Applications of realtime fMRI for non-invasive brain computer interface-decoding and neurofeedback

Non-invasive brain-computer interfaces (BCIs) seek to enable or restore brain function by using neuroimaging e.g. functional magnetic resonance imaging (fMRI), to engage brain activations without the need for explicit behavioural output or surgical implants. Brain activations are converted into output signals, for use in communication interfaces, motor prosthetics, or to directly shape brain function via a feedback loop. The aim of this thesis was to develop cognitive BCIs using realtime fMRI (rt-fMRI), with the potential for use as a communication interface, or for initiating neural plasticity to facilitate neurorehabilitation. Rt-fMRI enables brain activation to be manipulated directly to produce changes in function, such as perception. Univariate and multivariate classification approaches were used to decode brain activations produced by the deployment of covert spatial attention to simple visual stimuli. Primary and higher order visual areas were examined, as well as potential control regions. The classification platform was then developed to include the use of real-world visual stimuli, exploiting the use of category-specific visual areas, and demonstrating real-world applicability as a communications interface. Online univariate classification of spatial attention was successfully achieved, with individual classification accuracies for 4-quadrant spatial attention reaching 70%. Further, a novel implementation of m-sequences enabled the use of the timing of stimuli presentation to enhance signal characterisation. An established rt-fMRI analysis loop was then used for neurofeedback-led manipulation of category-specific visual brain regions, modulating their functioning, and, as a result, biasing visual perception during binocular rivalry. These changes were linked with functional and effective connectivity changes in trained regions, as well as in a putative top-down control region. The work presented provides proof-of-principle for non-invasive BCIs using rt-fMRI, with the potential for translation into the clinical environment. Decoding and 4 neurofeedback applied to non-invasive and implantable BCIs form an evolving continuum of options for enabling and restoring brain function

Interpolative multidimensional scaling techniques for the identification of clusters in very large sequence sets

<p>Abstract</p> <p>Background</p> <p>Modern pyrosequencing techniques make it possible to study complex bacterial populations, such as <it>16S rRNA</it>, directly from environmental or clinical samples without the need for laboratory purification. Alignment of sequences across the resultant large data sets (100,000+ sequences) is of particular interest for the purpose of identifying potential gene clusters and families, but such analysis represents a daunting computational task. The aim of this work is the development of an efficient pipeline for the clustering of large sequence read sets.</p> <p>Methods</p> <p>Pairwise alignment techniques are used here to calculate genetic distances between sequence pairs. These methods are pleasingly parallel and have been shown to more accurately reflect accurate genetic distances in highly variable regions of <it>rRNA </it>genes than do traditional multiple sequence alignment (MSA) approaches. By utilizing Needleman-Wunsch (NW) pairwise alignment in conjunction with novel implementations of interpolative multidimensional scaling (MDS), we have developed an effective method for visualizing massive biosequence data sets and quickly identifying potential gene clusters.</p> <p>Results</p> <p>This study demonstrates the use of interpolative MDS to obtain clustering results that are qualitatively similar to those obtained through full MDS, but with substantial cost savings. In particular, the wall clock time required to cluster a set of 100,000 sequences has been reduced from seven hours to less than one hour through the use of interpolative MDS.</p> <p>Conclusions</p> <p>Although work remains to be done in selecting the optimal training set size for interpolative MDS, substantial computational cost savings will allow us to cluster much larger sequence sets in the future.</p

Multi-thematic delineation of 'natural zones' of arable fields and their correspondence to spatial yield variation

