Direct electron detection yields cryo-EM reconstructions at resolutions beyond 3/4 Nyquist frequency

Bammes, Benjamin; Rochat, Ryan H.; Jakana, Joanita; Chen, Donghua; Chiu, Wah

doi:10.1016/j.jsb.2012.01.008

Cited by 142 publications

(118 citation statements)

References 55 publications

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“…This result, together with the information obtained through docking of the atomic models of the core sRNP protein components, is consistent with the previously proposed hypothesis that the sRNAs run perpendicular to the Nop5-fibrillarin heterotetramer (Bleichert et al 2009). Innovations in automation (Lander et al 2009;Voss et al 2009;Wiedenheft et al 2011) and in camera resolution (Bammes et al 2012) in cryo-electron microscopy are both designed to permit resolution sufficient to visualize the sR8 RNA. Localizing the sR8 RNA in future studies will reveal how the core proteins discriminate between di-and mono-sRNP conformations through the conserved presence of the internal sRNA loop.…”

Section: Discussionmentioning

confidence: 99%

The box C/D sRNP dimeric architecture is conserved across domain Archaea

Bower-Phipps

Taylor²,

Wang³

et al. 2012

RNA

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Box C/D small (nucleolar) ribonucleoproteins [s(no)RNPs] catalyze RNA-guided 29-O-ribose methylation in two of the three domains of life. Recent structural studies have led to a controversy over whether box C/D sRNPs functionally assemble as monomeric or dimeric macromolecules. The archaeal box C/D sRNP from Methanococcus jannaschii (Mj) has been shown by glycerol gradient sedimentation, gel filtration chromatography, native gel analysis, and single-particle electron microscopy (EM) to adopt a di-sRNP architecture, containing four copies of each box C/D core protein and two copies of the Mj sR8 sRNA. Subsequently, investigators used a two-stranded artificial guide sRNA, CD45, to assemble a box C/D sRNP from Sulfolobus solfataricus with a short RNA methylation substrate, yielding a crystal structure of a mono-sRNP. To more closely examine box C/D sRNP architecture, we investigate the role of the omnipresent sRNA loop as a structural determinant of sRNP assembly. We show through sRNA mutagenesis, native gel electrophoresis, and single-particle EM that a di-sRNP is the near exclusive architecture obtained when reconstituting box C/D sRNPs with natural or artificial sRNAs containing an internal loop. Our results span three distantly related archaeal species-Sulfolobus solfataricus, Pyrococcus abyssi, and Archaeoglobus fulgidus-indicating that the di-sRNP architecture is broadly conserved across the entire archaeal domain.

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Section: Discussionmentioning

confidence: 99%

The box C/D sRNP dimeric architecture is conserved across domain Archaea

Bower-Phipps

Taylor²,

Wang³

et al. 2012

RNA

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“…It can be notice in Fig. 2 indicated that the normalized frequency is schemed against power spectral density possessing a range from 0 to 1 knowing that 1 indicating 50 Nyquist frequency [19].…”

Section: A Power Spectral Densitymentioning

confidence: 91%

Mathematical Modeling of Sampling, Quantization, and Coding in Sigma Delta Converter using Matlab

Barzinjy

Ismail

Ameen

2017

UHDJST

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It can be stated that sigma-delta [3] analog-to-digital adapter is a most common approach of over-sampling analog-to-digital adapter. The map processor of a sigma-delta analog-to-digital adapter is displayed in Fig. 1 [4].The sigma-delta analog-to-digital adapter can be divided into two lumps, the quantizing and the decimating parts. Essentially, decimation is the act of decreasing the data rate down from the over-sampling rate without losing information. The quantizing part contains the analog integrator, the 1-bit analog-to-digital adapter, and the 1-bit digital-to-analog adapter [5]. The task of the quantizing part is to adapt the data in the analog input into digital shape. The input-output relationship of the sigma-delta quantizer is A B S T R A C TThe received analog signal must be digitized before the digital signal processing can demodulate it. Sampling, quantization, and coding are the separate stages for the analog-to-digital adaptation procedure. The procedure of adapting an unceasing time-domain signal into a separate time-domain signal is called sampling. While, the procedure of adapting a separatetime, continuous-valued signal into a discrete-time, discrete-valued signal is known as quantization. Thus, quantization error is the mismatch between the unquantized sample and the quantized sample. The method of demonstrating the quantized samples in binary form is known as coding. This investigation utilized Matlab® program to recommend a proper scheme for a wireless-call button network of input signal, normalized frequency, and over-sampling ratio against signalto-quantization noise ratio. Two vital characteristics of this wireless network design are cost-effective and low-power utilization. This investigation, through reducing the in-band quantization error, also studied how oversampling can enhance the accomplishment of an analog-to-digital adapter.

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“…A number of works have characterized the performance of these new cameras [42]- [44]. Interestingly, due to the small exposure times of these new devices, it has been realized that the specimen actually moves inside the ice matrix [45] due to its interaction with the electron beam (most likely, this has been one of the most resolution limiting factors in years).…”

Section: ) High-resolution Electron Imaging: Direct Detection Devicementioning

confidence: 99%

A Guided Tour of Selected Image Processing and Analysis Methods for Fluorescence and Electron Microscopy

Kervrann

Sorzano

Acton

et al. 2016

IEEE J. Sel. Top. Signal Process.

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Abstract-Microscopy imaging, including fluorescence microscopy and electron microscopy, has taken a prominent role in life science research and medicine due to its ability to investigate the 3D interior of live cells and organisms. A long-term research in bio-imaging at the sub-cellular and cellular scales consists then in inferring the relationships between the dynamics of macromolecules and their functions. In this area, image processing and analysis methods are now essential to understand the dynamic organization of groups of interacting molecules inside molecular machineries and to address issues in fundamental biology driven by advances in molecular biology, optics and technology. In this paper, we present recent advances in fluorescence and electron microscopy and we focus on dedicated image processing and analysis methods required to quantify phenotypes for a limited number but typical studies in cell imaging.

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Direct electron detection yields cryo-EM reconstructions at resolutions beyond 3/4 Nyquist frequency

Cited by 142 publications

References 55 publications

The box C/D sRNP dimeric architecture is conserved across domain Archaea

The box C/D sRNP dimeric architecture is conserved across domain Archaea

Mathematical Modeling of Sampling, Quantization, and Coding in Sigma Delta Converter using Matlab

A Guided Tour of Selected Image Processing and Analysis Methods for Fluorescence and Electron Microscopy

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