 Gbps.

This research examines the relationship between firms' corporate social performance (CSP) and the implied cost of equity capital using a sample of 25,938 firm-year observation from 49 countries during the period from 2002 to 2021. Using estimates of the firms' ex ante cost of equity capital, we examine its relationship with industry-relative measures of the firms' CSP, its environmental and social pillars and sub-pillars. We find that increased overall CSP reduces a firm's cost of equity capital up until a point, beyond which the marginal benefits of further CSP investment decrease. We find that the social pillar is the main driver of the reductions in cost of capital, in particular, a firm's performance in relation to its workforce, and that higher performance is increasingly rewarded with a lower cost of capital. Finally, we find that this relationship differs depending on the institutional context, with greater reductions in the cost of capital evident in countries with stronger institutional environments.

Convolutional Neural Networks and Deep Learning have revolutionized every field since their inception. Agriculture has also been reaping the fruits of developments in mentioned fields. Technology is being revolutionized to increase yield, save water wastage, take care of diseased weeds, and also increase the profit of farmers. Grapes are among the highest profit-yielding and important fruit related to the juice industry. Pakistan being an agricultural country, can widely benefit by cultivating and improving grapes per hectare yield. The biggest challenge in harvesting grapes to date is to detect their cluster successfully; many approaches tend to answer this problem by harvest and sort technique where the foreign objects are separated later from grapes after harvesting them using an automatic harvester. Currently available systems are trained on data that is from developed or grape-producing countries, thus showing data biases when used at any new location thus it gives rise to a need of creating a dataset from scratch to verify the results of research. Grape is available in different sizes, colors, seed sizes, and shapes which makes its detection, through simple Computer vision, even more challenging. This research addresses this issue by bringing the solution to this problem by using CNN and Neural Networks using the newly created dataset from local farms as the other research and the methods used don’t address issues faced locally by the farmers. YOLO has been selected to be trained on the locally collected dataset of grapes.

International audience; Consumption habits are changing due to the development of new technologies around renewable energy, environmental awareness, and new incentive policies. Smart grids are seen as an effective way to accommodate more renewable energy, achieve better control of demand, and improve the operating conditions of the electrical system. However, electric vehicles, which are an environmentally friendly alternative, have very high market penetration and require efficient electrical management at charging stations. Among the factors that have a significant impact on electrical energy consumption are traffic conditions, which can seriously impact the efficiency of electric vehicles. Therefore, the focus is on developing charging infrastructure and reducing vehicle waiting time by optimally allocating electric vehicles to charging stations. To this end, an optimization approach is presented, based on the traffic conditions collected by the SUMO simulator. This approach enables each vehicle to be assigned to the appropriate station while maintaining its battery state of charge at a higher level.

International audience; Background: Smoking has been associated with higher morbidity and mortality following myocardial infarction (MI), but reports of the impact on morbidity and mortality for females and elderly patients experiencing MI complicated with left ventricular dysfunction or overt heart failure are limited. Methods: In an individual patient data meta-analysis of high-risk MI patients, the association of smoking with hospitalizations and death were investigated. Weighted Cox proportional hazard modelling were used to study the risks of smoking on adjudicated endpoints among different sex and age categories. Results: 28,735 patients from the CAPRICORN, EPHESUS, OPTIMAAL and VALIANT trials were assessed. After weighting, smokers (N=18,148) were unfrequently women (29.2%) and a minority were above ≥80 years (9.8%). Smoking was significantly more associated with all-cause hospitalizations in women (hazard ratio (HR) 1.24; 95% confidence interval (CI) 1.16-1.32) than in men (HR=1.10; CI 1.05-1.16) resulting in a significant interaction between smoking and sex (p=0.005). Smoking was predictive of all-cause mortality homogenously across age categories (p for interaction=0.25) and sex (p for interaction=0.58). Conclusions: The influence of smoking on morbidity differed according to sex following high-risk MI. The deleterious impact of smoking on hospitalization appeared particularly potent in women, which should further reinforce preventive strategies in females.

The investment of time and resources for developing better strategies is key to dealing with future pandemics. In this work, we recreated the situation of COVID-19 across the year 2020, when the pandemic started spreading worldwide. We conducted experiments to predict the coronavirus cases for the 50 countries with the most cases during 2020. We compared the performance of state-of-the-art machine learning algorithms, such as long-short-term memory networks, against that of online incremental machine learning algorithms. To find the best strategy, we performed experiments to test three different approaches. In the first approach (single-country), we trained each model using data only from the country we were predicting. In the second one (multiple-country), we trained a model using the data from the 50 countries, and we used that model to predict each of the 50 countries. In the third experiment, we first applied clustering to calculate the nine most similar countries to the country that we were predicting. We consider two countries to be similar if the differences between the curve that represents the COVID-19 time series are small. To do so, we used time series similarity measures (TSSM) such as Euclidean Distance (ED) and Dynamic Time Warping (DTW). TSSM return a real value that represents the distance between the points in two time series which can be interpreted as how similar they are. Then, we trained the models with the data from the nine more similar countries to the one that was predicted and the predicted one. We used the model ARIMA as a baseline for our results. Results show that the idea of using TSSM is a very effective approach. By using it with the ED, the obtained RMSE in the singlecountry and multiple-country approaches was reduced by 74.21% and 74.70%, respectively. And by using the DTW, the RMSE was reduced by 74.89% and 75.36%. The main advantage of our methodology is that it is very simple and fast to apply since it is only based on time series data, as opposed to more complex methodologies that require a deep and thorough study to consider the number of parameters involved in the spread of the virus and their corresponding values. We made our code public to allow other researchers to explore our proposed methodology.

