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Results for “Bioengineering”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

GlucoPush: A Do-It-Yourself Add-On for Online Tracking of Personal Glucometer Use

Current public health guidelines on diabetes management recommend frequent self-monitoring and physician tracking of blood glucose to reduce complications caused by this condition. While Internet-of-Things glucometers exist to aid in this goal, most commercial glucometers seen in wide-spread use worldwide do not provide these online tracking capabilities or allow for convenient data sharing with health care providers. This situation is caused, among several factors, due to premium pricing of IoT glucometers and resistance from older users to dispose of their previous glucometers. We propose an add-on strategy to enable IoT integration in glucometers to increase the use of wireless connectivity features in this sector. Here we describe and test a simple do-it-yourself system fabricated with low-cost commercially available wireless connectivity components that aim to demonstrate this IoT augmentation strategy to track glucose readings from previously unconnected standard glucometers.

bioengineering↗

bioRxiv2018

A 3D Topographical Model of Parenchymal Infiltration and Perivascular Invasion in Glioblastoma

Glioblastoma (GBM) is the most common and invasive primary brain cancer. GBM tumors are characterized by diffuse infiltration, with tumor cells invading slowly through the hyaluronic acid (HA)-rich parenchyma toward vascular beds and then migrating rapidly along microvasculature. Progress in understanding local infiltration, vascular homing, and perivascular invasion is limited by an absence of culture models that recapitulate these hallmark processes. Here we introduce a platform for GBM invasion consisting of a tumor-like cell reservoir and a parallel open channel \"vessel\" embedded in 3D HA-RGD matrix. We show that this simple paradigm is sufficient to capture multi-step invasion and transitions in cell morphology and speed reminiscent of those seen in GBM. Specifically, seeded tumor cells grow into multicellular masses that expand and invade the surrounding HA-RGD matrices while extending long (10-100 {micro}m), thin protrusions resembling those observed for GBM in vivo. Upon encountering the channel, cells orient along the channel wall, adopt a 2D-like morphology, and migrate rapidly along the channel. Structured illumination microscopy reveals distinct cytoskeletal architectures for cells invading through the HA matrix versus those migrating along the vascular channel. Substitution of collagen I in place of HA-RGD supports the same sequence of events but with faster local invasion and a more mesenchymal morphology. These results indicate that topographical effects are generalizable across matrix formulations, but that mechanisms underlying invasion are matrix-dependent. We anticipate that our reductionist paradigm should speed the development of mechanistic hypotheses that could be tested in more complex tumor models.

bioengineering↗

bioRxiv2018

Revolutionising the design and analysis of protein engineering experiments using fractional factorial design

Protein engineering is one of the foundations of biotechnology, used to increase protein stability, re-assign the catalytic properties of enzymes or increase the interaction affinity between antibody and target. To date, strategies for protein engineering have focussed on systematic, random or computational methods for introducing new mutations. Here, we introduce the statistical approach of fractional factorial design as a convenient and powerful tool for the design and analysis of protein mutations, allowing sampling of a large mutational space whilst minimising the tests to be done. Our test case is the integral membrane protein, Acridine resistance subunit B (AcrB), part of the AcrAB-TolC multi-protein complex, a multi-drug efflux pump of Gram-negative bacteria. E. coli AcrB is naturally histidine-rich, meaning that it is a common contaminant in the purification of recombinantly expressed, histidine-tagged membrane proteins. Coupled with the ability of AcrB to crystallise from picogram quantities causing false positives in 2-D and 3-D crystallisation screening, AcrB contamination represents a significant hindrance to the determination of new membrane protein structures. Here, we demonstrate the use of fractional factorial design for protein engineering, identifying the most important residues involved in the interaction between AcrB and nickel resin. We demonstrate that a combination of spatially close, but sequentially distant histidine residues are important for nickel binding, which were different from those predicted a priori. Fractional factorial methodology has the ability to decrease the time and material costs associated with protein engineering whilst expanding the depth of mutational space explored; a revolutionary concept.\n\nSignificance statementProtein engineering is important for the production of enzymes for bio-manufacturing, stabilised protein for research and production of therapeutic antibodies against human diseases. Here, we introduce a statistical method that can reduce the time and cost required to perform protein engineering. We validate our approach experimentally using the multi-drug efflux pump AcrB, a target for understanding drug-resistance in pathogenic bacteria, but also a persistent contaminant in the purification of membrane proteins from E. coli. This provides a general method for increasing the efficiency of protein engineering.

