HESAM Université

The number of human genomes being genotyped or sequenced increases exponentially and efficient haplotype estimation methods able to handle this amount of data are now required. Here we present a method, SHAPEIT4, which substantially improves upon other methods to process large genotype and high coverage sequencing datasets. It notably exhibits sub-linear running times with sample size, provides highly accurate haplotypes and allows integrating external phasing information such as large reference panels of haplotypes, collections of pre-phased variants and long sequencing reads. We provide SHAPEIT4 in an open source format and demonstrate its performance in terms of accuracy and running times on two gold standard datasets: the UK Biobank data and the Genome In A Bottle.

The masks have always been mentioned as an effective tool against environmental threats. They are considered as protective equipment to preserve the respiratory system against the non-desirable air droplets and aerosols such as the viral or pollution particles. The aerosols can be pollution existence in the air, or the infectious airborne viruses initiated from the sneezing, coughing of the infected people. The filtration efficiency of the different masks against these aerosols are not the same, as the particles have different sizes, shapes, and properties. Therefore, the challenge is to fabricate the filtration masks with higher efficiency to decrease the penetration percentage at the nastiest conditions. To achieve this concept, knowledge about the mechanisms of the penetration of the aerosols through the masks at different effective environmental conditions is necessary. In this paper, the literature about the different kinds of face masks and respiratory masks, common cases of their application, and the advantages and disadvantages of them in this regard have been reviewed. Moreover, the related mechanisms of the penetration of the aerosols through the masks are discussed. The environmental conditions affecting the penetration as well as the quality of the fabrication are studied. Finally, special attention was given to the numerical simulation related to the different existing mechanisms.

Three-dimensional (3D) bioprinting, an additive manufacturing based technique of biomaterials fabrication, is an innovative and auspicious strategy in medical and pharmaceutical fields. The ability of producing regenerative tissues and organs has made this technology a pioneer to the creation of artificial multi-cellular tissues/organs. A broad variety of biomaterials is currently being utilized in 3D bioprinting as well as multiple techniques employed by researchers. In this review, we demonstrate the most common and novel biomaterials in 3D bioprinting technology further with introducing the related techniques that are commonly taking into account by researchers. In addition, an attempt has been accomplished to hand over the most relevant application of 3D bioprinting techniques such as tissue regeneration, cancer investigations, etc. by presenting the most important works. The main aim of this review paper is to emphasis on strengths and limitations of existence biomaterials and 3D bioprinting techniques in order to carry out a comparison through them.

Polylactic acid (PLA), one of the well-known bioabsorbable and compostable polyesters has rapidly evolved into a competitive commodity material over the last decades. To provide efficient therapy for the biomedical application domain understanding the PLA properties is the key tenets to achieve suitable chemical and biological properties. In most instances, PLA can be blended or copolymerized with other polymeric or nonpolymeric components. Devices made of PLA and its copolymers tend to slow degradation in vivo without inflammatory reaction and infection. This work intends to give an overview of the characterization and properties of PLA (i.e. crystallization behavior, physical properties, solubility and miscibility, degradation, and thermal properties). Also, we present some information about key elements to enhance PLA and its copolymers/composites/blends properties to optimize their fit with worldwide application requirements. This paper also emphasizes PLA applications in the pharmaceutical and biomedical industries, as well as recent PLA-based biomedical implant developed with Additive manufacturing (AM) devices.

Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain. In this domain, the different areas of interest concern the Omics (study of the genome—genomics—and proteins—transcriptomics, proteomics, and metabolomics), bioimaging (study of biological cell and tissue), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM). This paper reviews the major deep learning concepts pertinent to such biomedical applications. Concise overviews are provided for the Omics and the BBMI. We end our analysis with a critical discussion, interpretation and relevant open challenges.

Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community. PySINDy is a Python package that provides tools for applying the sparse identification of nonlinear dynamics (SINDy) approach to data-driven model discovery. In this major update to PySINDy, we implement several advanced features that enable the discovery of more general differential equations from noisy and limited data. The library of candidate terms is extended for the identification of actuated systems, partial differential equations (PDEs), and implicit differential equations. Robust formulations, including the integral form of SINDy and ensembling techniques, are also implemented to improve performance for real-world data. Finally, we provide a range of new optimization algorithms, including several sparse regression techniques and algorithms to enforce and promote inequality constraints and stability. Together, these updates enable entirely new SINDy model discovery capabilities that have not been reported in the literature, such as constrained PDE identification and ensembling with different sparse regression optimizers.

3D printing (additive manufacturing (AM)) has enormous potential for rapid tooling and mass production due to its design flexibility and significant reduction of the timeline from design to manufacturing. The current state-of-the-art in 3D printing focuses on material manufacturability and engineering applications. However, there still exists the bottleneck of low printing resolution and processing rates, especially when nanomaterials need tailorable orders at different scales. An interesting phenomenon is the preferential alignment of nanoparticles that enhance material properties. Therefore, this review emphasizes the landscape of nanoparticle alignment in the context of 3D printing. Herein, a brief overview of 3D printing is provided, followed by a comprehensive summary of the 3D printing-enabled nanoparticle alignment in well-established and in-house customized 3D printing mechanisms that can lead to selective deposition and preferential orientation of nanoparticles. Subsequently, it is listed that typical applications that utilized the properties of ordered nanoparticles (e.g., structural composites, heat conductors, chemo-resistive sensors, engineered surfaces, tissue scaffolds, and actuators based on structural and functional property improvement). This review's emphasis is on the particle alignment methodology and the performance of composites incorporating aligned nanoparticles. In the end, significant limitations of current 3D printing techniques are identified together with future perspectives.

International audience

The growing development of lithium-ion battery technology goes along with the new energy storage era across various sectors, e.g., mobility (electric vehicles), power generation and dispatching. The need for sophisticated modeling approaches has become a crucial tool to predict and optimize battery behavior given the demand of ever-higher performance, longevity, and safety. This review integrates the state-of-the-art in lithium-ion battery modeling, covering various scales, from particle-level simulations to pack-level thermal management systems, involving particle scale simplifications, microscale electrochemical models, and battery scale electrical models with thermal and heat generation prediction. Beyond that, authors highlight the growing trend in integrating highly accurate physics-based with thermal approaches such as the electrochemical-thermal coupled model to fully answer the multiscale challenges. Through capturing the electrochemical phenomena and thermal dynamics, and developing a comprehensive understanding of battery kinetics, safety risks such as thermal runaway can be thoroughly mitigated. Authors emphasize the trade-offs between computational efficiency and model complexity, explaining the limitations, strengths, and applications of diverse modeling approaches. This review illuminates the integration of battery management systems and cooling strategies. • Lithium-ion battery electrochemical and thermal dynamics are comprehensively reviewed. • Multiscale modeling is analyzed, considering physical limits and computational costs. • Systematic physics-based model comparison: strengths and limitations are detailed. • Scale-specific physical complexities are schematized for clarity.

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.

Navigation in a 3D immersive virtual environment is known to be prone to visually induced motion sickness (VIMS). Several psychophysiological and behavioral methods have been used to measure the level of sickness of a user, among which is postural instability. This study investigates all the features that can be extracted from the body postural sway: area of the projection of the center of gravity (mainly considered in past studies) and its shape and the frequency components of the signal’s spectrum, in order to estimate and predict the occurrence of sickness in a typical virtual reality (VR) application.After modeling and simulation of the body postural sway, an experiment on 17 subjects identified a relation between the level of sickness and the variation both in the time and frequency domains of the body sway signal. The results support and go further into detail of findings of past studies using postural instability as an efficient indicator of sickness, giving insight to better monitor VIMS in a VR application.

Abstract Fused filament fabrication is considered one of the most used processes in additive manufacturing rapid prototypes out of polymeric material. Poor strength of the deposited layers is still one of the main critical problems in this process, which affects the mechanical properties of the final parts. To improve the mechanical strength, investigation into various process parameters must be considered. In this article, the influence of different process parameters has been experimentally investigated by means of physicochemical and mechanical characterizations. Special attention was given to the thermal aspect. In that respect, the in situ measurement of temperature profile during deposition indicated that several parameters affect the cooling rate of material and consequently have an influence on the final parts. It was found that the influence of increasing the extruder temperature is more significant in comparison with other process parameters.

