OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis.

Cryo-electron microscopy (cryo-EM) captures snapshots of dynamic macromolecules, collectively illustrating the involved structural landscapes. This provides an exciting opportunity to explore the structural variations of macromolecules under study. However, traditional cryo-EM single-particle analysis often yields static structures. Here we describe OPUS-DSD, an algorithm capable of efficiently reconstructing the structural landscape embedded in cryo-EM data. OPUS-DSD uses a three-dimensional convolutional encoder-decoder architecture trained with cryo-EM images, thereby encoding structural variations into a smooth and easily analyzable low-dimension space. This space can be traversed to reconstruct continuous dynamics or clustered to identify distinct conformations. OPUS-DSD can offer meaningful insights into the structural variations of macromolecules, filling in the gaps left by traditional cryo-EM structural determination, and potentially improves the reconstruction resolution by reliably clustering similar particles within the dataset. These functionalities are especially relevant to the study of highly dynamic biological systems. OPUS-DSD is available at https://github.com/alncat/opusDSD .

OPUS-Fold3: a gradient-based protein all-atom folding and docking framework on TensorFlow.

For refining and designing protein structures, it is essential to have an efficient protein folding and docking framework that generates a protein 3D structure based on given constraints. In this study, we introduce OPUS-Fold3 as a gradient-based, all-atom protein folding and docking framework, which accurately generates 3D protein structures in compliance with specified constraints, such as a potential function as long as it can be expressed as a function of positions of heavy atoms. Our tests show that, for example, OPUS-Fold3 achieves performance comparable to pyRosetta in backbone folding and significantly better in side-chain modeling. Developed using Python and TensorFlow 2.4, OPUS-Fold3 is user-friendly for any source-code level modifications and can be seamlessly combined with other deep learning models, thus facilitating collaboration between the biology and AI communities. The source code of OPUS-Fold3 can be downloaded from http://github.com/OPUS-MaLab/opus_fold3. It is freely available for academic usage.

Wafer-Scale Patterning Integration of Chiral 3D Perovskite Single Crystals toward High-Performance Full-Stokes Polarimeter.

Chiral three-dimensional (3D) perovskites exhibit exceptional optoelectronic characteristics and inherent chiroptical activity, which may overcome the limitations of low-dimensional chiral optoelectronic devices and achieve superior performance. The integrated chip of high-performance arbitrary polarized light detection is one of the aims of chiral optoelectronic devices and may be achieved by chiral 3D perovskites. Herein, we first fabricate the wafer-scale integrated full-Stokes polarimeter by the synergy of unprecedented chiral 3D perovskites (R/S-PyEA)Pb2Br6 and one-step capillary-bridge assembly technology. Compared with the chiral low-dimensional perovskites, chiral 3D perovskites present smaller exciton binding energies of 57.3 meV and excellent circular dichroism (CD) absorption properties, yielding excellent circularly polarized light (CPL) photodetectors with an ultrahigh responsivity of 86.7 A W-1, an unprecedented detectivity exceeding 4.84 × 1013 Jones, a high anisotropy factor of 0.42, and high-fidelity CPL imaging with 256 pixels. Moreover, the anisotropic crystal structure also enables chiral 3D perovskites to have a large linear-polarization response with a polarized ratio of 1.52. The combination of linear-polarization and circular-polarization discrimination capabilities guarantees the achievement of a full-Stokes polarimeter. Our study provides new research insights for the large-scale patterning wafer integration of high-performance chiroptical devices.

OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors.

