Proinsulin slows retinal degeneration and vision loss in the P23H rat model of retinitis pigmentosa

Proinsulin has been characterized as a neuroprotective molecule. In this work we assess the therapeutic potential of proinsulin on photoreceptor degeneration, synaptic connectivity, and functional activity of the retina in the transgenic P23H rat, an animal model of autosomal dominant retinitis pigmentosa (RP). P23H homozygous rats received an intramuscular injection of an adeno-associated viral vector serotype 1 (AAV1) expressing human proinsulin (hPi+) or AAV1-null vector (hPi−) at P20. Levels of hPi in serum were determined by enzyme-linked immunosorbent assay (ELISA), and visual function was evaluated by electroretinographic (ERG) recording at P30, P60, P90, and P120. Preservation of retinal structure was assessed by immunohistochemistry at P120. Human proinsulin was detected in serum from rats injected with hPi+ at all times tested, with average hPi levels ranging from 1.1 nM (P30) to 1.4 nM (P120). ERG recordings showed an amelioration of vision loss in hPi+ animals. The scotopic b-waves were significantly higher in hPi+ animals than in control rats at P90 and P120. This attenuation of visual deterioration correlated with a delay in photoreceptor degeneration and the preservation of retinal cytoarchitecture. hPi+ animals had 48.7% more photoreceptors than control animals. Presynaptic and postsynaptic elements, as well as the synaptic contacts between photoreceptors and bipolar or horizontal cells, were preserved in hPi+ P23H rats. Furthermore, in hPi+ rat retinas the number of rod bipolar cell bodies was greater than in control rats. Our data demonstrate that hPi expression preserves cone and rod structure and function, together with their contacts with postsynaptic neurons, in the P23H rat. These data strongly support the further development of proinsulin-based therapy to counteract retinitis pigmentosa.

Teaching as a fractal: from experience to model

The aim of this work is to improve students’ learning by designing a teaching model that seeks to increase student motivation to acquire new knowledge. To design the model, the methodology is based on the study of the students’ opinion on several aspects we think importantly affect the quality of teaching (such as the overcrowded classrooms, time intended for the subject or type of classroom where classes are taught), and on our experience when performing several experimental activities in the classroom (for instance, peer reviews and oral presentations). Besides the feedback from the students, it is essential to rely on the experience and reflections of lecturers who have been teaching the subject several years. This way we could detect several key aspects that, in our opinion, must be considered when designing a teaching proposal: motivation, assessment, progressiveness and autonomy. As a result we have obtained a teaching model based on instructional design as well as on the principles of fractal geometry, in the sense that different levels of abstraction for the various training activities are presented and the activities are self-similar, that is, they are decomposed again and again. At each level, an activity decomposes into a lower level tasks and their corresponding evaluation. With this model the immediate feedback and the student motivation are encouraged. We are convinced that a greater motivation will suppose an increase in the student’s working time and in their performance. Although the study has been done on a subject, the results are fully generalizable to other subjects.

Historical performance of different auto manufacturers in the New Car Assessment Program tests. Report.

Mode of access: Internet.

A geometrical model for the synthesis of interval covers,

"Supported by Contract AT(11-1)-2118 with the U.S. Atomic Energy Commission."

The specification of vector autoregressive moving average models

In this paper we propose a new identification method based on the residual white noise autoregressive criterion (Pukkila et al. , 1990) to select the order of VARMA structures. Results from extensive simulation experiments based on different model structures with varying number of observations and number of component series are used to demonstrate the performance of this new procedure. We also use economic and business data to compare the model structures selected by this order selection method with those identified in other published studies.

SVMtm: Support vector machines to predict transmembrane segments

A new method has been developed for prediction of transmembrane helices using support vector machines. Different coding schemes of protein sequences were explored, and their performances were assessed by crossvalidation tests. The best performance method can predict the transmembrane helices with sensitivity of 93.4% and precision of 92.0%. For each predicted transmembrane segment, a score is given to show the strength of transmembrane signal and the prediction reliability. In particular, this method can distinguish transmembrane proteins from soluble proteins with an accuracy of similar to99%. This method can be used to complement current transmembrane helix prediction methods and can be Used for consensus analysis of entire proteomes . The predictor is located at http://genet.imb.uq.edu.au/predictors/ SVMtm. (C) 2004 Wiley Periodicals, Inc.

A physiological product-release model for baculovirus infected insect cells

Existing models describe the product release from baculovirus infected insect cells as an unspecific protein leakage occurring in parallel with protein production. The model presented here shows that the observed product release of normally non-secreted proteins can be described through cell death alone. This model avoids the implicit non-physiological assumption of previous models that cells permeable to recombinant protein as well as trypan blue continue to produce protein. (c) 2005 Wiley Periodicals, Inc.

