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Supervised machine learning is a traditionally remaining useful life (RUL) estimation tool, which requires a lot of prior knowledge. For the situation lacking labeled data, supervised methods are invalid for the issue of domain shift in data distribution. In this paper, a adversarial-based domain adaptation (ADA) architecture with convolution neural networks (CNN) for RUL estimation of bearings under different conditions and platforms, referred to as ADACNN, is proposed. Specifically, ADACNN is trained in source labeled data and fine-tunes to similar target unlabeled data via an adversarial training and parameters shared mechanism. Besides a feature extractor and source domain regressive predictor, ADACNN also includes a domain classifier that tries to guide feature extractor find some domain-invariant features, which differents with traditional methods and belongs to a unsupervised learning in target domain, which has potential application value and far-reaching significance in academia. In addition, according to different first predictive time (FPT) detection mechanisms, we also explores the impact of different FPT detection mechanisms on RUL estimation performance. Finally, according to extensive experiments, the results of RUL estimation of bearing in cross-condition and cross-platform prove that ADACNN architecture has satisfactory generalization performance and great practical value in industry.

Current base editors (BEs) catalyze only base transitions (C to T and A to G) and cannot produce base transversions. Here we present BEs that cause C-to-A transversions in Escherichia coli and C-to-G transversions in mammalian cells. These glycosylase base editors (GBEs) consist of a Cas9 nickase, a cytidine deaminase and a uracil-DNA glycosylase (Ung). Ung excises the U base created by the deaminase, forming an apurinic/apyrimidinic (AP) site that initiates the DNA repair process. In E. coli , we used activation-induced cytidine deaminase (AID) to construct AID-nCas9-Ung and found that it converts C to A with an average editing specificity of 93.8% ± 4.8% and editing efficiency of 87.2% ± 6.9%. For use in mammalian cells, we replaced AID with rat APOBEC1 (APOBEC-nCas9-Ung). We tested APOBEC-nCas9-Ung at 30 endogenous sites, and we observed C-to-G conversions with a high editing specificity at the sixth position of the protospacer between 29.7% and 92.2% and an editing efficiency between 5.3% and 53.0%. APOBEC-nCas9-Ung supplements the current adenine and cytidine BEs (ABE and CBE, respectively) and could be used to target G/C disease-causing mutations. New base editors change C to A in bacteria and C to G in mammalian cells.

The bearing is a component of the support shaft that guides the rotational movement of the shaft, widely used in the mechanical industry and also called a mechanical joint. In bearing fault diagnosis, the accuracy much depends on the feature extraction, which always needs a lot of training samples and classification in the commonly used methods. Neural networks are good at latent feature extraction and fault classification, however, they have problems with instability and over-fitting, and more labeled samples must be trained. Switchable normalization and semi-supervised learning are introduced to solve the above obstacles in this paper, which proposes a novel bearing fault diagnosis method based on switchable normalization semi-supervised generative adversarial networks (SN-SSGAN) with 1-dimensional representation of vibration signals as input. Experimental results showed that the proposed method has a desirable 99.93% classification accuracy in the case of less labeled data from the public data set of West Reserve University, which is better than the state-of-the-art methods.

Bearings are ubiquitous in rotating machinery and bearings in good working conditions are essential for the availability and safety of the machine. Various intelligent fault diagnosis models have been widely studied aiming to prevent system failures. These data-driven fault diagnosis models work well when training data and testing data are from the same distribution, which is not easy to sustain in industry since the working environment of rotating machinery is often subject to change. Recently, the domain adaptation methods for fault diagnosis between different working conditions have been extensively researched, which fully utilize the labeled data from the same machine under different working conditions to address this domain shift diploma. However, for a target machine with seldom occurred faulty data under any working conditions, the domain adaptation approaches between working conditions are not applicable. Hence, the cross-machine fault diagnosis tasks are recently proposed to utilize the labeled data from related but not identical machines. The larger domain shift between machines makes the cross-machine fault diagnosis a more challenging task. The large domain shift may cause the well-trained model on source domain deteriorates on target domain, and the ambiguous samples near the decision boundary are prone to be misclassified. In addition, the sparse faulty samples in target domain make a class-imbalanced scenario. To address the two issues, in this paper we propose a semi-supervised adversarial domain adaptation approach for cross-machine fault diagnosis which incorporates the virtual adversarial training and batch nuclear-norm maximization to make the fault diagnosis robust and discriminative. Experiments of transferring between three bearing datasets show that the proposed method is able to effectively learn a discriminative model given only a labeled faulty sample of each class in target domain. The research provides a feasible approach for knowledge transfer in fault diagnosis scenarios.

