Skyrmion-skyrmion and skyrmion-edge repulsions in skyrmion-based racetrack memory

Magnetic skyrmions are promising for building next-generation magnetic memories and spintronic devices due to their stability, small size and the extremely low currents needed to move them. In particular, skyrmion-based racetrack memory is attractive for information technology, where skyrmions are used to store information as data bits instead of traditional domain walls. Here we numerically demonstrate the impacts of skyrmion-skyrmion and skyrmion-edge repulsions on the feasibility of skyrmion-based racetrack memory. The reliable and practicable spacing between consecutive skyrmionic bits on the racetrack as well as the ability to adjust it are investigated. Clogging of skyrmionic bits is found at the end of the racetrack, leading to the reduction of skyrmion size. Further, we demonstrate an effective and simple method to avoid the clogging of skyrmionic bits, which ensures the elimination of skyrmionic bits beyond the reading element. Our results give guidance for the design and development of future skyrmion-based racetrack memory.Comment: 15 pages, 6 figure

Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure

Study on Evolvement Complexity in an Artificial Stock Market

An artificial stock market is established based on multi-agent . Each agent has a limit memory of the history of stock price, and will choose an action according to his memory and trading strategy. The trading strategy of each agent evolves ceaselessly as a result of self-teaching mechanism. Simulation results exhibit that large events are frequent in the fluctuation of the stock price generated by the present model when compared with a normal process, and the price returns distribution is L\'{e}vy distribution in the central part followed by an approximately exponential truncation. In addition, by defining a variable to gauge the "evolvement complexity" of this system, we have found a phase cross-over from simple-phase to complex-phase along with the increase of the number of individuals, which may be a ubiquitous phenomenon in multifarious real-life systems.Comment: 4 pages and 4 figure

Porcine aminopeptidase N binds to F4(+) enterotoxigenic Escherichia coli fimbriae

Citation: Xia, P. P., Wang, Y. T., Zhu, C. R., Zou, Y. J., Yang, Y., Liu, W., . . . Zhu, G. Q. (2016). Porcine aminopeptidase N binds to F4(+) enterotoxigenic Escherichia coli fimbriae. Veterinary Research, 47, 7. doi:10.1186/s13567-016-0313-5F4(+) enterotoxigenic Escherichia coli (ETEC) strains cause diarrheal disease in neonatal and post-weaned piglets. Several different host receptors for F4 fimbriae have been described, with porcine aminopeptidase N (APN) reported most recently. The FaeG subunit is essential for the binding of the three F4 variants to host cells. Here we show in both yeast two-hybrid and pulldown assays that APN binds directly to FaeG, the major subunit of F4 fimbriae, from three serotypes of F4(+) ETEC. Modulating APN gene expression in IPEC-J2 cells affected ETEC adherence. Antibodies raised against APN or F4 fimbriae both reduced ETEC adherence. Thus, APN mediates the attachment of F4(+) E. coli to intestinal epithelial cells

mTCTScan: a comprehensive platform for annotation and prioritization of mutations affecting drug sensitivity in cancers

Cancer therapies have experienced rapid progress in recent years, with a number of novel small-molecule kinase inhibitors and monoclonal antibodies now being widely used to treat various types of human cancers. During cancer treatments, mutations can have important effects on drug sensitivity. However, the relationship between tumor genomic profiles and the effectiveness of cancer drugs remains elusive. We introduce Mutation To Cancer Therapy Scan (mTCTScan) web server (http://jjwanglab.org/mTCTScan) that can systematically analyze mutations affecting cancer drug sensitivity based on individual genomic profiles. The platform was developed by leveraging the latest knowledge on mutation-cancer drug sensitivity associations and the results from large-scale chemical screening using human cancer cell lines. Using an evidence-based scoring scheme based on current integrative evidences, mTCTScan is able to prioritize mutations according to their associations with cancer drugs and preclinical compounds. It can also show related drugs/compounds with sensitivity classification by considering the context of the entire genomic profile. In addition, mTCTScan incorporates comprehensive filtering functions and cancer-related annotations to better interpret mutation effects and their association with cancer drugs. This platform will greatly benefit both researchers and clinicians for interrogating mechanisms of mutation-dependent drug response, which will have a significant impact on cancer precision medicine.published_or_final_versio

Longitudinal Schottky spectra of a bunched Ne10+ ion beam at the CSRe

The longitudinal Schottky spectra of a radio-frequency (RF) bunched and electron cooled 22Ne10+ ion beam at 70 MeV/u have been studied by a newly installed resonant Schottky pick-up at the experimental cooler storage ring (CSRe), at IMP. For an RF-bunched ion beam, a longitudinal momentum spread of has been reached with less than 107 stored ions. The reduction of momentum spread compared with coasting ion beam was observed from Schottky noise signal of the bunched ion beam. In order to prepare the future laser cooling experiment at the CSRe, the RF-bunching power was modulated at 25th, 50th and 75th harmonic of the revolution frequency, effective bunching amplitudes were extracted from the Schottky spectrum analysis. Applications of Schottky noise for measuring beam lifetime with ultra-low intensity of ion beams are presented, and it is relevant to upcoming experiments on laser cooling of relativistic heavy ion beams and nuclear physics at the CSRe.Comment: to be published in Chinese Physics

Quantitative hepatitis B core antibody levels in the natural history of hepatitis B virus infection

AbstractWe previously demonstrated that pretreatment quantitative anti–hepatitis B core protein (qAnti-HBc) levels can predict the treatment response for both interferon and nucleoside analogue therapy, but the characteristics of qAnti-HBc during chronic hepatitis B virus (HBV) infection remain poorly understood. To understand this issue, the qAnti-HBc levels were evaluated in individuals with past HBV infection, occult HBV infection and chronic HBV infection in the immune tolerance phase, immune clearance phase, low-replicative phase and hepatitis B e antigen (HBeAg)-negative hepatitis phase. Individuals with hepatitis B surface antigen (n = 598, 3.74 ± 0.90 log10 IU/mL) had significantly higher (p < 0.001, approximately 1000-fold) serum qAnti-HBc levels than those who had occult HBV, and serum qAnti-HBc levels were significantly higher in the occult HBV group than in the past HBV infection group (p < 0.001). qAnti-HBc levels were positively correlated with alanine aminotransferase levels (R = 0.663, p < 0.001), and subjects with an abnormal alanine aminotransferase level had a higher qAnti-HBc level (p < 0.001). Serum qAnti-HBc level varied in different phases of HBV infection, as determined by host immune status. Serum qAnti-HBc level is strongly associated with hepatitis activity in subjects with chronic HBV infection