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Abstract The kidney is vulnerable to ischemia and reperfusion (I/R) injury that can be fatal after major surgery. Currently, there are no effective treatments for I/R-induced kidney injury. Trimethylamine N-oxide (TMAO) is a gut-derived metabolite linked to many diseases, but its role in I/R-induced kidney injury remains unclear. Here, our clinical data reveals an association between preoperative systemic TMAO levels and postoperative kidney injury in patients after post-cardiopulmonary bypass surgery. By genetic deletion of TMAO-producing enzyme flavin-containing monooxygenase 3 (FMO3) and dietary supplementation of choline to modulate TMAO levels, we found that TMAO aggravated acute kidney injury through the triggering of endoplasmic reticulum (ER) stress and worsened subsequent renal fibrosis through TGFβ/Smad signaling activation. Together, our study underscores the negative role of TMAO in I/R-induced kidney injury and highlights the therapeutic potential through the modulation of TMAO levels by targeting FMO3, thereby mitigating acute kidney injury and preventing subsequent renal fibrosis.

Abstract Serous cavity effusion is a prevalent pathological condition encountered in clinical settings. Fluid samples obtained from these effusions are vital for diagnostic and therapeutic purposes. Traditionally, cytological examination of smears is a common method for diagnosing serous cavity effusion, renowned for its convenience. However, this technique presents limitations that can compromise its efficiency and diagnostic accuracy. This study aims to overcome these challenges and introduce an improved method for the precise detection of malignant cells in serous cavity effusions. We have developed a transformer-based classification framework, specifically employing the vision transformer (ViT) model, to fulfill this objective. Our research involved collecting smear images and corresponding cytological reports from 161 patients who underwent serous cavity drainage. We meticulously annotated 4836 patches from these images, identifying regions with and without malignant cells, thus creating a unique dataset for smear image classification. The findings of our study reveal that deep learning models, particularly the ViT model, exhibit remarkable accuracy in classifying patches as malignant or non-malignant. The ViT model achieved an impressive area under the receiver operating characteristic curve (AUROC) of 0.99, surpassing the performance of the convolutional neural network (CNN) model, which recorded an AUROC of 0.86. Additionally, we validated our models using an external cohort of 127 patients. The ViT model sustained its high-level screening performance, achieving an AUROC of 0.98 at the patient level, compared to the CNN model’s AUROC of 0.84. The visualization of our ViT models confirmed their capability to precisely identify regions containing malignant cells in multiple serous cavity effusion smear images. In summary, our study demonstrates the potential of deep learning models, particularly the ViT model, in automating the screening process for serous cavity effusions. These models offer significant assistance to cytologists in enhancing diagnostic accuracy and efficiency. The ViT model stands out for its advanced self-attention mechanism, making it exceptionally suitable for tasks that necessitate detailed analysis of small, sparsely distributed targets like cellular clusters in serous cavity effusions.

Abstract Emerging evidence has demonstrated the profound impact of the gut microbiome on cardiovascular diseases through the production of diverse metabolites. Using an animal model of myocardial ischemia–reperfusion (I/R) injury, we found that the prophylactic administration of a well‐known probiotic, Bifidobacterium infantis (B. infantis), exhibited cardioprotective effects in terms of preserving cardiac contractile function and preventing adverse cardiac remodeling following I/R and that these cardioprotective effects were recapitulated by its metabolite inosine. Transcriptomic analysis further revealed that inosine mitigated I/R‐induced cardiac inflammation and cell death. Mechanistic investigations elucidated that inosine suppressed the production of pro‐inflammatory cytokines and reduced the numbers of dendritic cells and natural killer cells, achieved through the activation of the adenosine A2A receptor (A2AR) that when inhibited abrogated the cardioprotective effects of inosine. Additionally, in vitro studies using C2C12 myoblasts revealed that inosine attenuated cell death by serving as an alternative carbon source for adenosine triphosphate (ATP) generation through the purine salvage pathway when subjected to oxygen‐glucose deprivation/reoxygenation that simulated myocardial I/R injury. Likewise, inosine reversed the I/R‐induced decrease in ATP levels in mouse hearts. Taken together, our findings indicate that B. infantis or its metabolite inosine exerts cardioprotective effects against I/R by suppressing cardiac inflammation and attenuating cardiac cell death, suggesting prophylactic therapeutic options for acute ischemic cardiac injury.

