New magnetic-resonance-imaging-visible poly(epsilon-caprolactone)-based polyester for biomedical applications

A great deal of effort has been made since the 1990s to enlarge the field of magnetic resonance imaging. Better tissue contrast, more biocompatible contrast agents and the absence of any radiation for the patient are some of the many advantages of using magnetic resonance imaging (MRI) rather than X-ray technology. But implantable medical devices cannot be visualized by conventional MRI and a tool therefore needs to be developed to rectify this. The synthesis of a new MRI-visible degradable polymer is described by grafting an MR contrast agent (DTPA-Gd) to a non-water-soluble, biocompatible and degradable poly(epsilon-caprolactone) (PCL). The substitution degree, calculated by H-1 nuclear magnetic resonance and inductively coupled plasma-mass spectrometry, is close to 0.5% and proves to be sufficient to provide a strong and clear T1 contrast enhancement. This new MRI-visible polymer was coated onto a commercial mesh for tissue reinforcement using an airbrush system and enabled in vitro MR visualization of the mesh for at least 1 year. A stability study of the DTPA-Gd-PCL chelate in phosphate-buffered saline showed that a very low amount of gadolinium was released into the medium over 52 weeks, guaranteeing the safety of the device. This study shows that this new MRI-visible polymer has great potential for the MR visualization of implantable medical devices and therefore the post-operative management of patients. (C) 2011 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved

Marked long-term decline in ambient CO mixing ratio in SE England, 1997–2014:Evidence of policy success in improving air quality

Atmospheric CO at Egham in SE England has shown a marked and progressive decline since 1997, following adoption of strict controls on emissions. The Egham site is uniquely positioned to allow both assessment and comparison of ‘clean Atlantic background’ air and CO-enriched air downwind from the London conurbation. The decline is strongest (approximately 50ppb per year) in the 1997–2003 period but continues post 2003. A ‘local CO increment’ can be identified as the residual after subtraction of contemporary background Atlantic CO mixing ratios from measured values at Egham. This increment, which is primarily from regional sources (during anticyclonic or northerly winds) or from the European continent (with easterly air mass origins), has significant seasonality, but overall has declined steadily since 1997. On many days of the year CO measured at Egham is now not far above Atlantic background levels measured at Mace Head (Ireland). The results are consistent with MOPITT satellite observations and ‘bottom-up’ inventory results. Comparison with urban and regional background CO mixing ratios in Hong Kong demonstrates the importance of regional, as opposed to local reduction of CO emission. The Egham record implies that controls on emissions subsequent to legislation have been extremely successful in the UK

A web-based system for statistical shape analysis in temporomandibular joint osteoarthritis

This study presents a web-system repository: Data Storage for Computation and Integration (DSCI) for Osteoarthritis of the temporomandibular joint (TMJ OA). This environment aims to maintain and allow contributions to the database from multi-clinical centers and compute novel statistics for disease classification. For this purpose, imaging datasets stored in the DSCI consisted of three-dimensional (3D) surface meshes of condyles from CBCT, clinical markers and biological markers in healthy and TMJ OA subjects. A clusterpost package was included in the web platform to be able to execute the jobs in remote computing grids. The DSCI application allowed runs of statistical packages, such as the Multivariate Functional Shape Data Analysis to compute global correlations between covariates and the morphological variability, as well as local p-values in the 3D condylar morphology. In conclusion, the DSCI allows interactive advanced statistical tools for non-statistical experts

SVA: Shape variation analyzer

Temporo-mandibular osteo arthritis (TMJ OA) is characterized by progressive cartilage degradation and subchondral bone remodeling. The causes of this pathology remain unclear. Current research efforts are concentrated in finding new biomarkers that will help us understand disease progression and ultimately improve the treatment of the disease. In this work, we present Shape Variation Analyzer (SVA), the goal is to develop a noninvasive technique to provide information about shape changes in TMJ OA. SVA uses neural networks to classify morphological variations of 3D models of the mandibular condyle. The shape features used for training include normal vectors, curvature and distances to average models of the condyles. The selected features are purely geometric and are shown to favor the classification task into 6 groups generated by consensus between two clinician experts. With this new approach, we were able to accurately classify 3D models of condyles. In this paper, we present the methods used and the results obtained with this new tool

EFECTO DEL GENOTIPO ANIMAL SOBRE EL RENDIMIENTO DE LA RES Y CALIDAD DE LA CARNE DE NOVILLOS ALIMENTADOS SOBRE PASTURA Y SUPLEMENTADOS EN EL PERIODO INVERNAL

El estudio fue realizado con el objetivo de determinar el rendimiento de la res y caracterizar la calidad de carne de cuatro genotipos de bovino de carne. Fueron extraídos al azar cuatro cabezas de un total de 30 animales terminados por tratamiento, sometidos a similares condiciones de manejo, sanidad y alimentación, con edad promedio de 24 meses. Los tratamientos fueron: Criollo (Cr, Bos taurus adaptado), Indico (1, Bos indicus), Trihíbrido (Tr, Bos taurus adaptado x Bos indicus x Bos taurus continental) e Híbrido (H, Bos taurus ingles x Bos indicus). El Diseño experimental fue el de bloques completos al azar, los resultados obtenidos fueron sometidos a ANAVA, las diferencias fueron analizadas por el test de Tukey al 5%. Los resultados evidencian pesos superiores de los H y Tr (sin ayuno) con respecto al Cr, mientras con ayuno solamente el H fue estadísticamente superior al Cr. Los tratamientos H, Tre I fueron estadísticamente superiores en el rendimiento en peso de la res al Cr. Los rendimientos como porcentaje del peso vivo sin y con ayuno promediaron 52.3 y 56.3 %, resp. El desbaste promedio fué de 7 %. Los tratamientos H y Tr fueron superiores al Cr en relación al cuarto pistola corto y paleta+costillar, los mismos no arrojaron diferencia como porcentaje del peso vivo sin y con ayuno. El residuo blando y el residuo duro representan el 17.1 y e113.5 % del peso vivo sin ayuno. En cuanto a la calidad de carne los parámetros espesor de grasa y color de carne presentaron diferencias estadísticas, siendo el Cr superior a los demás tratamientos (6 mm y grado 5, resp.), no se encontró diferencias en los otros parámetros. El marmoreado obtenido fue pobre, aunque el color de carne y de grasa se cualificó como muy buena (Según Standard Japonés). Los pesos de cortes de lomito (Psoas mayor) y peceto (Semitendinosus) están en relación directa a los pesos vivos de los animales de cada tratamiento (H>Tr>I>Cr), encontrándose diferencia estadística solamente en el peceto entre H y Cr.

Diurnal, seasonal, and annual trends in atmospheric CO₂ at southwest London during 2000-2012:Wind sector analysis and comparison with Mace Head, Ireland

In-situ measurements of atmospheric CO have been made at Royal Holloway University of London (RHUL) in Egham (EGH), Surrey, UK from 2000 to 2012. The data were linked to the global scale using NOAA-calibrated gases. Measured CO varies on time scales that range from minutes to inter-annual and annual cycles. Seasonality and pollution episodes occur each year. Diurnal cycles vary with daylight and temperature, which influence the biological cycle of CO and the degree of vertical mixing. Anthropogenic emissions of CO dominate the variability during weekdays when transport cycles are greater than at weekends. Seasonal cycles are driven by temporal variations in biological activity and changes in combustion emissions. Maximum mole fractions (μmol/mol) (henceforth referred to by parts per million, ppm) occur in winter, with minima in late summer. The smallest seasonal amplitude observed, peak to trough, was 17.0ppm CO in 2003, whereas the largest amplitude observed was 27.1ppm CO in 2008.Meteorology can strongly modify the CO mole fractions at different time scales. Analysis of eight 45° wind sectors shows that the highest CO mole fractions were recorded from the E and SE sectors. Lowest mole fractions were observed for air masses from the S and SW. Back-trajectory and meteorological analyses of the data confirm that the dominant sources of CO are anthropogenic emissions from London and SE England. The largest annual rate of increase in the annual average of CO, 3.26ppmyr (

Decreased Axon Caliber Underlies Loss of Fiber Tract Integrity, Disproportional Reductions in White Matter Volume, and Microcephaly in Angelman Syndrome Model Mice

Angelman syndrome (AS) is a debilitating neurodevelopmental disorder caused by loss of function of the maternally inherited UBE3A allele. It is currently unclear how the consequences of this genetic insult unfold to impair neurodevelopment. We reasoned that by elucidating the basis of microcephaly in AS, a highly penetrant syndromic feature with early postnatal onset, we would gain new insights into the mechanisms by which maternal UBE3A loss derails neurotypical brain growth and function. Detailed anatomical analysis of both male and female maternal Ube3a-null mice reveals that microcephaly in the AS mouse model is primarily driven by deficits in the growth of white matter tracts, which by adulthood are characterized by densely packed axons of disproportionately small caliber. Our results implicate impaired axon growth in the pathogenesis of AS and identify noninvasive structural neuroimaging as a potentially valuable tool for gauging therapeutic efficacy in the disorder

Shape variation analyzer: A classifier for temporomandibular joint damaged by osteoarthritis

We developed a deep learning neural network, the Shape Variation Analyzer (SVA), that allows disease staging of bony changes in temporomandibular joint (TMJ) osteoarthritis (OA). The sample was composed of 259 TMJ CBCT scans for the training set and 34 for the testing dataset. The 3D meshes had been previously classified in 6 groups by 2 expert clinicians. We improved the robustness of the training data using data augmentation, SMOTE, to alleviate over-fitting and to balance classes. We combined geometrical features and a shape descriptor, heat kernel signature, to describe every shape. The results were compared to nine different supervised machine learning algorithms. The deep learning neural network was the most accurate for classification of TMJ OA. In conclusion, SVA is a 3D Slicer extension that classifies pathology of the temporomandibular joint osteoarthritis cases based on 3D morphology

A web-based system for neural network based classification in temporomandibular joint osteoarthritis

Objective: The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). Methods: This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA. For the image analysis classification, 34 right and left condyles from 17 patients (39.9 ± 11.7 years), who experienced signs and symptoms of the disease for less than 5 years, were included as the testing dataset. For the integrative statistical model of clinical, biological and imaging markers, the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects (39.4 ± 15.4 years), who did not show any sign or symptom of OA. For these 34 subjects, a standardized clinical questionnaire, blood and saliva samples were also collected. The technological methodologies in this study include a deep neural network classifier of 3D condylar morphology (ShapeVariationAnalyzer, SVA), and a flexible web-based system for data storage, computation and integration (DSCI) of high dimensional imaging, clinical, and biological data. Results: The DSCI system trained and tested the neural network, indicating 5 stages of structural degenerative changes in condylar morphology in the TMJ with 91% close agreement between the clinician consensus and the SVA classifier. The DSCI remotely ran with a novel application of a statistical analysis, the Multivariate Functional Shape Data Analysis, that computed high dimensional correlations between shape 3D coordinates, clinical pain levels and levels of biological markers, and then graphically displayed the computation results. Conclusions: The findings of this study demonstrate a comprehensive phenotypic characterization of TMJ health and disease at clinical, imaging and biological levels, using novel flexible and versatile open-source tools for a web-based system that provides advanced shape statistical analysis and a neural network based classification of temporomandibular joint osteoarthritis