2,409 research outputs found
On Applying the Lackadaisical Quantum Walk Algorithm to Search for Multiple Solutions on Grids
Quantum computing holds the promise of improving the information processing
power to levels unreachable by classical computation. Quantum walks are heading
the development of quantum algorithms for searching information on graphs more
efficiently than their classical counterparts. A quantum-walk-based algorithm
that is standing out in the literature is the lackadaisical quantum walk. The
lackadaisical quantum walk is an algorithm developed to search two-dimensional
grids whose vertices have a self-loop of weight . In this paper, we address
several issues related to the application of the lackadaisical quantum walk to
successfully search for multiple solutions on grids. Firstly, we show that only
one of the two stopping conditions found in the literature is suitable for
simulations. We also demonstrate that the final success probability depends on
the space density of solutions and the relative distance between solutions.
Furthermore, this work generalizes the lackadaisical quantum walk to search for
multiple solutions on grids of arbitrary dimensions. In addition, we propose an
optimal adjustment of the self-loop weight for such scenarios of arbitrary
dimensions. It turns out the other fits of found in the literature are
particular cases. Finally, we observe a two-to-one relation between the steps
of the lackadaisical quantum walk and the ones of Grover's algorithm, which
requires modifications in the stopping condition. In conclusion, this work
deals with practical issues one should consider when applying the lackadaisical
quantum walk, besides expanding the technique to a wider range of search
problems.Comment: Extended version of the conference paper available at
https://doi.org/10.1007/978-3-030-61377-8_9 . 21 pages, 6 figure
Multiself-loop Lackadaisical Quantum Walk with Partial Phase Inversion
Quantum walks are the quantum counterpart of classical random walks and
provide an intuitive framework for building new quantum algorithms. The
lackadaisical quantum walk, which is a quantum analog of the lazy random walk,
is obtained by adding a self-loop transition to each state allowing the walker
to stay stuck in the same state, being able to improve the performance of the
quantum walks as search algorithms. However, the high dependence of a weight
makes it a key parameter to reach the maximum probability of success in the
search process. Although many advances have been achieved with search
algorithms based on quantum walks, the number of self-loops can also be
critical for search tasks. Believing that the multiple self-loops have not yet
been properly explored, this article proposes the quantum search algorithm
Multiself-loop Lackadaisical Quantum Walk with Partial Phase Inversion, which
is based on a lackadaisical quantum walk with multiple self-loops where the
target state phase is partially inverted. Each vertex has self-loops, with
weights , where is a real parameter. The phase inversion is based
on Grover's algorithm and acts partiality, modifying the phase of a given
quantity of self-loops. On a hypercube structure, we analyzed
the situation where and and investigated its
effects in the search for 1 to 12 marked vertices. Based on two ideal weights
used in the literature, we propose two new weight values. As a result, with
the proposal of the Multiself-loop Lackadaisical Quantum Walk with partial
phase inversion of target states and the new weight values for the self-loop,
this proposal improved the maximum success probabilities to values close to 1.
