Structural behavior of suspension bridge with a stabilizing cable

Suspended structures are commonly used in construction of motorway and pedestrian bridges. These structures allow wide spans without the need for intermediate supports. Suspension bridges are noted for lower structural stiffness as compared to beam bridges and arc bridges. The stiffness control depending on the environment and external effects (moving loads, wind, seismic forces, etc.) is a real-life challenge [1, 2]. The authors of this paper have evaluated the use of stabilizing cable installed in the central span under the stiffening girder as the means of stiffness control. A plane 3D model of a suspension bridge was developed using the ANSYS software. The study compared the stress deformed state and dynamic properties of the models with and without a stabilizing cable. The displacement in the model equipped with a stabilizing cable, as compared to the one without, was noted to be lower in all relevant sections: 2.6 times in the middle of the central span of the stiffening girder; 15 times in the middle of the end span; and displacement of the tower top was 3.5 times lower

Performance of a Suspension Bridge with Active Vibration Dampers

Suspended structures that are used extensively in construction of motorway and pedestrian bridges allow bridging wide spans without having to install intermediate supports. Being less stiff in comparison to girder and arch bridges, suspension bridges require their dynamic properties to be controlled [1, 2]. This is a vital task when it comes to suspension bridges. Several engineering arrangements are available to control the dynamic properties of the structures [3]. This paper addresses the use of active dampers [4] installed on the tops of the towers as the means to control vibrations of a suspension bridge. To this end, a planar 3D model of suspension bridge was built using ANSYS software. The authors compared stress-strain behavior and dynamic properties of the models with and without active vibration dampers. In contrast to the initial model, the model of a bridge equipped with active dampers exhibits less displacement in all cross-sections. Thus, the displacements are reduced 1.7 times in the middle of the central span of suspended stiffening truss; 2.7 times in the middle of the end span; and displacements of the top of the bridge tower are 1.6 times less. The modal analysis has shown that in the model with active dampers the frequency of transverse vibrations at the tower tops has increased 1.9 times, while vertical vibrations have increased within 23%. Under maximum applied overpressure in the active damper, torsional vibrations of the structure have increased 2.4 times as compared to the initial model. The results obtained by the authors allow for the conclusion that active dampers are useful tools for controlling the dynamic properties of a suspension bridge

Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach

<div><p>The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example.</p> </div

Core network of predicted drug targets in cancers.

<p>(A) shows the commonly predicted drug targets (within the top 100 predictions) for colorectal cancer, thyroid cancer, ovarian cancer, melanoma, acute myeloid leukemia, and hepatocellular carcinoma. Yellow stars represent known disease biomarkers for neoplasms obtained from the Metabase resource. Cyan stars highlight genes that are known drug targets for at least one of the six types of cancer. (B) shows diseases that are significantly associated with the predicted drug targets. The diseases are ordered by the percentage of genes they cover. Neoplasms are found to cover all of the predicted drug targets. (C) shows the most enriched KEGG pathways for the predicted drug targets <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060618#pone.0060618-Kanehisa1" target="_blank">[45]</a>. Cancer-related pathways are most enriched followed by pathways for specific cancers as well as cancer-related signaling pathways.</p

Network reconstruction for c-Myc as a common drug target in different cancers.

<p>The blue, green and magenta boxes show uniquely up-regulated genes that were predicted as drug targets (within the top 100 predictions) for the indicated cancer type and that contribute to the regulation of cell proliferation. c-Myc (in the middle) is the top drug target prediction for all three cancer types and is involved in the regulation of cell proliferation as well. Downstream targets of c-Myc are shown in the gray box below c-Myc and are uniformly up-regulated in all three cancer types. Cyan stars represent known drug targets for the respective cancer type. Purple stars correspond to drug targets that have been associated with other diseases and can be readily repositioned to the treatment of this type of cancer, while yellow stars indicate unexploited drug targets that can be used for the development of novel treatment strategies. Red thermometers show significantly up-regulated genes in (1) Thyroid Cancer, (2) Colon Cancer, and (3) Melanoma.</p

An integrated map of HIV-human protein complexes that facilitate viral infection.

Recent proteomic and genetic studies have aimed to identify a complete network of interactions between HIV and human proteins and genes. This HIV-human interaction network provides invaluable information as to how HIV exploits the host machinery and can be used as a starting point for further functional analyses. We integrated this network with complementary datasets of protein function and interaction to nominate human protein complexes with likely roles in viral infection. Based on our approach we identified a global map of 40 HIV-human protein complexes with putative roles in HIV infection, some of which are involved in DNA replication and repair, transcription, translation, and cytoskeletal regulation. Targeted RNAi screens were used to validate several proteins and complexes for functional impact on viral infection. Thus, our HIV-human protein complex map provides a significant resource of potential HIV-host interactions for further study

Consensus method performance.

<p>(A) The plot shows the median AUC for each disease model. The highest AUC of 93.19% is achieved for hyperplastic polyposis syndrome and the lowest for ischemic stroke with 63.27%. (B) and (C) show the ROC curves for hyperplastic polyposis syndrome and periodontitis, which achieved the highest performance. The blue areas around the AUC curves represent the 95% confidence intervals.</p

Network reconstruction for COX-2 as repositioning candidate for diabetes type 1 therapy.

<p>Over-expression of COX-2 in monocytes leads to an increased production of prostaglandin E2. Prostaglandin E2 activates T-cell signaling through the PGE2 receptor resulting in increased cAMP levels and activation of the transcription factors CREB1 and CREM. cAMP inactivates the IL-2 receptor of T-cells, while CREM acts as repressor for IL-2. The inhibition of IL-2 and the IL-2 receptor result in immune regulation dysfunction leading to autoimmunity and ultimately the death of beta-cells, which is the cause of diabetes type 1. Predicted drug targets (within the top 100) for diabetes are highlighted with colored stars, where the numbers correspond to the rank in the drug target predictions. Purple stars correspond to drug targets that have been associated with other diseases and can be readily repositioned to the treatment of diabetes type 1. Red thermometers show significantly up-regulated genes in diabetes type 1. Green arrows correspond to activation edges, red arrows represent inhibition edges.</p

Top drug target predictions for the 30 diseases.

<p>For each disease, the name of the predicted drug target obtained from MetaBase and the AUC performance together with the respective baseline AUC performance (based on permutation testing) is shown.</p