Christopher C. Marett

Distribution of the Soybean Cyst Nematode, Heterodera glycines, in the United States and Canada: 1954 to 2014

The soybean cyst nematode (Heterodera glycines) is considered the most damaging pathogen of soybean in the USA and Canada, and causes considerable yield loss in many other soybean-producing countries. It is believed to have been introduced into North America from Asia. The map of the known distribution of H. glycines in the USA and Canada has been updated for 2014. Maps of its known distribution in past years illustrate the spread of the pathogen since its initial discovery in the United States in 1954.

An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection

Recent work has presented max-equivocation as a measure of the resistance of a cryptosystem to attacks when the attacker is aware of the encoder function and message distribution. Here we consider the vulnerability of a cryptosystem in the one-try attack scenario when the attacker has incomplete information about the encoder function and message distribution. We show that encoder functions alone yield information to the attacker, and combined with inferable information about the ciphertexts, information about the message distribution can be discovered. We show that the whole encoder function need not be fixed or shared a priori for an effective cryptosystem, and this can be exploited to increase the equivocation over an a priori shared encoder. Finally we present two algorithms that operate in these scenarios and achieve good equivocation results, ExPad that demonstrates the key concepts, and ShortPad that has less overhead than ExPad.We introduce a novel playlist generation algorithm that focuses on the quality of transitions using a recurrent neural network (RNN). The proposed model assumes that optimal transitions between tracks can be modelled and predicted by internal transitions within music tracks. We introduce modelling sequences of high-level music descriptors using RNNs and discuss an experiment involving different similarity functions, where the sequences are provided by a musical structural analysis algorithm. Qualitative observations show that the proposed approach can effectively model transitions of music tracks in playlists.In today’s landscape of more and more software-driven functionalities, spanning more and more fields, model-driven engineering (MDE) promises to ease the development of software. To accomplish this goal, MDE employs domain-specific languages (DSLs). The problem is that, on one hand, DSLs are not easy to create, and, on the other hand, as a result of the increased software-driven functionalities, they need to deal with bigger models. In dealing with these big models, modularity mechanisms are employed regularly by DSLs. These mechanisms need to be introduced over and over again into the developed DSLs, adding to the effort of creating them. To ease the development of DSLs, we propose to introduce a modularisation of models that is independent of the DSLs. We do so via two mechanisms, groups and fragment abstractions, that comprise many modularity use cases found in DSLs. These two mechanisms have been implemented in a prototype tool, MetaMod.This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called GraphPrints. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets -- small induced subgraphs that describe local topology. By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out. Initial testing of GraphPrints is performed on real network data with an implanted anomaly. Evaluation shows false positive rates bounded by 2.84% at the time-interval level, and 0.05% at the IP-level with 100% true positive rates at both.Finding commonalities between descriptions of data or knowledge is a fundamental task in Machine Learning. The formal notion characterizing precisely such commonalities is known as least general generalization of descriptions and was introduced by G. Plotkin in the early 70s, in First Order Logic. Identifying least general generalizations has a large scope of database applications ranging from query optimization (e.g., to share commonalities between queries in view selection or multi-query optimization) to recommendation in social networks (e.g., to establish connections between users based on their commonalities between profiles or searches). To the best of our knowledge, this is the first work that re-visits the notion of least general generalizations in the entire Resource Description Framework (RDF) and popular con-junctive fragment of SPARQL, a.k.a. Basic Graph Pattern (BGP) queries. Our contributions include the definition and the computation of least general generalizations in these two settings, which amounts to finding the largest set of com-monalities between incomplete databases and conjunctive queries, under deductive constraints. We also provide an experimental assessment of our technical contributions.Previous studies have shown the efficiency of using quasi-random mutations on the well-know CMA evolution strategy [13]. Quasi-random mutations have many advantages, in particular their application is stable, efficient and easy to use. In this article, we extend this principle by applying quasi-random mutations on several well known continuous evolutionary algorithms (SA, CMSA, CMA) and do it on several old and new test functions, and with several criteria. The results point out a clear improvement compared to the baseline, in all cases, and in particular for moderate computational budget.We extend the definition and study the algebraic properties of the polylogarithm Li(T) , where T is rational series over the alphabet X = {x 0 , x 1 } belonging to suitable subalgebras of rational series.Multi-task algorithms typically use task similarity information as a bias to speed up learning. We argue that, when the classification problem is unbalanced, task dissimilarity information provides a more effective bias, as rare class labels tend to be better separated from the frequent class labels. In particular, we show that a multi-task extension of the label propagation algorithm for graph-based classification works much better on protein function prediction problems when the task relatedness information is represented using a dissimilarity matrix as opposed to a similarity matrix. CCS Concepts •Computing methodologies → Multi-task learning; Semi-supervised learning settings; •Applied computing → Bioinformatics;This paper proposes an end-to-end convolutional selective autoencoder approach for early detection of combustion instabilities using rapidly arriving flame image frames. The instabilities arising in combustion processes cause significant deterioration and safety issues in various human-engineered systems such as land and air based gas turbine engines. These properties are described as self-sustaining, large amplitude pressure oscillations and show varying spatial scales periodic coherent vortex structure shedding. However, such instability is extremely difficult to detect before a combustion process becomes completely unstable due to its sudden (bifurcation-type) nature. In this context, an autoencoder is trained to selectively mask stable flame and allow unstable flame image frames. In that process, the model learns to identify and extract rich descriptive and explanatory flame shape features. With such a training scheme, the selective autoencoder is shown to be able to detect subtle instability features as a combustion process makes transition from stable to unstable region. As a consequence, the deep learning tool-chain can perform as an early detection framework for combustion instabilities that will have a transformative impact on the safety and performance of modern engines.Content-based publish/subscribe is an attractive option for disseminating event data in cyber-physical systems. To this end, we propose MothPad: a monitoring and visualization tool to demonstrate the performance of various pub/sub solutions within the context of location-based applications. MothPad consists of Mammoth, an online game research framework used as a cyber-physical system simulator, and PADRES, the publish/subscribe dissemination substrate. Both are instrumented and the performance is displayed in real-time using a monitoring client. We show the applicability of our approach through two case studies: network engines for online games and self-evolving subscriptions.This paper presents a multi-modal hoax detection system composed of text, source, and image analysis. As hoax can be very diverse, we want to analyze several modalities to better detect them. This system is applied in the context of the Verifying Multimedia Use task of MediaEval 2016. Experiments show the performance of each separated modality as well as their combination.

