Induction Heating in Underwater Wet Welding—Thermal Input, Microstructure and Diffusible Hydrogen Content

Hydrogen-assisted cracking is a major challenge in underwater wet welding of high-strength steels with a carbon equivalent larger than 0.4 wt%. In dry welding processes, post-weld heat treatment can reduce the hardness in the heat-affected zone while simultaneously lowering the diffusible hydrogen concentration in the weldment. However, common heat treatments known from atmospheric welding under dry conditions are non-applicable in the wet environment. Induction heating could make a difference since the heat is generated directly in the workpiece. In the present study, the thermal input by using a commercial induction heating system under water was characterized first. Then, the effect of an additional induction heating was examined with respect to the resulting microstructure of weldments on structural steels with different strength and composition. Moreover, the diffusible hydrogen content in weld metal was analyzed by the carrier gas hot extraction method. Post-weld induction heating could reduce the diffusible hydrogen content by −34% in 30 m simulated water depth

C2M: Configurable Chemical Middleware

One of the vexing problems that besets concurrent use of multiple, heterogeneous resources is format multiplicity. C2M aims to equip scientists with a wrapper generator on their desktop. The wrapper generator can build wrappers, or converters that can convert data from or into different formats, from a high-level description of the formats. The language in which such a high-level description is expressed is easy enough for scientists to be able to write format descriptions at minimal cost. In C2M, wrappers and documentation for human reading are automatically obtained from the same user-supplied specifications. Initial experiments demonstrate that the idea can, indeed, lead to the advent of usergoverned wrapper generators. Future research will consolidate the code and extend the approach to a realistic variety of formats

High-throughput miRNA profiling of human melanoma blood samples

<p>Abstract</p> <p>Background</p> <p>MicroRNA (miRNA) signatures are not only found in cancer tissue but also in blood of cancer patients. Specifically, miRNA detection in blood offers the prospect of a non-invasive analysis tool.</p> <p>Methods</p> <p>Using a microarray based approach we screened almost 900 human miRNAs to detect miRNAs that are deregulated in their expression in blood cells of melanoma patients. We analyzed 55 blood samples, including 20 samples of healthy individuals, 24 samples of melanoma patients as test set, and 11 samples of melanoma patients as independent validation set.</p> <p>Results</p> <p>A hypothesis test based approch detected 51 differentially regulated miRNAs, including 21 miRNAs that were downregulated in blood cells of melanoma patients and 30 miRNAs that were upregulated in blood cells of melanoma patients as compared to blood cells of healthy controls. The tets set and the independent validation set of the melanoma samples showed a high correlation of fold changes (0.81). Applying hierarchical clustering and principal component analysis we found that blood samples of melanoma patients and healthy individuals can be well differentiated from each other based on miRNA expression analysis. Using a subset of 16 significant deregulated miRNAs, we were able to reach a classification accuracy of 97.4%, a specificity of 95% and a sensitivity of 98.9% by supervised analysis. MiRNA microarray data were validated by qRT-PCR.</p> <p>Conclusions</p> <p>Our study provides strong evidence for miRNA expression signatures of blood cells as useful biomarkers for melanoma.</p

Imagine a world without cancer

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Abstract Background Since the War on Cancer was declared in 1971, the United States alone has expended some $300 billion on research, with a heavy focus on the role of genomics in anticancer therapy. Voluminous data have been collected and analyzed. However, in hindsight, any achievements made have not been realized in clinical practice in terms of overall survival or quality of life extended. This might be justified because cancer is not one disease but a conglomeration of multiple diseases, with widespread heterogeneity even within a single tumor type. Discussion Only a few types of cancer have been described that are associated with one major signaling pathway. This enabled the initial successful deployment of targeted therapy for such cancers. However, soon after this targeted approach was initiated, it was subverted as cancer cells learned and reacted to the initial treatments, oftentimes rendering the treatment less effective or even completely ineffective. During the past 30 plus years, the cancer classification used had, as its primary aim, the facilitation of communication and the exchange of information amongst those caring for cancer patients with the end goal of establishing a standardized approach for the diagnosis and treatment of cancers. This approach should be modified based on the recent research to affect a change from a service-based to an outcome-based approach. The vision of achieving long-term control and/or eradicating or curing cancer is far from being realized, but not impossible. In order to meet the challenges in getting there, any newly proposed anticancer strategy must integrate a personalized treatment outcome approach. This concept is predicated on tumor- and patient-associated variables, combined with an individualized response assessment strategy for therapy modification as suggested by the patients own results. As combined strategies may be outcome-orientated and integrate tumor-, patient- as well as cancer-preventive variables, this approach is likely to result in an optimized anticancer strategy. Summary Herein, we introduce such an anticancer strategy for all cancer patients, experts, and organizations: Imagine a World without Cancer

Classification of current anticancer immunotherapies

During the past decades, anticancer immunotherapy has evolved from a promising therapeutic option to a robust clinical reality. Many immunotherapeutic regimens are now approved by the US Food and Drug Administration and the European Medicines Agency for use in cancer patients, and many others are being investigated as standalone therapeutic interventions or combined with conventional treatments in clinical studies. Immunotherapies may be subdivided into “passive” and “active” based on their ability to engage the host immune system against cancer. Since the anticancer activity of most passive immunotherapeutics (including tumor-targeting monoclonal antibodies) also relies on the host immune system, this classification does not properly reflect the complexity of the drug-host-tumor interaction. Alternatively, anticancer immunotherapeutics can be classified according to their antigen specificity. While some immunotherapies specifically target one (or a few) defined tumor-associated antigen(s), others operate in a relatively non-specific manner and boost natural or therapy-elicited anticancer immune responses of unknown and often broad specificity. Here, we propose a critical, integrated classification of anticancer immunotherapies and discuss the clinical relevance of these approaches