Accurate protein side-chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue. However, most of them depend on the discrete samples from the rotamer library, which may have limitations on their accuracies and usages. In this study, we report an open-source toolkit for protein side-chain modeling, named OPUS-Rota4. It consists of three modules: OPUS-RotaNN2, which predicts protein side-chain dihedral angles; OPUS-RotaCM, which measures the distance and orientation information between the side chain of different residue pairs and OPUS-Fold2, which applies the constraints derived from the first two modules to guide side-chain modeling. OPUS-Rota4 adopts the dihedral angles predicted by OPUS-RotaNN2 as its initial states, and uses OPUS-Fold2 to refine the side-chain conformation with the side-chain contact map constraints derived from OPUS-RotaCM. Therefore, we convert the side-chain modeling problem into a side-chain contact map prediction problem. OPUS-Fold2 is written in Python and TensorFlow2.4, which is user-friendly to include other differentiable energy terms. OPUS-Rota4 also provides a platform in which the side-chain conformation can be dynamically adjusted under the influence of other processes. We apply OPUS-Rota4 on 15 FM predictions submitted by AlphaFold2 on CASP14, the results show that the side chains modeled by OPUS-Rota4 are closer to their native counterparts than those predicted by AlphaFold2 (e.g. the residue-wise RMSD for all residues and core residues are 0.588 and 0.472 for AlphaFold2, and 0.535 and 0.407 for OPUS-Rota4).

Studying protein-protein interaction through side-chain modeling method OPUS-Mut.

Protein side chains are vitally important to many biological processes such as protein-protein interaction. In this study, we evaluate the performance of our previous released side-chain modeling method OPUS-Mut, together with some other methods, on three oligomer datasets, CASP14 (11), CAMEO-Homo (65) and CAMEO-Hetero (21). The results show that OPUS-Mut outperforms other methods measured by all residues or by the interfacial residues. We also demonstrate our method on evaluating protein-protein docking pose on a dataset Oligomer-Dock (75) created using the top 10 predictions from ZDOCK 3.0.2. Our scoring function correctly identifies the native pose as the top-1 in 45 out of 75 targets. Different from traditional scoring functions, our method is based on the overall side-chain packing favorableness in accordance with the local packing environment. It emphasizes the significance of side chains and provides a new and effective scoring term for studying protein-protein interaction.

Sparseness and Smoothness Regularized Imaging for improving the resolution of Cryo-EM single-particle reconstruction.

In this paper, we present a refinement method for cryo-electron microscopy (cryo-EM) single-particle reconstruction, termed as OPUS-SSRI (Sparseness and Smoothness Regularized Imaging). In OPUS-SSRI, spatially varying sparseness and smoothness priors are incorporated to improve the regularity of electron density map, and a type of real space penalty function is designed. Moreover, we define the back-projection step as a local kernel regression and propose a first-order method to solve the resulting optimization problem. On the seven cryo-EM datasets that we tested, the average improvement in resolution by OPUS-SSRI over that from RELION 3.0, the commonly used image-processing software for single-particle cryo-EM, was 0.64 Å, with the largest improvement being 1.25 Å. We expect OPUS-SSRI to be an invaluable tool to the broad field of cryo-EM single-particle analysis. The implementation of OPUS-SSRI can be found at https://github.com/alncat/cryoem.

Advances for pharmacological activities of Polygonum cuspidatum - A review.

CONTEXT: Polygonum cuspidatum Sieb. et Zucc (Polygonaceae), the root of which is included in the Chinese Pharmcopoeia under the name 'Huzhang', has a long history as a medicinal plant and vegetable. Polygonum cuspidatum has been used in traditional Chinese medicine for the treatment of inflammation, hyperlipemia, etc. OBJECTIVE: This article reviews the pharmacological action and the clinical applications of Polygonum cuspidatum and its extracts, whether in vivo or in vitro. We also summarized the main phytochemical constituents and pharmacokinetics of Polygonum cuspidatum and its extracts. METHODS: The data were retrieved from major medical databases, such as CNKI, PubMed, and SinoMed, from 2014 to 2022. Polygonum cuspidatum, pharmacology, toxicity, clinical application, and pharmacokinetics were used as keywords. RESULTS: The rhizomes, leaves, and flowers of Polygonum cuspidatum have different phytochemical constituents. The plant contains flavonoids, anthraquinones, and stilbenes. Polygonum cuspidatum and the extracts have anti-inflammatory, antioxidation, anticancer, heart protection, and other pharmacological effects. It is used in the clinics to treat dizziness, headaches, traumatic injuries, and water and fire burns. CONCLUSIONS: Polygonum cuspidatum has the potential to treat many diseases, such as arthritis, ulcerative colitis, asthma, and cardiac hypertrophy. It has a broad range of medicinal applications, but mainly focused on root medication; its aerial parts should receive more attention. Pharmacokinetics also need to be further investigated.

