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Abstract This paper mainly studies the issue of fractional parameter identification of generalized bilinear-in-parameter system(GBIP) with colored noise. Hierarchical fractional least mean square algorithm based on the key term separation principle(K-HFLMS) and multi-innovation hierarchical fractional least mean square algorithm based on the key term separation principle (K-MHFLMS) are presented for the effective parameter estimation of GBIP system. The K-MHFLMS expands the scalar innovation into the vector innovation by making full use of the system input and output data information at each recursive step. The detailed performance analyses of the K-MHFLMS strategy are compared with the K-HFLMS algorithm for GBIP identification model based on the Fitness metrics, the mean square error metrics and the average predicted output error. The effectiveness and reliability of K-HFLMS and K-MHFLMS algorithms are further verified through the simulation experimentation under different noise variances, fractional orders and innovation lengths, and the K-MHFLMS yields faster convergence speed than the K-HFLMS by increasing the innovation length.

BackgroundChimeric antigen receptor (CAR) engineered natural killer (NK) cells exhibit advantages such as MHC-independent recognition and strong anti-tumor functions. However, allogeneic CAR-NK cells derived from human tissues are heterogeneous and susceptible to clearance by hosts.MethodsWe generated a B2M knockout, HLA-E and CD19 CAR ectopic expressing embryonic stem cell (ESC) line, which differentiated normally and gave rise to homogeneous CD19 CAR-NK (CD19 CAR-UiNK) cells using an organoid aggregate induction method. The CD19 CAR-UiNK were co-cultured with T cells or NK cells derived from peripheral blood mononuclear cells (PBMC) with the mismatched HLA to evaluate the immunogenicity of CD19 CAR-UiNK cells. We further assessed the therapeutic effects of CD19 CAR-UiNK cells on CD19+ tumor cells through in vitro cytotoxicity assays and in vivo animal models.ResultsThe CD19 CAR-UiNK cells exhibited typical expression patterns of activating and inhibitory receptors, and crucial effector molecules of NK cells, similar to those of unmodified NK cells. In co-culture assays, the CD19 CAR-UiNK cells evaded allogeneic T cell response and suppressed allogeneic NK cell response. Functionally, the CD19 CAR-UiNK cells robustly secreted IFN-γ and TNF-α, and upregulated CD107a upon stimulation with Nalm-6 tumor cells. The CD19 CAR-UiNK cells effectively eliminated CD19+ tumor cells in vitro, including B-cell cancer cell lines and primary tumor cells from human B-cell leukemia and lymphoma. Further, the CD19 CAR-UiNK cells exhibited strong anti-tumor activity in xenograft animals.ConclusionWe offer a strategy for deriving homogeneous and hypoimmunogenic CD19 CAR-iNK cells with robust anti-tumor effects from ESCs. Our study has significant implications for developing hypoimmunogenic CD19 CAR-NK cell therapy using human ESC as an unlimited cell source.

Remote photo-plethysmography (rPPG) is a useful camera-based health motioning method that can measure the heart rhythm from facial videos. Many well-established deep learning models can provide highly accurate and robust results in measuring heart rate (HR) and heart rate variability (HRV). However, these methods are unable to effectively eliminate illumination variation and motion artifact disturbances, and their substantial computational resource requirements significantly limit their applicability in real-world scenarios. Hence, we propose a lightweight multi-frequency network named MFF-Net to measure heart rhythm via facial videos in a short time. Firstly, we propose a multi-frequency mode signal fusion (MFF) mechanism, which can separate the characteristics of different modes of the original rPPG signals and send them to a processor with independent parameters, helping the network recover blood volume pulse (BVP) signals accurately under a complex noise environment. In addition, in order to help the network extract the characteristics of different modal signals effectively, we designed a temporal multiscale convolution module (TMSC-module) and spectrum self-attention module (SSA-module). The TMSC-module can expand the receptive field of the signal-refining network, obtain more abundant multiscale information, and transmit it to the signal reconstruction network. The SSA-module can help a signal reconstruction network locate the obvious inferior parts in the reconstruction process so as to make better decisions when merging multi-dimensional signals. Finally, in order to solve the over-fitting phenomenon that easily occurs in the network, we propose an over-fitting sampling training scheme to further improve the fitting ability of the network. Comprehensive experiments were conducted on three benchmark datasets, and we estimated HR and HRV based on the BVP signals derived by MFF-Net. Compared with state-of-the-art methods, our approach achieves better performance both on HR and HRV estimation with lower computational burden. We can conclude that the proposed MFF-Net has the opportunity to be applied in many real-world scenarios.

