VIOLET: Visual Analytics for Explainable Quantum Neural Networks.

With the rapid development of Quantum Machine Learning, quantum neural networks (QNN) have experienced great advancement in the past few years, harnessing the advantages of quantum computing to significantly speed up classical machine learning tasks. Despite their increasing popularity, the quantum neural network is quite counter-intuitive and difficult to understand, due to their unique quantum-specific layers (e.g., data encoding and measurement) in their architecture. It prevents QNN users and researchers from effectively understanding its inner workings and exploring the model training status. To fill the research gap, we propose VIOLET, a novel visual analytics approach to improve the explainability of quantum neural networks. Guided by the design requirements distilled from the interviews with domain experts and the literature survey, we developed three visualization views: the Encoder View unveils the process of converting classical input data into quantum states, the Ansatz View reveals the temporal evolution of quantum states in the training process, and the Feature View displays the features a QNN has learned after the training process. Two novel visual designs, i.e., satellite chart and augmented heatmap, are proposed to visually explain the variational parameters and quantum circuit measurements respectively. We evaluate VIOLET through two case studies and in-depth interviews with 12 domain experts. The results demonstrate the effectiveness and usability of VIOLET in helping QNN users and developers intuitively understand and explore quantum neural networks.

Investigation on Mechanism of Microstructure Evolution during Multi-Process Hot Forming of GH4169 Superalloy Forging.

Typically, in the manufacturing of GH4169 superalloy forgings, the multi-process hot forming that consists of pre-deformation, heat treatment and final deformation is required. This study focuses on the microstructural evolution throughout hot working processes. Considering that δ phase can promote nucleation and limit the growth of grains, a process route was designed, including pre-deformation, aging treatment (AT) to precipitate sufficient δ phases, high temperature holding (HTH) to uniformly heat the forging, and final deformation. The results show that the uneven strain distribution after pre-deformation has a significant impact on the subsequent refinement of the grain microstructure due to the complex coupling relationship between the evolution of the δ phase and recrystallization behavior. After the final deformation, the fine-grain microstructure with short rod-like δ phases as boundaries is easy to form in the region with a large strain of the pre-forging. However, necklace-like mixed grain microstructure is formed in the region with a small strain of the pre-forging. In addition, when the microstructure before final deformation consists of mixed grains, dynamic recrystallization (DRX) nucleation behavior preferentially depends on kernel average misorientation (KAM) values. A large KAM can promote the formation of DRX nuclei. When the KAM values are close, a smaller average grain size of mixed-grain microstructure is more conductive to promote the DRX nucleation. Finally, the interaction mechanisms between δ phase and DRX nucleation are revealed.

Lead remediation by geological fluorapatite combined with Penicillium Oxalicum and Red yeast.

Phosphate solubilizing fungi Penicillium oxalicum (POX) and Red yeast Rhodotorula mucilaginosa (Rho) have been applied in Pb remediation with the combination of fluorapatite (FAp), respectively. The secretion of oxalic acid by POX and the production of extracellular polymers (EPS) by Rho dominate the Pb remediation. In this study, the potential of Pb remediation by the fungal combined system (POX and Rho) with FAp was investigated. After six days of incubation, the combination of POX and Rho showed the highest Pb remove ratio (99.7%) and the lowest TCLP-Pb concentration (2.9 mg/L). The EPS combined with POX also enhanced Pb remediation, which has a 99.3% Pb removal ratio and 5.5 mg/L TCLP-Pb concentration. Meanwhile, Rho and EPS can also stimulate POX to secrete more oxalic acid, which reached 1510.1 and 1450.6 mg/L in six days, respectively. The secreted oxalic acid can promote FAp dissolution and the formation of lead oxalate and pyromorphite. Meanwhile, the EPS produced by Rho can combine with Pb to form EPS-Pb. In the combined system of POX + Rho and POX + EPS, all of the lead oxalate, pyromorphite, and EPS-Pb were observed. Our findings suggest that the combined application of POX and Rho with FAp is an effective approach for enhancing Pb remediation.

