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At least 883 records · Page 49

Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes

Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such as antimicrobial resistance genes. Many microbial genomes have been sequenced by short read sequencers and have resulted in a mix of contigs that derive from plasmids or chromosomes. New tools that accurately identify plasmids are needed to elucidate new plasmid-borne genes of high biological importance. We have developed Deeplasmid, a deep learning tool for distinguishing plasmids from bacterial chromosomes based on the DNA sequence and its encoded biological data. It requires as input only assembled sequences generated by any sequencing platform and assembly algorithm and its runtime scales linearly with the number of assembled sequences. Deeplasmid achieves an AUC–ROC of over 89%, and it was more accurate than five other plasmid classification methods. Finally, as a proof of concept, we used Deeplasmid to predict new plasmids in the fish pathogen Yersinia ruckeri ATCC 29473 that has no annotated plasmids. Deeplasmid predicted with high reliability that a long assembled contig is part of a plasmid. Using long read sequencing we indeed validated the existence of a 102 kb long plasmid, demonstrating Deeplasmid's ability to detect novel plasmids.

59 BASIC BIOLOGICAL SCIENCES↗

Characterization of Metalloproteins by High-throughput X-ray Absorption Spectroscopy

High-throughput X-ray absorption spectroscopy was used to measure transition metal content based on quantitative detection of X-ray fluorescence signals for 3879 purified proteins from several hundred different protein families generated by the New York SGX Research Center for Structural Genomics. Approximately 9% of the proteins analyzed showed the presence of transition metal atoms (Zn, Cu, Ni, Co, Fe, or Mn) in stoichiometric amounts. The method is highly automated and highly reliable based on comparison of the results to crystal structure data derived from the same protein set. To leverage the experimental metalloprotein annotations, we used a sequence-based de novo prediction method, MetalDetector, to identify Cys and His residues that bind to transition metals for the redundancy reduced subset of 2411 sequences sharing <70% sequence identity and having at least one His or Cys. As the HT-XAS identifies metal type and protein binding, while the bioinformatics analysis identifies metal-binding residues, the results were combined to identify putative metal-binding sites in the proteins and their associated families. We explored the combination of this data with homology models to generate detailed structure models of metal-binding sites for representative proteins. Finally, we used extended X-ray absorption fine structure data from two of the purified Zn metalloproteins to validate predicted metalloprotein binding site structures. This combination of experimental and bioinformatics approaches provides comprehensive active site analysis on the genome scale for metalloproteins as a class, revealing new insights into metalloprotein structure and function.

59 BASIC BIOLOGICAL SCIENCES↗

Scintillation has minimal impact on far-field Bennett-Brassard 1984 protocol quantum key distribution

The effect of scintillation, arising from propagation through atmospheric turbulence, on the sift and error probabilities of a quantum key distribution (QKD) system that uses the weak-laser-pulse version of the Bennett-Brassard 1984 (BB84) protocol is evaluated. Two earth-space scenarios are examined: satellite-to-ground and ground-to-satellite transmission. Both lie in the far-field power-transfer regime. This work complements previous analysis of turbulence effects in near-field terrestrial BB84 QKD [J. H. Shapiro, Phys. Rev. A 67, 022309 (2003)]. More importantly, it shows that scintillation has virtually no impact on the sift and error probabilities in earth-space BB84 QKD, something that has been implicitly assumed in prior analyses for that application. This result contrasts rather sharply with what is known for high-speed laser communications over such paths, in which deep, long-lived scintillation fades present a major challenge to high-reliability operation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

X-ray free electron laser tuning for variable-gap undulators

A tuning method for hard x-ray free electron lasers is presented which combines electron-beam-based alignment (EBBA) with undulator radiation spectrum analysis. This method is conducive to achieving a dispersion-free orbit, finding the undulator field center, and determining the accurate value of the gap distance for the operating undulator K for each undulator segment, and it proves to be highly reliable and robust for free electron laser lasing with variable-gap undulators. The effect of the natural focusing of the undulator on the EBBA is confirmed with measurements and a theoretical analysis. The tilt of an undulator segment subject to ground movement introduces an undulator field change which the electron beam interacts with and can be corrected by K tuning to find a precise compensating gap distance. Lattice matching with a wire-scanner-based Twiss parameter measurement along the undulator line enables the EBBA reference orbit to coincide with the e-beam distribution.

