OBJECTIVE: We describe a detailed solution for maintaining high-capacity, data-intensive network flows (eg, 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations. MATERIALS AND METHODS: High-end networking, packet-filter firewalls, network intrusion-detection systems. RESULTS: We describe a "Medical Science DMZ" concept as an option for secure, high-volume transport of large, sensitive datasets between research institutions over national research networks, and give 3 detailed descriptions of implemented Medical Science DMZs. DISCUSSION: The exponentially increasing amounts of "omics" data, high-quality imaging, and other rapidly growing clinical datasets have resulted in the rise of biomedical research "Big Data." The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large datasets. Maintaining data-intensive flows that comply with the Health Insurance Portability and Accountability Act (HIPAA) and other regulations presents a new challenge for biomedical research. We describe a strategy that marries performance and security by borrowing from and redefining the concept of a Science DMZ, a framework that is used in physical sciences and engineering research to manage high-capacity data flows. CONCLUSION: By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements.

OBJECTIVE: We describe use cases and an institutional reference architecture for maintaining high-capacity, data-intensive network flows (e.g., 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations. MATERIALS AND METHODS: High-end networking, packet filter firewalls, network intrusion detection systems. RESULTS: We describe a "Medical Science DMZ" concept as an option for secure, high-volume transport of large, sensitive data sets between research institutions over national research networks. DISCUSSION: The exponentially increasing amounts of "omics" data, the rapid increase of high-quality imaging, and other rapidly growing clinical data sets have resulted in the rise of biomedical research "big data." The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large data sets. Maintaining data-intensive flows that comply with HIPAA and other regulations presents a new challenge for biomedical research. Recognizing this, we describe a strategy that marries performance and security by borrowing from and redefining the concept of a "Science DMZ"-a framework that is used in physical sciences and engineering research to manage high-capacity data flows. CONCLUSION: By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements.

In previous work we demonstrated the use of temporal image sequences to identify broad land use classes [1]. The approach aims to provide information critical to modeling land use impacts while minimizing reliance on collecting ground control for individual images. Here, we extend the method to include spectral information taken at the peak NDVI stage for each field. Results show the level of spectral separability of various key crops and pastures, and how we have grouped certain crops that are not spectrally separable. Whereas we obtained only 42% classification accuracy when attempting to classify crops individually, the classification accuracy for our crop groups was 81%. A major challenge is that image datasets are typically sparse - due to cloud cover in New Zealand - so the growth stage, and therefore appearance, of individual crops can vary widely in the ‘peak’ NDVI image.

Use of temporal signatures to classify the complex land use patterns in Canterbury New Zealand is investigated. Two datasets of detailed field histories spanning 5-7 years are used to develop a rule set for classifying from satellite imagery (SPOT/Landsat). While many individual crops proved inseparable, a broader set of land use classes such as `winter forage' or `summer arable crop' were defined that could be separated with accuracies between 71% and 96% (averaging 82% over 8 classifications). These broader land use classes will be used in a second stage of the project to guide and constrain classifications incorporating spectral information from the crop's peak NDVI phase.

Identification of irrigated farm land is critical to a variety of environmental models. For this work, it is needed to model the impacts of irrigation on groundwater quality and flow rates. As the ground cover appearance for many land uses is quite dynamic, we take a multi-temporal remote sensing approach and classify each location as likely irrigated if it appears irrigated at any stage. Our model also uses a landform analysis to determine where irrigation is feasible. Either the presence of visible infrastructure or NDVI values higher than expected from rainfall data are then used as evidence for irrigated land. Paddocks identified by fieldwork as irrigated were 75.6% correctly classified, rising to 88.9% for a pasture subset. Cropping fields have proved more difficult as their NDVI also depends on their growth stage and the specific crop type.

Abstract Summary: Multiple sequence alignment remains a crucial method for understanding the function of groups of related nucleic acid and protein sequences. However, it is known that automatic multiple sequence alignments can often be improved by manual editing. Therefore, tools are needed to view and edit multiple sequence alignments. Due to growth in the sequence databases, multiple sequence alignments can often be large and difficult to view efficiently. The Jalview Java alignment editor is presented here, which enables fast viewing and editing of large multiple sequence alignments. Availability: The Jar file and source code for Jalview is freely available via the World Wide Web at http://www.jalview.org. A Jalview mailing list is also available by e-mailing [email protected] with subscribe Jalview in the body of the mail.

MOTIVATION: An automatic sequence searching method (ProtEST) is described which constructs multiple protein sequence alignments from protein sequences and translated expressed sequence tags (ESTs). ProtEST is more effective than a simple TBLASTN search of the query against the EST database, as the sequences are automatically clustered, assembled, made non-redundant, checked for sequence errors, translated into protein and then aligned and displayed. RESULTS: A ProtEST search found a non-redundant, translated, error- and length-corrected EST sequence for > 58% of sequences when single sequences from 1407 Pfam-A seed alignments were used as the probe. The average family size of the resulting alignments of translated EST sequences contained > 10 sequences. In a cross-validated test of protein secondary structure prediction, alignments from the new procedure led to an improvement of 3.4% average Q3 prediction accuracy over single sequences. AVAILABILITY: The ProtEST method is available as an Internet World Wide Web service http://barton.ebi.ac.uk/servers/protest.html+ ++ The Wise2 package for protein and genomic comparisons and the ProtESTWise script can be found at http://www.sanger.ac.uk/Software/Wise2 CONTACT: [email protected]

UNLABELLED: An interactive protein secondary structure prediction Internet server is presented. The server allows a single sequence or multiple alignment to be submitted, and returns predictions from six secondary structure prediction algorithms that exploit evolutionary information from multiple sequences. A consensus prediction is also returned which improves the average Q3 accuracy of prediction by 1% to 72.9%. The server simplifies the use of current prediction algorithms and allows conservation patterns important to structure and function to be identified. AVAILABILITY: http://barton.ebi.ac.uk/servers/jpred.h tml CONTACT: [email protected]