Band structure of semimagnetic Hg1-yMnyTe quantum wells

The band structure of semimagnetic Hg_1-yMn_yTe/Hg_1-xCd_xTe type-III quantum wells has been calculated using eight-band kp model in an envelope function approach. Details of the band structure calculations are given for the Mn free case (y=0). A mean field approach is used to take the influence of the sp-d exchange interaction on the band structure of QW's with low Mn concentrations into account. The calculated Landau level fan diagram and the density of states of a Hg_0.98Mn_0.02Te/Hg_0.3Cd_0.7Te QW are in good agreement with recent experimental transport observations. The model can be used to interpret the mutual influence of the two-dimensional confinement and the sp-d exchange interaction on the transport properties of Hg_1-yMn_yTe/Hg_1-xCd_xTe QW's.Comment: 12 pages, 4 figure

Five-year outcome in 18 010 patients from the German Aortic Valve Registry

OBJECTIVES: To determine the 5-year outcome in patients treated by isolated transcatheter aortic valve implantation (TAVI) or surgical aortic valve replacement (sAVR)—a prospective observational cohort study. METHODS: A total of 18 010 patients were included (n = 8942 TAVI and n = 9068 sAVR) in the German Aortic Valve Registry (GARY) who were treated in 2011 and 2012 at 92 sites in central Germany. Eligible patients with TAVI and sAVR were matched using propensity scores in a nearest-neighbour approach. Patients with repeat procedures or unequivocal indication for one treatment option (e.g. frailty) were excluded (n = 4785 for TAVI and n = 2 for sAVR). This led to 13 223 patients (4157 TAVI and 9066 sAVR) as an unmatched subcohort. The main outcome measure was the 5-year all-cause mortality. RESULTS: TAVI patients were significantly older (80.9 ± 6.1 vs 68.5 ± 11.1 years, P < 0.001), had a higher Society of Thoracic Surgeons (STS) score (6.3 ± 4.9 vs 2.6 ± 3.0, P < 0.001) and a higher 5-year all-cause mortality (49.8% vs 16.5%, P < 0.0001). There was no major difference in in-hospital stroke, in-hospital myocardial infarction, or temporary and chronic dialysis. In the propensity score-matched group (n = 3640), there were 763 deaths (41.9%) among 1820 TAVI patients compared with 552 (30.3%) among 1820 treated with sAVR during the 5-year follow-up (hazard ratio 1.51, 95% confidence interval 1.35–1.68; P < 0.0001). New pacemaker implantation was performed in 448 patients (24.6%) after TAVI and in 201 (11.0%) after sAVR (P < 0.0001). CONCLUSIONS: The 5-year follow-up data show that TAVI patients were significantly older and had a higher STS score than sAVR patients. After propensity score matching, TAVI with early-generation prosthesis was associated with significantly higher 5-year all-cause mortality than sAVR

Genome-Wide DNA Methylation Profiling in Early Stage I Lung Adenocarcinoma Reveals Predictive Aberrant Methylation in the Promoter Region of the Long Noncoding RNA PLUT: An Exploratory Study

Introduction: Surgical procedure is the treatment of choice in early stage I lung adenocarcinoma. However, a considerable number of patients experience recurrence within the first 2 years after complete resection. Suitable prognostic biomarkers that identify patients at high risk of recurrence (who may probably benefit from adjuvant treatment) are still not available. This study aimed at identifying methylation markers for early recurrence that may become important tools for the development of new treatment modalities. Methods: Genome-wide DNA methylation profiling was performed on 30 stage I lung adenocarcinomas, comparing 14 patients with early metastatic recurrence with 16 patients with a long-term relapse-free survival period using methylated-CpG-immunoprecipitation followed by high-throughput next-generation sequencing. The differentially methylated regions between the two subgroups were validated for their prognostic value in two independent cohorts using the MassCLEAVE assay, a high-resolution quantitative methylation analysis. Results: Unsupervised clustering of patients in the discovery cohort on the basis of differentially methylated regions identified patients with shorter relapse-free survival (hazard ratio: 2.23; 95% confidence interval: 0.66-7.53; p = 0.03). In two validation cohorts, promoter hypermethylation of the long noncoding RNA PLUT was significantly associated with shorter relapse-free survival (hazard ratio: 0.54; 95% confidence interval: 0.31-0.93; p < 0.026) and could be reported as an independent prognostic factor in the multivariate Cox regression analysis. Conclusions: Promoter hypermethylation of the long noncoding RNA PLUT is predictive in patients with early stage I adenocarcinoma at high risk for early recurrence. Further studies are needed to validate its role in carcinogenesis and its use as a biomarker to facilitate patient selection and risk stratification