bioengineering↗

bioRxiv2018

Multiplex Enrichment and Detection of Rare KRAS Mutations in Liquid Biopsy Samples using Digital Droplet Pre-Amplification

Oncology research is increasingly incorporating molecular detection of circulating tumor DNA (ctDNA) as a tool for cancer surveillance and early detection. However, non-invasive monitoring of conditions with low tumor burden remains challenging, as the diagnostic sensitivity of most ctDNA assays is inversely correlated with total DNA concentration and ctDNA abundance. Here we present the Multiplex Enrichment using Droplet Pre-Amplification (MED-Amp) method, which com-bines single-molecule emulsification and short-round PCR preamplification with digital droplet PCR (ddPCR) detection of mutant DNA template. The MED-Amp assay increased mutant signal by over 50-fold with minimal distortion in allelic frequency. We demonstrate detection of as few as 3 mutant copies in wild-type DNA concentrations ranging from 5 to 50ng. The MED-Amp assay successfully detected KRAS mutant ctDNA in 86% plasma samples obtained from patients with metastatic pancreatic ductal adenocarcinoma. This assay for high-sensitivity rare variant detection is appropriate for liquid biopsy samples, or other limited clinical biospecimens

bioengineering↗

bioRxiv2018

A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle

Large efforts have been deployed in developing methods to estimate methane emissions from cattle. For large scale applications, accurate and inexpensive methane predictors are required. Within a livestock precision farming context, the objective of this work was to integrate real-time data on animal feeding behaviour with an in silico model for predicting the individual dynamic pattern of methane emission in cattle. The integration of real-time data with a mathematical model to predict variables that are not directly measured constitutes a software sensor. We developed a dynamic parsimonious grey-box model that uses as predictor variables either dry matter intake (DMI) or the intake time (IT). The model is described by ordinary differential equations. Model building was supported by experimental data of methane emissions from respiration chambers. The data set comes from a study with finishing beef steers (cross-bred Charolais and purebred Luing finishing). DMI and IT were recorded with load cells. A total of 37 individual dynamic patterns of methane production were analysed. Model performance was assessed by concordance analysis between the predicted methane output and the methane measured in respiration chambers. The model predictors DMI and IT performed similarly with a Lins concordance correlation coefficient (CCC) of 0.78 on average. When predicting the daily methane production, the CCC was 0.99 for both DMI and IT predictors. Consequently, on the basis of concordance analysis, our model performs very well compared with reported literature results for methane proxies and predictive models. Since IT measurements are easier to obtain than DMI measurements, this study suggests that a software sensor that integrates our in silico model with a real-time sensor providing accurate IT measurements is a viable solution for predicting methane output in a large scale context.\n\nImplicationsReducing methane emissions from ruminants is a major target for sustainable and efficient livestock farming. For the animal, methane production represents a loss of feed energy. For the environment, methane exerts a potent greenhouse effect. Methane mitigation strategies require accurate, non-invasive and inexpensive techniques for estimating individual methane emissions on farm. In this study, we integrate measurements of feeding behaviour in cattle and a mathematical model to estimate individual methane production. Together, model and measurements form a software sensor that efficiently predicts methane output. Our software sensor is a promising approach for estimating methane emissions at large scale.

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bioRxiv2018

Coordinating the in vivo processes for minicircle production - a novel approach to large scale manufacturing

Safety as well as efficiency issues in connection with bacterial backbone sequences should be carefully considered when designing new DNA vaccines or non-viral gene therapy approaches. Bacterial backbone sequences like antibiotic resistance markers or regulatory bacterial elements constitute biological safety risks and reduce the overall efficiency of the DNA agent. To overcome these problems the minicircle technology has been developed. But, despite all the obvious advantages, minicircles have so far not replaced their problem laden conventional counterpart in gene transfer applications what can be contributed to efficiency issues in large scale manufacturing. In this article we describe the combined efforts of experts in the field of minicircle development, large scale biomanufacturing and downstream process development to provide a new approach. The Recombination Based Plasmid Separation (RBPS) Technology, which has already solved crucial problems associated with minicircle-DNA production, has been developed further for this purpose. A novel parental plasmid exploiting advanced in vivo process coordination for restriction and subsequent degradation of miniplasmid-DNA will be introduced. Furthermore we describe the scale-up of minicircle-DNA production by fermentation in combination with high performance downstream processes including purification by ion exchange and hydrophobic interaction chromatography on monolithic material.

