Under what conditions do parallel channel networks occur?

2010 Fall.Includes bibliographic references (pages 53-57).Covers not scanned.Print version deaccessioned 2022.Geologists have long recognized that channel networks can deviate from a typical dendritic form when they develop under certain geologic or topographic constraints. One such deviation is the so-called parallel form, which is thought to develop when the pre-existing surface is sloping. The objectives of this research are to determine the specific conditions under which parallel networks occur and the nature of the transition between dendritic and parallel networks. Both real and simulated channel networks are analyzed in this study. The real networks were obtained from the digital elevation models of basins that include large areas of the pre-existing topographic surface. Such areas were identified as locations with small drainage areas and topographic curvatures that are close to zero. For each basin, the average slope of the pre-existing surface was calculated by averaging the local slopes for all points that are part of the pre-existing surface. Each channel network was then classified using a recently published method that can distinguish five different network types (including dendritic and parallel) based on three measures that are derived from scaling-invariance. These measures focus on the increments of drainage area along a channel, the irregularity of channel courses, and 111 the angles formed by merging tributaries. Based on these classifications, it is observed that natural networks become abruptly parallel when the average slope of the pre-existing surface exceeds about 3%. Simulated channel networks were also generated using a detachment-limited model for fluvial erosion and a slope-dependent model for hillslope processes. The parameters of the model were determined to imitate the real basins, and the average slope of the pre-existing surface was used for the slope of the initial surface. Based on these simulations, the model can also produce a transition between dendritic and parallel networks for an initial slope around 3%, but this threshold depends on the roughness of the initial surface and the boundary conditions

Spatio-Temporal Variability of Atmospheric CO2 as Observed from In-Situ Measurements over North America during NASA Field Campaigns (2004-2008)

Regional-scale measurements were made over the eastern United States (Intercontinental Chemical Transport Experiment - North America (INTEX-NA), summer 2004); Mexico (Megacity Initiative: Local and Global Research Observations (MILAGRO), March 2006); the eastern North Pacific and Alaska (INTEX-B May 2006); and the Canadian Arctic (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS), spring and summer 2008). For these field campaigns, instrumentation for the in situ measurement of CO2 was integrated on the NASA DC-8 research aircraft providing high-resolution (1 second) data traceable to the WMO CO2 mole fraction scale. These observations provide unique and definitive data sets via their intermediate-scale coverage and frequent vertical profiles (0.1 - 12 km) for examining the variability CO2 exhibits above the Earth s surface. A bottom-up anthropogenic CO2 emissions inventory (1deg 1deg) and processing methodology has also been developed for North America in support of these airborne science missions. In this presentation, the spatio-temporal distributions of CO2 and CO column values derived from the campaign measurements will be examined in conjunction with the emissions inventory and transport histories to aid in the interpretation of the CO2 observations

Efficacy and Tolerability of GCSB-5 for Hand Osteoarthritis: A Randomized, Controlled Trial

AbstractPurposeThe aim of this study was to investigate the efficacy and tolerability of GCSB-5, a mixture of 6 purified herbal extracts, in treating hand osteoarthritis (OA).MethodsA randomized, double-blind, placebo-controlled trial enrolled 220 patients with hand OA who had baseline a visual analog scale joint pain score of >30 of 100 mm at 3 hospitals between September 2013 and November 2014. After randomization, patients were allocated to receive oral GCSB-5 600 mg or placebo, bid for 12 weeks. The primary end point was the change in the Australian/Canadian OA Hand Index (AUSCAN)-defined pain score at 4 weeks relative to baseline. Secondary end points included the frequency Outcome Measures in Rheumatology–OA Research Society International (OMERACT-OARSI)-defined response at 4, 8, 12, and 16 weeks after randomization.FindingsThe allocated treatment was received by 109 and 106 patients in the GCSB-5 and placebo groups, respectively. At 4 weeks, the median (interquartile range) change in AUSCAN pain score relative to baseline was significantly greater in the GCSB-5 group than in the placebo group (–9.0 [–23.8 to –0.4] vs –2.2 [–16.7 to 6.0]; P = 0.014), with sustained improvement at 8, 12, and 16 weeks (P = 0.039). The GCSB-5 group also had a significantly greater OMERACT-OARSI–defined response rate than did the placebo group at 4 weeks (44.0% vs 30.2%), 8 weeks (51.4% vs 35.9%), 12 weeks (56.9% vs 40.6%), and 16 weeks (50.5% vs 37.7%) (P = 0.0074). The 2 treatments exhibited comparable safety profiles.ImplicationsGCSB-5 was associated with improved symptoms of hand OA, with good tolerability, in these patients. GCSB-5 may be a well-tolerated alternative of, or addition to, the treatment of hand OA. ClinicalTrials.gov identifier: NCT01910116

