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The traditional diagnostic process for autism spectrum disorder (ASD) is subjective, where early and accurate diagnosis significantly affects treatment outcomes and life quality. Thus, improving ASD diagnostic methods is critical. This paper proposes ASD-SWNet, a new shared-weight feature extraction and classification network. It resolves the issue found in previous studies of inefficiently integrating unsupervised and supervised learning, thereby enhancing diagnostic precision. The approach utilizes functional magnetic resonance imaging to improve diagnostic accuracy, featuring an autoencoder (AE) with Gaussian noise for robust feature extraction and a tailored convolutional neural network (CNN) for classification. The shared-weight mechanism utilizes features learned by the AE to initialize the convolutional layer weights of the CNN, thereby integrating AE and CNN for joint training. A novel data augmentation strategy for time-series medical data is also introduced, tackling the problem of small sample sizes. Tested on the ABIDE-I dataset through nested ten-fold cross-validation, the method achieved an accuracy of 76.52% and an AUC of 0.81. This approach surpasses existing methods, showing significant enhancements in diagnostic accuracy and robustness. The contribution of this paper lies not only in proposing new methods for ASD diagnosis but also in offering new approaches for other neurological brain diseases. [ABSTRACT FROM AUTHOR]

Biochar is an inexpensive alternative to activated carbon for adsorption of phenolic pollutants. Dissolved solids released from biochar, including organic and inorganic compounds, may affect the stable adsorption efficiency of biochar as an adsorbent. Using saccharification residue of rice straw to prepare biochar (biochar-SR) can improve the stability of biochar and reduce its cost. Biochar-SR and biochar prepared directly from rice straw (biochar-O) contained basically the same types of surface functional groups but the numbers of lactone, phenolic, and carbonyl groups in biochar-SR were 55.56%, 50.57%, and 311.56% higher than those in biochar-O, respectively. The total volume and diameter of pores and specific surface area of biochar-SR were 38%, 51%, and 167% higher than those of biochar-O, respectively. The adsorption data of biochar-SR fit better to a Freundlich model, indicating non-uniform multilayer adsorption. Compared with biochar-O, the equilibrium adsorption capacity of biochar-SR was 41.63% higher, and it had significantly better stability and reusability. These results demonstrated that the preparation method of biochar-SR could significantly improve the adsorption capacity, stability, and environmental and economic benefits of biochar as an adsorbent. [ABSTRACT FROM AUTHOR]

Misfolding and aggregation of α-Synuclein (α-Syn), which are hallmark pathological features of neurodegenerative diseases such as Parkinson's disease (PD) and dementia with Lewy Bodies, continue to be significant areas of research. Among the diverse forms of α-Syn – monomer, oligomer, and fibril, the oligomer is considered the most toxic. However, the mechanisms governing α-Syn oligomerization are not yet fully understood. In this study, we utilized genome-wide CRISPR/Cas9 loss-of-function screening in human HEK293 cells to identify negative regulators of α-Syn oligomerization. We found that tetraspanin 3 (TSPAN3), a presumptive four-pass transmembrane protein, but not its homolog TSPAN7, significantly modulates α-Syn oligomer levels. TSPAN3 was observed to interact with α-Syn oligomers, regulate the amount of α-Syn oligomers on the cell membrane, and promote their degradation via the clathrin-AP2 mediated endo-lysosome pathway. Our findings highlight TSPAN3 as a potential regulator of α-Syn oligomers, presenting a promising target for future PD prevention and treatment strategies. [ABSTRACT FROM AUTHOR]

