Inducing lasing in organic materials with low optical gain by three-dimensional plasmonic nanocavity arrays

Lasing in organic media with very low gain has been pursued for a long time in optoelectronics. Here, we experimentally demonstrate that plasmonic lasing in the visible regime at room temperature can be achieved by hybridizing active media of very low optical gain such as ionic liquid and polymethylmethacrylate with three-dimensional (3D) plasmonic metamaterials. The 3D nanostructure consists of a double-layer N-shaped silver wire-hole array with strongly coupled multiple hot spots densely packed in each unit cell. These hot spots overlap perfectly with the gain media, allowing efficient gain-plasmon coupling in subwavelength volumes. The periodic arrangement of hot spots, as the metal and dielectric are distributed in an alternate manner along both transverse and vertical directions, results in ultrastrong suppression of scattering losses. In addition, the lasing characteristics, including threshold, intensity and polarization can be controlled by the lattice constant and geometry of metamaterials. Such a plasmonic nanolaser proves to be of low threshold and low gain requirement, providing an essential step towards easy-processing organic based optoelectronics. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreemen

Joint study of genetic regulators for expression traits related to breast cancer

The mRNA expression levels of genes have been shown to have discriminating power for the classification of breast cancer. Studying the heritability of gene expression levels on breast cancer related transcripts can lead to the identification of shared common regulators and inter-regulation patterns, which would be important for dissecting the etiology of breast cancer. We applied multilocus association genome-wide scans to 18 breast cancer related transcripts and combined the results with traditional linkage scans. Regulatory hotspots for these transcripts were identified and some inter-regulation patterns were observed. We also derived evidence on interacting genetic regulatory loci shared by a number of these transcripts. In this paper, by restricting to a set of related genes, we were able to employ a more detailed multilocus approach that evaluates both marginal and interaction association signals at each single-nucleotide polymorphism. Interesting inter-regulation patterns and significant overlaps of genetic regulators between transcripts were observed. Interaction association results returned more expression quantitative trait locus hotspots that are significant

3,4-Dicyano­phenyl 2,3,4,6-tetra-O-acetyl-α-d-glucopyran­oside

The title compound, C22H22N2O10, was prepared by the glycosidation method through nitrite displacement on substituted nitro­phthalonitrile. The mol­ecule contains a benzene ring, two nitrile groups and an acetyl-protected d-glucose fragment which adopts a chair conformation. The absolute configuration was determined by the use of d-glucose as starting material. All substituents of the protected sugar are in equatorial positions, with the exclusive presence of the α-anomer. The crystal packing is stabilized by C—H⋯O and C—H⋯N hydrogen-bonding inter­actions

Effects of multi-organ crosstalk on the physiology and pathology of adipose tissue

In previous studies, adipocytes were found to play an important role in regulating whole-body nutrition and energy balance, and are also important in energy metabolism, hormone secretion, and immune regulation. Different adipocytes have different contributions to the body, with white adipocytes primarily storing energy and brown adipocytes producing heat. Recently discovered beige adipocytes, which have characteristics in between white and brown adipocytes, also have the potential to produce heat. Adipocytes interact with other cells in the microenvironment to promote blood vessel growth and immune and neural network interactions. Adipose tissue plays an important role in obesity, metabolic syndrome, and type 2 diabetes. Dysfunction in adipose tissue endocrine and immune regulation can cause and promote the occurrence and development of related diseases. Adipose tissue can also secrete multiple cytokines, which can interact with organs; however, previous studies have not comprehensively summarized the interaction between adipose tissue and other organs. This article reviews the effect of multi-organ crosstalk on the physiology and pathology of adipose tissue, including interactions between the central nervous system, heart, liver, skeletal muscle, and intestines, as well as the mechanisms of adipose tissue in the development of various diseases and its role in disease treatment. It emphasizes the importance of a deeper understanding of these mechanisms for the prevention and treatment of related diseases. Determining these mechanisms has enormous potential for identifying new targets for treating diabetes, metabolic disorders, and cardiovascular diseases

A new method for safety helmet detection based on convolutional neural network.

Considering practical issues such as cost control of hardware facilities in engineering projects, it is a challenge to design a robust safety helmet detection method, which can be implemented on mobile or embedded devices with limited computing power. This paper presents an approach to optimize the BottleneckCSP structure in the YOLOv5 backbone network, which can greatly reduce the complexity of the model without changing the size of the network input and output. To eliminate the information loss caused by upsampling and enhance the semantic information of the feature map on the reverse path, this paper designs an upsampling feature enhancement module. Besides, To avoid the negative impact of redundant information generated by feature fusion on the detection results, this paper introduces a self-attention mechanism. That is, using the designed channel attention module and location attention module, adjacent shallow feature maps and upsampled feature maps are adaptively fused to generate new feature maps with strong semantics and precise location information. Compared with the existing methods with the fastest inference speed, under the same compute capability, the proposed method not only has faster inference speed, the FPS can reach 416, but also has better performance with mAP of 94.2%

Ablation experiments.

Considering practical issues such as cost control of hardware facilities in engineering projects, it is a challenge to design a robust safety helmet detection method, which can be implemented on mobile or embedded devices with limited computing power. This paper presents an approach to optimize the BottleneckCSP structure in the YOLOv5 backbone network, which can greatly reduce the complexity of the model without changing the size of the network input and output. To eliminate the information loss caused by upsampling and enhance the semantic information of the feature map on the reverse path, this paper designs an upsampling feature enhancement module. Besides, To avoid the negative impact of redundant information generated by feature fusion on the detection results, this paper introduces a self-attention mechanism. That is, using the designed channel attention module and location attention module, adjacent shallow feature maps and upsampled feature maps are adaptively fused to generate new feature maps with strong semantics and precise location information. Compared with the existing methods with the fastest inference speed, under the same compute capability, the proposed method not only has faster inference speed, the FPS can reach 416, but also has better performance with mAP of 94.2%.</div

Comparison between SHDet method and other safety helmet detection methods on SHWD dataset.

Comparison between SHDet method and other safety helmet detection methods on SHWD dataset.</p

Comparison experiments with other safety helmet detection methods was conducted on a self-made dataset.

Comparison experiments with other safety helmet detection methods was conducted on a self-made dataset.</p

Positional attention module structure.

Considering practical issues such as cost control of hardware facilities in engineering projects, it is a challenge to design a robust safety helmet detection method, which can be implemented on mobile or embedded devices with limited computing power. This paper presents an approach to optimize the BottleneckCSP structure in the YOLOv5 backbone network, which can greatly reduce the complexity of the model without changing the size of the network input and output. To eliminate the information loss caused by upsampling and enhance the semantic information of the feature map on the reverse path, this paper designs an upsampling feature enhancement module. Besides, To avoid the negative impact of redundant information generated by feature fusion on the detection results, this paper introduces a self-attention mechanism. That is, using the designed channel attention module and location attention module, adjacent shallow feature maps and upsampled feature maps are adaptively fused to generate new feature maps with strong semantics and precise location information. Compared with the existing methods with the fastest inference speed, under the same compute capability, the proposed method not only has faster inference speed, the FPS can reach 416, but also has better performance with mAP of 94.2%.</div