Residual Attention Network for Image Classification

In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion. Our Residual Attention Network is built by stacking Attention Modules which generate attention-aware features. The attention-aware features from different modules change adaptively as layers going deeper. Inside each Attention Module, bottom-up top-down feedforward structure is used to unfold the feedforward and feedback attention process into a single feedforward process. Importantly, we propose attention residual learning to train very deep Residual Attention Networks which can be easily scaled up to hundreds of layers. Extensive analyses are conducted on CIFAR-10 and CIFAR-100 datasets to verify the effectiveness of every module mentioned above. Our Residual Attention Network achieves state-of-the-art object recognition performance on three benchmark datasets including CIFAR-10 (3.90% error), CIFAR-100 (20.45% error) and ImageNet (4.8% single model and single crop, top-5 error). Note that, our method achieves 0.6% top-1 accuracy improvement with 46% trunk depth and 69% forward FLOPs comparing to ResNet-200. The experiment also demonstrates that our network is robust against noisy labels.Comment: accepted to CVPR201

The genus Neurigona Rondani, 1856 (Diptera, Dolichopodidae) from Yunnan, China, with descriptions of seven new species and a key to the species of China

Previously, only three species of the genus Neurigona Rondani of the subfamily Neurigoninae were known from Yunnan Province. Here, we reviewed the species of Neurigona from Yunnan and added the following seven new species: N. apicilata sp. nov., N. basicurva sp. nov., N. brevidigitata sp. nov., N. convexa sp. nov., N. huanglianshana sp. nov., N. quadrimaculata sp. nov., and N. ventriprocessa sp. nov. All seven new species are sympatric and were collected from below a reservoir in the Huanglianshan Nature Reserve in Yunnan using three Malaise traps in 2019. This suggests a very high species richness in the Yunnan fauna. A key to the species of Neurigona from Chinese mainland is provided

Role of GM-CSF in lung balance and disease

Granulocyte-macrophage colony-stimulating factor (GM-CSF) is a hematopoietic growth factor originally identified as a stimulus that induces the differentiation of bone marrow progenitor cells into granulocytes and macrophages. GM-CSF is now considered to be a multi-origin and pleiotropic cytokine. GM-CSF receptor signals activate JAK2 and induce nuclear signals through the JAK-STAT, MAPK, PI3K, and other pathways. In addition to promoting the metabolism of pulmonary surfactant and the maturation and differentiation of alveolar macrophages, GM-CSF plays a key role in interstitial lung disease, allergic lung disease, alcoholic lung disease, and pulmonary bacterial, fungal, and viral infections. This article reviews the latest knowledge on the relationship between GM-CSF and lung balance and lung disease, and indicates that there is much more to GM-CSF than its name suggests

Integrative Multi-Omics Analysis of Identified NUF2 as a Candidate Oncogene Correlates With Poor Prognosis and Immune Infiltration in Non-Small Cell Lung Cancer

BackgroundLung cancer is one of the most common malignant tumors and the leading causes of cancer-related deaths worldwide. As a component of the nuclear division cycle 80 complex, NUF2 is a part of the conserved protein complex related to the centromere. Although the high expression of NUF2 has been reported in many different types of human cancers, the multi-omics analysis in non-small cell lung cancer (NSCLC) of NUF2 remains to be elucidated.MethodsIn this analysis, NUF2 expression difference analysis in non-small cell lung cancer was evaluated by Oncomine, TIMER, GEO, and TCGA database. And the prognosis analysis of NUF2 based on Kaplan-Meier was performed. R language was used to analyze the differential expression genes, functional annotation and protein-protein interaction (PPI). GSEA analysis of differential expression genes was also carried out. Mechanism analysis about exploring the characteristic of NUF2, multi-omics, and correlation analysis was carried out using UALCAN, cBioportal, GEPIA, TIMER, and TISIDB, respectively.ResultsThe expression of NUF2 in NSCLC, both lung adenocarcinoma (LUAD) and squamous lung cancer (LUSC), was significantly higher than that in normal tissues. The analysis of UALCAN database samples proved that NUF2 expression was connected with stage and smoking habits. Meanwhile, the overall survival curve also validated that high expression of NUF2 has a poorer prognosis in NSCLC. GO, KEGG, GSEA, subcellular location from COMPARTMENTS indicated that NUF2 may regulate the cell cycle. Correlation analysis also showed that NUF2 was mainly positively associated with cell cycle and tumor-related genes. NUF2 altered group had a poorer prognosis than unaltered group in NSCLC. Immune infiltration analysis showed that the NUF2 expression mainly have negatively correlation with immune cells and immune subtypes in LUAD and LUSC. Furthermore, quantitative PCR was used to validate the expression difference of NUF2 in LUAD and LUSC.ConclusionOur findings elucidated that NUF2 may play an important role in cell cycle, and significantly associated with tumor-related gene in NSCLC; we consider that NUF2 may be a prognostic biomarkers in NSCLC

Game Theoretic Training Enabled Deep Learning Solutions for Rapid Discovery of Satellite Behaviors

