University at Buffalo

About: University at Buffalo is a education organization based out in Buffalo, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 33773 authors who have published 63840 publications receiving 2278954 citations. The organization is also known as: UB & State University of New York at Buffalo.

Chronic thyroiditis and autoimmunization.

Abstract: • An explanation of chronic thyroiditis in man was sought in observations made on rabbits, dogs, guinea pigs, and human subjects. The rabbits received injections of saline extracts of rabbit thyroid glands. The tests for circulating autoantibodies utilized the phenomena of precipitation, complement fixation, and tanned-cell hemagglutination. The third of these, based upon the principle of altering the surface of the red blood cells by dilute tannic acid so that they absorb proteins, was particularly sensitive. The three tests were applied to serums from 35 rabbits injected with rabbit thyroid extract, and thyroid autoantibodies were found in 32. Structural damage was found in the thyroid roughly in proportion to the autoantibody titer in the serum. Similar studies were carried out in dogs and guinea pigs with canine and guinea pig thyroid extracts respectively. The three tests were then applied to serums from patients with chronic thyroiditis. Twelve such patients were found whose serums contained circulating antibodies specifically directed against extracts of human thyroid glands. Three typical case histories are given, with histological findings on thyroid tissue removed during surgery. Six other patients with chronic thyroiditis, proved histologically, were not found to have autoantibodies in their serums at the time of study. These findings lead to the hypotheses that some types of chronic thyroiditis are related to an autoimmunization process within the patient against his own thyroid tissue, and that thyroid autoantibodies are at once indicators and links in the chain of pathological processes.

THE COMMUNITY INTEGRATION QUESTIONNAIRE: A Comparative Examination

Abstract: The community integration questionnaire (CIQ) was designed to assess home integration, social integration and productive activity in persons with acquired brain injury. The instrument consists of 15 items and can be completed by self report or with the assistance of a family member or caregiver familiar with the person's health status and social activities. Previous research has demonstrated adequate test-retest reliability and internal consistency. This study was designed to examine further, the psychometric characteristics of the CIQ and begin the process of establishing the instrument's validity. The CIQ was administered to 341 persons with traumatic brain injury (TBI) and 210 persons without TBI or any other apparent disabilities. The results indicate that the total scores are normally distributed for both persons with and without TBI. A statistically significant difference (P < 0.01) was found between subjects with TBI and those without TBI for all three subscales of the CIQ and for total scores. Data analysis also revealed that the total CIQ scores statistically differentiated among subjects with TBI living in three different settings: (1) living independently; (2) living in a supported community situation and (3) living in an institution. Intercorrelations among the three subscales (home, social and productivity) demonstrated that the CIQ provides unique information in the assessment of community integration for persons with TBI. Areas of future research necessary to expand the research and program evaluation usefulness of the CIQ are identified.

Constructing the Spatial Weights Matrix Using a Local Statistic

Abstract: Spatial weights matrices are necessary elements in most regression models where a representation of spatial structure is needed. We construct a spatial weights matrix, W, based on the principle that spatial structure should be considered in a two-part framework, those units that evoke a distance effect, and those that do not. Our two-variable local statistics model (LSM) is based on the G i * local statistic. The local statistic concept depends on the designation of a critical distance, d c , defined as the distance beyond which no discernible increase in clustering of high or low values exists. In a series of simulation experiments LSM is compared to well-known spatial weights matrix specifications – two different contiguity configurations, three different inverse distance formulations, and three semi-variance models. The simulation experiments are carried out on a random spatial pattern and two types of spatial clustering patterns. The LSM performed best according to the Akaike Information Criterion, a spatial autoregressive coefficient evaluation, and Moran’s I tests on residuals. The flexibility inherent in the LSM allows for its favorable performance when compared to the rigidity of the global models.

Computational and performance aspects of PCA-based face-recognition algorithms.

Abstract: Algorithms based on principal component analysis (PCA) form the basis of numerous studies in the psychological and algorithmic face-recognition literature. PCA is a statistical technique and its incorporation into a face-recognition algorithm requires numerous design decisions. We explicitly state the design decisions by introducing a generic modular PCA-algorithm. This allows us to investigate these decisions, including those not documented in the literature. We experimented with different implementations of each module, and evaluated the different implementations using the September 1996 FERET evaluation protocol (the de facto standard for evaluating face-recognition algorithms). We experimented with (i) changing the illumination normalization procedure; (ii) studying effects on algorithm performance of compressing images with JPEG and wavelet compression algorithms; (iii) varying the number of eigenvectors in the representation; and (iv) changing the similarity measure in the classification process. We performed two experiments. In the first experiment, we obtained performance results on the standard September 1996 FERET large-gallery image sets. In the second experiment, we examined the variability in algorithm performance on different sets of facial images. The study was performed on 100 randomly generated image sets (galleries) of the same size. Our two most significant results are (i) changing the similarity measure produced the greatest change in performance, and (ii) that difference in performance of +/- 10% is needed to distinguish between algorithms.