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High-accuracy GNSS positioning in urban environments is important for applications like safe autonomous driving, however, dynamic errors in complex urban environments limit positioning performances. Recently, deep learning-based (DL) approaches can obtain better GNSS positioning solutions in complex urban environments than model-based ones. However, DL-based approaches simply concentrate one-view GNSS observations as inputs, which are insufficient to model vehicle states accurately, and temporally continuous observations are highly correlated, leading to inaccurate positioning correction results. To solve the challenge, we propose a Sparse Representation-based Multiview Deep Reinforcement Learning model for positioning correction, which employs attention-based multiview fusion to process multiview observations, and uses sparse representation to alleviate disturbances from highly correlated observations. To represent the vehicle state sufficiently, we build a multiview positioning correction environment, and develop an attention-weighted multiview fusion module to fuse temporal features as belief states based on adaptively learned attention weights. To effectively process redundant and correlated multiview features, we impose the ℓ1 norm regularizer to learn sparse hidden representations and improve the precision of value estimation. Finally, we construct a sparse representation-driven multiview actor-critic positioning correction model to achieve high-accuracy GNSS positioning in complex urban environments. We validate performances in both Google Smartphone Decimeter Challenge (GSDC) datasets and our collected GNSS datasets in the Guangzhou area (GZGNSS). Experimental results show that our algorithm can improve localization performances with 27% improvements from WLS+KF in GSDC trajectories, 16% from RTK, and 6% from DL-based methods in GZGNSS trajectories. [ABSTRACT FROM AUTHOR]

This paper presents a learning-based control policy design for point-to-point vehicle positioning in the urban environment via BeiDou navigation. While navigating in urban canyons, the multipath effect is a kind of interference that causes the navigation signal to drift and thus imposes severe impacts on vehicle localization due to the reflection and diffraction of the BeiDou signal. Here, the authors formulated the navigation control system with unknown vehicle dynamics into an optimal control-seeking problem through a linear discrete-time system, and the point-to-point localization control is modeled and handled by leveraging off-policy reinforcement learning for feedback control. The proposed learning-based design guarantees optimality with prescribed performance and also stabilizes the closed-loop navigation system, without the full knowledge of the vehicle dynamics. It is seen that the proposed method can withstand the impact of the multipath effect while satisfying the prescribed convergence rate. A case study demonstrates that the proposed algorithms effectively drive the vehicle to a desired setpoint under the multipath effect introduced by actual experiments of BeiDou navigation in the urban environment. [ABSTRACT FROM AUTHOR]

GDP-amylose transporter protein 1 (SLC35C1) plays an important role in many types of cancer. Therefore, it is clinically important to further investigate the expression profile of SLC35C1 in human tumors to provide new molecular clues for the pathogenesis of glioma. In this study, we performed a comprehensive pan-cancer analysis of SLC35C1 using a series of bioinformatics approaches and validated its differential tissue expression and biological function. The results showed that SLC35C1 was aberrantly expressed in different types of tumors and significantly correlated with overall survival (OS) and progression-free interval (PFI). More importantly, the expression level of SLC35C1 was closely correlated with Tumor Microenvironment (TME), immune infiltration and immune-related genes. In addition, we found that SLC35C1 expression was also closely related to Tumor Mutation Burden (TMB), Microsatellite Instability (MSI) and antitumor drug sensitivity in various cancer types. Functional bioinformatics analysis indicated that SLC35C1 may be involved in multiple signaling pathways and biological processes in glioma. Based on SLC35C1 expression, a risk factor model was found to predict OS of glioma. In addition, in vitro experiments showed that SLC35C1 knockdown significantly inhibited the proliferation, migration and invasive ability of glioma cells, while SLC35C1 overexpression promoted proliferation, migration, invasion and colony formation of glioma cells. Finally, quantitative real-time PCR confirmed that SLC35C1 was highly expressed in gliomas. [ABSTRACT FROM AUTHOR]

