The rapid destruction of the planet's biodiversity has prompted the nations of the world to set a target of achieving a significant reduction in the rate of loss of biodiversity by 2010. However, we do not yet have an adequate way of monitoring progress towards achieving this target. Here we present a method for producing indices based on the IUCN Red List to chart the overall threat status (projected relative extinction risk) of all the world's bird species from 1988 to 2004. Red List Indices (RLIs) are based on the number of species in each Red List category, and on the number changing categories between assessments as a result of genuine improvement or deterioration in status. The RLI for all bird species shows that their overall threat status has continued to deteriorate since 1988. Disaggregated indices show that deteriorations have occurred worldwide and in all major ecosystems, but with particularly steep declines in the indices for Indo-Malayan birds (driven by intensifying deforestation of the Sundaic lowlands) and for albatrosses and petrels . RLIs complement indicators based on species population trends and habitat extent for quantifying global trends in the status of biodiversity. Their main weaknesses are that the resolution of status changes is fairly coarse and that delays may occur before some status changes are detected. Their greatest strength is that they are based on information from nearly all species in a taxonomic group worldwide, rather than a potentially biased subset. At present, suitable data are only available for birds, but indices for other taxonomic groups are in development, as is a sampled index based on a stratified sample from all major taxonomic groups. An index is developed from the IUCN Red List that measures the overall threat status of taxa and reveals that the status of the world's birds has deteriorated since 1988 The world's biodiversity is diminishing rapidly . At the The European Union has adopted the more ambitious target of halting the loss of biodiversity by 2010 . We do nHere we address the third of this suite of indicators, using changes in the threat status of species as measured by the categories of the World Conservation Union (IUCN) Red List. The IUCN Red List is widely recognised as the most objective and authoritative listing of species that are globally at risk of extinction . SpeciesOne of the goals of the programme is to provide a global index of the changing state of biodiversity . HoweverThe total number of extant threatened and Near Threatened birds listed on the IUCN Red List has changed relatively little over the four complete assessments of all the world's birds, increasing from 1,664 species in 1988 to 1,990 species in 2004. However, large numbers of species have moved between categories, particularly in the earlier assessments . Most ofCorvus hawaiiensis] and Spix's macaw [Cyanopsitta spixii]), and five Endangered species became Critically Endangered, but this was offset by seven species that improved in status as a result of conservation efforts , the index was calculated using weights for each category related to the relative extinction risk associated with them . This shThe RLI can be broken down by biogeographic realm , ecosystBird species are being driven Extinct by increasing human impacts on the planet. In total, 129 bird species have been classified as Extinct since 1500, with an additional four species listed as Extinct in the Wild, but surviving in captive populations , 2004c. By contrast, the RLIs presented here provide a robust, sensitive measure of the rate at which the world's birds are changing in relative projected extinction risk, as classified using the categories of the IUCN Red List. The indices show that the overall threat status of the world's birds has deteriorated steadily over the last 16 y. The RLI value has changed by −6.90% over this period. However, it should be noted that owing to the arbitrary nature of the weights applied to each category to calculate the score, this percentage decline is not directly comparable with percentage declines reported for population-based indices such as the Living Planet Index  or the UC. hawaiiensis]), and another is highly likely to have done so  and measures of the rate of change in this state . For indices based on proportional change in a measure , if the measure is one of state, a significant diminution in downward trend would show that the target has been met. If the measure is one of rate of change of state, however, the target is not met until we see a positive trend, not just a decelerating decline. Some of the Red List criteria are based on absolute population size or range size, while others are based on rates of decline in these values or combinations of absolute size and rates of decline. These criteria are used to assign species to Red List categories that can be ranked according to relative projected extinction risk, and the RLI is calculated from changes between these categories. Hence an RLI value relates to the rate at which species are slipping towards extinction at a particular time. To show that the 2010 target has been met, the RLI must therefore show a positive trend. A downward trend, even if diminishing, shows that the slide of species towards extinction is accelerating, not slowing down. The negative trends in the RLI values see  thus shoThe RLIs show some interesting regional variations. The index for birds in the Indo-Malayan realm shows a sharp decline during the 1990s see . This waRLIs can be calculated for particular species groups that have specific conservation or policy significance. For example, there are particularly active conservation networks for game birds , raptors , and parrots , and the threat status of all three of these species groups is deteriorating, with steeper declines in the index value for parrots in the earlier part of the period see . In addiIt should be noted that setting all disaggregated index values to a common baseline in 1988 obscures any changes prior to this period  to reflect the ordinal ranks of the categories, whereby each step from Least Concern to Extinct indicates that at least one measure of extinction risk has become worse. The advantage of this approach is that it is simple, and the trends in the resulting index are driven by a relatively large number of species (hence producing a more robust and representative index). This is because a species moving from Least Concern to Near Threatened contributes just as much to the changing score as a Critically Endangered species going Extinct, and the numbers of species in each category (and the number moving in and out of each category) increases disproportionately from Critically Endangered to Least Concern see .However, steps between lower categories of threat represent smaller increases in extinction risk than steps between higher categories. Therefore we also tested an ‘extinction risk' approach, with each category weighted according to its relative extinction risk based on the quantitative thresholds for each of its criteria . AlthougThe most important difference between the two approaches is the effect of status changes in less-threatened or nonthreatened species. The equal-steps approach gives an index that is heavily influenced by movements of species among the lower categories of threat. The extinction risk approach gives an index that is largely influenced by movements of species among the higher threat categories. For example, if a Vulnerable species improves in status and becomes Near Threatened, and at the same time, a Critically Endangered species goes Extinct, the RLI based on equal-steps weights registers no change, but the index based on extinction risk weights shows a substantial decrease. Downlisting of a Vulnerable species to Near Threatened might represent a very substantial population increase, whereas extinction of a Critically Endangered species might represent the loss of very few individuals. The latter is arguably more significant in terms of genetic diversity, but the former might be more important as an indicator of wider biodiversity trends. Thus, the extinction risk weights emphasise the loss of biodiversity owing to imminent or potential extinctions of species, whereas the equal-steps weights allow the index to capture large changes in the populations of less-threatened species.For the RLI for complete taxonomic groups, and for disaggregating the index to show trends for subsets of species, for example, in particular realms or ecosystems we used the equal-steps approach because the number of species moving between the higher threat categories (those effectively driving trends when an ‘extinction risk' weighting is used) was too small to be meaningful in disaggregated indices. Only 23% of all genuine status changes  involved moves in or out of the highest threat categories. However, for examining trends in the species closest to extinction, we used the weights based on relative extinction risk.The usefulness of the IUCN Red List as an indicator of trends in the status of biodiversity e.g.,  has beenThe first of these problems has been addressed since 1994, when quantitative and objective categories and criteria for the IUCN Red List were introduced , 2001. TRLIs have a fairly coarse level of resolution of status changes because of the broad nature of Red List categories. Populations in the wild may have to undergo quite significant changes in size, trend, or distribution before crossing the thresholds to qualify for a higher or lower Red List category and, hence, before changing the RLI value. This is inherent in using the Red List categories rather than more precise parameters such as estimates of population size. It is not always true, however: The Red List criteria allow for species to be assessed as threatened on the basis of projected declines, and thus changes in status can reflect new or emerging threats in anticipation of population or range changes. We suggest that the disadvantage of coarse resolution is outweighed by the advantage of using a system that allows all the world's species in a taxonomic group to be assessed, rather than just a  subset for which adequately detailed information is available.Insensitivity of the index to status changes may also arise from time lags between changes in a species' population or range and changes in the RLI value, because of delays before detection of the status change, and/or before this knowledge becomes available to assessors. This is potentially more problematic, but several factors act to mitigate it. The Red List Programme partners have a large and expanding network of scientists across the world providing detailed and up-to-date information for an increasing number of species. Furthermore, with improving channels of communication , these status changes are the most important among the world's birds in terms of changes in projected extinction risk. We therefore suggest that the declines shown by the RLI since 1988 represent very significant losses to global biodiversity.Relatively large numbers of species changed categories in 1994 and 2000 owing to improvements in knowledge and improved consistency of interpretation of information against the Red List criteria see . This waThe greatest strength of the RLIs presented here is that they are based on comprehensive and complete assessments of nearly all species in a taxonomic group across the world . Most other global indicators based on, for example, population estimates, are derived from data biased towards common, well-studied species in the developed world, particularly Europe and North America. For example, the Living Planet Index  is basedAt present, indicators based on more representative suites of species are only available for particular countries or regions, such as the United Kingdom