Helicobacter pylori infection induces STAT3 phosphorylation on Ser727 and autophagy in human gastric epithelial cells and mouse stomach

© 2020, The Author(s).Helicobacter pylori (H. pylori) infection is considered as one of the principal risk factors of gastric cancer. Constitutive activation of the signal transducer and activator of transcription 3 (STAT3) plays an important role in inflammation-associated gastric carcinogenesis. In the canonical STAT3 pathway, phosphorylation of STAT3 on Tyr705 is a major event of STAT3 activation. However, recent studies have demonstrated that STAT3 phosphorylated on Ser727 has an independent function in mitochondria. In the present study, we found that human gastric epithelial AGS cells infected with H. pylori resulted in localization of STAT3 phosphorylated on Ser727 (P-STAT3Ser727), predominantly in the mitochondria. Notably, H. pylori-infected AGS cells exhibited the loss of mitochondrial integrity and increased expression of the microtubule-associated protein light chain 3 (LC3), the autophagosomal membrane-associated protein. Treatment of AGS cells with a mitophagy inducer, carbonyl cyanide 3-chlorophenylhydrazone (CCCP), resulted in accumulation of P-STAT3Ser727 in mitochondria. In addition, the elevated expression and mitochondrial localization of LC3 induced by H. pylori infection were attenuated in AGS cells harboring STAT3 mutation defective in Ser727 phosphorylation (S727A). We also observed that both P-STAT3Ser727 expression and LC3 accumulation were increased in the mitochondria of H. pylori-inoculated mouse stomach.

Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment: Meeting new challenges

© 2014 Elsevier Ltd All rights reserved. Despite tremendous successes of GPCR crystallography, the receptors with available structures represent only a small fraction of human GPCRs. An important role of the modeling community is to maximize structural insights for the remaining receptors and complexes. The community-wide GPCR Dock assessment was established to stimulate and monitor the progress in molecular modeling and ligand docking for GPCRs. The four targets in the present third assessment round presented new and diverse challenges for modelers, including prediction of allosteric ligand interaction and activation states in 5-hydroxytryptamine receptors 1B and 2B, and modeling by extremely distant homology for smoothened receptor. Forty-four modeling groups participated in the assessment. State-of-the-art modeling approaches achieved close-to-experimental accuracy for small rigid orthosteric ligands and models built by close homology, and they correctly predicted protein fold for distant homology targets. Predictions of long loops and GPCR activation states remain unsolved problems

Long-term efficacy, safety and immunogenicity in patients with rheumatoid arthritis continuing on an etanercept biosimilar (LBEC0101) or switching from reference etanercept to LBEC0101: an open-label extension of a phase III multicentre, randomised, double-blind, parallel-group study

Background To evaluate the long-term efficacy, safety and immunogenicity of continuing LBEC0101; the etanercept (ETN) biosimilar; or switching from the ETN reference product (RP) to LBEC0101 in patients with rheumatoid arthritis (RA). Methods This multicentre, single-arm, open-label extension study enrolled patients who had completed a 52-week randomised, double-blind, parallel phase III trial of LBEC0101 vs ETN-RP. Patients treated with ETN-RP during the randomised controlled trial switched to LBEC0101; those treated with LBEC0101 continued to receive LBEC0101 in this study. LBEC0101 (50 mg) was administered subcutaneously once per week for 48 weeks with a stable dose of methotrexate. Efficacy, safety and immunogenicity of LBEC0101 were assessed up to week 100. Results A total of 148 patients entered this extension study (70 in the maintenance group and 78 in the switch group). The 28-joint disease activity scores (DAS28)-erythrocyte sedimentation rate (ESR) were maintained in both groups from week 52 to week 100 (from 3.068 to 3.103 in the maintenance group vs. from 3.161 to 3.079 in the switch group). ACR response rates at week 100 for the maintenance vs. switch groups were 79.7% vs. 83.3% for ACR20, 65.2% vs. 66.7% for ACR50 and 44.9% vs. 42.3% for ACR70. The incidence of adverse events and the proportion of patients with newly developed antidrug antibodies were similar in the maintenance and switch groups (70.0% and 70.5%, 1.4% and 1.3%, respectively). Conclusions Administration of LBEC0101 showed sustained efficacy and acceptable safety in patients with RA after continued therapy or after switching from ETN-RP to LBEC0101. Trial registration ClinicalTrials.gov, NCT02715908. Registered 22 March 2016.This extension study was funded by LG Chem, Ltd. (formerly, LG Life Sciences, Ltd), Mochida Pharmaceutical Co., Ltd. and Korea Health Industry Development Institute

Correction: Park, S.; Jung, K. Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment. <i>ISPRS Int. J. Geo-Inf.</i> 2021, <i>10</i>, 574

Missing Acknowledgments [...

Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment

Seismic activities are serious disasters that induce natural hazards resulting in an incalculable amount of damage to properties and millions of deaths. Typically, seismic risk assessment can be performed by means of structural damage information computed based on the maximum displacement of the structure. In this study, machine learning models based on GPR are developed in order to estimate the maximum displacement of the structures from seismic activities and then used to construct fragility curves as an application. During construction of the models, 13 features of seismic waves are considered, and six wave features are selected to establish the seismic models with the correlation analysis normalizing the variables with the peak ground acceleration. Two models for six-floor and 13-floor buildings are developed, and a sensitivity analysis is performed to identify the relationship between prediction accuracy and sampling size. A 10-fold cross-validation method is used to evaluate the model performance, using the R-squared, root mean squared error, Nash criterion, and mean bias. Results of the six-parameter-based model apparently indicate a similar performance to that of the 13-parameter-based model for the two types of buildings. The model for the six-floor building affords a steadily enhanced performance by increasing the sampling size, while the model for the 13-floor building shows a significantly improved performance with a sampling size of over 200. The results indicate that the heighted structure requires a larger sampling size because it has more degrees of freedom that can influence the model performance. Finally, the proposed models are successfully constructed to estimate the maximum displacement, and applied to obtain fragility curves with various performance levels. Then, the regional seismic damage is assessed in Gyeonjgu city of South Korea as an application of the developed models. The damage assessment with the fragility curve provides the structural response from the seismic activities, which can assist in minimizing damage

Estimation of Low-Flow in South Korean River Basins Using a Canonical Correlation Analysis and Neural Network (CCA-NN) Based Regional Frequency Analysis

Low-flow quantiles at ungauged locations are generally estimated based on hydrological methods, such as the drainage area ratio and frequency analysis methods. In practice, the drainage area ratio approach is a popular but simple linear model. When hydrologically nonlinear characteristics govern the runoff process, the linear approach leads to significant bias. This study was conducted to develop an improved nonlinear approach using a canonical correlation analysis and neural network (CCA-NN)-based regional frequency analysis (RFA) for low-flow estimation. The jackknife technique was utilized to validate the two methods. The approaches were applied to 33 river basins in South Korea. In this work, we focused on two-year and five-year return periods. For the two-year return period, the BIAS, RMSE, and R2 were 0.013, 0.511, and 0.408 with the RFA, respectively, and &minus;0.042, 1.042, and 0.114 with the drainage area ratio method, respectively; whereas for the five-year return period, the respective indices were &minus;0.018, 0.316, and 0.573 with RFA, respectively, and 0.166, 0.536, and 0.044 with the drainage area ratio method, respectively. RFA outperformed the drainage area ratio method based on its high prediction accuracy and ability to avoid the bias problem. This study indicates that machine learning-based nonlinear techniques have the potential for use in estimating reliable low-flows at ungauged sites

Development of Models for Prompt Responses from Natural Disasters

This study aims to provide an enhanced model for rapid responses from natural disasters by estimating the maximum structural displacement. The linear regression, support vector machine, and Gaussian process regression (GPR) models were applied to obtain displacement estimates. Further, normalization (NM) and standardization (SD) of variables, and principal component analysis (PCA) were applied to improve model performance. The k-fold cross-validation approach was used to assess the results from the models based on the root-mean-square error and the R-squared indices. According to the results, the GPR model with NM and SD tended to provide the best estimates among the three models. The model that was based on a PCA value of 97% yielded better displacement estimation than the models with PCA values of 95% and 100%. Based on the displacement estimation, the maximum inter-story drift ratio was used to produce the fragility curve that can be used for risk assessment. The fragility curve parameters obtained from the actual numerical and predicted models were investigated and yielded similar responses. The proposed model can thus provide accurate and quick responses in disaster case by rapidly predicting the structural damage information