Method for Incomplete and Imbalanced Data Based on Multivariate Imputation by Chained Equations and Ensemble Learning.

The classification analysis of incomplete and imbalanced data is still a challenging task since these issues could negatively impact the training of classifiers, which were also found in our study on the physical fitness assessments of patients. And in fields such as healthcare, there are higher requirements for the accuracy of the generated imputation values. To train a high-performance classifier and pursue high accuracy, we attempted to resolve any potential negative impact by using a novel algorithmic approach based on the combination of multivariate imputation by chained equations and the ensemble learning method (MICEEN), which can solve the two problems simultaneously. We used multivariate imputation by chained equations to generate more accurate imputation values for the training set passed to ensemble learning to build a predictor. On the other hand, missing values were introduced into minority classes and used them to generate new samples belonging to the minority classes in order to balance the distribution of classes. On real-world datasets, we perform extensive experiments to assess our method and compare it to other state-of-the-art approaches. The advantages of the proposed method are demonstrated by experimental results for the benchmark datasets and self-collected datasets of physical fitness assessment of tumor patients with varying missing rates.

Toxicity prediction and classification of Gunqile-7 with small sample based on transfer learning method.

Drug-induced diseases are the most important component of iatrogenic disease. It is the duty of doctors to provide a reasonable and safe dose of medication. Gunqile-7 is a Mongolian medicine with analgesic and anti-inflammatory effects. As a foreign substance in the body, even with reasonable medication, it may produce varying degrees of adverse reactions or toxic side effects. Since the cost of collecting Gunqile-7 for pharmacological animal trials is high and the data sample is small, this paper employs transfer learning and data augmentation methods to study the toxicity of Gunqile-7. More specifically, to reduce the necessary number of training samples, the data augmentation approach is employed to extend the data set. Then, the transfer learning method and one-dimensional convolutional neural network are utilized to train the network. In addition, we use the support vector machine-recursive feature elimination method for feature selection to reduce features that have adverse effects on model predictions. Furthermore, due to the important role of the pre-trained model of transfer learning, we select a quantitative toxicity prediction model as the pre-trained model, which is consistent with the purpose of this paper. Lastly, the experimental results demonstrate the efficiency of the proposed method. Our method can improve accuracy by up to 9 percentage points compared to the method without transfer learning on a small sample set.

Glutathione-dependent redox homeostasis is critical for chlorothalonil detoxification in tomato leaves.

Glutathione plays a critical role in plant growth, development and response to stress. It is a major cellular antioxidant and is involved in the detoxification of xenobiotics in many organisms, including plants. However, the role of glutathione-dependent redox homeostasis and associated molecular mechanisms regulating the antioxidant system and pesticide metabolism remains unclear. In this study, endogenous glutathione levels were manipulated by pharmacological treatments with glutathione synthesis inhibitors and oxidized glutathione. The application of oxidized glutathione enriched the cellular oxidation state, reduced the activity and transcript levels of antioxidant enzymes, upregulated the expression level of nitric oxide and Ca2+ related genes and the content, and increased the residue of chlorothalonil in tomato leaves. Further experiments confirmed that glutathione-induced redox homeostasis is critical for the reduction of pesticide residues. RNA sequencing analysis revealed that miRNA156 and miRNA169 that target transcription factor SQUAMOSA-Promoter Binding Proteins (SBP) and NUCLEAR FACTOR Y (NFY) potentially participate in glutathione-mediated pesticide degradation in tomato plants. Our study provides important clues for further dissection of pesticide degradation mechanisms via miRNAs in plants.

Dataglove for Sign Language Recognition of People with Hearing and Speech Impairment via Wearable Inertial Sensors.

