Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition

Automated medical image classification is the key component in intelligent diagnosis systems. However, most medical image datasets contain plenty of samples of common diseases and just a handful of rare ones, leading to major class imbalances. Currently, it is an open problem in intelligent diagnosis to effectively learn from imbalanced training data. In this paper, we propose a simple yet effective framework, named \textbf{C}lass \textbf{A}ttention to \textbf{RE}gions of the lesion (CARE), to handle data imbalance issues by embedding attention into the training process of \textbf{C}onvolutional \textbf{N}eural \textbf{N}etworks (CNNs). The proposed attention module helps CNNs attend to lesion regions of rare diseases, therefore helping CNNs to learn their characteristics more effectively. In addition, this attention module works only during the training phase and does not change the architecture of the original network, so it can be directly combined with any existing CNN architecture. The CARE framework needs bounding boxes to represent the lesion regions of rare diseases. To alleviate the need for manual annotation, we further developed variants of CARE by leveraging the traditional saliency methods or a pretrained segmentation model for bounding box generation. Results show that the CARE variants with automated bounding box generation are comparable to the original CARE framework with \textit{manual} bounding box annotations. A series of experiments on an imbalanced skin image dataset and a pneumonia dataset indicates that our method can effectively help the network focus on the lesion regions of rare diseases and remarkably improves the classification performance of rare diseases.Comment: Accepted by Neurocomputing on July 2023. 37 page

Prominent edge detection with deep metric expression and multi-scale features

Abstract(#br)Edge detection is one of today’s hottest computer vision issues with widely applications. It is beneficial for improving the capability of many vision systems, such as semantic segmentation, salient object detection and object recognition. Deep convolution neural networks (CNNs) recently have been employed to extract robust features, and have achieved a definite improvement. However, there is still a long run to study this hotspot with the main reason that CNNs-based approaches may cause the edges thicker. To address this problem, a novel semantic edge detection algorithm using multi-scale features is proposed. Our model is deep symmetrical metric learning network, which includes 3 key parts. Firstly, the deep detail layer, as a preprocessing layer and a guide module, is..

Circulating microRNAs as novel biomarkers for dilated cardiomyopathy

Background: Circulating microRNAs (miRNAs) potentially carry disease-specific information. In the current study, we aim to characterize the miRNA signature in plasma from dilated cardiomyopathy (DCM) patients and assess the possible correlation between expression levels of circulating miRNAs and symptom severity in DCM patients. Methods: Using microarray-based miRNA expression profiling, we compared the miRNA expression levels in plasma samples from 4 DCM patients and 3 healthy controls. The expression levels of selected differentially expressed, upregulated miRNAs (miR-3135b, miR-3908 and miR-5571-5p) were validated independently in plasma samples from 19 DCM patients and 20 controls. Results: We observed that plasma miR-3135b (p < 0.001), miR-3908 (p < 0.001) and miR-5571-5p (p < 0.001) were significantly upregulated in DCM patients. The area under receiver operating characteristic (ROC) curves for the 3 miRNAs ranged from 0.83 to 1.00. Moreover, miR-5571-5p levels in plasma were significantly upregulated with severe New York Heart Association (NYHA) classification (p < 0.05). Conclusions: The circulating miRNAs (miR-3135b, miR-3908 and miR-5571-5p) have potential as diagnostic biomarkers for DCM. Additionally, miR-5571-5p correlated with NYHA classification.

Secondary infection with Streptococcus suis serotype 7 increases the virulence of highly pathogenic porcine reproductive and respiratory syndrome virus in pigs

<p>Abstract</p> <p>Background</p> <p>Porcine reproductive and respiratory syndrome virus (PRRSV) and <it>Streptococcus suis </it>are common pathogens in pigs. In samples collected during the porcine high fever syndrome (PHFS) outbreak in many parts of China, PRRSV and <it>S. suis </it>serotype 7 (SS7) have always been isolated together. To determine whether PRRSV-SS7 coinfection was the cause of the PHFS outbreak, we evaluated the pathogenicity of PRRSV and/or SS7 in a pig model of single and mixed infection.</p> <p>Results</p> <p>Respiratory disease, diarrhea, and anorexia were observed in all infected pigs. Signs of central nervous system (CNS) disease were observed in the highly pathogenic PRRSV (HP-PRRSV)-infected pigs (4/12) and the coinfected pigs (8/10); however, the symptoms of the coinfected pigs were clearly more severe than those of the HP-PRRSV-infected pigs. The mortality rate was significantly higher in the coinfected pigs (8/10) than in the HP-PRRSV- (2/12) and SS7-infected pigs (0/10). The deceased pigs of the coinfected group had symptoms typical of PHFS, such as high fever, anorexia, and red coloration of the ears and the body. The isolation rates of HP-PRRSV and SS7 were higher and the lesion severity was greater in the coinfected pigs than in monoinfected pigs.</p> <p>Conclusion</p> <p>HP-PRRSV infection increased susceptibility to SS7 infection, and coinfection of HP-PRRSV with SS7 significantly increased the pathogenicity of SS7 to pigs.</p

