Showing papers by "Conservatoire national des arts et métiers published in 2020"

Estimating the burden of SARS-CoV-2 in France.

Abstract: France has been heavily affected by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and went into lockdown on 17 March 2020. Using models applied to hospital and death data, we estimate the impact of the lockdown and current population immunity. We find that 2.9% of infected individuals are hospitalized and 0.5% of those infected die (95% credible interval: 0.3 to 0.9%), ranging from 0.001% in those under 20 years of age to 8.3% in those 80 years of age or older. Across all ages, men are more likely to be hospitalized, enter intensive care, and die than women. The lockdown reduced the reproductive number from 2.90 to 0.67 (77% reduction). By 11 May 2020, when interventions are scheduled to be eased, we project that 3.5 million people (range: 2.1 million to 6.0 million), or 5.3% of the population (range: 3.3 to 9.3%), will have been infected. Population immunity appears to be insufficient to avoid a second wave if all control measures are released at the end of the lockdown.

Age-specific mortality and immunity patterns of SARS-CoV-2 infection in 45 countries

Abstract: The number of COVID-19 deaths is often used as a key indicator of SARS-CoV-2 epidemic size. 42 However, heterogeneous burdens in nursing homes and variable reporting of deaths in elderly 43 individuals can hamper comparisons of deaths and the number of infections associated with them 44 across countries. Using age-specific death data from 45 countries, we find that relative differences 45 in the number of deaths by age amongst individuals aged

Determination of the fine-structure constant with an accuracy of 81 parts per trillion

Abstract: The standard model of particle physics is remarkably successful because it is consistent with (almost) all experimental results. However, it fails to explain dark matter, dark energy and the imbalance between matter and antimatter in the Universe. Because discrepancies between standard-model predictions and experimental observations may provide evidence of new physics, an accurate evaluation of these predictions requires highly precise values of the fundamental physical constants. Among them, the fine-structure constant α is of particular importance because it sets the strength of the electromagnetic interaction between light and charged elementary particles, such as the electron and the muon. Here we use matter-wave interferometry to measure the recoil velocity of a rubidium atom that absorbs a photon, and determine the fine-structure constant α−1 = 137.035999206(11) with a relative accuracy of 81 parts per trillion. The accuracy of eleven digits in α leads to an electron g factor1,2—the most precise prediction of the standard model—that has a greatly reduced uncertainty. Our value of the fine-structure constant differs by more than 5 standard deviations from the best available result from caesium recoil measurements3. Our result modifies the constraints on possible candidate dark-matter particles proposed to explain the anomalous decays of excited states of 8Be nuclei4 and paves the way for testing the discrepancy observed in the magnetic moment anomaly of the muon5 in the electron sector6. The fine-structure constant is determined with an accuracy of 81 parts per trillion using matter-wave interferometry to measure the rubidium atom recoil velocity.

A comparison of four serological assays for detecting anti-SARS-CoV-2 antibodies in human serum samples from different populations.

Abstract: It is of paramount importance to evaluate the prevalence of both asymptomatic and symptomatic cases of SARS-CoV-2 infection and their differing antibody response profiles. Here, we performed a pilot study of four serological assays to assess the amounts of anti-SARS-CoV-2 antibodies in serum samples obtained from 491 healthy individuals before the SARS-CoV-2 pandemic, 51 individuals hospitalized with COVID-19, 209 suspected cases of COVID-19 with mild symptoms, and 200 healthy blood donors. We used two ELISA assays that recognized the full-length nucleoprotein (N) or trimeric spike (S) protein ectodomain of SARS-CoV-2. In addition, we developed the S-Flow assay that recognized the S protein expressed at the cell surface using flow cytometry, and the luciferase immunoprecipitation system (LIPS) assay that recognized diverse SARS-CoV-2 antigens including the S1 domain and the carboxyl-terminal domain of N by immunoprecipitation. We obtained similar results with the four serological assays. Differences in sensitivity were attributed to the technique and the antigen used. High anti-SARS-CoV-2 antibody titers were associated with neutralization activity, which was assessed using infectious SARS-CoV-2 or lentiviral-S pseudotype virus. In hospitalized patients with COVID-19, seroconversion and virus neutralization occurred between 5 and 14 days after symptom onset, confirming previous studies. Seropositivity was detected in 32% of mildly symptomatic individuals within 15 days of symptom onset and in 3% of healthy blood donors. The four antibody assays that we used enabled a broad evaluation of SARS-CoV-2 seroprevalence and antibody profiling in different subpopulations within one region.

