Identification of the DNA methylation signature of Mowat-Wilson syndrome.

Mowat-Wilson syndrome (MOWS) is a rare congenital disease caused by haploinsufficiency of ZEB2, encoding a transcription factor required for neurodevelopment. MOWS is characterized by intellectual disability, epilepsy, typical facial phenotype and other anomalies, such as short stature, Hirschsprung disease, brain and heart defects. Despite some recognizable features, MOWS rarity and phenotypic variability may complicate its diagnosis, particularly in the neonatal period. In order to define a novel diagnostic biomarker for MOWS, we determined the genome-wide DNA methylation profile of DNA samples from 29 individuals with confirmed clinical and molecular diagnosis. Through multidimensional scaling and hierarchical clustering analysis, we identified and validated a DNA methylation signature involving 296 differentially methylated probes as part of the broader MOWS DNA methylation profile. The prevalence of hypomethylated CpG sites agrees with the main role of ZEB2 as a transcriptional repressor, while differential methylation within the ZEB2 locus supports the previously proposed autoregulation ability. Correlation studies compared the MOWS cohort with 56 previously described DNA methylation profiles of other neurodevelopmental disorders, further validating the specificity of this biomarker. In conclusion, MOWS DNA methylation signature is highly sensitive and reproducible, providing a useful tool to facilitate diagnosis.

AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing.

The application of data science in cancer research has been boosted by major advances in three primary areas: (1) Data: diversity, amount, and availability of biomedical data; (2) Advances in Artificial Intelligence (AI) and Machine Learning (ML) algorithms that enable learning from complex, large-scale data; and (3) Advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build in silico ML models that can provide transformative insights from data including: molecular dynamics simulations, next-generation sequencing, omics, imaging, and unstructured clinical text documents. Unique challenges persist, however, in building ML models related to cancer, including: (1) access, sharing, labeling, and integration of multimodal and multi-institutional data across different cancer types; (2) developing AI models for cancer research capable of scaling on next generation high performance computers; and (3) assessing robustness and reliability in the AI models. In this paper, we review the National Cancer Institute (NCI) -Department of Energy (DOE) collaboration, Joint Design of Advanced Computing Solutions for Cancer (JDACS4C), a multi-institution collaborative effort focused on advancing computing and data technologies to accelerate cancer research on three levels: molecular, cellular, and population. This collaboration integrates various types of generated data, pre-exascale compute resources, and advances in ML models to increase understanding of basic cancer biology, identify promising new treatment options, predict outcomes, and eventually prescribe specialized treatments for patients with cancer.

Development of a Transformation Method for Metschnikowia borealis and other CUG-Serine Yeasts.

Yeasts belonging to the Metschnikowia genus are particularly interesting for the unusual formation of only two needle-shaped ascospores during their mating cycle. Presently, the meiotic process that can lead to only two spores from a diploid zygote is poorly understood. The expression of fluorescent nuclear proteins should allow the meiotic process to be visualized in vivo; however, no large-spored species of Metschnikowia has ever been transformed. Accordingly, we aimed to develop a transformation method for Metschnikowiaborealis, a particularly large-spored species of Metschnikowia, with the goal of enabling the genetic manipulations required to study biological processes in detail. Genetic analyses confirmed that M. borealis, and many other Metschnikowia species, are CUG-Ser yeasts. Codon-optimized selectable markers lacking CUG codons were used to successfully transform M. borealis by electroporation and lithium acetate, and transformants appeared to be the result of random integration. Mating experiments confirmed that transformed-strains were capable of generating large asci and undergoing recombination. Finally, random integration was used to transform an additional 21 yeast strains, and all attempts successfully generated transformants. The results provide a simple method to transform many yeasts from an array of different clades and can be used to study or develop many species for various applications.

Simultaneous analysis and quality assurance for diffusion tensor imaging.

