From on-road to off : transfer learning within a deep convolutional neural network for segmentation and classification of off-road scenes.

Real-time road-scene understanding is a challenging computer vision task with recent advances in convolutional neural networks (CNN) achieving results that notably surpass prior traditional feature driven approaches. Here, we take an existing CNN architecture, pre-trained for urban road-scene understanding, and retrain it towards the task of classifying off-road scenes, assessing the network performance within the training cycle. Within the paradigm of transfer learning we analyse the effects on CNN classification, by training and assessing varying levels of prior training on varying sub-sets of our off-road training data. For each of these configurations, we evaluate the network at multiple points during its training cycle, allowing us to analyse in depth exactly how the training process is affected by these variations. Finally, we compare this CNN to a more traditional approach using a feature-driven Support Vector Machine (SVM) classifier and demonstrate state-of-the-art results in this particularly challenging problem of off-road scene understanding

Is thirty-seven years sufficient for full return of the ant biota following restoration?

Introduction: An assessment of whether rehabilitated mine sites have resulted in natural or novel ecosystems requires monitoring over considerable periods of time or the use of space-for-time substitution (chronosequence) approaches. Methods: To provide an assessment of ecosystem recovery in areas mined for bauxite in 1975, the ant fauna of one area planted with Eucalyptus resinifera, one seeded with mixed native species, one topsoiled but unrestored, and a forest reference was subjected to a ‘long-term’ study by sampling monthly and latterly annually between 1976 and 1989 using pitfall traps. These plots were resampled in 2012. A companion ‘short-term’ chronosequence study was performed in 1979 in 28 bauxite mines of various ages and restored by a range of different methods, plus three forest references. In order to examine the assertion that the observed differences between restored areas and forest references will lessen with time, sampling using comparable methods was repeated in 2012 in seven of the original plots, representing progressive advances in rehabilitation technology: planted pines; planted eastern states eucalypts; planted native eucalypts; planted eucalypts over seeded understorey; and planted eucalypts on fresh, double-stripped topsoil, plus two forest reference sites. Results: Ant and other invertebrate richness in the long-term study was initially superior in the seeded plot, with little difference between the planted and unplanted plots. It was concluded that although composition of the ant fauna had converged on that of the forest over the 14-year period, differences still persisted.The 2012 resampling revealed that ant species richness and composition had deteriorated in the seeded plot, while values in the unplanted plot, which now supported naturally colonised trees and an understorey, had increased. Differences between all rehabilitated plots and forest still persisted. As with the long-term study, the rate of fauna return and the type of ants present in the short-term study plots differed with the method of rehabilitation used, and, in 1979, no plots had converged on the forest in terms of the ant assemblage. By 2012 ant richness increased, and more so with each advance in rehabilitation technology, except for seeding, in which the understorey had collapsed. Double-stripping of topsoil resulted in the greatest improvements in ant species richness, although none of the areas had converged on the forest reference areas in terms of assemblage composition or ant functional group profiles. Furthermore, assemblage composition in the forest had changed over time, possibly due to reductions in rainfall, which further complicates rehabilitation objectives. Conclusions: It is concluded that although rehabilitation can achieve its objective of restoring diversity, the original assemblage has still not been achieved after 37 years, suggesting that a degree of novelty has been introduced into these older-style rehabilitated areas. The company’s current rehabilitation practices reflect multiple advances in their approach, lending optimism that current restoration may achieve something close to the original ecosystem, an outcome that can only be verified by extended studies like the one described here

Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data

Background. Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics. Methodology/Principal Findings. We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005. Conclusions/Significance. Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling

An automated, broad-based, near real-time public health surveillance system using presentations to hospital Emergency Departments in New South Wales, Australia

BACKGROUND: In a climate of concern over bioterrorism threats and emergent diseases, public health authorities are trialling more timely surveillance systems. The 2003 Rugby World Cup (RWC) provided an opportunity to test the viability of a near real-time syndromic surveillance system in metropolitan Sydney, Australia. We describe the development and early results of this largely automated system that used data routinely collected in Emergency Departments (EDs). METHODS: Twelve of 49 EDs in the Sydney metropolitan area automatically transmitted surveillance data from their existing information systems to a central database in near real-time. Information captured for each ED visit included patient demographic details, presenting problem and nursing assessment entered as free-text at triage time, physician-assigned provisional diagnosis codes, and status at departure from the ED. Both diagnoses from the EDs and triage text were used to assign syndrome categories. The text information was automatically classified into one or more of 26 syndrome categories using automated "naïve Bayes" text categorisation techniques. Automated processes were used to analyse both diagnosis and free text-based syndrome data and to produce web-based statistical summaries for daily review. An adjusted cumulative sum (cusum) was used to assess the statistical significance of trends. RESULTS: During the RWC the system did not identify any major public health threats associated with the tournament, mass gatherings or the influx of visitors. This was consistent with evidence from other sources, although two known outbreaks were already in progress before the tournament. Limited baseline in early monitoring prevented the system from automatically identifying these ongoing outbreaks. Data capture was invisible to clinical staff in EDs and did not add to their workload. CONCLUSION: We have demonstrated the feasibility and potential utility of syndromic surveillance using routinely collected data from ED information systems. Key features of our system are its nil impact on clinical staff, and its use of statistical methods to assign syndrome categories based on clinical free text information. The system is ongoing, and has expanded to cover 30 EDs. Results of formal evaluations of both the technical efficiency and the public health impacts of the system will be described subsequently

Gaviscon® vs. omeprazole in symptomatic treatment of moderate gastroesophageal reflux. a direct comparative randomised trial

<p>Abstract</p> <p>Background</p> <p>Medical management of GERD mainly uses proton pump inhibitors. Alginates also have proven efficacy. The aim of this trial was to compare short-term efficacy of an alginate (Gaviscon^®, 4 × 10 mL/day) and omeprazole (20 mg/day) on GERD symptoms in general practice.</p> <p>Methods</p> <p>A 14-day multicentre randomised double-blind double-dummy non-inferiority trial compared Gaviscon^®(4 × 10 mL/day) and omeprazole (20 mg/day) in patients with 2-6 day heartburn episodes weekly without alarm signals. The primary outcome was the mean time to onset of the first 24-h heartburn-free period after initial dosing. Secondary outcomes were the proportion of patients without heartburn by D7, pain relief by D7, and reduction in pain intensity by D7 and D14.</p> <p>Results</p> <p>278 patients were recruited; 120 were included in the Gaviscon^®group and 121 in the omeprazole group for the per protocol non-inferiority analysis. The mean time to onset of the first 24-h heartburn-free period after initial dosing was 2.0 (± 2.2) days for Gaviscon^®and 2.0 (± 2.3) days for omeprazole (<it>p </it>= 0.93); mean intergroup difference was 0.01 ± 1.55 days (95% CI = -0.41 to 0.43): i.e., less than the lower limit of the 95% CI of -0.5 days predetermined to demonstrate non-inferiority. The mean number of heartburn-free days by D7 was significantly greater in the omeprazole group: 3.7 ± 2.3 days vs. 3.1 ± 2.1 (<it>p </it>= 0.02). On D7, overall quality of pain relief was slightly in favour of omeprazole (<it>p </it>= 0.049). There was no significant difference in the reduction in pain intensity between groups by D7 (<it>p = </it>0.11) or D14 (<it>p = </it>0.08). Tolerance and safety were good and comparable in both groups.</p> <p>Conclusion</p> <p>Gaviscon^®was non-inferior to omeprazole in achieving a 24-h heartburn-free period in moderate episodic heartburn, and is a relevant effective alternative treatment in moderate GERD in primary care.</p> <p>Trial registration</p> <p><a href="http://www.controlled-trials.com/ISRCTN62203233">ISRCTN62203233</a>.</p

Causal Measures of Structure and Plasticity in Simulated and Living Neural Networks

A major goal of neuroscience is to understand the relationship between neural structures and their function. Recording of neural activity with arrays of electrodes is a primary tool employed toward this goal. However, the relationships among the neural activity recorded by these arrays are often highly complex making it problematic to accurately quantify a network's structural information and then relate that structure to its function. Current statistical methods including cross correlation and coherence have achieved only modest success in characterizing the structural connectivity. Over the last decade an alternative technique known as Granger causality is emerging within neuroscience. This technique, borrowed from the field of economics, provides a strong mathematical foundation based on linear auto-regression to detect and quantify “causal” relationships among different time series. This paper presents a combination of three Granger based analytical methods that can quickly provide a relatively complete representation of the causal structure within a neural network. These are a simple pairwise Granger causality metric, a conditional metric, and a little known computationally inexpensive subtractive conditional method. Each causal metric is first described and evaluated in a series of biologically plausible neural simulations. We then demonstrate how Granger causality can detect and quantify changes in the strength of those relationships during plasticity using 60 channel spike train data from an in vitro cortical network measured on a microelectrode array. We show that these metrics can not only detect the presence of causal relationships, they also provide crucial information about the strength and direction of that relationship, particularly when that relationship maybe changing during plasticity. Although we focus on the analysis of multichannel spike train data the metrics we describe are applicable to any stationary time series in which causal relationships among multiple measures is desired. These techniques can be especially useful when the interactions among those measures are highly complex, difficult to untangle, and maybe changing over time

Linkage map construction involving a reciprocal translocation

This paper is concerned with a novel statistical–genetic approach for the construction of linkage maps in populations obtained from reciprocal translocation heterozygotes of barley (Hordeum vulgare L.). Using standard linkage analysis, translocations usually lead to ‘pseudo-linkage’: the mixing up of markers from the chromosomes involved in the translocation into a single linkage group. Close to the translocation breakpoints recombination is severely suppressed and, as a consequence, ordering markers in those regions is not feasible. The novel strategy presented in this paper is based on (1) disentangling the “pseudo-linkage” using principal coordinate analysis, (2) separating individuals into translocated types and normal types and (3) separating markers into those close to and those more distant from the translocation breakpoints. The methods make use of a consensus map of the species involved. The final product consists of integrated linkage maps of the distal parts of the chromosomes involved in the translocation

CAGO: A Software Tool for Dynamic Visual Comparison and Correlation Measurement of Genome Organization

CAGO (Comparative Analysis of Genome Organization) is developed to address two critical shortcomings of conventional genome atlas plotters: lack of dynamic exploratory functions and absence of signal analysis for genomic properties. With dynamic exploratory functions, users can directly manipulate chromosome tracks of a genome atlas and intuitively identify distinct genomic signals by visual comparison. Signal analysis of genomic properties can further detect inconspicuous patterns from noisy genomic properties and calculate correlations between genomic properties across various genomes. To implement dynamic exploratory functions, CAGO presents each genome atlas in Scalable Vector Graphics (SVG) format and allows users to interact with it using a SVG viewer through JavaScript. Signal analysis functions are implemented using R statistical software and a discrete wavelet transformation package waveslim. CAGO is not only a plotter for generating complex genome atlases, but also a platform for exploring genome atlases with dynamic exploratory functions for visual comparison and with signal analysis for comparing genomic properties across multiple organisms. The web-based application of CAGO, its source code, user guides, video demos, and live examples are publicly available and can be accessed at http://cbs.ym.edu.tw/cago