Background CO 2 is fixed by all living organisms with an autotrophic metabolism, among which the Calvin–Benson–Bassham (CBB) cycle is the most important and widespread carbon fixation pathway. Thus, studying and engineering the CBB cycle with the associated energy providing pathways to increase the CO 2 fixation efficiency of cells is an important subject of biological research with significant application potential. Results In this work, the autotrophic microbe Ralstonia eutropha (Cupriavidus necator ) was selected as a research platform for CBB cycle optimization engineering. By knocking out either CBB operon genes on the operon or mega-plasmid of R. eutropha , we found that both CBB operons were active and contributed almost equally to the carbon fixation process. With similar knock-out experiments, we found both soluble and membrane-bound hydrogenases (SH and MBH), belonging to the energy providing hydrogenase module, were functional during autotrophic growth of R. eutropha. SH played a more significant role. By introducing a heterologous cyanobacterial RuBisCO with the endogenous GroES/EL chaperone system(A quality control systems for proteins consisting of molecular chaperones and proteases, which prevent protein aggregation by either refolding or degrading misfolded proteins) and RbcX(A chaperone in the folding of Rubisco), the culture OD 600 of engineered strain increased 89.2% after 72 h of autotrophic growth, although the difference was decreased at 96 h, indicating cyanobacterial RuBisCO with a higher activity was functional in R. eutropha and lead to improved growth in comparison to the host specific enzyme. Meanwhile, expression of hydrogenases was optimized by modulating the expression of MBH and SH, which could further increase the R. eutropha H16 culture OD 600 to 93.4% at 72 h. Moreover, the autotrophic yield of its major industrially relevant product, polyhydroxybutyrate (PHB), was increased by 99.7%. Conclusions To our best knowledge, this is the first report of successfully engineering the CBB pathway and hydrogenases of R. eutropha for improved activity, and is one of only a few cases where the efficiency of CO 2 assimilation pathway was improved. Our work demonstrates that R. eutropha is a useful platform for studying and engineering the CBB for applications.

The Markov method is a common reliability assessment method. It is often used to describe the dynamic characteristics of a system, such as its repairability, fault sequence and multiple degradation states. However, the “curse of dimensionality”, which refers to the exponential growth of the system state space with the increase in system complexity, presents a challenge to reliability assessments for complex systems based on the Markov method. In response to this challenge, a novel reliability assessment method for complex systems based on non-homogeneous Markov processes is proposed. This method entails the decomposition of a complex system into multilevel subsystems, each with a relatively small state space, in accordance with the system function. The homogeneous Markov model or the non-homogeneous Markov model is established for each subsystem/system from bottom to top. In order to utilize the outcomes of the lower-level subsystem models as inputs to the upper-level subsystem model, an algorithm is proposed for converting the unavailability curve of a subsystem into its corresponding 2×2 dynamic state transition probability matrix (STPM). The STPM is then employed as an input to the upper-level system’s non-homogeneous Markov model. A case study is presented using the reliability assessment of the Reactor Protection System (RPS) based on the proposed method, which is then compared with the models based on the other two contrast methods. This comparison verifies the effectiveness and accuracy of the proposed method.

Background Parkinson’s disease (PD) is a progressive neurodegenerative disorder. Its molecular mechanism is still unclear, and pharmacological treatments are unsatisfactory. Transient receptor potential vanilloid 4 (TRPV4) is a nonselective Ca 2+ channel. It has recently emerged as a critical risk factor in the pathophysiology of neuronal injuries and cerebral diseases. Our previous study reported that TRPV4 contributed to endoplasmic reticulum (ER) stress in the MPP + -induced cell model of PD. In the present study, we detected the role and the mechanism of TRPV4 in 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD mice. Methods Intracerebral injection of an adeno-associated virus (AAV) into the substantia nigra (SN) of mice was used to knockdown or upregulate the expression of TRPV4 and intraperitoneal injection of MPTP. Rotarod and pole tests were used to evaluate the locomotor ability of mice. We used immunohistochemistry, Nissl staining and Western blot to detect the alterations in the number of tyrosine hydroxylase (TH)-positive neurons, Nissl-positive neurons, the levels of ER stress-associated molecules and proinflammatory cytokines in the SN. Results The SN was transfected with AAV for 3 weeks and expressed the target protein with green fluorescence. Knockdown of TRPV4 via injection of a constructed AAV-TRPV4 shRNAi into the SN alleviated the movement deficits of PD mice. Upregulation of TRPV4 via injection of a constructed AAV-TRPV4 aggravated the above movement disorders. The expression of TRPV4 was upregulated in the SN of MPTP-treated mice. Injection of AAV-TRPV4 shRNAi into the SN rescued the number of TH-positive and Nissl-positive neurons in the SN decreased by MPTP, while injection of AAV-TRPV4 induced the opposite effect. Moreover, MPTP-decreased Sarco/endoplasmic reticulum Ca 2+ -ATPase 2 (SERCA2) and pro-cysteinyl aspartate specific proteinase-12 (procaspase-12), MPTP-increased Glucose-regulated protein 78 (GRP78), Glucose-regulated protein 94 (GRP94) and C/EBP homologous protein (CHOP) were inhibited by AAV-TRPV4 shRNAi infection, and enhanced by AAV-TRPV4. In the same way, MPTP-decreased procaspase-1, MPTP-increased Interleukin-18 (IL-18), Cyclooxgenase-2 (COX-2) and 5-Lipoxygenase (5-LOX) were inhibited by AAV-TRPV4 shRNAi, or further exacerbated by AAV-TRPV4. Conclusions These results suggest that TRPV4 mediates ER stress and inflammation pathways, contributing to the loss of dopamine (DA) neurons in the SN and movement deficits in PD mice. Moreover, this study provides a new perspective on molecular targets and gene therapies for the treatment of PD in the future.

