On Directional Selectivity in Vertebrate Retina: An Experimental and Computational Study

This thesis describes an investigation of retinal directional selectivity. We show intracellular (whole-cell patch) recordings in turtle retina which indicate that this computation occurs prior to the ganglion cell, and we describe a pre-ganglionic circuit model to account for this and other findings which places the non-linear spatio-temporal filter at individual, oriented amacrine cell dendrites. The key non-linearity is provided by interactions between excitatory and inhibitory synaptic inputs onto the dendrites, and their distal tips provide directionally selective excitatory outputs onto ganglion cells. Detailed simulations of putative cells support this model, given reasonable parameter constraints. The performance of the model also suggests that this computational substructure may be relevant within the dendritic trees of CNS neurons in general.

Reaction time and replenishment time of SP and CGRP after incision in rat skin

Background. the skin neurogenic inflammation is mainly related to Substance P (SP) and Calcitonin Gene-related Peptide (CGRP). There is no data on their availability in the dynamics of skin nerve endings, concerning their release and replenishment after a nociceptive stimulus, so this was investigated. Materials and methods. 25 rats were randomly distributed in 5 groups. the animals of the control group (CG) determined the baseline levels of neuropeptides in the skin. the groups S0 and S30 did not receive any cutaneous stimulus at 30 and 60 minutes, respectively. in the group S1, an incision stimulus was made at 30 minutes. in the group S31, a nociceptive stimulus was performed by subdermal scratching at 30 minutes and, at 60 minutes, the incision stimulus was carried out in the same location (nociceptive hyperstimulation). the skin samples of the other animals were harvested from the back 1 minute after their death. SP, pro-CGRP and CGRP were quantified by Western Blotting. Results. the incision stimulus released SP, S1 compared to S0 (p < 0.05) detected in the first minute, and the replenishment time was more than 30 minutes. Also, it cleaved pro-CGRP, S1 compared to S31 (p < 0.05) in the first minute, and its replenishment time less than 30 minutes. Release of CGRP was not detected. Conclusion. the incision released SP already detected in the first minute; its replenishment time is more than 30 minutes. the incision decreased pro-CGRP, also detected in the first minute; and its replenishment time is less than 30 minutes.

Modelowanie i przetwarzanie wiedzy czaso-przestrzennej. Model XCDC – aspekty teoretyczne oraz rozwiązania aplikacyjne

Wydział Matematyki i Informatyki: Zakład Lingwistyki Informatycznej i Sztucznej Inteligencji

Detour-Based Mobility Coordination in DTNs

Commonly, research work in routing for delay tolerant networks (DTN) assumes that node encounters are predestined, in the sense that they are the result of unknown, exogenous processes that control the mobility of these nodes. In this paper, we argue that for many applications such an assumption is too restrictive: while the spatio-temporal coordinates of the start and end points of a node's journey are determined by exogenous processes, the specific path that a node may take in space-time, and hence the set of nodes it may encounter could be controlled in such a way so as to improve the performance of DTN routing. To that end, we consider a setting in which each mobile node is governed by a schedule consisting of a ist of locations that the node must visit at particular times. Typically, such schedules exhibit some level of slack, which could be leveraged for DTN message delivery purposes. We define the Mobility Coordination Problem (MCP) for DTNs as follows: Given a set of nodes, each with its own schedule, and a set of messages to be exchanged between these nodes, devise a set of node encounters that minimize message delivery delays while satisfying all node schedules. The MCP for DTNs is general enough that it allows us to model and evaluate some of the existing DTN schemes, including data mules and message ferries. In this paper, we show that MCP for DTNs is NP-hard and propose two detour-based approaches to solve the problem. The first (DMD) is a centralized heuristic that leverages knowledge of the message workload to suggest specific detours to optimize message delivery. The second (DNE) is a distributed heuristic that is oblivious to the message workload, and which selects detours so as to maximize node encounters. We evaluate the performance of these detour-based approaches using extensive simulations based on synthetic workloads as well as real schedules obtained from taxi logs in a major metropolitan area. Our evaluation shows that our centralized, workload-aware DMD approach yields the best performance, in terms of message delay and delivery success ratio, and that our distributed, workload-oblivious DNE approach yields favorable performance when compared to approaches that require the use of data mules and message ferries.

