DNA deformability changes of single base pair mutants within CDE binding sites in S. Cerevisiae centromere DNA correlate with measured chromosomal loss rates and CDE binding site symmetries

BACKGROUND: The centromeres in yeast (S. cerevisiae) are organized by short DNA sequences (125 bp) on each chromosome consisting of 2 conserved elements: CDEI and CDEIII spaced by a CDEII region. CDEI and CDEIII are critical sequence specific protein binding sites necessary for correct centromere formation and following assembly with proteins, are positioned near each other on a specialized nucleosome. Hegemann et al. BioEssays 1993, 15: 451–460 reported single base DNA mutants within the critical CDEI and CDEIII binding sites on the centromere of chromosome 6 and quantitated centromere loss of function, which they measured as loss rates for the different chromosome 6 mutants during cell division. Olson et al. Proc Natl Acad Sci USA 1998, 95: 11163–11168 reported the use of protein-DNA crystallography data to produce a DNA dinucleotide protein deformability energetic scale (PD-scale) that describes local DNA deformability by sequence specific binding proteins. We have used the PD-scale to investigate the DNA sequence dependence of the yeast chromosome 6 mutants' loss rate data. Each single base mutant changes 2 PD-scale values at that changed base position relative to the wild type. In this study, we have utilized these mutants to demonstrate a correlation between the change in DNA deformability of the CDEI and CDEIII core sites and the overall experimentally measured chromosome loss rates of the chromosome 6 mutants. RESULTS: In the CDE I and CDEIII core binding regions an increase in the magnitude of change in deformability of chromosome 6 single base mutants with respect to the wild type correlates to an increase in the measured chromosome loss rate. These correlations were found to be significant relative to 10(5 )Monte Carlo randomizations of the dinucleotide PD-scale applied to the same calculation. A net loss of deformability also tends to increase the loss rate. Binding site position specific, 4 data-point correlations were also created using the wild type sequence and the 3 associated alternate base mutants at each binding site position. These position specific slope magnitudes, or sensitivities, correlated with and reflected the underlying position symmetry of the DNA binding sequences. CONCLUSION: These results suggest the utility of correlating quantitative aspects of sequence specific protein-DNA complex single base mutants with changes in the easily calculated PD-deformability scale of the individual DNA sequence mutants. Using this PD approach, it may be possible in the future to understand the magnitude of biological or energetic functional effects of specific DNA sequence mutants within DNA-protein complexes in terms of their effect on DNA deformability

Mixing layer height derived from radiosoundings and ground-based lidar - comparison and assessment

The inversion on top of the atmospheric boundary layer is a strong barrier for the transport of heat, momentum and matter from or to the earth's surface. Regarding aerosols and gaseous constituents like water vapour which originate from the surface, the concentration of those parameters within the boundary layer strongly depends on the height of the layer, the mixing height. During daytime the mixing height over land increases and reaches a maximum value in situations with constant synoptic conditions. In many applications, e.g. the comparison of model output with observations the mixing height is taken from radiosoundings. Often this value is - due to lack of other measurements - also taken as the height of the fully developed convective boundary layer. Since the mixing height is strongly varying both in time and space an observation along a single line like a radiosonde track represents only an estimate of the mixing height. Quasi-continuous measurements of the backscatter signal with a ground-based lidar from several field campains offer the opportunity to estimate the error associated with a mixing-height determination from radiosoundings. A method to determine the mixing height from the backscattered signal is presented. Data from several field campaigns are used, namely the Nauru99 campaign in the tropical western Pacific in June/July 1999, three campaigns at the ARM-site in Oklahoma (USA) in September/October 1999, September/October 2000 and November/December 2000 and three campaigns in the frame of the German EVA-GRIPS (Regional Evaporation at Grid/Pixel Scale) project near Lindenberg (Brandenburg, Germany) in September 2002, April 2003 and May/June 2003 (LITFASS-2003). From these campaigns measurements simultaneous to approximatly 50 radiosoundings exist and allow a statistical analysis of the results. The comparison of radiosonde mixing heights with lidar mixing heights over 10 min time intervalls reveal a good agreement, the better the shorter the distance between radiosonde launch point and lidar location. Lidar mixing heights averaged over 1 h, which are more representative for an area, may deviate up to 300 m from radiosonde mixing heights. The standard deviation within the averaging interval fairly represents the variability of the mixing layer height. The maximum mixing height which is given by an afternoon radiosounding is also compared with lidar measurements

Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks

Deep learning requires large labeled datasets that are difficult to gather in medical imaging due to data privacy issues and time-consuming manual labeling. Generative Adversarial Networks (GANs) can alleviate these challenges enabling synthesis of shareable data. While 2D GANs have been used to generate 2D images with their corresponding labels, they cannot capture the volumetric information of 3D medical imaging. 3D GANs are more suitable for this and have been used to generate 3D volumes but not their corresponding labels. One reason might be that synthesizing 3D volumes is challenging owing to computational limitations. In this work, we present 3D GANs for the generation of 3D medical image volumes with corresponding labels applying mixed precision to alleviate computational constraints. We generated 3D Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) patches with their corresponding brain blood vessel segmentation labels. We used four variants of 3D Wasserstein GAN (WGAN) with: 1) gradient penalty (GP), 2) GP with spectral normalization (SN), 3) SN with mixed precision (SN-MP), and 4) SN-MP with double filters per layer (c-SN-MP). The generated patches were quantitatively evaluated using the Fréchet Inception Distance (FID) and Precision and Recall of Distributions (PRD). Further, 3D U-Nets were trained with patch-label pairs from different WGAN models and their performance was compared to the performance of a benchmark U-Net trained on real data. The segmentation performance of all U-Net models was assessed using Dice Similarity Coefficient (DSC) and balanced Average Hausdorff Distance (bAVD) for a) all vessels, and b) intracranial vessels only. Our results show that patches generated with WGAN models using mixed precision (SN-MP and c-SN-MP) yielded the lowest FID scores and the best PRD curves. Among the 3D U-Nets trained with synthetic patch-label pairs, c-SN-MP pairs achieved the highest DSC (0.841) and lowest bAVD (0.508) compared to the benchmark U-Net trained on real data (DSC 0.901; bAVD 0.294) for intracranial vessels. In conclusion, our solution generates realistic 3D TOF-MRA patches and labels for brain vessel segmentation. We demonstrate the benefit of using mixed precision for computational efficiency resulting in the best-performing GAN-architecture. Our work paves the way towards sharing of labeled 3D medical data which would increase generalizability of deep learning models for clinical use

Toward sharing brain images: Differentially private TOF-MRA images with segmentation labels using generative adversarial networks

Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging, this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been shown to be partially reversible. Here, synthetic data using a Generative Adversarial Network (GAN) with differential privacy guarantees could be a solution to ensure the patient's privacy while maintaining the predictive properties of the data. In this study, we implemented a Wasserstein GAN (WGAN) with and without differential privacy guarantees to generate privacy-preserving labeled Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) image patches for brain vessel segmentation. The synthesized image-label pairs were used to train a U-net which was evaluated in terms of the segmentation performance on real patient images from two different datasets. Additionally, the Fréchet Inception Distance (FID) was calculated between the generated images and the real images to assess their similarity. During the evaluation using the U-Net and the FID, we explored the effect of different levels of privacy which was represented by the parameter ϵ. With stricter privacy guarantees, the segmentation performance and the similarity to the real patient images in terms of FID decreased. Our best segmentation model, trained on synthetic and private data, achieved a Dice Similarity Coefficient (DSC) of 0.75 for ϵ = 7.4 compared to 0.84 for ϵ = ∞ in a brain vessel segmentation paradigm (DSC of 0.69 and 0.88 on the second test set, respectively). We identified a threshold of ϵ <5 for which the performance (DSC <0.61) became unstable and not usable. Our synthesized labeled TOF-MRA images with strict privacy guarantees retained predictive properties necessary for segmenting the brain vessels. Although further research is warranted regarding generalizability to other imaging modalities and performance improvement, our results mark an encouraging first step for privacy-preserving data sharing in medical imaging

Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks

Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging, this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been shown to be partially reversible. Here, synthetic data using a Generative Adversarial Network (GAN) with differential privacy guarantees could be a solution to ensure the patient's privacy while maintaining the predictive properties of the data. In this study, we implemented a Wasserstein GAN (WGAN) with and without differential privacy guarantees to generate privacy-preserving labeled Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) image patches for brain vessel segmentation. The synthesized image-label pairs were used to train a U-net which was evaluated in terms of the segmentation performance on real patient images from two different datasets. Additionally, the Fréchet Inception Distance (FID) was calculated between the generated images and the real images to assess their similarity. During the evaluation using the U-Net and the FID, we explored the effect of different levels of privacy which was represented by the parameter ϵ. With stricter privacy guarantees, the segmentation performance and the similarity to the real patient images in terms of FID decreased. Our best segmentation model, trained on synthetic and private data, achieved a Dice Similarity Coefficient (DSC) of 0.75 for ϵ = 7.4 compared to 0.84 for ϵ = ∞ in a brain vessel segmentation paradigm (DSC of 0.69 and 0.88 on the second test set, respectively). We identified a threshold of ϵ <5 for which the performance (DSC <0.61) became unstable and not usable. Our synthesized labeled TOF-MRA images with strict privacy guarantees retained predictive properties necessary for segmenting the brain vessels. Although further research is warranted regarding generalizability to other imaging modalities and performance improvement, our results mark an encouraging first step for privacy-preserving data sharing in medical imaging

Wearable devices can predict the outcome of standardized 6-minute walk tests in heart disease

Wrist-worn devices with heart rate monitoring have become increasingly popular. Although current guidelines advise to consider clinical symptoms and exercise tolerance during decision-making in heart disease, it remains unknown to which extent wearables can help to determine such functional capacity measures. In clinical settings, the 6-minute walk test has become a standardized diagnostic and prognostic marker. We aimed to explore, whether 6-minute walk distances can be predicted by wrist-worn devices in patients with different stages of mitral and aortic valve disease. A total of n = 107 sensor datasets with 1,019,748 min of recordings were analysed. Based on heart rate recordings and literature information, activity levels were determined and compared to results from a 6-minute walk test. The percentage of time spent in moderate activity was a predictor for the achievement of gender, age and body mass index-specific 6-minute walk distances (p < 0.001; R2 = 0.48). The uncertainty of these predictions is demonstrated

Net precipitation over the Baltic Sea for one year using several methods

Precipitation and evaporation over the Baltic Sea are calculated for a one-year period from September 1998 to August 1999 by four different tools, the two atmospheric regional models HIRLAM and REMO, the oceanographic model PROBE-Baltic in combination with the SMHI (1 × 1)° database and Interpolated Fields, based essentially on ship measurements. The investigated period is slightly warmer and wetter than the climatological mean. Correlation coefficients of the differently calculated latent heat fluxes vary between 0.81 (HIRLAM and REMO) and 0.56 (SMHI/PROBE-Baltic and Interpolated Fields), while the correlation coefficients between model fluxes and measured fluxes range from 0.61 and 0.78. Deviations of simulated and interpolated monthly precipitation over the Baltic Sea are less than ±5 mm in the southern Baltic and up to 20 mm near the Finnish coast for the one-year period. The methods simulate the annual cycle of precipitation and evaporation of the Baltic Proper in a similar manner with a broad maximum of net precipitation in spring and early summer and a minimum in late summer. The annual averages of net precipitation of the Baltic Proper range from 57 mm (REMO) to 262 mm (HIRLAM) and for the Baltic Sea from 96 mm (SMHI/PROBE-Baltic) to 209 mm (HIRLAM). This range is considered to give the uncertainty of present-day determination of the net precipitation over the Baltic Sea