The tip–tilt mirror (TTM) is an important component of adaptive optics (AO) to achieve beam stabilization and pointing tracking. In many practical applications, the information of accurate TTM dynamics, complete system state, and noise characteristics is difficult to achieve due to the lack of sufficient sensors, which then restricts the implementation of high precision tracking control for TTM. To this end, this paper proposes a new method based on noisy-output feedback Q-learning. Without relying on neural networks or additional sensors, it infers the dynamics of the controlled system and reference jitter using only noisy measurements, thereby achieving optimal tracking control for the TTM system. We have established a modified Bellman equation based on estimation theory, directly linking noisy measurements to system performance. On this basis, a fast iterative learning of the control law is implemented through the adaptive transversal predictor and experience replay technique, making the algorithm more efficient. The proposed algorithm has been validated with an application to a TTM tracking control system, which is capable of quickly learning near-optimal control law under the interference of random noise. In terms of tracking performance, the method reduces the tracking error by up to 98.7% compared with the traditional integral control while maintaining a stable control process. Therefore, this approach may provide an intelligent solution for control issues in AO systems.

Abstract The gut microbiome has emerged as a potential target for the treatment of cardiovascular disease. Ischemia/reperfusion (I/R) after myocardial infarction is a serious complication and whether certain gut bacteria can serve as a treatment option remains unclear. Lactobacillus reuteri (L. reuteri) is a well‐studied probiotic that can colonize mammals including humans with known cholesterol‐lowering properties and anti‐inflammatory effects. Here, the prophylactic cardioprotective effects of L. reuteri or its metabolite γ‐aminobutyric acid (GABA) against acute ischemic cardiac injury caused by I/R surgery are demonstrated. The prophylactic gavage of L. reuteri or GABA confers cardioprotection mainly by suppressing cardiac inflammation upon I/R. Mechanistically, GABA gavage results in a decreased number of proinflammatory macrophages in I/R hearts and GABA gavage no longer confers any cardioprotection in I/R hearts upon the clearance of macrophages. In vitro studies with LPS‐stimulated bone marrow‐derived macrophages (BMDM) further reveal that GABA inhibits the polarization of macrophages toward the proinflammatory M1 phenotype by inhibiting lysosomal leakage and NLRP3 inflammasome activation. Together, this study demonstrates that the prophylactic oral administration of L. reuteri or its metabolite GABA attenuates macrophage‐mediated cardiac inflammation and therefore alleviates cardiac dysfunction after I/R, thus providing a new prophylactic strategy to mitigate acute ischemic cardiac injury.

Biomedical magnesium is an ideal material for hard tissue repair and replacement. However, its rapid degradation and infection after implantation significantly hindersclinical applications. To overcome these two critical drawbacks, we describe an integrated strategybased on the changes in pH and Mg2+ triggered by magnesiumdegradation. This system can simultaneously offer anticorrosion and antibacterial activity. First, nanoengineered peptide-grafted hyperbranched polymers (NPGHPs) with excellent antibacterial activity were introduced to sodium alginate (SA) to construct a sensitive NPGHPs/SA hydrogel. The swelling degree, responsiveness, and antibacterial activity were then investigated,indicating that the system can perform dual stimulation of pH and Mg2+ with controllable antimicrobial properties. Furthermore, an intelligent platform was constructed by coating hydrogels on magnesium with polydopamine as the transition layer. The alkaline environment generated by the corrosion of magnesium reduces the swelling degree of the coatingso that the liquid is unfavorable for contacting the substrate, thus exhibiting superior corrosion resistance. Antibacterial testing shows that the material can effectively fight against bacteria, while hemolytic and cytotoxicity testing suggest that it is highly biocompatible. Thus, this work realizes the smart integration of anticorrosion and antibacterial properties of biomedical magnesium, thereby providing broader prospects for the use of magnesium.

