Temporal characteristics of astrocytic activation in the TNC in a mice model of pain induced by recurrent dural infusion of inflammatory soup

Background Astrocytic activation might play a significant role in the central sensitization of chronic migraine (CM). However, the temporal characteristics of the astrocytic activation in the trigeminal nucleus caudalis (TNC) and the molecular mechanism under the process remain not fully understood. Therefore, this study aims to investigate the duration and levels change of astrocytic activation and to explore the correlation between astrocytic activation and the levels change of cytokines release. Methods We used a mice model induced by recurrent dural infusion of inflammatory soup (IS). The variation with time of IS-induced mechanical thresholds in the periorbital and hind paw plantar regions were evaluated using the von Frey filaments test. We detected the expression profile of glial fibrillary acidic protein (GFAP) in the TNC through immunofluorescence staining and western blot assay. We also investigated the variation with time of the transcriptional levels of GFAP and ionized calcium binding adapter molecule 1 (Iba1) through RNAscope in situ hybridization analysis. Then, we detected the variation with time of cytokines levels in the TNC tissue extraction and serum, including c-c motif chemokine ligand 2 (CCL2), c-c motif chemokine ligand 5 (CCL5), c-c motif chemokine ligand 7 (CCL7), c-c motif chemokine ligand 12 (CCL12), c-x-c motif chemokine ligand 1 (CXCL1), c-x-c motif chemokine ligand 13 (CXCL13), interferon gamma (IFN-γ), tumor necrosis factor alpha (TNF-α), macrophage colony-stimulating factor (M-CSF), interleukin 1beta (IL-1β), interleukin 6 (IL-6), interleukin 10 (IL-10), interleukin 17A (IL-17A). Results Recurrent IS infusion resulted in cutaneous allodynia in both the periorbital region and hind paw plantar, ranging from 5 d (after the second IS infusion) to 47 d (28 d after the last infusion) and 5 d to 26 d (7 d after the last infusion), respectively. The protein levels of GFAP and messenger ribonucleic acid (mRNA) levels of GFAP and Iba1 significantly increased and sustained from 20 d to 47 d (1 d to 28 d after the last infusion), which was associated with the temporal characteristics of astrocytic activation in the TNC. The CCL7 levels in the TNC decreased from 20 d to 47 d. But the CCL7 levels in serum only decreased on 20 d (1 d after the last infusion). The CCL12 levels in the TNC decreased on 22 d (3 d after the last infusion) and 33 d (14 d after the last infusion). In serum, the CCL12 levels only decreased on 22 d. The IL-10 levels in the TNC increased on 20 d. Conclusions Our results indicate that the astrocytic activation generated and sustained in the IS-induced mice model from 1 d to 28 d after the last infusion and may contribute to the pathology through modulating CCL7, CCL12, and IL-10 release.

Spatio-Temporal Characteristics and Variation Pattern of the Atmospheric Particulate Matter Concentration: A Case Study of the Beijing–Tianjin–Hebei Region, China

Based on measurement data from air quality monitoring stations, the spatio-temporal characteristics of the concentrations of particles with aerodynamic equivalent diameters smaller than 2.5 and 10 μm (PM2.5 and PM10, respectively) in the Beijing–Tianjin–Hebei (BTH) region from 2015 to 2018 were analysed at yearly, seasonal, monthly, daily and hourly scales. The results indicated that (1) from 2015 to 2018, the annual average values of PM2.5 and PM10 concentrations and the PM2.5/PM10 ratio in the study area decreased each year; (2) the particulate matter (PM) concentration in winter was significantly higher than that in summer, and the PM2.5/PM10 ratio was highest in winter and lowest in spring; (3) the PM2.5 and PM10 concentrations exhibited a pattern of double peaks and valleys throughout the day, reaching peak values at night and in the morning and valleys in the morning and afternoon; and (4) with the use of an improved sine function to simulate the change trend of the monthly mean PM concentration, the fitting R2 values for PM2.5 and PM10 in the whole study area were 0.74 and 0.58, respectively. Moreover, the high-value duration was shorter, the low-value duration was longer, and the concentration decrease rate was slower than the increase rate.

Микроволновая диагностика разрядов в искусственном облаке заряженных водяных капель

The microwave diagnostics of discharges occurring in an artificial cloud of charged water droplets created in an open air simulating the environment of thunderclouds is implemented. An artificial cloud with a droplet size of about 1 microns is opaque in the visible range, so intra-cloud discharges are not available for investigation by traditional methods in the spark discharge physics based on the registration of visible discharge radiation. Microwaves pass through such a cloud without noticeable attenuation, they interact only with the plasma of discharges occurring in the cloud. The probing microwave radiation had a wavelength of 8 mm. The attenuation of microwaves passed through the cloud was measured with temporary resolution of about 10 ns. The temporal characteristics of intra-cloud discharges were investigated.

