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A dual-labeled fluorescence quenching lateral flow assay based on one-pot, enzyme-free isothermal cascade amplification for the rapid and ultrasensitive detection of pathogens.

We present SizePairs, a new technique to create stable and balanced treemap layouts that visualize values changing over time in hierarchical data. To achieve an overall high-quality result across all time steps in terms of stability and aspect ratio, SizePairs employs a new hierarchical size-based pairing algorithm that recursively pairs two nodes that complement their size changes over time and have similar sizes. SizePairs maximizes the visual quality and stability by optimizing the splitting orientation of each internal node and flipping leaf nodes, if necessary. We also present a comprehensive comparison of SizePairs against the state-of-the-art treemaps developed for visualizing time-dependent data. SizePairs outperforms existing techniques in both visual quality and stability, while being faster than the local moves technique.

Purpose The purpose of this paper is to lay a theoretical foundation for identifying operational information units for library and information professional activities in the context of scholarly communication. Design/methodology/approach The study adopts a deduction-verification approach to formulate a typology of units for scholarly information. It first deduces possible units from an existing conceptualization of information, which defines information as the combined product of data and meaning, and then tests the usefulness of these units via two empirical investigations, one with a group of scholarly papers and the other with a sample of scholarly information users. Findings The results show that, on defining an information unit as a piece of information that is complete in both data and meaning, to such an extent that it remains meaningful to its target audience when retrieved and displayed independently in a database, it is then possible to formulate a hierarchical typology of units for scholarly information. The typology proposed in this study consists of three levels, which in turn, consists of 1, 5 and 44 units, respectively. Research limitations/implications The result of this study has theoretical implications on both the philosophical and conceptual levels: on the philosophical level, it hinges on, and reinforces the objective view of information; on the conceptual level, it challenges the conceptualization of work by IFLA’s Functional Requirements for Bibliographic Records and Library Reference Model but endorses that by Library of Congress’s BIBFRAME 2.0 model. Practical implications It calls for reconsideration of existing operational units in a variety of library and information activities. Originality/value The study strengthens the conceptual foundation of operational information units and brings to light the primacy of “one work” as an information unit and the possibility for it to be supplemented by smaller units.

BACKGROUND AND PURPOSE: To develop a novel deep learning algorithm of sequential analysis, Seq2Seq, for predicting weekly anatomical changes of lung tumor and esophagus during definitive radiotherapy, incorporate the potential tumor shrinkage into a predictive treatment planning paradigm, and improve the therapeutic ratio. METHODS AND MATERIALS: Seq2Seq starts with the primary tumor and esophagus observed on the planning CT to predict their geometric evolution during radiotherapy on a weekly basis, and subsequently updates the predictions with new snapshots acquired via weekly CBCTs. Seq2Seq is equipped with convolutional long short term memory to analyze the spatial-temporal changes of longitudinal images, trained and validated using a dataset including sixty patients. Predictive plans were optimized according to each weekly prediction and made ready for weekly deployment to mitigate the clinical burden of online weekly replanning. RESULTS: Seq2Seq tracks structural changes well: DICE between predicted and actual weekly tumor and esophagus were (0.83±0.10, 0.79±0.14, 0.78±0.12, 0.77±0.12, 0.75±0.12, 0.71±0.17), and (0.72 ± 0.16, 0.73 ± 0.11, 0.75 ± 0.08,0.74 ± 0.09, 0.72 ± 0.14, 0.71 ± 0.14), respectively, while the average Hausdorff distances were within 2mm. Evaluating dose to the actual weekly tumor and esophagus, a 4.2Gy reduction in esophagus mean dose while maintaining 60Gy tumor coverage was achieved with the predictive weekly plans, compared to the plan optimized using the initial tumor and esophagus alone, primarily due to noticeable tumor shrinkage during radiotherapy. CONCLUSION: It is feasible to predict the longitudinal changes of tumor and esophagus with the Seq2Seq, which could lead to improving the efficiency and effectiveness of lung adaptive radiotherapy.