Use of machine learning to shorten observation-based screening and diagnosis of autism

The Autism Diagnostic Observation Schedule-Generic (ADOS) is one of the most widely used instruments for behavioral evaluation of autism spectrum disorders. It is composed of four modules, each tailored for a specific group of individuals based on their language and developmental level. On average, a module takes between 30 and 60 min to deliver. We used a series of machine-learning algorithms to study the complete set of scores from Module 1 of the ADOS available at the Autism Genetic Resource Exchange (AGRE) for 612 individuals with a classification of autism and 15 non-spectrum individuals from both AGRE and the Boston Autism Consortium (AC). Our analysis indicated that 8 of the 29 items contained in Module 1 of the ADOS were sufficient to classify autism with 100% accuracy. We further validated the accuracy of this eight-item classifier against complete sets of scores from two independent sources, a collection of 110 individuals with autism from AC and a collection of 336 individuals with autism from the Simons Foundation. In both cases, our classifier performed with nearly 100% sensitivity, correctly classifying all but two of the individuals from these two resources with a diagnosis of autism, and with 94% specificity on a collection of observed and simulated non-spectrum controls. The classifier contained several elements found in the ADOS algorithm, demonstrating high test validity, and also resulted in a quantitative score that measures classification confidence and extremeness of the phenotype. With incidence rates rising, the ability to classify autism effectively and quickly requires careful design of assessment and diagnostic tools. Given the brevity, accuracy and quantitative nature of the classifier, results from this study may prove valuable in the development of mobile tools for preliminary evaluation and clinical prioritization—in particular those focused on assessment of short home videos of children—that speed the pace of initial evaluation and broaden the reach to a significantly larger percentage of the population at risk

Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study

In this work, we formulated a real-world problem related to sewer pipeline gas detection using the classification-based approaches. The primary goal of this work was to identify the hazardousness of sewer pipeline to offer safe and non-hazardous access to sewer pipeline workers so that the human fatalities, which occurs due to the toxic exposure of sewer gas components, can be avoided. The dataset acquired through laboratory tests, experiments, and various literature sources was organized to design a predictive model that was able to identify/classify hazardous and non-hazardous situation of sewer pipeline. To design such prediction model, several classification algorithms were used and their performances were evaluated and compared, both empirically and statistically, over the collected dataset. In addition, the performances of several ensemble methods were analyzed to understand the extent of improvement offered by these methods. The result of this comprehensive study showed that the instance-based learning algorithm performed better than many other algorithms such as multilayer perceptron, radial basis function network, support vector machine, reduced pruning tree. Similarly, it was observed that multi-scheme ensemble approach enhanced the performance of base predictors

A voting approach to identify a small number of highly predictive genes using multiple classifiers

<p>Abstract</p> <p>Background</p> <p>Microarray gene expression profiling has provided extensive datasets that can describe characteristics of cancer patients. An important challenge for this type of data is the discovery of gene sets which can be used as the basis of developing a clinical predictor for cancer. It is desirable that such gene sets be compact, give accurate predictions across many classifiers, be biologically relevant and have good biological process coverage.</p> <p>Results</p> <p>By using a new type of multiple classifier voting approach, we have identified gene sets that can predict breast cancer prognosis accurately, for a range of classification algorithms. Unlike a wrapper approach, our method is not specialised towards a single classification technique. Experimental analysis demonstrates higher prediction accuracies for our sets of genes compared to previous work in the area. Moreover, our sets of genes are generally more compact than those previously proposed. Taking a biological viewpoint, from the literature, most of the genes in our sets are known to be strongly related to cancer.</p> <p>Conclusion</p> <p>We show that it is possible to obtain superior classification accuracy with our approach and obtain a compact gene set that is also biologically relevant and has good coverage of different biological processes.</p