scholarly journals Evaluation of Structurally Distorted Split GFP Fluorescent Sensors for Cell-Based Detection of Viral Proteolytic Activity

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 24
Author(s):  
Miguel R. Guerreiro ◽  
Ana R. Fernandes ◽  
Ana S. Coroadinha
Keyword(s):  
Proteolytic Activity ◽  
Self Assembly ◽  
Signal To Noise Ratio ◽  
Functional Characterization ◽  
Coiled Coils ◽  
Tobacco Etch Virus Protease ◽  
Structural Distortions ◽  
Powerful Approach ◽  
Single Protein ◽  
Selection Of

Cell-based assays are essential for virus functional characterization in fundamental and applied research. Overcoming the limitations of virus-labelling strategies while allowing functional assessment of critical viral enzymes, virus-induced cell-based biosensors constitute a powerful approach. Herein, we designed and characterized different cell-based switch-on split GFP sensors reporting viral proteolytic activity and virus infection. Crucial to these sensors is the effective—yet reversible—fluorescence off-state, through protein distortion. For that, single (protein embedment or intein-mediated cyclization) or dual (coiled-coils) distortion schemes prevent split GFP self-assembly, until virus-promoted proteolysis of a cleavable sequence. All strategies showed their applicability in detecting viral proteolysis, although with different efficiencies depending on the protease. While for tobacco etch virus protease the best performing sensor was based on coiled-coils (signal-to-noise ratio, SNR, 97), for adenovirus and lentivirus proteases it was based on GFP11 cyclization (SNR 3.5) or GFP11 embedment distortion (SNR 6.0), respectively. When stably expressed, the sensors allowed live cell biosensing of adenovirus infection, with sensor fluorescence activation 24 h post-infection. The structural distortions herein studied are highly valuable in the development of cellular biosensing platforms. Additionally highlighted, selection of the best performing strategy is highly dependent on the unique properties of each viral protease.

Automated phase attribute-based picking applied to reflection seismics

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. V141-V150 ◽  
Author(s):  
Emanuele Forte ◽  
Matteo Dossi ◽  
Michele Pipan ◽  
Anna Del Ben
Keyword(s):  
Signal To Noise Ratio ◽  
Phase Method ◽  
Data Sets ◽  
Reflection Seismic ◽  
Instantaneous Phase ◽  
Whole Process ◽  
Specific Analysis ◽  
Reflection Seismics ◽  
Selection Of ◽  
Energy Package

We have applied an attribute-based autopicking algorithm to reflection seismics with the aim of reducing the influence of the user’s subjectivity on the picking results and making the interpretation faster with respect to manual and semiautomated techniques. Our picking procedure uses the cosine of the instantaneous phase to automatically detect and mark as a horizon any recorded event characterized by lateral phase continuity. A patching procedure, which exploits horizon parallelism, can be used to connect consecutive horizons marking the same event but separated by noise-related gaps. The picking process marks all coherent events regardless of their reflection strength; therefore, a large number of independent horizons can be constructed. To facilitate interpretation, horizons marking different phases of the same reflection can be automatically grouped together and specific horizons from each reflection can be selected using different possible methods. In the phase method, the algorithm reconstructs the reflected wavelets by averaging the cosine of the instantaneous phase along each horizon. The resulting wavelets are then locally analyzed and confronted through crosscorrelation, allowing the recognition and selection of specific reflection phases. In case the reflected wavelets cannot be recovered due to shape-altering processing or a low signal-to-noise ratio, the energy method uses the reflection strength to group together subparallel horizons within the same energy package and to select those satisfying either energy or arrival time criteria. These methods can be applied automatically to all the picked horizons or to horizons individually selected by the interpreter for specific analysis. We show examples of application to 2D reflection seismic data sets in complex geologic and stratigraphic conditions, critically reviewing the performance of the whole process.

scholarly journals DRPnet - Automated Particle Picking in Cryo-Electron Micrographs using Deep Regression

2019 ◽  
Author(s):  
Nguyen P. Nguyen ◽  
Jacob Gotberg ◽  
Ilker Ersoy ◽  
Filiz Bunyak ◽  
Tommi White
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Electron Micrographs ◽  
Signal To Noise Ratio ◽  
Particle Analysis ◽  
User Interactions ◽  
Distance Map ◽  
Protein Particles ◽  
Selection Of ◽  
Specific Particle

