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News Article | May 8, 2017

CINCINNATI--(BUSINESS WIRE)--Belcan, LLC (“Belcan”), a global supplier of engineering, technical recruiting, and information technology (IT) services to the aerospace, defense, industrial and government services markets, announced that it has completed its acquisition of Schafer Intermediate Holdings, LLC (“Schafer”). The transaction was initially announced on April 6, 2017. Terms were not disclosed. Belcan is a portfolio company of AE Industrial Partners, LLC (“AEI”), a private investor in aerospace, power generation, and specialty industrial companies. Schafer represents the fifth acquisition completed by Belcan in less than two years under AEI’s ownership. Belcan is a global supplier of engineering, technical recruiting, and IT services to customers in the aerospace, defense, industrial, and government sectors. Belcan engineers better outcomes through adaptive and integrated services—from jet engines, airframe, and avionics to heavy vehicles, chemical processing, and cybersecurity, Belcan takes a partnering approach to provide customer-driven solutions that are flexible, scalable, and cost-effective. Belcan’s unique capabilities have led to continuous growth and success for nearly 60 years. For more information, please visit Schafer is a leading provider of scientific, engineering, and technical services and products applied to defeating national security threats. Schafer provides innovative engineering and technology solutions to the military, intelligence community, DHS, NASA, and others. Schafer has been widely recognized for its technical expertise and ability to provide objective analysis that leads to the development of innovative, problem-solving solutions. For more information, please visit AE Industrial Partners, LLC is a leading private investment firm in the aerospace, power generation and specialty industrial sectors, focusing on highly technical manufacturing, distribution and supply chain management, MRO (maintenance, repair, and overhaul) and industrial service-based businesses. AE Industrial’s team includes partners with C-suite operating experience in organizations such as GE, Gulfstream Aerospace, Power Systems Manufacturing, Hawker Beechcraft, Landmark Aviation, Bombardier, Aviall, B/E Aerospace, NetJets, and Grand Prairie. For more information, please visit

Phillips P.J.,U.S. National Institute of Standards and Technology | Scruggs W.T.,SAIC | O'Toole A.J.,University of Texas at Dallas | Flynn P.J.,University of Notre Dame | And 3 more authors.
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2010

This paper describes the large-scale experimental results from the Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006. The FRVT 2006 looked at recognition from high-resolution still frontal face images and 3D face images, and measured performance for still frontal face images taken under controlled and uncontrolled illumination. The ICE 2006 evaluation reported verification performance for both left and right irises. The images in the ICE 2006 intentionally represent a broader range of quality than the ICE 2006 sensor would normally acquire. This includes images that did not pass the quality control software embedded in the sensor. The FRVT 2006 results from controlled still and 3D images document at least an order-of-magnitude improvement in recognition performance over the FRVT 2002. The FRVT 2006 and the ICE 2006 compared recognition performance from high-resolution still frontal face images, 3D face images, and the single-iris images. On the FRVT 2006 and the ICE 2006 data sets, recognition performance was comparable for high-resolution frontal face, 3D face, and the iris images. In an experiment comparing human and algorithms on matching face identity across changes in illumination on frontal face images, the best performing algorithms were more accurate than humans on unfamiliar faces. © 2006 IEEE.

Buus S.,Copenhagen University | Rockberg J.,Albanova University Center | Forsstrom B.,Albanova University Center | Nilsson P.,KTH Royal Institute of Technology | And 3 more authors.
Molecular and Cellular Proteomics | Year: 2012

Antibodies empower numerous important scientific, clinical, diagnostic, and industrial applications. Ideally, the epitope(s) targeted by an antibody should be identified and characterized, thereby establishing antibody reactivity, highlighting possible cross-reactivities, and perhaps even warning against unwanted (e.g. autoimmune) reactivities. Antibodies target proteins as either conformational or linear epitopes. The latter are typically probed with peptides, but the cost of peptide screening programs tends to prohibit comprehensive specificity analysis. To perform high-throughput, high-resolution mapping of linear antibody epitopes, we have used ultrahigh-density peptide microarrays generating several hundred thousand different peptides per array. Using exhaustive length and substitution analysis, we have successfully examined the specificity of a panel of polyclonal antibodies raised against linear epitopes of the human proteome and obtained very detailed descriptions of the involved specificities. The epitopes identified ranged from 4 to 12 amino acids in size. In general, the antibodies were of exquisite specificity, frequently disallowing even single conservative substitutions. In several cases, multiple distinct epitopes could be identified for the same target protein, suggesting an efficient approach to the generation of paired antibodies. Two alternative epitope mapping approaches identified similar, although not necessarily identical, epitopes. These results show that ultrahigh-density peptide microarrays can be used for linear epitope mapping. With an upper theoretical limit of 2,000,000 individual peptides per array, these peptide microarrays may even be used for a systematic validation of antibodies at the proteomic level. © 2012 by The American Society for Biochemistry and Molecular Biology, Inc.

Stauch J.,Schafer Corporation | Jah M.,U.S. Air force
Journal of Guidance, Control, and Dynamics | Year: 2015

The unscented Schmidt-Kalman filter is developed, paralleling the unscented Kalman filter algorithm using an augmented-state approach and a practical easy-to-implement algorithm is presented alongside the familiar UKF algorithm. The difference between a consider analysis approach (consider analysis unscented Kalman filter, CAUKF) and an augmented-state consider filter approach (USKF) are demonstrated using a simple example. In the CAUKF, only an addition to the UKF covariance is computed, as opposed to the USKF, which includes the consider parameters directly in the filter via an augmented state and covariance matrix. An important aspect of the USKF, missing from the CAUKF, is that the estimated state is implicitly altered by the consider uncertainties, thus potentially improving the estimate itself, in addition to providing a more realistic covariance. The USKF is shown to provide dramatically better results than the UKF when dynamic model errors are present.

Hansen L.B.,Copenhagen University | Hansen L.B.,Schafer Corporation | Buus S.,Copenhagen University | Schafer-Nielsen C.,Schafer Corporation
PLoS ONE | Year: 2013

We have recently developed a high-density photolithographic, peptide array technology with a theoretical upper limit of 2 million different peptides per array of 2 cm2. Here, we have used this to perform complete and exhaustive analyses of linear B cell epitopes of a medium sized protein target using human serum albumin (HSA) as an example. All possible overlapping 15-mers from HSA were synthesized and probed with a commercially available polyclonal rabbit anti-HSA antibody preparation. To allow for identification of even the weakest epitopes and at the same time perform a detailed characterization of key residues involved in antibody binding, the array also included complete single substitution scans (i.e. including each of the 20 common amino acids) at each position of each 15-mer peptide. As specificity controls, all possible 15-mer peptides from bovine serum albumin (BSA) and from rabbit serum albumin (RSA) were included as well. The resulting layout contained more than 200.000 peptide fields and could be synthesized in a single array on a microscope slide. More than 20 linear epitope candidates were identified and characterized at high resolution i.e. identifying which amino acids in which positions were needed, or not needed, for antibody interaction. As expected, moderate cross-reaction with some peptides in BSA was identified whereas no cross-reaction was observed with peptides from RSA. We conclude that high-density peptide microarrays are a very powerful methodology to identify and characterize linear antibody epitopes, and should advance detailed description of individual specificities at the single antibody level as well as serologic analysis at the proteome-wide level. © 2013 Hansen et al.

Slater J.M.,Schafer Corporation | Edwards B.,Lincoln Laboratory
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2010

This paper discusses methods for characterization of high power lasers. Specifically, these methods have been developed for the High Energy Laser Joint Technology Office and used for independent, government-sponsored testing in the 25 and 100 kW phases of the Joint High Power Solid State Laser program. Primarily this paper addresses measurement of power and beam quality. © 2010 Copyright SPIE - The International Society for Optical Engineering.

Andreatta M.,Technical University of Denmark | Schafer-Nielsen C.,Schafer Corporation | Lund O.,Technical University of Denmark | Buus S.,Copenhagen University | Nielsen M.,Technical University of Denmark
PLoS ONE | Year: 2011

Recent advances in high-throughput technologies have made it possible to generate both gene and protein sequence data at an unprecedented rate and scale thereby enabling entirely new "omics"-based approaches towards the analysis of complex biological processes. However, the amount and complexity of data that even a single experiment can produce seriously challenges researchers with limited bioinformatics expertise, who need to handle, analyze and interpret the data before it can be understood in a biological context. Thus, there is an unmet need for tools allowing non-bioinformatics users to interpret large data sets. We have recently developed a method, NNAlign, which is generally applicable to any biological problem where quantitative peptide data is available. This method efficiently identifies underlying sequence patterns by simultaneously aligning peptide sequences and identifying motifs associated with quantitative readouts. Here, we provide a web-based implementation of NNAlign allowing non-expert end-users to submit their data (optionally adjusting method parameters), and in return receive a trained method (including a visual representation of the identified motif) that subsequently can be used as prediction method and applied to unknown proteins/peptides. We have successfully applied this method to several different data sets including peptide microarray-derived sets containing more than 100,000 data points. NNAlign is available online at © 2011 Andreatta et al.

Burckel W.P.,Schafer Corporation | Gray R.N.,Schafer Corporation
Applied Optics | Year: 2013

We show that the filtered white noise process applied to logarithmically sampled turbulence spectra executed in cylindrical spatial frequency coordinates produces accurate phase screens that are free of the shortcomings associated with uniform sampling schemes. Decoupled from the sampling requirements of the wave-optics computational mesh in which they are used, the screens have isotropic statistics, do not require low spatial frequency augmentation, have prescribed resolution with more optimum sampling for the simulation, and feature an economical method of achieving screen motion that minimizes memory requirements. © 2013 Optical Society of America.

Schafer Corporation | Date: 2012-06-18

Condoms. Latex for use in the manufacture of condoms.

Schafer Corporation | Date: 2013-03-22

Fish and meat preserves; Fish burger patties; Fish cakes; Fish fillets; Fish sausages; Fish, not live; Meat, fish, poultry and game; Pickled fish; Processed fish; Processed seafood, namely, fish; Seasoned Fish Products.

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