A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance

Various animals exhibit accurate navigation using environment cues. The Earth's magnetic field has been proved a reliable information source in long-distance fauna migration. Inspired by animal navigation, this work proposes a bionic and data-driven approach for long-distance underwater navigation. The proposed approach uses measured geomagnetic data for the navigation, and requires no GPS systems or geographical maps. Particularly, we construct and train a Temporal Attention-based Long Short-Term Memory (TA-LSTM) network to predict the heading angle during the navigation. To mitigate the impact of geomagnetic anomalies, we develop the mechanism to detect and quantify the anomalies based on Maximum Likelihood Estimation. We integrate the developed mechanism with the TA-LSTM, and calibrate the predicted heading angles to gain resistance against geomagnetic anomalies. Using the retrieved data from the WMM model, we conduct numerical simulations with diversified navigation conditions to test our approach. The simulation results demonstrate a resilience navigation against geomagnetic anomalies by our approach, along with precision and stability of the underwater navigation in single and multiple destination missions

Machine learning-based prediction of off-pump coronary artery bypass grafting-associated acute kidney injury

background: the cardiac surgery-associated acute kidney injury (CSA-AKI) occurs in up to 1 out of 3 patients. off-pump coronary artery bypass grafting (OPCABG) is one of the major cardiac surgeries leading to CSA-AKI. early identification and timely intervention are of clinical significance for CSA-AKI. In this study, we aimed to establish a prediction model of off-pump coronary artery bypass grafting-associated acute kidney injury (OPCABG-AKI) after surgery based on machine learning methods. methods: the preoperative and intraoperative data of 1,041 patients who underwent OPCABG in chest hospital, tianjin university from June 1, 2021 to april 30, 2023 were retrospectively collected. the definition of OPCABG-AKI was based on the 2012 kidney disease improving global outcomes (KDIGO) criteria. the baseline data and intraoperative time series data were included in the dataset, which were preprocessed separately. a total of eight machine learning models were constructed based on the baseline data: logistic regression (LR), gradient-boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and decision tree (DT). the intraoperative time series data were extracted using a long short-term memory (LSTM) deep learning model. the baseline data and intraoperative features were then integrated through transfer learning and fused into each of the eight machine learning models for training. based on the calculation of accuracy and area under the curve (AUC) of the prediction model, the best model was selected to establish the final OPCABG-AKI risk prediction model. the importance of features was calculated and ranked by DT model, to identify the main risk factors. results: among 701 patients included in the study, 73 patients (10.4%) developed OPCABG-AKI. The GBDT model was shown to have the best predictions, both based on baseline data only (AUC =0.739, accuracy: 0.943) as well as based on baseline and intraoperative datasets (AUC =0.861, accuracy: 0.936). the ranking of importance of features of the GBDT model showed that use of insulin aspart was the most important predictor of OPCABG-AKI, followed by use of acarbose, spironolactone, alfentanil, dezocine, levosimendan, clindamycin, history of myocardial infarction, and gender. conclusions: A GBDT-based model showed excellent performance for the prediction of OPCABG-AKI. the fusion of preoperative and intraoperative data can improve the accuracy of predicting OPCABG-AKI

Quantum confined peptide assemblies with tunable visible to near-infrared spectral range

Quantum confined materials have been extensively studied for photoluminescent applications. Due to intrinsic limitations of low biocompatibility and challenging modulation, the utilization of conventional inorganic quantum confined photoluminescent materials in bio-imaging and bio-machine interface faces critical restrictions. Here, we present aromatic cyclo-dipeptides that dimerize into quantum dots, which serve as building blocks to further self-assemble into quantum confined supramolecular structures with diverse morphologies and photoluminescence properties. Especially, the emission can be tuned from the visible region to the near-infrared region (420 nm to 820 nm) by modulating the self-assembly process. Moreover, no obvious cytotoxic effect is observed for these nanostructures, and their utilization for in vivo imaging and as phosphors for light-emitting diodes is demonstrated. The data reveal that the morphologies and optical properties of the aromatic cyclo-dipeptide self-assemblies can be tuned, making them potential candidates for supramolecular quantum confined materials providing biocompatible alternatives for broad biomedical and opto-electric applications

Study on brain damage patterns of COVID-19 patients based on EEG signals

ObjectiveThe coronavirus disease 2019 (COVID-19) is an acute respiratory infectious disease caused by the SARA-CoV-2, characterized by high infectivity and incidence. Clinical data indicates that COVID-19 significantly damages patients’ perception, motor function, and cognitive function. However, the electrophysiological mechanism by which the disease affects the patient’s nervous system is not yet clear. Our aim is to investigate the abnormal levels of brain activity and changes in brain functional connectivity network in patients with COVID-19.MethodsWe compared and analyzed electroencephalography signal sample entropy, energy spectrum, and brain network characteristic parameters in the delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands of 15 patients with COVID-19 and 15 healthy controls at rest.ResultsAt rest, energy values of the four frequency bands in the frontal and temporal lobes of COVID-19 patients were significantly reduced. At the same time, the sample entropy value of the delta band in COVID-19 patients was significantly increased, while the value of the beta band was significantly decreased. However, the average value of the directed transfer function of patients did not show any abnormalities under the four frequency bands. Furthermore, node degree in the temporal lobe of patients was significantly increased, while the input degree of the frontal and temporal lobes was significantly decreased, and the output degree of the frontal and occipital lobes was significantly increased.ConclusionThe level of brain activity in COVID-19 patients at rest is reduced, and the brain functional network undergoes a rearrangement. These results preliminarily demonstrate that COVID-19 patients exhibit certain brain abnormalities during rest, it is feasible to explore the neurophysiological mechanism of COVID-19’s impact on the nervous system by using EEG signals, which can provide a certain technical basis for the subsequent diagnosis and evaluation of COVID-19 using artificial intelligence and the prevention of brain nervous system diseases after COVID-19 infection

Additional file 1 of Characteristics and outcome of patients with left atrial appendage closure in China: a single-center experience

Supplementary Material 1: WATCHMAN Access Sheath Advancement into LSP