Electronic Flat Band in Distorted Colouring Triangle Lattice.

Dispersionless flat bands (FBs) in momentum space, given rise to electron destructive interference in frustrated lattices, offer opportunities to enhance electronic correlations and host exotic many-body phenomena, such as Wigner crystal, fractional quantum hall state, and superconductivity. Despite successes in theory, great challenges remain in experimentally realizing FBs in frustrated lattices due to thermodynamically structural instability. Here, the observation of electronic FB in a potassium distorted colouring triangle (DCT) lattice is reported, which is supported on a blue phosphorene-gold network. It is verified that the interaction between potassium and the underlayer dominates and stabilizes the frustrated structures. Two-dimensional electron gas is modulated by the DCT lattice, and in turn results in a FB dispersion due to destructive quantum interferences. The FB exhibits suppressed bandwidth with high density of states, which is directly observed by scanning tunneling microscopy and confirmed by the first-principles calculation. This work demonstrates that DCT lattice is a promising platform to study FB physics and explore exotic phenomena of correlation and topological matters.

PA-Seg: Learning From Point Annotations for 3D Medical Image Segmentation Using Contextual Regularization and Cross Knowledge Distillation.

The success of Convolutional Neural Networks (CNNs) in 3D medical image segmentation relies on massive fully annotated 3D volumes for training that are time-consuming and labor-intensive to acquire. In this paper, we propose to annotate a segmentation target with only seven points in 3D medical images, and design a two-stage weakly supervised learning framework PA-Seg. In the first stage, we employ geodesic distance transform to expand the seed points to provide more supervision signal. To further deal with unannotated image regions during training, we propose two contextual regularization strategies, i.e., multi-view Conditional Random Field (mCRF) loss and Variance Minimization (VM) loss, where the first one encourages pixels with similar features to have consistent labels, and the second one minimizes the intensity variance for the segmented foreground and background, respectively. In the second stage, we use predictions obtained by the model pre-trained in the first stage as pseudo labels. To overcome noises in the pseudo labels, we introduce a Self and Cross Monitoring (SCM) strategy, which combines self-training with Cross Knowledge Distillation (CKD) between a primary model and an auxiliary model that learn from soft labels generated by each other. Experiments on public datasets for Vestibular Schwannoma (VS) segmentation and Brain Tumor Segmentation (BraTS) demonstrated that our model trained in the first stage outperformed existing state-of-the-art weakly supervised approaches by a large margin, and after using SCM for additional training, the model's performance was close to its fully supervised counterpart on the BraTS dataset.

MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images.

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on the myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. Note that MyoPS refers to both myocardial pathology segmentation and the challenge in this paper. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore the potential of solutions, as well as to provide a benchmark for future research. The average Dice scores of submitted algorithms were 0.614±0.231 and 0.644±0.153 for myocardial scars and edema, respectively. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/).

PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation.

BACKGROUND AND OBJECTIVE: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models that are essential for computer-assisted diagnosis and treatment procedures. Existing toolkits mainly focus on fully supervised segmentation that assumes full and accurate pixel-level annotations are available. Such annotations are time-consuming and difficult to acquire for segmentation tasks, which makes learning from imperfect labels highly desired for reducing the annotation cost. We aim to develop a new deep learning toolkit to support annotation-efficient learning for medical image segmentation, which can accelerate and simplify the development of deep learning models with limited annotation budget, e.g., learning from partial, sparse or noisy annotations. METHODS: Our proposed toolkit named PyMIC is a modular deep learning library for medical image segmentation tasks. In addition to basic components that support development of high-performance models for fully supervised segmentation, it contains several advanced components that are tailored for learning from imperfect annotations, such as loading annotated and unannounced images, loss functions for unannotated, partially or inaccurately annotated images, and training procedures for co-learning between multiple networks, etc. PyMIC is built on the PyTorch framework and supports development of semi-supervised, weakly supervised and noise-robust learning methods for medical image segmentation. RESULTS: We present several illustrative medical image segmentation tasks based on PyMIC: (1) Achieving competitive performance on fully supervised learning; (2) Semi-supervised cardiac structure segmentation with only 10% training images annotated; (3) Weakly supervised segmentation using scribble annotations; and (4) Learning from noisy labels for chest radiograph segmentation. CONCLUSIONS: The PyMIC toolkit is easy to use and facilitates efficient development of medical image segmentation models with imperfect annotations. It is modular and flexible, which enables researchers to develop high-performance models with low annotation cost. The source code is available at:https://github.com/HiLab-git/PyMIC.

A cloud-based consultation and collaboration system for radiotherapy: Remote decision support services for community radiotherapy centers.

