Genome-wide association studies for complex diseases will produce genotypes on hundreds of thousands of single nucleotide polymorphisms (SNPs). A logical first approach to dealing with massive numbers of SNPs is to use some test to screen the SNPs, retaining only those that meet some criterion for futher study. For example, SNPs can be ranked by p-value, and those with the lowest p-values retained. When SNPs have large interaction effects but small marginal effects in a population, they are unlikely to be retained when univariate tests are used for screening. However, model-based screens that pre-specify interactions are impractical for data sets with thousands of SNPs. Random forest analysis is an alternative method that produces a single measure of importance for each predictor variable that takes into account interactions among variables without requiring model specification. Interactions increase the importance for the individual interacting variables, making them more likely to be given high importance relative to other variables. We test the performance of random forests as a screening procedure to identify small numbers of risk-associated SNPs from among large numbers of unassociated SNPs using complex disease models with up to 32 loci, incorporating both genetic heterogeneity and multi-locus interaction.Keeping other factors constant, if risk SNPs interact, the random forest importance measure significantly outperforms the Fisher Exact test as a screening tool. As the number of interacting SNPs increases, the improvement in performance of random forest analysis relative to Fisher Exact test for screening also increases. Random forests perform similarly to the univariate Fisher Exact test as a screening tool when SNPs in the analysis do not interact.In the context of large-scale genetic association studies where unknown interactions exist among true risk-associated SNPs or SNPs and environmental covariates, screening SNPs using random forest analyses can significantly reduce the number of SNPs that need to be retained for further study compared to standard univariate screening methods. Genome-wide association studies for complex diseases such as asthma, schizophrenia, diabetes, and hypertension will soon produce genotypes on hundreds of thousands of single nucleotide polymorphisms (SNPs). Due to the large number of SNPs tested and the potential for both genetic and environmental interactions, determining which SNPs modify the risk of disease is a methodological challenge. While the number of genotypes produced by candidate gene approaches will be somewhat less daunting, on the order of hundreds to thousands of SNPs, it will still be a considerable challenge to weed out the noise and identify the SNPs contributing to complex traits.A logical first approach to dealing with massive numbers of SNPs is to first conduct univariate association tests on each individual SNP, in order to screen-out those with no evidence for disease association. The primary goal of such a procedure is not to prove that a particular variant or set of variants influences disease risk, but to prioritize SNPs for further study. Using a univariate test at this stage will result in low power for SNPs with very small marginal effects in the population, even if the SNPs have large interaction effects. Of course, in addition to taking all individual SNPs, all SNP pairs could also be tested for association. However, when dealing with multiple thousands of SNPs at the outset, such an approach is cumbersome, and raises the question of where to stop: why not all sets of three, four, or even five SNPs as well?Many model-building methods exist for dealing with large numbers of predictors. For example, stochastic search variable selection (SSVS) , a form Multivariate adaptive regression splines (MARS) models have also been explored in the context of genetic linkage and association studies ,6 with sCombinatorial partitioning and multifactor dimensionality reduction -10 are ce.g., [a priori, traditional regression models, which lump all individuals into a single group and estimate average effects over the entire sample, are unlikely to successfully identify the genetic causes of diseases.An additional concern to be considered is genetic heterogeneity. We define genetic heterogeneity to mean that there are multiple possible ways to acquire a disease or trait that can involve different subsets of genes. Traditional regression models are limited in their ability to deal with underlying genetic heterogeneity (see, e.g., ). If gene.g., . When thTree-based methods consist of non-parametric statistical approaches for conducting regression and classification analyses by recursive partitioning . These The ease of interpretation of classification trees, along with their flexibility in accommodating large numbers of predictors and ability to handle heterogeneity, has resulted in increasing interest in their application to genetic association and linkage studies. Classification trees have been adapted for use with sibling pairs to subdivide pairs into more homogenous subgroups defined by non-genetic covariates , thus inClassification trees are grown by recursively partitioning the observations into subgroups with a more homogeneous categorical response . At eachUsing ensembles of trees built in this manner increases the probability that some trees will capture interactions among variables with no strong main effect. Unlike variable selection methods, interactions among predictors do not need to be explicitly specified in order to be utilized by a forest of trees. Instead, any interactions between variables serve to increase the importance of the individual interacting variables, making them more likely to be given high importance relative to other variables. Thus, random forests appear to be particularly well-suited to address a primary problem posed by large scale association studies. In preliminary studies, we have shown the potential of random forests in the context of linkage analysis . Other iTo fully understand the basis of complex disease, it is important to identify the critical genetic factors involved, and to understand the complex relationships between genotypes, environment, and phenotypes. The few successes to date in identifying genes for complex disease suggest that despite carefully collected large samples, novel approaches are needed in the pursuit to dissect the multiple and varying factors that lead to complex human traits. Ultimately, the challenge in identifying polymorphisms that modulate the risk of complex disease is to find methods that can seamlessly handle large numbers of predictors, capitalize on and identify interactions, and tease apart the multiple heterogeneous etiologies. Here, we explore the use of the Random Forest methodology ,29 as a λs) fixed at 2.0 and population disease prevalence Kp equal to 0.10. These values are consistent with or lower than estimates from known complex genetic traits, such as Alzheimer disease, where estimates of cumulative prevalence in siblings of affected range from 30–40%, compared to a population prevalence of 10% at age 80 [λs = 2 and Kp = .10 for models H4M4 and H8M2, and 32 loci are responsible for models H8M4 and H16M2. Table We simulated complex diseases with sibling recurrence risk ratio for the disease , independent of disease status, with allele frequencies distributed equally across the range .01–.99. To simulate the results of an association study, in which we do not expect to be lucky enough to genotype all polymorphisms related to a trait, we included only a subset of the total number of rSNPs in each analysis. We denote the analysis design using the shorthand KkSsNn, where K = k is the total number of rSNPs genotyped in the analysis, S = s is the number of SNPs within each interaction system genotyped, and N = n is the total number of SNPs genotyped in the design. Thus, N-K is the total number of nSNPs in the analysis. For example, suppose the genetic model is H8M4, and the design is K4S2N100. Then out of the total of 8 × 4 = 32 rSNPs that contribute to the trait, four are genotyped: two interacting SNPs from within each of two heterogeneity systems. Six heterogeneity systems are not represented at all in the analysis. In addition to the four genotyped rSNPs, 100-4 = 96 total nSNPs are also genotyped in the design.IT) and standardized (ZT) variable importance scores (see Methods section for definitions of the scores). Little is known about the properties of importance indices under different distributions of the predictor variables. We use simulation to investigate their properties in the context of discrete predictors such as genetic polymorphisms conferring susceptibility to a complex trait.Random forests version 5 software  producesIT and ZT are highly correlated; the average correlation coefficient over 100 replicate data sets ranged from .93 (H8M4) to >.99 (H2M2 and H4M2)  to ranking by Fisher Exact test p-value using K4S2N100 and K4S2N1000 analysis designs, where two SNPs from each of the first two interaction systems are in the analysis. Figure T criterion ranks the four rSNPs as the most significant SNPs more often than the univariate Fisher Exact test association p-value under all genetic models. The difference between the random forest and association p-value ranking is less extreme for N1000. For the H8M4 genetic model, the results do not suggest that one ranking system is better than the other overall. Figure T contain all of the rSNPs. Thus, for a given probability of retaining all of the rSNPs, more SNPs can be eliminated using the ZT criterion than the Fisher exact test p-value. For example, for model H16M2, only 15 SNPs must be retained to have 80% probability that the 4 rSNPs are in the retained set, while 44 SNPs must be retained if the p-value criterion is used. The difference is less dramatic for H8M4: 37 SNPs give 80% probability that the four genotyped rSNPs are in the retained set for the ZT criterion, compared to 43 for the p-value criterion. For N1000, the advantage of the ZT criterion is clear for the H8M2 and H16M2 models. For H4M4, the advantage of ZT is minor, while for H8M4, ranking by ZT appears to give poorer results than the p-value criterion for the higher cutoff values of N. A second interpretation of Figure T criterion than for the univariate p-value criterion for all but the H8M4 model with 1000 total SNPs.We next compared the ranking of rSNPs by importance score  and a constant 96 nSNPs, we looked at the effect of increasing S, the number of rSNPs from each interaction system that were genotyped. Thus, for K8S1, along with 96 nSNPs, one SNP from each of the first 8 systems was included in the analysis, while for K8S4, all four SNPs in the first two systems were included in the analysis. For K8S3, three SNPs from the first two systems, and one from the third were included. Assuming that the random forest analysis was taking advantage of the interactions among the rSNPs, and that this was responsible for the improved performance of the random forests over the univariate tests, we expected the Fisher p-values and random forest importance ZT to perform similarly when only a single rSNP was genotyped from each system, and the random forests to perform increasingly better than the univariate Fisher tests as S increased from 1 to 4. Figures T, the S = 1 analyses for each K were similar to the Fisher results, while for each increase in S, the proportion of replicates for which the N most significant SNPs were rSNPs increases  had a more substantial improvement in ranking using ZT ranks interacting rSNPs higher than the univariate test, but also that the differences in rank correspond to differences in magnitude of ZT that are meaningful. In other words, we want to know how much "better" in terms of ZT  the rSNPs are than the nSNPs. Toward this goal, we computed the difference between the importance ZT of the top ranked rSNP and the top ranked nSNP:Beyond simply ranking SNPs, we may wish to use the magnitude of the difference in importance or p-value to determine which subset of top-ranked SNPs should be prioritized for further study. Thus, particularly for the cases where the rSNPs are among the top-ranked SNPs, we want to determine not just that Zmax(ZT) = maxrSNP(ZT) - maxnSNP(ZT),Das well as the lowest ranked rSNP and the top ranked nSNP:min(ZT) = minrSNP(ZT) - maxnSNP(ZT).Dmin(ZT) is positive when the lowest ranked rSNP is larger than the highest ranked nSNP, and negative when the lowest ranked rSNP is smaller than the highest ranked nSNP.Thus, Dmax(-log p) and Dmin(-log p), for the -log10 transformed Fisher Exact test p-values. In Figure T that is significantly higher than the highest ranking nSNP for both N100 and N1000, while the difference in -log10(p) is not significantly different from 0 for N100, and is significantly less than 0 for N1000. These plots illustrate that the positive differences are typically more extreme for ZT than for -log p, and that the negative differences are less extreme.We computed the analogous quantities, Da priori, such as the underlying genetic model and the ratio of associated risk SNPs to noise SNPs in an analysis.A key advantage of the random forest approach is that the investigator does not have to propose a model of any kind. This is important in an initial genome-wide or candidate region association study, where little is known about the genetic architecture of the trait. If interactions among SNPs exist, they will be exploited within the trees, and the variable importance scores will reflect the interactions. Therefore, we expect that when unknown interactions between true risk SNPs exist, the random forest approach to screening large numbers of SNPs will outperform a univariate ranking method in finding the risk SNPs among a large number of irrelevant SNPs. Our genetic models for simulation feature both multiplicative interaction and genetic heterogeneity. The multiplicative interaction results in a marginal effect in the population, the size of which is dependent on many factors, including the amount of heterogeneity. Thus, we have highlighted models in which univariate tests still have power, and shown that the random forest analysis can outperform these tests for selecting subsets of SNPs for further study. For models with genetic heterogeneity and interactions resulting in no main effect, similar to the models described by Ritchie et al. , the perOur results from analyses with four risk SNPs among 1000 SNPs suggest that even when a high proportion of the analyzed SNPs are unassociated, a random forest can rank interacting SNPs considerably higher than a univariate test, and that the proportional difference in importance between the risk SNPs and the best of the noise SNPs can be larger on average for a random forest. In our scale-up from 100 to 1000 total SNPs, we kept the number of risk SNPs constant. In practice, as we increase the number of SNPs genotyped, we expect that we will also increase the number of risk SNPs (or SNPs in linkage disequilibrium with risk SNPs) that are captured in an analysis. Thus, as a larger and large proportion of the genome or candidate region is captured by a scan, the more likely we will be to have all or most of sets of SNPs that interact, and thus the more likely we are to be in situations where random forest screening will outperform univariate screening of SNP data.It is important to consider the tuning parameters for such analyses. Consistent with the recommendations made by Breiman and Cutler ,29, the We have examined the use of random forests in the context of association studies for complex disease with uncorrelated SNP predictors. Random forests can also be used when predictors are correlated, as is the case with multiple SNPs in linkage disequilibrium within a small genetic region. For any analysis procedure, the more highly correlated variables are, the more they can serve as surrogates for each other, weakening the evidence for association for any one of the correlated variables to the outcome. In a random forest analysis, limited simulations suggest that correlated variables lead to diminished variable importance for each correlated risk SNP (data not shown). One way to limit the problems presented by SNPs in linkage disequilibrium is to use haplotypes instead of SNPs as predictor variables in a random forest. Future challenges include quantifying more completely the effect of linkage disequilibrium among SNPs submitted to a random forest analysis, and developing random forests in the context of haplotypes.With the increasing size of association studies, two-stage analyses, in which in the first stage a subset of the loci are retained for further analyses, are becoming more common. The most frequently voiced concern for these analyses is that variables that interact to increase disease risk but have minimal main effects in the population will be missed. Random forest analyses address this concern by presenting a summary importance of each SNP that takes into account its interactions with other SNPs. Current implementations of random forests can accommodate up to one thousand of SNPs in one analysis with the computation of importance. Further, there is no reason to restrict the input variables to SNPs. Potential environmental covariates can also be easily accommodated, allowing for SNPs with no strong main effect, but environmental interactions, to be distinguished from unassociated SNPs. We have shown that when unknown interactions among SNPs exist in a data set consisting of hundreds to thousands of SNPs, random forest analysis can be significantly more efficient than standard univariate screening methods in ranking the true disease-associated SNPs highly. After identifying the top-ranked SNPs and other variables, and weeding out those unlikely to be associated with the phenotype, more thorough statistical analyses, including model building procedures, can be performed.Xi represent the vector of predictor variable values, yi its true class, Vj(Xi) the vote of tree j and tij an indicator taking value 1 when individual i is out-of-bag for tree j and 0 otherwise. Let X(A,j) = (X1(A,j),..., XN(A,j)) represent the vector of predictor variables with the value of variable A randomly permuted among the out-of-bag observations for tree j, and X(A) the collection of X(A,j) for all trees where N is the total number of individuals in the sample. Letting 1(C) denote the indicator function taking value 1 when the condition C is true and 0 otherwise, the importance index averages over the trees of the forest, and is defined as:Rather than selecting variables for modeling, a random forest uses all available covariates to predict response. Here, we use measures of variable importance to determine which covariates (SNPs, in our case) or sets of covariates are important in the prediction. Breiman  proposedNj represents the number of out-of-bag individuals for tree j and T is the total number of trees.where IT, . The standardized index is defined as:The importance index can be standardized by dividing by a standard error derived from the between-tree variance of the raw index  represents the tree to tree variance of IT, rather than the variance of IT due to the sampling of the individuals from a population: the magnitude of ZT increases with the number of trees in the forest, and the number of trees is limited only by computing time. Thus, this standardized index cannot be treated as a Z-score in the traditional sense.The variance 0, q1, q2) represent the penetrance factors for 0, 1, or 2 risk alleles for an individual locus in a given model. Let G = {g11, g12, . ., gHM} be the multilocus genotype for an individual, where ghm indicates the individual's genotype at locus m (=1, . ., M) of heterogeneity system h (=1, . ., H). Then the penetrance for genotype G is defined as:For simplicity, assume each locus has the same effect, and let (qPG = 1 - (1 - q0q1)2 = 2q0q1 - (q0q1)2.For example, for model H2M2, an individual with genotype G = {0101} would have penetrance 0, q1, q2) and risk allele frequencies, as well as other features of our genetic models, are listed in Table λs and Kp, there is a unique allele frequency when we make the assumption that each SNP subunit has equal effect (the given penetrance factor vector) in the population. We chose penetrance factors such that the risk alleles at each locus for the H2M2, H4M2, H8M2, and H16M2 models are approximately additive in effect on the penetrance factor scale. For H4M4 and H8M4, we chose penetrance factor vectors such that the risk alleles show a moderate degree of dominance.The penetrance factors q0, q, q2 and The marginal genotype relative risks (GRRs) listed in Table All analyses were performed on 100 replicate data sets of 500 cases and 500 controls. We treated the SNPs as ordinal predictors. Random forests have one primary tuning parameter: "mtry" the number of randomly picked covariates to choose among for each split. The Random Forest v5 manual  recommenIT, ZT,), which can be used to rank order the importance of SNPs much as p-values from association tests are used. Using either method, the ranking of the SNPs in an analysis can be used to prioritize which sets of SNPs (or genome regions) should be followed up with further genotyping and/or additional analyses. We use the convention that a rank of 1 is the highest ranking SNP.The random forest analysis produces the raw and standardized importance indices (We compare the ranking of the raw and standardized importance measures, and further compare these with the rank based on p-values from a test of association, the Fisher Exact test, to determine whether the random forest can better discriminate susceptibility SNPs from SNPs unrelated to disease status when there is interaction and heterogeneity among and between SNPs.KLL conceived of and led the design of the study, coordinated all phases of simulation and analysis, and drafted the manuscript. LBH and JS automated the simulation and random forest analysis procedures, and participated in the design and analysis of study results. PVE added critical insight to the development of the genetic models and participated in the interpretation of study results. All authors provided comments on a draft manuscript and read and approved the final manuscript.