bioengineering↗

bioRxiv2018

Mass Action Kinetic Model of Apoptosis by TRAIL-Functionalized Leukocytes

1 AbstractO_ST_ABSBackgroundC_ST_ABSMetastasis through the bloodstream contributes to poor prognosis in many types of cancer. A unique approach to target and kill colon, prostate, and other epithelial-type cancer cells in the blood has been recently developed that causes circulating leukocytes to present the cancer-specific, liposome-bound Tumor Necrosis Factor (TNF)-related apoptosis inducing ligand (TRAIL) on their surface along with E - selectin adhesion receptors. This approach, demonstrated both in vitro with human blood and in mice, mimics the cytotoxic activity of natural killer cells. The resulting liposomal TRAIL-coated leukocytes hold promise as an effective means to neutralize circulating tumor cells that enter the bloodstream with the potential to form new metastases.\n\nResultsThe computational biology study reported here examines the mechanism of this effective signal delivery, by considering the kinetics of the coupled reaction cascade, from TRAIL binding death receptor to eventual apoptosis. In this study, a collision of bound TRAIL with circulating tumor cells (CTCs) is considered and compared to a prolonged exposure of CTCs to soluble TRAIL. An existing computational model of soluble TRAIL treatment was modified to represent the kinetics from a diffusion-limited 3D reference frame into a 2D collision frame with advection and adhesion to mimic the E - selectin and membrane bound TRAIL treatment. Thus, the current model recreates the new approach of targeting cancer cells within the blood. The model was found to faithfully reproduce representative observations from experiments of liposomal TRAIL treatment under shear. The model predicts apoptosis of CTCs within 2 hr when treated with membrane bound TRAIL, while apoptosis in CTCs treated with soluble TRAIL proceeds much more slowly over the course of 10 hrs, consistent with previous experiments. Given the clearance rate of soluble TRAIL in vivo, this model predicts that the soluble TRAIL method would be rendered ineffective, as found in previous experiments.\n\nConclusionThis study therefore indicates that the kinetics of the coupled reaction cascade of liposomal E - selectin and membrane bound TRAIL colliding with CTCs can explain why this new approach to target and kill cancer cells in blood is much more effective than its soluble counterpart.

bioengineering↗

bioRxiv2018

Design of Transcranial Magnetic Stimulation Coils with Optimal Trade-off between Depth, Focality, and Energy

BackgroundTranscranial magnetic stimulation (TMS) is a noninvasive brain stimulation technique used for research and clinical applications. Existent TMS coils are limited in their precision of spatial targeting (focality), especially for deeper targets.\n\nObjectiveThis paper presents a methodology for designing TMS coils to achieve optimal trade-off between the depth and focality of the induced electric field (E-field), as well as the energy required by the coil.\n\nMethodsA multi-objective optimization technique is used for computationally designing TMS coils that achieve optimal trade-offs between stimulation focality, depth, and energy (fdTMS coils). The fdTMS coil winding(s) maximize focality (minimize stimulated volume) while reaching a target at a specified depth and not exceeding predefined peak E-field strength and required coil energy. Spherical and MRI-derived head models are used to compute the fundamental depth-focality trade-off as well as focality-energy trade-offs for specific target depths.\n\nResultsAcross stimulation target depths of 1.0-3.4 cm from the brain surface, the stimulated volume can be theoretically decreased by 42%-55% compared to existing TMS coil designs. The stimulated volume of a figure-8 coil can be decreased by 36%, 44%, or 46%, for matched, doubled, or quadrupled energy. For matched focality and energy, the depth of a figure-8 coil can be increased by 22%.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=153 SRC=\"FIGDIR/small/300616_fig8.gif\" ALT=\"Figure 8\">\nView larger version (14K):\norg.highwire.dtl.DTLVardef@af0b5org.highwire.dtl.DTLVardef@4118d8org.highwire.dtl.DTLVardef@1c61f80org.highwire.dtl.DTLVardef@3e23fa_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 8.C_FLOATNO Energy, W, vs. spread, S1/2, curves for hand knob stimulation for a conventional figure-8 coil and optimized fdTMS coil on a 32 x 32 cm square plane.\n\nC_FIG ConclusionComputational design of TMS coils could enable more selective targeting of the induced E-field. The presented results appear to be the first significant advancement in the depth-focality trade-off of TMS coils since the introduction of the figure-8 coil three decades ago, and likely represent the fundamental physical limit.\n\nDECLARATION OF INTERESTThe fdTMS technology described in this paper is subject to a provisional patent application by Duke University with the authors as inventors. Additionally: L.J.G. is inventor on a patent pertaining to the design of focal multicoil TMS systems. S.M.G. is inventor on patents and patent applications; he has received royalties from Rogue Research, TU Muenchen, and Porsche; furthermore, he has been provided with research support and patent fee reimbursement from Magstim Co. A.V.P. is inventor on patents and patent applications, and has received research and travel support as well as patent royalties from Rogue Research; research and travel support, consulting fees, as well as equipment loan from Tal Medical / Neurex; patent application and research support from Magstim; and equipment loans from MagVenture, all related to technology for transcranial magnetic stimulation.

