From the late 1980s onward, the term “bioinformatics” mostly has been used to refer to computational methods for comparative analysis of genome data. However, the term was originally more widely defined as the study of informatic processes in biotic systems. In this essay, I will trace this early history  and I will argue that the original meaning of the term is re-emerging. In the beginning of the 1970s, Ben Hesper and I started to use the term “bioinformatics” for the research we wanted to do, defining it as “the study of informatic processes in biotic systems”. .Indeed, at the birth of molecular biology it was recognized that a central research theme should be how living systems gather, process, store, and use information We were embedded then within theoretical biology. At the time, after general systems theory Stuart Kaufman was there, presenting his work on random Boolean networks, which introduced the concept of large-scale transcription regulation networks and viewed a cell type as an attractor in a multidimensional dynamical system At this same meeting, models and experiments in such areas as oscillatory enzyme dynamics e.g., , positioAlso at about that time, John Maynard Smith gave a lecture in Utrecht and posed a similar challenge with respect to evolutionary biology as Turing's challenge relative to developmental biology. While evolutionary models mainly dealt with invasion of mutants and changing allele frequencies, the question of how evolution leads to complex organisms was not addressed. As Maynard Smith expressed it: “As good evolutionary biologists we should go once a year to the zoo and visit the elephant. We should greet it and say ‘Elephant, I believe you got about by random mutation’”. To meet the challenge of a “constructive evolutionary biology” became another long-term goal of bioinformatics as we envisioned it.Research in artificial intelligence at this time was exploring new representations of information processing systems, often inspired by biological systems, e.g. neural network models for learning and pattern recognition We felt that the re-introduction of biologically inspired computational ideas back into biology was needed in order to begin to understand biological systems as information processing systems. In particular, a focus on local interaction leading to emergent phenomena at multiple scales seemed to be missing in most biological models.At the time, molecular biology was of course not a heavily “data-driven” science, as it would become with the advent of massive sequencing projects. Indeed, data-driven science was looked down upon, both in molecular biology and in theoretical biology. However, data-driven research was being done in the more traditional parts of biology, ecology, and taxonomy. I had just finished a data collection survey on water plant vegetation in India, Czechoslovakia, and The Netherlands and had become dissatisfied by the local state of the art of data processing, which comprised shuffling large tables by hand. At the same time, pattern recognition methods had already been introduced as “numerical taxonomy” In short, under the heading of bioinformatics we wanted to combine pattern analysis and dynamic modeling and apply them to the challenge of unraveling pattern generation and informatic processes in biotic systems at multiple scales.But what could actually be done given the scarcity of data and paucity of computing power?In fact, many of the basic pattern analysis methods now used in bioinformatics were pioneered in the 1960s  basic interactions are implemented, and the resulting dynamics are analyzed in multiple ways and at multiple levels. If and only if various seemingly unrelated and unforeseen consequences of the model correspond to the modeled system, this gives truly novel insight (and confidence in the model) I recall the excitement when, in 1982, the first European Molecular Biology Laboratory sequence tape was delivered. Typing in data (on punch cards) from the Dayhoff atlases was cumbersome, even though many aligned sequences were provided. But what to do with this “mess” of data? Just for fun, we clustered species on nucleotide and dinucleotide content. To our surprise , a more or less decent classification emerged! This, in spite of our mantra that simple “amounts” would not take us very far in biology and we needed to look at patterns/information. But now we were back in the situation of almost a decade before: people trying to make sense of data by shuffling it around and finding by “eye/hand” some optimal arrangement, now with respect to aligning sets of sequences.By developing an iterative guide tree-based multiple alignment method Propelled by the exponential increase of sequence data, the term bioinformatics became mainstream in the late 