Identification of Quantitative Trait Loci Determining Vegetative Growth Traits in Coffea Canephor

Recently the use of molecular markers has been successfully applied for some crops. For coffee, new opportunities have been opened since Nestlé R&D Centre in collaboration with ICCRI completed the first genetic map of Coffea canephora. This study was aimed both to evaluate the phenotypic trait and also to identify the quantitative trait loci (QTLs) controlling the vegetative growth in Robusta coffee. Present study used three C. canephora populations and six genetic maps developed based on these populations using simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs) markers. A total of 17 different quantitative data were used for the detection of QTLs on each of three populations. Present result showed that most of these traits were not heritable. The nine vegetative traits have been identified and distributed over seven different linkage groups. Due to some QTLs determining one given trait were overlapping on the same linkage group and were coming from the same favourable parent, a total of 19 QTLs detected for vegetative traits might finally be considered as only 12 QTLs involved. However, only two of them were shared for different traits. One involved for the number/length of primary branches and width of the canopy while the other for length of internodes and width of canopy. These two QTLs might determine the size of the tree canopy in this species

Quantitative testing

We investigate the problem of specification based testing with dense sets of inputs and outputs, in particular with imprecision as they might occur due to errors in measurements, numerical instability or noisy channels. Using quantitative transition systems to describe implementations and specifications, we introduce implementation relations that capture a notion of correctness “up to ε”, allowing deviations of implementation from the specification of at most ε. These quantitative implementation relations are described as Hausdorff distances between certain sets of traces. They are conservative extensions of the well-known ioco relation. We develop an on-line and an off-line algorithm to generate test cases from a requirement specification, modeled as a quantitative transition system. Both algorithms are shown to be sound and complete with respect to the quantitative implementation relations introduced

Quantitative analysis of pixel crosstalk in AMOLED displays

The resolution of organic light-emitting diode (OLED) displays is increasing steadily as these displays are adopted for mobile and virtual reality (VR) devices. This leads to a stronger pixel crosstalk effect, where the neighbors of active pixels unintentionally emit light due to a lateral electric current between the pixels. Recently, the crosstalk was quantified by measuring the current flowing through the common hole transport layer between the neighboring pixels and comparing it to the current through the active pixel diode. The measurements showed that the crosstalk is more crucial for low light levels. In such cases, the intended and parasitic currents are similar. The simulations performed in this study validated these measurement results. By simulations, we quantify the crosstalk current through the diode. The luminous intensity can be calculated with the measured current efficiency of the diodes. For low light levels, the unintended luminance can reach up to 40% of the intended luminance. The luminance due to pixel crosstalk is perceivable by humans. This effect should be considered for OLED displays with resolutions higher than 300 PPI

Quantitative abstraction theory

A quantitative theory of abstraction is presented. The central feature of this is a growth formula defining the number of abstractions which may be formed by an individual agent in a given context. Implications of the theory for artificial intelligence and cognitive psychology are explored. Its possible applications to the issue of implicit v. explicit learning are also discussed

Research Center for Quantitative Renal Imaging

poster abstractMission: The overall mission of the Research Center for Quantitative Renal Imaging is to provide a focused research environment and resource for the development, implementation, and dissemination of innovative, quantitative imaging methods designed to assess the status of and mechanisms associated with acute and chronic kidney disease and evaluate efficacy of therapeutic interventions. Nature of the Center: IUPUI has several established research programs focused on understanding the fundamental mechanisms associated with kidney diseases along with established groups of investigators dedicated to the development of advanced imaging methods and quantitative analyses. This Research Center provides a formal mechanism to link these independently successful research efforts into a focused effort dedicated toward the development and implementation of quantitative renal imaging methods. The goals of the IUPUI Research Center for Quantitative Renal Imaging are to: Identify, develop, and implement innovative imaging methods that provide quantitative imaging biomarkers for assessing and inter-relating renal structure, function, hemodynamics and underlying tissue micro-environmental factors contributing to kidney disease. Establish an environment that facilitates and encourages interdisciplinary collaborations among investigators and offers research support to investigators focused on developing and utilizing innovative quantitative imaging methods in support of kidney disease research. Provide a resource to inform the greater research and healthcare communities of advances in quantitative renal imaging and its potential for enhanced patient management and care. Offer an imaging research resource to companies engaged in product development associated with the diagnosis and treatment of kidney diseases. Further Information: For further information regarding the IUPUI Research Center for Quantitative Renal Imaging and its funding programs please visit http://www.renalimaging.iupui.edu/ or contact the Center at [email protected]. Acknowledgments: The IUPUI Research Center for Quantitative Renal Imaging is supported by contributions from the IUPUI Signature Center Initiative, the Department of Radiology & Imaging Sciences; the Division of Nephrology, the IUPUI School of Science, the IUPUI School of Engineering & Technology, and the Indiana Clinical and Translational Sciences Institute (CTSI)

Quantitative Intensity Harmonization of Dopamine Transporter SPECT Images Using Gamma Mixture Models

PURPOSE: Differences in site, device, and/or settings may cause large variations in the intensity profile of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) images. However, the current standard to evaluate these images, the striatal binding ratio (SBR), does not efficiently account for this heterogeneity and the assessment can be unequivalent across distinct acquisition pipelines. In this work, we present a voxel-based automated approach to intensity normalize such type of data that improves on cross-session interpretation. PROCEDURES: The normalization method consists of a reparametrization of the voxel values based on the cumulative density function (CDF) of a Gamma distribution modeling the specific region intensity. The harmonization ability was tested in 1342 SPECT images from the PPMI repository, acquired with 7 distinct gamma camera models and at 24 different sites. We compared the striatal quantification across distinct cameras for raw intensities, SBR values, and after applying the Gamma CDF (GDCF) harmonization. As a proof-of-concept, we evaluated the impact of GCDF normalization in a classification task between controls and Parkinson disease patients. RESULTS: Raw striatal intensities and SBR values presented significant differences across distinct camera models. We demonstrate that GCDF normalization efficiently alleviated these differences in striatal quantification and with values constrained to a fixed interval [0, 1]. Also, our method allowed a fully automated image assessment that provided maximal classification ability, given by an area under the curve (AUC) of AUC = 0.94 when used mean regional variables and AUC = 0.98 when used voxel-based variables. CONCLUSION: The GCDF normalization method is useful to standardize the intensity of DAT SPECT images in an automated fashion and enables the development of unbiased algorithms using multicenter datasets. This method may constitute a key pre-processing step in the analysis of this type of images.Instituto de Salud Carlos III FI14/00497 MV15/00034Fondo Europeo de Desarrollo Regional FI14/00497 MV15/00034ISCIII-FEDER PI16/01575Wellcome Trust UK Strategic Award 098369/Z/12/ZNetherland Organization for Scientific Research NWO-Vidi 864-12-00

Quantitative Treatment of Decoherence

We outline different approaches to define and quantify decoherence. We argue that a measure based on a properly defined norm of deviation of the density matrix is appropriate for quantifying decoherence in quantum registers. For a semiconductor double quantum dot qubit, evaluation of this measure is reviewed. For a general class of decoherence processes, including those occurring in semiconductor qubits, we argue that this measure is additive: It scales linearly with the number of qubits.Comment: Revised version, 26 pages, in LaTeX, 3 EPS figure