Early motor and electrophysiological changes in transgenic mouse model of amyotrophic lateral sclerosis and gender differences on clinical outcome

Amyotrophic lateral sclerosis (ALS) is a progressive degenerative disorder affecting motoneurons and the SOD1(G93A) transgenic mice are widely employed to study disease physiopathology and therapeutic strategies. Despite the cellular and biochemical evidences of an early motor system dysfunction, the conventional behavioral tests do not detect early motor impairments in SOD1 mouse model. We evaluated early changes in motor behavior of ALS mice by doing the analyses of tail elevation, footprint, automatic recording of motor activities by means of an infrared motion sensor activity system and electrophysiological measurements in male and female wild-type (WT) and SOD1(G93A) mice from postnatal day (P) 20 up to endpoint. The classical evaluations of mortality, weight loss, tremor, rotometer, hanging wire and inclined plane were also employed. There was a late onset (after P90) of the impairments of classical parameters and the outcome varied between genders of ALS mice, being tremor, cumulative survival, weight loss and neurological score about 10 days earlier in male than female ALS mice and also about 20 days earlier in ALS males regarding rotarod and hanging wire performances. While diminution of hindpaw base was 10 days earlier in ALS males (P110) compared to females, the steep length decreased 40 days earlier in ALS females (P60) than ALS males. The automatic analysis of motor impairments showed substantial late changes (after P90) of motility and locomotion in the ALS females, but not in the ALS males. It was surprising that the scores of tail elevation were already decreased in ALS males and females by P40, reaching the minimal values at the endpoint. The electrophysiological analyses showed early changes of measures in the ALS mouse sciatic nerve, i.e., decreased values of amplitude (P40) and nerve conduction velocity (P20), and also an increased latency (P20) reaching maximal level of impairments at the late disease phase. The early changes were not accompanied by reductions of neuronal protein markers of neurofilament 200 and ChAT in the ventral part of the lumbar spinal cord of P20 and P60 ALS mice by means of Western blot technique, despite remarkable decreases of those protein levels in P120 ALS mice. In conclusion, early changes of motor behavior and electrophysiological parameters in ALS mouse model must be taken into attention in the analyses of disease mechanisms and therapeutic effects. (C) 2011 Published by Elsevier B.V.

Comorbidity adjustment index for the international classification of diseases, 10

OBJECTIVE: To develop a Charlson-like comorbidity index based on clinical conditions and weights of the original Charlson comorbidity index. METHODS: Clinical conditions and weights were adapted from the International Classification of Diseases, 10th revision and applied to a single hospital admission diagnosis. The study included 3,733 patients over 18 years of age who were admitted to a public general hospital in the city of Rio de Janeiro, southeast Brazil, between Jan 2001 and Jan 2003. The index distribution was analyzed by gender, type of admission, blood transfusion, intensive care unit admission, age and length of hospital stay. Two logistic regression models were developed to predict in-hospital mortality including: a) the aforementioned variables and the risk-adjustment index (full model); and b) the risk-adjustment index and patient's age (reduced model). RESULTS: Of all patients analyzed, 22.3% had risk scores >1, and their mortality rate was 4.5% (66.0% of them had scores >1). Except for gender and type of admission, all variables were retained in the logistic regression. The models including the developed risk index had an area under the receiver operating characteristic curve of 0.86 (full model), and 0.76 (reduced model). Each unit increase in the risk score was associated with nearly 50% increase in the odds of in-hospital death. CONCLUSIONS: The risk index developed was able to effectively discriminate the odds of in-hospital death which can be useful when limited information is available from hospital databases.

A model of gender prejudice, power, and discrimination: How hierarchy-enhancing factors predominate over hierarchy-attenuating factors

Gender inequalities remain an issue in our society and particularly in the workplace. Several factors can explain this gender difference in top-level managerial positions such as career ambitions but also biases against women. In our chapter, we propose a model explaining why gender inequalities and particularly discrimination against women is still present in our societies despite social norms and existing legislation on gender equality. To this purpose, we review research on discrimination through two different approaches, (a) a prejudice approach through the justification-suppression model developed by Crandall and Eshleman (2003) and (b) a power approach through the social dominance theory (Pratto, Sidanius, Stallworth, & Malle, 1994; Sidanius & Pratto, 1999). In our work, we integrate these two approaches and propose a model of gender prejudice, power and discrimination. The integration of these two approaches contributes to a better understanding of how discrimination against women is formed and maintained over time.

Breathing disorders in congestive heart failure: gender, etiology and mortality

We investigated the relationship between sleep-disordered breathing (SDB) and Cheyne-Stokes respiration (CSR) while awake as well as mortality. Eighty-nine consecutive outpatients (29 females) with congestive heart failure (CHF; left ventricular ejection fraction, LVEF <45%) were prospectively evaluated. The presence of SDB and of CSR while awake before sleep onset was investigated by polysomnography. SDB prevalence was 81 and 56%, using apnea-hypopnea index cutoffs >5 and >15, respectively. CHF etiologies were similar according to the prevalence of SDB and sleep pattern. Males and females were similar in age, body mass index, and LVEF. Males presented more SDB (P = 0.01), higher apnea-hypopnea index (P = 0.04), more light sleep (stages 1 and 2; P < 0.05), and less deep sleep (P < 0.001) than females. During follow-up (25 ± 10 months), 27% of the population died. Non-survivors had lower LVEF (P = 0.01), worse New York Heart Association (NYHA) functional classification (P = 0.03), and higher CSR while awake (P < 0.001) than survivors. As determined by Cox proportional model, NYHA class IV (RR = 3.95, 95%CI = 1.37-11.38, P = 0.011) and CSR while awake with a marginal significance (RR = 2.96, 95%CI = 0.94-9.33, P = 0.064) were associated with mortality. In conclusion, the prevalence of SDB and sleep pattern of patients with Chagas' disease were similar to that of patients with CHF due to other etiologies. Males presented more frequent and more severe SDB and worse sleep quality than females. The presence of CSR while awake, but not during sleep, may be associated with a poor prognosis in patients with CHF.

Visual Segmentation without Classification in a Model of the Primary Visual Cortex

Stimuli outside classical receptive fields significantly influence the neurons' activities in primary visual cortex. We propose that such contextual influences are used to segment regions by detecting the breakdown of homogeneity or translation invariance in the input, thus computing global region boundaries using local interactions. This is implemented in a biologically based model of V1, and demonstrated in examples of texture segmentation and figure-ground segregation. By contrast with traditional approaches, segmentation occurs without classification or comparison of features within or between regions and is performed by exactly the same neural circuit responsible for the dual problem of the grouping and enhancement of contours.

Model-based vehicle detection and classification using orthographic approximations

This paper reports the current state of work to simplify our previous model-based methods for visual tracking of vehicles for use in a real-time system intended to provide continuous monitoring and classification of traffic from a fixed camera on a busy multi-lane motorway. The main constraints of the system design were: (i) all low level processing to be carried out by low-cost auxiliary hardware, (ii) all 3-D reasoning to be carried out automatically off-line, at set-up time. The system developed uses three main stages: (i) pose and model hypothesis using 1-D templates, (ii) hypothesis tracking, and (iii) hypothesis verification, using 2-D templates. Stages (i) & (iii) have radically different computing performance and computational costs, and need to be carefully balanced for efficiency. Together, they provide an effective way to locate, track and classify vehicles.

Backward elimination model construction for regression and classification using leave-one-out criteria

A fundamental principle in practical nonlinear data modeling is the parsimonious principle of constructing the minimal model that explains the training data well. Leave-one-out (LOO) cross validation is often used to estimate generalization errors by choosing amongst different network architectures (M. Stone, "Cross validatory choice and assessment of statistical predictions", J. R. Stast. Soc., Ser. B, 36, pp. 117-147, 1974). Based upon the minimization of LOO criteria of either the mean squares of LOO errors or the LOO misclassification rate respectively, we present two backward elimination algorithms as model post-processing procedures for regression and classification problems. The proposed backward elimination procedures exploit an orthogonalization procedure to enable the orthogonality between the subspace as spanned by the pruned model and the deleted regressor. Subsequently, it is shown that the LOO criteria used in both algorithms can be calculated via some analytic recursive formula, as derived in this contribution, without actually splitting the estimation data set so as to reduce computational expense. Compared to most other model construction methods, the proposed algorithms are advantageous in several aspects; (i) There are no tuning parameters to be optimized through an extra validation data set; (ii) The procedure is fully automatic without an additional stopping criteria; and (iii) The model structure selection is directly based on model generalization performance. The illustrative examples on regression and classification are used to demonstrate that the proposed algorithms are viable post-processing methods to prune a model to gain extra sparsity and improved generalization.

Classification of instability modes in a model of aluminium reduction cells with a uniform magnetic field

A unified view on the interfacial instability in a model of aluminium reduction cells in the presence of a uniform, vertical, background magnetic field is presented. The classification of instability modes is based on the asymptotic theory for high values of parameter β, which characterises the ratio of the Lorentz force based on the disturbance current, and gravity. It is shown that the spectrum of the travelling waves consists of two parts independent of the horizontal cross-section of the cell: highly unstable wall modes and stable or weakly unstable centre, or Sele’s modes. The wall modes with the disturbance of the interface being localised at the sidewalls of the cell dominate the dynamics of instability. Sele’s modes are characterised by a distributed disturbance over the whole horizontal extent of the cell. As β increases these modes are stabilized by the field.