Since the discovery of the Quantum Spin Hall Effect, electronic and photonic topological insulators have made substantial progress, but phononic topological insulators in solids have received ...relatively little attention due to challenges in realizing topological states without spin-like degrees of freedom and with transverse phonon polarizations. Here we present a holey silicon-based topological insulator design, in which simple geometric control enables topologically protected in-plane elastic wave propagation up to GHz ranges with a submicron periodicity. By integrating a hexagonal lattice of six small holes with one central large hole and by creating a hexagonal lattice by themselves, our design induces zone folding to form a double Dirac cone. Based on the hole dimensions, breaking the discrete translational symmetry allows the six-petal holey silicon to achieve the topological phase transition, yielding two topologically distinct phononic crystals. Our numerical simulations confirm inverted band structures and demonstrate backscattering-immune elastic wave transmissions through defects including a cavity, a disorder, and sharp bends. Our design also offers robustness against geometric errors and potential fabrication issues, which shows up to 90% transmission of elastic waves even with 6% under-sized or 11% over-sized holes. These findings provide a detailed understanding of the relationship between geometry and topological properties and pave the way for developing future phononic circuits.
Immunosuppressive tumor microenvironment (TME) and ascites-derived spheroids in ovarian cancer (OC) facilitate tumor growth and progression, and also pose major obstacles for cancer therapy. The ...molecular pathways involved in the OC-TME interactions, how the crosstalk impinges on OC aggression and chemoresistance are not well-characterized. Here, we demonstrate that tumor-derived UBR5, an E3 ligase overexpressed in human OC associated with poor prognosis, is essential for OC progression principally by promoting tumor-associated macrophage recruitment and activation via key chemokines and cytokines. UBR5 is also required to sustain cell-intrinsic β-catenin-mediated signaling to promote cellular adhesion/colonization and organoid formation by controlling the p53 protein level. OC-specific targeting of UBR5 strongly augments the survival benefit of conventional chemotherapy and immunotherapies. This work provides mechanistic insights into the novel oncogene-like functions of UBR5 in regulating the OC-TME crosstalk and suggests that UBR5 is a potential therapeutic target in OC treatment for modulating the TME and cancer stemness.
Purpose
Accurate segmentation of lung and infection in COVID‐19 computed tomography (CT) scans plays an important role in the quantitative management of patients. Most of the existing studies are ...based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID‐19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics.
Methods
To promote the development of data‐efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID‐19 cases, which contain current active research areas, for example, few‐shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code.
Results
Based on the state‐of‐the‐art network, we provide more than 40 pretrained baseline models, which not only serve as out‐of‐the‐box segmentation tools but also save computational time for researchers who are interested in COVID‐19 lung and infection segmentation. We achieve average dice similarity coefficient (DSC) scores of 97.3%, 97.7%, and 67.3% and average normalized surface dice (NSD) scores of 90.6%, 91.4%, and 70.0% for left lung, right lung, and infection, respectively.
Conclusions
To the best of our knowledge, this work presents the first data‐efficient learning benchmark for medical image segmentation, and the largest number of pretrained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID‐19 CT segmentation with limited data.
Rhodium-catalyzed cycloaddition reactions are a powerful tool for the construction of polycyclic compounds. Combined experimental and DFT studies were used to investigate the temperature-controlled ...chemoselectivity of cationic rhodium-catalyzed intramolecular cycloaddition reactions of ene-vinylidenecyclopropanes. After a series of mechanistic studies, it was found that trace amounts of water in the reaction system play an important role in generating the product with
double bond located on a five-membered ring and revealed that trace amounts of water in the reaction system, including the rhodium catalyst, substrate and solvent, were sufficient to promote the formation of the product with
double bond located on a five-membered ring, and additional water could not further accelerate the reaction. DFT calculation results show that the addition of water indeed significantly lowers the energy barrier of the proton transfer step, making the formation of the product with
double bond located on a five-membered ring more likely to occur and confirming the rationality of water-assisted proton transfer occurring in the selective access to the product with
double bond located on a five-membered ring.
Climate change and tourism’s interaction and vulnerability have been among the most hotly debated topics recently. In this context, the study focuses on how CO
2
emissions, the primary cause of ...global warming and climate change, respond to changes in tourism development. In order to do so, the impact of tourism development on CO
2
emissions in the most visited countries is investigated. A panel data from 2000 to 2017 for top 70 tourist countries are analysed using a spatial econometric method to investigate the spatial effect of tourism on environmental pollution. The direct, indirect, and overall impact of tourism on CO
2
emissions are estimated using the most appropriate generalized nested spatial econometric (GNS) method. The findings reveal that tourism has a positive direct effect and a negative indirect effect; both are significant at the 1% level. The negative indirect effect of tourism is greater than its direct positive effect, implying an overall significantly negative impact. Further, the outcome of financial development and CO
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emissions have an inverted U-shaped and U-shaped relationship in direct and indirect impacts. Population density, trade openness, and economic growth significantly influence environmental pollution. In addition, education expenditure and infrastructure play a significant moderating role among tourism and environmental pollution. The results have important policy implications as they establish an inverted-U-shaped relationship among tourism and CO
2
emissions and indicate that while a country’s emissions initially rise with the tourism industry’s growth, it begins declining after a limit.
•Ⅰ Reveal the spatiotemporal changes in land-use carbon emissions and carbon intensities of 41 cities in the Yangtze River Delta region from 1995 to 2018.•Ⅱ Construction of land-use carbon emissions ...spatial correlation network in the Yangtze River Delta region in six different periods.•III Analyze the overall characteristics of the spatial correlation network and clarify the roles of cities in the network.•Ⅳ Characterize the spatial spillover effects of land-use carbon emissions and highlight key cities.
The use and transformation of land by humans are the main cause of the increase in the concentration of carbon dioxide in the atmosphere. Using land-use data and socioeconomic statistics for 1995, 2000, 2005, 2010, 2015, and 2018, this study examines the carbon emissions from land use and their intensity in the entire Yangtze River Delta Region. We constructed a spatial correlation network of land-use carbon emissions by using a modified gravity model to analyze the characteristics of the spatial correlations and spillover effects of land-use carbon emissions. Results reveal that: (1) the spatial differences in land-use carbon emissions gradually increased, whereas those in land-use carbon emission intensity gradually narrowed from 1995 to 2018; (2) the high-degree centrality of Shanghai, Wuxi, and Suzhou indicated that they had always played leading roles in the network from 1995 to 2018. Moreover, Shanghai and Wuxi had large land-use carbon radiation ranges and together with Suzhou, Hangzhou, Changzhou, and Nanjing, exhibited above-average betweenness centrality from 1995 to 2018 and strong bridging capabilities across the entire network; (3) the land-use carbon emissions had obvious spatial correlations and spillover effects. Our results can provide a scientific basis from an urban agglomeration perspective for the transformation of China’s current economy into a low-carbon one, as well as the realization of regionally differentiated and coordinated emission reduction.
We demonstrate enhanced acoustic sensing arising from the synergy between resonator-based acoustic sensor and deep learning. We numerically verify that both vibration amplitude and phase are enhanced ...and preserved at and off the resonance in our compact acoustic sensor housing three cavities. In addition, we experimentally measure the response of our sensor to single-frequency and siren signals, based on which we train convolutional neural networks (CNNs). We observe that the CNN trained by using both amplitude and phase features achieve the best accuracy on predicting the incident direction of both types of signals. This is even though the signals are broadband and affected by noise thought to be difficult for resonators. We attribute the improvement to a complementary effect between the two features enabled by the combination of resonant effect and deep learning. This observation is further supported by comparing to the CNNs trained by the features extracted from signals measured on reference sensor without resonators, whose performances fall far behind. Our results suggest the advantage of this synergetic approach to enhance the sensing performance of compact acoustic sensors on both narrow- and broad-band signals, which paves the way for the development of advanced sensing technology that has potential applications in autonomous driving systems to detect emergency vehicles.
Breast cancer (BC) is one of the most diagnosed cancers worldwide and is the second cause of cancer related death in women. The most frequent cause of BC-related deaths, like many cancers, is ...metastasis. However, metastasis is a complicated and poorly understood process for which there is a shortage of accurate prognostic indicators and effective treatments. With the rapid and ever-evolving development and application of genomic sequencing technologies, many novel molecules were identified that play previously unappreciated and important roles in the various stages of metastasis. In this review, we summarize current advancements in the functional genomic analysis of BC metastasis and discuss about the potential prognostic and therapeutic implications from the recent genomic findings.
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