Diffusion tensor imaging (DTI) enables non-invasive, cyto-architectural mapping of in vivo tissue microarchitecture through voxel-wise mathematical modeling of multiple magnetic resonance imaging (MRI) acquisitions, each differently sensitized to water diffusion. DTI computations are fundamentally estimation processes and are sensitive to noise and artifacts. Despite widespread adoption in the neuroimaging community, maintaining consistent DTI data quality remains challenging given the propensity for patient motion, artifacts associated with fast imaging techniques, and the possibility of hardware changes/failures. Furthermore, the quantity of data acquired per voxel, the non-linear estimation process, and numerous potential use cases complicate traditional visual data inspection approaches. Currently, quality inspection of DTI data has relied on visual inspection and individual processing in DTI analysis software programs (e.g. DTIPrep, DTI-studio). However, recent advances in applied statistical methods have yielded several different metrics to assess noise level, artifact propensity, quality of tensor fit, variance of estimated measures, and bias in estimated measures. To date, these metrics have been largely studied in isolation. Herein, we select complementary metrics for integration into an automatic DTI analysis and quality assurance pipeline. The pipeline completes in 24 hours, stores statistical outputs, and produces a graphical summary quality analysis (QA) report. We assess the utility of this streamlined approach for empirical quality assessment on 608 DTI datasets from pediatric neuroimaging studies. The efficiency and accuracy of quality analysis using the proposed pipeline is compared with quality analysis based on visual inspection. The unified pipeline is found to save a statistically significant amount of time (over 70%) while improving the consistency of QA between a DTI expert and a pool of research associates. Projection of QA metrics to a low dimensional manifold reveal qualitative, but clear, QA-study associations and suggest that automated outlier/anomaly detection would be feasible.

Correcting power and p-value calculations for bias in diffusion tensor imaging.

Diffusion tensor imaging (DTI) provides quantitative parametric maps sensitive to tissue microarchitecture (e.g., fractional anisotropy, FA). These maps are estimated through computational processes and subject to random distortions including variance and bias. Traditional statistical procedures commonly used for study planning (including power analyses and p-value/alpha-rate thresholds) specifically model variability, but neglect potential impacts of bias. Herein, we quantitatively investigate the impacts of bias in DTI on hypothesis test properties (power and alpha-rate) using a two-sided hypothesis testing framework. We present theoretical evaluation of bias on hypothesis test properties, evaluate the bias estimation technique SIMEX for DTI hypothesis testing using simulated data, and evaluate the impacts of bias on spatially varying power and alpha rates in an empirical study of 21 subjects. Bias is shown to inflame alpha rates, distort the power curve, and cause significant power loss even in empirical settings where the expected difference in bias between groups is zero. These adverse effects can be attenuated by properly accounting for bias in the calculation of power and p-values.

Assessment of bias in experimentally measured diffusion tensor imaging parameters using SIMEX.

Diffusion tensor imaging enables in vivo investigation of tissue cytoarchitecture through parameter contrasts sensitive to water diffusion barriers at the micrometer level. Parameters are derived through an estimation process that is susceptible to noise and artifacts. Estimated parameters (e.g., fractional anisotropy) exhibit both variability and bias relative to the true parameter value estimated from a hypothetical noise-free acquisition. Herein, we present the use of the simulation and extrapolation (SIMEX) approach for post hoc assessment of bias in a massively univariate imaging setting and evaluate the potential of a SIMEX-based bias correction. Using simulated data with known truth models, spatially varying fractional anisotropy bias error maps are evaluated on two independent and highly differentiated case studies. The stability of SIMEX and its distributional properties are further evaluated on 42 empirical diffusion tensor imaging datasets. Using gradient subsampling, an empirical experiment with a known true outcome is designed and SIMEX performance is compared to the original estimator. With this approach, we find SIMEX bias estimates to be highly accurate offering significant reductions in parameter bias for individual datasets and greater accuracy in averaged population-based estimates.

Integration of XNAT/PACS, DICOM, and Research Software for Automated Multi-modal Image Analysis.

Traumatic brain injury (TBI) is an increasingly important public health concern. While there are several promising avenues of intervention, clinical assessments are relatively coarse and comparative quantitative analysis is an emerging field. Imaging data provide potentially useful information for evaluating TBI across functional, structural, and microstructural phenotypes. Integration and management of disparate data types are major obstacles. In a multi-institution collaboration, we are collecting electroencephalogy (EEG), structural MRI, diffusion tensor MRI (DTI), and single photon emission computed tomography (SPECT) from a large cohort of US Army service members exposed to mild or moderate TBI who are undergoing experimental treatment. We have constructed a robust informatics backbone for this project centered on the DICOM standard and eXtensible Neuroimaging Archive Toolkit (XNAT) server. Herein, we discuss (1) optimization of data transmission, validation and storage, (2) quality assurance and workflow management, and (3) integration of high performance computing with research software.