With the increasing demand for secure biometric identification systems, finger vein recognition has received widespread attention. Recent studies have made progress in the verification of finger veins, but the extraction of the discriminative features of finger veins from images remains challenging. Although the traditional method of extracting features by combining softmax function and cross-entropy loss function can achieve separation between classes, it lacks discriminability. In addition, the method of setting a fixed margin yields good results, but it may cause some features to overlap. We argue that when some features are separated well from other features, the margin set needs to be reduced. Therefore, a dynamic margin softmax loss (dynamic softmax) is proposed in this study to obtain discriminative image features. Features and weight vectors are normalized, and the loss function dynamics are subsequently adjusted to achieve different cosine intervals for different classes. The main idea of this method is to maximize the distance between inter-class and minimize the distance between intra-class. This method can keep features separated without increasing the complexity of optimizing the neural network model. It is simpler and more effective than other loss functions. Experiments prove the effectiveness of the proposed method for finger vein recognition. [ABSTRACT FROM AUTHOR]

There are many stars that are rotating spheroids in the Universe, and studying them is of very important significance. Since the times of Newton, many astronomers and physicists have researched gravitational properties of stars by considering the moment equations derived from Eulerian hydrodynamic equations. In this paper we study the scattering of spinors of the Dirac equation, and in particular investigate the scattering issue in the limit case of rotating Maclaurin spheroids. Firstly we give the metric of a rotating ellipsoid star, then write the Dirac equation under this metric, and finally derive the scattering solution to the Dirac equation and establish a relation between differential scattering cross-section, σ , and stellar matter density, μ . It is found that the sensitivity of σ to the change in μ is proportional to the density μ . Because of the weak gravitational field and the constant mass density, our results are reasonable. The results can be applied to white dwarfs, main sequence stars, red giants, supergiant stars and so on, as long as their gravitational fields are so weak that they can be treated in the Newtonan approximations, and the fluid is assumed to be incompressible. Notice that we take the star's matter density to be its average density and the star is not taken to be compact. Obviously our results cannot be used to study neutron stars and black holes. In particular, our results are suitable for white dwarfs, which have average densities of about 10 5 – 10 6 g cm - 3 , corresponding to a range of mass of about 0.21 - 0.61 M ⨀ and a range of radius of about 6000 - 10 , 000 km. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by the loss of motor neurons in the brain and spinal cord. ALS neuropathology is associated with increased oxidative stress, excitotoxicity, and inflammation. We and others reported that the anti-aging and cognition-enhancing protein Klotho is a neuroprotective, antioxidative, anti-inflammatory, and promyelinating protein. In mice, its absence leads to an extremely shortened life span and to multiple phenotypes resembling human aging, including motor and hippocampal neurodegeneration and cognitive impairment. In contrast, its overexpression extends life span, enhances cognition, and confers resistance against oxidative stress; it also reduces premature mortality and cognitive and behavioral abnormalities in an animal model for Alzheimer's disease (AD). These pleiotropic beneficial properties of Klotho suggest that Klotho could be a potent therapeutic target for preventing neurodegeneration in ALS. Klotho overexpression in the SOD1 mouse model of ALS resulted in delayed onset and progression of the disease and extended survival that was more prominent in females than in males. Klotho reduced the expression of neuroinflammatory markers and prevented neuronal loss with the more profound effect in the spinal cord than in the motor cortex. The effect of Klotho was accompanied by reduced expression of proinflammatory cytokines and enhanced the expression of antioxidative and promyelinating factors in the motor cortex and spinal cord of Klotho × SOD1 compared to SOD1 mice. Our study provides evidence that increased levels of Klotho alleviate ALS-associated pathology in the SOD1 mouse model and may serve as a basis for developing Klotho-based therapeutic strategies for ALS. [ABSTRACT FROM AUTHOR]

This work aims to propose an event-triggered synchronization adaptive learning scheme for uncertain nonlinear multi-agent systems with communication link faults. In reality, the link faults are ubiquitous and responsible for modeling delays and disturbances in communications and also capturing time-varying communication weights between any two nodes in the network. To address this challenge, based on Lyapunov analysis, adaptive learning neural networks are employed to settle unknown link faults and system uncertainties. To reduce the consumption of communication resources, an event-triggered control strategy is introduced on the input side of the controller, whose triggering events are relative thresholds that can be spontaneously modified with the system states. In addition, a set of smooth functions are inserted in the Lyapunov function that assure the asymptotic stability of the system even with corrupted link faults. Meanwhile, it is rigorously verified that the synchronous tracking errors converge to a user-defined interval. Finally, the effectiveness of the proposed control protocol is demonstrated by a numerical simulation. [ABSTRACT FROM AUTHOR]

Multiple lines of evidence show that the anti-aging and cognition-enhancing protein Klotho fosters neuronal survival, increases the anti-oxidative stress defense, and promotes remyelination of demyelinated axons. Thus, upregulation of the Klotho gene can potentially alleviate the symptoms and/or prevent the progression of age-associated neurodegenerative diseases such as Alzheimer's disease and demyelinating diseases such as multiple sclerosis. Here we used a CRISPR-dCas9 complex to investigate single-guide RNA (sgRNA) targeting the Klotho promoter region for efficient transcriptional activation of the Klotho gene. We tested the sgRNAs within the - 1 to - 300 bp of the Klotho promoter region and identified two sgRNAs that can effectively enhance Klotho gene transcription. We examined the transcriptional activation of the Klotho gene using three different systems: a Firefly luciferase (FLuc) and NanoLuc luciferase (NLuc) coincidence reporter system, a NLuc knock-in in Klotho 3'-UTR using CRISPR genomic editing, and two human cell lines: neuronal SY5Y cells and kidney HK-2 cells that express Klotho endogenously. The two sgRNAs enhanced Klotho expression at both the gene and protein levels. Our results show the feasibility of gene therapy for targeting Klotho using CRISPR technology. Enhancing Klotho levels has a therapeutic potential for increasing cognition and treating age-associated neurodegenerative, demyelinating and other diseases, such as chronic kidney disease and cancer. [ABSTRACT FROM AUTHOR]

Fused deposition modeling (FDM) is a process of fabricating three-dimensional physical models by layered manufacturing. However, the surface quality of acrylonitrile butadiene styrene (ABS) built with FDM technologies is not acceptable and is not satisfactory for most general engineering purposes. In this study, a polishing system was demonstrated to enhance surface finish of ABS parts fabricated by FDM. Polishing mechanism for ABS parts was investigated. The features of this system include high polishing efficiency, no waste chemicals, ease of operation, with excellent dimensional accuracy, and low equipment costs. Improving surface roughness of ABS parts by the use of acetone vapor not only has required dimensional accuracy but also has high process stability. A great reduction in average surface roughness for ABS parts about 98% was obtained. The polishing mechanisms for ABS parts fabricated by additive manufacturing were investigated in this study. [ABSTRACT FROM AUTHOR]

This paper presents a new approximate analytical method for the performance evaluation of inhomogeneous assembly/disassembly (A/D) systems with machines subject to failures and finite buffers. Processing times of machines in these systems are deterministic but not identical. Times between failures and times to repair are both assumed to be exponentially distributed. The method developed in this paper provides a generalized approach for analyzing A/D systems. Unlike the previous work on this subject, generalized exponential distributions are adopted instead of exponential distributions to approximate the repair time distributions of the fictitious machines. As a result the proposed method can handle the situation where the orders of magnitude of machines' reliability parameters (mean times between failures and mean times to repair) are not at the same level. A new algorithm is also developed to improve the convergence reliability and efficiency of the method. Extensive numerical experiments and simulation have been carried out to illustrate that the method proposed for A/D systems is quite accurate and efficient. [ABSTRACT FROM AUTHOR]