Accurately forecasting the degradation trajectory of proton exchange membrane fuel cells (PEMFCs) across a spectrum of operational scenarios is indispensable for effective maintenance scheduling and robust health surveillance. However, this task is highly intricate due to the fluctuating nature of dynamic operating conditions and the limitations inherent in short-term forecasting techniques, which collectively pose significant challenges to achieving reliable predictions. To enhance the accuracy of PEMFC degradation forecasting, this research proposes an integrated approach that combines the complete ensemble empirical mode decomposition with the variational mode decomposition (CEEMD-VMD) and triple echo state network (TriESN) to predict the deterioration process precisely. Decomposition can filter out high-frequency noise and retain low-frequency degradation information effectively. Among data-driven methods, the echo state network (ESN) is capable of estimating the degradation performance of PEMFCs. To tackle the problem of low prediction accuracy, this study proposes a novel TriESN that builds upon the classical ESN. The proposed enhancement method seeks to refine the ESN architecture by reducing the impact of surrounding neurons and sub-reservoirs on active neurons, thus realizing partial decoupling of the ESN. On this basis of decoupling, the method takes into account the multi-timescale aging characteristics of PEMFCs to achieve precise prediction of remaining useful life. Overall, combining CEEMD-VMD with the TriESN strengthens feature depiction, fosters sparsity, diminishes the likelihood of overfitting, and augments the network’s capacity for generalization. It has been shown that the TriESN markedly improved the accuracy of long-term PEMFC degradation predictions in three different dynamic contexts.

We have investigated different Fourier domain approaches to calculate the magnetic anomaly of an arbitrarily shaped geological body with arbitrary magnetic susceptibility distribution, and compared different Fourier domain solutions of each approach with respect to accuracy and speed for any model. Both standard fast Fourier transform (FFT) and nonuniform fast Fourier transform (NUFFT) were used in the Fourier domain solution technique. The simulation results demonstrated that the standard FFT method with grid expansion has a great advantage in computing performance. However, the influence of aliasing, edge effect, and imposed periodicity limit the accuracy. The NUFFT method with a suitable wavenumber optimization scheme can avoid these problems, especially in minimizing the influence of the edge effect. In addition, a fast algorithm for undulating terrain was proposed, and these results showed that the more the number of grid nodes interpolated between the lowest and highest points of the undulating terrain, the greater the speed advantage of the fast algorithm. Finally, simple, complex, and real undulating topography models were designed to further reveal the reliability and stability of the NUFFT method.

Base editing technology is an ideal solution for treating pathogenic single-nucleotide variations (SNVs). No gene editing therapy has yet been approved for eye diseases, such as retinitis pigmentosa (RP). Here, we show, in the rd10 mouse model, which carries an SNV identified as an RP-causing mutation in human patients, that subretinal delivery of an optimized dual adeno-associated virus system containing the adenine base editor corrects the pathogenic SNV in the neuroretina with up to 49% efficiency. Light microscopy showed that a thick and robust outer nuclear layer (photoreceptors) was preserved in the treated area compared with the thin, degenerated outer nuclear layer without treatment. Substantial electroretinogram signals were detected in treated rd10 eyes, whereas control treated eyes showed minimal signals. The water maze experiment showed that the treatment substantially improved vision-guided behavior. Together, we construct and validate a translational therapeutic solution for the treatment of RP in humans. Our findings might accelerate the development of base-editing based gene therapies. Base editing technology has great potential in treating pathogenic single-nucleotide variations. Using a dual-AAV base editing system, Wu et al. restored visual functions in a mouse model of retinitis pigmentosa.