Service Provisioning in Mobile Networks Through Distributed Coordinated Resource Management

The pervasiveness of personal computing platforms offers an unprecedented opportunity to deploy large-scale services that are distributed over wide physical spaces. Two major challenges face the deployment of such services: the often resource-limited nature of these platforms, and the necessity of preserving the autonomy of the owner of these devices. These challenges preclude using centralized control and preclude considering services that are subject to performance guarantees. To that end, this thesis advances a number of new distributed resource management techniques that are shown to be effective in such settings, focusing on two application domains: distributed Field Monitoring Applications (FMAs), and Message Delivery Applications (MDAs). In the context of FMA, this thesis presents two techniques that are well-suited to the fairly limited storage and power resources of autonomously mobile sensor nodes. The first technique relies on amorphous placement of sensory data through the use of novel storage management and sample diffusion techniques. The second approach relies on an information-theoretic framework to optimize local resource management decisions. Both approaches are proactive in that they aim to provide nodes with a view of the monitored field that reflects the characteristics of queries over that field, enabling them to handle more queries locally, and thus reduce communication overheads. Then, this thesis recognizes node mobility as a resource to be leveraged, and in that respect proposes novel mobility coordination techniques for FMAs and MDAs. Assuming that node mobility is governed by a spatio-temporal schedule featuring some slack, this thesis presents novel algorithms of various computational complexities to orchestrate the use of this slack to improve the performance of supported applications. The findings in this thesis, which are supported by analysis and extensive simulations, highlight the importance of two general design principles for distributed systems. First, a-priori knowledge (e.g., about the target phenomena of FMAs and/or the workload of either FMAs or DMAs) could be used effectively for local resource management. Second, judicious leverage and coordination of node mobility could lead to significant performance gains for distributed applications deployed over resource-impoverished infrastructures.

Orientational Harmonic Model for Rotation, Translation, and Scale Invariant Pattern Representation in Biological Vision

The proposed model, called the combinatorial and competitive spatio-temporal memory or CCSTM, provides an elegant solution to the general problem of having to store and recall spatio-temporal patterns in which states or sequences of states can recur in various contexts. For example, fig. 1 shows two state sequences that have a common subsequence, C and D. The CCSTM assumes that any state has a distributed representation as a collection of features. Each feature has an associated competitive module (CM) containing K cells. On any given occurrence of a particular feature, A, exactly one of the cells in CMA will be chosen to represent it. It is the particular set of cells active on the previous time step that determines which cells are chosen to represent instances of their associated features on the current time step. If we assume that typically S features are active in any state then any state has K^S different neural representations. This huge space of possible neural representations of any state is what underlies the model's ability to store and recall numerous context-sensitive state sequences. The purpose of this paper is simply to describe this mechanism.

ART-EMAP: A Neural Network Architecture for Object Recognition by Evidence Accumulation

A new neural network architecture is introduced for the recognition of pattern classes after supervised and unsupervised learning. Applications include spatio-temporal image understanding and prediction and 3-D object recognition from a series of ambiguous 2-D views. The architecture, called ART-EMAP, achieves a synthesis of adaptive resonance theory (ART) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). ART-EMAP extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage 1 introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory (MTM). Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Each ART-EMAP stage is illustrated with a benchmark simulation example, using both noisy and noise-free data. A concluding set of simulations demonstrate ART-EMAP performance on a difficult 3-D object recognition problem.

Monitoring the vegetation start of season (SOS) across the island of Ireland using the MERIS global vegetation index

