Efficient Iris Localization via Optimization Model

Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method) algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square) is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective

MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

Pretraining with large-scale 3D volumes has a potential for improving the segmentation performance on a target medical image dataset where the training images and annotations are limited. Due to the high cost of acquiring pixel-level segmentation annotations on the large-scale pretraining dataset, pretraining with unannotated images is highly desirable. In this work, we propose a novel self-supervised learning strategy named Volume Fusion (VF) for pretraining 3D segmentation models. It fuses several random patches from a foreground sub-volume to a background sub-volume based on a predefined set of discrete fusion coefficients, and forces the model to predict the fusion coefficient of each voxel, which is formulated as a self-supervised segmentation task without manual annotations. Additionally, we propose a novel network architecture based on parallel convolution and transformer blocks that is suitable to be transferred to different downstream segmentation tasks with various scales of organs and lesions. The proposed model was pretrained with 110k unannotated 3D CT volumes, and experiments with different downstream segmentation targets including head and neck organs, thoracic/abdominal organs showed that our pretrained model largely outperformed training from scratch and several state-of-the-art self-supervised training methods and segmentation models. The code and pretrained model are available at https://github.com/openmedlab/MIS-FM.Comment: 13 pages, 8 figure

Tumor-Secreted Exosomal miR-222 Promotes Tumor Progression via Regulating P27 Expression and Re-Localization in Pancreatic Cancer

Background/Aims: MicroRNAs (miRNAs) or exosomes have recently been shown to play vital regulatory or communication roles in cancer biology. However, the roles and mechanisms of exosomal miRNAs in pancreatic ductal adenocarcinoma (PDAC) remain unknown. We aimed to investigate the detailed roles and mechanisms of tumor-generated exosomal miRNAs in progression of PDAC. Methods: miR-222 was identified by miRNA microarray studies in exosomes of PDAC cells, and further analyzed in plasma exosomes of PDAC patients. The regulatory mechanisms of miR-222 were explored by qRT-PCR, WB, dual-luciferase assays and immunofluorescence or confocal analysis. Other biological assays include transwell, xenograft models and so on. Results: miR-222 is significantly high in tumor exosomes or highly invasive PDAC cells. miR-222 could directly regulate p27 to promote cell invasion and proliferation. miR-222 could also activate AKT by inhibiting PPP2R2A expression, thus inducing p27 phosphorylation and cytoplasmic p27 expression to promote cell survival, invasion and metastasis. Expressions of miR-222 and p27 were significantly inversely correlated, and cytoplasmic p27, instead of nuclear p27, was associated with tumor malignancy. miR-222 could be transmitted between PDAC cells via exosome communication, and the exosomal miR-222 communication is functional. Plasma exosomal miR-222 in PDAC patients was high and significantly correlated to tumor size and TNM stage, and was an independent risk factor for PDAC patient survival. Conclusion: Tumor-generated exosomes could promote invasion and proliferation of neighboring tumor cells via miR-222 transmission, the plasma exosomal miR-222 plays important roles and may be a useful prognostic maker in PDAC

Predicted Electronic Markers for Polytypes of LaOBiS\u3csub\u3e2\u3c/sub\u3e Examined via Angle-Resolved Photoemission Spectroscopy

The natural periodic stacking of symmetry-inequivalent planes in layered compounds can lead to the formation of natural superlattices; albeit close in total energy, (thus in their thermodynamic stability), such polytype superlattices can exhibit different structural symmetries, thus have markedly different electronic properties which can in turn be used as “structural markers”. We illustrate this general principle on the layered LaOBiS2 compound where density-functional theory (DFT) calculations on the (BiS2)/(LaO)/(BiS2) polytype superlattices reveal both qualitatively and quantitatively distinct electronic structure markers associated with the Rashba physics, yet the total energies are only ∼ 0.1 meV apart. This opens the exciting possibility of identifying subtle structural features via electronic markers. We show that the pattern of removal of band degeneracies in different polytypes by the different forms of symmetry breaking leads to Rashba “minigaps” with characteristic Rashba parameters that can be determined from spectroscopy, thereby narrowing down the physically possible polytypes. By identifying these distinct DFT-predicted fingerprints via angle-resolved photoemission spectroscopy (ARPES) measurements on LaBiOS2 we found the dominant polytype with small amounts of mixtures of other polytypes. This conclusion, consistent with neutron scattering results, establishes ARPES detection of theoretically established electronic markers as a powerful tool to delineate energetically quasidegenerate polytypes

Multiparametric MR imaging in diagnosis of chronic prostatitis and its differentiation from prostate cancer

AbstractChronic prostatitis is a heterogeneous condition with high prevalence rate. Chronic prostatitis has overlap in clinical presentation with other prostate disorders and is one of the causes of high serum prostate specific antigen (PSA) level. Chronic prostatitis, unlike acute prostatitis, is difficult to diagnose reliably and accurately on the clinical grounds alone. Not only this, it is also challenging to differentiate chronic prostatitis from prostate cancer with imaging modalities like TRUS and conventional MR Imaging, as the findings can mimic those of prostate cancer. Even biopsy doesn't play promising role in the diagnosis of chronic prostatitis as it has limited sensitivity and specificity. As a result of this, chronic prostatitis may be misdiagnosed as a malignant condition and end up in aggressive surgical management resulting in increased morbidity. This warrants the need of reliable diagnostic tool which has ability not only to diagnose it reliably but also to differentiate it from the prostate cancer. Recently, it is suggested that multiparametric MR Imaging of the prostate could improve the diagnostic accuracy of the prostate cancer. This review is based on the critically published literature and aims to provide an overview of multiparamateric MRI techniques in the diagnosis of chronic prostatitis and its differentiation from prostate cancer

