Analysis of the Improvement Effect of Combined Application of Oral Rehabilitation Training and Neuromuscular Electrical Stimulation on Pediatric Swallowing Disorders.

Pediatric swallowing disorders are common yet often overlooked neuro-muscular system diseases that significantly impact the quality of life and development of affected children. This study aims to explore the effect of combined application of oral rehabilitation training and neuromuscular electrical stimulation on improving pediatric swallowing disorders. Children meeting the inclusion criteria for swallowing disorders were divided into control and experimental groups based on different intervention protocols. The experimental group received combined oral rehabilitation training and neuromuscular electrical stimulation, while the control group received only oral rehabilitation training. Results showed that the intervention was more effective in the experimental group, with shorter recovery time for normal swallowing function and improved nutritional status and quality of life. This study provides scientific evidence for clinical treatment of pediatric swallowing disorders. In conclusion, the combined application of oral rehabilitation training and neuromuscular electrical stimulation effectively improves pediatric swallowing disorders, with superior efficacy compared to single treatment methods. Further research is needed to elucidate the mechanism of action and optimize treatment protocols to enhance the therapeutic outcomes and prognosis of pediatric swallowing disorders.

Multi-Source Feature-Fusion Method for the Seismic Data of Cultural Relics Based on Deep Learning.

The museum system is exposed to a high risk of seismic hazards. However, it is difficult to carry out seismic hazard prevention to protect cultural relics in collections due to the lack of real data and diverse types of seismic hazards. To address this problem, we developed a deep-learning-based multi-source feature-fusion method to assess the data on seismic damage caused by collected cultural relics. Firstly, a multi-source data-processing strategy was developed according to the needs of seismic impact analysis of the cultural relics in the collection, and a seismic event-ontology model of cultural relics was constructed. Additionally, a seismic damage data-classification acquisition method and empirical calculation model were designed. Secondly, we proposed a deep learning-based multi-source feature-fusion matching method for cultural relics. By constructing a damage state assessment model of cultural relics using superpixel map convolutional fusion and an automatic data-matching model, the quality and processing efficiency of seismic damage data of the cultural relics in the collection were improved. Finally, we formed a dataset oriented to the seismic damage risk analysis of the cultural relics in the collection. The experimental results show that the accuracy of this method reaches 93.6%, and the accuracy of cultural relics label matching is as high as 82.6% compared with many kinds of earthquake damage state assessment models. This method can provide more accurate and efficient data support, along with a scientific basis for subsequent research on the impact analysis of seismic damage to cultural relics in collections.

Interaction between SlMAPK3 and SlASR4 regulates drought resistance in tomato (Solanum lycopersicum L.).

Tomato is a leading vegetable in modern agriculture, and with global warming, drought has become an important factor threatening tomato production. Mitogen-activated protein kinase 3 (MAPK3) plays an important role in plant disease and stress resistance. To clarify the downstream target proteins of SlMAPK3 and the mechanism of stress resistance in tomato, this study was conducted with the SlMAPK3-overexpressing lines OE-1 and OE-2 and the CRISPR/Cas9-mediated mutant lines slmapk3-1 and slmapk3-2 under PEG 6000-simulated drought. The results of yeast two-hybrid (Y2H), pull-down, and coimmunoprecipitation (Co-IP) assays confirmed that SlASR4 (NP_001269248.1) interacted with SlMAPK3. Analyses of the SlASR4 protein structure and SlASR4 expression under PEG 6000 and BTH stress revealed that SlASR4 has a highly conserved protein structural domain involved in the drought stress response under PEG 6000 treatment. The function of the SlASR4 and SlMAPK3 downstream target protein, in drought resistance in tomato plants, was identified by virus-induced gene silencing (VIGS). This study clarified that SlMAPK3 interacts with SlASR4 to positively regulate drought resistance in tomato plants.

WLiT: Windows and Linear Transformer for Video Action Recognition.