Robust Inter-Modality Multi-Atlas Segmentation for PACS-based DTI Quality Control.

Anatomical contexts (spatial labels) are critical for interpretation of medical imaging content. Numerous approaches have been devised for segmentation, query, and retrieval within the Picture Archive and Communication System (PACS) framework. To date, application-based methods for anatomical localization and tissue classification have yielded the most successful results, but these approaches typically rely upon the availability of standardized imaging sequences. With the ever expanding scope of PACS archives - including multiple imaging modalities, multiple image types within a modality, and multi-site efforts, it is becoming increasingly burdensome to devise a specific method for each data type. To address the challenge of generalizing segmentations from one modality to another, we consider multi-atlas segmentation to transfer label information from labeled T1-weighted MRI data to unlabeled B0 data collected in a diffusion tensor imaging (DTI) experiment. The label transfer approach is fully automated and enables a generalizable cross-modality segmentation method. Herein, we propose a multi-tier multi-atlas segmentation framework for the segmentation of previously unlabeled imaging modalities (e.g., B0 images for DTI analysis). We show that this approach can be used to construct informed structure-wise noise estimates for fractional anisotropy (FA) measurements of DTI. Although this label transfer methodology is demonstrated in the context of quality control of DTI images, the proposed framework is applicable to any application where the segmentation of unlabeled modalities is limited due to the current collection of available atlases.

Towards Automatic Quantitative Quality Control for MRI.

Quality and consistency of clinical and research data collected from Magnetic Resonance Imaging (MRI) scanners may become suspect due to a wide variety of common factors including, experimental changes, hardware degradation, hardware replacement, software updates, personnel changes, and observed imaging artifacts. Standard practice limits quality analysis to visual assessment by a researcher/clinician or a quantitative quality control based upon phantoms which may not be timely, cannot account for differing experimental protocol (e.g. gradient timings and strengths), and may not be pertinent to the data or experimental question at hand. This paper presents a parallel processing pipeline developed towards experiment specific automatic quantitative quality control of MRI data using diffusion tensor imaging (DTI) as an experimental test case. The pipeline consists of automatic identification of DTI scans run on the MRI scanner, calculation of DTI contrasts from the data, implementation of modern statistical methods (wild bootstrap and SIMEX) to assess variance and bias in DTI contrasts, and quality assessment via power calculations and normative values. For this pipeline, a DTI specific power calculation analysis is developed as well as the first incorporation of bias estimates in DTI data to improve statistical analysis.

Biological parametric mapping accounting for random regressors with regression calibration and model II regression.

Massively univariate regression and inference in the form of statistical parametric mapping have transformed the way in which multi-dimensional imaging data are studied. In functional and structural neuroimaging, the de facto standard "design matrix"-based general linear regression model and its multi-level cousins have enabled investigation of the biological basis of the human brain. With modern study designs, it is possible to acquire multi-modal three-dimensional assessments of the same individuals--e.g., structural, functional and quantitative magnetic resonance imaging, alongside functional and ligand binding maps with positron emission tomography. Largely, current statistical methods in the imaging community assume that the regressors are non-random. For more realistic multi-parametric assessment (e.g., voxel-wise modeling), distributional consideration of all observations is appropriate. Herein, we discuss two unified regression and inference approaches, model II regression and regression calibration, for use in massively univariate inference with imaging data. These methods use the design matrix paradigm and account for both random and non-random imaging regressors. We characterize these methods in simulation and illustrate their use on an empirical dataset. Both methods have been made readily available as a toolbox plug-in for the SPM software.

Assessment of bias for MRI diffusion tensor imaging using SIMEX.

