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In recent years, deep convolutional neural networks (DCNNs) have shown their predominant place in building extraction. Generally, the performance of DCNNs depends on the quality of parameter learning. To improve the performance, many approaches chose to enlarge the network size or employ additional plug-in modules. However, there exists large redundancy in parameter learning of neural networks. We argue that the methods totally relying on parameter learning cannot fully exploit the useful information of the given data, and feature learned by such methods is not comprehensive enough. In this paper, we propose an efficient memory module, named EMM , to enhance the learning ability of DCNNs in building extraction. In our method, we encode each batch of data into memory key-value pairs and store them in memory banks, in which each memory will dynamically update during the training process. Inspired by the non-local module, we propose to use a tri-branch way for a more efficient memory retrieval process. By training with our memory module, DCNNs can learn more holistic and discriminative features efficiently and effectively. We conduct intensive experiments on three challenging public datasets. Compared with popular segmentation networks on all three datasets, our method can achieve competitive performance while keeping relatively fewer parameters. • Using deep neural networks to extracts building footprints automatically. • Integrated memory module enables network learning to have more distinguishing feature. • The proposed memory module consumes less computation than the original module. • Implicit relation between features is established to capture discriminant features. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) causes great suffering to patients because of its heterogeneity, poor prognosis, and chemotherapy resistance. Ferroptosis is characterized by iron-dependent oxidative damage by accumulating intracellular lipid peroxides to lethal levels, and plays a vital role in the treatment of TNBC based on its intrinsic characteristics. To identify the relationship between chemotherapy resistance and ferroptosis in TNBC, we analyzed the single cell RNA-sequencing public dataset of GSE205551. It was found that the expression of Gpx4 in DOX-resistant TNBC cells was significantly higher than that in DOX-sensitive TNBC cells. Based on this finding, we hypothesize that inducing ferroptosis by inhibiting the expression of Gpx4 can reduce the resistance of TNBC to DOX and enhance the therapeutic effect of chemotherapy on TNBC. Herein, dihydroartemisinin (DHA)-loaded polyglutamic acid-stabilized Fe 3 O 4 magnetic nanoparticles (Fe 3 O 4 -PGA-DHA) was combined with DOX-loaded polyaspartic acid-stabilized Fe 3 O 4 magnetic nanoparticles (Fe 3 O 4 -PASP-DOX) for ferroptosis-enhanced chemotherapy of TNBC. Compared with Fe 3 O 4 -PASP-DOX, Fe 3 O 4 -PGA-DHA + Fe 3 O 4 -PASP-DOX demonstrated significantly stronger cytotoxicity against different TNBC cell lines and achieved significantly more intracellular accumulation of reactive oxygen species and lipid peroxides. Furthermore, transcriptomic analyses demonstrated that Fe 3 O 4 -PASP-DOX-induced apoptosis could be enhanced by Fe 3 O 4 -PGA-DHA-induced ferroptosis and Fe 3 O 4 -PGA-DHA + Fe 3 O 4 -PASP-DOX might trigger ferroptosis in MDA-MB-231 cells by inhibiting the PI3K/AKT/mTOR/GPX4 pathway. Fe 3 O 4 -PGA-DHA + Fe 3 O 4 -PASP-DOX showed superior anti-tumor efficacy on MDA-MB-231 tumor-bearing mice, providing great potential for improving the therapeutic effect of TNBC. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • 3,3-Disubstituted isoindolin-1-ones were prepared by radical addition and cyclization in moderate to good yields. • Toluene derivatives were used as radical precursors and two new C–C bonds were formed in one step. • The reactions were carried out under environmentally friendly conditions with excellent atom economy. Abstract Copper(I) catalyzed radical benzylation and cyclization reaction of tertiary enamides was investigated and 3,3-disubstituted N-Alkyl isoindolin-1-ones were obtained in moderate to good yields. In this reaction, two new C–C bonds were formed in one step with high atom economy. Possible reaction pathway for the formation of the products was also discussed in this paper. [ABSTRACT FROM AUTHOR]