The emergence of Transformer has led to the rapid development of video understanding, but it also brings the problem of high computational complexity. Previously, there were methods to divide the feature maps into windows along the spatiotemporal dimensions and then calculate the attention. There are also methods to perform down-sampling during attention computation to reduce the spatiotemporal resolution of features. Although the complexity is effectively reduced, there is still room for further optimization. Thus, we present the Windows and Linear Transformer (WLiT) for efficient video action recognition, by combining Spatial-Windows attention with Linear attention. We first divide the feature maps into multiple windows along the spatial dimensions and calculate the attention separately inside the windows. Therefore, our model further reduces the computational complexity compared with previous methods. However, the perceptual field of Spatial-Windows attention is small, and global spatiotemporal information cannot be obtained. To address this problem, we then calculate Linear attention along the channel dimension so that the model can capture complete spatiotemporal information. Our method achieves better recognition accuracy with less computational complexity through this mechanism. We conduct extensive experiments on four public datasets, namely Something-Something V2 (SSV2), Kinetics400 (K400), UCF101, and HMDB51. On the SSV2 dataset, our method reduces the computational complexity by 28% and improves the recognition accuracy by 1.6% compared to the State-Of-The-Art (SOTA) method. On the K400 and two other datasets, our method achieves SOTA-level accuracy while reducing the complexity by about 49%.

MSFFAL: Few-Shot Object Detection via Multi-Scale Feature Fusion and Attentive Learning.

Few-shot object detection (FSOD) is proposed to solve the application problem of traditional detectors in scenarios lacking training samples. The meta-learning methods have attracted the researchers' attention for their excellent generalization performance. They usually select the same class of support features according to the query labels to weight the query features. However, the model cannot possess the ability of active identification only by using the same category support features, and feature selection causes difficulties in the testing process without labels. The single-scale feature of the model also leads to poor performance in small object detection. In addition, the hard samples in the support branch impact the backbone's representation of the support features, thus impacting the feature weighting process. To overcome these problems, we propose a multi-scale feature fusion and attentive learning (MSFFAL) framework for few-shot object detection. We first design the backbone with multi-scale feature fusion and channel attention mechanism to improve the model's detection accuracy on small objects and the representation of hard support samples. Based on this, we propose an attention loss to replace the feature weighting module. The loss allows the model to consistently represent the objects of the same category in the two branches and realizes the active recognition of the model. The model no longer depends on query labels to select features when testing, optimizing the model testing process. The experiments show that MSFFAL outperforms the state-of-the-art (SOTA) by 0.7-7.8% on the Pascal VOC and exhibits 1.61 times the result of the baseline model in MS COCO's small objects detection.

Trim14 promotes osteoclastogenesis and noncanonical NF-κB activation by targeting p100/p52 in chronic periodontitis.

BACKGROUND: Periodontitis disease infection initiates host immune response, and alveolar bone damage is a hallmark of periodontitis. Bone damage occurs due to changes in osteoclast activity in response to local inflammation. Nuclear factor κB (NF-κB) signaling is essential for inflammatory responses and plays a pivotal role in osteoclast formation and activation. Tripartite motif 14 (Trim14) is a crucial regulator of the noncanonical NF-κB signaling. Here, we investigated the role of Trim14 in chronic periodontitis. METHODS: The development of immune cells and osteoclast formation was evaluated with flow cytometry, qRT-PCR, and histochemical staining. Proinflammatory cytokines were checked by ELISA and qRT-PCR. Protein expression was determined by immunoblotting. Also, the cemento-enamel junction-alveolar bone crest distance was evaluated in the mouse model. RESULTS: Development of innate and adaptive cells was not impaired from the deletion of Trim14. However, the genetic loss of Trim14 remarkably suppressed RANKL-induced osteoclastogenesis, without affecting TLR-induced proinflammatory cytokines except for Il-23a expression. The Trim14 deletion also suppressed the activation of noncanonical NF-κB signaling by targeting p100/p52. Importantly, the deletion of NIK diminished the effects of Trim14 on the inflammatory responses in vivo on chronic periodontitis responses. CONCLUSION: TRIM14 may be a positive regulator to promote osteoclastogenesis and proinflammatory cytokine secretion.

Adaptive Pedestrian Stride Estimation for Localization: From Multi-Gait Perspective.

Accurate and reliable stride length estimation modules play a significant role in Pedestrian Dead Reckoning (PDR) systems, but the accuracy of stride length calculation suffers from individual differences. This paper presents a stride length prediction strategy for PDR systems that can be adapted across individuals and broad walking velocity fields. It consists of a multi-gait division algorithm, which can divide a full stride into push-off, swing, heel-strike, and stance based on multi-axis IMU data. Additionally, based on the acquired gait phases, the correlation between multiple features of distinct gait phases and the stride length is analyzed, and multi regression models are merged to output the stride length value. In experimental tests, the gait segmentation algorithm provided gait phases division with the F-score of 0.811, 0.748, 0.805, and 0.819 for stance, push-off, swing, heel-strike, respectively, and IoU of 0.482, 0.69, 0.509 for push-off, swing, heel-strike, respectively. The root means square error (RMSE) of our proposed stride length estimation was 151.933, and the relative error for total distance in varying walking speed tests was less than 2%. The experimental results validated that our proposed gait phase segmentation algorithm can accurately recognize gait phases for individuals with wide walking speed ranges. With no need for parameter modification, the stride length method based on the fusion of multiple predictions from different gait phases can provide better accuracy than the estimations based on the full stride.