Construction and validation of a clinical prediction model for deep vein thrombosis in patients with digestive system tumors based on a machine learning.

This study developed a deep vein thrombosis (DVT) risk prediction model based on multiple machine learning methods for patients with digestive system tumors undergoing surgical treatment. Data of 1048 patients with digestive system tumors admitted to Shanxi Provincial People's Hospital (College of Shanxi Medical University) from January 2020 to January 2023 were retrospectively analyzed, and 845 cases were screened according to the inclusion and exclusion criteria. The patients were divided into a training group (586 patients), and a validation group (259 patients), then feature selection was performed using six models, including Lasso regression, XGBoost, Random Forest, Decision Tree, Support Vector Machine, and Logistics. Predictive models were subsequently constructed from column-line plots, and the predictive validity of the models was assessed using receiver operating characteristic curves, precision-recall curves, and decision-curve analysis. In the model comparison, the XGBoost model showed the largest area under the curve (AUC) on the validation set (P < 0.05), demonstrating excellent predictive performance and generalization ability. We selected the common characteristic factors in the six models to further develop the column line plots to assess the DVT risk. The model performed well in clinical validation and effectively differentiated high-risk and low-risk patients. The differences in BMI, procedure time, and D-dimer were statistically significant between patients in the thrombus group and those in the non-thrombus group (P < 0.05). However, the AUC of the Xgboost model was found to be greater than that of the column chart model by the Delong test (P < 0.05). BMI, procedure time, and D-dimer are critical predictors of DVT risk in patients with digestive system tumors. Our model is an adequate assessment tool for DVT risk, which can help improve the prevention and treatment of DVT.

Kinect-based objective assessment of the acute levodopa challenge test in parkinsonism: a feasibility study.

INTRODUCTION: The acute levodopa challenge test (ALCT) is an important and valuable examination but there are still some shortcomings with it. We aimed to objectively assess ALCT based on a depth camera and filter out the best indicators. METHODS: Fifty-nine individuals with parkinsonism completed ALCT and the improvement rate (IR, which indicates the change in value before and after levodopa administration) of the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) was calculated. The kinematic features of the patients' movements in both the OFF and ON states were collected with an Azure Kinect depth camera. RESULTS: The IR of MDS-UPDRS III was significantly correlated with the IRs of many kinematic features for arising from a chair, pronation-supination movements of the hand, finger tapping, toe tapping, leg agility, and gait (rs = - 0.277 ~ - 0.672, P < 0.05). Moderate to high discriminative values were found in the selected features in identifying a clinically significant response to levodopa with sensitivity, specificity, and area under the curve (AUC) in the range of 50-100%, 47.22%-97.22%, and 0.673-0.915, respectively. The resulting classifier combining kinematic features of toe tapping showed an excellent performance with an AUC of 0.966 (95% CI = 0.922-1.000, P < 0.001). The optimal cut-off value was 21.24% with sensitivity and specificity of 94.44% and 87.18%, respectively. CONCLUSION: This study demonstrated the feasibility of measuring the effect of levodopa and objectively assessing ALCT based on kinematic data derived from an Azure Kinect-based system.

Early-onset and late-onset Parkinson's disease exhibit a different profile of gait and posture features based on the Kinect.

INTRODUCTION: Gait and posture abnormalities are the common disabling motor symptoms in Parkinson's disease (PD). This study aims to investigate the differential characteristics of gait and posture in early-onset PD (EOPD) and late-onset PD (LOPD) using the Kinect depth camera. METHODS: Eighty-eight participants, including two subgroups of 22 PD patients and two subgroups of 22 healthy controls (HC) matched for age, sex, and height, were enrolled. Gait and posture features were quantitatively assessed using a Kinect-based system. A two-way analysis of variance was used to compare the difference between different subgroups. RESULTS: EOPD had a significantly higher Gait score than LOPD (p = 0.031). Specifically, decreased swing phase (p = 0.034) was observed in the EOPD group. Although the Posture score was similar between the two groups, LOPD was characterized by an increased forward flexion angle of the trunk at the thorax (p = 0.042) and a decreased forward flexion angle of the head relative to the trunk (p = 0.009). Additionally, age-independent features were observed in both PD subgroups, and post hoc tests revealed that EOPD generally performed worse gait features. In comparison, LOPD was characterized by worse performance in posture features. CONCLUSIONS: EOPD and LOPD exhibit different profiles of gait and posture features. The phenotype-specific characteristics likely reflect the distinct neurodegenerative processes between them.

