Neurons, and realistic models of neurons, typically express several different types of voltage-gated conductances. These conductances are subject to continual regulation. Therefore it is essential to understand how changes in the conductances of a neuron affect its intrinsic properties, such as burst period or delay to firing after inhibition of a particular duration and magnitude. Even in model neurons, it can be difficult to visualize how the intrinsic properties vary as a function of their underlying maximal conductances. We used a technique, called clutter-based dimension reordering (CBDR), which enabled us to visualize intrinsic properties in high-dimensional conductance spaces. We applied CBDR to a family of models with eight different types of voltage- and calcium-dependent channels. CBDR yields images that reveal structure in the underlying conductance space. CBDR can also be used to visualize the results of other types of analysis. As examples, we use CBDR to visualize the results of a connected-components analysis, and to visually evaluate the results of a separating-hyperplane (i.e., linear classifier) analysis. We believe that CBDR will be a useful tool for visualizing the conductance spaces of neuronal models in many systems.

We start with a mathematical definition of a real interval as a closed, connected set of reals. Interval arithmetic operations (addition, subtraction, multiplication and division) are likewise defined mathematically and we provide algorithms for computing these operations assuming exact real arithmetic. Next, we define interval arithmetic operations on intervals with IEEE 754 floating point endpoints to be sound and optimal approximations of the real interval operations and we show that the IEEE standard's specification of operations involving the signed infinities, signed zeros, and the exact/inexact flag are such as to make a correct and optimal implementation more efficient. From the resulting theorems we derive data that are sufficiently detailed to convert directly to a program for efficiently implementing the interval operations. Finally we extend these results to the case of general intervals, which are defined as connected sets of reals that are not necessarily closed.

Determining volumes of convex n-dimensional polyhedra defined by a linear system of inequalities is useful in program analysis Two methods for computing these volumes are proposed (1) summing the volumes of stmphces which form the polyhedron, and (2) summing the volumes of (increasingly smaller) paralleleplpeds which can be fit into the polyhedron Assuming that roundoff errors are small, the first method is analytically exact whereas the second one converges to the exact solution at the expense of addmonal computer time Examples of polyhedra whose volumes were computed by programs representing the algorithms are also provided KEY WORDS AND PHRASES' comblnatorics, slmphces, Monte Carlo CR CATEGORIES 5 25, 5 30, 5 40

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Pulmonary vascular sequestration of leukocytes has been reported to increase in some models of lung injury, including that induced by gram-negative bacterial lipopolysaccharide (LPS). Neutrophils recruited to the lung likely participate in LPS-induced lung inflammation and associated injury, but the functional activities of these pulmonary vascular neutrophils have not been directly assessed. In the current study, cells were recovered by pulmonary vascular lavage (PVL) of isolated rat lungs, harvested 2 h after intravenous infusion of LPS (3 mg/kg) or saline in intact rats, at which time LPS-induced neutrophil recruitment to the lung could be appreciated histologically but not by airway lavage. Relative concentrations of leukocytes recovered from the pulmonary vasculature by PVL were compared with those present in circulating blood, normalizing for lavage dilution on the basis of erythrocyte counts. Excess neutrophils, lymphocytes, monocytes, and eosinophils were recovered from the pulmonary vasculature of controls, and LPS infusion increased recovery of neutrophils (most prominently), lymphocytes, and monocytes. Compared with cells recovered from controls, PVL neutrophils from LPS-infused animals were primed for increased zymosan-stimulated superoxide generation, determined by ferricytochrome C reduction, and were more adherent to nylon wool columns. Northern blots of extracted RNA demonstrated that LPS infusion also upregulated interleukin-1 beta (IL-1 beta) mRNA expression in PVL leukocyte samples, but not BAL or circulating blood samples. Ficoll-hypaque separation demonstrated that the LPS-induced IL-1 beta signal in PVL leukocytes was derived primarily from polymorphonuclear rather than mononuclear leukocytes. In conclusion, all circulating leukocyte populations are sequestered in rat lungs, and LPS increases pulmonary vascular sequestration of leukocytes, recruiting most prominently an activated pool of neutrophils that are more adherent, primed for increased oxygen radical production, and expressing increased IL-1 beta message. These findings suggest a more prominent role than previously appreciated for sequestered neutrophils in sepsis-induced lung inflammation.

The first part ofthe paper shows that previous theoretical work on the semantics ofprobabilistic programs (Kozen) and on the correctness of performance annotated programs (Ramshaw) can be used to automate the average-case analysis of simple programs containing assignments, conditionals, and loops. A performance compiler has been developed using this theoretical foundation. The compiler is described, and it is shown that special cases of symbolic simplifications of formulas play a major role in rendering the system usable. The performance compiler generates a system of recurrence equations derived from a given program whose efficiency one wishes to analyze. This generation is always possible, but the problem of solving the resulting equations may be complex. The second part of the paper presents an original method that generalizes the previous approach and is applicable to functional programs that make use of recursion and complex data structures. Several examples are presented, including an analysis of binary tree sort. A key feature of the analysis of such programs is that distributions on complex data structures are represented using attributed probabilistic grammars.

The human brain is excellent at integrating information from different sources across multiple sensory modalities. To examine one particularly important form of multisensory interaction, we manipulated the temporal correlation between visual and auditory stimuli in a first-person fisherman video game. Subjects saw rapidly swimming fish whose size oscillated, either at 6 or 8 Hz. Subjects categorized each fish according to its rate of size oscillation, while trying to ignore a concurrent broadband sound seemingly emitted by the fish. In three experiments, categorization was faster and more accurate when the rate at which a fish oscillated in size matched the rate at which the accompanying, task-irrelevant sound was amplitude modulated. Control conditions showed that the difference between responses to matched and mismatched audiovisual signals reflected a performance gain in the matched condition, rather than a cost from the mismatched condition. The performance advantage with matched audiovisual signals was remarkably robust over changes in task demands between experiments. Performance with matched or unmatched audiovisual signals improved over successive trials at about the same rate, emblematic of perceptual learning in which visual oscillation rate becomes more discriminable with experience. Finally, analysis at the level of individual subjects' performance pointed to differences in the rates at which subjects can extract information from audiovisual stimuli.

Objective: The mismatch negativity (MMN) is considered as a promising biomarker that can inform future therapeutic studies. However, there is a large variability among patients with first episode psychosis (FEP). Also, most studies report a single electrode site and on comparing case-control group differences. Few have taken advantage of the full wealth of multi-channel EEG signals to examine observable patterns. None, to our knowledge, have used machine learning (ML) approaches to investigate neurophysiological derived subgroups with distinct cognitive and functional outcome characteristics. In this study, we applied ML to empirically stratify individuals into homogeneous subgroups based on multi-channel MMN data. We then characterized the functional, cognitive, and clinical profiles of these neurobiologically derived subgroups. We also explored the underlying low frequency range responses (delta, theta, alpha) during MMN. Methods: Clinical, neurocognitive, functioning data of 33 healthy controls and 20 FEP patients were collected. 90% of the patients had 6-month follow-up data. Neurocognition, social cognition, and functioning measures were assessed using the NCCB Cognitive Battery, the Awareness of Social Inference Test, UCSD Performance-Based Skills Assessment, and Multnomah Community Ability Scale. Symptom severity was collected using the PANSS. MMN amplitude and single-trial derived low frequency activity across 24 frontocentral channels were used as main variables in the ML kmeans clustering analyses.