Quantum Robot: Structure, Algorithms and Applications

A kind of brand-new robot, quantum robot, is proposed through fusing quantum theory with robot technology. Quantum robot is essentially a complex quantum system and it is generally composed of three fundamental parts: MQCU (multi quantum computing units), quantum controller/actuator, and information acquisition units. Corresponding to the system structure, several learning control algorithms including quantum searching algorithm and quantum reinforcement learning are presented for quantum robot. The theoretic results show that quantum robot can reduce the complexity of O(N^2) in traditional robot to O(N^(3/2)) using quantum searching algorithm, and the simulation results demonstrate that quantum robot is also superior to traditional robot in efficient learning by novel quantum reinforcement learning algorithm. Considering the advantages of quantum robot, its some potential important applications are also analyzed and prospected.Comment: 19 pages, 4 figures, 2 table

Control of non-controllable quantum systems: A quantum control algorithm based on Grover iteration

A new notion of controllability, eigenstate controllability, is defined for finite-dimensional bilinear quantum mechanical systems which are neither strongly completely controllably nor completely controllable. And a quantum control algorithm based on Grover iteration is designed to perform a quantum control task of steering a system, which is eigenstate controllable but may not be (strongly) completely controllable, from an arbitrary state to a target state.Comment: 7 pages, no figures, submitte

Information-technology approach to quantum feedback control

Quantum control theory is profitably reexamined from the perspective of quantum information, two results on the role of quantum information technology in quantum feedback control are presented and two quantum feedback control schemes, teleportation-based distant quantum feedback control and quantum feedback control with quantum cloning, are proposed. In the first feedback scheme, the output from the quantum system to be controlled is fed back into the distant actuator via teleportation to alter the dynamics of system. The result theoretically shows that it can accomplish some tasks such as distant feedback quantum control that Markovian or Bayesian quantum feedback can't complete. In the second feedback strategy, the design of quantum feedback control algorithms is separated into a state recognition step, which gives "on-off" signal to the actuator through recognizing some copies from the cloning machine, and a feedback (control) step using another copies of cloning machine. A compromise between information acquisition and measurement disturbance is established, and this strategy can perform some quantum control tasks with coherent feedback.Comment: 10 pages,submitte

Quantum mechanics helps in learning for more intelligent robot

A learning algorithm based on state superposition principle is presented. The physical implementation analysis and simulated experiment results show that quantum mechanics can give helps in learning for more intelligent robot.Comment: 9 pages, 2 figure

A Refined Study of FCRL Genes from a Genome-Wide Association Study for Graves’ Disease

To pinpoint the exact location of the etiological variant/s present at 1q21.1 harboring FCRL1-5 and CD5L genes, we carried out a refined association study in the entire FCRL region in 1,536 patients with Graves’ disease (GD) and 1,516 sex-matched controls by imputation analysis, logistic regression, and cis-eQTL analysis. Among 516 SNPs with P<0.05 in the initial GWAS scan, the strongest signals associated with GD and correlated to FCRL3 expression were located at a cluster of SNPs including rs7528684 and rs3761959. And the allele-specific effects for rs3761959 and rs7528684 on FCRL3 expression level revealed that the risk alleles A of rs3761959 and C of rs7528684 were correlated with the elevated expression level of FCRL3 whether in PBMCs or its subsets, especially in $CD19^+$ B cells and $CD8^+$ T subsets. Next, the combined analysis with 5,300 GD cases and 4,916 control individuals confirmed FCRL3 was a susceptibility gene of GD in Chinese Han populations, and rs3761959 and rs7528684 met the genome-wide association significance level ($P_{combined}$ = 2.27×$10^{−12}$ and 7.11×$10^{−13}$, respectively). Moreover, the haplotypes with the risk allele A of rs3761959 and risk allele C of rs7528684 were associated with GD risk. Finally, our epigenetic analysis suggested the disease-associated C allele of rs7528684 increased affinity for NF-KB transcription factor. Above data indicated that FCRL3 gene and its proxy SNP rs7528684 may be involved in the pathogenesis of GD by excessive inhibiting B cell receptor signaling and the impairment of suppressing function of Tregs

Comparison of intravascular ultrasound guided versus angiography guided drug eluting stent implantation: a systematic review and meta-analysis

