4,949 research outputs found
Effect of pulse magnetic field on solidification structure and properties of pure copper
The application of pulse magnetic field to metal solidification is an advanced technique which can remarkably refine solidification structure. In this paper, the effect of pulse magnetic field on solidification structure, mechanical properties and conductivity of pure copper was experimentally investigated. The results showed that the solidification structure transformed from coarse columnar crystal to fine globular crystal with increasing pulse voltage. Increasing pulse voltage also improved the tensile strength. However, with the increase of pulse voltage, the elongation and electrical resistivity firstly decreased, then increased when the pulse voltage beyond a critical value. Moreover, in some conditions, pulse magnetic field can simultaneously improve the conductivity and mechanical property of pure copper
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Transcriptional activation of CBFβ by CDK11p110 is necessary to promote osteosarcoma cell proliferation.
BACKGROUND:Aberrant expression of cyclin-dependent protein kinases (CDK) is a hallmark of cancer. CDK11 plays a crucial role in cancer cell growth and proliferation. However, the molecular mechanisms of CDK11 and CDK11 transcriptionally regulated genes are largely unknown. METHODS:In this study, we performed a global transcriptional analysis using gene array technology to investigate the transcriptional role of CDK11 in osteosarcoma. The promoter luciferase assay, chromatin immunoprecipitation assay, and Gel Shift assay were used to identify direct transcriptional targets of CDK11. Clinical relevance and function of core-binding factor subunit beta (CBFβ) were further accessed in osteosarcoma. RESULTS:We identified a transcriptional role of protein-DNA interaction for CDK11p110, but not CDK11p58, in the regulation of CBFβ expression in osteosarcoma cells. The CBFβ promoter luciferase assay, chromatin immunoprecipitation assay, and Gel Shift assay confirmed that CBFβ is a direct transcriptional target of CDK11. High expression of CBFβ is associated with poor outcome in osteosarcoma patients. Expression of CBFβ contributes to the proliferation and metastatic behavior of osteosarcoma cells. CONCLUSIONS:These data establish CBFβ as a mediator of CDK11p110 dependent oncogenesis and suggest that targeting the CDK11- CBFβ pathway may be a promising therapeutic strategy for osteosarcoma treatment
URL: Combating Label Noise for Lung Nodule Malignancy Grading
Due to the complexity of annotation and inter-annotator variability, most
lung nodule malignancy grading datasets contain label noise, which inevitably
degrades the performance and generalizability of models. Although researchers
adopt the label-noise-robust methods to handle label noise for lung nodule
malignancy grading, they do not consider the inherent ordinal relation among
classes of this task. To model the ordinal relation among classes to facilitate
tackling label noise in this task, we propose a Unimodal-Regularized
Label-noise-tolerant (URL) framework. Our URL contains two stages, the
Supervised Contrastive Learning (SCL) stage and the Memory pseudo-labels
generation and Unimodal regularization (MU) stage. In the SCL stage, we select
reliable samples and adopt supervised contrastive learning to learn better
representations. In the MU stage, we split samples with multiple annotations
into multiple samples with a single annotation and shuffle them into different
batches. To handle label noise, pseudo-labels are generated using the
similarity between each sample and the central feature of each class, and
temporal ensembling is used to obtain memory pseudo-labels that supervise the
model training. To model the ordinal relation, we introduce unimodal
regularization to keep the ordinal relation among classes in the predictions.
Moreover, each lung nodule is characterized by three orthographic views.
Experiments conducted on the LIDC-IDRI dataset indicate the superiority of our
URL over other competing methods. Code is available at
https://github.com/axz520/UR.Comment: 11 pages, accepted by DALI@MICCAI202
Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation
Deep learning-based medical image segmentation models suffer from performance
degradation when deployed to a new healthcare center. To address this issue,
unsupervised domain adaptation and multi-source domain generalization methods
have been proposed, which, however, are less favorable for clinical practice
due to the cost of acquiring target-domain data and the privacy concerns
associated with redistributing the data from multiple source domains. In this
paper, we propose a \textbf{C}hannel-level \textbf{C}ontrastive \textbf{S}ingle
\textbf{D}omain \textbf{G}eneralization (\textbf{CSDG}) model for medical
image segmentation. In CSDG, the shallower features of each image and its
style-augmented counterpart are extracted and used for contrastive training,
resulting in the disentangled style representations and structure
representations. The segmentation is performed based solely on the structure
representations. Our method is novel in the contrastive perspective that
enables channel-wise feature disentanglement using a single source domain. We
evaluated CSDG against six SDG methods on a multi-domain joint optic cup
and optic disc segmentation benchmark. Our results suggest the effectiveness of
each module in CSDG and also indicate that CSDG outperforms the
baseline and all competing methods with a large margin. The code will be
available at \url{https://github.com/ShishuaiHu/CCSDG}.Comment: 12 pages, 5 figure
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