headline indicator for birds  and the Species-based indicators such as the RLI arguably provide far more powerful measures of biodiversity loss than other indicators proposed for measuring progress towards the 2010 target . Trends At present, data are only available for birds to produce the sorts of indices shown here. By 2010 at least two complete global assessments will also be available, and RLIs calculated for all the world's mammals , amphibians , and hopefully some plant and marine groups. Additional indices, and an aggregation of RLI trends in multiple groups, will provide a more representative picture of the changing state of biodiversity. In recognition that this will take some time to implement, the IUCN Red List Programme is also developing a sampled RLI based on a stratified sample of about 3,000 species from all major taxonomic groups, biogeographic realms, ecosystems, and Red List categories. This will provide an index that may be more representative of trends in the threat status of all biodiversity. We suggest that RLIs will have a key role to play alongside other types of indicators in assessing progress towards reducing the rate of, or halting, the loss of biodiversity.BirdLife International  has been responsible for providing the assessments of the world's 10,000 or so species of birds for the IUCN Red List since 1963. Since 1988, BirdLife has assessed every species of bird on a regular basis, and birds are regarded as the most comprehensively documented class of organisms on the Red List. BirdLife is the official Red Listing Authority for birds, and assessments are based on data gathered from the BirdLife Partnership of organisations in over 100 countries around the world, from published and unpublished literature, and from information provided by a worldwide network of over 1,000 species experts , 2004c.The principal categories on the IUCN Red List are: Extinct, Extinct in the Wild, Critically Endangered, Endangered, Vulnerable, Near Threatened, and Least Concern . Since aChlamydotis undulata] and Saker falcon [Falco cherrug]) whose categories were revised for the 2004 IUCN Red List too late to be included in Data from which to calculate the indices were derived from four complete assessments of the status of the world's birds by Published lists of numbers of species in different Red List categories cannot simply be used to calculate the index, for several different reasons. For example, changing knowledge and taxonomy result in many category changes, but such revisions are not indicative of changes in the conservation status of species' populations. Hence, to identify those species changing categories between assessments for relevant reasons, a ‘reason for change' code was assigned for each recategorisation. These mutually exclusive codes were (1) recent genuine status change, (2) genuine status change since first assessment, (3) knowledge, (4) criteria revision, and (5) taxonomy. These last three codes were used for changes not relevant for calculating the indices.‘Recent genuine status change' was applied to species that had undergone a genuine improvement or deterioration in status in the period since the previous assessment. This included species qualifying because of population declines (under IUCN Red List criterion A), particularly those with long generation times, for which more than half of the period during which the change occurred was subsequent to the previous assessment. This code was also applied in a few cases where species entered the Red List as a result of their elevation from subspecies to species level, but for which there had been a genuine status change when the taxon was compared to the equivalent population of the original species. For species categorised as threatened in 1988 and Critically Endangered, Endangered, or Vulnerable in 1994, genuine changes in status between these two assessments were identified by searching the account in Serinus syriacus) was uplisted from Near Threatened to Vulnerable in 2004 because of the discovery that populations had declined during a drought in 1998–1999. This information was unavailable during the 2000 assessment, so the species was recategorised in 2004 and given this ‘reason for change' code  was uplisted from Near Threatened to Vulnerable in 2004 because its population was estimated to number as few as 750 individuals. However, the evidence suggests that the population may have been this small since before 1988 . (2) Over each period between complete assessments  the net number of genuine changes to the total in each category was calculated, multiplied by the weights above (with Possibly Extinct and Extinct = 5), and summed to calculate the proportional change in the total score, P. (3) The value of the index (I) was set to 100 in 1988. For subsequent assessments I was calculated by multiplying −P for the previous period by the previous index value .The number of species in each Red List category for each complete assessment and the number of species that changed categories as a result of genuine status changes were used to determine the index value in the following way: (1) For species assessed in two consecutive assessments , the total numbers of species in each category in the earlier assessment  were multiplied by a weight, and these were summed to give a total score, T is total score; c (ti)N is the number of species in category c at time i,t where it is the year of the ith assessment (assessments are not necessarily made every year); cW is the weight for category c; P is proportional genuine change; tiI is the value of the index at time i; Catt is the category of species s at time i; Wct is the weight for category sc; G = 1 if change (from ti−1)( to it) in category of species s is genuine (otherwise sG = 0).Mathematically, the method can be described as follows, where ti−1I = 100 for the first year of assessment.where relative extinction risk associated with each category. Several of the quantitative thresholds in the Red List criteria can be used to obtain approximate values for the relative risk of extinction (for species at the lower boundary of that category). The most obvious is criterion E, which is based on quantitative analysis of extinction probability. The quantitative thresholds in criterion E change for both extinction probability and time frame for the three categories, and depend on generation length . Taking a 3-generation time frame, a generation length set arbitrarily at 5 y, and assuming a constant annual risk of extinction, the 3-generation probabilities are approximately 0.5, 0.13, and 0.016 for Critically Endangered, Endangered, and Vulnerable, respectively . However, most extinctions do not occur as a result of random catastrophes, as implied by the assumption of the constant annual risk of extinction. Many are preceded by declines, resulting in sigmoid extinction risk curves (with probability of extinction as a function of time). For such cases, the 3-generation probabilities are approximately 0.5, 0.1, and 0.025 for Critically Endangered, Endangered, and Vulnerable, respectively  translate to smaller increases in extinction risk than steps between higher categories . Therefore we also calculated the aggregated RLI using weights based on the vely see . Comparavely see . Based ovely see . The weiP  from these simulations gave the lower error bar for the 2004 RLI value. Similarly, we ran 10,000 simulations of six species moving to categories of lower extinction risk (with probabilities for each number of category steps set by the distribution of category changes for ten ‘downlisted' species in 2000–2004), and took the minimum value for P to give the upper error bar. These are very similar to the minimum and maximum values for P derived by simulating an additional six species moving in a direction (and by a number of categories) in proportion to the distribution of these values for all 45 species that underwent genuine status changes in 2000–2004  RLI value owing to time lags before genuine status changes are detected . We esti2004 see .(T) for each of those realms. This is because a species could potentially undergo genuine changes in status in any or all realms in which it occurs. However, so that trends in indices for particular realms reflect changes in the threatening processes operating within each particular realm (rather than threats operating elsewhere in the range of the species), species were only included in the calculation of P for a particular realm if the genuine status change had been driven by factors operating in that realm. For example, Saker falcon (F. cherrug) occurs in the Palearctic, Indo-Malayan, and Afrotropical realms and was included in the score calculations for each of these. However, recent declines have been driven by factors operating on the breeding grounds in Central Asia  has declined as a result of incidental capture in commercial longline fisheries in oceans in the Afrotropical, Neotropical, and Australasian/Oceanic realms  to one or more biogeographic realms  following the boundaries mapped by c realms , and so T for all ecosystems and habitats for which they were scored, but only included in the calculation of P for a particular ecosystem or habitat if the genuine status change had been driven by threatening processes operating in that ecosystem or habitat. Species were only assigned to a habitat type if this was of critical or major importance .The index was disaggregated for ecosystem  and for two terrestrial habitat types  in a similar way. Species were included in the calculation of Macronectes spp. and Procellaria spp.) covered by the ACAP under the CMS.To exemplify how the approach can be used for taxonomic subsets of species, RLIs were also calculated for several high-profile species groups with specific conservation interest groups: raptors , game birds , and parrots (Psittaciformes); and for species groups relevant to particular international conservation treaties: waterbirds  and the total number of species in categories Extinct in the Wild, Critically Endangered, Endangered, Vulnerable, and Near Threatened in at least one assessment during the period .Table S1(66 KB DOC).Click here for additional data file.Table S2p-values  based on the assumption that an additional six genuine changes occurred from 2000 to 2004 but have not yet been identified owing to time lags in knowledge , with a maximum of two species from each category. For each species, the number and direction of category changes were randomly assigned with probabilities (1) based on the change in categories for the ten species that underwent genuine status changes and were downlisted to a lower category of threat during 2000–2004 (‘only down'); (2) based only on the 35 species that were uplisted to a higher category of threat (‘only up'); (3) based on all 45 species (‘both up and down'); and (4) based on all 45 species with probabilities that were set individually for each threat category . In each case, the procedure was repeated 10,000 times to calculate the minimum, maximum, mean, and standard deviation of the simulated p-value.Lists the simulated edge see . Six spep-value for cases when all six species were downlisted to lower categories of threat (shown in red in the table). The lower error bars were determined by the maximum simulated p-value for cases when all six species were uplisted to higher categories of threat (shown in blue in the table). In both cases the values are close to those calculated if the six species changed category, with probabilities based on the direction and number of category changes for all 45 species, and encompass those derived using the method based on category-dependent probabilities.The upper error bars for the RLI were determined by the minimum simulated (35 KB DOC).Click here for additional data file.


