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.

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.

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.

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.

Human-muscle-inspired single fibre actuator with reversible percolation.

Artificial muscles are indispensable components for next-generation robotics capable of mimicking sophisticated movements of living systems. However, an optimal combination of actuation parameters, including strain, stress, energy density and high mechanical strength, is required for their practical applications. Here we report mammalian-skeletal-muscle-inspired single fibres and bundles with large and strong contractive actuation. The use of exfoliated graphene fillers within a uniaxial liquid crystalline matrix enables photothermal actuation with large work capacity and rapid response. Moreover, the reversible percolation of graphene fillers induced by the thermodynamic conformational transition of mesoscale structures can be in situ monitored by electrical switching. Such a dynamic percolation behaviour effectively strengthens the mechanical properties of the actuator fibres, particularly in the contracted actuation state, enabling mammalian-muscle-like reliable reversible actuation. Taking advantage of a mechanically compliant fibre structure, smart actuators are readily integrated into strong bundles as well as high-power soft robotics with light-driven remote control.

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.

Nanosheet-Stabilized Emulsions: Near-Minimum Loading and Surface Energy Design of Conductive Networks.

Here, we develop a framework for assembly, understanding, and application of functional emulsions stabilized by few-layer pristine two-dimensional (2D) nanosheets. Liquid-exfoliated graphene and MoS2 are demonstrated to stabilize emulsions at ultralow nanosheet volume fractions, approaching the minimum loading achievable with 2D materials. These nanosheet-stabilized emulsions allow controlled droplet deposition free from the coffee ring effect to facilitate single-droplet devices from minute quantities of material or assembly into large-area films with high network conductivity. To broaden the range of compositions and subsequent applications, an understanding of emulsion stability and orientation in terms of surface energy of the three phases is developed. Importantly, this model facilitates determination of the surface energies of the nanosheets themselves and identifies strategies based on surface tension and pH to allow design of emulsion structures. Finally, this approach is used to prepare conductive silicone emulsion composites with a record-low loading level and excellent electromechanical sensitivity. The versatility of these nanosheet-stabilized emulsions illustrates their potential for low-loading composites, thin-film formation and surface energy determination, and the design of functional structures for a range of segregated network applications.

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.

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.

Liquid Crystal-Mediated 3D Printing Process to Fabricate Nano-Ordered Layered Structures.

The emergence of three-dimensional (3D) printing promises a disruption in the design and on-demand fabrication of smart structures in applications ranging from functional devices to human organs. However, the scale at which 3D printing excels is within macro- and microlevels and principally lacks the spatial ordering of building blocks at nanolevels, which is vital for most multifunctional devices. Herein, we employ liquid crystal (LC) inks to bridge the gap between the nano- and microscales in a single-step 3D printing. The LC ink is prepared from mixtures of LCs of nanocellulose whiskers and large sheets of graphene oxide, which offers a highly ordered laminar organization not inherently present in the source materials. LC-mediated 3D printing imparts the fine-tuning required for the design freedom of architecturally layered systems at the nanoscale with intricate patterns within the 3D-printed constructs. This approach empowered the development of a high-performance humidity sensor composed of self-assembled lamellar organization of NC whiskers. We observed that the NC whiskers that are flat and parallel to each other in the laminar organization allow facile mass transport through the structure, demonstrating a significant improvement in the sensor performance. This work exemplifies how LC ink, implemented in a 3D printing process, can unlock the potential of individual constituents to allow macroscopic printing architectures with nanoscopic arrangements.

Highly conductive colloidal carbon based suspension for flow-assisted electrochemical systems.

Carbon suspension electrodes are promising for flow-assisted electrochemical energy storage systems. They serve as flowable electrodes in electrolyte solutions of flow batteries, or flow capacitors. They can also be used for other applications such as capacitive deionization of water. However, developments of such suspensions remain challenging. The suspensions should combine low viscosity and high electronic conductivity for optimized performances. In this work, we report a flowable aqueous carbon dispersion which exhibits a viscosity of only 2 Pa.s at a shear rate of 5 s-1 for a concentration of particles of 7 wt%. This suspension displays an electronic conductivity of 65 mS/cm, nearly two orders of magnitude greater than previously investigated related materials. The investigated suspensions are stabilized by sodium alginate and arabic gum in the presence of ammonium sulfate. Their use in flowable systems for the storage and discharge of electrical charges is demonstrated.

Integration of a soft dielectric composite into a cantilever beam for mechanical energy harvesting, comparison between capacitive and triboelectric transducers.

