Anticonvulsant activities of α-asaronol ((E)-3'-hydroxyasarone), an active constituent derived from α-asarone.

BACKGROUND: Epilepsy is one of chronic neurological disorders that affects 0.5-1.0% of the world's population during their lifetime. There is a still significant need to develop novel anticonvulsant drugs that possess superior efficacy, broad spectrum of activities and good safety profile. METHODS: α-Asaronol and two current antiseizure drugs (α-asarone and carbamazepine (CBZ)) were assessed by in vivo anticonvulsant screening with the three most employed standard animal seizure models, including maximal electroshock seizure (MES), subcutaneous injection-pentylenetetrazole (PTZ)-induced seizures and 3-mercaptopropionic acid (3-MP)-induced seizures in mice. Considering drug safety evaluation, acute neurotoxicity was assessed with minimal motor impairment screening determined in the rotarod test, and acute toxicity was also detected in mice. RESULTS: In our results, α-asaronol displayed a broad spectrum of anticonvulsant activity (ACA) and showed better protective indexes (PI = 11.11 in MES, PI = 8.68 in PTZ) and lower acute toxicity (LD50 = 2940 mg/kg) than its metabolic parent compound (α-asarone). Additionally, α-asaronol displayed a prominent anticonvulsant profile with ED50 values of 62.02 mg/kg in the MES and 79.45 mg/kg in the sc-PTZ screen as compared with stiripentol of ED50 of 240 mg/kg and 115 mg/kg in the relevant test, respectively. CONCLUSION: The results of the present study revealed α-asaronol can be developed as a novel molecular in the search for safer and efficient anticonvulsants having neuroprotective effects as well as low toxicity. Meanwhile, the results also suggested that α-asaronol has great potential to develop into another new aromatic allylic alcohols type anticonvulsant drug for add-on therapy of Dravet's syndrome

Slicing Resource Allocation for eMBB and URLLC in 5G RAN

This paper investigates the network slicing in the virtualized wireless network. We consider a downlink orthogonal frequency division multiple access system in which physical resources of base stations are virtualized and divided into enhanced mobile broadband (eMBB) and ultrareliable low latency communication (URLLC) slices. We take the network slicing technology to solve the problems of network spectral efficiency and URLLC reliability. A mixed-integer programming problem is formulated by maximizing the spectral efficiency of the system in the constraint of users’ requirements for two slices, i.e., the requirement of the eMBB slice and the requirement of the URLLC slice with a high probability for each user. By transforming and relaxing integer variables, the original problem is approximated to a convex optimization problem. Then, we combine the objective function and the constraint conditions through dual variables to form an augmented Lagrangian function, and the optimal solution of this function is the upper bound of the original problem. In addition, we propose a resource allocation algorithm that allocates the network slicing by applying the Powell–Hestenes–Rockafellar method and the branch and bound method, obtaining the optimal solution. The simulation results show that the proposed resource allocation algorithm can significantly improve the spectral efficiency of the system and URLLC reliability, compared with the adaptive particle swarm optimization (APSO), the equal power allocation (EPA), and the equal subcarrier allocation (ESA) algorithm. Furthermore, we analyze the spectral efficiency of the proposed algorithm with the users’ requirements change of two slices and get better spectral efficiency performance

Estimating Artificial Impervious Surface Percentage in Asia by Fusing Multi-Temporal MODIS and VIIRS Nighttime Light Data

Impervious surfaces have important effects on the natural environment, including promoting hydrological run-off and impeding evapotranspiration, as well as increasing the urban heat island effect. Obtaining accurate and timely information on the spatial distribution and dynamics of urban surfaces is, thus, of paramount importance for socio-economic analysis, urban planning, and environmental modeling and management. Previous studies have indicated that the fusion of multi-source remotely sensed imagery can increase the accuracy of prediction for impervious surface information across large areas. However, the majority of them are limited to the use of specific data sources to construct a few features with which it can be challenging to characterize adequately the variation in impervious surfaces over large areas. Thus, impervious surface maps are often presented with high uncertainty. In response to this problem, we proposed the use of multi-temporal MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data to construct a more general and robust feature set for large-area artificial impervious surface percentage (AISP) prediction. Three fusion methods were proposed for application to multi-temporal MODIS surface reflectance product (MOD09A1) and Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) data to construct three different types of features: spectral features, index features (band calculations), and fusion features. These features were then used as variables in a random-forest-based AISP prediction model. The model was fitted to China and then applied to predict AISP across Asia. Fifteen typical cities from different regions of Asia were selected to assess the accuracy of the prediction model. The use of multi-temporal MODIS and VIIRS DNB data was found to significantly increase the accuracy of prediction for large-area AISP. The feature set constructed in this research was demonstrated to be suitable for large-area AISP prediction, and the random forest model based on optimization of the selected features achieved the highest accuracy, amongst benchmarks, with testing R2 of 0.690, and testing RMSE of 0.044 in 2018, respectively. In addition, to further test the performance of the proposed method, three existing impervious products (GAIA, HBASE, and NUACI) were used to compare quantitatively. The results showed that the predicted AISP achieved superior performance in comparison with others in some areas (e.g., arid areas and cloudy areas)

www.mdpi.com/journal/ijms Detection of Promyelocytic Leukemia/Retinoic Acid Receptor α (PML/RARα) Fusion Gene with Functionalized Graphene Oxide

Abstract: An attempt was made to use functionalized graphene oxide (GO) to detect the Promyelocytic leukemia/Retinoic acid receptor α fusion gene (PML/RARα fusion gene), a marker gene of acute promyelocytic leukemia. The functionalized GO was prepared by chemical exfoliation method, followed by a polyethylene glycol grafting. It is found that the functionalized GO can selectively adsorb the fluorescein isothiocyanate (FITC)-labeled single-stranded DNA probe and quench its fluorescence. The probe can be displaced by the PML/RARα fusion gene to restore the fluorescence, which can be detected by laser confocal microscopy and flow cytometry. These can be used to detect the presence of the PML/RARα fusion gene. This detection method is verified to be fast, simple and reliable

Impact behavior of nylon kernmantle ropes for high-altitude fall protection

Aiming at the problem that the existing rope falling device can only detect the impact force and cannot synchronously detect the impact displacement, this paper introduces a large-range high-precision displacement sensor and constructs a rope impact force-displacement detection device. Taking the nylon kernmantle rope for high-altitude fall protection commonly used in aerial work and rock climbing as the research object, the impact response behavior of the rope when drop mass is dropped once and repeatedly is systematically studied, and the impact force and impact displacement are discussed. Further, the evolution of the elastic modulus of the rope is discussed and this could provide theoretical support for the design of the impact-resistant rope structure and the rope impact protection

Frequencies, Laboratory Features, and Granulocyte Activation in Chinese Patients with CALR-Mutated Myeloproliferative Neoplasms.

Somatic mutations in the CALR gene have been recently identified as acquired alterations in myeloproliferative neoplasms (MPNs). In this study, we evaluated mutation frequencies, laboratory features, and granulocyte activation in Chinese patients with MPNs. A combination of qualitative allele-specific polymerase chain reaction and Sanger sequencing was used to detect three driver mutations (i.e., CALR, JAK2V617F, and MPL). CALR mutations were identified in 8.4% of cases with essential thrombocythemia (ET) and 5.3% of cases with primary myelofibrosis (PMF). Moreover, 25% of polycythemia vera, 29.5% of ET, and 48.1% of PMF were negative for all three mutations (JAK2V617F, MPL, and CALR). Compared with those patients with JAK2V617F mutation, CALR-mutated ET patients displayed unique hematological phenotypes, including higher platelet counts, and lower leukocyte counts and hemoglobin levels. Significant differences were not found between Chinese PMF patients with mutants CALR and JAK2V617F in terms of laboratory features. Interestingly, patients with CALR mutations showed markedly decreased levels of leukocyte alkaline phosphatase (LAP) expression, whereas those with JAK2V617F mutation presented with elevated levels. Overall, a lower mutant rate of CALR gene and a higher triple-negative rate were identified in the cohort of Chinese patients with MPNs. This result indicates that an undiscovered mutant gene may have a significant role in these patients. Moreover, these pathological features further imply that the disease biology varies considerably between mutants CALR and JAK2V617F