International audience; The use of telemonitoring solutions via wearable sensors is believed to play a major role in the prevention and therapy of physical weakening in older adults. Despite the various studies found in the literature, some elements are still not well addressed, such as the study cohort, the experimental protocol, the type of research design, as well as the relevant features in this context. To this end, the objective of this pilot study was to investigate the efficacy of data-driven systems to characterize older individuals over 80 years of age with impaired physical function, during their daily routine and under unsupervised conditions. We propose a fully automated process which extracts a set of heterogeneous time-domain features from 24-hour files of acceleration and barometric data. After being statistically tested, the most discriminant features fed a group of machine learning classifiers to distinguish frail from non-frail subjects, achieving an accuracy up to 93.51%. Our analysis, conducted over 570 days of recordings, shows that a longitudinal study is important while using the proposed features, in order to ensure a highly specific diagnosis. This work may serve as a basis for the paradigm of future monitoring systems.

International audience; (1) Background: Transcutaneous oxygen pressure (TcpO(2)) is used to determine the severity of lower extremity arterial disease (LEAD). Many authors used a ratio of limb to chest TcpO(2), also called the regional perfusion index (RPI), which should be independent of variations in oxygen delivery and reflective of local limb oxygen supply. The relevance of a reference probe-positioned TcpO(2) electrode is debated. We aimed to review the relevance of the reference probe in previous studies using rest TcpO(2). (2) Methods: We searched Medline and the Cochrane Central Register of Controlled Trials on 22 September 2022 using keywords related to TcpO(2), reference probe and LEAD. (3) Results/Discussion: Fifteen studies were included in the review. Nine studies investigated LEAD severity (n = 9), amputation healing predication (n = 4), surgical outcome prediction (n = 2), therapeutic effect (n = 3) and difference according to diabetic status (n = 1). Four studies investigated more than 1 indication. Among 12 (16.7%) studies using RPI, only two authors found a benefit of using RPI instead of absolute TcpO(2). Using only univariate analysis, one author reported that RPI was significantly related to viability at 1 year, while distal TcpO(2) was not, on 13 limbs. The following year, the same author published a new study including 118 limbs that reported that RPI and absolute TcPO2 were both prognostic factors for limb viability at 1 year using a multivariate model. (4) Conclusions: Only one study firmly supporting the use of RPI, calculated using a reference probe on the arm, to predict BKA healing. Prospective studies are needed to validate this result; for other indications there is insufficient data supporting the use of a TcpO(2) reference probe at rest.

Fractional calculus tools have been exploited to effectively model variety of engineering, physics and applied sciences problems. The concept of fractional derivative has been incorporated in the optimization process of least mean square (LMS) iterative adaptive method. This study exploits the recently introduced enhanced fractional derivative based LMS (EFDLMS) for parameter estimation of power signal formed by the combination of different sinusoids. The EFDLMS addresses the issue of fractional extreme points and provides faster convergence speed. The performance of EFDLMS is evaluated in detail by taking different levels of noise in the composite sinusoidal signal as well as considering various fractional orders in the EFDLMS. Simulation results reveal that the EDFLMS is faster in convergence speed than the conventional LMS (i.e., EFDLMS for unity fractional order).

While the psychological phenomena that affect group decisionmaking have been thoroughly investigated for decades, how these phenomena apply to decision-making by judges on panel courts is under-examined. This article examines the main psychological phenomena of group decision-making, both positive and negative, and considers their implications for panel courts and other groups of professional legal decision-makers such as adjudicators serving on tribunals. This article argues that experimental studies on judges and adjudicators testing the effects of these phenomena would improve understanding of legal decision-making by these groups and could help to devise ways to improve their decision-making processes to reach higher quality decisions. Some ideas and outlines for experimental studies are presented.

