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 90.01$\pm$1.23 and a mean absolute error of 2.72$\pm$0.58 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