Stepwise Feature Fusion: Local Guides Global

Colonoscopy, currently the most efficient and recognized colon polyp detection technology, is necessary for early screening and prevention of colorectal cancer. However, due to the varying size and complex morphological features of colonic polyps as well as the indistinct boundary between polyps and mucosa, accurate segmentation of polyps is still challenging. Deep learning has become popular for accurate polyp segmentation tasks with excellent results. However, due to the structure of polyps image and the varying shapes of polyps, it is easy for existing deep learning models to overfit the current dataset. As a result, the model may not process unseen colonoscopy data. To address this, we propose a new state-of-the-art model for medical image segmentation, the SSFormer, which uses a pyramid Transformer encoder to improve the generalization ability of models. Specifically, our proposed Progressive Locality Decoder can be adapted to the pyramid Transformer backbone to emphasize local features and restrict attention dispersion. The SSFormer achieves state-of-the-art performance in both learning and generalization assessment

MicroRNA-212-5p Prevents Dopaminergic Neuron Death by Inhibiting SIRT2 in MPTP-Induced Mouse Model of Parkinson’s Disease

Recently, emerging evidences show that sirtuins (SIRTs) modulate aging progress and affect neurodegenerative diseases. For example, inhibition of SIRT2 has been recognized to exert neuroprotective effects in Parkinson’s disease (PD). However, current SIRT2 inhibitors are lack of selective property distinguished from its homolog. In this study, we found that SIRT2 protein level was highly increased in PD model, which was negatively regulated by miR-212-5p. In detail, miR-212-5p transfection reduced SIRT2 expression and inhibited SIRT2 activity. In vivo study, miR-212-5p treatment prevented dopaminergic neuron loss and DAT reduction by targeting SIRT2, which means miR-212-5p shows neuroprotective effect in PD. Mechanismly, we found nuclear acetylated p53 was up-regulation according to p53 is a major deacetylation substrate of SIRT2. Furthermore, decreased cytoplasmic p53 promoted autophagy in PD model, which was showed as autophagosomes, autophagic flux, LC3 B and p62 expression. Meanwhile, we also found miR-212-5p treatment somehow alleviated apoptosis in PD model, which might have some underlying mechanisms. In conclusions, our study provides a direct link between miR-212-5p and SIRT2-mediated p53-dependent programmed cell death in the pathogenesis of PD. These findings will give us an insight into the development of highly specifically SIRT2 inhibitor of opening up novel therapeutic avenues for PD

A New Convolutional Neural Network Architecture for Automatic Segmentation of Overlapping Human Chromosomes

In clinical diagnosis, karyotyping is carried out to detect genetic disorders due to chromosomal aberrations. Accurate segmentation is crucial in this process that is mostly operated by experts. However, it is time-consuming and labor-intense to segment chromosomes and their overlapping regions. In this research, we look into the automatic segmentation of overlapping pairs of chromosomes. Different from standard semantic segmentation applications that mostly detect object regions or boundaries, this study attempts to predict not only non-overlapping regions but also the order of superposition and opaque regions of the underlying chromosomes. We propose a novel convolutional neural network called Compact Seg-UNet with enhanced deep feature learning capability and training efficacy. To address the issue of unrealistic images in use characterized by overlapping regions of higher color intensities, we propose a novel method to generate more realistic images with opaque overlapping regions. On the segmentation performance of overlapping chromosomes for this new dataset, our Compact Seg-UNet model achieves an average IOU score of 93.44% ± 0.26 which is significantly higher than the result of a simplified U-Net reported by literature by around 6.08%. The corresponding F1 score also increases from 0.9262 ± 0.1188 to 0.9596 ± 0.0814

A Novel Application of Image-to-Image Translation: Chromosome Straightening Framework by Learning from a Single Image

In medical imaging, chromosome straightening plays a significant role in the pathological study of chromosomes and in the development of cytogenetic maps. Whereas different approaches exist for the straightening task, typically geometric algorithms are used whose outputs are characterized by jagged edges or fragments with discontinued banding patterns. To address the flaws in the geometric algorithms, we propose a novel framework based on image-to-image translation to learn a pertinent mapping dependence for synthesizing straightened chromosomes with uninterrupted banding patterns and preserved details. In addition, to avoid the pitfall of deficient input chromosomes, we construct an augmented dataset using only one single curved chromosome image for training models. Based on this framework, we apply two popular image-to-image translation architectures, U-shape networks and conditional generative adversarial networks, to assess its efficacy. Experiments on a dataset comprised of 642 real-world chromosomes demonstrate the superiority of our framework, as compared to the geometric method in straightening performance, by rendering realistic and continued chromosome details. Furthermore, our straightened results improve the chromosome classification by 0.98%-1.39% mean accuracy.Comment: This work has been accepted by CISP-BMEI202

Numerical Study of the Effect of Primary Nozzle Geometry on Supersonic Gas-Solid Jet of Bypass Injected Dry Powder Fire Extinguishing Device

A two-way coupled model between polydisperse particle phases with compressible gases and a density-based coupling implicit solution method, combining the third-order MUSCL with QUICK spatial discretization scheme and the second-order temporal discretization scheme, are constructed based on the discrete-phase model (DPM) and the stochastic wander model (DRWM) in the Eulerian–Lagrangian framework in conjunction with a unitary particulate source (PSIC) approach and the SST k-ω turbulence model. The accuracy of the numerical prediction method is verified using previous supersonic nozzle gas-solid two-phase flow experiments. Numerical simulation of a two-phase jet of dry powder extinguishing agent gas with pilot-type supersonic nozzle was performed to analyze the influence of geometrical parameters, such as the length ratio rL and the area ratio rA of the main nozzle on the two-phase flow field, as well as on the jet performance indexes, such as the particle mean velocity vp,a, velocity inhomogeneity Φvp, particle dispersion Ψp, particle mean acceleration ap,a, etc. By analyzing the parameters, we indicate the requirements for the combination of jet performance metrics for different flame types such as penetrating, spreading, and dispersing

Transcriptional Stages of Conidia Germination and Associated Genes in Aspergillus flavus: An Essential Role for Redox Genes

Aflatoxin is a threatening mycotoxin primarily present in the agricultural environment, especially in food and feedstuff, and poses significant global health risks. Aflatoxins are produced mainly by Aspergillus flavus. Conidia germination is the first step for A. flavus development. In this study, the transcriptome of A. flavus conidia was analyzed at three different stages of conidia germination, which were characterized by two different microscopes. Dormant conidia grew isotropically with the cell size increasing up to 5 h of after being inoculated in a liquid medium. Conidia changed towards polarized growth from 5 to 10 h of germination, during which germ tubes formed. Moreover, transcriptome analyses revealed that a larger number of genes changed in the isotropic growth stages compared to polarized growth, with 1910 differentially expressed genes (DEGs) up-regulated and 969 DEGs down-regulated in isotropic growth. GO and KEGG pathway analyses and pathway enrichment demonstrated that, in the isotropic growth stage, the top three pathways were translation, amino acid and carbohydrate metabolism. The ribosome was a key pathway in translation, as RPS28e, RPL53 and RPL36e were the top three DEGs. For polarized growth stage, lipid metabolism, amino acid metabolism and carbohydrate metabolism were the top three most active pathways. POX1 from alpha-linolenic acid metabolism was a DEG in lipid metabolism as well. Genes related to the antioxidant system were crucial for conidia germination. Furthermore, RT-PCR results showed the same trends as the transcriptome for redox genes, and essential oils have a significant inhibitory effect on germination rate and redox gene expression. Therefore, redox genes play an important role during germination, and the disruption of redox genes is involved in the mechanism of action of coumalic acid and geraniol against A. flavus spore germination

Effect of Water Activity on Conidia Germination in Aspergillus flavus

In this study, we explored the mechanism underlying Aspergillus flavus conidia germination inhibited by decreased water activity. The impact of low water activity was analyzed at 4 h, 8 h and 12 h. Additionally, we demonstrated that low water activity affected cell shape and decreased cell sizes. Transcriptomics found numerous differentially expressed genes (DEGs) during the first 12 h of germination, with 654 DEGs observed among 4 h, 8 h and 12 h. In particular, more DEGs were detected at 8 h of germinating. Therefore, proteomics was performed at 8 h, and 209 differentially expressed proteins (DEPs) were speculated, with 94 up-regulated and 115 down-regulated. Combined analysis of KEGG of transcriptomics and proteomics demonstrated that the dominant pathways were nutrient metabolism and translation. We also found several DEGs and DEPs in the Mitogen Activated Protein Kinase (MAPK) pathway. Therefore, we concluded that low water activity inhibited conidia germination, causing unregular morphology. In addition, low water activity influenced expression of creA, TreB in carbohydrate metabolism, Clr4, RmtA in amino acid metabolism and RPL37, RPL3 in translation in Aspergillus flavus

A Robust Framework of Chromosome Straightening with VIT-Patch GAN

Chromosomes exhibit non-rigid and non-articulated nature with varying degrees of curvature. Chromosome straightening is an essential step for subsequent karyotype construction, pathological diagnosis and cytogenetic map development. However, robust chromosome straightening remains challenging, due to the unavailability of training images, distorted chromosome details and shapes after straightening, as well as poor generalization capability. We propose a novel architecture, ViT-Patch GAN, consisting of a motion transformation generator and a Vision Transformer-based patch (ViT-Patch) discriminator. The generator learns the motion representation of chromosomes for straightening. With the help of the ViT-Patch discriminator, the straightened chromosomes retain more shape and banding pattern details. The proposed framework is trained on a small dataset and is able to straighten chromosome images with state-of-the-art performance for two large datasets.Comment: This work has been submitted to Springer for possible publicatio