The droop mechanism is widely utilized in a stand-alone microgrid (MG) to regulate power-sharing among distributed generators (DG). However, over the years, the droop phenomena are continually modified to lessen the deviation in voltage and frequency parameters caused due to classical droop. This study suggests computing the droop coefficient for voltage–current (V–I) droop to take into account for proportional power distribution among DGs. In grid utility networks, the conservation voltage reduction (CVR) strategy is widely used to curtail the use of energy. Hence, this paper investigates the CVR's performance for stand-alone MG by performing the adaptive vector control scheme in the two-phase d–q reference frame. In addition to it, the paper is intended to develop a coordinated control strategy for stand-alone MG involving classical P–f droop and self-sustained V–I droop employed to perform the function of CVR during peak demand and overloading conditions. Further, a load-shedding control approach is also considered to cut out some segments of load during overloading conditions to operate the MG in the stable zone. The validation of the proposed strategy is conducted on MATLAB/Simulink software.

The purpose of this qualitative cross-cultural case study was to better understand how Muslim students living in Australia, Ireland, and the United States navigated racism to identify ways in which school leaders and teachers can better address the structural, historical, and socioeconomic roots of racial injustice, discrimination, and ongoing oppression. Data collection was guided by a shared interview protocol that asked questions regarding family background, personal interests, identity, and friendships with a focus on their experiences of anti-Muslim racism in secondary schools. Findings suggested that Muslim students navigated racialization by (de)constructing their Muslimness, seeking voice, navigating between inclusion and exclusion, and responding to hate. This paper contributes to the bourgeoning literature exploring anti-Muslim racialization and makes a foundational empirical cross-cultural contribution with its identification of essential practices for anti-Muslim racism in schooling.

In this paper, we examine deflation-based algebraic multigrid methods for solving large systems of linear equations. Aggregation of the unknown terms is applied for coarsening, while deflation techniques are proposed for improving the rate of convergence. More specifically, the V-cycle strategy is adopted, in which, at each iteration, the solution is computed by initially decomposing it utilizing two complementary subspaces. The approximate solution is formed by combining the solution obtained using multigrids and deflation. In order to improve performance and convergence behavior, the proposed scheme was coupled with the Modified Generic Factored Approximate Sparse Inverse preconditioner. Furthermore, a parallel version of the multigrid scheme is proposed for multicore parallel systems, improving the performance of the techniques. Finally, characteristic model problems are solved to demonstrate the applicability of the proposed schemes, while numerical results are given.

Company news Bend Bioscience invests in expansion of drug-delivery capabilities Bend Bioscience (OR, USA) and NovaQuest Private Equity, have announced plans on 11 October 2022 to invest in a new facility in Bend, Oregon. The group will focus on developing new R&D partnerships, and early phase manufacturing and development activities based on proprietary particle engineering-based drug-delivery technologies. This new facility will consist of approximately 20,000 square feet of laboratory and processing areas and it is anticipated that manufacturing and analytical capabilities will operational by the end of Q2 2023. Announcing the investment decision, Dan Dobry, Bend Bioscience’s co-founder said “We feel Bend Bioscience will fill an unmet need, offering problem solving, formulation, and manufacturing for valuable but challenging therapies and collaborating to develop new technologies for the rapidly evolving problem statements in the in pharmaceutical industry” [1].

This ethnographic study concentrates on the co-creation of experiential value between the tourist and tour guide in a single historic tourism site; Huntingdon Castle, Ireland. Built upon the principles of service dominant logic, the research explores how storytelling acts as an engagement platform and value enhancing strategic resource. In doing so, it impels the value co-creation journey and shapes the tourist’s experience. Observation is coupled with qualitative interviews to capture the dual perspective of both guides and tourists. Findings exhibit the co-creation process through the performance of stories; how and when people derive pleasure (value); the influencing aspects of the environment or place; and guide/tourist perspectives on how they feel and think during the experience. The research contributes by taking a practical operational view of how co-creation occurs. It goes beyond the guide’s perspective and exhibits the importance of co-creation of lived experience in the story enhanced tourism experience framework.

International audience; Tick-borne encephalitis (TBE) is a viral disease endemic in Eurasia. The virus is mainly transmitted to humans via ticks and occasionally via the consumption of unpasteurized milk products. The European Centre for Disease Prevention and Control reported an increase in TBE incidence over the past years in Europe as well as the emergence of the disease in new areas. To better understand this phenomenon, we investigated the drivers of TBE emergence and increase in incidence in humans through an expert knowledge elicitation. We listed 59 possible drivers grouped in eight domains and elicited forty European experts to: (i) allocate a score per driver, (ii) weight this score within each domain, and (iii) weight the different domains and attribute an uncertainty level per domain. An overall weighted score per driver was calculated, and drivers with comparable scores were grouped into three terminal nodes using a regression tree analysis. The drivers with the highest scores were: (i) changes in human behavior/activities; (ii) changes in eating habits or consumer demand; (iii) changes in the landscape; (iv) influence of humidity on the survival and transmission of the pathogen; (v) difficulty to control reservoir(s) and/or vector(s); (vi) influence of temperature on virus survival and transmission; (vii) number of wildlife compartments/groups acting as reservoirs or amplifying hosts; (viii) increase of autochthonous wild mammals; and (ix) number of tick species vectors and their distribution. Our results support researchers in prioritizing studies targeting the most relevant drivers of emergence and increasing TBE incidence.