Fibroblasts and monocyte macrophages contract and degrade three-dimensional collagen gels in extended co-culture

BACKGROUND: Inflammatory cells are believed to play a prominent role during tissue repair and remodeling. Since repair processes develop and mature over extended time frames, the present study was designed to evaluate the effect of monocytes and fibroblasts in prolonged culture in three-dimensional collagen gels. METHODS: Blood monocytes from healthy donors and human fetal lung fibroblasts were cast into type I collagen gels and maintained in floating cultures for three weeks. RESULTS: Fibroblast-mediated gel contraction was initially inhibited by the presence of monocytes (P < 0.01). However, with extended co-culture, contraction of the collagen gels was greatly augmented (P < 0.01). In addition, with extended co-culture, degradation of collagen in the gels occurred. The addition of neutrophil elastase to the medium augmented both contraction and degradation (P < 0.01). Prostaglandin E(2) production was significantly increased by co-culture and its presence attenuated collagen degradation. CONCLUSION: The current study, therefore, demonstrates that interaction between monocytes and fibroblasts can contract and degrade extracellular matrix in extended culture

Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification

Convolutional neural networks excel in histopathological image classification, yet their pixel-level focus hampers explainability. Conversely, emerging graph convolutional networks spotlight cell-level features and medical implications. However, limited by their shallowness and suboptimal use of high-dimensional pixel data, GCNs underperform in multi-class histopathological image classification. To make full use of pixel-level and cell-level features dynamically, we propose an asymmetric co-training framework combining a deep graph convolutional network and a convolutional neural network for multi-class histopathological image classification. To improve the explainability of the entire framework by embedding morphological and topological distribution of cells, we build a 14-layer deep graph convolutional network to handle cell graph data. For the further utilization and dynamic interactions between pixel-level and cell-level information, we also design a co-training strategy to integrate the two asymmetric branches. Notably, we collect a private clinically acquired dataset termed LUAD7C, including seven subtypes of lung adenocarcinoma, which is rare and more challenging. We evaluated our approach on the private LUAD7C and public colorectal cancer datasets, showcasing its superior performance, explainability, and generalizability in multi-class histopathological image classification

Collaborative interactions between neutrophil elastase and metalloproteinases in extracellular matrix degradation in three-dimensional collagen gels

BACKGROUND: Extended culture of monocytes and fibroblasts in three-dimensional collagen gels leads to degradation of the gels (see linked study in this issue, "Fibroblasts and monocytes contract and degrade three-dimensional collagen gels in extended co-culture"). The current study, therefore, was designed to evaluate production of matrix-degrading metalloproteinases by these cells in co-culture and to determine if neutrophil elastase could collaborate in the activation of these enzymes. Since co-cultures produce prostaglandin E(2) (PGE(2)), the role of PGE(2) in this process was also evaluated. METHODS: Blood monocytes from healthy donors and human fetal lung fibroblasts were cast into type I collagen gels and maintained in floating cultures for three weeks. Matrix metalloproteinases (MMPs) were assessed by gelatin zymography (MMPs 2 and 9) and immunoblotting (MMPs 1 and 3). The role of PGE(2) was explored by direct quantification, and by the addition of exogenous indomethacin and/or PGE(2). RESULTS: Gelatin zymography and immunoblots revealed that MMPs 1, 2, 3 and 9 were induced by co-cultures of fibroblasts and monocytes. Neutrophil elastase added to the medium resulted in marked conversion of latent MMPs to lower molecular weight forms consistent with active MMPs, and was associated with augmentation of both contraction and degradation (P < 0.01). PGE(2) appeared to decrease both MMP production and activation. CONCLUSION: The current study demonstrates that interactions between monocytes and fibroblasts can mediate tissue remodeling

Predicted electronic markers for polytypes of LaOBiS2 examined via angular resolved photoemission spectroscopy

The natural periodic stacking of symmetry-inequivalent planes in layered compounds can lead to the formation of natural superlattices; albeit close in total energy, (thus in their thermodynamic stability), such polytype superlattices can exhibit different structural symmetries, thus have markedly different electronic properties which can in turn be used as "structural markers". We illustrate this general principle on the layered LaOBiS2 compound where density-functional theory (DFT) calculations on the (BiS2)/(LaO)/(BiS2) polytype superlattices reveal both qualitatively and quantitatively distinct electronic structure markers associated with the Rashba physics, yet the total energies are only ~ 0.1 meV apart. This opens the exciting possibility of identifying subtle structural features via electronic markers. We show that the pattern of removal of band degeneracies in different polytypes by the different forms of symmetry breaking leads to new Rashba "mini gaps" with characteristic Rashba parameters that can be determined from spectroscopy, thereby narrowing down the physically possible polytypes. By identifying these distinct DFT-predicted fingerprints via ARPES measurements on LaBiOS2 we found the dominant polytype with small amounts of mixtures of other polytypes. This conclusion, consistent with neutron scattering results, establishes ARPES detection of theoretically established electronic markers as a powerful tool to delineate energetically quasidegenerate polytypes.Comment: 13 pages, 4 figure