Human motion tracking based on complementary Kalman filter

Miniaturized Inertial Measurement Unit (IMU) has been widely used in many motion capturing applications. In order to overcome stability and noise problems of IMU, a lot of efforts have been made to develop appropriate data fusion method to obtain reliable orientation estimation from IMU data. This article presents a method which models the errors of orientation, gyroscope bias and magnetic disturbance, and compensate the errors of state variables with complementary Kalman filter in a body motion capture system. Experimental results have shown that the proposed method significantly reduces the accumulative orientation estimation errors

Beat-to-beat ambulatory blood pressure estimation based on random forest

Ambulatory blood pressure is critical in predicting some major cardiovascular events; therefore, cuff-less and noninvasive beat-to-beat ambulatory blood pressure measure-ment is of great significance. Machine-learning methods have shown the potential to derive the relationship between physio-logical signal features and ABP. In this paper, we apply random forest method to systematically explorer the inherent connections between photoplethysmography signal, electrocardiogram signal and ambulatory blood pressure. To archive this goal, 18 features were extracted from PPG and ECG signals. Several models with most significant features as inputs and beat-to-beat ABP as outputs were trained and tested on data from the Multi-Parameter Intelligent Monitoring in Intensive Care II database. Results indicate that compared with the common pulse transit time method, the RF method gives a better performance for one-hour continuous estimation of diastolic blood pressure and systolic blood pressure under both the Association for the Advancement of Medical Instrumentation and British Hyper-tension Society standard

Robust Target Positioning for Reconfigurable Intelligent Surface Assisted MIMO Radar Systems

The direction of arrival (DOA) based multiple-input multiple-output (MIMO) radar technique has been widely utilized for ubiquitous positioning due to its advantage of simple implementability. On the other hand, reconfigurable intelligent surface (RIS) has received considerable attention, which can be deployed on the walls and objects to strengthen the positioning performance. However, RIS is usually not equipped with a perception module, which results in the tremendous challenge for RIS-assisted positioning. To tackle this challenge, this paper propose the fundamental problem of DOA-based target positioning in RIS-assisted MIMO radar system. Unlike conventional DOA estimation systems, the beneficial role of RIS is investigated in MIMO radar system, where a nonconvex $\ell _{p}$ promoting function is exploited to estimate DOA task. By adjusting the reflecting elements of the RIS, the proximal projection iterative strategy is developed to obtain the feasible solution. Both theoretical analysis and simulation results illustrate that the proposed scheme can achieve remarkable positioning performance and shed light on the benefits offered by the adoption of the RIS in terms of positioning performance

Biotic responses to volatile volcanism and environmental stresses over the Guadalupian-Lopingian (Permian) transition

Biotic extinction during the Guadalupian-Lopingian (G-L) transition is actively debated, with its timing, validity, and causality all questioned. Here, we show, based on detailed sedimentary, paleoecologic, and geochemical analyses of the Penglaitan section in South China, that this intra-Permian biotic crisis began with the demise of a metazoan reef system and extinction of corals and alatoconchid bivalves in the late Guadalupian. A second crisis, among nektonic organisms, occurred around the G-L boundary. Mercury concentration/total organic carbon (Hg/TOC) ratios show two anomalies. The first Hg/TOC peak broadly coincides with the reef collapse and a positive shift in Δ199Hg values during a lowstand interval, which was followed by microbial proliferation. A larger Hg/TOC peak is found just above the G-L boundary and speculatively represents a main eruption episode of the Emeishan large igneous province (ELIP). This volatile volcanism coincided with nektonic extinction, a negative δ13Ccarb excursion, anoxia, and sea-level rise. The temporal coincidence of these phenomena supports a cause-andeffect relationship and indicates that the eruption of the ELIP likely triggered the G-L crisis

Unsupervised Monocular Depth Estimation for Night-time Images using Adversarial Domain Feature Adaptation

In this paper, we look into the problem of estimating per-pixel depth maps from unconstrained RGB monocular night-time images which is a difficult task that has not been addressed adequately in the literature. The state-of-the-art day-time depth estimation methods fail miserably when tested with night-time images due to a large domain shift between them. The usual photo metric losses used for training these networks may not work for night-time images due to the absence of uniform lighting which is commonly present in day-time images, making it a difficult problem to solve. We propose to solve this problem by posing it as a domain adaptation problem where a network trained with day-time images is adapted to work for night-time images. Specifically, an encoder is trained to generate features from night-time images that are indistinguishable from those obtained from day-time images by using a PatchGAN-based adversarial discriminative learning method. Unlike the existing methods that directly adapt depth prediction (network output), we propose to adapt feature maps obtained from the encoder network so that a pre-trained day-time depth decoder can be directly used for predicting depth from these adapted features. Hence, the resulting method is termed as "Adversarial Domain Feature Adaptation (ADFA)" and its efficacy is demonstrated through experimentation on the challenging Oxford night driving dataset. Also, The modular encoder-decoder architecture for the proposed ADFA method allows us to use the encoder module as a feature extractor which can be used in many other applications. One such application is demonstrated where the features obtained from our adapted encoder network are shown to outperform other state-of-the-art methods in a visual place recognition problem, thereby, further establishing the usefulness and effectiveness of the proposed approach.Comment: ECCV 202

Analysis of Kif5b Expression during Mouse Kidney Development

published_or_final_versio

Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction

Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on experimental protein structure data and fail in scenarios where these data are unavailable. Predicted protein structures from AI tools (e.g., AlphaFold2) were utilized as alternatives. However, we observed that current practices, which simply employ accurately predicted structures during inference, suffer from notable degradation in prediction accuracy. While similar phenomena have been extensively studied in general fields (e.g., Computer Vision) as model robustness, their impact on protein property prediction remains unexplored. In this paper, we first investigate the reason behind the performance decrease when utilizing predicted structures, attributing it to the structure embedding bias from the perspective of structure representation learning. To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures. Extensive experiments have shown that our framework is model-agnostic and effective in improving the property prediction of both predicted structures and experimental structures. The benchmark datasets and codes will be released to benefit the community

Artificial intelligence approaches to predict coronary stenosis severity using non-invasive fractional flow reserve

Fractional flow reserve is the current reference standard in the assessment of the functional impact of a stenosis in coronary heart disease. In this study, three models of artificial intelligence of varying degrees of complexity were compared to fractional flow reserve measurements. The three models are the multivariate polynomial regression, which is a statistical method used primarily for correlation; the feed-forward neural network; and the long short-term memory, which is a type of recurrent neural network that is suited to modelling sequences. The models were initially trained using a virtual patient database that was generated from a validated one-dimensional physics-based model. The feed-forward neural network performed the best for all test cases considered, which were a single vessel case from a virtual patient database, a multi-vessel network from a virtual patient database, and 25 clinically invasive fractional flow reserve measurements from real patients. The feed-forward neural network model achieved around 99% diagnostic accuracy in both tests involving virtual patients, and a respectable 72% diagnostic accuracy when compared to the invasive fractional flow reserve measurements. The multivariate polynomial regression model performed well in the single vessel case, but struggled on network cases as the variation of input features was much larger. The long short-term memory performed well for the single vessel cases, but tended to have a bias towards a positive fractional flow reserve prediction for the virtual multi-vessel case, and for the patient cases. Overall, the feed-forward neural network shows promise in successfully predicting fractional flow reserve in real patients, and could be a viable option if trained using a large enough data set of real patients

Influence of Ketotifen, Cromolyn Sodium, and Compound 48/80 on the survival rates after intestinal ischemia reperfusion injury in rats

<p>Abstract</p> <p>Background</p> <p>Mast cells were associated with intestinal ischemia-reperfusion injury, the study was to observe the influence of Ketotifen, Cromolyn Sdium(CS), and Compound 48/80(CP) on the survival rates on the third day after intestinal ischemia-reperfusion injury in rats.</p> <p>Methods</p> <p>120 healthy Sprague-Dawley rats were randomly divided into 5 groups, Sham-operated group (group S), model group (group M), group K, group C and group CP. Intestinal damage was triggered by clamping the superior mesenteric artery for 75 minutes, group K, C, and CP were treated with kotifen 1 mg·kg^-1, CS 50 mg·kg^-1, and CP 0.75 mg·kg^-1i.v. at 5 min before reperfusion and once daily for three days following reperfusion respectively. Survival rate in each group was recorded during the three days after reperfusion. All the surviving rats were killed for determining the concentration of serum glutamic-oxaloacetic transaminase(AST), glutamic pyruvic transaminase(ALT), the ratio of AST compare ALT(S/L), total protein(TP), albumin(ALB), globulin(GLB), the ratio of ALB compare GLB(A/G), phosphocreatine kinase(CK), lactate dehydrogenase(LDH), urea nitrogen(BUN) and creatinine(CRE) at the 3^rdday after reperfusion. And ultrastructure of IMMC, Chiu's score, lung histology, IMMC counts, the levels of TNF-α, IL-1β, IL-6 and IL-10 of the small intestine were detected at the same time.</p> <p>Results</p> <p>Intestinal ischemia-reperfusion injury reduced the survival rate. The concentrations of TP, ALB and level of IL-10 in intestine in group M decreased significantly while the concentrations of S/L, LDH and the levels of IL-6 and TNF-α in intestine increased significantly compared with group S (<it>P </it>< 0.05). Treatment with Ketotifen and CS increased the survival rate compared with group M (<it>P </it>< 0.05), attenuated the down-regulation or up-regulation of the above index (<it>P </it>< 0.05). Treatment with CP decreased the survival rate on the 3^rdday after reperfusion compared with group M(<it>P </it>< 0.05). Group K and C had better morphology in IMMC in the small intestine and in the lungs than in group M and CP, although the Chiu's score and IMMC counts remained the same in the five groups(<it>P </it>> 0.05).</p> <p>Conclusion</p> <p>Mast cell inhibition after ischemia prior to reperfusion and following reperfusion may decrease the multi-organ injury induced by intestine ischemia reperfusion, and increase the survival rates.</p