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

Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.Comment: This paper is submitted to IEEE WCCI 2018 Conference for revie

Manipulating Multiple Order Parameters via Oxygen Vacancies: The case of Eu0.5Ba0.5TiO3-{\delta}

Controlling functionalities, such as magnetism or ferroelectricity, by means of oxygen vacancies (VO) is a key issue for the future development of transition metal oxides. Progress in this field is currently addressed through VO variations and their impact on mainly one order parameter. Here we reveal a new mechanism for tuning both magnetism and ferroelectricity simultaneously by using VO. Combined experimental and density-functional theory studies of Eu0.5Ba0.5TiO3-{\delta}, we demonstrate that oxygen vacancies create Ti3+ 3d1 defect states, mediating the ferromagnetic coupling between the localized Eu 4f7 spins, and increase an off-center displacement of Ti ions, enhancing the ferroelectric Curie temperature. The dual function of Ti sites also promises a magnetoelectric coupling in the Eu0.5Ba0.5TiO3-{\delta}.Comment: Accepted by Physical Review B, 201

PACIAE 2.0: An updated parton and hadron cascade model (program) for the relativistic nuclear collisions

We have updated the parton and hadron cascade model PACIAE for the relativistic nuclear collisions, from based on JETSET 6.4 and PYTHIA 5.7 to based on PYTHIA 6.4, and renamed as PACIAE 2.0. The main physics concerning the stages of the parton initiation, parton rescattering, hadronization, and hadron rescattering were discussed. The structures of the programs were briefly explained. In addition, some calculated examples were compared with the experimental data. It turns out that this model (program) works well.Comment: 23 pages, 7 figure

The Management of Debris Flow in Disaster Prevention using an Ontology-based Knowledge Management System

In recently years, the government, academia and business have applied different information technologies to disaster prevention and diverse web sites have been developed. Although these web sites provide a large number of data about disaster-prevention, they are knowledge poor in nature. Furthermore, disaster-prevention is a knowledge-intensive task and a potential knowledge management system can overcome the shortcoming of knowledge poor. On the other hand, ontology design plays the key role toward designing a successful knowledge management system. In this paper, we introduce a three-stage life cycle for ontology design for supporting the service of disaster prevention of debris flow and propose a framework of an ontology-based knowledge management system with the KAON API environment. In addition, by appealing to the technology of component reuse, the system is developed at lower cost thus knowledge workers can focus on the design of ontology and knowledge objects. The objectives of the proposed system is to facilitate knowledge accumulation, knowledge reuse and dissemination for the management of disaster prevention. This work is expected to enable the promotion of the traditional disaster management of debris flow towards the so-called knowledge-driven decision support services

Transforming Music Education Through Artificial Intelligence: A Systematic Literature Review on Enhancing Music Teaching and Learning

The advent of artificial intelligence (AI) has brought significant and transformative alterations to traditional music education. This study examines the progress of AI technology in music education by conducting a systematic review using the PRISMA methodology. Articles were selected for inclusion based on the criterion of specifically describing the utilization of AI in the instruction and acquisition of music. The search was performed on April 9, 2024, via the Web of Science and SCOPUS databases. The search terms “music education” and “artificial intelligence” were employed to ascertain relevant scholarly research. The group of papers underwent scrutiny by various researchers to ascertain their adherence to the established criteria. The articles that were verified by a minimum of two researchers were chosen. 31 articles were finally screened, and the results were divided into two sections: the development of AI in music education and innovative music pedagogy based on AI. A key finding is that the implementation of bibliometric analyses suggests that AI research in music education is still in its infancy. Prior research has primarily concentrated on music instruction at the university level, with a particular emphasis on the integration of AI in music education in China. In addition, this study identifies four specific facets of AI through the reshaping of music pedagogy: enhancing personalized music teaching, providing timely feedback on learning, supporting interactive experiences, and providing organized digital materials

An Illustration of Using Adaptive Data Mining to Develop Strategic Knowledge Bases for Student Retention

Technological development has engaged educational institutions in fierce global competition. To be competitive in meeting the changing needs of today’s student population, educational institutions find it imperative to prioritize student retention efforts and to develop strategies that interact with students to effectively provide additional value and service. In this study we developed a two-module system: a decision tree for predicting a student’s decision to stay until graduation and an affinity analysis algorithm for identifying the relationship between student attributes and student decisions. We followed a three-phase-six-stage adaptive data mining cycle in developing a knowledge base for student retention strategies. The affinity analysis initially identified more than 400 association relationships with student retention. By applying inductive inference, the association rule set was refined iteratively down to less than 30 rules, and useful strategic implications were developed regarding how the selected factors were associated with a student’s decision. This set of implications and factors was then integrated into the development of strategies for student retention

Exploring interdisciplinary aspects for conservation management: The case of land hermit crab wildlife trade in Taiwan

Most conservation policies and management primarily focus on vertebrate animals. However, considering the high demand for invertebrate species in the exotic pet markets, it is crucial to give them great consideration. This research explores Coenobita purpureus, a land hermit crab newly recorded in Taiwan in 2017. We noticed that it has gained popularity in the online pet market recently, despite limited studies confirming its population. To mitigate the potential risks associated with this species, our study investigated online wildlife trade markets, conducted field surveys for its distribution and scrutinised relevant regulations in Taiwan. The price of the species increased significantly following its scientific record, suggesting a growing demand in the exotic pet market, potentially driven by an advertisement effect. Additionally, both the sales platform and the individuals' coloration were found to influence market prices. Furthermore, we discovered that C. purpureus is more widely distributed in Taiwan than initially described in the literature, confirming its native status, though the population may be small. We also identified limitations in current Taiwanese regulations and policies regarding the risk of unsustainable trade in potentially threatened invertebrate species. Moreover, we found evidence of individuals being smuggled from China through e‐commerce channels. Regulatory measures addressing the smuggling of small amounts of wildlife are also insufficient, potentially posing invasion risks from alien species. Finally, we drew upon the conclusions from these aspects to provide integrated and practical management implications for policymakers. Additionally, we aim to offer this valuable case study to spotlight the state of the global invertebrate trade. Read the free Plain Language Summary for this article on the Journal blog