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Abstract 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.

Mountain ecosystems exhibit rapid changes in vegetation cover and climate conditions along elevation gradients, offering an ideal natural experiment to examine variations in the molecular composition of soil organic carbon (SOC) responsing to environmental change. We examined molecular composition, diversity, and network complexity of SOC using the pyrolysis-gas chromatography/mass spectrometry technology and the Shannon diversity index (SHDI) in Changbai Mountain (CBS). Results indicated that molecular composition of SOC differed greatly across different vegetation zones, which were predominated by mixed coniferous and broad-leaved forests (HJL), mixed coniferous forests (ZYL), birch forest covered (YHL), and tundra vegetation (TYD). ZYL had remarkably more abundant moieties with aromatic structures, 36% on average, than those in HJL, TYD, and YHL, 18.31%, 12.75% and 16.51% on average, respectively. O-alkyl, alkyl, and terpene compounds showed an increasing trend, whereas aromatic, lignin and nitrogen-containing compounds exhibited a notable decrease with elevation to a significant level. Either alpha diversity or beta diversity of SOC varied significantly among TYD, YHL, ZYL, and HJL, with HJL exhibiting the lowest alpha diversity. SOC from ZYL was the most complex, showing the highest number of edges and graph density. Leaf litter elements greatly regulated the molecular diversity and network complexity of SOC. Positive correlations between SHDI and SOC contents, as well as ratios of SOC to total iron, suggested that high molecular diversity promoted SOC persistence in mountain ecosystems.

Hydrological connectivity is crucial for supporting aquatic biodiversity and serving ecosystem function in river floodplain wetlands. Although the effects of longitudinal connectivity in the composition of snail (Mollusca: Gastropoda) assemblages is well understood, the effects of lateral connectivity are less evaluated. Here, we evaluated the effects of lateral and longitudinal connectivity on multiple facets of alpha and beta diversity (i.e., taxonomic, functional, and phylogenetic) of freshwater snails in 38 floodplain wetlands in the Da Xing'an Mountains of Northeast China. A total of 9,784 snails subjected to 5 orders, 14 families, 24 genera, and 42 species were collected. Overall, the snail alpha diversity was higher in the nearly isolated wetlands and downstream areas. Multivariate analyses revealed that the taxonomic, functional, and phylogenetic structures of snail assemblages in floodplain wetlands differed significantly across the lateral and longitudinal gradients. Waterscape and climate variables appeared to equivalently explain a large proportion of variations in the taxonomic, functional, and phylogenetic structures of snail assemblages. Lateral connectivity and river order were paramount variables that explain the greatest variation in taxonomic, functional, and phylogenetic structures of the snail assemblages. Our study suggests that hydrological connectivity is a key factor controlling the multiple facets of snail diversity in floodplains. Attention should be paid to the effects of changing climate and waterscape on the multi-faceted diversity of snail assemblages. Due to the extant patterns in assemblage structures, floodplain restoration and management should consider the full spectrum of longitudinal and laterally connectedness to maximize aquatic biodiversity.

Superpixel-based hyperspectral unmixing (HU) can effectively reduce spectral variability’s influence on unmixing performance. In the superpixel-based HU method, this study proposes a segmentation scale determination method to improve the accuracy of endmembers and fully constrained least squares based on distance strategy (D-FCLS) to improve the efficiency of abundance estimation. In the segmentation-scale determination method, this study establishes a segmentation scale division criterion to divide segmented images with similar quality into the same segmentation scale. The optimal segmentation scale is selected according to the actual situation of hyperspectral images. Moreover, the distance strategy is applied to fully constrained least squares (FCLS) using the spatial relationship between endmembers and the mixed pixel in abundance estimation. The proposed methods are evaluated on the synthetic and real datasets. The results show that the validity of the segmentation-scale determination method is verified by quantitative and qualitative evaluation on all datasets. In terms of abundance estimation, compared with FCLS, D-FCLS improves the efficiency by more than 10.30% on the synthetic dataset and 18.71% on the real dataset. In addition, this study’s proposed abundance estimation method and unsupervised superpixel-based HU method are superior to the other comparison methods.

Abstract 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.

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 β 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.