Properties such as soil apparent electric conductivity (ECa), topography and other site-related data (e.g. canopy reflectance from aerial images) vary across field. The agronomic effects of such variability can sometimes be seen in the spatial variations of crop yield on that field. However, yield maps do not always represent the natural boundaries based on site characteristics. Identification of these boundaries as “management zones” (MZ) can be beneficial in crop management and improving crop input use efficiency. A simple methodology is required to delineate such zones. This research presents an effective methodology to delineate MZ in an irrigated and a non-irrigated (rain-fed) arable maize field in New Zealand. Elevation data for the sites were acquired from Google Earth images and a soil survey. Soil ECa was collected from a soil survey with an electromagnetic device. Yield values (t/ha) were obtained from combine harvesters equipped with yield monitor and Global Positioning System (GPS), over the course of four years for the irrigated site, and two years for the non-irrigated site. The yield data was quality controlled using a filtering system to remove outliers and technically non-plausible data. The data sources were combined in Geographic Information Systems (GIS) and three MZ were delineated for each field through standard clustering methods. The maize yields were aggregated per derived MZ to compare yields between different MZ-classes. The results showed that there was some consistency in yields related to the MZ, derived without yield data. In both the non-irrigated and irrigated fields, the lowest yield consistently occurred in the same class each year, however, the MZ-class with the highest yield varied year to year. The results show that it is possible for the studied type of fields to delineate ‘natural’ clusters or zones of site properties that can be used as MZ-classes as they represent different yield levels. The required inputs are freely available and easily obtained data

A Study of the Relationship between Ecological Network Patch metrics and Landscape connectivity; with reference to case of Colombo Wetlands

Landscape fragmentation and habitat loss are emergent issues in Sri Lanka, which is a result of rapid urban development and inadequate concern of managing Landscape connectivity. Inland Wetlands and forests are the most valuable ecosystems effected from the fragmentation. Considering those habitats, Wetlands are among the most productive, active, diverse, and beneficial ecosystems in nature threatened by the urbanization. Therefore, this study aimed to introduce spatial strategies to locate landscape developments to restore landscape connectivity; referring to Colombo wetlands. Though the fragmentation causes alteration of wetland structure, fragments does serves to landscape connectivity functioning as an ecological network. Therefore the study aims to understand how the habitat/ ecological network patch metrics contribute to landscape connectivity. Two main methods used; to calculate the patch metrics; Patch area, Total edge, Perimeter-area ratio, Core area index and Inter-patch distances; GIS data have been used. Further, GIS enables least-cost path tool to be used to measure the Landscape connectivity and calculate number of species flow paths per wetland patch. With the data analysis, the study investigated the relationship between the patch metrics and the landscape connectivity. Referring to the findings, the study discussed; increasing the patch area, maintaining a mean perimeter-area ratio or core area index, reducing the inter patch distances could enhance landscape connectivity within in the urban wetland habitat patches. At the end, the study introduces strategies for Landscape architects to select most suitable locations to implement ecological based landscape developments adjacent to the existing urban habitat in order to enhance patch metrics and to restore the landscape connectivityKeywords: Landscape fragmentation, Ecological networks, Patch metrics, Landscape connectivit

Urine patch detection using LiDAR and RPAS/UAV produced photogrammetry

In grazed dairy pastures, the largest N source for both nitrate (NO3-) leaching and nitrous oxide (N2O) emissions is urine-N excreted by the animals. Additional application of N on urine patches as fertilizer may increase these losses so adapting N-fertilisation in these areas is necessary. The objective of this study was to examine the use of a tractor mounted LiDAR (Light Detection and Ranging) system to accurately identify and quantify areas affect by excess N, such as urine and dung. To do so, a controlled experiment was designed in a paddock with no recent exposure to animals or N fertilisation. Synthetic urine was randomly applied within two 20m x 20m blocks and weekly LiDAR scans were taken for 5 weeks. LiDAR based contour maps of the pasture canopy were shown to accurately detect the asymmetric urine patches as well as calculate a percent area of urine based high N as early as one week after a simulated grazing event. Further, weekly flights were taken with a remotely piloted aircraft system (RPAS/UAV) to have aerial footage of the trial. Resulting mosaic of RGB and NIR images were used to create photogrammetric based contour maps. Both approaches (LiDAR and photogrammetry) show no significant difference in the identification and sizing of urine patch cluster

EQ-5D-3L Derived Population Norms for Health Related Quality of Life in Sri Lanka

