Molecular cytogenetic detection of paternal chromosome fragments in allogynogenetic gibel carp, Carassius auratus gibelio Bloch

In gynogenesis, sperm from related species activates egg and embryonic development, but normally does not contribute genetically to the offspring. In gibel carp, Carassius auratus gibelio Bloch, however, gynogenetic offspring often show some phenotypes apparently derived from the heterologous sperm donor. This paternal effect of allogynogenesis is outstanding in an artificial clone F produced by cold treatment of clone E eggs after insemination with blunt-nose black bream (Megaloabrama amblycephala Yin) sperm. Karyotype analysis revealed 5-15 supernumerary microchromosomes in different individuals of clone F in addition to 156 normal chromosomes inherited from the maternal clone E. A painting probe was prepared from the microdissected microchromosomes, and used to investigate the origin of these microchromosomes. Strong positive signals were detected on each microchromosomes of clone F and on 4 pairs of chromosomes in blunt-nose black bream, whereas no signals were detected on the chromosomes of clone E. This result indicates that some paternal chromosome fragments of blunt-nose black bream have been incorporated into the artificial clone F. Therefore, the manipulation of allogynogenesis may provide a unique method to transfer DNA between diverse species for fish breeding.

Growth and feed utilization in two strains of gibel carp, Carassius auratus gibelio: paternal effects in a gynogenetic fish

Gibel carp (Carassius auratus gibelio Bloch) is a natural gynogenetic fish which requires sperm of the same or related species to activate egg development. The eggs of one gibel carp were divided into two batches. One batch was 'fertilized' with sperm from gibel carp (strain DD), and the other 'fertilized' with sperm from red common carp (Cyprinus carpio red variety) (strain DR). The juveniles were transferred to the laboratory 36 days post-hatch. Triplicate groups of each strain were fed a formulated diet at either 3% or satiation ration for 8 weeks. At both the restricted and satiation rations, specific growth rate was significantly higher in strain DR than in strain DD. At the 3% ration, there was no significant difference in feeding rate or feed conversion efficiency between the two strains. At the satiation ration, strain DR had a significantly lower feeding rate but higher feed conversion efficiency than strain DD. At the satiation ration, strain DR had a significantly lower intake protein, but higher recovered protein than strain DD. There was no significant difference in faecal protein loss between the two strains. At the 3% ration, strain had no significant effects on intake protein, faecal protein or recovered protein. Neither faecal energy loss nor recovered energy was affected by strain or ration. At both the 3% and satiation ration, final body contents of dry matter and lipid were significantly lower in strain DR than strain DD, while there was no significant difference in protein and energy content between the two strains at either ration level. The results suggested that gibel carp 'fertilized' with sperm of common carp grew faster than those 'fertilized' with sperm of gibel carp through increased feed conversion efficiency and protein retention.

Phase uncertainty in calibrating microwave test fixtures

The problem of phase uncertainty arising in calibration of the test fixtures is investigated in this paper, It is shown that the problem exists no matter what kinds of calibration standards are used. It is also found that there is no need to determine the individual S-parameters of the test fixtures. In order to eliminate the problem of phase uncertainty, three different precise (known) reflection standards or one known reflection standard plus one known transmission standard should be used to calibrate symmetrical test fixtures. For the asymmetrical cases, three known standards, including at least one transmission standard, should be used. The thru-open-match (TOM) and thru-short-match (TSM) techniques are the simplest methods, and they have no bandwidth limitation. When the standards are imprecise (unknown), it is recommended to use any suitable technique, such as the thru-reflect-line, line-reflect-line, thru-short-delay, thru-open-delay,line-reflect-match, line-reflect-reflect-match, or multiline methods, to accurately determine the values of the required calibration terms and, in addition, to use the TOM or TSM method with the same imprecise standards to resolve the phase uncertainty.

Temperature variation through 2000 years in China: An uncertainty analysis of reconstruction and regional difference

Chinese Academy of Sciences [KZCX2-YW-315, KZCX2-YW-Q1-01]; National Natural Science Foundation of China [40625002, 90502009, 200905006]; Office of Science (BER), U. S. Department of Energy ; EU/FP7 [212250]

Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: Accuracy and uncertainty analysis

Mapping the spatial distribution of contaminants in soils is the basis of pollution evaluation and risk control. Interpolation methods are extensively applied in the mapping processes to estimate the heavy metal concentrations at unsampled sites. The performances of interpolation methods (inverse distance weighting, local polynomial, ordinary kriging and radial basis functions) were assessed and compared using the root mean square error for cross validation. The results indicated that all interpolation methods provided a high prediction accuracy of the mean concentration of soil heavy metals. However, the classic method based on percentages of polluted samples, gave a pollution area 23.54-41.92% larger than that estimated by interpolation methods. The difference in contaminated area estimation among the four methods reached 6.14%. According to the interpolation results, the spatial uncertainty of polluted areas was mainly located in three types of region: (a) the local maxima concentration region surrounded by low concentration (clean) sites, (b) the local minima concentration region surrounded with highly polluted samples; and (c) the boundaries of the contaminated areas. (C) 2010 Elsevier Ltd. All rights reserved.

Study of remote sensing based parameter uncertainty in Production Efficiency Models

The remote sensing based Production Efficiency Models (PEMs), springs from the concept of "Light Use Efficiency" and has been applied more and more in estimating terrestrial Net Primary Productivity (NPP) regionally and globally. However, global NPP estimates vary greatly among different models in different data sources and handling methods. Because direct observation or measurement of NPP is unavailable at global scale, the precision and reliability of the models cannot be guaranteed. Though, there are ways to improve the accuracy of the models from input parameters. In this study, five remote sensing based PEMs have been compared: CASA, GLO-PEM, TURC, SDBM and VPM. We divided input parameters into three categories, and analyzed the uncertainty of (1) vegetation distribution, (2) fraction of photosynthetically active radiation absorbed by the canopy (fPAR) and (3) light use efficiency (e). Ground measurements of Hulunbeier typical grassland and meteorology measurements were introduced for accuracy evaluation. Results show that a real-time, more accurate vegetation distribution could significantly affect the accuracy of the models, since it's applied directly or indirectly in all models and affects other parameters simultaneously. Higher spatial and spectral resolution remote sensing data may reduce uncertainty of fPAR up to 51.3%, which is essential to improve model accuracy.

Robust Learning Control for Manipulators with Uncertainty Disturbances Based on Internal Model

In this paper, a disturbance controller is designed for making robotic system behave as a decoupled linear system according to the concept of internal model. Based on the linear system, the paper presents an iterative learning control algorithm to robotic manipulators. A sufficient condition for convergence is provided. The selection of parameter values of the algorithm is simple and easy to meet the convergence condition. The simulation results demonstrate the effectiveness of the algorithm..

Feature Matching for Object Localization in the Presence of Uncertainty

We consider the problem of matching model and sensory data features in the presence of geometric uncertainty, for the purpose of object localization and identification. The problem is to construct sets of model feature and sensory data feature pairs that are geometrically consistent given that there is uncertainty in the geometry of the sensory data features. If there is no geometric uncertainty, polynomial-time algorithms are possible for feature matching, yet these approaches can fail when there is uncertainty in the geometry of data features. Existing matching and recognition techniques which account for the geometric uncertainty in features either cannot guarantee finding a correct solution, or can construct geometrically consistent sets of feature pairs yet have worst case exponential complexity in terms of the number of features. The major new contribution of this work is to demonstrate a polynomial-time algorithm for constructing sets of geometrically consistent feature pairs given uncertainty in the geometry of the data features. We show that under a certain model of geometric uncertainty the feature matching problem in the presence of uncertainty is of polynomial complexity. This has important theoretical implications by demonstrating an upper bound on the complexity of the matching problem, an by offering insight into the nature of the matching problem itself. These insights prove useful in the solution to the matching problem in higher dimensional cases as well, such as matching three-dimensional models to either two or three-dimensional sensory data. The approach is based on an analysis of the space of feasible transformation parameters. This paper outlines the mathematical basis for the method, and describes the implementation of an algorithm for the procedure. Experiments demonstrating the method are reported.

Error Detection and Recovery for Robot Motion Planning with Uncertainty

Robots must plan and execute tasks in the presence of uncertainty. Uncertainty arises from sensing errors, control errors, and uncertainty in the geometry of the environment. The last, which is called model error, has received little previous attention. We present a framework for computing motion strategies that are guaranteed to succeed in the presence of all three kinds of uncertainty. The motion strategies comprise sensor-based gross motions, compliant motions, and simple pushing motions.

On Motion Planning with Uncertainty

Robots must successfully plan and execute tasks in the presence of uncertainty. Uncertainty arises from errors in modeling, sensing, and control. Planning in the presence of uncertainty constitutes one facet of the general motion planning problem in robotics. This problem is concerned with the automatic synthesis of motion strategies from high level task specification and geometric models of environments. In order to develop successful motion strategies, it is necessary to understand the effect of uncertainty on the geometry of object interactions. Object interactions, both static and dynamic, may be represented in geometrical terms. This thesis investigates geometrical tools for modeling and overcoming uncertainty. The thesis describes an algorithm for computing backprojections o desired task configurations. Task goals and motion states are specified in terms of a moving object's configuration space. Backprojections specify regions in configuration space from which particular motions are guaranteed to accomplish a desired task. The backprojection algorithm considers surfaces in configuration space that facilitate sliding towards the goal, while avoiding surfaces on which motions may prematurely halt. In executing a motion for a backprojection region, a plan executor must be able to recognize that a desired task has been accomplished. Since sensors are subject to uncertainty, recognition of task success is not always possible. The thesis considers the structure of backprojection regions and of task goals that ensures goal recognizability. The thesis also develops a representation of friction in configuration space, in terms of a friction cone analogous to the real space friction cone. The friction cone provides the backprojection algorithm with a geometrical tool for determining points at which motions may halt.

Modelling uncertainty in agricultural image analysis

C.M. Onyango, J.A. Marchant and R. Zwiggelaar, 'Modelling uncertainty in agricultural image analysis', Computers and Electronics in Agriculture 17 (3), 295-305 (1997)