Diadochokinetic rate in Saudi and Bahraini arabic speakers : dialect and the influence of syllable type

Arabic is spoken by more than 420 million people worldwide and still there are a limited number of studies on dialects of the Gulf Arabic regions where most selected respondents are male speakers. This study aimed to explore and establish normative data for the Diadochokinetic Rate (DDK) for two dialects (Saudi Arabia’s Najdi and Bahrain’s Bahraini) speakers. Furthermore, it aimed to investigate whether there are differences between the two dialects and whether sex differences are evident. In addition, it investigated syllable type differences. The study used the monosyllables /ba, da, ga/ and the multisyllabic sequence /badaga/ to analyse the DDK rates. Acoustic analysis was carried out to obtain DDK rates for the syllables. A mixed model ANOVA was performed to investigate dialect and sex differences, in addition, to syllable type. The study included 40 males and 40 female speakers from each of the two dialects. Results showed that for DDK, Saudi speakers had faster DDK rates for the monosyllables /ba/, /da/, /ga/, than Bahrainis, while, no significant differences were observed for the multisyllabic sequences. However, there were no differences between male and female speakers with regard to the DDK rates. The syllable /ga/ showed the slowest DDK rate among the monosyllables while the multisyllabic sequences displayed the slowest DDK rates. In brief, normative data for DDK rates for clinic were determined for the Arabic Nadji and Bahrain’s Bahraini dialects. DDK rate was shown to be more sensitive to dialect differences for the monosyllable tasks. However, no sex differences were observed for the Arabic dialects in this study across all DDK tasks

Production of chemical alarm cues in convict cichlids: the effects of diet, body condition and ontogeny

While much is known regarding the role of chemical alarm cues in the mediation of predator–prey dynamics within aquatic ecosystems, little is known regarding the production of these critically important information sources. In a series of laboratory experiments, we tested the possible effects of diet, body condition and ontogeny on the production of chemical alarm cues in juvenile convict cichlids (Archocentrus nigrofasciatus, Cichlidae, Acanthopterygii). Juvenile cichlids were fed one of two diets, tubifex worms (Tubifex spp.) or brine shrimp (Artemia spp.). Shrimp fed cichlids grew longer and heavier and were in better condition than were tubifex fed cichlids. In Experiment 1, cichlids exhibited a stronger antipredator response to conspecific skin extracts from donors fed shrimp versus tubifex, regardless of test cichlid diet. In Experiment 2, juvenile cichlids were exposed to the skin extracts of high versus low condition donors fed either tubifex or shrimp diets. Cichlids exhibited a significantly stronger antipredator response to skin extracts of high condition donors, regardless of donor diet. Finally, in Experiment 3, juvenile cichlids were exposed to skin extracts of similar sized juvenile conspecifics, adult conspecifics, swordtail (Xiphophorus helleri) or a distilled water control. We found no evidence of an ontogenetic change in the production of alarm cues as cichlids exhibited similar intensity antipredator responses when exposed to juvenile and adult conspecific alarm cues. Taken together, these data suggest that individual diet may influence body condition with the consequence of influencing chemical alarm cue production in juvenile cichlids

Computational Methods for Complex Stochastic Systems: A Review of Some Alternatives to MCMC.

We consider analysis of complex stochastic models based upon partial information. MCMC and reversible jump MCMC are often the methods of choice for such problems, but in some situations they can be difficult to implement; and suffer from problems such as poor mixing, and the difficulty of diagnosing convergence. Here we review three alternatives to MCMC methods: importance sampling, the forward-backward algorithm, and sequential Monte Carlo (SMC). We discuss how to design good proposal densities for importance sampling, show some of the range of models for which the forward-backward algorithm can be applied, and show how resampling ideas from SMC can be used to improve the efficiency of the other two methods. We demonstrate these methods on a range of examples, including estimating the transition density of a diffusion and of a discrete-state continuous-time Markov chain; inferring structure in population genetics; and segmenting genetic divergence data