Search

Abstract Background Breast cancer (BC) is the most common cancer in women, and its progression is closely related to the phenomenon of anoikis. Anoikis, the specific programmed death resulting from a lack of contact between cells and the extracellular matrix, has recently been recognized as playing a critical role in tumor initiation, maintenance, and treatment. The ability of cancer cells to resist anoikis leads to cancer progression and metastatic colonization. However, the impact of anoikis on the prognosis of BC patients remains unclear. Method This study utilized data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to collect transcriptome and clinical data of BC patients. Anoikis-related genes (ARGs) were classified into subtypes A and B through consensus clustering. Subsequently, survival prognosis analysis, immune cell infiltration analysis, and functional enrichment analysis were performed for both subtypes. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a set of 10 ARGs related to prognosis was identified. Immune cell infiltration and tumor microenvironment analyses were conducted on these 10 ARGs to develop a prognostic model. Furthermore, single-cell data analysis and real-time polymerase chain reaction (RT-PCR) analysis were employed to study the expression of the 10 identified prognostic ARGs in BC cells. Results One hundred thirty-five ARGs were identified as differentially expressed genes in the TCGA and GEO databases, with 42 of them associated with the survival prognosis of BC patients. Analyses involving Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) revealed distinct expression patterns of ARGs between types A and B. Patients in type A exhibited worse survival prognosis and lower immune cell infiltration compared to type B. Subsequent analyses identified 10 key ARGs (YAP1, PIK3R1, BAK1, PHLDA2, EDA2R, LAMB3, CD24, SLC2A1, CDC25C, and SLC39A6) relevant to BC prognosis. Kaplan–Meier analysis indicated that high-risk patients based on these ARGs had a poorer BC prognosis. Additionally, Cox regression analysis established gender, age, T (tumor), N (nodes), and risk score as predictive factors in a nomogram model for BC. The model demonstrated diagnostic value for BC patients at 1, 3, and 5 years. Decision curve analysis (DCA) verified the risk score as a reliable predictor of BC patient survival rates. Moreover, RT-PCR results confirmed differential expressions of YAP1, PIK3R1, BAK1, PHLDA2, CD24, SLC2A1, and CDC25C in BC cells, with SLC39A6, EDA2R, and LAMB3 showing low expression levels. Conclusion ARGs markers can be used as BC biomarkers for risk stratification and survival prediction in BC patients. Besides, ARGs can be used as stratification factors for individualized and precise treatment of BC patients.

An analog PID controller-based galvanometer scanner is widely used by fractional laser medical equipment (FLME) to scan lasers across tissue surfaces, achieving the desired therapeutic effect. This type of driver, primarily composed of passive components and operational amplifiers, can only accept commands from the central controller of the FLME, with a simple hardware circuit-based fault diagnosis; thus, the safety of the FLME is compromised. To address these issues, the failure mechanisms of galvanometers and their impact on the safety of FLME are thoroughly analyzed first. Then, an adaptive limit protection method, a coil open circuit fault diagnosis, a communication timeout protection based on two handshakes, and a galvanometer control timeout protection are proposed, respectively, based on a digital driver platform, to supplement the deficiencies in the original fault diagnosis and protection system. This ensures the safety of the FLME. Finally, the effectiveness of the proposed strategies is validated through experiments.

The rough structure may affect the flow field and electromagnetic (EM) scattering characteristics of a hypersonic vehicle. In this study, the EM scattering characteristics of the hypersonic vehicle RAM C-II with a microrough surface were investigated in the millimeter wave band. We first simulated the flow field of a smooth RAM C-II vehicle and calculated the radar cross section (RCS) in the wideband using the physical optics method. The calculation results show that many factors contribute to the variation in the RCS; however, it is occasionally difficult to predict. We then used a one-dimensional Gaussian random rough surface to generate a RAM C-II with a microrough surface through a point-coordinate transformation and simulated its flow field. The electron density flow field of the rough surfaced aircraft fluctuates greatly on the wall. Finally, we calculated the RCS of RAM C-II with different values of roughness in the millimeter wave band. The change of surface structure can be reflected in millimeter waves, and the RCS is determined by both plasma and rough structure.

In this study, we obtained the intensity and orbital angular momentum (OAM) spectral distribution of the scattering fields of vortex electromagnetic beams illuminating electrically large targets composed of different materials. We used the angular spectral decomposition method to decompose a vortex beam into plane waves in the spectral domain at different elevations and azimuths. We combined this method with the physical optics algorithm to calculate the scattering field distribution. The OAM spectra of the scattering field along different observation radii were analyzed using the spiral spectrum expansion method. The numerical results indicate that for beams with different parameters (such as polarization, topological charge, half-cone angle, and frequency) and targets with different characteristics (such as composition), the scattering field intensity distribution and OAM spectral characteristics varied considerably. When the beam parameters change, the results of scattering from different materials show similar changing trends. Compared with beams scattered by uncoated metal and dielectric targets, the scattering field of the coating target can better maintain the shape and OAM mode of beams from the incident field. The scattering characteristics of metal targets were the most sensitive to beam-parameter changes. The relationship between the beam parameters, target parameters, the scattering field intensity, and the OAM spectra of the scattering field was constructed, confirming that the spiral spectrum of the scattering field carries the target information. These findings can be used in remote sensing engineering to supplement existing radar imaging, laying the foundation for further identification of beam or target parameters.

