Edward C. Wack

Robust detection of individual forensic profiles in DNA mixtures

For a forensic identification method to be admissible in international courts, the probability of false match must be quantified. For comparison of individuals against complex mixtures using a panel of single nucleotide polymorphisms (SNPs), the probability of a random man not excluded, P(RMNE) is one admissible standard. While the P(RMNE) of SNP alleles has been previously studied, it remains to be rigorously defined and calculated for experimentally genotyped mixtures. In this report, exact P(RMNE) values were calculated for a range of complex mixtures, verified with Monte Carlo simulations, and compared alongside experimentally determined detection probabilities.

A procedure for embedding effluent plumes into LWIR imagery

Longwave Infrared (LWIR) data sets collected from airborne platforms provide opportunities for study of atmospheric and surface features in the emissive spectral regime. The transfer of radiation for LWIR scenes can be formulated in a manner that allows recovery of the surface-leaving radiance (a result of atmospheric compensation). Using a forward radiative transfer model, a number of modifications to the atmospheric component of the scene can be made and applied to the surface-leaving radiance to predict sensor radiance that reflects a desired scenario. One such modification is the inclusion of a layer of effluent, the structure of which can be simulated by a plume model. Additionally, a different set of atmospheric conditions can be modeled and used to replace the conditions present in the scene. The resultant scene radiance field can be used to test algorithms for effluent characterization since the composition of the effluent layer and the intervening atmosphere is known. This approach allows for the embedding of a plume layer containing any combination of effluents from a set of over 400 gas spectra, the dispersion of which can be simulated using various plume models. Examples of simulated plume scenes are given, one of which contains an existing plume which is replicated using known emission information. Comparison of the real and simulated plume brightness temperatures yielded differences on the order of 0.2 K.

Architectural trades for an advanced geostationary atmospheric sounding instrument

The process of formulating a remote sensing instrument design from a set of observational requirements involves a series of trade studies during which judgments are made between available design options. The outcome of this process is a system architecture which drives the size, weight, power consumption, cost, and technological risk of the instrument. In this paper, a set of trade studies are described which guided the development of a baseline sensor design to provide vertical profiles (soundings) of atmospheric temperature and humidity from future Geostationary Operational Environmental Satellite (GOES) platforms. Detailed trade studies presented include the choice between an interferometric versus a dispersive spectrometer, the optical design of the IR interferometer and visible imaging channel, the optimization of the instrument spatial response, the selection of detector array materials, operating temperatures, and array size, the thermal design for detector and optics cooling, and the electronics required to process detected interferograms into spectral radiance. The trade study process was validated through simulations of the radiometric performance of the instrument, and through simulated retrievals of vertical profiles of atmospheric temperature and humidity. The flexibility of these system trades is emphasized, highlighting the differing outcomes that occur from this process as system requirements evolve. Observations are made with respect to the reliability and readiness of key technologies. The results of this study were disseminated to industry to assist their interpretation of, and responses to, system requirements provided by the U.S. Government.

Sherlock's Toolkit: A forensic DNA analysis system

DNA sequence analysis has multiple forensic applications. The justice system currently uses sizes of STRs as well as mitochondrial DNA (mtDNA) for DNA evidence. With recent advancements in DNA sequencing technologies, inclusion of additional polymorphic loci, including SNPs, enable new useful analyses while maintaining backwards compatibility with STR sizing. Sherlocks Toolkit, developed by MIT Lincoln Laboratory, is an open source, scalable system for the integration and automation of STR and SNP-based analysis for high-throughput sequence data. The toolkit includes modules for a range of kinship, biogeographic ancestry, replicate, and mixture analyses.

KinLinks: Software Toolkit for kinship analysis and pedigree generation from HTS datasets

The ability to predict familial relationships from source DNA in multiple samples has a number of forensic and medical applications. Kinship testing of suspect DNA profiles against relatives in a law enforcement database can provide valuable investigative leads, determination of familial relationships can inform immigration decisions, and remains identification can provide closure to families of missing individuals. The proliferation of High-Throughput Sequencing technologies allows for enhanced capabilities to accurately predict familial relationships to the third degree and beyond. KinLinks, developed by MIT Lincoln Laboratory, is a software tool that predicts pairwise relationships and reconstructs kinship pedigrees for multiple input samples using single-nucleotide polymorphism (SNP) profiles. The software has been trained and evaluated on a set of 175 subjects (30,450 pairwise relationships), consisting of three multi-generational families and 52 geographically diverse subjects. Though a panel of 5396 SNPs was selected for kinship prediction, KinLinks is highly modular, allowing for the substitution of expanded SNP panels and additional training models as sequencing capabilities continue to progress. KinLinks builds on the SNP-calling capabilities of Sherlocks Toolkit, and is fully integrated with the Sherlocks Toolkit pipeline.

Radiometric error in GOES 8 Imager data due to sensor MTF

Understanding the sources of uncertainty in GOES Imager IR data is important to meteorologists and scientists who develop meteorological products. One component of radiometric uncertainty that is not well characterized, unlike noise and calibration errors, arises from the sensorss MTF. To understand this effect it is necessary to know the amount of power at high spatial frequencies in a typical scene. The sensor MTF, however, acts as a lower pass filter on the scene spatial frequency content, passing low frequencies and attenuating higher frequencies. To study the effect of the higher spatial frequencies in a scene, a model of both sensor MTF and scene spatial frequency content has been developed. The scene model is based on data from the Modis Airborne Simulator (MAS), a 50 channel radiometer- imager flown aboard a NASA ER-2. The MAS sensor has a 50 m IFOV at nadir, compared to the GOES channel radiometer- imaging flown aboard a NASA ER-2. The MAS sensor has a 50 m IFOV at nadir, compared to the GOES 4 km IFOV. The data sets from which the scene model was developed contain various combinations of land and clouds from several flights. Overlapping power spectral densities from the two sensors validate the use of MAS data for a GOES scene model at high spatial frequencies. The sensor MTF model is based both on measurements made during pre-launch testing and on theoretical calculations from sensor f-number, detector size and electronics filtering. The MTFs of Imager channels 2 and 4 are compared. Their difference is applied to the scene power spectra to evaluate the average radiometric error due to MTF differences.

Pupil apodization as a means of mitigating diffraction effects in remote sensing instruments

The Geostationary Operational Environmental Satellite (GOES) platform carries an infrared atmospheric sounding instrument which is used to obtain vertical profiles of atmospheric temperature and humidity throughout much of the western hemisphere. These profiles are numerically retrieved from measured nadir-viewing spectral radiances. The opacity of clouds to IR radiance makes such instruments functional only in clear-air regions. Because severe weather is associated with clouded regions, it is highly desirable to obtain soundings through holes in the cloud cover and up to the edge of frontal boundaries. There is much difficulty in performing this task with the existing GOES sounder because cloud cover gives rise to radiance errors in adjacent, and more distant, clear-air fields-of-view. A primary cause for this problem is diffraction, which introduces optical crosstalk between fields-of-view, and which is exacerbated by the large radiance contrast between clouds and clear air. This paper describes a novel application of tapered, or apodized, aperture illumination which may be employed in future GOES sounding instruments to mitigate the effects of diffraction. Tapering the aperture illumination at the edges (or applying this taper at accessible pupils, which are images of the aperture stop) reduces the subsidiary rings of the point-spread function. The benefits of pupil apodization are quantified, as are the penalties incurred by effectively making the aperture smaller. The construction of a graded-transmission spatial filter is described, and its optimal location in a sounding instrument based on a Michelson spectrometer is defined. Finally, the results of measurements taken on a fabricated filter are presented.

Human CODIS STR loci profiling from HTS data

Human DNA identification is currently performed by amplifying a small, defined set of short tandem repeat (STR) loci (e.g. CODIS) and analyzing the size of the alleles present at those loci by capillary electrophoresis. High-throughput DNA sequencing (HTS) could enable the simultaneous analysis of many additional STR and single nucleotide polymorphism (SNP) loci, improving accuracy and discrimination. However, it is necessary to demonstrate that HTS can generate accurate data on the CODIS loci to enable backwards compatibility with the FBI NDIS database. Sequencing can also detect novel polymorphisms within alleles that migrate with identical sizes by capillary electrophoresis, improving allele discrimination, and enhancing human identification analysis. All CODIS alleles from an individual can be amplified in a single, multiplex PCR reaction, and combined with additional barcoded samples prior to sequencing. A computational tool for allele identification from multiplexed sequence data has been developed. With longer-read-length platforms, 99.6% allele calling accuracy can be achieved. In the course of STR sequencing protocol development, 12 novel allele sequences have been identified for multiple loci. Sequencing STR loci combined with SNPs will enable new forensic applications.

Multimodal biometric collection and evaluation architecture

The size and scope of standoff multimodal biometric datasets can be increased through the adoption of a common architecture to collect, describe, archive, and analyze subject traits. The Extendable Multimodal Biometric Evaluation Range (EMBER) system developed by MIT Lincoln Laboratory is a field-ready, easily adaptable architecture to streamline collections requiring multiple biometric devices in environments of interest. Its data architecture includes a fully featured metadata-rich relational database that supports the aggregation of biometric data collected with proliferated systems into a single corpus for analytical use.

Measurements of scene spectral radiance variability

Detection performance of LWIR passive standoff chemical agent sensors is strongly influenced by various scene parameters, such as atmospheric conditions, temperature contrast, concentration-path length product (CL), agent absorption coefficient, and scene spectral variability. Although temperature contrast, CL, and agent absorption coefficient affect the detected signal in a predictable manner, fluctuations in background scene spectral radiance have less intuitive consequences. The spectral nature of the scene is not problematic in and of itself; instead it is spatial and temporal fluctuations in the scene spectral radiance that cannot be entirely corrected for with data processing. In addition, the consequence of such variability is a function of the spectral signature of the agent that is being detected and is thus different for each agent. To bracket the performance of background-limited (low sensor NEDN), passive standoff chemical sensors in the range of relevant conditions, assessment of real scene data is necessary1. Currently, such data is not widely available2. To begin to span the range of relevant scene conditions, we have acquired high fidelity scene spectral radiance measurements with a Telops FTIR imaging spectrometer3. We have acquired data in a variety of indoor and outdoor locations at different times of day and year. Some locations include indoor office environments, airports, urban and suburban scenes, waterways, and forest. We report agent-dependent clutter measurements for three of these backgrounds.