Logit-linear Model Research Articles

Accounting for uncertainty when estimating drivers of imperfect detection: An integrated approach illustrated with snorkel surveys for riverine fishes

Imperfect detection is a common issue affecting the accuracy of surveys that quantify animal abundance and distribution. To quantify detectability, counts are often calibrated to independent measures of abundance (e.g., via mark-recapture) but stochastic sampling variability in both data types is not typically accounted for. This practice may cause detectability to be quantified inaccurately and lead to overly confident predictions for out-of-sample applications. Our objective was to develop, apply, and simulation-test an integrated approach for quantifying detectability that better-accommodates uncertainty in the data. The method assumes mark-recapture and count surveys sample the same local abundance with error, allowing the construction of a joint likelihood function for both data sets. The model estimates coefficients that link detection probability to local covariates through a logit-linear model, which enables correcting counts for imperfect detection in locations where mark-recapture data are unavailable. We illustrate the application of the model with an empirical data set of over 100 paired snorkel count and mark-recapture electrofishing surveys for riverine salmonids in northeastern Oregon. Covariates that best explained heterogeneity in detectability included species, visibility, and channel unit type and depth, though substantial variability was attributed to site-level random effects. Estimated detection probability ranged from 0.02 to 0.92 among surveys and was higher for Chinook Salmon (Oncorhynchus tshawytscha) juveniles (mean: 0.38) than for steelhead/Rainbow Trout (O. mykiss; mean: 0.24). Simulation analyses revealed that our integrated model performed better (relative to a method that treated mark-recapture abundance estimates as known without error) with respect to (i) selection of covariates, (ii), credible interval coverage, (iii) accuracy of estimated random variability terms, and (iv) reduced sensitivity to violated mark-recapture assumptions surrounding behavioral effects. This model represents an improvement over simpler calibration methods, particularly for snorkel surveys, by applying more a rigorous statistical treatment of sources of variability while explicitly describing the mechanistic link between local conditions and detectability. The analytical methods we illustrate are general and could be broadly applied to quantify detectability in other biological surveys with paired abundance and count data.

Differences in perception of regional pro-entrepreneurial policy: does obtaining support change a prospect?

Research background: In the paper, we presented a most crucial aspect of the entrepreneurial ecosystem in a regional context that seems to be more visible than the general approach. The role of entrepreneurs and support institutions as one of the main actors of a regional entrepreneurship ecosystem was underscored. We also stressed the significance of entrepreneurs? opinions related to Business Support Institutions (BSI) as sources of feedback and potential boosters of delivery and promotion that can be used by regional stakeholders and policymakers.
 Purpose of the article: The aim of this paper is to investigate significant differences in a perception of regional pro-entrepreneurial institutions between businesses that obtained or did not obtain support.
 Methods: Our study is based on data collected in Lubelskie Voivodeship, hence they refer to a single region in Poland. The sample totalled 386 responses, and we conducted numerical analyses by using the logit linear regression model. As the dependent variable, we used a dual-variable (not supported; supported) that describes two groups of enterprises, i.e. those that do not get and those that got some sort of public support. As independent variables, we adopted 18 factors that depict ordinary scale perception of various aspects of regional pro-entrepreneurial policy.
 Findings & Value added: Analyses revealed nine statistically significant relationships between perception of regional BSI impact on enterprises functioning and regional entrepreneurial ecosystem depending on whether particular enterprises got or did not get support, e.g. a favourable experience in obtaining support boosts positive perception that support from BSI influences positively the decision to set up one's own business. BSI at Lubelskie Voivodeship acts in favour to create bonds between enterprises, and support from BSI increases opportunities to survive in the market. Moreover, supported enterprises perceived better aspects, such as positive influence on enterprises? innovativeness thanks to BSI, and more are convinced that support programmes of BSI are available for a broad group of companies.

Optimization of Planning Layout of Urban Building Based on Improved Logit and PSO Algorithms

There is a huge amount of data in the opportunity of “turning waste into treasure” with the arrival of the big data age. Urban layout is very important for the development of urban transportation and building system. Once the layout of the city is finalized, it will be difficult to start again. Therefore, the urban architectural layout planning and design have a very important impact. This paper uses the urban architecture layout big data for building layout optimization using advanced computation techniques. Firstly, a big data collection and storage system based on the Hadoop platform is established. Then, the evaluation model of urban building planning based on improved logit and PSO algorithm is established. The PSO algorithm is used to find the suitable area for this kind of building layout, and then through five impact indicators: land prices, rail transit, historical protection, road traffic capacity, and commercial potential have been established by using the following logit linear regression model. Then, the bridge between logit and PSO algorithm is established by the fitness value of particle. The particle in the particle swarm is assigned to the index parameter of logit model, and then the logit model in the evaluation system is run. The performance index corresponding to the set of parameters is obtained. The performance index is passed to the PSO as the fitness value of the particle to search for the best adaptive position. The reasonable degree of regional architectural planning is obtained, and the rationality of urban architectural planning layout is determined.

Evaluation of an online family history tool for identifying hereditary and familial colorectal cancer

Identifying a hereditary colorectal cancer (CRC) syndrome or familial CRC (FCC) in a CRC patient may enable the patient and relatives to enroll in surveillance protocols. As these individuals are insufficiently recognized, we evaluated an online family history tool, consisting of a patient-administered family history questionnaire and an automated genetic referral recommendation, to facilitate the identification of patients with hereditary CRC or FCC. Between 2015 and 2016, all newly diagnosed CRC patients in five Dutch outpatient clinics, were included in a trial with a stepped-wedge design, when first visiting the clinic. Each hospital continued standard procedures for identifying patients at risk (control strategy) and then, after a predetermined period, switched to offering the family history tool to included patients (intervention strategy). After considering the tool-based recommendation, the health care provider could decide on and arrange the referral. Primary outcome was the relative number of CRC patients who received screening or surveillance recommendations for themselves or relatives because of hereditary CRC or FCC, provided by genetic counseling. The intervention effect was evaluated using a logit-linear model. With the tool, 46/489 (9.4%) patients received a screening or surveillance recommendation, compared to 35/292 (12.0%) in the control group. In the intention-to-treat-analysis, accounting for time trends and hospital effects, this difference was not statistically significant (p = 0.58). A family history tool does not necessarily assist in increasing the number of CRC patients and relatives enrolled in screening or surveillance recommendations for hereditary CRC or FCC. Other interventions should be considered.

Race and Assisted Housing

This article explores racial disparities between assisted housing outcomes of black and white and white households with children. We compare the assisted housing occupied by black and white households with children, and examine whether young adult education, employment, and earnings outcomes in 2011 differ between blacks and whites who spent part of their childhood in assisted housing in the 2000s. We use a special version of the Panel Study of Income Dynamics (PSID) that has been address-matched to federally assisted housing, and the PSID’s Transition to Adulthood supplement, along with geocode-matched data from the U.S. Census Bureau, American Community Survey (ACS), CoreLogic real estate data, and U.S. Department of Housing and Urban Development (HUD). Statistical methods include difference in means, logit and general linear models. We find no evidence of racial disparities in the type of assisted housing program, the physical quality of project-based developments, or the management of public housing developments in the 2000 decade. But black households with children are more likely to live in assisted housing that is located in poorer quality neighborhoods. Multivariate tests reveal that the worse outcomes of black young adults compared with whites disappear once socioeconomic differences are taken into account. The discrepancy in assisted housing neighborhood quality experienced by black and white children makes no additional contribution to predicting young adult outcomes. Nonetheless, black children living in relatively better assisted housing neighborhoods tend to have better outcomes in young adulthood than those who live in poorer quality assisted housing neighborhoods. We discuss sources of racial disparity in neighborhood quality, and the policies enacted and proposed to address it.

A hierarchical zero‐inflated model for species compositional data—from individual taxon responses to community response

Healthy macroinvertebrate communities are typically composed of a few abundant taxa and many relatively rare taxa. These rare taxa tend to be sensitive to pollution and, in aggregate, are important indicators of water quality (e.g., EPT taxa richness that combines Ephemeroptera, Plecoptera, and Trichoptera taxa). Rare taxa lead to community data sets that contain large numbers of zero values that are often a source of difficulty in data processing and analysis. A hierarchical zero‐inflated Poisson model is developed to study the responses to watershed urbanization of a group of macroinvertebrates taxa (EPT taxa) often used as indicators of good water quality and a healthy stream ecosystem. The zero‐inflated Poisson model is parameterized with a unimodal curve for the abundance model and a logit‐linear model for the zero model. The hierarchical model is used to integrate responses of individual taxa into a community level response. The emphasis of the model is on estimating the probability that an observed 0 represents the true absence of a taxon. By modeling the probability of true absence as a function of watershed level urbanization intensity both at individual taxon level and at the community level, we are able to develop a stressor‐specific tolerance indicator to (1) separate truly sensitive EPT taxa from more tolerant taxa, and (2) compare the responses of different communities to the same stressor or responses of the same community in different regions. We document the development of the model and the process of integrating individual taxon models into a community model. The model is applied to a data set collected for study of the effect of urbanization on stream ecosystems.

Abstract P122: An Interactive Intervention to Increase Stroke Knowledge: Results of the SWIFT Study

Introduction: The majority of acute stroke patients do not seek care within the optimal 3 hour time frame. General informational strategies have not increased stroke preparedness, possibly due to a lack of attention to health literacy or cultural tailoring. Several interventions have successfully increased stroke knowledge yet there remains skepticism that knowledge alone can lead to behavior change. The Stroke Warning Information and Faster Treatment (SWIFT) trial is the first intervention to rigorously test whether an interactive educational intervention (II; 2 in-hospital educational sessions and health literate, culturally tailored materials), compared to an enhanced educational intervention (EE; only educational materials) can increase arrival times for stroke/TIA recurrence to the emergency department (ED) in 1193 participants. Aims: SWIFT found large, non-differential increases in the proportion of participants arriving to the ED within 3 hours in both the II and EE group. Here we assess the effect of SWIFT on stroke knowledge (SK), and stroke preparedness (SP), and assess differences by race. Methods: SK was assessed with the 29 item stroke knowledge scale and dichotomized as high/low SK (≥ 23/< 23 correct). SP was dichotomized into those that knew the three important things to tell a health professional after the onset of stroke symptoms and those that identified less than 3 things. These scales were measured at baseline, and one and twelve months after baseline. We fit logit-linear models for high SK and high SP using generalized estimating equations. Results: We observed no difference between groups at baseline in SK (p-value = 0.44) or SP (p-value = 0.36). For SK, we observed statistically significant effect modification such that knowledge increased over time (p-value = 0.02) and the effect was differential by intervention group (p-value = 0.04). At one month the II group had 1.63 (95%CI = 1.23, 2.15) times greater odds of having high SK compared to the EE group but by twelve months there was no difference between groups (OR=1.21; 0.87, 1.67). However, for SP there is not a statistically significant effect of time (p-value = 0.07) but there is a difference in the rate of change by group. The II group had greater odds of having high SP at one month (OR=3.36; 1.86, 6.10) and twelve months (OR=7.64; 2.49, 23.49) compared to the EE group. We did not observe differences in rates of change by race for SK (p-value = 0.21) or SP (p-value = 0.86) but SK (p-value < 0.01) and SP (p-value < 0.01) increased over time for all races. Overall, we find that both II and EE increase SK over 12 months but that only II was able to increase SP. Moreover, this effect was observed for all race ethnic groups. Conclusions: The provision of culturally tailored, health literate information alone was able to increase SK; it did not eliminate racial disparities in SK but did improve SK for all race/ethnic groups studied.

Genetic parameters for reproductive traits in female Nile tilapia (Oreochromis niloticus): I. Spawning success and time to spawn

Breeding programmes for Nile tilapia typically use nested mating designs with 2 females mated to 1 male to produce paternal half-sib families. This mating design can take up to 3months or longer to produce the desired number of half-sib family groups. Prolonged family production increases common environmental effects, and negatively affects estimation of genetic parameters. In this paper we investigated the hypothesis that prolonged family production is a consequence of selection for growth in Nile tilapia. We compare two mating systems: group mating with 7 males and 15 females (MM) and group mating with a single male and 10 females (SM), using females from generation 12 of the GIFT sub-strain kept at RIA-2, Vietnam. Spawning success was modelled as a threshold trait (SPAWN) using a linear repeatability animal model and a generalised logit linear repeatability model. All animals that spawned before 32days were labelled ‘spawn’ (1) and animals that did not spawn after 32days were considered as ‘no-spawn’ (0). We then changed the threshold and estimated heritability at each threshold point; e.g. with a threshold at 20days, all animals with ‘spawn’ records after 20days are considered ‘no-spawn’. Overall, the MM experiment yielded a higher proportion of successful spawn records than the SM experiment. However, in both experiments 85% of the successful spawns were produced within 20days. In the SM system, males frequently mated with more than one female during a mating period of 4days. Heritability estimates for SPAWN were 0.20 to 0.22 for linear model and 0.14–0.18 for logit model with thresholds from 20 to 32days. Estimates were consistent between linear and logit models. Estimates for ‘time to spawn’ were not different from zero (0.01±0.02). Genetic correlations of SPAWN with harvest weight ranged from 0.48 to 0.52 for thresholds of 20–32days. We conclude that Nile tilapia favour mating in groups and that spawning success as defined here is a heritable trait. Selection for harvest weight in GIFT should improve spawning success of Nile tilapia, provided the mating period is limited to 20–32days. To facilitate the rapid production of paternal half-sibs, we recommend using a group mating design with single males and multiple females in a single tank.

Evaluation of graphical diagnostics for assessing goodness of fit of logistic regression models

The aim of the current work was to evaluate graphical diagnostics for assessment of the fit of logistic regression models. Assessment of goodness of fit of a model to the data set is essential to ensure the model provides an acceptable description of the binary variables seen. For logistic regression the most common diagnostic used for this purpose is binning the data and comparing the empirical probability of the occurrence of a dependent variable with the model predicted probability against the mean covariate value in the bin. Although intuitively appealing this method, which we term simple binning, may not have consistent properties for diagnosing model problems. In this report we describe and evaluate two different diagnostic procedures, random binning and simplified Bayes marginal model plots. These procedures were assessed via simulation under three different designs. Design 1: studies which were balanced on binary variables and a continuous covariate. Design 2: studies that were balanced on binary variables but unbalanced on the continuous covariate. Design 3: studies that were unbalanced on both the binary variables and the covariate. Each simulated study consisted of 500 individuals. Thirty studies were simulated. The covariate of interest was dose which could range from 0 to 20 units. The data were simulated with the dose being related to the outcome according to an E (max) model on the logit scale. A logit E (max) model (correct model) and a logit linear model (wrong model) were fitted to all data sets. The performance of the above diagnostics, in addition to simple binning, was compared. For all designs the proposed diagnostics performed at least as well and in many instances better than simple binning. In case of design 1 random binning and simple binning are identical. In the case of designs 2 and 3 random binning and simplified Bayes marginal model plots were superior in assessing the model fit when compared to simple binning. For the examples tested, both random binning and simplified Bayesian marginal model plots performed acceptably.

Two-Part Models for Analysis of Agatston Scores with Possible Proportionality Constraints

ABSTRACT Logit-linear and probit-linear two-part models can be used to analyze data that are a mixture of zeros and positive continuous responses. The slopes in the linear part of a model can be constrained to be proportional to the slopes in the logit or probit part. In this article, it is shown that implementing such a constraint will decrease (in Loewner ordering) the asymptotic covariance matrix of the maximum likelihood estimates. A case study is provided using coronary artery calcification data from the Multi-Ethnic Study of Atherosclerosis.

Prognostic Impact of C-Kit Mutations in Core Binding Factor-Leukemia.

C-KIT gene mutations are not a rare event in Core Binding Factor Leukemia (CBFL). To investigate their prognostic impact in this setting , we attempted at correlating the KIT mutational status with the WBC count, the level of the WBC-index, the presence of extramedullary disease (EMD) and the outcome, in t(8;21)(n=30) and inv(16) (n=20) AML patients. On the basis of genomic DNA analysis of c-KIT gene exons 2, 8, 10, 11, 17 for mutation detection, we categorized 21 patients (42%) in the “KIT mutated group” (KIT+) and 29 patients (58%) in the “KIT unmutated group” (KIT−). The median age at diagnosis was 46.4 and 45.6 years for the KIT(+) and KIT(−) groups, respectively (P=0.872). Among the KIT(+) patients, we found mutations in exon 8 (n = 4), exon 10 (n =1), exon 11 (n = 1) and exon 17 (n = 15). We recorded mean WBC counts of 28.6 x 109/L vs 34.5 x 109/L (P=0.051), and a mean WBC-index, expressed as WBC x (% Bone Marrow blasts/100), of 37.2 vs 15.5 (P=0.0395), for KIT(+) and KIT(−), respectively. Seven out of 50 patients experienced an EMD: in 6 cases (28.57%) this event occurred in the KIT(+) group. 40 patients (age < 60 years) were evaluable for clinical response; 18 out of them were KIT mutated. At a median follow-up of 24 months (range 10–107), we recorded for KIT(+) and KIT(−), respectively: CR incidence of 88.8 % (16/18) vs 100% (22/22) (P=0.38); Relapse Incidence 81.3% (13/16) vs 31.81% (7/22), P=0.0072; OS 27.7% (5/18) vs 77.3% (17/22), P=0.0049; DFS 16.7% (3/18) vs 77.3% (17/22), P=0.0005. Using a log-it linear model for univariate and multivariate regression analysis, we found a significant contribution to death (P=0.048) and relapse (P= 0.045) exerted by mutation in the whole group of patients; this effect was more evident when the analysis was restricted to patients <60 years (P=0.003 and P=0.014, respectively). In conclusion, this study confirms the correlation between c-KIT mutational status and high WBC-index in CBFL patients. Furthermore, our data indicate a statistical correlation between the presence of c-KIT mutations and both the OS and the DFS.

Prediction of locally optimal splice sites in plant pre-mRNA with applications to gene identification in Arabidopsis thaliana genomic DNA.

Prediction of splice site selection and efficiency from sequence inspection is of fundamental interest (testing the current knowledge of requisite sequence features) and practical importance (genome annotation, design of mutant or transgenic organisms). In plants, the dominant variables affecting splice site selection and efficiency include the degree of matching to the extended splice site consensus and the local gradient of U- and G+C-composition (introns being U-rich and exons G+C-rich). We present a novel method for splice site prediction, which was particularly trained for maize and Arabidopsis thaliana. The method extends our previous algorithm based on logitlinear models by considering three variables simultaneously: intrinsic splice site strength, local optimality and fit with respect to the overall splice pattern prediction. We show that the method considerably improves prediction specificity without compromising the high degree of sensitivity required in gene prediction algorithms. Applications to gene identification are illustrated for Arabidopsis and suggest that successful methods must combine scoring for splice sites, coding potential and similarity with potential homologs in non-trivial ways. A WWW version of the SplicePredictor program is available at http:/gnomic.stanford.edu/volker/SplicePredi ctor.html/

GeneGenerator--a flexible algorithm for gene prediction and its application to maize sequences.

We developed GeneGenerator because of the need for a tool to predict gene structure without knowing in advance how to score potential exons and introns in order to obtain the best results, pertinent in particular to less well-studied organisms for which suitable training sets are small. GeneGenerator is a very flexible algorithm which for a given genomic sequence generates a number of feasible gene structures satisfying user-defined constraints. The specific implementation described in detail requires minimum scoring for translation start and donor and acceptor splice sites according to previously trained logitlinear models. In addition, potential exons and introns are required to exceed specified minimal lengths and threshold scores for coding or non-coding potential derived as log-likelihood ratios of appropriate Markov sequence models. A database of 46 non-redundant genomic sequences from maize is used for illustration. It is shown that the correct gene structures do not always maximize the considered target function. However, in most cases, the correct or nearly correct structures are found in a small set of high-scoring structures. A critical review of the generated structures sometimes allows the choices to be narrowed by considering additional variables such as predicted splice site strength or local optimality of splice site scores. Summary statistics for prediction accuracy over all 46 maize genes are derived under cross-validation and non-cross-validation training conditions for the Markov sequence models. The algorithm achieved exon sensitivity of 0.81 and specificity of 0.75 on an independent set of 14 novel maize genomic segments. GeneGenerator runs under Borland-Pascal 7.0 using MS-DOS and C on UNIX work stations. The source code is available upon request. jkleffe@euler.grumed.fu-berlin-de

Combined effects of heat shock, smoke and darkness on germination of Epacris stuartii Stapf., an endangered fire-prone Australian shrub.

Epacris stuartii (Epacridaceae) is an endangered heathland shrub in which seedling recruitment occurs almost exclusively after fire. Seed viability and multiple seed dormancy mechanisms were examined to explore why levels of seedling recruitment were low after some fires, despite high rates of pre-fire seed production. The individual and combined effects of heat shock, smoke derivatives and darkness on germination were tested in the laboratory using an orthogonal logit-linear model, a form of analysis ideally suited to hypotheses concerning multiple germination cues. Seed viability (56%) was found not to be limiting. After 89 days of incubation, germination was significantly enhanced by heat shock, smoke treatment and continuous darkness but there were no significant interactions. These effects were equal in magnitude and additive such that maximum germination (42% of viable seeds) was stimulated when all three treatments were combined, significantly less germination occurred in response to any two treatments combined (22-23%), any single treatment stimulated germination at levels (10-11%) significantly less than two treatments and the lowest levels of germination occurred among untreated seeds (4%). Relative to the untreated control, germination was stimulated by smoke derivatives in high concentrations but not in low concentrations. The effect of darkness diminished with time so that after 270 days of incubation darkness had no significant effect on germination, while heat and smoke still enhanced germination significantly, additively and interchangeably. More prolonged germination of seeds exposed to light on the soil surface than buried seed may spread the risk of desiccation of emerging seedlings over multiple rainfall events. The role of heat and smoke derivates suggests that fire management is a crucial tool for influencing seedling recruitment and hence the survival of E. stuartii at its only known location.