Juvenile test period [pre-pubertal locomotor test: Postnatal Day 11 (P11); pre-pubertal water maze test: P18] or Young-adult test period [post-pubertal locomotor test: P60; post-pubertal water maze test: P81].Administration of the N-methyl-D-aspartate (NMDA) antagonist ketamine during the perinatal period can produce a variety of behavioral and neuroanatomical changes. Our laboratory has reported reliable changes in learning and memory following a single dose of ketamine administered late in gestation. However, the nature of the drug-induced changes depends on the point during embryonic development when ketamine is administered. Embryonic day 18 (E18) rat fetuses pre-treated with ketamine  and taught a conditioned taste aversion (CTA) learn and remember the CTA, whereas E19 fetuses do not. The current study sought to determine if long-term behavioral effects could be detected in animals that received ketamine or a saline control injection on either E18 or E19. Rat behavior was evaluated on two different measures: spontaneous locomotion and water maze learning. Measurements were collected during 2 periods: Water maze performance of ketamine-treated rats was similar to that of controls when tested on P18. Likewise, the age of the animal at the time of ketamine/saline treatment did not influence learning of the maze. However, the young-adult water maze test (P81) revealed reliable benefits of prenatal ketamine exposure – especially during the initial re-training trial. On the first trial of the young adult test, rats treated with ketamine on E18 reached the hidden platform faster than any other group – including rats treated with ketamine on E19. Swim speeds of experimental and control rats were not significantly different. Spontaneous horizontal locomotion measured during juvenile testing indicated that ketamine-treated rats were less active than controls. However, later in development, rats treated with ketamine on E18 were more active than rats that received the drug on E19.These data suggest that both the day in fetal development when ketamine is administered and the timing of post-natal behavioral testing interact to influence behavioral outcomes. The data also indicate that the paradoxical age-dependent effects of early ketamine treatment on learning, previously described in fetuses and neonates, may also be detected later in young adult rats. Administration of ketamine or other NMDA receptor blocking drugs  may brinThe toxic effects of NMDA receptor blockade are apparently dependent on the dose of the drug, administration regimen, and the age of the animal treated. For example, vulnerability to MK-801-induced cortical vacuolization is not evident in fetal animals but rather begins at approximately the time of puberty . FurtherRecent experiments from our laboratory have focused on age-dependent effects of a single dose of ketamine on fetal learning and memory. Rat fetuses can learn conditioned taste aversions (CTAs) and exhibit taste-mediated conditioned motor responses (CMRs) ,20, whicThese previous studies have only tested the durability of the ketamine paradox over a period of up to 2 weeks  and haveThe current study extends our original observations and reports how a single injection of ketamine on either E18 or E19 modulates spontaneous locomotor activity and performance in a water maze. We tested the rats as juveniles and then as young adults. Our data suggest several age-dependent effects of early ketamine treatment – effects that depend on not only the length of time between drug administration and behavioral testing but also on the day in embryonic development when the NMDA receptor antagonist was administered.Over the10 trials of the water maze test, subjects significantly reduced their latencies to mount the hidden platform [F9, 102) = 4.233; p < 0.0001] indicating the gradual learning of the maze. However, as Figure  = 4.233;Time spent treading water at the side of the tank may be interpreted as an alternative, futile, escape strategy and not necessarily as an indicator of learning the position of the hidden platform. Subsequent analyses subtracted out stop-times in order to provide the most accurate portrayal of maze learning during the first 3 trials. With stop-time removed, the declining time-to-platform [F(2, 102) = 3.501; p = 0.034] and swimming distance [F(2, 102) = 4.632; p = 0.012] over the first 3 trials indicated a learning of the maze during this initial exposure to the apparatus. However the behavioral changes induced by drug or age manipulations were not statistically significant. The number of trials in which the rat failed to mount the platform within 90 seconds was not significantly different among the 4 treatment groups. Likewise, swim speed did not decrease significantly over the first 3 maze trials indicating that the animals were not fatiguing as they undertook multiple swims.Once the rat mounted the hidden platform we timed how long the subject remained there before it was removed (maximum of 30 seconds). An analysis of these data during the first 3 maze trials revealed neither a significant influence of subject age nor drug treatment.An analysis of the second water maze test indicated that there was a significant reduction in time [F(9, 157) = 26.868; p < 0.001] to mount the hidden platform over the 10 trials , P81 rats that were treated with ketamine on E18 did not swim significantly faster than any of the animals in the other treatment groups. For example, the swim speeds for trial 1 were: [E18/ketamine: 34.30 ± 3.59 cm/sec; E18/saline: 37.97 ± 1.85 cm/sec; E19/ketamine: 30.47 ± 2.40 cm/sec; E19/saline: 32.92 ± 2.23 cm/sec (Mean ± SEM)]. Fatigue did not seem to play a role in the group differences since swim speed remained stable in all groups over the first 3 water maze trials on P81.A single ketamine treatment during the perinatal period had long-lasting effects on spontaneous locomotor movements. Ketamine's effects depended on the age of behavioral testing as well as the age of the drug treatment. Horizontal movements (i.e., line crossings) were more prominently influenced by perinatal ketamine than were vertical movements (rearing).P11 rats treated prenatally with ketamine showed habituation to the open-field test chamber and exhibited reduced horizontal movements overall  = 7.79, p = 0.007]. However, there was a significant interaction between drug treatment and the block of time in which the locomotor measurement was made [F(5, 405) = 6.26, p = 0.0002]. At the end of 5 minutes, P11 rats reduced their spontaneous locomotion to approximately 20% of its original level. For this reason, we performed a minute-by-minute analysis of the first 5 minutes in the open field apparatus , rats treated with ketamine on E18 found the hidden platform more quickly than did animals receiving the same treatment on E19. Moreover, they exhibited enhanced maze performance compared to both groups of saline-treated rats. It is important to note that ketamine treatment on E18 did not induce faster swim speeds. Rather, the animals swam more direct routes to the hidden platform.in utero reduced subsequent spontaneous movement. This effect was subtle (i.e., only in evidence within the first 3-minutes of testing) and did not depend on the subject's age at the time of the drug's administration. However, when these animals were re-tested on P60, the rats that received ketamine on E18 both moved more than the rats that received ketamine on E19 and also moved more than saline-injected controls.Effects of ketamine on spontaneous open-field locomotion were also age-dependent. In neonatal animals (P11), ketamine administration in utero. Drug-induced effects on water maze learning were observed over 11 weeks after birth and locomotor effects were documented 9 weeks post partum. These findings are consistent with others indicating that early NMDA receptor blockade may produce behavioral alterations that are detectable in adulthood [First, these data reveal long-term behavioral effects of a single dose of ketamine administered dulthood ,25,26. Sdulthood . The samdulthood . Similardulthood ,27.The effects of early ketamine treatment on locomotion are apparently not consistent throughout postnatal development. Ketamine reduced locomotor movements in P11 rats but later (P60) selectively enhanced locomotion of animals that received the drug on E18. The reasons for this change in responding are unclear. In order to accommodate the different size of the animals at P11 and P60, there were differences in the dimensions of the open field chambers used at each test. Also, the chamber walls were clear during the P11 test and opaque at P60. But beyond these differences in apparatus, maturation clearly brings with it a variety of capabilities and propensities many of which can modulate motor responding. For example, at the end of 5 minutes, P11 rats reduce their spontaneous locomotion to approximately 20% of its original level. However, P60 rats are 80% as active during this same time period. These data indicate a general tendency for young rats to habituate (or fatigue) more rapidly than older rats. Other behavioral studies have revealed toxin-induced performance impairments that reveal themselves only at certain stages of early postnatal development but not at older ages . More reOur measures of swim speed may offer some insights regarding the relative motor capacities and motivation of our animals. Swim speeds did not significantly differ between animals previously treated with ketamine or saline. Likewise, fetal age at the time of the drug treatment did not influence speed of swimming. Instead, rats reduced their latencies to mount the platform by swimming more-direct routes. Thus, the water maze data reported here are less likely a reflection of the animal's capacity or motivation to get to the platform and more likely a reflection of learning ability.It should be noted that water maze performance may be influenced by a number of factors beyond cognitive ability. For example, drug-induced alteration of visual acuity, motivation or motor capacities can alter performance of this task . The litWe used accepted statistical methods to avoid spurious inflation of sample size and to control for litter effects ,36  – but only on the first trial. Likewise, P11 rats treated with ketamine as fetuses, exhibited fewer locomotor movements than did saline controls – but only during the first 5 minutes of our test. Our laboratory , as well3H] MK-801 (a potent/selective NMDA receptor antagonist) in synaptosomal membranes is differentially altered by glutamate (and other) agonists during various periods of development. These data build on previous findings [What neural, or other, mechanisms might subserve the behavioral phenomenon outlined