bioengineering↗

bioRxiv2018

Mode of Growth and Temperature Dependence on Expression of Atrazine-degrading Genes in Pseudomonas sp. strain ADP Biofilms

Bacterial strain Pseudomonas sp. strain ADP is capable of metabolizing atrazine, a synthetic herbicide, and uses atrazine as a sole nitrogen source for growth. The microbe completely mineralizes the substrate in a catabolic pathway comprised of six enzymatic steps. All enzymes, AtzA-AtzF, encoded by corresponding genes, AtzA-AtzF, are located on a self-transmissible plasmid, pADP-1. (Souza, M. L., Wackett, L.P., and Sadowsky, M.J Appl. and Environ. Microbiol. 64(6): 2323-2326, 1998) RT-qPCR was used to differentiate gene expression in atrazine-degrading genes in Pseudomonas sp. strain ADP cells grown as suspended cells and as biofilms. Relative gene expression was also evaluated for biofilms grown at 25{degrees}C, 30{degrees}C, and 37{degrees}C. Complementary atrazine kinetic data was collected using GC-MS for both modes of growth and temperature variance. No significant difference in expression was observed for all atrazine-degrading genes in biofilm-mediated cells relative to planktonic cells, suggesting neither decreased or increased catabolic activity at the mRNA level. In contrasting experiments concerning biofilm growth, expression was downregulated at 37{degrees}C for genes AtzA, AtzB, and AtzC and upregulated for genes AtzD, AtzE, AtzF, signifying Pseudomonas sp. strain ADP biofilms catabolic activity may change in response to substantial temperature changes. Gradual decreases in atrazine concentration were apparent in cells grown in shake flasks, while biofilm-mediated cells showed transient increases and decreases in reactor effluent. The complex extracellular matrix components, quorum sensing, and genetic transfer may account for accumulation and rapid degradation of atrazine. The data collected suggest biofilm-mediated bioremediation may give insight into catabolic activity and atrazine degradation potential.\n\nImportanceAtrazine is the second most applied herbicide in the United States. It is applied to crops including sorghum, corn, and sugarcane to prevent the growth of broad-leaved weeds. Once used, it can permeate the soil and contaminate proximal groundwater sources, which provide drinking water for over 90-million people. The Environmental Protection Agency sets the maximum contaminant level at 3 parts per billion for atrazine in drinking water, however this is frequently exceeded in rural regions which presents a public safety concern. Atrazine is an endocrine disruptor compound and a suspected teratogen in humans and freshwater species, respectively. This research is significant in evaluating the use an atrazine-degrading strain, Pseudomonas sp. strain ADP, grown in a biofilm mode of growth to increase the degradation potential compared to suspended cells. Our results concerning expression and kinetics will aid the development of biofilm reactors for ex situ bioremediation and understanding environmental biofilms.

bioengineering↗

bioRxiv2018

Development of an on-chip detection of Zika virus and antibodies simultaneously using array of nanowells

Zika virus (ZIKV) infections are an emerging health pandemic of significant medical importance. ZIKV appeared recently in the Americas from Africa via the South Pacific. The current outbreak has garnered attention by exhibiting unique characteristics of devastating neurodevelopmental defects in newborns of infected pregnant women. Current guidelines for ZIKV diagnostics developed by the Center of Diseases Control and Prevention (CDC) consist of nucleic acid testing, plaque reduction neutralization test (PRNT), and a serologic test for IgM detection. To better accommodate and comply with these guidelines, we developed a simultaneous on-chip detection of ZIKV and anti-ZIKV antibodies using an array of nanowells. Using on-chip microengraving, we were able to detect anti-ZIKV antibodies and their immunoglobulin isotypes. In parallel, applying on-chip real-time PCR with epifluorescence microscopy, we were able to quantify ZIKV viral load as low as one copy. To test clinical samples of patients at the postconvalescent stage, we analyzed samples from 8 patients. The on-chip nanowells could effectively identify antibodies that reacted against ZIKV envelope protein and their isotypes with high sensitivity and specificity. The small sample requirement with high specificity and sensitivity and combined molecular and serological tests could potentially be very advantageous and beneficial in accurate detection of Zika infection for better disease monitoring and management.