The chapter presents a game theoretic training model enabling a deep learning solution for rapid discovery of satellite behaviors from collected sensor data. The solution has two parts, namely, Part 1 and Part 2. Part 1 is a PE game model that enables data augmentation method, and Part 2 uses convolutional neural networks (CNNs) for satellite behavior classification. The sensor data are propagated with the various maneuver strategies from the proposed space game models. Under the PE game theoretic framework, various satellite behaviors are simulated to generate synthetic datasets with labels for the training to detect space object behaviors. To evaluate the performance of the proposed PE model, a CNN model is designed and implemented for satellite behavior classification. Python 3 and TensorFlow are used in this implementation. The simulation results show that the trained machine learning model can efficiently and correctly classify the satellite behaviors up to 99.8%

Synthesis of polyacrylamide-based aerosol fixative and its fixation effect on tellurium aerosol

The removal control of radioactive aerosols in a nuclear emergency is an important issue, and capture fixation is a parameter for studying the purification effect of aerosol fixatives on aerosols. Herein, PAM-g-PAA, PAM-g-PHEA, and PAM-g-PAA/PHEA were obtained by chemical grafting with polyacrylamide as the substrate, acrylic acid and 2-hydroxyethyl acrylate as grafting monomers. The grafting product was confirmed by infrared spectroscopy and the grafting rate was calculated. The microstructure of different products were compared and discussed by scanning electron microscope images of freeze-drying and film formation. The capture and sedimentation effects of tellurium (simulated polonium) aerosol were studied by surface tension and fixed sedimentation experiments (PAM, PAM-g-PAA, PAM-g-PHEA, PAM-g-PAA/PHEA aqueous solution), and the mechanism of aerosol fixation was discussed. The results showed that the surface tension of the grafted product was significantly lower than that of the substrate PAM. Among them, the aerosol fixing agent PAM-g-PHEA grafted with HEA modified polyacrylamide can more effectively capture and fix tellurium aerosol particles, and its fixed sedimentation efficiency is 94.34%, which provides a research idea for the purification of polonium radioactive aerosol by atomization fixation method

Gut bacterial species in late trimester of pregnant sows influence the occurrence of stillborn piglet through pro-inflammation response

Maternal gut microbiota is an important regulator for the metabolism and immunity of the fetus during pregnancy. Recent studies have indicated that maternal intestinal microbiota is closely linked to the development of fetus and infant health. Some bacterial metabolites are considered to be directly involved in immunoregulation of fetus during pregnancy. However, the detailed mechanisms are largely unknown. In this study, we exploited the potential correlation between the gut microbiota of pregnant sows and the occurrence of stillborn piglets by combining the 16S rRNA gene and metagenomic sequencing data, and fecal metabolome in different cohorts. The results showed that several bacterial species from Bacteroides, potential pathogens, and LPS-producing bacteria exhibited significantly higher abundances in the gut of sows giving birth to stillborn piglets. Especially, Bacteroides fragilis stood out as the key driver in both tested cohorts and showed the most significant association with the occurrence of stillborn piglets in the DN1 cohort. However, several species producing short-chain fatty acids (SCFAs), such as Prevotella copri, Clostridium butyricum and Faecalibacterium prausnitzii were enriched in the gut of normal sows. Functional capacity analysis of gut microbiome revealed that the pathways associated with infectious diseases and immune diseases were enriched in sows giving birth to stillborn piglets. However, energy metabolism had higher abundance in normal sows. Fecal metabolome profiling analysis found that Lysophosphatidylethanolamine and phosphatidylethanolamine which are the main components of cell membrane of Gram-negative bacteria showed significantly higher concentration in stillbirth sows, while SCFAs had higher concentration in normal sows. These metabolites were significantly associated with the stillborn-associated bacterial species including Bacteroides fragilis. Lipopolysaccharide (LPS), IL-1β, IL-6, FABP2, and zonulin had higher concentration in the serum of stillbirth sows, indicating increased intestinal permeability and pro-inflammatory response. The results from this study suggested that certain sow gut bacterial species in late trimester of pregnancy, e.g., an excess abundance of Bacteroides fragilis, produced high concentration of LPS which induced sow pro-inflammatory response and might cause the death of the relatively weak piglets in a farrow. This study provided novel evidences about the effect of maternal gut microbiota on the fetus development and health

Postpartum depression in mothers and fathers: a structural equation model

open access articleBackground Post-partum depression (PPD) is a growing mental health concern worldwide. There is little evidence in the Chinese context of the relationship between paternal PPD and maternal PPD. Given the growing global concerns this relationship requires further exploration. Methods A survey was conducted with 950 total couples from March 2017 to December 2018. The study was conducted using a standardized questionnaire that included basic demographic information, information on the relationship between the mother-in-law and daughter-in-law, marital satisfaction (both maternal and paternal), and PPD symptoms. Structural Equation Modelling (SEM) analysis was used to explore the underlying mechanism for PPD symptoms in mothers and fathers. Results In 4.4% of the couples both the wife and the husband showed depressive symptoms. Maternal marital satisfaction showed a significant mediating effect on paternal PPD (B = -0.114, p < 0.01), and there was a direct effect of maternal PPD on paternal PPD (B = 0.31, p < 0.001). Conclusions This is the first study to investigate the possible correlation between maternal PPD, mother-in-law and daughter-in-law relationship satisfaction, maternal marital satisfaction, paternal marital satisfaction, and paternal PPD. It is important for future PPD interventions to target both maternal and paternal mental health, as well as the mechanisms identified that can lead to PPD