Sensitivity and applicability of a rapid micro detection system in the enumeration of bacterial spores
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Abstract We present our follow-up observations to search for an electromagnetic counterpart of the IceCube high-energy neutrino IceCube-170922A. Monitoring observations of a likely counterpart, TXS 0506+056, are also described. First, we quickly took optical and near-infrared images of seven flat-spectrum radio sources within the IceCube error region right after the neutrino detection and found a rapid flux decline of TXS 0506+056 in Kanata/HONIR J-band data. Motivated by this discovery, intensive follow-up observations of TXS 0506+056 were continuously performed, including our monitoring imaging observations, spectroscopic observations, and polarimetric observations in optical and near-infrared wavelengths. TXS 0506+056 showed a large-amplitude (∼1.0 mag) variability in a time scale of several days or longer, although no significant variability was detected in a time scale of a day or shorter. TXS 0506+056 also showed a bluer-when-brighter trend in optical and near-infrared wavelengths. Structure functions of the variabilities were examined and indicate that TXS 0506+056 is not a special blazar in terms of optical variability. Polarization measurement results of TXS 0506+056 are also discussed.
Engineering of carbohydrate-active enzymes such as glycosynthases to enable chemoenzymatic synthesis of bespoke oligosaccharides has been limited by the lack of suitable ultrahigh-throughput screening methods capable of robustly detecting either starting substrates or end-products of the glycosidic bond formation reaction. Currently, there are limited screening methods available for rapid and highly sensitive single-cell-based screening of glycosynthase enzymes employing azido sugars as activated donor glycosyl substrates. Here, we report a fluorescence-based approach employing click-chemistry for the selective detection of glycosyl azides as substrates versus free inorganic azides as reaction products that facilitated an ultrahigh-throughput in vivo single-cell-based assay of glycosynthase activity. This assay was developed based on the distinct differences observed in relative fluorescence intensity of the triazole-containing fluorophore product formed during the click-chemistry reaction of organic glycosyl azides versus inorganic azides. This discovery formed the basis for proof of concept validation of a directed evolution methodology for screening and sorting glycosynthase mutants capable of synthesis of targeted fucosylated oligosaccharides. Our screening approach facilitated fluorescence-activated cell sorting of an error-prone polymerase chain reaction-based mutant library of fucosynthases expressed in Escherichia coli to identify several novel mutants that showed increased activity for ß-fucosyl azide-activated donor sugars toward desired acceptor sugars (e.g., pNP-xylose and lactose). Finally, we discuss avenues for improving this proof of concept in vivo assay method to identify better glycosynthase mutants and further demonstrate the broader applicability of this screening methodology for synthesis of bespoke glycans.
In some embodiments, a plurality of monitoring nodes each generate a series of current monitoring node values over time that represent a current operation of the industrial asset. An attack detection computer platform may receive the series of current monitoring node values and generate a set of current feature vectors including a current feature for capturing transients (e.g., local transients and/or global transients). The attack detection computer platform may also access an attack detection model having at least one decision boundary that was created using at least one of a set of normal feature vectors and/or a set of attacked feature vectors. The attack detection model may then be executed such that an attack alert signal is transmitted by the attack detection computer platform, when appropriate, based on the set of current feature vectors (including the current feature to capture transients) and the at least one decision boundary.
PCR-based assays have become the benchmark for detecting pathogens of poultry and other livestock; however, these techniques are limited in their ability to detect multiple infecting agents, provide limited genetic information on the pathogen, and, for RNA viruses, must be reviewed frequently to assure high sensitivity and specificity. In contrast, untargeted, high-throughput sequencing can rapidly detect all infecting agents in a sample while providing genomic sequence information to allow more in-depth characterization of viruses. Although next-generation sequencing (NGS) offers many advantages, one of its primary limitations is low sensitivity to pathogens given the abundance of host and other non-target sequences in sequencing libraries. We explored methods for improving the sensitivity of NGS to detect respiratory and enteric viruses in poultry from RNA extracts of swab samples. We employed commercial and custom-designed negative enrichment strategies to selectively deplete the most abundant rRNA reads from the host and non-target bacteria; host RNA was diminished from up to 40% of total reads to as low as 3%, and the total number of reads assigned to abundant bacterial classes were reduced greatly. Our treatment resulted in up to a 700-fold increase in the number of viral reads, detection of a greater number of viral agents, and higher average genome coverage for pathogens. Depletion assays added only 2 h to the NGS library preparation workflow. Custom depletion probe design offered significant cost savings (US$7–12 per sample) compared to commercial kits (US$30–50 per sample).
Responders need tools to rapidly detect and identify airborne alpha radioactivity during consequence management scenarios. Traditional continuous air monitor systems used for this purpose compute the net counts in various energy windows to determine the presence of specified isotopes, such as 235U, 239Pu, and 241Am. These calculations rely on having a well-calibrated detector, which is challenging in low-background environments. Here an alternative approach of using artificial neural networks to classify alpha spectra is presented. Two network architectures, fully connected and convolutional networks, were trained to classify alpha spectra into four categories: background and background plus the three isotopes above. Sources were injected into measured background at various fractions of the derived response level (DRL) corresponding to early-phase Protective Action Guides. The convolutional network identifies all sources at 1% of the DRL with average probability of detection of 95% and false alarm probability of 1%. Further, the network identifies sources ranging between 0.25% and 1% of the DRL with higher than 80% probability of detection and lower than 7% false alarm probability. Most significantly, the network performance improves in low-count background conditions, increasing its minimum probability of detection to 93% and reducing the false alarm probabilities to lower than 0.25%. These results show that, once trained on datasets representing a range of detection scenarios, artificial neural networks can accurately identify alpha isotopes of interest without the need for detector calibration.
A rendezvous of an unmanned probe to an earth-approaching asteroid has been given a high priority in the planning of interplanetary missions for the 1990s. Even without a space mission, much could be learned about the history of asteroids and comet nuclei if more information were available concerning asteroids with orbits which cross or approach the orbit of earth. It is estimated that the total number of earth-crossers accessible to ground-based survey telescopes should be approximately 1000. However, in connection with the small size and rapid angular motion expected of many of these objects an average of only one object is discovered per year. Attention is given to the development of the software necessary to distinguish such rapidly moving asteroids from stars and noise in continuously scanned CCD exposures of the night sky. Model and input parameters are considered along with detector sensitivity, aspects of minimum detectable displacement, and the point-spread function of the CCD.
Nuclear security is one of the defining challenges of our time. Nuclear threats range from deliberate dispersal of radioactive material to contaminate the vital infrastructure to diversion and smuggling of special nuclear material for clandestine nuclear programs and nuclear terrorism, respectively. There is an associated need to develop and sustain the nuclear forensics capabilities, which requires the understanding of complex processes that occur in plasmas of nuclear materials. The area of nuclear safety has seen a resurgence of public interest, and there is a concomitant need to safely store used nuclear fuel and detect structural material failure in nuclear power systems, especially in innovative reactor designs envisioned for future adoption. Laser-produced plasmas are complicated extreme environments that can generate intense and rich, highly specific signatures of nuclear and radiological materials, which can then be explored in a wide range of applications. They include interdiction and rapid detection of nuclear materials, including their isotopic composition, detection over long distances, laboratory simulation of weapons effects, monitoring the condition of structural materials in dry cask storage containers, and novel instrumentation for nuclear power systems. We present a compilation of recent representative examples of the application of laser spectroscopy, and laser-induced breakdown spectroscopy in particular, to nuclear safety and security problems. A case is made that spectroscopic techniques based on laser-produced plasmas offer complementary, and sometimes unique, capabilities that motivate the continued exploration of their efficient production and understanding of the signatures they produce
A data scaling technique is proposed to improve the performance of a rapid estimation scheme for analyzing rover rangefinder data in the specific area of obstacle outline characterization. The scaling transformation involves multiplying the measured beam length times a scale factor and taking the logarithm in a certain base. Although analysis shows that introducing the scaling transformation has no primary effect on the detection ability and that there is no apparent signal-to-noise ratio improvement, computer simulations show a reduction in the number of false jumps. In the 40-m range the use of data scaling cleared the foreground of obstacles of noise and obtained definition of the near edge of a crater.
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The overall objective of this project is the development of a lab-on-a-chip (LOC) electrochemical sensor capable of accurately measuring heavy metal (lead (Pb), cadmium (Cd), and arsenic (As)) concentrations in complex aqueous streams such as wastewater. The specific goals include: i) identify how heavy metal speciate and transform during the different stages of the wastewater treatment train, and explore pre-treatment methods that will de-complex heavy metals, making them available to ASV, ii) develop appropriate electrodes for ASV using organic/inorganic nanomaterials, and iii) integrate pre-treatment steps and specialized electrodes into an LOC sensor device capable of autonomously determining heavy metal concentrations in complex wastewater streams.
Abstract not provided.
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Small leaks in vacuum lines are detected by applying liquid-crystal coating, warming suspected area, and observing color change due to differential cooling by leak jet. Technique is used on inside or outside walls of vacuum-jacketed lines.
The objective of this study was to evaluate proposed design characteristics and applications of automated biomonitoring devices for real-time toxicity detection in water quality control on-board permanent space stations. Simulated tests in downlinking transmissions of automated biomonitoring data to Earth-receiving stations were simulated using satellite data transmissions from remote Earth-based stations.