Flexible dielectrics that harvest mechanical energy via electrostatic effects are excellent candidates as power sources for wearable electronics or autonomous sensors. The integration of a soft dielectric composite (polydimethylsiloxane PDMS-carbon black CB) into two mechanical energy harvesters is here presented. Both are based on a similar cantilever beam but work on different harvesting principles: variable capacitor and triboelectricity. We show that without an external bias the triboelectric beam harvests a net density power of 0.3 [Formula: see text] under a sinusoidal acceleration of 3.9g at 40 Hz. In a variable capacitor configuration, a bias of 0.15 [Formula: see text] is required to get the same energy harvesting performance under the same working conditions. As variable capacitors' harvesting performance are quadratically dependent on the applied bias, increasing the bias allows the system to harvest energy much more efficiently than the triboelectric one. The present results make CB/PDMS composites promising for autonomous portable multifunctional systems and intelligent sensors.

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.

Common misconceptions, hidden biases and modern challenges of dMRI tractography.

The human brain is a complex and organized network, where the connection between regions is not achieved with single axons crisscrossing each other but rather millions of densely packed and well-ordered axons. Reconstruction from diffusion MRI tractography is only an attempt to capture the full complexity of this network, at the macroscale. This review provides an overview of the misconceptions, biases and pitfalls present in structural white matter bundle and connectome reconstruction using tractography. The goal is not to discourage readers, but rather to inform them of the limitations present in the methods used by researchers in the field in order to focus on what they can do and promote proper interpretations of their results. It also provides a list of open problems that could be solved in future research projects for the next generation of PhD students.

Shape memory nanocomposite fibers for untethered high-energy microengines.

Classic rotating engines are powerful and broadly used but are of complex design and difficult to miniaturize. It has long remained challenging to make large-stroke, high-speed, high-energy microengines that are simple and robust. We show that torsionally stiffened shape memory nanocomposite fibers can be transformed upon insertion of twist to store and provide fast and high-energy rotations. The twisted shape memory nanocomposite fibers combine high torque with large angles of rotation, delivering a gravimetric work capacity that is 60 times higher than that of natural skeletal muscles. The temperature that triggers fiber rotation can be tuned. This temperature memory effect provides an additional advantage over conventional engines by allowing for the tunability of the operation temperature and a stepwise release of stored energy.

Tractography and machine learning: Current state and open challenges.

Supervised machine learning (ML) algorithms have recently been proposed as an alternative to traditional tractography methods in order to address some of their weaknesses. They can be path-based and local-model-free, and easily incorporate anatomical priors to make contextual and non-local decisions that should help the tracking process. ML-based techniques have thus shown promising reconstructions of larger spatial extent of existing white matter bundles, promising reconstructions of less false positives, and promising robustness to known position and shape biases of current tractography techniques. But as of today, none of these ML-based methods have shown conclusive performances or have been adopted as a de facto solution to tractography. One reason for this might be the lack of well-defined and extensive frameworks to train, evaluate, and compare these methods. In this paper, we describe several datasets and evaluation tools that contain useful features for ML algorithms, along with the various methods proposed in the recent years. We then discuss the strategies that are used to evaluate and compare those methods, as well as their shortcomings. Finally, we describe the particular needs of ML tractography methods and discuss tangible solutions for future works.

Integrated Electromechanical Transduction Schemes for Polymer MEMS Sensors.

Polymer Micro ElectroMechanical Systems (MEMS) have the potential to constitute a powerful alternative to silicon-based MEMS devices for sensing applications. Although the use of commercial photoresists as structural material in polymer MEMS has been widely reported, the integration of functional polymer materials as electromechanical transducers has not yet received the same amount of interest. In this context, we report on the design and fabrication of different electromechanical schemes based on polymeric materials ensuring different transduction functions. Piezoresistive transduction made of carbon nanotube-based nanocomposites with a gauge factor of 200 was embedded within U-shaped polymeric cantilevers operating either in static or dynamic modes. Flexible resonators with integrated piezoelectric transduction were also realized and used as efficient viscosity sensors. Finally, piezoelectric-based organic field effect transistor (OFET) electromechanical transduction exhibiting a record sensitivity of over 600 was integrated into polymer cantilevers and used as highly sensitive strain and humidity sensors. Such advances in integrated electromechanical transduction schemes should favor the development of novel all-polymer MEMS devices for flexible and wearable applications in the future.

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.

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.