Background. NKG2A (KLRC1) belongs to the NKG2 family, which has been shown to affect the activity of natural killer (NK) cells and CD8T cells. However, a comprehensive biological analysis and exploration of NKG2A in different cancers is lacking and this needs to be further investigated. Methods. A comprehensive pan-cancer analysis of NKG2A was performed based on multiple databases. The Cancer Genome Atlas (TCGA) and Genotype–Tissue Expression (GTEx) databases were used to analyze the expression profile of NKG2A in pan-cancer. The relevance of NKG2A to the prognosis of different cancers was assessed using Kaplan–Meier survival analysis. In addition, we explored the correlation between NKG2A expression and gene mutations, pathological staging, tumor-infiltrating immune cells (TIICs), DNA methyltransferase (DNMT) genes, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), and immune checkpoints (ICPs). Finally, the expression levels of NKG2A in several cancer cell lines were verified by qRT-PCR. Results. Pan-cancer comprehensive analysis showed that NKG2A expression levels were significantly different between multiple cancers and corresponding normal tissues. The differential expression of NKG2A was related to the prognosis and pathological staging of patients with multiple cancers, and was closely related to the excessive infiltration of immune cells and the regulation of ICP genes in the tumor microenvironment (TME). In addition, TMB, MSI, MMR, and DNMT genes in many cancer types are also affected by NKG2A expression. Gene set enrichment analysis (GSEA) showed that NKG2A was associated with multiple immune-related functions and pathways in malignant tumors. qRT-PCR results showed that NKG2A was underexpressed in liver, gastric, and colon cancer cell lines compared to normal cells, which was consistent with bioinformatics analysis. Conclusion. The present study suggests that NKG2A may be a potential predictive biomarker for cancer immune response and prognosis.

A variety of modern AI products essentially require raw user data for training diverse machine learning models. With the increasing concern on data privacy, federated learning, a decentralized learning framework, enables privacy-preserving training of models by iteratively aggregating model updates from participants, instead of aggregating raw data. Since all the participants, i.e., mobile devices, need to transfer their local model updates concurrently and iteratively over mobile edge networks, the network is easily overloaded, leading to a high risk of transmission failures. Although previous works on transmission protocols have already tried their best to avoid transmission collisions, the number of iterative concurrent transmissions should be fundamentally decreased. Inspired by the fact that raw data are often generated unevenly among devices, those devices with a small proportion of data could be properly excluded since they have little effect on the convergence of models. To further guarantee the accuracy of models, we propose to properly select a subset of devices as participants to ensure the given proportion of involved data. Correspondingly, we propose to minimize the risk against the transmission failures during model updates. Afterwards, we design a randomized algorithm ($ran$ RFL) to choose suitable participants by using a series of delicately calculated probabilities, and prove that the result is concentrated on its optimum with high probability. Extensive simulations show that through delicate participant selection, $ran$ RFL decreases the maximal error rate of model updates by up to 38.3% compared with the state-of-the-art schemas.

The understanding of circuit diagram is very important for the study of electrical engineering. Existing circuit diagram simulation tools are mostly based on GUI interface and rely on users to click or drag icons with mouse, which requires them to be familiar with the software and distracts a great deal of their attention from the circuit diagram itself. Although a lot of previous works have devoted to designing algorithmic solution to recognize hand-drawn circuit diagrams automatically, there still exists strict constraints on users' drawing habits and stroke orders. In order to address these inconveniences, this paper proposes a novel sketch recognition algorithm named LS4D. It uses graph to model the input strokes and their relationships, and leverages cycles by local strokes to detect some circuit components. Theoretical derivations have demonstrated that LS4D can efficiently recognize diagrams with different drawing styles and arbitrary stroke orders. To furthermore illustrate the practical value of the proposed approach, we construct a prototype of pen-based circuit diagram system based on LS4D , which enables users to draw circuit diagrams directly on the digital screen without any other restriction. An experiment of 158 samples collected from 17 users is conducted on the designed platform. Our approach has achieved 93.04% recognition accuracy and overall 4.53 from a 5-scale user satisfaction rating, which outperforms previous state-of-the-art methods numerically. It is shown that the same approach can also be generalized to many other sketch recognition applications with minor modifications. To facilitate future researches and applications, we publish our source code, model, and training data at https://github.com/Huage001/ Graph-Based-Circuit-Painter.

In this paper, we obtain the intensity and phase distributions of the scattering and external fields of a vector Bessel–Gaussian vortex beam in the far-field region after being scattered by a particle. In our analysis, we use the Generalized Lorenz–Mie theory (GLMT) and the angular spectrum decomposition method (ASDM). The orbital angular momentum (OAM) spectra of the fields are analyzed by using the spiral spectrum expansion method, which is a frequently used tool for studying the propagation of vortex beams in turbulent atmospheres. Both scattered and external fields show a significant difference in spiral spectra for particles with different characteristic parameters, such as the size and complex refractive index. We also examine sampling the phase along with a circle and show that it is unable to fully express the information of the fields. This study can provide a theoretical basis for the inversion of characteristic parameters of the Bessel–Gaussian vortex beam and spherical particle by OAM spectra with applications in remote sensing engineering.

In this paper, information energy metric (IEM) is obtained by similarity computing for high-dimensional samples in a reproducing kernel Hilbert space (RKHS). Firstly, similar/dissimilar subsets and their corresponding informative energy functions are defined. Secondly, IEM is proposed for similarity measure of those subsets, which converts the non-metric distances into metric ones. Finally, applications of this metric is introduced, such as classification problems. Experimental results validate the effectiveness of the proposed method.