345 research outputs found
Edge-aware Multi-task Network for Integrating Quantification Segmentation and Uncertainty Prediction of Liver Tumor on Multi-modality Non-contrast MRI
Simultaneous multi-index quantification, segmentation, and uncertainty
estimation of liver tumors on multi-modality non-contrast magnetic resonance
imaging (NCMRI) are crucial for accurate diagnosis. However, existing methods
lack an effective mechanism for multi-modality NCMRI fusion and accurate
boundary information capture, making these tasks challenging. To address these
issues, this paper proposes a unified framework, namely edge-aware multi-task
network (EaMtNet), to associate multi-index quantification, segmentation, and
uncertainty of liver tumors on the multi-modality NCMRI. The EaMtNet employs
two parallel CNN encoders and the Sobel filters to extract local features and
edge maps, respectively. The newly designed edge-aware feature aggregation
module (EaFA) is used for feature fusion and selection, making the network
edge-aware by capturing long-range dependency between feature and edge maps.
Multi-tasking leverages prediction discrepancy to estimate uncertainty and
improve segmentation and quantification performance. Extensive experiments are
performed on multi-modality NCMRI with 250 clinical subjects. The proposed
model outperforms the state-of-the-art by a large margin, achieving a dice
similarity coefficient of 90.011.23 and a mean absolute error of
2.720.58 mm for MD. The results demonstrate the potential of EaMtNet as a
reliable clinical-aided tool for medical image analysis
Galactic Phylogenetics
Phylogenetics is a widely used concept in evolutionary biology. It is the
reconstruction of evolutionary history by building trees that represent
branching patterns and sequences. These trees represent shared history, and it
is our intention for this approach to be employed in the analysis of Galactic
history. In Galactic archaeology the shared environment is the interstellar
medium in which stars form and provides the basis for tree-building as a
methodological tool.
Using elemental abundances of solar-type stars as a proxy for DNA, we built
in Jofre et al 2017 such an evolutionary tree to study the chemical evolution
of the solar neighbourhood. In this proceeding we summarise these results and
discuss future prospects.Comment: Contribution to IAU Symposium No. 334: Rediscovering our Galax
Anomalous quantum scattering and transport of electrons with Mexican-hat dispersion induced by electrical potential
We theoretically study the quantum scattering and transport of electrons with
Mexican-hat dispersion through both step and rectangular potential barriers by
using the transfer matrix method. Owing to the torus-like iso-energy lines of
the Mexican-hat dispersion, we observe the presence of double reflections and
double transmissions in both two different barrier scenarios, i.e., the normal
reflection (NR), retro-reflection (RR), normal transmission (NT), and specular
transmission (ST).For the step potential with electrons incident from the large
wavevector, the transmission is primarily governed by NT with nearly negligible
ST, while the reflection is dominant by RR (NR) within (outside) the critical
angle. Additionally, for electrons incident from the small wavevector, the NT
can be reduced to zero by adjusting the barrier, resulting in a significant
enhancement of ST and RR. For the rectangular barrier, the transmission and
reflection spectra resemble those of the step barrier, but there are two kinds
of resonant tunneling which can lead to perfect NT or ST. There exists a
negative differential conductance (NDC) effect in the conductance spectrum. The
conductance and the peak-to-valley ratio of the NDC effect can be effectively
controlled by adjusting the height and width of the barrier as well as the
incident energy. Our results provide a deeper understanding of the electron
states governed by the Mexican-hat dispersion.Comment: 8 pages, 5 figure
A Pivotal Role of Hormones in Regulating Cotton Fiber Development
Cotton is the main source of renewable fiber in the world and is primarily used for textile production. Cotton fibers are single cells differentiated from the ovule epidermis and are an excellent model system for studying cell elongation, polyploidization, and cell wall biosynthesis. Plant hormones, which are present in relatively low concentrations, play important roles in various developmental processes, and recently, multiple reports have revealed the pivotal roles of hormones in regulating cotton fiber development. For example, exogenous application of hormones has been shown to promote the initiation and growth of fiber cells. However, a comprehensive understanding about phytohormone regulating fiber development is still unknown. Here, we focus on recent advances in elucidating the roles of multiple phytohormones in the control of fiber development, namely auxin, gibberellin, brassinosteroid, ethylene, cytokinin, abscisic acid, and strigolactones. We not only review the identification of genes involved in hormone biosynthetic and signaling pathways but also discuss the mechanisms of these phytohormones in regulating the initiation and elongation of fiber cells in cotton. Auxin, gibberellin, brassinosteroid, ethylene, jasmonic acid, and strigolactones play positive roles in fiber development, whereas cytokinin and abscisic acid inhibit fiber growth. Our aim is to provide a comprehensive review of the role of phytohormones in cotton fiber development that will serve as the basis for further elucidation of the mechanisms by which plant hormones regulate fiber growth
Predicting Mitral Valve mTEER Surgery Outcomes Using Machine Learning and Deep Learning Techniques
Mitral Transcatheter Edge-to-Edge Repair (mTEER) is a medical procedure
utilized for the treatment of mitral valve disorders. However, predicting the
outcome of the procedure poses a significant challenge. This paper makes the
first attempt to harness classical machine learning (ML) and deep learning (DL)
techniques for predicting mitral valve mTEER surgery outcomes. To achieve this,
we compiled a dataset from 467 patients, encompassing labeled echocardiogram
videos and patient reports containing Transesophageal Echocardiography (TEE)
measurements detailing Mitral Valve Repair (MVR) treatment outcomes. Leveraging
this dataset, we conducted a benchmark evaluation of six ML algorithms and two
DL models. The results underscore the potential of ML and DL in predicting
mTEER surgery outcomes, providing insight for future investigation and
advancements in this domain.Comment: 5 pages, 1 figur
Mode-matching metasurfaces: coherent reconstruction and multiplexing of surface waves
Metasurfaces are promising two-dimensional metamaterials that are engineered
to provide unique properties or functionalities absent in naturally occurring
homogeneous surfaces. Here, we report a type of metasurface for tailored
reconstruction of surface plasmon waves from light. The design is generic in a
way that one can selectively generate different surface plasmon waves through
simple variation of the wavelength or the polarization state of incident light.
The ultra-thin metasurface demonstrated in this paper provides a versatile
interface between the conventional free-space optics and a two-dimensional
platform such as surface plasmonics.Comment: 7 figures, supplementary information at the end of the documen
Development and Validation of a 6-Gene Hypoxia-Related Prognostic Signature For Cholangiocarcinoma
Cholangiocarcinoma (CHOL) is highly malignant and has a poor prognosis. This study is committed to creating a new prognostic model based on hypoxia related genes. Here, we established a novel tumor hypoxia-related prognostic model consisting of 6 hypoxia-related genes by univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) algorithm to predict CHOL prognosis and then the risk score for each patient was calculated. The results showed that the patients with high-risk scores had poor prognosis compared with those with low-risk scores, which was verified as an independent predictor by multivariate analysis. The hypoxia-related prognostic model was validated in both TCGA and GEO cohorts and exhibited excellent performance in predicting overall survival in CHOL. The PPI results suggested that hypoxia-related genes involved in the model may play a central role in regulating the hypoxic state. In addition, the presence of IDH1 mutations in the high-risk group was high, and GSEA results showed that some metabolic pathways were upregulated, but immune response processes were generally downregulated. These factors may be potential reasons for the high-risk group with worse prognosis. The analysis of different immune regulation-related processes in the high- and low-risk groups revealed that the expression of genes related to immune checkpoints would show differences between these two groups. We further verified the expression of the oncogene PPFIA4 in the model, and found that compared with normal samples, CHOL patients were generally highly expressed, and the patients with high-expression of PPFIA4 had a poor prognosis. In summary, the present study may provide a valid prognostic model for bile duct cancer to inform better clinical management of patients
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