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CDC researchers have developed a novel acoustic whole body plethysmograph (AWBP) that allows measurement of tidal volume in lab animals, independent of gas compression in the lung. This system provides particular advantages over the traditional whole body plethysmograph (WBP) when measuring model animals with increased gas compression due to increased airway resistance or increased acceleration in the breathing pattern.

CDC researchers have developed the Portable Exposure Assessment System (PEAS), a field-based, remotely deployed tool to monitor and provide early warning of working conditions that have a high likelihood of musculoskeletal injury. PEAS is a noninvasive, real-time, instrument-based system. Sensor technology simultaneously measures and collects data regarding the body loads and awkward postures imposed by package handling as well as driving-related, low-frequency vibrations.

This CDC-developed technology entails a system for monitoring and assessing the risk of auditory damage from exposure to impulse noise, such as noise created by construction machinery and firearms. Noise dosimeters have been used extensively over the past two decades to document personal exposure to noise and assure workplaces comply with permissible noise exposure levels. However, due to older methods of calculating "noise dose," current noise dosimeters often inaccurately determine the risk of an impulse event.

Software is has been developed and available for licensing that fully automates image processing for the quantification of myocardial blood flow (MBF) pixel maps from firstpass contrast-enhanced cardiac magnetic resonance (CMR) perfusion images. The system removes the need for laborious manual quantitative CMR perfusion pixel map processing and can process prospective and retrospective studies acquired from various imaging protocols. In full automation, arterial input function (AIF) images are processed for motion correction and myocardial perfusion images are corrected for intensity bias.

One of four deaths in the United States is due to cancer despite an emphasis on prevention, early detection, and treatment that has lowered cancer death rates by 20% in the past two decades. Further improvements in survival rates are likely to come from improving the limits of detection sensitivity at earlier stages of cancer. New approaches that rely heavily on genomic information, however, may change future testing strategies.

Cancer represents the leading cause of morbidity and mortality worldwide, with approximately 14 million new cases and 8.2 million cancer related deaths in 2012. This number is predicted to rise by approximately 70% over the next two decades according to the World Health Organization. Prognosis depends heavily on both early detection and frequent monitoring of the patient's response to treatment. Cancerous tumors shed nucleic acids into blood, which can be detected by ultra-deep sequencing of mitochondrial DNA (mtDNA).

Scientists at NCATS have developed a novel Cellular Thermal Shift Assay (CETSA), named “Real-time CETSA” in which temperature-induced aggregation of proteins can be monitored in cells in real time across a range of compound concentrations and simultaneously across a temperature gradient in a high-throughput manner. Real-time CETSA streamlines the thermal shift assay and allows investigators to capture full aggregation profiles for every sample.

This technology includes a novel computational algorithm and software implementation to map and display biological pathways and their relationship on the surface of a globe in a three-dimensional space. Currently, biological pathways and genes are represented as two-dimensional networks, which is not effective for displaying complicated relationships between pathways and genes.

This technology relates to a software package called NIMH DAO Toolbox that uses multithreading and a unique buffer structure to shorten gaps in sample readouts. Data acquisition devices running in continuous sampling mode collect data samples at a given sampling rate. The samples are typically stored in a memory buffer and read out at a regular interval. If the sampling rate is short enough, there can be a gap between the time the first sample is acquired and the time that sample is available to the user. This gap is typically on the order of tens of milliseconds.

This technology relates to a closed-loop controller that is being developed as a phone app for emotional self-regulation in real-time. There is a significant association between emotion dysregulation and symptoms of depression, anxiety, eating pathology, and substance abuse, affecting millions worldwide. Consisting of a closed-loop controller that adjusts reward values in real-time according to individual mood response, the Mood Machine Interface technology compensates for adaptation to stimuli over time allowing it to generate substantial mood changes in the user.