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Parkinson’s disease (PD) is characterized pathologically by alpha-synuclein (α-Syn) aggregates and clinically by the motor as well as cognitive deficits, including impairments in sequence learning and habit learning. Using intracerebral injection of WT and A53T mutant α-Syn fibrils, we investigate the behavioral mechanism of α-Syn for procedure-learning deficit in PD by critically determining the α-Syn-induced effects on model-based goal-directed behavior, model-free (probability-based) habit learning, and hierarchically organized sequence learning. 1) Contrary to the widely held view of habit-learning deficit in early PD, α-Syn aggregates in the dorsomedial striatum (DMS) and dorsolateral striatum (DLS) did not affect acquisition of habit learning, but selectively impaired goal-directed behavior with reduced value sensitivity. 2) α-Syn in the DLS (but not DMS) and SNc selectively impaired the sequence learning by affecting sequence initiation with the reduced first-step accuracy. 3) Adenosine A2A receptor (A2AR) antagonist KW6002 selectively improved sequence learning by preferentially improving sequence initiation and shift of sequence learning as well as behavioral reactivity. These findings established a casual role of α-Syn in the SN-DLS pathway in sequence-learning deficit and DMS α-Syn in goal-directed behavior deficit and suggest a novel therapeutic strategy to improve sequence-learning deficit in PD with enhanced sequence initiation by A2AR antagonists.

Chromatin interactions functionally affect genome architecture and gene regulation, but to date, only fresh samples must be used in High-through chromosome conformation capture (Hi-C) to keep natural chromatin conformation intact. This requirement has impeded the advancement of 3D genome research by limiting sample collection and storage options for researchers and severely limiting the number of samples that can be processed in a short time. Here, we develop a freeze substitution Hi-C (FS-Hi-C) technique that overcomes the need for fresh samples. FS-Hi-C can be used with samples stored in liquid nitrogen (LN2): the water in a vitreous form in the sample cells is replaced with ethanol via automated freeze substitution. After confirming that the FS step preserves the natural chromosome conformation during sample thawing, we tested the performance of FS-Hi-C with Drosophila melanogaster and Gossypium hirsutum. Beyond allowing the use of frozen samples and confirming that FS-Hi-C delivers robust data for generating contact heat maps and delineating A/B compartments and topologically associating domains, we found that FS-Hi-C outperforms the in situ Hi-C in terms of library quality, reproducibility, and valid interactions. Thus, FS-Hi-C will probably extend the application of 3D genome structure analysis to the vast number of experimental contexts in biological and medical research for which Hi-C methods have been unfeasible to date.

The choroid plexus (CP) is one of the key gateways regulating the entry of peripheral immune cells into the CNS. However, the neuromodulatory mechanisms of maintaining its gateway activity are not fully understood. Here, we identified adenosine A(2A) receptor (A(2A)R) activity as a regulatory signal for the activity of CP gateway under physiological conditions. In association with a tightly closed CP gateway, we found that A(2A)R was present at low density in the CP. The RNA-seq analysis revealed that the A(2A)R antagonist KW6002 affected the expression of the cell adhesion molecules’ (CAMs) pathway and cell response to IFN-γ in the CP. Furthermore, blocking or activating A(2A)R signaling in the CP resulted in a decreased and an increased, respectively, expression of lymphocyte trafficking determinants and disruption of the tight junctions (TJs). Furthermore, A(2A)R signaling regulates the CP permeability. Thus, A(2A)R activity in the CP may serve as a therapeutic target for remodeling the immune homeostasis in the CNS with implications for the treatment of neuroimmunological disorders. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11302-022-09847-5.