Complex brains have evolved a highly efficient network architecture whose structural connectivity is capable of generating a large repertoire of functional states. We detect characteristic network building blocks  in neuroanatomical data sets and identify a small set of structural motifs that occur in significantly increased numbers. Our analysis suggests the hypothesis that brain networks maximize both the number and the diversity of functional motifs, while the repertoire of structural motifs remains small. Using functional motif number as a cost function in an optimization algorithm, we obtain network topologies that resemble real brain networks across a broad spectrum of structural measures, including small-world attributes. These results are consistent with the hypothesis that highly evolved neural architectures are organized to maximize functional repertoires and to support highly efficient integration of information. Analysis of characteristic patterns of connectivity in neuroanatomical datasets suggests that nervous systems evolved to maximize functional repertoires and support highly efficient integration of information The complex vertebrate brain has evolved from simpler networks of neurons over a time span of many millions of years. Brain networks have increased in size and complexity , as haveCaenorhabditis elegans  of the motif, equal to the number of nodes (vertices), and the number and pattern of interconnections. For a more formal definition of motifs and related concepts, see What rules underlie the organization of the particular types of networks that we see in complex brains? It is likely that, as networks become more complex, already existing simpler networks are largely preserved, extended, and combined, while it is less likely that complex structures are generated entirely de novo. One hypothesis states that complex and highly evolved networks arise from the addition of network elements in positions where they maximize the overall processing power of the neural architecture. This could be achieved by increasing the number of existing processing configurations or by introducing new processing configurations that add to the robustness or range of cognitive and behavioral repertoires. We may gain insight into the rules governing the structure of complex networks by investigating their composition from smaller network building blocks. Those building blocks are called “motifs” , and they have been examined in the context of gene regulatory, metabolic, and other biological and artificial networks . Motifs M, and the motif diversity, defined as the number of classes that are represented within the network by at least one example.While the most common definition of network motifs is based on their structural characteristics , structu(N) and edges (K) within a large network has a strong effect on the motif number and diversity of its constituent structural and functional motifs. But even if N and K are held constant, different connection patterns will result in different repertoires of such network motifs, expressed in terms of both number and diversity. These considerations lead us to formulate hypotheses concerning the rules for brain network organization in terms of network motifs. We hypothesize that neuronal networks have evolved such that their repertoire of potential functional interactions  is both large and highly diverse, while their physical architecture is constructed from structural motifs that are less numerous and less diverse. A large functional repertoire facilitates flexible and dynamic processing, while a small structural repertoire promotes efficient encoding and assembly.Clearly, the number of vertices We investigate this hypothesis first by performing an analysis of structural and functional motifs in various brain networks. We compare the motif properties of real brain networks with random networks and with networks that follow specific connection rules such as neighborhood connectivity (lattice networks). We identify some motif classes that occur more frequently in real brain networks, as compared to random or lattice topologies. Second, by rewiring random networks and imposing a cost function that maximizes functional motif number, network topologies are generated that resemble real brain networks across a broad spectrum of structural measures, including small-world attributes. The results of our analyses are consistent with the hypothesis that complex brain networks maximize functional motif number and diversity while maintaining relatively low structural motif number and diversity.M = 2, 3, 4, and 5 (estimations). In addition, we obtained motif frequency spectra for the matrix of interneuronal connections  of C. elegans, for motif sizes M = 2, 3, and 4 (estimations). For each neural connectivity matrix we generated equivalent  random and lattice matrices, preserving degree distributions . All neuronal networks  showed maximal functional motif diversity for all motif sizes examined . Their structural motif diversity tended to be submaximal. For example, the structural motif diversity of macaque cortex was significantly reduced in comparison to random matrices . This tendency was especially pronounced for higher values of M .M = 3, M = 4) found within the network of the macaque visual cortex and C. elegans and their corresponding reference cases. Spectra of macaque and C. elegans networks are both less similar to random networks than to lattice networks. For M = 3, in the case of the macaque visual cortex, some motif counts appear decreased over random networks  while other motif counts appear increased  over both random and lattice networks. M = 2, 3, and 4. Given motif frequencies from samples of n = 100 random or lattice networks, we calculated z-scores for the corresponding motifs in neuronal networks. Only structural motifs that were significantly increased  in real networks as compared to both random and lattice networks are tabulated. Despite variations in size, areal composition, species, and collating authors, specific motif classes consistently emerged across several different cortical networks. M = 3) in all cortical matrices examined in this study. The appearance of this motif cannot be explained by a higher proportion of reciprocal  edges (a motif of size M = 2): While random networks contain fewer such edges, lattice networks contain an equally high proportion of such edges . No motif of size M = 2 was significantly increased in frequency for any of the connection matrices in this study  occurred more frequently than expected.is study . FurtherM that the area participates in. Motif fingerprints characterize brain areas, as do other structural and functional features (Each vertex (brain area) participates in a subset of the structural motifs that compose the entire network. We asked whether individual brain areas participate in similar or different sets of motifs and whether motif participation might reveal functional relationships. We define the motif fingerprint of a brain area as the number of distinct structural motifs of size features , and theM = 3) for several visual areas of macaque visual cortex. Motif ID = 9 was the only motif found to be significantly increased over both random and lattice networks, but it was increased for only five visual areas . All of these areas showed highly similar motif fingerprints characterized by a specific ratio of motif classes 9, 12, and 13 , in generations of 10 individuals, with a low rewiring rate of one connection per generation and a survivor rate of one network per generation, over 2,000 generations. Convergence was robust and consistent structural features of optimized connection matrices were observed.We hypothesized that high functional motif number and diversity represent important ingredients in the global organization of cortical networks, and that a selective advantage for these two properties might contribute to the generation of other significant structural properties. To test this hypothesis we applied an evolutionary algorithm  that selM = 3) as well as at lower and higher motif sizes . These motifs essentially form of a chain of reciprocally connected units, while pairs of connections linking the ends of the chain are absent. In functional terms, units in these motifs are highly integrated with their neighbors, while some pairs of units remain more segregated from each other and do not communicate directly. Thus, this motif type combines two major principles of cortical functional organization, integration and segregation  are increased. Our results suggest that large-scale cortical connection matrices form a distinct family ; rather, optimized networks routinely exhibit functional motif numbers that exceed those of ideal lattices, and they belong to a general class of networks that maintain a mixture of “local” and “long-range” connectivity. Importantly, even though structural and functional motifs are directly related , optimizing structural and functional motif number yielded strikingly different connection topologies.M = 3; unpublished data). In turn, networks optimized for functional motif number have significantly higher complexity than random networks, while those optimized for structural motif number are much less complex. Thus, it appears that several criteria for optimality  favor similar global network architectures that are all characterized by two coexisting organizational principles, functional segregation and functional integration. The functional motif frequency spectrum provides a sophisticated way of characterizing subtypes of such networks geared at more specific functional modes of information processing.Optimizing functional  motif number produced a tendency toward the emergence of small-world attributes (high clustering coefficient and short path length), a mode of connectivity that promotes functional cooperation, recurrent processing, and efficient information exchange . High clAll networks and network motifs in this paper are described as graphs of units  with directed  connections .M vertices and a set of edges  forming a subgraph of a larger network. For each M there is a limited set of distinct motif classes. For M = 2, 3, 4, and 5, the corresponding numbers of motif classes are 2, 13, 199, and 9,364 . Thus, the functional motif frequency spectrum is easily obtained from the structural motif frequency spectrum, without the need for additional motif detection.A structural motif provides the complete anatomical substrate for possible functional interactions among its constituent vertices. However, in real neuronal networks, not all structural connections participate in functional interactions at all times. As different edges or connections become functionally engaged, different “functional motifs” emerge within a single structural motif. The former  refers to “processing modes” or “effective circuits,” while the latter  refers to “anatomical elements” or “building blocks.” The existence of different functional motifs greatly enhances the processing power of any neuronal architecture. We then distinguish structural motifs from functional motifs that form a set of subgraphs of the structural motif. All such functional motifs consist of the original C. elegans are provided in This definition implies that functional motifs are more naturally applied to networks with vertices that contain multiple neurons or neuronal populations. In the present study, our main focus is on motifs of large-scale connection matrices; data for the single neuron network of M = 3, motifs with ID = 7, 9, 10, 12, and 13 are connected motifs.A “connected motif” is a structural motif that forms a strongly connected graph. In a connected motif, all constituent vertices can be reached from all other constituent vertices. Such a motif, in principle, allows all vertices to exert causal effects on each other. For M. The motif frequency spectrum for structural motifs is obtained by motif detection. The motif frequency spectrum for functional motifs can be obtained from the structural spectrum by simple multiplication with the characteristic number of functional motifs for the respective structural motif.A “motif frequency spectrum” records the number of occurrences of each motif of a given class for a size M) encountered in a network. The motif number is obtained as the sum over the motif frequency spectrum, either structural or functional.