The mechanism of reaction HCNO + HO2 is investigated by means of CCSD(T)/6-311 + G(d,p)//B3LYP/6-311 + G(d,p) method, in which Pi with the i = 1, 2, 3, ..., 7 are involved, to determine a more reasonable pathway. Among four possible entrance patterns, the attack of oxygen to carbon is the most energetically feasible leading to complex a1. Starting from the a1, the rupture of O2–O3 bond to form P3 (HC(O)NO + OH) is the most favorable pathway. Alternatively, the a1 undergoes N–O1 bond rotation to form the isomer a2. A concerted O2–O3 bond and C–N bond cleavage leading to P4 (OH + HCO + NO) is the secondary feasible pathway. Other products are not energetically accessible because of higher barrier-consumed and/or complicated processes. We hope that the present work would be helpful to experimentally identify the product distributions and deeply understand the mechanism of the title reaction. [ABSTRACT FROM AUTHOR]

This paper studies the problem of scheduling a multiple-load carrier which is used to deliver parts to line-side buffers of a general assembly (GA) line. In order to maximize the reward of the GA line, both the throughput of the GA line and the material handling distance are considered as scheduling criteria. After formulating the scheduling problem as a reinforcement learning (RL) problem by defining state features, actions and the reward function, we develop a Q( $$\lambda $$ ) RL algorithm based scheduling approach. To improve performance, forecasted information such as quantities of parts required in a look-ahead horizon is used when we define state features and actions in formulation. Other than applying traditional material handling request generating policy, we use a look-ahead based request generating policy with which material handling requests are generated based not only on current buffer information but also on future part requirement information. Moreover, by utilizing a heuristic dispatching algorithm, the approach is able to handle future requests as well as existing ones. To evaluate the performance of the approach, we conduct simulation experiments to compare the proposed approach with other approaches. Numerical results demonstrate that the policies obtained by the RL approach outperform other approaches. [ABSTRACT FROM AUTHOR]

The current study examined whether overexpression of Klotho (KL) in transgenic mice can enhance remyelination following cuprizone-induced demyelination and improves the clinical outcome in experimental autoimmune encephalomyelitis (EAE). Demyelination was achieved by feeding transgenic mice overexpressing the transmembrane form of Klotho (KL-OE) and wild-type (WT) littermates cuprizone-containing chow for 6 weeks. The animals were then allowed to remyelinate for 3 weeks. Paraphenylenediamine staining and platelets-derived growth factor receptor α (PDGFRα) and glutathione S-transferase pi (GSTpi) immunohistochemistry were performed on corpus callosum (CC) sections for quantification of myelin and progenitor and mature oligodendrocytes, respectively. The EAE model was induced with the MOG peptide. The animals were scored daily for clinical symptoms for 30 days. Following 6 weeks of demyelination, both KL-OE mice and WT littermates demonstrated almost complete and comparable demyelination of the CC. However, the level of spontaneous remyelination was increased approximately two-fold in KL-OE mice, although no significant differences in the numbers of PDGFRα and GSTpi-positive cells were observed. Following EAE induction, Klotho overexpression did not affect the clinical scores, likely due to the different roles Klotho plays in the brain and spinal cord. Thus, increasing Klotho expression should be considered as a therapy for enhancing remyelination in the brains of individuals with multiple sclerosis. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive control approach to bridge the gap between unknown time-varying actuator nonlinearities and identical control directions in the area of robotic systems. Technically, a novel Nussbaum gain and its properties are first proposed to pave the way for the formulation of a more advanced tool. Subsequently, a newly developed theorem is summarized and integrated into the stability analysis such that all control units automatically and simultaneously estimate the unknown actuator nonlinearities and directions. Finally, it is rigorously proved that robotic systems asymptotically converge to the desired trajectories. [ABSTRACT FROM AUTHOR]

The control problem of the dual arm robot rigidly grasping an object with unknown nonsymmetric deadzone input is investigated in this paper. Due to the factor that the deadzone nonlinearity is widespread in the actuators, a smooth inverse adaptive deadzone is incorporated to minimize the effect of the deadzone nonlinearity in the dual arm robot system to guarantee a high accuracy tracking. Since these type of robots are usually applied in complex environments, a multi-input multi-output fuzzy logic unit is adopted to approximate the manipulator's dynamics to achieve a accuracy tracking performances. Moreover, a decentralized robust fuzzy adaptive control scheme is constructed to make the motion and internal forces track a reference trajectories in the presence of parameters uncertainty and external disturbance. By using the Lyapunov method, the stability of the signals in the closed-loop system is proved. Simulation result demonstrates that the proposed controller is effective to the dual arm robot system with deadzone nonlinearity. [ABSTRACT FROM AUTHOR]