Retinopathy of prematurity (ROP) remains one of the major causes of blindness in children worldwide. While current ROP treatments are mostly disruptive to reduce proliferative neovascularization by targeting the hypoxic phase, protection against early hyperoxia-induced retinal vascular loss represents an effective therapeutic window, but no such therapeutic strategy is available. Built upon our recent demonstration that the protection against oxygen-induced retinopathy by adenosine A2A receptor (A2A R) antagonists is most effective when administered at the hyperoxia (not hypoxic) phase, we here uncovered the cellular mechanism underlying the A2A R-mediated protection against early hyperoxia-induced retinal vascular loss by reversing the inhibition of cellular proliferation via possibly multiple signaling pathways. Specifically, we revealed two distinct stages of the hyperoxia phase with greater cellular proliferation and apoptosis activities and upregulation of adenosine signaling at postnatal 9 day (P9) but reduced cellular activities and adenosine-A2A R signaling at P12. Importantly, the A2A R-mediated protection at P9 was associated with the reversal of hyperoxia-induced inhibition of progenitor cells at the peripheral retina at P9 and of retinal endothelial proliferation at P9 and P12. The critical role of cellular proliferation in the hyperoxia-induced retinal vascular loss was validated by the increased avascular areas by siRNA knockdown of the multiple signaling molecules involved in modulation of cellular proliferation, including activin receptor-like kinase 1, DNA-binding protein inhibitor 1, and vascular endothelial growth factor-A.

Background: Multiple sclerosis (MS) is one of the most common autoimmune disorders characterized by the infiltration of immune cells into the brain and demyelination. The unwanted immunosuppressive side effect of therapeutically successful natalizumab led us to focus on choroid plexus (CP), a key site for the first wave of immune cell infiltration in experimental autoimmune encephalomyelitis (EAE), for the control of immune cells trafficking. Adenosine A2A receptor (A2AR) is emerging as a potential pharmacological target to control EAE pathogenesis; however, the cellular basis for the A2AR-mediated protection remains undetermined. Methods: In EAE model, we assessed A2AR expression and leukocyte trafficking determinants in CP by immunohistochemistry and qPCR analyses. We determined the effect of the A2AR antagonist KW6002 treatment at days 8-12 or 8-14 post-immunization on T cell infiltration across CP and EAE pathology. We determined the critical role of the CP-A2AR on T cell infiltration and EAE pathology by focal knockdown the CP-A2AR with intracerebroventricular injection of CRE-TAT recombinase into the A2ARflox/flox mice. In cultured CP epithelium, we also evaluated the effect of overexpression of A2ARs or the A2AR agonist CGS21680 treatment on CP permeability and lymphocytes migration. Results: We found the specific upregulation of A2AR in CP in association with enhanced CP gateway activity peaked at day 12 post-immunization in EAE mice. Furthermore, the KW6002 treatment at days 8-12 or 8-14 post-immunization reduced T-cell trafficking across CP and attenuated EAE pathology. Importantly, focal CP-A2AR knockdown attenuated pathogenic infiltration of Th17+ cells across CP via inhibiting CCR6-CCL20 axis through NFκB / STAT3 pathway and protected against EAE pathology. Lastly, activation of A2AR in cultured epithelium by A2AR overexpression or CGS21680 treatment increased the permeability of CP epithelium and facilitated lymphocytes migration. Conclusion: These findings define the CP niche as one of the primary sites of A2AR action whereby A2AR antagonists confer protection against EAE pathology. Thus, pharmacological targeting of the CP-A2AR represents a novel therapeutic strategy for MS by controlling immune cell trafficking across CP.

Multiple sclerosis (MS) is one of the most common autoimmune disorders characterized by the infiltration of immune cells into the brain and demyelination. The unwanted immunosuppressive side effect of therapeutically successful natalizumab led us to focus on the choroid plexus (CP), a key site for the first wave of immune cell infiltration in experimental autoimmune encephalomyelitis (EAE), for the control of immune cells trafficking. Adenosine AIn the EAE model, we assessed AWe found the specific upregulation of AThese findings define the CP niche as one of the primary sites of A

Retinopathy of prematurity (ROP) remains one of the major causes of blindness in children worldwide. While current ROP treatments are mostly disruptive to reduce proliferative neovascularization by targeting the hypoxic phase, protection against early hyperoxia-induced retinal vascular loss represents an effective therapeutic window, but no such therapeutic strategy is available. Built upon our recent demonstration that the protection against oxygen-induced retinopathy by adenosine A

Recently, the ever-increasing vehicle population has become a severe challenge to traffic efficiency and air quality in modern city life. Signal-controlled intersection is a bottleneck of the urban traffic system. Platooning has the potential to improve the intersection throughput, and a typical V2X application, green light optimal speed advisory (GLOSA), can also reduce vehicles’ stopping at intersections to enhance traffic efficiency. However, there is scarcely any literature regarding GLOSA system for platoon to increase intersection throughput. In this paper, the composite platoon trajectory planning strategy (CPTPS) is proposed to maximize the intersection throughput. Since the intersection throughput maximization problem can be transformed into minimizing vehicles’ intersection arrival time in a green phase by optimizing vehicles’ trajectories, three portions are designed in CPTPS to solve the problem, which are a GLOSA-based trajectory planning method for platoon leaders, a reinforcement learning-based trajectory planning method for platoon followers, and a flexible platoon management protocol to ensure the smooth operations in a platoon. Simulation results show that, compared to the non-optimized cases, CPTPS can effectively increase intersection throughput while reduce stoppings at intersections and gas emissions. Especially in saturation cases, CPTPS can achieve a near-ideal throughput performance. And the communication delay within 100 ms scarcely affects CPTPS's headway maintenance performance. Moreover, CPTPS outperforms the existing works in headway maintenance when vehicles drive in a platoon. These results validate the effectiveness, robustness, and practical feasibility of CPTPS, for solving the signal-controlled intersection traffic efficiency problem.

Vehicle-to-infrastructure (V2I) communication enables vehicles to acquire surrounding traffic information in real time, which significantly improves the driving safety and comfort. The roadside unit (RSU) which transfers information among vehicles plays an indispensable role in V2I communication. For a long time, researchers have been focusing on choosing proper RSU deployment locations to deploy fixed RSUs. Meanwhile, unmanned aerial vehicle (UAV) technology has developed rapidly. In terms of its unique mobility and flexibility, the UAV-assisted RSU (u-RSU) is able to cover a larger area. If the flight trajectory is designed properly, fewer u-RSUs can solve the RSU deployment problem and ensure the network performance. Therefore, the large-scale u-RSU collaborative trajectories design problem is investigated in this paper. We propose a u-RSU flight trajectory strategy and solve the problem in three steps. An improved greedy algorithm and an ant colony optimization (ACO) algorithm are used in the solution. Under different conditions, the proposed u-RSU flight strategy is analyzed and compared with other deployment strategies. Simulation results show that our strategy always has better network performance and deploys fewer u-RSUs. Thus, the proposed strategy is more economical and effective compared with the traditional deployment strategy.

Recently, many researches based on millimeter wave (mmWave) together with analog beamforming technology have been done to provide higher transmission throughput in vehicle-to-everything (V2X) communication. While hybrid beamforming technology, which can generate multi radio frequency (RF) chains with reduced hardware complexity, continues attracting attention. Hence, this paper focuses on a hybrid-beamforming-based roadside unit (RSU) beam resource scheduling problem in vehicle-to-infrastructure (V2I) communication and intends to improve transmission fairness. Then, this paper proposes an indicator Q to evaluate transmission fairness. Furthermore, a Power-allocated-based Beam resource Scheduling strategy (PBS) and a Time-allocated-based Beam resource Scheduling strategy (TBS) are designed for the multi-RF-chain based V2I communication to enhance transmission fairness. Simulation results prove that, the two proposed beam resource scheduling strategies can effectively improve the transmission fairness in V2I communication.