QuantumEyes: Towards Better Interpretability of Quantum Circuits.

Quantum computing offers significant speedup compared to classical computing, which has led to a growing interest among users in learning and applying quantum computing across various applications. However, quantum circuits, which are fundamental for implementing quantum algorithms, can be challenging for users to understand due to their underlying logic, such as the temporal evolution of quantum states and the effect of quantum amplitudes on the probability of basis quantum states. To fill this research gap, we propose QuantumEyes, an interactive visual analytics system to enhance the interpretability of quantum circuits through both global and local levels. For the global-level analysis, we present three coupled visualizations to delineate the changes of quantum states and the underlying reasons: a Probability Summary View to overview the probability evolution of quantum states; a State Evolution View to enable an in-depth analysis of the influence of quantum gates on the quantum states; a Gate Explanation View to show the individual qubit states and facilitate a better understanding of the effect of quantum gates. For the local-level analysis, we design a novel geometrical visualization dandelion chart to explicitly reveal how the quantum amplitudes affect the probability of the quantum state. We thoroughly evaluated QuantumEyes as well as the novel dandelion chart integrated into it through two case studies on different types of quantum algorithms and in-depth expert interviews with 12 domain experts. The results demonstrate the effectiveness and usability of our approach in enhancing the interpretability of quantum circuits.

Combination of mineral protection and molecular characteristics rather than alone to govern soil organic carbon stability in Qinghai-Tibetan plateau wetlands.

Wetlands in the Yarlung Tsangpo River Basin (YTR) on the Qinghai-Tibet Plateau provide immense soil organic carbon (SOC) storage, which is highly susceptible to climate warming and requires urgent deciphering SOC stabilization mechanisms of long-term protection of SOC against decomposition. Conflicting views exist regarding whether persistent SOC is controlled by molecular features or by mineral protection. As such, this study quantified SOC stability using two thermal indices (TG-T50, and DSC), described molecular features of SOC using pyrolysis-gas chromatography-mass spectrometry, and measured SOC protection by minerals using a chemical extraction method. Results indicated SOC of topsoils had higher thermal stability, with TG-T50 and DSC-T50 of 337.61 °C and 384.58 °C, than that of subsoils with TG-T50 and DSC-T50 of 337.32 and 382.67 °C, respectively. We found subsoils had significantly higher proportions of aliphatic and aromatic compounds, while existed higher SOC associated with minerals. It seemed SOC stabilization differed with soil depths, in which mineral protection dictated SOC thermal stability in topsoils while molecular features posed a more important constraint on SOC stabilization in subsoils. Overall, our findings support the hypothesis of physical and chemical protection but emphasized that SOC thermal stability largely depended on to extent of the combination between molecular features and mineral protection, which explained 55% in topsoils and 73% in subsoils, respectively.

Kinect-based objective assessment for early frailty identification in patients with Parkinson's disease.

BACKGROUND: Frailty is common in Parkinson's disease (PD) and increases vulnerability to adverse outcomes. Early detection of this syndrome aids in early intervention. AIMS: To objectively identify frailty at an early stage during routine motor tasks in PD patients using a Kinect-based system. METHODS: PD patients were recruited and assessed with the Fried criteria to determine their frailty status. Each participant was recorded performing the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) extremity tasks with a Kinect-based system. Statistically significant kinematic parameters were selected to discriminate the pre-frail from the non-frail group. RESULTS: Of the fifty-two participants, twenty were non-frail and thirty-two were pre-frail. Decreased frequency in finger tapping (P = 0.005), hand grasping (P = 0.002), toe tapping (P = 0.002), and leg agility (P = 0.019) alongside reduced hand grasping speed (P = 0.030), lifting (P < 0.001) and falling speed (P < 0.001) in leg agility were observed in the pre-frail group. Amplitude in leg agility (P = 0.048) and amplitude decrement rate (P = 0.046) in hand grasping showed marginally significant differences between two groups. Moderate discriminative values were found in frequency and speed of the extremity tasks to identify pre-frailty with sensitivity, specificity, and area under the curve (AUC) in the range of 45.00-85.00%, 68.75-100%, and 0.701-0.836, respectively. The combination of frequency and speed in extremity tasks showed moderate to high discriminatory ability, with AUC of 0.775 (95% CI 0.637-0.913, P < 0.001) for upper limb tasks and 0.909 (95% CI 0.832-0.987, P < 0.001) for lower limb tasks. When combining these features in both upper and lower limb tasks, the AUC increased to 0.942 (95% CI 0.886-0.999, P < 0.001). CONCLUSIONS: Our findings demonstrated the promise of utilizing Kinect-based kinematic data from MDS-UPDRS III tasks as early indicators of frailty in PD patients.

Differential distribution patterns and assembly processes of soil microbial communities under contrasting vegetation types at distinctive altitudes in the Changbai Mountain.

Diversity patterns and community assembly of soil microorganisms are essential for understanding soil biodiversity and ecosystem processes. Investigating the impacts of environmental factors on microbial community assembly is crucial for comprehending the functions of microbial biodiversity and ecosystem processes. However, these issues remain insufficiently investigated in related studies despite their fundamental significance. The present study aimed to assess the diversity and assembly of soil bacterial and fungal communities to altitude and soil depth variations in mountain ecosystems by using 16S and ITS rRNA gene sequence analyses. In addition, the major roles of environmental factors in determining soil microbial communities and assembly processes were further investigated. The results showed a U-shaped pattern of the soil bacterial diversity at 0-10 cm soil depth along altitudes, reaching a minimum value at 1800 m, while the fungal diversity exhibited a monotonically decreasing trend with increasing altitude. At 10-20 cm soil depth, the soil bacterial diversity showed no apparent changes along altitudinal gradients, while the fungal Chao1 and phylogenetic diversity (PD) indices exhibited hump-shaped patterns with increasing altitude, reaching a maximum value at 1200 m. Soil bacterial and fungal communities were distinctively distributed with altitude at the same depth of soil, and the spatial turnover rates in fungi was greater than in bacteria. Mantel tests suggested soil physiochemical and climate variables significantly correlated with the ß diversity of microbial community at two soil depths, suggesting both soil and climate heterogeneity contributed to the variation of bacterial and fungal community. Correspondingly, a novel phylogenetic null model analysis demonstrated that the community assembly of soil bacterial and fungal communities were dominated by deterministic and stochastic processes, respectively. The assembly processes of bacterial community were significantly related to the soil DOC and C:N ratio, while the fungal community assembly processes were significantly related to the soil C:N ratio. Our results provide a new perspective to assess the responses of soil microbial communities to variations with altitude and soil depth.

Kinect-based objective evaluation of bradykinesia in patients with Parkinson's disease.

Objective: To quantify bradykinesia in Parkinson's disease (PD) with a Kinect depth camera-based motion analysis system and to compare PD and healthy control (HC) subjects. Methods: Fifty PD patients and twenty-five HCs were recruited. The Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) was used to evaluate the motor symptoms of PD. Kinematic features of five bradykinesia-related motor tasks were collected using Kinect depth camera. Then, kinematic features were correlated with the clinical scales and compared between groups. Results: Significant correlations were found between kinematic features and clinical scales (P < 0.05). Compared with HCs, PD patients exhibited a significant decrease in the frequency of finger tapping (P < 0.001), hand movement (P < 0.001), hand pronation-supination movements (P = 0.005), and leg agility (P = 0.003). Meanwhile, PD patients had a significant decrease in the speed of hand movements (P = 0.003) and toe tapping (P < 0.001) compared with HCs. Several kinematic features exhibited potential diagnostic value in distinguishing PD from HCs with area under the curve (AUC) ranging from 0.684-0.894 (P < 0.05). Furthermore, the combination of motor tasks exhibited the best diagnostic value with the highest AUC of 0.955 (95% CI = 0.913-0.997, P < 0.001). Conclusion: The Kinect-based motion analysis system can be applied to evaluate bradykinesia in PD. Kinematic features can be used to differentiate PD patients from HCs and combining kinematic features from different motor tasks can significantly improve the diagnostic value.