DeePMD-kit v2: A software package for Deep Potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.Comment: 51 pages, 2 figure

EFFECTS OF THERMAL MODIFICATION ON MECHANICAL AND SWELLING PROPERTIES AND COLOR CHANGE OF LUMBER KILLED BY MOUNTAIN PINE BEETLE

To extend the application of mountain pine beetle (MPB) killed lumber for decking, siding, and landscaping materials, it is essential to improve its dimensional stability. Thermal treatment is one of the well-established processes used to improve wood stability by modifying chemical compounds and masking blue-stains by darkening the fibre color. In this study, the MPB lumber was subjected to thermal treatment at three temperatures (195, 205, or 215°C) and three exposure times (1.5, 2, or 3 h). Based on Duncan's multiple range test, the results indicated that the volumetric swelling after thermal treatment, either from oven-dry to air-conditioned or from oven-dry to water-saturated, was significantly reduced after thermal treatment. Modulus of elasticity was increased when specimens were treated at a temperature of 195°C, and then decreased as the temperature increased. Modulus of rupture was significantly reduced as treatment temperature increased. The hardness of lumber thermal-treated at 195°C was significantly increased compared to that of the untreated lumber. At higher temperatures, hardness started to decrease slightly. With the treatment temperature increasing to 215°C for 3 h, the color difference between stained and clear wood was reduced by 75%. As a result, the blue-stains vanished gradually

Bound Water Content and Pore Size Distribution of Thermally Modified Wood Studied by NMR

The physical and mechanical properties of thermally modified wood (TMW) have been comprehensively studied; however, the quantitative analysis of water states and cell wall pores of TMW is limited. In this work, Douglas fir and Norway spruce were thermally modified at 180, 200 and 220 &deg;C, and then studied by NMR cryoporometry method. The results show that thermally modified samples had lower fiber saturation point and the bound water content than the reference samples at all the experimental temperatures, indicating the reduced hygroscopicity due to thermal modification (TM). In addition, TM decreased number of hygroscopic groups, which can be implied by the decreased proportion of bound water sites, and TM also increased the proportion of small voids for bound water clusters. An increase in TM intensity resulted in lower bound water content and a smaller number of hygroscopic groups. In summary, the NMR method detected the water states and pore size distribution and confirmed that TM decreased the fiber saturation point and hygroscopicity of wood by reducing the bound water content and proportion of bound water sites in wood cell walls

Parallel Hybrid Particle Swarm Algorithm for Workshop Scheduling Based on Spark

In hybrid mixed-flow workshop scheduling, there are problems such as mass production, mass manufacturing, mass assembly and mass synthesis of products. In order to solve these problems, combined with the Spark platform, a hybrid particle swarm algorithm that will be parallelized is proposed. Compared with the existing intelligent algorithms, the parallel hybrid particle swarm algorithm is more conducive to the realization of the global optimal solution. In the loader manufacturing workshop, the optimization goal is to minimize the maximum completion time and a parallelized hybrid particle swarm algorithm is used. The results show that in the case of relatively large batches, the parallel hybrid particle swarm algorithm can effectively obtain the scheduling plan and avoid falling into the local optimal solution. Compared with algorithm serialization, algorithm parallelization improves algorithm efficiency by 2–4 times. The larger the batches, the more obvious the algorithm parallelization improves computational efficiency

Numerical Simulation of Dynamic Response and Evaluation of Flexural Damage of RC Columns under Horizontal Impact Load

By using the ABAQUS finite element (FE) model, which has been verified by experiments, the deformation and internal force changes of RC columns during the impact process are investigated, and a parametric analysis is conducted under different impact kinetic energies Ek. According to the development path of the bottom bending moment-column top displacement curve under impact, the member is in a slight damage state when the curve rebounds before reaching the peak and in a moderate or severe damage state when the curve exceeds the peak, in which case the specific damage state of the member needs to be determined by examining whether there is a secondary descending stage in the curve. Accordingly, a qualitative method for evaluating the bending failure of RC column members under impact is obtained. In addition, the damage state of RC columns under impact can also be quantitatively evaluated by the ratio of the equivalent static load Feq and the ultimate static load-bearing capacity Fsu