Synthesis and structural characterization of (pyrazolyl)alkenyl Fischer carbene complexes of chromium and tungsten

Michael addition of substituted pyrazoles 2 to 1-alkynyl Fischer carbene complexes (CO)(5)M=C(OEt)(CdropCPh) (1) (a, M = Cr and b M = W) afforded (pyrazolyl)alkenyl Fischer carbene complexes (CO)(5)M=C(OEt)(CH=C(R(1)R(2)R(3)pz)Ph) (R(1)R(2)R(3)pz = pyrazolyl) 3 (M = Cr) and 4 (M = W), respectively, with an exclusive (E)-configuration in mild to excellent yields. The reaction of la and 3,5-dimethylpyrazole (2b) was monitored to demonstrate the formation and decomposition of complex 3b by H-1 NMR measurements in CDCl3 at 23degreesC. Complexes 3 and 4 were characterized with H-1, C-13{H-1} NMR, IR spectroscopies and elemental analysis. When the substituted pyrazoles were 3-methylpyrazole (2a) and 3,5-di-tert-butylpyrazole (2d), molecular structures of the corresponding (pyrazolyl)alkenyl Fischer carbene complexes 3a and 4d were characterized by X-ray crystallographic study. (C) 2004 Elsevier Ltd. All rights reserved.

Models of Backwardness versus Transformation in Eastern Europe. Review Article

This paper is critical analysis of book by Anna Sosnowska, "Zrozumieć zacofanie. Spory historyków o Europę Wschodnią (1947-1994)" [To Understand Backwardness: Historians' Deabates about Eastern Europe (1947-1994)]. Warszawa 2004.

Structural Analysis of Network Traffic Flows

Network traffic arises from the superposition of Origin-Destination (OD) flows. Hence, a thorough understanding of OD flows is essential for modeling network traffic, and for addressing a wide variety of problems including traffic engineering, traffic matrix estimation, capacity planning, forecasting and anomaly detection. However, to date, OD flows have not been closely studied, and there is very little known about their properties. We present the first analysis of complete sets of OD flow timeseries, taken from two different backbone networks (Abilene and Sprint-Europe). Using Principal Component Analysis (PCA), we find that the set of OD flows has small intrinsic dimension. In fact, even in a network with over a hundred OD flows, these flows can be accurately modeled in time using a small number (10 or less) of independent components or dimensions. We also show how to use PCA to systematically decompose the structure of OD flow timeseries into three main constituents: common periodic trends, short-lived bursts, and noise. We provide insight into how the various constituents contribute to the overall structure of OD flows and explore the extent to which this decomposition varies over time.

Adaptive Neural Models of Queuing and Timing in Fluent Action

Temporal structure in skilled, fluent action exists at several nested levels. At the largest scale considered here, short sequences of actions that are planned collectively in prefrontal cortex appear to be queued for performance by a cyclic competitive process that operates in concert with a parallel analog representation that implicitly specifies the relative priority of elements of the sequence. At an intermediate scale, single acts, like reaching to grasp, depend on coordinated scaling of the rates at which many muscles shorten or lengthen in parallel. To ensure success of acts such as catching an approaching ball, such parallel rate scaling, which appears to be one function of the basal ganglia, must be coupled to perceptual variables, such as time-to-contact. At a fine scale, within each act, desired rate scaling can be realized only if precisely timed muscle activations first accelerate and then decelerate the limbs, to ensure that muscle length changes do not under- or over-shoot the amounts needed for the precise acts. Each context of action may require a much different timed muscle activation pattern than similar contexts. Because context differences that require different treatment cannot be known in advance, a formidable adaptive engine-the cerebellum-is needed to amplify differences within, and continuosly search, a vast parallel signal flow, in order to discover contextual "leading indicators" of when to generate distinctive parallel patterns of analog signals. From some parts of the cerebellum, such signals controls muscles. But a recent model shows how the lateral cerebellum, such signals control muscles. But a recent model shows how the lateral cerebellum may serve the competitive queuing system (in frontal cortex) as a repository of quickly accessed long-term sequence memories. Thus different parts of the cerebellum may use the same adaptive engine system design to serve the lowest and the highest of the three levels of temporal structure treated. If so, no one-to-one mapping exists between levels of temporal structure and major parts of the brain. Finally, recent data cast doubt on network-delay models of cerebellar adaptive timing.

Neural Models of Motion Integration, Segmentation, and Probablistic Decision-Making

When brain mechanism carry out motion integration and segmentation processes that compute unambiguous global motion percepts from ambiguous local motion signals? Consider, for example, a deer running at variable speeds behind forest cover. The forest cover is an occluder that creates apertures through which fragments of the deer's motion signals are intermittently experienced. The brain coherently groups these fragments into a trackable percept of the deer in its trajectory. Form and motion processes are needed to accomplish this using feedforward and feedback interactions both within and across cortical processing streams. All the cortical areas V1, V2, MT, and MST are involved in these interactions. Figure-ground processes in the form stream through V2, such as the seperation of occluding boundaries of the forest cover from the boundaries of the deer, select the motion signals which determine global object motion percepts in the motion stream through MT. Sparse, but unambiguous, feauture tracking signals are amplified before they propogate across position and are intergrated with far more numerous ambiguous motion signals. Figure-ground and integration processes together determine the global percept. A neural model predicts the processing stages that embody these form and motion interactions. Model concepts and data are summarized about motion grouping across apertures in response to a wide variety of displays, and probabilistic decision making in parietal cortex in response to random dot displays.

Neural Models of Temporally Organized Behaviors: Handwriting Production and Working Memory

Advanced Research Projects Agency (ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309)

Differential and numerical models of hysteretic systems with stochastic and deterministic inputs

Many deterministic models with hysteresis have been developed in the areas of economics, finance, terrestrial hydrology and biology. These models lack any stochastic element which can often have a strong effect in these areas. In this work stochastically driven closed loop systems with hysteresis type memory are studied. This type of system is presented as a possible stochastic counterpart to deterministic models in the areas of economics, finance, terrestrial hydrology and biology. Some price dynamics models are presented as a motivation for the development of this type of model. Numerical schemes for solving this class of stochastic differential equation are developed in order to examine the prototype models presented. As a means of further testing the developed numerical schemes, numerical examination is made of the behaviour near equilibrium of coupled ordinary differential equations where the time derivative of the Preisach operator is included in one of the equations. A model of two phenotype bacteria is also presented. This model is examined to explore memory effects and related hysteresis effects in the area of biology. The memory effects found in this model are similar to that found in the non-ideal relay. This non-ideal relay type behaviour is used to model a colony of bacteria with multiple switching thresholds. This model contains a Preisach type memory with a variable Preisach weight function. Shown numerically for this multi-threshold model is a pattern formation for the distribution of the phenotypes among the available thresholds.

Expression and function of the neurotrophic factors GDF5 and GDNF in the nigrostriatal system during development and in rat models of Parkinson's disease

Growth/differentiation factor 5 (GDF5) and glial cell line-derived neurotrophic factor (GDNF) are neurotrophic factors that promote the survival of midbrain dopaminergic neurons in vitro and in vivo. Both factors have potent neurotrophic and neuroprotective effects in rat models of Parkinson's disease (PD), and may represent promising new therapies for PD. The aim of the present study was to investigate the endogenous expression and function of GDF5 and GDNF in the nigrostriatal dopaminergic system during development and in rat models of PD. Examination of the temporal expression patterns of endogenous GDF5, GDNF, and their respective receptors, in the developing and adult nigrostriatal dopaminergic system suggest that these factors play important roles in promoting the survival and maturation of midbrain dopaminergic neurons during the period of postnatal programmed cell death. The relative levels of GDF5 and GDNF mRNAs in the midbrain and striatum, and their individual temporal expression patterns during development, suggest that their modes of actions are quite distinct in vivo. Furthermore, the sustained expression of GDF5, GDNF, and their receptors into adulthood suggest roles for these factors in the continued support and maintenance of mature nigrostriatal dopaminergic neurons. The present study found that endogenous GDF5, GDNF, and their receptors are differentially expressed in two 6-hydroxydopamine-induced lesion adult rat models of PD. In both terminal and axonal lesion models of PD, GDF5 mRNA levels in the striatum increased at 10 days post-lesion, while GDNF mRNA levels in the nigrostriatal system decreased at 10 and 28 days post-lesion. Thus, despite the fact that exogenous GDF5 and GDNF have similar effects on midbrain dopaminergic neurons in vitro and in vivo, their endogenous responses to a neurotoxic injury are quite distinct. These results highlight the importance of studying the temporal dynamic changes in neurotrophic factor expression during development and in animal models of PD.