Abstract 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 (r s = 0.369, p = 0.002), MDS-UPDRS-III total score (r s = 0.431, p

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

BackgroundDecreased myocardial uptake of 131I-metaiodobenzylguanidine (MIBG) is known to be an important feature to diagnose Parkinson’s disease (PD). However, the diagnosis accuracy of myocardial MIBG scintigraphy alone is often unsatisfying. Recent studies have found that the MIBG uptake of the major salivary glands was reduced in PD patients as well.PurposeTo evaluate the diagnostic value of major salivary gland MIBG scintigraphy in PD, and explore the potential role of myocardial MIBG scintigraphy combined with salivary gland MIBG scintigraphy in distinguishing PD from non-PD (NPD).MethodsThirty-seven subjects were performed with 131I-MIBG scintigraphy. They were classified into the PD group (N = 18) and the NPD group (N = 19), based on clinical diagnostic criteria, DAT PET and 18F-FDG PET imaging findings. Images of salivary glands and myocardium were outlined to calculated the MIBG uptake ratios.ResultsThe combination of left parotid and left submandibular gland early images had a good performance in distinguishing PD from NPD, with sensitivity, specificity, and accuracy of 50.00, 94.74, and 72.37%, respectively. Combining the major salivary gland and myocardial scintigraphy results in the early period showed a good diagnostic value with AUC, sensitivity and specificity of 0.877, 77.78, and 94.74%, respectively. Meanwhile, in the delayed period yield an excellent diagnostic value with AUC, sensitivity and specificity of 0.904, 88.89, and 84.21%, respectively.Conclusion131I-MIBG salivary gland scintigraphy assisted in the diagnosis and differential diagnosis of PD. The combination of major salivary gland and myocardial 131I-MIBG scintigraphy further increased the accuracy of PD diagnosis.

Abstract Background Automated and accurate assessment for postural abnormalities is necessary to monitor the clinical progress of Parkinson’s disease (PD). The combination of depth camera and machine learning makes this purpose possible. Methods Kinect was used to collect the postural images from 70 PD patients. The collected images were processed to extract three-dimensional body joints, which were then converted to two-dimensional body joints to obtain eight quantified coronal and sagittal features (F1-F8) of the trunk. The decision tree classifier was carried out over a data set established by the collected features and the corresponding doctors’ MDS-UPDRS-III 3.13 (the 13th item of the third part of Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale) scores. An objective function was implanted to further improve the human–machine consistency. Results The automated grading of postural abnormalities for PD patients was realized with only six selected features. The intraclass correlation coefficient (ICC) between the machine’s and doctors’ score was 0.940 (95%CI, 0.905–0.962), meaning the machine was highly consistent with the doctors’ judgement. Besides, the decision tree classifier performed outstandingly, reaching 90.0% of accuracy, 95.7% of specificity and 89.1% of sensitivity in rating postural severity. Conclusions We developed an intelligent evaluation system to provide accurate and automated assessment of trunk postural abnormalities in PD patients. This study demonstrates the practicability of our proposed method in the clinical scenario to help making the medical decision about PD.

BackgroundCarotid body tumor surgery is associated with various complications. However, intraoperative cardiac arrest is very rare and no more than 10 cases have been reported.Case descriptionA 58-year-old woman diagnosed with bilateral carotid body tumors underwent right carotid body tumor surgery. Sudden cardiac arrest occurred during the resection and was attributed to carotid sinus hypersensitivity. The patient recovered after prompt treatment and the tumor was removed completely with no complications.ConclusionCardiac arrest attributed to carotid sinus hypersensitivity during carotid body tumor resection is very rare. Proper treatments can reverse intraoperative cardiac arrest. If carotid sinus hypersensitivity is detected preoperatively, prophylactic temporary pacemaker implantation may be appropriate.

BackgroundAxial disturbances are the most disabling symptoms of Parkinson’s disease (PD). Kinect-based objective measures could extract motion characteristics with high reliability and validity.PurposeThe 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 methodsWe 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.ResultsAll 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 (rs = 0.345, P = 0.022; rs = 0.382, P = 0.010; rs = 0.314, P = 0.038; rs = 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 (β = 0.593, 95% CI = 0.023–1.164, P = 0.042).ConclusionAxial symptoms were not completely drug-resistant, and some kinematic features can be improved after the daily medication regimen of patients with PD.

BackgroundExercise plays an essential role in improving motor symptoms in Parkinson’s disease (PD), but the underlying mechanism in the central nervous system remains unclear.MethodsMotor 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.ResultsThe 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.ConclusionSN 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.

