Multiple influence of immune cells in the bone metastatic cancer microenvironment on tumors

Bone is a common organ for solid tumor metastasis. Malignant bone tumor becomes insensitive to systemic therapy after colonization, followed by poor prognosis and high relapse rate. Immune and bone cells in situ constitute a unique immune microenvironment, which plays a crucial role in the context of bone metastasis. This review firstly focuses on lymphatic cells in bone metastatic cancer, including their function in tumor dissemination, invasion, growth and possible cytotoxicity-induced eradication. Subsequently, we examine myeloid cells, namely macrophages, myeloid-derived suppressor cells, dendritic cells, and megakaryocytes, evaluating their interaction with cytotoxic T lymphocytes and contribution to bone metastasis. As important components of skeletal tissue, osteoclasts and osteoblasts derived from bone marrow stromal cells, engaging in ‘vicious cycle’ accelerate osteolytic bone metastasis. We also explain the concept tumor dormancy and investigate underlying role of immune microenvironment on it. Additionally, a thorough review of emerging treatments for bone metastatic malignancy in clinical research, especially immunotherapy, is presented, indicating current challenges and opportunities in research and development of bone metastasis therapies

Distributed Higher Order Association Rule Mining Using Information Extracted from Textual Data

The burgeoning amount of textual data in distributed sources combined with the obstacles involved in creating and maintaining central repositories motivates the need for effective distributed information extraction and mining techniques. Recently, as the need to mine patterns across distributed databases has grown, Distributed Association Rule Mining (D-ARM) algorithms have been developed. These algorithms, however, assume that the databases are either horizontally or vertically distributed. In the special case of databases populated from information extracted from textual data, existing D-ARM algorithms cannot discover rules based on higher-order associations between items in distributed textual documents that are neither vertically nor horizontally distributed, but rather a hybrid of the two. In this article we present D-HOTM, a framework for Distributed Higher Order Text Mining. D-HOTM is a hybrid approach that combines information extraction and distributed data mining. We employ a novel information extraction technique to extract meaningful entities from unstructured text in a distributed environment. The information extracted is stored in local databases and a mapping function is applied to identify globally unique keys. Based on the extracted information, a novel distributed association rule mining algorithm is applied to discover higher-order associations between items (i.e., entities) in records fragmented across the distributed databases using the keys. Unlike existing algorithms, D-HOTM requires neither knowledge of a global schema nor that the distribution of data be horizontal or vertical. Evaluation methods are proposed to incorporate the performance of the mapping function into the traditional support metric used in ARM evaluation. An example application of the algorithm on distributed law enforcement data demonstrates the relevance of D-HOTM in the fight against terrorism. Keywords Distributed data mining, distributed association rule mining, knowledge discovery, artificial intelligence, machine learning, data mining, association rule mining, text mining, evaluation, privacy-preserving, terrorism, law enforcement, criminal justice 1

Distinguiendo similitudes y diferencias; combinando Oriente y Occidente. Perspectivas presentes y futuras de la cultura china

Li Shenzhi (1923-2003), nacido en la antigua ciudad industrial de Wuxi, provincia de Jiangsu, en el seno de una familia de comerciantes, experimentó tempranamente los horrores de la guerra cuando, con sólo quince años de edad, su ciudad natal fue bombardeada por el ejército japonés y su primo de once años, asesinado frente a la puerta de su hogar en Zhoushilong

République populaire de Chine : cinquante années de troubles et de vicissitudes

Shenzhi Li, Becquelin Nicolas. République populaire de Chine : cinquante années de troubles et de vicissitudes. In: Perspectives chinoises, n°61, 2000. pp. 4-13

Mining Higher-Order Association Rules from Distributed Named Entity Databases

Abstract- The burgeoning amount of textual data in distributed sources combined with the obstacles involved in creating and maintaining central repositories motivates the need for effective distributed information extraction and mining techniques. Recently, as the need to mine patterns across distributed databases has grown, Distributed Association Rule Mining (D-ARM) algorithms have been developed. These algorithms, however, assume that the databases are either horizontally or vertically distributed. In the special case of databases populated from information extracted from textual data, existing D-ARM algorithms cannot discover rules based on higher-order associations between items in distributed textual documents that are neither vertically nor horizontally distributed, but rather a hybrid of the two. In this article we present D-HOTM, a framework for Distributed Higher Order Text Mining. Unlike existing algorithms, D-HOTM requires neither full knowledge of the global schema nor that the distribution of data be horizontal or vertical. D-HOTM discovers rules based on higher-order associations between distributed database records containing the extracted entities. In this paper, two approaches to the definition and discovery of higher order itemsets are presented. The implementation of D-HOTM is based on the TMI [20] and tested on a cluster at the National Center for Supercomputing Applications (NCSA). Results on a real-world dataset from th

On the spectral sidebands’ evolution of mode-locked fiber lasers

Funding This work was supported by the National Natural Science Foundation of China (Grant Nos. 62275060); Natural Science Foundation of Heilongjiang Province (Grant Nos. LH2023F029 and LH2019F012).Peer reviewedPostprin