Fault Diagnosis for Rolling Bearing of Combine Harvester Based on Composite-Scale-Variable Dispersion Entropy and Self-Optimization Variational Mode Decomposition Algorithm.

Because of the influence of harsh and variable working environments, the vibration signals of rolling bearings for combine harvesters usually show obvious characteristics of strong non-stationarity and nonlinearity. Accomplishing accurate fault diagnosis using these signals for rolling bearings is a challenging subject. In this paper, a novel fault diagnosis method based on composite-scale-variable dispersion entropy (CSvDE) and self-optimization variational mode decomposition (SoVMD) is proposed, systematically combining the nonstationary signal analysis approach and machine learning technology. Firstly, an improved SoVMD algorithm is developed to realize adaptive parameter optimization and to further extract multiscale frequency components from original signals. Subsequently, a CSvDE-based feature learning model is established to generate the multiscale fault feature space (MsFFS) of frequency components for the improvement of fault feature learning ability. Finally, the generated MsFFS can serve as the inputs of the Softmax classifier for fault category identification. Extensive experiments on the vibration datasets collected from rolling bearings of combine harvesters are conducted, and the experimental results demonstrate the more superior and robust fault diagnosis performance of the proposed method compared to other existing approaches.

OPUS-X: an open-source toolkit for protein torsion angles, secondary structure, solvent accessibility, contact map predictions and 3D folding.

MOTIVATION: The development of an open-source platform to predict protein 1D features and 3D structure is an important task. In this paper, we report an open-source toolkit for protein 3D structure modeling, named OPUS-X. It contains three modules: OPUS-TASS2, which predicts protein torsion angles, secondary structure and solvent accessibility; OPUS-Contact, which measures the distance and orientation information between different residue pairs; and OPUS-Fold2, which uses the constraints derived from the first two modules to guide folding. RESULTS: OPUS-TASS2 is an upgraded version of our previous method OPUS-TASS. OPUS-TASS2 integrates protein global structure information and significantly outperforms OPUS-TASS. OPUS-Contact combines multiple raw co-evolutionary features with protein 1D features predicted by OPUS-TASS2, and delivers better results than the open-source state-of-the-art method trRosetta. OPUS-Fold2 is a complementary version of our previous method OPUS-Fold. OPUS-Fold2 is a gradient-based protein folding framework based on the differentiable energy terms in opposed to OPUS-Fold that is a sampling-based method used to deal with the non-differentiable terms. OPUS-Fold2 exhibits comparable performance to the Rosetta folding protocol in trRosetta when using identical inputs. OPUS-Fold2 is written in Python and TensorFlow2.4, which is user-friendly to any source-code-level modification. AVAILABILITYAND IMPLEMENTATION: The code and pre-trained models of OPUS-X can be downloaded from https://github.com/OPUS-MaLab/opus_x. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Adaptive Stochastic Resonance-Based Processing of Weak Magnetic Slippage Signals of Bearings.

Slip is one of the most common forms of failure in aviation bearings, and it can pose a great threat to the stable operation of aviation bearings. Bearing cage speed monitoring methods based on weak magnetic detection can achieve nondestructive measurements. However, the method suffers from solid signal background noise due to the high sensitivity of the sensor. Therefore, in this paper, an adaptive stochastic resonance algorithm was proposed in response to the characteristics of the weak magnetic detection signal and the problem of solid noise. In addition, by adaptively adjusting the coefficients of the stochastic resonance system-by an improved moth flame optimization algorithm-the drawback in which the stochastic resonance method required artificially set parameters for extracting the feature frequencies of the weak magnetic signals was solved. In this process, we used parameters, such as general refined composite multi-scale sample entropy, as the adaptation function of the optimization algorithm. In the end, simulation and experimental outcomes verified the efficacy of the approach put forward.

