Reduction of Motion Artifacts and Improvement of R Peak Detecting Accuracy Using Adjacent Non-Intrusive ECG Sensors

Non-intrusive electrocardiogram (ECG) monitoring has many advantages: easy to measure and apply in daily life. However, motion noise in the measured signal is the major problem of non-intrusive measurement. This paper proposes a method to reduce the noise and to detect the R peaks of ECG in a stable manner in a sitting arrangement using non-intrusive sensors. The method utilizes two capacitive ECG sensors (cECGs) to measure ECG, and another two cECGs located adjacent to the sensors for ECG are added to obtain the information on motion. Then, active noise cancellation technique and the motion information are used to reduce motion noise. To verify the proposed method, ECG was measured indoors and during driving, and the accuracy of the detected R peaks was compared. After applying the method, the sum of sensitivity and positive predictivity increased 8.39% on average and 26.26% maximally in the data. Based on the results, it was confirmed that the motion noise was reduced and that more reliable R peak positions could be obtained by the proposed method. The robustness of the new ECG measurement method will elicit benefits to various health care systems that require noninvasive heart rate or heart rate variability measurements.1145Ysciescopu

Design Rules for Self-Assembly of 2D Nanocrystal/Metal-Organic Framework Superstructures.

We demonstrate the guiding principles behind simple two dimensional self-assembly of MOF nanoparticles (NPs) and oleic acid capped iron oxide (Fe3 O4 ) NCs into a uniform two-dimensional bi-layered superstructure. This self-assembly process can be controlled by the energy of ligand-ligand interactions between surface ligands on Fe3 O4 NCs and Zr6 O4 (OH)4 (fumarate)6 MOF NPs. Scanning transmission electron microscopy (TEM)/energy-dispersive X-ray spectroscopy and TEM tomography confirm the hierarchical co-assembly of Fe3 O4 NCs with MOF NPs as ligand energies are manipulated to promote facile diffusion of the smaller NCs. First-principles calculations and event-driven molecular dynamics simulations indicate that the observed patterns are dictated by combination of ligand-surface and ligand-ligand interactions. This study opens a new avenue for design and self-assembly of MOFs and NCs into high surface area assemblies, mimicking the structure of supported catalyst architectures, and provides a thorough fundamental understanding of the self-assembly process, which could be a guide for designing functional materials with desired structure

Superconductivity below 20 K in heavily electron-doped surface layer of FeSe bulk crystal

A superconducting transition temperature (T-c) as high as 100 K was recently discovered in one monolayer FeSe grown on SrTiO3. The discovery ignited efforts to identify the mechanism for the markedly enhanced T-c from its bulk value of 8 K. There are two main views about the origin of the T-c enhancement: interfacial effects and/or excess electrons with strong electron correlation. Here, we report the observation of superconductivity below 20 K in surface electron-doped bulk FeSe. The doped surface layer possesses all the key spectroscopic aspects of the monolayer FeSe on SrTiO3. Without interfacial effects, the surface layer state has a moderate T-c of 20 K with a smaller gap opening of 4.2 meV. Our results show that excess electrons with strong correlation cannot induce the maximum T-c, which in turn reveals the need for interfacial effects to achieve the highest T-c in one monolayer FeSe on SrTiO3.1116Ysciescopu

Classification of time series by shapelet transformation

Time-series classification (TSC) problems present a specific challenge for classification algorithms: how to measure similarity between series. A \emph{shapelet} is a time-series subsequence that allows for TSC based on local, phase-independent similarity in shape. Shapelet-based classification uses the similarity between a shapelet and a series as a discriminatory feature. One benefit of the shapelet approach is that shapelets are comprehensible, and can offer insight into the problem domain. The original shapelet-based classifier embeds the shapelet-discovery algorithm in a decision tree, and uses information gain to assess the quality of candidates, finding a new shapelet at each node of the tree through an enumerative search. Subsequent research has focused mainly on techniques to speed up the search. We examine how best to use the shapelet primitive to construct classifiers. We propose a single-scan shapelet algorithm that finds the best $k$ shapelets, which are used to produce a transformed dataset, where each of the $k$ features represent the distance between a time series and a shapelet. The primary advantages over the embedded approach are that the transformed data can be used in conjunction with any classifier, and that there is no recursive search for shapelets. We demonstrate that the transformed data, in conjunction with more complex classifiers, gives greater accuracy than the embedded shapelet tree. We also evaluate three similarity measures that produce equivalent results to information gain in less time. Finally, we show that by conducting post-transform clustering of shapelets, we can enhance the interpretability of the transformed data. We conduct our experiments on 29 datasets: 17 from the UCR repository, and 12 we provide ourselve

Signal transducer and activator of transcription 3-mediated CD133 up-regulation contributes to promotion of hepatocellular carcinoma

published_or_final_versio

Flagellin is a strong vaginal adjuvant of a therapeutic vaccine for genital cancer

Cervical cancer is a high-incidence female cancer most commonly caused by human papilloma virus (HPV) infection of the genital mucosa. Immunotherapy targeting HPV-derived tumor antigens (TAs) has been widely studied in animal models and in patients. Because the female genital tract is a portal for the entry of HPV and a highly compartmentalized system, the development of topical vaginal immunotherapy in an orthotopic cancer model would provide an ideal therapeutic. Thus, we examined whether flagellin, a potent mucosal immunomodulator, could be used as an adjuvant for a topical therapeutic vaccine for female genital cancer. Intravaginal (IVAG) co-administration of the E6/E7 peptides with flagellin resulted in tumor suppression and long-term survival of tumor-bearing mice. In contrast to IVAG vaccination, intranasal (IN) or subcutaneous (SC) immunization did not induce significant tumor suppression in the same model. The vaginal adjuvant effect of the flagellin was completely abolished in Toll-like receptor-5 (TLR5) knock-out mice. IVAG immunization with the E6/E7 peptides plus flagellin induced the accumulation of CD4(+) and CD8(+) cells and the expression of T cell activation-related genes in the draining genital lymph nodes (gLNs). The co-administered flagellin elicited antigen-specific IFN gamma production in the gLNs and spleen. The intravaginally administered flagellin was found in association with CD11c(+) cells in the gLNs. Moreover, after immunization with a flagellin and the E6/E7 peptides, the TLR5 expression in gLN cells was significantly upregulated. These results suggest that flagellin serves as a potent vaginal adjuvant for a therapeutic peptide cancer vaccine through the activation of TLR5 signaling.1166sciescopu

Polynomial T-depth quantum solvability of noisy binary linear problem: from quantum-sample preparation to main computation

The noisy binary linear problem (NBLP) is known as a computationally hard problem, and therefore, it offers primitives for post-quantum cryptography. An efficient quantum NBLP algorithm that exhibits a polynomial quantum sample and time complexities has recently been proposed. However, the algorithm requires a large number of samples to be loaded in a highly entangled state and it is unclear whether such a precondition on the quantum speedup can be obtained efficiently. Here, we present a complete analysis of the quantum solvability of the NBLP by considering the entire algorithm process, namely from the preparation of the quantum sample to the main computation. By assuming that the algorithm runs on 'fault-tolerant' quantum circuitry, we introduce a reasonable measure of the computational time cost. The measure is defined in terms of the overall number of T gate layers, referred to as T-depth complexity. We show that the cost of solving the NBLP can be polynomial in the problem size, at the expense of an exponentially increasing logical qubits

Increased entropy of signal transduction in the cancer metastasis phenotype

Studies into the statistical properties of biological networks have led to important biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes. Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis. Further exploration of such integrated cancer expression and protein interaction networks will therefore be a fruitful endeavour.Comment: 5 figures, 2 Supplementary Figures and Table

Influences of H on the Adsorption of a Single Ag Atom on Si(111)-7 × 7 Surface

The adsorption of a single Ag atom on both clear Si(111)-7 × 7 and 19 hydrogen terminated Si(111)-7 × 7 (hereafter referred as 19H-Si(111)-7 × 7) surfaces has been investigated using first-principles calculations. The results indicated that the pre-adsorbed H on Si surface altered the surface electronic properties of Si and influenced the adsorption properties of Ag atom on the H terminated Si surface (e.g., adsorption site and bonding properties). Difference charge density data indicated that covalent bond is formed between adsorbed Ag and H atoms on 19H-Si(111)-7 × 7 surface, which increases the adsorption energy of Ag atom on Si surface

The interplay of microscopic and mesoscopic structure in complex networks

Not all nodes in a network are created equal. Differences and similarities exist at both individual node and group levels. Disentangling single node from group properties is crucial for network modeling and structural inference. Based on unbiased generative probabilistic exponential random graph models and employing distributive message passing techniques, we present an efficient algorithm that allows one to separate the contributions of individual nodes and groups of nodes to the network structure. This leads to improved detection accuracy of latent class structure in real world data sets compared to models that focus on group structure alone. Furthermore, the inclusion of hitherto neglected group specific effects in models used to assess the statistical significance of small subgraph (motif) distributions in networks may be sufficient to explain most of the observed statistics. We show the predictive power of such generative models in forecasting putative gene-disease associations in the Online Mendelian Inheritance in Man (OMIM) database. The approach is suitable for both directed and undirected uni-partite as well as for bipartite networks