Finding ways to enable seamless communication between deaf and able-bodied individuals has been a challenging and pressing issue. This paper proposes a solution to this problem by designing a low-cost data glove that utilizes multiple inertial sensors with the purpose of achieving efficient and accurate sign language recognition. In this study, four machine learning models-decision tree (DT), support vector machine (SVM), K-nearest neighbor method (KNN), and random forest (RF)-were employed to recognize 20 different types of dynamic sign language data used by deaf individuals. Additionally, a proposed attention-based mechanism of long and short-term memory neural networks (Attention-BiLSTM) was utilized in the process. Furthermore, this study verifies the impact of the number and position of data glove nodes on the accuracy of recognizing complex dynamic sign language. Finally, the proposed method is compared with existing state-of-the-art algorithms using nine public datasets. The results indicate that both the Attention-BiLSTM and RF algorithms have the highest performance in recognizing the twenty dynamic sign language gestures, with an accuracy of 98.85% and 97.58%, respectively. This provides evidence for the feasibility of our proposed data glove and recognition methods. This study may serve as a valuable reference for the development of wearable sign language recognition devices and promote easier communication between deaf and able-bodied individuals.

Prediction of Joint Angles Based on Human Lower Limb Surface Electromyography.

Wearable exoskeletons can help people with mobility impairments by improving their rehabilitation. As electromyography (EMG) signals occur before movement, they can be used as input signals for the exoskeletons to predict the body's movement intention. In this paper, the OpenSim software is used to determine the muscle sites to be measured, i.e., rectus femoris, vastus lateralis, semitendinosus, biceps femoris, lateral gastrocnemius, and tibial anterior. The surface electromyography (sEMG) signals and inertial data are collected from the lower limbs while the human body is walking, going upstairs, and going uphill. The sEMG noise is reduced by a wavelet-threshold-based complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) reduction algorithm, and the time-domain features are extracted from the noise-reduced sEMG signals. Knee and hip angles during motion are calculated using quaternions through coordinate transformations. The random forest (RF) regression algorithm optimized by cuckoo search (CS), shortened as CS-RF, is used to establish the prediction model of lower limb joint angles by sEMG signals. Finally, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) are used as evaluation metrics to compare the prediction performance of the RF, support vector machine (SVM), back propagation (BP) neural network, and CS-RF. The evaluation results of CS-RF are superior to other algorithms under the three motion scenarios, with optimal metric values of 1.9167, 1.3893, and 0.9815, respectively.

MEMS Devices-Based Hand Gesture Recognition via Wearable Computing.

Gesture recognition has found widespread applications in various fields, such as virtual reality, medical diagnosis, and robot interaction. The existing mainstream gesture-recognition methods are primarily divided into two categories: inertial-sensor-based and camera-vision-based methods. However, optical detection still has limitations such as reflection and occlusion. In this paper, we investigate static and dynamic gesture-recognition methods based on miniature inertial sensors. Hand-gesture data are obtained through a data glove and preprocessed using Butterworth low-pass filtering and normalization algorithms. Magnetometer correction is performed using ellipsoidal fitting methods. An auxiliary segmentation algorithm is employed to segment the gesture data, and a gesture dataset is constructed. For static gesture recognition, we focus on four machine learning algorithms, namely support vector machine (SVM), backpropagation neural network (BP), decision tree (DT), and random forest (RF). We evaluate the model prediction performance through cross-validation comparison. For dynamic gesture recognition, we investigate the recognition of 10 dynamic gestures using Hidden Markov Models (HMM) and Attention-Biased Mechanisms for Bidirectional Long- and Short-Term Memory Neural Network Models (Attention-BiLSTM). We analyze the differences in accuracy for complex dynamic gesture recognition with different feature datasets and compare them with the prediction results of the traditional long- and short-term memory neural network model (LSTM). Experimental results demonstrate that the random forest algorithm achieves the highest recognition accuracy and shortest recognition time for static gestures. Moreover, the addition of the attention mechanism significantly improves the recognition accuracy of the LSTM model for dynamic gestures, with a prediction accuracy of 98.3%, based on the original six-axis dataset.

A new deep convolutional neural network incorporating attentional mechanisms for ECG emotion recognition.