A key challenge of hazard risk management is finding novel ways to respond to future extremes amid increasing vulnerability. Societal transformation in the context of multi-functional protection schemes offers potential in this regard. However, the drivers and barriers of societal transformation in hazard management are poorly understood. Here we interrogate drivers and barriers of societal transformation in natural hazard management through case studies in Austria, France and Ireland focusing on attempts to integrate multi-functional protection schemes in the context of flood and avalanche hazards. We conducted qualitative semi-structured interviews with key stakeholders connected to proposed transformative strategies in the selected case studies. We find that transformative approaches have been mainly supported by local initiatives instigated by local governments, residents, or NGOs with the aim of complementing conventional hazard management policies. Our analysis shows that local actors and stakeholders often pursue initiatives to address local problems or to seize local opportunities rather than to contribute to a broader societal transformation. According to our findings, key drivers of community-based initiatives with multiple functionality and use include: (i) lack of funding, (ii) lack of legal protection or (iii) lack of space, where classical risk management measures can no longer respond to new circumstances. In contrast, key barriers relate to: (i) lack of local capacities, (ii) lack of local political support and (iii) technological challenges in the implementation phase. These insights support European regions currently working on the implementation of climate change adaptation strategies arising from natural hazards.

Polymer membranes are central to the proper operation of several processes used in a wide range of applications. The production of these membranes relies on processes such as phase inversion, stretching, track etching, sintering, or electrospinning. A novel and competitive strategy in membrane production is the use of additive manufacturing that enables the easier manufacture of tailored membranes. To achieve the future development of better membranes, it is necessary to compare this novel production process to that of more conventional techniques, and clarify the advantages and disadvantages. This review article compares a conventional method of manufacturing polymer membranes to additive manufacturing. A review of 3D printed membranes is also done to give researchers a reference guide. Membranes from these two approaches were compared in terms of cost, materials, structures, properties, performance. and environmental impact. Results show that very few membrane materials are used as 3D-printed membranes. Such membranes showed acceptable performance, better structures, and less environmental impact compared with those of conventional membranes.

The presence of defects like gas bubble in fabricated parts is inherent in the selective laser sintering process and the prediction of bubble shrinkage dynamics is crucial. In this paper, two artificial intelligence (AI) models based on Decision Trees algorithm were constructed in order to predict bubble dissolution time, namely the Ensemble Bagged Trees (EDT Bagged) and Ensemble Boosted Trees (EDT Boosted). A metadata including 68644 data were generated with the help of our previously developed numerical tool. The AI models used the initial bubble size, external domain size, diffusion coefficient, surface tension, viscosity, initial concentration, and chamber pressure as input parameters, whereas bubble dissolution time was considered as output variable. Evaluation of the models’ performance was achieved by criteria such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and coefficient of determination (R2). The results showed that EDT Bagged outperformed EDT Boosted. Sensitivity analysis was then conducted thanks to the Monte Carlo approach and it was found that three most important inputs for the problem were the diffusion coefficient, initial concentration, and bubble initial size. This study might help in quick prediction of bubble dissolution time to improve the production quality from industry.

Abstract Lithium‐ion batteries (LIBs) have significantly impacted the daily lives, finding broad applications in various industries such as consumer electronics, electric vehicles, medical devices, aerospace, and power tools. However, they still face issues (i.e., safety due to dendrite propagation, manufacturing cost, random porosities, and basic & planar geometries) that hinder their widespread applications as the demand for LIBs rapidly increases in all sectors due to their high energy and power density values compared to other batteries. Additive manufacturing (AM) is a promising technique for creating precise and programmable structures in energy storage devices. This review first summarizes light, filament, powder, and jetting‐based 3D printing methods with the status on current trends and limitations for each AM technology. The paper also delves into 3D printing‐enabled electrodes (both anodes and cathodes) and solid‐state electrolytes for LIBs, emphasizing the current state‐of‐the‐art materials, manufacturing methods, and properties/performance. Additionally, the current challenges in the AM for electrochemical energy storage (EES) applications, including limited materials, low processing precision, codesign/comanufacturing concepts for complete battery printing, machine learning (ML)/artificial intelligence (AI) for processing optimization and data analysis, environmental risks, and the potential of 4D printing in advanced battery applications, are also presented.

