Ribosomal trafficking is reduced in Schwann cells following induction of myelination.

Local synthesis of proteins within the Schwann cell periphery is extremely important for efficient process extension and myelination, when cells undergo dramatic changes in polarity and geometry. Still, it is unclear how ribosomal distributions are developed and maintained within Schwann cell projections to sustain local translation. In this multi-disciplinary study, we expressed a plasmid encoding a fluorescently labeled ribosomal subunit (L4-GFP) in cultured primary rat Schwann cells. This enabled the generation of high-resolution, quantitative data on ribosomal distributions and trafficking dynamics within Schwann cells during early stages of myelination, induced by ascorbic acid treatment. Ribosomes were distributed throughout Schwann cell projections, with ~2-3 bright clusters along each projection. Clusters emerged within 1 day of culture and were maintained throughout early stages of myelination. Three days after induction of myelination, net ribosomal movement remained anterograde (directed away from the Schwann cell body), but ribosomal velocity decreased to about half the levels of the untreated group. Statistical and modeling analysis provided additional insight into key factors underlying ribosomal trafficking. Multiple regression analysis indicated that net transport at early time points was dependent on anterograde velocity, but shifted to dependence on anterograde duration at later time points. A simple, data-driven rate kinetics model suggested that the observed decrease in net ribosomal movement was primarily dictated by an increased conversion of anterograde particles to stationary particles, rather than changes in other directional parameters. These results reveal the strength of a combined experimental and theoretical approach in examining protein localization and transport, and provide evidence of an early establishment of ribosomal populations within Schwann cell projections with a reduction in trafficking following initiation of myelination

Characterizing a Defect in a One-Dimensional Bar

We examine the inverse problem of locating and describing an internal point defect in a one-dimensional rod $\Omega$ by controlling the heat inputs and measuring the subsequent temperatures at the boundary of $\Omega$. We use a variation of the forward heat equation to model heat flow through $\Omega$, then propose algorithms for locating an internal defect and quantifying the effect the defect has on the heat flow. We implement these algorithms, analyze the stability of the procedures, and provide several computational examples

Quantitative Ultrasound and B-mode Image Texture Features Correlate with Collagen and Myelin Content in Human Ulnar Nerve Fascicles

We investigate the usefulness of quantitative ultrasound (QUS) and B-mode texture features for characterization of ulnar nerve fascicles. Ultrasound data were acquired from cadaveric specimens using a nominal 30 MHz probe. Next, the nerves were extracted to prepare histology sections. 85 fascicles were matched between the B-mode images and the histology sections. For each fascicle image, we selected an intra-fascicular region of interest. We used histology sections to determine features related to the concentration of collagen and myelin, and ultrasound data to calculate backscatter coefficient (-24.89 dB $\pm$ 8.31), attenuation coefficient (0.92 db/cm-MHz $\pm$ 0.04), Nakagami parameter (1.01 $\pm$ 0.18) and entropy (6.92 $\pm$ 0.83), as well as B-mode texture features obtained via the gray level co-occurrence matrix algorithm. Significant Spearman's rank correlations between the combined collagen and myelin concentrations were obtained for the backscatter coefficient (R=-0.68), entropy (R=-0.51), and for several texture features. Our study demonstrates that QUS may potentially provide information on structural components of nerve fascicles

Water Governance: Retheorizing Politics

This republished Special Issue highlights recent and emergent concepts and approaches to water governance that re-centers the political in relation to water-related decision making, use, and management. To do so at once is to focus on diverse ontologies, meanings and values of water, and related contestations regarding its use, or its importance for livelihoods, identity, or place-making. Building on insights from science and technology studies, feminist, and postcolonial approaches, we engage broadly with the ways that water-related decision making is often depoliticized and evacuated of political content or meaning—and to what effect. Key themes that emerged from the contributions include the politics of water infrastructure and insecurity; participatory politics and multi-scalar governance dynamics; politics related to emergent technologies of water (bottled or packaged water, and water desalination); and Indigenous water governance

CloudScent: a model for code smell analysis in open-source cloud

The low cost and rapid provisioning capabilities have made open-source cloud a desirable platform to launch industrial applications. However, as open-source cloud moves towards maturity, it still suffers from quality issues like code smells. Although, a great emphasis has been provided on the economic benefits of deploying open-source cloud, low importance has been provided to improve the quality of the source code of the cloud itself to ensure its maintainability in the industrial scenario. Code refactoring has been associated with improving the maintenance and understanding of software code by removing code smells. However, analyzing what smells are more prevalent in cloud environment and designing a tool to define and detect those smells require further attention. In this paper, we propose a model called CloudScent which is an open source mechanism to detect smells in open-source cloud. We test our experiments in a real-life cloud environment using OpenStack. Results show that CloudScent is capable of accurately detecting 8 code smells in cloud. This will permit cloud service providers with advanced knowledge about the smells prevalent in open-source cloud platform, thus allowing for timely code refactoring and improving code quality of the cloud platforms

The correlation between colposcopy, cervical cytology and histopathology in the diagnosis and management of cervical lesions: a cross sectional study

Background: The study was undertaken to see the correlation between cervical cytology, histopathology and colposcopy in the diagnosis and management of various cervical lesions.Methods: It is a cross sectional study conducted at a tertiary care hospital in Mumbai, in the department of obstetrics and gynecology from February 2007 to March 2008. A total 55 sexually active women were enrolled for the study who belonged to age group greater than 20 years with history of chronic leucorrhoea or post-coital bleeding/spotting, intermenstrual bleeding/spotting or examination findings of erosion, an unhealthy cervix, a lesion bleeding on touch or an abnormal or suspicious Papanicolaou smear. These women then underwent cytology, colposcopy and cervical biopsy.Results: The accuracy of cytology when compared to colposcopy was 81.82%. The accuracy of colpo-histopathology was 83.6%. The combined accuracy was 76.36%.Conclusions: The simultaneous use of cytological studies and screening colposcopy has been shown to increase the cervical cancer detection. Colposcopy offers an excellent tool in the hands of a gynaecologist to evaluate the uterine cervix and it is not possible to develop this kind of perspective by any other method

Knee menisci segmentation and relaxometry of 3D ultrashort echo time cones MR imaging using attention U-Net with transfer learning.

PurposeTo develop a deep learning-based method for knee menisci segmentation in 3D ultrashort echo time (UTE) cones MR imaging, and to automatically determine MR relaxation times, namely the T1, T1ρ , and T2∗ parameters, which can be used to assess knee osteoarthritis (OA).MethodsWhole knee joint imaging was performed using 3D UTE cones sequences to collect data from 61 human subjects. Regions of interest (ROIs) were outlined by 2 experienced radiologists based on subtracted T1ρ -weighted MR images. Transfer learning was applied to develop 2D attention U-Net convolutional neural networks for the menisci segmentation based on each radiologist's ROIs separately. Dice scores were calculated to assess segmentation performance. Next, the T1, T1ρ , T2∗ relaxations, and ROI areas were determined for the manual and automatic segmentations, then compared.ResultsThe models developed using ROIs provided by 2 radiologists achieved high Dice scores of 0.860 and 0.833, while the radiologists' manual segmentations achieved a Dice score of 0.820. Linear correlation coefficients for the T1, T1ρ , and T2∗ relaxations calculated using the automatic and manual segmentations ranged between 0.90 and 0.97, and there were no associated differences between the estimated average meniscal relaxation parameters. The deep learning models achieved segmentation performance equivalent to the inter-observer variability of 2 radiologists.ConclusionThe proposed deep learning-based approach can be used to efficiently generate automatic segmentations and determine meniscal relaxations times. The method has the potential to help radiologists with the assessment of meniscal diseases, such as OA