Multigradient field-active contour model for multilayer boundary detection of ultrasound rectal wall image

Extraction and reconstruction of rectal wall structures from an ultrasound image is helpful for surgeons in rectal clinical diagnosis and 3-D reconstruction of rectal structures from ultrasound images. The primary task is to extract the boundary of the muscular layers on the rectal wall. However, due to the low SNR from ultrasound imaging and the thin muscular layer structure of the rectum, this boundary detection task remains a challenge. An active contour model is an effective high-level model, which has been used successfully to aid the tasks of object representation and recognition in many image-processing applications. We present a novel multigradient field active contour algorithm with an extended ability for multiple-object detection, which overcomes some limitations of ordinary active contour models—"snakes." The core part in the algorithm is the proposal of multigradient vector fields, which are used to replace image forces in kinetic function for alternative constraints on the deformation of active contour, thereby partially solving the initialization limitation of active contour for rectal wall boundary detection. An adaptive expanding force is also added to the model to help the active contour go through the homogenous region in the image. The efficacy of the model is explained and tested on the boundary detection of a ring-shaped image, a synthetic image, and an ultrasound image. The experimental results show that the proposed multigradient field-active contour is feasible for multilayer boundary detection of rectal wall

Detecting and sorting targeting peptides wtih neural networks and support vector machines

This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).

Motion vector encryption in multimedia streaming

Streaming video application requires high security as well as high computational performance. In video encryption, traditional selective algorithms have been used to partially encrypt the relatively important data in order to satisfy the streaming performance requirement. Most video selective encryption algorithms are inherited from still image encryption algorithms, the encryption on motion vector data is not considered. The assumption is that motion vector data are not as important as pixel image data. Unfortunately, in some cases, motion vector itself may be sufficient enough to leak out useful video information. Normally motion vector data consume over half of the whole video stream bandwidth, neglecting their security may be unwise. In this paper, we target this security problem and illustrate attacks at two different levels that can restore useful video information using motion vectors only. Further, an information analysis is made and a motion vector information model is built. Based on this model, we describe a new motion vector encryption algorithm called MVEA. We show the experimental results of MVEA. The security strength and performance of the algorithm are also evaluated.

Anisotropy model for mantle convection

The paper presents a new theory for modeling flow in anisotropic, viscous rock. This theory has originally been developed for the simulation of large deformation processes including folding and kinking in multi-layered visco-elastic rock. The orientation of slip planes in the context of crystallographic slip is determined by the normal vector, the so-called director of these surfaces. The model is applied to simulate anisotropic natural mantle convection. We compare the evolution of the director and approximately steady states of isotropic and anisotropic convection. The isotropic case has a simple steady state solution, whereas the orthotropic convection model produces a continuously evolving patterning in tile core of the convection cell which makes only a near-steady condition possible, in which the thermal boundary layer appears to be well aligned with the flow and hence as observed in seismic tomomgraphy strong anistropic.

RESILIÊNCIA EM TRABALHADORES: IMPACTO DA AUTO- EFICÁCIA E DA PERCEPÇÃO DE SUPORTE SOCIAL

Resiliência representa o processo dinâmico envolvendo a adaptação positiva no contexto de adversidade significativa. Estudos sobre o conceito têm aumentado com o advento da Psicologia Positiva, pelos potenciais efeitos na saúde e no desempenho dos trabalhadores. Outros conceitos importantes para a saúde circunscritos no escopo da Psicologia Positiva no contexto de trabalho são os de auto-eficácia, definida como crenças das pessoas sobre suas capacidades e/ou seu exercício de controle sobre os eventos que afetam sua vida e o de suporte social no trabalho, que compreende a percepção do quanto o contexto laborativo oferece apoio aos trabalhadores. Pouca literatura existe sobre resiliência no contexto de trabalho e nenhum estudo envolvendo os três construtos foi encontrado. Por isto, esta investigação analisou o impacto da auto-eficácia e da percepção de suporte social no trabalho sobre a resiliência de trabalhadores. Participaram 243 universitários trabalhadores da região metropolitana de São Paulo, com idade média de 23 anos (DP=6,2 anos), em sua maioria do sexo feminino (69,5%), cristãos (católicos=51,5%; protestantes=18,1%), atuantes em cargos de apoio administrativo e técnico (49,1%), oriundos de organizações de diversos ramos. Foi aplicado um questionário para coletar dados sócio-demográficos dos participantes e três escalas brasileiras válidas para medir a percepção de suporte social no trabalho (Escala de Percepção de Suporte Social no Trabalho EPSST), as crenças de auto-eficácia (Escala de Auto-eficácia Geral Percebida) e nível de resiliência (Escala de Resiliência de Connor-Davidson CD-RISC-10). Foram realizadas análises estatísticas exploratórias e descritivas, análises de regressão stepwise, análises de variância (ANOVA) e teste t para descrever participantes, variáveis e testar o modelo. Os dados revelaram que os universitários trabalhadores apresentam níveis de resiliência e auto-eficácia acima da média e de suporte social no trabalho, na média. Auto-eficácia se confirmou como preditor significativo de resiliência ao contrário dos três tipos de percepção de suporte social no trabalho (informacional, emocional e instrumental). Os achados indicaram a necessidade de aprofundamento sobre o tema e foi apontada a necessidade de novos estudos que auxiliem na compreensão dos resultados desta investigação.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

Numerical Q parameter estimates for scalar and vector models in optical communication system simulations

The Q parameter scales differently with the noise power for the signal-noise and the noise-noise beating terms in scalar and vector models. Some procedures for including noise in the scalar model largely under-estimate the Q parameter. We propose a simple method for including noise within a scalar model which will allow both the noise-noise dominated limit and the signal-noise dominated limit to be treated consistently. © 2005 Elsevier B.V. All rights reserved.