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Purinergic signaling plays a causal role in the modulation of immune inflammatory response in the course of psoriasis, but its regulatory mechanism remains unclear. As a member of purinoceptors, P2Y6R mainly distributed in macrophages was significantly up-expressed in skin lesions from patients with psoriasis in the present study. Here, the severity of psoriasis was alleviated in imiquimod-treated mice with macrophages conditional knockout of P2Y6R, while the cell-chat algorithm showed there was a correlation between macrophage P2Y6R and Th1 cells mediated by IL-27. Mechanistically, P2Y6R enhanced PLCβ/p-PKC/MAPK activation to induce IL-27 release dependently, which subsequently regulated the differentiation of Th1 cells, leading to erythematous and scaly plaques of psoriasis. Interestingly, we developed a novel P2Y6R inhibitor FS-6, which bonds with the ARG266 side chain of P2Y6R, exhibited remarkable anti-psoriasis effects targeting P2Y6R. Our study provides insights into the role of P2Y6R in the pathogenesis of psoriasis and suggests its potential as a target for the development of therapeutic interventions. A novel P2Y6R inhibitor FS-6 could be developed as an anti-psoriasis drug candidate for the clinic.

This article presents a comprehensive study on the emerging near-space communications (NS-COM) within the context of space–air–ground–sea integrated network (SAGSIN). Specifically, we firstly explore the recent technical developments of NS-COM, followed by the discussions about motivations behind integrating NS-COM into SAGSIN. To further demonstrate the necessity of NS-COM, a comparative analysis between the NS-COM network and other counterparts in SAGSIN is conducted, covering aspects of deployment, coverage, channel characteristics, and unique problems of NS-COM network. Afterward, the technical aspects of NS-COM, including channel modeling, random access, channel estimation, array-based beam management, and joint network optimization, are examined in detail. Furthermore, we explore the potential applications of NS-COM, such as structural expansion in SAGSIN communication, civil aviation communication, remote and urgent communication, weather monitoring, and carbon neutrality. Finally, some promising research avenues are identified, including stratospheric satellite-to-ground direct links for mobile terminals, reconfigurable multiple-input multiple-output and holographic multiple-input multiple-output, federated learning in NS-COM networks, maritime communication, electromagnetic spectrum sensing and adversarial game, integrated sensing and communications, stratospheric-satellite-based radar detection and imaging, NS-COM-assisted enhanced global navigation system, NS-COM-assisted intelligent unmanned system, and free-space optical communication. Overall, this paper highlights that the NS-COM plays an indispensable role in the SAGSIN puzzle, providing substantial performance and coverage enhancement to the traditional SAGSIN architecture.

What will the wireless networks of the next generation look like? The latest advances in industrial and academic research have shed some light into that direction. With the finalization of the framework recommendation for the 6th generation (6G) networks by the International Telecommunication Union (ITU), the pillar usage scenarios, supporting capabilities and technical enablers are becoming clearer. The overarching motivation for the development of 6G is to continue to build an inclusive information society in a sustainable way. In this context, a range of user and application trends are foreseen to become an integral part of 6G, including ubiquitous intelligence, immersive multimedia and multi-sensory interactions, digital twins, digital health, smart industries, ubiquitous connectivity, integration of sensing and communication, as well as sustainability. It is worth noting that many of the trending demands do not come from the traditional markets for private mobile users and instead, they are driven by strong needs from vertical industries including manufacturing, transportation, and health care.

The flight formation of unmanned aerial vehicles (UAVs) needs to be reconfigured whenever necessary to cope with complex environments and varying tasks. However, the continuity, nonlinearity and high dimensionality of the UAV formation control parameters bring significant challenges to the efficiency and safety of UAV formation reconfiguration. To this end, this paper proposes a reconfiguration strategy of the UAV formation based on a modified Artificial Bee Colony (ABC) algorithm, which ensures superior efficiency and safety level simultaneously. Specifically, we first formulate the formation reconfiguration problem minimizing the time consumed for reconfiguration under the constraints of safety and connection. Then the continuous optimization problem is discretized by using the control parameterization and time discretization (CPTD) method. Finally, we use a modified ABC algorithm to find the solution of formation reconfiguration. Extensive performance evaluations are conducted to verify the superiority of the proposed method. It is concluded that the proposed algorithm achieves a better performance than the existing approaches in literature in solving the problem of 3-D formation reconfiguration.