Background Health Related Quality of Life (HRQoL) is an important outcome measure in health economic evaluation that guides health resource allocations. Population norms for HRQoL are an essential ingredient in health economics and in the evaluation of population health. The aim of this study was to produce EQ-5D-3L-derived population norms for Sri Lanka. Method A population sample (n =  780) was selected from four districts of Sri Lanka. A stratified cluster sampling approach with probability proportionate to size was employed. Twenty six clusters of 30 participants each were selected; each participant completed the EQ-5D-3L in a face-to-face interview. Utility weights for their EQ-5D-3L health states were assigned using the Sri Lankan EQ-5D-3L algorithm. The population norms are reported by age and socio-economic variables. Results The EQ-5D-3L was completed by 736 people, representing a 94% response rate. Sixty per cent of the sample reported being in full health. The percentage of people responding to any problems in the five EQ-5D-3L dimensions increased with age. The mean EQ-5D-3L weight was 0.85 (SD 0.008; 95%CI 0.84-0.87). The mean EQ-5D-3L weight was significantly associated with age, housing type, disease experience and religiosity. People above 70 years of age were 7.5 times more likely to report mobility problems and 3.7 times more likely to report pain/discomfort than those aged 18-29 years. Those with a tertiary education were five times less likely to report any HRQoL problems than those without a tertiary education. A person living in a shanty was 4.3 more likely to have problems in usual activities than a person living in a single house. Conclusion The population norms in Sri Lanka vary with socio-demographic characteristics. The socioeconomically disadvantaged have a lower HRQoL. The trends of population norms observed in this lower middle income country were generally similar to those previously reported in high income countries

Distributed Response Time Analysis of GSPN Models with MapReduce

widely used in the performance analysis of computer and communications systems. Response time densities and quantiles are often key outputs of such analysis. These can be extracted from a GSPN’s underlying semi-Markov process using a method based on numerical Laplace transform inversion. This method typically requires the solution of thousands of systems of complex linear equations, each of rank n, where n is the number of states in the model. For large models substantial processing power is needed and the computation must therefore be distributed. This paper describes the implementation of a Response Time Analysis module for the Platform Independent Petri net Editor (PIPE2) which interfaces with Hadoop, an open source implementation of Google’s MapReduce distributed programming environment, to provide distributed calculation of response time densities in GSPN models. The software is validated with analytically calculated results as well as simulated ones for larger models. Excellent scalability is shown. I

Enhanced Removal of Nutrients, Heavy Metals, and PAH from Synthetic Stormwater by Incorporating Different Adsorbents into a Filter Media

Stormwater harvesting and reuse is an attractive option to lower the demand placed on other sources of water supply. However, it contains a wide range of pollutants that need to be removed before it can be reused or even discharged to the waterways and receiving waters. An experimental protocol to estimate the efficiency of a soil-based-filter medium for the treatment of stormwater pollutants from 1 to 3 years rainfall experienced in the field was developed using a laboratory column-set-up over short-term duration. The filter removed substantial amounts of PO -P and NH -N for up to 8 h at a flow velocity of 100 mm/h which is a 1-year time-equivalent of rainfall at a locality in Sydney, Australia. An addition of 10% zeolite to the soil-based filter extended the column saturation period to 24 h. The breakthrough data for PO -P and NH -N were satisfactorily described by the Thomas model. The majority of the nine heavy metals tested were removed by more than 50% for up to 4 h in the soil-based filter. This level of removal increased to 16 h when 10% zeolite was added to the filter. The column with the soil-based filter + 10% zeolite had higher affinity for Pb, Cu, Zn, and As than Ni, with Pb having the highest percentage removal. Soil-based filter + 10% zeolite removed considerable amounts of 3 polycyclic aromatic hydrocarbons (PAHs) (30–50%), while soil-based filter + 10% zeolite + 0.3% granular activated carbon removed 65 to > 99% of the PAHs at 24-h operation. 4 4 4