Based on the recent oil and gas discoveries and geological understandings on the ultra-deep strata of sedimentary basins, the formation and occurrence of hydrocarbons in the ultra-deep strata were investigated with respect to the processes of basin formation, hydrocarbon generation, reservoir formation and hydrocarbon accumulation, and key issues in ultra-deep oil and gas exploration were discussed. The ultra-deep strata in China underwent two extensional-convergent cycles in the Meso-Neoproterozoic Era and the Early Paleozoic Era respectively, with the tectonic-sedimentary differentiation producing the spatially adjacent source-reservoir assemblages. There are diverse large-scale carbonate reservoirs such as mound-beach, dolomite, karst fracture-vug, fractured karst and faulted zone, as well as over-pressured clastic rock and fractured bedrock reservoirs. Hydrocarbons were accumulated in multiple stages, accompanied by adjusting and finalizing in the late stage. The distribution of hydrocarbons is controlled by high-energy beach zone, regional unconformity, paleo-high and large-scale fault zone. The ultra-deep strata endow oil and gas resources as 33% of the remaining total resources, suggesting an important successive domain for hydrocarbon development in China. The large-scale pool-forming geologic units and giant hydrocarbon enrichment zones in ultra-deep strata are key and promising prospects for delivering successive discoveries. The geological conditions and enrichment zone prediction of ultra-deep oil and gas are key issues of petroleum geology.

Background: Liver injury in pregnancy significantly impacts the physical and mental health of pregnant women, and finding a potential therapeutic target is crucial for early prediction and improving adverse pregnancy outcomes. This study aims to examine the relationship between miR-339-5p expression and early diagnosis and pregnancy outcomes in patients experiencing liver injury in pregnancy. Methods: A retrospective study of 63 patients with liver injury in pregnancy. The expression of miR-339-5p in plasma of patients with liver health and liver injury in pregnancy was detected using quantitative real-time polymerase chain reaction (qRT-PCR). The value of miR-339-5p in the diagnosis and pregnancy outcomes of patients with liver injury in pregnancy was evaluated by receiver operating characteristic (ROC) and Cox regression analysis. Results: The alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bile acids (TBA), total bilirubin (TBIL) levels and miR-339-5p expression of patients in the intrahepatic cholestasis of pregnancy (ICP), hemolysis, elevated liver enzymes, and low platelet count (HELLP) and acute fatty liver of pregnancy (AFLP) groups, respectively, were statistically significant compared with those in the healthy control (HC) group (p < 0.05). MiR-339-5p expression was significantly lower in patients with liver injury in pregnancy compared to healthy individuals. This difference could be used to distinguish between healthy individuals and those with liver injury in pregnancy (area under the curve (AUC) = 0.897, 95% confidence interval (95% CI) = 0.843–0.951). In addition, ALT (r = –0.686), AST (r = –0.699) and TBA (r = –0.706) were highly negatively correlated with miR-339-5p expression, respectively. MiR-339-5p can be used as a biomarker of liver injury in pregnancy to predict adverse pregnancy outcomes. Conclusions: MiR-339-5p could potentially be used as a potential molecular marker for early diagnosis of liver injury in pregnancy and the prediction of adverse pregnancy outcomes.

Network topology plays a key role in determining the characteristics and dynamical behaviors of a network. But in practice, network topology is sometimes hidden or uncertain ahead of time because of network complexity. In this paper, a robust-synchronization-based topology observer (STO) is proposed and applied to solve the problem of identifying the topology of complex delayed networks (TICDNs). In comparison to the existing literature, the proposed STO does not require any prior knowledge about the range of topological parameters and does not have strict limits on topology type. Furthermore, the proposed STO is suitable not only for networks with fixed coupling strength, but also for networks with adaptive coupling strength. Finally, a few comparison examples for TICDNs are used to verify the feasibility and efficiency of the proposed STO, and the results show that the proposed STO outperforms the other methods.