Diffusion Tensor Imaging (DTI) is a Magnetic Resonance Imaging method for measuring water diffusion in vivo. One powerful DTI contrast is fractional anisotropy (FA). FA reflects the strength of water's diffusion directional preference and is a primary metric for neuronal fiber tracking. As with other DTI contrasts, FA measurements are obscured by the well established presence of bias. DTI bias has been challenging to assess because it is a multivariable problem including SNR, six tensor parameters, and the DTI collection and processing method used. SIMEX is a modem statistical technique that estimates bias by tracking measurement error as a function of added noise. Here, we use SIMEX to assess bias in FA measurements and show the method provides; i) accurate FA bias estimates, ii) representation of FA bias that is data set specific and accessible to non-statisticians, and iii) a first time possibility for incorporation of bias into DTI data analysis.

Using the water signal to detect invisible exchanging protons in the catalytic triad of a serine protease.

Chemical Exchange Saturation Transfer (CEST) is an MRI approach that can indirectly detect exchange broadened protons that are invisible in traditional NMR spectra. We modified the CEST pulse sequence for use on high-resolution spectrometers and developed a quantitative approach for measuring exchange rates based upon CEST spectra. This new methodology was applied to the rapidly exchanging Hδ1 and Hε2 protons of His57 in the catalytic triad of bovine chymotrypsinogen-A (bCT-A). CEST enabled observation of Hε2 at neutral pH values, and also allowed measurement of solvent exchange rates for His57-Hδ1 and His57-Hε2 across a wide pH range (3-10). Hδ1 exchange was only dependent upon the charge state of the His57 (k (ex,Im+) = 470 s(-1), k (ex,Im) = 50 s(-1)), while Hε2 exchange was found to be catalyzed by hydroxide ion and phosphate base (k(OH)⁻ = 1.7 × 10(10) M(-1) s(-1), K(HPO)²â»4 = 1.7 × 10(6) M(-1) s(-1)), reflecting its greater exposure to solute catalysts. Concomitant with the disappearance of the Hε2 signal as the pH was increased above its pK (a), was the appearance of a novel signal (δ = 12 ppm), which we assigned to Hγ of the nearby Ser195 nucleophile, that is hydrogen bonded to Nε2 of neutral His57. The chemical shift of Hγ is about 7 ppm downfield from a typical hydroxyl proton, suggesting a highly polarized O-Hγ bond. The significant alkoxide character of Oγ indicates that Ser195 is preactivated for nucleophilic attack before substrate binding. CEST should be generally useful for mechanistic investigations of many enzymes with labile protons involved in active site chemistry.

Accounting for Random Regressors: A Unified Approach to Multi-modality Imaging.

Massively univariate regression and inference in the form of statistical parametric mapping have transformed the way in which multi-dimensional imaging data are studied. In functional and structural neuroimaging, the de facto standard "design matrix"-based general linear regression model and its multi-level cousins have enabled investigation of the biological basis of the human brain. With modern study designs, it is possible to acquire multiple three-dimensional assessments of the same individuals - e.g., structural, functional and quantitative magnetic resonance imaging alongside functional and ligand binding maps with positron emission tomography. Current statistical methods assume that the regressors are non-random. For more realistic multi-parametric assessment (e.g., voxel-wise modeling), distributional consideration of all observations is appropriate (e.g., Model II regression). Herein, we describe a unified regression and inference approach using the design matrix paradigm which accounts for both random and non-random imaging regressors.

Easy Multiplicity Control in Equivalence Testing Using Two One-sided Tests.

Equivalence testing is growing in use in scientific research outside of its traditional role in the drug approval process. Largely due to its ease of use and recommendation from the United States Food and Drug Administration guidance, the most common statistical method for testing equivalence is the two one-sided tests procedure (TOST). Like classical point-null hypothesis testing, TOST is subject to multiplicity concerns as more comparisons are made. In this manuscript, a condition that bounds the family-wise error rate using TOST is given. This condition then leads to a simple solution for controlling the family-wise error rate. Specifically, we demonstrate that if all pair-wise comparisons of k independent groups are being evaluated for equivalence, then simply scaling the nominal Type I error rate down by (k - 1) is sufficient to maintain the family-wise error rate at the desired value or less. The resulting rule is much less conservative than the equally simple Bonferroni correction. An example of equivalence testing in a non drug-development setting is given.