CryoTRANS: predicting high-resolution maps of rare conformations from self-supervised trajectories in cryo-EM.

Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, enabling efficient determination of structures at near-atomic resolutions. However, a common challenge arises from the severe imbalance among various conformations of vitrified particles, leading to low-resolution reconstructions in rare conformations due to a lack of particle images in these quasi-stable states. We introduce CryoTRANS, a method that predicts high-resolution maps of rare conformations by constructing a self-supervised pseudo-trajectory between density maps of varying resolutions.

Enzyme-free fluorescent DNA detection based on nucleic acid-templated click reaction via controllable synthesis of CuO as heterogeneous nanocatalyst.

In the field of nucleic acid amplification assays, developing enzyme-free, easy-to-use, and highly sensitive amplification approaches remains a challenge. In this work, we synthesized a heterogeneous CuO nanocatalyst (hnCuO) with different particle sizes and shapes, which was used for developing enzyme- and label-free nucleic acid amplification methods based on the nucleic acid-templated azide-alkyne cycloaddition (AAC) reaction catalyzed by hnCuO. The hnCuO exhibited size- and shape-dependent catalytic activity, with smaller sizes and spherical-like shapes exhibiting superior activity.

DNA-Templated Click Ligation Chain Reaction Catalyzed by Heterogeneous CuO for Enzyme-Free Amplification and Ultrasensitive Detection of Nucleic Acids.

Nucleic acids play a pivotal role in the diagnosis of diseases. However, rapid, cost-efficient, and ultrasensitive identification of nucleic acid targets still represents a significant challenge. Herein, we describe an enzyme-free DNA amplification method capable of achieving accurate and ultrasensitive nucleic acid detection via NA-emplated lick igation hain eaction (DT-CLCR) catalyzed by a eterogeneous anocatalyst made of CuO (hnCuO).

Enhancing Density Maps by Removing the Majority of Particles in Single Particle Cryogenic Electron Microscopy Final Stacks.

Over the past decade, advancements in technology and methodology within the field of cryogenic electron microscopy (cryo-EM) single-particle analysis (SPA) have substantially improved our capacity for high-resolution structural examination of biological macromolecules. This advancement has ushered in a new era of molecular insights, replacing X-ray crystallography as the dominant method and providing answers to longstanding questions in biology. Since cryo-EM does not depend on crystallization, which is a significant limitation of X-ray crystallography, it captures particles of varying quality.

A minority of final stacks yields superior amplitude in single-particle cryo-EM.

Cryogenic electron microscopy (cryo-EM) is widely used to determine near-atomic resolution structures of biological macromolecules. Due to the low signal-to-noise ratio, cryo-EM relies on averaging many images. However, a crucial question in the field of cryo-EM remains unanswered: how close can we get to the minimum number of particles required to reach a specific resolution in practice? The absence of an answer to this question has impeded progress in understanding sample behavior and the performance of sample preparation methods.

Diffusion Mechanism in Residual Neural Network: Theory and Applications.

Diffusion, a fundamental internal mechanism emerging in many physical processes, describes the interaction among different objects. In many learning tasks with limited training samples, the diffusion connects the labeled and unlabeled data points and is a critical component for achieving high classification accuracy. Many existing deep learning approaches directly impose the fusion loss when training neural networks.

Learn From Unpaired Data for Image Restoration: A Variational Bayes Approach.

Collecting paired training data is difficult in practice, but the unpaired samples broadly exist. Current approaches aim at generating synthesized training data from unpaired samples by exploring the relationship between the corrupted and clean data. This work proposes LUD-VAE, a deep generative method to learn the joint probability density function from data sampled from marginal distributions.

Self-Distillation: Towards Efficient and Compact Neural Networks.

Remarkable achievements have been obtained by deep neural networks in the last several years. However, the breakthrough in neural networks accuracy is always accompanied by explosive growth of computation and parameters, which leads to a severe limitation of model deployment. In this paper, we propose a novel knowledge distillation technique named self-distillation to address this problem.

Investigating energy-based pool structure selection in the structure ensemble modeling with experimental distance constraints: The example from a multidomain protein Pub1.

The structural variations of multidomain proteins with flexible parts mediate many biological processes, and a structure ensemble can be determined by selecting a weighted combination of representative structures from a simulated structure pool, producing the best fit to experimental constraints such as interatomic distance. In this study, a hybrid structure-based and physics-based atomistic force field with an efficient sampling strategy is adopted to simulate a model di-domain protein against experimental paramagnetic relaxation enhancement (PRE) data that correspond to distance constraints. The molecular dynamics simulations produce a wide range of conformations depicted on a protein energy landscape.

Apparent coherence loss in phase space tomography.

A sensor pixel integrates optical intensity across its extent, and we explore the role that this integration plays in phase space tomography. The literature is inconsistent in its treatment of this integration-some approaches model this integration explicitly, some approaches are ambiguous about whether this integration is taken into account, and still some approaches assume pixel values to be point samples of the optical intensity. We show that making a point-sample assumption results in apodization of and thus systematic error in the recovered ambiguity function, leading to underestimating the overall degree of coherence.

Cerebellar Functional Parcellation Using Sparse Dictionary Learning Clustering.

The human cerebellum has recently been discovered to contribute to cognition and emotion beyond the planning and execution of movement, suggesting its functional heterogeneity. We aimed to identify the functional parcellation of the cerebellum using information from resting-state functional magnetic resonance imaging (rs-fMRI). For this, we introduced a new data-driven decomposition-based functional parcellation algorithm, called Sparse Dictionary Learning Clustering (SDLC).

Dictionary Learning for Sparse Coding: Algorithms and Convergence Analysis.

In recent years, sparse coding has been widely used in many applications ranging from image processing to pattern recognition. Most existing sparse coding based applications require solving a class of challenging non-smooth and non-convex optimization problems. Despite the fact that many numerical methods have been developed for solving these problems, it remains an open problem to find a numerical method which is not only empirically fast, but also has mathematically guaranteed strong convergence.