Hyperspectral image classification is essential for satellite Internet of Things (IoT) to build a large scale land-cover surveillance system. After acquiring real-time land-cover information, the edge of the network transmits all the hyperspectral images by satellites with low-latency and high-efficiency to the cloud computing center, which are provided by satellite IoT. A gigantic amount of remote sensing data bring challenges to the storage and processing capacity of traditional satellite systems. When hyperspectral images are used in annotation of land-cover application, data dimension reduction for classifier efficiency often leads to the decrease of classifier accuracy, especially the region to be annotated consists of natural landform and artificial structure. This paper proposes encoding spectral-spatial features for hyperspectral image classification in the satellite Internet of Things system to extract features effectively, namely attribute profile stacked autoencoder (AP-SAE). Firstly, extended morphology attribute profiles EMAP is used to obtain spatial features of different attribute scales. Secondly, AP-SAE is used to extract spectral features with similar spatial attributes. In this stage the program can learn feature mappings, on which the pixels from the same land-cover class are mapped as closely as possible and the pixels from different land-cover categories are separated by a large margin. Finally, the program trains an effective classifier by using the network of the AP-SAE. Experimental results on three widely-used hyperspectral image (HSI) datasets and comprehensive comparisons with existing methods demonstrate that our proposed method can be used effectively in hyperspectral image classification.

In order to achieve high-quality entertainment services and large-capacity sensor sharing, it is imperative to improve the throughput of V2I communication systems. Millimeter wave communication is a promising technology, which is generally combined with beamforming techniques. When it comes to beam design, we need to focus on the tradeoff between system throughput and alignment overhead. However, conventional optimization schemes are limited by uniform beamwidth design. Considering the characteristics of V2I communication on the highway, this paper proposes a non-uniform beamwidth design idea. Firstly, an average throughput model based on beam training is established. Then, a recursive algorithm is used to achieve non-uniform beamwidth optimization. Finally, the simulation results prove that the non-uniform beamwidth scheme can significantly improve the throughput of the V2I system.

In vehicular network, high reliability and low latency of communication ensure the driving safety. In order to avoid traffic accidents, it is important to warn surrounding vehicles before a potential traffic accident occurs as soon as possible. In the case of short spectrum resource, a reasonable resource allocation for alarm information is critical in V2V (Vehicle to Vehicle) communication. In this paper, a modified scheme is introduced by taking account of vehicle's priority level and spectrum availability to allocate the frequency resources to upcoming cars. The connection among priority level vehicles will be ensured and channel congestion caused by the surrounding vehicles will be drastically reduced while spectrum resources are insufficient. Simulation results show that the combinative use of sharing vehicle level and available spectrum resource improves the efficiency of spectrum resources, shortens the access-time of alarm information on the premise of successful communication, and improves the safety of vehicle driving.

In the group sequential (GS) approach with a fixed sample size design, the Type I error is controlled by the additivity of exit spending values. However, in a flexible sample size design where the sample size will be recalculated using the interim data, the overall Type I error rate can be inflated. Therefore, the predefined GS stopping boundaries have to be adjusted to maintain the Type I error level at each interim analysis and the at the overall level. The modified α spending function adjusted for sample size reestimation (SSR) is proposed to maintain the Type I error level. We use a unified approach and mathematically quantify the Type I error with and without sample size adjustment constraints. As a result, stopping boundaries can be obtained by inversely solving the exact Type I error functions. This unified approach, using Brownian motion theory, can be applied to normal, survival, and binary endpoints. Extensive simulations show the adjusted stopping boundaries can control the Type I error at each...

In this article, we study nonparametric conditional quantile estimation via neural network structure. We proposed an estimation method that combines quantile regression and neural network (robust neural network, RNN). It provides good smoothing performance in the presence of outliers and can be used to construct prediction bands. A Majorization-Minimization (MM) algorithm was developed for optimization. Monte Carlo simulation study is conducted to assess the performance of RNN. Comparison with other nonparametric regression methods (e.g., local linear regression and regression splines) in real data application demonstrate the advantage of the newly proposed procedure.

Flexible sample size designs based on interim efficacy results can ensure adequate power by adjusting the sample size, which potentially saves time and resources. However, the Type I error can often be inflated due to such adjustments. We use a unified approach to quantify the Type I error rate and to adjust the stopping boundary accordingly to maintain the overall Type I error. This unified approach can be applied to normal, survival, and binary endpoints. Several aspects of sample size adjustments are considered based on information time. The Type I error inflation can be well controlled by giving up some unrealistic power. Simulations show the proposed method works well for survival and binary endpoints.