“Motif number” is the total number of all motifs of all classes (for a given size M) encountered in a network. The motif diversity is obtained as the number of all motif classes for which the frequency spectrum is greater than zero.“Motif diversity” is the total number of all motif classes . We used data of all chemical synapses from both individuals, discarding data on gap junctions , resulting in a matrix of N = 197 neurons and K = 1,974 directed connections. Other studies used matrices with N = 282 , N = 280  and by imposing equal degree distributions on all comparison networks . see also . We notesee also .i1,j1) and  is selected for which cij11 = 1, cij22 = 1, cij21 = 0, and cij12 = 0. Then we set cij11 = 0, cij22 = 0, cij21 = 1, and cij12 = 1. This is repeated until the connection topology of the original matrix is randomized.Random and lattice matrices that preserve the in-degree and out-degree for each vertex are generated from the original anatomical connection matrices by a Markov-chain algorithm . For ranFor lattice matrices, the same Markov procedure is employed but swaps are only carried out if the resulting matrix has nonzero entries that are located closer to the main diagonal (thus approximating a lattice or ring topology). This algorithm is implemented as a probabilistic optimization using a weighted cost function.http://www.indiana.edu/~cortex/CCNL.html. In some cases, for large networks or high values of M, we employed random sampling to estimate motif frequency spectra and their associated values for motif number and diversity. We selected different sample sizes to ensure convergence of these estimates and performed up to ten separate runs to generate good estimates. The evolutionary algorithms used in this study for optimizing structural and functional motif numbers of networks were similar to the algorithm described in N, K, and the original degree distribution were conserved.All graph theory methods used in this paper—including those for calculating clustering coefficients and path lengths —as well


Because deleterious alleles arising from mutation are filtered by natural selection, mutations that create such alleles will be underrepresented in the set of common genetic variation existing in a population at any given time. Here, we describe an approach based on this idea called VERIFY , which can be used to assess the extent of natural selection acting on an oligonucleotide motif or set of motifs predicted to have biological activity. As an application of this approach, we analyzed a set of 238 hexanucleotides previously predicted to have exonic splicing enhancer (ESE) activity in human exons using the relative enhancer and silencer classification by unanimous enrichment (RESCUE)-ESE method. Aligning the single nucleotide polymorphisms (SNPs) from the public human SNP database to the chimpanzee genome allowed inference of the direction of the mutations that created present-day SNPs. Analyzing the set of SNPs that overlap RESCUE-ESE hexamers, we conclude that nearly one-fifth of the mutations that disrupt predicted ESEs have been eliminated by natural selection (odds ratio = 0.82 ± 0.05). This selection is strongest for the predicted ESEs that are located near splice sites. Our results demonstrate a novel approach for quantifying the extent of natural selection acting on candidate functional motifs and also suggest certain features of mutations/SNPs, such as proximity to the splice site and disruption or alteration of predicted ESEs, that should be useful in identifying variants that might cause a biological phenotype. A computational analysis of single nucleotide polymorphisms showed that one-fifth of mutations that disrupt predicted exonic splicing enhancers have been eliminated by natural selection Exonic splicing enhancers (ESEs) were identified about a decade ago as short oligonucleotide sequences that enhance exon recognition by the splicing machinery -ESE which identifies ESEs in human genomic sequences using statistical properties of the oligonucleotide composition and splice site strengths of large datasets of exons and introns . This meA variety of other selection-based methods have been used to identify sets of sequences that are capable of functioning as ESEs. These SELEX methods isolate ESEs from a complex pool of random sequence by iteratively selecting and amplifying the fraction of molecules that can function as ESEs in a reporter assay . These mDespite this, a few previous studies have identified several disease alleles where the disruption of a conserved splicing enhancer corresponds to observed splicing defects, a noteworthy example being splicing mutations in the breast cancer gene BRCA1 . To dateHere we test the hypothesis that, in addition to protein-coding requirements, human exon sequences are also significantly constrained by the requirement to encode ESEs. We have developed a population genetic approach  which exploits the simple principle that, because they are selected against, deleterious alleles will tend to be underrepresented in the pool of sequence variants that are common in a population . Taking To assess the relationship between the locations of common genetic variation in human genes and ESEs, we screened the public SNP database  for SNPs that mapped to human exons. A set of biallelic reference SNPs was used, excluding entries that (1) mapped to multiple regions in the human genome, (2) mapped to repetitive elements, or (3) were derived from transcript sequence data, e.g., through comparison of expressed sequence tags (ESTs) see . This seRecording the position of each SNP within the corresponding exon revealed that SNP density is not uniform along exons . ConsistThe frequency of ESE disruption for simulated  mutations was compared to the frequency of ESE disruption observed in SNPs, which represent mutations that have survived selection to become reasonably frequent in the human population. SNPs are shaped by the interplay between the mutation process and the process of natural selection. Comparing simulated mutations to natural variations is an effective way to decouple these processes, and this approach has been used by others to study selective pressure in protein-coding genes . Here, wWhen considering SNPs, in order to distinguish mutations that disrupt predicted ESEs from those that create predicted ESEs, the identity of the ancestral allele must be established. Since the mutations that created most human polymorphisms occurred less than 1 million years ago , long afPan troglodytes, accessed through the NCBI trace archives (ftp://ftp.ncbi.nih.gov/pub/TraceDB/pan_troglodytes/). As the trace archives represented several-fold coverage of the chimp genomic sequence, most SNPs matched to several sequence reads. Whenever one allele of a human SNP consistently matched the chimpanzee sequence in all high-quality alignments, that allele was designated as the ancestral allele, and the other allele at that position was designated as a variant allele. The 8,408 SNPs that satisfied this criterion were then annotated for predicted ESEs in both the ancestral and variant sequence, simply by comparing the six overlapping hexanucleotides that differed between the two alleles to the set of RESCUE-ESE hexamers and recording the number of matching hexamers.Each of the 9,862 mapped human SNPs described previously was aligned (using a 33-nucleotide sequence window centered around the polymorphic position) to unassembled reads from the genome of the chimpanzee It is well known that current SNP databases contain a certain rate of error. The SNP consortium estimated that about five percent of their submissions were false positives attributed to base calling errors . Incorretrans-factor(s). The relative frequencies of these four outcomes are listed in The annotation of RESCUE-ESE hexamers in a biallelic SNP results in one of four possible outcomes: no ESE hexamers in either allele , one or more ESE hexamers only in the ancestral allele , one or more ESE hexamers only in the variant allele , or one or more ESE hexamers in both alleles . The latter category is referred to as ESE alteration because the sets of RESCUE-ESE hexamers in the ancestral and variant alleles are, of course, different, and therefore may not necessarily be recognized with the same affinity by the same Mutations were simulated at random in human exons using nucleotide substitution frequencies that reflect the mutational biases observed in unselected regions of the genome with similar nucleotide composition. In vertebrates, the greatest biases in nucleotide-to-nucleotide substitution frequencies are related to the hypermutability of C residues  and the Using the substitution frequencies derived from a recent large-scale study of nucleotide substitution patterns in processed pseudogenes , mutatioTo assess the degree of selection on ESEs, one standard measure is the relative risk (RR), defined in this instance as the ratio of the frequency of ESE disruption in SNPs to the frequency of ESE disruption for unselected mutations . This observation confirms our previous result and alleviates the concern that the analysis might have been confounded by the effects of selection acting at the protein level. The summary OR  