Dynamic environments of fungus-farming termite mounds exert growth-modulating effects on fungal crop parasites.

This study investigated for the first time the impact of the internal mound environment of fungus-growing termites on the growth of fungal crop parasites. Mounds of the termite Odontotermes obesus acted as (i) temperature and relative humidity (RH) 'stabilisers' showing dampened daily variation and (ii) 'extreme environments' exhibiting elevated RH and CO2 levels, compared to the outside. Yet, internal temperatures exhibited seasonal dynamics as did daily and seasonal CO2 levels. During in situ experiments under termite-excluded conditions within the mound, the growth of the crop parasite Pseudoxylaria was greater inside than outside the mound, i.e., Pseudoxylaria is 'termitariophilic'. Also, ex situ experiments on parasite isolates differing in growth rates and examined under controlled conditions in the absence of termites revealed a variable effect with fungal growth decreasing only under high CO2 and low temperature conditions, reflecting the in situ parasite growth fluctuations. In essence, the parasite appears to be adapted to survive in the termite mound. Thus the mound microclimate does not inhibit the parasite but the dynamic environmental conditions of the mound affect its growth to varying extents. These results shed light on the impact of animal-engineered structures on parasite ecology, independent of any direct role of animal engineers.

Fungus-Farming Termites Selectively Bury Weedy Fungi that Smell Different from Crop Fungi.

Mutualistic associations such as the fungal farms of insects are prone to parasitism and are consequently vulnerable to attack by weeds and pests. Therefore, efficient farm management requires quick detection of weeds for their elimination. Furthermore, if the available weedicides are non-specific, then the ability of insects to discriminate between crop and weeds becomes essential for targeted application of such compounds. Here, we demonstrate for the first time in fungus-farming insects, that worker castes of the fungus-growing termite Odontotermes obesus discriminate between their crop (Termitomyces) and the weedy (Pseudoxylaria) fungi, even if exposed to only fungal scents. Termites respond to the presence of fungal mycelium or scent alone, by burying the weed with the offered material such as soil or agar, possibly anointing the weed with chemicals in the process. The scent profiles of crop and weedy fungi are distinct and the differences are likely exploited by termites to selectively mount their defences. Sesquiterpene compounds such as aristolene and viridiflorol, which are absent from crop odours, may constitute the "weedy scent". Our results provide a general mechanism of how other fungus-farming insects could avoid indiscriminate application of non-specific fungicides which could lead to poisoning their crops, and have bearing on the stability of the mutualism between termites and their crop fungus in the face of parasitism by weedy fungi.

Insight: An ontology-based integrated database and analysis platform for epilepsy self-management research.

We present Insight as an integrated database and analysis platform for epilepsy self-management research as part of the national Managing Epilepsy Well Network. Insight is the only available informatics platform for accessing and analyzing integrated data from multiple epilepsy self-management research studies with several new data management features and user-friendly functionalities. The features of Insight include, (1) use of Common Data Elements defined by members of the research community and an epilepsy domain ontology for data integration and querying, (2) visualization tools to support real time exploration of data distribution across research studies, and (3) an interactive visual query interface for provenance-enabled research cohort identification. The Insight platform contains data from five completed epilepsy self-management research studies covering various categories of data, including depression, quality of life, seizure frequency, and socioeconomic information. The data represents over 400 participants with 7552 data points. The Insight data exploration and cohort identification query interface has been developed using Ruby on Rails Web technology and open source Web Ontology Language Application Programming Interface to support ontology-based reasoning. We have developed an efficient ontology management module that automatically updates the ontology mappings each time a new version of the Epilepsy and Seizure Ontology is released. The Insight platform features a Role-based Access Control module to authenticate and effectively manage user access to different research studies. User access to Insight is managed by the Managing Epilepsy Well Network database steering committee consisting of representatives of all current collaborating centers of the Managing Epilepsy Well Network. New research studies are being continuously added to the Insight database and the size as well as the unique coverage of the dataset allows investigators to conduct aggregate data analysis that will inform the next generation of epilepsy self-management studies.

A scalable neuroinformatics data flow for electrophysiological signals using MapReduce.

Data-driven neuroscience research is providing new insights in progression of neurological disorders and supporting the development of improved treatment approaches. However, the volume, velocity, and variety of neuroscience data generated from sophisticated recording instruments and acquisition methods have exacerbated the limited scalability of existing neuroinformatics tools. This makes it difficult for neuroscience researchers to effectively leverage the growing multi-modal neuroscience data to advance research in serious neurological disorders, such as epilepsy. We describe the development of the Cloudwave data flow that uses new data partitioning techniques to store and analyze electrophysiological signal in distributed computing infrastructure. The Cloudwave data flow uses MapReduce parallel programming algorithm to implement an integrated signal data processing pipeline that scales with large volume of data generated at high velocity. Using an epilepsy domain ontology together with an epilepsy focused extensible data representation format called Cloudwave Signal Format (CSF), the data flow addresses the challenge of data heterogeneity and is interoperable with existing neuroinformatics data representation formats, such as HDF5. The scalability of the Cloudwave data flow is evaluated using a 30-node cluster installed with the open source Hadoop software stack. The results demonstrate that the Cloudwave data flow can process increasing volume of signal data by leveraging Hadoop Data Nodes to reduce the total data processing time. The Cloudwave data flow is a template for developing highly scalable neuroscience data processing pipelines using MapReduce algorithms to support a variety of user applications.

Insight: Semantic Provenance and Analysis Platform for Multi-center Neurology Healthcare Research.

Insight is a Semantic Web technology-based platform to support large-scale secondary analysis of healthcare data for neurology clinical research. Insight features the novel use of: (1) provenance metadata, which describes the history or origin of patient data, in clinical research analysis, and (2) support for patient cohort queries across multiple institutions conducting research in epilepsy, which is the one of the most common neurological disorders affecting 50 million persons worldwide. Insight is being developed as a healthcare informatics infrastructure to support a national network of eight epilepsy research centers across the U.S. funded by the U.S. Centers for Disease Control and Prevention (CDC). This paper describes the use of the World Wide Web Consortium (W3C) PROV recommendation for provenance metadata that allows researchers to create patient cohorts based on the provenance of the research studies. In addition, the paper describes the use of descriptive logic-based OWL2 epilepsy ontology for cohort queries with "expansion of query expression" using ontology reasoning. Finally, the evaluation results for the data integration and query performance are described using data from three research studies with 180 epilepsy patients. The experiment results demonstrate that Insight is a scalable approach to use Semantic provenance metadata for context-based data analysis in healthcare informatics.

Prevalence and associated features of gastroesophageal reflux symptoms in a Caucasian-predominant adolescent school population.

OBJECTIVE: To determine the prevalence of esophageal symptoms and associated symptoms of gastroesophageal reflux symptoms in 14-18 year-old high school students and the percentage of symptomatic adolescents who saw a physician or received medications. DESIGN AND SETTING: A cross-sectional questionnaire administered to students at two high schools. PARTICIPANTS: 1,286 completed questionnaires were analyzed. The study sample consisted of 57% Caucasians and 57% males, with a mean age of 15.7 (+/-1.3) years. INTERVENTION: No intervention was used. Participants completed questionnaires only. RESULTS: 714 students (56%) reported at least one esophageal or respiratory symptom. Esophageal symptoms reported were: heartburn (22%), regurgitation (21%), and dysphagia (15%). Respiratory symptoms reported were: shortness of breath (24%), wheezing (20%), and cough (18%). Students with at least one esophageal symptom were more likely to experience at least one respiratory symptom than were students with no esophageal symptoms (52% vs. 25%; p < 0.001). Cigarette, alcohol, and non-steroidal anti-inflammatory drug use were risk factors for both respiratory and esophageal symptoms. Of those experiencing one or more of these symptoms, 4% reported that the symptoms affected their daily activities, 23% visited a physician, and 25% took medication in the past year. CONCLUSIONS: Esophageal symptoms of gastroesophageal reflux are frequent in adolescents. Fewer than 25% of students with gastroesophageal reflux symptoms consulted a physician and/or took medications.