Integrating methods for determining length-at-age to improve growth estimates for two large scombrids

Fish growth is commonly estimated from length-at-age data obtained from otoliths. There are several techniques for estimating length-at-age from otoliths including 1) direct observed counts of annual increments; 2) age adjustment based on a categorization of otolith margins; 3) age adjustment based on known periods of spawning and annuli formation; 4) back-calculation to all annuli, and 5) back-calculation to the last annulus only. In this study we compared growth estimates (von Bertalanffy growth functions) obtained from the above five methods for estimating length-at-age from otoliths for two large scombrids: narrow-barred Spanish mackerel (Scomberomorus commerson) and broad-barred king mackerel (Scomberomorus semifasciatus). Likelihood ratio tests revealed that the largest differences in growth occurred between the back-calculation methods and the observed and adjusted methods for both species of mackerel. The pattern, however, was more pronounced for S. commerson than for S. semifasciatus, because of the pronounced effect of gear selectivity demonstrated for S. commerson. We propose a method of substituting length-at-age data from observed or adjusted methods with back-calculated length-at-age data to provide more appropriate estimates of population growth than those obtained with the individual methods alone, particularly when faster growing young fish are disproportionately selected for. Substitution of observed or adjusted length-at-age data with back-calculated length-at-age data provided more realistic estimates of length for younger ages than observed or adjusted methods as well as more realistic estimates of mean maximum length than those derived from backcalculation methods alone.

Estimates of growth and comparisons of growth rates determined from length- and age-based models for populations of purple wrasse (Notolabrus fucicola)

Growth of a temperate reefa-ssociated fish, the purple wrasse (Notolabrus fucicola), was examined from two sites on the east coast of Tasmania by using age- and length-based models. Models based on the von Bertalanffy growth function, in the standard and a reparameterized form, were constructed by using otolith-derived age estimates. Growth trajectories from tag-recaptures were used to construct length-based growth models derived from the GROTAG model, in turn a reparameterization of the Fabens model. Likelihood ratio tests (LRTs) determined the optimal parameterization of the GROTAG model, including estimators of individual growth variability, seasonal growth, measurement error, and outliers for each data set. Growth models and parameter estimates were compared by bootstrap confidence intervals, LRTs, and randomization tests and plots of bootstrap parameter estimates. The relative merit of these methods for comparing models and parameters was evaluated; LRTs combined with bootstrapping and randomization tests provided the most insight into the relationships between parameter estimates. Significant differences in growth of purple wrasse were found between sites in both length- and age-based models. A significant difference in the peak growth season was found between sites, and a large difference in growth rate between sexes was found at one site with the use of length-based models.

Reproductive biology of carpenter seabream (Argyrozona argyrozona) (Pisces: Sparidae) in a marine protected area

The carpenter seabream (Argyrozona argyrozona) is an endemic South African sparid that comprises an important part of the handline fishery. A three-year study (1998−2000) into its reproductive biology within the Tsitsikamma National Park revealed that these fishes are serial spawning late gonochorists. The size at 50% maturity (L50) was estimated at 292 and 297 mm FL for both females and males, respectively. A likelihood ratio test revealed that there was no significant difference between male and female L50 (P>0.5). Both monthly gonadosomatic indices and macroscopically determined ovarian stages strongly indicate that A. argyrozona within the Tsitsikamma National Park spawn in the astral summer between November and April. The presence of postovulatory follicles (POFs) confirmed a six-month spawning season, and monthly proportions of early (0−6 hour old) POFs showed that spawning frequency was highest (once every 1−2 days) from December to March. Although spawning season was more highly correlated to photoperiod (r = 0.859) than temperature (r = −0.161), the daily proportion of spawning fish was strongly correlated (r= 0.93) to ambient temperature over the range 9−22oC. These results indicate that short-term upwelling events, a strong feature in the Tsitsikamma National Park during summer, may negatively affect carpenter fecundity. Both spawning frequency and duration (i.e., length of spawning season) increased with fish length. As a result of the allometric relationship between annual fecundity and fish mass a 3-kg fish was calculated to produce fivefold more eggs per kilogram of body weight than a fish of 1 kg. In addition to producing more eggs per unit of weight each year, larger fish also produce significantly larger eggs.

A general framework for integrating environmental time series into stock assessment models: model description, simulation testing, and example

We present a method to integrate environmental time series into stock assessment models and to test the significance of correlations between population processes and the environmental time series. Parameters that relate the environmental time series to population processes are included in the stock assessment model, and likelihood ratio tests are used to determine if the parameters improve the fit to the data significantly. Two approaches are considered to integrate the environmental relationship. In the environmental model, the population dynamics process (e.g. recruitment) is proportional to the environmental variable, whereas in the environmental model with process error it is proportional to the environmental variable, but the model allows an additional temporal variation (process error) constrained by a log-normal distribution. The methods are tested by using simulation analysis and compared to the traditional method of correlating model estimates with environmental variables outside the estimation procedure. In the traditional method, the estimates of recruitment were provided by a model that allowed the recruitment only to have a temporal variation constrained by a log-normal distribution. We illustrate the methods by applying them to test the statistical significance of the correlation between sea-surface temperature (SST) and recruitment to the snapper (Pagrus auratus) stock in the Hauraki Gulf–Bay of Plenty, New Zealand. Simulation analyses indicated that the integrated approach with additional process error is superior to the traditional method of correlating model estimates with environmental variables outside the estimation procedure. The results suggest that, for the snapper stock, recruitment is positively correlated with SST at the time of spawning.

Comparative molecular population genetics of phycoerythrin locus in Prochlorococcus

As the only remainder type of phycobiliproteins in Prochlorococcus, the actual role of phycoerythrin still remains unknown. Previous studies revealed that two different forms of phycoerythrin gene were found in two ecotypes of Prochlorococcus that are specifically adapted to either high light (HL) or low light (LL) conditions. Here we analyze patterns of phycoerythrin nucleotide variation in the HL- and LL-Prochlorococcus populations. Our analyses reveal a significantly greater number of non-synonymous fixed substitutions in peB and peA than expected based on interspecific comparisons. This pattern of excess non-synonymous fixed substitutions is not seen in other five phycoerythrin-related genes (peZ/V/Y/T/S). Several neutrality statistical tests indicate an excess of rare frequency polymorphisms in the LL-Prochlorococcus data, but an excess of intermediate frequency polymorphisms in the HL-Prochlorococcus data. Distributions of the positively selected sites identified using the likelihood ratio test, when mapped onto the phycoerythrin tertiary structure, reveal that HL- and LL-phycoerythrin should be under different selective patterns. These findings may provide insights into the likely role of selection at the phycoerythrin locus and motivate further research to unveil the function of phycoerythrin in Prochlorococcus.

雨生红球藻中虾青素合成相关酶基因的转录调控序列分析

虾青素是一种具有极强抗氧化活性的类胡萝卜素，具有广泛的应用价值。雨生红球藻是一种单细胞绿藻，在逆境胁迫条件下能够大量合成并迅速积累虾青素，其积累量最高可达细胞干重的4%，从而成为目前首选的天然虾青素合成工具。但是，虾青素的大量积累总是发生在不适于生物量积累的环境胁迫条件下，虾青素积累与生物量积累之间成为一对矛盾，制约着虾青素大量生产。 异戊烯焦磷酸异构酶（IPI）、β-胡萝卜素酮化酶（BKT）和β-胡萝卜素氧化酶（CRTO）是虾青素合成过程中的相关酶。已有研究结果表明，这些酶基因的表达调控至少是部分发生在转录水平上的，这就为我们从转录水平上研究虾青素生物合成关键酶基因的调控机制提供了重要线索。 本文研究结果如下： 1. 利用基因组步移的方法克隆了两条异戊烯焦磷酸异构酶基因（ipi）的5’上游侧翼序列（1.8kb和2.5kb），预示着ipi的转录由不止一个启动子调控。 2. 利用上述方法克隆了两条β-胡萝卜素氧化酶基因（crtO）5'上游侧翼序列（1kb和2kb）。以lacZ为报告基因的瞬间表达实验结果表明，长度为320bp(-682/-363)的crtO 5'上游侧翼序列具有很强的启动转录活性，提示这段序列包含了启动子的结构。 3. 利用凝胶阻滞的方法研究了雨生红球藻中bkt强启动转录活性区域，即 309bp(-617/-309)启动子区域的转录因子结合位点，并发现在-396/-338的59bp区域内存在特异的核蛋白结合位点。通过序列分析，发现此59bp区域并不包含TATA或者CAAT-box，而是存在对光、缺氧、p-香豆酸及激素反应的G-box。 4. 根据国外已经获得的ipi和crtO全长cDNA序列，利用长距离PCR法从红球藻基因组中扩增到基因组序列。发现ipi和crtO均包含6个外显子和5个内含子，内含子的剪切位点基本符合GU-AG规律。并通过似然比检验（Likelihood ratio test）的方法发现两基因在进化过程中存在着正选择现象。 这些工作为下一步继续寻找与上述特定DNA调控区域特异结合的反式作用因子（蛋白质因子）奠定了基础。

The evaluation of evidence for exponentially distributed data

C.G.G. Aitken, Q. Shen, R. Jensen and B. Hayes. The evaluation of evidence for exponentially distributed data. Computational Statistics & Data Analysis, vol. 51, no. 12, pp. 5682-5693, 2007.

A Bayesian Approach for TCP to Distinguish Congestion from Wireless Losses

(This Technical Report revises TR-BUCS-2003-011) The Transmission Control Protocol (TCP) has been the protocol of choice for many Internet applications requiring reliable connections. The design of TCP has been challenged by the extension of connections over wireless links. In this paper, we investigate a Bayesian approach to infer at the source host the reason of a packet loss, whether congestion or wireless transmission error. Our approach is "mostly" end-to-end since it requires only one long-term average quantity (namely, long-term average packet loss probability over the wireless segment) that may be best obtained with help from the network (e.g. wireless access agent).Specifically, we use Maximum Likelihood Ratio tests to evaluate TCP as a classifier of the type of packet loss. We study the effectiveness of short-term classification of packet errors (congestion vs. wireless), given stationary prior error probabilities and distributions of packet delays conditioned on the type of packet loss (measured over a larger time scale). Using our Bayesian-based approach and extensive simulations, we demonstrate that congestion-induced losses and losses due to wireless transmission errors produce sufficiently different statistics upon which an efficient online error classifier can be built. We introduce a simple queueing model to underline the conditional delay distributions arising from different kinds of packet losses over a heterogeneous wired/wireless path. We show how Hidden Markov Models (HMMs) can be used by a TCP connection to infer efficiently conditional delay distributions. We demonstrate how estimation accuracy is influenced by different proportions of congestion versus wireless losses and penalties on incorrect classification.

On classical and other methods of discriminant analysis and estimation of log-odds

For two multinormal populations with equal covariance matrices the likelihood ratio discriminant function, an alternative allocation rule to the sample linear discriminant function when n1 ≠ n2 ,is studied analytically. With the assumption of a known covariance matrix its distribution is derived and the expectation of its actual and apparent error rates evaluated and compared with those of the sample linear discriminant function. This comparison indicates that the likelihood ratio allocation rule is robust to unequal sample sizes. The quadratic discriminant function is studied, its distribution reviewed and evaluation of its probabilities of misclassification discussed. For known covariance matrices the distribution of the sample quadratic discriminant function is derived. When the known covariance matrices are proportional exact expressions for the expectation of its actual and apparent error rates are obtained and evaluated. The effectiveness of the sample linear discriminant function for this case is also considered. Estimation of true log-odds for two multinormal populations with equal or unequal covariance matrices is studied. The estimative, Bayesian predictive and a kernel method are compared by evaluating their biases and mean square errors. Some algebraic expressions for these quantities are derived. With equal covariance matrices the predictive method is preferable. Where it derives this superiority is investigated by considering its performance for various levels of fixed true log-odds. It is also shown that the predictive method is sensitive to n1 ≠ n2. For unequal but proportional covariance matrices the unbiased estimative method is preferred. Product Normal kernel density estimates are used to give a kernel estimator of true log-odds. The effect of correlation in the variables with product kernels is considered. With equal covariance matrices the kernel and parametric estimators are compared by simulation. For moderately correlated variables and large dimension sizes the product kernel method is a good estimator of true log-odds.

Survival analysis of adult tuberculosis disease

Background: We conducted a survival analysis of all the confirmed cases of Adult Tuberculosis (TB) patients treated in Cork-City, Ireland. The aim of this study was to estimate Survival time (ST), including median time of survival and to assess the association and impact of covariates (TB risk factors) to event status and ST. The outcome of the survival analysis is reported in this paper. Methods: We used a retrospective cohort study research design to review data of 647 bacteriologically confirmed TB patients from the medical record of two teaching hospitals. Mean age 49 years (Range 18–112). We collected information on potential risk factors of all confirmed cases of TB treated between 2008–2012. For the survival analysis, the outcome of interest was ‘treatment failure’ or ‘death’ (whichever came first). A univariate descriptive statistics analysis was conducted using a non- parametric procedure, Kaplan -Meier (KM) method to estimate overall survival (OS), while the Cox proportional hazard model was used for the multivariate analysis to determine possible association of predictor variables and to obtain adjusted hazard ratio. P value was set at <0.05, log likelihood ratio test at >0.10. Data were analysed using SPSS version 15.0. Results: There was no significant difference in the survival curves of male and female patients. (Log rank statistic = 0.194, df = 1, p = 0.66) and among different age group (Log rank statistic = 1.337, df = 3, p = 0.72). The mean overall survival (OS) was 209 days (95%CI: 92–346) while the median was 51 days (95% CI: 35.7–66). The mean ST for women was 385 days (95%CI: 76.6–694) and for men was 69 days (95%CI: 48.8–88.5). Multivariate Cox regression showed that patient who had history of drug misuse had 2.2 times hazard than those who do not have drug misuse. Smokers and alcohol drinkers had hazard of 1.8 while patients born in country of high endemicity (BICHE) had hazard of 6.3 and HIV co-infection hazard was 1.2. Conclusion: There was no significant difference in survival curves of male and female and among age group. Women had a higher ST compared to men. But men had a higher hazard rate compared to women. Anti-TNF, immunosuppressive medication and diabetes were found to be associated with longer ST, while alcohol, smoking, RICHE, BICHE was associated with shorter ST.

Monitoring growth in asthmatic children treated with high dose inhaled glucocorticoids does not predict adrenal suppression

Aims: To determine whether routine outpatient monitoring of growth predicts adrenal suppression in prepubertal children treated with high dose inhaled glucocorticoid.

Methods: Observational study of 35 prepubertal children (aged 4–10 years) treated with at least 1000 µg/day of inhaled budesonide or equivalent potency glucocorticoid for at least six months. Main outcome measures were: changes in HtSDS over 6 and 12 month periods preceding adrenal function testing, and increment and peak cortisol after stimulation by low dose tetracosactrin test. Adrenal suppression was defined as a peak cortisol 500 nmol/l.

Results: The areas under the receiver operator characteristic curves for a decrease in HtSDS as a predictor of adrenal insufficiency 6 and 12 months prior to adrenal testing were 0.50 (SE 0.10) and 0.59 (SE 0.10). Prediction values of an HtSDS change of –0.5 for adrenal insufficiency at 12 months prior to testing were: sensitivity 13%, specificity 95%, and positive likelihood ratio of 2.4. Peak cortisol reached correlated poorly with change in HtSDS ( = 0.23, p = 0.19 at 6 months; = 0.33, p = 0.06 at 12 months).

Conclusions: Monitoring growth does not enable prediction of which children treated with high dose inhaled glucocorticoids are at risk of potentially serious adrenal suppression. Both growth and adrenal function should be monitored in patients on high dose inhaled glucocorticoids. Further research is required to determine the optimal frequency of monitoring adrenal function.

The structure of the Statistics Anxiety Rating Scale: A confirmatory factor analysis using UK psychology students

This study aimed to examine the structure of the statistics anxiety rating scale. Responses from 650 undergraduate psychology students throughout the UK were collected through an on-line study. Based on previous research three different models were specified and estimated using confirmatory factor analysis. Fit indices were used to determine if the model fitted the data and a likelihood ratio difference test was used to determine the best fitting model. The original six factor model was the best explanation of the data. All six subscales were intercorrelated and internally consistent. It was concluded that the statistics anxiety rating scale was found to measure the six subscales it was designed to assess in a UK population.

Rarity, pseudodiagnosticity and Bayesian reasoning

Three experiments investigated the effect of rarity on people's selection and interpretation of data in a variant of the pseudodiagnosticity task. For familiar (Experiment 1) but not for arbitrary (Experiment 3) materials, participants were more likely to select evidence so as to complete a likelihood ratio when the initial evidence they received was a single likelihood concerning a rare feature. This rarity effect with familiar materials was replicated in Experiment 2 where it was shown that participants were relatively insensitive to explicit manipulations of the likely diagnosticity of rare evidence. In contrast to the effects for data selection, there was an effect of rarity on confidence ratings after receipt of a single likelihood for arbitrary but not for familiar materials. It is suggested that selecting diagnostic evidence necessitates explicit consideration of the alternative hypothesis and that consideration of the possible consequences of the evidence for the alternative weakens the rarity effect in confidence ratings. Paradoxically, although rarity effects in evidence selection and confidence ratings are in the spirit of Bayesian reasoning, the effect on confidence ratings appears to rely on participants thinking less about the alternative hypothesis.

Background beliefs and evidence interpretation

In this paper we argue that it is often adaptive to use one’s background beliefs when interpreting information that, from a normative point of view, is incomplete. In both of the experiments reported here participants were presented with an item possessing two features and were asked to judge, in the light of some evidence concerning the features, to which of two categories it was more likely that the item belonged. It was found that when participants received evidence relevant to just one of these hypothesised categories (i.e. evidence that did not form a Bayesian likelihood ratio) they used their background beliefs to interpret this information. In Experiment 2, on the other hand, participants behaved in a broadly Bayesian manner when the evidence they received constituted a completed likelihood ratio. We discuss the circumstances under which participants, when making their judgements, consider the alternative hypothesis. We conclude with a discussion of the implications of our results for an understanding of hypothesis testing, belief revision, and categorisation.

Novel biomarkers in early diagnosis of acute myocardial infarction compared with cardiac troponin T

Aims: To evaluate the role of novel biomarkers in early detection of acute myocardial infarction (MI) in patients admitted with acute chest pain.
Methods and results: A prospective study of 664 patients presenting to two coronary care units with chest pain was conducted over 3 years from 2003. Patients were assessed on admission: clinical characteristics, ECG (electrocardiogram), renal function, cardiac troponin T (cTnT), heart fatty acid binding protein (H-FABP), glycogen phosphorylase-BB, NT-pro-brain natriuretic peptide, D-dimer, hsCRP (high sensitivity C-reactive protein), myeloperoxidase, matrix metalloproteinase-9, pregnancy associated plasma protein-A, soluble CD40 ligand. A =12 h cTnT sample was also obtained. MI was defined as cTnT = 0.03 µg/L. In patients presenting <4 h of symptom onset, sensitivity of H-FABP for MI was significantly higher than admission cTnT (73 vs. 55%; P = 0.043). Specificity of H-FABP was 71%. None of the other biomarkers challenged cTnT. Combined use of H-FABP and cTnT (either one elevated initially) significantly improved the sensitivities of H-FABP or cTnT (85%; P = 0.004). This combined approach also improved the negative predictive value, negative likelihood ratio, and the risk ratio.
Conclusion: Assessment of H-FABP within the first 4 h of symptoms is superior to cTnT for detection of MI, and is a useful additional biomarker for patients with acute chest pain.