Breast metastasis from rectal carcinoma: A case report and review of the literature

BackgroundMetastasis from extramammary primary tumor to breast is extremely rare. Case SummaryA 59-year-old woman with 1-year history of rectal cancer presented with asymptomatic breast mass. At 16 months after the diagnosis of rectal mucinous adenocarcinoma, a breast mass was confirmed by ultrasonography and identified by pathology and immunohistochemistry as a metastasis from the rectal cancer. Treatments included chemotherapy (6 cycles: 300 mg irinotecan on day 1, 4.5 mg raltitrexed on day 2, 450 mg bevacizumab on day 3), radiotherapy, and surgical resection. Two years of follow-up examinations (6-months intervals) showed no evidence of recurrence or novel distant metastasis. ConclusionBreast metastasis from rectal carcinoma is a rare secondary malignancy. Final diagnosis can be established by histopathology and immunohistochemistry

A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Medical Images

Self-supervised pre-training has become the priory choice to establish reliable models for automated recognition of massive medical images, which are routinely annotation-free, without semantics, and without guarantee of quality. Note that this paradigm is still at its infancy and limited by closely related open issues: 1) how to learn robust representations in an unsupervised manner from unlabelled medical images of low diversity in samples? and 2) how to obtain the most significant representations demanded by a high-quality segmentation? Aiming at these issues, this study proposes a knowledge-based learning framework towards enhanced recognition of medical images, which works in three phases by synergizing contrastive learning and generative learning models: 1) Sample Space Diversification: Reconstructive proxy tasks have been enabled to embed a priori knowledge with context highlighted to diversify the expanded sample space; 2) Enhanced Representation Learning: Informative noise-contrastive estimation loss regularizes the encoder to enhance representation learning of annotation-free images; 3) Correlated Optimization: Optimization operations in pre-training the encoder and the decoder have been correlated via image restoration from proxy tasks, targeting the need for semantic segmentation. Extensive experiments have been performed on various public medical image datasets (e.g., CheXpert and DRIVE) against the state-of-the-art counterparts (e.g., SimCLR and MoCo), and results demonstrate that: The proposed framework statistically excels in self-supervised benchmarks, achieving 2.08, 1.23, 1.12, 0.76 and 1.38 percentage points improvements over SimCLR in AUC/Dice. The proposed framework achieves label-efficient semi-supervised learning, e.g., reducing the annotation cost by up to 99% in pathological classification.Comment: 10 pages, 9 figures, 3 tables, submitted to IEEE-TM

Simulating dynamical quantum Hall effect with superconducting qubits

We propose an experimental scheme to simulate the dynamical quantum Hall effect and the related interaction-induced topological transition with a superconducting-qubit array. We show that a one-dimensional Heisenberg model with tunable parameters can be realized in an array of superconducting qubits. The quantized plateaus, which is a feature of the dynamical quantum Hall effect, will emerge in the Berry curvature of the superconducting qubits as a function of the coupling strength between nearest neighbor qubits. We numerically calculate the Berry curvatures of two-, four- and six-qubit arrays, and find that the interaction-induced topological transition can be easily observed with the simplest two-qubit array. Furthermore, we analyze some practical conditions in typical experiments for observing such dynamical quantum Hall effect.Comment: 9 pages, 6 figures, version accepted by PR

Synchronization of Diverse Agents via Phase Analysis

In this paper, the synchronization of heterogeneous agents interacting over a dynamical network is studied. The edge dynamics can model the inter-agent communications which are often heterogeneous by nature. They can also model the controllers of the agents which may be different for each agent or uniform for all the agents. Novel synchronization conditions are obtained for both cases from a phase perspective by exploiting a recently developed small phase theorem. The conditions scale well with the network and reveal the trade-off between the phases of node dynamics and edge dynamics. We also study the synchronizability problem which aims to characterize the allowable diversity of the agents for which controllers can be designed so as to achieve synchronization. The allowable diversity is captured in terms of phase conditions engaging the residue matrices of the agents at their persistent modes. Controller design algorithms are provided for the cases of agent-dependent and uniform controllers, respectively

Superfluid and magnetic states of an ultracold Bose gas with synthetic three-dimensional spin-orbit coupling in an optical lattice

We study ultracold bosonic atoms with the synthetic three-dimensional spin-orbit (SO) coupling in a cubic optical lattice. In the superfluidity phase, the lowest energy band exhibits one, two or four pairs of degenerate single-particle ground states depending on the SO-coupling strengths, which can give rise to the condensate states with spin-stripes for the weak atomic interactions. In the deep Mott-insulator regime, the effective spin Hamiltonian of the system combines three-dimensional Heisenberg exchange interactions, anisotropy interactions and Dzyaloshinskii-Moriya interactions. Based on Monte Carlo simulations, we numerically demonstrate that the resulting Hamiltonian with an additional Zeeman field has a rich phase diagram with spiral, stripe, vortex crystal, and especially Skyrmion crystal spin-textures in each xy-plane layer. The obtained Skyrmion crystals can be tunable with square and hexagonal symmetries in a columnar manner along the z axis, and moreover are stable against the inter-layer spin-spin interactions in a large parameter region.Comment: 9 pages, 4 figures; title modified, references and discussions added; accepted by PR