The Diverse Gait Dataset: Gait Segmentation Using Inertial Sensors for Pedestrian Localization with Different Genders, Heights and Walking Speeds.

Stride length estimation is one of the most crucial aspects of Pedestrian Dead Reckoning (PDR). Due to the measurement noise of inertial sensors, individual variances of pedestrians, and the uncertainty in pedestrians walking, there is a substantial error in the assessment of stride length, which causes the accumulated deviation of Pedestrian Dead Reckoning (PDR). With the help of multi-gait analysis, which decomposes strides in time and space with greater detail and accuracy, a novel and revolutionary stride estimating model or scheme could improve the performance of PDR on different users. This paper presents a diverse stride gait dataset by using inertial sensors that collect foot movement data from people of different genders, heights, and walking speeds. The dataset contains 4690 walking strides data and 19,083 gait labels. Based on the dataset, we propose a threshold-independent stride segmentation algorithm called SDATW and achieve an F-measure of 0.835. We also provide the detailed results of recognizing four gaits under different walking speeds, demonstrating the utility of our dataset for helping train stride segmentation algorithms and gait detection algorithms.

Inhibition of microRNA-17 enhances cisplatin-induced apoptosis of human tongue squamous carcinoma cell.

MicroRNA-17 (miR-17) was reported to promote cell proliferation and migration of various types of cancers. However, the mechanism remains unclear. This present study was designed to explore the potential mechanism. Downregulation of miR-17 in CAL-27 cells was performed by transfecting anti-miR-27 plasmids. Xenograft tumor model was carried out to detect the effect of inhibition of microRNA-17 on tongue squamous carcinoma growth. MiR-17 inhibition promotes cisplatin-induced apoptosis via regulating the expression of apoptotic molecules. MiR-17 inhibition promotes cisplatin-induced autophagy of CAL-27 cells. Mechanically, miR-17 inhibition promotes apoptosis and autophagy through STAT3 signaling pathway. Xenograft tumor model showed that miR-17 inhibition attenuates tongue squamous carcinoma growth and promotes tongue squamous carcinoma cell apoptosis in vivo. MiR-17 inhibition enhances cisplatin-induced apoptosis of human tongue squamous carcinoma cell. Our study supplies the evidence that miR-17 may serve as the potential target for human tongue squamous carcinoma treatment.

A novel ferroptosis-related lncRNA signature for prognosis prediction in gastric cancer.

BACKGROUND: Gastric cancer (GC) is a common malignant cancer with a poor prognosis. Ferroptosis has been shown to play crucial roles in GC development. Long non-coding RNAs (lncRNAs) is also associated with tumor progression in GC. This study aimed to screen the prognostic ferroptosis-related lncRNAs and to construct a prognostic risk model for GC. METHODS: Ferroptosis-related lncRNAs from The Cancer Genome Atlas (TCGA) GC expression data was downloaded. First, single factor Cox proportional hazard regression analysis was used to select seven prognostic ferroptosis-related lncRNAs from TCGA database. And then, the selected lncRNAs were further included in the multivariate Cox proportional hazard regression analysis to establish the prognostic model. A nomogram was constructed to predict individual survival probability. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the risk model. RESULTS: We constructed a prognostic ferroptosis-related lncRNA signature in this study. Kaplan-Meier curve analysis revealed a significantly better prognosis for the low-risk group than for the high-risk group (P = 2.036e-05). Multivariate Cox proportional risk regression analysis demonstrated that risk score was an independent prognostic factor [hazard ratio (HR) = 1.798, 95% confidence interval (CI) =1.410-2.291, P < 0.001]. A nomogram, receiver operating characteristic curve, and principal component analysis were used to predict individual prognosis. Finally, the expression levels of AP003392.1, AC245041.2, AP001271.1, and BOLA3-AS1 in GC cell lines and normal cell lines were tested by qRT-PCR. CONCLUSIONS: This risk model was shown to be a novel method for predicting prognosis for GC patients.

