Temporal lobe epilepsy and social behavior: An animal model

Social behavior depends on the integrity of social brain circuitry. The temporal lobe is an important part of the social brain, and manifests morphological and functional alterations in autism spectrum disorders (ASD). Rats with temporal lobe epilepsy (TLE), induced with pilocarpine, were subjected to a social discrimination test that has been used to investigate potential animal models of ASD, and the results were compared with those for the control group. Rats with TLE exhibited fewer social behaviors than controls. No differences were observed in nonsocial behavior between groups. The results suggest an important role for the temporal lobe in regulating social behaviors. This animal model might be used to explore some questions about ASD pathophysiology. (c) 2008 Elsevier Inc. All rights reserved.

CD147 overexpression allows an accurate discrimination of bladder cancer patients' prognosis

Background: Urothelial bladder carcinoma (UBC) is a chemo-sensitive tumour, but the response to treatment is heterogeneous. CD 147 has been associated with chemotherapy resistance. We aimed to define tumours with an aggressive phenotype by the combined analysis of clinicopathological and biological parameters.Methods: 77 patients with T1G3 or muscle-invasive UBC treated by radical cystectomy were studied. Immunohistochemistry was performed to detect CD147, heparanase, CD31 (blood vessels identification) and D2-40 (lymphatic vessels identification) expressions. The immunohistochemical reactions were correlated with the clinicopathological and the outcome parameters. 5-year disease-free survival (DFS) and overall survival (OS) rates were estimated using the Kaplan-Meier method. Multivariate analysis was performed by Cox proportional hazards analysis.Results: The 5-year DFS and OS rates were significantly influenced by the classical clinicopathological parameters, and by the occurrence of lymphovascular invasion. CD 147 and heparanase immunoexpression did not affect patients' outcome. However, patients with pT3/pT4 tumours had a median OS time of 14.7 months (95% CI 7.1-22.3, p = 0.003), which was reduced to 9.2 months (95% CI 1.5-17.0, p = 0.008) if the tumours were CD147 positive. We developed a model of tumour aggressiveness using parameters as stage, grade, lymphovascular invasion and CD147 immunoexpression, which separated a low aggressiveness from a high aggressiveness group, remaining as an independent prognostic factor of DFS (HR 3.746; 95% CI 1.244-11.285; p = 0.019) and OS (HR 3.247; 95% CI 1.015-10.388, p = 0.047).Conclusion: CD 147 overexpression, included in a model of UBC aggressiveness, may help surgeons to identify patients who could benefit from a personalized therapeutic regimen. Additional validation is needed. (C) 2011 Elsevier Ltd. All rights reserved.

Morphological prediction of worker size discrimination and relative abundance of sympartic species of Pseudacteon (Dipt., Phoridae) parasitoids of the fire ant, Solenopsis saevissima (Hym., Formicidae) in Brazil

Patterns of attack for collected species of phorids are predicted using multivariate morphometrics of female Pseudacteon species and worker size distributions of parasitized fire ants, Solenopsis saevissima. The model assumes that there is a direct correlation between phorid size and the size range of the worker ant attacked, and presumes that worker sizes are a resource that is divided by sympatric phorid species to minimize joint parasitism. These results suggest that the community of sympatric Pseudacteon species on only one host species coexists by restricting the size of workers attacked, and secondarily by differing diel patterns of ovipositional activity. When we compared relative abundance of species of Pseudacteon with the size distribution of foragers of S. saevissima, our observed distribution did not differ significantly from our predicted relative abundance of females of Pseudacteon. The activity of Pseudacteon may be a factor determining forager size distributions.

Discrimination of Basal Cell Carcinoma and Melanoma from Normal Skin Biopsies in Vitro Through Raman Spectroscopy and Principal Component Analysis

Objective: Raman spectroscopy has been employed to discriminate between malignant (basal cell carcinoma [BCC] and melanoma [MEL]) and normal (N) skin tissues in vitro, aimed at developing a method for cancer diagnosis. Background data: Raman spectroscopy is an analytical tool that could be used to diagnose skin cancer rapidly and noninvasively. Methods: Skin biopsy fragments of similar to 2 mm(2) from excisional surgeries were scanned through a Raman spectrometer (830 nm excitation wavelength, 50 to 200 mW of power, and 20 sec exposure time) coupled to a fiber optic Raman probe. Principal component analysis (PCA) and Euclidean distance were employed to develop a discrimination model to classify samples according to histopathology. In this model, we used a set of 145 spectra from N (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues. Results: We demonstrated that principal components (PCs) 1 to 4 accounted for 95.4% of all spectral variation. These PCs have been spectrally correlated to the biochemicals present in tissues, such as proteins, lipids, and melanin. The scores of PC2 and PC3 revealed statistically significant differences among N, BCC, and MEL (ANOVA, p < 0.05) and were used in the discrimination model. A total of 28 out of 30 spectra were correctly diagnosed as N, 93 out of 96 as BCC, and 13 out of 19 as MEL, with an overall accuracy of 92.4%. Conclusions: This discrimination model based on PCA and Euclidean distance could differentiate N from malignant (BCC and MEL) with high sensitivity and specificity.

Identification, discrimination, and selective adaptation of simultaneous musical intervals

Four experiments investigated perception of major and minor thirds whose component tones were sounded simultaneously. Effects akin to categorical perception of speech sounds were found. In the first experiment, musicians demonstrated relatively sharp category boundaries in identification and peaks near the boundary in discrimination tasks of an interval continuum where the bottom note was always an F and the top note varied from A to A flat in seven equal logarithmic steps. Nonmusicians showed these effects only to a small extent. The musicians showed higher than predicted discrimination performance overall, and reaction time increases at category boundaries. In the second experiment, musicians failed to consistently identify or discriminate thirds which varied in absolute pitch, but retained the proper interval ratio. In the last two experiments, using selective adaptation, consistent shifts were found in both identification and discrimination, similar to those found in speech experiments. Manipulations of adapting and test showed that the mechanism underlying the effect appears to be centrally mediated and confined to a frequency-specific level. A multistage model of interval perception, where the first stages deal only with specific pitches may account for the results.

Duration discrimination in a series of rhythmic events

Duration discrimination of the last of a series of four clicks was investigated. Examination of psychophysical functions from eight subjects revealed evidence for a Weber’s law model relating discrimination to base interclick interval. Also, the point of subjective equality was seen to change reliably as a function of base rate.

Mechanisms of perceptual learning of depth discrimination in random dot stereograms.

Perceptual learning is a training induced improvement in performance. Mechanisms underlying the perceptual learning of depth discrimination in dynamic random dot stereograms were examined by assessing stereothresholds as a function of decorrelation. The inflection point of the decorrelation function was defined as the level of decorrelation corresponding to 1.4 times the threshold when decorrelation is 0%. In general, stereothresholds increased with increasing decorrelation. Following training, stereothresholds and standard errors of measurement decreased systematically for all tested decorrelation values. Post training decorrelation functions were reduced by a multiplicative constant (approximately 5), exhibiting changes in stereothresholds without changes in the inflection points. Disparity energy model simulations indicate that a post-training reduction in neuronal noise can sufficiently account for the perceptual learning effects. In two subjects, learning effects were retained over a period of six months, which may have application for training stereo deficient subjects.

Regional climate model simulations for Europe at 6 and 0.2 k BP: sensitivity to changes in anthropogenic deforestation

This study aims to evaluate the direct effects of anthropogenic deforestation on simulated climate at two contrasting periods in the Holocene, ~6 and ~0.2 k BP in Europe. We apply We apply the Rossby Centre regional climate model RCA3, a regional climate model with 50 km spatial resolution, for both time periods, considering three alternative descriptions of the past vegetation: (i) potential natural vegetation (V) simulated by the dynamic vegetation model LPJ-GUESS, (ii) potential vegetation with anthropogenic land use (deforestation) from the HYDE3.1 (History Database of the Global Environment) scenario (V + H3.1), and (iii) potential vegetation with anthropogenic land use from the KK10 scenario (V + KK10). The climate model results show that the simulated effects of deforestation depend on both local/regional climate and vegetation characteristics. At ~6 k BP the extent of simulated deforestation in Europe is generally small, but there are areas where deforestation is large enough to produce significant differences in summer temperatures of 0.5–1 °C. At ~0.2 k BP, extensive deforestation, particularly according to the KK10 model, leads to significant temperature differences in large parts of Europe in both winter and summer. In winter, deforestation leads to lower temperatures because of the differences in albedo between forested and unforested areas, particularly in the snow-covered regions. In summer, deforestation leads to higher temperatures in central and eastern Europe because evapotranspiration from unforested areas is lower than from forests. Summer evaporation is already limited in the southernmost parts of Europe under potential vegetation conditions and, therefore, cannot become much lower. Accordingly, the albedo effect dominates in southern Europe also in summer, which implies that deforestation causes a decrease in temperatures. Differences in summer temperature due to deforestation range from −1 °C in south-western Europe to +1 °C in eastern Europe. The choice of anthropogenic land-cover scenario has a significant influence on the simulated climate, but uncertainties in palaeoclimate proxy data for the two time periods do not allow for a definitive discrimination among climate model results.

