AllerTOP - a server for in silico prediction of allergens

Background: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.Results: A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity.Conclusions: AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. © 2013 Dimitrov et al.; licensee BioMed Central Ltd.

The role of suicide risk in the decision for psychiatric hospitalization after a suicide attempt

BACKGROUND: Suicide prevention can be improved by knowing which variables physicians take into account when considering hospitalization or discharge of patients who have attempted suicide. AIMS: To test whether suicide risk is an adequate explanatory variable for predicting admission to a psychiatric unit after a suicide attempt. METHODS: Analyses of 840 clinical records of patients who had attempted suicide (66.3% women) at four public general hospitals in Madrid (Spain). RESULTS: 180 (21.4%) patients were admitted to psychiatric units. Logistic regression analyses showed that explanatory variables predicting admission were: male gender; previous psychiatric hospitalization; psychiatric disorder; not having a substance-related disorder; use of a lethal method; delay until discovery of more than one hour; previous attempts; suicidal ideation; high suicidal planning; and lack of verbalization of adequate criticism of the attempt. CONCLUSIONS: Suicide risk appears to be an adequate explanatory variable for predicting the decision to admit a patient to a psychiatric ward after a suicide attempt, although the introduction of other variables improves the model. These results provide additional information regarding factors involved in everyday medical practice in emergency settings.

Investigating the role of parent and child characteristics in healthy eating intervention outcomes

While numerous studies have investigated the efficacy of interventions at increasing children's vegetable consumption, little research has examined the effect of individual characteristics on intervention outcomes. In previous research, interventions consisting of modelling and rewards have been shown to increase children's vegetable intake, but differences were identified in terms of how much children respond to such interventions. With this in mind, the current study investigated the role of parental feeding practices, child temperament, and child eating behaviours as predictors of intervention success. Parents (N = 90) of children aged 2-4 years were recruited from toddler groups across Leicestershire, UK. Parents completed measures of feeding practices, child eating behaviours and child temperament, before participating in one of four conditions of a home-based, parent led 14 day intervention aimed at increasing their child's consumption of a disliked vegetable. Correlations and logistic regressions were performed to investigate the role of these factors in predicting intervention success. Parental feeding practices were not significantly associated with intervention success. However, child sociability and food fussiness significantly predicted intervention success, producing a regression model which could predict intervention success in 61% of cases. These findings suggest that future interventions could benefit from being tailored according to child temperament. Furthermore, interventions for children high in food fussiness may be better targeted at reducing fussiness in addition to increasing vegetable consumption.

Regression games

The solution of a TU cooperative game can be a distribution of the value of the grand coalition, i.e. it can be a distribution of the payo (utility) all the players together achieve. In a regression model, the evaluation of the explanatory variables can be a distribution of the overall t, i.e. the t of the model every regressor variable is involved. Furthermore, we can take regression models as TU cooperative games where the explanatory (regressor) variables are the players. In this paper we introduce the class of regression games, characterize it and apply the Shapley value to evaluating the explanatory variables in regression models. In order to support our approach we consider Young (1985)'s axiomatization of the Shapley value, and conclude that the Shapley value is a reasonable tool to evaluate the explanatory variables of regression models.

Modeling the Impacts of Climate Change on Phytogeographical Units. A Case Study of the Moesz Line

Regional climate models (RCMs) provide reliable climatic predictions for the next 90 years with high horizontal and temporal resolution. In the 21st century northward latitudinal and upward altitudinal shift of the distribution of plant species and phytogeographical units is expected. It is discussed how the modeling of phytogeographical unit can be reduced to modeling plant distributions. Predicted shift of the Moesz line is studied as case study (with three different modeling approaches) using 36 parameters of REMO regional climate data-set, ArcGIS geographic information software, and periods of 1961-1990 (reference period), 2011-2040, and 2041-2070. The disadvantages of this relatively simple climate envelope modeling (CEM) approach are then discussed and several ways of model improvement are suggested. Some statistical and artificial intelligence (AI) methods (logistic regression, cluster analysis and other clustering methods, decision tree, evolutionary algorithm, artificial neural network) are able to provide development of the model. Among them artificial neural networks (ANN) seems to be the most suitable algorithm for this purpose, which provides a black box method for distribution modeling.

The use of Poisson regression in the sociological study of suicide

This paper explains how Poisson regression can be used in studies in which the dependent variable describes the number of occurrences of some rare event such as suicide. After pointing out why ordinary linear regression is inappropriate for treating dependent variables of this sort, we go on to present the basic Poisson regression model and show how it fits in the broad class of generalized linear models. Then we turn to discussing a major problem of Poisson regression known as overdispersion and suggest possible solutions, including the correction of standard errors and negative binomial regression. The paper ends with a detailed empirical example, drawn from our own research on suicide.

Gender differences in behavioral and psychosocial correlates of substance abuse among adolescents in residential treatment

This dissertation reports the results of a study that examined differences between genders in a sample of adolescents from a residential substance abuse treatment facility. The sample included 72 males and 65 females, ages 12 through 17. The data were archival, having been originally collected for a study of elopement from treatment. The current study included 23 variables. The variables were from multiple dimensions, including socioeconomic, legal, school, family, substance abuse, psychological, social support, and treatment histories. Collectively, they provided information about problem behaviors and psychosocial problems that are correlates of adolescent substance abuse. The study hypothesized that these problem behaviors and psychosocial problems exist in different patterns and combinations between genders.^ Further, it expected that these patterns and combinations would constitute profiles important for treatment. K-means cluster analysis identified differential profiles between genders in all three areas: problem behaviors, psychosocial problems, and treatment profiles. In the dimension of problem behaviors, the predominantly female group was characterized as suicidal and destructive, while the predominantly male group was identified as aggressive and low achieving. In the dimension of psychosocial problems, the predominantly female group was characterized as abused depressives, while the male group was identified as asocial, low problem severity. A third group, neither predominantly female or male, was characterized as social, high problem severity. When these dimensions were combined to form treatment profiles, the predominantly female group was characterized as abused, self-harmful, and social, and the male group was identified as aggressive, destructive, low achieving, and asocial. Finally, logistic regression and discriminant analysis were used to determine whether a history of sexual and physical abuse impacted problem behavior differentially between genders. Sexual abuse had a substantially greater influence in producing self-mutilating and suicidal behavior among females than among males. Additionally, a model including sexual abuse, physical abuse, low family support, and low support from friends showed a moderate capacity to predict unusual harmful behavior (fire-starting and cruelty to animals) among males. Implications for social work practice, social work research, and systems science are discussed. ^

The effects of cultural distance on student socialization and departure decisions

Organizational socialization theory and university student retention literature support the concept that social integration influences new recruits' level of satisfaction with the organization and their decision to remain. This three-phase study proposes and tests a Cultural Distance Model of student retention based on Tinto's (1975) Student Integration Model, Louis' (1980) Model of Newcomer Experience, and Kuh and Love's (2000) theory relating cultural distance to departure from the organization. ^ The main proposition tested in this study was that the greater the cultural distance, the greater the likelihood of early departure from the organization. Accordingly, it was inferred that new recruits entering the university culture experience some degree of social and psychological distance. The extent of the distance correspondingly influences satisfaction with the institution and intent to remain for subsequent years. ^ The model was tested through two freshman student surveys designed to examine the effects of cultural distance on non-Hispanics at a predominantly Hispanic, urban, public university. The first survey was administered eight weeks into their first Fall semester and the second at the end of their first year. Student retention was determined through their re-enrollment for the second Fall semester. Path analysis tested the viability of the hypothesis relating cultural distance to satisfaction and retention as suggested in the model. Logistic regression tested the model's predictive power. ^ Correlations among variables were significant, accounting for 54% of variance in students' decisions to return for the second year with 96% prediction accuracy. Initial feelings of high cultural distance were related to increased dissatisfaction with social interactions and institutional choice at the end of the first year and students' intention not to re-enroll. Path analysis results supported the view that the construct of culture distance incorporates both social and psychological distance, and is composed of beliefs of institutional fit with one's cultural expectations, individual comfort with the fit, and the consequent sense of “belonging” or identifying with the institution. ^

A study of moderating influences of adolescent alcohol drinking conditioned by perception of peer alcohol drinking

Despite a long history of prevention efforts and federal laws prohibiting the consumption of alcohol for those below the age of 21 years, underage drinking continues at both a high prevalence rate and high incidence rate. The purpose of this research study is to explain underage drinking of alcohol conditioned by perception of peer drinking. An acquisition model is conjectured and then a relationship within the model is explained with a national sample of students. From a developmental perspective, drinking alcohol is acquired in a reasonably ordered fashion that reflects the influences over time of the culture, family, and peers. The study measures perceptions of alcohol drinking during early adolescence when alcohol use begins the maintenance phase of the behavior. The correlation between drinking alcohol and perception of classmate drinking can be described via social learning theory. Simultaneously the moderating effects of grade level, gender, and race/ethnicity are used to explain differences between groups. Multilevel logistic regression was used to analyze the relations. The researcher found support for an association between adolescent drinking and perceptions of classmate drinking. Gender and grade level moderated the relation. African-Americans consistently demonstrated less drinking and less perception of classmate drinking than either whites or other students not white nor African-American. The importance of a better understanding of the process of acquiring drinking behaviors is discussed in relation to future research models with longitudinal data. ^

The theory of margin as a predictor of success on the National Council Licensure Examination for Registered Nurses

The National Council Licensure Examination for Registered Nurses (NCLEX-RN) is the examination that all graduates of nursing education programs must pass to attain the title of registered nurse. Currently the NCLEX-RN passing rate is at an all-time low (81%) for first-time test takers (NCSBN, 2004); amidst a nationwide shortage of registered nurses (Glabman, 2001). Because of the critical need to supply greater numbers of professional nurses, and the potential accreditation ramifications that low NCLEX-RN passing rates can have on schools of nursing and graduates, this research study tests the effectiveness of a predictor model. This model is based upon the theoretical framework of McClusky's (1959) theory of margin (ToM), with the hope that students found to be at-risk for NCLEX-RN failure can be identified and remediated prior to taking the actual licensure examination. To date no theory based predictor model has been identified that predicts success on the NCLEX-RN. ^ The model was tested using prerequisite course grades, nursing course grades and scores on standardized examinations for the 2003 associate degree nursing graduates at a urban community college (N = 235). Success was determined through the reporting of pass on the NCLEX-RN examination by the Florida Board of Nursing. Point biserial correlations tested model assumptions regarding variable relationships, while logistic regression was used to test the model's predictive power. ^ Correlations among variables were significant and the model accounted for 66% of variance in graduates' success on the NCLEX-RN with 98% prediction accuracy. Although certain prerequisite course grades and nursing course grades were found to be significant to NCLEX-RN success, the overall model was found to be most predictive at the conclusion of the academic program of study. The inclusion of the RN Assessment Examination, taken during the final semester of course work, was the most significant predictor of NCLEX-RN success. Success on the NCLEX-RN allows graduates to work as registered nurses, reflects positively on a school's academic performance record, and supports the appropriateness of the educational program's goals and objectives. The study's findings support potential other uses of McClusky's theory of margin as a predictor of program outcome in other venues of adult education. ^

Alcohol use and sexual victimization among Hispanic college women

Sexual victimization of young women typically occurs within a context of alcohol use, such that women are more likely to be victimized on days on which they consume alcohol compared to days on which no alcohol is consumed. Additionally, most research on sexual victimization of women has focused on forced sexual acts; consequently, little is known about forms sexual victimization that college women typically experience, such as brief (e.g., unwanted touching) or verbally coerced experiences (e.g., doing sexual things to prevent a partner from leaving). Finally, there is a need for more research on the processes underlying college women's drinking and the specific mechanisms through which drinking increases risk for sexual victimization. This dissertation sought to replicate recent findings of a temporal association between alcohol use and sexual victimization, and to investigate whether or not binge use increased risk for victimization, within a sample of young Hispanic college women, using repeated-measures logistic regression. This study also aimed to identify and explore typologies of victimization experiences in order to better understand types of sexual victimization common among young college women. Finally, the validity of a model of alcohol use and sexual victimization was investigated using structural equation modeling techniques. The results confirmed and extended previous research by demonstrating an increase in the conditional probability of sexual victimization on days of alcohol consumption compared with days of no alcohol consumption, and on days of binge alcohol consumption compared with days of moderate alcohol consumption. Sexual victimization experiences reported in this study were diverse, and cluster analysis was used to identify and explore specific typologies of victimization experiences, including intimate relationship victimization, brief victimization with stranger, prolonged victimization with acquaintance, and workplace victimization. The results from structural equation modeling (SEM) analyses were complex and helped to illuminate the relationships between reasons for drinking, alcohol use, childhood sexual abuse, sexual victimization, psychopathology, and acculturation-related factors among Hispanic college women. These findings have implications for the design of university-based prevention and intervention efforts aimed at reducing rates of alcohol-related sexual victimization within Hispanic populations.

Correlates of voluntary HIV testing and counseling among middle aged and older Latinas

This study identifies and describes HIV Voluntary Counseling and Testing (VCT) of middle aged and older Latinas. The rate of new cases of HIV in people age 45 and older is rapidly increasing, with a 40.6% increase in the numbers of older Latinas infected with HIV between 1998 and 2002. Despite this increase, there is paucity of research on this population. This research seeks to address the gap through a secondary data analysis of Latina women. The aim of this study is twofold: (1) Develop and empirically test a multivariate model of VCT utilization for middle aged and older Latinas; (2) To test how the three individual components of the Andersen Behavioral Model impact VCT for middle aged and older Latinas. The study is organized around the three major domains of the Andersen Behavioral Model of service use that include: (a) predisposing factors; (b) enabling characteristics and (c) need. Logistic regression using structural equation modeling techniques were used to test multivariate relationships of variables on VCT for a sample of 135 middle age and older Latinas residing in Miami-Dade County, Florida. Over 60% of participants had been tested for HIV. Provider endorsement was found to he the strongest predictor of VCT (odds ration [OR] 6.38), followed by having a clinic as a regular source of healthcare (OR=3.88). Significant negative associations with VCT included self rated health status (OR=.592); Age (OR=.927); Spanish proficiency (OR=.927); number of sexual partners (OR=.613) and consumption of alcohol during sexual activity (.549). As this line of inquiry provides a critical glimpse into the VCT of older Latinas, recommendations for enhanced service provision and research will he offered.

Cognitive and noncognitive variables that predict Florida community college radiography program graduates' success on the Registry

This study examined the predictive merits of selected cognitive and noncognitive variables on the national Registry exam pass rate using 2008 graduates (n = 175) from community college radiography programs in Florida. The independent variables included two GPAs, final grades in five radiography courses, self-efficacy, and social support. The dependent variable was the first-attempt results on the national Registry exam. The design was a retrospective predictive study that relied on academic data collected from participants using the self-report method and on perceptions of students' success on the national Registry exam collected through a questionnaire developed and piloted in the study. All independent variables except self-efficacy and social support correlated with success on the national Registry exam ( p < .01) using the Pearson Product-Moment Correlation analysis. The strongest predictor of the national Registry exam success was the end-of-program GPA, r = .550, p < .001. The GPAs and scores for self-efficacy and social support were entered into a logistic regression analysis to produce a prediction model. The end-of-program GPA (p = .015) emerged as a significant variable. This model predicted 44% of the students who failed the national Registry exam and 97.3% of those who passed, explaining 45.8% of the variance. A second model included the final grades for the radiography courses, self efficacy, and social support. Three courses significantly predicted national Registry exam success; Radiographic Exposures, p < .001; Radiologic Physics, p = .014; and Radiation Safety & Protection, p = .044, explaining 56.8% of the variance. This model predicted 64% of the students who failed the national Registry exam and 96% of those who passed. The findings support the use of in-program data as accurate predictors of success on the national Registry exam.

Predicting hurricane evacuation decisions: When, how many, and how far

Traffic from major hurricane evacuations is known to cause severe gridlocks on evacuation routes. Better prediction of the expected amount of evacuation traffic is needed to improve the decision-making process for the required evacuation routes and possible deployment of special traffic operations, such as contraflow. The objective of this dissertation is to develop prediction models to predict the number of daily trips and the evacuation distance during a hurricane evacuation. ^ Two data sets from the surveys of the evacuees from Hurricanes Katrina and Ivan were used in the models' development. The data sets included detailed information on the evacuees, including their evacuation days, evacuation distance, distance to the hurricane location, and their associated socioeconomic characteristics, including gender, age, race, household size, rental status, income, and education level. ^ Three prediction models were developed. The evacuation trip and rate models were developed using logistic regression. Together, they were used to predict the number of daily trips generated before hurricane landfall. These daily predictions allowed for more detailed planning over the traditional models, which predicted the total number of trips generated from an entire evacuation. A third model developed attempted to predict the evacuation distance using Geographically Weighted Regression (GWR), which was able to account for the spatial variations found among the different evacuation areas, in terms of impacts from the model predictors. All three models were developed using the survey data set from Hurricane Katrina and then evaluated using the survey data set from Hurricane Ivan. ^ All of the models developed provided logical results. The logistic models showed that larger households with people under age six were more likely to evacuate than smaller households. The GWR-based evacuation distance model showed that the household with children under age six, income, and proximity of household to hurricane path, all had an impact on the evacuation distances. While the models were found to provide logical results, it was recognized that they were calibrated and evaluated with relatively limited survey data. The models can be refined with additional data from future hurricane surveys, including additional variables, such as the time of day of the evacuation. ^

Contemporary aspects of dividends: Before and during the financial crisis

The number of dividend paying firms has been on the decline since the popularity of stock repurchases in the 1980s, and the recent financial crisis has brought about a wave of dividend reductions and omissions. This dissertation examined the U.S. firms and American Depository Receipts that are listed on the U.S. equity exchanges according to their dividend paying history in the previous twelve quarters. While accounting for the state of the economy, the firm’s size, profitability, earned equity, and growth opportunities, it determines whether or not the firm will pay a dividend in the next quarter. It also examined the likelihood of a dividend change. Further, returns of firms were examined according to their dividend paying history and the state of the economy using the Fama-French three-factor model. Using forward, backward, and step-wise selection logistic regressions, the results show that firms with a history of regular and uninterrupted dividend payments are likely to continue to pay dividends, while firms that do not have a history of regular dividend payments are not likely to begin to pay dividends or continue to do so. The results of a set of generalized polytomous logistic regressions imply that dividend paying firms are more likely to reduce dividend payments during economic expansions, as opposed to recessions. Also the analysis of returns using the Fama-French three factor model reveals that dividend paying firms are earning significant abnormal positive returns. As a special case, a similar analysis of dividend payment and dividend change was applied to American Depository Receipts that trade on the NYSE, NASDAQ, and AMEX exchanges and are issued by the Bank of New York Mellon. Returns of American Depository Receipts were examined using the Fama-French two-factor model for international firms. The results of the generalized polytomous logistic regression analyses indicate that dividend paying status and economic conditions are also important for dividend level change of American Depository Receipts, and Fama-French two-factor regressions alone do not adequately explain returns for these securities.