Percolation Theories for Quantum Networks

Quantum networks have experienced rapid advancements in both theoretical and experimental domains over the last decade, making it increasingly important to understand their large-scale features from the viewpoint of statistical physics. This review paper discusses a fundamental question: how can entanglement be effectively and indirectly (e.g., through intermediate nodes) distributed between distant nodes in an imperfect quantum network, where the connections are only partially entangled and subject to quantum noise? We survey recent studies addressing this issue by drawing exact or approximate mappings to percolation theory, a branch of statistical physics centered on network connectivity. Notably, we show that the classical percolation frameworks do not uniquely define the network's indirect connectivity. This realization leads to the emergence of an alternative theory called ``concurrence percolation,'' which uncovers a previously unrecognized quantum advantage that emerges at large scales, suggesting that quantum networks are more resilient than initially assumed within classical percolation contexts, offering refreshing insights into future quantum network design

YXQ-EQ Induces Apoptosis and Inhibits Signaling Pathways Important for Metastasis in Non-Small Cell Lung Carcinoma Cells

Background/Aims: Lung cancer is one of the most prevalent malignancies in the world. The 5-year survival rate for non-small cell lung cancer (NSCLC) patients is only approximately 15%, with metastasis as the primary cause of death. This study was aimed to investigate cytotoxic effect of external qi of Yan Xin Qigong (YXQ-EQ) toward human lung adenocarcinoma A549 cells as well as its effect on signaling pathways promoting migration, invasion and epithelial-to-mesenchymal transition (EMT) in A549 cells. Methods: Cytotoxic effect of YXQ-EQ was evaluated using MTS [3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, inner salt] and cologenic assays. Apoptosis of treated cells was determined by Annexin V/propidium iodide staining and flow cytometry analysis, while cell migration and invasion were determined using transwell assays and EMT was assessed by morphological changes in cells. Protein expression and phosphorylation were examined by immunoblot analyses. Results: YXQ-EQ induced apoptosis in A549 cells, resulting in a pronounced reduction in viability and clonogenic formation. This was associated with inhibition of phosphorylation of AKT and ERK1/2 and reduced expression of anti-apoptotic proteins BCL-xL, XIAP and survivin. Furthermore, YXQ-EQ inhibited EGF/EGFR signaling and EGF mediated migration and invasion of A549 cells. While TGF-β1 induced phosphorylation of SMAD2/3 and EMT in A549 cells, YXQ-EQ suppressed TGF-β/SMAD signaling and induced cell death in these cells in the presence of TGF-β1. Conclusion: Our findings suggest that YXQ-EQ could exert anti-lung cancer effects via inhibiting signaling pathways that are important for NSCLC cell survival and NSCLC metastasis

The complex hexaploid oil‐Camellia genome traces back its phylogenomic history and multi‐omics analysis of Camellia oil biosynthesis

Summary: Oil‐Camellia (Camellia oleifera), belonging to the Theaceae family Camellia, is an important woody edible oil tree species. The Camellia oil in its mature seed kernels, mainly consists of more than 90% unsaturated fatty acids, tea polyphenols, flavonoids, squalene and other active substances, which is one of the best quality edible vegetable oils in the world. However, genetic research and molecular breeding on oil‐Camellia are challenging due to its complex genetic background. Here, we successfully report a chromosome‐scale genome assembly for a hexaploid oil‐Camellia cultivar Changlin40. This assembly contains 8.80 Gb genomic sequences with scaffold N50 of 180.0 Mb and 45 pseudochromosomes comprising 15 homologous groups with three members each, which contain 135 868 genes with an average length of 3936 bp. Referring to the diploid genome, intragenomic and intergenomic comparisons of synteny indicate homologous chromosomal similarity and changes. Moreover, comparative and evolutionary analyses reveal three rounds of whole‐genome duplication (WGD) events, as well as the possible diversification of hexaploid Changlin40 with diploid occurred approximately 9.06 million years ago (MYA). Furthermore, through the combination of genomics, transcriptomics and metabolomics approaches, a complex regulatory network was constructed and allows to identify potential key structural genes (SAD, FAD2 and FAD3) and transcription factors (AP2 and C2H2) that regulate the metabolism of Camellia oil, especially for unsaturated fatty acids biosynthesis. Overall, the genomic resource generated from this study has great potential to accelerate the research for the molecular biology and genetic improvement of hexaploid oil‐Camellia, as well as to understand polyploid genome evolution

Heterogeneous temporal representation for diabetic blood glucose prediction

Background and aims: Blood glucose prediction (BGP) has increasingly been adopted for personalized monitoring of blood glucose levels in diabetic patients, providing valuable support for physicians in diagnosis and treatment planning. Despite the remarkable success achieved, applying BGP in multi-patient scenarios remains problematic, largely due to the inherent heterogeneity and uncertain nature of continuous glucose monitoring (CGM) data obtained from diverse patient profiles.Methodology: This study proposes the first graph-based Heterogeneous Temporal Representation (HETER) network for multi-patient Blood Glucose Prediction (BGP). Specifically, HETER employs a flexible subsequence repetition method (SSR) to align the heterogeneous input samples, in contrast to the traditional padding or truncation methods. Then, the relationships between multiple samples are constructed as a graph and learned by HETER to capture global temporal characteristics. Moreover, to address the limitations of conventional graph neural networks in capturing local temporal dependencies and providing linear representations, HETER incorporates both a temporally-enhanced mechanism and a linear residual fusion into its architecture.Results: Comprehensive experiments were conducted to validate the proposed method using real-world data from 112 patients in two hospitals, comparing it with five well-known baseline methods. The experimental results verify the robustness and accuracy of the proposed HETER, which achieves the maximal improvement of 31.42%, 27.18%, and 34.85% in terms of MAE, MAPE, and RMSE, respectively, over the second-best comparable method.Discussions: HETER integrates global and local temporal information from multi-patient samples to alleviate the impact of heterogeneity and uncertainty. This method can also be extended to other clinical tasks, thereby facilitating efficient and accurate capture of crucial pattern information in structured medical data

Landscape Characteristics Affecting Spatial Patterns of Water Quality Variation in a Highly Disturbed Region

Spatial patterns of water quality trends for 45 stations in control units of the Shandong Province, China during 2009–2017 were examined by a non-parametric seasonal Mann-Kendall’s test (SMK) for dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), permanganate index (CODMn), total phosphorus (TP) and ammonia nitrogen (NH3-N). The DO concentration showed significant upward trends at approximately half of the stations, while other parameters showed significant downward trends at more than 40% of stations. The stations with downward trends presented significant spatial autocorrelation, and were mainly concentrated in the northwest and southwest regions. The relationship between the landscape characteristics and water quality was explored using stepwise multiple regression models, which indicated the water quality was better explained using landscape pattern metrics compared to the percentage of land use types. Decreased mean patch area and connectedness of farmland will promote the control of BOD, COD and CODMn, whereas the increased landscape percentage of urban areas were not conducive to the water quality improvement, which suggested the sprawling of farmland and urban land was not beneficial to pollution control. Increasing the grassland area was conducive to the reduction of pollutants, while the effect of grassland fragmentation was reversed

Analysis of Land-Use Emergy Indicators Based on Urban Metabolism: A Case Study for Beijing

The correlation of urban metabolism and changes in land use is an important issue in urban ecology, but recent research lacks consideration of the mechanisms and interactions between them. In this research, we did an emergy analysis of the flows of materials, energy, and capital within the socioeconomic system of Beijing. We calculated emergy-based evaluation indices of urban metabolism and land use change, to analyze the relationship between urban metabolism and land use by correlation analysis and regression analysis. Results indicate that the socio-economic activities on built-up land depend on local, non-renewable resource exploitation and external resource inputs. The emergy utilization efficiency of farmland has consistently decreased, but there remains significant utilization potential there. Urban development in Beijing relies on production activities on built-up land, which is subjected to great environmental pressure during extraction of material resources. To keep the economy developing effectively, we suggest that Beijing should commit to development of a circular economy, and change the land-use concept to “Smart Growth”. In this paper, we efficaciously solve the problem of conflicting measurement units, and avoid the disadvantages of subjective assignment. Consequently, this work provides not only a more scientific way to study land problems, but also provides a reliable reference for ecological construction and economic development in Beijing

Quantum Measurement of Single Electron State by a Mesoscopic Detector

A realistic measurement setup for a system such system measured by a mesoscopie detector,is theoretically as a charged two-state （qubit） or multi-state quantum studied. To properly describe the measurement-induced back-action,a detailed-balance preserved quantum master equation treatment is developed. The established framework is applicable for arbitrary voltages and temperatures

The Beneficial Effects of Soybean Proteins and Peptides on Chronic Diseases

With lifestyle changes, chronic diseases have become a public health problem worldwide, causing a huge burden on the global economy. Risk factors associated with chronic diseases mainly include abdominal obesity, insulin resistance, hypertension, dyslipidemia, elevated triglycerides, cancer, and other characteristics. Plant-sourced proteins have received more and more attention in the treatment and prevention of chronic diseases in recent years. Soybean is a low-cost, high-quality protein resource that contains 40% protein. Soybean peptides have been widely studied in the regulation of chronic diseases. In this review, the structure, function, absorption, and metabolism of soybean peptides are introduced briefly. The regulatory effects of soybean peptides on a few main chronic diseases were also reviewed, including obesity, diabetes mellitus, cardiovascular diseases (CVD), and cancer. We also addressed the shortcomings of functional research on soybean proteins and peptides in chronic diseases and the possible directions in the future

Innovative Application of Metabolomics on Bioactive Ingredients of Foods

Metabolomics, as a new omics technology, has been widely accepted by researchers and has shown great potential in the field of nutrition and health in recent years. This review briefly introduces the process of metabolomics analysis, including sample preparation and extraction, derivatization, separation and detection, and data processing. This paper focuses on the application of metabolomics in food-derived bioactive ingredients. For example, metabolomics techniques are used to analyze metabolites in food to find bioactive substances or new metabolites in food materials. Moreover, bioactive substances have been tested in vitro and in vivo, as well as in humans, to investigate the changes of metabolites and the underlying metabolic pathways, among which metabolomics is used to find potential biomarkers and targets. Metabolomics provides a new approach for the prevention and regulation of chronic diseases and the study of the underlying mechanisms. It also provides strong support for the development of functional food or drugs. Although metabolomics has some limitations such as low sensitivity, poor repeatability, and limited detection range, it is developing rapidly in general, and also in the field of nutrition and health. At the end of this paper, we put forward our own insights on the development prospects of metabolomics in the application of bioactive ingredients in food

A method for the evaluation of image quality according to the recognition effectiveness of objects in the optical remote sensing image using machine learning algorithm.

Objective and effective image quality assessment (IQA) is directly related to the application of optical remote sensing images (ORSI). In this study, a new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment