FIESTA: Autoencoders for accurate fiber segmentation in tractography.

White matter bundle segmentation is a cornerstone of modern tractography to study the brain's structural connectivity in domains such as neurological disorders, neurosurgery, and aging. In this study, we present FIESTA (FIbEr Segmentation in Tractography using Autoencoders), a reliable and robust, fully automated, and easily semi-automatically calibrated pipeline based on deep autoencoders that can dissect and fully populate white matter bundles. This pipeline is built upon previous works that demonstrated how autoencoders can be used successfully for streamline filtering, bundle segmentation, and streamline generation in tractography. Our proposed method improves bundle segmentation coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle. A latent space of streamlines is learned using autoencoder-based modeling combined with contrastive learning. Using an atlas of bundles in standard space (MNI), our proposed method segments new tractograms using the autoencoder latent distance between each tractogram streamline and its closest neighbor bundle in the atlas of bundles. Intra-subject bundle reliability is improved by recovering hard-to-track streamlines, using the autoencoder to generate new streamlines that increase the spatial coverage of each bundle while remaining anatomically correct. Results show that our method is more reliable than state-of-the-art automated virtual dissection methods such as RecoBundles, RecoBundlesX, TractSeg, White Matter Analysis and XTRACT. Our framework allows for the transition from one anatomical bundle definition to another with marginal calibration efforts. Overall, these results show that our framework improves the practicality and usability of current state-of-the-art bundle segmentation framework.

In situcontrol of graphene oxide dispersions with a small impedance sensor.

Carbon-based nanomaterials (CBNs), such as graphene and carbon nanotubes, display advanced physical and chemical properties, which has led to their widespread applications. One of these applications includes the incorporation of CBNs into cementitious materials in the form of aqueous dispersions. The main issue that arises in this context is that currently no established protocol exists as far as characterizing the dispersions. In the present article, an innovative method for quick evaluation and quantification of graphene oxide (GO) dispersions is proposed. The proposed method is electrical impedance spectroscopy (EIS) with an impedance sensor. The novelty lies on the exploitation of a small sensor for on-site (field) direct dielectric measurements with the application of alternating current. Five different concentrations of GO dispersions were studied by applying EIS and for various accumulated ultrasonic energies. The low GO concentration leads to high impedance values due to low formed current network. Two opposing mechanisms were revealed during the accumulation of ultrasonic energy, that are taking place simultaneously: breakage of the agglomerates that facilitates the flow of the electric current due to the formation of a better dispersed network, nevertheless the surface hydrophilic structure of the GO is damaged with the high accumulated ultrasonic energy. The dielectric measurements were exploited to express an appropriate quantitative 'quality index' to facilitate with the dispersion control of the nanostructures. An intermediate concentration of GO is suggested (about 0.15 wt% of the binder materials) to be optimal for the specific engineering application, ultrasonicated at approximately 30 to 65 kJ. The investigated methodology is highly novel and displays a high potential to be applied in-field applications where CBNs must be incorporated in building materials.

Tractostorm: The what, why, and how of tractography dissection reproducibility.

Investigative studies of white matter (WM) brain structures using diffusion MRI (dMRI) tractography frequently require manual WM bundle segmentation, often called "virtual dissection." Human errors and personal decisions make these manual segmentations hard to reproduce, which have not yet been quantified by the dMRI community. It is our opinion that if the field of dMRI tractography wants to be taken seriously as a widespread clinical tool, it is imperative to harmonize WM bundle segmentations and develop protocols aimed to be used in clinical settings. The EADC-ADNI Harmonized Hippocampal Protocol achieved such standardization through a series of steps that must be reproduced for every WM bundle. This article is an observation of the problematic. A specific bundle segmentation protocol was used in order to provide a real-life example, but the contribution of this article is to discuss the need for reproducibility and standardized protocol, as for any measurement tool. This study required the participation of 11 experts and 13 nonexperts in neuroanatomy and "virtual dissection" across various laboratories and hospitals. Intra-rater agreement (Dice score) was approximately 0.77, while inter-rater was approximately 0.65. The protocol provided to participants was not necessarily optimal, but its design mimics, in essence, what will be required in future protocols. Reporting tractometry results such as average fractional anisotropy, volume or streamline count of a particular bundle without a sufficient reproducibility score could make the analysis and interpretations more difficult. Coordinated efforts by the diffusion MRI tractography community are needed to quantify and account for reproducibility of WM bundle extraction protocols in this era of open and collaborative science.

Superflexibility of graphene oxide.

Graphene oxide (GO), the main precursor of graphene-based materials made by solution processing, is known to be very stiff. Indeed, it has a Young's modulus comparable to steel, on the order of 300 GPa. Despite its very high stiffness, we show here that GO is superflexible. We quantitatively measure the GO bending rigidity by characterizing the flattening of thermal undulations in response to shear forces in solution. Characterizations are performed by the combination of synchrotron X-ray diffraction at small angles and in situ rheology (rheo-SAXS) experiments using the high X-ray flux of a synchrotron source. The bending modulus is found to be 1 kT, which is about two orders of magnitude lower than the bending rigidity of neat graphene. This superflexibility compares with the fluidity of self-assembled liquid bilayers. This behavior is discussed by considering the mechanisms at play in bending and stretching deformations of atomic monolayers. The superflexibility of GO is a unique feature to develop bendable electronics after reduction, films, coatings, and fibers. This unique combination of properties of GO allows for flexibility in processing and fabrication coupled with a robustness in the fabricated structure.

Shear Rheology Control of Wrinkles and Patterns in Graphene Oxide Films.

Drying graphene oxide (GO) films are subject to extensive wrinkling, which largely affects their final properties. Wrinkles were shown to be suitable in biotechnological applications; however, they negatively affect the electronic properties of the films. Here, we report on wrinkle tuning and patterning of GO films under stress-controlled conditions during drying. GO flakes assemble at an air-solvent interface; the assembly forms a skin at the surface and may bend due to volume shrinkage while drying. We applied a modification of evaporative lithography to spatially define the evaporative stress field. Wrinkle alignment is achieved over cm2 areas. The wavelength (i.e., wrinkle spacing) is controlled in the µm range by the film thickness and GO concentration. Furthermore, we propose the use of nanoparticles to control capillary forces to suppress wrinkling. An example of a controlled pattern is given to elucidate the potential of the technique. The results are discussed in terms of classical elasticity theory. Wrinkling is the result of bending of the wet solid skin layer assembled on a highly elastic GO dispersion. Wavelength selection is the result of energy minimization between the bending of the skin and the elastic deformation of the GO supporting dispersion. The results strongly suggest the possibility to tune wrinkles and patterns by simple physicochemical routes.

A conductive hydrogel based on alginate and carbon nanotubes for probing microbial electroactivity.

Some bacteria can act as catalysts to oxidize (or reduce) organic or inorganic matter with the potential of generating electrical current. Despite their high value for sustainable energy, organic compound production and bioremediation, a tool to probe the natural biodiversity and to select most efficient microbes is still lacking. Compartmentalized cell culture is an ideal strategy for achieving such a goal but the appropriate compartment allowing cell growth and electron exchange must be tailored. Here, we develop a conductive composite hydrogel made of a double network of alginate and carbon nanotubes. Homogeneous mixing of carbon nanotubes within the polyelectrolyte is obtained by a surfactant assisted dispersion followed by a desorption step for triggering electrical conductivity. Dripping the mixture in a gelling bath through simple extrusion or a double one allows the formation of either plain hydrogel beads or liquid core hydrogel capsules. The process is shown to be compatible with the bacterial culture (Geobacter sulfurreducens). Bacteria can indeed colonize the outer wall of plain beads or the inner wall of the conductive capsules' shell that function as an anode from which electrons produced by the cells are collected.

Giant Electrostrictive Response and Piezoresistivity of Emulsion Templated Nanocomposites.

Using an emulsion road and optimizing the dispersion process, we prepare polymer carbone nanotubes (CNT) and polymer reduced graphene oxide (rGO) composites. The introduction of conductive nanoparticles into polymer matrices modifies the electronic properties of the material. We show that these materials exhibit giant electrostriction coefficients in the intermediate filler concentration (below 1 wt %). This makes them very promising for applications such as capacitive sensors and actuators. In addition, the values of the piezoresistivity measured in the high filler concentration situation are at least an order of magnitude greater than the one reported in the literature. This opens the way to use these materials for stress or strain sensor applications considering their giant responses to mechanical deformations.

Investigation of the dynamics of growth of polymer materials obtained by combined pervaporation and micro-moulding.

We report an extensive study of the concentration process of aqueous polymer solutions confined within microfluidic channels, owing to the pervaporation of water through the matrix of the chip. Concentration of polymer continuously increases up to the formation of a dense material which eventually invades the channel. This technology can be used to fabricate micro-composites of different shapes starting from dilute inks. We use both theory and screening experiments to show that the dynamics of growth can be predicted by simple conservation equations. More precisely, we establish a quantitative prediction of the growth dynamics taking into account deformations of the soft channels of the moulds, and the solvent chemical activity of the polymer solution. The present results based on general transport equations for binary mixtures provide direct guidance for the design of micro-fabricated materials considering their shape, dimensions, time scale of fabrication, and chemical composition. This quantitative framework is indeed essential to engineer integrated polymer-based micro-devices by using combined pervaporation and microfluidic moulding.

Effect of the rheological properties of carbon nanotube dispersions on the processing and properties of transparent conductive electrodes.

Transparent conductive films are made from aqueous surfactant stabilized dispersions of carbon nanotubes using an up-scalable rod coating method. The processability of the films is governed by the amount of surfactant which is shown to alter strongly the wetting and viscosity of the ink. The increase of viscosity results from surfactant mediated attractive interactions between the carbon nanotubes. Links between the formulation, ink rheological properties, and electro-optical properties of the films are determined. The provided guidelines are generalized and used to fabricate optimized electrodes using conductive polymers and carbon nanotubes. In these electrodes, the carbon nanotubes act as highly efficient viscosifiers that allow the optimized ink to be homogeneously spread using the rod coating method. From a general point of view and in contrast to previous studies, the CNTs are optimally used in the present approach as conductive additives for viscosity enhancements of electronic inks.

Giant Permittivity Polymer Nanocomposites Obtained by Curing a Direct Emulsion.

Near-percolated CNT-polymer composites are promising high-permittivity materials. The main challenge in the field consists of finding compromises that allow high permittivity and low losses in frequency ranges of interest. Using an emulsion approach and optimizing the size of the droplets and the curing procedure, we obtain unprecedented performances and measure giant permittivity larger than 20,000 at 100 Hz along with a conductivity below 10(-4) S/m.

Competing mechanisms and scaling laws for carbon nanotube scission by ultrasonication.

Dispersion of carbon nanotubes (CNTs) into liquids typically requires ultrasonication to exfoliate individuals CNTs from bundles. Experiments show that CNT length drops with sonication time (or energy) as a power law t(-m). Yet the breakage mechanism is not well understood, and the experimentally reported power law exponent m ranges from approximately 0.2 to 0.5. Here we simulate the motion of CNTs around cavitating bubbles by coupling brownian dynamics with the Rayleigh-Plesset equation. We observe that, during bubble growth, CNTs align tangentially to the bubble surface. Surprisingly, we find two dynamical regimes during the collapse: shorter CNTs align radially, longer ones buckle. We compute the phase diagram for CNT collapse dynamics as a function of CNT length, stiffness, and initial distance from the bubble nuclei and determine the transition from aligning to buckling. We conclude that, depending on their length, CNTs can break due to either buckling or stretching. These two mechanisms yield different power laws for the length decay (0.25 and 0.5, respectively), reconciling the apparent discrepancy in the experimental data.

How polymers lose memory with age.

Uniquely in the world of materials, polymers deformed at high temperature and subsequently quenched at low temperature, memorize the temperature at which they have been processed. Polymers can even memorize multiple temperatures. This temperature memory is reflected by a maximum of residual stress restored at the temperature of initial processing. It has been speculated that this capability could arise from the presence of dynamical heterogeneities in glassy domains of polymers. Processing the material at a given temperature would result in the selection of certain heterogeneities that participate in the storage of mechanical stress. Because dynamical heterogeneities are associated with particular relaxation times, the temperature memory of polymers should depend on the time, for example, the glass transition temperature depends on the frequency. The first experimental study of temporal effects on the temperature memory of polymers is presently reported. It is found that aging at high temperature shifts the maximum of residual stress towards greater temperatures. The corresponding loss of memory is explained by the relaxation of dynamical heterogeneities with short characteristic times. The present results clarify the origin of the temperature memory and provide insights into their efficient exploitation in applications.

Data Representation Structure to Support Clinical Decision-Making in the Pediatric Intensive Care Unit: Interview Study and Preliminary Decision Support Interface Design.