BACKGROUND: Intravascular ultrasound (IVUS) can be a useful tool during drug-eluting stents (DES) implantation as it allows accurate assessment of lesion severity and optimal treatment planning. However, numerous reports have shown that IVUS guided percutaneous coronary intervention is not associated with improved clinical outcomes, especially in non-complex patients and lesions. METHODS: We searched the literature in Medline, the Cochrane Library, and other internet sources to identify studies that compare clinical outcomes between IVUS-guided and angiography-guided DES implantation. Random-effects model was used to assess treatment effect. RESULTS: Twenty eligible studies with a total of 29,068 patients were included in this meta-analysis. The use of IVUS was associated with significant reductions in major adverse cardiovascular events (MACE, odds ratios [OR] 0.77, 95 % confidence intervals [CI] 0.71-0.83, P < 0.001), death (OR 0.62, 95 % CI 0.54-0.71, p < 0.001), and stent thrombosis (OR 0.59, 95 % CI: 0.47-0.73, P < 0.001). The benefit was also seen in the repeated analysis of matched and randomized studies. In stratified analysis, IVUS guidance appeared to be beneficial not only in patients with complex lesions or acute coronary syndromes (ACS) but also patients with mixed lesions or presentations (MACE: OR 0.69, 95 % CI: 0.60-0.79, p < 0.001, OR 0.81, 95 % CI 0.74-0.90, p < 0.001, respectively). By employing meta-regression analysis, the benefit of IVUS is significantly pronounced in patients with complex lesions or ACS with respect to death (p = 0.048). CONCLUSIONS: IVUS guidance was associated with improved clinical outcomes, especially in patients with complex lesions admitted with ACS. Large, randomized clinical trials are warranted to identify populations and lesion characteristics where IVUS guidance would be associated with better outcomes

Assessing the genetic architecture of epithelial ovarian cancer histological subtypes.