This article presents an innovative approach for fault diagnosis based on an optimized generalized regression neural network (GRNN) by integrating with dissolved gas analysis, cuckoo search algorithm (CSA), and rough set theory (RS). In the proposed method, the high dimensioned data will be simplified and reduced by RS to generate better features or attributes for the GRNN input. Meanwhile, to enhance the network performance, the smoothing factor of GRNN is optimized by CSA with Levy flight, which leads to a good global convergence. As a consequence, CSA can provide a good solution to effectively improve the fault diagnosis performance. To validate and demonstrate the proposed method, we applied it to a real‐world fault diagnosis application, power transformer fault diagnosis, by comparing the results with those of other methods. From the experimental results obtained from the evaluation, it is obvious that the proposed fault diagnosis method enabled with RS‐CSA‐GRNN can provide a useful solution for power transformer fault diagnosis because it outperformed other GRNN‐based methods that deployed different optimizing algorithms such as the particle swarm optimization and genetic algorithm. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

This paper presents a granular computing method (GrC) for the economic dispatch (ED) problems with valve-point effects to reduce the computational dimension and improve the solving accuracy. The computing process of the GrC is composed of the coarse granular computing and the fine granular computing which are connected with the load demand and optimized with the intelligent algorithm. For the purpose of improving the computational accuracy, a new granularity partition strategy is introduced in this work to cluster generating units into several fine granules based on their own peculiarities. In order to demonstrate the effectiveness of the GrC method, it is tested on 13- and 40-unit test systems with the valve-point effects and transmission losses considered. The obtained simulation results show that the proposed GrC method has significant superiority in accuracy and robustness compared with other previous optimization algorithms, which can be a potential method for solving the ED problems with valve-point effects.

This paper presents a hybrid machine-learning method based on oil-immersed power transformer fault diagnosis Probability Neural Network (PNN) optimized via a Multi-Verse Optimizer (MVO) algorithm. PNN is a radial basis function prefeedback neural network based on Bayesian decision theory. It has strong fault tolerance and has significant advantages in pattern classification. However, the performance of PNN is greatly affected by the hidden-layer unit-smoothing factor, and the classification result is affected. MVO is a metaheuristic algorithm with strong global convergence. Therefore, the smoothing factor of MVO-optimized PNN (MVO-PNN) can effectively improve the fault diagnosis ability. Recent studies have demonstrated the MVO algorithm. We utilize an experiment about the oil data in the power transformer in Jiangxi Province, China. The results show that MVO-PNN can significantly improve the accuracy of power transformer fault classification and is more efficient than the Cuckoo search algorithm, Bat algorithm, Genetic Algorithm optimization, and other algorithms capabilities in some cases.

Objective 1,25(OH)2D3 has been reported to attenuate liver steatosis; however, its exact mechanism of action remains poorly understood. This study aimed to determine whether 1,25(OH)2D3 can attenuate hepatic steatosis by inducing autophagy. Methods Male C57BL/6 mice fed a high-fat diet (HFD) were injected with 1,25(OH)2D3 for 4 weeks. These mice were given 3-methyladenine (3-MA) to inhibit autophagy. HepG2 cells were preincubated with a free fatty acid (FFA) and then treated with 1,25(OH)2D3. Vitamin D receptor (VDR) shRNA and autophagy-related 16-like 1 (ATG16L1) siRNA were used for VDR knockdown or ATG16L1 silencing, respectively. Results 1,25(OH)2D3 diminished HFD-induced liver damage and steatosis, changes accompanied by autophagy and ATG16L1 expression upregulation. Inhibition of 1,25(OH)2D3-induced autophagy mediated by 3-MA blocked the protective effects of 1,25(OH)2D3 on hepatic steatosis. Additionally, 1,25(OH)2D3-induced autophagy appeared to play a role in anti-inflammation and lipid metabolism modulation in the liver. In HepG2 cells, 1,25(OH)2D3 reduced lipid accumulation and increased autophagy and ATG16L1 expression; however, this effect was abrogated after VDR knockdown. The protective effects of 1,25(OH)2D3-mediated autophagy against lipid accumulation were abolished by 3-MA. Furthermore, siRNA-mediated ATG16L1 knockdown prevented 1,25(OH)2D3-induced autophagy, resulting in increased fat accumulation. Conclusions The data suggest that 1,25(OH)2D3 may ameliorate hepatic steatosis by inducing autophagy by upregulating ATG16L1.