Leaf spot disease is a dangerous disease that affects peanut growth, and its severity can significantly impact peanut yield. Hyperspectral-based disease detection technology is a popular non-destructive technique due to its high efficiency, objectivity, and accuracy. In this study, peanut leaf spectra at different levels of severity of leaf spot disease were collected in Liaoning Province, China, in mid-August. This study analyzed the differences in wavelengths using mean spectral reflectance and sensitivity. Using improved principal component analysis loading (I-PCA loading) based on the contribution weight assignment approach, we identified three feature wavelengths of 570 nm, 671 nm, and 750 nm. We evaluated the ability of these feature wavelengths to detect the severity of leaf spot disease using k-nearest neighbor (KNN), support vector machine (SVM), and back-propagation (BP) neural network classifiers. Our experimental results showed that our improved PCA loading method achieved higher classification accuracy with fewer wavelengths than the seven commonly used feature selection methods. Among these classifiers, the SVM achieved the highest accuracy, with an overall accuracy (OA) of 96.88% and a Kappa of 95.81%. Therefore, our proposed method can accurately detect the severity of peanut leaf spot disease and provide scientific and technical support for accurately managing peanut crops.

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.

The sound-evoked electrical compound potential known as auditory brainstem response (ABR) represents the firing of a heterogenous population of auditory neurons in response to sound stimuli, and is often used for clinical diagnosis based on wave amplitude and latency. However, recent ABR applications to detect human cochlear synaptopathy have led to inconsistent results, mainly due to the high variability of ABR wave-1 amplitude. Here, rather than focusing on the amplitude of ABR wave 1, we evaluated the use of ABR wave curvature to detect cochlear synaptic loss. We first compared four curvature quantification methods using simulated ABR waves, and identified that the cubic spline method using five data points produced the most accurate quantification. We next evaluated this quantification method with ABR data from an established mouse model with cochlear synaptopathy. The data clearly demonstrated that curvature measurement is more sensitive and consistent in identifying cochlear synaptic loss in mice compared to the amplitude and latency measurements. We further tested this curvature method in a different mouse model presenting with otitis media. The change in curvature profile due to middle ear infection in otitis media is different from the profile of mice with cochlear synaptopathy. Thus, our study suggests that curvature quantification can be used to address the current ABR variability issue, and may lead to additional applications in the clinic diagnosis of hearing disorders.

In recent years, the relative importance of the processes driving metacommunity composition has aroused extensive attention and become a powerful approach to identify community patterns and their regulatory mechanisms. We investigated variations in the composition of benthic community in restored wetlands and natural wetlands in the Yellow River Delta (Shandong Province, China). First, spatial structures within each wetland were modeled with Moran eigenvector maps. Next, the variation in community structure among local environmental and spatial variables was partitioned using constrained ordination, and the “elements of metacommunity structure” analysis was used to determine the patterns of best fit for species distributions within metacommunities. Finally, the null model was used to analyze non-random patterns of species co-occurrence. The community structure of benthic invertebrates in restored wetlands and natural wetlands differed significantly. The benthic invertebrate metacommunity structure showed a nested distribution in restored wetlands and a quasi-Clementsian structure in natural wetlands. Pure environmental fractions and pure spatial fractions were critical in regulating benthic invertebrate metacommunities of restored wetlands. In natural wetlands, pure spatial fractions and the interaction between environmental and spatial factors (shared fractions) played a major role in the metacommunity. A species co-occurrence analysis showed that species co-occurred more frequently than expected by chance, demonstrating that biotic interactions were not the main driver of metacommunity structures in both wetland types. Accordingly, the benthic invertebrate metacommunity in estuarine wetlands following freshwater releases was mostly determined by environmental and spatial effects, which resulted in a metacommunity with nested distribution. These results are important for biodiversity protection and ecosystem management of estuarine wetlands in the Yellow River Delta.

