Age-Related Neural Dedifferentiation and Cognition

This review focuses on possible contributions of neural dedifferentiation to age-related cognitive decline. Neural dedifferentiation is held to reflect a breakdown in the functional specificity of brain regions and networks that compromises the fidelity of neural representations supporting episodic memory and related cognitive functions. The evidence for age-related dedifferentiation is robust when it is operationalized as neural selectivity for different categories of perceptual stimuli or as decreased segregation or modularity of resting-state functional brain networks. Neural dedifferentiation for perceptual categories appears to demonstrate a negative, age-invariant relationship with performance on tests of memory and fluid processing. Whether this pattern extends to network-level measures of dedifferentiation cannot currently be determined due to insufficient evidence. The existing data highlight the importance of further examination of neural dedifferentiation as a factor contributing to episodic memory and to cognitive performance more generally.

Spectrally-invariant behavior of zenith radiance around cloud edges simulated by radiative transfer

In a previous paper, we discovered a surprising spectrally-invariant relationship in shortwave spectrometer observations taken by the Atmospheric Radiation Measurement (ARM) program. The relationship suggests that the shortwave spectrum near cloud edges can be determined by a linear combination of zenith radiance spectra of the cloudy and clear regions. Here, using radiative transfer simulations, we study the sensitivity of this relationship to the properties of aerosols and clouds, to the underlying surface type, and to the finite field-of-view (FOV) of the spectrometer. Overall, the relationship is mostly sensitive to cloud properties and has little sensitivity to other factors. At visible wavelengths, the relationship primarily depends on cloud optical depth regardless of cloud phase function, thermodynamic phase and drop size. At water-absorbing wavelengths, the slope of the relationship depends primarily on cloud optical depth; the intercept, by contrast, depends primarily on cloud absorbing and scattering properties, suggesting a new retrieval method for cloud drop effective radius. These results suggest that the spectrally-invariant relationship can be used to infer cloud properties near cloud edges even with insufficient or no knowledge about spectral surface albedo and aerosol properties.

Spectral invariant behavior of zenith radiance around cloud edges simulated by radiative transfer

A previous paper discovered a surprising spectral-invariant relationship in shortwave spectrometer observations taken by the Atmospheric Radiation Measurement (ARM) program. Here, using radiative transfer simulations, we study the sensitivity of this relationship to the properties of aerosols and clouds, to the underlying surface type, and to the finite field-of-view (FOV) of the spectrometer. Overall, the relationship is mostly sensitive to cloud properties and has little sensitivity to the other factors. At visible wavelengths, the relationship primarily depends on cloud optical depth regardless of cloud thermodynamic phase and drop size. At water-absorbing wavelengths, the slope of the spectral-invariant relationship depends primarily on cloud optical depth; the intercept, by contrast, depends primarily on cloud absorption properties, suggesting a new retrieval method for cloud drop effective radius. These results suggest that the spectral-invariant relationship can be used to infer cloud properties even with insufficient or no knowledge about spectral surface albedo and aerosol properties.

FEATURES OF DERIVATIVE CONTINUITY IN SHAPE

Derivative continuity is a distributed invariant relationship between parts of flowing shapes. The original techniques presented here were developed for making the behavioral dynamics of complex processes more recognizable, but are equally applicable to assisting in the recognition of shapes in images. Regularizing a sequence using a constraint of derivative continuity is equivalent to using a bimodal smoothing kernel, producing a distinct bias for reducing variation on higher derivative levels, sharply defining shape with minimal suppression of shape. To help determine where reconstructing shapes in this way is valid, a test was developed to help distinguish combinations of noise and smooth flows from random walks. This helps distinguish between illusory and genuine, data shapes but also exposes a flair in using this and other measures of scaling behavior for diagnostic purposes. Gaussian scale space techniques in use for some time in image recognition, for identifying reliable landmarks in the shapes of outlines, are demonstrated for use in identifying key features of shape in time series.

Sengupta's invariant relationship and its application to waiting time inference

This paper presents a new proof of Sengupta's invariant relationship between virtual waiting time and attained sojourn time and its application to estimating the virtual waiting time distribution by counting the number of arrivals and departures of a G/G/1 FIFO queue. Since this relationship does not require any parametric assumptions, our method is non-parametric. This method is expected to have applications, such as call processing in communication switching systems, particularly when the arrival or service process is unknown.

Sengupta's invariant relationship and its application to waiting time inference

This paper presents a new proof of Sengupta's invariant relationship between virtual waiting time and attained sojourn time and its application to estimating the virtual waiting time distribution by counting the number of arrivals and departures of a G/G/1 FIFO queue. Since this relationship does not require any parametric assumptions, our method is non-parametric. This method is expected to have applications, such as call processing in communication switching systems, particularly when the arrival or service process is unknown.

A predictable sequential determinant spreading cascade invariably accompanies progression of experimental autoimmune encephalomyelitis: a basis for peptide-specific therapy after onset of clinical disease.

The development of autoimmune disease is accompanied by the acquired recognition of new self-determinants, a process commonly referred to as determinant spreading. In this study, we addressed the question of whether determinant spreading is pathogenic for progression of chronic-relapsing experimental autoimmune encephalomyelitis (EAE), a disease with many similarities to multiple sclerosis (MS). Our approach involved a systematic epitope mapping of responses to myelin proteolipid protein (PLP) as well as assaying responses to known encephalitogenic determinants of myelin basic protein (MBP 87-89) and myelin oligodendrocyte glycoprotein (MOG 92-106) at various times after induction of EAE in (SWR X SJL)F1 mice immunized with PLP 139-151. We found that the order in which new determinants are recognized during the course of disease follows a predictable sequential pattern. At monthly intervals after immunization with p139-151, responses to PLP 249-273, MBP 87-99, and PLP 137-198 were sequentially accumulated in al mice examined. Three lines of evidence showed that determinant spreading is pathogenic for disease progression: (a) spreading determinants mediate passive transfer of acute EAE in naive (SWR X SJL)F1 recipients; (b) an invariant relationship exists between the development of relapse/progression and the spreading of recognition to new immunodominant encephalitogenic determinants; and (c) after EAE onset, the induction of peptide-specific tolerance to spreading but not to nonspreading encephalitogenic determinants prevents subsequent progression of EAE. Thus, the predictability of acquired self-determinant recognition provides a basis for sequential determinant-specific therapeutic intervention after onset of the autoimmune disease process.