Re-analysis of microarray data reveals insights into altered transcriptional activity of TH17 and TReg signalling in psoriasis

Identifying differential expression of genes in psoriatic and healthy skin by microarray data analysis is a key approach to understand the pathogenesis of psoriasis. Analysis of more than one dataset to identify genes commonly upregulated reduces the likelihood of false positives and narrows down the possible signature genes. Genes controlling the critical balance between T helper 17 and regulatory T cells are of special interest in psoriasis. Our objectives were to identify genes that are consistently upregulated in lesional skin from three published microarray datasets. We carried out a reanalysis of gene expression data extracted from three experiments on samples from psoriatic and nonlesional skin using the same stringency threshold and software and further compared the expression levels of 92 genes related to the T helper 17 and regulatory T cell signaling pathways. We found 73 probe sets representing 57 genes commonly upregulated in lesional skin from all datasets. These included 26 probe sets representing 20 genes that have no previous link to the etiopathogenesis of psoriasis. These genes may represent novel therapeutic targets and surely need more rigorous experimental testing to be validated. Our analysis also identified 12 of 92 genes known to be related to the T helper 17 and regulatory T cell signaling pathways, and these were found to be differentially expressed in the lesional skin samples.

Data Reduction of Power System Disturbances using Wavelet Transforms for Network Operation and Control

Wavelet transforms provide basis functions for time-frequency analysis and have properties that are particularly useful for compression of analogue point on wave transient and disturbance power system signals. This paper evaluates the reduction properties of the wavelet transform using real power system data and discusses the application of the reduction method for information transfer in network communications.

Interpreting pedestrian behaviour by visualising and clustering movement data

Recent technological advances have increased the quantity of movement data being recorded. While valuable knowledge can be gained by analysing such data, its sheer volume creates challenges. Geovisual analytics, which helps the human cognition process by using tools to reason about data, offers powerful techniques to resolve these challenges. This paper introduces such a geovisual analytics environment for exploring movement trajectories, which provides visualisation interfaces, based on the classic space-time cube. Additionally, a new approach, using the mathematical description of motion within a space-time cube, is used to determine the similarity of trajectories and forms the basis for clustering them. These techniques were used to analyse pedestrian movement. The results reveal interesting and useful spatiotemporal patterns and clusters of pedestrians exhibiting similar behaviour.

Bayesian network data imputation with application to survival tree analysis

Retrospective clinical datasets are often characterized by a relatively small sample size and many missing data. In this case, a common way for handling the missingness consists in discarding from the analysis patients with missing covariates, further reducing the sample size. Alternatively, if the mechanism that generated the missing allows, incomplete data can be imputed on the basis of the observed data, avoiding the reduction of the sample size and allowing methods to deal with complete data later on. Moreover, methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain. The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification. Survival tree methods usually address this problem by using surrogate splits, that is, splitting rules that use other variables yielding similar results to the original ones. Instead, our methodology consists in modeling the dependencies among the clinical variables with a Bayesian network, which is then used to perform data imputation, thus allowing the survival tree to be applied on the completed dataset. The Bayesian network is directly learned from the incomplete data using a structural expectation–maximization (EM) procedure in which the maximization step is performed with an exact anytime method, so that the only source of approximation is due to the EM formulation itself. On both simulated and real data, our proposed methodology usually outperformed several existing methods for data imputation and the imputation so obtained improved the stratification estimated by the survival tree (especially with respect to using surrogate splits).

An analysis of implied volatility jump dynamics: Novel functional data representation in crude oil markets

The predominant fear in capital markets is that of a price spike. Commodity markets differ in that there is a fear of both upward and down jumps, this results in implied volatility curves displaying distinct shapes when compared to equity markets. The use of a novel functional data analysis (FDA) approach, provides a framework to produce and interpret functional objects that characterise the underlying dynamics of oil future options. We use the FDA framework to examine implied volatility, jump risk, and pricing dynamics within crude oil markets. Examining a WTI crude oil sample for the 2007–2013 period, which includes the global financial crisis and the Arab Spring, strong evidence is found of converse jump dynamics during periods of demand and supply side weakness. This is used as a basis for an FDA-derived Merton (1976) jump diffusion optimised delta hedging strategy, which exhibits superior portfolio management results over traditional methods.

Analysis of groundwater-level response to rainfall and recharge estimates in fractured hard rock aquifers, NW Ireland

Despite fractured hard rock aquifers underlying over 65% of Ireland, knowledge of key processes controlling groundwater recharge in these bedrock systems is inadequately constrained. In this study, we examined 19 groundwater-level hydrographs from two Irish hillslope sites underlain by hard rock aquifers. Water-level time-series in clustered monitoring wells completed at the subsoil, soil/bedrock interface, shallow and deep bedrocks were continuously monitored hourly over two hydrological years. Correlation methods were applied to investigate groundwater-level response to rainfall, as well as its seasonal variations. The results reveal that the direct groundwater recharge to the shallow and deep bedrocks on hillslope is very limited. Water-level variations within these geological units are likely dominated by slow flow rock matrix storage. The rapid responses to rainfall (⩽2 h) with little seasonal variations were observed to the monitoring wells installed at the subsoil and soil/bedrock interface, as well as those in the shallow or deep bedrocks at the base of the hillslope. This suggests that the direct recharge takes place within these units. An automated time-series procedure using the water-table fluctuation method was developed to estimate groundwater recharge from the water-level and rainfall data. Results show the annual recharge rates of 42–197 mm/yr in the subsoil and soil/bedrock interface, which represent 4–19% of the annual rainfall. Statistical analysis of the relationship between the rainfall intensity and water-table rise reveal that the low rainfall intensity group (⩽1 mm/h) has greater impact on the groundwater recharge rate than other groups (>1 mm/h). This study shows that the combination of the time-series analysis and the water-table fluctuation method could be an useful approach to investigate groundwater recharge in fractured hard rock aquifers in Ireland.

Generalized collisional radiative model for light elements: C: Data for the B isonuclear sequence

A first stage collision database is assembled which contains electron-impact excitation, ionization,\r and recombination rate coefficients for B, B + , B 2+ , B 3+ , and B 4+ . The first stage database\r is constructed using the R-matrix with pseudostates, time-dependent close-coupling, and perturbative\r distorted-wave methods. A second stage collision database is then assembled which contains\r generalized collisional-radiative ionization, recombination, and power loss rate coefficients as a\r function of both temperature and density. The second stage database is constructed by solution of\r the collisional-radiative equations in the quasi-static equilibrium approximation using the first\r stage database. Both collision database stages reside in electronic form at the IAEA Labeled Atomic\r Data Interface (ALADDIN) database and the Atomic Data Analysis Structure (ADAS) open database.

The Value of Full Scale Prototype Data - Testing Oyster 800 at EMEC, Orkney

This paper demonstrates the unparalleled value of full scale data which has been acquired from ocean trials of Aquamarine Power’s Oyster 800 Wave Energy Converter (WEC) at the European Marine Energy Centre (EMEC), Orkney, Scotland.
High quality prototype and wave data were simultaneously recorded in over 750 distinct sea states (comprising different wave height, wave period and tidal height combinations) and include periods of operation where the hydraulic Power Take-Off (PTO) system was both pressurised (damped operation) and de-pressurised (undamped operation).
A detailed model-prototype correlation procedure is presented where the full scale prototype behaviour is compared to predictions from both experimental and numerical modelling techniques via a high temporal resolution wave-by-wave reconstruction. This unquestionably provides the definitive verification of the capabilities of such research techniques and facilitates a robust and meaningful uncertainty analysis to be performed on their outputs.
The importance of a good data capture methodology, both in terms of handling and accuracy is also presented. The techniques and procedures implemented by Aquamarine Power for real-time data management are discussed, including lessons learned on the instrumentation and infrastructure required to collect high-value data.

Volatile terpenoids and C13 norisoprenoids in wines: development of rapid methods of analysis and evaluation of the sesquiterpenoids biological potential

Vitis vinifera L., the most widely cultivated fruit crop in the world, was the starting point for the development of this PhD thesis. This subject was exploited following on two actual trends: i) the development of rapid, simple, and high sensitive methodologies with minimal sample handling; and ii) the valuation of natural products as a source of compounds with potential health benefits. The target group of compounds under study were the volatile terpenoids (mono and sesquiterpenoids) and C13 norisoprenoids, since they may present biological impact, either from the sensorial point of view, as regards to the wine aroma, or by the beneficial properties for the human health. Two novel methodologies for quantification of C13 norisoprenoids in wines were developed. The first methodology, a rapid method, was based on the headspace solid-phase microextraction combined with gas chromatography-quadrupole mass spectrometry operating at selected ion monitoring mode (HS-SPME/GC-qMS-SIM), using GC conditions that allowed obtaining a C13 norisoprenoid volatile signature. It does not require any pre-treatment of the sample, and the C13 norisoprenoid composition of the wine was evaluated based on the chromatographic profile and specific m/z fragments, without complete chromatographic separation of its components. The second methodology, used as reference method, was based on the HS-SPME/GC-qMS-SIM, allowing the GC conditions for an adequate chromatographic resolution of wine components. For quantification purposes, external calibration curves were constructed with β-ionone, with regression coefficient (r2) of 0.9968 (RSD 12.51 %) and 0.9940 (RSD of 1.08 %) for the rapid method and for the reference method, respectively. Low detection limits (1.57 and 1.10 μg L-1) were observed. These methodologies were applied to seventeen white and red table wines. Two vitispirane isomers (158-1529 L-1) and 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN) (6.42-39.45 μg L-1) were quantified. The data obtained for vitispirane isomers and TDN using the two methods were highly correlated (r2 of 0.9756 and 0.9630, respectively). A rapid methodology for the establishment of the varietal volatile profile of Vitis vinifera L. cv. 'Fernão-Pires' (FP) white wines by headspace solid-phase microextraction combined with comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (HS-SPME/GCxGC-TOFMS) was developed. Monovarietal wines from different harvests, Appellations, and producers were analysed. The study was focused on the volatiles that seem to be significant to the varietal character, such as mono and sesquiterpenic compounds, and C13 norisoprenoids. Two-dimensional chromatographic spaces containing the varietal compounds using the m/z fragments 93, 121, 161, 175 and 204 were established as follows: 1tR = 255-575 s, 2tR = 0,424-1,840 s, for monoterpenoids, 1tR = 555-685 s, 2tR = 0,528-0,856 s, for C13 norisoprenoids, and 1tR = 695-950 s, 2tR = 0,520-0,960 s, for sesquiterpenic compounds. For the three chemical groups under study, from a total of 170 compounds, 45 were determined in all wines, allowing defining the "varietal volatile profile" of FP wine. Among these compounds, 15 were detected for the first time in FP wines. This study proposes a HS-SPME/GCxGC-TOFMS based methodology combined with classification-reference sample to be used for rapid assessment of varietal volatile profile of wines. This approach is very useful to eliminate the majority of the non-terpenic and non-C13 norisoprenic compounds, allowing the definition of a two-dimensional chromatographic space containing these compounds, simplifying the data compared to the original data, and reducing the time of analysis. The presence of sesquiterpenic compounds in Vitis vinifera L. related products, to which are assigned several biological properties, prompted us to investigate the antioxidant, antiproliferative and hepatoprotective activities of some sesquiterpenic compounds. Firstly, the antiradical capacity of trans,trans-farnesol, cis-nerolidol, α-humulene and guaiazulene was evaluated using chemical (DPPH• and hydroxyl radicals) and biological (Caco-2 cells) models. Guaiazulene (IC50= 0.73 mM) was the sesquiterpene with higher scavenger capacity against DPPH•, while trans,trans-farnesol (IC50= 1.81 mM) and cis-nerolidol (IC50= 1.48 mM) were more active towards hydroxyl radicals. All compounds, with the exception of α-humulene, at non-cytotoxic levels (≤ 1 mM), were able to protect Caco-2 cells from oxidative stress induced by tert-butyl hydroperoxide. The activity of the compounds under study was also evaluated as antiproliferative agents. Guaiazulene and cis-nerolidol were able to more effectively arrest the cell cycle in the S-phase than trans,trans-farnesol and α-humulene, being the last almost inactive. The relative hepatoprotection effect of fifteen sesquiterpenic compounds, presenting different chemical structures and commonly found in plants and plant-derived foods and beverages, was assessed. Endogenous lipid peroxidation and induced lipid peroxidation with tert-butyl hydroperoxide were evaluated in liver homogenates from Wistar rats. With the exception of α-humulene, all the sesquiterpenic compounds under study (1 mM) were effective in reducing the malonaldehyde levels in both endogenous and induced lipid peroxidation up to 35% and 70%, respectively. The developed 3D-QSAR models, relating the hepatoprotection activity with molecular properties, showed good fit (R2LOO > 0.819) with good prediction power (Q2 > 0.950 and SDEP < 2%) for both models. A network of effects associated with structural and chemical features of sesquiterpenic compounds such as shape, branching, symmetry, and presence of electronegative fragments, can modulate the hepatoprotective activity observed for these compounds. In conclusion, this study allowed the development of rapid and in-depth methods for the assessment of varietal volatile compounds that might have a positive impact on sensorial and health attributes related to Vitis vinifera L. These approaches can be extended to the analysis of other related food matrices, including grapes and musts, among others. In addition, the results of in vitro assays open a perspective for the promising use of the sesquiterpenic compounds, with similar chemical structures such as those studied in the present work, as antioxidants, hepatoprotective and antiproliferative agents, which meets the current challenges related to diseases of modern civilization.

A robótica educativa no ensino e aprendizagem de conceitos de programação e algoritmos

Relatório da Prática de Ensino Supervisionada, Ensino de Informática, Universidade de Lisboa, 2013

Knowledge representation for data integration and exploration in translational medicine

Tese de doutoramento, Informática (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2014

Design and Qualitative/Quantitative Analysis of Multi-Agent Spatial Simulation Library

Thesis (Master's)--University of Washington, 2012

Some reflections on acquisition, processing and analysis of statistical data in forest soils

Beyond the classical statistical approaches (determination of basic statistics, regression analysis, ANOVA, etc.) a new set of applications of different statistical techniques has increasingly gained relevance in the analysis, processing and interpretation of data concerning the characteristics of forest soils. This is possible to be seen in some of the recent publications in the context of Multivariate Statistics. These new methods require additional care that is not always included or refered in some approaches. In the particular case of geostatistical data applications it is necessary, besides to geo-reference all the data acquisition, to collect the samples in regular grids and in sufficient quantity so that the variograms can reflect the spatial distribution of soil properties in a representative manner. In the case of the great majority of Multivariate Statistics techniques (Principal Component Analysis, Correspondence Analysis, Cluster Analysis, etc.) despite the fact they do not require in most cases the assumption of normal distribution, they however need a proper and rigorous strategy for its utilization. In this work, some reflections about these methodologies and, in particular, about the main constraints that often occur during the information collecting process and about the various linking possibilities of these different techniques will be presented. At the end, illustrations of some particular cases of the applications of these statistical methods will also be presented.

Quantifying consistency of Event Related Potentials across subjects based on single-trial topographic analysis

Introduction: Non-invasive brain imaging techniques often contrast experimental conditions across a cohort of participants, obfuscating distinctions in individual performance and brain mechanisms that are better characterised by the inter-trial variability. To overcome such limitations, we developed topographic analysis methods for single-trial EEG data [1]. So far this was typically based on time-frequency analysis of single-electrode data or single independent components. The method's efficacy is demonstrated for event-related responses to environmental sounds, hitherto studied at an average event-related potential (ERP) level. Methods: Nine healthy subjects participated to the experiment. Auditory meaningful sounds of common objects were used for a target detection task [2]. On each block, subjects were asked to discriminate target sounds, which were living or man-made auditory objects. Continuous 64-channel EEG was acquired during the task. Two datasets were considered for each subject including single-trial of the two conditions, living and man-made. The analysis comprised two steps. In the first part, a mixture of Gaussians analysis [3] provided representative topographies for each subject. In the second step, conditional probabilities for each Gaussian provided statistical inference on the structure of these topographies across trials, time, and experimental conditions. Similar analysis was conducted at group-level. Results: Results show that the occurrence of each map is structured in time and consistent across trials both at the single-subject and at group level. Conducting separate analyses of ERPs at single-subject and group levels, we could quantify the consistency of identified topographies and their time course of activation within and across participants as well as experimental conditions. A general agreement was found with previous analysis at average ERP level. Conclusions: This novel approach to single-trial analysis promises to have impact on several domains. In clinical research, it gives the possibility to statistically evaluate single-subject data, an essential tool for analysing patients with specific deficits and impairments and their deviation from normative standards. In cognitive neuroscience, it provides a novel tool for understanding behaviour and brain activity interdependencies at both single-subject and at group levels. In basic neurophysiology, it provides a new representation of ERPs and promises to cast light on the mechanisms of its generation and inter-individual variability.

Structural analysis of Turtle mountain (Alberta) using digital elevation model: Toward a progressive failure

In 1903, the eastern slope of Turtle Mountain (Alberta) was affected by a 30 M m3-rockslide named Frank Slide that resulted in more than 70 casualties. Assuming that the main discontinuity sets, including bedding, control part of the slope morphology, the structural features of Turtle Mountain were investigated using a digital elevation model (DEM). Using new landscape analysis techniques, we have identified three main joint and fault sets. These results are in agreement with those sets identified through field observations. Landscape analysis techniques, using a DEM, confirm and refine the most recent geology model of the Frank Slide. The rockslide was initiated along bedding and a fault at the base of the slope and propagated up slope by a regressive process following a surface composed of pre-existing discontinuities. The DEM analysis also permits the identification of important geological structures along the 1903 slide scar. Based on the so called Sloping Local Base Level (SLBL) an estimation was made of the present unstable volumes in the main scar delimited by the cracks, and around the south area of the scar (South Peak). The SLBL is a method permitting a geometric interpretation of the failure surface based on a DEM. Finally we propose a failure mechanism permitting the progressive failure of the rock mass that considers gentle dipping wedges (30°). The prisms or wedges defined by two discontinuity sets permit the creation of a failure surface by progressive failure. Such structures are more commonly observed in recent rockslides. This method is efficient and is recommended as a preliminary analysis prior to field investigation.