The aim of this study was to develop a methodology, based on satellite remote sensing, to estimate the vegetation Start of Season (SOS) across the whole island of Ireland on an annual basis. This growing body of research is known as Land Surface Phenology (LSP) monitoring. The SOS was estimated for each year from a 7-year time series of 10-day composited, 1.2 km reduced resolution MERIS Global Vegetation Index (MGVI) data from 2003 to 2009, using the time series analysis software, TIMESAT. The selection of a 10-day composite period was guided by in-situ observations of leaf unfolding and cloud cover at representative point locations on the island. The MGVI time series was smoothed and the SOS metric extracted at a point corresponding to 20% of the seasonal MGVI amplitude. The SOS metric was extracted on a per pixel basis and gridded for national scale coverage. There were consistent spatial patterns in the SOS grids which were replicated on an annual basis and were qualitatively linked to variation in landcover. Analysis revealed that three statistically separable groups of CORINE Land Cover (CLC) classes could be derived from differences in the SOS, namely agricultural and forest land cover types, peat bogs, and natural and semi-natural vegetation types. These groups demonstrated that managed vegetation, e.g. pastures has a significantly earlier SOS than in unmanaged vegetation e.g. natural grasslands. There was also interannual spatio-temporal variability in the SOS. Such variability was highlighted in a series of anomaly grids showing variation from the 7-year mean SOS. An initial climate analysis indicated that an anomalously cold winter and spring in 2005/2006, linked to a negative North Atlantic Oscillation index value, delayed the 2006 SOS countrywide, while in other years the SOS anomalies showed more complex variation. A correlation study using air temperature as a climate variable revealed the spatial complexity of the air temperature-SOS relationship across the Republic of Ireland as the timing of maximum correlation varied from November to April depending on location. The SOS was found to occur earlier due to warmer winters in the Southeast while it was later with warmer winters in the Northwest. The inverse pattern emerged in the spatial patterns of the spring correlates. This contrasting pattern would appear to be linked to vegetation management as arable cropping is typically practiced in the southeast while there is mixed agriculture and mostly pastures to the west. Therefore, land use as well as air temperature appears to be an important determinant of national scale patterns in the SOS. The TIMESAT tool formed a crucial component of the estimation of SOS across the country in all seven years as it minimised the negative impact of noise and data dropouts in the MGVI time series by applying a smoothing algorithm. The extracted SOS metric was sensitive to temporal and spatial variation in land surface vegetation seasonality while the spatial patterns in the gridded SOS estimates aligned with those in landcover type. The methodology can be extended for a longer time series of FAPAR as MERIS will be replaced by the ESA Sentinel mission in 2013, while the availability of full resolution (300m) MERIS FAPAR and equivalent sensor products holds the possibility of monitoring finer scale seasonality variation. This study has shown the utility of the SOS metric as an indicator of spatiotemporal variability in vegetation phenology, as well as a correlate of other environmental variables such as air temperature. However, the satellite-based method is not seen as a replacement of ground-based observations, but rather as a complementary approach to studying vegetation phenology at the national scale. In future, the method can be extended to extract other metrics of the seasonal cycle in order to gain a more comprehensive view of seasonal vegetation development.

Intracardiac acoustic radiation force impulse (ARFI) and shear wave imaging in pigs with focal infarctions.

Four pigs, three with focal infarctions in the apical intraventricular septum (IVS) and/or left ventricular free wall (LVFW), were imaged with an intracardiac echocardiography (ICE) transducer. Custom beam sequences were used to excite the myocardium with focused acoustic radiation force (ARF) impulses and image the subsequent tissue response. Tissue displacement in response to the ARF excitation was calculated with a phase-based estimator, and transverse wave magnitude and velocity were each estimated at every depth. The excitation sequence was repeated rapidly, either in the same location to generate 40 Hz M-modes at a single steering angle, or with a modulated steering angle to synthesize 2-D displacement magnitude and shear wave velocity images at 17 points in the cardiac cycle. Both types of images were acquired from various views in the right and left ventricles, in and out of infarcted regions. In all animals, acoustic radiation force impulse (ARFI) and shear wave elasticity imaging (SWEI) estimates indicated diastolic relaxation and systolic contraction in noninfarcted tissues. The M-mode sequences showed high beat-to-beat spatio-temporal repeatability of the measurements for each imaging plane. In views of noninfarcted tissue in the diseased animals, no significant elastic remodeling was indicated when compared with the control. Where available, views of infarcted tissue were compared with similar views from the control animal. In views of the LVFW, the infarcted tissue presented as stiff and non-contractile compared with the control. In a view of the IVS, no significant difference was seen between infarcted and healthy tissue, whereas in another view, a heterogeneous infarction was seen to be presenting itself as non-contractile in systole.

Spectrotemporal CT data acquisition and reconstruction at low dose.

