SUREVEG - recirkulering af planteaffald som kompost og samdyrkning af grøntsager

Poster præsenterer SureVeg projektet

Partitioned Graph Convolution Using Adversarial and Regression Networks for Road Travel Speed Prediction

Access to quality travel time information for roads in a road network has become increasingly important with the rising demand for real-time travel time estimation for paths within road networks. In the context of the Danish road network (DRN) dataset used in this paper, the data coverage is sparse and skewed towards arterial roads, with a coverage of 23.88% across 850,980 road segments, which makes travel time estimation difficult. Existing solutions for graph-based data processing often neglect the size of the graph, which is an apparent problem for road networks with a large amount of connected road segments. To this end, we propose a framework for predicting road segment travel speed histograms for dataless edges, based on a latent representation generated by an adversarially regularized convolutional network. We apply a partitioning algorithm to divide the graph into dense subgraphs, and then train a model for each subgraph to predict speed histograms for the nodes. The framework achieves an accuracy of 71.5% intersection and 78.5% correlation on predicting travel speed histograms using the DRN dataset. Furthermore, experiments show that partitioning the dataset into clusters increases the performance of the framework. Specifically, partitioning the road network dataset into 100 clusters, with approximately 500 road segments in each cluster, achieves a better performance than when using 10 and 20 clusters.Comment: This thesis was completed 2020-06-12 and defended 2020-06-2

Sensitive Melting Analysis after Real Time- Methylation Specific PCR (SMART-MSP): high-throughput and probe-free quantitative DNA methylation detection

DNA methylation changes that are recurrent in cancer have generated great interest as potential biomarkers for the early detection and monitoring of cancer. In such situations, essential information is missed if the methylation detection is purely qualitative. We describe a new probe-free quantitative methylation-specific PCR (MSP) assay that incorporates evaluation of the amplicon by high-resolution melting (HRM) analysis. Depending on amplicon design, different types of information can be obtained from the HRM analysis. Much of this information cannot be obtained by electrophoretic analysis. In particular, identification of false positives due to incomplete bisulphite conversion or false priming is possible. Heterogeneous methylation can also be distinguished from homogeneous methylation. As proof of principle, we have developed assays for the promoter regions of the CDH1, DAPK1, CDKN2A (p16INK4a) and RARB genes. We show that highly accurate quantification is possible in the range from 100% to 0.1% methylated template when 25 ng of bisulphite-modified DNA is used as a template for PCR. We have named this new approach to quantitative methylation detection, Sensitive Melting Analysis after Real Time (SMART)-MSP

Methylation profiling of normal individuals reveals mosaic promoter methylation of cancer-associated genes

Epigenetic silencing by promoter methylation of genes associated with cancer initiation and progression is a hallmark of tumour cells. As a consequence, testing for DNA methylation biomarkers in plasma or other body fluids shows great promise for detection of malignancies at early stages and/or for monitoring response to treatment. However, DNA from normal leukocytes may contribute to the DNA in plasma and will affect biomarker specificity if there is any methylation in the leukocytes. DNA from 48 samples of normal peripheral blood mononuclear cells was evaluated for the presence of methylation of a panel of DNA methylation biomarkers that have been implicated in cancer. SMART-MSP, a methylation specific PCR (MSP) methodology based on real time PCR amplification, high-resolution melting and strategic primer design, enabled quantitative detection of low levels of methylated DNA. Methylation was observed in all tested mononuclear cell DNA samples for the CDH1 and HIC1 promoters and in the majority of DNA samples for the TWIST1 and DAPK1 promoters. APC and RARB promoter methylation, at a lower average level, was also detected in a substantial proportion of the DNA samples. We found no BRCA1, CDKN2A, GSTP1 and RASSF1A promoter methylation in this sample set. Several individuals had higher levels of methylation at several loci suggestive of a methylator phenotype. In conclusion, methylation of many potential DNA methylation biomarkers can be detected in normal peripheral blood mononuclear cells, and is likely to affect their specificity for detecting low level disease. However, we found no evidence of promoter methylation for other genes indicating that panels of analytically sensitive and specific methylation biomarkers in body fluids can be obtained

Comparing the Scandinavian automobile taxation systems and their CO2 mitigation effects

Despite their similarities, Scandinavian countries have adopted starkly different automobile tax regimes. The Danish system entails very high and convex tax rates with moderate CO2 differentiation. In Norway, tax rates are high and convex with strong CO2 differentiation and total exemptions for zero emission vehicles, even from value added tax. Sweden practices feebates – CO2 dependent subsidization along with moderate taxation. Relying on a disaggregate discrete choice model of automobile purchase, we simulate the demand for passenger cars as of 2016 in Norway under a set of conditions resembling, respectively, the Danish, Norwegian or Swedish fiscal incentives before and after recent reforms. In all cases, implications are derived in terms of energy technology market shares, average type approval CO2 emission rates, and aggregate fiscal revenue. The automobile taxation system is seen to have remarkable impacts on all three accounts. In essence, among the three jurisdictions examined, the Norwegian fiscal regime has by far the strongest CO2 abatement effect. The Danish system is less effective in terms of CO2 abatement, but provides twice as much government revenue. The Swedish feebate strategy is by far the least effective in terms of both CO2 mitigation and revenue collection.publishedVersio

Handlingsreguleringer i danske lægebøger

In this article we present a corpus linguistic project called Syntactic Stylistics. The goal of the project is to combine pragmatics and syntax in a stylistic description of a text. Our corpus consists of 73 texts written by doctors and other health care personnel to layman. The texts are gathered from both written books and from the Internet. The methodology is from the outset primarily quantitative, that is in the text we count the occurrences of e.g. different constituents, their material, their frequency and their combination with other constituents and so on. Many of our findings show a pattern that we try to explain through qualitative studies of e.g. word frequency, language change, and so on

Entanglement-enhanced quantum rectification

Quantum mechanics dictates the band-structure of materials that is essential for functional electronic components. With increased miniaturization of devices it becomes possible to exploit the full potential of quantum mechanics through the principles of superpositions and entanglement. We propose a new class of quantum rectifiers that can leverage entanglement to dramatically increase performance by coupling two small spin chains through an effective double-slit interface. Simulations show that rectification is enhanced by several orders of magnitude even in small systems and should be realizable using several of the quantum technology platforms currently available.Comment: 5+15 pages, 3+6 figure

Information flow in parameterized quantum circuits

In this work, we introduce a new way to quantify information flow in quantum systems, especially for parameterized quantum circuits. We use a graph representation of the circuits and propose a new distance metric using the mutual information between gate nodes. We then present an optimization procedure for variational algorithms using paths based on the distance measure. We explore the features of the algorithm by means of the variational quantum eigensolver, in which we compute the ground state energies of the Heisenberg model. In addition, we employ the method to solve a binary classification problem using variational quantum classification. From numerical simulations, we show that our method can be successfully used for optimizing the parameterized quantum circuits primarily used in near-term algorithms. We further note that information-flow based paths can be used to improve convergence of existing stochastic gradient based methods