High PSQI score is associated with the development of dyskinesia in Parkinson's disease.

Dyskinesia is one of the most disabling motor complications in Parkinson's Disease (PD). Sleep is crucial to keep neural circuit homeostasis, and PD patients often suffer from sleep disturbance. However, few prospective studies have been conducted to investigate the association of sleep quality with dyskinesia in PD. The objective of the current study is to investigate the association between sleep quality and dyskinesia and build a prediction model for dyskinesia in PD. We prospectively followed a group of PD patients without dyskinesia at baseline for a maximum of 36 months. Univariable and multivariable Cox regression with stepwise variable selection was used to investigate risk factors for dyskinesia. The performance of the model was assessed by the time-dependent area under the receiver-operating characteristic curve (AUC). At the end of follow-up, 32.8% of patients developed dyskinesia. Patients with bad sleep quality had a significantly higher proportion of dyskinesia compared with those with good sleep quality (48.1% vs. 20.6%, p = 0.023). Multivariable Cox regression selected duration of PD, sleep quality, cognition, mood, and levodopa dose. Notably, high Pittsburgh sleep quality index (PSQI) score was independently associated with an increased risk of dyskinesia (HR = 2.96, 95% CI 1.05-8.35, p = 0.041). The model achieved a good discriminative ability, with the highest AUC being 0.83 at 35 months. Our results indicated that high PSQI score may increase the risk of developing dyskinesia in PD, implying that therapeutic intervention targeting improving sleep quality may be a promising approach to prevent or delay the development of dyskinesia in PD.

Technology-based therapy-response evaluation of axial motor symptoms under daily drug regimen of patients with Parkinson's disease.

Background: Axial disturbances are the most disabling symptoms of Parkinson's disease (PD). Kinect-based objective measures could extract motion characteristics with high reliability and validity. Purpose: The present research aimed to quantify the therapy-response of axial motor symptoms to daily medication regimen and to explore the correlates of the improvement rate (IR) of axial motor symptoms based on a Kinect camera. Materials and methods: We enrolled 44 patients with PD and 21 healthy controls. All 65 participants performed the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III and the Kinect-based kinematic evaluation to assess arising from a chair, gait, posture, and postural stability before and after medication. Spearman's correlation analysis and multiple linear regression model were performed to explore the relationships between motor feature IR and clinical data. Results: All the features arising from a chair (P = 0.001), stride length (P = 0.001), velocity (P < 0.001), the height of foot lift (P < 0.001), and turning time (P = 0.001) improved significantly after a daily drug regimen in patients with PD. In addition, the anterior trunk flexion (lumbar level) exhibited significant improvement (P = 0.004). The IR of the axial motor symptoms score was significantly correlated with the IRs of kinematic features for gait velocity, stride length, foot lift height, and sitting speed (r s = 0.345, P = 0.022; r s = 0.382, P = 0.010; r s = 0.314, P = 0.038; r s = 0.518, P < 0.001, respectively). A multivariable regression analysis showed that the improvement in axial motor symptoms was associated with the IR of gait velocity only (ß = 0.593, 95% CI = 0.023-1.164, P = 0.042). Conclusion: Axial symptoms were not completely drug-resistant, and some kinematic features can be improved after the daily medication regimen of patients with PD.

A summary index derived from Kinect to evaluate postural abnormalities severity in Parkinson's Disease patients.

