Linear and nonlinear trending and prediction for AVHRR time series data

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse

High-Frequency network activity, global increase in Neuronal Activity, and Synchrony Expansion Precede Epileptic Seizures In Vitro

How seizures start is a major question in epilepsy research. Preictal EEG changes occur in both human patients and animal models, but their underlying mechanisms and relationship with seizure initiation remain unknown. Here we demonstrate the existence, in the hippocampal CA1 region, of a preictal state characterized by the progressive and global increase in neuronal activity associated with a widespread buildup of low-amplitude high-frequency activity (HFA) (100 Hz) and reduction in system complexity.HFAis generated by the firing of neurons, mainly pyramidal cells, at much lower frequencies. Individual cycles ofHFAare generated by the near-synchronous (within 5 ms) firing of small numbers of pyramidal cells. The presence of HFA in the low-calcium model implicates nonsynaptic synchronization; the presence of very similar HFA in the high-potassium model shows that it does not depend on an absence of synaptic transmission. Immediately before seizure onset, CA1 is in a state of high sensitivity in which weak depolarizing or synchronizing perturbations can trigger seizures. Transition to seizure is haracterized by a rapid expansion and fusion of the neuronal populations responsible for HFA, associated with a progressive slowing of HFA, leading to a single, massive, hypersynchronous cluster generating the high-amplitude low-frequency activity of the seizure

Regional and inter-regional effects in evolving climate networks

Peer reviewedPublisher PD

Measuring Information Transfer

An information theoretic measure is derived that quantifies the statistical coherence between systems evolving in time. The standard time delayed mutual information fails to distinguish information that is actually exchanged from shared information due to common history and input signals. In our new approach, these influences are excluded by appropriate conditioning of transition probabilities. The resulting transfer entropy is able to distinguish driving and responding elements and to detect asymmetry in the coupling of subsystems.Comment: 4 pages, 4 Figures, Revte

On directed information theory and Granger causality graphs

Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.Comment: accepted for publications, Journal of Computational Neuroscienc

Climate Dynamics: A Network-Based Approach for the Analysis of Global Precipitation

Precipitation is one of the most important meteorological variables for defining the climate dynamics, but the spatial patterns of precipitation have not been fully investigated yet. The complex network theory, which provides a robust tool to investigate the statistical interdependence of many interacting elements, is used here to analyze the spatial dynamics of annual precipitation over seventy years (1941-2010). The precipitation network is built associating a node to a geographical region, which has a temporal distribution of precipitation, and identifying possible links among nodes through the correlation function. The precipitation network reveals significant spatial variability with barely connected regions, as Eastern China and Japan, and highly connected regions, such as the African Sahel, Eastern Australia and, to a lesser extent, Northern Europe. Sahel and Eastern Australia are remarkably dry regions, where low amounts of rainfall are uniformly distributed on continental scales and small-scale extreme events are rare. As a consequence, the precipitation gradient is low, making these regions well connected on a large spatial scale. On the contrary, the Asiatic South-East is often reached by extreme events such as monsoons, tropical cyclones and heat waves, which can all contribute to reduce the correlation to the short-range scale only. Some patterns emerging between mid-latitude and tropical regions suggest a possible impact of the propagation of planetary waves on precipitation at a global scale. Other links can be qualitatively associated to the atmospheric and oceanic circulation. To analyze the sensitivity of the network to the physical closeness of the nodes, short-term connections are broken. The African Sahel, Eastern Australia and Northern Europe regions again appear as the supernodes of the network, confirming furthermore their long-range connection structure. Almost all North-American and Asian nodes vanish, revealing that extreme events can enhance high precipitation gradients, leading to a systematic absence of long-range patterns

An ECVAG† trial on assessment of oxidative damage to DNA measured by the comet assay

The increasing use of single cell gel electrophoresis (the comet assay) highlights its popularity as a method for detecting DNA damage, including the use of enzymes for assessment of oxidatively damaged DNA. However, comparison of DNA damage levels between laboratories can be difficult due to differences in assay protocols (e.g. lysis conditions, enzyme treatment, the duration of the alkaline treatment and electrophoresis) and in the end points used for reporting results (e.g. %DNA in tail, arbitrary units, tail moment and tail length). One way to facilitate comparisons is to convert primary comet assay end points to number of lesions/106 bp by calibration with ionizing radiation. The aim of this study was to investigate the inter-laboratory variation in assessment of oxidatively damaged DNA by the comet assay in terms of oxidized purines converted to strand breaks with formamidopyrimidine DNA glycosylase (FPG). Coded samples with DNA oxidation damage induced by treatment with different concentrations of photosensitizer (Ro 19-8022) plus light and calibration samples irradiated with ionizing radiation were distributed to the 10 participating laboratories to measure DNA damage using their own comet assay protocols. Nine of 10 laboratories reported the same ranking of the level of damage in the coded samples. The variation in assessment of oxidatively damaged DNA was largely due to differences in protocols. After conversion of the data to lesions/106 bp using laboratory-specific calibration curves, the variation between the laboratories was reduced. The contribution of the concentration of photosensitizer to the variation in net FPG-sensitive sites increased from 49 to 73%, whereas the inter-laboratory variation decreased. The participating laboratories were successful in finding a dose–response of oxidatively damaged DNA in coded samples, but there remains a need to standardize the protocols to enable direct comparisons between laboratories

The Role of Creativity in Entrepreneurship

This paper evaluates the contribution of creativity to entrepreneurship theory and practice in terms of building an holistic and transdisciplinary understanding of its impact. Acknowledgement is made of the subjectivist theory of entrepreneurship which embraces randomness, uncertainty and ambiguity but these factors should then be embedded in wider business and social contexts. The analysis is synthesised into a number of themes, from consideration of its definition, its link with personality and cognitive style, creativity as a process and the use of biography in uncovering data on creative entrepreneurial behaviour. Other relevant areas of discussion include creativity’s link with motivation, actualisation and innovation, as well as the interrogation of entrepreneurial artists as owner/managers. These factors are embedded in a critical evaluation of how creativity contributes to successful entrepreneurship practice. Modelling, measuring and testing entrepreneurial creativity are also considered and the paper includes detailed consideration of several models of creativity in entrepreneurship. Recommendations for future theory and practice are also made