PURPOSE: X-ray computed tomography (CT) is widely used, both clinically and preclinically, for fast, high-resolution anatomic imaging; however, compelling opportunities exist to expand its use in functional imaging applications. For instance, spectral information combined with nanoparticle contrast agents enables quantification of tissue perfusion levels, while temporal information details cardiac and respiratory dynamics. The authors propose and demonstrate a projection acquisition and reconstruction strategy for 5D CT (3D+dual energy+time) which recovers spectral and temporal information without substantially increasing radiation dose or sampling time relative to anatomic imaging protocols. METHODS: The authors approach the 5D reconstruction problem within the framework of low-rank and sparse matrix decomposition. Unlike previous work on rank-sparsity constrained CT reconstruction, the authors establish an explicit rank-sparse signal model to describe the spectral and temporal dimensions. The spectral dimension is represented as a well-sampled time and energy averaged image plus regularly undersampled principal components describing the spectral contrast. The temporal dimension is represented as the same time and energy averaged reconstruction plus contiguous, spatially sparse, and irregularly sampled temporal contrast images. Using a nonlinear, image domain filtration approach, the authors refer to as rank-sparse kernel regression, the authors transfer image structure from the well-sampled time and energy averaged reconstruction to the spectral and temporal contrast images. This regularization strategy strictly constrains the reconstruction problem while approximately separating the temporal and spectral dimensions. Separability results in a highly compressed representation for the 5D data in which projections are shared between the temporal and spectral reconstruction subproblems, enabling substantial undersampling. The authors solved the 5D reconstruction problem using the split Bregman method and GPU-based implementations of backprojection, reprojection, and kernel regression. Using a preclinical mouse model, the authors apply the proposed algorithm to study myocardial injury following radiation treatment of breast cancer. RESULTS: Quantitative 5D simulations are performed using the MOBY mouse phantom. Twenty data sets (ten cardiac phases, two energies) are reconstructed with 88 μm, isotropic voxels from 450 total projections acquired over a single 360° rotation. In vivo 5D myocardial injury data sets acquired in two mice injected with gold and iodine nanoparticles are also reconstructed with 20 data sets per mouse using the same acquisition parameters (dose: ∼60 mGy). For both the simulations and the in vivo data, the reconstruction quality is sufficient to perform material decomposition into gold and iodine maps to localize the extent of myocardial injury (gold accumulation) and to measure cardiac functional metrics (vascular iodine). Their 5D CT imaging protocol represents a 95% reduction in radiation dose per cardiac phase and energy and a 40-fold decrease in projection sampling time relative to their standard imaging protocol. CONCLUSIONS: Their 5D CT data acquisition and reconstruction protocol efficiently exploits the rank-sparse nature of spectral and temporal CT data to provide high-fidelity reconstruction results without increased radiation dose or sampling time.

Case history and persistence of the non-indigenous diatom Coscinodiscus wailesii in the north-east Atlantic

The introduction of non-indigenous marine plankton species can have a considerable ecological and economic effect on regional systems. Their presence, however, can go unnoticed until they reach nuisance status and as a consequence few case histories exist containing information on their initial appearance and their spatio-temporal patterns. Here we report on the occurrence of the non-indigenous diatom Coscinodiscus wailesii in 1977 in the English Channel, its subsequent geographical spread into European shelf seas, and its persistence as a significant member of the diatom community in the north-east Atlantic from 1977-1995.

Relating sardine recruitment in the Northern Benguela to satellite-derived sea surface height using a neural network pattern recognition approach

Processes of enrichment, concentration and retention are thought to be important for the successful recruitment of small pelagic fish in upwelling areas, but are difficult to measure. In this study, a novel approach is used to examine the role of spatio-temporal oceanographic variability on recruitment success of the Northern Benguela sardine Sardinops sagax. This approach applies a neural network pattern recognition technique, called a self-organising map (SOM), to a seven-year time series of satellite-derived sea level data. The Northern Benguela is characterised by quasi-perennial upwelling of cold, nutrient-rich water and is influenced by intrusions of warm, nutrient-poor Angola Current water from the north. In this paper, these processes are categorised in terms of their influence on recruitment success through the key ocean triad mechanisms of enrichment, concentration and retention. Moderate upwelling is seen as favourable for recruitment, whereas strong upwelling, weak upwelling and Angola Current intrusion appear detrimental to recruitment success. The SOM was used to identify characteristic patterns from sea level difference data and these were interpreted with the aid of sea surface temperature data. We found that the major oceanographic processes of upwelling and Angola Current intrusion dominated these patterns, allowing them to be partitioned into those representing recruitment favourable conditions and those representing adverse conditions for recruitment. A marginally significant relationship was found between the index of sardine recruitment and the frequency of recruitment favourable conditions (r super(2) = 0.61, p = 0.068, n = 6). Because larvae are vulnerable to environmental influences for a period of at least 50 days after spawning, the SOM was then used to identify windows of persistent favourable conditions lasting longer than 50 days, termed recruitment favourable periods (RFPs). The occurrence of RFPs was compared with back-calculated spawning dates for each cohort. Finally, a comparison of RFPs with the time of spawning and the index of recruitment showed that in years where there were 50 or more days of favourable conditions following spawning, good recruitment followed (Mann-Whitney U-test: p = 0.064, n = 6). These results show the value of the SOM technique for describing spatio-temporal variability in oceanographic processes. Variability in these processes appears to be an important factor influencing recruitment in the Northern Benguela sardine, although the available data time series is currently too short to be conclusive. Nonetheless, the analysis of satellite data, using a neural network pattern-recognition approach, provides a useful framework for investigating fisheries recruitment problems.