Leaf spot (LS) caused by Cercosporidium personatum is one of the most harmful peanut diseases in the late growth stage and severely affects the yield of peanuts. Hyperspectral disease detection technology is efficient, objective, and accurate and is suitable for large-scale crop management practices. To establish a multi-scale spectral index (SI) with high accuracy and stability for the detection of peanut LS disease, the spectral reflectance of different disease severity levels at leaf, plant, and field scales was collected, and the difference in wavelength caused by disease severity was analyzed using the mean, variance, and dispersion matrix of hyperspectral reflectance. Meanwhile, the feature weights at different scales were obtained using Relief-F, and the average feature weights identified 540, 660, and 770 nm as multi-scale sensitive wavelengths. Three new SIs were constructed by combining single, ratiometric, and normalized wavelengths. The new SIs were compared and analyzed with 35 commonly used SIs by correlation analysis and M-statistic values, and 6 SIs were significantly correlated with disease severity levels and had good separability. Finally, k-nearest neighbor (KNN) and multinomial logistic regression (MLR) were used to evaluate the ability of the above SIs to detect LS severity. The results showed that the leaf spot multi-scale spectral index (LS-MSSI) constructed in this study was superior to the other SIs and obtained high accuracy at different scales simultaneously. At the leaf and plant scales, the MLR obtained high accuracy, with the overall accuracy (OA) reaching 93.77% and 92.50% and Kappa reaching 91.59% and 89.97%, respectively. At the field scale, the KNN obtained high accuracy, with the OA and Kappa reaching 90.29% and 87.04%, respectively. The LS-MSSI proposed in this study has high accuracy, stability, and robustness in the detection of LS severity at multiple scales, providing a technical basis and scientific guidance for the detection and precise management of peanuts.

In this paper, we proposed an interval degradation model to improve the reliability of the classical single point degradation model. The interval degradation model is very flexible when model parameters follows different distributions. Twenty-five types of interval Gamma degradation models are considered and discussed under different conditions. The reliabilities of interval Gamma degradation models are obtained. The Monte Carlo method has been studied to compute the reliability and lifetime of interval Gamma degradation model. The numerical examples are conducted to compare the interval degradation model with the classical single point degradation model. Simulation results reveal that the performance of reliability and mean lifetime of interval Gamma degradation model are much better than those of the single Gamma degradation model. Finally, we applied our model to a real data example and demonstrated the effectiveness and feasibility of the interval Gamma degradation model.

Parkinson's disease (PD) is a common neurodegenerative disease with movement and balance impairments. Although studies have reported improvement of motor symptoms with physical exercise, the mechanisms by which exercise is beneficial remains poorly understood. Our study addresses the exercise-induced changes to peripheral immune cells by interrogating the transcriptome of blood-derived leukocytes in PD patients before and after exercise. Patients attended 1 h exercise classes twice a week for 12 weeks. Leukocytes were collected at the beginning and end of the study for gene expression analysis by RNA-seq or quantitative real-time PCR. We correlated differentially expressed genes after exercise with clinical measures and analyzed the potential functions of gene changes with Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analysis. Exercise improved measures of movement and balance when compared with scores before the exercise program. Among the gene changes, Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analysis suggests that T-cell receptor signaling, T-cell activation, and T-cell migration pathways were downregulated, while the T-cell receptor signaling pathway was the most significantly correlated with clinical measures. To further investigate T-cell-related changes in PD leukocytes, we reanalyzed the differentially expressed genes from publicly available microarray data and found that genes in the T-cell activation, differentiation, and migration pathways were upregulated in PD samples compared to controls in a time-dependent manner. Together, our findings suggest that exercise rehabilitation may improve movement and balance in PD patients by reversing the upregulated T-cell activation pathways associated with PD. This study was registered with the Chinese Clinical Trial Registry under ChiCTR-TRC-14004707. Registered on May 27, 2014.

Botulinum toxin type-A (Btx-A), a powerful therapeutic tool in various medical specialties, requires repeated injections to maintain its effect. Therefore, novel methods to prolong the effective duration time of Btx-A are highly needed. Rats were assigned to three major groups: control group (n = 30), Btx-A group (n = 30), and IGF-1 Ab groups. IGF-1 Ab groups were composed by sub-groups A1–A5 (each has 25 rats) for the subsequent IGF-1Ab dose-effect study. Muscle strength was determined by a survey system for rat lower limbs nerve and muscle function. Muscle-specific receptor tyrosine kinase (MuSK), Insulin-like growth factor binding protein-5 (IGFBP5), and growth-associated protein, 43-kDa (GAP43) were determined by real-time polymerase chain reactions (PCRs) and Western blot. We found that Btx-A decreased the muscle strength, with a paralysis maintained for 70 days. IGF-1Ab prolonged the effective duration time of Btx-A. Real-time PCRs and Western blot showed that IGF-1Ab delayed the increase of MuSK and IGFBP5 after Btx-A injection, without affecting GAP43. These results indicate that IGF-1Ab might prolong the effective duration time of Btx-A on muscle strength through delaying the increase of MuSK. It would be interesting to determine whether IGF-1Ab can be used as an auxiliary measure to the Btx-A treatment in the future.