Assessment of lumbar disc herniation-impaired gait by using IMU data fusion method.

The inertial motion unit (IMU) is an effective tool for monitoring and assessing gait impairment in patients with lumbar disc herniation(LDH). However, the current clinical assessment methods for LDH gait focus on patients' subjective scoring indicators and lack the assessment of kinematic ability; at the same time, individual differences in the motor function degradation of the healthy and affected lower limbs of LDH patients are also ignored. To solve this problem, we propose an LDH gait feature model based on multi-source adaptive Kalman data fusion of acceleration and angular velocity. The gait phase is segmented by using an adaptive Kalman data fusion algorithm to estimate the attitude angle, and obtaining gait events through a zero-velocity update technique and a peak detection algorithm. Two IMUs were used to analyze the gait characteristics of lumbar disc patients and healthy gait people, including 12 gait characteristics such as gait spatiotemporal parameters, kinematic parameters, gait variability and stability. Statistical methods were used to analyze the characteristic model and verify the biological differences between the healthy affected side of LDH and healthy subjects. Finally, feature engineering and machine learning technology were used to identify the gait pattern of inertial movement units in patients with lumbar intervertebral disc disease, and achieved a classification accuracy of 95.50%, providing an effective gait feature set and method for clinical evaluation of LDH.

Unveiling critical ADHD biomarkers in limbic system and cerebellum using a binary hypothesis testing approach.

Attention deficit hyperactivity disorder (ADHD) is a common childhood developmental disorder. In recent years, pattern recognition methods have been increasingly applied to neuroimaging studies of ADHD. However, these methods often suffer from limited accuracy and interpretability, impeding their contribution to the identification of ADHD-related biomarkers. To address these limitations, we applied the amplitude of low-frequency fluctuation (ALFF) results for the limbic system and cerebellar network as input data and conducted a binary hypothesis testing framework for ADHD biomarker detection. Our study on the ADHD-200 dataset at multiple sites resulted in an average classification accuracy of 93%, indicating strong discriminative power of the input brain regions between the ADHD and control groups. Moreover, our approach identified critical brain regions, including the thalamus, hippocampal gyrus, and cerebellum Crus 2, as biomarkers. Overall, this investigation uncovered potential ADHD biomarkers in the limbic system and cerebellar network through the use of ALFF realizing highly credible results, which can provide new insights for ADHD diagnosis and treatment.

Increased risk of heavy metal accumulation in mangrove seedlings in coastal wetland environments due to microplastic inflow.

The recovery phase of mangrove seedlings in coastal wetland ecosystems can be negatively affected by exposure to external pollutants. This study aimed to investigate the impact of microplastics (MPs) influx, specifically polystyrene (PS) and polymethyl methacrylate (PMMA), on the growth of Aegiceras corniculatum seedlings and their accumulation of heavy metals (HMs). PS and PMMA significantly increased HMs accumulation (up to 21.0-548%), particularly in the roots of seedlings, compared to the control treatment (CK). Additionally, elevated activities of malondialdehyde and catalase enzymes were observed in the leaves of seedlings, while peroxidase enzyme activity decreased. Topological analysis of the root sediment microbiota coexistence network revealed that the modularization data increased from 0.69 (CK treatment) to 1.07 (PS treatment) and 5.11 (PMMA treatment) under the combined stress of MPs and HMs. This suggests that the introduction of MPs intensifies microbial modularization. The primary cause of increased HMs accumulation in plants is the MPs input, which influences the secretion of organic acids by plants and facilitates the shift of HMs in sediment to bioavailable states. Furthermore, changes in microbial clustering may also contribute to the elevated HMs accumulation in plants. This study provides valuable insights into the effects of external pollutants on mangrove seedlings and offers new perspectives for the preservation and restoration of mangrove coastal wetlands.

Differential Nitrous oxide emission and microbiota succession in constructed wetlands induced by nitrogen forms.

Nitrous oxide (N2O) emission during the sewage treatment process is a serious environmental issue that requires attention. However, the N2O emission in constructed wetlands (CWs) as affected by different nitrogen forms in influents remain largely unknown. This study investigated the N2O emission profiles driven by microorganisms in CWs when exposed to two typical nitrogen sources (NH4+-N or NO3--N) along with different carbon source supply (COD/N ratios: 3, 6, and 9). The results showed that CWs receiving NO3--N caused a slight increase in total nitrogen removal (by up to 11.8 %). This increase was accomplished by an enrichment of key bacteria groups, including denitrifiers, dissimilatory nitrate reducers, and assimilatory nitrate reducers, which enhanced the stability of microbial interaction. Additionally, it led to a greater abundance of denitrification genes (e.g., nirK, norB, norC, and nosZ) as inferred from the database. Consequently, this led to a gradual increase in N2O emission from 66.51 to 486.77 ug-N/(m2·h) as the COD/N ratio increased in CWs. Conversely, in CWs receiving NH4+-N, an increasing influent COD/N ratio had a negative impact on nitrogen biotransformation. This resulted in fluctuating trend of N2O emissions, which decreased initially, followed by an increase at later stage (with values of 122.87, 44.00, and 148.59 ug-N/(m2·h)). Furthermore, NH4+-N in the aquatic improved the nitrogen uptake by plants and promoted the production of more root exudates. As a result, it adjusted the nitrogen-transforming function, ultimately reducing N2O emissions in CWs. This study highlights the divergence in microbiota succession and nitrogen transformation in CWs induced by nitrogen form and COD/N ratio, contributing to a better understanding of the microbial mechanisms of N2O emission in CWs with NH4+-N or NO3--N at different COD/N ratios.

Arsenic pollution, geochemical fractions, and leaching characteristics in soil samples from four contaminated sites in the Beijiang River Basin, South China.

To remediate historically polluted sites before their land-use changes, it is essential to understand the concentration distribution, geochemical fraction, and migratory behavior of As in soil with varied particle sizes for the use of a sieving procedure. This study investigated the amount and percentage of As in soil with different particle sizes (0.25, 0.25-1, and 1-2 mm) as well as its toxicity characteristic in leaching procedure at four previously contaminated sites in the Beijiang River Basin, South China. The results showed that the total As concentration in the collected soils ranged from 70.1 to 402.8 mg/kg, and only a few percent of soil particle samples had As contents below the local risk threshold value of 60 mg/kg. The amorphous hydrous oxide bound, crystalline hydrous oxide bound, and residual fractions (F3-F5) of the geochemical fraction of As in soil of polluted sites accounted for 82.2-95.7% of the total As distribution. However, the concentration of As in non-specifically bound fractions increased with the mass ratio of soil with coarse particle sizes due to the negative correlation of Fe-bearing minerals concentration with the mass ratio of soil with coarse particle size. According to redundancy analysis, soil with coarse particle sizes and non-specifically bound As were mostly responsible for the As concentration in the leachate. These findings confirmed that a sieving process was not suitable for the remediation of soil As at four historically contaminated sites in the Beijiang River Basin due to the high concentration of As in soil and non-negligible environmental risk of labile extractable As in soil with coarse particle size.

Static-Dynamic Temporal Networks for Parkinson's Disease Detection and Severity Prediction.

Most patients with Parkinson's disease (PD) have different degrees of movement disorders, and effective gait analysis has a huge potential for uncovering hidden gait patterns to achieve the diagnosis of patients with PD. In this paper, the Static-Dynamic temporal networks are proposed for gait analysis. Our model involves a Static temporal pathway and a Dynamic temporal pathway. In the Static temporal pathway, the time series information of each sensor is processed independently with a parallel one-dimension convolutional neural network (1D-Convnet) to extract respective depth features. In the Dynamic temporal pathway, the stitched surface of the feet is deemed to be an irregular "image", and the transfer of the force points at all levels on the sole is regarded as the "optical flow." Then, the motion information of the force points at all levels is extracted by 16 parallel two-dimension convolutional neural network (2D-Convnet) independently. The results show that the Static-Dynamic temporal networks achieved better performance in gait detection of PD patients than other previous methods. Among them, the accuracy of PD diagnosis reached 96.7%, and the accuracy of severity prediction of PD reached 92.3%.

Distribution fractions and potential ecological risk assessment of heavy metals in mangrove sediments of the Greater Bay Area.

The restoration of mangrove in coastal wetlands of China has been started since the 1990s. However, various pollutants, especially for heavy metals (HMs), contained in wastewater might present a significant risk to mangrove forests during the restoration. In this study, sediments of five typical mangrove wetlands with varying restoration years and management measures in the Greater Bay Area were collected to evaluate the distribution fractions and potential ecological risk of HMs. Cd (0.2-1.6 mg/kg) was found in high concentrations in the exchangeable fraction (37.8-71.5%), whereas Cu (54.2-94.8 mg/kg), Zn (157.6-332.6 mg/kg), Cr (57.7-113.6 mg/kg), Pb (36.5-89.9 mg/kg), and Ni (29.7-69.5 mg/kg) primarily presented in residual fraction (30.8-91.9%). According to the geo-accumulation index (Igeo) analysis, sediment Cd presented a high level of pollution (3 ≤ Igeo ≤ 4), while Zn and Cu were associated with moderately pollution (1 ≤ Igeo ≤ 2). Besides, high ecological risk of Cd was found in sediments of five mangroves, with risk assessment code (RAC) ranging from 45.9 to 84.2. Redundancy analysis revealed that the content of NO3--N was closely related to that of HMs in sediments and, pH value and NO3--N concentration affected the distribution of HMs geochemical fractions. High concentration of HMs in QA and NS sampling sites was caused by the formerly pollutants discharge, resulting in these sediments still with a higher HM pollution level after the plant of mangrove for a long period. Fortunately, strict drainage standards for industrial activities in Shenzhen significantly availed for decreasing HMs contents in mangrove sediments. Therefore, future works on mangrove conversion and restoration should be linked to the water purification in the GBA.

Reducing the inhibitive effect of fluorine and heavy metals on nitrate reduction by hydroxyapatite substrate in constructed wetlands.

Bio-toxic inorganic pollutants, e.g., fluorine (F) and heavy metals (HMs), in wastewaters are the potential threats to nitrate (NO3--N) reduction by microorganisms in constructed wetlands (CWs). Selection of suitable substrate with high F and HMs adsorption efficiency and capacity is a potential alternative for simultaneous removal of these pollutants in CWs. Herein, this study investigated the feasibility of applying hydroxyapatite (HA)-gravel media for F and HMs adsorption and its effect on NO3--N reduction in CWs (HA CWs) by comparing the CWs filled with gravel substrate (CK CWs). The results indicated that the removal efficiency of F, Cr, As, and NO3--N in HA CWs increased by 113.6-, 3.3-, 2.7-, and 0.6-folds, respectively, compared to CK CWs. The NO3--N reduction rate decreased by 11-46% in CK CWs after the presence of F and HMs in influent, while for HA CWs, it was only 13-22%. Excellent F and HMs adsorption capacity of HA substrate availed for wetland plants resisting F/HMs toxicity and making catalase activity lower. The HA substrate in CWs resulted in the certain succession of nitrogen-transforming bacteria, e.g., nitrifiers (Nitrospira) and denitrifiers (Thiobacillus and Desulfobacterium). More importantly, key functional genes, including nirK/nirS, korA/korB, ChrA/ChrD, arsA/arsB, catalyzing the processes of nitrogen biotransformation, energy metabolism, NO3--N and metal ions reduction were also enriched in HA CWs. This study highlights HA substrate reduce the inhibitive effect of F and HMs on NO3--N reduction, and provides new insights into how microbiota structurally and functionally respond to different substrates in CWs.

A Novel Segmentation Scheme with Multi-Probability Threshold for Human Activity Recognition Using Wearable Sensors.

In recent years, much research has been conducted on time series based human activity recognition (HAR) using wearable sensors. Most existing work for HAR is based on the manual labeling. However, the complete time serial signals not only contain different types of activities, but also include many transition and atypical ones. Thus, effectively filtering out these activities has become a significant problem. In this paper, a novel machine learning based segmentation scheme with a multi-probability threshold is proposed for HAR. Threshold segmentation (TS) and slope-area (SA) approaches are employed according to the characteristics of small fluctuation of static activity signals and typical peaks and troughs of periodic-like ones. In addition, a multi-label weighted probability (MLWP) model is proposed to estimate the probability of each activity. The HAR error can be significantly decreased, as the proposed model can solve the problem that the fixed window usually contains multiple kinds of activities, while the unknown activities can be accurately rejected to reduce their impacts. Compared with other existing schemes, computer simulation reveals that the proposed model maintains high performance using the UCI and PAMAP2 datasets. The average HAR accuracies are able to reach 97.71% and 95.93%, respectively.