Using ECG signals captured by wearable devices for emotion recognition is a feasible solution. We propose a deep convolutional neural network incorporating attentional mechanisms for ECG emotion recognition. In order to address the problem of individuality differences in emotion recognition tasks, we incorporate an improved Convolutional Block Attention Module (CBAM) into the proposed deep convolutional neural network. The deep convolutional neural network is responsible for capturing ECG features. Channel attention in CBAM is responsible for adding weight information to ECG features of different channels and spatial attention is responsible for the weighted representation of ECG features of different regions inside the channel. We used three publicly available datasets, WESAD, DREAMER, and ASCERTAIN, for the ECG emotion recognition task. The new state-of-the-art results are set in three datasets for multi-class classification results, WESAD for tri-class results, and ASCERTAIN for two-category results, respectively. A large number of experiments are performed, providing an interesting analysis of the design of the convolutional structure parameters and the role of the attention mechanism used. We propose to use large convolutional kernels to improve the effective perceptual field of the model and thus fully capture the ECG signal features, which achieves better performance compared to the commonly used small kernels. In addition, channel attention and spatial attention were added to the deep convolutional model separately to explore their contribution levels. We found that in most cases, channel attention contributed to the model at a higher level than spatial attention.

Research on the Efficiency of Working Status Based on Wearable Devices in Different Light Environments.

According to the working scenes, a proper light environment can enable people to maintain greater attention and meditation. A posture detection system in different working scenes is proposed in this paper, and different lighting conditions are provided for changes in body posture. This aims to stimulate the nervous system and improve work efficiency. A brainwave acquisition system was used to capture the participants' optimal attention and meditation. The posture data are collected by ten miniature inertial measurement units (IMUs). The gradient descent method is used for information fusion and updating the participant's attitude after sensor calibration. Compared with the optical capture system, the reliability of the system is verified, and the correlation coefficient of both joint angles is as high as 0.9983. A human rigid body model is designed for reconstructing the human posture. Five classical machine learning algorithms, including logistic regression, support vector machine (SVM), decision tree, random forest, and k-nearest neighbor (KNN), are used as classification algorithms to recognize different postures based on joint angles series. The results show that SVM and random forest achieve satisfactory classification effects. The effectiveness of the proposed method is demonstrated in the designed systematic experiment.

A Wearable Gait Analysis and Recognition Method for Parkinson's Disease Based on Error State Kalman Filter.

For the purpose of quantitative analysis, this paper proposes a wearable gait analysis method for Parkinson's disease (PD) to evaluates the motor ability. The error state Kalman filter (ESKF) is used for attitude estimation, and the gait parameters are modified by phase segmentation and zero velocity update (ZUPT) algorithm. In addition, this study uses gait parameters as classifier features to recognize abnormal gait, and compares the recognition effect with statistical features. The effect of our gait system is verified by comparison with the OptiTrack system, and the mean absolute error (MAE) of step length and foot clearance are 2.52 ±3.61 cm and 0.96 ±1.24 cm respectively. Forty Parkinson's patients and forty age-matched healthy people are recruited for gait comparison, the analysis results showed significant differences between the two groups. The abnormal gait recognition results show that gait features have stronger generalization ability than statistical features in leave-one-subject-out (LOSO) validation. The method proposed in this study can be applied to the gait analysis and objective evaluation of PD.

Adaptive Multi-Modal Fusion Framework for Activity Monitoring of People With Mobility Disability.

The development of activity recognition based on multi-modal data makes it possible to reduce human intervention in the process of monitoring. This paper proposes an efficient and cost-effective multi-modal sensing framework for activity monitoring, it can automatically identify human activities based on multi-modal data, and provide help to patients with moderate disabilities. The multi-modal sensing framework for activity monitoring relies on parallel processing of videos and inertial data. A new supervised adaptive multi-modal fusion method (AMFM) is used to process multi-modal human activity data. Spatio-temporal graph convolution network with adaptive loss function (ALSTGCN) is proposed to extract skeleton sequence features, and long short-term memory fully convolutional network (LSTM-FCN) module with adaptive loss function is adapted to extract inertial data features. An adaptive learning method is proposed at the decision level to learn the contribution of the two modalities to the classification results. The effectiveness of the algorithm is demonstrated on two public multi-modal datasets (UTD-MHAD and C-MHAD) and a new multi-modal dataset H-MHAD collected from our laboratory. The results show that the performance of the AMFM approach on three datasets is better than the performance of the video or the inertial-based single-modality model. The class-balanced cross-entropy loss function further improves the model performance based on the H-MHAD dataset. The accuracy of action recognition is 91.18%, and the recall rate of falling activity is 100%. The results illustrate that using multiple heterogeneous sensors to realize automatic process monitoring is a feasible alternative to the manual response.