OPUS-TASS: a protein backbone torsion angles and secondary structure predictor based on ensemble neural networks.

MOTIVATION: Predictions of protein backbone torsion angles (ϕ and ψ) and secondary structure from sequence are crucial subproblems in protein structure prediction. With the development of deep learning approaches, their accuracies have been significantly improved. To capture the long-range interactions, most studies integrate bidirectional recurrent neural networks into their models. In this study, we introduce and modify a recently proposed architecture named Transformer to capture the interactions between the two residues theoretically with arbitrary distance. Moreover, we take advantage of multitask learning to improve the generalization of neural network by introducing related tasks into the training process. Similar to many previous studies, OPUS-TASS uses an ensemble of models and achieves better results. RESULTS: OPUS-TASS uses the same training and validation sets as SPOT-1D. We compare the performance of OPUS-TASS and SPOT-1D on TEST2016 (1213 proteins) and TEST2018 (250 proteins) proposed in the SPOT-1D paper, CASP12 (55 proteins), CASP13 (32 proteins) and CASP-FM (56 proteins) proposed in the SAINT paper, and a recently released PDB structure collection from CAMEO (93 proteins) named as CAMEO93. On these six test sets, OPUS-TASS achieves consistent improvements in both backbone torsion angles prediction and secondary structure prediction. On CAMEO93, SPOT-1D achieves the mean absolute errors of 16.89 and 23.02 for ϕ and ψ predictions, respectively, and the accuracies for 3- and 8-state secondary structure predictions are 87.72 and 77.15%, respectively. In comparison, OPUS-TASS achieves 16.56 and 22.56 for ϕ and ψ predictions, and 89.06 and 78.87% for 3- and 8-state secondary structure predictions, respectively. In particular, after using our torsion angles refinement method OPUS-Refine as the post-processing procedure for OPUS-TASS, the mean absolute errors for final ϕ and ψ predictions are further decreased to 16.28 and 21.98, respectively. AVAILABILITY AND IMPLEMENTATION: The training and the inference codes of OPUS-TASS and its data are available at https://github.com/thuxugang/opus_tass. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Atomistic simulations indicate the functional loop-to-coiled-coil transition in influenza hemagglutinin is not downhill.

Influenza hemagglutinin (HA) mediates viral entry into host cells through a large-scale conformational rearrangement at low pH that leads to fusion of the viral and endosomal membranes. Crystallographic and biochemical data suggest that a loop-to-coiled-coil transition of the B-loop region of HA is important for driving this structural rearrangement. However, the microscopic picture for this proposed "spring-loaded" movement is missing. In this study, we focus on understanding the transition of the B loop and perform a set of all-atom molecular dynamics simulations of the full B-loop trimeric structure with the CHARMM36 force field. The free-energy profile constructed from our simulations describes a B loop that stably folds half of the postfusion coiled coil in tens of microseconds, but the full coiled coil is unfavorable. A buried hydrophilic residue, Thr59, is implicated in destabilizing the coiled coil. Interestingly, this conserved threonine is the only residue in the B loop that strictly differentiates between the group 1 and 2 HA molecules. Microsecond-scale constant temperature simulations revealed that kinetic traps in the structural switch of the B loop can be caused by nonnative, intramonomer, or intermonomer ß-sheets. The addition of the A helix stabilized the postfusion state of the B loop, but introduced the possibility for further ß-sheet structures. Overall, our results do not support a description of the B loop in group 2 HAs as a stiff spring, but, rather, it allows for more structural heterogeneity in the placement of the fusion peptides during the fusion process.