Interactive effects of aquatic nitrogen and plant biomass on nitrous oxide emission from constructed wetlands.

Understanding of mechanisms in nitrous oxide (N2O) emission from constructed wetland (CW) is particularly important for the establishment of related strategies to reduce greenhouse gas (GHG) production during its wastewater treatment. However, plant biomass accumulation, microbial communities and nitrogen transformation genes distribution and their effects on N2O emission from CW as affected by different nitrogen forms in aquatic environment have not been reported. This study investigated the interactive effects of aquatic nitrogen and plant biomass on N2O emission from subsurface CW with NH4+-N (CW-A) or NO3--N (CW-B) wastewater. The experimental results show that NH4+-N and NO3--N removal efficiencies from CW mesocosms were 49.4% and 87.6%, which indirectly lead to N2O emission fluxes of CW-A and CW-B maintained at 213 ± 67 and 462 ± 71 µg-N/(m2·h), respectively. Correlation analysis of nitrogen conversion dynamic indicated that NO2--N accumulation closely related to N2O emission from CW. Aquatic NH4+-N could up-regulate plant biomass accumulation by intensifying citric acid cycle, glycine-serine-threonine metabolism etc., resulting in more nitrogen uptake and lower N2O emission/total nitrogen (TN) removal ratio of CW-A compared to CW-B. Although the abundance of denitrifying bacteria and N2O reductase nosZ in CW-B were significantly higher than that of CW-A, after fed with mixed NH4+-N and NO3--N influent, N2O fluxes and N2O emission/TN removal ratio in CW-A were extremely close to that of CW-B, suggesting that nitrogen form rather than nitrogen transformation microbial communities and N2O reductase nosZ determines N2O emission from CW. Hence, the selection of nitrate-loving plants will play an important role in inhibiting N2O emission from CW.

Human Behavior Recognition Model Based on Feature and Classifier Selection.

With the rapid development of the computer and sensor field, inertial sensor data have been widely used in human activity recognition. At present, most relevant studies divide human activities into basic actions and transitional actions, in which basic actions are classified by unified features, while transitional actions usually use context information to determine the category. For the existing single method that cannot well realize human activity recognition, this paper proposes a human activity classification and recognition model based on smartphone inertial sensor data. The model fully considers the feature differences of different properties of actions, uses a fixed sliding window to segment the human activity data of inertial sensors with different attributes and, finally, extracts the features and recognizes them on different classifiers. The experimental results show that dynamic and transitional actions could obtain the best recognition performance on support vector machines, while static actions could obtain better classification effects on ensemble classifiers; as for feature selection, the frequency-domain feature used in dynamic action had a high recognition rate, up to 99.35%. When time-domain features were used for static and transitional actions, higher recognition rates were obtained, 98.40% and 91.98%, respectively.

Outlier Detection in Wireless Sensor Networks Using Model Selection-Based Support Vector Data Descriptions.

Wireless sensor networks (WSNs) are often deployed in harsh and unattended environments, which may cause the generation of abnormal or low quality data. The inaccurate and unreliable sensor data may increase generation of false alarms and erroneous decisions, so it's very important to detect outliers in sensor data efficiently and accurately to ensure sound scientific decision-making. In this paper, an outlier detection algorithm (TSVDD) using model selection-based support vector data description (SVDD) is proposed. Firstly, the Toeplitz matrix random feature mapping is used to reduce the time and space complexity of outlier detection. Secondly, a novel model selection strategy is realized to keep the algorithm stable under the low feature dimensions, this strategy can select a relatively optimal decision model and avoid both under-fitting and overfitting phenomena. The simulation results on SensorScope and IBRL datasets demonstrate that, TSVDD achieves higher accuracy and lower time complexity for outlier detection in WSNs compared with existing methods.

[The effects of 40 mg atorvastatin on serum lipids, inflammatory markers and clinical events in ACS patients post PCI].

OBJECTIVE: To assess the safety and effects of 40 mg atorvastatin on serum lipids, inflammatory markers and clinical events in ACS patients post PCI. METHODS: A total of 92 patients with ACS post successful PCI were randomly divided into atorvastatin 10 mg/d (group A) and atorvastatin 40 mg/d (group B) on top of the standard medical therapy. Blood were taken at baseline, 4, 12 and 24 weeks for serum alanine aminotransferase (ALT), lipids, high-sensitive C-reactive protein (hs-CRP) and matrix metalloprotease-9 (MMP-9) measurements. The major adverse cardiac events (MACE) were also observed. RESULTS: There was no significant difference in medication withdrawn (2 vs. 3 cases) due to increased ALT (3 times higher than normal) and incidence of MACE (5 vs. 7 cases) between the groups. TC and LDL were significantly reduced in both groups 4 weeks and thereafter post medication compared to pre-treatment (P < 0.05) and the reduction was more significant in group B than that in group A at 24 weeks post medication (P < 0.05) while TG and HDL remained unchanged. hs-CRP was significantly reduced at 12 and 24 weeks in both groups compared to baseline and the reduction was more significant in group B than that in group A at 24 weeks. MMP-9 was significantly reduced in both groups 4 weeks and thereafter post medication compared to pre-treatment (P < 0.05) and the reduction was more significant in group B than that in group A at 12 weeks post medication (P < 0.05). CONCLUSION: Both atorvastatin doses significantly reduced TC, LDL, hs-CRP and MMP-9 in ACS patients post PCI and the reduction was more significant in high dose atorvastatin group at 24 weeks while the MACE and drug withdraw rates were similar between the groups.

The effects of 40 mg atorvastatin on serum lipids, inflammatory markers and clinical events in ACS patients post PCI / 中华心血管病杂志

<p><b>OBJECTIVE</b>To assess the safety and effects of 40 mg atorvastatin on serum lipids, inflammatory markers and clinical events in ACS patients post PCI.</p><p><b>METHODS</b>A total of 92 patients with ACS post successful PCI were randomly divided into atorvastatin 10 mg/d (group A) and atorvastatin 40 mg/d (group B) on top of the standard medical therapy. Blood were taken at baseline, 4, 12 and 24 weeks for serum alanine aminotransferase (ALT), lipids, high-sensitive C-reactive protein (hs-CRP) and matrix metalloprotease-9 (MMP-9) measurements. The major adverse cardiac events (MACE) were also observed.</p><p><b>RESULTS</b>There was no significant difference in medication withdrawn (2 vs. 3 cases) due to increased ALT (3 times higher than normal) and incidence of MACE (5 vs. 7 cases) between the groups. TC and LDL were significantly reduced in both groups 4 weeks and thereafter post medication compared to pre-treatment (P < 0.05) and the reduction was more significant in group B than that in group A at 24 weeks post medication (P < 0.05) while TG and HDL remained unchanged. hs-CRP was significantly reduced at 12 and 24 weeks in both groups compared to baseline and the reduction was more significant in group B than that in group A at 24 weeks. MMP-9 was significantly reduced in both groups 4 weeks and thereafter post medication compared to pre-treatment (P < 0.05) and the reduction was more significant in group B than that in group A at 12 weeks post medication (P < 0.05).</p><p><b>CONCLUSION</b>Both atorvastatin doses significantly reduced TC, LDL, hs-CRP and MMP-9 in ACS patients post PCI and the reduction was more significant in high dose atorvastatin group at 24 weeks while the MACE and drug withdraw rates were similar between the groups.</p>

Study on Radicamine A as a ?-glucosidase inhibitor / 第三军医大学学报

Objective To study the inhibitory effect of radicamine A on ?-glucosidase and the glucose's absorption by small intestine.Methods Inhibitory effect of radicamine A was studied both in ?-glucosidase inhibition experiments and by a valgus cyst model of small in testine in vitro.Results Radicamine A inhibited ?-glucosidase and glucose's absorption in a dose-dependent manner(P