The difficulty of controlling the charging of electric buses (EBs) and their effects on network demand are discussed in this study. The solutions suggest a call for worldwide, complex infrastructures that manage EVs and EBs equally. Additionally, the Distribution Network (DN) must be prepared for an increased prevalence of reverse power flow caused by widespread distributed renewable generation. This paper focuses exclusively on EBs since they have higher capacity and predictable charging patterns, which makes them more significant for the DN in the context of a transition to complete vehicle electrification and technologies that are mature enough to be hosted. The proposed algorithm employs the Day-Ahead Energy Market (DAEM) in the Smart Charging (SC) to forecast the network operating circumstances. Additionally, the technique makes it possible to facilitate distributed photovoltaic (PV) generation, allowing network demand to be referenced depending on net demand. It also identifies an appropriate individual charger current per vehicle and per-time-step with load-levelling or peak-shaving as its primary goal. The final real demand demonstrates that a coarse correction of the demand is possible. According to the analysis of the DN voltage profile and associated line losses, the ideal node position location of the CS is dependent on PV penetration.

International audience; Background and Objectives: This study reviewed the literature to summarize the current and recent knowledge of temporomandibular joint osteoarthritis (TMJOA). Methods: Through a literature review, this work summarizes many concepts related to TMJOA. Results: Although many signaling pathways have been investigated, the etiopathogenesis of TMJOA remains unclear. Some clinical signs are suggestive of TMJOA; however, diagnosis is mainly based on radiological findings. Treatment options include noninvasive, minimally invasive, and surgical techniques. Several study models have been used in TMJOA studies because there is no gold standard model. Conclusion: More research is needed to develop curative treatments for TMJOA, which could be tested with reliable in vitro models, and to explore tissue engineering to regenerate damaged temporomandibular joints.

Innovation in both detection and treatment of cancer is necessary for the constant improvement in therapeutic strategies, especially in patients with novel or resistant variants of cancer. Cancer mortality rates have declined by almost 30% since 1991, however, depending on the cancer type, acquired resistance can occur to varying degrees. To combat this, researchers are looking towards advancing our understanding of cancer biology, in order to inform early detection, and guide novel therapeutic approaches. Through combination of these approaches, it is believed that a more complete and thorough intervention on cancer can be achieved. Here, we will discuss the advances and approaches in both detection and treatment of cancer, presented at the 58th Irish Association for Cancer Research (IACR) annual conference.

International audience; This paper presents a novel vehicular environment identification approach based on deep learning. It consists of exploiting the vehicular wireless channel characteristics in the form of Channel State Information (CSI) in the receiver side of a connected vehicle in order to identify the environment type in which the vehicle is driving, without any need to implement specific sensors such as cameras or radars. We consider environment identification as a classification problem, and propose a new convolutional neural network (CNN) architecture to deal with it. The estimated CSI is used as the input feature to train the model. To perform the identification process, the model is targeted for implementation in an autonomous vehicle connected to a vehicular network (VN). The proposed model is extensively evaluated, showing that it can reliably recognize the surrounding environment with high accuracy (96.48%). Our model is compared to related approaches and state-of-the-art classification architectures. The experiments show that our proposed model yields favorable performance compared to all other considered methods.

Global stock markets experienced a dual shock in 2020 due to the impact of the global health crisis, parallel to a simultaneous shock derived from the Saudi Arabia and Russia oil price war. The dual shock fueled oil market volatility with lasting effects as the global economy is immersed in an energy crisis combined with high inflationary pressures exacerbated by heightened energy costs. This research paper implemented GARCH and FIGARCH models on daily returns from 31 December 2015, to 9 December 2021, to examine volatility persistence and long memory processes. The world’s most prominent economies are represented by the G7, E7 and the GCC stock markets. Particular attention was devoted to the case of Kuwait as an example of a small oil-dependent economy. The research findings suggest evidence of volatility persistence across the markets, as reported by the GARCH (1,1) model. The FIGARCH (1,1) did not offer significant evidence of long memory processes except for the cases of FTSE 100, BIST 100, IDEX, BSE 100 and Bahrain.

This work is based on our patent number “US 2015/0280017 A1”.; International audience; The process of effective solar energy harvesting and conversion requires efficient photon absorption, followed by charge generation and separation, then electron transfer. Nanostructured materials have been considered as potential building blocks for the development of future generations of solar cells. Much attention has been given to wide-bandgap semiconductor nanowires, combined and sensitized with low-bandgap semiconductors effectively attached to the nanowires for low-cost and highly efficient solar cells. Here, the in situ growth of lead sulfide (PbS) nanoparticles on the surface of zinc oxide (ZnO) nanowires grown by the Successive Ionic Layer Adsorption and Reaction (SILAR) technique is presented for different numbers of cycles. The morphology and structure of PbS nanoparticles are confirmed by Scanning Electron Microscopy (SEM), revealing the decoration of the nanowires with the PbS nanoparticles, Transmission Electron Microscopy (TEM) and HR-TEM, showing the tight attachment of PbS nanoparticles on the surface of the ZnO nanowires. The Selected Area Electron Diffraction (SAED) confirms the crystallization of the PbS. Photoluminescence spectra show a broad and more intense deep-level emission band.

