Semantic segmentation-based semantic communication system for image transmission

With the rapid development of artificial intelligence and the widespread use of the Internet of Things, semantic communication, as an emerging communication paradigm, has been attracting great interest. Taking image transmission as an example, from the semantic communication's view, not all pixels in the images are equally important for certain receivers. The existing semantic communication systems directly perform semantic encoding and decoding on the whole image, in which the region of interest cannot be identified. In this paper, we propose a novel semantic communication system for image transmission that can distinguish between Regions Of Interest (ROI) and Regions Of Non-Interest (RONI) based on semantic segmentation, where a semantic segmentation algorithm is used to classify each pixel of the image and distinguish ROI and RONI. The system also enables high-quality transmission of ROI with lower communication overheads by transmissions through different semantic communication networks with different bandwidth requirements. An improved metric θPSNR is proposed to evaluate the transmission accuracy of the novel semantic transmission network. Experimental results show that our proposed system achieves a significant performance improvement compared with existing approaches, namely, existing semantic communication approaches and the conventional approach without semantics.

Rectal Cancer Stages T2 and T3 Identification Based on Asymptotic Hybrid Feature Maps

Many existing intelligent recognition technologies require huge datasets for model learning.However,it is not easy to collect rectal cancer images,so the performance is usually low with limited training samples.In addition,traditional rectal cancer staging is time-consuming,error-prone,and susceptible to physicians’subjective awareness as well as professional expertise.To settle these deficiencies,we propose a novel deep-learning model to classify the rectal cancer stages of T2 and T3.First,a novel deep learning model(RectalNet)is constructed based on residual learning,which combines the squeeze-excitation with the asymptotic output layer and new cross-convolution layer links in the residual block group.Furthermore,a two-stage data augmentation is designed to increase the number of images and reduce deep learning’s dependence on the volume of data.The experiment results demonstrate that the proposed method is superior to many existing ones,with an overall accuracy of 0.8583.Oppositely,other traditional techniques,such as VGG16,DenseNet121,EL,and DERNet,have an average accuracy of 0.6981,0.7032,0.7500,and 0.7685,respectively.

Synergistic Promotion of Electrical, Mechanical and Thermal Properties for Silicone Rubber-based Field Grading Material via Compound Modification

Use of nonlinearconductive SiC/silicone rubber(SR)field grading material(FGM)can improve the local field concentration of composite insulators.Adding large volume fraction and large-size SiC particles(SiCp)into SR can obtain a good field grading effect,but it is accompanied by the deterioration of mechanical properties.Compounding SiC with different shapes can solve this contradiction.By incorporating one-dimensional SiC whiskers(SiCw)to synergize with granular SiCp,SiC/SR FGM with better field-dependent conductivity,mechanical properties and thermal conductivity than large-size SiCp and large volume fraction filling case can be obtained by using smaller size SiCp and lower filling contents.The simulations of 500 kv line insulators show that the modified SiC/SR FGM can reduce the maximum field strength along the insulator surface and at sheath-core rod interfaces by 55%and 71.4%,respectively.The combined application of FGM and grading ring can achieve a complementary effect.Using FGM to partially replace the role of the grading rings,the field strength indicators can still meet the operational requirements after the tube radius and shielding depth of the grading rings at both ends are reduced by 36.2%and 40%separately,which is a benefit to alleviating the problems of high weight and large volume faced by traditional field grading methods.

Decitabine induces IRF7-mediated immune responses in p53-mutated triple-negative breast cancer:a clinical and translational study

p53 is mutated in half of cancer cases.However,no p53-targeting drugs have been approved.Here,we reposition decitabine for triple-negative breast cancer(TNBC),a subtype with frequent p53 mutations and extremely poor prognosis.In a retrospective study on tissue microarrays with 132 TNBC cases,DNMT1 overexpression was associated with p53 mutations(P=0.037)and poor overall survival(OS)(P=0.010).In a prospective DEciTabinE and Carboplatin in TNBC(DETECT)trial(NCT03295552),decitabine with carboplatin produced an objective response rate(ORR)of 42%in 12 patients with stage IV TNBC.Among the 9 trialed patients with available TP53 sequencing results,the 6 patients with p53 mutations had higher ORR(3/6 vs.0/3)and better OS(16.0 vs.4.0 months)than the patients with wild-type p53.In a mechanistic study,isogenic TNBC cell lines harboring DETECT-derived p53 mutations exhibited higher DNMT1 expression and decitabine sensitivity than the cell line with wild-type p53.In the DETECT trial,decitabine induced strong immune responses featuring the striking upregulation of the innate immune player IRF7 in the p53-mutated TNBC cell line(upregulation by 16-fold)and the most responsive patient with TNBC.Our integrative studies reveal the potential of repurposing decitabine for the treatment of p53-mutated TNBC and suggest IRF7 as a potential biomarker for decitabine-based treatments.

分子基导电聚合物吸波材料:进展与未来挑战

随着科技的发展,电磁辐射泄漏引起的电磁污染问题日趋严峻.电磁污染不仅会影响电子设备的功能,而且会对人体健康产生不良的影响.为解决这一问题,研发具有各种新颖结构的电磁波吸收材料具有重要的意义.分子基导电吸波材料因其独特的物理化学特性展现出良好的吸波潜能,吸引了研究者的广泛兴趣.本文综述了近年来有关分子基导电吸波材料的相关研究成果.首先,介绍了分子基导电吸波材料的吸波原理.然后,聚焦分子基导电吸波材料中的三大类材料,即导电高分子聚合物、导电共轭聚合物和导电金属-有机配合物吸波材料,讨论了其结构设计调控策略和吸波损耗机制.此外,本文还介绍了典型的制备分子基导电吸波材料的方法.最后,结合面向前沿的吸波材料的需求,指出了分子基导电吸波材料未来研究亟待解决的挑战.

Differentiation trajectories and biofunctions of symbiotic and un-symbiotic fate cells in root nodules of Medicago truncatula

The root nodule is a complex symbiotic nitrogen fixation factory,in which cells are highly heterogeneous.However,the differentiation trajectories and interconnection of nodule cells remain largely unknown.In this study,we set up a modified protocol for nodule protoplast preparation and performed a single-cell RNA sequencing profiling of the indeterminate Medicago truncatula nodule.We designated 13 cell clusters with specific expression patterns in 14-day post inoculation nodules and constructed a spatial and functional cellular map based on experimental data and bioinformatic analyses.Pseudotime analysis further revealed that two groups of apical meristematic cells develop into symbiotic and un-symbiotic fate cells along their particular trajectories.Biofunction analysis of each cell cluster revealed their particularity and interrelation,especially that the un-infected cells in nitrogen fixation zone are also involved in nitrogen assimilation by undertaking the asparagine synthesis.Collectively,our data offer an important resource for investigating the mechanism of nodule organogenesis and symbiotic nitrogen fixation.