Klotho functions as an aging suppressor, which, in mice, extends lifespan when overexpressed and accelerates development of aging-like phenotypes when disrupted. Klotho is mainly expressed in brain and kidney and is secreted into the serum and CSF. We have previously shown that Klotho is reduced in brains of old monkeys, rats, and mice. We further reported the ability of Klotho to enhance oligodendrocyte differentiation and myelination. Here, we examined the signaling pathways induced by Klotho in MO3.13, a human oligodendrocytic hybrid cell line. We show that exogenous Klotho affects the ERK and Akt signaling pathways, decreases the proliferative abilities and enhances differentiation of MO3.13 cells. Furthermore, microarray analysis of Klotho-treated MO3.13 cells reveals a massive change in gene expression with 80 % of the differentially expressed genes being downregulated. Using gene set enrichment analysis, we predicted potential transcription factors involved in regulating Klotho-treated MO3.13 cells and found that these cells are highly enriched in the gene sets, that are similarly observed in cancer, cardiovascular disease, stress, aging, and hormone-related chemical and genetic perturbations. Since Klotho is downregulated in all brain tumors tested to date, enhancing Klotho has therapeutic potential for treating brain and other malignancies. [ABSTRACT FROM AUTHOR]

As the hardware of FLIM technique becomes mature, the most important criterion for FLIM application is the correct interpretation of its data. In this research, first of all, a more orthogonal phasor approach, called as Modified Phasor Approach (MPA), is put forward. It is a way to calculate the lifetime of the complex fluorescent process, and a rule to measure how much the fluorescence process deviates from single exponential decay. Secondly, MPA is used to analysis the time-resolved fluorescence processes of the transfected CHO-K1 Cell lines expressing adenosine receptor AR tagged by CYP and YFP, measured in the channel of the acceptor. The image of the fluorescence lifetime and the multiplication of the fluorescence lifetime and deviation from single exponential decay reveal the details of the Homo-FRET. In one word, MPA provides the physical meaning in its whole modified phasor space, and broadens the way for the application of the fluorescence lifetime imaging. [ABSTRACT FROM AUTHOR]

In order to improve the test accuracy of CCD, a new type of CCD device is proposed. Several columns (rows) of photoelectric diodes (PDs) are combined together, and staggered with the distance of H= H/N, where H is the space between two adjacent PDs, and N is the number of columns (rows). The photoelectric signals are collected simultaneously by multi-channel A/D, and the accurate measurement result is obtained through appropriate signal processing. Without changing the size or space of PDs, more photographic pixels are arranged in the given direction within a finite length. Diameters of three standard poles are measured by a single CCD and two staggered CCDs, respectively with length of 30 mm and diameters of 5 mm, 8 mm and 12 mm, respectively. The results show that the accuracy of double staggered CCDs is two times of that of single CCD. The new type of CCDs can avoid the impact of PD space theoretically and higher measurement accuracy can be obtained. [ABSTRACT FROM AUTHOR]

A novel two-step transformation process for the production of androsta-l by microorganisms-diene-3,17-dione (ADD) from a high concetration of cholesterol by microorganisms is proposed. Cholesterol (20 g/l) was initially converted to cholest-4-en-3-one (cholestenone) by an inducible cholesterol oxidase-producing bacterium, Arthrobacter simplex U-S-A-18. The maximum productivity of cholestenone was 8 g/l per day and the molar conversion rate was 80%. Subsequently, a fine suspension of cholestenone (50 g/l), which was prepared directly from the fermentation broth of A. simplex, was converted to ADD by Mycobacterium sp. NRRL B-3683 in the presence of an androstenone adsorbent, Amberlite XAD-7. The maximum productivity of ADD was 0.91 g/l per day and the molar conversion rate was 35%. [ABSTRACT FROM AUTHOR]