First principles simulation of amorphous silicon bulk, interfaces, and nanowires for photovoltaics

Amorphous silicon has become the material of choice for many technologies, with major applications in large area electronics: displays, image sensing and thin film photovoltaic cells. This technology development has occurred because amorphous silicon is a thin film semiconductor that can be deposited on large, low cost substrates using low temperature. In this thesis, classical molecular dynamics and first principles DFT calculations have been performed to generate structural models of amorphous and hydrogenated amorphous silicon and interfaces of amorphous and crystalline silicon, with the ultimate aim of understanding the photovoltaic properties of core-shell crystalline amorphous Si nanowire structures. We have shown, unexpectedly, from the simulations, that our understanding of hydrogenated bulk a-Si needs to be revisited, with our robust finding that when fully saturated with hydrogen, bulk a-Si exhibits a constant optical energy gap, irrespective of the hydrogen concentration in the sample. Unsaturated a-Si:H, with a lower than optimum hydrogen content, shows a smaller optical gap, that increases with hydrogen content until saturation is reached. The mobility gaps obtained from an analysis of the electronic states show similar behavior. We also obtained that the optical and mobility gaps show a volcano curve as the H content is varied from 7% (undersaturation) to 18% (mild oversaturation). In the case of mild over saturation, the mid-gap states arise exclusively from an increase in the density of strained Si-Si bonds. Analysis of our structures shows the extra H atoms in this case form a bridge between neighboring silicon atoms which increases the corresponding Si-Si distance and promotes bond length disorder in the sample. That has the potential to enhance the Staebler-Wronski effect. Planar interface models of amorphous-crystalline silicon have been generated in Si (100), (110) and (111) surfaces. The interface models are characterized by structure, RDF, electronic density of states and optical absorption spectrum. We find that the least stable (100) surface will result in the formation of the thickest amorphous silicon layer, while the most stable (110) surface forms the smallest amorphous region. We calculated for the first time band offsets of a-Si:H/c-Si heterojunctions from first principles and examined the influence of different surface orientations and amorphous layer thickness on the offsets and implications for device performance. The band offsets depend on the amorphous layer thickness and increase with thickness. By controlling the amorphous layer thickness we can potentially optimise the solar cell parameters. Finally, we have successfully generated different amorphous layer thickness of the a-Si/c-Si and a-Si:H/c-Si 5 nm nanowires from heat and quench. We perform structural analysis of the a-Si-/c-Si nanowires. The RDF, Si-Si bond length distributions, and the coordination number distributions of amorphous regions of the nanowires reproduce similar behaviour compared to bulk amorphous silicon. In the final part of this thesis we examine different surface terminating chemical groups, -H, - OH and –NH2 in (001) GeNW. Our work shows that the diameter of Ge nanowires and the nature of surface terminating groups both play a significant role in both the magnitude and the nature of the nanowire band gaps, allowing tuning of the band gap by up to 1.1 eV. We also show for the first time how the nanowire diameter and surface termination shifts the absorption edge in the Ge nanowires to longer wavelengths. Thus, the combination of nanowire diameter and surface chemistry can be effectively utilised to tune the band gaps and thus light absorption properties of small diameter Ge nanowires.

Estimating equilibrium models of sorting across locations

While there is growing interest in measuring the size and scope of local spillovers, it is well understood that such spillovers cannot be distinguished from unobservable local attributes using solely the observed location decisions of individuals or firms. We propose an empirical strategy for recovering estimates of spillovers in the presence of unobserved local attributes for a broadly applicable class of equilibrium sorting models. Our approach relies on an IV strategy derived from the internal logic of the sorting model itself. We show practically how the strategy is implemented, provide intuition for our instruments, discuss the role of effective choice-set variation in identifying the model, and carry-out a series of Monte Carlo simulations to demonstrate performance in small samples. © 2007 The Author(s). Journal compilation Royal Economic Society 2007.

Structural manifestation of the delocalization error of density functional approximations: C(4N+2) rings and C(20) bowl, cage, and ring isomers.

The ground state structure of C(4N+2) rings is believed to exhibit a geometric transition from angle alternation (N < or = 2) to bond alternation (N > 2). All previous density functional theory (DFT) studies on these molecules have failed to reproduce this behavior by predicting either that the transition occurs at too large a ring size, or that the transition leads to a higher symmetry cumulene. Employing the recently proposed perspective of delocalization error within DFT we rationalize this failure of common density functional approximations (DFAs) and present calculations with the rCAM-B3LYP exchange-correlation functional that show an angle-to-bond-alternation transition between C(10) and C(14). The behavior exemplified here manifests itself more generally as the well known tendency of DFAs to bias toward delocalized electron distributions as favored by Huckel aromaticity, of which the C(4N+2) rings provide a quintessential example. Additional examples are the relative energies of the C(20) bowl, cage, and ring isomers; we show that the results from functionals with minimal delocalization error are in good agreement with CCSD(T) results, in contrast to other commonly used DFAs. An unbiased DFT treatment of electron delocalization is a key for reliable prediction of relative stability and hence the structures of complex molecules where many structure stabilization mechanisms exist.

Animal models of soft-tissue sarcoma.

Soft-tissue sarcomas (STSs) are rare mesenchymal tumors that arise from muscle, fat and connective tissue. Currently, over 75 subtypes of STS are recognized. The rarity and heterogeneity of patient samples complicate clinical investigations into sarcoma biology. Model organisms might provide traction to our understanding and treatment of the disease. Over the past 10 years, many successful animal models of STS have been developed, primarily genetically engineered mice and zebrafish. These models are useful for studying the relevant oncogenes, signaling pathways and other cell changes involved in generating STSs. Recently, these model systems have become preclinical platforms in which to evaluate new drugs and treatment regimens. Thus, animal models are useful surrogates for understanding STS disease susceptibility and pathogenesis as well as for testing potential therapeutic strategies.

A comparison of dimensional models of emotion: evidence from emotions, prototypical events, autobiographical memories, and words.

The intensity and valence of 30 emotion terms, 30 events typical of those emotions, and 30 autobiographical memories cued by those emotions were each rated by different groups of 40 undergraduates. A vector model gave a consistently better account of the data than a circumplex model, both overall and in the absence of high-intensity, neutral valence stimuli. The Positive Activation - Negative Activation (PANA) model could be tested at high levels of activation, where it is identical to the vector model. The results replicated when ratings of arousal were used instead of ratings of intensity for the events and autobiographical memories. A reanalysis of word norms gave further support for the vector and PANA models by demonstrating that neutral valence, high-arousal ratings resulted from the averaging of individual positive and negative valence ratings. Thus, compared to a circumplex model, vector and PANA models provided overall better fits.

Contrasting Models of Posttraumatic Stress Disorder: Reply to Monroe and Mineka (2008)

The authors address the 4 main points in S. M. Monroe and S. Mineka's (2008) comment. First, the authors show that the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; American Psychiatric Association, 2000) posttraumatic stress disorder (PTSD) diagnosis includes an etiology and that it is based on a theoretical model with a distinguished history in psychology and psychiatry. Two tenets of this theoretical model are that voluntary (strategic) recollections of the trauma are fragmented and incomplete while involuntary (spontaneous) recollections are vivid and persistent and yield privileged access to traumatic material. Second, the authors describe differences between their model and other cognitive models of PTSD. They argue that these other models share the same 2 tenets as the diagnosis and show that these 2 tenets are largely unsupported by empirical evidence. Third, the authors counter arguments about the strength of the evidence favoring the mnemonic model. Fourth, they show that concerns about the causal role of memory in PTSD are based on views of causality that are generally inappropriate for the explanation of PTSD in the social and biological sciences. © 2008 American Psychological Association.

Mainland size variation informs predictive models of exceptional insular body size change in rodents.

The tendency for island populations of mammalian taxa to diverge in body size from their mainland counterparts consistently in particular directions is both impressive for its regularity and, especially among rodents, troublesome for its exceptions. However, previous studies have largely ignored mainland body size variation, treating size differences of any magnitude as equally noteworthy. Here, we use distributions of mainland population body sizes to identify island populations as 'extremely' big or small, and we compare traits of extreme populations and their islands with those of island populations more typical in body size. We find that although insular rodents vary in the directions of body size change, 'extreme' populations tend towards gigantism. With classification tree methods, we develop a predictive model, which points to resource limitations as major drivers in the few cases of insular dwarfism. Highly successful in classifying our dataset, our model also successfully predicts change in untested cases.