Increase in Soybean Cyst Nematode Virulence and Reproduction on Resistant Soybean Varieties in Iowa From 2001 to 2015 and the Effects on Soybean Yields

Management of the soybean cyst nematode (SCN) relies heavily on use of SCN-resistant soybean varieties to limit nematode reproduction and minimize yield loss. For Iowa, almost all SCN-resistant soybean varieties contain SCN resistance genes from a breeding line named Plant Introduction (PI) 88788. Iowa State University conducts experiments to evaluate numerous SCN-resistant and three to four SCN-susceptible soybean varieties in up to nine field experiments across Iowa each year. Data on SCN population density, virulence (SCN race and HG type), soybean yield, precipitation, and growing degree days from more than 25,000 four-row plots in field experiments conducted from 2001 to 2015 were analyzed to determine how these factors affected SCN reproduction and yield. SCN population densities were positively correlated with temperatures and negatively associated with precipitation during the growing seasons, indicating that SCN reproduction was greatest in hot, dry years. Over the years, virulence of SCN populations on PI 88788 increased in the fields in which the experiments were conducted, resulting in increased end-of-season SCN population densities and reduced yields of SCN-resistant soybean varieties with the PI 88788 source of resistance. These results indicate that soybean yield loss caused by SCN on resistant varieties with the common PI 88788 source of resistance likely will increase as virulence of SCN populations increases unless new sources of resistance become widely available and used in the future.

Testing for Plant-parasitic Nematodes that Feed on Corn in Iowa 2000-2010

The Iowa State University Plant and Insect Diagnostic Clinic analyzes soil and root samples for plant-parasitic nematodes. The results of samples associated with corn that were submitted from 2000 through 2010 were summarized. One or more genera of plant-parasitic nematodes were found in 92% of the samples. Spiral nematode and root-lesion nematode were most commonly found. Other nematodes recovered were dagger, lance, needle, pin, ring, and stunt nematodes. Nematodes recovered at damaging population densities were dagger, needle, ring, and spiral nematodes. An average of 15 samples were submitted per year from 2000 to 2004. Sample numbers increased nearly threefold since 2005, but overall sample numbers were low every year from 2000 through 2010. Samples were received from 53 of the 99 Iowa counties, and most samples were received in June and July, which is the recommended sampling time. Nematodes that have been associated with corn in Iowa in the past that were not recovered from the samples were sheath, sting, and stubby-root nematodes. The methods used to extract the nematodes from soil and roots and how the samples were handled during collection and processing may have affected the species and population densities recovered. Much more frequent and widespread sampling is needed in Iowa for plant-parasitic nematodes that feed on corn.

Soybean Cyst Nematode HG Type Test Results Differ Among Multiple Samples from the Same Field but the Management Implications Are the Same

Growing soybean cultivars that are resistant to the soybean cyst nematode (SCN), Heterodera glycines, is an effective way to preserve soybean yield and limit increases in population densities of the nematode. The ability of SCN populations to reproduce on germplasm lines that are used in soybean breeding programs to develop SCN-resistant cultivars was measured and classified originally by the race test (Golden et al. 1970; Riggs and Schmitt 1988) and more recently by the HG type test (Niblack et al. 2002). The “HG” represents the first letters in the genus and species names of SCN, Heterodera glycines. Seven different soybean germplasm lines or cultivars that have been registered in a scientific journal currently are the indicator lines in the HG type test. The indicator lines are assigned index numbers 1 through 7, and the ability of an SCN population to reproduce on each of the HG type indicator lines is determined by calculating a female index. The female index is the average number of SCN females produced on the HG type indicator line relative to the number produced on a standard, susceptible soybean cultivar in a 30-daylong HG type test conducted in a greenhouse (Niblack et al. 2002). Disciplines Agricultural Science | Agriculture | Plant Breeding and Genetics | Plant Pathology Comments This article is published as Beeman, A. Q., Harbach, C. J., Marett, C. C., and Tylka, G. L. 2016. Soybean cyst nematode HG type test results differ among multiple samples from the same field but the management implications are the same. Plant Health Prog. 17:160-162. doi: 10.1094/PHP-BR-16-0033. Posted with permission. This article is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/plantpath_pubs/202

Evaluation of Soybean Varieties Resistant to Soybean Cyst Nematode in Southeast Iowa in 2002

Use of resistant soybean varieties is a very effective strategy for managing soybean cyst nematode (SCN), and numerous SCN-resistant soybean varieties are available for Iowa soybean growers. Each year, public and private SCN-resistant soybean varieties are evaluated in SCN-infested and noninfested fields throughout Iowa by Iowa State University personnel. The research described in this report was performed to assess the agronomic performance of maturity group (MG) I and II SCN-resistant soybean varieties and to determine the effects of the varieties on SCN population densities.