Effect of age and cytoskeletal elements on the indentation-dependent mechanical properties of chondrocytes.

Articular cartilage chondrocytes are responsible for the synthesis, maintenance, and turnover of the extracellular matrix, metabolic processes that contribute to the mechanical properties of these cells. Here, we systematically evaluated the effect of age and cytoskeletal disruptors on the mechanical properties of chondrocytes as a function of deformation. We quantified the indentation-dependent mechanical properties of chondrocytes isolated from neonatal (1-day), adult (5-year) and geriatric (12-year) bovine knees using atomic force microscopy (AFM). We also measured the contribution of the actin and intermediate filaments to the indentation-dependent mechanical properties of chondrocytes. By integrating AFM with confocal fluorescent microscopy, we monitored cytoskeletal and biomechanical deformation in transgenic cells (GFP-vimentin and mCherry-actin) under compression. We found that the elastic modulus of chondrocytes in all age groups decreased with increased indentation (15-2000 nm). The elastic modulus of adult chondrocytes was significantly greater than neonatal cells at indentations greater than 500 nm. Viscoelastic moduli (instantaneous and equilibrium) were comparable in all age groups examined; however, the intrinsic viscosity was lower in geriatric chondrocytes than neonatal. Disrupting the actin or the intermediate filament structures altered the mechanical properties of chondrocytes by decreasing the elastic modulus and viscoelastic properties, resulting in a dramatic loss of indentation-dependent response with treatment. Actin and vimentin cytoskeletal structures were monitored using confocal fluorescent microscopy in transgenic cells treated with disruptors, and both treatments had a profound disruptive effect on the actin filaments. Here we show that disrupting the structure of intermediate filaments indirectly altered the configuration of the actin cytoskeleton. These findings underscore the importance of the cytoskeletal elements in the overall mechanical response of chondrocytes, indicating that intermediate filament integrity is key to the non-linear elastic properties of chondrocytes. This study improves our understanding of the mechanical properties of articular cartilage at the single cell level

Measurements of few-mode fiber photonic lanterns in emulated atmospheric conditions for a low earth orbit space to ground optical communication receiver application

Photonic lanterns are being evaluated as a component of a scalable photon counting real-time optical ground receiver for space-to-ground photon-starved communication applications. The function of the lantern as a component of a receiver is to efficiently couple and deliver light from the atmospherically distorted focal spot formed behind a telescope to multiple small-core fiber-coupled single-element super-conducting nanowire detectors. This architecture solution is being compared to a multimode fiber coupled to a multi-element detector array. This paper presents a set of measurements that begins this comparison. This first set of measurements are a comparison of the throughput coupling loss at emulated atmospheric conditions for the case of a 60 cm diameter telescope receiving light from a low earth orbit satellite. The atmospheric conditions are numerically simulated at a range of turbulence levels using a beam propagation method and are physically emulated with a spatial light modulator. The results show that for the same number of output legs as the single-mode fiber lantern, the few-mode fiber lantern increases the power throughput up to 3.92 dB at the worst emulated atmospheric conditions tested of D/r(sub 0)=8.6. Furthermore, the coupling loss of the few-mode fiber lantern approaches the capability of a 30 micron graded index multimode fiber chosen for coupling to a 16 element detector array

Capture and inception of bubbles near line vortices

Motivated by the need to predict vortex cavitation inception, a study has been conducted to investigate bubble capture by a concentrated line vortex of core size rcrc and circulation Γ0Γ0 under noncavitating and cavitating conditions. Direct numerical simulations that solve simultaneously for the two phase flow field, as well as a simpler one-way coupled point-particle-tracking model (PTM) were used to investigate the capture process. The capture times were compared to experimental observations. It was found that the point-particle-tracking model can successfully predict the capture of noncavitating small nuclei by a line vortex released far from the vortex axis. The nucleus grows very slowly during capture until the late stages of the process, where bubble/vortex interaction and bubble deformation become important. Consequently, PTM can be used to study the capture of cavitating nuclei by dividing the process into the noncavitating capture of the nucleus, and then the growth of the nucleus in the low-pressure core region. Bubble growth and deformation act to speed up the capture process.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87832/2/022105_1.pd

