Carvacrol, a Food-Additive, Provides Neuroprotection on Focal Cerebral Ischemia/Reperfusion Injury in Mice

Carvacrol (CAR), a naturally occurring monoterpenic phenol and food additive, has been shown to have antimicrobials, antitumor, and antidepressant-like activities. A previous study demonstrated that CAR has the ability to protect liver against ischemia/reperfusion injury in rats. In this study, we investigated the protective effects of CAR on cerebral ischemia/reperfusion injury in a middle cerebral artery occlusion mouse model. We found that CAR (50 mg/kg) significantly reduced infarct volume and improved neurological deficits after 75 min of ischemia and 24 h of reperfusion. This neuroprotection was in a dose-dependent manner. Post-treatment with CAR still provided protection on infarct volume when it was administered intraperitoneally at 2 h after reperfusion; however, intracerebroventricular post-treatment reduced infarct volume even when the mice were treated with CAR at 6 h after reperfusion. These findings indicated that CAR has an extended therapeutic window, but delivery strategies may affect the protective effects of CAR. Further, we found that CAR significantly decreased the level of cleaved caspase-3, a marker of apoptosis, suggesting the anti-apoptotic activity of CAR. Finally, our data indicated that CAR treatment increased the level of phosphorylated Akt and the neuroprotection of CAR was reversed by a PI3K inhibitor LY-294002, demonstrating the involvement of the PI3K/Akt pathway in the anti-apoptotic mechanisms of CAR. Due to its safety and wide use in the food industry, CAR is a promising agent to be translated into clinical trials

Activation of Epidermal Growth Factor Receptor Is Required for NTHi-Induced NF-κB-Dependent Inflammation

Inflammation is a hallmark of many serious human diseases. Nontypeable Haemophilus influenzae (NTHi) is an important human pathogen causing respiratory tract infections in both adults and children. NTHi infections are characterized by inflammation, which is mainly mediated by nuclear transcription factor-kappa B (NF-κB)-dependent production of proinflammatory mediators. Epidermal growth factor receptor (EGFR) has been shown to play important roles in regulating diverse biological processes, including cell growth, differentiation, apoptosis, adhesion, and migration. Its role in regulating NF-κB activation and inflammation, however, remains largely unknown.In the present study, we demonstrate that EGFR plays a vital role in NTHi-induced NF-κB activation and the subsequent induction of proinflammatory mediators in human middle ear epithelial cells and other cell types. Importantly, we found that AG1478, a specific tyrosine kinase inhibitor of EGFR potently inhibited NTHi-induced inflammatory responses in the middle ears and lungs of mice in vivo. Moreover, we found that MKK3/6-p38 and PI3K/Akt signaling pathways are required for mediating EGFR-dependent NF-κB activation and inflammatory responses by NTHi.Here, we provide direct evidence that EGFR plays a critical role in mediating NTHi-induced NF-κB activation and inflammation in vitro and in vivo. Given that EGFR inhibitors have been approved in clinical use for the treatment of cancers, current studies will not only provide novel insights into the molecular mechanisms underlying the regulation of inflammation, but may also lead to the development of novel therapeutic strategies for the treatment of respiratory inflammatory diseases and other inflammatory diseases

The dominant Anopheles vectors of human malaria in the Asia-Pacific region: occurrence data, distribution maps and bionomic précis

<p>Abstract</p> <p>Background</p> <p>The final article in a series of three publications examining the global distribution of 41 dominant vector species (DVS) of malaria is presented here. The first publication examined the DVS from the Americas, with the second covering those species present in Africa, Europe and the Middle East. Here we discuss the 19 DVS of the Asian-Pacific region. This region experiences a high diversity of vector species, many occurring sympatrically, which, combined with the occurrence of a high number of species complexes and suspected species complexes, and behavioural plasticity of many of these major vectors, adds a level of entomological complexity not comparable elsewhere globally. To try and untangle the intricacy of the vectors of this region and to increase the effectiveness of vector control interventions, an understanding of the contemporary distribution of each species, combined with a synthesis of the current knowledge of their behaviour and ecology is needed.</p> <p>Results</p> <p>Expert opinion (EO) range maps, created with the most up-to-date expert knowledge of each DVS distribution, were combined with a contemporary database of occurrence data and a suite of open access, environmental and climatic variables. Using the Boosted Regression Tree (BRT) modelling method, distribution maps of each DVS were produced. The occurrence data were abstracted from the formal, published literature, plus other relevant sources, resulting in the collation of DVS occurrence at 10116 locations across 31 countries, of which 8853 were successfully geo-referenced and 7430 were resolved to spatial areas that could be included in the BRT model. A detailed summary of the information on the bionomics of each species and species complex is also presented.</p> <p>Conclusions</p> <p>This article concludes a project aimed to establish the contemporary global distribution of the DVS of malaria. The three articles produced are intended as a detailed reference for scientists continuing research into the aspects of taxonomy, biology and ecology relevant to species-specific vector control. This research is particularly relevant to help unravel the complicated taxonomic status, ecology and epidemiology of the vectors of the Asia-Pacific region. All the occurrence data, predictive maps and EO-shape files generated during the production of these publications will be made available in the public domain. We hope that this will encourage data sharing to improve future iterations of the distribution maps.</p