Depression is a mood disorder that can affect people's psychological problems. The current medical approach is to detect depression by manual analysis of EEG signals, however, manual analysis of EEG signals is cumbersome and time-consuming, requiring a lot of experience. Therefore, we propose a short time series base on convolutional neural network (CNN), called DCLNet, for depression classification. Firstly, the sample size and diversity of the dataset are enhanced by the clipping strategy. Then we superimposes different frequency domains and put them into a two-dimensional matrix according to the electrode position of the EEG, which was input to CNN to extract important features. Finally, the extracted features are put into the Long short-term memory network (LSTM) to capture the temporal information. The experimental results shows that the Accuracy of the model is 99.15%, Specificity is 99.01%, and Sensitivity is 99.30%. Compared with the current popular machine learning (ML) methods and deep learning (DL) models, DCTNet has excellent performance in the evaluation indexes of Accuracy, Specificity and Sensitivity. [ABSTRACT FROM AUTHOR]

Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder. An increasing number of researchers have found extra motor features in ALS, which are also called ALS-plus syndromes. Besides, a great majority of ALS patients also have cognitive impairment. However, clinical surveys of the frequency and genetic background of ALS-plus syndromes are rare, especially in China. Methods: We investigated a large cohort of 1015 patients with ALS, classifying them into six groups according to different extramotor symptoms and documenting their clinical manifestations. Meanwhile, based on their cognitive function, we divided these patients into two groups and compared demographic characteristics. Genetic screening for rare damage variants (RDVs) was also performed on 847 patients. Results: As a result, 16.75% of patients were identified with ALS-plus syndrome, and 49.5% of patients suffered cognitive impairment. ALS-plus group had lower ALSFRS-R scores, longer diagnostic delay time, and longer survival times, compared to ALS pure group. RDVs occurred less frequently in ALS-plus patients than in ALS-pure patients (P = 0.042) but showed no difference between ALS-cognitive impairment patients and ALS-cognitive normal patients. Besides, ALS-cognitive impairment group tends to harbour more ALS-plus symptoms than ALS-cognitive normal group (P = 0.001). Conclusion: In summary, ALS-plus patients in China are not rare and show multiple differences from ALS-pure patients in clinical and genetic features. Besides, ALS-cognitive impairment group tends to harbour more ALS-plus syndrome than ALS-cognitive normal group. Our observations correspond with the theory that ALS involves several diseases with different mechanisms and provide clinical validation. [ABSTRACT FROM AUTHOR]

Focal epilepsy accounts for 60% of all forms of epilepsy, but the pathogenic mechanism is not well understood. In this study, three novel mutations in NPRL3 (nitrogen permease regulator-like 3), c.937_945del, c.1514dupC and 6,706-bp genomic DNA (gDNA) deletion, were identified in three families with focal epilepsy by linkage analysis, whole exome sequencing (WES) and Sanger sequencing. NPRL3 protein is a component of the GATOR1 complex, a major inhibitor of mTOR signaling. These mutations led to truncation of the NPRL3 protein and hampered the binding between NPRL3 and DEPDC5, which is another component of the GATOR1 complex. Consequently, the mutant proteins enhanced mTOR signaling in cultured cells, possibly due to impaired inhibition of mTORC1 by GATOR1. Knockdown of nprl3 in Drosophila resulted in epilepsy-like behavior and abnormal synaptic development. Taken together, these findings expand the genotypic spectrum of NPRL3-associated focal epilepsy and provide further insight into how NPRL3 mutations lead to epilepsy. [ABSTRACT FROM AUTHOR]

