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Background/Aims: Tegoprazan, a new, fast, and strong potassium-competitive acid blocker, has been approved for the treatment of gastric acid-related diseases in Korea. However, real-world clinical data regarding this drug are scarce. We aimed to compare the Helicobacter pylori eradication rates of tegoprazan- and rabeprazole-based triple therapy.Methods: We retrospectively reviewed data from patients who received first-line treatment for H. pylori infection using tegoprazan- or rabeprazole-based triple therapy for 2 weeks (50 mg tegoprazan or 20 mg rabeprazole+1,000 mg amoxicillin+500 mg clarithromycin twice daily). The primary endpoint was the eradication rate as determined by intention-to-treat analysis.Results: Of the 677 patients included in our study, 344 and 333 received tegoprazan-based and rabeprazole-based triple therapy, respectively. The eradication rate from intention-to-treat analysis was 76.7% (95% confidence interval [CI], 72.1% to 81.0%) for tegoprazan-based triple therapy and 75.4% (95% CI, 70.5% to 79.8%) for rabeprazole-based triple therapy. There was no significant difference in the eradication rates between the two groups (p>0.999). Per-protocol analysis also revealed no significant difference between the eradication rates of the two groups (tegoprazan 83.4% [95% CI, 79.0% to 87.2%] vs rabeprazole 83.5% [79.0% to 87.4%], p>0.999). Furthermore, there was no significant difference in adverse event rates between the two groups (tegoprazan, 27.6%; rabeprazole, 25.8%; p=0.604).Conclusions: The eradication rate of tegoprazan-based triple therapy was similar to that of rabeprazole-based triple therapy. Further studies on the dose-escalation effect of tegoprazan for H. pylori eradication and the efficacy of tegoprazan in regimens other than conventional triple therapy are needed.

Abstract It is known that winter Atlantic-Niño events can induce the El Niño–Southern oscillation (ENSO) in the following winter with a lag of 1-year during one period. On the other hand, summer Atlantic-Niño events can lead to the ENSO in the subsequent winter with a half-year lag during another period. In this study, we investigate the distinct interdecadal modulation of the effect of the Atlantic-Niños on ENSO by analyzing observational reanalysis datasets. During the mid-twentieth century, the winter Atlantic-Niño exhibited increased intensity and extended westward due to warmer conditions in the tropical western Atlantic. As a result, convection occurred from the Amazon to the Atlantic, triggering an atmospheric teleconnection that led to trade wind discharging and equatorial Kelvin waves, ultimately contributing to the development of ENSO. In contrast, during late twentieth century, summer Atlantic-Niño events were closely linked to the South America low-level jet in boreal spring. This connection led to the formation of widespread and intense convection over the Amazon to the Atlantic region. Then, the Walker circulation was effectively modulated, subsequently triggering ENSO events. Further analysis revealed that the interdecadal modulation of the Atlantic–South America–Pacific mean state plays a crucial role in shaping the impact of Atlantic-Niños on ENSO by modifying not only the characteristics of the Atlantic-Niños but also ocean–atmospheric feedback process. Therefore, improving our understanding of the interdecadal modulation of the climatological mean state over the Pacific to Atlantic regions enables better anticipation of the interaction between the Atlantic and Pacific Oceans.

Abstract The East Asian countries have experienced heavy rainfalls in boreal summer 2020. Here, we investigate the dynamical processes driving the rainfall extremes in East Asia during July and August. The Indian Ocean basin warming in June can be responsible for the anticyclonic anomalies in the western North Pacific (WNP), which modulate the zonally-elongated rainfalls in East Asia during July through an atmospheric Rossby wave train. In August, the East Asian rainfall increase is also related to the anticyclonic anomalies in the subtropical WNP, although it is located further north. The north tropical Atlantic warming in June partly contributes to the subtropical WNP rainfall decrease in August through a subtropical teleconnection. Then the subtropical WNP rainfall decrease drives the local anticyclonic anomalies that cause the rainfall increase in East Asia during August. The tropical Indian Ocean anomalously warmed in June and the subtropical WNP rainfall decreased in August 2020, which played a role in modulating the WNP anticyclonic anomalies. Therefore, the record-breaking rainfall extremes in East Asia that occurred during summer 2020 can be explained by the teleconnections associated with the tropical origins among the Indian, Pacific, and Atlantic Oceans and their interbasin interactions.

Demand response (DR) aims at improving the reliability and efficiency of the power grids by shaping the power demand over time. Given that building energy consumption constitutes a significant portion of the overall grid load, building energy management is a critical component for the DR portfolio. In this study, DR control policies for lighting and air-conditioner systems for the individual spaces in buildings are proposed. The policies are designed to achieve the energy reduction amount specified in the DR request while minimizing the user discomfort. A significant challenge is to cope with the uncertainty of various environmental factors such as the solar illuminance and ambient temperature, as well as the psycho-economic factors such as the energy usage preferences of the occupants. We employ a data-driven machine learning approach to tackle this challenge. Our novel idea is to take advantage of the structural similarity of the control policies across the spaces in a lifelong multi-task learning framework. To accommodate significant nonlinearity in efficient policies, a kernel-based learning approach is pursued. The dual decomposition method is employed to relax the constraint coupled across the spaces, which allows solving the overall learning problem via a series of unconstrained subproblems. The efficacy of the proposed method is verified by numerical experiments based on semi-real data sets.