Epithelial ovarian cancer (EOC) is one of the deadliest common cancers. The five most common types of disease are high-grade and low-grade serous, endometrioid, mucinous and clear cell carcinoma. Each of these subtypes present distinct molecular pathogeneses and sensitivities to treatments. Recent studies show that certain genetic variants confer susceptibility to all subtypes while other variants are subtype-specific. Here, we perform an extensive analysis of the genetic architecture of EOC subtypes. To this end, we used data of 10,014 invasive EOC patients and 21,233 controls from the Ovarian Cancer Association Consortium genotyped in the iCOGS array (211,155 SNPs). We estimate the array heritability (attributable to variants tagged on arrays) of each subtype and their genetic correlations. We also look for genetic overlaps with factors such as obesity, smoking behaviors, diabetes, age at menarche and height. We estimated the array heritabilities of high-grade serous disease ([Formula: see text] = 8.8 ± 1.1 %), endometrioid ([Formula: see text] = 3.2 ± 1.6 %), clear cell ([Formula: see text] = 6.7 ± 3.3 %) and all EOC ([Formula: see text] = 5.6 ± 0.6 %). Known associated loci contributed approximately 40 % of the total array heritability for each subtype. The contribution of each chromosome to the total heritability was not proportional to chromosome size. Through bivariate and cross-trait LD score regression, we found evidence of shared genetic backgrounds between the three high-grade subtypes: serous, endometrioid and undifferentiated. Finally, we found significant genetic correlations of all EOC with diabetes and obesity using a polygenic prediction approach.The Ovarian Cancer Association Consortium is supported by a grant from the Ovarian Cancer Research Fund thanks to donations by the family and friends of Kathryn Sladek Smith (PPD/RPCI.07). The Nurses’ Health Studies would like to thank the participants and staff of the Nurses' Health Study and Nurses' Health Study II for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data. Funding of the constituent studies was provided by the California Cancer Research Program (00-01389V-20170, N01-CN25403, 2II0200); the Canadian Institutes of Health Research (MOP-86727); Cancer Australia; Cancer Council Victoria; Cancer Council Queensland; Cancer Council New South Wales; Cancer Council South Australia; Cancer Council Tasmania; Cancer Foundation of Western Australia; the Cancer Institute of New Jersey; Cancer Research UK (C490/A6187, C490/A10119, C490/A10124); the Danish Cancer Society (94-222-52); the ELAN Program of the University of Erlangen-Nuremberg; the Eve Appeal; the Helsinki University Central Hospital Research Fund; Helse Vest; the Norwegian Cancer Society; the Norwegian Research Council; the Ovarian Cancer Research Fund; Nationaal Kankerplan of Belgium; the L & S Milken Foundation; the Polish Ministry of Science and Higher Education (4 PO5C 028 14, 2 PO5A 068 27); the Roswell Park Cancer Institute Alliance Foundation; the US National Cancer Institute (K07-CA095666, K07-CA80668, K07-CA143047, K22-CA138563, N01-CN55424, N01-PC67001, N01-PC067010, N01-PC035137, P01-CA017054, P01-CA087696, P30-CA072720, P30-CA15083, P30-CA008748, P50-CA159981, P50-CA105009, P50-CA136393, R01-CA149429, R01-CA014089, R01-CA016056, R01-CA017054, R01-CA049449, R01-CA050385, R01-CA054419, R01-CA058598, R01-CA058860, R01-CA061107, R01-CA061132, R01-CA063678, R01-CA063682, R01-CA067262, R01-CA071766, R01-CA074850, R01-CA080978, R01-CA083918, R01-CA087538, R01-CA092044, R01-CA095023, R01-CA122443, R01-CA112523, R01-CA114343, R01-CA126841, R01-CA136924, R03-CA113148, R03-CA115195, U01-CA069417, U01-CA071966, UM1-CA186107, UM1-CA176726 and Intramural research funds); the NIH/National Center for Research Resources/General Clinical Research Center (MO1-RR000056); the US Army Medical Research and Material Command (DAMD17-01-1-0729, DAMD17-02-1-0666, DAMD17-02-1-0669, W81XWH-07-0449, W81XWH-10-1-02802); the US Public Health Service (PSA-042205); the National Health and Medical Research Council of Australia (199600 and 400281); the German Federal Ministry of Education and Research of Germany Programme of Clinical Biomedical Research (01GB 9401); the State of Baden-Wurttemberg through Medical Faculty of the University of Ulm (P.685); the German Cancer Research Center; the Minnesota Ovarian Cancer Alliance; the Mayo Foundation; the Fred C. and Katherine B. Andersen Foundation; the Lon V. Smith Foundation (LVS-39420); the Oak Foundation; Eve Appeal; the OHSU Foundation; the Mermaid I project; the Rudolf-Bartling Foundation; the UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge, Imperial College London, University College Hospital ‘Womens Health Theme’ and the Royal Marsden Hospital; and WorkSafeBC 14. Investigator-specific funding: G.C.P receives scholarship support from the University of Queensland and QIMR Berghofer. Y.L. was supported by the NHMRC Early Career Fellowship. G.C.T. is supported by the National Health and Medical Research Council. S.M. was supported by an ARC Future Fellowship

Shared genetics underlying epidemiological association between endometriosis and ovarian cancer

Epidemiological studies have demonstrated associations between endometriosis and certain histotypes of ovarian cancer, including clear cell, low-grade serous and endometrioid carcinomas. We aimed to determine whether the observed associations might be due to shared genetic aetiology. To address this, we used two endometriosis datasets genotyped on common arrays with full-genome coverage (3194 cases and 7060 controls) and a large ovarian cancer dataset genotyped on the customized Illumina Infinium iSelect (iCOGS) arrays (10 065 cases and 21 663 controls). Previous work has suggested that a large number of genetic variants contribute to endometriosis and ovarian cancer (all histotypes combined) susceptibility. Here, using the iCOGS data, we confirmed polygenic architecture for most histotypes of ovarian cancer. This led us to evaluate if the polygenic effects are shared across diseases. We found evidence for shared genetic risks between endometriosis and all histotypes of ovarian cancer, except for the intestinal mucinous type. Clear cell carcinoma showed the strongest genetic correlation with endometriosis (0.51, 95% CI = 0.18-0.84). Endometrioid and low-grade serous carcinomas had similar correlation coefficients (0.48, 95% CI = 0.07-0.89 and 0.40, 95% CI = 0.05-0.75, respectively). High-grade serous carcinoma, which often arises from the fallopian tubes, showed a weaker genetic correlation with endometriosis (0.25, 95% CI = 0.11-0.39), despite the absence of a known epidemiological association. These results suggest that the epidemiological association between endometriosis and ovarian adenocarcinoma may be attributable to shared genetic susceptibility loci.Other Research Uni