Jasmonic acid promotes glutathione assisted degradation of chlorothalonil during tomato growth.

Glutathione (GSH) biosynthesis and regeneration play a significant role in the metabolism of chlorothalonil (CHT) in tomatoes. However, the specific regulatory mechanism of GSH in the degradation of CHT remains uncertain. To address this, we investigate the critical regulatory pathways in the degradation of residual CHT in tomatoes. The results revealed that the detoxification of CHT residue in tomatoes was inhibited by buthionine sulfoximine and oxidized glutathione pretreatment, which increased by 26% and 46.12% compared with control, respectively. Gene silencing of γECS, GS, and GR also compromised the CHT detoxification potential of plants, which could be alleviated by GSH application and decreased the CHT accumulation by 33%, 25%, and 21%, respectively. Notably, it was found that the jasmonic acid (JA) pathway participated in the degradation of CHT regulated by GSH. CHT residues reduced by 28% after application of JA. JA played a role downstream of the glutathione pathway by promoting the degradation of CHT residue in tomatoes via nitric oxide signaling and improving the gene expression of antioxidant and detoxification-related enzymes. This study unveiled a crucial regulatory mechanism of GSH via the JA pathway in CHT degradation in tomatoes and offered new insights for understanding residual pesticide degradation.

Machine Learning Based Healthcare System for Investigating the Association Between Depression and Quality of Life.

New technological innovations are changing the future of healthcare system. Identification of factors that are responsible for causing depression may lead to new experiments and treatments. Because depression as a disease is becoming a leading community health concern worldwide. Using machine learning techniques this article presents a complete methodological framework to process and explore the heterogenous data and to better understand the association between factors related to quality of life and depression. Subsequently, the experimental study is mainly divided into two parts. In the first part, a data consolidation process is presented. The relationship of data is formed and to uniquely identify each relation in data the concept of the Secure Hash Algorithm is adopted. Hashing is used to locate and index the actual items in the data. The second part proposed a model using both unsupervised and supervised machine learning techniques. The consolidation approach helped in providing a base for formulation and validation of the research hypothesis. The Self organizing map provided 08 cluster solution and the classification problems were taken from the clustered data to further validate the performance of the posterior probability multi-class Support Vector Machine. The expectations of the importance sampling resulted in factors responsible for causing depression. The proposed model was adopted to improve the classification performance, and the result showed classification accuracy of 91.16%.

Tumor-associated macrophages modulate angiogenesis and tumor growth in a xenograft mouse model of multiple myeloma.

Tumor-associated macrophages (TAMs) are closely associated with poor multiple myeloma (MM) prognosis. Therefore, in-depth understanding of the mechanism by which TAM supports MM progression may lead to its effective treatment. We used the MM nude mouse subcutaneous xenograft model to evaluate the efficacy of the macrophage-depleting agent clodronate liposome (Clo) against MM and elucidate the mode of action of this therapy. At the same time, observe whether the elimination of TAM in vivo while silencing the expression of VEGFA has the same effect as in vitro experiments. We also used Clo to eliminate macrophages and reinjected M1 or M2 TAM through mouse tail veins to investigate the effects of various macrophage subtypes on MM xenograft tumor growth. We applied qRT-PCR, immunohistochemistry, and enzyme-linked immunosorbent assay to quantify VEGFA, CD31, and CD163 expression in tumor tissues and sera. Removal of TAMs from the tumor microenvironment impeded tumor growth. The combination of Clo plus VEGFA siRNA had a stronger inhibitory effect on tumor growth than Clo alone, and M2 and M1 macrophages promoted and inhibited tumor growth, respectively. Macrophage depletion combined with cytokine blocking is a promising MM treatment. Targeted M2 macrophage elimination together with cytokine block may be more effective at inhibiting MM growth than either treatment alone. The results of the present study lay an empirical foundation for the development of novel therapeutic strategies for MM.

Exosome-derived miR-let-7c promotes angiogenesis in multiple myeloma by polarizing M2 macrophages in the bone marrow microenvironment.

