Salmon Calcitonin Exerts an Antidepressant Effect by Activating Amylin Receptors

Depressive disorder is defined as a psychiatric disease characterized by the core symptoms of anhedonia and learned helplessness. Currently, the treatment of depression still calls for medications with high effectiveness, rapid action, and few side effects, although many drugs, including fluoxetine and ketamine, have been approved for clinical usage by the Food and Drug Administration (FDA). In this study, we focused on calcitonin as an amylin receptor polypeptide, of which the antidepressant effect has not been reported, even if calcitonin gene-related peptides have been previously demonstrated to improve depressive-like behaviors in rodents. Here, the antidepressant potential of salmon calcitonin (sCT) was first evaluated in a chronic restraint stress (CRS) mouse model of depression. We observed that the immobility duration in CRS mice was significantly increased during the tail suspension test and forced swimming test. Furthermore, a single administration of sCT was found to successfully rescue depressive-like behaviors in CRS mice. Lastly, AC187 as a potent amylin receptor antagonist was applied to investigate the roles of amylin receptors in depression. We found that AC187 significantly eliminated the antidepressant effects of sCT. Taken together, our data revealed that sCT could ameliorate a depressive-like phenotype probably via the amylin signaling pathway. sCT should be considered as a potential therapeutic candidate for depressive disorder in the future

Structural and Lipidomic Alterations of Striatal Myelin in 16p11.2 Deletion Mouse Model of Autism Spectrum Disorder

Myelin abnormalities have been observed in autism spectrum disorder (ASD). In this study, we seek to discover myelin-related changes in the striatum, a key brain region responsible for core ASD features, using the 16p11.2 deletion (16p11.2±) mouse model of ASD. We found downregulated expression of multiple myelin genes and decreased myelin thickness in the striatum of 16p11.2± mice versus wild type controls. Moreover, given that myelin is the main reservoir of brain lipids and that increasing evidence has linked dysregulation of lipid metabolism to ASD, we performed lipidomic analysis and discovered decreased levels of certain species of sphingomyelin, hexosyl ceramide and their common precursor, ceramide, in 16p11.2± striatum, all of which are major myelin components. We further identified lack of ceramide synthase 2 as the possible reason behind the decrease in these lipid species. Taken together, our data suggest a role for myelin and myelin lipids in ASD development

Chronic salmon calcitonin exerts an antidepressant effect via modulating the p38 MAPK signaling pathway

Depression is a common recurrent psychiatric disorder with a high lifetime prevalence and suicide rate. At present, although several traditional clinical drugs such as fluoxetine and ketamine, are widely used, medications with a high efficiency and reduced side effects are of urgent need. Our group has recently reported that a single administration of salmon calcitonin (sCT) could ameliorate a depressive-like phenotype via the amylin signaling pathway in a mouse model established by chronic restraint stress (CRS). However, the molecular mechanism underlying the antidepressant effect needs to be addressed. In this study, we investigated the antidepressant potential of sCT applied chronically and its underlying mechanism. In addition, using transcriptomics, we found the MAPK signaling pathway was upregulated in the hippocampus of CRS-treated mice. Further phosphorylation levels of ERK/p38/JNK kinases were also enhanced, and sCT treatment was able only to downregulate the phosphorylation level of p38/JNK, with phosphorylated ERK level unaffected. Finally, we found that the antidepressant effect of sCT was blocked by p38 agonists rather than JNK agonists. These results provide a mechanistic explanation of the antidepressant effect of sCT, suggesting its potential for treating the depressive disorder in the clinic

Multiple Factors Involved in the Pathogenesis of White Matter Lesions

White matter lesions (WMLs), also known as leukoaraiosis (LA) or white matter hyperintensities (WMHs), are characterized mainly by hyperintensities on T2-weighted or fluid-attenuated inversion recovery (FLAIR) images. With the aging of the population and the development of imaging technology, the morbidity and diagnostic rates of WMLs are increasing annually. WMLs are not a benign process. They clinically manifest as cognitive decline and the subsequent development of dementia. Although WMLs are important, their pathogenesis is still unclear. This review elaborates on the advances in the understanding of the pathogenesis of WMLs, focusing on anatomy, cerebral blood flow autoregulation, venous collagenosis, blood brain barrier disruption, and genetic factors. In particular, the attribution of WMLs to chronic ischemia secondary to venous collagenosis and cerebral blood flow autoregulation disruption seems reasonable. With the development of gene technology, the effect of genetic factors on the pathogenesis of WMLs is gaining gradual attention

Robust optimisation of the streamlined shape of a high-speed train in crosswind conditions

Traditional deterministic aerodynamic optimisation cannot consider environmental uncertainty, which may lead to sensitivity issues. The present study proposes a robust design framework for the aerodynamic optimisation of high-speed trains, which accounts for the uncertain wind and its impact on crosswind stability. In this framework, a variance analysis method based on the Non-Intrusive Polynomial Chaos is proposed to determine the deformation area, and a parametric model is subsequently established. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used as the optimiser to minimise the mean and variance of the aerodynamic response. The mean and variance can be quickly predicted by an uncertainty analysis approach combining Monte Carlo simulation and Kriging model. The framework is then applied to the optimisation of a high-speed train under crosswind. The results of the robust optimisation are compared with those of the baseline geometry and deterministic optimisation. The mean and variance of the rolling moment under crosswind are reduced by 2.26% and 3.37% respectively after optimisation, indicating that the performance and robustness are both improved. The proposed framework is effective for the engineering design of high-speed trains and can also provide a reference for the robust design of other aerodynamic shapes

