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Due to the complex and diverse operating conditions of rolling bearings, it is difficult to accurately predict remaining useful life (RUL) of rolling bearings via traditional prediction models. Besides, when a rolling bearing malfunctions, only single channel or single-domain degradation information is considered to bear RUL prediction, and the prediction effect of existing RUL prediction methods is greatly limited. Therefore, in order to address the above issue, a novel method abbreviated as CNN-GRU-MSA with multi-channel feature fusion is proposed for RUL prediction of rolling bearings. Firstly, the statistical features related to the time series are calculated, and the similarity features are constructed based on the obtained statistical features. Then, the sensitive features are selected and fused through specific indicators. Finally, the dual-channel feature fusion is performed to construct a health indicator (HI), which is input into the proposed CNN-GRU-MSA model for training and achieving RUL prediction of rolling bearings. The effectiveness of the proposed method is validated using two benchmark datasets (i.e. IEEE PHM 2012 challenge datasets and XJTU-SY datasets). Experimental results demonstrate the superiority and effectiveness of the proposed method over other similar prediction methods in bearing RUL prediction. [ABSTRACT FROM AUTHOR]

Protein remote homology detection is one of the most fundamental research tool for protein structure and function prediction. Most search methods for protein remote homology detection are evaluated based on the Structural Classification of Proteins-extended (SCOPe) benchmark, but the diverse hierarchical structure relationships between the query protein and candidate proteins are ignored by these methods. In order to further improve the predictive performance for protein remote homology detection, a search framework based on the predicted protein hierarchical relationships (PHR-search) is proposed. In the PHR-search framework, the superfamily level prediction information is obtained by extracting the local and global features of the Hidden Markov Model (HMM) profile through a convolution neural network and it is converted to the fold level and class level prediction information according to the hierarchical relationships of SCOPe. Based on these predicted protein hierarchical relationships, filtering strategy and re-ranking strategy are used to construct the two-level search of PHR-search. Experimental results show that the PHR-search framework achieves the state-of-the-art performance by employing five basic search methods, including HHblits, JackHMMER, PSI-BLAST, DELTA-BLAST and PSI-BLASTexB. Furthermore, the web server of PHR-search is established, which can be accessed at http://bliulab.net/PHR-search. [ABSTRACT FROM AUTHOR]

Motivation Protein remote homology detection is a challenging task for the studies of protein evolutionary relationships. PSI-BLAST is an important and fundamental search method for detecting homology proteins. Although many improved versions of PSI-BLAST have been proposed, their performance is limited by the search processes of PSI-BLAST. Results For further improving the performance of PSI-BLAST for protein remote homology detection, a supervised two-layer search framework based on PSI-BLAST (S2L-PSIBLAST) is proposed. S2L-PSIBLAST consists of a two-level search: the first-level search provides high-quality search results by using SMI-BLAST framework and double-link strategy to filter the non-homology protein sequences, the second-level search detects more homology proteins by profile-link similarity, and more accurate ranking lists for those detected protein sequences are obtained by learning to rank strategy. Experimental results on the updated version of Structural Classification of Proteins-extended benchmark dataset show that S2L-PSIBLAST not only obviously improves the performance of PSI-BLAST, but also achieves better performance on two improved versions of PSI-BLAST: DELTA-BLAST and PSI-BLASTexB. Availability and implementation http://bliulab.net/S2L-PSIBLAST. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Protein remote homology detection is a fundamental and important task for protein structure and function analysis. Several search methods have been proposed to improve the detection performance of the remote homologues and the accuracy of ranking lists. The position-specific scoring matrix (PSSM) profile and hidden Markov model (HMM) profile can contribute to improving the performance of the state-of-the-art search methods. In this paper, we improved the profile-link (PL) information for constructing PSSM or HMM profiles, and proposed a PL-based search method (PL-search). In PL-search, more robust PLs are constructed through the double-link and iterative extending strategies, and an accurate similarity score of sequence pairs is calculated from the two-level Jaccard distance for remote homologues. We tested our method on two widely used benchmark datasets. Our results show that whether HHblits, JackHMMER or position-specific iterated-BLAST is used, PL-search obviously improves the search performance in terms of ranking quality as well as the number of detected remote homologues. For ease of use of PL-search, both its stand-alone tool and the web server are constructed, which can be accessed at http://bliulab.net/PL-search/. [ABSTRACT FROM AUTHOR]