Increasing evidence suggests that circadian dysfunction is related to Parkinson's disease (PD). However, the role of circadian clock genes in PD is still poorly understood. This study aimed to illustrate the association between genetic variants of circadian clock genes and PD in a large Chinese population cohort. Ten circadian clock genes were included in this study. Whole-exome sequencing (WES) was conducted in 1997 early-onset or familial PD patients and 1652 controls (WES cohort), and whole-genome sequencing (WGS) was conducted in 1962 sporadic late-onset PD patients and 1279 controls (WGS cohort). Analyses were completed using the optimized sequence kernel association test and regression analyses. In the burden analysis of the circadian clock gene set, we found suggestive significant associations between the circadian clock genes and PD in the WES cohort when considering missense, damaging missense (Dmis), and deleterious variants. Moreover, the burden analysis of single genes revealed suggestive significant associations between PD and the loss-of-function variants of the CRY1 gene, missense, Dmis, and deleterious variants of the PER1 gene, and Dmis and deleterious variants of the PER2 gene in the WES cohort. Rare variants in the WGS cohort and all common variants in the WGS and WES cohorts were unrelated to PD. Phenotypic analysis indicated that deleterious variants of the PER1 gene were associated with dyskinesia in the WES cohort. Our study provides evidence of a potential link between circadian clock genes and PD from a genetic perspective. [ABSTRACT FROM AUTHOR]

The development of advanced photocatalysts is crucial for efficient photocatalytic degradation of the antibiotic tetracycline (TC). In this article, a series of BiOI/Bi2WO6 (I/W) with different ratios (9:1, 7:3, 5:5, 3:7, 1:9) were synthesized by hydrothermal method and solvothermal method with BiOI as the primary material. XRD, SEM, FT-IR, XPS, and other analyses showed that the two monomers BiOI and Bi2WO6 were well combined together. The degradation experiment was considered that the 7:3I/W photocatalytic composite has the best degradation effect on TC, which can degrade 84.81% of TC within 120 min. At the same time, the 7:3I/W composite has good stability after five cycles. In the 7:3I/W composite, BiOI and Bi2WO6 were successfully recombined, and a p-n type heterojunction system was formed. This p-n type result not only greatly promoted the separation between e− and h+, but also significantly improved its photocatalytic performances. This paper provided a novel photocatalytic material for treating TC in water, and also created theoretical support for the composite of bismuth-based photocatalytic materials. [ABSTRACT FROM AUTHOR]

Molecular chaperones were reported to play an important role in PD pathogenesis. Recent studies revealed the association of several HSP40/DNAJ family genes with PD, but no genetic analysis of all the DNAJ family genes in PD has been conducted. To systematically analyze the genetic impact of all the DNAJ family genes in PD, we performed genetic analysis for these genes in a large case-control study. We analyzed the rare variants in 49 DNAJ family genes from 3879 PD patients and 2931 healthy controls by whole-exome sequencing and whole-genome sequencing. All rare missense variants and the subgroups of rare damaging missense (Dmis) and loss-of-function (LoF) variants were gathered to test the accumulated association of these variants in each gene with PD. In total, 1617 rare nonsynonymous variants of DNAJ family genes with minor allele frequency less than 1% were identified in our cohort. We identified 82 rare missense variants for DNAJC26 in sporadic early-onset PD (sEOPD) or familial PD (FPD), and 17 Dmis and one LoF variant were detected among them. Gene-based burden analysis showed that the rare Dmis variants alone or Dmis plus LoF variants together of DNAJC26 were significantly enriched in PD patients. We also found suggestive associations of DNAJB2 and DNAJC18 with PD in sEOPD or FPD and DNAJC2, DNAJC10, DNAJC22, DNAJC24, DNAJC27, DNAJC28, and DNAJC29 with PD in sporadic late-onset PD. In conclusion, rare missense variants of DNAJC26 were significantly enriched in FPD or sEOPD. Moreover, DNAJB2, DNAJC2, DNAJC10, DNAJC18, DNAJC22, DNAJC24, DNAJC27, DNAJC28, and DNAJC29 were suggestively associated with PD. [ABSTRACT FROM AUTHOR]

The article titled "HR-CycleGAN: Face highlight reduction based on improved cycle-consistent adversarial networks" has been retracted by the Editor-in-Chief and the publisher due to concerns identified during an investigation. These concerns include citations that do not support the claims made in the text, non-standard phrasing, and image irregularities. The Editor-in-Chief no longer has confidence in the results and conclusions of the article. One of the authors disagrees with the retraction, while two authors have not responded to correspondence. The online version of the article includes the retracted article as Supplementary Information. [Extracted from the article]