The meso-scale response of subarctic North Pacific seabird community structure to lower trophic level abundance and diversity

Understanding the mechanisms that structure communities and influence biodiversity are fundamental goals of ecology. To test the hypothesis that the abundance and diversity of upper-trophic level predators (seabirds) is related to the underlying abundance and diversity of their prey (zooplankton) and ecosystem-wide energy availability (primary production), we initiated a monitoring program in 2002 that jointly and repeatedly surveys seabird and zooplankton populations across a 7,500 km British Columbia-Bering Sea-Japan transect. Seabird distributions were recorded by a single observer (MH) using a strip-width technique, mesozooplankton samples were collected with a Continuous Plankton Recorder, and primary production levels were derived using the appropriate satellite parameters and the Vertically Generalized Production Model (Behrenfeld and Falkowski 1997). Each trophic level showed clear spatio-temporal patterns over the course of the study. The strongest relationship between seabird abundance and diversity and the lower trophic levels was observed in March/April ('spring') and significant relationships were also found through June/July ('summer'). No discernable relationships were observed during the September/October ('fall') months. Overall, mesozooplankton abundance and biomass explained the dominant portion of seabird abundance and diversity indices (richness, Simpson's Index, and evenness), while primary production was only related to seabird richness. These findings underscore the notion that perturbations of ocean productivity and lower trophic level ecosystem constituents influenced by climate change, such as shifts in timing (phenology) and synchronicity (match-mismatch), could impart far-reaching consequences throughout the marine food web.

Seasonal plankton dynamics along a cross-shelf gradient off northern Spain

The development of population models able to reproduce the dynamics of zooplankton is a central issue when trying to understand how a changing environment would affect zooplankton in the future. Using 10 years of monthly data on phytoplankton and zooplankton abundance in the Bay of Biscay from the IEO's RADIALES time-series programme, we built non-parametric Generalized Additive Models (GAMs) able to reproduce the dynamics of plankton on the basis of environmental factors (nutrients, temperature, upwelling and photoperiod). We found that the interaction between these two plankton components is approximately linear, whereas the effects of environmental factors are non-linear. With the inclusion of the environmental variability, the main seasonal and inter-annual dynamic patterns observed within the studied plankton assemblage indicate the prevalence of bottom-up regulatory control. The statistically deduced models were used to simulate the dynamics of the phytoplankton and zooplankton. A good agreement between observations and simulations was obtained, especially for zooplankton. We are presently developing spatio-temporal GAM models for the North Sea based on the Continuous Plankton Recorder database.

Non-linearities, regime shifts and recovery: The recent influence of climate on Black Sea chlorophyll

The Black Sea ecosystem experienced severe eutrophication-related degradation during the 1970s and 1980s. However, in recent years the Black Sea has shown some signs of recovery which are often attributed to a reduction in nutrient loading. Here, SeaWiFS chlorophyll a (chl a), a proxy for phytoplankton biomass, is used to investigate spatio-temporal patterns in Black Sea phytoplankton dynamics and to explore the potential role of climate in the Black Sea's recovery. Maps of chl a anomalies, calculated relative to the 8 year mean, emphasize spatial and temporal variability of phytoplankton biomass in the Black Sea, particularly between the riverine-influenced Northwest Shelf and the open Black Sea. Evolution of phytoplankton biomass has shown significant spatial variability of persistence of optimal bloom conditions between three major regions of the Black Sea. With the exception of 2001, chl a has generally decreased during our 8 year time-series. However, the winter of 2000–2001 was anomalously warm with low wind stress, resulting in reduced vertical mixing of the water column and retention of nutrients in the photic zone. These conditions were associated with anomalously high levels of chl a throughout much of the open Black Sea during the following spring and summer. The unusual climatic conditions occurring in 2001 may have triggered a shift in the Black Sea's chl a regime. The long-term significance of this recent shift is still uncertain but illustrates a non-linear response to climate forcing that makes future ecosystem changes in the pelagic Black Sea ecosystem difficult to predict.