BACKGROUND: Clinical decision-making is a complex cognitive process that relies on the interpretation of a large variety of data from different sources and involves the use of knowledge bases and scientific recommendations. The representation of clinical data plays a key role in the speed and efficiency of its interpretation. In addition, the increasing use of clinical decision support systems (CDSSs) provides assistance to clinicians in their practice, allowing them to improve patient outcomes. In the pediatric intensive care unit (PICU), clinicians must process high volumes of data and deal with ever-growing workloads. As they use multiple systems daily to assess patients' status and to adjust the health care plan, including electronic health records (EHR), clinical systems (eg, laboratory, imaging and pharmacy), and connected devices (eg, bedside monitors, mechanical ventilators, intravenous pumps, and syringes), clinicians rely mostly on their judgment and ability to trace relevant data for decision-making. In these circumstances, the lack of optimal data structure and adapted visual representation hinder clinician's cognitive processes and clinical decision-making skills. OBJECTIVE: In this study, we designed a prototype to optimize the representation of clinical data collected from existing sources (eg, EHR, clinical systems, and devices) via a structure that supports the integration of a home-developed CDSS in the PICU. This study was based on analyzing end user needs and their clinical workflow. METHODS: First, we observed clinical activities in a PICU to secure a better understanding of the workflow in terms of staff tasks and their use of EHR on a typical work shift. Second, we conducted interviews with 11 clinicians from different staff categories (eg, intensivists, fellows, nurses, and nurse practitioners) to compile their needs for decision support. Third, we structured the data to design a prototype that illustrates the proposed representation. We used a brain injury care scenario to validate the relevance of integrated data and the utility of main functionalities in a clinical context. Fourth, we held design meetings with 5 clinicians to present, revise, and adapt the prototype to meet their needs. RESULTS: We created a structure with 3 levels of abstraction-unit level, patient level, and system level-to optimize clinical data representation and display for efficient patient assessment and to provide a flexible platform to host the internally developed CDSS. Subsequently, we designed a preliminary prototype based on this structure. CONCLUSIONS: The data representation structure allows prioritizing patients via criticality indicators, assessing their conditions using a personalized dashboard, and monitoring their courses based on the evolution of clinical values. Further research is required to define and model the concepts of criticality, problem recognition, and evolution. Furthermore, feasibility tests will be conducted to ensure user satisfaction.

Carbon nanotube fiber microelectrodes show a higher resistance to dopamine fouling.

We have compared the properties and resistance to DA fouling of a carbon nanotube fiber (CNTF) microelectrode to a traditional carbon fiber (CF) microelectrode. These two materials show comparable electrochemical activities for outer-sphere and inner-sphere redox reactions. Although the CNTF might have a higher intrinsic RC constant, thus limiting its high-frequency behavior, the CNTF shows a significantly higher durability than the CF in terms of electrode stability. During constant oxidation of 100 µM DA, the signal measured by the CNTF microelectrode shows a 2-h window over which no decrease in current is observed. Under the same conditions, the current obtained at the CF microelectrode decreases by almost 50%. A model of the fouling process, assuming the formation of growing patches of insulator on the surface, has been compared to the data. This model is found to be in good agreement with our results and indicates a growth rate of the patches in the 0.1-2 nm s(-1) range.

Carbon nanotubes induced gelation of unmodified hyaluronic acid.

This work reports an experimental study of the kinetics and mechanisms of gelation of carbon nanotubes (CNTs)-hyaluronic acid (HA) mixtures. These materials are of great interest as functional biogels for future medical applications and tissue engineering. We show that CNTs can induce the gelation of noncovalently modified HA in water. This gelation is associated with a dynamical arrest of a liquid crystal phase separation, as shown by small-angle light scattering and polarized optical microscopy. This phenomenon is reminiscent of arrested phase separations in other colloidal systems in the presence of attractive interactions. The gelation time is found to strongly vary with the concentrations of both HA and CNTs. Near-infrared photoluminescence reveals that the CNTs remain individualized both in fluid and in gel states. It is concluded that the attractive forces interplay are likely weak depletion interactions and not strong van der Waals interactions which could promote CNT rebundling, as observed in other biopolymer-CNT mixtures. The present results clarify the remarkable efficiency of CNT at inducing the gelation of HA, by considering that CNTs easily phase separate as liquid crystals because of their giant aspect ratio.

Situation Awareness-Oriented Dashboard in ICUs in Support of Resource Management in Time of Pandemics.

In a pediatric intensive care unit (PICU) of 32 beds, clinicians manage resources 24 hours a day, 7 days a week, from a large-screen dashboard implemented in 2017. This resource management dashboard efficiently replaces the handwriting information displayed on a whiteboard, offering a synthetic view of the bed's layout and specific information on staff and equipment at bedside. However, in 2020 when COVID-19 hit, the resource management dashboard showed several limitations. Mainly, its visualization offered to the clinicians limited situation awareness (SA) to perceive, understand and predict the impacts on resource management and decision-making of an unusual flow of patients affected by the most severe form of coronavirus. To identify the SA requirements during a pandemic, we conducted goal-oriented interviews with 11 clinicians working in ICUs. The result is the design of an SA-oriented dashboard with 22 key indicators (KIs): 1 on the admission capacity, 15 at bedside and 6 displayed as statistics in the central area. We conducted a usability evaluation of the SA-oriented dashboard compared to the resource management dashboard with 6 clinicians. The results showed five usability improvements of the SA-oriented dashboard and five limitations. Our work contributes to new knowledge on the clinicians' SA requirements to support resource management and decision-making in ICUs in times of pandemics.