Utilizing the densely deployed light-emitting diodes (LEDs) as transmitters, multiple-input-single-output (MISO) becomes an attractive technique to improve the transmission throughput of light-fidelity (LiFi) systems. To fully exploit the benefits of joint transmission of multiple LEDs, beamforming techniques are usually employed to improve the downlink throughput, which requires the knowledge of channel state information (CSI) at transmitter. Since frequent CSI feedback leads to heavy overhead, the optimization of feedback interval, defined as the time period between two adjacent feedbacks, is investigated in this paper, which aims at maximizing the weighted bidirectional transmission throughput. Additionally, a quasi-optimal solution is calculated as a well approximation of the exact optimal solution in order to reduce the computational complexity. Simulation results show that, the proposed feedback interval optimization is capable of improving the weighted bidirectional throughput considerably thanks to the reduced CSI feedback overhead, compared with its conventional counterparts using classical feedback mechanisms. Besides, by employing the obtained quasi-optimal feedback interval, the weighted bidirectional throughput approaches that of the optimal interval with much lower computational complexity.

Angiotensin I-Converting Enzyme (ACE, CD143) Gene plays a crucial role in the pathology of carcinomas in many cancers including colorectal cancer (CRC). However, the methylation of ACE was rarely reported. In this study, our purpose was to investigate the methylation status of ACE and explored its prognostic value in CRC. The expression of ACE was detected by quantitative real-time polymerase chain reaction (qRT-PCR) analysis while the methylation status of ACE was measured via methylation-specific polymerase chain reaction (MSP). The result demonstrated that ACE expression was up-regulated in tumour tissues and HT-29 cells compared with the controls. ACE was also confirmed to be hypomethylated in CRC. Next, we evaluated the influence of ACE hypomethylation on cell growth. It was proved to be a favourable factor for the cell proliferation, cell colony forming, but an inhibitor for the cell apoptosis of CRC cells according to MST assay, colony forming assay and flow cytometry assay. ACE hypomethylation was also considered to be related to the prognosis of CRC through Cox regression analysis. Taken together, the over-expression of ACE was regulated by its hypomethylation and the ACE hypomethylation might be an independent prognostic indicator in CRC.

Traffic accidents have globally increased over the past decades, and the driving safety has become an important issue for human society. Despite great progress, the existing driving safety prediction algorithms hardly consider the sophisticated feature interactions between the driving information, traffic information, and driver information. Therefore, in order to solve this problem, a factorization machine combined neural network (FMCNN) is proposed in this paper to predict the driving safety in vehicular communication. In the proposed framework, the factorization machine and the deep neural network are used to learn the effects of low-order and high-order feature interactions from the driving information and the weather information in the pre-training phase, respectively. After the pre-training phase, the high-order feature interactions extracted by the last hidden layer of the deep neural network and the low-order feature interactions trained by the factorization machine are the input of a new deep neural network to predict the driving safety. Varying from the most machine learning algorithms, the proposed algorithm does not require manual extraction of features. It can automatically extract features from the driving information, traffic information, and driver information collected by the vehicle ad hoc network. The experiment results show that the prediction result of the proposed FMCNN is better than DNN and FM in AUC and Logloss.

With the rapid expansion of road traffic and vehicle scale, citizens have to face the serious life safety risks caused by vehicle accidents while enjoying an increasingly convenient life. Fortunately, vehicular ad hoc networks have provided us with important information about vehicle big data, which makes us have new methods to analyze vehicle traffic accidents. In this paper, we tackle the problem of predicting the risk of vehicle accidents and propose the trichotomy Adaboost with SMOTE and One-Hot encoding (AdaBoost-SO) algorithm to attain a vehicle accident risk prediction model. In this paper, predicting accident risk is mainly based on the use of big data mining and analysis of real-life accidents data. First, the experimental dataset is reconstructed by a synthetic minority oversampling technique. We complement the missing data and encode each sample feature to one-hot code. Second, the trichotomy AdaBoost algorithm is, respectively, used to train a series of weak classifiers from the experimental dataset and then combine them into a strong classifier to get the prediction model. Finally, the extensive simulation results illustrate that using the prediction model by trichotomy AdaBoost-SO algorithm can take the area under the curve of 0.77 and real-time into account. Through risk prediction and early warning, it provides the intelligent transportation system and driving safety assistance with a theoretical foundation.

Stress is recognized as risk factor for the development of type 2 diabetes. Here Balsevich et al. show that the stress responsive co-chaperone FKBP5 regulates glucose metabolism in mice by modulating AS160 phosphorylation, glucose transporter expression and muscle glucose uptake.