Corporate financial distress is related to the interests of the enterprise and stakeholders. Therefore, its accurate prediction is of great significance to avoid huge losses from them. Despite significant effort and progress in this field, the existing prediction methods are either limited by the number of input variables or restricted to those financial predictors. To alleviate those issues, both financial variables and non-financial variables are screened out from the existing accounting and finance theory to use as financial distress predictors. In addition, a novel method for financial distress prediction (FDP) based on sparse neural networks is proposed, namely FDP-SNN, in which the weight of the hidden layer is constrained with L 1 / 2 regularization to achieve the sparsity, so as to select relevant and important predictors, improving the predicted accuracy. It also provides support for the interpretability of the model. The results show that non-financial variables, such as investor protection and governance structure, play a key role in financial distress prediction than those financial ones, especially when the forecast period grows longer. By comparing those classic models proposed by predominant researchers in accounting and finance, the proposed model outperforms in terms of accuracy, precision, and AUC performance. [ABSTRACT FROM AUTHOR]

Endocannabinoid (eCB), 2-arachidonoyl-glycerol (2-AG), the most abundant eCB in the brain, regulates diverse neural functions. Here we linked multiple homozygous loss-of-function mutations in 2-AG synthase diacylglycerol lipase β (DAGLB) to an early onset autosomal recessive Parkinsonism. DAGLB is the main 2-AG synthase in human and mouse substantia nigra (SN) dopaminergic neurons (DANs). In mice, the SN 2-AG levels were markedly correlated with motor performance during locomotor skill acquisition. Genetic knockdown of Daglb in nigral DANs substantially reduced SN 2-AG levels and impaired locomotor skill learning, particularly the across-session learning. Conversely, pharmacological inhibition of 2-AG degradation increased nigral 2-AG levels, DAN activity and dopamine release and rescued the locomotor skill learning deficits. Together, we demonstrate that DAGLB-deficiency contributes to the pathogenesis of Parkinsonism, reveal the importance of DAGLB-mediated 2-AG biosynthesis in nigral DANs in regulating neuronal activity and dopamine release, and suggest potential benefits of 2-AG augmentation in alleviating Parkinsonism. 2- arachidonoylglycerol (2-AG), an abundant endocannabinoid in the brain, regulates diverse neural functions. Here, the authors identified four loss-of-function mutations in dicylglycerol lipase β (DAGLB) from six patients with early onset Parkinsonism. In mice, loss of DAGLB in dopamine neurons reduced neuronal activity and impaired locomotor function and augmentation of 2-AG levels boosted neuronal activity and rescued locomotor deficits. [ABSTRACT FROM AUTHOR]

Although the single-domain person re-identification (Re-ID) method has achieved great accuracy, the dependence on the label in the same image domain severely limits the scalability of this method. Therefore, cross-domain Re-ID has received more and more attention. In this paper, a novel cross-domain Re-ID method combining supervised and unsupervised learning is proposed, which includes two models: a triple-condition generative adversarial network (TC-GAN) and a dual-task feature extraction network (DFE-Net). We first use TC-GAN to generate labeled images with the target style, and then we combine supervised and unsupervised learning to optimize DFE-Net. Specifically, we use labeled generated data for supervised learning. In addition, we mine effective information in the target data from two perspectives for unsupervised learning. To effectively combine the two types of learning, we design a dynamic weighting function to dynamically adjust the weights of these two approaches. To verify the validity of TC-GAN, DFE-Net, and the dynamic weight function, we conduct multiple experiments on Market-1501 and DukeMTMC-reID. The experimental results show that the dynamic weight function can improve the performance of the models, and our method is better than many state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Purpose: Multi-modal vibration control often causes modal peaks' reductions unevenly, which makes the active vibration suppression system less effective to the broadband disturbance. To this end, a multi-mode vibration average suppression method for cylindrical shells with optimally distributed piezoelectric sensors and actuators (S/As) is proposed in this paper. Methods: Based on the equivalent homogenized parameters of the macro-fiber composite (MFC), the modal control force of MFC with a tilt angle is derived. Its form is similar to that of the modal sensing signal with a tilt angle. A modified S/As placement optimization criterion of maximum observability and controllability for average distribution of the control efficiencies of multi-mode is also proposed. Thus, an optimization model of considering the positions and orientations of orthotropic piezoelectric S/As for the cylindrical shell multi-mode vibration control is established. Result: The proposed method is applied to optimize the S/As distribution of a cantilever cylindrical shell. The results of optimization placement show that the S/As are placed near the free ends, their tilt angles tend to π/2 rad. To valid the effectiveness of optimized S/As distribution, a linear quadratic Gaussian (LQG) controller with Kalman filter is applied to control the response of cylindrical shell under the harmonic and transient excitations. Conclusion: The vibration control system with S/As distribution optimized by the modified criterion could suppress all the response peaks more averagely than that under the original criterion, and achieve better vibration control performance. In addition, including the tilt angle in the optimization problem, the efficiency and the uniformity of the multi-mode vibration suppression system could be improved. [ABSTRACT FROM AUTHOR]

Simple hydrothermal method and wet chemical method were both used to synthesize a novel SFO/BMO heterojunction, which was applied to the degradation of tetracycline (TC) in wastewater. Compared with the pure catalyst, the composite heterojunction catalyst can significantly improve the photodegradation efficiency. Under simulated sunlight, the degradation rate of TC by the composite with 30 wt% SFO mass could reach 77%, which was 20.8% higher than that of pure BMO and 50.4% higher than that of pure SFO. This is attributed to the low recombination rate of photogenerated electron-holes and the reduction in the band gap width caused by the synergy of BMO and SFO. At the same time, this phenomenon was confirmed by a series of characterizations, including XRD, SEM, XPS, FT-IR and UV–Vis, as well as analysis of the catalytic mechanism. The study provides a theoretical basis for the discovery of diverse photocatalysts. The heterojunction Bi2MoO6/SrFeO3-x is formed to reduce the recombination rate of photogenerated electron-holes, thereby improving the degradation efficiency of the original Bi2MoO6 to tetracycline wastewater. [ABSTRACT FROM AUTHOR]

The purpose of active learning is to significantly reduce the cost of annotation while ensuring the good performance of the model. In this paper, we propose a novel active learning method based on the combination of pool and synthesis named dual generative adversarial active learning (DGAAL), which includes the functions of image generation and representation learning. This method includes two groups of generative adversarial network composed of a generator and two discriminators. One group is used for representation learning, and then this paper performs sampling based on the predicted value of the discriminator. The other group is used for image generation. The purpose is to generate samples which are similar to those obtained from sampling, so that samples with rich information can be fully utilized. In the sampling process, the two groups of network cooperate with each other to enable the generated samples to participate in sampling process, and to enable the discriminator for sampling to co-evolve. Thus, in the later stage of sampling, the problem of insufficient information for selecting samples based on the pool method is alleviated. In this paper, DGAAL is evaluated extensively on three data sets, and the results show that DGAAL not only has certain advantages over the existing methods in terms of model performance but can also further reduces the annotation cost. [ABSTRACT FROM AUTHOR]