Angiogenesis is an integral part of the multiple myeloma (MM) microenvironment, and affects tumorigenesis, progression, invasion, and metastasis. Exosomes are essential for cell-cell communication and help in regulating the bone marrow microenvironment. Herein, we investigated macrophage polarization and angiogenesis in MM in vitro via exosome-derived miR-let-7c. We observed that exosomal miR-let-7c secreted by mesenchymal stem cells promoted M2 macrophage polarization, thereby enhancing angiogenesis in the bone marrow microenvironment. Suppressing miR-let-7c expression significantly inhibited vascular endothelial cell function in myeloma. Thus, exosomal miR-let-7c may be a reliable biomarker for early prediction of tumor progression and a promising therapeutic target for MM.

Ambulatory Human Gait Phase Detection Using Wearable Inertial Sensors and Hidden Markov Model.

Gait analysis, as a common inspection method for human gait, can provide a series of kinematics, dynamics and other parameters through instrumental measurement. In recent years, gait analysis has been gradually applied to the diagnosis of diseases, the evaluation of orthopedic surgery and rehabilitation progress, especially, gait phase abnormality can be used as a clinical diagnostic indicator of Alzheimer Disease and Parkinson Disease, which usually show varying degrees of gait phase abnormality. This research proposed an inertial sensor based gait analysis method. Smoothed and filtered angular velocity signal was chosen as the input data of the 15-dimensional temporal characteristic feature. Hidden Markov Model and parameter adaptive model are used to segment gait phases. Experimental results show that the proposed model based on HMM and parameter adaptation achieves good recognition rate in gait phases segmentation compared to other classification models, and the recognition results of gait phase are consistent with ground truth. The proposed wearable device used for data collection can be embedded on the shoe, which can not only collect patients' gait data stably and reliably, ensuring the integrity and objectivity of gait data, but also collect data in daily scene and ambulatory outdoor environment.

Paddle Stroke Analysis for Kayakers Using Wearable Technologies.

Proper stroke posture and rhythm are crucial for kayakers to achieve perfect performance and avoid the occurrence of sport injuries. The traditional video-based analysis method has numerous limitations (e.g., site and occlusion). In this study, we propose a systematic approach for evaluating the training performance of kayakers based on the multiple sensors fusion technology. Kayakers' motion information is collected by miniature inertial sensor nodes attached on the body. The extend Kalman filter (EKF) method is used for data fusion and updating human posture. After sensor calibration, the kayakers' actions are reconstructed by rigid-body model. The quantitative kinematic analysis is carried out based on joint angles. Machine learning algorithms are used for differentiating the stroke cycle into different phases, including entry, pull, exit and recovery. The experiment shows that our method can provide comprehensive motion evaluation information under real on-water scenario, and the phase identification of kayaker's motions is up to 98% validated by videography method. The proposed approach can provide quantitative information for coaches and athletes, which can be used to improve the training effects.

Synergistic effects of multiple myeloma cells and tumor-associated macrophages on vascular endothelial cells in vitro.

Angiogenesis is a prerequisite for multiple myeloma development. Tumor cells can stimulate angiogenesis by secreting vascular endothelial growth factor A (VEGFA), but we previously reported that tumor angiogenesis was not significantly reduced when VEGFA expression was inhibited in myeloma cells. Tumor-associated macrophages (TAMs) are important components of the tumor microenvironment and have been reported to be involved in the regulation of angiogenesis. In this study, we performed in vitro macrophage coculture studies and studies with RPMI 8226 and TAMs cell-conditioned media to explore their effects on the proliferation, migration, and tube formation of human umbilical vein endothelial cells (HUVECs). Our results showed that M2 macrophages and RPMI 8226 cells could synergistically promote HUVEC proliferation, migration, and tube formation, and that VEGFA depletion in both cell types suppressed HUVEC tube formation ability. Conversely, M1 macrophages inhibited the tube formation in HUVECs. Mechanistically, M2 macrophage secretion of VEGFA may affect vascular endothelial growth factor receptor 1 signaling to regulate angiogenesis. In summary, our results suggest that macrophage clearance or inducing of transformation of M2 macrophages into M1 macrophages are potential treatment strategies for multiple myeloma.

ciRS-7 Promotes the Proliferation and Migration of Papillary Thyroid Cancer by Negatively Regulating the miR-7/Epidermal Growth Factor Receptor Axis.

