Hierarchical Mutual Information Analysis: Towards Multi-view Clustering in The Wild

Multi-view clustering (MVC) can explore common semantics from unsupervised views generated by different sources, and thus has been extensively used in applications of practical computer vision. Due to the spatio-temporal asynchronism, multi-view data often suffer from view missing and are unaligned in real-world applications, which makes it difficult to learn consistent representations. To address the above issues, this work proposes a deep MVC framework where data recovery and alignment are fused in a hierarchically consistent way to maximize the mutual information among different views and ensure the consistency of their latent spaces. More specifically, we first leverage dual prediction to fill in missing views while achieving the instance-level alignment, and then take the contrastive reconstruction to achieve the class-level alignment. To the best of our knowledge, this could be the first successful attempt to handle the missing and unaligned data problem separately with different learning paradigms. Extensive experiments on public datasets demonstrate that our method significantly outperforms state-of-the-art methods on multi-view clustering even in the cases of view missing and unalignment

HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images

Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter- and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and two vision transformer models via five-fold cross validation. It achieves state-of-the-art classification performance with the best model interpretability. In the meanwhile, our proposed model outperforms two senior sonographers on the breast cancer diagnosis when only one BUS image is given

Synthesis of single-crystal La0.67Sr0.33MnO3 freestanding films with different crystal-orientation

We report the synthesis of single-crystal La0.67Sr0.33MnO3 (LSMO) freestanding films with different crystal orientations. By using pulsed laser deposition, water soluble perovskite-like sacrificial layers Sr3Al2O6 (SAO) followed by LSMO films are grown on differently oriented SrTiO3 substrates. Freestanding LSMO films with different orientations are obtained by etching the SAO in pure water. All the freestanding films show room-temperature ferromagnetism and metallicity, independent of the crystal orientation. Intriguingly, the Curie temperature (TC) of the freestanding films is increased due to strain relaxation after releasing from the substrates. Our results provide an additional degree of freedom to tailor the properties of freestanding perovskite oxide heterostructures by crystal orientation and an opportunity to further integrate different oriented films together

Causative agent distribution and antibiotic therapy assessment among adult patients with community acquired pneumonia in Chinese urban population

<p>Abstract</p> <p>Background</p> <p>Knowledge of predominant microbial patterns in community-acquired pneumonia (CAP) constitutes the basis for initial decisions about empirical antimicrobial treatment, so a prospective study was performed during 2003–2004 among CAP of adult Chinese urban populations.</p> <p>Methods</p> <p>Qualified patients were enrolled and screened for bacterial, atypical, and viral pathogens by sputum and/or blood culturing, and by antibody seroconversion test. Antibiotic treatment and patient outcome were also assessed.</p> <p>Results</p> <p>Non-viral pathogens were found in 324/610 (53.1%) patients among whom <it>M. pneumoniae </it>was the most prevalent (126/610, 20.7%). Atypical pathogens were identified in 62/195 (31.8%) patients carrying bacterial pathogens. Respiratory viruses were identified in 35 (19%) of 184 randomly selected patients with adenovirus being the most common (16/184, 8.7%). The nonsusceptibility of <it>S. pneumoniae </it>to penicillin and azithromycin was 22.2% (Resistance (R): 3.2%, Intermediate (I): 19.0%) and 79.4% (R: 79.4%, I: 0%), respectively. Of patients (312) from whom causative pathogens were identified and antibiotic treatments were recorded, clinical cure rate with β-lactam antibiotics alone and with combination of a β-lactam plus a macrolide or with fluoroquinolones was 63.7% (79/124) and 67%(126/188), respectively. For patients having mixed <it>M. pneumoniae </it>and/or <it>C. pneumoniae </it>infections, a better cure rate was observed with regimens that are active against atypical pathogens (e.g. a β-lactam plus a macrolide, or a fluoroquinolone) than with β-lactam alone (75.8% vs. 42.9%, <it>p </it>= 0.045).</p> <p>Conclusion</p> <p>In Chinese adult CAP patients, <it>M. pneumoniae </it>was the most prevalent with mixed infections containing atypical pathogens being frequently observed. With <it>S. pneumoniae</it>, the prevalence of macrolide resistance was high and penicillin resistance low compared with data reported in other regions.</p

Influence of solution temperature on microstructure and mechanical properties of DZ411 alloys with different Ta compositions

The influence of solution treatment temperature on microstructure and mechanical properties of DZ411 alloys with different Ta compositions (2.72, 3.10, and 4.00 wt.%) is studied in details. With the increase of Ta composition, the average values of the primary dendrite arm spacing (PDAS) in superalloys before heat treatment are found to increase from 145.9 ± 3.7 to 209.8 ± 7.6 μm, the area fractions of carbides increase from 0.59 ± 0.05% to 0.65 ± 0.05%, the area fractions of γ-γ′ eutectics increase from 4.6 ± 0.05% to 8.1 ± 0.05%, and the cubicity of the γ′ phase also increases. After heat treatment, since the γ′ phase shows more superior high temperature performance in alloys with higher Ta composition, the solution temperature of alloys with higher Ta composition should be increased to promote the dissolution of the coarse γ′ phase. The evolution of carbides is mainly divided into two types: the decomposition of MC carbides: MC + γ → M23C6 + γ′ and the precipitation of fine M23C6 carbides. The increase of cubicity of γ′ phase will increase the interfacial energy of two phases, which can lead to the pile-up of dislocation and improve the microhardness of alloys. The fine M23C6 carbides will hinder the initiation and propagation of cracks, and the interaction of fine carbide particles reduces the stress concentration and improve the fracture strength of alloys. Therefore, the addition of Ta increases the mechanical properties including both tensile properties and microhardness of alloys with higher Ta composition increases more obviously after heat treatment at higher temperature

HeterBot: A heterogeneous mobile manipulation robot for versatile operation

Abstract This study presents the overall architecture of HeterBot, a heterogeneous mobile manipulation robot developed in our lab, which is designed for versatile operation in hazardous environments. The most significant feature of HeterBot is the heterogeneous design created by adopting the idea of ‘big arm + small arm’ and ‘big car + mini car’. This combination has the advantage of functional complementation, which achieves performance promotion in both locomotion and manipulation capabilities, making HeterBot distinguished from other mobile manipulation robots. Besides, multiple novel technologies are integrated into HeterBot to expand its versatility and ease of use, including Virtual Robot Experimentation Platform‐based teleoperation, reconfigurable end effectors, laser‐aided grasping, and manipulation with customised tools. Experimental results validate the effectiveness of HeterBot in various locomotion and manipulation tasks. HeterBot displays huge potential in future applications, such as searching and rescue

Kernel Differential Subgraph Analysis to Reveal the Key Period Affecting Glioblastoma.

Glioblastoma (GBM) is a fast-growing type of malignant primary brain tumor. To explore the mechanisms in GBM, complex biological networks are used to reveal crucial changes among different biological states, which reflect on the development of living organisms. It is critical to discover the kernel differential subgraph (KDS) that leads to drastic changes. However, identifying the KDS is similar to the Steiner Tree problem that is an NP-hard problem. In this paper, we developed a criterion to explore the KDS (CKDS), which considered the connectivity and scale of KDS, the topological difference of nodes and function relevance between genes in the KDS. The CKDS algorithm was applied to simulated datasets and three single-cell RNA sequencing (scRNA-seq) datasets including GBM, fetal human cortical neurons (FHCN) and neural differentiation. Then we performed the network topology and functional enrichment analyses on the extracted KDSs. Compared with the state-of-art methods, the CKDS algorithm outperformed on simulated datasets to discover the KDSs. In the GBM and FHCN, seventeen genes (one biomarker, nine regulatory genes, one driver genes, six therapeutic targets) and KEGG pathways in KDSs were strongly supported by literature mining that they were highly interrelated with GBM. Moreover, focused on GBM, there were fifteen genes (including ten regulatory genes, three driver genes, one biomarkers, one therapeutic target) and KEGG pathways found in the KDS of neural differentiation process from activated neural stem cells (aNSC) to neural progenitor cells (NPC), while few genes and no pathway were found in the period from NPC to astrocytes (Ast). These experiments indicated that the process from aNSC to NPC is a key differentiation period affecting the development of GBM. Therefore, the CKDS algorithm provides a unique perspective in identifying cell-type-specific genes and KDSs

Placental Malfunction, Fetal Survival and Development Caused by Sow Metabolic Disorder: The Impact of Maternal Oxidative Stress

The energy and metabolic state of sows will alter considerably over different phases of gestation. Maternal metabolism increases dramatically, particularly in late pregnancy. This is accompanied by the development of an increase in oxidative stress, which has a considerable negative effect on the maternal and the placenta. As the only link between the maternal and the fetus, the placenta is critical for the maternal to deliver nutrients to the fetus and for the fetus’ survival and development. This review aimed to clarify the changes in energy and metabolism in sows during different pregnancy periods, as well as the impact of maternal oxidative stress on the placenta, which affects the fetus’ survival and development

Emergent ferromagnetism with tunable perpendicular magnetic anisotropy in short-periodic SrIrO3/SrRuO3 superlattices

Interface engineering is a promising method to trigger emergent magnetic order in oxide heterostructures. Here, we report on the electrical and magnetic properties of short-periodic superlattices (SLs) (SrIrO3)(n)/(SrRuO3)(n) (n=1-5) epitaxially grown on the (001)-oriented SrTiO3 substrate. Intriguingly, (SrIrO3)(n)/(SrRuO3)(n) superlattices show itinerant ferromagnetism with recovered Curie temperature and magnetic mom3). Moreover, perpendicular magnetic anisotropy (PMA) is observed and can be tuned by the layer thickness n in the superlattices. Enhanced PMA as high as 1.6x106 erg/cm(3) is obtained in the n=1 superlattice, which is considerably higher compared to that in n=4 and 5 SLs. Our systematic thickness-dependent studies reveal that the (SrIrO3)/(SrRuO3) interface plays a crucial role in both electrical and magnetic properties. These results indicate n as a knob to tune the PMA of superlattices, paving a way to design functional materials in transition metal oxides