This Letter proposes a universal wavefront reconstruction approach based on a coupled data set and neural network, aiming to overcome the limitations of current algorithms in terms of universality and wavefront sensing accuracy for variable imaging objects. First, a novel data set, Multi-Object Wavefront Coupling Dataset (MOCD-Dataset), is developed to provide diverse data and enable the network to learn universal wavefront features. Next, a new universal wavefront reconstruction network called Object-Independent Wavefront Decoupling Network (OIWD-Net) is introduced, aiming to separate imaging object information from multiple variable images. Our algorithm eliminates the need for specialized wavefront sensors, has a simple system, high light energy utilization, and does not require customized models for each different type of imaging objects, making it highly practical. By combining the MOCD-Dataset and the OIWD-Net, excellent accuracy in wavefront reconstruction of different imaging objects has been achieved. This research provides a new solution for high-resolution image restoration in fields such as solar structure observation and astronomical high-resolution imaging.

The precise estimation of fluid motion is critical across various fields, including aerodynamics, hydrodynamics, and industrial fluid mechanics. However, refraction at complex interfaces in the light path can cause image deterioration and lead to severe measurement errors if the aberration changes with time, e.g., at fluctuating air–water interfaces. This challenge is particularly pronounced in technical energy conversion processes such as bubble formation in electrolysis, droplet formation in fuel cells, or film flows. In this paper, a flow field estimation algorithm that can perform the aberration correction function is proposed, which integrates the flow field distribution estimation algorithm based on the Particle Image Velocimetry (PIV) technique and the novel actuator-free adaptive optics technique. Two different multi-input convolutional neural network (CNN) structures are established, with two frames of distorted PIV images and measured wavefront distortion information as inputs. The corrected flow field results are directly output, which are divided into two types based on different network structures: dense estimation and sparse estimation. Based on a series of models, a corresponding dataset synthesis model is established to generate training datasets. Finally, the algorithm performance is evaluated from different perspectives. Compared with traditional algorithms, the two proposed algorithms achieves reductions in the root mean square value of velocity residual error by 84% and 89%, respectively. By integrating both flow field measurement and novel adaptive optics technique into deep CNNs, this method lays a foundation for future research aimed at exploring more intricate distortion phenomena in flow field measurement.

To achieve the failure warning of marine systems and their equipment (MSAE), the threshold is one of the most prominent issues that should be solved first. In this study, a fusion model based on sparse Bayes and probabilistic statistical methods is applied to determine a new and more accurate adaptive alarm threshold. A multistep relevance vector machine (RVM) model is established to realize the parameter reconstruction in which the internal uncertainties caused by the degradation process and the external uncertainty caused by the loading, environment, and disturbances were considered. Then, a varying moving window (VMW) method is employed to determine the window size and achieve continuous data reconstruction. Further, the model based on Johnson distribution systems is utilized to complete the transformation of the residual parameters and calculate the adaptive threshold. Finally, the proposed adaptive decision threshold is successfully involved in the actual examples of the peak pressure and exhaust temperature of marine diesel engines. The results show that the proposed method can realize the continuous health condition monitoring of MSAE, successfully detect abnormal conditions in advance, achieve an early warning of failure, and reserve sufficient time for decision-making to prevent the occurrence of catastrophic disasters.

ObjectiveA ship's system is a complex mechanism composed of multiple pieces of equipment. Due to the dynamic and non-linear characteristics of the parameters of each component, fault diagnosis is complicated. This paper proposes a dynamic feature fusion method for performing efficient fault diagnosis on the system.MethodsFractal theory, dynamic theory and the kernel principal component analysis (KPCA) method are used to reconstruct, map and filter the system state data, and obtain the principal component characteristic data matrix, square prediction error (SPE) and corresponding control limits. An offline monitoring model based on the health data of a marine diesel engine intake and exhaust system is then constructed and used to diagnose and analyze ship system faults. In order to verify the validity of the model, the fault data of the intake and exhaust system of a marine diesel engine is selected for verification and analysis. ResultsThe results show that this method can effectively realize the accurate analysis of a system's dynamic nonlinear state data and efficient analysis and diagnosis of faults, with better fault diagnosis performance than the KPCA and support vector machine (SVM) method.ConclusionsThe method proposed in this paper can realize the detection and diagnosis of marine diesel engine intake and exhaust system failures, and improve the reliability and safety of system operation.

Lenticular galaxies (S0s) in the local Universe are generally absent of recent star formation and lack molecular gas. In this paper, we investigate one massive ( M _* ∼ 5 × 10 ^10 M _⊙ ) star-forming S0, PGC 39535, with the Northern Extended Millimeter Array (NOEMA). Using optical data from the Sloan Digital Sky Survey IV Mapping Nearby Galaxies at the Apache Point Observatory survey, we find star formation mainly concentrates in the central region of PGC 39535. The total star formation rate estimated using extinction-corrected H α flux is 1.57 M _⊙ yr ^−1 . The results of the NOEMA observation suggest that the molecular gas mainly concentrates in the central regions as a gaseous bar and a ring-like structure, and shows similar kinematics as the stellar and ionized gas components. The total molecular gas mass estimated from CO(1–0) is (5.42 ± 1.52) × 10 ^9 M _⊙ . We find PGC 39535 lies on the star-forming main sequence but falls below the Kennicutt–Schmidt relation of spiral galaxies, suggesting that the star formation efficiency may be suppressed by the massive bulge. The existence of a second Gaussian component in the CO spectrum of the central region indicates possible gas flows. Furthermore, our analyses suggest that PGC 39535 resides in the center of a massive group and the derived star formation history indicates it may experience a series of gas-rich mergers over the past 2–7 Gyr.

Abstract The study of histopathological phenotypes is vital for cancer research and medicine as it links molecular mechanisms to disease prognosis. It typically involves integration of heterogenous histopathological features in whole-slide images (WSI) to objectively characterize a histopathological phenotype. However, the large-scale implementation of phenotype characterization has been hindered by the fragmentation of histopathological features, resulting from the lack of a standardized format and a controlled vocabulary for structured and unambiguous representation of semantics in WSIs. To fill this gap, we propose the Histopathology Markup Language (HistoML), a representation language along with a controlled vocabulary (Histopathology Ontology) based on Semantic Web technologies. Multiscale features within a WSI, from single-cell features to mesoscopic features, could be represented using HistoML which is a crucial step towards the goal of making WSIs findable, accessible, interoperable and reusable (FAIR). We pilot HistoML in representing WSIs of kidney cancer as well as thyroid carcinoma and exemplify the uses of HistoML representations in semantic queries to demonstrate the potential of HistoML-powered applications for phenotype characterization.

Adaptive Optics (AO) technology is an effective means to compensate for wavefront distortion, but its inherent delay error will cause the compensation wavefront on the deformable mirror (DM) to lag behind the changes in the distorted wavefront. Especially when the change in the wavefront is higher than the Shack–Hartmann wavefront sensor (SHWS) sampling frequency, the multi-frame delay will seriously limit its correction performance. In this paper, a highly stable AO prediction network based on deep learning is proposed, which only uses 10 frames of prior wavefront information to obtain high-stability and high-precision open-loop predicted slopes for the next six frames. The simulation results under various distortion intensities show that the prediction accuracy of six frames decreases by no more than 15%, and the experimental results also verify that the open-loop correction accuracy of our proposed method under the sampling frequency of 500 Hz is better than that of the traditional non-predicted method under 1000 Hz.

Correction performance of an adaptive optics (AO) system is severely limited by its system latency under high temporal frequency distortions. Wavefront prediction methods has been proven to be an effective way to compensate system delay. A novel wavefront prediction method based on residual learning fusion network (RLFNet) is proposed in this paper. The network is able to eliminate redundant information between adjacent wavefront frames and fuse refined features, which provides a higher prediction accuracy under fewer priori wavefront input. The method is tested on a 1km laser atmospheric transmission experimental setup through high frequency atmospheric turbulence, where wavefront data is collected from a 1kHz Shack-Hartmann wavefront sensor (SHWS). We show that the proposed architecture is able to predict the second frame wavefront after the previous 6 frames with a root-mean-square (RMS) wavefront error reduction up to 53% compare to non-predictive method.

Objective In light of problems such as the untimely condition monitoring and alarm, excessively large threshold bandwidth and inaccurate condition evaluation parameters of intelligent ship power system equipment, an adaptive threshold method is proposed to monitor, alarm and evaluate the conditions of such equipment. Method First, a simulated annealing algorithm is used to optimize the support vector regression (SVR) machine prediction model to simulate the general state characteristic parameters of the power system equipment. Then, after the normal transformation of the modeling residual, combined with the sliding time window, the adaptive threshold model is constructed. Finally, the exhaust gas temperature of the ship's main propulsion diesel engine is selected as the research object for example verification. Results The results show that compared with the traditional fixed threshold, the adaptive threshold model has more compact bandwidth and good adaptability, and can identify abnormal phenomena in power system equipment in advance. Conclusion This method improves the efficiency and threshold accuracy of monitoring and alarm systems, and provides an effective means of early fault diagnosis and a more accurate basis for system status evaluation.

The senescence of cardiovascular endothelial cells (ECs) is a major risk factor in the development of aging-related cardiovascular diseases. However, the molecular dynamics in cardiovascular EC aging are poorly understood. Here, we characterized the transcriptomic landscape of cardiovascular ECs during aging and observed that ribosome biogenesis, inflammation, apoptosis and angiogenesis-related genes and pathways changed with age. We also highlighted the importance of collagen genes in the crosstalk between ECs and other cell types in cardiovascular aging. Moreover, transcriptional regulatory network analysis revealed Jun as a candidate transcription factor involved in murine cardiovascular senescence and we validated the upregulation of Jun in aged cardiovascular ECs both in vitro and in vivo. Altogether, our study reveals the transcriptomic reprogramming in the aging murine cardiovascular ECs, which deepens the understanding of the molecular mechanisms of cardiovascular aging and provides new insights into potential therapeutic targets against age-related cardiovascular diseases.

In 2015, Google began a new transportation demand management program designed to increase bike commuting to their two main corporate campuses in Mountain View and Sunnyvale, CA, United States by lending conventional and electric assisted bikes to employees at no cost to them. Following the lending period, Google incentivized bike purchases, among many other program co-benefits to increase bike commuting. Using a series of bivariate and multivariable analyses, we estimate the program led to average bike commute increases of approximately 1.7–2.3 days per week, roughly a tripling of prior bike commute rates for participating employees. After the program, bike rates of participants diminished slightly, but were still greater than baseline (increase of 1.3–1.9 days per week). Furthermore, nearly all the increases in bicycling are likely attributed to decreases in single occupancy vehicle (SOV) commuting. This study offers a first look at the potential for bike lending as a transportation demand management strategy for large employers in suburban settings which can help other employers design their own programs.

Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases in the elderly. The aim of this study was to explore the effects of AD on cardiac function and autonomic nervous function, and the feasibility of electrocardiogram (ECG) in monitoring the development of AD. APP/PS1 double transgenic mice were used in the Morris water maze (MWM) experiment to evaluate the changes of cognitive ability of AD mice, then the non-invasive ECG acquisition system was used and the changes of ECG intervals and heart rate variability (HRV) were analyzed. AD mice already had cognitive dysfunction at the age of 5 months, reaching the level of mild dementia, and the degree of dementia increased with the course of disease. There were no significant changes in ECG intervals in the AD group at each month. The mean square of successive RR interval differences, percentage of intervals >6 ms different from preceding interval, and normalized high frequency power component in the AD group were decreased and low-to-high frequency power ratio and normalized low frequency power component were increased. Combined with the results of the MWM, it was shown that the regulation mechanism of sympathetic and parasympathetic nerves in mice was already imbalanced in early stage AD, which was manifested as the increase of excessive activity of sympathetic nerves and the inhibition of parasympathetic activities. Therefore, ECG-based analysis of HRV may become a means of daily monitoring of AD and provide an auxiliary basis for clinical diagnosis.