Rolling Bearing Fault Diagnosis Based on Refined Composite Multi-Scale Approximate Entropy and Optimized Probabilistic Neural Network.

A rolling bearing early fault diagnosis method is proposed in this paper, which is derived from a refined composite multi-scale approximate entropy (RCMAE) and improved coyote optimization algorithm based probabilistic neural network (ICOA-PNN) algorithm. Rolling bearing early fault diagnosis is a time-sensitive task, which is significant to ensure the reliability and safety of mechanical fault system. At the same time, the early fault features are masked by strong background noise, which also brings difficulties to fault diagnosis. So, we firstly utilize the composite ensemble intrinsic time-scale decomposition with adaptive noise method (CEITDAN) to decompose the signal at different scales, and then the refined composite multi-scale approximate entropy of the first signal component is calculated to analyze the complexity of describing the vibration signal. Afterwards, in order to obtain higher recognition accuracy, the improved coyote optimization algorithm based probabilistic neural network classifiers is employed for pattern recognition. Finally, the feasibility and effectiveness of this method are verified by rolling bearing early fault diagnosis experiment.

A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction.

The early fault diagnosis of rolling bearings has always been a difficult problem due to the interference of strong noise. This paper proposes a new method of early fault diagnosis for rolling bearings with entropy participation. First, a new signal decomposition method is proposed in this paper: intrinsic time-scale decomposition based on time-varying filtering. It is introduced into the framework of complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN). Compared with traditional intrinsic time-scale decomposition, intrinsic time-scale decomposition based on time-varying filtering can improve frequency-separation performance. It has strong robustness in the presence of noise interference. However, decomposition parameters (the bandwidth threshold and B-spline order) have significant impacts on the decomposition results of this method, and they need to be artificially set. Aiming to address this problem, this paper proposes rolling-bearing fault diagnosis optimization based on an improved coyote optimization algorithm (COA). First, the minimal generalized refined composite multiscale sample entropy parameter was used as the objective function. Through the improved COA algorithm, optimal intrinsic time-scale decomposition parameters based on time-varying filtering that match the input signal are obtained. By analyzing generalized refined composite multiscale sample entropy (GRCMSE), whether the mode component is dominated by the fault signal is determined. The signal is reconstructed and decomposed again. Finally, the mode component with the highest energy in the central frequency band is selected for envelope spectrum variation for fault diagnosis. Lastly, simulated and experimental signals were used to verify the effectiveness of the proposed method.

Potential feedback mediated by soil microbiome response to warming in a glacier forefield.

Mountain glaciers are retreating at an unprecedented rate due to global warming. Glacier retreat is widely believed to be driven by the physiochemical characteristics of glacier surfaces; however, the current knowledge of such biological drivers remains limited. An estimated 130 Tg of organic carbon (OC) is stored in mountain glaciers globally. As a result of global warming, the accelerated microbial decomposition of OC may further accelerate the melting process of mountain glaciers by heat production with the release of greenhouse gases, such as carbon dioxide (CO2 ) and methane. Here, using short-term aerobic incubation data from the forefield of Urumqi Glacier No. 1, we assessed the potential climate feedback mediated by soil microbiomes at temperatures of 5°C (control), 6.2°C (RCP 2.6), 11°C (RCP 8.5), and 15°C (extreme temperature). We observed enhanced CO2 -C release and heat production under warming conditions, which led to an increase in near-surface (2 m) atmospheric temperatures, ranging from 0.9°C to 3.4°C. Warming significantly changed the structures of the RNA-derived (active) and DNA-derived (total) soil microbiomes, and active microbes were more sensitive to increased temperatures than total microbes. Considering the positive effects of temperature and deglaciation age on the CO2 -C release rate, the alterations in the active microbial community structure had a negative impact on the increased CO2 -C release rate. Our results revealed that glacial melting could potentially be significantly accelerated by heat production from increased microbial decomposition of OC. This risk might be true for other high-altitude glaciers under emerging warming, thus improving the predictions of the effects of potential feedback on global warming.