International audience; We present Microseek, a pipeline for virus identification and discovery based on RVDB-prot, a comprehensive, curated and regularly updated database of viral proteins. Microseek analyzes metagenomic Next Generation Sequencing (mNGS) raw data by performing quality steps, de novo assembly, and by scoring the Lowest Common Ancestor (LCA) from translated reads and contigs. Microseek runs on a local computer. The outcome of the pipeline is displayed through a user-friendly and dynamic graphical interface. Based on two representative mNGS datasets de-rived from human tissue and plasma specimens, we illustrate how Microseek works, and we report its performances. In silico spikes of known viral sequences, but also spikes of fake Neo-pneumovirus viral sequences generated with variable evolutionary distances from known mem-bers of the Pneumoviridae family, were used. Results were compared to Chan Zuckerberg ID (CZ ID), a reference cloud-based mNGS pipeline. We show that Microseek reliably identifies known viral sequences and performs well for the detection of distant pseudoviral sequences, especially in complex samples such as in human plasma, while minimizing non-relevant hits.

International audience; Pathogenic fungi require delicate gene regulation mechanisms to adapt to diverse living environments and escape host immune systems. Recent advances in sequencing technology have exposed the complexity of the fungal genome, thus allowing the gradual disentanglement of multiple layers of gene expression control. Alternative transcription start site (aTSS) usage, previously reported to be prominent in mammals and to play important roles in physiopathology, is also present in fungi to fine-tune gene expression. Depending on the alteration in their sequences, RNA isoforms arising from aTSSs acquire different characteristics that significantly alter their stability and translational capacity as well as the properties and biologic functions of the resulting proteins. Disrupted control of aTSS usage has been reported to severely impair growth, virulence, and the infectious capacity of pathogenic fungi. Here, we discuss principle concepts, mechanisms, and the functional implication of aTSS usage in fungi.

International audience; Sacrificial bonds have been observed in several biological materials and structures and can increase their toughness, i.e., their resistance to fracture. They provide a reversible mechanism for dissipating mechanical energy before the possible system rupture. From a structural point of view, sacrificial bonds consist of short polymer chains that short-circuit parts of a main macromolecular chain (generating hidden lengths) and absorb energy by breaking them instead of the main chain. The toughness increase due to the presence of sacrificial bonds is typically named extra-toughness. Here, we developed a statistical mechanics and thermodynamics-based theory able to estimate the force–extension relation for chains with sacrificial bonds and to calculate the corresponding extra-toughness. The model is useful to better understand the sacrificial bond effects in biomaterials but also to apply the biomimetic paradigm and foster the development of high-performance artificial polymeric materials.

Background There are few studies reporting on self-medication, perceptions or difficulties older adults have with their medications. This study aimed to describe the uses and the perceptions of medications among older adults in France and to identify patient groups based on that information. Methods We used data from the 2020 ‘French Health Barometer’ – a nationally-representative cross-sectional survey. We assessed polypharmacy (five or more medications), self-medication, and patient perceptions of medications. Robust Poisson regression was used to investigate socio-demographic and health-related factors associated with the outcomes. Latent class analysis was used to identify patient groups classified by the use and the perceptions of medications. Factors associated with group assignment were investigated by multinomial logistic regression. All analyses were weighted. Results The study sample comprised 1,623 respondents aged 70–85 years. Polypharmacy and self-medication were reported in 23.5 and 48.7% of the older population, respectively. Polypharmacy was associated with increasing age, low education, and impaired health status. Self-medication was associated with female sex and high education. Among individuals taking at least 1 medication, 8.2% reported not to understand all their medications, and 9.7% having difficulty taking medications as prescribed. Among individuals taking at least 2 medications, 23.2% thought that they took too many medications. Three patient groups were identified: ‘Non-polypharmacy, positive perceptions’ (62.5%), ‘Polypharmacy, positive perceptions’ (28.0%), and ‘Negative perceptions’ (9.5%). Conclusions Polypharmacy and self-medication are common in French older adults. One segment of people reported negative perceptions of their medications regardless of their polypharmacy status. This underlines the difference between the objective and perceived measures of polypharmacy.

COVID-19 and a low vitamin D state share common risk factors, which might explain why vitamin D deficiency has been linked with higher COVID-19 mortality. Moreover, measures of serum vitamin D may become lower during systemic inflammatory responses, further confounding the association via reverse causality. In this prospective study (recruited over 12 months), we examined whether the association between a low vitamin D state and in-hospital mortality due to SARS-CoV-2 pneumonia in unvaccinated subjects is explained by (i) the presence of shared risk factors (e.g., obesity, advanced age) or (ii) a reduction in serum 25(OH)D due to COVID-19 (i.e., reverse causality). In this cohort of 232 (mean age = 56 years) patients (all had SARS-CoV-2 diagnosed via PCR AND required supplemental oxygen therapy), we failed to find an association between serum vitamin D and levels of CRP, or other inflammatory markers. However, the hazard ratio for mortality for subjects over 70 years of age (13.2) and for subjects with a serum 25(OH)D level less than 30 nmol·L−1 (4.6) remained significantly elevated even after adjustment for gender, obesity and the presence of diabetes mellitus. Subjects 70 years had significantly higher mortality with a serum 25(OH)D less than 30 nmol·L−1 (11.8% and 55%), than with a serum 25(OH)D greater than 30 nmol·L−1 (2.2% and 25%). Unvaccinated Caucasian adults with a low vitamin D state have higher mortality due to SARS CoV-2 pneumonia, which is not explained by confounders and is not closely linked with elevated serum CRP.

Background: Hamstring strains are the most common moderate-major severity injuries in football. The majority of hamstring injuries occur during sprinting, with low eccentric hamstring strength being associated with an elevated risk. Objective: To examine the relationship between sprinting and eccentric hamstring strength by monitoring total weekly sprint distance and weekly efforts > 90% and >95% of maximum velocity. Methods: Fifty-eight professional male footballers were observed over one-and-a-half seasons. Players’ running was monitored during training and matches using GPS, and eccentric hamstring strength was measured weekly. Results: Weekly sprint distance (ρ = −0.13, p < 0.01) and weekly efforts >90% of maximum velocity (ρ = −0.08, p = 0.01) both displayed significant inverse relationships with the percentage change in eccentric hamstring strength; weekly efforts >95% of maximum velocity showed no relationship with hamstring strength (ρ = −0.02, p = 0.45). Only weekly efforts >90% of maximum velocity significantly influenced the mean percentage change in eccentric hamstring force, F(3,58) = 3.71, p = 0.01, with significant differences occurring when comparing 7–8 sprint efforts with 0–2 efforts (0.11%, p = 0.03) and 5–6 efforts (0.12%, p = 0.03). Conclusions: Eccentric hamstring strength levels significantly decrease when 7–8 weekly sprint efforts are completed at >90% of maximum velocity. Monitoring weekly sprint loading at velocities > 90% of maximum velocity may be valuable to help to reduce the risk of hamstring injuries in professional football.

A novel series of coumarin–thiadiazole hybrids, derived from substituted coumarin-3-carboxylic acids was isolated and fully characterized with the use of a number of spectroscopic techniques and XRD crystallography. Several of the novel compounds showed intensive fluorescence in the visible region, comparable to that of known coumarin-based fluorescence standards. Moreover, the new compounds were tested as potential antineurodegenerative agents via their ability to act as acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitors. Compared to the commercial standards, only a few compounds demonstrated moderate AChE and BuChE activities. Moreover, the novel derivatives were tested for their antimicrobial activity against a panel of pathogenic bacterial and fungal species. Their lack of activity and toxicity across a broad range of biochemical assays, together with the exceptional emission of some hybrid molecules, highlights the possible use of a number of the novel hybrids as potential fluorescence standards or fluorescence imaging agents.

Today, optical sensors are the subject of a very significant number of studies and applications. Many well-established technologies, including free-space optics, integrated photonics, and fiber optics approaches, have been developed in recent decades to fabricate and develop increasingly more efficient optical sensors for applications ranging from industrial control to monitoring the environment, biomedical use, and even as part of the Internet of Things.

International audience; Lentiviral vectors are among the most effective viral vectors for vaccination. In clear contrast to the reference adenoviral vectors, lentiviral vectors have a high potential for transducing dendritic cells in vivo. Within these cells, which are the most efficient at activating naive T cells, lentiviral vectors induce endogenous expression of transgenic antigens that directly access antigen presentation pathways without the need for external antigen capture or cross-presentation. Lentiviral vectors induce strong, robust, and long-lasting humoral, CD8+ T-cell immunity and effective protection against several infectious diseases. There is no pre-existing immunity to lentiviral vectors in the human population and the very low pro-inflammatory properties of these vectors pave the way for their use in mucosal vaccination. In this review, we have mainly summarized the immunological aspects of lentiviral vectors, their recent optimization to induce CD4+ T cells, and our recent data on lentiviral vector-based vaccination in preclinical models, including prophylaxis against flaviviruses, SARS-CoV-2, and Mycobacterium tuberculosis.