In this study, inspection planning of deteriorating bridges is optimized to determine the inspection application times and methods based on various objectives. These objectives can be formulated by considering the probabilistic structural performance and service life after inspection and maintenance. Probabilistic structural performance and service life prediction are generally based on the probability of failure (or reliability). However, there are difficulties associated with optimizing inspection planning when a low probability of failure is estimated. In this study, we address inspection planning using extrapolation approaches to efficiently compute a low probability of failure. The inspection planning method proposed in this study determines the inspection application times for a given inspection method. We investigated the applicability of direct Monte Carlo simulation (MCS), subset simulation, and two extrapolation approaches (i.e., kernel density estimation (KDE) and KDE combined with generalized Pareto distribution (GPD)) for inspection planning. The probability of failure for optimum inspection planning was based on the damage detection-based state function and extended service life-based state function. These state functions were formulated by considering damage propagation, damage detection by inspections, and service life extensions by maintenance. Illustrative applications to general examples and an existing bridge are provided to investigate the effects of approaches for computing the failure probability on the accuracy and variation of the optimum inspection application times. Finally, the most appropriate approach for optimum inspection planning was determined considering the accuracy and reliability of the solution, computational efficiency, and the applicability of the probabilistic optimization process. The presented investigations revealed that KDE is more appropriate than MCS and the combination of KDE and GPD for optimum inspection planning.

In wireless sensor networks, the accurate estimation of distances between sensor nodes is essential. In addition to the distance information available for immediate neighbors within a sensing range, the distance estimation of two-hop neighbors can be exploited in various wireless sensor network applications such as sensor localization, robust data transfer against hidden terminals, and geographic greedy routing. In this article, we propose a two-hop distance estimation method, which first obtains the region in which the two-hop neighbor nodes possibly exist and then takes the average of the distances to the points in that region. The improvement in the estimation accuracy achieved by the proposed method is analyzed in comparison with a simple summation method that adds two single-hop distances as an estimate of a two-hop distance. Numerical simulation results show that in comparison with other existing distance estimation methods, the proposed method significantly reduces the distance estimation error over a wide range of node densities.

In wireless sensor networks powered by battery-limited energy harvesting, sensor nodes that have relatively more energy can help other sensor nodes reduce their energy consumption by compressing the sensing data packets in order to consequently extend the network lifetime. In this article, we consider a data compression technique that can shorten the data packet itself to reduce the energies consumed for packet transmission and reception and to eventually increase the entire network lifetime. First, we present an energy consumption model, in which the energy consumption at each sensor node is derived. We then propose a data compression algorithm that determines the compression level at each sensor node to decrease the total energy consumption depending on the average energy level of neighboring sensor nodes while maximizing the lifetime of multihop wireless sensor networks with energy harvesting. Numerical simulations show that the proposed algorithm achieves a reduced average energy consumption while extending the entire network lifetime.

Network slicing to create multiple virtual networks, called network slice, is a promising technology to enable networking resource sharing among multiple tenants for the 5th generation (5G) networks. By offering a network slice to slice tenants, network slicing supports parallel services to meet the service level agreement (SLA). In legacy networks, every tenant pays a fixed and roughly estimated monthly or annual fee for shared resources according to a contract signed with a provider. However, such a fixed resource allocation mechanism may result in low resource utilization or violation of user quality of service (QoS) due to fluctuations in the network demand. To address this issue, we introduce a resource management system for network slicing and propose a dynamic resource adjustment algorithm based on reinforcement learning approach from each tenant’s point of view. First, the resource management for network slicing is modeled as a Markov Decision Process (MDP) with the state space, action space, and reward function. Then, we propose a Q-learning-based dynamic resource adjustment algorithm that aims at maximizing the profit of tenants while ensuring the QoS requirements of end-users. The numerical simulation results demonstrate that the proposed algorithm can significantly increase the profit of tenants compared to existing fixed resource allocation methods while satisfying the QoS requirements of end-users.

A smart grid facilitates more effective energy management of an electrical grid system. Because both energy consumption and associated building operation costs are increasing rapidly around the world, the need for flexible and cost-effective management of the energy used by buildings in a smart grid environment is increasing. In this paper, we consider an energy management system for a smart energy building connected to an external grid (utility) as well as distributed energy resources including a renewable energy source, energy storage system, and vehicle-to-grid station. First, the energy management system is modeled using a Markov decision process that completely describes the state, action, transition probability, and rewards of the system. Subsequently, a reinforcement-learning-based energy management algorithm is proposed to reduce the operation energy costs of the target smart energy building under unknown future information. The results of numerical simulation based on the data measured in real environments show that the proposed energy management algorithm gradually reduces energy costs via learning processes compared to other random and non-learning-based algorithms.

