Eighteenth Annual Research Articles

The association between frailty and outcomes following ruptured abdominal aortic aneurysm repair: Presented at the Eighteenth Annual Academic Surgical Congress, Houston, Texas, February 7-9, 2023.

The association between frailty and outcomes following ruptured abdominal aortic aneurysm repair: Presented at the Eighteenth Annual Academic Surgical Congress, Houston, Texas, February 7-9, 2023.

Name of Congress: Eighth International Congress and Eighteenth Annual Congress of Clinical Oncology

Name of Congress: Eighth International Congress and Eighteenth Annual Congress of Clinical Oncology

Meeting abstracts, Eighteenth Annual Themed Issue on Methods in Safety Pharmacology

Meeting abstracts, Eighteenth Annual Themed Issue on Methods in Safety Pharmacology

Animal and cellular models of human disease

In this eighteenth (2016) Annual Review Issue of The Journal of Pathology, we present a collection of 19 invited review articles that cover different aspects of cellular and animal models of disease. These include genetically-engineered models, chemically-induced models, naturally-occurring models, and combinations thereof, with the focus on recent methodological and conceptual developments across a wide range of human diseases.

Semantic Relations vs. Abstract Syntactic Relations: Evidence from Halkomelem

Proceedings of the Eighteenth Annual Meeting of the Berkeley Linguistics Society: General Session and Parasession on The Place of Morphology in a Grammar (1992), pp. 76-87

Financial Reporting and Business Communication Eighteenth Annual Conference

"Financial Reporting and Business Communication Eighteenth Annual Conference." Accounting Forum, 38(1), p. 90

Econometric Modelling: The ‘Consumption Function’ In Retrospect

Scottish Journal of Political EconomyVolume 60, Issue 5 p. 495-522 Article Econometric Modelling: The ‘Consumption Function’ In Retrospect† David F. Hendry, David F. Hendry Nuffield College, OxfordSearch for more papers by this author David F. Hendry, David F. Hendry Nuffield College, OxfordSearch for more papers by this author First published: 02 October 2013 https://doi.org/10.1111/sjpe.12021 † Eighteenth Annual Lecture of the Scottish Economic Society delivered at the University of Stirling on 5 February 1983. The Society acknowledges with gratitude the financial support provided by Shell International for the Annual Lecture. ‡ This article is reprinted from Scottish Journal of Political Economy, 1983; 30: 193–220. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume60, Issue5Special Issue: Special Anniversary IssueNovember 2013Pages 495-522 RelatedInformation

The Personal Note: The Eighteenth Annual Trieste Joyce School, Trieste, Italy, 29 June–5 July 2014

The Personal Note: The Eighteenth Annual Trieste Joyce School, Trieste, Italy, 29 June–5 July 2014

IHEA (International Health Economics Association) Eighteenth Annual Kenneth J. Arrow Award in Health Economics

<i>i</i>HEA (International Health Economics Association) Eighteenth Annual Kenneth J. Arrow Award in Health Economics

Abstracts from the Eighteenth Annual Meeting of the International Genetic Epidemiology Society

Genetic EpidemiologyVolume 33, Issue 8 p. 752-835 Abstract Abstracts from the Eighteenth Annual Meeting of the International Genetic Epidemiology Society First published: 23 November 2009 https://doi.org/10.1002/gepi.20463Citations: 1AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume33, Issue8December 2009Pages 752-835 RelatedInformation

Moving beyond convergence in the pheromone system of the moth

Introduction Male moths can sense and locate conspecific females releasing small amounts of pheromone from more than a mile away [1]. Integral to this behavior is the extraordinary sensitivity of the male moths olfactory apparatus to pheromone molecules. This apparatus involves the transduction of molecular binding events in large populations of olfactory receptor neurons (ORNs) and their convergent projections onto relatively small numbers of neurons in the macro glomerular complex (MGC). Schneider calculated a behavioral threshold of the pheromone system of 200 molecular binding events at the antennae [1]. Recently Angioy et al. have reported that fluctuations of the male moths cardiac rhythm were elicited with a calculated 6 molecular events at the antennae [2]. While it has been demonstrated that ORN are capable of producing spikes after only a single molecular binding event the overall sensitivity of the systems is limited by noise in the from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009

Behavioral inhibition during reversal learning in the limbic system: a computational model