Ship mechanical system health prognosis is one of the major tasks of ship intelligent operation and maintenance (O&M). However, current failure prediction methods are aimed at single pieces of equipment, and system-level monitoring remains an underexplored area. To address this issue, an integration method based on a synthesized health indicator (SHI) and dynamic hybrid prediction is proposed. To accurately reflect the changes in system health conditions, a multi-state parameter fusion method based on dynamic kernel principal component analysis (DKPCA) and the stacked autoencoder (SAE) is presented, along with construction of a system SHI. Taking into consideration that the system degradation process includes global degradation trends, local self-healing phenomena, and local interference, a dynamic hybrid prediction model is established after SHI decomposition. The performance of the proposed approach is applied to a ship fuel-oil system to show its effectiveness.

Prognostics and health management (PHM) is an essential means to optimize resource allocation and improve the intelligent operation and maintenance (O&M) efficiency of marine systems and equipment (MSAE). PHM generally consists of four technical processes, namely health condition motoring (HCM), fault diagnosis (FD), health prognosis (HP), and maintenance decision (MD). In recent years, a large amount of research has been implemented in each process. However, there is not any systematic review that covers the technical framework comprehensively. This article presents a review of the framework of PHM in the marine field to fill the gap. First, the essential HCM methods, which are widely observed in the academic literature, are introduced systematically. Then, the commonly used FD approaches and their applications in MSAE are summarized, and the implementation process of intelligent methods is systematically introduced. After that, the technologies of HP have been reviewed, including the construction of health indicator (HI), health stage (HS) division, and popular remaining useful life (RUL) prediction approaches. Afterwards, the evolution of maintenance strategy in the maritime field is reviewed. Finally, the challenges of implementing PHM for intelligent ships are put forward.

This paper proposes a new type of composite shear wall with concrete-filled steel tubular frames and artificial vertical slits. The seismic behaviour of this new shear wall is studied by using a cyclic loading test. The failure mode, load-carrying capacity, ductility, stiffness degradation, hysteresis behaviour and energy dissipating capacity shown in the course of the test are studied. And this paper proposes the calculation formula of normal section bearing capacity. The main research results are as follows. Firstly, the failure mode of this new shear wall is the flexural failure. Secondly, compared with the ordinary reinforced concrete shear wall with slits, the new shear walls with concrete-filled steel tubular frames and slits show a better performance in load-carrying capacity, ductility, stiffness degradation and energy dissipating capacity.

Objectives: We aimed to evaluate the effect of Shenfu injection in a rat model of ischemic heart failure and explore its mechanism.Methods: A rat model of ischemic heart failure after myocardial infarction was established by ligating the left anterior descending coronary artery. Forty-eight hours after surgery, the rats were intraperitoneally administered Shenfu injection for 7 weeks. Then, left ventricular fractional shortening and left ventricular ejection fraction were measured using transthoracic echocardiography, whereas heart rate and left ventricular end-diastolic pressure were measured using a MD3000 biosignal acquisition and processing system. The hearts and lungs of the rats were excised and weighed to measure the heart and lung weight indexes. In addition, cardiac histopathological changes were observed via hematoxylin–eosin and Masson’s trichrome staining, and serum cardiac troponin content was detected using a cardiac troponin ELISA kit. Furthermore, matrix-assisted laser desorption/ionization–mass spectrometry imaging was used to detect the levels and distribution of small molecules in the hearts of rats with ischemic heart failure.Results: We found that Shenfu injection can significantly increase left ventricular fractional shortening and left ventricular ejection fraction in rats with ischemic heart failure and significantly reduce the left ventricular end-diastolic pressure, heart and lung weight indexes, and cardiac troponin content; improve cardiac tissue morphology; and reduce infarct size. In addition, the matrix-assisted laser desorption/ionization–mass spectrometry imaging results demonstrated that 22:6 phospholipids were predominately distributed in the non-infarct zone, whereas 20:4 phospholipids tended to concentrate in the infarct zone. Shenfu injection significantly reduced taurine, glutathione, and phospholipids levels in the hearts of rats with ischemic heart failure and primarily changed the distribution of these molecules in the non-infarct zone.Conclusion: Shenfu injection induced obvious myocardial protective effects in rats with ischemic heart failure by stimulating antioxidation and changing the phospholipid levels and distribution.