here? NMDA receptor populations and physiology are neither static nor mature during the perinatal period and blockade of these receptors during particular days of development may produce quite different effects ,40. For findings  indicatiThe identification of several NMDA receptor subtypes with different functional roles and different patterns of expression during the perinatal period may also eventually reveal aspects of the ketamine paradox's physiological substrate -54. CoulIt should also be noted that ketamine can influence maternal and fetal physiology in ways that go beyond the drug's well-known effects on NMDA glutamate receptors . PulmonaIf early ketamine exposure influences NMDA receptor populations or functioning, post-synaptic second messenger pathways would also be engaged as mediators of behavioral change . DownstrThe variables of subject age and ketamine dose interact in complex ways to produce predictions of neurotoxicity. If NMDA antagonists are used to suppress neuronal activity during a critical developmental period of synaptogenesis, the timing and sequence of synaptic connection is disrupted . This caIn addition to subject age, the dose of ketamine is another important factor in determining the likelihood of apoptosis induction as well as the generalizability of our data to clinical settings. In order to produce an increase in apoptotic neurons in neonatal rats, ketamine must be administered in multiple injections over a period of 9 hours . Our stuThese studies were aimed at determining the long-term behavioral effects of ketamine administration on E18 and E19 as a means of assessing the durability, intensity and generalizability of the ketamine paradox . Our preDespite the reliable enhancement of CTA learning that has been reported in fetuses and neonates treated with ketamine on E18 , this saThe subjects were Sprague-Dawley rats  obtained from timed pregnant female rats supplied by Zivic Laboratories (Zelienople, PA). Litters (N = 2/treatment group) were not culled and ranged in size from 9 to 13 pups. [See behavioral testing sections below for details about the number of animals in each treatment group.] The variable number of subjects/group was due to different litter sizes and several logistical constraints that did not always allow the testing of all rats in each litter. Statistical adjustments were made in order to compensate for the unequal Ns in the treatment groups (see below). The date of conception  was designated as "embryonic day 0" (E0). Postnatal day 0 (P0) was the day of birth . The pregnant animals from which our subjects were derived were individually housed in plastic 'shoe-box' cages (44.45 cm long × 21.59 cm wide × 20.32 cm high). After birth, perinatal rats were housed with the dam until they were sexed and weaned between P21-25  [Pregnant dams received ketamine HCl , or an equal volume of physiological saline, (0.9% NaCl, i.p.) on either E18 or E19. Saline injections controlled for the stress of pre-natal manipulation. Thus, there were 4 treatment groups designated hereafter as follows: E18/ketamine, E18/saline, E19/ketamine, or E19/saline. Rats from two litters were used in each of the treatment groups. In order to separate out treatment effects from litter effects special statistical measures were employed (see , below) ,36. The , below)  in which, below) .Juvenile Period , within the first three weeks after birth and Young-adult Period , between 2–3 months of age. We selected these two behavioral test periods based on knowledge of the patterns of development of NMDA receptor subtypes. The NR2B receptor subtype has been implicated in learning and memory [We recorded performance in a water maze and also spontaneous locomotor movements in an open field. Each of these behavioral tests was conducted twice, i.e., once in each of two different time periods: d memory . These rd memory ,74. TherMost, but not all, animals were tested and then retested. However, statistical analyses indicated that there were no differences between the animals that were tested once or twice. Therefore, these groups were combined for subsequent statistical analyses. Additional behavioral testing (i.e., conditioned taste aversion) was performed on some of these animals but there were some logistical problems with the experiment. These data did not reveal reliable group differences and are not reported here.P18: E18/ketamine: (N = 12); E18/saline: (N = 16); E19/ketamine: (N = 14); E19/saline: (N = 15); P81: E18/ketamine: (N = 12); E18/saline: (N = 22); E19/ketamine: (N = 17); E19/saline: (N = 20). Our data corroborate other studies indicating that rats younger than P20 are capable of learning a water maze [At age P18, and then again at P81, we evaluated water maze performance by testing the following number of rats per group: ter maze ,76.® brand). The water temperature was maintained at 26 ± 1°C. The escape platform was a clear plastic disc (12.5 cm diameter × 1.2 cm) mounted on a stand. The platform remained in the same location (approximately 10 cm from the side of the tank) throughout the test session. The edges of the platform had white rubberized tape attached in order to aid the rats as they mounted it. The tank was in a room lighted with fluorescent lights and surrounded by a rich array of laboratory furnishings.The water maze was an oval stock-watering tank (manufactured by Rubbermaid, Inc.) measuring 94 cm × 74 cm × 60 cm deep. It was sized to shorten swim distances to the hidden platform and reduce the likelihood of fatigue in our young animals. The tank was filled to 39.5 cm (1 cm above a hidden platform) with water that was made opaque and white by adding 710 ml of evaporated milk . "Escape latency" was defined as the time (in seconds) it took the rat, once in the water, to mount and gain balance on the platform. "Stop time" was the total time/trial that subjects spent treading water (i.e., not making forward progress). "Time on the platform" was defined as the time (up to 30 sec) the animal remained on the platform after initial mounting. "Path length" was the total distance (cm) swum before the subject mounted the platform. "Swim speed" was expressed in cm/sec and reflected the rate of forward progress towards the platform.All test sessions were video recorded and the tapes were later used for analysis of latency to escape, stop time, time on platform, path length and swim speed . The holding cage sat upon a heating pad set at 33.5°C., producing a floor temperature of approximately 28.5 ± 1°C. If the animal did not reach the platform in 90 seconds it was removed from the water and returned to the cage. If the animal jumped off the platform before 30 seconds, it was removed from the water and returned to its holding cage. All rats were given a 60-second rest period before the next trial was initiated. Each rat experienced ten swim trials during each of the two test sessions. At the end of the 10 trials, each rat was thoroughly dried with a towel and a blow dryer and then returned to its home cage. Escape latencies and time on the hidden platform were recorded for each trial.At age P11, we measured the spontaneous locomotor activity of the following number of rat pups in each group: E18/ketamine: (N = 18); E18/saline: (N = 25); E19/ketamine: (N = 21); E19/saline: (N = 24). The test chamber consisted of a plastic 'shoe-box' cage (44.45 cm long × 21.59 cm wide × 20.32 cm high) with transparent walls and open top. This chamber had a grid on its floor composed of 3 × 6 squares (each measuring 7.1 cm × 7.1 cm). Young rats have limited abilities to thermoregulate . TherefoAt age P60, we again measured the spontaneous locomotor activity of the following number of rats in each group: E18/ketamine: (N = 12); E18/saline: (N = 21); E19/ketamine: (N = 17); E19/saline: (N = 22). For this second test, a larger test chamber (64 cm long × 46 cm wide × 42 cm high) was used. The walls were opaque plastic and the top open. This chamber had a grid on its floor that consisted of 3 × 3 rectangles (each measuring 21 cm × 15 cm). As before, rats were individually placed in the center square of the chamber at the beginning of the 30-minute test session. After each session, the animal was weighed and then returned to its home cage. In preparation for the next animal, the activity chamber was cleaned as described above.Videotapes of locomotor movements were later viewed and independently scored by observers blind to the experimental condition of the animal. We counted line crossings to assess the amount of horizontal locomotion exhibited by each animal. A "line-cross" was counted when any part of the rat, except the tail, crossed a line. Rearing was operationally defined as any time the rat raised both front paws from the chamber floor. In P11 rats, rearing was very rare and therefore not scored. However, this behavior was recorded during the P60 test.Escape latencies and time on the hidden platform were recorded for each trial in the water maze. Group differences in water maze performance were most evident early in training. After the first few trials, rats in all treatment groups moved promptly to the hidden platform with a latency of less than 20 seconds. For this reason, our statistical analyses focused on the initial trials of each session. Animals removed from the maze after not finding the hidden platform in 90 seconds were, nevertheless, assigned a time of 90 seconds for purposes of data analysis.Swim distances, swim speeds, and time spent treading water (stop times) were also calculated for the first 3 swim trials. Towards this end, videotapes were viewed and independently evaluated by raters blind to the experimental condition of the animal. Swim paths were hand-drawn on acetate placed on a video screen during playback of the videotape. The paths were digitized using a light pen providing input to NIH Image software (Bethesda, MD). The lengths of the paths were compared with a calibrated length on the video record in order to calculate the swim distance. An evaluation of the inter-rater reliability of our video scoring methods indicated a high correlation [r(10) = 0.924, p < 0.001]. These methods produced swim distances that were not significantly different [t(9) = 1.02, p > 0.05] between observers. For some of our swim speed analyses, we subtracted out any time that the animal stopped swimming mid-trial, and treaded water at the side of the tank. Dividing the swim distance by the adjusted time to mount the hidden platform produced swim speed.Two observers, blind to the experimental condition of the animals, evaluated each of the tapes of animals locomoting in the open field by counting line crossings and rears. The counts of these 2 observers were then averaged and this data point was used in our statistical