A biological model of the sub-cortical nuclei of the limbic system capable of performing reversal learning in a foodseeking task is presented. Contrary to other learning models, the reversal is modeled not by eliminating (unlearning) learned behaviors, but rather by the inhibiting the previously learned behavior. This allows for the re-instatement of behavior to be quickly re-established in such a way that animal behavior is simulated. In this model, the role of dopamine is different to standard models. The activity of dopaminergic (DA) neurons required for successful conditioning has commonly been identified as an error signal whereby an increased DA activity codes a positive error i.e. long term potentiation (LTP) and a decrease in dopamine concentration codes a negative error. In this model learning is achieved by implementing a form of differential Hebbian learning known as Isotropic Sequence Order learning and a third factor (ISO3). This third factor enables learning to be triggered at relevant moments. It is modeled by dopaminergic neurons which can be activated via a direct excitatory glutamatergic pathway, and an indirect disinhibitory GABAergic pathway. While the former generates phasic DA release which during acquisition enables long term potentiation (LTP) to occur, the latter produces in an increased population of tonically active DA neurons which generates long term depression (LTD) when an adjustment in learned behavior is required. The nucleus accumbens is divided into two shell and core subunits and has been modeled to function in distinct but complementary manners. Here, the core uses conditioned reinforcers to invigorate instrumental responding. The core has been modeled to learn to enable behavioral responding to reward predicting stimuli and as such undergoes minimal LTD so as not to eliminate these learned processes. On the other hand, LTD occurs in the shell that, through a shell-ventral pallido-medio dorsal pathway, influences the core and enables behavioral flexibility. A simple reversal-learning task will be used to demonstrate how this biophysically realistic pathway can be used to learn and reverse a simple food-seeking task. from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009

A kinetic theory network model to capture first and second order statistics of population activity in large-scale neuronal networks

We present a kinetic theory neuronal network model that captures how first and second order statistics of population activity depend on certain statistical patterns of connectivity. To capture pairwise correlations among neurons in each population, we model the activity of each population by a joint population density ρ(x1, x2, t), where (x1, x2) represents the state of any pair of neurons in the population. We derive the evolution equations of ρ(x1, x2, t) using the Chapman-Kolmolgorov formalism based on the assumption that the input to any pair of neurons is a multivariate Poisson point process. We then derive the network equations by coupling population equations. The coupling scheme is based on (1) the first and second order statistics of connectivity, (2) the approximation of delayed correlation as instantaneous correlation in each population activity, and (3) the inference of higher order statistics of population activity from lower order statistics via a conditional independence method. We test the validity of our network equations by comparing the numerical solutions to the Monte Carlo simulation under various network configurations. Finally, we discuss the success and failure of the implementation of our kinetic theory network model. from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009

Simulation of cytoskeleton influence on spatial Ca2+ dynamics in neuroendocrine cells

2 pages.-- Poster presentation from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009

Real-time activity-dependent drug microinjection

From Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009

GABAA receptor plasticity provides homeostasis of neuronal activity in a neocortical microcircuit model

During the eye-opening development phase in mice, in primary visual cortex, changes in GABAA postsynaptic receptor function appear to occur in a highly regulated manner. In particular, significant correlation [1,2] exists between the developmental reduction in the duration of inhibitory postsynaptic currents (IPSCs) and the concomitant developmental changes in total inhibitory synaptic weight (Figure 1). Using a computational model [3], we characterize the extent to which GABAA receptor plasticity and changes in synaptic weight affect the input-output transfer of a neocortical microcircuit consisting of pyramidal cells with reciprocal synaptic connections to perisomatic innervating interneurons. We found that when developmental GABAA receptor plasticity is matched by a gradual shift in overall inhibitory synaptic weight, inputoutput constancy is created with respect to both firing frequency (Figure 2) and spike train patterning. We propose that GABAA receptor plasticity matches the concomitant shift in synaptic weight per neuron in order to guarantee homeostasis of microcircuit function. We discuss the putative relevance of such a regulatory mechanism in terms of neocortical development. In addition to demonstrating homeostasis of neuronal activity during maturation of the GABAergic synaptic network, we show that IPSC decay affect burst firing, that accelerating the IPSC decay appears to reduce the extent of flicker fusion, and that changing the IPSC decay provides the microcircuit with a synaptic mechanism for gain modulation. from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009

Multi-objective evolutionary algorithms for model neuron parameter value selection matching biological behavior under different simulation scenarios