This article contributes with a new perspective on the use of quantum
interferences in the construction of new quantum search algorithms.Comment: 16 pages, 4 figures, 3 table
Effect of different intravenous iron preparations on lymphocyte intracellular reactive oxygen species generation and subpopulation survival
<p>Abstract</p> <p>Background</p> <p>Infections in hemodialysis (HD) patients lead to high morbidity and mortality rates and are associated with early cardiovascular mortality, possibly related to chronic inflammation. Intravenous (IV) iron is widely administered to HD patients and has been associated with increased oxidative stress and dysfunctional cellular immunity. The purpose of this study was to examine the effect of three commercially available IV iron preparations on intracellular reactive oxygen species generation and lymphocyte subpopulation survival.</p> <p>Methods</p> <p>Peripheral blood mononuclear cells (PBMC) were isolated from healthy donor buffy coat. PBMC were cultured and incubated with 100 μg/mL of sodium ferric gluconate (SFG), iron sucrose (IS) or iron dextran (ID) for 24 hours. Cells were then probed for reactive oxygen species (ROS) with dichlorofluorescein-diacetate. In separate studies, isolated PBMCs were incubated with the 25, 50 or 100 μg/mL iron concentrations for 72 hours and then stained with fluorescein conjugated monoclonal antibodies for lymphocyte subpopulation identification. Untreated PBMCs at 24 hours and 72 hours served as controls for each experiment.</p> <p>Results</p> <p>All three IV iron preparations induced time dependent increases in intracellular ROS with SFG and IS having a greater maximal effect than ID. The CD4+ lymphocytes were most affected by IV iron exposure, with statistically significant reduction in survival after incubation with all three doses (10, 25 and 100 μg/mL) of SFG, IS and ID.</p> <p>Conclusion</p> <p>These data indicate IV iron products induce differential deleterious effects on CD4+ and CD16+ human lymphocytes cell populations that may be mediated by intracellular reactive oxygen species generation. Further studies are warranted to determine the potential clinical relevance of these findings.</p
Local Difference Measures between Complex Networks for Dynamical System Model Evaluation
Acknowledgments We thank Reik V. Donner for inspiring suggestions that initialized the work presented herein. Jan H. Feldhoff is credited for providing us with the STARS simulation data and for his contributions to fruitful discussions. Comments by the anonymous reviewers are gratefully acknowledged as they led to substantial improvements of the manuscript.Peer reviewedPublisher PD
Telomere length analysis in amyotrophic lateral sclerosis using large-scale whole genome sequence data
BackgroundAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the loss of upper and lower motor neurons, leading to progressive weakness of voluntary muscles, with death following from neuromuscular respiratory failure, typically within 3 to 5 years. There is a strong genetic contribution to ALS risk. In 10% or more, a family history of ALS or frontotemporal dementia is obtained, and the Mendelian genes responsible for ALS in such families have now been identified in about 50% of cases. Only about 14% of apparently sporadic ALS is explained by known genetic variation, suggesting that other forms of genetic variation are important. Telomeres maintain DNA integrity during cellular replication, differ between sexes, and shorten naturally with age. Sex and age are risk factors for ALS and we therefore investigated telomere length in ALS. MethodsSamples were from Project MinE, an international ALS whole genome sequencing consortium that includes phenotype data. For validation we used donated brain samples from motor cortex from people with ALS and controls. Ancestry and relatedness were evaluated by principal components analysis and relationship matrices of DNA microarray data. Whole genome sequence data were from Illumina HiSeq platforms and aligned using the Isaac pipeline. TelSeq was used to quantify telomere length using whole genome sequence data. We tested the association of telomere length with ALS and ALS survival using Cox regression. ResultsThere were 6,580 whole genome sequences, reducing to 6,195 samples (4,315 from people with ALS and 1,880 controls) after quality control, and 159 brain samples (106 ALS, 53 controls). Accounting for age and sex, there was a 20% (95% CI 14%, 25%) increase of telomere length in people with ALS compared to controls (p = 1.1 x 10(-12)), validated in the brain samples (p = 0.03). Those with shorter telomeres had a 10% increase in median survival (p = 5.0x10(-7)). Although there was no difference in telomere length between sporadic ALS and familial ALS (p=0.64), telomere length in 334 people with ALS due to expanded C9orf72 repeats was shorter than in those without expanded C9orf72 repeats (p = 5.0x10(-4)). DiscussionAlthough telomeres shorten with age, longer telomeres are a risk factor for ALS and worsen prognosis. Longer telomeres are associated with ALS
Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context
Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts
Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas
Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN
Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas
This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing
molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and
correlation with overall survival. TIL map structural patterns were grouped using standard
histopathological parameters. These patterns are enriched in particular T cell subpopulations
derived from molecular measures. TIL densities and spatial structure were differentially enriched
among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial
infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic
patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for
the TCGA image archives with insights into the tumor-immune microenvironment
Bone mineral density loci specific to the skull portray potential pleiotropic effects on craniosynostosis
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