arXiv subjects

Ma Chao

Publications and source records attributed to Ma Chao.

4 recordsLinked to original sources

arXiv2020

An insight into acupoints and meridians in human body based on interstitial fluid circulation

The atlas of human acupoints and meridians has been utilized in clinical practice for almost a millennium although the anatomical structures and functions remain to be clarified. It has recently been reported that a long-distance interstitial fluid (ISF) circulatory pathway may originate from the acupoints in the extremities. As observed in living human subjects, cadavers and animals using magnetic resonance imaging and fluorescent tracers, the ISF flow pathways include at least 4 types of anatomical structures: the cutaneous-, perivenous-, periarterial-, and neural-pathways. Unlike the blood or lymphatic vessels, these ISF flow pathways are composed of highly ordered and topologically connected interstitial fibrous connective tissues that may work as guiderails for the ISF to flow actively over long distance under certain driving forces. Our experimental results demonstrated that most acupoints in the extremity endings connect with one or more ISF flow pathways and comprise a complex network of acupoint-ISF-pathways. We also found that this acupoint-ISF-pathway network can connect to visceral organs or tissues such as the pericardium and epicardium, even though the topographical geometry in human extremities does not totally match the meridian lines on the atlas that is currently used in traditional Chinese medicine. Based on our experimental data, the following working hypotheses are proposed. A comprehensive atlas will be constructed to systemically reveal the detailed anatomical structures of the acupoints-originated ISF circulation. Such an atlas may shed light on the mysteries shrouding the visceral correlations of acupoints and meridians, and inaugurate a new frontier for innovative medical applications.

q-bio.TO↗

arXiv2019

Active interfacial dynamic transport of fluid in fibrous connective tissues and a hypothesis of interstitial fluid circulatory system

Fluid in interstitial spaces accounts for ~20% of an adult body weight. Does it circulate around the body like vascular circulations besides a diffusive and short-ranged transport? This bold conjecture has been debated for decades. As a conventional physiological concept, interstitial space was the space between cells and a micron-sized space. Fluid in interstitial spaces is thought to be entrapped within interstitial matrix. However, our serial data have further defined an interfacial transport zone on a solid fiber of interstitial matrix. Within this fine space that is probably nanosized, fluid can transport along a fiber under a driving power. Since 2006, our imaging data from volunteers and cadavers have revealed a long-distance extravascular pathway for interstitial fluid flow, comprising four types of anatomic distributions at least. The framework of each extravascular pathway contains the longitudinally assembled and oriented fibers, working as a fibrous guiderail for fluid flow. Interestingly, our data showed that the movement of fluid in a fibrous pathway is in response to a dynamic driving source and named as dynamotaxis. By analysis of some representative studies and our experimental results, a hypothesis of interstitial fluid circulatory system is proposed.

q-bio.TO↗

arXiv2019

End-to-End Learning Deep CRF models for Multi-Object Tracking

Existing deep multi-object tracking (MOT) approaches first learn a deep representation to describe target objects and then associate detection results by optimizing a linear assignment problem. Despite demonstrated successes, it is challenging to discriminate target objects under mutual occlusion or to reduce identity switches in crowded scenes. In this paper, we propose learning deep conditional random field (CRF) networks, aiming to model the assignment costs as unary potentials and the long-term dependencies among detection results as pairwise potentials. Specifically, we use a bidirectional long short-term memory (LSTM) network to encode the long-term dependencies. We pose the CRF inference as a recurrent neural network learning process using the standard gradient descent algorithm, where unary and pairwise potentials are jointly optimized in an end-to-end manner. Extensive experimental results on the challenging MOT datasets including MOT-2015 and MOT-2016, demonstrate that our approach achieves the state of the art performances in comparison with published works on both benchmarks.

cs.CV↗

arXiv2013

Microstructure and Structural Phase Transitions in Iron-Based Superconductors

Crystal structures and microstructural features, such as structural phase transitions, defect structures, chemical and structural inhomogeneities, are known to have profound effects on the physical properties of superconducting materials. Recently, a large amount of works on the structural properties of Fe-based high-Tc superconductors have been published. This review article will mainly focus on typical microstructural features in samples that have been well characterized by physical measurements. (a) Certain common structural features are discussed. In particular, crystal structural features for different superconducting families, local structural distortions in the Fe2Pn2 (Pn=P, As, Sb) or Fe2Ch2 (Ch=S, Se, Te) blocks, and structural transformations in the 122 system. (b) In FeTe(Se) (11-family), the superconductivity, chemical and structural inhomogeneities are investigated and discussed in correlation with superconductivity. (c) In K0.8Fe1.6+xSe2 system, we focus on typical compounds with emphasis on Fe-vacancy order and phase separations. The microstructural features in other superconducting materials are also briefly discussed.

cond-mat.supr-con↗