A Novel Method for Source Tracking of Chemical Gas Leakage: Outlier Mutation Optimization Algorithm.

Chemical industrial parks, which act as critical infrastructures in many cities, need to be responsive to chemical gas leakage accidents. Once a chemical gas leakage accident occurs, risks of poisoning, fire, and explosion will follow. In order to meet the primary emergency response demands in chemical gas leakage accidents, source tracking technology of chemical gas leakage has been proposed and evolved. This paper proposes a novel method, Outlier Mutation Optimization (OMO) algorithm, aimed to quickly and accurately track the source of chemical gas leakage. The OMO algorithm introduces a random walk exploration mode and, based on Swarm Intelligence (SI), increases the probability of individual mutation. Compared with other optimization algorithms, the OMO algorithm has the advantages of a wider exploration range and more convergence modes. In the algorithm test session, a series of chemical gas leakage accident application examples with random parameters are first assumed based on the Gaussian plume model; next, the qualitative experiments and analysis of the OMO algorithm are conducted, based on the application example. The test results show that the OMO algorithm with default parameters has superior comprehensive performance, including the extremely high average calculation accuracy: the optimal value, which represents the error between the final objective function value obtained by the optimization algorithm and the ideal value, reaches 2.464e-15 when the number of sensors is 16; 2.356e-13 when the number of sensors is 9; and 5.694e-23 when the number of sensors is 4. There is a satisfactory calculation time: 12.743 s/50 times when the number of sensors is 16; 10.304 s/50 times when the number of sensors is 9; and 8.644 s/50 times when the number of sensors is 4. The analysis of the OMO algorithm's characteristic parameters proves the flexibility and robustness of this method. In addition, compared with other algorithms, the OMO algorithm can obtain an excellent leakage source tracing result in the application examples of 16, 9 and 4 sensors, and the accuracy exceeds the direct search algorithm, evolutionary algorithm, and other swarm intelligence algorithms.

Experimental Evaluation of the Packet Reception Performance of LoRa.

LoRa technology is currently one of the most popular Internet of Things (IoT) technologies. A substantial number of LoRa devices have been applied in a wide variety of real-world scenarios, and developers can adjust the packet reception performance of LoRa through physical layer parameter configuration to meet the requirements. However, since the important details of the relationship between the physical layer parameters and the packet reception performance of LoRa remain unknown, it is a challenge to choose the appropriate parameter configuration to meet the requirements of the scenarios. Moreover, with the increase in application scenarios, the requirements for energy consumption become increasingly high. Therefore, it is also a challenge to know how to configure the parameters to maximize the energy efficiency while maintaining a high data rate. In this work, a complex evaluation experiment on the communication capability under a negative Signal to Noise Ratio is presented, and the specific details of the relationship between physical layer parameters and the packet reception performance of LoRa are clarified. Furthermore, we study the impact of the packet length on the packet reception performance of LoRa, and the experimental results show that when there is a large amount of data to be transmitted, it is better to choose long packets instead of short packets. Finally, considering the influence of physical layer parameters and the packet length on the packet reception performance of LoRa, the optimal parameter combination is explored, so as to propose a transmission scheme with a balanced reliability, delay, and energy consumption. This scheme is the first to consider the physical layer parameters and packet length together to study the communication transmission scheme, which reduces the communication time by 50% compared with the traditional transmission scheme and greatly reduces the energy consumption.

Downregulation of miR-24-3p promotes osteogenic differentiation of human periodontal ligament stem cells by targeting SMAD family member 5.

Osteogenic differentiation is a complicated process that depends on various regulatory factors and signal pathways. In our research, the osteogenic differentiation capacity was analyzed by alizarin red staining, alkaline phosphatase activity, and protein levels of osteogenic differentiation markers including runt-related transcription factor 2, bone morphogenetic protein 2, and osteocalcin (OCN). We observed a notable decrease of miR-24-3p level in osteogenic-differentiated human periodontal ligament stem cells (hPDLSCs) by microarray analysis. In our gain- and loss-of-function experiments, we discovered that miR-24-3p has a suppression effect on hPDLSCs osteogenic differentiation. Moreover, SMAD family member 5 (Smad5), the critical osteogenic differentiation transcription factors, was predicted to be targets of miR-24-3p. In addition, luciferase reporter assay further proved that miR-24-3p directly targeted the 3'-untranslated region of Smad5. Similarly, we found that the overexpression of miR-24-3p significantly decreased the Smad5 messenger RNA level in hPDLSCs, which was detected by real-time quantitative polymerase chain reaction. Then hPDLSCs were transfected with miR-24-3p mimics to inhibit Smad5 expression; meanwhile, Smad5 RNA interference could significantly reverse the osteogenic differentiation inhibition effect of miR-24-3p. In brief, a series of data showed that miR-24-3p is a regulator of Smad5, playing an important role in osteogenic differentiation.

