TomTom (Germany)

One of the problems for the clinician who desires to measure the interventricular septum (IVS) in a high-risk fetus is to know where to make the measurement. The purpose of this study was to use speckle-tracking analysis to measure the IVS area, 24-segment widths, and length at end-diastole (ED) and end-systole (ES) in normal fetuses. From the 4-chamber view, speckle-tracking analysis was performed at ED and ES on the IVS in 200 normal fetuses. The following were computed and regressed against gestational age (GA) and fetal biometric (FB) measurements: area, length, and the 24-segment transverse widths from the apex to the crux. The 24-segment width/length ratio was also measured. The speckle-tracking measurements of the ED area and length were compared using a point-to-point measurement tool available on all ultrasound machines. The ED and ES areas, lengths, and 24-segment widths increased with GA and FB. The ED and ES areas were virtually identical. The 24-segment width/length ratio decreased from the apex to the crux of the septum. There was no significant difference in the measurement of the ED area and the length between speckle-tracking and the point-to-point measurements. Measurement of the area and length of the IVS are simple to obtain and provide a new diagnostic tool to evaluate the fetus at risk for IVS hypertrophy which may be observed in fetuses of mothers with pregestational and gestational diabetes.

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008–2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.

Evaluate the frame-by-frame systolic area change (SAC) of the right (RV) and left (LV) ventricles imaged in the 4-chamber view (4CV) in fetuses with structural and/or functional pathology of the heart. Using offline proprietary software, the endocardium of the RV and LV was traced. The computer program identified the endocardial contour for each frame of the cardiac cycle and computed the area. The end-diastolic area was displayed on a bullet graphic as a blue contour. Subsequent frames of the cardiac cycle were displayed as a red contour. End-systole was identified as a red rectangle on the red contour diagram (see figure 1). Fetuses with suspected abnormal cardiac anatomy and/or function were compared to normal fetuses. Fetuses with abnormal cardiac function and/or anatomical defects demonstrated the following abnormal area contours when compared to normal fetuses: (1) decreased area during the cardiac cycle (RV and LV, figure 1A; RV, figure 1B; LV, figure 1C), (2) asymmetrical contours (RV, figure 1B; RV and LV, figure 1C), (3) increased area when compared to the end-diastolic area that was greater than the end-diastolic area (RV, figure 1A, 1B; LV, figure 1C), or (4) delayed peak systole (RV, figure 1B). Supporting information can be found in the online version of this abstract Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

We describe an XML file format for storing data from computations in algebra and geometry. We also present a formal specification based on a RELAX-NG schema.

The recent horse meat scandal in Europe increased the demand for optical sensors that can identify meat type. Micro-Raman spectroscopy is a promising technique for the discrimination of meat types. Here, we present micro-Raman measurements of chicken, pork, turkey, mutton, beef and horse meat test samples. The data was analyzed with different combinations of data normalization and classification approaches. Our results show that Raman spectroscopy can discriminate between different meat types. Red and white meat are easily discriminated, however a sophisticated chemometric model is required to discriminate species within these groups.