The Madden--Julian oscillation (MJO) is closely related to the intraseasonal variability of surface temperature in East Asia. It has been shown that significant cold surface temperature anomalies are observed in East Asia during MJO phase 3. However, the cooling tendency develops prior to phase 3, suggesting that the cold surface anomalies in East Asia are a delayed and accumulated response to the MJO forcings prior to phase 3. Here, using a thermodynamic equation, it is shown that both meridional advection and adiabatic cooling terms associated with the MJO flow are the dominant contributors to the cooling tendency. The meridional cold advection initially manifests in East Asia in the form of northerly wind anomalies in the eastern part of anticyclonic circulation anomalies that are centered over eastern Europe and develop before the establishment of the cold anomalies. It is suggested that the enhanced convection in the western North Pacific Ocean is responsible for the anomalous anticyclonic flow over eastern Europe with about a 10-day lag via a meridionally propagating Rossby wave train. Further, cooling by the vertical wind component in East Asia is a result of adiabatic cooling interpreted as a reversed local overturning circulation, with downward motion in the tropics and upward motion in the subtropics. This anomalous meridional overturning circulation process initiated from suppressed convection spanning the tropical Indian Ocean to East Asia also takes about 10 days. Therefore, both the Rossby wave propagation and a local overturning circulation induced by the tropical convections play an important role in driving the lagged response of cold surface anomalies in East Asia. Interestingly, these tropical convection forcings are similar to the typical dipole pattern in convection during MJO phase 7, with suppressed Indian Ocean convection and enhanced western North Pacific convection. This implies that the dipole convective forcing during MJO phase 7 possibly leads to the cold anomalies in East Asia following phase 3. [ABSTRACT FROM AUTHOR]

A novel approach to evaluating safety of existing bridges based on monitored structural responses and component conditions is presented in this paper. A limit state equation is developed for the measured strain data from structural health monitoring (SHM). The new concepts of the condition function, α(s,t), and prediction function, ζ(s,t), are introduced. The condition function is utilized to estimate the strains at locations other than the strain gauge locations. This function is related to the structural condition assessment results, strain gauge locations, and failure modes under consideration. The prediction function is used to predict the extreme values of the SHM data in the future. An illustration of the proposed approach is provided on an existing highway bridge in Pennsylvania, which had been monitored from 2001 to 2005 by the Advanced Technology for Large Structural Systems Center, a National Engineering Research Center at Lehigh University, Bethlehem, Pa. This study provides the basis for integrating achievable SHM data into structural safety evaluation, and establishes a valid platform for life-cycle, cost-oriented, and reliability-based infrastructure management systems using structural health monitoring. [ABSTRACT FROM AUTHOR]

We propose a new method for identifying and validating drug targets by using gene networks, which are estimated from cDNA microarray gene expression profile data. We created novel gene disruption and drug response microarray gene expression profile data libraries for the purpose of drug target elucidation. We use two types of microarray gene expression profile data for estimating gene networks and then identifying drug targets. The estimated gene networks play an essential role in understanding drug response data and this information is unattainable from clustering methods, which are the standard for gene expression analysis. In the construction of gene networks, we use the Bayesian network model. We use an actual example from analysis of the Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information to drug discovery. [ABSTRACT FROM AUTHOR]

Proposes a statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. Estimation of the conditional distribution of each random variable; Fitting of the nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes.

The interaction of ebselen, 2-phenyl-1,2-benzisoselenazol-3(2H)-one, a novel neuroprotective agent, with cyclodextrins (CyDs) in aqueous solution was studied by the solubility method and spectroscopic methods. The ability of sulfobutyl ether β-CyD (SBE7-β-CyD, average degree of substitution= 6.2) to solubilize ebselen was greater, and its stability constant (> 2000 M-1) was significantly higher than those (< 1000 M-1) of other CyD complexes employed. The stability constant of the complexes rose as hydrophobicity of the substituents of CyDs increased, whereas it was negligibly affected by change in ionic strength of the medium, indicating a significant contribution of hydrophobic interaction in the complexation. SBE7-β-CyD gave positive and negative CD bands at around 320 and 350 nm, respectively, indicating that the guest is embedded in the asymmetric locus of the CyD cavity. 1H-NMR spectroscopic studies suggested that the mono-substituted benzene ring of ebselen is preferably included in the cavity of SBE7-β-CyD. The results indicate that SBE7-β-CyD is useful as a solubilizing agent for ebselen. [ABSTRACT FROM AUTHOR]

The article offers information on the assumptions of the discussers regarding the article "Bridge Reliability Assessment Based on Monitoring," including the statistical determination of steel trusses, the shear studs and deck, and the monitoring system measure.

We propose a new statistical method for constructing genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is in the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. A problem still remains to be solved in selecting an optimal graph, which gives the best representation of the system among genes. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes.