Learning complex texture discrimination.

Higher-order spatial correlations contribute strongly to visual structure and salience, and are common in the natural environment. One method for studying this structure has been through the use of highly controlled texture patterns whose obvious structure is defined entirely by third- and higher-order correlations. Here we examine the effects that longer-term training has on discrimination of 17 such texture types. Training took place in 14 sessions over 42 days. Discrimination performance increased at different rates for different textures. The time required to complete a visit reduced by 25.4% (p=0.0004). Factor analysis was applied to data from the learning and experienced phases of the experiment. This indicated that the gain in speed was accompanied by an increase in the number of mechanisms contributing to discrimination. Learning was not affected by sleep quality but was affected by extreme tiredness (p<0.01). The improved discrimination and speed were retained for 2.5 months. Overall, the effects were consistent with perceptual learning. The observed learning is likely related to the adaptation of innate mechanisms that underlie our ability to identify nonredundant, visually salient structure in natural images. It may involve cortical V2 and appears to involve increased strength, speed, and breadth of connections within our internal representation of this complex perceptual space.

Assessing the Effects of Software Platforms on Volumetric Segmentation of Glioblastoma.

BACKGROUND: Radiological assessments of biologically relevant regions in glioblastoma have been associated with genotypic characteristics, implying a potential role in personalized medicine. Here, we assess the reproducibility and association with survival of two volumetric segmentation platforms and explore how methodology could impact subsequent interpretation and analysis. METHODS: Post-contrast T1- and T2-weighted FLAIR MR images of 67 TCGA patients were segmented into five distinct compartments (necrosis, contrast-enhancement, FLAIR, post contrast abnormal, and total abnormal tumor volumes) by two quantitative image segmentation platforms - 3D Slicer and a method based on Velocity AI and FSL. We investigated the internal consistency of each platform by correlation statistics, association with survival, and concordance with consensus neuroradiologist ratings using ordinal logistic regression. RESULTS: We found high correlations between the two platforms for FLAIR, post contrast abnormal, and total abnormal tumor volumes (spearman's r(67) = 0.952, 0.959, and 0.969 respectively). Only modest agreement was observed for necrosis and contrast-enhancement volumes (r(67) = 0.693 and 0.773 respectively), likely arising from differences in manual and automated segmentation methods of these regions by 3D Slicer and Velocity AI/FSL, respectively. Survival analysis based on AUC revealed significant predictive power of both platforms for the following volumes: contrast-enhancement, post contrast abnormal, and total abnormal tumor volumes. Finally, ordinal logistic regression demonstrated correspondence to manual ratings for several features. CONCLUSION: Tumor volume measurements from both volumetric platforms produced highly concordant and reproducible estimates across platforms for general features. As automated or semi-automated volumetric measurements replace manual linear or area measurements, it will become increasingly important to keep in mind that measurement differences between segmentation platforms for more detailed features could influence downstream survival or radio genomic analyses.

Design and internal validation of an obstetric early warning score: secondary analysis of the Intensive Care National Audit and Research Centre Case Mix Programme database.

We designed and internally validated an aggregate weighted early warning scoring system specific to the obstetric population that has the potential for use in the ward environment. Direct obstetric admissions from the Intensive Care National Audit and Research Centre's Case Mix Programme Database were randomly allocated to model development (n = 2240) or validation (n = 2200) sets. Physiological variables collected during the first 24 h of critical care admission were analysed. Logistic regression analysis for mortality in the model development set was initially used to create a statistically based early warning score. The statistical score was then modified to create a clinically acceptable early warning score. Important features of this clinical obstetric early warning score are that the variables are weighted according to their statistical importance, a surrogate for the FI O2 /Pa O2 relationship is included, conscious level is assessed using a simplified alert/not alert variable, and the score, trigger thresholds and response are consistent with the new non-obstetric National Early Warning Score system. The statistical and clinical early warning scores were internally validated using the validation set. The area under the receiver operating characteristic curve was 0.995 (95% CI 0.992-0.998) for the statistical score and 0.957 (95% CI 0.923-0.991) for the clinical score. Pre-existing empirically designed early warning scores were also validated in the same way for comparison. The area under the receiver operating characteristic curve was 0.955 (95% CI 0.922-0.988) for Swanton et al.'s Modified Early Obstetric Warning System, 0.937 (95% CI 0.884-0.991) for the obstetric early warning score suggested in the 2003-2005 Report on Confidential Enquiries into Maternal Deaths in the UK, and 0.973 (95% CI 0.957-0.989) for the non-obstetric National Early Warning Score. This highlights that the new clinical obstetric early warning score has an excellent ability to discriminate survivors from non-survivors in this critical care data set. Further work is needed to validate our new clinical early warning score externally in the obstetric ward environment.

Effect of table tilt on ligamentum flavum length measured using ultrasonography in pregnant women*.

The purpose of this study was to assess whether applying dorsal table tilt to a seated parturient positioned for neuraxial anaesthesia (i.e. facing laterally with her legs over the side of the table) increased the size of the target area for the neuraxial needle. We performed lumbar ultrasonography on 20 pregnant women with the table level (0°) and tilted 8° and 15° dorsally. For each position, the size of the 'target area', defined as the visible length of the ligamentum flavum in the longitudinal paramedian view, and the interlaminar distance, were measured at the L3-4 interspace. There were significant increases in the mean (SD) size of the target area with increasing table tilt (10.7 (1.3) mm at 0°, 11.5 (1.3) mm at 8° and 12.2 (1.7) mm at 15°; p < 0.0001). Interlaminar distances were not significantly increased. These data show that using dorsal table tilt in obstetric patients increases the target area for neuraxial needles. Additional research is required to assess whether this eases the placement of neuraxial needles in clinical practice.

Gas chromatographic instrumentation for the analysis of aerosols and gases in Titan's atmosphere.

During the next decade or so, NASA, in conjunction with the European Space Agency, plans to send a spacecraft to the Saturnian system so that local studies of Saturn and its satellite, Titan, can be made. In order to study the atmosphere of Titan, analysis of both aerosols and gases will have to be made. To accomplish this, gas chromatographic instrumentation for the collection and analysis of organic gases and aerosols in Titan's atmosphere is being developed. The aerosols will be collected and then subjected to pyrolysis-gas chromatography. Results using a simple pyrolysis-GC system and tholin, made by subjecting a nominal Titan mixture (96.8% N2, 3% CH4, 0.2% H2) to laser-supported shocks, show that many compounds, including hydrocarbons and simple nitriles, can be identified by this technique. Atmospheric gases will be collected using large volume (>10 cm3) sample loops and then analyzed by gas chromatography. Large volume samples are required because the ambient pressures, where the probe instruments are first deployed, will be low (<10 mbar). Preliminary studies using a 20 cm3 sampling system and a very sensitive meta-stable ionization detector show that hydrocarbon components at the 10 ppb level can be detected. Work will continue to improve GC sensitivity, minimize analysis time, and develop interfaces with suitable sample collectors for analysis of atmospheres by future spacecraft.