#### 2SDR: Applying Kronecker Envelope PCA to denoise Cryo-EM Images

Principal component analysis (PCA) is arguably the most widely used dimension reduction method for vector type data. When applied to image data, PCA demands the images to be portrayed as vectors. The resulting computation is heavy because it will solve an eigenvalue problem of a huge covariance matrix due to the vectorization step. To mitigate the computation burden, multilinear PCA (MPCA) that generates each basis vector using a column vector and a row vector with a Kronecker product was introduced, for which the success was demonstrated on face image sets.

Expand

#### The generalized degrees of freedom of multilinear principal component analysis

Tensor data, such as image set, movie data, gene-environment interactions, or gene–gene interactions, have become a popular data format in many fields. Multilinear Principal Component Analysis (MPCA) has been recognized as an efficient dimension reduction method for tensor data analysis. However, a gratifying rank selection method for a general application of MPCA is not yet available. For example, both the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), arguably two of the most commonly used model selection methods, require more strict model assumptions when applying on the rank selection in MPCA.

Expand

#### Deriving a sub-nanomolar affinity peptide from TAP to enable smFRET analysis of RNA polymerase II complexes

Our capability to visualize protein complexes such as RNA polymerase II (pol II) by single-molecule imaging techniques has largely been hampered by the absence of a simple bio-orthogonal approach for selective labeling with a fluorescent probe. Here, we modify the existing calmodulin-binding peptide (CBP) in the widely used Tandem Affinity Purification (TAP) tag to endow it with a high affinity for calmodulin (CaM) and use dye-CaM to conduct site-specific labeling of pol II.

Expand

#### On the strengths of the self-updating process clustering algorithm

The self-updating process (SUP) is a clustering algorithm that stands from the viewpoint of data points and simulates the process how data points move and perform self-clustering. It is an iterative process on the sample space and allows for both time-varying and time-invariant operators.

Expand

#### γ-SUP: a clustering algorithm for cryo-electron microscopy images

of asymmetric particles

Cryo-electron microscopy (cryo-EM) has recently emerged as a powerful tool for obtaining three-dimensional (3D) structures of biological macromolecules in native states. A minimum cryo-EM image data set for deriving a meaningful reconstruction is comprised of thousands of randomly orientated projections of

Expand

#### Robust independent component analysis via minimum

divergence estimation

Independent component analysis (ICA) has been shown to be useful in many applications. However, most ICA methods are sensitive to data contamination and outliers. In this article we introduce a general minimum U-divergence framework for ICA, which covers some standard ICA methods as special cases.

Expand

#### On multilinear principal component analysis of order-two tensors

Principal component analysis is commonly used for dimension reduction in analyzing high dimensional data. Multilinear principal component analysis aims to serve a similar function for analyzing tensor structure data, and has empirically been shown effective in reducing dimensionality. In this paper, we investigate its

Expand

#### Regulation of mammalian transcription by Gdown1 through

a novel steric crosstalk revealed by cryo-EM

In mammals, a distinct RNA polymerase II form, RNAPII(G) contains a novel subunit Gdown1 (encoded by POLR2M), which represses gene activation, only to be reversed by the multisubunit Mediator co-activator. Here, we employed single-particle cryo-electron microscopy (cryo-EM) to disclose the architectures of

Expand

#### Toward automated denoising of single molecular Förster

resonance energy transfer data

A wide-field two-channel fluorescence microscope is a powerful tool as it allows for the study of conformation dynamics of hundreds to thousands of immobilized single molecules by Förster resonance energy transfer (FRET) signals. To date, the data reduction from a movie to a final set containing meaningful

Expand

#### Zernike phase plate cryoelectron microscopy facilitates single

particle analysis of unstained asymmetric protein complexes

Single particle reconstruction from cryoelectron microscopy images, though emerging as a powerful means in structural biology, is faced with challenges as applied to asymmetric proteins smaller than megadaltons due to low contrast. Zernike phase plate can improve the contrast by restoring the microscope

Expand