Disentangling Physical Dynamics From Unknown Factors for Unsupervised Video Prediction

Abstract: Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video forecasting models. Since physics is too restrictive for describing the full visual content of generic video sequences, we introduce PhyDNet, a two-branch deep architecture, which explicitly disentangles PDE dynamics from unknown complementary information. A second contribution is to propose a new recurrent physical cell (PhyCell), inspired from data assimilation techniques, for performing PDE-constrained prediction in latent space. Extensive experiments conducted on four various datasets show the ability of PhyDNet to outperform state-of-the-art methods. Ablation studies also highlight the important gain brought out by both disentanglement and PDE-constrained prediction. Finally, we show that PhyDNet presents interesting features for dealing with missing data and long-term forecasting.

Absolute airborne gravimetry with a cold atom sensor

Abstract: Measuring gravity from an aircraft is essential in geodesy, geophysics and exploration. It fills a gap between satellite techniques which have a low spatial resolution and traditional ground measurements which can only be performed on ground in accessible areas. Today, only relative sensors are available for airborne gravimetry. This is a major drawback because of the calibration and drift estimation procedures which lead to important operational constraints and measurement errors. Here, we report an absolute airborne gravimeter based on atom interferometry. This instrument has been first tested on a motion simulator leading to gravity measurements noise of 0.3 mGal for 75 s filtering time constant. Then, we realized an airborne campaign across Iceland in April 2017. From repeated line and crossing points, we obtain gravity measurements with an estimated error between 1.7 and 3.9 mGal. The airborne measurements have also been compared to upward continued ground gravity data and show differences with a standard deviation ranging from 3.3 to 6.2 mGal and a mean value ranging from − 0.7 to − 1.9 mGal.

Multi-Resource Allocation for Network Slicing

Abstract: Among the novelties introduced by 5G networks, the formalization of the ‘network slice’ as a resource allocation unit is an important one. In legacy networks, resources such as link bandwidth, spectrum, computing capacity are allocated independently of each other. In 5G environments, a network slice is meant to directly serve end-to-end services, or verticals: behind a network slice demand, a tenant expresses the need to access a precise service type, under a fully qualified set of computing and network requirements. The resource allocation decision encompasses, therefore, a combination of different resources. In this paper, we address the problem of fairly sharing multiple resources between slices, in the critical situation in which the network does not have enough resources to fully satisfy slice demands. We model the problem as a multi-resource allocation problem, proposing a versatile optimization framework based on the Ordered Weighted Average (OWA) operator, that takes into account different fairness approaches. We show how, adapting the OWA utility function, our framework can generalize classical single-resource allocation methods, existing multi-resource allocation solutions at the state of the art, and implement novel multi-resource allocation solutions. We compare analytically and by extensive simulations the different methods in terms of fairness and system efficiency.

Optimizing COVID-19 surveillance in long-term care facilities: a modelling study.

Abstract: Long-term care facilities (LTCFs) are vulnerable to outbreaks of coronavirus disease 2019 (COVID-19). Timely epidemiological surveillance is essential for outbreak response, but is complicated by a high proportion of silent (non-symptomatic) infections and limited testing resources. We used a stochastic, individual-based model to simulate transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) along detailed inter-individual contact networks describing patient-staff interactions in a real LTCF setting. We simulated distribution of nasopharyngeal swabs and reverse transcriptase polymerase chain reaction (RT-PCR) tests using clinical and demographic indications and evaluated the efficacy and resource-efficiency of a range of surveillance strategies, including group testing (sample pooling) and testing cascades, which couple (i) testing for multiple indications (symptoms, admission) with (ii) random daily testing. In the baseline scenario, randomly introducing a silent SARS-CoV-2 infection into a 170-bed LTCF led to large outbreaks, with a cumulative 86 (95% uncertainty interval 6–224) infections after 3 weeks of unmitigated transmission. Efficacy of symptom-based screening was limited by lags to symptom onset and silent asymptomatic and pre-symptomatic transmission. Across scenarios, testing upon admission detected just 34–66% of patients infected upon LTCF entry, and also missed potential introductions from staff. Random daily testing was more effective when targeting patients than staff, but was overall an inefficient use of limited resources. At high testing capacity (> 10 tests/100 beds/day), cascades were most effective, with a 19–36% probability of detecting outbreaks prior to any nosocomial transmission, and 26–46% prior to first onset of COVID-19 symptoms. Conversely, at low capacity (< 2 tests/100 beds/day), group testing strategies detected outbreaks earliest. Pooling randomly selected patients in a daily group test was most likely to detect outbreaks prior to first symptom onset (16–27%), while pooling patients and staff expressing any COVID-like symptoms was the most efficient means to improve surveillance given resource limitations, compared to the reference requiring only 6–9 additional tests and 11–28 additional swabs to detect outbreaks 1–6 days earlier, prior to an additional 11–22 infections. COVID-19 surveillance is challenged by delayed or absent clinical symptoms and imperfect diagnostic sensitivity of standard RT-PCR tests. In our analysis, group testing was the most effective and efficient COVID-19 surveillance strategy for resource-limited LTCFs. Testing cascades were even more effective given ample testing resources. Increasing testing capacity and updating surveillance protocols accordingly could facilitate earlier detection of emerging outbreaks, informing a need for urgent intervention in settings with ongoing nosocomial transmission.

Multitarget Therapeutic Strategies for Alzheimer's Disease: Review on Emerging Target Combinations.

Abstract: Neurodegenerative diseases represent nowadays one of the major health problems. Despite the efforts made to unveil the mechanism leading to neurodegeneration, it is still not entirely clear what triggers this phenomenon and what allows its progression. Nevertheless, it is accepted that neurodegeneration is a consequence of several detrimental processes, such as protein aggregation, oxidative stress, and neuroinflammation, finally resulting in the loss of neuronal functions. Starting from these evidences, there has been a wide search for novel agents able to address more than a single event at the same time, the so-called multitarget-directed ligands (MTDLs). These compounds originated from the combination of different pharmacophoric elements which endowed them with the ability to interfere with different enzymatic and/or receptor systems, or to exert neuroprotective effects by modulating proteins and metal homeostasis. MTDLs have been the focus of the latest strategies to discover a new treatment for Alzheimer's disease (AD), which is considered the most common form of dementia characterized by neurodegeneration and cognitive dysfunctions. This review is aimed at collecting the latest and most interesting target combinations for the treatment of AD, with a detailed discussion on new agents with favorable in vitro properties and on optimized structures that have already been assessed in vivo in animal models of dementia.

Blockchain for the Internet of Vehicles: A Decentralized IoT Solution for Vehicles Communication Using Ethereum.

Abstract: The concept of smart cities has become prominent in modern metropolises due to the emergence of embedded and connected smart devices, systems, and technologies. They have enabled the connection of every "thing" to the Internet. Therefore, in the upcoming era of the Internet of Things, the Internet of Vehicles (IoV) will play a crucial role in newly developed smart cities. The IoV has the potential to solve various traffic and road safety problems effectively in order to prevent fatal crashes. However, a particular challenge in the IoV, especially in Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications, is to ensure fast, secure transmission and accurate recording of the data. In order to overcome these challenges, this work is adapting Blockchain technology for real time application (RTA) to solve Vehicle-to-Everything (V2X) communications problems. Therefore, the main novelty of this paper is to develop a Blockchain-based IoT system in order to establish secure communication and create an entirely decentralized cloud computing platform. Moreover, the authors qualitatively tested the performance and resilience of the proposed system against common security attacks. Computational tests showed that the proposed solution solved the main challenges of Vehicle-to-X (V2X) communications such as security, centralization, and lack of privacy. In addition, it guaranteed an easy data exchange between different actors of intelligent transportation systems.

Use of proton pump inhibitors in adults in France: a nationwide drug utilization study.

Abstract: Proton pump inhibitor (PPI) drugs are approved for the management of gastric acid–related diseases, mainly treatment of gastroesophageal reflux disease, treatment of nonsteroidal anti-inflammatory drugs (NSAID)–related gastrointestinal complications and prevention in at-risk patients, Helicobacter pylori eradication, and treatment of ulcers. PPIs are one of the most commonly prescribed drug class worldwide, and off-label use is widespread. The aim of this study was to describe outpatient PPI use of the whole adult population in France, based on the French National Health Data System (SNDS). All individuals aged 18 years or older, with at least one dispensing for PPI between January 1, 2015 and December 31, 2015, were identified as PPI users. PPI users were considered as new users if they received no dispensing for PPI in the prior year. New users were followed until treatment discontinuation or up to 1 year, whichever occurred first. Characteristics of new users and of their PPI treatment were described, overall and separately by treatment indication. In total, 15,388,419 PPI users were identified in 2015 (57.0% women; mean age 57.0 years), accounting for 29.8% of the French adult population. Of them, 7,399,303 were new PPI users; mean treatment duration was 40.9 days, and 4.1% received a continuous PPI therapy lasting more than 6 months (10.2% among new users > 65 years versus 2.4% among those 18–65 years). For 53.5% of new users, indication for PPI therapy was a co-prescription with NSAID; in this indication, the large majority of patients (79.7%) had no measurable risk factor supporting a systematic prophylactic co-prescription of PPI. A proportion of 32.4% of new users did not have any identified comedication or inpatient diagnosis supporting an indication for PPI therapy; among them, only a small proportion (7.3% overall, and 8.4% of patients aged > 65 years) underwent a procedure investigating the digestive tract at the time of PPI initiation. The results of this study suggest PPI overuse in France, not always in line with the French guidelines. In particular, inappropriate co-prescription with NSAID was frequent. Efforts should be made to limit PPI treatment to appropriate indications and durations.

Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

Abstract: Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purely data-driven approaches are arguably insufficient in this context, standard physical modeling based approaches tend to be over-simplistic, inducing non-negligible errors. In this work, we introduce the APHYNITY framework, a principled approach for augmenting incomplete physical dynamics described by differential equations with deep data-driven models. It consists in decomposing the dynamics into two components: a physical component accounting for the dynamics for which we have some prior knowledge, and a data-driven component accounting for errors of the physical model. The learning problem is carefully formulated such that the physical model explains as much of the data as possible, while the data-driven component only describes information that cannot be captured by the physical model, no more, no less. This not only provides the existence and uniqueness for this decomposition, but also ensures interpretability and benefits generalization. Experiments made on three important use cases, each representative of a different family of phenomena, i.e. reaction-diffusion equations, wave equations and the non-linear damped pendulum, show that APHYNITY can efficiently leverage approximate physical models to accurately forecast the evolution of the system and correctly identify relevant physical parameters.

Human plague: An old scourge that needs new answers.

Abstract: Yersinia pestis, the bacterial causative agent of plague, remains an important threat to human health. Plague is a rodent-borne disease that has historically shown an outstanding ability to colonize and persist across different species, habitats, and environments while provoking sporadic cases, outbreaks, and deadly global epidemics among humans. Between September and November 2017, an outbreak of urban pneumonic plague was declared in Madagascar, which refocused the attention of the scientific community on this ancient human scourge. Given recent trends and plague's resilience to control in the wild, its high fatality rate in humans without early treatment, and its capacity to disrupt social and healthcare systems, human plague should be considered as a neglected threat. A workshop was held in Paris in July 2018 to review current knowledge about plague and to identify the scientific research priorities to eradicate plague as a human threat. It was concluded that an urgent commitment is needed to develop and fund a strong research agenda aiming to fill the current knowledge gaps structured around 4 main axes: (i) an improved understanding of the ecological interactions among the reservoir, vector, pathogen, and environment; (ii) human and societal responses; (iii) improved diagnostic tools and case management; and (iv) vaccine development. These axes should be cross-cutting, translational, and focused on delivering context-specific strategies. Results of this research should feed a global control and prevention strategy within a "One Health" approach.

A review of the use of time-varying covariates in the Fine-Gray subdistribution hazard competing risk regression model.

Abstract: In survival analysis, time-varying covariates are covariates whose value can change during follow-up. Outcomes in medical research are frequently subject to competing risks (events precluding the occurrence of the primary outcome). We review the types of time-varying covariates and highlight the effect of their inclusion in the subdistribution hazard model. External time-dependent covariates are external to the subject, can effect the failure process, but are not otherwise involved in the failure mechanism. Internal time-varying covariates are measured on the subject, can effect the failure process directly, and may also be impacted by the failure mechanism. In the absence of competing risks, a consequence of including internal time-dependent covariates in the Cox model is that one cannot estimate the survival function or the effect of covariates on the survival function. In the presence of competing risks, the inclusion of internal time-varying covariates in a subdistribution hazard model results in the loss of the ability to estimate the cumulative incidence function (CIF) or the effect of covariates on the CIF. Furthermore, the definition of the risk set for the subdistribution hazard function can make defining internal time-varying covariates difficult or impossible. We conducted a review of the use of time-varying covariates in subdistribution hazard models in articles published in the medical literature in 2015 and in the first 5 months of 2019. Seven percent of articles published included a time-varying covariate. Several inappropriately described a time-varying covariate as having an association with the risk of the outcome.