Aerodynamic performance and flow evolution of a high-speed train exiting a tunnel with crosswinds

Sudden changes in the aerodynamic loads acting on trains can result in derailment or overturning. The impacts of infrastructure scenarios on the aerodynamic performance of trains are significant. When high-speed trains travel from one infrastructure scenario to another one, the aerodynamic loads and flow field will change suddenly. It is a commonly in western China for HSTs to exit a tunnel with crosswinds. In order to investigate the aerodynamic loads and the flow evolution, a three-dimensional, compressible, unsteady Reynolds Averaged Navier-Stokes method was utilized to simulate the process of a train exiting a tunnel under crosswinds. Results show that the flow field and the pressure varied significantly in the horizonal plane while the train exited the tunnel under crosswinds. In addition, the aerodynamic loads of each carriage which varied abruptly resulted in complex dynamic responses of the train including lateral variation, snake-like locomotion, and pitching motion. Furthermore, the variation magnitudes of Delta C-side, Delta C-lift, and Delta C-RM for the head carriage were 4.1, 2.2 and 1.6 times for the middle carriage, and 7.9, 8.1 and 8.2 times for the rear carriage. Therefore, the aerodynamic performance of the head carriage was the worst and the risk of accidents was the highest under crosswinds

Analysis of aerodynamic effects and load spectrum characteristics in high-speed railway tunnels

To study the aerodynamic effects and load spectra caused by high-speed trains passing through double-track tunnels, this paper uses unsteady, viscous, compressible Navier-Stokes equations and the Re-Normalization Group k-e turbulence model with sliding grid technology for simulation. A dynamic model test is carried out to verify the calculation method and grid. This study considers the impact of the three main factors of the tunnel aerodynamic effect when the train passes through the tunnel. The peak of the aerodynamic load spectrum when the train is coupled to the tunnel is mainly concentrated in the range of 0-5 Hz. The results show that as the train speed increases, the peak pressure and pressure gradient increase significantly, and the maximum pressure gradient appears in the time between the peak and trough of the head wave. When two trains meet in the middle of the tunnel, the peak pressure and its position change significantly, and the maximum pressure gradient peaks reach 80.58 kPa/s. When two trains exit the tunnel, the pressure presents a fixed period of fluctuations, and the maximum pressure peak is only 0.09 kPa less than the peak when a single train passes through the tunnel at the same monitoring point

Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images

Deep learning models have brought great breakthroughs in building extraction from high-resolution optical remote-sensing images. Among recent research, the self-attention module has called up a storm in many fields, including building extraction. However, most current deep learning models loading with the self-attention module still lose sight of the reconstruction bias’s effectiveness. Through tipping the balance between the abilities of encoding and decoding, i.e., making the decoding network be much more complex than the encoding network, the semantic segmentation ability will be reinforced. To remedy the research weakness in combing self-attention and reconstruction-bias modules for building extraction, this paper presents a U-Net architecture that combines self-attention and reconstruction-bias modules. In the encoding part, a self-attention module is added to learn the attention weights of the inputs. Through the self-attention module, the network will pay more attention to positions where there may be salient regions. In the decoding part, multiple large convolutional up-sampling operations are used for increasing the reconstruction ability. We test our model on two open available datasets: the WHU and Massachusetts Building datasets. We achieve IoU scores of 89.39% and 73.49% for the WHU and Massachusetts Building datasets, respectively. Compared with several recently famous semantic segmentation methods and representative building extraction methods, our method’s results are satisfactory

Aerodynamic-Aeroacoustic Optimization of a Baseline Wing and Flap Configuration

Optimization design was widely used in the high-lift device design process, and the aeroacoustic reduction characteristic is an important objective of the optimization. The aerodynamic and aeroacoustic study on the baseline wing and flap configuration was performed numerically. In the current study, the three-dimensional Large Eddy Simulation (LES) equations coupled with dynamic Smagorinsky subgrid model and Ffowcs-William and Hawkings (FW-H) equation are employed to simulate the flow fields and carry out acoustic analogy. The numerical results show reasonable agreement with the experimental data. Further, the particle swarm optimization algorithm coupled with the Kriging surrogate model was employed to determine optimum location of the flap deposition. The Latin hypercube method is used for the generation of initial samples for optimization. In addition, the relationship between the design variables and the objective functions are obtained using the optimization sample points. The optimized maximum overall sound pressure level (OASPL) of far-field noise decreases by 3.99 dB with a loss of lift-drag ratio (L/D) of less than 1%. Meanwhile, the optimized performances are in good and reasonable agreement with the numerical predictions. The findings provide suggestions for the low-noise and high-lift configuration design and application in high-lift devices

Semisupervised Classification for Hyperspectral Imagery With Transductive Multiple-Kernel Learning

Natural Science Foundation of China [61371144]; European Space Agency-Ministry of Science and Technology (ESA-MOST) Dragon 3 Cooperation Project [10689]The classification of hyperspectral imagery is a challenging problem because few labeled pixels are available. In this letter, we propose a new semisupervised learning algorithm to combine both cluster and manifold assumptions to increase classification reliability and accuracy. The new method uses a concave-convex procedure and sequential minimization optimization technologies for transductive multiple-kernel learning (TMKL). Then, a one-against-all strategy is adopted to generalize the binary TMKL classifiers to solve the multiclass problem of remote sensing images. Experimental results on two real data sets indicate that the proposed method exhibits both high accuracy and good computational performance