An emerging prognostic and health management (PHM) technology has recently attracted a great deal of attention from academies, industries, and governments. The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management systems. PHM systems enable a pro-active fault prevention strategy through continuously monitoring the health of complex systems. Power transformer PHM will play a key role in securing and stabling electrical power supply to users, especially in the smart grid. In this paper, we present a novel approach for power transformer fault diagnosis based on cuckoo search optimized neural network, also named it as dissolved gas analysis (DGA) approach. The proposed approach uses the Cuckoo Search (CS) algorithm to select the best parameters of backpropagation (BP) neural network, which can approximate any nonlinear relationships. The paper validates the usefulness and efficiency of the proposed approach by conducting simulation to compare the results to Particle Swarm Optimization (PSO) and Genetic algorithm (GA). The results demonstrated that the proposed approach outperformed other methods such as BP neural network, SVM, GA-BP, and PSO-BP. It significantly improved the performance and accuracy of fault diagnosis/detection for power transformer PHM., 2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC), August 15-17, 2018, Xi'an, China

The burgeoning prognostic and health management (PHM) engineering technology with superior performance has lately received extensive attention in the academic circle. Nevertheless, the various types of faults of the power transformer often lead to less accurate predictions and the instability of the power system. To address these problems, a power transformer PHM model with a hybrid machine learning method-approach is proposed in this paper. The model uses intelligent sensors to obtain dissolved gas analysis (DGA) data for fault diagnosis of the power transformer system, so as to compress the complexity of features (gas types) in the power transformer. In particular, to enhance the robustness of the model, we adopt a modified differential evolution whale optimization algorithm (MDE-WOA) to optimize the probabilistic neural network (PNN), namely, the classification performance of the model is improved by updating the smoothing factor ( σ ) of PNN. In addition, compared with other optimization algorithms, the MDE-WOA algorithm has a lower complexity and more stable optimization process. Finally, we evaluate this model with real world data from the power transformer sensor in Jiangxi province, China. The results indicated that the proposed algorithm could achieve the highest diagnostic accuracy in the fourth iteration, its accuracy having reached 98.86%. Therefore, the proposed PNN parameter optimization meta heuristic algorithm could effectively enhance the accuracy and efficiency of the power transformer fault diagnosis.

This paper proposes a hybrid Bat-BP approach based on dissolved gas-in-oil data set (DGA) to optimize the structure of back propagation neural network (BPNN). BPNN is a multilayer feed forward neural network. The rule of local decline that BPNN used is easy to fall into local optimum. Bat algorithm is a metaheuristic bionic algorithm with great local performance, which is adopted to optimize the initial value of BPNN. The recommended Bat-BP method has been employed in power transformer fault diagnosis for the first time. To prove the proposed method has better ability of power transformer fault diagnosis, this paper compares the fitness of Bat-BP with BPNN and other optimized approaches including PSO-BP, GA-BP based on the same DGA data set. The mean squared error (MSE) is used in this paper to evaluate the performance of the total four methods. The experimental results show the Bat-BP has increased the fault diagnosis accuracy from 75.68% to 95.22%, which is higher than those optimized models.

FK866 exhibits a protective effect on D-galactosamine (GaIN)/lipopolysaccharide (LPS) and concanavalin A (ConA)-induced acute liver failure (ALF), but the mechanism by which FK866 affords this benefit has not yet been elucidated. Autophagy has a protective effect on acute liver injury. However, the contribution of autophagy to FK866-conferred hepatoprotection is still unclear. This study aimed to investigate whether FK866 could attenuate GaIN/LPS and ConA-induced ALF through c-jun-N-terminal kinase (JNK)-dependent autophagy. In vivo, Mice were pretreated with FK866 at 24, 12, and 0.5 h before treatment with GaIN/LPS and ConA. 3-methyladenine (3MA) or rapamycin were used to determine the role of autophagy in FK866-conferred hepatoprotection. In primary hepatocytes, autophagy was inhibited by 3MA or autophagy-related protein 7 (Atg7) small interfering RNA (siRNA). JNK was suppressed by SP600125 or Jnk siRNA. FK866 alleviated hepatotoxicity and increased autophagy while decreased JNK activation. Suppression of autophagy abolished the FK866-conferred protection. Inhibition of JNK increased autophagy and exhibited strongly protective effect. Collectively, FK866 could ameliorate GaIN/LPS and ConA-induced ALF through induction of autophagy while suppression of JNK. These findings suggest that FK866 acts as a simple and applicable preconditioning intervention to protect against ALF; autophagy and JNK may also provide therapeutic targets for ALF treatment.

1,25(OH)Male C57BL/6 mice fed a high-fat diet (HFD) were injected with 1,25(OH)1,25(OH)The data suggest that 1,25(OH)

An emerging prognostic and health management (PHM) technology has recently attracted a great deal of attention from academies, industries, and governments. The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management systems. PHM models depend on the smart sensors and data generated from sensors. This paper proposed a machine learning-based methods for developing PHM models from sensor data to perform fault diagnostic for transformer systems in a smart grid. In particular, we apply the Cuckoo Search (CS) algorithm to optimize the Back-propagation (BP) neural network in order to build high performance fault diagnostics models. The models were developed using sensor data called dissolved gas data in oil of the power transformer. We validated the models using real sensor data collected from power transformers in China. The results demonstrate that the developed meta heuristic algorithm for optimizing the parameters of the neural network is effective and useful, and machine learning-based models significantly improved the performance and accuracy of fault diagnosis/detection for power transformer PHM.

We previously demonstrated that baicalein could protect against liver ischemia/reperfusion (I/R) injury in mice. The exact mechanism of baicalein remains poorly understood. Autophagy plays an important role in protecting against I/R injury. This study was designed to determine whether baicalein could protect against liver I/R injury via induction of autophagy in rats. Baicalein was intraperitoneally injected 1 h before warm ischemia. Pretreatment with baicalein prior to I/R insult significantly blunted I/R-induced elevations of serum aminotransferase levels and significantly improved the histological status of livers. Electron microscopy and expression of the autophagic marker LC3B-II suggested induction of autophagy after baicalein treatment. Moreover, inhibition of the baicalein-induced autophagy using 3-methyladenine (3-MA) worsened liver injury. Furthermore, baicalein treatment increased heme oxygenase (HO)-1 expression and pharmacological inhibition of HO-1 with tin protoporphyrin IX (SnPP) abolished the baicalein-mediated autophagy and the hepatocellular protection. In primary rat hepatocytes, baicalein-induced autophagy also protected hepatocytes from hypoxia/reoxygenation injury in vitro and the beneficial effect was abrogated by 3-MA or Atg7 siRNA, respectively. Suppression of HO-1 activity by SnPP or HO-1 siRNA prevented the baicalein-mediated autophagy and resulted in increased hepatocellular injury. Collectively, these results suggest that baicalein prevents hepatocellular injury via induction of HO-1-mediated autophagy.

Several Nitrogen (N)-hydrogen(H), N-dangling bond (DB) and N-single vacancy (V) complexes as the possible donor centers in diamond have been investigated using both supercell and cluster methods within the frame of density functional theory. We have found that the H—N—N—H complex exhibits shallower donor character than that of the N—H—N center discussed by Miyazaki et al.1 and it is one of the possible effective shallow donor centers in crystalline diamond. We conclude that the N—V related complex demonstrates a character of shallow donors and it should be one of the possible donor centers for the ultrananocrystalline diamond (UNCD) films, which are responsible for the n-type high conductivity of these films.

We carried out a calculation of the Roper state with the Sequential Empirical Bayesian (SEB) method with overlap valence fermion on 2+1-flavor domain-wall fermion configurations on the 24^3 x 64 lattice with a^{-1} = 1.73 GeV. The light sea quark mass corresponds to a pion mass of 330 MeV. The mass of the Roper, chirally extrapolated to the physical pion mass, is 1404(112) MeV which is consistent with the experimental value at 1440 MeV. When compared to the Roper state calculation with variational method for Clover and twisted mass fermions, it is found that the Roper states from SEB with overlap fermion are systematically lower by 400 - 800 MeV for all the quark masses ranging from light to the strange mass region. We study the origin of the difference by exploring the size of the interpolation field in relation to the radial wavefunction of the Roper and also the dynamical influence of the higher Fock space., Comment: 7 pages, 6 figures, presented at the 31st International Symposium on Lattice Field Theory (LATTICE 2013), 29 July - 3 August 2013, Mainz, Germany

We present renormalization constants of overlap quark bilinear operators on 2+1-flavor domain wall fermion configurations. Both overlap and domain wall fermions have chiral symmetry on the lattice. The scale independent renormalization constant for the local axial vector current is computed using a Ward Identity. The renormalization constants for the scalar, pseudoscalar and vector current are calculated in the RI-MOM scheme. Results in the MS-bar scheme are obtained by using perturbative conversion ratios. The analysis uses in total six ensembles with lattice sizes 24^3x64 and 32^3x64., 7 pages, 10 figures, presented at the 31st International Symposium on Lattice Field Theory (LATTICE 2013), 29 July - 3 August 2013, Mainz, Germany