Other affiliations: Zhejiang University, Yangzhou University, China Academy of Engineering Physics ...read more
Bio: Yang Liu is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 82, co-authored 1695 publications receiving 33657 citations. Previous affiliations of Yang Liu include Zhejiang University & Yangzhou University.
Papers published on a yearly basis
TL;DR: It is demonstrated that Tsc1 deletion in the HSCs drives them from quiescence into rapid cycling, with increased mitochondrial biogenesis and elevated levels of reactive oxygen species (ROS), which may explain the well-documented association between quiescent and the “stemness” of H SCs.
Abstract: The tuberous sclerosis complex (TSC)–mammalian target of rapamycin (mTOR) pathway is a key regulator of cellular metabolism. We used conditional deletion of Tsc1 to address how quiescence is associated with the function of hematopoietic stem cells (HSCs). We demonstrate that Tsc1 deletion in the HSCs drives them from quiescence into rapid cycling, with increased mitochondrial biogenesis and elevated levels of reactive oxygen species (ROS). Importantly, this deletion dramatically reduced both hematopoiesis and self-renewal of HSCs, as revealed by serial and competitive bone marrow transplantation. In vivo treatment with an ROS antagonist restored HSC numbers and functions. These data demonstrated that the TSC–mTOR pathway maintains the quiescence and function of HSCs by repressing ROS production. The detrimental effect of up-regulated ROS in metabolically active HSCs may explain the well-documented association between quiescence and the “stemness” of HSCs.
TL;DR: The results reveal that the CD24–Siglec G pathway protects the host against a lethal response to pathological cell death and discriminates danger- versus pathogen-associated molecular patterns.
Abstract: Patten recognition receptors, which recognize pathogens or components of injured cells (danger), trigger activation of the innate immune system. Whether and how the host distinguishes between danger- versus pathogen-associated molecular patterns remains unresolved. We report that CD24-deficient mice exhibit increased susceptibility to danger- but not pathogen-associated molecular patterns. CD24 associates with high mobility group box 1, heat shock protein 70, and heat shock protein 90; negatively regulates their stimulatory activity; and inhibits nuclear factor kappaB (NF-kappaB) activation. This occurs at least in part through CD24 association with Siglec-10 in humans or Siglec-G in mice. Our results reveal that the CD24-Siglec G pathway protects the host against a lethal response to pathological cell death and discriminates danger- versus pathogen-associated molecular patterns.
TL;DR: Spatial imaging of current-induced spin accumulation at the edges of Bi2Se3 and BiSbTeSe2 topological insulators as well as Pt by a scanning photovoltage microscope at room temperature points towards a better understanding of the interaction between spins and circularly polarized light.
Abstract: Charge-to-spin conversion in various materials is the key for the fundamental understanding of spin-orbitronics and efficient magnetization manipulation. Here we report the direct spatial imaging of current-induced spin accumulation at the channel edges of Bi2Se3 and BiSbTeSe2 topological insulators as well as Pt by a scanning photovoltage microscope at room temperature. The spin polarization is along the out-of-plane direction with opposite signs for the two channel edges. The accumulated spin direction reverses sign upon changing the current direction and the detected spin signal shows a linear dependence on the magnitude of currents, indicating that our observed phenomena are current-induced effects. The spin Hall angle of Bi2Se3, BiSbTeSe2, and Pt is determined to be 0.0085, 0.0616, and 0.0085, respectively. Our results open up the possibility of optically detecting the current-induced spin accumulations, and thus point towards a better understanding of the interaction between spins and circularly polarized light.
TL;DR: A comprehensive study of different mechanisms of collaboration and defense in collaborative security, covering six types of security systems, with the goal of helping to make collaborative security systems more resilient and efficient.
Abstract: Security is oftentimes centrally managed. An alternative trend of using collaboration in order to improve security has gained momentum over the past few years. Collaborative security is an abstract concept that applies to a wide variety of systems and has been used to solve security issues inherent in distributed environments. Thus far, collaboration has been used in many domains such as intrusion detection, spam filtering, botnet resistance, and vulnerability detection. In this survey, we focus on different mechanisms of collaboration and defense in collaborative security. We systematically investigate numerous use cases of collaborative security by covering six types of security systems. Aspects of these systems are thoroughly studied, including their technologies, standards, frameworks, strengths and weaknesses. We then present a comprehensive study with respect to their analysis target, timeliness of analysis, architecture, network infrastructure, initiative, shared information and interoperability. We highlight five important topics in collaborative security, and identify challenges and possible directions for future research. Our work contributes the following to the existing research on collaborative security with the goal of helping to make collaborative security systems more resilient and efficient. This study (1) clarifies the scope of collaborative security, (2) identifies the essential components of collaborative security, (3) analyzes the multiple mechanisms of collaborative security, and (4) identifies challenges in the design of collaborative security.
TL;DR: It is demonstrated that the mechanisms of tumor regression by anti-HER2/neu antibody therapy also require the adaptive immune response, and the addition of chemotherapeutic drugs, although capable of enhancing the reduction of tumor burden, could abrogate antibody-initiated immunity leading to decreased resistance to rechallenge or earlier relapse.
Abstract: SUMMARY Anti-HER2/neu antibody therapy is reported to mediate tumor regression by interrupting oncogenic signals and/or inducing FcR-mediated cytotoxicity. Here, we demonstrate that the mechanisms of tumor regression by this therapyalsorequire the adaptive immune response.Activation of innate immunity and T cells, initiated by antibody treatment, was necessary. Intriguingly, the addition of chemotherapeutic drugs, although capable of enhancing the reduction of tumor burden, could abrogate antibody-initiated immunity leading to decreased resistance to rechallenge or earlier relapse. Increased influx of both innate and adaptive immune cells into the tumor microenvironment by a selected immunotherapy further enhanced subsequent antibody-induced immunity, leading toincreasedtumor eradication and resistance to rechallenge.This study proposes a model and strategy for anti-HER2/neu antibody-mediated tumor clearance.
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).