cfSNV: a software tool for the sensitive detection of somatic mutations from cell-free DNA.

Cell-free DNA (cfDNA) in blood, viewed as a surrogate for tumor biopsy, has many clinical applications, including diagnosing cancer, guiding cancer treatment and monitoring treatment response. All these applications depend on an indispensable, yet underdeveloped task: detecting somatic mutations from cfDNA. The task is challenging because of the low tumor fraction in cfDNA. Recently, we developed the computational method cfSNV, the first method that comprehensively considers the properties of cfDNA for the sensitive detection of mutations from cfDNA. cfSNV vastly outperformed the conventional methods that were developed primarily for calling mutations from solid tumor tissues. cfSNV can accurately detect mutations in cfDNA even with medium-coverage (e.g., ≥200×) sequencing, which makes whole-exome sequencing (WES) of cfDNA a viable option for various clinical utilities. Here, we present a user-friendly cfSNV package that exhibits fast computation and convenient user options. We also built a Docker image of it, which is designed to enable researchers and clinicians with a limited computational background to easily carry out analyses on both high-performance computing platforms and local computers. Mutation calling from a standard preprocessed WES dataset (~250× and ~70 million base pair target size) can be carried out in 3 h on a server with eight virtual CPUs and 32 GB of random access memory.

MMS SITL Ground Loop: Automating the Burst Data Selection Process.

Global-scale energy flow throughout Earth's magnetosphere is catalyzed by processes that occur at Earth's magnetopause (MP). Magnetic reconnection is one process responsible for solar wind entry into and global convection within the magnetosphere, and the MP location, orientation, and motion have an impact on the dynamics. Statistical studies that focus on these and other MP phenomena and characteristics inherently require MP identification in their event search criteria, a task that can be automated using machine learning so that more man hours can be spent on research and analysis. We introduce a Long-Short Term Memory (LSTM) Recurrent Neural Network model to detect MP crossings and assist studies of energy transfer into the magnetosphere. As its first application, the LSTM has been implemented into the operational data stream of the Magnetospheric Multiscale (MMS) mission. MMS focuses on the electron diffusion region of reconnection, where electron dynamics break magnetic field lines and plasma is energized. MMS employs automated burst triggers onboard the spacecraft and a Scientist-in-the-Loop (SITL) on the ground to select intervals likely to contain diffusion regions. Only low-resolution survey data is available to the SITL, which is insufficient to resolve electron dynamics. A strategy for the SITL, then, is to select all MP crossings. Of all 219 SITL selections classified as MP crossings during the first five months of model operations, the model predicted 166 (76%) of them, and of all 360 model predictions, 257 (71%) were selected by the SITL. Most predictions that were not classified as MP crossings by the SITL were still MP-like, in that the intervals contained mixed magnetosheath and magnetospheric plasmas. The LSTM model and its predictions are public to ease the burden of arduous event searches involving the MP, including those for EDRs. For MMS, this helps free up mission operation costs by consolidating manual classification processes into automated routines.

Assessing Engineering Resilience for Systems with Multiple Performance Measures.

Recently, efforts to model and assess a system's resilience to disruptions due to environmental and adversarial threats have increased substantially. Researchers have investigated resilience in many disciplines, including sociology, psychology, computer networks, and engineering systems, to name a few. When assessing engineering system resilience, the resilience assessment typically considers a single performance measure, a disruption, a loss of performance, the time required to recover, or a combination of these elements. We define and use a resilient engineered system definition that separates system resilience into platform and mission resilience. Most complex systems have multiple performance measures; this research proposes using multiple objective decision analysis to assess system resilience for systems with multiple performance measures using two distinct methods. The first method quantifies platform resilience and includes resilience and other "ilities" directly in the value hierarchy, while the second method quantifies mission resilience and uses the "ilities" in the calculation of the expected mission performance for every performance measure in the value hierarchy. We illustrate the mission resilience method using a transportation systems-of-systems network with varying levels of resilience due to the level of connectivity and autonomy of the vehicles and platform resilience by using a notional military example. Our analysis found that it is necessary to quantify performance in context with specific mission(s) and scenario(s) under specific threat(s) and then use modeling and simulation to help determine the resilience of a system for a given set of conditions. The example demonstrates how incorporating system mission resilience can improve performance for some performance measures while negatively affecting others.