Gene expression profiling for molecular distinction and characterization of laser captured primary lung cancers

<p>Abstract</p> <p>Methods</p> <p>We examined gene expression profiles of tumor cells from 29 untreated patients with lung cancer (10 adenocarcinomas (AC), 10 squamous cell carcinomas (SCC), and 9 small cell lung cancer (SCLC)) in comparison to 5 samples of normal lung tissue (NT). The European and American methodological quality guidelines for microarray experiments were followed, including the stipulated use of laser capture microdissection for separation and purification of the lung cancer tumor cells from surrounding tissue.</p> <p>Results</p> <p>Based on differentially expressed genes, different lung cancer samples could be distinguished from each other and from normal lung tissue using hierarchical clustering. Comparing AC, SCC and SCLC with NT, we found 205, 335 and 404 genes, respectively, that were at least 2-fold differentially expressed (estimated false discovery rate: < 2.6%). Different lung cancer subtypes had distinct molecular phenotypes, which also reflected their biological characteristics. Differentially expressed genes in human lung tumors which may be of relevance in the respective lung cancer subtypes were corroborated by quantitative real-time PCR.</p> <p>Genetic programming (GP) was performed to construct a classifier for distinguishing between AC, SCC, SCLC, and NT. Forty genes, that could be used to correctly classify the tumor or NT samples, have been identified. In addition, all samples from an independent test set of 13 further tumors (AC or SCC) were also correctly classified.</p> <p>Conclusion</p> <p>The data from this research identified potential candidate genes which could be used as the basis for the development of diagnostic tools and lung tumor type-specific targeted therapies.</p

Carbon inputs from Miscanthus displace older soil organic carbon without inducing priming

The carbon (C) dynamics of a bioenergy system are key to correctly defining its viability as a sustainable alternative to conventional fossil fuel energy sources. Recent studies have quantified the greenhouse gas mitigation potential of these bioenergy crops, often concluding that C sequestration in soils plays a primary role in offsetting emissions through energy generation. Miscanthus is a particularly promising bioenergy crop and research has shown that soil C stocks can increase by more than 2 t C ha−1 yr−1. In this study, we use a stable isotope (13C) technique to trace the inputs and outputs from soils below a commercial Miscanthus plantation in Lincolnshire, UK, over the first 7 years of growth after conversion from a conventional arable crop. Results suggest that an unchanging total topsoil (0–30 cm) C stock is caused by Miscanthus additions displacing older soil organic matter. Further, using a comparison between bare soil plots (no new Miscanthus inputs) and undisturbed Miscanthus controls, soil respiration was seen to be unaffected through priming by fresh inputs or rhizosphere. The temperature sensitivity of old soil C was also seen to be very similar with and without the presence of live root biomass. Total soil respiration from control plots was dominated by Miscanthus-derived emissions with autotrophic respiration alone accounting for ∼50 % of CO2. Although total soil C stocks did not change significantly over time, the Miscanthus-derived soil C accumulated at a rate of 860 kg C ha−1 yr−1 over the top 30 cm. Ultimately, the results from this study indicate that soil C stocks below Miscanthus plantations do not necessarily increase during the first 7 years

The use of immunosuppressive therapy in MDS: clinical outcomes and their predictors in a large international patient cohort

Most studies of immunosuppressive therapy (IST) in myelodysplastic syndromes (MDS) are limited by small numbers and their single-center nature, and report conflicting data regarding predictors for response to IST. We examined outcomes associated with IST and predictors of benefit in a large international cohort of patients with MDS. Data were collected from 15 centers in the United States and Europe. Responses, including red blood cell (RBC) transfusion independence (TI), were assessed based on the 2006 MDS International Working Group criteria, and overall survival (OS) was estimated by Kaplan-Meier methods. Logistic regression models estimated odds for response and TI, and Cox Proportional Hazard models estimated hazards ratios for OS. We identified 207 patients with MDS receiving IST, excluding steroid monotherapy. The most common IST regimen was anti-thymocyte globulin (ATG) plus prednisone (43%). Overall response rate (ORR) was 48.8%, including 11.2% (95% confidence interval [CI], 6.5%-18.4%) who achieved a complete remission and 30% (95% CI, 22.3%-39.5%) who achieved RBC TI. Median OS was 47.4 months (95% CI, 37-72.3 months) and was longer for patients who achieved a response or TI. Achievement of RBC TI was associated with a hypocellular bone marrow (cellularity < 20%); horse ATG plus cyclosporine was more effective than rabbit ATG or ATG without cyclosporine. Age, transfusion dependence, presence of paroxysmal nocturnal hemoglobinuria or large granular lymphocyte clones, and HLA DR15 positivity did not predict response to IST. IST leads to objective responses in nearly half the selected patients with the highest rate of RBC TI achieved in patients with hypocellular bone marrows

From design to test with UML: Applied to a roaming algorithm for bluetooth devices

The UML Testing Profile provides support for UML based model-driven testing. This paper introduces a methodology of how to use the testing profile in order to modify and extend an existing UML design model for test issues. As a case study, a new roaming algorithm for bluetooth devices has been developed at the University of Lübeck, is modelled using UML. The usability of the UML Testing Profile will be explained by applying it to this model

Practice and Experience using High Performance Computing and Quantum Computing to Speed-up Data Science Methods in Scientific Applications

High-Performance Computing (HPC) can quickly process scientific data and perform complex calculations at extremely high speeds. A vast increase in HPC use across scientific communities is observed, especially in using parallel data science methods to speed-up scientific applications. HPC enables scaling up machine and deep learning algorithms that inherently solve optimization problems. More recently, the field of quantum machine learning evolved as another HPC related approach to speed-up data science methods. This paper will address primarily traditional HPC and partly the new quantum machine learning aspects, whereby the latter specifically focus on our experiences on using quantum annealing at the Juelich Supercomputing Centre (JSC). Quantum annealing is particularly effective for solving optimization problems like those that are inherent in machine learning methods. We contrast these new experiences with our lessons learned of using many parallel data science methods with a high number of Graphical Processing Units (GPUs). That includes modular supercomputers such as JUWELS, the fastest European supercomputer at the time of writing. Apart from practice and experience with HPC co-design applications, technical challenges and solutions are discussed, such as using interactive access via JupyterLab on typical batch-oriented HPC systems or enabling distributed training tools for deep learning on our HPC systems