Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter

This paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data. © 2011 IEEE.

“Improved Feature Extraction and Classification Techniques for Multispectral Brain Magnetic Resonance Images”

Magnetic Resonance Imaging (MRI) is a multi sequence medical imaging technique in which stacks of images are acquired with different tissue contrasts. Simultaneous observation and quantitative analysis of normal brain tissues and small abnormalities from these large numbers of different sequences is a great challenge in clinical applications. Multispectral MRI analysis can simplify the job considerably by combining unlimited number of available co-registered sequences in a single suite. However, poor performance of the multispectral system with conventional image classification and segmentation methods makes it inappropriate for clinical analysis. Recent works in multispectral brain MRI analysis attempted to resolve this issue by improved feature extraction approaches, such as transform based methods, fuzzy approaches, algebraic techniques and so forth. Transform based feature extraction methods like Independent Component Analysis (ICA) and its extensions have been effectively used in recent studies to improve the performance of multispectral brain MRI analysis. However, these global transforms were found to be inefficient and inconsistent in identifying less frequently occurred features like small lesions, from large amount of MR data. The present thesis focuses on the improvement in ICA based feature extraction techniques to enhance the performance of multispectral brain MRI analysis. Methods using spectral clustering and wavelet transforms are proposed to resolve the inefficiency of ICA in identifying small abnormalities, and problems due to ICA over-completeness. Effectiveness of the new methods in brain tissue classification and segmentation is confirmed by a detailed quantitative and qualitative analysis with synthetic and clinical, normal and abnormal, data. In comparison to conventional classification techniques, proposed algorithms provide better performance in classification of normal brain tissues and significant small abnormalities.

Abnormal methylation at the KvDMR1 imprinting control region in clinically normal children conceived by assisted reproductive technologies

Genomic imprinting alterations have been shown to be associated with assisted reproductive technologies (ARTs) in animals. At present, data obtained in humans are inconclusive; however, some epidemiological studies have demonstrated an increased incidence of imprinting disorders in children conceived by ARTs. In the present study, we focused on the effect of ARTs [IVF and intracytoplasmic sperm injection (ICSI)] on the epigenetic reprogramming of the maternally methylated imprinting control region KvDMR1 in clinically normal children. Qualitative and quantitative methylation at KvDMR1 were assessed by the methylation-specific PCR approach and by the methylation-sensitive enzymatic digestion associated with real-time PCR method, respectively. DNA was obtained from peripheral blood of 12/18 and umbilical cord blood and placenta of 6/18 children conceived by IVF or ICSI. The methylation patterns observed in this group were compared with the patterns observed in 30 clinically normal naturally conceived children (negative controls) and in 3 naturally conceived Beckwith-Wiedemann syndrome patients (positive controls). Hypomethylation at KvDMR1 was observed in 3/18 clinically normal children conceived by ARTs (2 conceived by IVF and 1 by ICSI). A discordant methylation pattern was observed in the three corresponding dizygotic twins. Our findings corroborate the hypothesis of vulnerability of maternal imprinting to ARTs. Furthermore, the discordant methylation at KvDMR1 observed between dizygotic twins could be consequent to one of the following possibilities: (i) a differential vulnerability of maternal imprints among different embryos; or (ii) epimutations that occurred during gametogenesis resulting in the production of oocytes without the correct primary imprint at KvDMR1.

Determinants of hepato- and spleno-megaly in Hunan, China: cross-sectional survey data from areas endemic for schistosomiasis

In order to understand the determinants of schistosome-related hepato- and spleno-megaly better, 14 002 subjects aged 3-60 years (59% male; mean age =32 years) were randomly selected from 43 villages, all in Hunan province, China, where schistosomiasis caused by Schistosoma japonicum is endemic. The abdomen of each subject was examined along the mid-sternal (MSL) and mid-clavicular lines, for evidence of current hepato- and/or spleno-megaly, and a questionnaire was used to collect information on the medical history of each individual. Current infections with S. japonicum were detected by stool examination. Almost all (99.8%) of the subjects were ethnically Han by descent and most (77%) were engaged in farming. Although schistosomiasis appeared common (42% of the subjects claiming to have had the disease), only 45% of the subjects said they had received anti-schistosomiasis drugs. Overall, 1982 (14%) of the subjects had S. japonicum infections (as revealed by miracidium-hatching tests and/or Katon Katz smears) when examined and 22% had palpable hepatomegaly (i.e. enlargement of at least 3 cm along the MSL), although only 2.5% had any form of detectable splenomegaly (i.e. a Hackett's grade of at least 1). Multiple logistic regression revealed that male subjects, fishermen, farmers, subjects aged greater than or equal to 25 years, subjects with a history of schistosomiasis, and subjects who had had bloody stools in the previous 2 weeks were all at relatively high risk of hepato- and/or spleno-megaly. In areas moderately endemic for Schistosoma japonicum, occupational exposure and disease history appear to be good predictors of current disease status among older residents. These results reconfirm those reported earlier in the same region.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

The Deinotherium (Proboscidea, Mammalia): an abnormal tusk from Lisbon, the Miocene record in Portugal and the first appearance datum. Evidence from Lisbon, Portugal

An exceptionally favourable stratigraphic and chronologic context concerning the Miocene series in Lisbon allows us to stress that there are two successive data as far as the Proboscideans' immigration into western Europe is concerned: firstly, that of Gomphotheres, and later that of Deinotheres. The study of a Langhian (in age) tusk has shown that Deinotherinm havaricum was still present then. The time span of this species could be accurately recognized. A discussion on the genus Deinotherium is presented, as well as its occurrence in Portugal and on its ecologic meaning.

Early diabetes and abnormal postnatal pancreatic islet development in mice lacking Glut-2.

Glut-2 is a low-affinity transporter present in the plasma membrane of pancreatic beta-cells, hepatocytes and intestine and kidney absorptive epithelial cells of mice. In beta-cells, Glut-2 has been proposed to be active in the control of glucose-stimulated insulin secretion (GSIS; ref. 2), and its expression is strongly reduced in glucose-unresponsive islets from different animal models of diabetes. However, recent investigations have yielded conflicting data on the possible role of Glut-2 in GSIS. Whereas some reports have supported a specific role for Glut-2 (refs 5,6), others have suggested that GSIS could proceed normally even in the presence of low or almost undetectable levels of this transporter. Here we show that homozygous, but not heterozygous, mice deficient in Glut-2 are hyperglycaemic and relatively hypo-insulinaemic and have elevated plasma levels of glucagon, free fatty acids and beta-hydroxybutyrate. In vivo, their glucose tolerance is abnormal. In vitro, beta-cells display loss of control of insulin gene expression by glucose and impaired GSIS with a loss of first phase but preserved second phase of secretion, while the secretory response to non-glucidic nutrients or to D-glyceraldehyde is normal. This is accompanied by alterations in the postnatal development of pancreatic islets, evidenced by an inversion of the alpha- to beta-cell ratio. Glut-2 is thus required to maintain normal glucose homeostasis and normal function and development of the endocrine pancreas. Its absence leads to symptoms characteristic of non-insulin-dependent diabetes mellitus.

Truncated robust distance for clinical laboratory safety data monitoring and assessment.

Laboratory safety data are routinely collected in clinical studies for safety monitoring and assessment. We have developed a truncated robust multivariate outlier detection method for identifying subjects with clinically relevant abnormal laboratory measurements. The proposed method can be applied to historical clinical data to establish a multivariate decision boundary that can then be used for future clinical trial laboratory safety data monitoring and assessment. Simulations demonstrate that the proposed method has the ability to detect relevant outliers while automatically excluding irrelevant outliers. Two examples from actual clinical studies are used to illustrate the use of this method for identifying clinically relevant outliers.

The persistence of abnormal returns at industry and firm levels

The present paper proposes a model for the persistence of abnormal returnsboth at firm and industry levels, when longitudinal data for the profitsof firms classiffied as industries are available. The model produces a two-way variance decomposition of abnormal returns: (a) at firm versus industrylevels, and (b) for permanent versus transitory components. This variancedecomposition supplies information on the relative importance of thefundamental components of abnormal returns that have been discussed in theliterature. The model is applied to a Spanish sample of firms, obtainingresults such as: (a) there are significant and permanent differences betweenprofit rates both at industry and firm levels; (b) variation of abnormal returnsat firm level is greater than at industry level; and (c) firm and industry levelsdo not differ significantly regarding rates of convergence of abnormal returns.

Expertises croisées dans la dégénérescence maculaire liée à l'âge. Focus sur la physiopathologie, l'angiogenèse, les données pharmacologiques et cliniques [Review and expert opinion in age related macular degeneration. Focus on the pathophysiology, angiogenesis and pharmacological and clinical data].

Age related macular degeneration (AMD) is a pathological aging of the macula, brought about by the interaction of genetic and environmental factors. It induces geographic atrophy of the retina and/or choroidal neovascularization. In the latter, abnormal vessels develop from the choriocapillaris, with the involvement of VEGF (vascular endothelial growth factor). The VEGF family includes several factors, including VEGF-A, B, C, D, F and PlGF (placental growth factor). Their biological properties and their affinities to the VEGFR1, VEGFR2 and VEGFR3 receptors found on endothelial cells differ. Exudative AMD involves mainly VEGF-A and VEGF-R2. Anti-VEGF agents used in ophthalmology (ranibizumab, bevacizumab and aflibercept) are designed to primarily target this pathway. In vitro, all have sufficient affinity to their ligands. Their therapeutic efficacy must therefore be judged based on clinical criteria. In clinical practice, the minimum number of injections required for a satisfactory result appears to be comparable with all the three. The few available studies on therapeutic substitutions of anti-VEGF compounds suggest that some patients may benefit from substituting the anti-VEGF in cases of an unsatisfactory response to an initial molecule. Although local side effects, including increased risk of geographic atrophy, and systemic effects, including vascular accidents, have been suggested, these risks remain low, specially compared to the benefits of the treatment. Differences in safety between anti-VEGF are theoretically possible but unproven.

Integrated methodologies to study human transcriptional regulation from functional genomics data

Abstract : The human body is composed of a huge number of cells acting together in a concerted manner. The current understanding is that proteins perform most of the necessary activities in keeping a cell alive. The DNA, on the other hand, stores the information on how to produce the different proteins in the genome. Regulating gene transcription is the first important step that can thus affect the life of a cell, modify its functions and its responses to the environment. Regulation is a complex operation that involves specialized proteins, the transcription factors. Transcription factors (TFs) can bind to DNA and activate the processes leading to the expression of genes into new proteins. Errors in this process may lead to diseases. In particular, some transcription factors have been associated with a lethal pathological state, commonly known as cancer, associated with uncontrolled cellular proliferation, invasiveness of healthy tissues and abnormal responses to stimuli. Understanding cancer-related regulatory programs is a difficult task, often involving several TFs interacting together and influencing each other's activity. This Thesis presents new computational methodologies to study gene regulation. In addition we present applications of our methods to the understanding of cancer-related regulatory programs. The understanding of transcriptional regulation is a major challenge. We address this difficult question combining computational approaches with large collections of heterogeneous experimental data. In detail, we design signal processing tools to recover transcription factors binding sites on the DNA from genome-wide surveys like chromatin immunoprecipitation assays on tiling arrays (ChIP-chip). We then use the localization about the binding of TFs to explain expression levels of regulated genes. In this way we identify a regulatory synergy between two TFs, the oncogene C-MYC and SP1. C-MYC and SP1 bind preferentially at promoters and when SP1 binds next to C-NIYC on the DNA, the nearby gene is strongly expressed. The association between the two TFs at promoters is reflected by the binding sites conservation across mammals, by the permissive underlying chromatin states 'it represents an important control mechanism involved in cellular proliferation, thereby involved in cancer. Secondly, we identify the characteristics of TF estrogen receptor alpha (hERa) target genes and we study the influence of hERa in regulating transcription. hERa, upon hormone estrogen signaling, binds to DNA to regulate transcription of its targets in concert with its co-factors. To overcome the scarce experimental data about the binding sites of other TFs that may interact with hERa, we conduct in silico analysis of the sequences underlying the ChIP sites using the collection of position weight matrices (PWMs) of hERa partners, TFs FOXA1 and SP1. We combine ChIP-chip and ChIP-paired-end-diTags (ChIP-pet) data about hERa binding on DNA with the sequence information to explain gene expression levels in a large collection of cancer tissue samples and also on studies about the response of cells to estrogen. We confirm that hERa binding sites are distributed anywhere on the genome. However, we distinguish between binding sites near promoters and binding sites along the transcripts. The first group shows weak binding of hERa and high occurrence of SP1 motifs, in particular near estrogen responsive genes. The second group shows strong binding of hERa and significant correlation between the number of binding sites along a gene and the strength of gene induction in presence of estrogen. Some binding sites of the second group also show presence of FOXA1, but the role of this TF still needs to be investigated. Different mechanisms have been proposed to explain hERa-mediated induction of gene expression. Our work supports the model of hERa activating gene expression from distal binding sites by interacting with promoter bound TFs, like SP1. hERa has been associated with survival rates of breast cancer patients, though explanatory models are still incomplete: this result is important to better understand how hERa can control gene expression. Thirdly, we address the difficult question of regulatory network inference. We tackle this problem analyzing time-series of biological measurements such as quantification of mRNA levels or protein concentrations. Our approach uses the well-established penalized linear regression models where we impose sparseness on the connectivity of the regulatory network. We extend this method enforcing the coherence of the regulatory dependencies: a TF must coherently behave as an activator, or a repressor on all its targets. This requirement is implemented as constraints on the signs of the regressed coefficients in the penalized linear regression model. Our approach is better at reconstructing meaningful biological networks than previous methods based on penalized regression. The method is tested on the DREAM2 challenge of reconstructing a five-genes/TFs regulatory network obtaining the best performance in the "undirected signed excitatory" category. Thus, these bioinformatics methods, which are reliable, interpretable and fast enough to cover large biological dataset, have enabled us to better understand gene regulation in humans.

Steganalytic methods for the detection of histogram shifting data-hiding schemes

In this paper, some steganalytic techniques designed to detect the existence of hidden messages using histogram shifting methods are presented. Firstly, some techniques to identify specific methods of histogram shifting, based on visible marks on the histogram or abnormal statistical distributions are suggested. Then, we present a general technique capable of detecting all histogram shifting techniques analyzed. This technique is based on the effect of histogram shifting methods on the "volatility" of the histogram of differences and the study of its reduction whenever new data are hidden.

Steganalytic Methods for the Detection of Histogram Shifting Data Hiding Schemes

Peer-reviewed

Classification and inventory management of abnormal demand items

The main objective of the study is to form a framework that provides tools to recognise and classify items whose demand is not smooth but varies highly on size and/or frequency. The framework will then be combined with two other classification methods in order to form a three-dimensional classification model. Forecasting and inventory control of these abnormal demand items is difficult. Therefore another object of this study is to find out which statistical forecasting method is most suitable for forecasting of abnormal demand items. The accuracy of different methods is measured by comparing the forecast to the actual demand. Moreover, the study also aims at finding proper alternatives to the inventory control of abnormal demand items. The study is quantitative and the methodology is a case study. The research methods consist of theory, numerical data, current state analysis and testing of the framework in case company. The results of the study show that the framework makes it possible to recognise and classify the abnormal demand items. It is also noticed that the inventory performance of abnormal demand items differs significantly from the performance of smoothly demanded items. This makes the recognition of abnormal demand items very important.

Abnormal Returns of Dividend Announcements during a Boom and a Recession. Empirical Evidence from U.S. from the years of 2000 - 2002 and 2005 - 2007, including Finnish Extra Dividends

This study examines the short time price effect of dividend announcements during a boom and a recession. The data being used here is gathered from the years of 2000 - 2002 when it was a recession after the techno bubble burst and from the years 2005 - 2007 when investors experienced large capital gains all around the world. The data consists of dividend increases and intact observations. The aim is to find out differences in abnormal returns between a boom and a recession. Second, the study examines differences between different dividend yield brackets. Third, Finnish extra dividends, mainly being delivered to shareholders in 2004 are included to the empirical test. Generally stated, the aim is to find out do investors respect dividends more during a recession than a boom and can this be proved by using dividend yield brackets. The empirical results from U.S shows that the abnormal returns of dividend increase announcements during the recession in the beginning of this decade were larger than during the boom. Thus, investors seem to respect dividend increases more when stock prices are falling. Substantial abnormal returns of dividend increases during the time period of 2005 - 2007 could not be found. The results from the overall samples state that the abnormal returns during the recession were positively slightly higher than during the boom. No clear and strong evidence was found between different dividend yield brackets. In Finland, there were substantial abnormal returns on the announcement day of the extra dividends. Thus, indicating that investors saw the extra dividends as a good thing for shareholders' value. This paper is mostly in line with the theory that investors respect dividends more during bad times than good times.