Breast cancer is a highly aggressive type of cancer with very low median survival. Accurate prognosis prediction of breast cancer can spare a significant number of patients from receiving unnecessary ...adjuvant systemic treatment and its related expensive medical costs. Previous work relies mostly on selected gene expression data to create a predictive model. The emergence of deep learning methods and multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of breast cancer and therefore can improve diagnosis, treatment, and prevention. In this study, we propose a Multimodal Deep Neural Network by integrating Multi-dimensional Data (MDNNMD) for the prognosis prediction of breast cancer. The novelty of the method lies in the design of our method's architecture and the fusion of multi-dimensional data. The comprehensive performance evaluation results show that the proposed method achieves a better performance than the prediction methods with single-dimensional data and other existing approaches. The source code implemented by TensorFlow 1.0 deep learning library can be downloaded from the Github: https://github.com/USTC-HIlab/MDNNMD.
Accurate brain magnetic resonance imaging (MRI) tumor segmentation continues to be an active research topic in medical image analysis since it provides doctors with meaningful and reliable ...quantitative information in diagnosing and monitoring neurological diseases. Successful deep learning-based proposals have been designed, and most of them are built upon image patches. In this paper, a novel end-to-end brain tumor segmentation method is developed using an improved fully convolutional network by modifying the U-Net architecture. In our network, an innovative structure referred to as an up skip connection is first proposed between the encoding path and decoding path to enhance information flow. Moreover, an inception module is adopted in each block to help our network learn richer representations, and an efficient cascade training strategy is introduced to segment brain tumor subregions sequentially. In contrast to those patchwise methods, our model can automatically generate segmentation maps slice by slice. We have validated our proposal by using imaging data from the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2015 and BRATS 2016. Experimental results compared with U-Net suggest that our method is 2.6%, 3.9%, and 5.2% higher (by using the BRATS 2015 training dataset) as well as 2.8%, 3.7%, and 8.1% (by using the BRATS 2017 training dataset) higher in terms of complete, core and enhancing tumor regions, respectively. Quantitative and visual evaluation of our method has revealed the effectiveness of the proposed improvements and indicated that our end-to-end segmentation method can achieve a performance that can compete with state-of-the-art brain tumor segmentation approaches.
•An end-to-end brain tumor segmentation method based on image slices in both training and testing phases.•The method is built upon U-Net with innovative up skip connections and modified Inception modules.•A cascaded training strategy can help improve segmentation performance of the proposed method.•The method achieves competitive performance as state-of-the-art brain tumor segmentation methods.
Circular RNAs were recently identified as a novel type of noncoding RNAs. An increasing number of reports have demonstrated their essential regulatory roles in various biological processes and human ...diseases, including cancer. However, the role of circRNA in cervical cancer (CC) remains largely unknown. In the current study, we investigated the physiological functions of circ_0067934 during CC development and progression. We found that circ_0067934 was overexpressed in CC tissues and cell lines. Circ_0067934 upregulation was associated with advanced stage, lymph node metastasis, and poor prognosis in CC patients. Knockdown of circ_0067934 suppressed the proliferation, colony formation, migration, invasion, and epithelial‐mesenchymal transition of CC cells in vitro. Circ_0067934 loss also inhibited CC tumor growth in vivo. Mechanistically, silencing circ_0067934 increased miR‐545 expression. MiR‐545 repressed EIF3C expression through targeting its 3′‐untranslated region. MiR‐545 suppressed the proliferation, migration, and invasion of CC cells, whereas restoration of EIF3C could rescue the effects of circ_0067934 knockdown. Taken together, our findings revealed that circ_0067934 promotes CC progression via miR‐545/EIF3C axis. Our study may provide a new insight into the pathogenesis of CC.
Our findings revealed that circ_0067934 promotes CC progression via miR‐545/EIF3C axis. Our study may provide a new insight into the pathogenesis of CC.