PURPOSE: The incidence of papillary thyroid cancer (PTC) is increasing, and traditional diagnostic methods are unsatisfactory. Therefore, identifying novel prognostic markers is very important. ciRS-7 has been found to play an important role in many cancers, but its role in PTC has not been reported. This study was performed to evaluate the biological role and mechanism of ciRS-7 in PTC. Material and Methods. The expression of ciRS-7 in PTC tissues and the matched adjacent tissues was determined by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The PTC cell lines (TPC-1 and BCPAP) were used to evaluate the role of ciRS-7. ciRS-7-siRNA and overexpression plasmid were constructed and transfected into PTC cells. A CCK-8 assay and colony formation assay were performed to explore the effects of ciRS-7 on cell proliferation. Annexin V/PI staining and FACS detection were used to detect cell apoptosis. Wound healing assay was performed to detect cell migration. A transwell assay was conducted to explore the effects of ciRS-7 on invasion and migration. Western blotting was performed to evaluate protein expression. The luciferase reporter system was used to determine the underlying mechanism of miR-7. RESULT: ciRS-7 was highly expressed in PTC tissues and cell lines compared with the corresponding controls. In vitro study showed that ciRS-7 silencing suppressed proliferation, migration, and invasion of TPC-1 and BCPAP. Mechanistically, the effects of ciRS-7 on invasion and migration may be related to epithelial-mesenchymal transition (EMT). ciRS-7 silencing could attenuate effects on PTC cells induced by miR-7 knockdown. Epidermal growth factor receptor (EGFR), which was demonstrated to be a target of miR-7, decreased significantly in ciRS-7-siRNA PTC cells. Overexpression of EGFR also attenuated effects of PTC cells induced by silencing ciRS-7. CONCLUSION: ciRS-7 was significantly upregulated in PTC tissues, and it promoted the progression of PTC by regulating the miR-7/EGFR axis. ciRS-7 is a promising prognostic biomarker and therapeutic target in PTC.

Canoeing Motion Tracking and Analysis via Multi-Sensors Fusion.

Coaches and athletes are constantly seeking novel training methodologies in an attempt to improve athletic performance. This paper proposes a method of rowing sport capture and analysis based on Inertial Measurement Units (IMUs). A canoeist's motion was collected by multiple miniature inertial sensor nodes. The gradient descent method was used to fuse data and obtain the canoeist's attitude information after sensor calibration, and then the motions of canoeist's actions were reconstructed. Stroke quality was performed based on the estimated joint angles. Machine learning algorithm was used as the classification method to divide the stroke cycle into different phases, including propulsion-phase and recovery-phase, a quantitative kinematic analysis was carried out. Experiments conducted in this paper demonstrated that our method possesses the capacity to reveal the similarities and differences between novice and coach, the whole process of canoeist's motions can be analyzed with satisfactory accuracy validated by videography method. It can provide quantitative data for coaches or athletes, which can be used to improve the skills of rowers.

Towards Wearable-Inertial-Sensor-Based Gait Posture Evaluation for Subjects with Unbalanced Gaits.

Human gait reflects health condition and is widely adopted as a diagnostic basisin clinical practice. This research adopts compact inertial sensor nodes to monitor the functionof human lower limbs, which implies the most fundamental locomotion ability. The proposedwearable gait analysis system captures limb motion and reconstructs 3D models with high accuracy.It can output the kinematic parameters of joint flexion and extension, as well as the displacementdata of human limbs. The experimental results provide strong support for quick access to accuratehuman gait data. This paper aims to provide a clue for how to learn more about gait postureand how wearable gait analysis can enhance clinical outcomes. With an ever-expanding gait database,it is possible to help physiotherapists to quickly discover the causes of abnormal gaits, sports injuryrisks, and chronic pain, and provides guidance for arranging personalized rehabilitation programsfor patients. The proposed framework may eventually become a useful tool for continually monitoringspatio-temporal gait parameters and decision-making in an ambulatory environment.