PURPOSE: This study aimed to establish a cloud-based radiotherapy consultation and collaboration system, then investigated the practicability of remote decision support for community radiotherapy centers using the system. METHODS AND MATERIALS: A cloud-based consultation and collaboration system for radiotherapy, OncoEvidance®, was developed to provide remote services of LINAC modeling, simulation CT data import/export, target volume and organ-at-risk delineation, prescription, and treatment planning. The system was deployed on a hybrid cloud. A federate of public nodes, each corresponding to a medical institution, are managed by a central node where a group of consultants have registered. Users can access the system through network using computing devices. The system has been tested at three community radiotherapy centers. One accelerator was modeled. 12 consultants participated the remote radiotherapy decision support and 77 radiation treatment plans had been evaluated remotely. RESULTS: All the passing rates of per-beam dose verification are > 94% and all the passing rates of composite beam dose verification are > 99%. The average downloading time for one set of simulation CT data for one patient from Internet was within 1 min under the cloud download bandwidth of 8 Mbps and local network bandwidth of 100 Mbps. The average response time for one consultant to contour target volumes and make prescription was about 24 h. And that for one consultant to design and optimize a IMRT treatment plan was about 36 h. 100% of the remote plans passed the dosimetric criteria and could be imported into the local TPS for further verification. CONCLUSION: The cloud-based consultation and collaboration system saved the travel time for consultants and provided high quality radiotherapy to patients in community centers. The under-staffed community radiotherapy centers could benefit from the remote system with lower cost and better treatment quality control.

Identification and stress function verification of the HAK/KUP/KT family in Gossypium hirsutum.

The potassium transporter family HAK/KUP/KT is a large group of proteins that are important in plant potassium transport and play a crucial role in plant growth and development. The members of the family play an important role in the response of plants to abiotic stress by maintaining osmotic balance. However, the function of the family in cotton is unclear. In this study, whole genome identification and characterization of the HAK/KUP/KT family from upland cotton (Gossypium hirsutum) were carried out. Bioinformatics methods were used to identify HAK/KUP/KT family members from the G. hirsutum genome and to analyse the physical and chemical properties, basic characteristics, phylogeny, chromosome location and expression of HAK/KUP/KT family members. A total of 41 HAK/KUP/KT family members were identified in the G. hirsutum genome. Phylogenetic analysis grouped these genes into four clusters (I, II, III, IV), containing 6, 10, 3 and 22 genes, respectively. Chromosomal distribution, gene structure and conserved motif analyses of the 41 GhHAK genes were subsequently performed. The RNA-seq data and qRT-PCR results showed that the family had a wide range of tissue expression patterns, and they responded to certain drought stresses. Through expression analysis, seven HAK/KUP/KT genes involved in drought stress were screened, and four genes with obvious phenotypes under drought stress were obtained by VIGS verification, which laid a theoretical foundation for the function of the cotton HAK/KUP/KT family.

Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis From Lung CT Scans With Multi-Scale Guided Dense Attention.

Computed Tomography (CT) plays an important role in monitoring radiation-induced Pulmonary Fibrosis (PF), where accurate segmentation of the PF lesions is highly desired for diagnosis and treatment follow-up. However, the task is challenged by ambiguous boundary, irregular shape, various position and size of the lesions, as well as the difficulty in acquiring a large set of annotated volumetric images for training. To overcome these problems, we propose a novel convolutional neural network called PF-Net and incorporate it into a semi-supervised learning framework based on Iterative Confidence-based Refinement And Weighting of pseudo Labels (I-CRAWL). Our PF-Net combines 2D and 3D convolutions to deal with CT volumes with large inter-slice spacing, and uses multi-scale guided dense attention to segment complex PF lesions. For semi-supervised learning, our I-CRAWL employs pixel-level uncertainty-based confidence-aware refinement to improve the accuracy of pseudo labels of unannotated images, and uses image-level uncertainty for confidence-based image weighting to suppress low-quality pseudo labels in an iterative training process. Extensive experiments with CT scans of Rhesus Macaques with radiation-induced PF showed that: 1) PF-Net achieved higher segmentation accuracy than existing 2D, 3D and 2.5D neural networks, and 2) I-CRAWL outperformed state-of-the-art semi-supervised learning methods for the PF lesion segmentation task. Our method has a potential to improve the diagnosis of PF and clinical assessment of side effects of radiotherapy for lung cancers.

A novel angular dependency model for MatriXX response and its application to true composite dose verification for IMRT plans.

