Discrimination of gyrodactylids based on landmark data

First paragraph: There are many different species of the genus Gyrodactylus. One particular form, Gyrodactylus salaris, is known to be highly pathogenic to stocks of Atlantic salmon, whereas other species that infect salmonids have a generally low pathogenicity. Gyrodactylus salaris is responsible for the catastrophic decline in salmon stocks in Norway and has been demonstrated to be widespread in Norwegian rivers. It has also caused problems in Portugal and France. In order to prevent its entry into the UK, G. salaris was made a notifiable disease in 1988 under the 1937 and 1983 Diseases of Fish Acts of the UK. While the UK is thought to be free of G. salaris there is another species, G. thymalli which has been found in the UK and some think is a variant of G. salaris. It is important to find a means of identification of G. salaris via routine microscopic monitoring of samples of parasites. Hence the main motivation for this work is the development of a statistical method which could be used to discriminate G. salaris from other species of Gyrodactylus, while a secondary aim is the discrimination of the other species of Gyrodactylus from each othe

Activation functions, computational goals, and learning rules for local processors with contextual guidance

Information about context can enable local processors to discover latent variables that are relevant to the context within which they occur, and it can also guide short-term processing. For example, Becker and Hinton (1992) have shown how context can guide learning, and Hummel and Biederman (1992) have shown how it can guide processing in a large neural net for object recognition. This article studies the basic capabilities of a local processor with two distinct classes of inputs: receptive field inputs that provide the primary drive and contextual inputs that modulate their effects. The contextual predictions are used to guide processing without confusing them with receptive field inputs. The processor's transfer function must therefore distinguish these two roles. Given these two classes of input, the information in the output can be decomposed into four disjoint components to provide a space of possible goals in which the unsupervised learning of Linsker (1988) and the internally supervised learning of Becker and Hinton (1992) are special cases. Learning rules are derived from an information-theoretic objective function, and simulations show that a local processor trained with these rules and using an appropriate activation function has the elementary properties required

Reactions to an Initial Attempt At Implementing Interactive Videodisc in Extension

The study focused on evaluating (a) the computer-directed videodisc technology as a delivery method for Extension, and (b) Ihe effectiveness of using it to teach cash flow planning to farmers/ranchers. Extension agents in 12 states completed open-ended questionnaires explaining how they prepared for the videodisc and how they used it They also completed anecdotal notes on the participants. Farmer responses were recorded in a computer file when they used the program

The use of statistical classifiers for the discrimination of species of the genus Gyrodactylus (Monogenea) parasitizing salmonids

This study applies flexible statistical methods to morphometric measurements obtained via light and scanning electron microscopy (SEM) to discriminate closely related species of Gyrodactylus parasitic on salmonids. For the first analysis, morphometric measurements taken from the opisthaptoral hooks and bars of 5 species of gyrodactylid were derived from images obtained by SEM and used to assess the prediction performance of 4 statistical methods (nearest neighbours; feed-forward neural network; projection pursuit regression and linear discriminant analysis). The performance of 2 methods, nearest neighbours and a feed-forward neural network provided perfect discrimination of G. salaris from 4 other species of Gyrodactylus when using measurements taken from only a single structure, the marginal hook. Data derived from images using light microscopy taken from the full complement of opisthaptoral hooks and bars were also tested and nearest neighbours and linear discriminant analysis gave perfect discrimination of G. salaris from G. derjavini Mikailov, 1975 and G. truttae Gläser, 1974. The nearest neighbours method had the least misclassifications and was therefore assessed further for the analysis of individual hooks. Five morphometric parameters from the marginal hook subset (total length, shaft length, sickle length, sickle proximal width and sickle distal width) gave near perfect discrimination of G. salaris. For perfect discrimination therefore, larger numbers of parameters are required at the light level than at the SEM level

Exact partial information decompositions for Gaussian systems based on dependency constraints

The Partial Information Decomposition (PID) [arXiv:1004.2515] provides a theoretical framework to characterize and quantify the structure of multivariate information sharing. A new method (Idep) has recently been proposed for computing a two-predictor PID over discrete spaces. [arXiv:1709.06653] A lattice of maximum entropy probability models is constructed based on marginal dependency constraints, and the unique information that a particular predictor has about the target is defined as the minimum increase in joint predictor-target mutual information when that particular predictor-target marginal dependency is constrained. Here, we apply the Idep approach to Gaussian systems, for which the marginally constrained maximum entropy models are Gaussian graphical models. Closed form solutions for the Idep PID are derived for both univariate and multivariate Gaussian systems. Numerical and graphical illustrations are provided, together with practical and theoretical comparisons of the Idep PID with the minimum mutual information PID (Immi). [arXiv:1411.2832] In particular, it is proved that the Immi method generally produces larger estimates of redundancy and synergy than does the Idep method. In discussion of the practical examples, the PIDs are complemented by the use of deviance tests for the comparison of Gaussian graphical models.Comment: 39 pages, 9 figures, 9 table