Logitlinear models for the prediction of splice sites in plant pre-mRNA sequences.

Pre-mRNA splicing in plants, while generally similar to the processes in vertebrates and yeast, is thought to involve plant specific cis-acting elements. Both monocot and dicot introns are typically strongly enriched in U nucleotides, and AU- or U-rich segments are thought to be involved in intron recognition, splice site selection, and splicing efficiency. We have applied logitlinear models to find optimal combinations of splice site variables for the purpose of separating true splice sites from a large excess of potential sites. It is shown that plant splice site prediction from sequence inspection is greatly improved when compositional contrast between exons and introns is considered in addition to degree of matching to the splice site consensus (signal quality). The best model involves subclassification of splice sites according to the identity of the base immediately upstream of the GU and AG signals and gives substantial performance gains compared with conventional profile methods.

Model-based estimation of population attributable risk under cross-sectional sampling.

The covariate-adjusted population attributable risk (PAR) measures the proportionate reduction in disease prevalence in the target population when the putative risk factor is removed, after adjusting for covariate effects. This paper extends the model-based approach developed for retrospective and cohort studies to the cross-sectional sampling design. An appropriate logit linear model is utilized to estimate the covariate-adjusted attributable risk. The asymptotic variance of this complex ratio estimate is obtained using Taylor series expansions which incorporate the sampling variation of the estimated model parameters and the appropriate estimates of risk factor prevalence. These methods are illustrated with cardiovascular disease risk factor data from the second National Health and Nutrition Examination Survey (NHANES II).

Bayesian analysis of binary survey data

First a comprehensive treatment of the hierarchical-conjugate Bayesian predictive approach to binary survey data is presented, encompassing simple random, stratified, cluster, and two-stage sampling, as well as two-stage sampling within strata. For the case of two-stage sampling within strata when there is more than one variable of stratification, analysis using an unsaturated logit linear model on the prior means is proposed. This allows there to be cells containing no sampled clusters. Formulas for posterior predictive means, variances, and covariances of numbers of successes in unsampled portions of clusters are presented in terms of posterior expectations of certain functions of hyperparameters; these may be evaluated by existing methods. The technique is illustrated using a small subset of Canada Youth & AIDS Study data. A sample of students within each of various selected school boards was chosen and interviewed via questionnaire. The boards were stratified/poststratified in two dimensions, but some of the resulting cells contained no data. The additive logit linear model on the prior means produced estimates and posterior variances for boards in all cells. Data showed the additive model to be plausible.

Application of Logistic Regression and Logistic Discrimination in Medical Decision Making

AbstractThe logit linear model has been shown to be very useful for investigations in many fields of biology and medicine. One kind of application of this model is that of logistic discriminant analysis. To conquer some theoretical disadvantages of logistic discriminant analysis and Fisher's linear discriminant analysis too, it has been recommended to augment their model equations with appropriate interaction terms. The present paper discusses experiences with epidemiologic data going this way.

Salinity tolerance of Mytilocypris henricae (Chapman) (Crustacea, Ostracoda)

Salinity tolerance, and the effects of temperature upon it, of the Australian ostracod Mytilocypris henricae (Chapman) was determined in direct transfer experiments using adults. Animals were subjected to a combination of 11 salinities (ranging between 0.0 g · 1−1 and 45.0 g · 1−1) and 4 temperatures (10, 15, 20 and 25 °C). Survival was analysed using two statistical techniques: the logit linear model and the proportional hazards model. Results show that both salinity and temperature have a significant effect on survival, but there is no significant interaction between temperature and salinity.

Determinants of contraceptive use in Nepal.

SummaryThis study, based on the Nepal Contraceptive Prevalence Survey carried out in 1981, is the first of its kind. Analysis using a logit-linear model showed that the prevalence of current contraception was directly related to number and sex of living children, and the age of the woman. The prevalence rates are higher among women who did not want more children and women who had discussed family size with their husbands. The prevalence varies according to women's education and work-status. Urban women have a higher prevalence than rural women. Women who have access to the family planning service within a short distance have higher prevalence than those who do not.