CHOLINERGIC MODULATION OF THALAMIC INPUT INTO THE CINGULATE CORTEX

This research demonstrates cholinergic modulation of thalamic input into the limbic cortex. A projection from the mediodorsal thalamus (MD) to the anterior cingulate cortex was defined anatomically and physiologically. Injections of horse-radish peroxidase into the anterior cingulate cortex labels neurons in the lateral, parvocellular, region of MD. Electrical Stimulation of this area produces a complex field potential in the anterior cingulate cortex which was further characterized by current density analysis and single cell recordings.^ The monsynaptic component of the response was identified as a large negative field which is maximal in layer IV of the anterior cingulate cortex. This response shows remarkable tetanic potentiation of frequencies near 7 Hz. During a train of 50 or more stimuli, the response would grow quickly and remain at a fairly stable potentiated level throughout the train.^ Cholinergic modulation of this thalamic response was demonstrated by iontophoretic application of the cholinergic agonist carbachol decreased the effectiveness of the thalamic imput by rapidly attenuation the response during a train of stimuli. The effect was apparently mediated by muscarinic receptors since the effect of carbachol was blocked by atropine but not by hexamethonium.^ To determine the source of the cingulate cortex cholinergic innervation, lesions were made in the anterior and medial thalamus and in the nucleus of the diagonal band of Broca. The effects of these lesions on choline acetyltranferase activity in the cingulate cortex were determined by a micro-radio-enzymatical assay. Only the lesions of the nucleus of the diagonal band significantly decreased the choline acetyltransferase activity in the cingulate cortex regions. Therefore, the diagonal band appears to be a major source of sensory cholinergic innervation and may be involved in gating of sensory information from the thalamus into the limbic cortex. Attempts to modulate the cingulate response to MD stimulation with electrical stimulation of the diagonal band, however were not successful.^

Measuring Scale Efficiency from the Translog Multi-Input, Multi-Output Distance Function

Ray (1998) developed measures of input- and output-oriented scale efficiency that can be directly computed from an estimated Translog frontier production function. This note extends the earlier results from Ray (1998) to the multiple-output multiple input case.

Comparing Input- and Output-Oriented Measures of Technical Efficiency to Determine Local Returns to Scale in DEA Models

This paper shows how one can infer the nature of local returns to scale at the input- or output-oriented efficient projection of a technically inefficient input-output bundle, when the input- and output-oriented measures of efficiency differ.

Input Aggregation in Models of Data Envelopment Analysis: A Statistical Test with an Application to Indian Manufacturing

A problem frequently encountered in Data Envelopment Analysis (DEA) is that the total number of inputs and outputs included tend to be too many relative to the sample size. One way to counter this problem is to combine several inputs (or outputs) into (meaningful) aggregate variables reducing thereby the dimension of the input (or output) vector. A direct effect of input aggregation is to reduce the number of constraints. This, in its turn, alters the optimal value of the objective function. In this paper, we show how a statistical test proposed by Banker (1993) may be applied to test the validity of a specific way of aggregating several inputs. An empirical application using data from Indian manufacturing for the year 2002-03 is included as an example of the proposed test.

Optimal Test for Parameter Instability When the Unstable Process and the Error Distribution are Unknown

This paper proposes asymptotically optimal tests for unstable parameter process under the feasible circumstance that the researcher has little information about the unstable parameter process and the error distribution, and suggests conditions under which the knowledge of those processes does not provide asymptotic power gains. I first derive a test under known error distribution, which is asymptotically equivalent to LR tests for correctly identified unstable parameter processes under suitable conditions. The conditions are weak enough to cover a wide range of unstable processes such as various types of structural breaks and time varying parameter processes. The test is then extended to semiparametric models in which the underlying distribution in unknown but treated as unknown infinite dimensional nuisance parameter. The semiparametric test is adaptive in the sense that its asymptotic power function is equivalent to the power envelope under known error distribution.

Input Price Variation Across Locations and a Generalized Measure of Cost Efficiency

We propose a nonparametric model for global cost minimization as a framework for optimal allocation of a firm's output target across multiple locations, taking account of differences in input prices and technologies across locations. This should be useful for firms planning production sites within a country and for foreign direct investment decisions by multi-national firms. Two illustrative examples are included. The first example considers the production location decision of a manufacturing firm across a number of adjacent states of the US. In the other example, we consider the optimal allocation of US and Canadian automobile manufacturers across the two countries.

An unknown Khazar document

by S. Schechter

Theory of uncertainty in illness in patients with cancer of unknown primary origin: Structure providers as antecedents of uncertainty in a population of cancer patients with an unknown primary diagnosis

Uncertainty has been found to be a major component of the cancer experience and can dramatically affect psychosocial adaptation and outcomes of a patient's disease state (McCormick, 2002). Patients with a diagnosis of Carcinoma of Unknown Primary (CUP) may experience higher levels of uncertainty due to the unpredictability of current and future symptoms, limited treatment options and an undetermined life expectancy. To date, only one study has touched upon uncertainty and its' effects on those with CUP but no information exists concerning the effects of uncertainty regarding diagnosis and treatment on the distress level and psychosocial adjustment of this population (Parker & Lenzi, 2003). ^ Mishel's Uncertainty in Illness Theory (1984) proposes that uncertainty is preceded by three variables, one of which being Structure Providers. Structure Providers include credible authority, the degree of trust and confidence the patient has with their doctor, education and social support. It was the goal of this study to examine the relationship between uncertainty and Structure Providers to support the following hypotheses: (1) There will be a negative association between credible authority and uncertainty, (2) There will be a negative association between education level and uncertainty, and (3) There will be a negative association between social support and uncertainty. ^ This cross-sectional analysis utilized data from 219 patients following their initial consultation with their oncologist. Data included the Mishel Uncertainty in Illness Scale (MUIS) which was used to determine patients' uncertainty levels, the Medical Outcomes Study-Social Support Scale (MOSS-SSS) to assess patients, levels of social support, the Patient Satisfaction Questionnaire (PSQ-18) and the Cancer Diagnostic Interview Scale (CDIS) to measure credible authority and general demographic information to assess age, education, marital status and ethnicity. ^ In this study we found that uncertainty levels were generally higher in this sample as compared to other types of cancer populations. And while our results seemed to support most of our hypothesis, we were only able to show significant associations between two. The analyses indicated that credible authority measured by both the CDIS and the PSQ was a significant predictor of uncertainty as was social support measured by the MOSS-SS. Education has shown to have an inconsistent pattern of effect in relation to uncertainty and in the current study there was not enough data to significantly support our hypothesis. ^ The results of this study generally support Mishel's Theory of Uncertainty in Illness and highlight the importance of taking into consideration patients, psychosocial factors as well as employing proper communication practices between physicians and their patients.^

Dynamic population coding in primary visual cortex

More than a century ago Ramon y Cajal pioneered the description of neural circuits. Currently, new techniques are being developed to streamline the characterization of entire neural circuits. Even if this 'connectome' approach is successful, it will represent only a static description of neural circuits. Thus, a fundamental question in neuroscience is to understand how information is dynamically represented by neural populations. In this thesis, I studied two main aspects of dynamical population codes. ^ First, I studied how the exposure or adaptation, for a fraction of a second to oriented gratings dynamically changes the population response of primary visual cortex neurons. The effects of adaptation to oriented gratings have been extensively explored in psychophysical and electrophysiological experiments. However, whether rapid adaptation might induce a change in the primary visual cortex's functional connectivity to dynamically impact the population coding accuracy is currently unknown. To address this issue, we performed multi-electrode recordings in primary visual cortex, where adaptation has been previously shown to induce changes in the selectivity and response amplitude of individual neurons. We found that adaptation improves the population coding accuracy. The improvement was more prominent for iso- and orthogonal orientation adaptation, consistent with previously reported psychophysical experiments. We propose that selective decorrelation is a metabolically inexpensive mechanism that the visual system employs to dynamically adapt the neural responses to the statistics of the input stimuli to improve coding efficiency. ^ Second, I investigated how ongoing activity modulates orientation coding in single neurons, neural populations and behavior. Cortical networks are never silent even in the absence of external stimulation. The ongoing activity can account for up to 80% of the metabolic energy consumed by the brain. Thus, a fundamental question is to understand the functional role of ongoing activity and its impact on neural computations. I studied how the orientation coding by individual neurons and cell populations in primary visual cortex depend on the spontaneous activity before stimulus presentation. We hypothesized that since the ongoing activity of nearby neurons is strongly correlated, it would influence the ability of the entire population of orientation-selective cells to process orientation depending on the prestimulus spontaneous state. Our findings demonstrate that ongoing activity dynamically filters incoming stimuli to shape the accuracy of orientation coding by individual neurons and cell populations and this interaction affects behavioral performance. In summary, this thesis is a contribution to the study of how dynamic internal states such as rapid adaptation and ongoing activity modulate the population code accuracy. ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Calcium-dependent mechanisms and long-term potentiation: Role of NMDA receptors, voltage -dependent calcium channels and persistent protein kinase activity

Long-term potentiation (LTP) is a rapidly induced and long lasting increase in synaptic strength and is the leading cellular model for learning and memory in the mammalian brain. LTP was first identified in the hippocampus, a structure implicated in memory formation. LTP induction is dependent on postsynaptic Ca2+ increases mediated by N-methyl-D-aspartate (NMDA) receptors. Activation of other postsynaptic routes of Ca2+ entry, such as voltage-dependent Ca2+ channels (VDCCs) have subsequently been shown to induce a long-lasting increase in synaptic strength. However, it is unknown if VDCC-induced LTP utilized similar cellular mechanisms as the classical NMDA receptor-dependent LTP and if these two forms of LTP display similar properties. This dissertation determines the similarities and differences in VDCC and NMDA receptor-dependent LTP in area CA1 of hippocampal slices and demonstrates that VDCCs and NMDA receptors activate similar cellular mechanisms, such as protein kinases, to induce LTP. However, VDCC and NMDA receptor activated LTP induction mechanisms are compartmentalized in the postsynaptic neuron, such that they do not interact. Consistent with activation properties of NMDA receptors and VDCCs, NMDA receptor and VDCC-dependent LTP have different induction properties. In contrast to NMDA-dependent LTP, VDCC-induced potentiation does not require evoked presynaptic stimulation or display input specificity. These results indicate that there are two different routes of postsynaptic Ca2+ which can induce LTP and the compartmentation of VDCCs and NMDA receptors and/or their resulting Ca2+ increases may account for the distinction between these LTP induction mechanisms.^ One of the molecular targets for postsynaptic Ca2+ that is required for the induction of LTP is protein kinases. Evidence for the role of protein kinase activity in LTP expression is either correlational or controversial. We have utilized a broad range and potent inhibitors of protein kinases to systematically examine the temporal requirement for protein kinases in the induction and expression of LTP. Our results indicate that there is a critical period of persistent protein kinase activity required for LTP induction activated by tetanic stimulation and extending until 20 min after HFS. In addition, our results suggest that protein kinase activity during and immediately after HFS is not sufficient for LTP induction. These results provide evidence for persistent and/or Ca2+ independent protein kinase activity involvement in LTP induction. ^