Individual differences in working memory capacity explain the relationship between general discrimination ability and psychometric intelligence

The close association between psychometric intelligence and general discrimination ability (GDA), conceptualized as latent variable derived from performance on different sensory discrimination tasks, is empirically well-established but theoretically widely unclear. The present study contrasted two alternative explanations for this association. The first explanation is based on what Spearman (1904) referred to as a central function underlying this relationship in the sense of the g factor of intelligence and becoming most evident in GDA. In this case, correlations between different aspects of cognitive abilities, such as working memory (WM) capacity, and psychometric intelligence should be mediated by GDA if their correlation is caused by g. Alternatively, the second explanation for the relationship between psychometric intelligence and GDA proceeds from fMRI studies which emphasize the role of WM functioning for sensory discrimination. Given the well-known relationship between WM and psychometric intelligence, the relationship between GDA and psychometric intelligence might be attributed to WM. The present study investigated these two alternative explanations at the level of latent variables. In 197 young adults, a model in which WM mediated the relationship between GDA and psychometric intelligence described the data better than a model in which GDA mediated the relationship between WM and psychometric intelligence. Moreover, GDA failed to explain portions of variance of psychometric intelligence above and beyond WM. These findings clearly support the view that the association between psychometric intelligence and GDA must be understood in terms of WM functioning.

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^

Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of mild cognitive impaired conditions

Whole brain resting state connectivity is a promising biomarker that might help to obtain an early diagnosis in many neurological diseases, such as dementia. Inferring resting-state connectivity is often based on correlations, which are sensitive to indirect connections, leading to an inaccurate representation of the real backbone of the network. The precision matrix is a better representation for whole brain connectivity, as it considers only direct connections. The network structure can be estimated using the graphical lasso (GL), which achieves sparsity through l1-regularization on the precision matrix. In this paper, we propose a structural connectivity adaptive version of the GL, where weaker anatomical connections are represented as stronger penalties on the corre- sponding functional connections. We applied beamformer source reconstruction to the resting state MEG record- ings of 81 subjects, where 29 were healthy controls, 22 were single-domain amnestic Mild Cognitive Impaired (MCI), and 30 were multiple-domain amnestic MCI. An atlas-based anatomical parcellation of 66 regions was ob- tained for each subject, and time series were assigned to each of the regions. The fiber densities between the re- gions, obtained with deterministic tractography from diffusion-weighted MRI, were used to define the anatomical connectivity. Precision matrices were obtained with the region specific time series in five different frequency bands. We compared our method with the traditional GL and a functional adaptive version of the GL, in terms of log-likelihood and classification accuracies between the three groups. We conclude that introduc- ing an anatomical prior improves the expressivity of the model and, in most cases, leads to a better classification between groups.

Kinetic discrimination in T-cell activation.

We propose a quantitative model for T-cell activation in which the rate of dissociation of ligand from T-cell receptors determines the agonist and antagonist properties of the ligand. The ligands are molecular complexes between antigenic peptides and proteins of the major histocompatibility complex on the surfaces of antigen-presenting cells. Binding of ligand to receptor triggers a series of biochemical reactions in the T cell. If the ligand dissociates after these reactions are complete, the T cell receives a positive activation signal. However, dissociation of ligand after completion of the first reaction but prior to generation of the final products results in partial T-cell activation, which acts to suppress a positive response. Such a negative signal is brought about by T-cell ligands containing the variants of antigenic peptides referred to as T-cell receptor antagonists. Results of recent experiments with altered peptide ligands compare favorably with T-cell responses predicted by this model.

Freedom for individual development : an implementation model for reducing minority and sex role stereotyping /

WI docs. no.: Ed.3/2:9267

Making public employment a model of equal opportunity : a report of the proceedings of Regional Civil Rights Conference II, sponsored by the U.S. Commission on Civil Rights in Boston, Massachusetts, September 22-24, 1974.

Mode of access: Internet.

Repelling class discrimination: ephrin-A5 binds to and activates EphB2 receptor signaling

The interactions between Eph receptor tyrosine kinases and their ephrin ligands regulate cell migration and axon pathfinding. The EphA receptors are generally thought to become activated by ephrin-A ligands, whereas the EphB receptors interact with ephrin-B ligands. Here we show that two of the most widely studied of these molecules, EphB2 and ephrin-A5, which have never been described to interact with each other, do in fact bind one another with high affinity. Exposure of EphB2-expressing cells to ephrin-A5 leads to receptor clustering, autophosphorylation and initiation of downstream signaling. Ephrin-A5 induces EphB2-mediated growth cone collapse and neurite retraction in a model system. We further show, using X-ray crystallography, that the ephrin-A5-EphB2 complex is a heterodimer and is architecturally distinct from the tetrameric EphB2-ephrin-B2 structure. The structural data reveal the molecular basis for EphB2-ephrin-A5 signaling and provide a framework for understanding the complexities of functional interactions and crosstalk between A- and B-subclass Eph receptors and ephrins.