International audience; Currently, face recognition technology is the most widely used method for verifying an individual’s identity. Nevertheless, it has increased in popularity, raising concerns about face presentation attacks, in which a photo or video of an authorized person’s face is used to obtain access to services. Based on a combination of background subtraction (BS) and convolutional neural network(s) (CNN), as well as an ensemble of classifiers, we propose an efficient and more robust face presentation attack detection algorithm. This algorithm includes a fully connected (FC) classifier with a majority vote (MV) algorithm, which uses different face presentation attack instruments (e.g., printed photo and replayed video). By including a majority vote to determine whether the input video is genuine or not, the proposed method significantly enhances the performance of the face anti-spoofing (FAS) system. For evaluation, we considered the MSU MFSD, REPLAY-ATTACK, and CASIA-FASD databases. The obtained results are very interesting and are much better than those obtained by state-of-the-art methods. For instance, on the REPLAY-ATTACK database, we were able to attain a half-total error rate (HTER) of 0.62% and an equal error rate (EER) of 0.58%. We attained an EER of 0% on both the CASIA-FASD and the MSU MFSD databases.

To outline the development and implementation of a food science and nutrition module for dental undergraduate students that provides basic knowledge and clinical skills for improving oral health outcomes and understanding their importance for overall health.Interdisciplinary discussions with professionals with expertise in food science and nutrition, including dentists, dietitians and nutritionists, were held to agree on core subject areas in line with the evidence base. The module was delivered online to 2nd-year dental students due to COVID-19 restrictions. Students completed an online evaluation on completing the module. Final examination consisted of one essay question.Subject areas and learning outcomes were derived from current and previous approaches to curriculum development. A total of 14 prerecorded lectures, including healthy eating guidelines, dietary assessment, specific oral effects of diet and food constituents were delivered and tutorials provided. The evaluation survey had a 90% (n = 39/43) response rate. A majority indicated that the course was "interesting," "worth doing" (59%) and "provided a good evidence base to understand nutrition and oral health" (87%). Nearly all students (92%) agreed that the course was "sufficiently structured to allow understanding of the key topics" and that "a good understanding of nutrition is important for a dentist" (95%).A food science and nutrition module developed by a multidisciplinary team enabled dental students to gain an understanding of the role of diet in oral and overall health. The module facilitated the development of skills that enable students to utilise dietary assessment techniques and promote dietary interventions beneficial to oral health. The approach taken may act as a template for other institutions.

International audience; Mesenchymal stem cells (MSC) are involved in the regeneration of various missing or compromised periodontal tissues, including bone. MSC-derived conditioned medium (CM) has recently been explored as a favorable surrogate for stem cell therapy, as it is capable of producing comparable therapeutic effects. This study aimed to evaluate the influence of periodontal ligament stem cells (PDLSC)-CM on osteoblasts (OB) and its potential as a therapeutic tool for periodontal regeneration. Human PDLSC were isolated and characterized, and CM from these cells was collected. The presence of exosomes in the culture supernatant was observed by immunofluorescence and by transmission electron microscopy. CM was added to a cultured osteoblastic cell line (Saos-2 cells) and viability (MTT assay) and gene expression analysis (real-time PCR) were examined. A cell line derived from the periodontal ligament and showing all the characteristics of MSC was successfully isolated and characterized. The addition of PDLSC-CM to Saos-2 cells led to an enhancement of their proliferation and an increased expression of some osteoblastic differentiation markers, but this differentiation was not complete. Saos-2 cells were involved in the initial inflammation process by releasing IL-6 and activating COX2. The effects of PDLSC-CM on Saos-2 appear to arise from a cumulative effect of different effective components rather than a few factors present at high levels.

International audience; In mammalian fertilization, the link between the oocyte plasma membrane and underneath cytoskeleton has often been associated to key elements of successful gamete fusion, like microvilli shaping or CD9 function, but its effective role has poorly been studied. EWI-2 and EWI-F as cis partners of CD9, and ERM proteins (Ezrin, Radixin and Moesin) that both attach to the actin cytoskeleton and to the EWI are part of the molecules that make the link between the oocyte membrane and its cytoskeleton. This study aims to assay through siRNA inhibition, the involvement of these ERM and EWI molecules in mouse fertilization, their role in the microvilli morphology of the egg but also their possible contribution to the cortical tension, a parameter that reflects the mechanical behavior of the oocyte cortex. Whereas inhibiting separately the expression of each protein had no effect on fertilization, the combined inhibition of either EWI-2/EWI-F or the three ERM triggered a significant decrease of the fertilization index. This inhibition seems to correlate with an increase in the radius of curvature of the oocyte microvilli. It also causes a decrease of the oocyte cortical tension. These results show the importance of EWI-2 and EWI–F and ERM proteins in the smooth running of a fertilization event and support their involvement in the microvilli architecture of the oocyte and in its mechanical properties.