analysis. There was high degree of correlation between the ratings of our 2 observers. P11 locomotor test: r(85) = 0.91, p < 0.01; P60 locomotor test: r(72) = 0.90, p < 0.01.Unless otherwise stated, locomotor data for the two different test periods (P11 and P60) and water maze data for the 2 different test periods (P18 and P81) were analyzed via separate repeated-measures, three-way ANOVAs [Drug  X Treatment age (E18, E19) X Time] with time blocks as the repeated factor and compensation for unequal Ns.We used several rats from each litter and employed statistical corrections in order to avoid spurious inflation of sample size . Since aAn initial inclusion of subject sex as a factor in our statistical analyses indicated no significant differences between male and female subjects. Therefore, the subsequent analyses reported here were run without this factor.Platform navigation for juvenile rats might be more challenging than the task presented to older rats. A small water maze was used in these studies and the same start-to-platform distance was used for all tests. Still, the P18 rat may have found it significantly more challenging than the P81 rat to traverse this distance. For this reason, we intentionally avoided comparing the water maze data of our juvenile and young-adult rats. Likewise, we did not make statistical comparisons between the locomotor responses of animals run at the 2 different ages since the use of different-sized apparatuses would presumably influence these data.a priori hypotheses regarding possible behavioral differences between rats treated with ketamine on E18 versus E19 [a priori planned comparisons were made, one-tail probabilities were computed. An α = 0.05 was used throughout these analyses presented here.If a repeated-measure ANOVA revealed a significant trial effect (indicating a change over time) a two-way ANOVA [Drug  X Treatment age (E18, E19)] was run to analyze the group differences during a particular trial. Individual group comparisons were accomplished by using either the Tukey-Kramer Multiple Comparisons Test or t-tests  using thrsus E19 . When a GAM designed the studies, performed some of the behavioral work, conceptualized the statistical analyses and drafted the manuscript. CK helped design the studies, performed most of the behavioral work, assisted with statistical analyses and the drafting of the manuscript. CM supervised most of the behavioral work, assisted with statistical analyses and the drafting of the manuscript. AS supervised and performed much of the behavioral work. AY performed most of the behavioral work. DL-F assisted with the statistical analyses. EV assisted with the behavioral work. BW assisted with the behavioral work. JMB performed aspects of the behavioral work and assisted with the drafting of the manuscript.


Heterochromatin is usually thought of as a stable and inactive region of the genome. Not so, according to a study published earlier this year Science  . HP1 is Science, both groups generated cell lines stably expressing HP1 fused to green fluorescent protein (GFP) so that they could watch the behaviour of HP1 in living cells. Specifically, they used photobleaching techniques to study the in vivo mobility of HP1. In a defined region of a cell, fluorescently tagged proteins are bleached by a laser pulse. Recovery of fluorescence in the bleached area can then only occur if bleached molecules are replaced with unbleached molecules from regions outside the bleached area. The technique is called fluorescence recovery after photobleaching (FRAP) and provides information about the mobility and stability of the cellular structures and proteins. For HP1–GFP, the speed at which fluorescence recovers depends on how tightly it is bound within heterochromatic regions. Heitz (and many others) might have expected that heterochromatin-bound HP1 shows little turnover and therefore recovery should take place very slowly.In the two articles in As discussed by the authors of both studies, several important conclusions can be drawn. In striking contrast to previous models, HP1 appears to be a very mobile molecule. The formation of heterochromatin appears not to be based on a stable oligomeric network of HP1 molecules. Furthermore, heterochromatin is accessible. There is no obvious constraint shielding these transcriptionally inactive compartments from factors residing outside. Given the rapid exchange of HP1 in heterochromatic clusters, any other soluble nuclear protein, such as a transcription factor, should be able to gain access, compete with silencing factors, and potentially activate genes located within heterochromatin. Taken together, heterochromatin appears to be a surprisingly dynamic compartment even though it forms morphologically stable entities. This dynamic situation could imply that heterochromatic silencing is not just a switch, but rather a continuous and active process.Although the new work suggests that heterochromatin is more dynamic than was thought, some caveats remain. It is still possible that a stable “mark” of heterochromatin does exist. As I discussed above, this mark might be an undetected fraction of immobile HP1 molecules. In addition, one cannot exclude that HP1 is a downstream factor that is dynamically tethered to a stable binding site, the most likely candidate being the methylated histone tail. Perhaps the role of HP1 in heterochromatic silencing has simply been overinterpreted. The formation of facultative heterochromatin by X inactivation in mammals, for example, does not involve HP1 even though appropriate histone methylation marks are set. This indicates that heterochromatin formation does not always follow the same rules and suggests that our definitions of heterochromatin must be refined. In any case, it could well be that a silent state is marked by signals that have a slow turnover. In fact, histone methylation is believed to generate a quite stable “code” . Until tDrosophila  have also been proposed to consist of dynamic collections of components . Personaosophila . This pr


Scientists debating how to assess global fisheries are now including studies of long-term ecosystem effects and options for recovery efforts. But is it possible to both conserve and farm the sea? It is becoming increasingly apparent that the vast blue expanse of ocean—the last frontier—is not as inexhaustible as it once seemed. While we have yet to fully explore the reaches of the sea, technology has granted humans the ability to harvest its wealth. We can now fish anywhere, at any depth, for any species. Like the American frontier range's bison and wolf populations brought to the brink of extinction swordfish and sharks are the ocean's most pursued prizes. The disadvantages associated with the depth and dimensions of this open range, however, have long obscured the real consequences of fishing. Indeed, scientists have the formidable challenge of assessing the status of species whose home covers over 75% of the earth.Three recent highly publicized papers—a trifecta detailing troubled waters—call attention to overfishing's contributions to the dramatic declines in global fisheries. Delving into the past, Recent reports by the United Nation's Food and Agriculture Organization (FAO) which maintains the world's most complete global fisheries database, appear to validate the conclusions of these studies. The most recent FAO report states that 28% of global stocks are significantly depleted or overexploited, and 47% are either fully exploited or meet the target maximum sustainable yield. Only 24% of global stocks are either under- or moderately exploited. As the sea is increasingly harvested, many ecologists wonder how the ecosystem will continue to function . AlthougThe FAO began keeping fisheries records in 1950. Unfortunately, an enormous amount of data comes directly from each country's fishing industry, which is often biased as a result of unreported discarding, illegal fishing, and the misreporting of harvests. For example, mid-level Chinese government officials seeking promotions systematically enhanced China's fisheries numbers in recent years—which inflated and skewed international catch rates.www.worldfishcenter.org/trawl/) confirms that the region's commercial species have been depleted to 10%–30% of what they were 30–40 years ago.The FAO data show that catches, excluding a recent surge in anchoveta and China's suspect numbers, reached a peak of 80 million metric tons in the late 1980s and have since begun to decline. Regional studies validate these trends. “Most of the line fish around the coast of South Africa are depleted to 5%–15% of pristine levels,” says George Branch, a marine biologist from the University of Cape Town (Cape Town, South Africa). Meryl Williams, Director General of WorldFish in Penang, Malaysia, notes that the Asia-specific database called TrawlBase . And that is where fisheries have adequate access to current monitoring programs. “With the recent expansion of the Taiwanese and Chinese fleets, we don't have the kind of sampling programs needed for those kinds of fisheries,” says Rick Deriso, a fisheries scientist with the Inter-American Tropical Tuna Commission (IATTC) .Couple these inadequacies with previously unknown bycatch rates (i.e., the fish caught in addition to the target catch) and illegal catches, and it is easy to see that the task is formidable. The FAO estimates that roughly one-quarter of the marine commercial catch destined for human consumption—some 18–40 million metric tons of fish—is thrown back in the sea, a harvested catch that is never utilized or counted. It is estimated that the illegal, unreported, and unregulated (IUU) fisheries surpass allowed fishing quotas by 300%. IUU fishers operate in areas where fishing is not permitted, use banned technologies or outlawed net types, or underreport catches. “The IUU fishery for Patagonian toothfish expanded rapidly in the mid-1990s, likely on the order of 20–30 vessels,” says Andrew Constable, an ecological modeler at the Australian Antarctic Division , who also works with the Scientific Committee of the Commission for the Conservation of Antarctic Marine Living Resources . “These rates of IUU fishing could reduce stocks to threshold levels in some areas in two to five years,” he adds.Often overlooked is the inescapable fact that even sustainable harvest rates reduce fish populations quickly. “If the goal is a productive fishery, we're automatically talking about up to a 70% decline in population across the board,” says Deriso. The FAO's Chief of Marine Resource Services, Jorge Csirke, states that “from a stock point of view, there is no way to preserve integrity of wild stocks and exploit them at the same time.” Indeed, the United States' National Marine Fisheries Service (NMFS) considers optimal harvest rates to be between 40%–60% of virgin levels. But once fish populations dip below the 10%–20% mark, declines are of serious concern.Atlantic cod in Canadian waters suffered a total population collapse and are now on Canada's endangered species list . From 2 Given the task of compiling data on only the economically important species, fisheries biologists developed a single-species management approach in the 1960s, which assumed that fisheries affect each species in isolation. This approach, although now rife with problems, served the community and the politicians well during the decades of abundant resources. “They brought the approach of single-species management to near-perfection,” says Boris Worm, a marine ecologist at the Institute for Marine Science in Kiel, Germany. A growing discontent with the model, in addition to greater awareness of ecological interactions, however, prompted Worm and his Dalhousie University colleague Ransom Myers to question the sustainability of the single-species approach. Attempting a comprehensive assessment, their widely cited recent paper  indicateHowever, this new approach to assess fish stocks is not without its critics. Fisheries biologists point out that the nuances of management contained in fisheries data—such as altered fisher behavior, the variable “catchability” of individual species, and altered gear use—were discounted in the Despite the controversy, most agree that the large predators, particularly sharks, skates, rays, and marlin, are in the most dire straits. Unlike other lower-trophic order species, the wholesale removal of top predators has enormous effects on the rest of the ecosystem. One consequence is that overall reproduction rates can potentially suffer. Fish size, gender, and age at maturity have a substantial impact on individual species' reproduction rates. Since larger fish are the most susceptible to fishing, the population's age structure can shift as individuals, particularly females, are fished out. For example, a 23-inch (59-cm) female vermilion rockfish can produce 17 times the young of a 14-inch (36-cm) fish. Given uncertainties with population dynamics, the fact that basic biological data are missing makes the job even harder. While knowledge of these components is still quite spotty, tuna inventories, for example, have started collecting gender data on catches.Daniel Pauly, a fisheries biologist at the University of Vancouver (Vancouver, British Columbia, Canada), has shown that increased fishing has caused the industry to “fish down the food web,” or systematically move to lower trophic levels over time as higher ones were depleted . The impFisheries science has taken steps to increase the quality of data in recent years. “Traditional fishery models assumed that a fishery was a homogenous thing—like bacteria in a bottle—rather than a spatially diverse system,” says Pierre Kleiber, a fisheries biologist with the Pacific Islands Fisheries Science Center of the NMFS (Honolulu, Hawaii, United States). He adds that recent work accounts for spatial diversity. In addition, fisheries are now dealing with the inherent uncertainty of their work and are factoring that into models and decision-making. “Uncertainty didn't used to be dealt with at all in formulating fishery management advice,” confirms Keith Sainsbury, a marine ecologist with the Commonwealth Scientific and Industrial Research Organisation (CSIRO) , adding that its absence gave rise to an awful lot of troubles. “Traditional models tended to assume perfect data with no holes in it,” says Kleiber. “Now we've tried to craft a model to fit the realities of missing data.”As well as incorporating spatial diversity and uncertainty, researchers are beginning to comprehend the ecological damage caused by different types of fishing gear. Indeed, trawling the bottom of the seafloor for groundfish can destroy a half-acre footprint of habitat . Detaile“Science is getting more realistic, but it is getting more difficult,” says Branch. Ecological models are far more complex than traditional fisheries models, says Csirke, adding that more model variables make it more difficult to apply to fisheries, an industry whose focus is, understandably, not conservation. Despite its incorporation into national fisheries policies, ecosystem-based management remains a loosely defined term. It is not a well-defined concept because it is not possible to optimize every species, says Deriso.An additional concern to scientists is that of biomass resilience in the face of environmental changes. Francisco Chavez, a biologist with the Monterey Bay Aquarium Research Institute , recently demonstrated that over a 25-year period, warmer and cooler Pacific waters tilt the distribution of anchoveta versus sardines, both open-ocean dwellers . Indeed,In the end, many scientists have their doubts about the influence of science on decision-making. “My personal view is that it's naïve to think that modifying and improving models will necessarily lead to improved natural resource management,” says Simon Jennings, a fisheries biologist with the United Kingdom's Centre for Environment, Fisheries and Aquaculture Science in Lowestoft. Indeed, the International Council for the Exploration of the Seas (Copenhagen, Denmark) recently recommended a total ban on North Sea and Irish Sea cod stocks, based on single-species assessment. Although the more intensive ecosystem-based models could not have produced a more stringent recommendation, politicians allowed harvests at roughly half of last year's catch.While lowering fisheries' effort seems the most logical approach to the recovery of depleted fisheries, social and economic concerns often stymie political action. Yet demand for seafood continues. Therefore, scientists also are investigating both conservation and alternative production options.Given the social, economic, and political problems associated with that, managers have often used closures to help a hard-hit species recover. In many cases, however, the recovery time for exploited species is longer than once thought . “Based One management strategy to recover species is to create marine protected areas (MPAs), zones that restrict all removal of marine life A review of 89 studies of MPAs by Ben Halpern, a student at the University of California, Santa Barbara , demonstrated that the average number of fish inside a reserve increases between 60%– and 150% . In addiIn terms of simply producing fish for global food needs, aquaculture  is another, increasingly popular, option. In 2001, the European Union produced 17% of total fishery's production via aquaculture. These numbers are projected to steadily increase, but some question whether aquaculture would be sufficient to supply what has been lost by overexploited fisheries.Concentrated in coastal areas, aquaculture has aroused numerous concerns. Indeed, in developed countries, most operations grow carnivorous fish, which necessitates growing fish to feed fish. While the process has become more efficient in recent years, due in part to a growing reliance on vegetarian diets, it still takes about 3 pounds (1.36 kg) of fish to create 2.2 pounds (1 kg) of desirable meat . Yet, thTo alleviate many of these concerns, open-ocean aquaculture is now being considered. Indeed, the NMFS is set to propose a Code of Conduct for Offshore Aquaculture, which would open up the 200-mile (322-km) United States Exclusive Economic Zone to net pens seaward of coastal state boundaries and authorities. The Sea Grant program in conjunction with interested business, is also currently assessing the carrying capacity of open-water pens as well as their potential environmental impact. Given increased industrial interest and unchanging demand for seafood, many think farming the sea may be around the corner.Undoubtedly, scientific effort will continue to inform both conservationists and industry about fisheries' capacity and potential recovery options. As attitudes towards fisheries continue to change, increased understanding of the ecological underpinnings should help strike a more informed balance between fisheries' conservation and production. “The big mistake is suggesting that you can manage fish stocks,” says Niels Daan, a biologist with the Netherlands Institute for Fisheries Research (IJmuiden, The Netherlands). “In my opinion, we can only manage human activity.”While industrial-scale fishing is a growing concern to fisheries biologists, the management of subsistence fishing in developing countries is equally complex. Indonesia alone has 1.3 million fishers. Given the lack of alternative economic options for subsistence fishers, it is much more difficult to reduce fishing because it meets immediate food and resource needs. Local scientists, often lacking in resources, have a much more difficult time assessing the effects and offering advice to governmental fisheries regulators, who have limited political influence. Kenyan researcher Tim McClanahan notes that a main problem is a lack of coordination and respect between traditional and national programs of management. Therefore, he focuses on the fishing gear used. By reconciling the impact of certain fishing gear with traditional knowledge, McClanahan has developed a basis for suggested restrictions deemed acceptable to the local community.While MPAs are heavily touted as one of the best management tools to address both conservation and fisheries management, few have been enacted. In 2001, following a strong mandate by the Australian Minister to the Environment and overwhelming political will, the Great Barrier Reef Marine Park Authority (GBRMPA) in Australia established a network of marine protected, or no-take, areas as an ecosystem-based management approach.In setting up the reserve networks, scientists determined the most effective areas to protect biodiversity with little impact to productivity. “We tried to avoid peak use areas, while protecting at least one-third of each bioregion and minimizing the impact to users of the Great Barrier Reef Park,” says Phil Cadwallader, Director of Fisheries at the GBRMPA.Off the coast of California, the Channel Islands network of marine reserves, established in April 2003, consists of 13 areas designed to protect biodiversity and critical habitat for breeding fish and to maintain biodiversity. The area has suffered serious declines of red snapper, angel sharks, and abalone, once plentiful off the California coast, over the past decade. Scientists designed the network to protect those productive habitats that would help ensure that larval dispersal was maintained between the individual reserves. Totaling 132 nautical square miles , 11 of the areas are no-take reserves—allowing no fishing or harvest of any kind.


Carcinoma of the esophagus is an aggressive malignancy with an increasing incidence. Its virulence, in terms of symptoms and mortality, justifies a continued search for optimal therapy. A clinical practice guideline was developed based on a systematic review investigating neoadjuvant or adjuvant therapy on resectable thoracic esophageal cancer.A systematic review with meta-analysis was developed and clinical recommendations were drafted. External review of the practice guideline report by practitioners in Ontario, Canada was obtained through a mailed survey, and incorporated. Final approval of the practice guideline was obtained from the Practice Guidelines Coordinating Committee.The systematic review was developed and recommendations were drafted, and the report was mailed to Ontario practitioners for external review. Ninety percent of respondents agreed with both the evidence summary and the draft recommendations, while only 69% approved of the draft recommendations as a practice guideline. Based on the external review, a revised document was created. The revised practice guideline was submitted to the Practice Guidelines Coordinating Committee for review. All 11 members of the PGCC returned ballots. Eight PGCC members approved the practice guideline report as written and three members approved the guideline conditional on specific concerns being addressed. After these recommended changes were made, the final practice guideline report was approved.In consideration of the systematic review, external review, and subsequent Practice Guidelines Coordinating Committee revision suggestions, and final approval, the Gastrointestinal Cancer Disease Site Group recommends the following:For adult patients with resectable thoracic esophageal cancer for whom surgery is considered appropriate, surgery alone (i.e., without neoadjuvant or adjuvant therapy) is recommended as the standard practice. Carcinoma of the esophagus is an aggressive malignancy with an increasing incidence. Its virulence, in terms of symptoms and mortality, justifies a continued search for optimal therapy. The large and growing number of patients affected, the high mortality rates, the worldwide geographic variation in practice, and the large body of good quality research warrants a clinical practice guideline.This clinical practice guideline was developed by the Gastrointestinal Cancer Disease Site Group (DSG) of Cancer Care Ontario's Program in Evidence-based Care (PEBC), using the methods of the Practice Guidelines Development Cycle . This prThe systematic review on neoadjuvant or adjuvant therapy for resectable esophageal cancer, which forms the basis for this clinical practice guideline, is available in a companion document . Based oA systematic review with meta-analysis on neoadjuvant or adjuvant therapy for resectable esophageal cancer was developed by the Gastrointestinal Cancer DSG of Cancer Care Ontario's Program in Evidence-based Care . The eviIn discussions regarding the completed systematic review, the Gastrointestinal Cancer DSG agreed that the evidence did not support a recommendation for neoadjuvant or adjuvant chemotherapy or radiotherapy for resectable thoracic esophageal cancer. A recommendation that surgery alone should be the standard of care for this patient population was drafted, and it was recommended that the draft practice guideline be sent out to Ontario practitioners for external review.Combined modality radiotherapy and chemotherapy in the non-surgical management of localized carcinoma of the esophagus [The role of radiotherapy alone and chemoradiation alone without surgery is addressed in a separate Gastrointestinal Cancer DSG Clinical Practice Guideline: sophagus .Practitioner feedback was obtained through a mailed survey of 163 practitioners in Ontario . The survey consisted of items evaluating the methods, results, and interpretative summary used to inform the draft recommendations and whether the draft recommendations should be approved as a practice guideline. Written comments were invited. Follow-up reminders were sent at two weeks (post card) and four weeks (complete package mailed again). The Gastrointestinal Cancer DSG reviewed the results of the survey.Eighty-six surveys (58%) were returned. Twenty-nine respondents (34%)  indicated that the report was relevant to their clinical practice and completed the survey. Key results of the practitioner feedback survey are summarized below.1. Number surveyed: 163 practitioners in Ontario, Canada involved in the care of cancer patients2. Return rate: 58% 3. Written comments attached: 10%4. Agreement with the summary of evidence: 90%5. Agreement with the recommendation: 90%6. Approval of the recommendation as a practice guideline: 69%Three (10%) respondents provided written comments. One practitioner hypothesized that preoperative chemoradiation might have a role in adenocarcinoma of the lower third of the esophagus  esophageal cancer for whom surgery is considered appropriate.• If surgery is considered appropriate, then surgery alone (i.e., without neoadjuvant or adjuvant therapy) is recommended as the standard practice for resectable thoracic esophageal cancer. for a complete list of current and on-going projects.This Clinical Practice Guideline report is based on work completed in October, 2003. All approved PEBC Clinical Practice Guideline reports are updated regularly. Please see the PEBC's web site The author(s) declare that they have no competing interests.In order of appearance:DSG, Disease Site Group; PEBC, Program in Evidence-based Care; PGCC, Practice Guidelines Coordinating Committee.RM, RW, and LZ created the initial drafts of this clinical practice guideline with input from other members of the Gastrointestinal Cancer DSG. RM, RW, and BR created the final draft of this clinical practice guideline. Creation of the submitted manuscript was performed by BR and RM.The pre-publication history for this paper can be accessed here:


Lipoprotein lipase (LPL) has a central role in the catabolism of triglyceride-rich lipoproteins. The enzyme is anchored to the vascular endothelium through interaction with heparan sulphate proteoglycans and is displaced from this interaction by heparin. When heparin is infused, there is a peak of LPL activity accompanied by a reduction in triglycerides (TG) during the first hour, followed by a decrease in LPL activity to a stable plateau during the remaining session while TG increase towards and beyond baseline. This suggests that tissue stores of LPL become depleted. It has been argued that low molecular weight (LMW) heparins cause less disturbance of the LPL system than conventional heparin does.We have followed LPL activity and TG during a dialysis-session with a LMW heparin  using the same patients and regime as in a previous study with conventional heparin, i.e. a primed infusion.The shape of the curve for LPL activity resembled that during the earlier dialyses with conventional heparin, but the values were lower during dialysis with dalteparin. The area under the curve for LPL activity during the peak period (0–180 minutes) was only 27% and for the plateau period (180–240 minutes) it was only 36% of that observed with conventional heparin (p < 0.01). These remarkably low plasma LPL activities prompted us to re-analyze LPL activity and to measure LPL mass in frozen samples from our earlier studies. There was excellent correlation between the new and old values which rules out the possibility of assay variations as a confounding factor. TG increased from 2.14 mmol/L before, to 2.59 mmol/L after the dialysis (p < 0.01). From 30 minutes on, the TG values were significantly higher after dalteparin compared to conventional heparin (p < 0.05).These results indicate that LMW heparins disturb the LPL system as much or more than conventional heparin does. Lipoprotein lipase (LPL) hydrolyses triglycerides (TG) in circulating lipoproteins ,2. This et al. found that on a weight basis, decasaccharides released more LPL from perfused rat hearts than conventional heparin did [In recent years, conventional heparin has increasingly been replaced by various low molecular weight (LMW) heparins. A major argument is the ease of administration ,14. A siarin did . Severalarin did ,21.In a recent study we infused a LMW heparin  for eight hours to healthy volunteers to explore the influence on LPL activity and TG response . The peaThe study design was based on the protocol used in a previous investigation in which nine HD-patients were studied during a dialysis-session using conventional heparin as anticoagulant . The pre3H]-oleic acid-labelled triolein, 100 mg soybean TG and 10 mg egg yolk phospholipids per mL, prepared by Fresenius-Kabi, Uppsala, Sweden. Hepatic lipase was inhibited by pre-incubation of the plasma samples with immunoglobulins from a rabbit antiserum to human hepatic lipase. The assay medium contained a relatively high concentration of heparin, and possible differences in the heparin concentration or type in the sample would not affect the activity. All assays were made in triplicate and the mean value was used. A standard sample of human post-heparin plasma was run on each assay day and the value was used to calibrate for between-assay-variations. LPL protein mass was determined with an enzyme-linked immunosorbent assay, as previously described [®, Chromogenix AB, Mölndal, Sweden).Blood samples for measurement of LPL activity were collected in heparinized tubes. They were immediately chilled in ice water and centrifuged in a cooling centrifuge within 15 minutes. The plasma was frozen at -20°C and then stored at -70°C until analyses. LPL activity was measured as described  using anescribed , using iescribed . AntifacThe values are expressed in terms of median and range and were examined for significant differences by paired Wilcoxon signed-rank test. Simple linear regression and the Spearman rank correlation test were used to evaluate relationships between variables. Two-tailed P values below 0.05 were considered to be statistically significant.Table During the dialysis with dalteparin, the anti-Factor Xa activity was between 0.52 and 0.87 IU/mL. The target value recommended by the manufacturer is 0.5–1.0 IU/mL. Hence, the plasma dalteparin concentration remained well within the range for effective anticoagulation throughout the dialysis-session.The LPL-activity rose rapidly when dalteparin was administered. The highest value was at 15 minutes, median 15 mU/mL (range 9–32). The activity remained high at 30 minutes, but then decreased so that at 120 minutes the median was only 9 mU/mL (range 5–15)  at the start, to 2.59 mmol/L (range 1.49–5.04) at the end of the dialysis represents a 21 % increase (p < 0.01). Compared to the values during the earlier dialysis with conventional heparin, there was no statistically significant difference at the start of dialysis, but from 30 minutes and through the remaining session TG values were significantly higher during the dialysis with dalteparin (p < 0.05)  at start, to 6.1 mmol/L (range 3.8–8.4) at the end (32%, p < 0.05). This differs from the earlier dialysis with conventional heparin, when total cholesterol did not change from baseline .HDL-cholesterol did not change from baseline. Again, this differs from the dialysis with conventional heparin when HDL-cholesterol increased from 1.09 mmol/L (range 0.67–2.06) at start to 1.19 mmol/L (range 0.67–2.31) at the end (p < 0.05). No correlation was found between LPL activity and total or HDL-cholesterol changes during any of the dialysis-sessions.The median LDL-cholesterol, calculated by the Friedewald formula, was 2.9 mmol/L (range 2.1–4.9) before the dialysis and increased to 3.75 (2.1–5.6) at 240 min. This corresponds to an increase of 29% (p < 0.05).This study shows the same pattern of plasma LPL activity in HD patients given dalteparin as observed in previous studies with control subjects given dalteparin  or conveet al. found that the early LPL activity was only about half as high compared to values observed after conventional heparin [There are several earlier studies that show that administration of LMW heparin results in lower plasma levels of LPL than conventional heparin -30. LMW  heparin ,29. This heparin .et al. have, however, found that the amount of LPL released by a bolus of heparin is restored within 24 hours [The LPL activities were lower in HD patients than observed in controls given dalteparin . A simil24 hours . In rats24 hours  and chyl24 hours . A more 24 hours . Yet ano24 hours ,35.et al. found that the clearance of injected radioactively labeled chylomicron TG was dramatically increased five minutes after rats had been given conventional heparin or LMW heparin [Administration of heparin causes a temporary derangement of lipoprotein metabolism. In our studies with control subjects given conventional heparin or dalteparin the TG concentration decreased after heparin and then gradually increased again so that at the end of the study period TG exceeded the baseline level  declare that they have no competing interests.BN participated in the design of the study, carried out the patient studies, assembled the data, did the statistical analyses, and participated in writing of the manuscript; BS and GO conceived of the study and coordinated the work. TO participated in the design of the study and in writing of the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Conventional total hip replacement (THR) may be felt to carry too high a risk of failure over a patient's lifetime, especially in young people. There is increasing interest in metal on metal hip resurfacing arthroplasty (MoM) as this offers a bone-conserving option for treating those patients who are not considered eligible for THR. We aim to evaluate the effectiveness of MoM for treatment of hip disease, and compare it with alternative treatments for hip disease offered within the UK.A systematic review was carried out to identify the relevant literature on MoM published before 2002. As watchful waiting and total hip replacement are alternative methods commonly used to alleviate the symptoms of degenerative joint disease of the hip, we compared MoM with these.The data on the effectiveness of MoM are scarce, as it is a relatively new technique and at present only short-term results are available.It is not possible to make any firm conclusions about the effectiveness of MoM based on these early results. While the short-term results are promising, it is unclear if such results would be replicated in more rigorous studies, and what the long-term performance might be. Further research is needed which ideally should involve long-term randomised comparisons of MoM with alternative approaches to the clinical management of hip disease. The treatment of younger people with disease of the hip joint presents a difficult clinical problem. Conventional total hip replacement (THR) may be felt to carry too high a risk of failure over a patient's lifetime. Overall, long-term results of THR in younger patients with a variety of underlying conditions indicate that 25–30% may require revision by 15 years . Data onMetal on metal hip resurfacing arthroplasty (MoM) offers a bone-conserving option for treating those patients who are not considered eligible for THR. MoM may also represent a more attractive alternative to other procedures such as osteotomy, bone fusion and arthroscopy, which have previously been used or been advocated as means of delaying or preventing the need for a THR. MoM involves the removal and replacement of the surface of the femoral head with a hollow metal hemisphere, which fits into a metal acetabular cup. This technique conserves femoral bone , maintains normal femoral loading and stresses, and may not therefore compromise future total hip replacements. Data on the use of MoM within the NHS in England and Wales could not be obtained in this review. Never the less, because of increasing interest in MoM, we conducted a systematic review of the evidence of effectiveness aiming to compare it with THR and watchful waiting.Initial searches failed to identify any randomised or comparative observational studies comparing MoM with any of the chosen alternatives. A structured search was conducted to identify evidence relating to the clinical effectiveness and cost-effectiveness of MoM for treatment of hip disease. The search strategy comprised of: (1) A free text search to identify any potentially relevant papers evaluating MoM (free text search terms were used because of the anticipated scarcity of published literature); and (2) A search for RCTs and systematic reviews of RCTs for THR using a modified version of the search strategy used for a recent review . The seaAll identified abstracts were assessed for subject relevance independently by two reviewers. Full papers were then obtained and formally assessed for inclusion. It was agreed at the outset of the review that the search strategy would not be limited by language. It was agreed that non-English studies would be identified, but due to time and resource limitations would not be translated and assessed for their relevance to the review. No restrictions on the type of patient were imposed. Comprehensive systematic reviews of THR was carried out in Health Technology Assessment in 1998. These reviews were updated by the National Institute of Clinical Excellence (NICE) in 2000. Therefore, in this review a search for systematic reviews and RCTs published subsequent to the completion of the systematic reviews was carried out. Table Two reviewers independently abstracted data and quality assessed the included studies. Where a difference in opinion occurred, an arbiter was consulted. A data abstraction form was developed to record details of trial methods, participants, interventions, patient's characteristics and pre-specified outcomes  therapy, physiotherapy and treatment of side effects of medications) would be about £640 . WhetherThe use of MoM in the UK is still relatively rare. However, there has been increasing interest from younger people with hip disease who are not currently considered eligible for THR and amongst surgeons who strive for better ways to treat the patients whom they see. However, only very limited evidence are currently available on MoM and although the procedure does appear promising the lack of robust comparisons with the other treatment options and of long term data make it virtually impossible to draw robust conclusions about its relative effectiveness. Given the early promise shown by MoM there is a real need for more rigorous research. Such research would be challenging, not least because of ethical considerations, but should attempt some form of prospective, preferably randomised, comparison of MoM with a policy of delayed selective surgery. These studies should preferably be large-scale, long-term, and use standard outcome measures, both pre- and post-operatively.The author(s) declare that they have no competing interests.LW carried out the critical appraisal of the included studies and assisted in the writing up. LV coordinated the project and assisted in the writing up. KM developed the methodology for the literature search and assisted in the writing up. AG participated in the design and coordination of the study. MB assisted in the critical appraisal of the included studies. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:Search strategies. The search strategies used to search electronic databases to identify studies relevant to this review.Click here for file