In our previous work, we systematically explored a 12dimensional parameter space of a 2-compartment model of the AB (anterior burster) neuron, which is one of the two cells that form the pacemaker kernel in the pyloric network in the lobster stomatogastric ganglion (STG). Our computational exploration started with a hand-tuned AB model [1] and systematically varied maximal conductances of membrane currents, one at a time with all the other conductances kept constant at their hand-tuned values, to determine ranges and variation steps that produced physiologically realistic behavior. Every parameter set representing an individual model neuron in this gridbased parameter space was simulated and classified as functional if it produced biologically realistic bursting activity. Specifically, we looked at the period, burst duration, spike and slow wave amplitude, number of spikes per burst, spike frequency, and after-hyperpolarization potential, which all had to be within limits determined in physiological experiments. Furthermore, in order to be classified as good, the models had to exhibit proper responses to STG deafferentation (i.e., neuromodulator deprivation) as well as current injections both in the presence and in the absence of neuromodulation. After applying all the criteria, not only have we determined that many different parameter sets performed successfully under all tested conditions, but we have also found several very interesting relationships between parameters, which may indicate the existence of co-regulations in the investigated system [2]. However, the question has always remained: are the ranges determined by varying just one maximum conductance at a time suitable and representative of the solution space of the real AB neuron? Perhaps, if two or more conductances were varied at the same time, the ranges would have been different. Unfortunately, the answer to this question cannot be found by simply extending the ranges of parameter values to some arbitrary limits and simulating all the resulting combinations; this would be unrealistic computationally. Therefore, we utilize an approach alternative to the strict grid determination of the parameter search space, based on an application of Multi-Objective Evolutionary Algorithms (MOEA) to generate a population of neuron models meeting the above selection criteria. In MOEA, the algorithm searches for pseudo-optimal solutions by trying to optimize several, sometimes conflicting, objectives at the same time. In this case, all the selection criteria were implemented as separate objectives in the end-VEGA (elitist non-dominated Vector-Evaluated Genetic Algorithm), previously proposed by the authors [3]. The ranges of variation from the hand-tuned values were set between -100% and +400%. This allowed for an extensive and ample exploration of the parameter space. The results from the simulation of the spontaneous activity alone [4], as well as all the remaining scenarios, on the one hand confirmed our observations about the possible existence of coregulations between some currents, but on the other hand, some of the relationships seen before seem to have disappeared over the extended ranges of values. This may indicate that from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009

Influence of external input on waxing and waning of neuronal network oscillations

Introduction Experimental observations have reported modulation of cortical oscillations as phases of high synchronization (waxing) followed by periods of reduced synchronization (waning) [1-3]. Although the phenomenon is present in almost all frequency bands, it is still not understood how this is driven. Here we study whether this phenomenon can occur in a network of inhibitory (I) and excitatory (E) cells and what effect external inputs have. Methods Using NEURON, we model a network of Ne excitatory and Ni inhibitory cells such that Ne/Ni = 4. The cells have a single compartment, and include passive channels and voltage dependent Na+, K+ channels. Synaptic connections are random, projecting GABA synapses from I to I and I to E cells and AMPA synapses from E to E and E to I cells. To stimulate the network, each cell receives a baseline of current and a stream of spikes delivered at random intervals across the simulated period. from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009

From quantification of 3D morphology of coincidence detector neurons in the MSO to a multi-compartmental model

In the medial superior olive (MSO), neurons compute the azimuthal location of low frequency sound sources by a temporally precise mechanism of coincidence detection. This type of neuronal processing has been subject to various model predictions based on in vivo and in vitro data obtained in mammalian MSO and an avian analog brain structure. It is assumed that dendritic morphology of MSO neurons affects this computational process. However, few quantitative data about the morphology of these neuronal coincidence detectors are available from mammals, limiting theoretical approaches. We used single cell electroporation, microscopic reconstruction and compartmentalization to extract anatomical parameters of MSO neurons and quantitatively describe their morphology and their development between postnatal day 9 and 36. The dendritic arborization of higher order dendrites decreases between postnatal day 9 and 21, whereas the dendritic radius increased until P27. The developmental profile of the morphology of MSO neurons shows that maturation is reached two weeks after hearing onset. In general, we find that at postnatal days older than P27 the neurons of the gerbil MSO are bipolar, morphologically compact, cylinder-like cells with axons originating from the soma. The obtained geometry of these cells was then used to establish multi-compartmental models to quantify the influence of morphological and developmental changes on the integration of synaptic currents. To increase computational and programming efficiency of the simulations, we developed a Python-based object-oriented library. from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009

RTBiomanager: a software platform to expand the applications of real-time technology in neuroscience

From Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009.