A Monte-Carlo-Based Network Method for Source Positioning in Bioluminescence Tomography

We present an approach based on the improved Levenberg Marquardt (LM) algorithm of backpropagation (BP) neural network to estimate the light source position in bioluminescent imaging. For solving the forward problem, the table-based random sampling algorithm (TBRS), a fast Monte Carlo simulation method we developed before, is employed here. Result shows that BP is an effective method to position the light source

Numerical Simulation and Experimental Study of Deep Bed Corn Drying Based on Water Potential

The concept and the model of water potential, which were widely used in agricultural field, have been proved to be beneficial in the application of vacuum drying model and have provided a new way to explore the grain drying model since being introduced to grain drying and storage fields. Aiming to overcome the shortcomings of traditional deep bed drying model, for instance, the application range of this method is narrow and such method does not apply to systems of which pressure would be an influential factor such as vacuum drying system in a way combining with water potential drying model. This study established a numerical simulation system of deep bed corn drying process which has been proved to be effective according to the results of numerical simulation and corresponding experimental investigation and has revealed that desorption and adsorption coexist in deep bed drying

Transcriptomic profiling of mature embryo from an elite super-hybrid rice LYP9 and its parental lines

<p>Abstract</p> <p>Background</p> <p>The mature embryo of rice (<it>Oryza sativa, L</it>.) is a synchronized and integrated tissue mass laying the foundation at molecular level for its growth, development, and differentiation toward a developing and ultimately a mature plant. We carried out an EST (expressed-sequence-tags)-based transcriptomic study, aiming at gaining molecular insights into embryonic development of a rice hybrid triad–an elite hybrid rice <it>LYP</it>9 and its parental lines (<it>93-11 </it>and <it>PA64s</it>)–and possible relatedness to heterosis.</p> <p>Results</p> <p>We generated 27,566 high-quality ESTs from cDNA libraries made from mature rice embryos. We classified these ESTs into 7,557 unigenes (2,511 contigs and 5,046 singletons) and 7,250 (95.9%) of them were annotated. We noticed that the high-abundance genes in mature rice embryos belong to two major functional categories, stress-tolerance and preparation-for-development, and we also identified 191 differentially-expressed genes (General Chi-squared test, <it>P</it>-value <= 0.05) between <it>LYP9 </it>and its parental lines, representing typical expression patterns including over-dominance, high- and low-parent dominance, additivity, and under-dominance. In <it>LYP9</it>, the majority of embryo-associated genes were found not only abundantly and specifically enriched but also significantly up-regulated.</p> <p>Conclusion</p> <p>Our results suggested that massively strengthening tissue-(or stage-) characteristic functions may contribute to heterosis rather than a few simple mechanistic explanations at the individual gene level. In addition, the large collection of rice embryonic ESTs provides significant amount of data for future comparative analyses on plant development, especially for the important crops of the grass family.</p

Effects of 5-aminolevulinic Acid on the Photosynthesis, Antioxidant System, and α-Bisabolol Content of Matricaria recutita

Matricaria recutita is a widely used medicinal plant with broad pharmacological effects, and α-bisabolol is the main active ingredient of this plant. To improve its α-bisabolol content, M. recutita was sprayed with different concentrations (1.0, 2.0,and 4.0 mmol.L−1) of 5-aminolevulinic acid (ALA) or with water as a control to study the effects of ALA treatment on the photosynthesis, antioxidant system, and α-bisabolol content of M. recutita. Results showed that the photosynthetic rate, transpiration rate, stomatal conductance, intercellular CO2 concentration, soluble protein, total amino acids, soluble sugar, and α-bisabolol of M. recutita were significantly increased. Moreover, the activities of superoxide dismutase, peroxidase, and catalase of M. recutita were also enhanced by ALA treatment. Optimal results were obtained when the concentration of ALA was 2.0 mmol.L−1. Results showed that ALA treatment could improve the α-bisabolol content of M. recutita, and the underlying physiological mechanism was analyzed. ALA treatment was an effective measure for improving the medicinal value of M. recutita

Compound Attention and Neighbor Matching Network for Multi-contrast MRI Super-resolution

Multi-contrast magnetic resonance imaging (MRI) reflects information about human tissue from different perspectives and has many clinical applications. By utilizing the complementary information among different modalities, multi-contrast super-resolution (SR) of MRI can achieve better results than single-image super-resolution. However, existing methods of multi-contrast MRI SR have the following shortcomings that may limit their performance: First, existing methods either simply concatenate the reference and degraded features or exploit global feature-matching between them, which are unsuitable for multi-contrast MRI SR. Second, although many recent methods employ transformers to capture long-range dependencies in the spatial dimension, they neglect that self-attention in the channel dimension is also important for low-level vision tasks. To address these shortcomings, we proposed a novel network architecture with compound-attention and neighbor matching (CANM-Net) for multi-contrast MRI SR: The compound self-attention mechanism effectively captures the dependencies in both spatial and channel dimension; the neighborhood-based feature-matching modules are exploited to match degraded features and adjacent reference features and then fuse them to obtain the high-quality images. We conduct experiments of SR tasks on the IXI, fastMRI, and real-world scanning datasets. The CANM-Net outperforms state-of-the-art approaches in both retrospective and prospective experiments. Moreover, the robustness study in our work shows that the CANM-Net still achieves good performance when the reference and degraded images are imperfectly registered, proving good potential in clinical applications.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

LiveRetro: Visual Analytics for Strategic Retrospect in Livestream E-Commerce

Livestream e-commerce integrates live streaming and online shopping, allowing viewers to make purchases while watching. However, effective marketing strategies remain a challenge due to limited empirical research and subjective biases from the absence of quantitative data. Current tools fail to capture the interdependence between live performances and feedback. This study identified computational features, formulated design requirements, and developed LiveRetro, an interactive visual analytics system. It enables comprehensive retrospective analysis of livestream e-commerce for streamers, viewers, and merchandise. LiveRetro employs enhanced visualization and time-series forecasting models to align performance features and feedback, identifying influences at channel, merchandise, feature, and segment levels. Through case studies and expert interviews, the system provides deep insights into the relationship between live performance and streaming statistics, enabling efficient strategic analysis from multiple perspectives.Comment: Accepted by IEEE VIS 202

PHI-SMFE: spatial multi-scale feature extract neural network based on physical heterogeneous interaction for solving passive scalar advection in a 2-D unsteady flow

Fluid dynamic calculations play a crucial role in understanding marine biochemical dynamic processes, impacting the behavior, interactions, and distribution of biochemical components in aquatic environments. The numerical simulation of fluid dynamics is a challenging task, particularly in real-world scenarios where fluid motion is highly complex. Traditional numerical simulation methods enhance accuracy by increasing the resolution of the computational grid. However, this approach comes with a higher computational demand. Recent advancements have introduced an alternative by leveraging deep learning techniques for fluid dynamic simulations. These methods utilize discretized learned coefficients to achieve high-precision solutions on low-resolution grids, effectively reducing the computational burden while maintaining accuracy. Yet, existing fluid numerical simulation methods based on deep learning are limited by their single-scale analysis of spatially correlated physical fields, which fails to capture the diverse scale characteristics inherent in flow fields governed by complex laws in different physical space. Additionally, these models lack an effective approach to enhance correlation interactions among dynamic fields within the same system. To tackle these challenges, we propose the Spatial Multi-Scale Feature Extract Neural Network based on Physical Heterogeneous Interaction (PHI-SMFE). The PHI module is designed to extract heterogeneity and interaction information from diverse dynamic fields, while the SMFE module focuses on capturing multi-scale features in fluid dynamic fields. We utilize channel-biased convolution to implement a separation strategy, reducing the processing of redundant feature information. Furthermore, the traditional solution module based on the finite volume method is integrated into the network to facilitate the numerical solution of the discretized dynamic field in subsequent time steps. Comparative analysis with the current state-of-the-art model reveals that our proposed method offers a 41% increase in simulation accuracy and a 12.7% decrease in inference time during the iterative evolution of unsteady flow. These results underscore the superior performance of our model in terms of both simulation accuracy and computational speedup, establishing it as a state-of-the-art solution

Genetic variants in the calcium signaling pathway genes are associated with cutaneous melanoma-specific survival

Remodeling or deregulation of the calcium signaling pathway is a relevant hallmark of cancer including cutaneous melanoma (CM). In this study, using data from a published genome-wide association study (GWAS) from The University of Texas M.D. Anderson Cancer Center, we assessed the role of 41,377 common single-nucleotide polymorphisms (SNPs) of 167 calcium signaling pathway genes in CM survival. We used another GWAS from Harvard University as the validation dataset. In the single-locus analysis, 1830 SNPs were found to be significantly associated with CM-specific survival (CMSS; P ≤ 0.050 and false-positive report probability ≤ 0.2), of which 9 SNPs were validated in the Harvard study (P ≤ 0.050). Among these, three independent SNPs (i.e. PDE1A rs6750552 T>C, ITPR1 rs6785564 A>G and RYR3 rs2596191 C>A) had a predictive role in CMSS, with a meta-analysis-derived hazards ratio of 1.52 (95% confidence interval = 1.19–1.94, P = 7.21 × 10−4), 0.49 (0.33–0.73, 3.94 × 10−4) and 0.67 (0.53–0.86, 0.0017), respectively. Patients with an increasing number of protective genotypes had remarkably improved CMSS. Additional expression quantitative trait loci analysis showed that these genotypes were also significantly associated with mRNA expression levels of the genes. Taken together, these results may help us to identify prospective biomarkers in the calcium signaling pathway for CM prognosis

A Hippo-like Signaling Pathway Controls Tracheal Morphogenesis in Drosophila melanogaster

Hippo-like pathways are ancient signaling modules first identified in yeasts. The best-defined metazoan module forms the core of the Hippo pathway, which regulates organ size and cell fate. Hippo-like kinase modules consist of a Sterile 20-like kinase, an NDR kinase, and non-catalytic protein scaffolds. In the Hippo pathway, the upstream kinase Hippo can be activated by another kinase, Tao-1. Here, we delineate a related Hippo-like signaling module that Tao-1 regulates to control tracheal morphogenesis in Drosophila melanogaster. Tao-1 activates the Sterile 20-like kinase GckIII by phosphorylating its activation loop, a mode of regulation that is conserved in humans. Tao-1 and GckIII act upstream of the NDR kinase Tricornered to ensure proper tube formation in trachea. Our study reveals that Tao-1 activates two related kinase modules to control both growth and morphogenesis. The Hippo-like signaling pathway we have delineated has a potential role in the human vascular disease cerebral cavernous malformation