PURPOSE: This paper proposes a model for the angular dependency of MatriXX response and investigates whether MatriXX, with the angular-model-based approach can be applied to true composite dose verification for IMRT plans. METHOD: This model attributes the angular dependence of MatriXX response to dynamical translation of its effective measurement plane (EMP) due to the change of beam angle. Considering this mechanism, true composite dose verifications for IMRT plans specified in AAPM TG 119 report using both MatriXX and Gafchromic EBT3 films were undertook and compared to validate the applicability of MatriXX for patient specific QA of composite beam IMRT plans. Dose verifications using MatriXX with and without angular-model-based approach were performed. RESULTS: MatriXX with angular-model-based approach achieved gamma passing rates with 3%/3 mm and 3%/2 mm criteria better than 98.3% and 98.1% respectively for true composite dose verification of plans in AAPM TG 119 report. The 3%/3 mm and 3%/2 mm gamma passing rates using MatriXX without angular-model-based approach ranged from 85.8% to 98.2% and from 81.3% to 96.5%, respectively. The p-values from the single sided paired t-test indicated no statistical difference between the passing rates from MatriXX with angular-model-based approach and from films, and significant difference between the passing rates from uncorrected MatriXX and from films. CONCLUSION: The proposed model for angular dependent MatriXX response is necessary and effective. Dose verification using MatriXX with angular-model-based approach is acceptable for true composite beam IMRT plans with required accuracy to simplify patient specific QA.

Comprehensive evaluation of microRNA-10b in digestive system cancers reveals prognostic implication and signaling pathways associated with tumor progression.

Background: Digestive system cancers (DSCs) have been recognized to be linked with high morbidity and mortality. Recent studies have reported that microRNA-10b (miR-10b) is abnormally expressed in DSCs and associated with prognosis. However, the inconclusive results and unknown underlying mechanisms promoted us to perform this study. Methods: We systematic searched several databases for eligible studies and conducted quantitative analysis for evidence regarding the associations between miR-10b and survival outcome of DSCs. We also performed a series of bioinformatics analyses to uncover the potential mechanisms. Results: A total of 32 eligible studies with 3392 patients were included. Increased miR-10b expression was linked with unfavorable overall survival (OS) in DSCs (HR=1.72; 95% CI: 1.30-2.27; P <0.001). When stratified by tumor type, the impact of miR-10b overexpression on poor prognosis was observed in colorectal cancer, gastric cancer, hepatocellular carcinoma, and esophageal carcinoma, but not in pancreatic cancer. Subsequently, we predicted the targets of miR-10b and conducted functional enrichment analyses. The results disclosed that miR-10b targets were predominantly enriched in some vital biological terms and pivotal signaling pathways associated with tumor progression including cell cycle, FoxO, proteoglycans, central carbon metabolism, p53, Notch, HIF-1, focal adhesion, AMPK, and pancreatic cancer. Moreover, a protein-protein interaction (PPI) network was also constructed to identify the top ten hub genes and significant modules and demonstrated the underlying interactions among them. Conclusion: Our results indicated that miR-10b could act as a significant biomarker in the prognosis DSCs. However, more research should be performed to test these findings.

First-principles screening of single transition metal atoms anchored on two-dimensional C9N4 for the nitrogen reduction reaction.

Compared to the Haber-Bosch process, the electrochemical nitrogen reduction reaction (NRR) can convert N2 into NH3 under ambient conditions, and thus has attracted considerable attention in recent years. However, it remains a challenge to fabricate NRR catalysts with high faradaic efficiency and yield rate. In this work, by systematic first-principles calculations, we investigate the structure, stability and catalytic performance of single metal atoms anchored on porous monolayer C9N4 (M@C9N4) for the electrochemical NRR. A total of 25 transition metals (Sc-Zn, Zr-Mo, Ru-Ag, Hf-Au) were explored, and we screened out four promising systems, i.e., Nb, Ta, Re and W@C9N4, which not only exhibit high catalytic activity with low limiting potentials of -0.3, -0.42, -0.49 and -0.25 V, respectively, but also have superior selectivity that suppresses the competitive hydrogen evolution reaction. The physical origin lies in the coupling between the d orbitals of the transition metals and the 2π* orbital of N2, which activates the N2 molecule and facilitates the reduction process. Our proposed systems are kinetically and thermodynamically stable, which may shed light on future design and fabrication of high-efficiency single atom catalysts for various technologically important chemical reactions.

Time-Domain Ab Initio Insights into the Reduced Nonradiative Electron-Hole Recombination in ReSe2/MoS2 van der Waals Heterostructure.

Two-dimensional (2D) ReSe2 has attracted considerable interest due to its unique anisotropic mechanical, optical, and exitonic characteristics. Recent transient absorption experiments demonstrated a prolonged lifetime of photoexcited charge carriers by stacking ReSe2 with MoS2, but the underlying mechanism remains elusive. Here, by combining time-domain density functional theory with nonadiabatic molecular dynamics, we investigate the electronic properties and charge carrier dynamics of 2D ReSe2/MoS2 van der Waals (vdW) heterostructure. ReSe2/MoS2 has a type II band alignment that exhibits spatially distinguished conduction and valence band edges, and a built-in electric field is formed due to interface charge transfer. Remarkably, in spite of the decreased band gap and increased decoherence time, we demonstrate that the photocarrier lifetime of ReSe2/MoS2 is ∼5 times longer than that of ReSe2, which originates from the greatly reduced nonadiabatic coupling that suppresses electron-hole recombination, perfectly explaining the experimental results. These findings not only provide physical insights into experiments but also shed light on future design and fabrication of functional optoelectronic devices based on 2D vdW heterostructures.