The 7:3LaCoO3/Bi2WO6 composite synthesized in this experiment had a significant effect on the degradation of TC in the solution under visible light (85.5% at 150min). The experimental results proved that the composite material not only had a high removal rate of TC in the water, but also could be recycled and met the requirements of environmental protection. XRD, SEM, FT-IR, XPS and other analysis showed that in the 7:3LaCoO3/Bi2WO6 composite, the two monomers LaCoO3 and Bi2WO6 were well combined together. The two active groups h+ and •O2- played an important role in the photocatalytic degradation process of the composite material. The electrons of the composite material moved from the conduction band of Bi2WO6 to the valence band of LaCoO3, which further enhanced the photocatalytic activity of the composite material. This research provided a new type of photocatalytic material for treating TC in water, also provided a certain theoretical support for the composite of lanthanide-based photocatalytic materials and bismuth-based photocatalytic materials. [ABSTRACT FROM AUTHOR]

The noble metal-free ternary Fe3O4/MoS2/BiVO4 p–n heterojunctions photocatalyst were prepared via a simply hydrothermal method, which possessed the unique nanospheres-on-microspheres heterostructure. The Fe3O4/MoS2/BiVO4 composite photocatalyst exhibited higher photocatalytic degradation of tetracycline hydrochloride (TCH) than those of MoS2, BiVO4 under visible light irradiation (λ > 420 nm). Especially, when the Bi/Mo molar ratio reached 30:1 (FMB3), the FMB3 displayed the highest photocatalytic activity, which can degrade 83% TCH (20 mg/L) within 90 min. The enhanced photocatalytic activity of the ternary photocatalyst could be attributed to the energy band matching and reduction of the charge transfer resistance to promote the spatial separation and reduce the recombination of photogenerated charge carriers due to the formation of built-in electric field by p–n heterojunctions between MoS2 and BiVO4. Additionally, MoS2 can be used as cocatalyst to enhance the separation efficiency of BiVO4 catalyst carrier and thereby improving the photocatalytic activity. Furthermore, the photocatalyst displayed highly stable recycling performances, which can be separated rapidly with an external magnetic field and be reused for five cycles and remain 91.8% of the first time. [ABSTRACT FROM AUTHOR]

Accurate localization of a moving object is important in many robotic tasks. Often an elaborate motion capture system is used to realize it. While high precision is guaranteed, such a complicated system is costly and limited to specified small size workspace. This paper describes a lightweight and scalable visual-inertial approach, which leverages paper printable, known size and unknown pose, artificial landmarks, as called fiducials, to obtain motion state estimates, including pose and velocity. Visual-inertial joint optimization using incremental smoother over factor graph and the IMU preintegration technique make our method efficient and accurate. No special hardware is required except a monocular camera and an IMU, making our system lightweight and easy to deploy. Using paper printable landmarks, as well as the efficient incremental inference algorithm, renders it nearly constant-time complexity and scalable to large-scale environment. We perform an extensive evaluation of our method on public datasets and real-world experiments. Results show our method achieves accurate state estimates and is scalable to large-scale environment and robust to fast motion and changing light condition. Besides, our method has the ability to recover from intermediate failure. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a common neurodegenerative disorder with multiple factors contributing to disease pathogenesis. Previous studies implicated the involvement of the transcription factor hypoxia inducible factor 1 alpha (HIF1A) in PD through its transcriptional regulation of PD-associated genes. This study uses molecular inversion probes (MIPs) followed by high-throughput sequencing for the genetic analysis of HIF1A in a large cohort including 1692 ethnic Han Chinese PD patients and 1419 neurologically normal control subjects matched for age, gender, and ethnicity. Common HIF1A variant rs11549465 was found to be associated with increased late-onset PD (LOPD) risk (OR (95%CI) = 1.531(1.068-2.194), P = 0.03828 for trend test, P = 0.03948 for analyses using the allelic model and P = 0.04196 for logistic regression analyses (sex + age as covariates)). Though the gene-based variants burden test is negative, seven rare non-synonymous, predicted-pathogenic point variants were identified. In conclusion, our study further indicates that HIF1A plays a role in PD pathogenesis. [ABSTRACT FROM AUTHOR]