A User’s Guide to the Cornish Fisher Expansion

Abstract: Using the Cornish Fisher expansion is a relatively easy and parsimonious way of dealing with non-normality in asset price or return distributions, in such fields as insurance asset liability management or portfolio optimization with assets such as derivatives. It also allows to implement portfolio optimization with a risk measure more sophisticated than variance, such as Value-at-Risk or Conditional Value-at-Risk The use of Cornish Fisher expansion should avoid two pitfalls: (i) exiting the domain of validity of the formula; (ii) confusing the skewness and kurtosis parameters of the formula with the actual skewness and kurtosis of the distribution.This paper provides guidelines for a proper use of the Cornish Fisher expansion.

Applications of Sensitized-Semiconductors as Heterogeneous Visible-Light Photocatalysts in Organic Synthesis

Abstract: The industrial transition to more sustainable chemical manufacturing requires the development of a variety of high-performance heterogeneous catalysts. Recently, new classes of heterogeneous and re...

Toward the understanding of temperature effect on bonding strength, dimensions and geometry of 3D-printed parts

Abstract: Fused filament fabrication (FFF), which is an additive manufacturing technique, opens alternative possibilities for complex geometries fabrication. However, its use in functional products is limited due to anisotropic strength issues. Indeed, the strength of FFF fabricated parts across successive layers in the build direction (Z direction) can be significantly lower than the strength in X–Y directions. This strength weakness has been attributed to poor bonding between printed layers. This bonding depends on the temperature of the current layer being deposited—at melting temperature (Tm)—and the temperature of the previously deposited layer. It is assumed that depositing a layer at Tm on a layer at temperature around crystallization temperature (Tc) would enable higher material crystallinity and thus better bonding between previous and present layers. On the contrary, if the previous layer temperature is below Tc, material crystallinity will be low and bonding strength weak. This paper aims at studying the significant effect of temperature difference (ΔT) between previous and current deposited layers temperatures on (1) inter-layers bonding strength improvement and (2) part dimensions, geometry and structure stability. A 23% increase in the inter-layers bonding strength for previous layer temperature slightly higher than Tc reported here confirms the above assumption and offers a first solution toward the increase in inter-layers bonding strength in FFF.

Serologic responses to SARS-CoV-2 infection among hospital staff with mild disease in eastern France

Abstract: Background: The serologic response of individuals with mild forms of SARS-CoV-2 infection is poorly characterized. Methods: Hospital staff who had recovered from mild forms of PCR-confirmed SARS-CoV-2 infection were tested for anti-SARS-CoV-2 antibodies using two assays: a rapid immunodiagnostic test (99.4% specificity) and the S-Flow assay (~99% specificity).The neutralizing activity of the sera was tested with a pseudovirus-based assay. Results: Of 162 hospital staff who participated in the investigation, 160 reported SARS-CoV- 2 infection that had not required hospital admission and were included in these analyses. The median time from symptom onset to blood sample collection was 24 days (IQR: 21-28, range 13-39). The rapid immunodiagnostic test detected antibodies in 153 (95.6%) of the samples and the S-Flow assay in 159 (99.4%), failing to detect antibodies in one sample collected 18 days after symptom onset (the rapid test did not detect antibodies in that patient). Neutralizing antibodies (NAbs) were detected in 79%, 92% and 98% of samples collected 13-20, 21-27 and 28-41 days after symptom onset, respectively (P=0.02). Conclusion: Antibodies against SARS-CoV-2 were detected in virtually all hospital staff sampled from 13 days after the onset of COVID-19 symptoms. This finding supports the use of serologic testing for the diagnosis of individuals who have recovered from SARS-CoV-2 infection. The neutralizing activity of the antibodies increased overtime. Future studies will help assess the persistence of the humoral response and its associated neutralization capacity in recovered patients.

Gene expression profiling reveals a conserved microglia signature in larval zebrafish.