TCIANet: Transformer-Based Context Information Aggregation Network for Remote Sensing Image Change Detection

Change detection based on remote sensing data is an important method to detect the earth surface changes. With the development of deep learning, convolutional neural networks have excelled in the field of change detection. However, the existing neural network models are susceptible to external factors in the change detection process, leading to pseudo change and missed detection in the detection results. In order to better achieve the change detection effect and improve the ability to discriminate pseudo change, this article proposes a new method, namely, transformer-based context information aggregation network for remote sensing image change detection. First, we use a filter-based visual tokenizer to segment each temporal feature map into multiple visual semantic tokens. Second, the addition of the progressive sampling vision transformer not only effectively excludes the interference of irrelevant changes, but also uses the transformer encoder to obtain compact spatiotemporal context information in the token set. Then, the tokens containing rich semantic information are fed into the pixel space, and the transformer decoder is used to acquire pixel-level features. In addition, we use the feature fusion module to fuse low-level semantic feature information to complete the extraction of coarse contour information of the changed region. Then, the semantic relationships between object regions and contours are captured by the contour-graph reasoning module to obtain feature maps with complete edge information. Finally, the prediction model is used to discriminate the change of feature information and generate the final change map. Numerous experimental results show that our method has more obvious advantages in visual effect and quantitative evaluation than other methods

A Visual Tracking Method Based on an Adaptive Overlapping Correlation Filter for Robotic Real-Time Cognitive Imaging

Computer vision is a very important research direction in the cognitive computing field. Robots encounter various target-tracking problems with computer vision systems. Robust scale estimation is an important issue in tracking algorithms. Most of the available methods have difficulty addressing even reasonable changes of scale in complex videos. In this paper, we propose a visual tracking method based on robust scale estimation, which uses a discriminant correlation filter based on a time-dependent scale-space filter and an adaptive cross-correlation filter. The tracker uses separate essential filters for sample migration and scale estimation. Furthermore, the built-in scale estimation method can be introduced into other tracking algorithms. We validate the proposed method on the UAV123 dataset. The results of comparison experiments with the traditional correlation filter tracking method demonstrate that the proposed method improves the success rate and tracking accuracy while controlling the computational complexity; its success rate measured by the area under the curve is 0.638, while at a location error precision of 20%, it is 0.649

RREV: A Robust and Reliable End-to-End Visual Navigation

With the development of deep learning, more and more attention has been paid to end-to-end autonomous driving. However, affected by the nature of deep learning, end-to-end autonomous driving is currently facing some problems. First, due to the imbalance between the “junctions” and “non-junctions” samples of the road scene, the model is overfitted to a large class of samples during training, resulting in insufficient learning of the ability to turn at intersections; second, it is difficult to evaluate the confidence of the deep learning model, so it is impossible to determine whether the model output is reliable, and then make further decisions, which is an important reason why the end-to-end autonomous driving solution is not recognized; and third, the deep learning model is highly sensitive to disturbances, and the predicted results of the previous and subsequent frames are prone to jumping. To this end, a more robust and reliable end-to-end visual navigation scheme (RREV navigation) is proposed in this paper, which was used to predict a vehicle’s future waypoints from front-view RGB images. First, the scheme adopted a dual-model learning strategy, using two models to independently learn “junctions” and “non-junctions” to eliminate the influence of sample imbalance. Secondly, according to the smoothness and continuity of waypoints, a model confidence quantification method of “Independent Prediction-Fitting Error” (IPFE) was proposed. Finally, IPFE was applied to weight the multi-frame output to eliminate the influence of the prediction jump of the deep learning model and ensure the coherence and smoothness of the output. The experimental results show that the RREV navigation scheme in this paper was more reliable and robust, especially, the steering performance of the model intersection could be greatly improved

Characterization of Nano-Scale Parallel Lamellar Defects in RDX and HMX Single Crystals by Two-Dimension Small Angle X-ray Scattering

Nano-scale crystal defects extremely affect the security and reliability of explosive charges of weapons. In this work, the nano-scale crystal defects of 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX) and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) single crystals were characterized by two-dimension SAXS. Deducing from the changes of SAXS pattern with sample stage rotating, we firstly found the parallel lamellar nano-scale defects in both RDX and HMX single crystals. Further analysis shows that the average diameter and thickness of nano-scale lamellar defects for RDX single crystal are 66.4 nm and 19.3 nm, respectively. The results of X-ray diffraction (XRD) indicate that the lamellar nano-scale defects distribute along the (001) in RDX and the (011) in HMX, which are verified to be the crystal planes with the lowest binding energy by the theoretical calculation