47 OTHER INSTRUMENTATION↗

Asymptotic-state prediction for fast flavor transformation in neutron star mergers

Neutrino flavor instabilities appear to be omnipresent in dense astrophysical environments, thus presenting a challenge to large-scale simulations of core-collapse supernovae and neutron star mergers (NSMs). Subgrid models offer a path forward, but require an accurate determination of the local outcome of such conversion phenomena. Focusing on “fast” instabilities, related to the existence of a crossing between neutrino and antineutrino angular distributions, we consider a range of analytical mixing schemes, including a new, fully three-dimensional one, and also introduce a new machine learning (ML) model. We compare the accuracy of these models with the results of several thousands of local dynamical calculations of neutrino evolution from the conditions extracted from classical NSM simulations. Our ML model shows good overall performance, but struggles to generalize to conditions from a NSM simulation not used for training. The multidimensional analytic model performs and generalizes even better, while other analytic models (which assume axisymmetric neutrino distributions) do not have reliably high performances, as they notably fail as expected to account for effects resulting from strong anisotropies. As a result, the ML and analytic subgrid models extensively tested here are both promising, with different computational requirements and sources of systematic errors.

79 ASTRONOMY AND ASTROPHYSICS↗

Effect of Back Contact and Rapid Thermal Processing Conditions on Flexible CdTe Device Performance

Flexible CdTe solar cells on ultra-thin glass substrates can enable new applications that require high specific power, unique form-factors, and low manufacturing costs. To be successful, these cells must be cost competitive, have high efficiency, and have high reliability. Here we present back contact processing conditions that enabled us to achieve over 16% efficiency on flexible Corning (R) Willow (R) Glass substrates. We used co-evaporated ZnTe:Cu and Au as our back contact and used rapid thermal processing (RTP) to activate the back contact. Both the ZnTe to Cu ratio and the RTP activation temperature provide independent control over the device performance. We have investigated the influence of various RTP conditions to Cu activation and distribution. Current density-voltage, capacitance-voltage measurements along with device simulations were used to examine the device performance in terms of ZnTe to Cu ratio and rapid thermal activation temperature.

14 SOLAR ENERGY↗

Evaluating Model Robustness for Defect Identification and Classification in a Composite Aerostructure Material

Aircraft structures are required to have a high level of quality to satisfy their need for light weight, efficient flight, and withstanding high loads over their lifespan. These aerostructures are typically made from a composite material due to their good tensile strength and resistance to compression. To ensure their structural integrity, the composite material requires inspection for common flaws such as porosity, delaminations, voids, foreign object debris, and other defects. Ultrasonic testing (UT) is a popular non-destructive inspection (NDI) technique used for effectively evaluating the composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods in the identification and classification of defects. Deep learning has shown great promise in numerous fields, and we show its effectiveness in the evaluation of the composite aerostructure material. The methods developed here are both highly reliable with a top recall value of 98.64% as well as extremely efficient requiring an average of 4 s during the inferencing stage to evaluate new composites. Lastly, we investigate model robustness to concept drift by measuring its performance over time.

36 MATERIALS SCIENCE↗

Comparative Study on Deep Learning Methods for Defect Identification and Classification in Composite Aerostructure Material

Aircraft structures are required to have a high level of quality to satisfy their need for light weight, efficient flight, and withstanding high loads over their lifespan. These aerostructures are typically made from composite material due to their good tensile strength and resistance to compression. To ensure their structural integrity, the composite material requires inspection for common flaws such as porosity, delaminations, voids, foreign object debris, and other defects. Ultrasonic testing (UT) is a popular non-destructive inspection (NDI) technique used for effectively evaluating composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods in the identification and classification of defects. Deep learning has shown great promise in numerous fields, and we show its effectiveness in the evaluation of composite aerostructure material. Furthermore, the methods developed here are both highly reliable with a top recall value of 98.63% as well as extremely efficient requiring an average of 4 seconds during the inferencing stage to evaluate new composites.

composites↗

Transfer of autocollimator calibration for use with scanning gantry profilometers for accurate determination of surface slope and curvature of state-of-the-art x-ray mirrors

X-ray optics, desired for beamlines at free-electron-laser and diffraction-limited-storage-ring x-ray light sources, must have almost perfect surfaces, capable of delivering light to experiments without significant degradation of brightness and coherence. To accurately characterize such optics at an optical metrology lab, two basic types of surface slope profilometers are used: The long trace profilers (LTPs) and nanometer optical measuring (NOM) like angular deflectometers, based on electronic autocollimator (AC) ELCOMAT-3000. The inherent systematic errors of the instrument's optical sensors set the principle limit to their measuring performance. Where autocollimator of a NOM-like profiler may be calibrated at a unique dedicated facility, this is for a particular configuration of distance, aperture size, and angular range that does not always match the exact use in a scanning measurement with the profiler. Here we discuss the developed methodology, experimental set-up, and numerical methods of transferring the calibration of one reference AC to the scanning AC of the Optical Surface Measuring System (OSMS), recently brought to operation at the ALS Xray Optics Laboratory. We show that precision calibration of the OSMS performed in three steps, allows us to provide high confidence and accuracy low-spatial-frequency metrology and not â€print into' measurements the inherent systematic error of tool in use. Here, with the examples of the OSMS measurements with a state-of-The-Art x-ray aspherical mirror, available from one of the most advanced vendors of X-ray optics, we demonstrate the high efficacy of the developed calibration procedure. The results of our work are important for obtaining high reliability data, needed for sophisticated numerical simulations of beamline performance and optimization of beamline usage of the optics. This work was supported by the U. S. Department of Energy under contract number DE-AC02-05CH11231.

47 OTHER INSTRUMENTATION↗

Magnetic resonance visualization of conductive structures by sequence-triggered direct currents and spin-echo phase imaging

Purpose: Instrument visualization in interventional magnetic resonance imaging (MRI) is commonly performed via susceptibility artifacts. Unfortunately, this approach suffers from limited conspicuity in inhomogeneous tissue and disturbed spatial encoding. Also, susceptibility artifacts are controllable only by sequence parameters. This work presents the basics of a new visualization method overcoming such problems by applying sequence-triggered direct current (DC) pulses in spin-echo (SE) imaging. SE phase images allow for background free current path localization. Methods: Application of a sequence-triggered DC pulse in SE imaging, e.g., during a time period between radiofrequency excitation and refocusing, results in transient field inhomogeneities. Dependent on the additional z-magnetic field from the DC, a phase offset results despite the refocusing pulse. False spatial encoding is avoided by DC application during periods when read-out or slice-encoding gradients are inactive. A water phantom containing a brass conductor (water equivalent susceptibility) and a titanium needle (serving as susceptibility source) was used to demonstrate the feasibility. Artifact dependence on current strength and orientation was examined. Results: Without DC, the brass conductor was only visible due to its water displacement. The titanium needle showed typical susceptibility artifacts. Applying triggered DC pulses, the phase offset of spins near the conductor appeared. Because SE phase images are homogenous also in regions of persistent field inhomogeneities, the position of the conductor could be determined with high reliability. Artifact characteristic could be easily controlled by amperage leaving sequence parameters unchanged. For an angle of 30° between current and static field visualization was still possible. Conclusions: SE phase images display the position of a conductor carrying pulsed DC free from artifacts caused by persistent field inhomogeneities. Magnitude and phase images are acquired simultaneously under the same conditions without the use of extra measurement time. The presented technique offers many advantages for precise instrument localization in interventional MRI.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

SU-E-J-211: Design and Study of In-House Software Based Respiratory Motion Monitoring, Controlling and Breath-Hold Device for Gated Radiotherapy

Purpose: The purpose of this present work was to fabricate an in-house software based respiratory monitoring, controlling and breath-hold device using computer software programme which guides the patient to have uniform breath hold in response to request during the gated radiotherapy. Methods: The respiratory controlling device consists of a computer, inhouse software, video goggles, a highly sensitive sensor for measurement of distance, mounting systems, a camera, a respiratory signal device, a speaker and a visual indicator. The computer is used to display the respiratory movements of the patient with digital as well as analogue respiration indicators during the respiration cycle, to control, breath-hold and analyze the respiratory movement using indigenously developed software. Results: Studies were conducted with anthropomophic phantoms by simulating the respiratory motion on phantoms and recording the respective movements using the respiratory monitoring device. The results show good agreement between the simulated and measured movements. Further studies were conducted for 60 cancer patients with several types of cancers in the thoracic region. The respiratory movement cycles for each fraction of radiotherapy treatment were recorded and compared. Alarm indications are provided in the system to indicate when the patient breathing movement exceeds the threshold level. This will help the patient to maintain uniform breath hold during the radiotherapy treatment. Our preliminary clinical test results indicate that our device is highly reliable and able to maintain the uniform respiratory motion and breathe hold during the entire course of gated radiotherapy treatment. Conclusion: An indigenous respiratory monitoring device to guide the patient to have uniform breath hold device was fabricated. The alarm feature and the visual waveform indicator in the system guide the patient to have normal respiration. The signal from the device can be connected to the radiation unit in near future to carry out the gated radiotherapy treatment.

60 APPLIED LIFE SCIENCES↗

Evaluation of MRI and cannabinoid type 1 receptor PET templates constructed using DARTEL for spatial normalization of rat brains

Purpose: Image registration is one prerequisite for the analysis of brain regions in magnetic-resonance-imaging (MRI) or positron-emission-tomography (PET) studies. Diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) is a nonlinear, diffeomorphic algorithm for image registration and construction of image templates. The goal of this small animal study was (1) the evaluation of a MRI and calculation of several cannabinoid type 1 (CB1) receptor PET templates constructed using DARTEL and (2) the analysis of the image registration accuracy of MR and PET images to their DARTEL templates with reference to analytical and iterative PET reconstruction algorithms. Methods: Five male Sprague Dawley rats were investigated for template construction using MRI and [{sup 18}F]MK-9470 PET for CB1 receptor representation. PET images were reconstructed using the algorithms filtered back-projection, ordered subset expectation maximization in 2D, and maximum a posteriori in 3D. Landmarks were defined on each MR image, and templates were constructed under different settings, i.e., based on different tissue class images [gray matter (GM), white matter (WM), and GM + WM] and regularization forms (“linear elastic energy,” “membrane energy,” and “bending energy”). Registration accuracy for MRI and PET templates was evaluated by means of the distance between landmark coordinates. Results: The best MRI template was constructed based on gray and white matter images and the regularization form linear elastic energy. In this case, most distances between landmark coordinates were <1 mm. Accordingly, MRI-based spatial normalization was most accurate, but results of the PET-based spatial normalization were quite comparable. Conclusions: Image registration using DARTEL provides a standardized and automatic framework for small animal brain data analysis. The authors were able to show that this method works with high reliability and validity. Using DARTEL templates together with nonlinear registration algorithms allows for accurate spatial normalization of combined MRI/PET or PET-only studies.

60 APPLIED LIFE SCIENCES↗

Net-zero emissions energy systems

Some energy services and industrial processes - such as long-distance freight transport, air travel, highly reliable electricity, and steel and cement manufacturing - are particularly difficult to provide without adding carbon dioxide (CO 2 ) to the atmosphere. Rapidly growing demand for these services, combined with long lead times for technology development and long lifetimes of energy infrastructure, make decarbonization of these services both essential and urgent. We examine barriers and opportunities associated with these difficult-to-decarbonize services and processes, including possible technological solutions and research and development priorities. A range of existing technologies could meet future demands for these services and processes without net addition of CO 2 to the atmosphere, but their use may depend on a combination of cost reductions via research and innovation, as well as coordinated deployment and integration of operations across currently discrete energy industries.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Structure of a complex of uridine phosphorylase from Yersinia pseudotuberculosis with the modified bacteriostatic antibacterial drug determined by X-ray crystallography and computer analysis

Pseudotuberculosis and bubonic plague are acute infectious diseases caused by the bacteria Yersinia pseudotuberculosis and Yersinia pestis. These diseases are treated, in particular, with trimethoprim and its modified analogues. However, uridine phosphorylases (pyrimidine nucleoside phosphorylases) that are present in bacterial cells neutralize the action of trimethoprim and its modified analogues on the cells. In order to reveal the character of the interaction of the drug with bacterial uridine phosphorylase, the atomic structure of the unligated molecule of uridine-specific pyrimidine nucleoside phosphorylase from Yersinia pseudotuberculosis (YptUPh) was determined by X-ray diffraction at 1.7 Å resolution with high reliability (R{sub work} = 16.2, R{sub free} = 19.4%; r.m.s.d. of bond lengths and bond angles are 0.006 Å and 1.005°, respectively; DPI = 0.107 Å). The atoms of the amino acid residues of the functionally important secondary-structure elements—the loop L9 and the helix H8—of the enzyme YptUPh were located. The three-dimensional structure of the complex of YptUPh with modified trimethoprim—referred to as 53I—was determined by the computer simulation. It was shown that 53I is a pseudosubstrate of uridine phosphorylases, and its pyrimidine-2,4-diamine group is located in the phosphate-binding site of the enzyme YptUPh.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Cryogenic control system operational experience at SNS

Abstract The helium cryogenic system at Spallation Neutron Source (SNS) provides cooling to 81 superconducting radio frequency cavities. To support the operation of the cryogenic facility, a highly reliable control system consisting of software, hardware, and Human Machine Interface (HMI) has been developed and improved during the first fifteen years of operation. Integrating the cryogenic control system with other subsystems of the SNS complex is an important aspect to the success of the operation. The operating experience, lessons learned and recommendations to consider for future facilities will be detailed in this paper.

Howell, M. (ORCID:0000000310360707)↗

Implementing and evaluating a Gaussian mixture framework for identifying gene function from TnSeq data

The rapid acceleration of microbial genome sequencing increases opportunities to understand bacterial gene function. Unfortunately, only a small proportion of genes have been studied. Recently, TnSeq has been proposed as a cost-effective, highly reliable approach to predict gene functions as a response to changes in a cell’s fitness before-after genomic changes. However, major questions remain about how to best determine whether an observed quantitative change in fitness represents a meaningful change. To address the limitation, we develop a Gaussian mixture model framework for classifying gene function from TnSeq experiments. In order to implement the mixture model, we present the Expectation-Maximization algorithm and a hierarchical Bayesian model sampled using Stan’s Hamiltonian Monte-Carlo sampler. We compare these implementations against the frequentist method used in current TnSeq literature. From simulations and real data produced by E.coli TnSeq experiments, we show that the Bayesian implementation of the Gaussian mixture framework provides the most consistent classification results.

Li, Kevin↗

SierraNet: monitoring the snowpack in the Sierra Nevada: demo

© 2016 Copyright held by the owner/author(s). Next-generation hydrologic science and monitoring requires real-time, spatially distributed measurements of key variables including: soil moisture, air/soil temperature, snow depth, and air relative humidity. The SierraNet project provides these measurements by deploying low-power mesh networks across the California Sierra Nevada. This demo presents a replica of the end-to-end SierraNet monitoring system deployed in the Southern Sierra. This system is a highly reliable, low-power turn-key solution for environmental monitoring.

Brun-Laguna, Keoma↗

foresee™ (SWR-17-15)

foresee™ is a home energy management system (HEMS) that provides a user centric energy automation solution for residential building occupants. Built upon advanced control and machine learning algorithms, foresee™ intelligently manages the home appliances and distributed energy resources (DERs) such as photovoltaics and battery storage in a home. Unlike existing HEMS in the market, foresee™ provides a tailored home automation solution for individual occupants by learning and adapting to their preferences on cost, comfort, convenience and carbon. foresee™ improves not only the energy efficiency of the home but also its capability to provide grid services such as demand response. Highly reliable demand response services are likely to be incentivized by utility companies, making foresee™ economically viable for most homes.

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