Discrimination of the notifiable pathogen Gyrodactylus salaris from G-thymalli (Monogenea) using statistical classifiers applied to morphometric data

The identification and discrimination of 2 closely related and morphologically similar species of Gyrodactylus, G. salaris and G. thymalli, were assessed using the statistical classification methodologies Linear Discriminant Analysis (LDA) and k-Nearest Neighbours (KNN). These statistical methods were applied to morphometric measurements made on the gyrodactylid attachment hooks. The mean estimated classification percentages of correctly identifying each species were 98±1% (LDA) and 97±9% (KNN) for G. salaris and 99±9% (LDA) and 73±2% (KNN) for G. thymalli. The analysis was expanded to include another 2 closely related species and the new classification efficiencies were 94±6%(LDA) and 98±0% (KNN) for G. salaris; 98±2% (LDA) and 72±6% (KNN) for G. thymalli; 86±7% (LDA) and 91±8% (KNN) for G. derjavini ; and 76±5% (LDA) and 77±7% (KNN) for G. truttae. The higher correct classification scores of G. salaris and G. thymalli by the LDA classifier in the 2-species analysis over the 4-species analysis suggested the development of a 2-stage classifier. The mean estimated correct classification scores were 99±97% (LDA) and 99±99% (KNN) for the G. salaris±G. thymalli pairing and 99±4% (LDA) and 99±92% (KNN) for the G. derjavini±G. truttae pairing. Assessment of the 2-stage classifier using only marginal hook data was very good with classification efficiencies of 100% (LDA) and 99±6%(KNN) for the G. salaris±G. thymalli pairing and 97±2%(LDA) and 9±2%(KNN) for the G. derjavini±G. truttae pairing. Paired species were then discriminated individually in the second stage of the classifier using data from the full set of hooks. These analyses demonstrate that using the methods of LDA and KNN statistical classification, the discrimination of closely related and pathogenic species of Gyrodactylus may be achieved using data derived from light microscope studies

Assessing Interactive Videodisc in Extension

If Extension is going to use interactive videodisc as a program delivery method in the future, the technology must be explored and systematically evaluated in a variety of learning situations. Studying the adoption of the technology in Extension challenges us to (a) identify those interested in exploring the medium, (b) develop an overall strategy for testing the technology, and (e) organize a method for delivering the evaluative information to decision makers. This article poses a considerable number of questions to be addressed as interactive videodisc is tested for its application in transferring information in Extension

Assessing Interactive Videodisc In Extension

If Extension is going to use interactive videodisc as a program delivery method in the future, the technology must be explored and systematically evaluated in a variety of learning situations

Building an Academic Community SmallSat Program

The US Coast Guard Academy (CGA), which educates future officers for service in the US Coast Guard, is developing a multifaceted program in SmallSats. The CGA space initiative includes undergraduate courses, such as a recently-created Remote Sensing course. It incorporates Virginia Space\u27s ThinSat Educational Program, providing hands-on experience in designing, building, and testing miniature satellites through space mission engineering; this involves collaboration between Science and Engineering and is extended to local high schools through their participation in SmallSat projects with CGA faculty and students. An important addition to the CGA space initiative is an MC3 ground station atop the Science Department building at CGA. It will be used to train cadets in satellite operations and ground station hardware, and to acquire data from satellites, allowing for training in cyber security and data analysis, and for use in student and faculty research projects. Sequential year-long senior student capstone projects in the Engineering Department to design, build, and test CubeSats, including new components and innovations, have already begun and will continue. Additional capstone projects involving CubeSats sensors, the use of UAVs, and remote sensing data analysis, including data from the CGA ground station, are planned in the Science Department with the 2020 implementation of a revised Science major

Partial and Entropic Information Decompositions of a Neuronal Modulatory Interaction

Information processing within neural systems often depends upon selective amplification of relevant signals and suppression of irrelevant signals. This has been shown many times by studies of contextual effects but there is as yet no consensus on how to interpret such studies. Some researchers interpret the effects of context as contributing to the selective receptive field (RF) input about which neurons transmit information. Others interpret context effects as affecting transmission of information about RF input without becoming part of the RF information transmitted. Here we use partial information decomposition (PID) and entropic information decomposition (EID) to study the properties of a form of modulation previously used in neurobiologically plausible neural nets. PID shows that this form of modulation can affect transmission of information in the RF input without the binary output transmitting any information unique to the modulator. EID produces similar decompositions, except that information unique to the modulator and the mechanistic shared component can be negative when modulating and modulated signals are correlated. Synergistic and source shared components were never negative in the conditions studied. Thus, both PID and EID show that modulatory inputs to a local processor can affect the transmission of information from other inputs. Contrary to what was previously assumed, this transmission can occur without the modulatory inputs becoming part of the information transmitted, as shown by the use of PID with the model we consider. Decompositions of psychophysical data from a visual contrast detection task with surrounding context suggest that a similar form of modulation may also occur in real neural systems