The use and abuse of population viability analysis

A recent study by Brook ef al. empirically tested the performance of population viability analysis (PVA) using data from 21 populations across a wide range of species. The study concluded that PVAs are good at predicting the future dynamics of populations. We suggest that this conclusion is a result of a bias in the studies that Brook et al, included in their analyses, We present arguments that PVAs can only be accurate at predicting extinction probabilities if data are extensive and reliable, and if the distribution of vital rates between individuals and years can be assumed stationary in the future, or if any changes can be accurately predicted. In particular, we note th at although catastrophes are likely to have precipitated many extinctions, estimates of the probability of catastrophes are unreliable.

Analysis of an array of monopoles with the use of a radial waveguide approach

A radial guide field matching method (RGFMM) is used to analyze a circular array antenna consisting of one active monopole surrounded by a concentric array of passive monopoles terminated in arbarary loads. An equivalent admittance matrix for this antenna system is determined to study the input admittance of the active monopole when the peripheral elements are terminated in open or short circuits. RGFMM results are compared with free-space method of moments (FS-MoM) results for a small switched-beam array a seven monopoles. Good agreement is noted. (C) 2002 Wiley Periodicals, Inc.

The use of stochastic dynamic programming in optimal landscape reconstruction for metapopulations

A decision theory framework can be a powerful technique to derive optimal management decisions for endangered species. We built a spatially realistic stochastic metapopulation model for the Mount Lofty Ranges Southern Emu-wren (Stipiturus malachurus intermedius), a critically endangered Australian bird. Using diserete-time Markov,chains to describe the dynamics of a metapopulation and stochastic dynamic programming (SDP) to find optimal solutions, we evaluated the following different management decisions: enlarging existing patches, linking patches via corridors, and creating a new patch. This is the first application of SDP to optimal landscape reconstruction and one of the few times that landscape reconstruction dynamics have been integrated with population dynamics. SDP is a powerful tool that has advantages over standard Monte Carlo simulation methods because it can give the exact optimal strategy for every landscape configuration (combination of patch areas and presence of corridors) and pattern of metapopulation occupancy, as well as a trajectory of strategies. It is useful when a sequence of management actions can be performed over a given time horizon, as is the case for many endangered species recovery programs, where only fixed amounts of resources are available in each time step. However, it is generally limited by computational constraints to rather small networks of patches. The model shows that optimal metapopulation, management decisions depend greatly on the current state of the metapopulation,. and there is no strategy that is universally the best. The extinction probability over 30 yr for the optimal state-dependent management actions is 50-80% better than no management, whereas the best fixed state-independent sets of strategies are only 30% better than no management. This highlights the advantages of using a decision theory tool to investigate conservation strategies for metapopulations. It is clear from these results that the sequence of management actions is critical, and this can only be effectively derived from stochastic dynamic programming. The model illustrates the underlying difficulty in determining simple rules of thumb for the sequence of management actions for a metapopulation. This use of a decision theory framework extends the capacity of population viability analysis (PVA) to manage threatened species.

The use of 16s rDNA restriction fragment length polymorphism analysis for the identification of non-fermenting gram negative bacilli in a cystic fibrosis microbiology referral service

The utility of 16s rDNA restriction fragment length polymorphism (RFLP) analysis for the partial genomovar differentiation of Burkholderia cepacia complex bacterium is well documented. We compared the 16s rDNA RFLP signatures for a number of non-fermenting gram negative bacilli (NF GNB) LMG control strains and clinical isolates pertaining to the genera Burkholderia, Pseudomonas, Achromobacter (Alcaligenes), Ralstonia, Stenotrophomonas and Pandoraea. A collection of 24 control strain (LMG) and 25 clinical isolates were included in the study. Using conventional PCR, a 1.2 kbp 16s rDNA fragment was generated for each organism. Following restriction digestion and electrophoresis, each clinical isolate RFLP signature was compared to those of the control strain panel. Nineteen different RFLP signatures were detected from the 28 control strains included in the study. TwentyoneyTwenty- five of the clinical isolates could be classified by RFLP analysis into a single genus and species when compared to the patterns produced by the control strain panel. Four clinical B. pseudomallei isolates produced RFLP signatures which were indistinguishable from B. cepacia genomovars I, III and VIII. The identity of these four isolates were confirmed using B. pseudomallei specific PCR. 16s rDNA RFLP analysis can be a useful identification strategy when applied to NF GNB, particularly for those which exhibit colistin sulfate resistance. The use of this molecular based methodology has proved very useful in the setting of a CF referral laboratory particularly when utilised in conjunction with B. cepacia complex and genomovar specific PCR techniques. Species specific PCR or sequence analysis should be considered for selected isolates; especially where discrepancies between epidemiology, phenotypic and genotypic characteristics occur.

Effects of dopamine receptor D4 variation on alcohol and tobacco use and on novelty seeking: Multivariate linkage and association analysis

The dopamine D4 receptor gene contains a polymorphic sequence consisting of a variable number of 48-base-pair (bp) repeats, and there have been a number of reports that this polymorphism is associated with variation in novelty seeking or in substance abuse and addictive behaviors. In this study we have assessed the linkage and association of DRD4 genotype with novelty seeking, alcohol use, and smoking in a sample of 377 dizygotic twin pairs and 15 single twins recruited from the Australian Twin Registry (ATR). We found no evidence of linkage or association of the DRD4 locus with any of the phenotypes. We made use of repeated measures for some phenotypes to increase power by multivariate genetic analysis, but allelic effects were still non-significant. Specifically, it has been suggested that the DRD4 7-repeat allele is associated with increased novelty seeking in males but we found no evidence for this, despite considerable power to do so. We conclude that DRD4 variation does not have an effect on use of alcohol and the problems that arise from it, on smoking, or on novelty seeking behavior. (C) 2003 Wiley-Liss, Inc.

Use of stable-isotope probing, full-cycle rRNA analysis, and fluorescence in situ hybridization-microautoradiography to study a methanol-fed denitrifying microbial community

A denitrifying microbial consortium was enriched in an anoxically operated, methanol-fed sequencing batch reactor (SBR) fed with a mineral salts medium containing methanol as the sole carbon source and nitrate as the electron acceptor. The SBR was inoculated with sludge from a biological nutrient removal activated sludge plant exhibiting good denitrification. The SBR denitrification rate improved from less than 0.02 mg of NO3-.N mg of mixed-liquor volatile suspended solids (MLVSS)(-1) h(-1) to a steady-state value of 0.06 mg of NO3-.N mg of MLVSS-1 h(-1) over a 7-month operational period. At this time, the enriched microbial community was subjected to stable-isotope probing (SIP) with [C-13] methanol to biomark the DNA of the denitrifiers. The extracted [C-13]DNA and [C-12]DNA from the SIP experiment were separately subjected to full-cycle rRNA analysis. The dominant 16S rRNA gene phylotype (group A clones) in the [C-13]DNA clone library was closely related to those of the obligate methylotrophs Methylobacillus and Methylophilus in the order Methylophilales of the Betaproteobacteria (96 to 97% sequence identities), while the most abundant clone groups in the [C-12]DNA clone library mostly belonged to the family Saprospiraceae in the Bacteroidetes phylum. Oligonucleotide probes for use in fluorescence in situ hybridization (FISH) were designed to specifically target the group A clones and Methylophilales (probes DEN67 and MET1216, respectively) and the Saprospiraceae clones (probe SAP553). Application of these probes to the SBR biomass over the enrichment period demonstrated a strong correlation between the level of SBR denitrification and relative abundance of DEN67-targeted bacteria in the SBR community. By contrast, there was no correlation between the denitrification rate and the relative abundances of the well-known denitrifying genera Hyphomicrobium and Paracoccus or the Saprospiraceae clones visualized by FISH in the SBR biomass. FISH combined with microautoradiography independently confirmed that the DEN67-targeted cells were the dominant bacterial group capable of anoxic [C-14] methanol uptake in the enriched biomass. The well-known denitrification lag period in the methanol-fed SBR was shown to coincide with a lag phase in growth of the DEN67-targeted denitrifying population. We conclude that Methylophilales bacteria are the dominant denitrifiers in our SBR system and likely are important denitrifiers in full-scale methanol-fed denitrifying sludges.

Effects of reduction in heroin supply on injecting drug use: analysis of data from needle and syringe programmes

n early 2001 there was a dramatic decline in the availability of heroin in New South Wales (NSW), Australia, where previously heroin had been readily available at a low price and high purity.1 The decline was confirmed by Australia's strategic early warning system, which revealed a reduction in heroin supply across Australia and a considerable increase in price,2 particularly from January to April 2001. This "heroin shortage" provided a natural experiment in which to examine the effect of substantial changes in price and availability on injecting drug use and its associated harms in Australia's largest heroin market,2 a setting in which harm reduction strategies were widely used. Publicly funded needle and syringe programmes were introduced to Australia in 1987, and methadone maintenance programmes, which were established in the 1970s, were significantly expanded in 1985 and again in 1999.

The use of cusum analysis in the early detection and management of hospital bed occupancy crises

Objective: To assess the value of cusum analysis in hospital bed management. Design: Comparative analysis of medical patient flows, bed occupancy, and emergency department admission rates and access block over 2 years. Setting: Internal Medicine Services and Emergency Department in a teaching hospital. Interventions: Improvements in bed use and changes in the level of available beds. Main outcome measures: Average length of stay; percentage occupancy of available beds; number of patients waiting more than 8 hours for admission (access block); number of medical patients occupying beds in non-medical wards; and number of elective surgical admissions. Results: Cusum analysis provided a simple means of revealing important trends in patient flows that were not obvious in conventional time-series data. This prompted improvements in bed use that resulted in a decrease of 9500 occupied bed-days over a year. Unfortunately and unexpectedly, after some initial improvement, the levels of access block, medical ward congestion and elective surgical admissions all then deteriorated significantly. This was probably caused by excessive bed closures in response to the initial improvement in bed use. Conclusion: Cusum analysis is a useful technique for the early detection of significant changes in patient flows and bed use, and in determining the appropriate number of beds required for a given rate of patient flow.

Use of the EM algorithm to detect QTL affecting multiple-traits in an across half-sib family analysis

QTL detection experiments in livestock species commonly use the half-sib design. Each male is mated to a number of females, each female producing a limited number of progeny. Analysis consists of attempting to detect associations between phenotype and genotype measured on the progeny. When family sizes are limiting experimenters may wish to incorporate as much information as possible into a single analysis. However, combining information across sires is problematic because of incomplete linkage disequilibrium between the markers and the QTL in the population. This study describes formulae for obtaining MLEs via the expectation maximization (EM) algorithm for use in a multiple-trait, multiple-family analysis. A model specifying a QTL with only two alleles, and a common within sire error variance is assumed. Compared to single-family analyses, power can be improved up to fourfold with multi-family analyses. The accuracy and precision of QTL location estimates are also substantially improved. With small family sizes, the multi-family, multi-trait analyses reduce substantially, but not totally remove, biases in QTL effect estimates. In situations where multiple QTL alleles are segregating the multi-family analysis will average out the effects of the different QTL alleles.

The applicability of recurrent neural networks for biological sequence analysis

Selection of machine learning techniques requires a certain sensitivity to the requirements of the problem. In particular, the problem can be made more tractable by deliberately using algorithms that are biased toward solutions of the requisite kind. In this paper, we argue that recurrent neural networks have a natural bias toward a problem domain of which biological sequence analysis tasks are a subset. We use experiments with synthetic data to illustrate this bias. We then demonstrate that this bias can be exploitable using a data set of protein sequences containing several classes of subcellular localization targeting peptides. The results show that, compared with feed forward, recurrent neural networks will generally perform better on sequence analysis tasks. Furthermore, as the patterns within the sequence become more ambiguous, the choice of specific recurrent architecture becomes more critical.