Abstract: This study compares the effect of the extra-thoracic airways on the flow field through the lower airways by carrying out computational fluid dynamics (CFD) simulations of the airflow through the human respiratory tract. In order to facilitate this comparison, two geometries were utilized. The first was a realistic nine-generation lower airway geometry derived from computed tomography (CT) images, while the second included an additional component, i.e., an idealized extra-thoracic airway (ETA) coupled with the same nine-generation CT model. Another aspect of this study focused on the impact of breathing transience on the flow field. Consequently, simulations were carried out for transient breathing in addition to peak inspiration and expiration. Physiologically-appropriate regional ventilation for two different flow rates was induced at the distal boundaries by imposing appropriate lobar specific flow rates. The scope of these simulations was limited to the modeling of tidal breathing at rest. The typical breathing rates for these cases range from 7.5 to 15 breaths per minute with a tidal volume of 0.5L. For comparison, the flow rates for constant inspiration/expiration were selected to be identical to the peak flow rates during the transient breathing. Significant differences were observed from comparing the peak inspiration and expiration with transient breathing in the entire airway geometry. Differences were also observed for the lower airway geometry. These differences point to the fact that simulations that utilize constant inspiration or expiration may not be an appropriate approach to gain better insight into the flow patterns present in the human respiratory system. Consequently, particle trajectories derived from these flow fields might be misleading in their applicability to the human respiratory system.

Abstract: Modeling of fluid mechanics for the vascular system is of great value as a source of knowledge about development, progression, and treatment of cardiovascular disease. Full three-dimensional simulation of blood flow in the whole human body is a hard computational problem. We discuss parallel decomposition of blood flow simulation as a graph partitioning problem. The detailed model of full human arterial tree and some simpler geometries are discussed. The effectiveness of coarse-graining as well as pure spectral approaches is studied. Published data can be useful for development of parallel hemodynamic applications as well as for estimation of their effectiveness and scalability.

Abstract: We describe an algorithm to generate a normative infant cranial model from the input of 3D meshes that are extracted from CT scans of normal infant skulls. We generate a correspondence map between meshes based on a registration algorithm. Then we apply our averaging algorithm to construct the normative model. The goal of this normal model is to assist an objective evaluating system to analyze the efficacy of plastic surgeries.

Abstract: Targeting deep regions in the brain is a key challenge in noninvasive transcranial electrical neuromodulation. We explore this problem by means of computer simulations within a detailed seven-tissue finite element head model (2 millions tetrahedrons) constructed from high resolution MRI and CT volumes. We solve the forward electrical stimulation and EEG problems governed by the quasi-static Poisson equation numerically using the first order Finite Element Method (FEM) with the Galerking approach. Given a dense array of EEG-electrode layout and location of regions of interest inside the brain, we compute optimal current injection patterns based on the reciprocity principle in EEG and compare results with optimization based on the Least Squares (LS) or Linearly Constrained Minimum Variance (LCMV) algorithms. It is found that the reciprocity algorithms show good performance comparable to the LCMV and LS solutions for deep brain targets which are generally computationally more expensive to obtain.

Abstract: Supermodeling is an interesting and non-standard concept used recently for simulation of complex and chaotic systems such as climate and weather dynamics. It consists in coupling of many imperfect models to create a single supermodel. We discuss here supermodeling strategy in the context of tumor growth. To check its adaptive flexibility we have developed a basic, but still computationally complex, modeling framework of melanoma growth. The supermodel of melanoma consists of a few coupled sub-models, which differ in values of a parameter responsible for tumor cells and extracellular matrix interactions. We demonstrate that due to synchronization of sub-models, the supermodel is able to simulate qualitatively different modes of cancer growth than those observed for a single model. These scenarios correspond to the basic types of melanoma cancer. This property makes the supermodel very flexible to be fit to real data. On the basis of preliminary simulation results, we discuss the prospects of supermodeling strategy as a promising coupling factor between both formal and data-based models of tumor.

Abstract: We are reporting on a strong capacity boost in storing digital data in synthetic DNA. In principle, synthetic DNA is an ideal media to archive digital data for very long times because the achievable data density and longevity outperforms today’s digital data storage media by far. On the other hand, neither the synthesis, nor the amplification and the sequencing of DNA strands can be performed error-free today and in the foreseeable future. In order to make synthetic DNA available as digital data storage media, forward-error-correction schemes have to be applied. In order to realize DNA data storage, we have developed an efficient and robust forward-error-correcting scheme adapted to the DNA channel. We based the design of the needed DNA channel model on data from a proof-of-concept conducted 2012 by a team from the Harvard Medical School*. Our forward error correction scheme is able to cope with all error types of today DNA synthesis, amplification and sequencing processes, e.g. insertion, deletion, and swap errors. In a successful experiment, we were able to store and retrieve error-free 22MByte of digital data in synthetic DNA recently. The found residual error probability is already in the same order as it is in hard disk drives and can be easily improved. This proves the feasibility to use synthetic DNA as a long-term digital data storage media. In an already planned next development step we will increase the amount of stored data into the GByte range. The presented forward error correction scheme is already designed for such and even much higher volumes of data. *) Church, G. M.; Gao, Y.; Kosuri, S. (2012). "Next-Generation Digital Information Storage in DNA". Science 337 (6102): 1628

Abstract: In this paper, we describe some available high-confident call sets that have been developed to test the accuracy of called single nucleotide polymorphisms (SNPs) from next-generation sequencing. We use these calls to test and parameterize the GATK best practice pipeline on the high-performance computing cluster at the University of Kentucky. Automated script to run the pipeline can be found at https://github.com/sallyrose0425/GATKBP. This study demonstrates the usefulness of high-confident call sets in validating and optimizing bioinformatics pipelines, estimates computational needs for genomic analysis, and provides scripts for an automated GATK best practices pipeline.

Abstract: There are many legitimate reasons why standards for formatting of biomedical research data are lengthy and complex (Souza, Kush, & Evans, 2007). However, the common scenario of a biostatistician simply needing to import a given dataset into their statistical software is at best under-served by these standards. Statisticians are forced to act as amateur database administrators to pivot and join their data into a usable form before they can even begin the work that they specialize in doing. Or worse, they find their choice of statistical tools dictated not by their own experience and skills, but by remote standards bodies or inertial administrative choices. This may limit academic freedom. If the formats in question require the use of one proprietary software package, it also raises concerns about vendor lock-in (DeLano, 2005) and stewardship of public resources. The logistics and transparency of data sharing can be made more tractable by an appreciation of the differences between structural, semantic, and syntactic levels of data interoperability. The semantic level is legitimately a complex problem. Here we make the case that, for the limited purpose of statistical analysis, a simplifying assumption can be made about structural level: the needs of a large number of statistical models can often be met with a modified variant of the first normal form or 1NF (Codd, 1979). Once data is merged into one such table, the syntactic level becomes a solved problem, with many text based formats available and robustly supported by virtually all statistical software without the need for any custom or third-party client-side add-ons. We implemented our denormalization approach in DataFinisher, an open source server-side add-on for i2b2 (Murphy et al., 2009), which we use at our site to enable self-service pulls of de-identified data by researchers.