A influência do e-learning no capital humano

Na Europa e nas últimas décadas do Século XX, a emergência da Sociedade de Informação veio impor às organizações a necessidade de que, para além das inovações tecnológicas, haja uma preocupação relativamente aos bens intangíveis como a informação, as novas metodologias de trabalho e o know how (Batista, 2002). Paralelamente a estas inovações, as Instituições de Ensino Superior têm contribuído para a evolução do Capital Humano, como ativo intangível intrínseco ao Homem. Em Portugal e no contexto do Ensino/Formação a Distância parecem continuar a existir, ainda, em algumas instituições, problemas de identificação, e de descriminação das vantagens no que concerne à estrutura aberta e flexível, com o estudante/formando a ter algumas dificuldades em adaptar o seu perfil e interesses profissionais ao tipo de aprendizagem que mais se lhe adequa. O e-learning surge como um método de Ensino/Formação a Distância, só possível com a especificidade dos processos pedagógicos e em complementaridade com as Tecnologias de Informação e Comunicação (TIC), uma vez que são estas que lhe dão o suporte necessário à sua concretização. O e-learning ao proporcionar novas formas de comunicação, de interação e de confronto de ideias, permite uma aprendizagem baseada na partilha de saberes, tendo em consideração as experiências e os objetivos profissionais dos formandos. Dentro destes pressupostos, achámos importante fazer uma investigação a partir de Instituições de Ensino Superior Portuguesas, de modo a percebermos qual o papel e a influência que o e-learning desempenha nos objetivos das organizações académicas em geral e no Capital Humano dos seus Estudantes/Formandos em particular. A partir da questão da investigação foram definidos os objetivos e hipóteses de investigação de modo a que ao ser enunciada uma metodologia esta englobe fatores que foquem os elementos necessários à confirmação, ou não, dos pressupostos enunciados. Foi analisada documentação diversa, criado um questionário e conduzidas entrevistas, de modo a obter e potenciar a informação necessária e suficiente para o efeito. A recolha de dados para posterior análise e os resultados depois de interpretados, permitirão responder aos propósitos expressos desde o início da investigação.

Reduced development of CD4-8-B220+ T cells but normal autoantibody production in lpr/lpr mice lacking major histocompatibility complex class I molecules.

The lpr gene has recently been shown to encode a functional mutation in the Fas receptor, a molecule involved in transducing apoptotic signals. Mice homozygous for the lpr gene develop an autoimmune syndrome accompanied by massive accumulation of double-negative (DN) CD4-8-B220+ T cell receptor-alpha/beta+ cells. In order to investigate the origin of these DN T cells, we derived lpr/lpr mice lacking major histocompatibility complex (MHC) class I molecules by intercrossing them with beta 2-microglobulin (beta 2m)-deficient mice. Interestingly, these lpr beta 2m-/- mice develop 13-fold fewer DNT cells in lymph nodes as compared to lpr/lpr wild-type (lprWT) mice. Analysis of anti-DNA antibodies and rheumatoid factor in serum demonstrates that lpr beta 2m-/- mice produce comparable levels of autoantibodies to lprWT mice. Collectively our data indicate that MHC class I molecules control the development of DN T cells but not autoantibody production in lpr/lpr mice and support the hypothesis that the majority of DN T cells may be derived from cells of the CD8 lineage.

Statistical learning theory for geospatial data. Case study: Aral sea

In recent years there has been an explosive growth in the development of adaptive and data driven methods. One of the efficient and data-driven approaches is based on statistical learning theory (Vapnik 1998). The theory is based on Structural Risk Minimisation (SRM) principle and has a solid statistical background. When applying SRM we are trying not only to reduce training error ? to fit the available data with a model, but also to reduce the complexity of the model and to reduce generalisation error. Many nonlinear learning procedures recently developed in neural networks and statistics can be understood and interpreted in terms of the structural risk minimisation inductive principle. A recent methodology based on SRM is called Support Vector Machines (SVM). At present SLT is still under intensive development and SVM find new areas of application (www.kernel-machines.org). SVM develop robust and non linear data models with excellent generalisation abilities that is very important both for monitoring and forecasting. SVM are extremely good when input space is high dimensional and training data set i not big enough to develop corresponding nonlinear model. Moreover, SVM use only support vectors to derive decision boundaries. It opens a way to sampling optimization, estimation of noise in data, quantification of data redundancy etc. Presentation of SVM for spatially distributed data is given in (Kanevski and Maignan 2004).

TACI, unlike BAFF-R, is solely activated by oligomeric BAFF and APRIL to support survival of activated B cells and plasmablasts.

The cytokine BAFF binds to the receptors TACI, BCMA, and BAFF-R on B cells, whereas APRIL binds to TACI and BCMA only. The signaling properties of soluble trimeric BAFF (BAFF 3-mer) were compared with those of higher-order BAFF oligomers. All forms of BAFF bound BAFF-R and TACI, and elicited BAFF-R-dependent signals in primary B cells. In contrast, signaling through TACI in mature B cells or plasmablasts was only achieved by higher-order BAFF and APRIL oligomers, all of which were also po-tent activators of a multimerization-dependent reporter signaling pathway. These results indicate that, although BAFF-R and TACI can provide B cells with similar signals, only BAFF-R, but not TACI, can respond to soluble BAFF 3-mer, which is the main form of BAFF found in circulation. BAFF 60-mer, an efficient TACI agonist, was also detected in plasma of BAFF transgenic and nontransgenic mice and was more than 100-fold more active than BAFF 3-mer for the activation of multimerization-dependent signals. TACI supported survival of activated B cells and plasmablasts in vitro, providing a rational basis to explain the immunoglobulin deficiency reported in TACI-deficient persons.

NLRC5 deficiency selectively impairs MHC class I- dependent lymphocyte killing by cytotoxic T cells.

Nucleotide-binding oligomerization domain-like receptors (NLRs) are intracellular proteins involved in innate-driven inflammatory responses. The function of the family member NLR caspase recruitment domain containing protein 5 (NLRC5) remains a matter of debate, particularly with respect to NF-κB activation, type I IFN, and MHC I expression. To address the role of NLRC5, we generated Nlrc5-deficient mice (Nlrc5(Δ/Δ)). In this article we show that these animals exhibit slightly decreased CD8(+) T cell percentages, a phenotype compatible with deregulated MHC I expression. Of interest, NLRC5 ablation only mildly affected MHC I expression on APCs and, accordingly, Nlrc5(Δ/Δ) macrophages efficiently primed CD8(+) T cells. In contrast, NLRC5 deficiency dramatically impaired basal expression of MHC I in T, NKT, and NK lymphocytes. NLRC5 was sufficient to induce MHC I expression in a human lymphoid cell line, requiring both caspase recruitment and LRR domains. Moreover, endogenous NLRC5 localized to the nucleus and occupied the proximal promoter region of H-2 genes. Consistent with downregulated MHC I expression, the elimination of Nlrc5(Δ/Δ) lymphocytes by cytotoxic T cells was markedly reduced and, in addition, we observed low NLRC5 expression in several murine and human lymphoid-derived tumor cell lines. Hence, loss of NLRC5 expression represents an advantage for evading CD8(+) T cell-mediated elimination by downmodulation of MHC I levels-a mechanism that may be exploited by transformed cells. Our data show that NLRC5 acts as a key transcriptional regulator of MHC I in lymphocytes and support an essential role for NLRs in directing not only innate but also adaptive immune responses.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Geomodelling of a fluvial system with semi-supervised support vector regression

Fluvial deposits are a challenge for modelling flow in sub-surface reservoirs. Connectivity and continuity of permeable bodies have a major impact on fluid flow in porous media. Contemporary object-based and multipoint statistics methods face a problem of robust representation of connected structures. An alternative approach to model petrophysical properties is based on machine learning algorithm ? Support Vector Regression (SVR). Semi-supervised SVR is able to establish spatial connectivity taking into account the prior knowledge on natural similarities. SVR as a learning algorithm is robust to noise and captures dependencies from all available data. Semi-supervised SVR applied to a synthetic fluvial reservoir demonstrated robust results, which are well matched to the flow performance

Example-based support vector machine for drug concentration analysis

Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Building World-Class Schools for Iowa: A Legislative Brief, January 14, 2013

The Iowa Department of Education has joined the Office of the Governor to prepare a set of legislative proposals that will bring Iowa closer to its goal of providing a world-class education to all children, no mater where they live. This legislative brief serves as an overview of the legislation, which I encourage you to read and discuss in greater detail. the goals behind these policies are straightforward: Comprehensive and systematically raise and support the teaching profession while expanding efforts to customize instruction to every student's passion and talents. Iowa's children deserve the best education can provide so they leave our schools with the knowledge and skills necessary for successful and rewarding lives. Iowa has many good schools with hard-working, talented educators who deserves our respect and appreciation. While we honor the past work of generations of Iowans who built a strong foundation, it is our responsibility - and our turn - to make a focused, dedicated effort to improve Iowa's schools. We stand at a pivotal moment in Iowa storied education history, in which we have the opportunity and will as community to make the transition from being "good" to being "great".

Learning wind fields with multiple kernels

This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool. The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains. Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region.

Supporting interdisciplinary case studies: development and implementation of a joint learning environment for students in forensic science and criminal law

Scientific reporting and communication is a challenging topic for which traditional study programs do not offer structured learning activities on a regular basis. This paper reports on the development and implementation of a web application and associated learning activities that intend to raise the awareness of reporting and communication issues among students in forensic science and law. The project covers interdisciplinary case studies based on a library of written reports about forensic examinations. Special features of the web framework, in particular a report annotation tool, support the design of various individual and group learning activities that focus on the development of knowledge and competence in dealing with reporting and communication challenges in the students' future areas of professional activity.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Auditory cues support place navigation in rats when associated with a visual cue.

Rats, like other crepuscular animals, have excellent auditory capacities and they discriminate well between different sounds [Heffner HE, Heffner RS, Hearing in two cricetid rodents: wood rats (Neotoma floridana) and grasshopper mouse (Onychomys leucogaster). J Comp Psychol 1985;99(3):275-88]. However, most experimental literature concerning spatial orientation almost exclusively emphasizes the use of visual landmarks [Cressant A, Muller RU, Poucet B. Failure of centrally placed objects to control the firing fields of hippocampal place cells. J Neurosci 1997;17(7):2531-42; and Goodridge JP, Taube JS. Preferential use of the landmark navigational system by head direction cells in rats. Behav Neurosci 1995;109(1):49-61]. To address the important issue of whether rats are able to achieve a place navigation task relative to auditory beacons, we designed a place learning task in the water maze. We controlled cue availability by conducting the experiment in total darkness. Three auditory cues did not allow place navigation whereas three visual cues in the same positions did support place navigation. One auditory beacon directly associated with the goal location did not support taxon navigation (a beacon strategy allowing the animal to find the goal just by swimming toward the cue). Replacing the auditory beacons by one single visual beacon did support taxon navigation. A multimodal configuration of two auditory cues and one visual cue allowed correct place navigation. The deletion of the two auditory or of the one visual cue did disrupt the spatial performance. Thus rats can combine information from different sensory modalities to achieve a place navigation task. In particular, auditory cues support place navigation when associated with a visual one.

Ensemble methods for environmental data modelling with support vector regression

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computationally efficient than training a single SVR model while reducing error. Boosting, however, does not improve results on this specific problem.