Development and Use of a Tool for Automated Alignments of Genes in the Rice BAC\u27s GenBank Card Against Other Species

In many cases, the analysis of the genetic bases of any trait requires molecular markers and if possible co-dominant PCR-based ones. In perennial fodder species, the number of publicly available markers (microsatellites and Sequence Tagged Site (STS)) is limited. Our goal is to use sequences from model grass species, i.e. rice, wheat, maize, barley, in L. perenne in order to develop STS markers in interesting regions such as under a QTL (Quantitative Trait Loci) or around a candidate gene,. As the genome sequence of rice is now available, the objective was to use the sequences of genes included in the BAC’s GenBank card from rice. As there are almost no available sequences in L. perenne, we are designing consensus primers from an alignment of at least two different species. The problem is that for all the genes included in a BAC, just a few have their sequences known in at least two species. It is very laborious to check “by hand” if each gene has an homologous sequence known in another species

Approaches to address the Data Skew Problem in Federated Learning

A Federated Learning approach consists of creating an AI model from multiple data sources, without moving large amounts of data across to a central environment. Federated learning can be very useful in a tactical coalition environment, where data can be collected individually by each of the coalition partners, but network connectivity is inadequate to move the data to a central environment. However, such data collected is often dirty and imperfect. The data can be imbalanced, and in some cases, some classes can be completely missing from some coalition partners. Under these conditions, traditional approaches for federated learning can result in models that are highly inaccurate. In this paper, we propose approaches that can result in good machine learning models even in the environments where the data may be highly skewed, and study their performance under different environments

QTLs for Morphogenetic Traits in Medicago Truncatula

Plant morphogenesis that includes growth, development and flowering date, drives a large number of agronomical important traits in both grain and forage crops. Quantitative trait locus (QTL) mapping is a way to locate zones of the genome that are involved in the variations observed in a segregating population. Co-location of QTLs and candidate genes is an indication of the involvement of the genes in the variation. The objective of this study was to analyse segregation of aerial morphogenetic traits in a mapping population of recombinant inbred lines of the model legume species M. truncatula , to locate QTLs and candidate genes

Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighbourhood. It is therefore important to select the most appropriate filter to estimate the position of these persons. This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue

Towards a situated, multimodal interface for multiple UAV control

Multiple autonomous Unmanned Aerial Vehicles (UAVs) can be used to complement human teams. This paper presents the results of an exploratory study to investigate gesture/ speech interfaces for interaction with robots in a situated manner and the development of three iterations of a prototype command set. A command set was compiled from observing users interacting with a simulated interface in a virtual reality environment. We discovered that users find this type of interface intuitive and their commands tend to naturally group into both 'High-Level' and 'Low-Level' instructions. However, as the robots moved further away, the loss of depth perception and direct feedback was inimical to the interaction. In a second experiment we found that using simple heads up display elements could mitigate these issues. ©2010 IEEE

The modern pollen-vegetation relationship of a tropical forest-savannah mosaic landscape, Ghana, West Africa

Transitions between forest and savannah vegetation types in fossil pollen records are often poorly understood due to over-production by taxa such as Poaceae and a lack of modern pollen-vegetation studies. Here, modern pollen assemblages from within a forest-savannah transition in West Africa are presented and compared, their characteristic taxa discussed, and implications for the fossil record considered. Fifteen artificial pollen traps were deployed for 1 year, to collect pollen rain from three vegetation plots within the forest-savannah transition in Ghana. High percentages of Poaceae and Melastomataceae/Combretaceae were recorded in all three plots. Erythrophleum suaveolens characterised the forest plot, Manilkara obovata the transition plot and Terminalia the savannah plot. The results indicate that Poaceae pollen influx rates provide the best representation of the forest-savannah gradient, and that a Poaceae abundance of >40% should be considered as indicative of savannah-type vegetation in the fossil record

Unscented Kalman filter with parameter identifiability analysis for the estimation of multiple parameters in kinetic models

In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF), rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison

Designing attractive models via automated identification of chaotic and oscillatory dynamical regimes

Chaos and oscillations continue to capture the interest of both the scientific and public domains. Yet despite the importance of these qualitative features, most attempts at constructing mathematical models of such phenomena have taken an indirect, quantitative approach, for example, by fitting models to a finite number of data points. Here we develop a qualitative inference framework that allows us to both reverse-engineer and design systems exhibiting these and other dynamical behaviours by directly specifying the desired characteristics of the underlying dynamical attractor. This change in perspective from quantitative to qualitative dynamics, provides fundamental and new insights into the properties of dynamical systems