A core-shell surface-imprinted polymer was successfully synthesized on the surface of silica spheres by precipitation polymerization. The polymers were used to selectively identify dichlorophen in complex environmental system. The physical-chemical properties of the silica sphere molecularly imprinted polymers were analyzed by scanning electron microscopy and transmission electron microscopy scans characterization. The adsorption properties of core-shell polymers were examined by adsorption isotherms, kinetics and selectivity experiments. The Langmuir adsorption isotherm well fitted the adsorption experimental data, and an increase of temperature enhanced the adsorption capacity. The maximum binding capacity of imprinted polymers reached 72.46 mg g-1 at 318 K. Meanwhile, the adsorption data of binding experiments were well-fitting by the pseudo-second-order equation. Compared with nonimprinted polymer, the imprinted polymer not only has specific selectivity towards dichlorophen but also has higher adsorption performance. Moreover, via the regeneration experiments, it has been demonstrated that the MIPs have certain stability to the adsorption for the target, which provides a good reference for the adsorption of dichlorophen by the imprinted polymer in water. [ABSTRACT FROM AUTHOR]

In this work, Fe-series (Fe, Co, Ni) doped titanate nanotubes were synthesized via a simple, one pot hydrothermal process using commercial TiO powder as the precursor. The samples were characterized by X-ray diffraction, nitrogen adsorption, scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy, and UV-Vis. SEM and TEM results show that regularly uniform nanotubes were obtained and they possess homogeneous tubular structures with open ends. There is no significantly morphological and structural difference between TiNTs and Fe-series doped TiNTs. Furthermore, the photocatalytic properties of TiNTs and Fe-series doped TiNTs were studied using the degradation of ciprofloxacin. Fe-series doped TiNTs exhibit higher catalytic activities than those of TiNTs and commercial TiO. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is the second most common neurodegenerative disorder. The presence of Lewy bodies is a major pathological change of PD. α-synuclein is the main component of Lewy bodies and is encoded by the SNCA gene. Mutations in the SNCA gene mainly result in rare familial forms of PD, while genetic variability in the SNCA gene modulates susceptibility to sporadic PD. Recent studies have suggested that levels of α-synuclein in extracellular biological fluid are associated with PD and implicated α-synuclein as a potential biomarker for PD diagnosis and severity. We studied serum α-synuclein concentration and two polymorphic variants of SNCA (Rep1 and rs11931074) in 110 sporadic PD patients and 136 controls. We further explored the influence of the two polymorphisms on the expression levels of serum α-synuclein. Soluble α-synuclein was detected in serum in all subjects, with no statistically significant difference between PD patients and controls ( p = 0.611). Different Rep1 alleles and genotypes did not influence the expression of serum α-synuclein. The frequency of allele T of rs11931074 was significantly elevated in PD patients ( p = 0.041), and was correlated with decreased serum α-synuclein in both dominant ( p = 0.011) and additive ( p = 0.008) models of association. [ABSTRACT FROM AUTHOR]

Synthesis and design of output variable structure controller for time-invariant linear time-delay singular system are studied. In the case that the system is regular and the system index is one, switching function with integral compensator and variable structure controller are designed, which guarantee that the sliding mode is asymptotically stable and the solution trajectory of the system arrives at the switching manifold in limited time. The design method is applicable to the systems which can be regularized. Finally, a numerical example is given to demonstrate effectiveness and simplicity of the design method. [ABSTRACT FROM AUTHOR]

The existence of linear quadratic optimal control of ship automatic steering instruments is studied. Firstly, the sufficient conditions for the quadratic integrability of the solutions of linear second order time-variant differential equations are developed. Secondly, the optimal control form of the ship automatic steering instrument is obtained by using the dynamic programming method, which guarantees a minimal ship sway range, during long-distance navigation, by using as little energy as possible. Finally, based on the above mentioned sufficient conditions, the conditions for the realization of optimal control are obtained, which provides a foundation for choosing the weighted coefficients for optimal control in engineering. [ABSTRACT FROM AUTHOR]