for ESE disruption was 0.82 ± 0.05 (p < 0.001).As expected, the SNP dataset was greatly enriched for synonymous variation relative to the simulated mutation dataset. There is a 1.3:1 ratio of synonymous:nonsynonymous changes in the SNP dataset compared to a 0.5:1 ratio of synonymous:nonsynonymous changes in the simulated dataset. This difference, which has been observed many times, suggests that more than 60% of mutations that change the amino acid sequence are eliminated by natural selection . In orde2 ± 0.05 B, which Base changes that alter one or more predicted ESE hexamers but result in creation of other RESCUE-ESE hexamers  are also selected against, but to a somewhat lesser degree . SR protAt first glance, the overrepresentation of some categories in this analysis may seem surprising. However, this is simply a consequence of the underrepresentation of disruption and alteration mutations in the SNP pool causing the remaining two categories of variation, ESE neutrality and ESE creation, to appear slightly overrepresented in SNPs relative to unselected mutations B. As mutp < 0.05) relative to predicted ESEs located further from splice sites  was calculated for each hexamer and provided a means of assessing the selective pressure on each hexamer. The RR for a hexamer that was under no additional selective pressure would therefore be equal to 1.0, and a hexamer under increased selection would have an RR of less than 1. Consistent with our previous analysis  and compared this frequency to the value we would expect in the absence of selection (simulated mutations). For this analysis we used a simple RR measure, defined as the frequency with which a hexamer overlaps with SNPs relative to the frequency with which the same hexamer overlaps with simulated mutations. This ratio , about half of the hexamers predicted to be ESEs in all four vertebrates examined  were significantly conserved in the SNP analysis. In the future, with a larger SNP database and more genomes available, it should be possible to use these methods to analyze more individual hexamers for evidence of selective pressure.A comparison of RESCUE-ESE predictions for four different vertebrates  revealed a strong correspondence between within-species conservation  and cross-species conservation B. In othAs mentioned previously, the public SNP database used in this study contains a significant fraction of entries that could not be validated by resequencing. If these unvalidated entries in dbSNP were errors, they would not be expected to specifically avoid ESEs, or particular exon positions. Therefore, SNP artifacts are likely to reduce, rather than increase, the apparent significance of the biases in the nature and distribution of SNPs that we have observed.Here we have shown, using polymorphism data, that RESCUE-ESE hexamers have been preferentially conserved in the recent evolutionary history of the human lineage and that the strength of this conservation increases with increasing proximity to the splice sites. These results imply that splicing imposes important constraints on the evolution of human exons.As the size and quality of the SNP database is rapidly increasing, measures of selection deriving from SNP data are likely to become increasingly useful for evaluating the function of short, degenerate sequence elements like ESEs. For example, it should be possible to use the VERIFY method to analyze selection on motifs that are postulated to control transcription, polyadenylation, messenger RNA (mRNA) stability, or translation. The current build of dbSNP contains 360,000 SNPs that are located in a “locus region” within 2 kb upstream of the transcript start site or 500 bp downstream of the polyadenylation site . There aIn addition, this work identifies two new categories of mutations that are under selective pressure: mutations that disrupt or alter RESCUE-ESE hexamers. This increased selective pressure presumably reflects the increased likelihood that such variations will alter splicing and, therefore, gene activity. Several cases of polymorphisms which result in allele-specific differences in splicing have been reported . Here, wftp.ncbi.nih.gov) and parsed from the XML format, which integrates data  from several sources (http://ncbi.nih.gov/). To limit the contribution of erroneous SNPs, several filters were applied to produce the set of SNPs used in this analysis. First, SNPs located within a coding region in the assembly annotation were required to align to an internal exon in the GENOA exon dataset. GENOA is a genome annotation script that annotates exons by spliced alignment of mRNA/cDNA sequence to an assembled genome, applying a number of checks on the quality of the resulting alignments (see below). SNPs that mapped to multiple genomic regions, to known repetitive elements, to regions where the reading frame differed in the genomic/transcript alignment, or to an entry that had been contributed in the context of spliced transcripts  were excluded. We also removed entries where both versions of a SNP contained a match to either the human exon database or the chimpanzee (Pan troglodytes) genome (ftp://ftp.ncbi.nih.gov/pub/TraceDB/pan_troglodytes/). From the resulting pool of reference SNPs, entries associated with genotype data (population frequency) were used to build a validated SNP set. Unless otherwise indicated, SNPs were aligned to other sequences only when a perfect (33/33) base match was obtained using BLAST (Build 112 of dbSNP was downloaded from the NCBI ftp server ( sources . A curreng BLAST  with onehttp://genome.ucsc.edu) using the genome annotation script GENOA . The exon/intron structures were inferred from at least two cDNA/genomic alignments, and the reading frame was determined from the CDS annotation of the GenBank cDNAs. As RESCUE-ESE hexamers were identified in constitutively spliced exons and alternative splicing sometimes results in ambiguous splice site positions, we confined our analysis to exons which showed no (cDNA) evidence of alternative splicing. Therefore, exons present in some, but not all, cDNA alignments overlapping a genomic region were excluded. In addition, it was required that each exon (1) be flanked by introns whose ends matched the consensus terminal dinucleotides of U2-type or U12-type introns  and (2) be in an open reading frame that spanned at least three exons (the exon being considered and at least one flanking exon on either side).Datasets of internal human exons were generated by spliced alignment of cDNA sequences from GenBank (release no. 134) to the assembled, masked human genome sequence  whether a mutation would occur in an exon, (2) the position where a mutation would occur, and (3) the identity of the variant allele. Mutations were simulated in present-day internal coding exons with a probability of occurrence proportional to the length of the exon and no a priori strand or position bias. The output mutations were stored in reference SNP format and annotated with predicted ESEs as described in the text.The mutation probabilities and the pattern of substitution are functions of the input sequence . The values of all 12 nucleotide-to-nucleotide substitution probabilities used here were considered for all four possible 3′ neighboring-base contexts. The distribution of dinucleotide substitution probabilities (48 possible) used in this work was derived from a large study of mutation in ribosomal protein pseudogenes , ESE creation (− +), ESE alteration (+ +), or ESE neutrality (− −). In the annotations, the first position refers to the wild-type or ancestral allele, where “+” indicates one or more ESE hexamers and “−” indicates no ESE hexamers. The ESE alteration category can include mutations in which a net gain or a net loss of ESE hexamers is annotated. All annotations used the set of 238 RESCUE-ESE hexamers described previously .ORs (rather than RRs) were used to quantify the difference between the set of experimentally validated SNPs and simulated mutations in where P(outcome|selection) represents the probability that SNP (a mutation under selection) will have a particular ESE annotation outcome . The extent of selection was measured separately for synonymous and nonsynonymous mutations and standard methods were used to calculate 95% confidence intervals . The MH Experimentally validated SNPs that were located in the first or last 70 nucleotides of an exon were evaluated for ESE disruption as a function of position. As no restrictions were placed on exon size, it was possible for SNPs in small exons to be placed in multiple categories . ORs were used to measure the extent of selection, and significance was assessed at an α level of 0.05 (one-tailed) for ESE disruption mutations in the four different regions of an exon.p value for each of the 238 hexamers.Bootstrap sampling was used to determine hexamers that were significantly avoided by SNPs. RR was used to compare the selection on individual hexamers over the course of 5,000 trials. For each trial, 8,408 SNPs were sampled with replacement from the set of 8,408 SNPs described in the text. The frequency of cases where an ESE coincided with an SNP was calculated for each of the 238 RESCUE-ESE hexamers and divided by the expected frequency for that hexamer (determined through simulation). This ratio of frequencies was used to estimate RR for each hexamer. Dividing the instances where RR was greater than 1 by the number of trials  provided a bootstrap The interspecies comparisons considered the output of RESCUE-ESE on four vertebrate genomes: human, mouse, zebrafish, and pufferfish. The set of human hexamers was divided into subsets according to the degree of conservation across vertebrates in the following manner: Hexamers that were only present in humans defined set 1; hexamers present in human and mouse, set 2; hexamers present in human, mouse, and one of the fish species, set 3; hexamers present in human, mouse, zebrafish, and pufferfish, set 4. The degree of overlap between these hexamer sets and the RESCUE-ESE hexamers with an RR of significantly less than 1 was recorded and displayed in −4 rather than 2.4 × 10−4), and so a particular ESE would be expected to be interrupted by a SNP at any one of six positions with a frequency of 6 × 3.7 × 10−4 = 2.2 × 10−3. This probability was used to estimate the number of variations that would interrupt  an ESE hexamer with and without selection for the different SNP database sizes and degrees of selection (ORs) described in the text.Power calculations for single hexamer analysis were performed as described previously (http://genes.mit.edu/burgelab/rescue-ese/. An online tool to annotate RESCUE-ESE hexamers in exons.http://genes.mit.edu/burgelab/Supplementary/fairbrother04/. Contains exon, SNP, RR, and ancestral allele databases used and/or generated in this study.


Methylphenidate (MPH) is commonly prescribed in the treatment of Attention-Deficit/Hyperactivity Disorder or ADHD. Concerta and Metadate CD are once-daily formulations of MPH using different delivery mechanisms resulting in different pharmacokinetic profiles. A recent study (COMACS) showed that for near-milligram (mg) equivalent daily doses, Metadate CD provides greater symptom control in the morning (1.5 through 4.5 hours post-dose), while Concerta provides greater control in the early evening (12 hours post-dose). Non-inferential comparison of effects for different dose levels of the two formulations suggested that equivalent levels of morning symptom control could be obtained with lower daily doses of Metadate CD than Concerta; the situation being reversed in the evening. The current paper presents a secondary analysis that provides a statistical test of these observations.post-hoc comparisons across dose levels presented here, total SKAMP scores with the active treatments (adjusted for placebo response) were analyzed using an analysis of covariance, with a combined measure modeling placebo response across all time period as the covariate.The COMACS study was a multi-center, double-blind crossover study of Metadate CD, Concerta and placebo with each treatment administered for 1 week. Children were assigned on the basis of their pre-trial dosage to either high , medium  or low doses  of MPH, and attended a laboratory school on the 7th day for assessment at 7 sessions across the day. For the Symptom control from 1.5 through 6.0 hours post-dose was as good with lower doses of Metadate CD (20 and 40 mg) as with higher doses of Concerta . Lower daily doses of Concerta (18 and 36 mg) and higher doses of Metadate CD  gave equivalent control at 7.5 and 12 hours with Metadate CD giving better control from1.5 through 6.0 hours post-dose.Different delivery profiles of Metadate CD and Concerta can be exploited to limit total daily exposure to MPH while at the same targeting a specific, especially clinically significant, period of the day. These results need to be confirmed in a study in which children are randomly allocated to different dose levels of the two formulations and plasma MPH concentrations are assessed simultaneously. Attention Deficit /Hyperactivity Disorder (ADHD) is a relatively common early onset developmental condition characterised by a pervasive and persistent pattern of age inappropriate and debilitating inattention, impulsiveness and overactivity. It is reported to affect between 3 and 6 percent of the childhood population and, if untreated, to be associated with a poor prognosis in adolescence and adulthood ,2. Methymax). Recently a 10 and 30 mg dose of MCD and a 27 mg dose of CON were licensed, but these were not available during the COMACS study. As a result of the design differences, MCD releases 50% more IR MPH in the initial bolus delivery process than CON for a near mg-equivalent total daily doses. Furthermore although the amount of ER MPH is the same for near mg-equivalent doses the pattern of delivery differs with MPH release front-loaded with MCD (starting at 1 hour) and more back-loaded with CON.We recently reported the results of a randomised, placebo-controlled, head-to-head comparison of the pharmacodynamic (PD) properties of near mg-equivalent daily doses of two safe and effective ,11 ER foThe different IR/ER ratios and time-course for dissolution of the two extended-release portions of the formulations result in distinctly different plasma concentration vs. time profiles in adults when compared at near-mg equivalent total doses . ConcentThe publication of the main analysis of the COMACS study data has led to a number of suggestions for alternative analyses that would usefully address questions of the clinical utility of the two formulations. Although from a scientific point of view the aim of the COMACS study – to compare across-the-day PD profiles of the two formulations – was best served by a comparison of bio-equivalent  total daily doses, other strategies for selecting comparator doses could have been legitimately followed. For instance, it has been suggested that comparators could have been matched on the basis of the size of the IR component dose rather than the total daily dose  of the formulations. Thus this primary analysis of the data did not include a cross-dose analysis, which would allow the relative efficacy of different daily doses of MCD and CON to be directly tested at different times of the day. The present paper presents a statistical cross-dose analysis of the efficacy of MCD and CON at different times of the day in order to test these observations directly.post-hoc exploration of the PD profiles of different doses of MCD and CON. Furthermore, the inclusion of the placebo arm in the COMACS design enabled us to directly address one of the possible major confounds associated with across dose comparisons in this sort of stratified design. An inspection of the published COMACS data revealed that the placebo scores measured at different sessions across the day were higher in the high dose group, than the medium or low dose groups. This suggests that children on higher doses may have had a more severe expression of the disorder making cross-group comparisons complicated. In the analysis reported in the current paper these placebo scores were used to adjust the treatment outcome scores to take account of this.The ideal way to test across-dose comparisons of different medications is to randomise patients to different dosing strata. In the COMACS study children were assigned to dosing strata on the basis of pre-trial dose levels. However, because the COMACS study was designed to have similar and substantial numbers of children in each dosing strata the data set still offers the opportunity for a preliminary A number of specific across-dose comparisons were made. First, the lower daily doses (20 mg and 40 mg) of MCD were compared to the higher daily doses of CON (36 mg and 54 mg respectively). The comparison between MCD 20 mg and CON 36 mg is of particular interest to clinicians as these are the suggested dose substitutions for IR MPH 10 mg BID and 10 mg TID, respectively. On the basis of the initial observations of the COMACS study it was predicted that MCD 20 and CON 36 mg, and MCD 40 and CON 54 mg would produce equivalent control in the morning (from 1.5 through 4.5 hours post-dose). In contrast, at these dose levels it was predicted that CON would produce significantly greater effect as the day wore on (from 6 hours onwards). Second, lower daily doses (18 mg and 36 mg) of CON were compared with higher doses of MCD (40 mg and 60 mg respectively). We tested the prediction that at these dosing levels MCD would demonstrate greater efficacy only in the morning and early afternoon (from 1.5 through 6 hours); so that despite the lower total daily dosing levels, CON, designed to be effective over a 12 hour period, would still be more effective than MCD, designed to be effective over an 8 hour period, at reducing symptoms in the early evening .® CD  Extended-Release Capsules (MCD) were obtained from Eurand Americas, Inc , while Concerta® (methylphenidate HCl) Extended-release Tablets (CON) were obtained from Alza Corporation . For a detailed description of the preparation of clinical materials see Swanson et al., 2004 [Metadatel., 2004 .Six to 12 year old children, with interview-confirmed-diagnoses of ADHD who were being treated with MPH in doses of between 10 to 60 mg/day  were recruited for the trial. Children were deemed otherwise healthy on the basis of an extensive medical history and physical examination. Children were excluded if they had an IQ below 80 or the inability to follow or understand study instructions. Other standard exclusion criteria for MPH drug trials applied [This was a ten-site, double-blind, placebo-controlled crossover study comparing three treatment conditions: MCD, CON, and placebo (PLA). The study was conducted in accordance with the principle of the Declaration of Helsinki and its amendments and the International Committee on Harmonization Guidelines on Good Clinical Practice. Dose-level assignment was made according to pre-existing daily dosing requirement for MPH and children remained at the dose-level assigned for the entire study duration. Children treated with low doses (≤ 15 mg/day IR or 20 mg/day ER) of MPH were randomized to receive a daily dose of MCD 20 mg, CON 18 mg or PLA; those treated with medium doses (>10 to ≤ 30 mg/day IR or >20 to ≤ 40 mg/day ER) of MPH were randomized to receive MCD 40 mg, CON 36 mg or PLA; and children treated with high doses (> 30 mg/day IR or >40 mg/day ER) of MPH were randomized to receive MCD 60 mg, CON 54 mg, or PLA. Each of the three treatments was administered for 7 days (in randomized order) without an intervening washout period. Assessments took place in the laboratory school on Days 7, 14, and 21 . Two trA preliminary factorial Analysis of Variance (ANOVA) using the SPSS General Linear Model was conducted in order to confirm that the pattern of effects of Treatment , Dose  and Session  found in the Swanson et al. (2004) paper [Table The PLA adjusted scores for the total SKAMP score relating to the specific cross-dose comparisons are presented in Figures The comparison of MCD 20 mg and CON 36 mg revealed no overall difference between treatments, F  = 0.001; ns. There was an effect of session, F  = 15.08; p < 0.001 and an interaction between treatment and session F  = 3.98; p < 0.01. The two treatments did not differ at the time of dose administration or 3, 4.5, 6 and 7.5 hours after it (Fs < 2.5). MCD was superior to CON at 1.5 hours, F  = 4.48; p < 0.05, while CON was superior to MCD at 12 hours, F  = 3.94; p < 0.05. The comparison of MCD 40 mg and CON 54 mg revealed no overall difference between treatments, F  = 1.05; ns. There was an effect of session F  = 36.20; p < 0.001 and an interaction between treatment and session, F  = 3.72; p < 0.01. There was no difference between treatments at the time of administration or at 1.5, 3, 4.5 and 6 hours . However, CON was superior to MCD at 7.5, F  = 6.26; p < 0.05, and 12 hours, F  = 4.55; p < 0.05.For the comparison of MCD 40 mg and CON 18 mg there was a main effect of treatment with MCD being associated with lower  SKAMP scores than CON, F = 6.50, p < 0.05. There was also an effect of session, F = 18.61, p < 0.001 and an interaction between treatment and session, F = 8.32; p < 0.001. MCD gave lower (better) scores at 1.5  = 10.02; p < 0.005), 3  = 18.36; p < 0.001), 4.5  = 16.16; p < 0.001), and 6  = 5.94; p < 0.05) hours. However, there was no difference between the two formulations at 7.5 and 12 hours, Fs < 2.75. MCD 60 mg also gave significantly lower scores than CON 36, F  = 14.03; p < 0.001. Once again there was an effect of session, F  = 35.38; p < 0.001, and an interaction between treatment and session, F  = 10.55; p < 0.001. MCD gave significantly lower scores at 1.5  = 35.01; p < 0.001), 3  = 30.99; p < 0.001), 4.5  = 18.23; p < 0.001) and 6  = 9.66; p < 0.005) hours but not at 7.5 and 12 hours, Fs < 0.55.CON and MCD are once-daily formulations of MPH using different drug delivery mechanisms resulting in different plasma concentration vs. time profiles in both adults  and chilThere was only partial support for these predictions. Not only was there no significant difference between placebo adjusted total SKAMP scores for MCD 40 mg and CON 54 mg from 1.5 through 4.5 hours, but also at 6.0 hours post-dose. In addition, comparison at the lower dose levels (MCD 20 mg and CON 36 mg) gave a surprising result: MCD appeared to be associated with a stronger effect and a more rapid onset of action than CON during the very early period post-dose. This was indicated both by the absolute values  for the two formulations at 1.5 hours post-dose and the steeper slopes for MCD than CON between 0 and 1.5 hours. This was a particularly unexpected finding because at the dose levels being compared, MPH available from the IR component of MCD (6 mg) was less than that available for CON (8 mg). Given that the active drug is the same in the two formulations and assuming that clinical efficacy reflects MPH serum concentrations as has been proposed by Swanson  this wouThe predictions relating to the efficacy of these different dose comparisons during the latter part of the day were confirmed only at the 12 hour testing session for the lower doses (CON 36 mg vs. MCD 20 mg) and the 7.5 and 12 hours sessions for the higher doses (CON 54 mg vs. MCD 40 mg). Given their expected plasma concentration vs. time profiles in children, one would expect greater efficacy from CON than MCD whatever the dose comparison being made. The 7.5 hour post-dose period is less clear-cut in terms of the relative benefit of the two formulations.The second set of predictions related to the value of lower daily doses of CON (18 mg and 36 mg) to provide equivalent symptom coverage compared to higher doses of MCD  in the late afternoon and superior coverage in the evening. The predictions for late afternoon were supported at both the higher and lower cross-dose comparisons with lower doses of CON giving equivalent control at 7.5 hours. However, interestingly, at 12 hours the higher doses of MCD remained equivalent to CON. This result is in keeping with the idea, supported by the expected plasma concentration and known dissolution data that MCD continues to release MPH up to 12 hours post-dose. As expected, the higher doses of MCD had a greater effect between dosing through 6 hours post dose.Given design limitations it is possible that these effects are not 'real' effects related to dose and treatment type but are related to differences between the types of children assigned to different dose levels. The way in which the children were assigned to dose level meant that across-dose comparisons of different treatments could be subject to a number of confounds. First, it could be that children were placed on higher doses because they had a more severe form of ADHD. In the present study we attempted to deal with the possible confounds that such an approach to dose assignment might bring by using the placebo SKAMP score  as a covariate. This score was included as a proxy for severity of ADHD in order to control for the possibility that children assigned to the higher-dose level had a more severe form of the condition.A second possible confound, not corrected for by controlling for placebo scores, relates to the child's sensitivity to MPH: Children may have been prescribed higher pre-study doses of MPH because they were less sensitive to MPH and did not respond to the lower dose. Such variations in sensitivity could be independent of overall severity of the disorder and therefore constitute a second possible confound. If this were the case then an across-dose comparison would be between more- and less-sensitive children. There are two pieces of evidence that argue against this as an explanation for the current results. First, Swanson et al.  reportedTaken together, the existence of these confounding factors and complications means that while these data provide an initial indication of the relative efficacy of different doses of CON and MCD at different points across the day they should be treated with a certain degree of caution, and the results should be confirmed in a study where subjects are randomized to dose level rather than being assigned to it on the basis of their pre-study MPH daily dose and plasma levels of MPH are assessed simultaneously.From the point of view of the practicing clinician the results of this study further highlight the value of having available different ER MPH formulations with different expected plasma concentration vs. time profiles offering different patterns of efficacy over different periods of the day. Given the established dose-response relationship between MPH and side effects, clinicians and parents may wish to limit total daily MPH intake as far as possible while maintaining a tolerable level of efficacy over the day as a whole and/or targeting a particularly important period of the day for a particular patient. This head-to-head study makes a first step towards providing the systematic evidence base on which to make such decisions.EJB and DC are consultant for and/or have received support from Celltech, McNeil, Jannsen Cilag and Eli Lilly. JMS is a consultant for Celltech, McNeil and Eli Lilly. SJH and HHD are employees of Celltech Americas, Inc.JMS, DC, SJH and EJB participated in the design of this post-hoc analysis while EJB performed the statistical analysis. HHD and EJB prepared the manuscript. All authors contributed discussion and read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Understanding why some species are at high risk of extinction, while others remain relatively safe, is central to the development of a predictive conservation science. Recent studies have shown that a species' extinction risk may be determined by two types of factors: intrinsic biological traits and exposure to external anthropogenic threats. However, little is known about the relative and interacting effects of intrinsic and external variables on extinction risk. Using phylogenetic comparative methods, we show that extinction risk in the mammal order Carnivora is predicted more strongly by biology than exposure to high-density human populations. However, biology interacts with human population density to determine extinction risk: biological traits explain 80% of variation in risk for carnivore species with high levels of exposure to human populations, compared to 45% for carnivores generally. The results suggest that biology will become a more critical determinant of risk as human populations expand. We demonstrate how a model predicting extinction risk from biology can be combined with projected human population density to identify species likely to move most rapidly towards extinction by the year 2030. African viverrid species are particularly likely to become threatened, even though most are currently considered relatively safe. We suggest that a preemptive approach to species conservation is needed to identify and protect species that may not be threatened at present but may become so in the near future. As human populations increase, the importance of intrinsic biological factors in determining which carnivores become extinct will also rise - an interaction that makes some species more threatened than previously thought Mammals have been severely affected by the current extinction crisis: around a quarter of extant species are considered to be threatened with extinction . UnderstAlthough exposure to threatening processes is the ultimate cause of extinction, a species' biology determines how well it is able to withstand the threats to which it is exposed. Biological traits that confer ecological flexibility and allow populations to recover rapidly from depletion may offer a degree of protection from external threats. A number of recent studies have linked variation in extinction risk or decline among species to biological traits , and, inHere we present a global-scale analysis of biological and external predictors of extinction risk in the mammal order Carnivora. As well as including many symbols of conservation such as the giant panda, tiger, and sea otter, carnivores in general are a good model taxon for the development of a predictive science of conservation: their biology and phylogeny are well studied, they are near-global in distribution, they represent a wide range of biological strategies, and they include species at all levels of extinction risk. Our analysis emphasizes those threatened species that have suffered measurable declines, rather than those simply with small populations or ranges that may be considered “naturally” rare. We use human population density (HPD) as a summary measure of anthropogenic impact. Although not all types of impact are necessarily associated with high-density human populations, on a global scale HPD is more reliably quantified than direct threatening processes such as habitat loss or hunting, which are difficult to measure accurately in ways that are consistent across regions and biomes. Therefore, HPD represents one of the best available means of summarizing the impact faced by mammal species on a global scale. At local or regional scales, high HPD is often associated with some measure of mammal decline . Here we2. With the exception of the last two of these, all showed significant nonlinear effects on extinction risk as separate predictors  is the most threatened species of the Canidae family despite the fact that its population density is not especially low, nor its gestation especially long, compared to other Canis species. However, it has a geographic distribution only a fraction of the size of its congenerics, and within that distribution is limited to afroalpine habitat , life history did not predict extinction risk, and the only variables included in the model were those that determine the maximum population size a species can achieve (geographic range size and species population density). For “high-exposure” species, life history (gestation length) became additionally significant, and the explanatory power of the model as a whole was very high (80%). Evidently, a small species population contributes to high extinction risk no matter what the threat level, but where exposure to human populations is high, the disadvantage of a small species population is compounded by the disadvantage of a slow life history. This might suggest a difference in the major threat types experienced by carnivore species with different levels of exposure to human populations. For example, habitat loss, the predominant threat type for the great majority of mammals .If species in regions of high HPD are threatened by direct persecution and exploitation as well as habitat loss, we should expect top-level predators to be particularly threatened in these regions . Why, thThe strong effect of biological traits on extinction risk status for “high-exposure” species suggests that, as human populations increase globally over coming decades, the importance of biology in determining which species persist and which decline will also increase. This is worrying for species that possess traits making them more vulnerable to external threats, as their extinction risk can be expected to increase more sharply. Of particular concern are species that not only have unfavorable biology, but also live in regions of rapid human population growth. We have identified the carnivore species with the greatest expected increase in extinction risk over the next few decades, given HPD projected to the year 2030 based on recent growth rates. We acknowledge the problems with converting the ordinal categories of the Red List to an interval scale, and these have been discussed previously . HoweverWe conclude that there is no room for complacency about the security of species simply because they are not currently considered globally threatened. There is a strong case to be made for preemptive conservation of species, such as the African viverrids, that live in regions of rapid human population growth and have a biology predisposing them to decline. Preemptive action could include, for example, establishing population-monitoring programs, or listing species under national species protection laws on the basis of potential future susceptibility. Arguably, maintaining the stability of particularly susceptible species before they become threatened could be more cost-effective in the long term than postdecline attempts to rescue them from the brink of extinction.As the response variable in our analyses, we followed 2. Geographic variation in both HPD and the distribution of threatened species may be confounded with net primary productivity  amount of variance to the model. Previously dropped predictors were then once again reintroduced in turn and the model retested each time. It should be noted that this method cannot guarantee to find the best-fitting model: it is essentially a heuristic search for the best model, and simulations on a dataset in which associations between variables are known would be needed to fully test the accuracy of the method.We first carried out univariate regressions of each HPD predictor against extinction risk .Click here for additional data file.Dataset S2(38 KB TDS).Click here for additional data file.


We have previously demonstrated that NF-Y and Sp1 interact with the human telomerase RNA (hTR) promoter and play a central role in its regulation. We have also shown that pRB activates the hTR promoter, but the mechanism of pRb directed activation is unknown. It has recently been reported that pRB induces Sp1 activity by relieving inhibition mediated by mdm2. The aim was to investigate possible roles for mdm2 in hTR promoter regulation.Chromatin immunoprecipitation was used to determine binding of mdm2 to the hTR promoter. Transfection and luciferase assays were used to investigate mdm2 repression of the promoter activity and interaction with known transcriptional modulators.Here we show using chromatin immunoprecipitation that mdm2 specifically binds the hTR promoter in vivo. Transient co-transfection experiments using an hTR promoter luciferase reporter construct show that hTR promoter activity is inhibited by over-expression of mdm2 in 5637 bladder carcinoma cells . Titration of mdm2 was able to antagonise activation of hTR promoter activity mediated by pRB or Sp1 over-expression, although in the presence of pRB, mdm2 could not repress promoter activity below basal levels. Using an Sp1 binding site mutation construct we showed that mdm2 repression did not absolutely require Sp1 binding sites in the hTR promoter, suggesting the possibility of pRB/Sp1 independent mechanisms of repression. Finally, we show that NF-Y mediated transactivation of the hTR promoter was also suppressed by mdm2 in a dose-dependent manner.These studies suggest that mdm2 may inhibit the hTR promoter by multiple mechanisms. Mdm2 may directly repress activation by both pRB and Sp1, or activation by NF-Y. Furthermore, the ability of mdm2 to interact and interfere with components of the general transcription machinery might partly explain the general repressive effect seen here. Elucidation of new regulators affecting hTR basal promoter activity in cancer cells provides a basis for future studies aimed at improving our understanding of the differential hTR expression between normal and cancer cells. Telomerase is a ribonucleoprotein complex that consists of an essential RNA molecule, hTR, with a template domain for telomeric DNA synthesis and of a catalytic protein, hTERT, with reverse transcriptase activity. Functional telomerase is minimally composed of both hTR and hTERT ,2. The tin vivo [The molecular regulation of hTR gene transcription in cancer cells remains poorly understood. The previously identified core promoter region in the hTR gene has several features utilised by the basal RNA PolII transcription machinery, including one CCAAT-box and four Sp1 sites termed Sp1.1-Sp1.4. The activity of the hTR promoter is controlled by NF-Y, Sp1 and Sp3 in bladder cancer cells in vitro and we have recently shown that an Sp1 site mutation in the hTR promoter detected in a blood sample taken from a paroxysmal nocturnal haemoglobinuria (PNH) patient can alter core promoter activity in vitro, raising the possibility that mutation might affect hTR gene transcription in hematopoietic cells in vivo -14. Sevein vivo ,13.The mechanism whereby pRB activates hTR remains unknown though pRB is not known to interact directly in a specific fashion with DNA, relying instead on recruitment to genes through interaction with other transcriptional regulators including Sp1 and mdm2. The recent finding that pRB induces Sp1 activity by binding to mdm2 resulting in the physical release of Sp1 from mdm2 and enhancement of its binding to consensus sequence implies that mdm2 might inhibit promoters such as hTR that are positively regulated by pRb and Sp1 . In this2 in 1640 medium supplemented with 10% foetal bovine serum, penicillin, and streptomycin.Antibodies to Sp1, TFIIB and mdm2 were purchased from Santa Cruz Biotechnology Inc. . Rabbit polyclonal antibodies directed against NF-YA, B and C were obtained from R. Mantovani . The 5637 cell line, originally established from the primary bladder carcinoma of a 68-year-old man in 1974, was purchased from DSMZ (No: ACC 35). 5637 cells were maintained at 37°C in 5% COConstruction of the promoter fragment hProm867 and subcloning as an Xho I/Hind III fragment in the luciferase reporter pGL3-Basic  was previously reported . The repRenilla luciferase control, pRL-SV40 (Promega) using Superfect Transfection Reagent (Qiagen) as previously described [Renilla luciferase was determined 48 h later using the Dual Luciferase Assay kit (Promega). A minimum of three independent transfections were performed in duplicate and specific hTR promoter activity was normalized to protein as described elsewhere [The hTR promoter plasmids containing firefly luciferase reporters were cotransfected into tumour cells with an internal escribed ,13. 5637escribed ), titratlsewhere ,13.Formaldehyde cross-linking and chromatin immunoprecipitation were performed as described previously . In briein vivo , we performed chromatin immunoprecipitation experiments using antibodies to Sp1, TFIIB and mdm2. The presence of the hTR promoter in chromatin immunoprecipitates was detected by semi-quantitative PCR. Antibodies to Sp1 and TFIIB, both of which have previously been detected at the hTR promoter in chromatin immunoprecipitation, and to mdm2 all precipitated the hTR core promoter DNA sequence but failed to precipitate the negative control GAPDH coding sequence from 5637 cells  was used in transient co-transfections with a full-length mdm2 expression vector. As shown in Fig. To address whether mdm2 affects the activity of the hTR promoter in vitro and in vivo and to inhibit transactivation of Sp1-activated promoters [Mdm2 has been shown to interact with the transcription factor Sp1 romoters ,20. We nromoters . As showin vitro. Furthermore, gel shift analysis using a probe corresponding to the Sp1.3 site did not produce an mdm2 specific supershift, suggesting that mdm2 may not directly bind the hTR promoter DNA (data not shown).To investigate whether this repression of the hTR promoter by mdm2 was entirely dependent on Sp1, we transfected a core promoter construct harboring functional mutations in all Sp1 binding sites. It should be noted the construct used here contains a shorter promoter fragment than that used throughout. Our previous study showed that mutation of all four Sp1 binding sites does not impair the activity of this hTR core promoter or its ability to be transactivated by NF-Y, but disrupts Sp1 binding and activation . As showSince mdm2 can influence the basal activity of the hTR reporter without targeting its Sp1 binding sites, it is likely that at least part of the repressive effect is directed through another pathway. One alternative mechanism by which mdm2 could regulate the construct is through interfering with the NF-Y-CCAAT-box complex. Therefore, we next tested whether mdm2 could repress NF-Y function. As shown in Fig. The retinoblastoma gene product pRb acts as a positive regulator of hTR promoter activity by an unknown mechanism while Sp1 and NF-Y activate the hTR promoter by directly binding to DNA. Recent studies have suggested that pRb can mediate stimulatory effects at Sp1 stimulated promoters by liberating Sp1 from negative regulation by mdm2 ,21-23. Min vivo  declare that they have no competing interests.JZ, AB and KJ carried out the transfection and genetic analysis. AB helped draft the manuscript. WNK conceived the study, participated in design and coordination and helped draft the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here: