Breast cancer (BC) is highly malignant and its mortality rate remains high. The development of immunotherapy has gradually improved the prognosis and survival rate of patients. Therefore, identifying ...molecular markers concerned with BC immunity is of great importance for the treatment of this disease. The Cancer Genome Atlas-breast invasive carcinoma (TCGA-BRCA) was utilized as the training set while the BC expression dataset from the gene expression omnibus database was taken as the validation set here. Weighted gene co-expression network analysis combined with Pearson analysis and Tumor immune estimation resource (TIMER) was used to obtain immune cell-related hub gene module. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on this module. Then, receiver operating characteristic curves combining Kaplan–Meier was used to evaluate the effectiveness of the model. Feature genes were screened and the independence of risk score was evaluated by univariate and multivariate Cox analyses. Differences in immune characteristics were analyzed via single-sample gene set enrichment analysis and CIBERSORT, and differences in gene mutation frequency were assessed via GenVisR analysis. Finally, the expression levels of prognostic feature genes in BC cells were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). In this study, cell immune-related gene modules in TCGA-BRCA were successfully excavated, and a five-gene (TNFRSF14, NFKBIA, DLG3, IRF2, and CYP27A1) prognostic model was established. The prognostic model could effectively forecast the prognosis and survival rate of BC patients. The result showed that human leukocyte antigen-related proteins and macrophage M2 scores were remarkably highly expressed in the high-risk group, whereas CD8+ T cells, natural killer cells, M1, and other anti-tumor cells were lowly expressed. The model could be used as an independent prognostic factor to predict the prognosis of BC patients. The results of qRT-PCR validation were consistent with the results in the database, that is, except DLG3, the other four feature genes were lowly expressed in BC. The five-gene model established in this study can predict the prognostic and immune mode of BC patients effectively, which is anticipated to become a feasible molecular target for BC therapy.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
An innovative three-dimensional hazard detection algorithm is proposed for a planetary safe landing mission. Typically, plane fitting is carried out to approximate the surface of the planetary ...terrain. Current state-of-the-art plane estimation methods with a low breakdown point do not meet the requirements of future pinpoint landing missions. In this paper, a novel robust scale estimator called Preprocessing and Double Iterative Median Scale Estimator (PDIMSE) is proposed, which can achieve a breakdown point of more than 50%. It can be applied to future Mars landing missions.
Notch1 is a multifunctional transmembrane receptor that regulates cellular differentiation, development, proliferation, and
survival in a variety of contexts. We have previously shown that Notch1 may ...function as a tumor suppressor and that histone
deacetylase (HDAC) inhibitors can induce Notch1 expression in some endocrine cancers. Here, we showed that although there
was minimal Notch1 expression in follicular thyroid cancer FTC236 and papillary thyroid cancer DRO cells, transfection of
constitutive Notch1 plasmid into these cells led to growth inhibition, down-regulation of cyclin D1, and up-regulation of
p21. Treatment of FTC236 cells with HDAC inhibitors valproic acid (1–4 mmol/L) or suberoyl bishydroxamic acid (10–30 μmol/L)
induced functional Notch1 protein expression and suppressed cell growth in a dose-dependent manner. Notch1 siRNA interference
blocked the antiproliferative effect of HDAC inhibitors. Western blot analysis revealed the reduction of cyclin D1 and the
increase of p21 in HDAC inhibitor–treated cells. These results indicate that HDAC inhibitors activate Notch1 signaling in
thyroid cancer cells and lead to the suppression of proliferation by cell cycle arrest. Our findings provide the first documentation
of the role of Notch1 signaling as a tumor suppressor in DRO and FTC236 cells, suggesting that Notch1 activation may be a
potential therapeutic target for papillary and follicular thyroid cancers. Mol Cancer Ther 2009;8(2):350–6
Martian rock segmentation aims to separate rock pixels from background, which plays a crucial role in downstream tasks, such as traversing and geologic analysis by Mars rovers. The U-Nets have ...achieved certain results in rock segmentation. However, due to the inherent locality of convolution operations, U-Nets are inadequate in modeling global context and long-range spatial dependencies. Although emerging Transformers can solve this, they suffer from difficulties in extracting and retaining sufficient low-level local information. These shortcomings limit the performance of existing networks for Martian rocks that are variable in shape, size, texture and color. Therefore, we propose RockFormer, the first U-shaped Transformer framework for Mars rock segmentation, consisting of a hierarchical encoder-decoder architecture with a Feature Refining Module (FRM) connected between them. Specifically, the encoder hierarchically generates multi-scale features using an improved vision Transformer (improved-ViT), where both abundant local information and long-range contexts are exploited. The FRM removes less representative features and captures global dependencies between multi-scale features, improving RockFormer's robustness to Martian rocks with diverse appearances. The decoder is responsible for aggregating these features for pixel-wise rock prediction. For evaluation, we establish two Mars rock datasets, including both real and synthesized images. One is MarsData-V2, an extension of our previously published MarsData collected from real Mars rocks. The other is SynMars, a synthetic dataset sequentially photographed from a virtual terrain built referring to the TianWen-1 dataset. Extensive experiments on the two datasets show the superiority of RockFormer for Martian rock segmentation, achieving state-of-the-art performance with decent computational simplicity.