A wideband compact magnetoelectric (ME) dipole antenna is investigated for millimeter-wave applications. First, an aperture coupled ME dipole is proposed with wideband and low profile. Next, ...transverse slots are added to miniaturize the antenna. The radiation performance of the higher-order mode is also improved. The antenna is finally miniaturized to <inline-formula> <tex-math notation="LaTeX">2.5\times3.3 </tex-math></inline-formula> mm 2 (<inline-formula> <tex-math notation="LaTeX">0.27\,\,\lambda _{0} \times 0.35\,\,\lambda _{0} </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">\lambda _{0} </tex-math></inline-formula> is the wavelength in free space at center frequency) when it is used in the array environment. A bandwidth of 48.8% (24.3-40 GHz) for SWR < 2 can be achieved, with unidirectional radiation performance over the operating band. By combining the proposed compact antenna with an eight-way substrate integrated coaxial line (SICL) feed network, a <inline-formula> <tex-math notation="LaTeX">1 \times 8 </tex-math></inline-formula> linear array is designed, fabricated, and measured. Good beam scanning capability is also verified by active simulation. With the advantages of wide bandwidth, compact size, promising radiation pattern and wide-angle beam scanning potential, the proposed antenna would be attractive for millimeter-wave devices and antenna in package (AiP) applications.
In view of the large amount of data collected by an edge server, when compression technology is used for data compression, data classification accuracy is reduced and data loss is large. This paper ...proposes a data compression algorithm based on the chaotic mutation adaptive sparrow search algorithm (CMASSA). Constructing a new fitness function, CMASSA optimizes the hyperparameters of the Convolutional Auto-Encoder Network (CAEN) on the cloud service center, aiming to obtain the optimal CAEN model. The model is sent to the edge server to compress the data at the lower level of edge computing. The effectiveness of CMASSA performance is tested on ten high-dimensional benchmark functions, and the results show that CMASSA outperforms other comparison algorithms. Subsequently, experiments are compared with other literature on the Multi-class Weather Dataset (MWD). Experiments show that under the premise of ensuring a certain compression ratio, the proposed algorithm not only has better accuracy in classification tasks than other algorithms but also maintains a high degree of data reconstruction.
The design and synthesis of uranium sorbent materials with high uptake efficiency, capacity and selectivity, as well as excellent hydrolytic stability and radiation resistance remains a challenge. ...Herein, a polyoxometalate (POM)–organic framework material (SCU‐19) with a rare inclined polycatenation structure was designed, synthesized through a solvothermal method, and tested for uranium separation. Under dark conditions, SCU‐19 can efficiently capture uranium through ligand complexation using its exposed oxo atoms and partial chemical reduction from UVI to UIV by the low‐valent Mo atoms in the POM. An additional UVI photocatalytic reduction mechanism can occur under visible light irradiation, leading to a higher uranium removal without saturation and faster sorption kinetics. SCU‐19 is the only uranium sorbent material with three distinct sorption mechanisms, as further demonstrated by X‐ray photoelectron spectroscopy (XPS) and X‐ray absorption near edge structure (XANES) analysis.
Stuck on U: Uranium capture by a polyoxometalate–organic framework is possible through three different mechanisms, these are complexation, chemical reduction, and photocatalytic reduction. The material features a unique 2D+2D→3D polycatenation structure, resulting in excellent stability toward hydrolysis and ionization irradiation.
Cluster-based channel modeling has been an important trend in the development of channel model, as it maintains accuracy while reducing complexity. Whereas a large number of channel measurements have ...shown that multipath components (MPCs) are distributed as groups, i.e., clusters, existing clustering algorithms have various drawbacks with respect to complexity, threshold choices, and/or assumptions about prior knowledge. In this paper, a kernel-power-density (KPD)-based algorithm is proposed for MPC clustering. It uses the kernel density of MPCs to incorporate the modeled behavior of MPCs and takes into account the power of the MPCs. Furthermore, the KPD algorithm only considers the K nearest MPCs in the density estimation to better identify the local density variations of MPCs. A heuristic approach of cluster merging is used to improve the performance. Both simulation and channel measurements validate the KPD algorithm, and almost no performance degradation is found even with a large number of clusters and large cluster angular spread, which outperforming other algorithms. The KPD algorithm enables applications in multipleinput-multiple-output channels with no prior knowledge about the clusters, such as number and initial locations. It also has a fairly low computational complexity and can be used for clusterbased channel modeling.