Abstract: Microglia are the resident macrophages of the brain. Over the past decade, our understanding of the function of these cells has significantly improved. Microglia do not only play important roles in the healthy brain but are involved in almost every brain pathology. Gene expression profiling allowed to distinguish microglia from other macrophages and revealed that the full microglia signature can only be observed in vivo. Thus, animal models are irreplaceable to understand the function of these cells. One of the popular models to study microglia is the zebrafish larva. Due to their optical transparency and genetic accessibility, zebrafish larvae have been employed to understand a variety of microglia functions in the living brain. Here, we performed RNA sequencing of larval zebrafish microglia at different developmental time points: 3, 5, and 7 days post fertilization (dpf). Our analysis reveals that larval zebrafish microglia rapidly acquire the core microglia signature and many typical microglia genes are expressed from 3 dpf onwards. The majority of changes in gene expression happened between 3 and 5 dpf, suggesting that differentiation mainly takes place during these days. Furthermore, we compared the larval microglia transcriptome to published data sets of adult zebrafish microglia, mouse microglia, and human microglia. Larval microglia shared a significant number of expressed genes with their adult counterparts in zebrafish as well as with mouse and human microglia. In conclusion, our results show that larval zebrafish microglia mature rapidly and express the core microglia gene signature that seems to be conserved across species.

Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators

Abstract: Hydrogenerators are strategic assets for power utilities. Their reliability and availability can lead to significant benefits. For decades, monitoring and diagnosis of hydrogenerators have been at the core of maintenance strategies. A significant part of generator diagnosis relies on Partial Discharge (PD) measurements, because the main cause of hydrogenerator breakdown comes from failure of its high voltage stator, which is a major component of hydrogenerators. A study of all stator failure mechanisms reveals that more than 85% of them involve the presence of PD activity. PD signal can be detected from the lead of the hydrogenerator while it is running, thus allowing for on-line diagnosis. Hydro-Quebec has been collecting more than 33 000 unlabeled PD measurement files over the last decades. Up to now, this diagnostic technique has been quantified based on global PD amplitudes and integrated PD energy irrespective of the source of the PD signal. Several PD sources exist and they all have different relative risk, but in order to recognize the nature of the PD, or its source, the judgement of experts is required. In this paper, we propose a new method based on visual data analysis to build a PD source classifier with a minimum of labeled data. A convolutional variational autoencoder has been used to help experts to visually select the best training data set in order to improve the performances of the PD source classifier.

Subtype Selective γ-Aminobutyric Acid Type A Receptor (GABA A R) Modulators Acting at the Benzodiazepine Binding Site: An Update

Abstract: γ-Aminobutyric acid (GABA) is the major inhibitory neurotransmitter within the central nervous system (CNS) with fast, transsynaptic, and modulatory extrasynaptic effects being mediated by the ionotropic GABA type A receptors (GABAARs). These receptors are of particular interest because they are the molecular target of a number of pharmacological agents, of which the benzodiazepines (BZDs), such as diazepam, are the best described. The anxiolytic, sedating, and myorelaxant effects of BZDs are mediated by separate populations of GABAARs containing either α1, α2, α3, or α5 subunits and the molecular dissection of the pharmacology of BZDs indicates that subtype-selective GABAAR modulators will have novel pharmacological profiles. This is best exemplified by α2/α3-GABAAR positive allosteric modulators (PAMs) and α5-GABAAR negative allosteric modulators (NAMs), which were originally developed as nonsedating anxiolytics and cognition enhancers, respectively. This review aims to summarize the current state of the field of subtype-selective GABAAR modulators acting via the BZD binding site and their potential clinical indications.

Non-intrusive reduced order modelling for the dynamics of geometrically nonlinear flat structures using three-dimensional finite elements

Abstract: Non-intrusive methods have been used since two decades to derive reduced-order models for geometrically nonlinear structures, with a particular emphasis on the so-called STiffness Evaluation Procedure (STEP), relying on the static application of prescribed displacements in a finite-element context. We show that a particularly slow convergence of the modal expansion is observed when applying the method with 3D elements, because of nonlinear couplings occurring with very high frequency modes involving 3D thickness deformations. Focusing on the case of flat structures, we first show by computing all the modes of the structure that a converged solution can be exhibited by using either static condensation or normal form theory. We then show that static modal derivatives provide the same solution with fewer calculations. Finally, we propose a modified STEP, where the prescribed displacements are imposed solely on specific degrees of freedom of the structure, and show that this adjustment also provides efficiently a converged solution.