Motivation As one of the most important and widely used mainstream iterative search tool for protein sequence search, an accurate Position-Specific Scoring Matrix (PSSM) is the key of PSI-BLAST. However, PSSMs containing non-homologous information obviously reduce the performance of PSI-BLAST for protein remote homology. Results To further study this problem, we summarize three types of Incorrectly Selected Homology (ISH) errors in PSSMs. A new search tool Supervised-Manner-based Iterative BLAST (SMI-BLAST) is proposed based on PSI-BLAST for solving these errors. SMI-BLAST obviously outperforms PSI-BLAST on the Structural Classification of Proteins-extended (SCOPe) dataset. Compared with PSI-BLAST on the ISH error subsets of SCOPe dataset, SMI-BLAST detects 1.6–2.87 folds more remote homologous sequences, and outperforms PSI-BLAST by 35.66% in terms of ROC1 scores. Furthermore, this framework is applied to JackHMMER, DELTA-BLAST and PSI-BLASTexB, and their performance is further improved. Availability and implementation User-friendly webservers for SMI-BLAST, JackHMMER, DELTA-BLAST and PSI-BLASTexB are established at http://bliulab.net/SMI-BLAST/ , by which the users can easily get the results without the need to go through the mathematical details. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background: Electrocardiogram (ECG) is a powerful tool for studying cardiac activity and diagnosing various cardiovascular diseases, including arrhythmia. While machine learning and deep learning algorithms have been applied to ECG interpretation, there is still room for improvement. For instance, the commonly used Recurrent Neural Networks (RNNs), reply on its previous state to update and is therefore ineffective for parallel computing. RNN also struggles to efficiently address the issue of long‐distance reliance. Purpose: To reduce computational complexity by dimensionality reduction of ECG signals we constructed a Stacked Auto‐encoders model using Transformer for ECG‐based arrhythmia detection. And overcome the challenges of long‐term dependencies and limited parallelizability in traditional RNNs when applied to ECG signal processing. Methods: In this paper, a Transformer‐Based ECG Dimensionality Reduction Stacked Auto‐encoders model is proposed for ECG‐based arrhythmia detection. The transformer is used to encode ECG signals into a feature matrix, which is then dimensionally reduced using unsupervised greedy training through the four linear layers. This resulted in a low‐dimensional representation of ECG features, which are subsequently classified using support vector machines (SVM) to minimize overfitting. Results: The proposed method is benchmarked on the MIT‐BIH Arrhythmia database. In the 10‐fold cross validation of beat‐based arrhythmia detection, the average accuracy, sensitivity, specificity and F1 score of the proposed method are 99.83%, 98.84%, 99.84% and 99.13%, respectively, for the record‐based arrhythmia detection which refers to the approach where the training and testing sets use ECG data from independent recorded patients are 88.10%, 49.79%, 91.56% and 39.95%, respectively. Conclusions: Compared to other existing ECG‐based arrhythmia detection methods, our proposed approach exhibits improved detection accuracy and stronger generalization for arrhythmia beats. Additionally, the use of the record‐based data division method makes our approach more suitable for clinical practice. [ABSTRACT FROM AUTHOR]

Motivation Protein function annotation is fundamental to understanding biological mechanisms. The abundant genome-scale protein–protein interaction (PPI) networks, together with other protein biological attributes, provide rich information for annotating protein functions. As PPI networks and biological attributes describe protein functions from different perspectives, it is highly challenging to cross-fuse them for protein function prediction. Recently, several methods combine the PPI networks and protein attributes via the graph neural networks (GNNs). However, GNNs may inherit or even magnify the bias caused by noisy edges in PPI networks. Besides, GNNs with stacking of many layers may cause the over-smoothing problem of node representations. Results We develop a novel protein function prediction method, CFAGO, to integrate single-species PPI networks and protein biological attributes via a multi-head attention mechanism. CFAGO is first pre-trained with an encoder–decoder architecture to capture the universal protein representation of the two sources. It is then fine-tuned to learn more effective protein representations for protein function prediction. Benchmark experiments on human and mouse datasets show CFAGO outperforms state-of-the-art single-species network-based methods by at least 7.59%, 6.90%, 11.68% in terms of m-AUPR, M-AUPR, and Fmax, respectively, demonstrating cross-fusion by multi-head attention mechanism can greatly improve the protein function prediction. We further evaluate the quality of captured protein representations in terms of Davies Bouldin Score, whose results show that cross-fused protein representations by multi-head attention mechanism are at least 2.7% better than that of original and concatenated representations. We believe CFAGO is an effective tool for protein function prediction. Availability and implementation The source code of CFAGO and experiments data are available at: http://bliulab.net/CFAGO/. [ABSTRACT FROM AUTHOR]

The influence of three kinds of alloy adding 0.3–1.8 wt% Zn on the mechanical properties and corrosion resistance of medium Mg content 4.0–4.5 wt% (5083 type) hot rolled alloy was investigated. The results showed that the addition of Zn content could further dissolve the second phases into the matrix. The hardness and tensile strength were reduced when annealing at 250 °C × 2 h. The mechanical behavior of the alloys showed a tendency that increases first and then decreases and the peak reached its maximum when 1.6 wt% Zn is added at both annealed and unannealed states. Through quantitative analysis, the improvement of mechanical properties mainly comes from dislocation strengthening and Al–Mg–Zn ternary strengthening phases. Meanwhile, with the increase of Zn content, the intergranular corrosion (IGC) of the alloy improved greatly after annealing at 250 °C × 2 h. The electrochemical corrosion potential (ECP) of the alloys increased first and then decreased. [ABSTRACT FROM AUTHOR]

Protein function prediction is critical for understanding the cellular physiological and biochemical processes, and it opens up new possibilities for advancements in fields such as disease research and drug discovery. During the past decades, with the exponential growth of protein sequence data, many computational methods for predicting protein function have been proposed. Therefore, a systematic review and comparison of these methods are necessary. In this study, we divide these methods into four different categories, including sequence-based methods, 3D structure-based methods, PPI network-based methods and hybrid information-based methods. Furthermore, their advantages and disadvantages are discussed, and then their performance is comprehensively evaluated and compared. Finally, we discuss the challenges and opportunities present in this field. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of privacy-preserving data synthesis (PPDS) for tabular data in a distributed multi-party environment. In a decentralized setting, for PPDS, federated generative models with differential privacy are used by the existing methods. Unfortunately, the existing models apply only to images or text data and not to tabular data. Unlike images, tabular data usually consist of mixed data types (discrete and continuous attributes) and real-world datasets with highly imbalanced data distributions. Existing methods hardly model such scenarios due to the multimodal distributions in the decentralized continuous columns and highly imbalanced categorical attributes of the clients. To solve these problems, we propose a federated generative model for decentralized tabular data synthesis (HT-Fed-GAN). There are three important parts of HT-Fed-GAN: the federated variational Bayesian Gaussian mixture model (Fed-VB-GMM), which is designed to solve the problem of multimodal distributions; federated conditional one-hot encoding with conditional sampling for global categorical attribute representation and rebalancing; and a privacy consumption-based federated conditional GAN for privacy-preserving decentralized data modeling. The experimental results on five real-world datasets show that HT-Fed-GAN obtains the best trade-off between the data utility and privacy level. For the data utility, the tables generated by HT-Fed-GAN are the most statistically similar to the original tables and the evaluation scores show that HT-Fed-GAN outperforms the state-of-the-art model in terms of machine learning tasks. [ABSTRACT FROM AUTHOR]

Abstract: The influence of chlorinated polyethylene (CPE) content on properties of poly (vinyl chloride) (PVC)/poly (α-methylstyrene-acrylonitrile) (α-MSAN) blends (70/30) was investigated. Addition of CPE contributed to improvement in toughness but played a negative role in strength and modulus. With the addition of 15 phr CPE, the impact strength increased by nearly four-fold compared with that of pure PVC/α-MSAN. A transition of mechanical properties was observed in the range of 5–8 phr CPE. The morphology observed by scanning electron microscopy was heterogeneous and correlated well with the mechanical properties. Thermogravimetric analysis revealed improvement in thermal stability. Glass transition temperature measured by differential scanning calorimetry and dynamic mechanical thermal analysis was constant, as corresponded with PVC/α-MSAN matrix, indicating the immiscibility of the ternary blends. Heat distortion temperature remained almost unchanged over all the compositions. Our study showed that addition of CPE enhanced the toughness and thermal stability without sacrificing heat resistance of the blends. [ABSTRACT FROM AUTHOR]

The effect of multi-stage solution on microstructure and properties was studied in Al–10.5Zn–2.05Mg–1.02Cu–0.2Zr–0.923Ti alloy extrusion. The microstructure, corrosion resistance, plasticity, hardness, and tensile properties of alloy were evaluated using metallographic microscope, X-ray diffraction analysis, micro-hardness tester, and microcomputer-controlled universal testing machine. The results show that the tensile strength and corrosion resistance of the alloy improve first and then deteriorate with increase of solution temperature and holding time, and 2# sample has the best mechanical properties (tensile strength is 711.159 MPa and elongation is 13%) and highest corrosion resistance of alloys. Furthermore, 2# sample has more uniform grains, more equiaxed crystals, and the low-angle grain boundaries, and the dislocation density and the dislocation strengthening contribution of alloy are relatively high. However, the grain grows and the dislocations were consumed with the increase of solution time and temperature, resulting in a decrease in dislocation density and dislocation strengthening contribution. It is indicated that alloy can achieve better comprehensive properties under solution treatment process (450 °C × 2 h + 460 °C × 2 h + 470 °C × 2 h + 480 °C × 2 h). [ABSTRACT FROM AUTHOR]