Index modulation has become a promising technique in the context of orthogonal frequency division multiplexing (OFDM), whereby the specific activation of the frequency domain subcarriers is used for implicitly conveying extra information, hence improving the achievable throughput at a given bit error ratio (BER) performance. In this paper, a dual-mode OFDM technique (DM-OFDM) is proposed, which is combined with index modulation and enhances the attainable throughput of conventional index-modulation-based OFDM. In particular, the subcarriers are divided into several subblocks, and in each subblock, all the subcarriers are partitioned into two groups, modulated by a pair of distinguishable modem-mode constellations, respectively. Hence, the information bits are conveyed not only by the classic constellation symbols, but also implicitly by the specific activated subcarrier indices, representing the subcarriers' constellation mode. At the receiver, a maximum likelihood (ML) detector and a reduced-complexity near optimal log-likelihood ratio-based detector are invoked for demodulation. The minimum distance between the different legitimate realizations of the OFDM subblocks is calculated for characterizing the performance of DM-OFDM. Then, the associated theoretical analysis based on the pairwise error probability is carried out for estimating the BER of DM-OFDM. Furthermore, the simulation results confirm that at a given throughput, DM-OFDM achieves a considerably better BER performance than other OFDM systems using index modulation, while imposing the same or lower computational complexity. The results also demonstrate that the performance of the proposed low-complexity detector is indistinguishable from that of the ML detector, provided that the system's signal to noise ratio is sufficiently high.

Orthogonal frequency-division multiplexing (OFDM) with index modulation has been emerged as a promising technique for next-generation networks, where the specific activation pattern of the OFDM subcarriers is capable of conveying additional information implicitly without additional energy consumption, hence improving the system energy efficiency. In this paper, zero-padded tri-mode OFDM with index modulation (ZTM-OFDM-IM) is proposed, which is capable of achieving high spectral and energy efficiency. In ZTM-OFDM-IM, subcarriers are partitioned into subblocks. In each OFDM subblock, only a fraction of subcarriers are modulated by two distinguishable constellation alphabets, while the others remain empty, which reduces the energy consumption. With such an arrangement, extra information bits can be carried by the subcarrier activation pattern. At the receiver, a maximum-likelihood (ML) detector as well as a reduced-complexity two-stage log-likelihood ratio (LLR) detector are developed for signal demodulation. Theoretical performance analysis based on Euclidean distance metric and pairwise error probability are conducted. Besides, in order to attain better BER performance, the design strategies for the two distinguishable constellation alphabets are investigated, which is followed by brief discussions on generalization methods for the proposed ZTM-OFDM-IM. It is demonstrated via Monte Carlo simulations that ZTM-OFDM-IM is capable of enhancing the BER performance compared with the conventional OFDM and other conventional index-modulated OFDM benchmarks, which provides high-rate data transmission with low energy consumption. Moreover, the LLR detection of ZTM-OFDM-IM only suffers from slight performance loss in comparison with the ML detection and achieves significant complexity reduction.

The primary objective of this study is to compare pedestrian evacuation strategies in the large-scale public space (LPS) using microscopic model. Data were collected by video recording from Tian-yi square for 36 hours in city of Ningbo, China. A pedestrian evacuation simulation model was developed based on the social force model (SFM). The simulation model parameters, such as reaction time, elasticity coefficient, sliding coefficient, et al, were calibrated using the real data extracted from the video. Five evacuation strategies, strategy 1 (S1) to strategy 5 (S5) involving distance, density and capacity factors were simulated and compared by indicators of evacuation time and channel utilization rate, as well as the evacuation efficiency. The simulation model parameters calibration results showed that a) the pedestrians walking speed is 1.0 ~ 1.5m/s; b) the pedestrians walking diameter is 0.3 ~ 0.4m; c) the frequency of pedestrian arrival and departure followed multi-normal distribution. The simulation results showed that, (a) in terms of total evacuation time, the performance of S4 and S5 which considering the capacity and density factors were best in all evacuation scenarios, the performance of S3 which only considering the density factor was the worst, relatively, and S1 and S2 which considering the distance factor were in the middle. (b) the utilization rate of channels under S5 strategy was better than other strategies, which performs best in the balance of evacuation. S3 strategy was the worst, and S1, S2 and S4 were in the middle. (c) in terms of the evacuation efficiency, when the number of evacuees is within 2, 500 peds, the S1 and S2 strategy which considering the distance factor have best evacuation efficiency than other strategies. And when the number of evacuees is above 2, 500 peds, the S4 and S5 strategy which considering the capacity factor are better than others.