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Complex oxide heterointerfaces and heterostructures have demonstrated enormous emergent phenomena over the last decades, attributed to the reconstructions of mis‐matched crystalline structure, polarity, and spin ordering across the heterointerfaces. This work employs the heterostructures of La0.7Sr0.3MnO3 and CaFeO2.5 as model system to demonstrate an interface‐specific oxygen migration/reconstruction across the interfaces due to the mismatched chemical potential, which dramatically influences the ferromagnetic and electronic states of La0.7Sr0.3MnO3 layer. Specifically, the alternative stacking of octahedral (Oh) and tetrahedral (Td) layers in CaFeO2.5 are used to form two distinct heterointerfaces, namely the Oh‐Td and the Oh‐Oh interfaces with the adjacent La0.7Sr0.3MnO3 layer. Interestingly, the oxygen ion migrates toward opposite directions across the interface for these two cases, in which the CaFeO2.5 layer acts as an "oxygen pump" and manipulates the oxygen contents of its adjacent La0.7Sr0.3MnO3 layers. Such manipulation leads to a dramatically changed ferromagnetic transition temperature for the heterostructure with the Oh‐Td and Oh‐Oh interface. This work establishes a feasible and efficient strategy to control the oxygen ionic distribution through atomic‐scale interface design and opens up new opportunities to exploit emergent states at the complex oxide heterostructures through selective oxygen ion evolution. [ABSTRACT FROM AUTHOR]

This article was to explore the adoption value of deep learning combined with computed tomography (CT) imaging omics in the prediction of metastatic lymph nodes of nasopharyngeal carcinoma (NPC). An end‐to‐end neural network architecture was designed based on the fully convolutional neural network (FCNN), which was applied to the CT image analysis of 52 patients with lymphatic metastasis and 36 patients without lymphatic metastasis. Patient's lymph node volume (V), the largest cross‐sectional shortest diameter (d‐value), and other macro characteristics were recorded. The microscopic features of its CT imaging omics were extracted. Moreover, receiver operating characteristic (ROC) curve was utilized to analyse the prediction performance (accuracy, area under the curve [AUC], and Youden index) of each feature for lymphatic metastasis. The results showed that the lymph node volume (4.37 ± 0.67) and the shortest diameter of the largest cross section (12.35 ± 2.31) of patients with lymph node metastasis were greatly larger than those without lymph node metastasis (1.84 ± 0.65, 7.98 ± 2.04) (P < 0.05). There were five features that met the conditions of AUC > 0.7 and Yoden index>0.5, including lymph node volume (AUC area 0.945, Youden index 0.597), the shortest diameter of the largest cross section (AUC area 0.746, Youden index 0.539), Surface Area Density (AUC area 0.809, Youden index 0.552), Compactness1 (AUC area 0.751, Youden index 0.537), and Convex Hull Volume (AUC area 0.751, Youden index 0.537). The AUC of V+ Surface Area Density + Compactness1 + Convex Hull Volume was 0.876, and the prediction accuracy was 92.11%. In short, the prediction model composed of the macroscopic features of CT images and some imaging omics features based on deep learning showed high accuracy and AUC for the prediction of NPC metastatic lymph nodes. Moreover, V + Surface Area Density + Compactness1 + Convex Hull Volume can be used as the optimal feature combination model for predicting NPC lymphatic metastasis. [ABSTRACT FROM AUTHOR]

Spin‐orbit‐coupled Mott iridates show great similarity with parent compounds of superconducting cuprates, attracting extensive research interest especially for their electron‐doped states. However, previous experiments have been largely limited within a small doping range due to the absence of effective dopants, and therefore the electron‐doped phase diagram remains elusive. Here, an ionic‐liquid‐gating‐induced protonation method is utilized to achieve electron‐doping into a 5d Mott‐insulator built with a SrIrO3/SrTiO3 superlattice (SL), and a systematic mapping of its electron‐doped phase diagram is achieved with the evolution of the iridium valence state from 4+ to 3+, equivalent to doping of one electron per iridium ion. Along increasing doping level, the parent Mott‐insulator is first turned into a localized metallic state with gradually suppressed magnetic ordering, and then further evolves into a nonmagnetic band insulating state. This work forms an important step forward for the study of electron‐doped Mott iridate systems, and the strategy of manipulating the band filling in an artificially designed SL structure can be readily extended into other systems with more exotic states to explore. [ABSTRACT FROM AUTHOR]