Environmental Epidemiology of Intestinal Schistosomiasis in Uganda: Population Dynamics of Biomphalaria (Gastropoda: Planorbidae) in Lake Albert and Lake Victoria with Observations on Natural Infections with Digenetic Trematodes

This study documented the population dynamics of Biomphalaria and associated natural infections with digenetic trematodes, along the shores of Lake Albert and Lake Victoria, recording local physicochemical factors. Over a two-and-a-half-year study period with monthly sampling, physicochemical factors were measured at 12 survey sites and all freshwater snails were collected. Retained Biomphalaria were subsequently monitored in laboratory aquaria for shedding trematode cercariae, which were classified as either human infective (Schistosoma mansoni) or nonhuman infective. The population dynamics of Biomphalaria differed by location and by lake and had positive relationship with pH (P < 0.001) in both lakes and negative relationship with conductivity (P = 0.04) in Lake Albert. Of the Biomphalaria collected in Lake Albert (N = 6,183), 8.9% were infected with digenetic trematodes of which 15.8% were shedding S. mansoni cercariae and 84.2% with nonhuman infective cercariae. In Lake Victoria, 2.1% of collected Biomphalaria (N = 13,172) were infected with digenetic trematodes with 13.9% shedding S. mansoni cercariae, 85.7% shedding nonhuman infective cercariae, and 0.4% of infected snails shedding both types of cercariae. Upon morphological identification, species of Biomphalaria infected included B. sudanica, B. pfeifferi, and B. stanleyi in Lake Albert and B. sudanica, B. pfeifferi, and B. choanomphala in Lake Victoria. The study found the physicochemical factors that influenced Biomphalaria population and infections. The number and extent of snails shedding S. mansoni cercariae illustrate the high risk of transmission within these lake settings. For better control of this disease, greater effort should be placed on reducing environmental contamination by improvement of local water sanitation and hygiene

Type A Behavior and Savoring Among College Undergraduates: Enjoy Achievements Now—Not Later

Recent research tested the a priori hypothesis that Type A Behavior (TAB) undermines enjoyment of leisure time, and that this effect is mediated by savoring responses which hamper enjoyment.1 Findings suggested that the hypothesized A-B differences in savoring reflect differences in perfectionism rather than in time urgency. The present study uses the same sample to compare 117 extreme Type A and 131 extreme B undergraduates on ten dimensions of savoring assessed for a performance-related stimulus. Findings revealed Type As focus on how proud they are and impressed others are, but are only moderately to weakly involved in actively storing positive memories for later recall, or in reminiscing about prior positive events

Calculated Scattering Cross Sections for He☒He at Thermal Energies

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/71197/2/JCPSA6-40-3-917-1.pd

Finding Joy in the Past, Present, and Future: The Relationship Between Type A Behavior and Savoring Beliefs Among College Undergraduates

Prior research investigating savoring behaviors and Type A behavior (TAB) found that extreme Type A undergraduates are most likely to score in the highest quintile on self-congratulation, and in the lowest three quintiles on memory-building. This study used scores on past-, present-, and future-focused savoring beliefs to discriminate 117 extreme Type A versus 131 extreme Type B college undergraduates. Univariate statistical analysis conducted via UniODA revealed that compared to extreme Type Bs, extreme Type As had significantly greater reminiscence (past focus) and anticipation (future focus) scores, and also had marginally greater savor the moment (present focus) scores. Multivariate analysis via CTA identified a singleattribute model involving a three-branch parse: extreme Type Bs are substantially more likely than extreme Type As to score at lowest levels on anticipation; extreme As and Bs are comparably likely to score at moderate levels on anticipation; and extreme Type As are modestly more likely than extreme Type Bs to score at the highest levels on anticipation

Analysis Involving Categorical Attributes Having Many Response Categories

Attributes measured on a categorical response scale are common in the literature. Categorical scales for attributes such as, for example, political affiliation, ethnic origin, marital status, state of residence, or diagnosis may consist of many qualitative response categories. Such disorganized variables rarely appear in multivariable models: some effects are missed in analysis due to inadequate statistical power for the many categories, and some findings are dismissed due to inability of the investigator to recognize the dimension(s) underlying segmented categories. This research note recommends that such multi-categorical attributes are replaced by a new set of attributes created via content analysis. In this approach observations are scored on new dimensions all theoretically motivated to predict the class variable. The methodology is illustrated using a hypothetical example in the field of investment realty

Obtaining an Enumerated CTA Model via Automated CTA Software

The use of automated CTA software to obtain an enumerated optimal (maximum-accuracy) classification tree analysis (EO-CTA) model is demonstrated and the resulting model is compared with a HO-CTA model developed using the same data

The Role of Residuals in Optimal and Suboptimal Statistical Modeling

This note contrasts the importance of the analysis of model residual values in assessing the invalidity of estimated Type I error rates for parametric methods, versus in determinin

How to Create an ASCII Input Data File for UniODA and CTA Software

UniODA and CTA software require an ASCII (unformatted text) file as input data. Arguably the most difficult task an operator faces in conducting analyses is converting the original data file from (a) whatever software package was used to enter the data, into (b) an ASCII file for analysis. This article first highlights critical issues concerning missing data, variable labels, and variable types that users must address in order to convert their data into an ASCII file for analysis using ODA software. Specific steps needed to convert a data set from its original file-type into a space-delimited ASCII file are then discussed. The process of converting data into ASCII files for use as input data is illustrated for three leading statistical software packages: SPSS, SAS, and STATISTICA

Type A Behavior, Pessimism, and Optimism Among College Undergraduates

This study used scores on measures of dispositional optimism and pessimism to discriminate 117 extreme Type A versus 131 extreme Type B college undergraduates. Consistent with a priori hypotheses the analysis revealed that Type As were significantly less pessimistic, and significantly more optimistic, than Type Bs

Obtaining a Hierarchically Optimal CTA Model Via uniODA Software

The use of UniODA software to obtain a hierarchically optimal (maximum-accuracy) classification tree analysis (HO-CTA) model is demonstrated