AbstractSelection of individual protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based method to automatically detect particle centers from cryoEM micrographs. This is a challenging task because of the low signal-to-noise ratio of cryoEM micrographs and the size, shape, and grayscale-level variations in particles. We propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined (or classified) to reduce false particle detections by the second CNN. This approach, entitled Deep Regression Picker Network or “DRPnet”, is simple but very effective in recognizing different grayscale patterns corresponding to 2D views of 3D particles. Our experiments showed that DRPnet’s first CNN pretrained with one dataset can be used to detect particles from a different datasets without retraining. The performance of this network can be further improved by re-training the network using specific particle datasets. The second network, a classification convolutional neural network, is used to refine detection results by identifying false detections. The proposed fully automated “deep regression” system, DRPnet, pretrained with TRPV1 (EMPIAR-10005) [1], and tested on β-galactosidase (EMPIAR-10017) [2] and β-galactosidase (EMPIAR-10061) [3], was then compared to RELION’s interactive particle picking. Preliminary experiments resulted in comparable or better particle picking performance with drastically reduced user interactions and improved processing time.

scholarly journals Collecting Microplastics in Gardens: Case Study (i) of Soil

2021 ◽  
Vol 9 ◽  
Author(s):  
Zahra Sobhani ◽  
Yunlong Luo ◽  
Christopher T. Gibson ◽  
Youhong Tang ◽  
Ravi Naidu ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Point Of View ◽  
Signal To Noise ◽  
Statistical Point ◽  
Image Mapping ◽  
Noise Ratio ◽  
In The Beginning ◽  
Raman Image ◽  
Selection Of

As an emerging contaminant, microplastic is receiving increasing attention. However, the contamination source is not fully known, and new sources are still being identified. Herewith, we report that microplastics can be found in our gardens, either due to the wrongdoing of leaving plastic bubble wraps to be mixed with mulches or due to the use of plastic landscape fabrics in the mulch bed. In the beginning, they were of large sizes, such as > 5 mm. However, after 7 years in the garden, owing to natural degradation, weathering, or abrasion, microplastics are released. We categorize the plastic fragments into different groups, 5 mm–0.75 mm, 0.75 mm–100 μm, and 100–0.8 μm, using filters such as kitchenware, meaning we can collect microplastics in our gardens by ourselves. We then characterized the plastics using Raman image mapping and a logic-based algorithm to increase the signal-to-noise ratio and the image certainty. This is because the signal-to-noise ratio from a single Raman spectrum, or even from an individual peak, is significantly less than that from a spectrum matrix of Raman mapping (such as 1 vs. 50 × 50) that contains 2,500 spectra, from the statistical point of view. From the 10 g soil we sampled, we could detect the microplastics, including large (5 mm–100 μm) fragments and small (<100 μm) ones, suggesting the degradation fate of plastics in the gardens. Overall, these results warn us that we must be careful when we do gardening, including selection of plastic items for gardens.

Solvent-tuning of ordered mesoporous silicas prepared through evaporation-induced self-assembly templated by poly(ethylene oxide-b-ε-caprolactone)

RSC Advances ◽  
2014 ◽  
Vol 4 (105) ◽  
pp. 61012-61021 ◽  
Author(s):  
Wei-Cheng Chu ◽  
Chen-Xin Lin ◽  
Shiao-Wei Kuo
Keyword(s):  
Ethylene Oxide ◽  
Self Assembly ◽  
Mesoporous Silicas ◽  
Size Distributions ◽  
Ordered Mesoporous ◽  
Pore Size Distributions ◽  
Evaporation Induced Self Assembly ◽  
Poly Ethylene Oxide ◽  
Poly Ethylene ◽  
Selection Of

Highly ordered mesoporous silicas having narrow pore size distributions and various morphologies merely through selection of an appropriate solvent.

scholarly journals Integration of Rotation and Piston Motions in Coiled-Coil Signal Transduction

2007 ◽  
Vol 189 (16) ◽  
pp. 6048-6056 ◽  
Author(s):  
Rong Gao ◽  
David G. Lynn
Keyword(s):  
Coiled Coil ◽  
Dynamic Environment ◽  
Coiled Coils ◽  
Signal Integration ◽  
Signaling Mechanisms ◽  
Protein Receptor ◽  
Single Protein ◽  
Environmental Responses ◽  
Multiple Signaling ◽  
Host Signals

ABSTRACT A coordinated response to a complex and dynamic environment requires an organism to simultaneously monitor and interpret multiple signaling cues. In bacteria and some eukaryotes, environmental responses depend on the histidine autokinases (HKs). For example, VirA, a large integral membrane HK from Agrobacterium tumefaciens, regulates the expression of virulence genes in response to signals from multiple molecular classes (phenol, pH, and sugar). The ability of this pathogen to perceive inputs from different known host signals within a single protein receptor provides an opportunity to understand the mechanisms of signal integration. Here we exploited the conserved domain organization of the HKs and engineered chimeric kinases to explore the signaling mechanisms of phenol sensing and pH/sugar integration. Our data implicate a piston-assisted rotation of coiled coils for integration of multiple inputs and regulation of critical responses during pathogenesis.

Multispectral Imaging in Light Microscopy

1998 ◽  
Vol 4 (S2) ◽  
pp. 126-127
Author(s):  
K. R. Spring
Keyword(s):  
Light Microscopy ◽  
Multispectral Imaging ◽  
Signal To Noise Ratio ◽  
Monochromatic Light ◽  
Tunable Filters ◽  
Excitation Wavelength ◽  
Wavelength Selection ◽  
Emission Signal ◽  
Excitation Laser ◽  
Selection Of

Many recent applications in light microscopy involve the use of multiple fluorophores or the delineation of signals arising from spectrally distinct sources. In microspectroscopy, it is always desirable to illuminate fluorescently-labeled microscopic specimens with monochromatic light as the narrowest possible excitation wavelength range usually results in the highest emission signal-to-noise ratio. Generation of polychromatic light from an arc lamp and selection of the excitation wavelength by interference filters or monochrometers are the most common techniques for excitation microspectrofluorometry. Emission spectroscopy is usually done with filter wheels, monochrometers, or interferometers inserted between the microscope detection port and the detector. This presentation will be directed toward other, less frequently-used, approaches for spectral scanning of the specimen in the light microscope. Three topics will be considered: 1) the use of acousto-optical tunable filters and lasers for rapid, narrow-band, excitation wavelength selection; 2) the use of holographic notch filters for rejection of unwanted excitation laser light; 3) using liquid-crystal tunable filters for emission scanning.

Review of Recent Research on the Selection of Frequency-Gain Characteristics for Hearing Aids

1980 ◽  
Vol 89 (5_suppl) ◽  
pp. 79-83
Author(s):  
Richard Lippmann
Keyword(s):  
Hearing Aids ◽  
Speech Intelligibility ◽  
Signal To Noise Ratio ◽  
Hearing Aid ◽  
Linear Amplification ◽  
Percentage Points ◽  
Gain Characteristic ◽  
The Relationship ◽  
Fitting Hearing Aids ◽  
Selection Of

Following the Harvard master hearing aid study in 1947 there was little research on linear amplification. Recently, however, there have been a number of studies designed to determine the relationship between the frequency-gain characteristic of a hearing aid and speech intelligibility for persons with sensorineural hearing loss. These studies have demonstrated that a frequency-gain characteristic that rises at a rate of 6 dB/octave, as suggested by the Harvard study, is not optimal. They have also demonstrated that high-frequency emphasis of 10–40 dB above 500–1000 Hz is beneficial. Most importantly, they have demonstrated that hearing aids as they are presently being fit do not provide maximum speech intelligibility. Percent word correct scores obtained with the best frequency-gain characteristics tested in various studies have been found to be 9 to 19 percentage points higher than scores obtained with commercial aids owned by subjects. This increase in scores is equivalent to an increase in signal-to-noise ratio of 10 to 20 dB. This is a significant increase which could allow impaired listeners to communicate in many situations where they presently cannot. These results demonstrate the need for further research on linear amplification aimed at developing practical suggestions for fitting hearing aids.

Image Matching Optimization Based on Taguchi Method and Adaptive Spatial Clustering with SIFT Features

2017 ◽  
Vol 31 (11) ◽  
pp. 1755014 ◽  
Author(s):  
Yuan Xu ◽  
Hehui Lu ◽  
Defu Zhou ◽  
Jiongbin Zheng ◽  
Jianguo Zhang
Keyword(s):  
Taguchi Method ◽  
Image Matching ◽  
Spatial Clustering ◽  
Signal To Noise Ratio ◽  
Matching Accuracy ◽  
Scale Invariant ◽  
Effect Factor ◽  
Optimum Parameters ◽  
Scale Invariant Feature ◽  
Selection Of

A novel image matching algorithm based on both Taguchi method and spatial clustering is proposed to optimize the Scale Invariant Feature Transform (SIFT) matching results. To improve the matching accuracy, adaptive spatial clustering is used. What is more, in order to get the fitting parameters to balance matching accuracy and quantity, Taguchi method is adopted to optimize the key parameter combination including the ratio threshold of Euclidean distance and the constrain parameters in the process of adaptive spatial clustering. Moreover, signal-to-noise ratio (SNR) results are analyzed by variance to get the effect factor which is taken as the basis for the selection of optimized parameters. The optimum parameters combination is obtained eventually. The final experimental results show that the matching quality based on SIFT feature are improved significantly.

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