Intrinsic Valley Polarization and High-Temperature Ferroelectricity in Two-Dimensional Orthorhombic Lead Oxide.

Recent years have witnessed a surge of research in two-dimensional (2D) ferroelectric structures that may circumvent the depolarization effect in conventional perovskite oxide films. Herein, by first-principles calculations, we predict that an orthorhombic phase of lead(II) oxide, PbO, serves as a promising candidate for 2D ferroelectrics with good stability. With a semiconducting nature, 2D ferroelectric PbO exhibits intrinsic valley polarization, which leads to robust ferroelectricity with an in-plane spontaneous polarization of 2.4 × 10-10 C/m and a Curie temperature of 455 K. Remarkably, we reveal that the ferroelectricity is strain-tunable, and ferroelasticity coexists in the PbO film, implying the realization of 2D multiferroics. The underlying physical mechanism is generally applicable and can be extended to other oxide films such as ferroelectric SnO and GeO, thus paving an avenue for future design and fabrication of functional ultrathin devices that are compatible with Si-based technology.

Functionalization induced quantum spin Hall to quantum anomalous Hall phase transition in monolayer jacutingaite.

As novel states of quantum matter, quantum spin Hall (QSH) and quantum anomalous Hall (QAH) states have attracted considerable interest in condensed matter and materials science communities. Recently, a monolayer of the naturally occurring mineral jacutingaite (Pt2HgSe3), was theoretically proposed to be a large-gap QSH insulator and experimentally confirmed. Here, based on first-principles calculations and tight-binding modeling, we demonstrate QSH to QAH phase transition in jacutingaite by chemical functionalization with chalogen. We show that two-dimensional (2D) chalogenated jacutingaite, Pt2HgSe3-X (X = S, Se, Te), is ferromagnetic with Curie temperature up to 316 K, and it exhibits QAH effect with chiral edge states inside a sizeable topological gap. The physical mechanism lies in the adsorption induced transformation of the original Kane-Mele model into an effective four-band model involving (px, py) orbitals on a hexagonal lattice, so that the topological gap size can be controlled by spin-orbit coupling strength of the chalogen (0.28 eV for Pt2HgSe3-Te). These results not only show the promise of functionalization in orbital-engineering of 2D functional structures, but also provide an ideal and practical platform for achieving exotic topological phases for dissipationless transport and quantum computing.

Epitaxial Growth of Flat, Metallic Monolayer Phosphorene on Metal Oxide.

In recent years, two-dimensional (2D) group VA elemental materials have attracted considerable interest from physics/chemistry and materials science communities, with particular attention paid to honeycomb blue phosphorene. To date, phosphorene is limited to its α-phase and small sizes because it can only be produced by exfoliating black phosphorus crystals. Here, we report the direct synthesis of high-quality phosphorene on a nonmetallic copper oxide substrate by molecular beam epitaxy. By combining scanning tunneling microscopy/spectroscopy, X-ray photoelectron spectroscopy, and first-principles calculations, we demonstrate the growth intermediates and electronic structures of phosphorene on Cu3O2/Cu(111). Surprisingly, the grown phosphorene has a flat honeycomb lattice, similar to graphene, which exhibits a metallic nature. We reveal that the growth mechanism and morphology of phosphorene are strongly correlated with the surface structures of prepared copper oxide, and the resulting phosphorene can be stabilized after high-temperature annealing above 600 K even in oxygen gas. The high stability is closely related to the irregular Moiré pattern and structural corrugations of phosphorene on Cu3O2/Cu(111) that efficiently relieve the surface strain. These results shed light on future fabrication of large-scale, versatile 2D structures for interconnect and device integration.

Large-Scale Synthesis of Strain-Tunable Semiconducting Antimonene on Copper Oxide.

Controlled synthesis of 2D structures on nonmetallic substrate is challenging, yet an attractive approach for the integration of 2D systems into current semiconductor technologies. Herein, the direct synthesis of high-quality 2D antimony, or antimonene, on dielectric copper oxide substrate by molecular beam epitaxy is reported. Delicate scanning tunneling microscopy imaging on the evolution intermediates reveals a segregation growth process on Cu3 O2 /Cu(111), from ordered dimer chains to packed dot arrays, and finally to monolayer antimonene. First-principles calculations demonstrate the strain-modulated band structures in antimonene, which interacts weakly with the oxide surface so that its semiconducting nature is preserved, in perfect agreement with spectroscopic measurements. This work paves the way for large-scale growth and processing of antimonene for practical implementation.