OPUS-Rota3: Improving Protein Side-Chain Modeling by Deep Neural Networks and Ensemble Methods.

Side-chain modeling is critical for protein structure prediction since the uniqueness of the protein structure is largely determined by its side-chain packing conformation. In this paper, differing from most approaches that rely on rotamer library sampling, we first propose a novel side-chain rotamer prediction method based on deep neural networks, named OPUS-RotaNN. Then, on the basis of our previous work OPUS-Rota2, we propose an open-source side-chain modeling framework, OPUS-Rota3, which integrates the results of different methods into its rotamer library as the sampling candidates. By including OPUS-RotaNN into OPUS-Rota3, we conduct our experiments on three native backbone test sets and one non-native backbone test set. On the native backbone test set, CAMEO-Hard61 for example, OPUS-Rota3 successfully predicts 51.14% of all side-chain dihedral angles with a tolerance criterion of 20° and outperforms OSCAR-star (50.87%), SCWRL4 (50.40%), and FASPR (49.85%). On the non-native backbone test set DB379-ITASSER, the accuracy of OPUS-Rota3 is 52.49%, better than OSCAR-star (48.95%), FASPR (48.69%), and SCWRL4 (48.29%). All the source codes including the training codes and the data we used are available at https://github.com/thuxugang/opus_rota3.

Three classes of response elements for human PRC2 and MLL1/2-Trithorax complexes.

Polycomb group (PcG) and Trithorax group (TrxG) proteins are essential for maintaining epigenetic memory in both embryonic stem cells and differentiated cells. To date, how they are localized to hundreds of specific target genes within a vertebrate genome had remained elusive. Here, by focusing on short cis-acting DNA elements of single functions, we discovered three classes of response elements in human genome: Polycomb response elements (PREs), Trithorax response elements (TREs) and Polycomb/Trithorax response elements (P/TREs). In particular, the four PREs (PRE14, 29, 39 and 48) are the first set of, to our knowledge, bona fide vertebrate PREs ever discovered, while many previously reported Drosophila or vertebrate PREs are likely P/TREs. We further demonstrated that YY1 and CpG islands are specifically enriched in the four TREs (PRE30, 41, 44 and 55), but not in the PREs. The three classes of response elements as unraveled in this study should guide further global investigation and open new doors for a deeper understanding of PcG and TrxG mechanisms in vertebrates.

The diffuse involvement of anti-N-methyl-D-aspartate receptor encephalitis in brain: a case report.

BACKGROUND: Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a severe and most common autoimmune encephalitis in patients under 40 years old. Anti-NMDAR encephalitis has various clinical and neuroimaging findings. Here we report a special case of an anti-NMDAR encephalitis who had diffuse lesions in bilateral hemispheres with mild mass effects in left basal ganglia area. CASE PRESENTATIONS: A 28-year-old female anti-NMDAR encephalitis patient mainly presented with headache and fever. Brain magnetic resonance image (MRI) showed slightly contrasted diffuse lesions, involving the left temporal and frontal lobes, left basal ganglia area and splenium of corpus callosum, as well as the right frontal lobe, with mild edema surrounded in the left basal ganglia area. Cerebrospinal fluid (CSF) revealed a moderate pleocytosis with normal protein and glucose levels. Anti-NMDAR antibodies were identified in CSF. Transvaginal ovarian ultrasound did not reveal an ovarian teratoma. The patient was treated with immunoglobulin and steroid, and had a good recovery. CONCLUSIONS: Anti-NMDAR encephalitis has no special clinical manifestations and brain MRI is highly variable, which could be unremarkable or abnormal involving white and grey matters. The extensive lesions in frontal and temporal lobes, and basal ganglia area, with mild mass effects, have not been described previously. Recognition of various changes in brain MRI will enable the early detection of anti-NMDAR antibody and then effective treatments.