Mood Induction in Depressive Patients: A Comparative Multidimensional Approach

Anhedonia, reduced positive affect and enhanced negative affect are integral characteristics of major depressive disorder (MDD). Emotion dysregulation, e.g. in terms of different emotion processing deficits, has consistently been reported. The aim of the present study was to investigate mood changes in depressive patients using a multidimensional approach for the measurement of emotional reactivity to mood induction procedures. Experimentally, mood states can be altered using various mood induction procedures. The present study aimed at validating two different positive mood induction procedures in patients with MDD and investigating which procedure is more effective and applicable in detecting dysfunctions in MDD. The first procedure relied on the presentation of happy vs. neutral faces, while the second used funny vs. neutral cartoons. Emotional reactivity was assessed in 16 depressed and 16 healthy subjects using self-report measures, measurements of electrodermal activity and standardized analyses of facial responses. Positive mood induction was successful in both procedures according to subjective ratings in patients and controls. In the cartoon condition, however, a discrepancy between reduced facial activity and concurrently enhanced autonomous reactivity was found in patients. Relying on a multidimensional assessment technique, a more comprehensive estimate of dysfunctions in emotional reactivity in MDD was available than by self-report measures alone and this was unsheathed especially by the mood induction procedure relying on cartoons. The divergent facial and autonomic responses in the presence of unaffected subjective reactivity suggest an underlying deficit in the patients' ability to express the felt arousal to funny cartoons. Our results encourage the application of both procedures in functional imaging studies for investigating the neural substrates of emotion dysregulation in MDD patients. Mood induction via cartoons appears to be superior to mood induction via faces and autobiographical material in uncovering specific emotional dysfunctions in MDD

A two-stage classifier approach for network intrusion detection

Network Intrusion Detection Systems (NIDS) are essential to combat security threats in network environments. These systems monitor and detect malicious behavior to provide automated methods of identifying and dealing with attacks or security breaches in a network. Machine learning is a promising approach in the development of effective NIDS. One of the problems faced in the development of such systems is that the datasets used in the construction of classifiers are typically imbalanced. This is because the classification categories do not have relatively equal representation in the datasets. This study investigates a two-stage classifier approach to NIDS based on imbalanced intrusion detection datasets by separating the training and detection of minority and majority intrusion classes. The purpose of this is to allow flexibility in the classification process, for example, two different classifiers can be used for detecting minority and majority classes respectively. In this paper, we performed experiments using the random forests classifier and the contemporary UNSW-NB15 dataset was used to evaluate the effectiveness of the proposed approach

Molecular evaluation of Glypican 3 gene expression in Egyptian patients with Hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most deadly cancers worldwide and it represents a major world health problem. The burdenof hepatocellular carcinoma (HCC) has been increasing in Egypt with a doubling in the incidence rate in the past 10 years. . The genetic alterations that take place in the transformation of normal cells to neoplastic cells give an insight into the molecular mechanism of the disease. Glypican 3 (GPC3) is protein that plays a role in the control of cell division and growth regulation. It acts as a tumor suppressorgene in some tissues, while acting as an oncofetal protein in othertissues. The aim of this work was to study gene expression of GPC 3 inHCC patients in Egypt. This was an internally controlled case series where HCC tumor tissue and non-tumor tissue from the same patient were  analyzed for the expression of GPC 3. This study showed that statistically no significant positive correlation between AFP and GPC3 was observed.So, GPC 3 may serve as a potential marker for the diagnosis and prognosis HCC.Keywords: HCC, Glypican

Tinea pedis na cidade de Manaus - AM, Brasil

De 1981 a 1985, foram atendidos 96 acientes nas unidades de saúde: Instituto de Dermatologia Troppical e Venereologia "Alfredo da Matta" IDTV-AM e Serviço Médico do Instituto nacional de Pesquisas da Amazônia (INPA), Manaus-AM.Os portadores de diagnóstico clínico de Tiena pedis (pé-de-atleta), foram submetidos ao exame micológico, os quais revelaram que dos segmentos podais mais afetados foram os da Região plantar (42,0%) e interdigital (26,0%). Das 99 espécies fúngicas, 74% eram dermatófitos e 26% de leveduras. Dentre as espécies observadas, verificou-se que as mais frequentes por número de ocorrência foram: Trichophyton rubrum (335,5%), Trichopthyton mentagrophytes (25,00% e Candida sp. (25,0%)

Curve-based multivariate distance matrix regression analysis: application to genetic association analyses involving repeated measures

Most, if not all, human phenotypes exhibit a temporal, dosage-dependent, or age effect. Despite this fact, it is rare that data are collected over time or in sequence in relevant studies of the determinants of these phenotypes. The costs and organizational sophistication necessary to collect repeated measurements or longitudinal data for a given phenotype are clearly impediments to this, but greater efforts in this area are needed if insights into human phenotypic expression are to be obtained. Appropriate data analysis methods for genetic association studies involving repeated or longitudinal measures are also needed. We consider the use of longitudinal profiles obtained from fitted functions on repeated data collections from a set of individuals whose similarities are contrasted between sets of individuals with different genotypes to test hypotheses about genetic influences on time-dependent phenotype expression. The proposed approach can accommodate uncertainty of the fitted functions, as well as weighting factors across the time points, and is easily extended to a wide variety of complex analysis settings. We showcase the proposed approach with data from a clinical study investigating human blood vessel response to tyramine. We also compare the proposed approach with standard analytic procedures and investigate its robustness and power via simulation studies. The proposed approach is found to be quite flexible and performs either as well or better than traditional statistical methods