Here I detail the effects of the abiotic/captive environment of an adult wandering spider, Pardosa saltans (Lycosidae) on its behaviour. These studies focused on spiders collected as adults in their natural environment and spiders developed in the laboratory under controlled conditions. Wild-caught spiders were tested either immediately after capture or after being housed for 15 days post-collection. Laboratory reared spiders were kept in different environments: small or large space combined with the presence or absence of litter. Two tests evaluated by sex show the influence of these rearing conditions: an open-field test and a radial-arm maze test. The results show that wild caught spiders of both sexes tested immediately after capture weighed significantly less and were significantly more active than spiders housed in the laboratory for 15 days and spiders reared in the laboratory. Laboratory conditions induced a positive impact on body mass and negative impact on behaviour activities. The locomotor and exploratory activities of spiders of both sexes kept in container without substrate showed lower. My results suggest that the physical enrichment of the environment can reduce these negative effects for females, but not for males that seem to be more affected by being reared under controlled conditions.

Simple Summary In vertebrates, each of the two hemispheres of the brain controls the opposite side of the body. Consequently, lateralized actions in animals may reflect that one hemisphere processes particular functions underlying the behavior. In humans, for instance, the left-brain hemisphere is specialized for language functions. In several species of non-human primates, communicative gestures are preferentially produced with the right hand. It has been hypothesized that the human language lateralization has common evolutionary origins with a left-hemispheric specialization for gestures in African-Asian primates. Therefore, it is particularly interesting to describe the gestural laterality of non-human primates to understand the evolutionary history of intentional communication. Previous studies reported different factors affecting the gestural laterality of great apes, but this has rarely been investigated in more distant species of monkeys. In the present study, we observed the manual gestures produced by captive mangabeys. We found that, although monkeys were not lateralized when considering their gestures, on the whole, they preferentially gesture with their right hand in contexts of aggression or submission, and that the position of their receiver also affected the hand used. As for chimpanzees, gorillas, or humans, the gestural laterality of mangabeys depends on the context in which they communicate. Abstract Catarrhine primates gesture preferentially with their right hands, which led to the hypothesis of a gestural origin of human left-hemispheric specialization for language. However, the factors influencing this gestural laterality remain understudied in non-hominoid species, particularly in intraspecific contexts, although it may bring valuable insights into the proximate and ultimate causes of language lateralization. We present here a preliminary investigation of intraspecific gestural laterality in catarrhine monkeys, red-capped mangabeys (Cercocebus torquatus). We described the spontaneous production of brachio-manual intentional gestures in twenty-five captive subjects. Although we did not evidence any significant gestural lateralization neither at the individual- nor population-level, we found that mangabeys preferentially use their right hands to gesture in negative social contexts, such as aggressions, suggesting an effect of emotional lateralization, and that they adapt to the position of their receiver by preferentially using their ipsilateral hand to communicate. These results corroborate previous findings from ape studies. By contrast, factors related to gesture form and socio-demographic characteristics of signaler and receiver did not affect gestural laterality. To understand better the relationships between gestural laterality and brain lateralization from an evolutionary perspective, we suggest that the gestural communication of other monkey species should be examined with a multifactorial approach.

International audience; Healthcare is undergoing large transformations, and it is imperative to leverage new technologies to support the advent of personalized medicine and disease prevention. It is now well accepted that the levels of certain biological molecules found in blood and other bodily fluids, as well as in exhaled breath, are an indication of the onset of many human diseases and reflect the health status of the person. Blood, urine, sweat, or saliva biomarkers can therefore serve in early diagnosis of diseases such as cancer, but also in monitoring disease progression, detecting metabolic disfunctions, and predicting response to a given therapy. For most point-of-care sensors, the requirement that patients themselves can use and apply them is crucial not only regarding the diagnostic part, but also at the sample collection level. This has stimulated the development of such diagnostic approaches for the non-invasive analysis of disease-relevant analytes. Considering these timely efforts, this review article focuses on novel, sensitive, and selective sensing systems for the detection of different endogenous target biomarkers in bodily fluids as well as in exhaled breath, which are associated with human diseases.

Functionalised holograms are important for applications utilising smart diffractive optical elements for light redirection, shaping and in the development of sensors/indicators. This paper reports on holographic recording in novel magnetic nanocomposites and the observed temperature change in dry layers and liquid samples exposed to alternating magnetic field (AMF). The nanocomposite consists of N-isopropylacrylamide (NIPA)-based polymer doped with magnetic nanoparticles (MNPs), and local heating is achieved through magnetic induction. Here, volume transmission holographic gratings (VTHGs) are recorded with up to 24% diffraction efficiency (DE) in the dry layers of magnetic nanocomposites. The dry layers and liquid samples are then exposed to AMF. Efficient heating was observed in the liquid samples doped with Fe3O4 MNPs of 20 nm average size where the temperature increased from 27 °C to 64 °C after 300 s exposure to 111 mT AMF. The temperature increase in the dry layers doped with the same nanoparticles after exposure to 4.4 mT AMF was observed to be 6 °C. No temperature change was observed in the undoped layers. Additionally, we have successfully recorded Denisyuk holograms in the magnetic nanocomposite materials. The results reveal that the magnetic nanocomposite layers are suitable for recording holograms and need further optimisation in developing holographic indicators for mapping AMFs.