Postural abnormalities are common disabling motor complications affecting patients with Parkinson's disease (PD). We proposed a summary index for postural abnormalities (IPA) based on Kinect depth camera and explored the clinical value of this indicator. Seventy individuals with PD and thirty age-matched healthy controls (HCs) were enrolled. All participants were tested using a Kinect-based system with IPA automatically obtained by algorithms. Significant correlations were detected between IPA and the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) total score (rs = 0.369, p = 0.002), MDS-UPDRS-III total score (rs = 0.431, p < 0.001), MDS-UPDRS-III 3.13 score (rs = 0.573, p < 0.001), MDS-UPDRS-III-bradykinesia score (rs = 0.311, p = 0.010), the 39-item Parkinson's Disease Questionnaire (PDQ-39) (rs = 0.272, p = 0.0027) and the Berg Balance Scale (BBS) score (rs = -0.350, p = 0.006). The optimal cut-off value of IPA for distinguishing PD from HCs was 12.96 with a sensitivity of 97.14%, specificity of 100.00%, area under the curve (AUC) of 0.999 (0.997-1.002, p < 0.001), and adjusted AUC of 0.998 (0.993-1.000, p < 0.001). The optimal cut-off value of IPA for distinguishing between PD with and without postural abnormalities was 20.14 with a sensitivity, specificity, AUC and adjusted AUC of 77.78%, 73.53%, 0.817 (0.720-0.914, p < 0.001), and 0.783 (0.631-0.900, p < 0.001), respectively. IPA was significantly correlated to the clinical manifestations of PD patients, and could reflect the global severity of postural abnormalities in PD with important value in distinguishing PD from HCs and distinguishing PD with postural abnormalities from those without.

Transcriptional Profiling Reveals Brain Region-Specific Gene Networks Regulated in Exercise in a Mouse Model of Parkinson's Disease.

Background: Exercise plays an essential role in improving motor symptoms in Parkinson's disease (PD), but the underlying mechanism in the central nervous system remains unclear. Methods: Motor ability was observed after 12-week treadmill exercise on a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced mouse model of PD. RNA-sequencing on four brain regions (cerebellum, cortex, substantia nigra (SN), and striatum) from control animals, MPTP-induced PD, and MPTP-induced PD model treated with exercise for 12 weeks were performed. Transcriptional networks on the four regions were further identified by an integrative network biology approach. Results: The 12-week treadmill exercise significantly improved the motor ability of an MPTP-induced mouse model of PD. RNA-seq analysis showed SN and striatum were remarkably different among individual region's response to exercise in the PD model. Especially, synaptic regulation pathways about axon guidance, synapse assembly, neurogenesis, synaptogenesis, transmitter transport-related pathway, and synaptic regulation genes, including Neurod2, Rtn4rl2, and Cd5, were upregulated in SN and striatum. Lastly, immunofluorescence staining revealed that exercise rescued the loss of TH+ synapses in the striatal region in PD mice, which validates the key role of synaptic regulation pathways in exercise-induced protective effects in vivo. Conclusion: SN and striatum are important brain regions in which critical transcriptional changes, such as in synaptic regulation pathways, occur after the exercise intervention on the PD model.

MANF Inhibits α-Synuclein Accumulation through Activation of Autophagic Pathways.

Progressive accumulation of misfolded SNCA/α-synuclein is key to the pathology of Parkinson's disease (PD). Drugs aiming at degrading SNCA may be an efficient therapeutic strategy for PD. Our previous study showed that mesencephalic astrocyte-derived neurotrophic factor (MANF) facilitated the removal of misfolded SNCA and rescued dopaminergic (DA) neurons, but the underlying mechanisms remain unknown. In this study, we showed that AAV8-MANF relieved Parkinsonian behavior in rotenone-induced PD model and reduced SNCA accumulation in the substantia nigra. By establishing wildtype (WT) SNCA overexpression cellular model, we found that chaperone-mediated-autophagy (CMA) and macroautophagy were both participated in MANF-mediated degradation of SNCAWT. Nuclear factor erythroid 2-related factor (Nrf2) was activated to stimulating macroautophagy activity when CMA pathway was impaired. Using A53T mutant SNCA overexpression cellular model to mimic CMA dysfunction situation, we concluded that macroautophagy rather than CMA was responsible to the degradation of SNCAA53T, and this degradation was mediated by Nrf2 activation. Hence, our findings suggested that MANF has potential therapeutic value for PD. Nrf2 and its role in MANF-mediated degradation may provide new sights that target degradation pathways to counteract SNCA pathology in PD.

