Malignant phyllodes tumors display mesenchymal stem cell features and aldehyde dehydrogenase/disialoganglioside identify their tumor stem cells.

IntroductionAlthough breast phyllodes tumors are rare, there is no effective therapy other than surgery. Little is known about their tumor biology. A malignant phyllodes tumor contains heterologous stromal elements, and can transform into rhabdomyosarcoma, liposarcoma and osteosarcoma. These versatile properties prompted us to explore their possible relationship to mesenchymal stem cells (MSCs) and to search for the presence of cancer stem cells (CSCs) in phyllodes tumors.MethodsParaffin sections of malignant phyllodes tumors were examined for various markers by immunohistochemical staining. Xenografts of human primary phyllodes tumors were established by injecting freshly isolated tumor cells into the mammary fat pad of non-obese diabetic-severe combined immunodeficient (NOD-SCID) mice. To search for CSCs, xenografted tumor cells were sorted into various subpopulations by flow cytometry and examined for their in vitro mammosphere forming capacity, in vivo tumorigenicity in NOD-SCID mice and their ability to undergo differentiation.ResultsImmunohistochemical analysis revealed the expression of the following 10 markers: CD44, CD29, CD106, CD166, CD105, CD90, disialoganglioside (GD2), CD117, Aldehyde dehydrogenase 1 (ALDH), and Oct-4, and 7 clinically relevant markers (CD10, CD34, p53, p63, Ki-67, Bcl-2, vimentin, and Globo H) in all 51 malignant phyllodes tumors examined, albeit to different extents. Four xenografts were successfully established from human primary phyllodes tumors. In vitro, ALDH+ cells sorted from xenografts displayed approximately 10-fold greater mammosphere-forming capacity than ALDH- cells. GD2+ cells showed a 3.9-fold greater capacity than GD2- cells. ALDH+/GD2+cells displayed 12.8-fold greater mammosphere forming ability than ALDH-/GD2- cells. In vivo, the tumor-initiating frequency of ALDH+/GD2+ cells were up to 33-fold higher than that of ALDH+ cells, with as few as 50 ALDH+/GD2+ cells being sufficient for engraftment. Moreover, we provided the first evidence for the induction of ALDH+/GD2+ cells to differentiate into neural cells of various lineages, along with the observation of neural differentiation in clinical specimens and xenografts of malignant phyllodes tumors. ALDH+ or ALDH+/GD2+ cells could also be induced to differentiate into adipocytes, osteocytes or chondrocytes.ConclusionsOur findings revealed that malignant phyllodes tumors possessed many characteristics of MSC, and their CSCs were enriched in ALDH+ and ALDH+/GD2+ subpopulations

Anomaly detection in spatiotemporal data via regularized non-negative tensor analysis

Anomaly detection in multidimensional data is a challenging task. Detecting anomalous mobility patterns in a city needs to take spatial, temporal, and traffic information into consideration. Although existing techniques are able to extract spatiotemporal features for anomaly analysis, few systematic analysis about how different factors contribute to or affect the anomalous patterns has been proposed. In this paper, we propose a novel technique to localize spatiotemporal anomalous events based on tensor decomposition. The proposed method employs a spatial-feature-temporal tensor model and analyzes latent mobility patterns through unsupervised learning. We first train the model based on historical data and then use the model to capture the anomalies, i.e., the mobility patterns that are significantly different from the normal patterns. The proposed technique is evaluated based on the yellow-cab dataset collected from New York City. The results show several interesting latent mobility patterns and traffic anomalies that can be deemed as anomalous events in the city, suggesting the effectiveness of the proposed anomaly detection method

Competitive and Weighted Evolving Simplicial Complexes

A simplex-based network is referred to as a higher-order network, in which describe that the interactions can include more than two nodes. This paper first proposes a competitive evolving model of higher-order networks. We notice the batch effect of low-dim simplices during the growth of such a network. We obtain an analytical expression for the distribution of higher-order degrees by employing the theory of Poisson processes and the mean field method and use computers to simulate higher-order networks of competitions. The established results indicate that the scale-free behavior for the (d-1)-dim simplex with respect to the d-order degree is controlled by the competitiveness factor. As the competitiveness increases, the d-order degree of the (d-1)-dim simplex is bent under the logarithmic coordinates. Second, by considering the weight changes of the neighboring simplices, as triggered by the selected simplex, a new weighted evolving model in higher-order networks is proposed. The results of the competitive evolving model of higher-order networks are used to analyze the weighted evolving model so that obtained are the analytical expressions of the higher-order degree distribution and higher-order strength density function of weighted higher-order networks. The outcomes of the simulation experiments are consistent with the theoretical analysis. Therefore, the weighted network belongs to the collection of competition networks

Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions

Deep learning has seen rapid growth in recent years and achieved state-of-the-art performance in a wide range of applications. However, training models typically requires expensive and time-consuming collection of large quantities of labeled data. This is particularly true within the scope of medical imaging analysis (MIA), where data are limited and labels are expensive to be acquired. Thus, label-efficient deep learning methods are developed to make comprehensive use of the labeled data as well as the abundance of unlabeled and weak-labeled data. In this survey, we extensively investigated over 300 recent papers to provide a comprehensive overview of recent progress on label-efficient learning strategies in MIA. We first present the background of label-efficient learning and categorize the approaches into different schemes. Next, we examine the current state-of-the-art methods in detail through each scheme. Specifically, we provide an in-depth investigation, covering not only canonical semi-supervised, self-supervised, and multi-instance learning schemes, but also recently emerged active and annotation-efficient learning strategies. Moreover, as a comprehensive contribution to the field, this survey not only elucidates the commonalities and unique features of the surveyed methods but also presents a detailed analysis of the current challenges in the field and suggests potential avenues for future research.Comment: Update Few-shot Method

Nanoparticles enabled pump-free direct absorption solar collectors

Developing renewable energy technologies, especially solar energy-based, is of great importance to secure our energy future. Current solar thermal systems, however, have relatively low utilization efficiencies, limited not only by their low solar energy capture efficiency but also the auxiliary pumping power to circulate the working fluid. Here an innovative nanoparticle enabled pump-free direct absorption solar collector concept is presented, which combines the advantages of volumetric solar harvesting and oscillating heat pipes. Two different flow modes have been observed when the concentration of nanofluid is different. There is an optimum filling ratio when the thermal resistance reaches the minimum. Validation experiments show that the proposed concept can efficiently harvest solar energy and spontaneously transfer the heat into targeted areas, providing a novel approach for efficient solar energy utilization