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.

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.

Prism-based excitation wavelength selection for multicolor fluorescence coincidence measurements

2005 ◽  
Vol 44 (6) ◽  
pp. 893 ◽  
Author(s):  
Roel Kassies ◽  
Aufried Lenferink ◽  
Ine Segers-Nolten ◽  
Cees Otto
Keyword(s):  
Excitation Wavelength ◽  
Wavelength Selection ◽  
Selection For ◽  
Multicolor Fluorescence ◽  
Coincidence Measurements

Raman spectra of twisted bilayer graphene close to the magic angle

2D Materials ◽  
2022 ◽  
Author(s):  
Tiago Campolina Barbosa ◽  
Andreij C. Gadelha ◽  
Douglas A. A. Ohlberg ◽  
Kenji Watanabe ◽  
Takashi Taniguchi ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Laser Excitation ◽  
Bilayer Graphene ◽  
Geometrical Model ◽  
Excitation Wavelength ◽  
Magic Angle ◽  
Phonon Interaction ◽  
Electron Phonon Interaction ◽  
Excitation Laser ◽  
Twisted Bilayer Graphene

Abstract In this work, we study the Raman spectra of twisted bilayer graphene samples as a function of their twist-angles (θ), ranging from 0.03º to 3.40º, where local θ are determined by analysis of their associated moiré superlattices, as imaged by scanning microwave impedance microscopy. Three standard excitation laser lines are used (457, 532, and 633 nm wavelengths), and the main Raman active graphene bands (G and 2D) are considered. Our results reveal that electron-phonon interaction influences the G band's linewidth close to the magic angle regardless of laser excitation wavelength. Also, the 2D band lineshape in the θ < 1º regime is dictated by crystal lattice and depends on both the Bernal (AB and BA) stacking bilayer graphene and strain soliton regions (SP) [1]. We propose a geometrical model to explain the 2D lineshape variations, and from it, we estimate the SP width when moving towards the magic angle.

Thin-Section Preparation and Transmitted-Light Microscopy

2010 ◽  
pp. 115-135
Keyword(s):  
Light Microscopy ◽  
Polymeric Composites ◽  
Glass Slide ◽  
Transmitted Light ◽  
Fiber Reinforced ◽  
Transmitted Light Microscopy ◽  
Ultrathin Sections ◽  
Microscopy Techniques ◽  
Surface Mounting ◽  
Selection Of

Abstract Transmitted-light methods reveal more details of the morphology of fiber-reinforced polymeric composites than are observable using any other available microscopy techniques. This chapter describes the various aspects relating to the selection and preparation of ultrathin-section specimens of fiber-reinforced polymeric composites for examination by transmitted-light microscopy techniques. The preparation steps covered are a selection of the rough section, preparation of the rough section for preliminary mounting, grinding and polishing the primary-mount first surface, mounting the first surface on a glass slide, and preparing the second surface (top surface). The optimization of microscope conditions and analysis of specimens by microscopy techniques are also covered. In addition, examples of composite ultrathin sections that are analyzed using transmitted-light microscopy contrast methods are shown throughout.

scholarly journals Optimization of Nonspherical Gold Nanoparticles for Photothermal Therapy

2019 ◽  
Vol 9 (20) ◽  
pp. 4300
Author(s):  
Paerhatijiang Tuersun ◽  
Xiayiding Yakupu ◽  
Xiang’e Han ◽  
Yingzeng Yin
Keyword(s):  
Gold Nanoparticles ◽  
Refractive Index ◽  
Photothermal Therapy ◽  
Therapeutic Agents ◽  
Tissue Type ◽  
Excitation Wavelength ◽  
Objective Functions ◽  
Design Tolerance ◽  
T Matrix ◽  
Selection Of

Previous investigations devoted to the optimization of nonspherical gold nanoparticles for photothermal therapy (PTT) encountered two issues, namely, the appropriate selection of objective functions and the processing of particle random orientations. In this study, these issues were resolved, and accurate optimization results were obtained for the three typical nonspherical gold nanoparticles (nanospheroid, nanocylinder, and nanorod) by using the T-matrix method. The dependence of the optimization results on the excitation wavelength and the refractive index of tissue was investigated. Regardless of the excitation wavelength and tissue type, gold nanospheroids were found to be the most effective therapeutic agents for PTT. The light absorption ability of optimized nanoparticles could be enhanced by using a laser with a longer wavelength. Finally, the design tolerance for the different sizes of nanoparticles was provided.

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.

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