bioengineering↗

bioRxiv2018

An optimized toolkit for precision base editing

CRISPR base editing is a potentially powerful technology that enables the creation of genetic mutations with single base pair resolution. By re-engineering both DNA and protein sequences, we developed a collection of constitutive and inducible base editing vector systems that dramatically improve the ease and efficiency by which single nucleotide variants can be created. This new toolkit is effective in a wide range of model systems, and provides a means for efficient in vivo somatic base editing.

bioengineering↗

bioRxiv2018

Skeletal Muscle Metrics on Clinical 18F- FDG PET/CT Predict Health Outcomes in Patients with Sarcoma

The aim of this study was to determine the association of measures of skeletal muscle determined from 18F-FDG PET/CT with health outcomes in patients with soft-tissue sarcoma. 14 patients (8 women and 6 men; mean age 66.5 years) with sarcoma had PET/CT examinations. On CTs of the abdomen and pelvis, skeletal muscle was segmented, and cross-sectional muscle area, muscle volume, and muscle attenuation were determined. Within the segmented muscle, intramuscular fat area, volume, and density were derived. On PET images the standardized uptake value (SUV) of muscle was determined. Regression analyses were conducted to determine the association between the imaging measures and health outcomes including overall survival (OS), local recurrence-free survival (LRFS), distant cancer recurrence (DCR), and major surgical complications (MSC). The association between imaging metrics and pre-therapy levels of serum C-reactive protein (CRP), creatinine, hemoglobin, and albumin was determined. Decreased volumetric muscle CT attenuation was associated with increased DCR. Increased PET SUV of muscle was associated with decreased OS and LRFS. Lower muscle SUV was associated with lower serum hemoglobin and albumin. Muscle measurements obtained on routine 18F-FDG PET/CT is associated with outcomes and serum hemoglobin and albumin in patients with sarcoma.

bioengineering↗

bioRxiv2018

Particle-templated emulsification for microfluidics-free digital biology

The compartmentalization of reactions in monodispersed droplets is valuable for applications across biology. However, the requirement of microfluidics to partition the sample into monodispersed droplets is a significant barrier that impedes implementation. Here, we introduce particle-templated emulsification, a method to encapsulate samples in monodispersed emulsions without microfluidics. By vortexing a mixture of hydrogel particles and sample solution, we encapsulate the sample in monodispersed emulsions that are useful for most droplet applications. We illustrate the method with ddPCR and single cell culture. The ability to encapsulate samples in monodispersed droplets without microfluidics should facilitate the implementation of compartmentalized reactions in biology.

bioengineering↗

bioRxiv2018

Glial cells in the heart? Replicating the diversity of the myocardium with low-cost 3D models

Excitation-contraction (EC) coupling in the heart has, until recently, been solely accredited to cardiomyocytes. The inherent complexities of the heart make it difficult to examine nonmuscle contributions to contraction in vivo, and conventional in vitro models fail to capture multiple features and cellular heterogeneity of the myocardium. Here, we report on the development of a 3D cardiac Tissue towards recapitulating the architecture and composition of native myocardium in vitro. Cells are encapsulated within micropatterned gelatin-based hydrogels formed via visible light photocrosslinking. This system enables spatial control of cardiac microarchitecture, perturbation of the cellular composition, and functional measures of EC coupling via video microscopy and a custom algorithm to quantify beat frequency and degree of coordination. To demonstrate the robustness of these tools and evaluate the impact of altered cell population densities on cardiac Tissues, contractility and cell morphology were assessed with the inclusion of exogenous non-myelinating Schwann cells (SCs). Results demonstrate that the addition of exogenous SCs alter cardiomyocyte EC, profoundly inhibiting the response to electrical pacing. Computational modeling of connexin-mediated coupling suggests that SCs impact cardiomyocyte resting potential and rectification following depolarization. Cardiac Tissues hold potential for examining the role of cellular heterogeneity in heart health, pathologies, and cellular therapies.

bioengineering↗

bioRxiv2018

Discovering novel calcineurin inhibitors through quantitative mapping of protein-peptide affinity landscapes

Transient, regulated binding of globular protein domains to Short Linear Motifs (SLiMs) in disordered regions of other proteins drives cellular signaling. Mapping the energy landscapes of these interactions is essential for deciphering and therapeutically perturbing signaling networks, but is challenging due to their weak affinities. We present a powerful technology, MRBLE-pep, that simultaneously quantifies protein binding to a library of peptides directly synthesized on beads containing unique spectral codes. Using computational modeling and MRBLE-pep, we systematically probe binding of calcineurin (CN), a conserved protein phosphatase essential for the immune response and target of immunosuppressants, to the PxIxIT SLiM. We establish that flanking residues and post- translational modifications critically contribute to PxIxIT-CN affinity, and discover CN-inhibitory peptides with unprecedented affinity and therapeutic potential. The quantitative measurements provided by this approach will improve computational modeling efforts, elucidate a broad range of weak protein-SLiM interactions, and revolutionize our understanding of signaling networks.

bioengineering↗

bioRxiv2018

Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentations

ObjectiveOverlapping measures are often utilized to quantify the similarity between two binary regions. However, modern segmentation algorithms output a probability or confidence map with continuous values in the zero-to-one interval. Moreover, these binary overlapping measures are biased to structures size. Addressing these challenges is the objective of this work.\n\nMethodsWe extend the definition of the classical Dice coefficient (DC) overlap to facilitate the direct comparison of a ground truth binary image with a probabilistic map. We call the extended method continuous Dice coefficient (cDC) and show that 1) cDC [≤]1 and cDC = 1 if-and-only-if the structures overlap is complete, and; 2) cDC is monotonically decreasing with the amount of overlap. We compare the classical DC and the cDC in a simulation of partial volume effects that incorporates segmentations of common targets for deep-brain-stimulation. Lastly, we investigate the cDC for an automatic segmentation of the subthalamic-nucleus.\n\nResultsPartial volume effect simulation on thalamus (large structure) resulted with DC and cDC averages (SD) of 0.98 (0.006) and 0.99 (0.001), respectively. For subthalamic-nucleus (small structure) DC and cDC were 0.86 (0.025) and 0.97 (0.006), respectively. The DC and cDC for automatic STN segmentation were 0.66 and 0.80, respectively.\n\nConclusionThe cDC is well defined for probabilistic segmentation, less biased to structures size and more robust to partial volume effects in comparison to DC. Significance: The proposed method facilitates a better evaluation of segmentation algorithms. As a better measurement tool, it opens the door for the development of better segmentation methods.

bioengineering↗

bioRxiv2018

Measuring Clinically Relevant Knee Motions With A Self-Calibrated Wearable Sensor

Low-cost sensors provide a unique opportunity to continuously monitor patient progress during rehabilitation; however, these sensors have yet to demonstrate the fidelity and lack the calibration paradigms necessary to be viable tools for clinical research. Therefore, the purpose of this study was to validate a low-cost wearable sensor that accurately measured peak knee extension during clinical exercises and needed no additional equipment for calibration. Knee flexion was quantified using a 9-axis motion sensor and directly compared to motion capture data. Peak extension values during seated knee extensions were accurate within 5 degrees across all subjects (RMS error: 2.6 degrees, P = 0.29) but less accurate during sit-to-stand exercises (RMS error: 16.6 degrees, P = 0.48). Knee flexion during gait strongly correlated (0.84 [≤] rxy [≤] 0.99) with motion capture measurements but demonstrated average errors of 10 degrees. This study demonstrated a low-cost sensor that satisfied our criteria: a simple calibration procedure resulting in accurate measures of joint function during clinical exercises, making it a feasible tool for continuous patient monitoring to guide regenerative rehabilitation.

bioengineering↗

bioRxiv2018

Deep learning achieves super-resolution in fluorescence microscopy

AbtsractWe present a deep learning-based method for achieving super-resolution in fluorescence microscopy. This data-driven approach does not require any numerical models of the imaging process or the estimation of a point spread function, and is solely based on training a generative adversarial network, which statistically learns to transform low resolution input images into super-resolved ones. Using this method, we super-resolve wide-field images acquired with low numerical aperture objective lenses, matching the resolution that is acquired using high numerical aperture objectives. We also demonstrate that diffraction-limited confocal microscopy images can be transformed by the same framework into super-resolved fluorescence images, matching the image resolution acquired with a stimulated emission depletion (STED) microscope. The deep network rapidly outputs these super-resolution images, without any iterations or parameter search, and even works for types of samples that it was not trained for.

bioengineering↗