On the sum of the two largest signless Laplacian eigenvalues

Let $G$ be a simple connected graph and let $S_k(G)$ be the sum of the first $k$ largest signless Laplacian eigenvalues of $G$. It was conjectured by Ashraf, Omidi and Tayfeh-Rezaibe in 2013 that $S_k(G)\leq e(G)+\binom{k+1}{2}$ holds for $1\leq k\leq n-1$. They gave a proof for the conjecture when $k = 2$, but applied an incorrect key lemma. Therefore, the conjecture is still open when $k = 2$. In this paper, we prove that $S_2(G)<e(G)+3$ is true for any graphs which also confirm the conjecture when $k = 2$.Comment: 15 pages, 5 figure

Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search

Mobile landmark search (MLS) recently receives increasing attention for its great practical values. However, it still remains unsolved due to two important challenges. One is high bandwidth consumption of query transmission, and the other is the huge visual variations of query images sent from mobile devices. In this paper, we propose a novel hashing scheme, named as canonical view based discrete multi-modal hashing (CV-DMH), to handle these problems via a novel three-stage learning procedure. First, a submodular function is designed to measure visual representativeness and redundancy of a view set. With it, canonical views, which capture key visual appearances of landmark with limited redundancy, are efficiently discovered with an iterative mining strategy. Second, multi-modal sparse coding is applied to transform visual features from multiple modalities into an intermediate representation. It can robustly and adaptively characterize visual contents of varied landmark images with certain canonical views. Finally, compact binary codes are learned on intermediate representation within a tailored discrete binary embedding model which preserves visual relations of images measured with canonical views and removes the involved noises. In this part, we develop a new augmented Lagrangian multiplier (ALM) based optimization method to directly solve the discrete binary codes. We can not only explicitly deal with the discrete constraint, but also consider the bit-uncorrelated constraint and balance constraint together. Experiments on real world landmark datasets demonstrate the superior performance of CV-DMH over several state-of-the-art methods

Lasing oscillation condition and group delay control in gain-assisted plasmon-induced transparency

A gain-assisted plasmonic waveguide with two detuned resonators is investigated in the plasmon-induced transparency window. Phase map is employed to study power transmittance and group delay for varying gain coefficients and frequency detunings of the two resonators. The gain coefficient for lasing oscillation condition is analytically shown to vary quadratically with the frequency detuning. In the amplification regime below the lasing threshold, the spectrum implies not only large group delay, but also high transmittance and narrow linewidth. This is in contrast to those in the loss-compensation regime and the passive case in which there always exists a trade-off between the linewidth and the peak transmittance.Comment: 15 pages, 4 figure

Improving mobility of silicon metal-oxide-semiconductor devices for quantum dots by high vacuum activation annealing

To improve mobility of fabricated silicon metal-oxide-semiconductor (MOS) quantum devices, forming gas annealing is a common method used to mitigate the effects of disorder at the Si/SiO2 interface. However, the importance of activation annealing is usually ignored. Here, we show that a high vacuum environment for implantation activation is beneficial for improving mobility compared to nitrogen atmosphere. Low-temperature transport measurements of Hall bars show that peak mobility can be improved by a factor of two, reaching 1.5 m^2/(Vs) using high vacuum annealing during implantation activation. Moreover, the charge stability diagram of a single quantum dot is mapped, with no visible disturbance caused by disorder, suggesting possibility of fabricating high-quality quantum dots on commercial wafers. Our results may provide valuable insights into device optimization in silicon-based quantum computing.Comment: 13 pages, 4 figure

PlantES: A plant electrophysiological multi-source data online analysis and sharing platform

At present, plant electrophysiological data volumes and complexity are increasing rapidly. It causes the demand for efficient management of big data, data sharing among research groups, and fast analysis. In this paper, we proposed PlantES (Plant Electrophysiological Data Sharing), a distributed computing-based prototype system that can be used to store, manage, visualize, analyze, and share plant electrophysiological data. We deliberately designed a storage schema to manage the multi-source plant electrophysiological data by integrating distributed storage systems HDFS and HBase to access all kinds of files efficiently. To improve the online analysis efficiency, parallel computing algorithms on Spark were proposed and implemented, e.g., plant electrical signals extraction method, the adaptive derivative threshold algorithm, and template matching algorithm. The experimental results indicated that Spark efficiently improves the online analysis. Meanwhile, the online visualization and sharing of multiple types of data in the web browser were implemented. Our prototype platform provides a solution for web-based sharing and analysis of plant electrophysiological multi-source data and improves the comprehension of plant electrical signals from a systemic perspective

