Coexistence of Two Forms of LTP in ACC Provides a Synaptic Mechanism for the Interactions between Anxiety and Chronic Pain

SummaryChronic pain can lead to anxiety and anxiety can enhance the sensation of pain.　Unfortunately, little is known about the synaptic mechanisms that mediate these re-enforcing interactions. Here we characterized two forms of long-term potentiation (LTP) in the anterior cingulate cortex (ACC); a presynaptic form (pre-LTP) that requires kainate receptors and a postsynaptic form (post-LTP) that requires N-methyl-D-aspartate receptors. Pre-LTP also involves adenylyl cyclase and protein kinase A and is expressed via a mechanism involving hyperpolarization-activated cyclic nucleotide-gated (HCN) channels. Interestingly, chronic pain and anxiety both result in selective occlusion of pre-LTP. Significantly, microinjection of the HCN blocker ZD7288 into the ACC in vivo produces both anxiolytic and analgesic effects. Our results provide a mechanism by which two forms of LTP in the ACC may converge to mediate the interaction between anxiety and chronic pain

MKANet: An Efficient Network with Sobel Boundary Loss for Land-Cover Classification of Satellite Remote Sensing Imagery

Land cover classification is a multiclass segmentation task to classify each pixel into a certain natural or human-made category of the earth’s surface, such as water, soil, natural vegetation, crops, and human infrastructure. Limited by hardware computational resources and memory capacity, most existing studies preprocessed original remote sensing images by downsampling or cropping them into small patches less than 512 × 512 pixels before sending them to a deep neural network. However, downsampling incurs a spatial detail loss, renders small segments hard to discriminate, and reverses the spatial resolution progress obtained by decades of efforts. Cropping images into small patches causes a loss of long-range context information, and restoring the predicted results to their original size brings extra latency. In response to the above weaknesses, we present an efficient lightweight semantic segmentation network termed MKANet. Aimed at the characteristics of top view high-resolution remote sensing imagery, MKANet utilizes sharing kernels to simultaneously and equally handle ground segments of inconsistent scales, and also employs a parallel and shallow architecture to boost inference speed and friendly support image patches more than 10× larger. To enhance boundary and small segment discrimination, we also propose a method that captures category impurity areas, exploits boundary information, and exerts an extra penalty on boundaries and small segment misjudgments. Both visual interpretations and quantitative metrics of extensive experiments demonstrate that MKANet obtains a state-of-the-art accuracy on two land-cover classification datasets and infers 2× faster than other competitive lightweight networks. All these merits highlight the potential of MKANet in practical applications

Selective controlled precipitation mechanism of canasite and xonotlite in glass-ceramics from silica slag

In this paper, Na2O-CaO-SiO2-X(NCSX) glass-ceramics was prepared to use industrial silicon slag as the main raw material. We employed XRD, FTIR and NMR to analyze the influence of Na2O/SiO2 on the composition of microstructure units Qn in the glass-ceramics. It revealed the precipitation mechanism of xonotlite and canasite. Spectroscopy study indicated that sodium atoms could selectively destroy the glass network to form Q(3) units while silicon atoms could rebuild the network to form Q(4) units. Q(3) units can combine with calcium atoms to form Si-O-Ca bonds that generated the precipitation of xonotlite phase. However, Si-O-Si bond wrapped the Si-O-Ca bond to form an interlayer structure in canasite crystal. And the effective method for controlling the formation of canasite crystal was adopted cautiously. That is, synergistic regulation of Na2O/SiO2 and heat treatment to precipitate the canasite phase exclusively

The Supramolecular Organogel Formed by Self-Assembly of Ursolic Acid Appended with Aromatic Rings

Ursolic acid (UA) as a natural ursane-triterpenoid has rich pharmacological activities. We have found that it possesses aggregation properties and could self-assemble into organogels. Based on the aggregation property of ursolic acid in suitable solvents, its derivative appended with aromatic rings by amide groups was synthesized. The property of self-assembly into organogel was studied in this paper. The results revealed that this derivative could form supramolecular gel in halogenated benzene and also gelate chloroform in the presence of toluene or p-xylene. By Fourier-transform infrared spectra (FT-IR) and variable temperature proton nuclear magnetic resonance (1H NMR), it was proved that intermolecular hydrogen bonding and π⁻π stacking interaction were the primary driving forces for the aggregation to form organogel

MKANet: An Efficient Network with Sobel Boundary Loss for Land-Cover Classification of Satellite Remote Sensing Imagery

Land cover classification is a multiclass segmentation task to classify each pixel into a certain natural or human-made category of the earth’s surface, such as water, soil, natural vegetation, crops, and human infrastructure. Limited by hardware computational resources and memory capacity, most existing studies preprocessed original remote sensing images by downsampling or cropping them into small patches less than 512 × 512 pixels before sending them to a deep neural network. However, downsampling incurs a spatial detail loss, renders small segments hard to discriminate, and reverses the spatial resolution progress obtained by decades of efforts. Cropping images into small patches causes a loss of long-range context information, and restoring the predicted results to their original size brings extra latency. In response to the above weaknesses, we present an efficient lightweight semantic segmentation network termed MKANet. Aimed at the characteristics of top view high-resolution remote sensing imagery, MKANet utilizes sharing kernels to simultaneously and equally handle ground segments of inconsistent scales, and also employs a parallel and shallow architecture to boost inference speed and friendly support image patches more than 10× larger. To enhance boundary and small segment discrimination, we also propose a method that captures category impurity areas, exploits boundary information, and exerts an extra penalty on boundaries and small segment misjudgments. Both visual interpretations and quantitative metrics of extensive experiments demonstrate that MKANet obtains a state-of-the-art accuracy on two land-cover classification datasets and infers 2× faster than other competitive lightweight networks. All these merits highlight the potential of MKANet in practical applications

Sinterability, microstructure and compressive strength of porous glass ceramics from metallurgical silicon slag and waste glass

Porous glass-ceramics have been prepared by the direct sintering of powder mixtures of metallurgical silicon slag and waste glass. The thermal behavior of silicon slag was examined by differential thermal analysis and thermogravimetry to clarify the foaming mechanism of porous glass-ceramics. The mass loss of silicon slag below 700 degrees C was attributed to the oxidation of amorphous carbon from residual metallurgical coke in the silicon slag, and the mass gain above 800 degrees C to the passive oxidation of silicon carbide. The porosity of sintered glass-ceramics was characterized in terms of the apparent density and pore size. By simply adjusting the content of waste glass and sintering parameters (i.e. temperature, time and heating rate), the apparent density changed from 0.4 g/cm(3) to 0.5 g/cm(3), and the pore size from 0.7 mm to 1.4 mm. In addition to the existing crystalline phases in the silicon slag, the gehlenite phase appeared in the sintered glass-ceramics. The compressive strength of porous glass-ceramics firstly increased and then decreased with the sintering temperature, reaching a maximal value of 1.8 MPa at 750 degrees C. The mechanical strength was primarily influenced by the crystallinity of glass-ceramics and the interfaces between the crystalline phases and the glassy matrix. These sintered porous glass-ceramics exhibit superior properties such as light-weight, heat-insulation and sound-absorption, and could found their potential applications in the construction decoration. (C) 2016 Elsevier Ltd and Techna Group S.r.l. All rights reserved