Poly(dodecano-12-lactam) (commercially known as polyamide “PA12”) is one of the most resourceful materials used in the selective laser sintering (SLS) process due to its chemical and physical properties. The present work examined the influence of two SLS parameters, namely, laser power and hatch orientation, on the tensile, structural, thermal, and morphological properties of the fabricated PA12 parts. The main objective was to evaluate the suitable laser power and hatching orientation with respect to obtaining better final properties. PA12 powders and SLS-printed parts were assessed through their particle size distributions, X-ray diffraction (XRD), Fourier Transform Infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), a scanning electron microscope (SEM), and their tensile properties. The results showed that the significant impact of the laser power while hatching is almost unnoticeable when using a high laser power. A more significant condition of the mechanical properties is the uniformity of the powder bed temperature. Optimum factor levels were achieved at 95% laser power and parallel/perpendicular hatching. Parts produced with the optimized SLS parameters were then subjected to an annealing treatment to induce a relaxation of the residual stress and to enhance the crystallinity. The results showed that annealing the SLS parts at 170 °C for 6 h significantly improved the thermal, structural, and tensile properties of 3D-printed PA12 parts.

Anticorrosive protection efficiency of novel tetrafunctional epoxy prepolymer, namely 2,3,4,5-tetraglycidyloxy pentanal (TGP), for mild steel in 1 M HCl medium was assessed through potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), scanning electron microscope (SEM), energy dispersive X-ray spectroscopy (EDS), contact angle (CA), adsorption isotherm model, temperature effect and thermodynamic parameters. The synthesized TGP was characterized and confirmed by Fourier transform infrared (FTIR) spectroscopy and nuclear magnetic resonance (NMR). The inhibitory efficiencies found at lower concentration of the prepolymer TGP were85% (PDP) and 87.17% (EIS). PDP measurement illustrated that the TGP behaved as a mixed-type inhibitor in the realized solution. SEM and EDS analysis showeda significant decrease in the corrosion of the MS surface in the presence of the inhibitory prepolymer compared with the blank (1 M HCl). Langmuir adsorption isotherm is the most acceptable modelto describe the TGP epoxy prepolymer on the MS area.

Abstract In this study, polyurethane‐films loaded with diclofenac were used to analyze the drug release kinetics and mechanisms. For this purpose, the experimental procedures were developed under static and dynamic conditions with different initial drug loads of 10, 20, and 30%. In the dynamic condition, to better simulate the biological flow, drug release measurements were investigated at flow rates of 7.5 and 23.5 ml/s. These values indicate the flow rate of the internal carotid artery (ICA) for a normal state of a body and for a person during the exercise, respectively. The experimental data were analyzed and adjusted by Higuchi, Korsmeyer–Peppas, First‐order, zero‐order, and Peppas–Sahlin models in order to understand the mechanisms contributed. Finally, drug release mechanisms were specified by investigating the model correlation coefficients. Experimental results showed that increasing the flow rate and initial drug loads enhance drug liberation. In addition, the rate of release is more influenced by the drug dosage in the static state. The analysis revealed that diffusion, burst, and osmotic pressure are the principal mechanisms contributed. Moreover, Fickian type was the dominant mechanism at all duration of release. However, it was discovered using Peppas–Sahlin model that the contribution of the diffusion mechanism decreases with increasing flow rate and initial dosage. Furthermore, the tests at different drug dosages showed that the number of stages in medication release profile is independent of the flow rate and the medicine percentage. One can conclude that the drug release kinetic in static state is more influenced by drug dosage compared with dynamic state.

In recent years, technologies have evolved towards social, universal and collaborative uses involving multiple users. However, methods and models from user centered design are focused on single-user design and do not take into account the impact of other users on intentions and behaviors to use the technology. The objective of this article is to provide paths of reflection on how to integrate a multi-user centered approach to existing user centered design methods and models. Each phase of user-centered design has been rethought to implement this new framework. Guidelines for moving from user experience to Multi-User eXperience (MUX) are provided. In the same way, we recommend adding a multi-user variable in the technology acceptance models to become the Multi-user Acceptance Model (MAM). User research and user testing have also been rethought to a multi-user reach and a multi-user testing. All these considerations are discussed, and lead to a proposal of a future Multi-user Centered Design (MCD) approach, specifically adapted to manage multi-user digital technology development projects. Finally, it is therefore necessary and important to direct research in the fields (acceptance, user experience, user research and user testing) to assist designers with the development of new methods of product design more respectful of social, environmental and collaborative values.