Semantic segmentation of Martian terrain is crucial for the route planning and autonomous navigation of rovers on Mars. However, existing methods are restricted to structured or semi-structured ...scenes, performing poorly on Mars that is a completely unstructured environment. Therefore, we propose a novel hybrid attention semantic segmentation (HASS) network, which contains a global intra-class attention branch, a local inter-class attention branch and a representation merging module. Specifically, the global attention branch draws the consistencies of all homogeneous pixels in the whole image, and the local attention branch models the relationships between specific heterogeneous pixels with the supervision of elaborately designed loss function. The merging module aggregates the contexts from the two branches for the final segmentation. Furthermore, we establish a panorama semantic segmentation dataset of Martian landforms, named MarsScapes, which provides fine-grained annotations for eight semantic categories. Extensive experiments on our MarsScapes and the public AI4Mars datasets show the superiority of the proposed method.
•We design a hybrid attention semantic segmentation method with a dual-branch network.•We establish a panorama dataset of Martian landforms with detailed annotations.•We demonstrate HASS outperforms existing approaches through extensive experiments.
Martian terrain segmentation aims to assign all pixels of an input image with various terrain labels, which provides a firm support for the downstream research on rover traversing and geologic ...analysis tasks. However, existing studies in this field suffer from limitations in two aspects: one is the lack of large-scale and high-quality Martian terrain datasets, and the other is the over-reliance on purely supervised learning that is very data-hungry and sensitive to domain shifts among different datasets. In this paper, we overcome these from the perspective of both data and methodology. First, we publish MarsScapes, a panorama dataset with appreciable data volume and fine-grained annotations for Martian terrain understanding. The dataset contains 195 terrain panoramas composed of 3779 sub-images, and all pixels in the panoramas are split into 9 semantic categories. Then, we propose the first Transformer-based unsupervised domain adaptation (UDA) framework (UDAFormer) for the cross-domain terrain segmentation on Mars, which consists of a teacher-student model and an output-guided biased sampling (OGBS) module. The teacher-student model performs knowledge distillation to explore robust cross-domain features, where a modified augmentation regularization is designed to alleviate the interference of undesirable augmentations to domain adaption. The OGBS helps the teacher-student network to emphasize the categories that tend to be ambiguous or submerged during the training, elevating the overall accuracy for the UDA segmentation of Martian terrains. Extensive experiments on the MarsScapes and another dataset called Mars-Seg demonstrate the superiority of UDAFormer over state-of-the-art methods in UDA Martian terrain segmentation.
Auto-semantic segmentation is important for robots on unstructured and dynamic environments like planets where ambient conditions cannot be controlled and the scale is larger than that found indoors. ...Current learning-based methods have achieved breathtaking improvements on this topic. For onboard applications, however, all those methods still suffer from huge computational costs and are difficult to deploy on edge devices. In this paper, unlike previous transformer-based SOTA approaches that heavily relied on complex design, we proposed Light4Mars, a lightweight model with minimal computational complexity while maintaining high segmenting accuracy. We designed a lightweight squeeze window transformer module that focuses on window-scale feature extraction and is more effective in learning global and local contextual information. The aggregated local attention decoder is utilized to fuse semantic information at different scales, especially for unstructured scenes. Since there are few all-terrain datasets for semantic segmentation of unstructured scenes like Mars, we built a synthetic dataset SynMars-TW, referencing images collected by the ZhuRong rover on the Tianwen-1 mission and the Curiosity rover. Extensive experiments on SynMars-TW and the real Mars dataset, MarsScapes show that our approach achieves state-of-the-art performance with favorable computational simplicity. To the best of our knowledge, the proposed Light4Mars-T network is the first segmentation model for Mars image segmentation with parameters lower than 0.1M. Code and datasets are available at https://github.com/CVIR-Lab/Light4Mars.