Abstract Human pluripotent stem cell (hPSC)-induced NK (iNK) cells are a source of off-the-shelf cell products for universal immune therapy. Conventional methods for iNK cell regeneration from hPSCs include embryoid body (EB) formation and feeder-based expansion steps, which are time-consuming and cause instability and high costs of manufacturing. Here, we develop an EB-free, organoid aggregate method for NK cell regeneration from hPSCs. In a short time-window of 27-day induction, millions of hPSC input can output over billions of iNK cells without the necessity of NK cell expansion feeders. The iNK cells highly express classical toxic granule proteins, apoptosis-inducing ligands, as well as abundant activating and inhibitory receptors. Functionally, the iNK cells eradicate human tumor cells via mechanisms of direct cytotoxicity, apoptosis, and antibody-dependent cellular cytotoxicity. This study provides a reliable scale-up method for regenerating human NK cells from hPSCs, which promotes the universal availability of NK cell products for immune therapy.

Gaze is a significant behavioral characteristic that can be used to reflect a person’s attention. In recent years, there has been a growing interest in estimating gaze from facial videos. However, gaze estimation remains a challenging problem due to variations in appearance and head poses. To address this, a framework for 3D gaze estimation using appearance cues is developed in this study. The framework begins with an end-to-end approach to detect facial landmarks. Subsequently, we employ a normalization method and improve the normalization method using orthogonal matrices and conduct comparative experiments to prove that the improved normalization method has a higher accuracy and a lower computational time in gaze estimation. Finally, we introduce a dual-branch convolutional neural network, named FG-Net, which processes the normalized images and extracts eye and face features through two branches. The extracted multi-features are then integrated and input into a fully connected layer to estimate the 3D gaze vectors. To evaluate the performance of our approach, we conduct ten-fold cross-validation experiments on two public datasets, namely MPIIGaze and EyeDiap, achieving remarkable accuracies of 3.11° and 2.75°, respectively. The results demonstrate the high effectiveness of our proposed framework, showcasing its state-of-the-art performance in 3D gaze estimation.

Arabidopsis Casparian strip membrane domain proteins (CASPs) form a transmembrane scaffold to recruit lignin biosynthetic enzymes for Casparian strip (CS) formation. Rice is a semi-aquatic plant with a more complex root structure than Arabidopsis to adapt its growing conditions, where the different deposition of lignin and suberin is crucial for adaptive responses. Here, we observed the structure of rice primary and small lateral roots (SLRs), particularly the deposition patterns of lignin and suberin in wild type and Oscasp1 mutants. We found that the appearance time and structure of CS in the roots of rice are different from those of Arabidopsis and observed suberin deposition in the sclerenchyma in wild type roots. Rice CASP1 is highly similar to AtCASPs, but its expression is concentrated in SLR tips and can be induced by salt stress especially in the steles. The loss of OsCASP1 function alters the expression of the genes involved in suberin biosynthesis and the deposition of suberin in the endodermis and sclerenchyma and leads to delayed CS formation and uneven lignin deposition in SLRs. These different depositions may alter nutrient uptake, resulting in ion imbalance in plant, withered leaves, fewer tillers, and reduced tolerance to salt stress. Our findings suggest that OsCASP1 could play an important role in nutrient homeostasis and adaptation to the growth environment.

Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases. However, BP values are mainly acquired through a contact-sensing method, which is inconvenient and unfriendly for BP monitoring. This paper proposes an efficient end-to-end network for estimating BP values from a facial video to achieve remote BP estimation in daily life. The network first derives a spatiotemporal map of a facial video. Then, it regresses the BP ranges with a designed blood pressure classifier and simultaneously calculates the specific value with a blood pressure calculator in each BP range based on the spatiotemporal map. In addition, an innovative oversampling training strategy was developed to handle the problem of unbalanced data distribution. Finally, we trained the proposed blood pressure estimation network on a private dataset, MPM-BP, and tested it on a popular public dataset, MMSE-HR. As a result, the proposed network achieved a mean absolute error (MAE) and root mean square error (RMSE) of 12.35 mmHg and 16.55 mmHg on systolic BP estimations, and those for diastolic BP were 9.54 mmHg and 12.22 mmHg, which were better than the values obtained in recent works. It can be concluded that the proposed method has excellent potential for camera-based BP monitoring in the indoor scenarios in the real world.