Novel targeting of PEGylated liposomes for codelivery of TGF-β1 siRNA and four antitubercular drugs to human macrophages for the treatment of mycobacterial infection: a quantitative proteomic study

Tuberculosis (TB) is still a major public health issue in developing countries, and its chemotherapy is compromised by poor drug compliance and severe side effects. This study aimed to synthesize and characterize new multimodal PEGylated liposomes encapsulated with clinically commonly used anti-TB drugs with linkage to small interfering RNA (siRNA) against transforming growth factor-&beta;1 (TGF-&beta;1). The novel NP-siRNA liposomes could target THP-1-derived human macrophages that were the host cells of mycobacterium infection. The biological effects of the NP-siRNA liposomes were evaluated on cell cycle distribution, apoptosis, autophagy, and the gene silencing efficiency of TGF-&beta;1 siRNA in human macrophages. We also explored the proteomic responses to the newly synthesized NP-siRNA liposomes using the stable isotope labeling with amino acids in cell culture approach. The results showed that the multifunctional PEGylated liposomes were successfully synthesized and chemically characterized with a mean size of 265.1 nm. The novel NP-siRNA liposomes functionalized with the anti-TB drugs and TGF-&beta;1 siRNA were endocytosed efficiently by human macrophages as visualized by transmission electron microscopy and scanning electron microscopy. Furthermore, the liposomes showed a low cytotoxicity toward human macrophages. There was no significant effect on cell cycle distribution and apoptosis in THP-1-derived macrophages after drug exposure at concentrations ranging from 2.5 to 62.5 &mu;g/mL. Notably, there was a 6.4-fold increase in the autophagy of human macrophages when treated with the NP-siRNA liposomes at 62.5 &mu;g/mL. In addition, the TGF-&beta;1 and nuclear factor-&kappa;B expression levels were downregulated by the NP-siRNA liposomes in THP-1-derived macrophages. The Ingenuity Pathway Analysis data showed that there were over 40 signaling pathways involved in the proteomic responses to NP-siRNA liposome exposure in human macrophages, with 160 proteins mapped. The top five canonical signaling pathways were eukaryotic initiation factor 2 signaling, actin cytoskeleton signaling, remodeling of epithelial adherens junctions, epithelial adherens junction signaling, and Rho GDP-dissociation inhibitor signaling pathways. Collectively, the novel synthetic targeting liposomes represent a promising delivery system for anti-TB drugs to human macrophages with good selectivity and minimal cytotoxicity

Raw Garlic Consumption and Risk of Liver Cancer: A Population-Based Case-Control Study in Eastern China.

Although the major risk factors for liver cancer have been established, preventive factors for liver cancer have not been fully explored. We evaluated the association between raw garlic consumption and liver cancer in a large population-based case-control study in Eastern China. The study was conducted in Jiangsu, China, from 2003 to 2010. A total of 2011 incident liver cancer cases and 7933 randomly selected population-controls were interviewed. Epidemiological data including raw garlic intake and other exposures were collected, and serum markers of hepatitis B virus (HBV) and hepatitis C virus (HCV) infection were assayed. Overall, eating raw garlic twice or more per week was inversely associated with liver cancer, with an adjusted odds ratio (aOR) of 0.77 (95% confidence interval (CI): 0.62-0.96) compared to those ingesting no raw garlic or less than twice per week. In stratified analyses, high intake of raw garlic was inversely associated with liver cancer among Hepatitis B surface antigen (HBsAg) negative individuals, frequent alcohol drinkers, those having history of eating mold-contaminated food or drinking raw water, and those without family history of liver cancer. Marginal interactions on an additive scale were observed between low raw garlic intake and HBsAg positivity (attributable proportion due to interaction (AP) = 0.31, 95% CI: -0.01-0.62) and heavy alcohol drinking (AP = 0.28, 95% CI: 0.00-0.57). Raw garlic consumption is inversely associated with liver cancer. Such an association shed some light on the potential etiologic role of garlic intake on liver cancer, which in turn might provide a possible dietary intervention to reduce liver cancer in Chinese population