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Continuous imaging of cardiac functions is highly desirable for the assessment of long-term cardiovascular health, detection of acute cardiac dysfunction and clinical management of critically ill or surgical patients 1 – 4 . However, conventional non-invasive approaches to image the cardiac function cannot provide continuous measurements owing to device bulkiness 5 – 11 , and existing wearable cardiac devices can only capture signals on the skin 12 – 16 . Here we report a wearable ultrasonic device for continuous, real-time and direct cardiac function assessment. We introduce innovations in device design and material fabrication that improve the mechanical coupling between the device and human skin, allowing the left ventricle to be examined from different views during motion. We also develop a deep learning model that automatically extracts the left ventricular volume from the continuous image recording, yielding waveforms of key cardiac performance indices such as stroke volume, cardiac output and ejection fraction. This technology enables dynamic wearable monitoring of cardiac performance with substantially improved accuracy in various environments. Innovations in device design, material fabrication and deep learning are described, leading to a wearable ultrasound transducer capable of dynamic cardiac imaging in various environments and under different conditions.

Multiple studies have established associations between human gut bacteria and host physiology, but determining the molecular mechanisms underlying these associations has been challenging 1 – 3 . Akkermansia muciniphila has been robustly associated with positive systemic effects on host metabolism, favourable outcomes to checkpoint blockade in cancer immunotherapy and homeostatic immunity 4 – 7 . Here we report the identification of a lipid from A. muciniphila ’s cell membrane that recapitulates the immunomodulatory activity of A. muciniphila in cell-based assays 8 . The isolated immunogen, a diacyl phosphatidylethanolamine with two branched chains (a15:0-i15:0 PE), was characterized through both spectroscopic analysis and chemical synthesis. The immunogenic activity of a15:0-i15:0 PE has a highly restricted structure–activity relationship, and its immune signalling requires an unexpected toll-like receptor TLR2–TLR1 heterodimer 9 , 10 . Certain features of the phospholipid’s activity are worth noting: it is significantly less potent than known natural and synthetic TLR2 agonists; it preferentially induces some inflammatory cytokines but not others; and, at low doses (1% of EC 50 ) it resets activation thresholds and responses for immune signalling. Identifying both the molecule and an equipotent synthetic analogue, its non-canonical TLR2–TLR1 signalling pathway, its immunomodulatory selectivity and its low-dose immunoregulatory effects provide a molecular mechanism for a model of A. muciniphila’ s ability to set immunological tone and its varied roles in health and disease. Overall, this study describes the molecular mechanism of a druggable pathway that recapitulates in cellular assays the immunomodulatory effects associated with Akkermansia muciniphila , a prominent member of the gut microbiota.

Rolling bearings are a crucial component in rotating machinery, essential for ensuring the smooth functioning of the entire system. However, their vulnerability to damage necessitates the implementation of effective fault diagnosis. Traditional deep learning methods often struggle due to the scarcity of fault samples, leading to issues like overfitting and inadequate generalization. To address this problem, a novel Siamese Neural Network (SNN) model, integrating Deep Convolutional Neural Networks with Wide First-layer Kernel (WDCNN) and Bidirectional Long Short-Term Memory (BiLSTM) network is proposed. This model constructs a feature extraction system that combines WDCNN and BiLSTM to extract local spatial features and global temporal dependencies from vibration signals. Additionally, the SNN framework is introduced to build a feature space under small sample conditions through metric learning, enhancing the ability of model to discern sample similarities. Experiments on the CWRU and HUST datasets indicate that with only 90 training samples, the model achieves diagnostic accuracy of 83.47% and 61.48%, respectively, significantly surpassing CNN, BiLSTM, and their combined models. Furthermore, the model also shows robustness against severe noise interference, making it a viable tool for efficient fault diagnosis in rolling bearings with limited data.

Electronic patches, based on various mechanisms, allow continuous and noninvasive monitoring of biomolecules on the skin surface. However, to date, such devices are unable to sense biomolecules in deep tissues, which have a stronger and faster correlation with the human physiological status than those on the skin surface. Here, we demonstrate a photoacoustic patch for three-dimensional (3D) mapping of hemoglobin in deep tissues. This photoacoustic patch integrates an array of ultrasonic transducers and vertical-cavity surface-emitting laser (VCSEL) diodes on a common soft substrate. The high-power VCSEL diodes can generate laser pulses that penetrate >2 cm into biological tissues and activate hemoglobin molecules to generate acoustic waves, which can be collected by the transducers for 3D imaging of the hemoglobin with a high spatial resolution. Additionally, the photoacoustic signal amplitude and temperature have a linear relationship, which allows 3D mapping of core temperatures with high accuracy and fast response. With access to biomolecules in deep tissues, this technology adds unprecedented capabilities to wearable electronics and thus holds significant implications for various applications in both basic research and clinical practice. The authors present a wearable photoacoustic patch, which integrates laser diodes and piezoelectric transducers for three-dimensional imaging of hemoglobin and temperature in deep tissues.

This study systematically explores the sources and influencing factors of resistance encountered by magnetic flux leakage (MFL) detectors in natural gas pipelines through a theoretical analysis, experimental investigation, and numerical simulation. The research methodology involves the development of a fluid–structure interaction model using ABAQUS 2023 finite element software, complemented by the design and implementation of a pull-testing platform for MFL detectors. This platform simulates detector operation under various interference conditions and quantifies the resulting frictional resistance. The findings reveal that the primary source of frictional resistance is the contact interaction between the MFL detector and the pipeline wall. Key factors influencing the magnitude of this resistance include the detector’s mass, the structural design and materials of the sealing cups and support plates, as well as the surface roughness of the pipeline. Both experimental results and numerical simulations demonstrate a pronounced increase in frictional resistance with heightened interference levels. The theoretical model exhibits strong agreement with experimental data, though deviations are observed under conditions of severe interference. This study provides a detailed understanding of frictional resistance patterns under diverse structural and operational scenarios, offering both theoretical guidance and practical recommendations for the design of low-resistance MFL detectors.

The plastic zone range is an important parameter in the analysis of damage characteristics and the degree of damage to the rock surrounding a roadway. Based on the establishment of a plastic zone calculation model considering the creep effect, this paper obtains the characteristics of the change in the plastic zone damage range with time by solving the model. Additionally, the validity of the model is verified by field experiments. The research results can provide guidance for gas pressure measurement and gas drainage in coal mines.

The relationship between psoriasis and site-specific cancers remains unclear. Here, we aim to investigate whether psoriasis is causally associated with site-specific cancers. We use observational and genetic data from the UK Biobank, obtaining GWAS summary data, eQTL analysis data, TCGA data, and GTEx data from public datasets. We perform PheWAS, polygenic risk score analysis, and one-sample and two-sample Mendelian randomization analyses to investigate the potential causal associations between psoriasis and cancers. In the unselected PheWAS analysis, psoriasis is associated with higher risks of 16 types of cancer. Using one-sample Mendelian randomization analyses, it is found that genetically predicted psoriasis is associated with higher risks of anal canal cancer, breast cancer, follicular non-Hodgkin’s lymphoma and nonmelanoma skin cancer in women; and lung cancer and kidney cancer in men. Our two-sample Mendelian randomization analysis indicates that psoriasis is causally associated with breast cancer and lung cancer. Gene annotation shows that psoriasis-related genes, such as ERAP1, are significantly different in lung and breast cancer tissues. Taken together, clinical attention to lung cancer and breast cancer may be warranted among patients with psoriasis. The relationship between psoriasis and cancer remains unclear. Here, the authors use PheWAS, polygenic risk scores, and Mendelian randomization analyses to demonstrate that psoriasis is causally associated with lung and breast cancer.

The measurement of the injecting water into coal is commonly used to prevent and control methane disasters, which will increase moisture in the coal, and the characteristics of methane desorption in coal will be changed. The methane desorption of different metamorphic degree coal was tested after injecting water on homemade device. The results show that the methane desorption quantity gradually decreased with the amount of injected water. The maximum effect was obtained from the DL coal (a decrease of 82.48 % after injecting water compared with the dry sample), and moderate effects were obtained from the QN coal and the YH coal, and the lowest effect was obtained from the GJZ coal (a decrease of 37.97–47.59 % after injecting water compared with the dry sample). The impact of the injected water on the methane desorption velocity is obvious in the first 40 min, and the methane diffusion coefficient decreases gradually with the amount of injected water. The injected water can reduce the gas outburst disasters by impacting on the methane desorption quantity, methane desorption velocity, and methane diffusion coefficient.

This article combines physical experiments and molecular simulations to explore the influence of added water on CH 4 adsorption in different metamorphism grade coal. Specifically, under different pressures, when the added water content increases from 0% to 6.26%, the CH 4 adsorption amount in high metamorphic coal decreases by approximately 76.72%, 63.82%, 59.53%, 56.10%, and 50.94%. However, the decrease in adsorption amount of gas fat coal and long flame coal is lower than that of anthracite, indicating that the effect of adding water on CH 4 adsorption is most significant in high metamorphic coal, while the effect on medium and low metamorphic coal is relatively small. The average adsorption heat of CH 4 molecules in anthracite is 1.66 times of that of gas fat coal and 2.40 times of that of long flame coal, and the higher the added moisture content, the lower the adsorption heat of CH 4 , the more significant the inhibitory effect of added moisture on CH 4 adsorption by coal.

Background The escalation of viruses over the past decade has highlighted the need to determine their respective hosts, particularly for emerging ones that pose a potential menace to the welfare of both human and animal life. Yet, the traditional means of ascertaining the host range of viruses, which involves field surveillance and laboratory experiments, is a laborious and demanding undertaking. A computational tool with the capability to reliably predict host ranges for novel viruses can provide timely responses in the prevention and control of emerging infectious diseases. The intricate nature of viral-host prediction involves issues such as data imbalance and deficiency. Therefore, developing highly accurate computational tools capable of predicting virus-host associations is a challenging and pressing demand. Results To overcome the challenges of virus-host prediction, we present HostNet, a deep learning framework that utilizes a Transformer-CNN-BiGRU architecture and two enhanced sequence representation modules. The first module, k-mer to vector, pre-trains a background vector representation of k-mers from a broad range of virus sequences to address the issue of data deficiency. The second module, an adaptive sliding window, truncates virus sequences of various lengths to create a uniform number of informative and distinct samples for each sequence to address the issue of data imbalance. We assess HostNet's performance on a benchmark dataset of “Rabies lyssavirus” and an in-house dataset of “Flavivirus”. Our results show that HostNet surpasses the state-of-the-art deep learning-based method in host-prediction accuracies and F1 score. The enhanced sequence representation modules, significantly improve HostNet's training generalization, performance in challenging classes, and stability. Conclusion HostNet is a promising framework for predicting virus hosts from genomic sequences, addressing challenges posed by sparse and varying-length virus sequence data. Our results demonstrate its potential as a valuable tool for virus-host prediction in various biological contexts. Virus-host prediction based on genomic sequences using deep neural networks is a promising approach to identifying their potential hosts accurately and efficiently, with significant impacts on public health, disease prevention, and vaccine development.