Lithium-ion batteries are a green and environmental energy storage component, which have become the first choice for energy storage due to their high energy density and good cycling performance. Lithium-ion batteries will experience an irreversible process during the charge and discharge cycles, which can cause continuous decay of battery capacity and eventually lead to battery failure. Accurate remaining useful life (RUL) prediction technology is important for the safe use and maintenance of energy storage components. This paper reviews the progress of domestic and international research on RUL prediction methods for energy storage components. Firstly, the failure mechanism of energy storage components is clarified, and then, RUL prediction method of the energy storage components represented by lithium-ion batteries are summarized. Next, the application of the data–model fusion-based method based on kalman filter and particle filter to RUL prediction of lithium-ion batteries are analyzed. The problems faced by RUL prediction of the energy storage components and the future research outlook are discussed.

In this paper, aiming at the problem of control and obstacle avoidance in quadrotor formation when mathematical modeling is not accurate, the artificial potential field method with virtual force is used to plan the obstacle avoidance path of quadrotor formation to solve the problem that the artificial potential field method may fall into local optimal. The adaptive predefined-time sliding mode control algorithm based on RBF neural networks enables the quadrotor formation to track the planned trajectory in a predetermined time and also adaptively estimates the unknown interference in the mathematical model of the quadrotor to improve the control performance. Through theoretical derivation and simulation experiments, this study verified that the proposed algorithm can make the planned trajectory of the quadrotor formation avoid obstacles and make the error between the true trajectory and the planned trajectory converge within a predetermined time under the premise of adaptive estimation of unknown interference in the quadrotor model.

Currently, there are some problems in the electrocorticogram (EEG) emotion recognition research, such as single feature, redundant signal, which make it impossible to achieve high-precision recognition accuracy when used a few channel signals. To solve the abovementioned problems, the authors proposed a method for emotion recognition based on long short-term memory (LSTM) neural network and convolutional neural network (CNN) combined with neurophysiological knowledge. First, the authors selected emotion-sensitive signals based on the physiological function of EEG regions and the active scenario of the band signals, and then merged temporal and spatial features extracted from sensitive signals by LSTM and CNN. Finally, merged features were classified to recognize emotion. The method was experimented on the DEAP dataset, the average accuracy in the valence and arousal dimensions were 92.87% and 93.23%, respectively. Compared with similar studies, it not only improved the recognition accuracy, but also greatly reduced the calculation channel, which proved the superiority of the method.

The eyeball distortions caused by eye diseases, such as myopia and strabismus, can lead to the deviations of eye-tracking data. In this paper, a model-based method with geometric solutions is proposed for gaze correction. The deviations of estimated gaze points are geometrically analyzed based on the individual eyeball models with considerations of the distortions caused by myopia and strabismus. A set of integrated geometric solutions is derived from the varied situations including the case of strabismus and the case of myopia and strabismus, and then used for gaze correction in eyetracking. The experimental results demonstrate that this model-based method is effective to reduce deviations in estimated gaze points, and can be used to correct the modeling error in eye-tracking. Moreover, the proposed method has the potential to provide a simple approach to correct the eyetracking data for various populations with eye diseases.

When developing a Wi-Fi indoor positioning system in a real-world environment, the problems we have to face are that some access points’ signal strength fluctuates extensively or even loses contact due to the cybersecurity threats, leading to the fact that the indoor location system cannot get reliable application in a real-world environment. To solve this problem, we propose a new integrated model based on signal anomaly detector and signal distance corrector to provide reliable position estimation when the access points’ signal is lost under cybersecurity threats. The signal anomaly detector improves recognition capability of the uncertain signal and noise, while the signal distance corrector improves the robustness and fault tolerance of the highly variable Wi-Fi signals. To fully reflect the performance of the proposed method, experiments have been carried out in the real environment of indoor parking lots. The results show that the proposed integrated model successfully provides reliable position estimation when the access points are lost under cybersecurity threats.