This article presents two kernel-based rock detection methods for a Mars rover. Rock detection on planetary surfaces is particularly pivotal for planetary vehicles regarding navigation and obstacle ...avoidance. However, the diverse morphologies of Martian rocks, the sparsity of pixel-wise features, and engineering constraints are great challenges to current pixel-wise object detection methods, resulting in inaccurate and delayed object location and recognition. We therefore propose a region-wise rock detection framework and design two detection algorithms, kernel principle component analysis (KPCA)-based rock detection (KPRD) and kernel low-rank representation (KLRR)-based rock detection (KLRD), using hypotheses of feature and sub-spatial separability. KPRD is based on KPCA and is expert in real-time detection yet with less accurate performance. KLRD is based on KPRD with KLRR which can generate more precise rock detection results with less delay. To validate the efficiency of the proposed methods, we build a small-scale Martian rock dataset, MarsData, containing various rocks. Preliminary experimental results show that our methods are efficient in dealing with complex images containing rocks, shadows, and gravel. The code and data are available at: https://github.com/CVIR-Lab/MarsData .
With the increase in the number of types of spectrometers in use, calibration models cannot be shared among different instruments; however, this problem can be solved via calibration transfer (CT). ...In this study, a variety of modern process analysis technology (PAT) data are taken as the research object. After preprocessing the spectra data using principal component analysis (PCA) and cubic spline interpolation, the TrAdaBoost algorithm in transfer learning combined with extreme learning machine (ELM), i.e., TrAdaBoost-ELM, is used to transfer the master model to slave instruments and to make comparisons with the transfer via an extreme learning machine auto-encoder method (TEAM) and the semisupervised parameter-free framework for calibration enhancement (SS-PFCE) method. After the master model is transferred by the TrAdaBoost-ELM algorithm for the prediction dataset of slave instruments, the mean coefficient of determination of prediction (Formula Omitted) increases from 0.7843 to 0.8707, and the mean root-mean-square error of prediction (RMSEP) decreases from 2.7508 to 2.3112. Furthermore, variable combination population analysis (VCPA) in combination with a genetic algorithm (VCPA-IGA) was used to select characteristic wavelengths in molecular and atomic spectra, respectively. For the same type of laser-induced breakdown spectroscopy (LIBS) instruments K1 and K2, after processing by the VCPA-IGA algorithm, the LIBS calibration model established on K1 was transferred successfully to K2, and for the major elements, the mean Formula Omitted = 0.9563 and the mean RMSEP = 1.3796. After processing by the VCPA algorithm, the near-infrared (NIR) model for instrument L was transferred to a different instrument J, and the prediction results were Formula Omitted = 0.9110 and RMSEP = 0.4044 °Brix. The results demonstrated that an appropriate variable selection method combined with the TrAdaBoost-ELM algorithm can be effectively used for CT for spectrometers of the same and different types, thus achieving model sharing between different spectrometers.
Semantic segmentation of Mars scenes has a crucial role in Mars rovers science missions. Current convolutional neural network (CNN)-based composition of U-Net has powerful information extraction ...capabilities; however, convolutional localization suffers from the limited global context modeling capability. Although transformer global modeling has performed well, it still encounters obstacles in the extraction and retention of low-level features. This issue is particularly relevant for Martian rocks with their varying shapes, textures, and sizes in Mars scenes. In this article, we propose a novel transformer semantic segmentation framework for Martian rock images, called MarsFormer, that consists of an encoder-decoder structure connected through a feature enhancement module (FEM) and a window transformer block (WTB). Specifically, multiscale hierarchical features are generated by the mix transformer (MiT) encoders, upgraded-FFN decoder (UFD) fuse and filter features at different scales, preserving the rich local and global contextual information. FEM enhances the inter-multiscale feature correlation from both spatial and channel perspectives. WTB captures the long-range contexts and preserves the local features. We built two datasets of synthetic and real Martian rocks. The synthetic dataset is SynMars, referencing data from the ZhuRong rover taken from its virtual terrain engine. The other dataset is MarsData-V2, from real Mars scenes, and published recently in our previous study. Extensive experiments conducted on both datasets showed that MarsFormer achieves superiority in Martian rock segmentation, obtaining state-of-the-art performance with favorable computational simplicity. The data are available at: https://github.com/CVIR-Lab/SynMars .
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