Development and proof-of-concept demonstration of a clinical metagenomics method for the rapid detection of bloodstream infection.

BACKGROUND: The timely and accurate diagnosis of bloodstream infection (BSI) is critical for patient management. With longstanding challenges for routine blood culture, metagenomics is a promising approach to rapidly provide sequence-based detection and characterisation of bloodborne bacteria. Long-read sequencing technologies have successfully supported the use of clinical metagenomics for syndromes such as respiratory illness, and modified approaches may address two requisite factors for metagenomics to be used as a BSI diagnostic: depletion of the high level of host DNA to then detect the low abundance of microbes in blood. METHODS: Blood samples from healthy donors were spiked with different concentrations of four prevalent causative species of BSI. All samples were then subjected to a modified saponin-based host DNA depletion protocol and optimised DNA extraction, whole genome amplification and debranching steps in preparation for sequencing, followed by bioinformatical analyses. Two related variants of the protocol are presented: 1mL of blood processed without bacterial enrichment, and 5mL of blood processed following a rapid bacterial enrichment protocol-SepsiPURE. RESULTS: After first identifying that a large proportion of host mitochondrial DNA remained, the host depletion process was optimised by increasing saponin concentration to 3% and scaling the reaction to allow more sample volume. Compared to non-depleted controls, the 3% saponin-based depletion protocol reduced the presence of host chromosomal and mitochondrial DNA < 106 and < 103 fold respectively. When the modified depletion method was further combined with a rapid bacterial enrichment method (SepsiPURE; with 5mL blood samples) the depletion of mitochondrial DNA improved by a further > 10X while also increasing detectable bacteria by > 10X. Parameters during DNA extraction, whole genome amplification and long-read sequencing were also adjusted, and subsequently amplicons were detected for each input bacterial species at each of the spiked concentrations, ranging from 50-100 colony forming units (CFU)/mL to 1-5 CFU/mL. CONCLUSION: In this proof-of-concept study, four prevalent BSI causative species were detected in under 12 h to species level (with antimicrobial resistance determinants) at concentrations relevant to clinical blood samples. The use of a rapid and precise metagenomic protocols has the potential to advance the diagnosis of BSI.

Terahertz imaging system performance model for concealed-weapon identification.

The U.S. Army Night Vision and Electronic Sensors Directorate (NVESD) and the U.S. Army Research Laboratory have developed a terahertz (THz) -band imaging system performance model for detection and identification of concealed weaponry. The MATLAB-based model accounts for the effects of all critical sensor and display components and for the effects of atmospheric attenuation, concealment material attenuation, and active illumination. The model is based on recent U.S. Army NVESD sensor performance modeling technology that couples system design parameters to observer-sensor field performance by using the acquire methodology for weapon identification performance predictions. This THz model has been developed in support of the Defense Advanced Research Project Agencies' Terahertz Imaging Focal-Plane Technology (TIFT) program and is currently being used to guide the design and development of a 0.650 THz active-passive imaging system. This paper will describe the THz model in detail, provide and discuss initial modeling results for a prototype THz imaging system, and outline plans to calibrate and validate the model through human perception testing.

Sparse Detector Imaging Sensor with Two-Class Silhouette Classification.

This paper presents the design and test of a simple active near-infrared sparse detector imaging sensor. The prototype of the sensor is novel in that it can capture remarkable silhouettes or profiles of a wide-variety of moving objects, including humans, animals, and vehicles using a sparse detector array comprised of only sixteen sensing elements deployed in a vertical configuration. The prototype sensor was built to collect silhouettes for a variety of objects and to evaluate several algorithms for classifying the data obtained from the sensor into two classes: human versus non-human. Initial tests show that the classification of individually sensed objects into two classes can be achieved with accuracy greater than ninety-nine percent (99%) with a subset of the sixteen detectors using a representative dataset consisting of 512 signatures. The prototype also includes a Webservice interface such that the sensor can be tasked in a network-centric environment. The sensor appears to be a low-cost alternative to traditional, high-resolution focal plane array imaging sensors for some applications. After a power optimization study, appropriate packaging, and testing with more extensive datasets, the sensor may be a good candidate for deployment in vast geographic regions for a myriad of intelligent electronic fence and persistent surveillance applications, including perimeter security scenarios.

Modeling the target acquisition performance of active imaging systems.

Recent development of active imaging system technology in the defense and security community have driven the need for a theoretical understanding of its operation and performance in military applications such as target acquisition. In this paper, the modeling of active imaging systems, developed at the U.S. Army RDECOM CERDEC Night Vision & Electronic Sensors Directorate, is presented with particular emphasis on the impact of coherent effects such as speckle and atmospheric scintillation. Experimental results from human perception tests are in good agreement with the model results, validating the modeling of coherent effects as additional noise sources. Example trade studies on the design of a conceptual active imaging system to mitigate deleterious coherent effects are shown.

Atmospheric modulation transfer function in the infrared.

In high-resolution ultranarrow field-of-view thermal imagers, image quality over relatively long path lengths is typically limited by atmospheric degradation, especially atmospheric blur. We report our results and analyses of infrared images from two sites, Fort A. P. Hill and Aberdeen Proving Ground. The images are influenced by the various atmospheric phenomena: scattering, absorption, and turbulence. A series of experiments with high-resolution equipment in both the 3-5- and 8-13-microm regions at the two locations indicate that, as in the visible, image quality is limited much more by atmosphere than by the instrumentation for ranges even of the order of only a few kilometers. For paths close to the ground, turbulence is more dominant, whereas for paths involving higher average elevation, aerosol modulation transfer function (MTF) is dominant. As wavelength increases, turbulence MTF also increases, thus permitting aerosol MTF to become more dominant. A critical role in aerosol MTF in the thermal infrared is attributed to absorption, which noticeably decreases atmospheric transmission much more than in the visible, thereby reducing high-spatial-frequency aerosol MTF. These measurements indicate that atmospheric MTF should be a basic component in imaging system design and analysis even in the infrared, especially as higher-resolution hardware becomes available.