Orientational order of carbon nanotube guests in a nematic host suspension of colloidal viral rods.

In order to investigate the coupling between the degrees of alignment of elongated particles in binary nematic dispersions, surfactant stabilized single-wall carbon nanotubes (CNTs) have been added to nematic suspensions of colloidal rodlike viruses in aqueous solution. We have independently measured the orientational order parameter of both components of the guest-host system by means of polarized Raman spectroscopy and by optical birefringence, respectively. Our system allows us therefore to probe the regime where the guest particles (CNTs) are shorter and thinner than the fd virus host particles. We show that the degree of order of the CNTs is systematically smaller than that of the fd virus particles for the whole nematic range. These measurements are in good agreement with predictions of an Onsager-type second-viral theory, which explicitly includes the flexibility of the virus particles, and the polydispersity of the CNTs.

Piezoelectric Fibers: Processing and Challenges.

Integration of piezoelectric materials in composite and textile structures is promising for creating smart textiles with sensing or energy harvesting functionalities. The most direct integration that combines wearability, comfort, and piezoelectric efficiency consists of using fibers made of piezoelectric materials. The latter include inorganic ceramics or organic polymers. Ceramics have outstanding piezoelectric properties but can not be easily melted or solubilized in a solvent to be processed in the form of fibers. They have to be spun from precursor materials and thermally treated afterward for densification and sintering. These delicate processes have to be carefully controlled to optimize the piezoelectric properties of the fibers. On the other hand, organic piezoelectric polymers, such as polyvinylidene fluoride (PVDF), can be spun by more conventional textile fibers technologies. In addition to enjoy an easier manufacturing, organic piezoelectric fibers display flexibility that facilitates their integration and use in smart textiles. However, organic fibers suffer from a low piezoelectric efficiency. This reviews looks at the processing techniques and their specific limitations and advantages to realize single-component or coaxial piezofibers. Fundamental challenges related to the use of composite fibers are discussed. The latter include challenges for poling and electrically wiring the fibers to collect charges under operation or to apply electrical fields. The electromechanical properties of these fibers processed by different manufacturing techniques are compared. Recent studies of structures used to integrate such fibers in textiles and composites with conventional techniques and their potential applications are discussed.

3D Printing Graphene Oxide Soft Robotics.

We propose a universal strategy to 3D printing the graphene oxide (GO) complex structure with GO highly aligned and densely compacted, by the combination of direct ink writing and constrained drying. The constraints not only allow the generation of a huge capillary force accompanied by water evaporation at nanoscale, which induces the high compaction and alignment of GO, but also limit the shrinkage of the extruded filaments only along the wall thickness direction, therefore, successfully maintaining the uniformity of the structure at macroscale. We discover that the shrinkage stress gradually increased during the drying process, with the maximum exceeding ∼0.74 MPa, significantly higher than other colloidal systems. Interestingly, because of the convergence between plates with different orientations of the constraints, a gradient of porosity naturally formed across the thickness direction at the corner. This allows us to 3D print humidity sensitive GO based soft robotics.

TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography.

TractoInferno is the world's largest open-source multi-site tractography database, including both research- and clinical-like human acquisitions, aimed specifically at machine learning tractography approaches and related ML algorithms. It provides 284 samples acquired from 3 T scanners across 6 different sites. Available data includes T1-weighted images, single-shell diffusion MRI (dMRI) acquisitions, spherical harmonics fitted to the dMRI signal, fiber ODFs, and reference streamlines for 30 delineated bundles generated using 4 tractography algorithms, as well as masks needed to run tractography algorithms. Manual quality control was additionally performed at multiple steps of the pipeline. We showcase TractoInferno by benchmarking the learn2track algorithm and 5 variations of the same recurrent neural network architecture. Creating the TractoInferno database required approximately 20,000 CPU-hours of processing power, 200 man-hours of manual QC, 3,000 GPU-hours of training baseline models, and 4 Tb of storage, to produce a final database of 350 Gb. By providing a standardized training dataset and evaluation protocol, TractoInferno is an excellent tool to address common issues in machine learning tractography.