PTP1B inhibition ameliorates inflammatory injury and dysfunction in ox-LDL-induced HUVECs by activating the AMPK/SIRT1 signaling pathway via negative regulation of KLF2.

Atherosclerosis is a key pathogenic factor of cardiovascular diseases. However, the role of protein tyrosine phosphatase 1B (PTP1B) in oxidized low-density lipoprotein (ox-LDL)-treated vascular endothelial cells remains unclear. The aim of the present study was to explore the possible physiological roles and mechanism of PTP1B in atherosclerosis using HUVECs as an in vitro model. PTP1B expression was assessed by reverse transcription-quantitative PCR. Cell viability was measured using the Cell Counting Kit-8 and lactate dehydrogenase activity assays. Levels of inflammatory factors, including IL-1ß, IL-6 and TNF-α, and oxidative stress factors, including malondialdehyde, superoxide dismutase and glutathione peroxidase, were assessed using ELISA and commercially available kits, respectively. Furthermore, TUNEL assay and western blotting were performed to assess the extent of apoptosis-related factors, including Bcl-2, Bax, Cleaved caspase-3 and Caspase-3. Tube formation assay was used to assess tubule formation ability and western blotting was to analyze VEGFA protein level. Binding sites for the transcription factor Kruppel-like factor 2 (KLF2) on the PTP1B promoter were predicted using the JASPAR database and verified using luciferase reporter assays and chromatin immunoprecipitation. The protein levels of phosphorylated 5'AMP-activated protein kinase (p-AMPK), AMPK and SIRT1 were measured using western blotting. The results demonstrated that the PTP1B mRNA and protein expression levels were significantly upregulated in oxidized low-density lipoprotein (ox-LDL)-induced HUVECs. In addition, ox-LDL-induced HUVECs transfected with short hairpin RNA against PTP1B exhibited a significant increase in cell viability, reduced inflammatory factor levels, apoptosis and oxidative stress, as well as increased tubule formation ability. KLF2 was found to negatively regulate the transcriptional activity of PTP1B. KLF2 knockdown reversed the protective effects of PTP1B knockdown on ox-LDL-induced HUVECs. KLF2 knockdown also abolished PTP1B knockdown-triggered AMPK/SIRT1 signaling pathway activation in ox-LDL-induced HUVECs. To conclude, the results of the present study suggested that PTP1B knockdown can prevent ox-LDL-induced inflammatory injury and dysfunction in HUVECs, which is regulated at least in part by the AMPK/SIRT1 signaling pathway through KLF2.

Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm.

Leaf blast is a disease of rice leaves caused by the Pyricularia oryzae. It is considered a significant disease is affecting rice yield and quality and causing economic losses to food worldwide. Early detection of rice leaf blast is essential for early intervention and limiting the spread of the disease. To quickly and non-destructively classify rice leaf blast levels for accurate leaf blast detection and timely control. This study used hyperspectral imaging technology to obtain hyperspectral image data of rice leaves. The descending dimension methods got rice leaf disease characteristics of different disease classes, and the disease characteristics obtained by screening were used as model inputs to construct a model for early detection of leaf blast disease. First, three methods, ElasticNet, principal component analysis loadings (PCA loadings), and successive projections algorithm (SPA), were used to select the wavelengths of spectral features associated with leaf blast, respectively. Next, the texture features of the images were extracted using a gray level co-occurrence matrix (GLCM), and the texture features with high correlation were screened by the Pearson correlation analysis. Finally, an adaptive-weight immune particle swarm optimization extreme learning machine (AIPSO-ELM) based disease level classification method is proposed to further improve the model classification accuracy. It was also compared and analyzed with a support vector machine (SVM) and extreme learning machine (ELM). The results show that the disease level classification model constructed using a combination of spectral characteristic wavelengths and texture features is significantly better than a single disease feature in terms of classification accuracy. Among them, the model built with ElasticNet + TFs has the highest classification accuracy, with OA and Kappa greater than 90 and 87%, respectively. Meanwhile, the AIPSO-ELM proposed in this study has higher classification accuracy for leaf blast level classification than SVM and ELM classification models. In particular, the AIPSO-ELM model constructed with ElasticNet+TFs as features obtained the best classification performance, with OA and Kappa of 97.62 and 96.82%, respectively. In summary, the combination of spectral characteristic wavelength and texture features can significantly improve disease classification accuracy. At the same time, the AIPSO-ELM classification model proposed in this study has sure accuracy and stability, which can provide a reference for rice leaf blast disease detection.

Changes of T lymphocyte subpopulations and their roles in predicting the risk of Parkinson's disease.

T lymphocytes are involved in the pathogenesis of Parkinson's disease (PD), while the heterogeneity of T-cell subpopulations remains elusive. In this study, we analyzed up to 22 subpopulations of T lymphocytes in 115 PD patients and 60 matched healthy controls (HC) using flow cytometry. We found that PD patients exhibited decreased naïve CD8+ T cells (CD3+ CD8+ CD45RA+ CD45RO-) and increased late-differentiated CD4+ T cells (CD3+ CD4+ CD28- CD27-), compared to HC, which were not affected by anti-parkinsonism medication administration. The proportion of naïve CD8+ T cells in PD patients was positively correlated with their severity of autonomic dysfunction and psychiatric complications, but negatively associated with the severity of rapid eye movement and sleep behavior disorder. The proportion of late-differentiated CD4+ T cells was negatively correlated with the onset age of the disease. We further developed individualized PD risk prediction models with high reliability and accuracy on the base of the T lymphocyte subpopulations. These data suggest that peripheral cellular immunity is disturbed in PD patients, and changes in CD8+ T cells and late-differentiated CD4+ T cells are representative and significant. Therefore, we recommend naïve CD8 + and late-differentiated CD4+ T cells as candidates for multicentric clinical study and pathomechanism study of PD.

Effects of Repetitive Transcranial Magnetic Stimulation on Motor Symptoms in Parkinson's Disease: A Meta-Analysis.

BACKGROUND: Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that has been closely examined as a possible treatment for Parkinson's disease (PD). Owing to various rTMS protocols and results, the optimal mode and suitable PD symptoms have yet to be established. OBJECTIVES: This study intends to systematically evaluate the efficacy of rTMS intervention and identify optimal stimulation protocol of rTMS for specific motor symptoms. METHODS: PubMed and web of Science databases were searched before January 2022. Eligible studies included sham-controlled and randomized clinical trials of rTMS intervention for motor dysfunction in patients with PD. Standard mean difference (SMD) was calculated with random-effects models. The effects of rTMS on motor symptoms were mainly estimated by the UPDRS-III. RESULTS: A total of 1172 articles were identified, of which 32 articles met the inclusion criteria for meta-analysis. The pooled evidence suggested that rTMS relieves motor symptoms of patients with PD (SMD 0.64, 95%CI [0.47, 0.80]). High frequency stimulation on M1 is the most effective mode of intervention (SMD 0.79, 95%CI [0.52, 1.07]). HF rTMS has significant therapeutic effects on limbs motor function (SMD 1.93, 95%CI [0.73, 3.12] for upper limb function and SMD 0.88, 95%CI [0.43, 1.33] for lower limb function), akinesia (SMD 1.17, 95%CI [0.43, 1.92), rigidity (SMD 1.02, 95%CI [0.12, 1.92]) and tremor(SMD 0.91, 95%CI [0.15, 1.67]). CONCLUSION: rTMS therapy is an effective treatment for motor symptoms of PD and the individualized stimulation protocols for different symptoms would further improve its clinical efficacy.