Photocaging allows for precise spatiotemporal control over the release of biologically active compounds with light. Most photocaged molecules employ organic photolabile protecting groups; however, ...biologically active compounds often contain functionalities such as nitriles and aromatic heterocycles that cannot be caged with organic groups. Despite their prevalence, only a few studies have reported successful caging of nitriles and aromatic heterocycles. Recently, Ru(ii)-based photocaging has emerged as a powerful method for the release of bioactive molecules containing these functional groups, in many cases providing high levels of spatial and temporal control over biological activity. This Feature Article discusses recent developments in applying Ru(ii)-based photocaging towards biological problems. Our groups designed and synthesized Ru(ii)-based platforms for the photoinduced delivery of cysteine protease and cytochrome P450 inhibitors in order to achieve selective control over enzyme inhibition. We also reported Ru(ii) photocaging groups derived from higher-denticity ancillary ligands that possess photophysical and photochemical properties distinct from more traditional Ru(ii)-based caging groups. In addition, for the first time, we are able to rapidly synthesize and screen Ru(ii) polypyridyl complexes that elicit desired properties by solid-phase synthesis. Finally, our work also defined steric and orbital mixing effects that are important factors in controlling photoinduced ligand exchange.
Accumulating evidences have indicated that lncRNAs play an important role in various human complex diseases. However, known disease-related lncRNAs are still comparatively small in number, and ...experimental identification is time-consuming and labor-intensive. Therefore, developing a useful computational method for inferring potential associations between lncRNAs and diseases has become a hot topic, which can significantly help people to explore complex human diseases at the molecular level and effectively advance the quality of disease diagnostics, therapy, prognosis and prevention. In this paper, we propose a novel prediction of lncRNA-disease associations via lncRNA-disease-gene tripartite graph (TPGLDA), which integrates gene-disease associations with lncRNA-disease associations. Compared to previous studies, TPGLDA can be used to better delineate the heterogeneity of coding-non-coding genes-disease association and can effectively identify potential lncRNA-disease associations. After implementing the leave-one-out cross validation, TPGLDA achieves an AUC value of 93.9% which demonstrates its good predictive performance. Moreover, the top 5 predicted rankings of lung cancer, hepatocellular carcinoma and ovarian cancer are manually confirmed by different relevant databases and literatures, affording convincing evidence of the good performance as well as potential value of TPGLDA in identifying potential lncRNA-disease associations. Matlab and R codes of TPGLDA can be found at following: https://github.com/USTC-HIlab/TPGLDA .
•Plant species for accumulating Hg studied in the past 20 years are reviewed.•The type of Hg accelerators studied in the past 20 years are reviewed.•Transgenic plants for accumulating studied in the ...past 20 years are reviewed.•Phytoremediation and assisted phytoremediation are compared and discussed.•Some significant future perspectives are proposed.
Mercury (Hg) and its compounds are one of the most dangerous environmental pollutants and Hg pollution exists in soils in different degrees over the world. Phytoremediation of Hg-contaminated soils has attracted increasing attention for the advantages of low investment, in-situ remediation, potential economic benefits and so on. Searching for the hyperaccumulator of Hg and its application in practice become a research hotspot. In this context, we review the current literatures that introduce various experimental plant species for accumulating Hg and aided techniques improving the phytoremediation of Hg-contaminated soils. Experimental plant species for accumulating Hg and accumulation or translocation factor of Hg are listed in detail. The translocation factor (TF) is greater than 1.0 for some plant species, however, the bioaccumulation factor (BAF) is greater than 1.0 for Axonopus compressus only. Plant species, soil properties, weather condition, and the bioavailability and heterogeneity of Hg in soils are the main factors affecting the phytoremediation of Hg-contaminated soils. Chemical accelerator kinds and promoting effect of chemical accelerators for accumulating and transferring Hg by various plant species are also discussed. Potassium iodide, compost, ammonium sulphate, ammonium thiosulfate, sodium sulfite, sodium thiosulfate, hydrochloric acid and sulfur fertilizer may be selected to promote the absorption of Hg by plants. The review introduces transgenic gene kinds and promoting effect of transgenic plants for accumulating and transferring Hg in detail. Some transgenic plants can accumulate more Hg than non-transgenic plants. The composition of rhizosphere microorganisms of remediation plants and the effect of rhizosphere microorganisms on the phytoremediation of Hg-contaminated soils are also introduced. Some rhizosphere microorganisms can increase the mobility of Hg in soils and are beneficial for the phytoremediation.
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