Nanocomposite Scaffold for Chondrocyte Growth and Cartilage Tissue Engineering: Effects of Carbon Nanotube Surface Functionalization

The goal of this study was to assess the long-term biocompatibility of single-wall carbon nanotubes (SWNTs) for tissue engineering of articular cartilage. We hypothesized that SWNT nanocomposite scaffolds in cartilage tissue engineering can provide an improved molecular-sized substrate for stimulation of chondrocyte growth, as well as structural reinforcement of the scaffold\u27s mechanical properties. The effect of SWNT surface functionalization (-COOH or -PEG) on chondrocyte viability and biochemical matrix deposition was examined in two-dimensional cultures, in three-dimensional (3D) pellet cultures, and in a 3D nanocomposite scaffold consisting of hydrogels + SWNTs. Outcome measures included cell viability, histological and SEM evaluation, GAG biochemical content, compressive and tensile biomechanical properties, and gene expression quantification, including extracellular matrix (ECM) markers aggrecan (Agc), collagen-1 (Col1a1), collagen-2 (Col2a1), collagen-10 (Col10a1), surface adhesion proteins fibronectin (Fn), CD44 antigen (CD44), and tumor marker (Tp53). Our findings indicate that chondrocytes tolerate functionalized SWNTs well, with minimal toxicity of cells in 3D culture systems (pellet and nanocomposite constructs). Both SWNT-PEG and SWNT-COOH groups increased the GAG content in nanocomposites relative to control. The compressive biomechanical properties of cell-laden SWNT-COOH nanocomposites were significantly elevated relative to control. Increases in the tensile modulus and ultimate stress were observed, indicative of a tensile reinforcement of the nanocomposite scaffolds. Surface coating of SWNTs with -COOH also resulted in increased Col2a1 and Fn gene expression throughout the culture in nanocomposite constructs, indicative of increased chondrocyte metabolic activity. In contrast, surface coating of SWNTs with a neutral -PEG moiety had no significant effect on Col2a1 or Fn gene expression, suggesting that the charged nature of the -COOH surface functionalization may promote ECM expression in this culture system. The results of this study indicate that SWNTs exhibit a unique potential for cartilage tissue engineering, where functionalization with bioactive molecules may provide an improved substrate for stimulation of cellular growth and repair

The Two-Phase Flow Separator Experiment Breadboard Model: Reduced Gravity Aircraft Results

Life support systems in space depend on the ability to effectively separate gas from liquid. Passive cyclonic phase separators use the centripetal acceleration of a rotating gas-liquid mixture to carry out phase separation. The gas migrates to the center, while gas-free liquid may be withdrawn from one of the end plates. We have designed, constructed and tested a breadboard that accommodates the test sections of two independent principal investigators and satisfies their respective requirements, including flow rates, pressure and video diagnostics. The breadboard was flown in the NASA low-gravity airplane in order to test the system performance and design under reduced gravity conditions

Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount

The precision of the two-layer cloud height fields derived from the Atmospheric Infrared Sounder (AIRS) is explored and quantified for a five-day set of observations. Coincident profiles of vertical cloud structure by CloudSat, a 94 GHz profiling radar, and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), are compared to AIRS for a wide range of cloud types. Bias and variability in cloud height differences are shown to have dependence on cloud type, height, and amount, as well as whether CloudSat or CALIPSO is used as the comparison standard. The CloudSat-AIRS biases and variability range from −4.3 to 0.5±1.2–3.6 km for all cloud types. Likewise, the CALIPSO-AIRS biases range from 0.6–3.0±1.2–3.6 km (−5.8 to −0.2±0.5–2.7 km) for clouds ≥7 km (<7 km). The upper layer of AIRS has the greatest sensitivity to Altocumulus, Altostratus, Cirrus, Cumulonimbus, and Nimbostratus, whereas the lower layer has the greatest sensitivity to Cumulus and Stratocumulus. Although the bias and variability generally decrease with increasing cloud amount, the ability of AIRS to constrain cloud occurrence, height, and amount is demonstrated across all cloud types for many geophysical conditions. In particular, skill is demonstrated for thin Cirrus, as well as some Cumulus and Stratocumulus, cloud types infrared sounders typically struggle to quantify. Furthermore, some improvements in the AIRS Version 5 operational retrieval algorithm are demonstrated. However, limitations in AIRS cloud retrievals are also revealed, including the existence of spurious Cirrus near the tropopause and low cloud layers within Cumulonimbus and Nimbostratus clouds. Likely causes of spurious clouds are identified and the potential for further improvement is discussed

Hierarchical reinforcement learning for efficient and effective automated penetration testing of large networks

Penetration testing (PT) is a method for assessing and evaluating the security of digital assets by planning, generating, and executing possible attacks that aim to discover and exploit vulnerabilities. In large networks, penetration testing becomes repetitive, complex and resource consuming despite the use of automated tools. This paper investigates reinforcement learning (RL) to make penetration testing more intelligent, targeted, and efficient. The proposed approach called Intelligent Automated Penetration Testing Framework (IAPTF) utilizes model-based RL to automate sequential decision making. Penetration testing tasks are treated as a partially observed Markov decision process (POMDP) which is solved with an external POMDP-solver using different algorithms to identify the most efficient options. A major difficulty encountered was solving large POMDPs resulting from large networks. This was overcome by representing networks hierarchically as a group of clusters and treating each cluster separately. This approach is tested through simulations of networks of various sizes. The results show that IAPTF with hierarchical network modeling outperforms previous approaches as well as human performance in terms of time, number of tested vectors and accuracy, and the advantage increases with the network size. Another advantage of IAPTF is the ease of repetition for retesting similar networks, which is often encountered in real PT. The results suggest that IAPTF is a promising approach to offload work from and ultimately replace human pen testing

Evaluation of Turbulence Models Performance in Predicting Incipient Cavitation in an Enlarged Step-Nozzle

Predictive capability of RANS and LES models to calculate incipient cavitation of water in a step nozzle is assessed. The RANS models namely, Realizable k-?, SST k-? and Reynolds Stress Model did not predict any cavitation, due to the limitation of RANS models to predict the low pressure vortex cores. LES WALE model was able to predict the cavitation by capturing the shear layer instability and vortex shedding. The performance of a barotropic cavitation model and Rayleigh-Plesset-based cavitation models was compared using WALE model. Although the phase change formulation is different in these models, the predicted cavitation and flow field were not significantly different

Measurements of Few-Mode Fiber Photonic Lanterns in Emulated Atmospheric Conditions for a Low Earth Orbit Space to Ground Optical Communication Receiver Application

Photonic lanterns are being evaluated as a component of a scalable photon counting real-time optical ground receiver for space-to-ground photon-starved communication applications. The function of the lantern as a component of a receiver is to efficiently couple and deliver light from the atmospherically distorted focal spot formed behind a telescope to multiple small-core fiber-coupled single-element super-conducting nanowire detectors. This architecture solution is being compared to a multimode fiber coupled to a multi-element detector array. This paper presents a set of measurements that begins this comparison. This first set of measurements are a comparison of the throughput coupling loss at emulated atmospheric conditions for the case of a 60 cm diameter telescope receiving light from a low earth orbit satellite. The atmospheric conditions are numerically simulated at a range of turbulence levels using a beam propagation method and are physically emulated with a spatial light modulator. The results show that for the same number of output legs as the single-mode fiber lantern, the few mode fiber lantern increases the power throughput up to 3.92 dB at the worst emulated atmospheric conditions tested of D/r0=8.6. Furthermore, the coupling loss of the few mode fiber lantern approaches the capability of a 30 micron graded index multimode fiber chosen for coupling to a 16 element detector array

Applications of artificial intelligence to prostate multiparametric MRI (mpMRI): Current and emerging trends

Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists\u27 accuracy and speed