APPLICATION OF THE ATTITUDE IDENTIFICATION MODEL TO THE OBJECT BASED ON THE SOCIAL MEDIA PUBLICATIONS ANALYSIS FOR SOLVING APPLIED PROBLEMS IN ASSESSING THE EFFECTIVENESS OF THE ACTIVITIES OF SENIOR RUSSIAN FEDERATION SUBJECTS OFFICIALS

В статье изложена оценка эффективности модели идентификации отношения к целевому объекту на основе публикаций в социальных сетях, использующей искусственные показатели публикаций социальных сетей, полученные на основе структурно-временных характеристик последних. The article evaluates the effectiveness of the attitude identification model to the target object based on social networks publications, using social network publications artificial indicators obtained on their structural and temporal characteristics basis.

Analysis on Spatio-Temporal Characteristics and Influencing Factors of Industrial Green Innovation Efficiency—From the Perspective of Innovation Value Chain

Green innovation has become an important combination of high-quality economic growth and ecological sustainability. In this paper, the super-efficiency network SBM model was used to measure the two-stage green innovation efficiency of the industrial technology research and development (R&D) stage and achievement transformation stage in China (30 provinces and cities) from 2009 to 2019. The results show the following points. Firstly, in terms of temporal series, the efficiency of technology R&D and achievement transformation has experienced three stages of “upward-declining-revitalized period”. Secondly, in terms of spatial trend, the industrial green innovation efficiency gradually increases from northwest to southeast. The high-efficiency areas are still concentrated in the eastern coastal region, with a clear trend towards balanced development in the central and western regions. Finally, openness, industrial structure, government technical expenditures, enterprise scale, and environmental regulation all have different degrees of impact on the efficiency of green innovation in the two stages. Based on the above, this paper is helpful for the government to formulate laws and regulations and coordinate the level of regional economic development and clarify the spatio-temporal characteristics and influencing factors of the efficiency of green innovation.

A Simulation Model of the Confrontation between an Organized Attacker and an Information Security System in the Implementation of an Attack on a Network Management System of Clock Network Synchronization

This article provides an overview of the interaction between the warring parties and the main stages of the confrontation between the organized attacker and the information security system in the implementation of an attack on the network management system of clock network synchronization. A simulation model has been developed that reflects all stages of the struggle, which allows, depending on the resources of an organized attacker and the information security system, to obtain probabilistic and temporal characteristics of the results of the confrontation. Simulation has been carried out for various scenarios of organizing an attack at all stages of the confrontation, from the overwhelming advantage of an organized malefactor to the overwhelming advantage of an information security system. The results obtained in the general case can be used by security administrators and network administrators to make adjustments to the strategy of organizing the protection of the network management system of clock network synchronization.

Spatial-Temporal 3D Residual Correlation Network for Urban Traffic Status Prediction

Accurate traffic status prediction is of great importance to improve the security and reliability of the intelligent transportation system. However, urban traffic status prediction is a very challenging task due to the tight symmetry among the Human–Vehicle–Environment (HVE). The recently proposed spatial–temporal 3D convolutional neural network (ST-3DNet) effectively extracts both spatial and temporal characteristics in HVE, but ignores the essential long-term temporal characteristics and the symmetry of historical data. Therefore, a novel spatial–temporal 3D residual correlation network (ST-3DRCN) is proposed for urban traffic status prediction in this paper. The ST-3DRCN firstly introduces the Pearson correlation coefficient method to extract a high correlation between traffic data. Then, a dynamic spatial feature extraction component is constructed by using 3D convolution combined with residual units to capture dynamic spatial features. After that, based on the idea of long short-term memory (LSTM), a novel architectural unit is proposed to extract dynamic temporal features. Finally, the spatial and temporal features are fused to obtain the final prediction results. Experiments have been performed using two datasets from Chengdu, China (TaxiCD) and California, USA (PEMS-BAY). Taking the root mean square error (RMSE) as the evaluation index, the prediction accuracy of ST-3DRCN on TaxiCD dataset is 21.4%, 21.3%, 11.7%, 10.8%, 4.7%, 3.6% and 2.3% higher than LSTM, convolutional neural network (CNN), 3D-CNN, spatial–temporal residual network (ST-ResNet), spatial–temporal graph convolutional network (ST-GCN), dynamic global-local spatial–temporal network (DGLSTNet), and ST-3DNet, respectively.