QSAR e dinâmica molecular no estudo de sistemas biomoleculares: predição da atividade biológica de antagonistas do receptor sigma-1 e simulações de bicamadas lipídicas

Diferentes abordagens teóricas têm sido utilizadas em estudos de sistemas biomoleculares com o objetivo de contribuir com o tratamento de diversas doenças. Para a dor neuropática, por exemplo, o estudo de compostos que interagem com o receptor sigma-1 (Sig-1R) pode elucidar os principais fatores associados à atividade biológica dos mesmos. Nesse propósito, estudos de Relações Quantitativas Estrutura-Atividade (QSAR) utilizando os métodos de regressão por Mínimos Quadrados Parciais (PLS) e Rede Neural Artificial (ANN) foram aplicados a 64 antagonistas do Sig-1R pertencentes à classe de 1-arilpirazóis. Modelos PLS e ANN foram utilizados com o objetivo de descrever comportamentos lineares e não lineares, respectivamente, entre um conjunto de descritores e a atividade biológica dos compostos selecionados. O modelo PLS foi obtido com 51 compostos no conjunto treinamento e 13 compostos no conjunto teste (r² = 0,768, q² = 0,684 e r²teste = 0,785). Testes de leave-N-out, randomização da atividade biológica e detecção de outliers confirmaram a robustez e estabilidade dos modelos e mostraram que os mesmos não foram obtidos por correlações ao acaso. Modelos também foram gerados a partir da Rede Neural Artificial Perceptron de Multicamadas (MLP-ANN), sendo que a arquitetura 6-12-1, treinada com as funções de transferência tansig-tansig, apresentou a melhor resposta para a predição da atividade biológica dos compostos (r²treinamento = 0,891, r²validação = 0,852 e r²teste = 0,793). Outra abordagem foi utilizada para simular o ambiente de membranas sinápticas utilizando bicamadas lipídicas compostas por POPC, DOPE, POPS e colesterol. Os estudos de dinâmica molecular desenvolvidos mostraram que altas concentrações de colesterol induzem redução da área por lipídeo e difusão lateral e aumento na espessura da membrana e nos valores de parâmetro de ordem causados pelo ordenamento das cadeias acil dos fosfolipídeos. As bicamadas lipídicas obtidas podem ser usadas para simular interações entre lipídeos e pequenas moléculas ou proteínas contribuindo para as pesquisas associadas a doenças como Alzheimer e Parkinson. As abordagens usadas nessa tese são essenciais para o desenvolvimento de novas pesquisas em Química Medicinal Computacional.

Near Real-Time Detection Of Pipe Bursts in Water Distribution Systems

The research work presented in the thesis describes a new methodology for the automated near real-time detection of pipe bursts in Water Distribution Systems (WDSs). The methodology analyses the pressure/flow data gathered by means of SCADA systems in order to extract useful informations that go beyond the simple and usual monitoring type activities and/or regulatory reporting , enabling the water company to proactively manage the WDSs sections. The work has an interdisciplinary nature covering AI techniques and WDSs management processes such as data collection, manipulation and analysis for event detection. Indeed, the methodology makes use of (i) Artificial Neural Network (ANN) for the short-term forecasting of future pressure/flow signal values and (ii) Rule-based Model for bursts detection at sensor and district level. The results of applying the new methodology to a District Metered Area in Emilia- Romagna’s region, Italy have also been reported in the thesis. The results gathered illustrate how the methodology is capable to detect the aforementioned failure events in fast and reliable manner. The methodology guarantees the water companies to save water, energy, money and therefore enhance them to achieve higher levels of operational efficiency, a compliance with the current regulations and, last but not least, an improvement of customer service.

Paleoceanography on sediment core MD01-2416

IMAGES core MD01-2416 (51°N, 168°E) provides the first centennial-scale multiproxy record of Holocene variation in North Pacific sea-surface temperature (SST), salinity, and biogenic productivity. Our results reveal a gradual decrease in subarctic SST by 3-5 °C from 11.1 to 4.2 ka and a stepwise long-term decrease in sea surface salinity (SSS) by 2-3 p.s.u. Early Holocene SSS were as high as in the modern subtropical Pacific. The steep halocline and stratification that is characteristic of the present-day subarctic North Pacific surface ocean is a fairly recent feature, developed as a product of mid-Holocene environmental change. High SSS matched a salient productivity maximum of biogenic opal during Bølling-to-Early Holocene times, reaching levels similar to those observed during preglacial times in the warm mid-Pliocene prior to 2.73 Ma. Similar productivity spikes marked every preceding glacial termination of the last 800 ka, indicating recurrent short-term events of mid-Pliocene-style intense upwelling of nutrient-rich Pacific Deepwater in the Pleistocene. Such events led to a repeated exposure of CO2-rich deepwater at the ocean surface facilitating a transient CO2 release to the atmosphere, but the timing and duration of these events repudiate a long-term influence of the subarctic North Pacific on global atmospheric CO2 concentration.

Sea surface temperture reconstruction and planktonic foraminifera of ODP Hole 117-723A

In the western Arabian Sea (WAS), the highest seasonal sea surface temperature (SST) difference presently occurs between May and August. In order to gain an understanding on how monsoonal upwelling modulates the SST difference between these two months, we have computed SST for the months of May and August based on census counts of planktonic foraminifers by using the artificial neural network (ANN) technique. The SST difference between May and August exhibits three distinct phases: i) a moderate SST difference in the late Holocene (0-3.5 ka) is attributable to intense upwelling during August, ii) a minimum SST difference from 4 to 12 ka is due to weak upwelling during the month of August, and iii) the highest SST difference during the last glacial interval (19 to 22 ka) with high Globigerina bulloides % could have been caused by the occurrence of a prolonged upwelling season (from May through July) and maximum difference in the incoming solar radiation between May and August. Overall, variations in the SST difference between May and August show that the timing of intense upwelling in the Western Arabian Sea over the last 22 kyr has been variable over the months of June, July and August.

Centennial-scale evolution of Dansgaard-Oeschger events in sediment core MD95-2006

There is much uncertainty surrounding the mechanisms that forced the abrupt climate fluctuations found in many palaeoclimate records during Marine Isotope Stage (MIS)-3. One of the processes thought to be involved in these events is the Atlantic Meridional Overturning Circulation (MOC), which exhibited large changes in its dominant mode throughout the last glacial period. Giant piston core MD95-2006 from the northeast Atlantic Ocean records a suite of palaeoceanographic proxies related to the activity of both surface and deep water masses through a period of MIS-3 when abrupt climate fluctuations were extremely pronounced. A two-stage progression of surface water warming during interstadial warm events is proposed, with initial warming related to the northward advection of a thin warm surface layer within the North Atlantic Current, which only extended into deeper surface layers as the interstadial progressed. Benthic foraminifera isotope data also show millennial-scale oscillations but of a different structure to the abrupt surface water changes. These changes are argued to partly be related to the influence of low-salinity deepwater brines. The influence of deepwater brines over the site of MD95-2006 reached a maximum at times of rapid warming of surface waters. This observation supports the suggestion that brine formation may have helped to destabilize the accumulation of warm, saline surface waters at low latitudes, helping to force the MOC into a warm mode of operation. The contribution of deepwater brines relative to other mechanisms proposed to alter the state of the MOC needs to be examined further in future studies.

Wind power forecasts using Gaussian processes and numerical weather prediction

Since wind at the earth's surface has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safe and economic use of wind energy. In this paper, we investigated a combination of numeric and probabilistic models: a Gaussian process (GP) combined with a numerical weather prediction (NWP) model was applied to wind-power forecasting up to one day ahead. First, the wind-speed data from NWP was corrected by a GP, then, as there is always a defined limit on power generated in a wind turbine due to the turbine controlling strategy, wind power forecasts were realized by modeling the relationship between the corrected wind speed and power output using a censored GP. To validate the proposed approach, three real-world datasets were used for model training and testing. The empirical results were compared with several classical wind forecast models, and based on the mean absolute error (MAE), the proposed model provides around 9% to 14% improvement in forecasting accuracy compared to an artificial neural network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data. © 2013 IEEE.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

A study of stock market fluctuations and their relation to business conditions

Most research on stock prices is based on the present value model or the more general consumption-based model. When applied to real economic data, both of them are found unable to account for both the stock price level and its volatility. Three essays here attempt to both build a more realistic model, and to check whether there is still room for bubbles in explaining fluctuations in stock prices. In the second chapter, several innovations are simultaneously incorporated into the traditional present value model in order to produce more accurate model-based fundamental prices. These innovations comprise replacing with broad dividends the more narrow traditional dividends that are more commonly used, a nonlinear artificial neural network (ANN) forecasting procedure for these broad dividends instead of the more common linear forecasting models for narrow traditional dividends, and a stochastic discount rate in place of the constant discount rate. Empirical results show that the model described above predicts fundamental prices better, compared with alternative models using linear forecasting process, narrow dividends, or a constant discount factor. Nonetheless, actual prices are still largely detached from fundamental prices. The bubblelike deviations are found to coincide with business cycles. The third chapter examines possible cointegration of stock prices with fundamentals and non-fundamentals. The output gap is introduced to form the nonfundamental part of stock prices. I use a trivariate Vector Autoregression (TVAR) model and a single equation model to run cointegration tests between these three variables. Neither of the cointegration tests shows strong evidence of explosive behavior in the DJIA and S&P 500 data. Then, I applied a sup augmented Dickey-Fuller test to check for the existence of periodically collapsing bubbles in stock prices. Such bubbles are found in S&P data during the late 1990s. Employing econometric tests from the third chapter, I continue in the fourth chapter to examine whether bubbles exist in stock prices of conventional economic sectors on the New York Stock Exchange. The ‘old economy’ as a whole is not found to have bubbles. But, periodically collapsing bubbles are found in Material and Telecommunication Services sectors, and the Real Estate industry group.