High-Resolution Imaging as a Tool for Identifying Quantitative Trait Loci that Regulate Photomorphogenesis in \u3ci\u3eArabidopsis thaliana\u3c/i\u3e

A primary component of seedling establishment is the photomorphogenic response as seedlings emerge from the soil. This process is characterized by a reduced growth rate in the hypocotyl, increased root growth, opening of the apical hook and expansion of the cotyledons as photosynthetic organs. While fundamental to plant success, the photomorphogenic response can be highly variable. Additionally, studies of Arabidopsis thaliana are made difficult by subtle differences in growth rate between individuals. High-resolution imaging and computational processing have emerged as useful tools for quantification of such phenotypes. This study sought to: (i) develop an imaging methodology which could capture changes in growth rate as seedlings transition from darkness to blue light in real time, and (ii) apply this methodology to single-quantitative trait locus (QTL) analysis using the Cvi × Ler recombinant inbred line (RIL) mapping population. Significant differences in the photomorphogenic response were observed between the parent lines and analysis of 158 RILs revealed a wide range of growth rate phenotypes. Quantitative trait locus analysis detected significant loci associated with dark growth rate on chromosome 5 and significant loci associated with light growth rate on chromosome 2. Candidate genes associated with these loci, such as the previously characterized ER locus, highlight the application of this approach for QTL analysis. Genetic analysis of Landsberg lines without the erecta mutation also supports a role for ER in modulating the photomorphogenic response, consistent with previous QTL analyses of this population. Strengths and limitations of this methodology are presented, as well as means of improvement

Alien Registration- Deslauriers, Eugene D. (Lewiston, Androscoggin County)

https://digitalmaine.com/alien_docs/29608/thumbnail.jp

Scaling and Suppression of Anomalous Heating in Ion Traps

We measure and characterize anomalous motional heating of an atomic ion confined in the lowest quantum levels of a novel rf ion trap that features moveable electrodes. The scaling of heating with electrode proximity is measured, and when the electrodes are cooled from 300 to 150 K, the heating rate is suppressed by an order of magnitude. This provides direct evidence that anomalous motional heating of trapped ions stems from microscopic noisy potentials on the electrodes that are thermally driven. These observations are relevant to decoherence in quantum information processing schemes based on trapped ions and perhaps other charge-based quantum systems

Towards a Cloud Native Big Data Platform using MiCADO

In the big data era, creating self-managing scalable platforms for running big data applications is a fundamental task. Such self-managing and self-healing platforms involve a proper reaction to hardware (e.g., cluster nodes) and software (e.g., big data tools) failures, besides a dynamic resizing of the allocated resources based on overload and underload situations and scaling policies. The distributed and stateful nature of big data platforms (e.g., Hadoop-based cluster) makes the management of these platforms a challenging task. This paper aims to design and implement a scalable cloud native Hadoop-based big data platform using MiCADO, an open-source, and a highly customisable multi-cloud orchestration and auto-scaling framework for Docker containers, orchestrated by Kubernetes. The proposed MiCADO-based big data platform automates the deployment and enables an automatic horizontal scaling (in and out) of the underlying cloud infrastructure. The empirical evaluation of the MiCADO-based big data platform demonstrates how easy, efficient, and fast it is to deploy and undeploy Hadoop clusters of different sizes. Additionally, it shows how the platform can automatically be scaled based on user-defined policies (such as CPU-based scaling)

T-junction ion trap array for two-dimensional ion shuttling, storage and manipulation

We demonstrate a two-dimensional 11-zone ion trap array, where individual laser-cooled atomic ions are stored, separated, shuttled, and swapped. The trap geometry consists of two linear rf ion trap sections that are joined at a 90 degree angle to form a T-shaped structure. We shuttle a single ion around the corners of the T-junction and swap the positions of two crystallized ions using voltage sequences designed to accommodate the nontrivial electrical potential near the junction. Full two-dimensional control of multiple ions demonstrated in this system may be crucial for the realization of scalable ion trap quantum computation and the implementation of quantum networks.Comment: 3 pages, 5 figure

A myoelectric digital twin for fast and realistic modelling in deep learning

Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces

Science Gateways with Embedded Ontology-based E-learning Support

Science gateways are widely utilised in a range of scientific disciplines to provide user-friendly access to complex distributed computing infrastructures. The traditional approach in science gateway development is to concentrate on this simplified resource access and provide scientists with a graphical user interface to conduct their experiments and visualise the results. However, as user communities behind these gateways are growing and opening their doors to less experienced scientists or even to the general public as “citizen scientists”, there is an emerging need to extend these gateways with training and learning support capabilities. This paper describes a novel approach showing how science gateways can be extended with embedded e-learning support using an ontology-based learning environment called Knowledge Repository Exchange and Learning (KREL). The paper also presents a prototype implementation of a science gateway for analysing earthquake data and demonstrates how the KREL can extend this gateway with ontology-based embedded e-learning support