Identification of differentially expressed circRNAs and a novel hsa_circ_0000144 that promote tumor growth in gastric cancer.

BACKGROUND: Circular RNAs (circRNAs) are involved in regulating tumor pathogenesis. The mechanism of circRNAs in gastric cancer (GC) is still unknown. Our study aimed to identify differentially expressed circRNAs and assess a novel circRNA (hsa_circ_0000144) in the proliferation, migration, and invasion in GC. METHODS: Gene ontology (GO) enrichment and analyses of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, pathway network, and the ceRNA regulatory network of hsa_circ_0000144 targeting miRNAs and mRNAs were performed with the help of bioinformatics using R language and Perl software. hsa_circ_0000144 expression and circRNA knockdown in GC cell lines were detected using quantitative PCR (qPCR) in vitro. Cell proliferation, migration, and invasion after circRNA knockdown were measured using the cell counting kit-8 assay and Transwell assay. RESULTS: The circRNA expression profile GSE78092 downloaded from the Gene Expression Omnibus database included three GC patients and three normal tissues. Thirty-two differentially expressed circRNAs comprised six upregulated circRNAs and 26 downregulated circRNAs. In particular, the ErbB signaling pathway, neurotrophin signaling pathway, cellular senescence, and pathways in bladder cancer and GC played the most important roles in the pathway network. The expression of hsa_circ_0000144 was upregulated in GC cell lines. Hsa_circ_0000144 knockdown suppressed tumor growth in vitro. CONCLUSIONS: Hsa_circ_0000144 promotes GC cell proliferation, migration, and invasion, and the ceRNA regulatory network of hsa_circ_0000144 targeting miRNAs and mRNAs might be biomarkers for GC diagnosis and targeted therapy.

Measurement and Analysis of Near-Ground Propagation Models under Different Terrains for Wireless Sensor Networks.

The propagation model is an essential component in the design and deployment of a wireless sensor network (WSN). Although much attention has been given to near-ground propagation models, few studies place the transceiver directly on the ground with the height of antennas at the level of a few centimeters, which is a more realistic deployment scenario for WSNs. We measured the Received Signal Strength Indication (RSSI) of these truly near-ground WSNs at 470 MHz under four different terrains, namely flat concrete road, flat grass and two derived scenarios, and obtained the corresponding path loss models. By comprehensive analysis of the influence of different antenna heights and terrain factors, we showed the limit of existing theoretical models and proposed a propagation model selection strategy to more accurately reflect the true characteristics of the near-ground wireless channels for WSNs. In addition, we implemented these models on Cooja simulator and showed that simplistic theoretical models would induce great inaccuracy of network connectivity estimation.

A Robust Dynamic Heart-Rate Detection Algorithm Framework During Intense Physical Activities Using Photoplethysmographic Signals.

Dynamic accurate heart-rate (HR) estimation using a photoplethysmogram (PPG) during intense physical activities is always challenging due to corruption by motion artifacts (MAs). It is difficult to reconstruct a clean signal and extract HR from contaminated PPG. This paper proposes a robust HR-estimation algorithm framework that uses one-channel PPG and tri-axis acceleration data to reconstruct the PPG and calculate the HR based on features of the PPG and spectral analysis. Firstly, the signal is judged by the presence of MAs. Then, the spectral peaks corresponding to acceleration data are filtered from the periodogram of the PPG when MAs exist. Different signal-processing methods are applied based on the amount of remaining PPG spectral peaks. The main MA-removal algorithm (NFEEMD) includes the repeated single-notch filter and ensemble empirical mode decomposition. Finally, HR calibration is designed to ensure the accuracy of HR tracking. The NFEEMD algorithm was performed on the 23 datasets from the 2015 IEEE Signal Processing Cup Database. The average estimation errors were 1.12 BPM (12 training datasets), 2.63 BPM (10 testing datasets) and 1.87 BPM (all 23 datasets), respectively. The Pearson correlation was 0.992. The experiment results illustrate that the proposed algorithm is not only suitable for HR estimation during continuous activities, like slow running (13 training datasets), but also for intense physical activities with acceleration, like arm exercise (10 testing datasets).

Weighted Global Artificial Bee Colony Algorithm Makes Gas Sensor Deployment Efficient.

This paper proposes an improved artificial bee colony algorithm named Weighted Global ABC (WGABC) algorithm, which is designed to improve the convergence speed in the search stage of solution search equation. The new method not only considers the effect of global factors on the convergence speed in the search phase, but also provides the expression of global factor weights. Experiment on benchmark functions proved that the algorithm can improve the convergence speed greatly. We arrive at the gas diffusion concentration based on the theory of CFD and then simulate the gas diffusion model with the influence of buildings based on the algorithm. Simulation verified the effectiveness of the WGABC algorithm in improving the convergence speed in optimal deployment scheme of gas sensors. Finally, it is verified that the optimal deployment method based on WGABC algorithm can improve the monitoring efficiency of sensors greatly as compared with the conventional deployment methods.

Interleukin-1 receptor-associated kinase-2 genetic variant rs708035 increases NF-κB activity through promoting TRAF6 ubiquitination.

The IL-1 receptor-associated kinases (IRAKs) are key regulators of Toll-like receptor (TLR)/IL-1 signaling, which are critical regulators of mammalian inflammation and innate immune response. Single nucleotide polymorphisms (SNPs) within the IRAK genes have been discovered recently. However, the functions of these IRAK SNPs remain largely unknown. Here, we found that the non-synonymous IRAK2 variant rs708035 (coding D431E) increases NF-κB activity and leads to more expression of NF-κB-dependent proinflammatory cytokines compared with IRAK2 wild type. Moreover, when IRAK2 knockdown cells reconstituted with siRNA-resistant WT-IRAK2 or D431E-IRAK2 were infected with influenza virus, a more obvious induction of IL-6 and a stronger anti-apoptosis effect were observed in D431E-IRAK2 expressing cells. Notably, we also found that the levels of proinflammatory cytokine-IL-6 were indeed higher in people carrying D431E-IRAK2 than those carrying WT-IRAK2. Further study demonstrated that elevated NF-κB activation mediated by the IRAK2 variant was due to increased TRAF6 ubiquitination and faster IκBα degradation. Our study provides important insight of IRAK2 SNP in the regulation of NF-κB activation and indicates that IRAK2 rs708035 might be associated with human diseases caused by hyper-activation of NF-κB.

The Role of CD36 in the Effect of Arginine in Atherosclerotic Rats.

BACKGROUND: The aim of this study was to investigate the effects of arginine in the development of atherosclerosis in rats fed a high-fat diet supplemented with arginine and to evaluate the role of CD36 in this process. MATERIAL AND METHODS: A total of 40 Sprague-Dawley rats were randomly assigned to 4 groups: control group, fat diet group, simvastatin group, and arginine group. They were fed for 12 weeks and were then sacrificed. Immunohistochemical CD36 expression and pathology was investigated in the aorta; CD36 expression in mononuclear cells was detected by Western blot and RT-PCR. RESULTS: The thickness of the aortal intima, media, and I/M significantly decreased in the arginine group rats compared with those in the fat diet group (P<0.05). CD36 expression was up-regulated in rats in the fat diet group compared with the control group and was down-regulated in rats in the arginine group compared with rats in the fat diet group. CONCLUSIONS: The addition of arginine has a significant effect on reducing rat atherosclerosis development, which may be attributed to both the down-regulation of CD36 expression in rat aortic endothelial and blood mononuclear cells and the NO pathway.

A robust method to detect zero velocity for improved 3D personal navigation using inertial sensors.

This paper proposes a robust zero velocity (ZV) detector algorithm to accurately calculate stationary periods in a gait cycle. The proposed algorithm adopts an effective gait cycle segmentation method and introduces a Bayesian network (BN) model based on the measurements of inertial sensors and kinesiology knowledge to infer the ZV period. During the detected ZV period, an Extended Kalman Filter (EKF) is used to estimate the error states and calibrate the position error. The experiments reveal that the removal rate of ZV false detections by the proposed method increases 80% compared with traditional method at high walking speed. Furthermore, based on the detected ZV, the Personal Inertial Navigation System (PINS) algorithm aided by EKF performs better, especially in the altitude aspect.