1980s, coming to mean the development and use of computational methods for data management and data analysis of sequence data, protein structure determination, homology-based function prediction, and phylogeny. But the rich insights obtained from the massive sequencing projects, and the related bioinformatic analysis to unravel function and evolution, is not really the “roots of bioinformatics”, but rather the “trunk of bioinformatics”, and not the subject of this article.In 2002, I received a surprising e-mail from Oxford University Press: “It appears that you may be responsible for the term ‘bioinformatics’. I am preparing an entry for the word in the Oxford English Dictionary, and in this connection am investigating its history. . .” This led to our 1978 papers on chaotic dynamics in ecological models The set of fully sequenced genomes (including human) was expanding, and high-throughput “omics” data entered the field, adding new dimensions to data-driven comparative research. Organisms were no longer just a “bag of genes or proteins” but also, e.g., a “bag of transcriptomes”, “a bag of interactomes”, and “a bag of metabolomes”. Integrating these various data is a marvelous opportunity and great challenge for bioinformatics in whatever sense of the word!Indeed, the insight has again taken hold that organisms are not just a bag full of anything, but rather complex dynamical systems, and that an understanding of their functioning requires dynamical modeling. Under the heading “systems biology”, modeling efforts have been revived, and some of these efforts reflect the problems and dilemmas encountered already in the 1970s. How far can models be simplified and still be relevant?  How can models be sensibly scaled up so as to meet the complexity revealed by the genomic data and still be manageable? As was the case in the 1970s with respect to “whole ecosystem” modeling Individual-based (agent-based) bottom-up modeling is still rare, but the detailed agent-based models of cell division DictyosteliumSo what about the long-term goals we set for bioinformatics in the 1970s, i.e., what of the “horse part” and the “elephant”? Some progress has been made in modeling morphogenesis in a strict sense (the “horse part”), through the use of cell-based models that incorporate some of the physical properties of cells Constructive models of evolution (“the elephant”) have progressed from studies on the evolutionary consequences of non-linear “physical” genotype/phenotype mapping as exemplified by RNA folding Guardian newspaper As I am writing this, a video of Nobel laureate Paul Nurse has been posted in the science supplement of the


Phenotypic traits may exert causal effects between them. For example, on the one hand, high yield in dairy cows may increase the liability to certain diseases and, on the other hand, the incidence of a disease may affect yield negatively. Likewise, the transcriptome may be a function of the reproductive status in mammals and the latter may depend on other physiological variables. Knowledge of phenotype networks describing such interrelationships can be used to predict the behavior of complex systems, e.g. biological pathways underlying complex traits such as diseases, growth and reproduction. Structural Equation Models (SEM) can be used to study recursive and simultaneous relationships among phenotypes in multivariate systems such as genetical genomics, system biology, and multiple trait models in quantitative genetics. Hence, SEM can produce an interpretation of relationships among traits which differs from that obtained with traditional multiple trait models, in which all relationships are represented by symmetric linear associations among random variables, such as covariances and correlations. In this review, we discuss the application of SEM and related techniques for the study of multiple phenotypes. Two basic scenarios are considered, one pertaining to genetical genomics studies, in which QTL or molecular marker information is used to facilitate causal inference, and another related to quantitative genetic analysis in livestock, in which only phenotypic and pedigree information is available. Advantages and limitations of SEM compared to traditional approaches commonly used for the analysis of multiple traits, as well as some indication of future research in this area are presented in a concluding section. In animal breeding and quantitative genetics, relationships among phenotypic traits are traditionally studied via probabilistic relationships between them, using standard Multiple Trait Models (MTM) - see, for example, ,2. Althoy1 and y2 can be due to a direct effect of y1 on y2 (or y2 on y1) or to extraneous variables that jointly affect y1 and y2. Knowledge about the causal structure underlying phenotypic relationships is necessary to predict the effect of interventions  applied to trait y1 or y2. For example, if trait y1 affects y2, and y2 has no effect on y1, an intervention on y1 will cause changes on y2, but the reverse would not hold true.Information regarding phenotype networks describing such interrelationships can be used to predict the behavior of complex systems, e.g. biological pathways underlying complex traits such as diseases, growth and reproduction, and ultimately it can be used to optimize management practices and multi-trait selection strategies in livestock. For instance, a correlation between traits g) on two traits (y1 and y2), as illustrated in Figure y1, and the phenotypic change on trait y1 affects trait y2; (b) the expression of the gene acts on trait y2, and the phenotypic changes on trait y2 modify trait y1; or (c) the expression of the gene changes both traits directly, which may or may not have a phenotypic causal effect between them. Knowledge about these different sources of genetic correlation between traits could be used to further improve selection decisions and increase the genetic progress of breeding programs.Similar situations can be considered from a genetic improvement standpoint. Conventionally, genetic correlation is defined as the proportion of variance that two traits share due to genetic causes, and it indicates how much of the genetic influence on two traits is common to both, e.g., due to pleiotropism. However, different scenarios can cause a pleiotropic effect of a specific gene  can b can b6])In the last few years, genetics has been used as a means to infer phenotype networks, including causal relationships among them , and SEMx and some additional  variables e directly affect variables y, which have also some causal relationships among them.Structural Equation Models ,4 providThe graph in Figure β's are model parameters representing the "fixed effects" of the x covariates on y's, and λ's are structural coefficients representing the magnitude of the casual effects among y's. Hence, in matrix notation, a SEM can be represented as y = Λy + Xβ + e, where Λ is a quadratic matrix with zeroes in the diagonal and with structural coefficients λ or zeroes in the off-diagonal, and y, X, β and e are appropriate vectors or matrices with the observations y's, exogenous variables x's, model parameters β's and residuals e's, respectively. Competing networks representing different causal structures among y's may be compared using some model selection criteria, such as likelihood ratio tests (LRT), Akaike information criterion , Bayesion , or Bayon .Structural equation models have been intensively used in many fields, such as economics, psychometrics, social statistics, and biological sciences. In genetics, they have been used, for example, to study the relationships between phenotypic traits in humans, especially in the context of twin designs e.g. ,21). Mor. Mor21])Thomas and Conti  have poig, y1 and y2 denote the cis-acting eQTL, the transcriptional activity of the gene, and the complex trait, respectively. Model C depicted in Figure y1, in which allelic variations in g change y2 by changing the transcriptional activity y1. Model R , g and y1 are unconditionally correlated, and by conditioning on y2 their dependence vanishes. When the QTL g and both traits y1 and y2 are causally connected, as in model (1)-(3), the raw and partial correlations between them will all be nonzero, but they will change in magnitude depending on the signs of the path coefficients  y = Xβ + Zu + e, so that an equivalent reduced model can be obtained as [where ained as :y given the location parameters and the residual covariance matrix is:The resulting sampling distribution of Ψ = Ψ0⊗In.where By reducing the SEM, the location and dispersion parameters are transformed into parameters of a standard MTM ,25, as iwhere with Here, y. Conversely, SEM carries extra parameters in Λ, resulting in an unidentifiable likelihood function. Nevertheless, it is possible to introduce constraints in SEM to achieve parameter identifiability [Ψ0 to be diagonal, as in the examples discussed below. After defining the causal structure and achieving parameter identifiability, one may apply standard statistical methodologies  to makeSEM models have been used to study simultaneous and recursive relationships between phenotypes in various species and breeds, such as dairy goats , LandracDe los Campos et al. ,27 have Also working with MY and SCS data in dairy cattle, Wu et al.  have extAnother study concerning the relationships between MY and SCS has been conducted by Jamrozik et al.  with CanTo infer simultaneous and recursive relationships between binary and Gaussian characters, Wu et al.  have proA related application of Bayesian linear-threshold SEM has been presented by König et al. , who havOther applications of Gaussian-threshold SEM with heterogeneous structural coefficients have been presented by de Maturana et al. ,29 to exWorking with health and fertility traits in dairy cows, Heringstad et al.  have empFinally, Varona et al.  have preΛ of coefficients defining the causal structure must be specified. In all applications of SEM in quantitative genetics so far, the causal structure was assumed known a priori , or , or Λ ofi  in the path. Colliders block the flow of dependency in a path, which makes a and b independent in the structure above. Conditioning on a variable that is not in the extremes of the path switches its status regarding the flow of dependence through it, i.e. if the variable is a collider it allows the flow, whereas if it is a non-collider it blocks the flow. Two variables a and b in a DAG are said to be d-separated conditionally on a subset S of remaining variables if there are no path between a and b such that all its nodes allow the flow of dependence . Under some assumptions, d-separations in the causal structure of a SEM result in conditional independencies in the joint probability distribution of y. This is used to guide the selection of a causal structure or a class of equivalent causal structures  that is compatible with the joint distribution of the data [A recursive causal structure can be represented by a Directed Acyclic Graph (DAG), which is a set of variables (or nodes) connected by directed edges (arrows). Pairs of connected nodes represent direct causal relationships. A path in the causal structure is a sequence of connected variables. Unconditionally, flows of dependence between variables in the extremes of paths may take place, unless there is a collider , search for a set Sab that contains c such that a is independent of b given Sab. If this set does not exist, then add arrowheads pointing at c (a → c← b). If this set exists, then continue.2. For each pair of non-adjacent variables 3. In the resulting partially-oriented graph, orient as many undirected edges as possible in such a way that it does not result in new colliders or in cycles.The goal of the first step of the algorithm is to obtain a graph that specifies pairs of traits that are directly connected by an edge, because variables that are adjacent in the underlying causal structure are not d-separated  given any possible set of variables. The second step aims to orient edges by searching for unshielded colliders (structures where a collider is directly caused by two non-adjacent variables). Non-adjacent parents of a collider variable are d-separated given at least one set of variables, but not if conditioned to any set of variables that contains the collider. The observational consequence of this is the probabilistic dependence between the non-adjacent parents conditionally on every possible set of variables that contains the common child. The third step performs every further edge orienting that does not result in a new collider or in a cycle. Additional constraining of the output may be achieved by incorporating background knowledge like time precedence or other prior beliefs ,23.The decisions about declaring pairs of variables as conditionally dependent or not are based on partial correlations inferred from a sample, which involves some degree of uncertainty. To account for that, decisions may be made by testing null hypotheses of vanishing partial correlations or, in a Bayesian approach, using highest posterior density (HPD) intervals for the partial correlations.Ψ0 to be diagonal) is already adopted in recent applications of SEM in animal breeding in order to achieve model identifiability. Therefore, the assumptions of the IC algorithms are not stronger than the assumptions considered in recent application of SEM in quantitative genetics. In those applications, not only covariance matrices of random variables are assumed to be structured , but the causal structure itself is assumed to be known.The IC algorithm was developed based on the connection between causal structure and joint distribution, which requires some assumptions . Maybe tValente et al.  have adoy1 through y5, with uncorrelated residuals  and correlated additive genetic effects . The connection between the causal structure among phenotypes and their joint probability distribution does not hold in a model where genetic effects are uncontrolled hidden variables. For example, given such causal structure y1 would be expected to be independent of y3 given y2, but this may not hold because of the correlation between u1 and u3.Take as an example the causal structure depicted in Figure yi can be expressed as:Nonetheless, as indicated by , genetic(It - Λ)-1 G0 (It - Λ)-1 and (It - Λ)-1 Ψ0 (It - Λ)-1 are the covariance matrices of additive genetic effects  and of residuals  obtained from a standard multiple trait mixed model that accounts for covariance between genetic effects and residuals from different traits, but not for causal relationships between phenotypes [yi can be then rewritten as Note that enotypes ,25. The In Valente et al. , the  Obtain the posterior distribution of residual partial correlation ρa,b|S.b) Compute the 95% HPD interval for the posterior distribution of ρa,b|S as null. Otherwise, declare a and b as conditionally dependent.c) If the HPD interval contains 0, declare 3. Lastly, a SEM using the selected causal structure  is fitted, as in , such thValente et al.  have valStructural equation models are able to express causality among traits. However, one may fit a SEM with causal structures that do not express the actual causal relationship among traits. The inference of the causal structure is a much harder task than just describing data by a stochastic model. As discussed in this review, using the IC algorithm and related techniques involves accepting specific assumptions, from which the causal sufficiency seems to be the strongest one. In this regard, applying the IC algorithm may be regarded as a causal structure inference only if one is willing to accept the causal assumptions. Otherwise, the application of such algorithms can be viewed simply as a causal structure selection for SEM constructed with diagonal residual covariance matrices.Nonetheless, the latter applications may still produce interesting and useful results such as the generation of causality hypotheses for further research and investigation. Such hypotheses can then be supported or dismissed by additional data collected from other studies, or they might be tested experimentally through controlled interventions. In genetics, for example, a putative causal mutation could be ultimately tested using gene knockout or knockdown methodologies. However, quite often, randomized experiments are not an alternative due to logistic or ethical constrains, and one is restricted to the analysis of observational studies. In this context, SEM and causal search tools like the IC algorithm are handy. Moreover, in genetics and genomics studies, causal inference is aided by the concept of Mendelian randomization , in whicIn addition to DNA polymorphism information and knowledge about genes or QTL that can be used as parent nodes in phenotype network reconstruction, the joint analysis of multilayer large-scale "omics" data such as transcriptome, metabolome and proteome can certainly provide added information and enhance the ability to infer causal phenotype relationships, although it also brings another level of statistical, computational and data mining challenge . MoreoveA constructed from pedigree information has been considered for such task. As an alternative, if high density molecular marker data is available , more efficient genetic merit prediction approaches can be employed such as Bayesian regression techniques [SEM have also been used in the context of quantitative genetics analysis of multiple phenotypic traits when QTL or genomic information is not available , allowinchniques  or kernechniques . This isSome other areas related to phenotype network inference that would also warrant additional research refers to the development of (parametric or non-parametric) methods to deal with non-Gaussian traits, as well as search algorithms and software suitable to handle huge number (on the level of thousands) of variables. Lastly, and specifically in the context of animal and plant breeding, extra research is required to study how knowledge regarding causal effects between traits could be explored for the development of more efficient breeding programs and agricultural production enterprises.In summary, SEM provide a flexible and insightful approach for the genetic analysis of multiple traits, allowing the characterization of pleiotropic and heterogeneous genetic effects of multiple loci on multiple traits, as well as causal relationships among phenotypes, which can be used to predict behavior of complex systems, e.g. biological pathways underlying disease traits. More specifically with livestock, SEM can be used to infer phenotype networks in the genetic analysis of quantitative traits, such that the effect of external interventions can be better predicted. This may foster the development of more efficient breeding programs and optimal decision-making strategies regarding farm management practices.The authors declare that they have no competing interests.GJMR and BDV wrote the manuscript; GC, XLW, DG and MAS provided critical insights and helped revising the manuscript. All authors read and approved the final manuscript.


The properties (or labels) of nodes in networks can often be predicted based on their proximity and their connections to other labeled nodes. So-called “label propagation algorithms” predict the labels of unlabeled nodes by propagating information about local label density iteratively through the network. These algorithms are fast, simple and scale to large networks but nonetheless regularly perform better than slower and much more complex algorithms on benchmark problems. We show here, however, that these algorithms have an intrinsic limitation that prevents them from adapting to some common patterns of network node labeling; we introduce a new algorithm, 3Prop, that retains all their advantages but is much more adaptive. As we show, 3Prop performs very well on node labeling problems ill-suited to label propagation, including predicting gene function in protein and genetic interaction networks and gender in friendship networks, and also performs slightly better on problems already well-suited to label propagation such as labeling blogs and patents based on their citation networks. 3Prop gains its adaptability by assigning separate weights to label information from different steps of the propagation. Surprisingly, we found that for many networks, the third iteration of label propagation receives a negative weight.The code is available from the authors by request. In protein interaction networks, proteins linked by short paths of interactions tend to have similar functions i.e., “positive examples” or simply “positives”, these algorithms assign a score to each node in the network according to how likely it is to also have the queried label. For example, given a set of proteins with known functions in mitochondrial biogenesis, these algorithms can use the network to find other proteins likely to have the same function  of interest, on e.g.,; or givee.g., people linked by sexual contact tend to be of different genders) or networks where nodes with many shared neighbors are more likely to have the same label than nodes that are directly connected to one another . This framework allows us to highlight a limiting underlying assumption shared by all algorithms that fall under this class. Further, using this framework, we introduce a new algorithm called 3Prop that retains all of the advantages of label propagation but can adapt to diverse node labeling patterns. In particular, 3Prop gains this adaptivity by learning independent weights for the first three steps of label propagation, thereby overcoming an inherent limitation that we show restricts other label propagation algorithms. 3Prop can be applied to large networks, computes node scores quickly, and is easy to implement. Furthermore, as we will show, because the topological structure of many real world networks limits the amount of node label information available through label propagation, 3Prop will likely perform well on a wide range of network labeling problems. Specifically, 3Prop predicts node labels more accurately (and sometimes much more accurately) than label propagation on five separate social and biological network labeling problems where the networks range in size from 750 to 3 million nodes and the proportion of labeled nodes ranges from 0.0002% to 40%.symmetric and asymmetric. In both classes, the scores assigned to a node can be derived from probabilities that, starting from the node, random walks of different lengths will end in a positive node. These two classes have direct correspondence, in that algorithms from one class can be used to calculate scores for the other, and vice versa.To motivate our 3Prop algorithm and to illustrate why label propagation fails for some networks, we introduce a general framework, which we will refer to as Generic Label Propagation (GLP), that encompasses common variations of label propagation algorithms. As we show, these algorithms fall into one of two classes, which we call Below, we will first establish the GLP framework, and then use the random walk interpretation of the GLP scores to illustrate the intrinsic limitation of algorithms that fall into this framework.n nodes and a set of positive examples of nodes with the label of interest  as input, score all nodes in the network so as to rank them according to how likely they are to be positives. Note that in our formulation, we are not using negative examples, as in most problems that we consider  negative labels are rarely available. We will represent the input network using an A, where i and j (i and j are not connected). We will also assume that the network has no self-links so i. We represent the positive nodes using a label vector i is labeled (a positive) and i and j are connected, in this case A is an adjacency matrix. In many real-world networks, only a small proportion of the node pairs are linked, making A a sparse matrix.Label propagation algorithms address the following problem: given an undirected, possibly weighted, network over Label propagation assigns scores to nodes by an iterative process which propagates “evidence for positiveness” out from positive nodes through the links in the network to nearby nodes; this process is often compared to heat diffusion i, given by i’s neighbors at the previous iteration, along with i’s initial label. Typically, to ensure convergence of the updates, the original matrix A is normalized to generate a matrix M that has the same pattern of non-zero elements and, therefore, corresponds to a network with the same links but different link weights. These normalizations are described later. Using M, the update rule for node i is given by:R iterations, In particular, in each iteration of GLP, the score of node i.e.,I is the identity matrix) and then either solving a linear system with coefficient matrix A ensure convergence and correspond to the asymmetric and symmetric variants of GLP .Two different normalizations of i. In matrix notation, we can write S to refer to D is a diagonal matrix whose diagonal elements In SLP, the matrix i.e.,P refers to S, P is not symmetric. However, each row of P can be interpreted as a probability distribution over the neighbors of the corresponding node, i.e., P is a singly stochastic matrix. ALP methods include random walk with restart In ALP, i is positive and P can be interpreted as random walk probabilities.The solutions of SLP and ALP are closely related–a slightly modified version of the former can be used to compute node scores for the latter (and vice versa). This similarity arises because i ends in node j, then the i will end in node j, and by induction, r random walk starting from node i ends in node j. Under this interpretation, if we write r-th term in the summation in i-th element r from node i will end in a positive node  in e.g.,r-th element equal to the average score assigned to the positive and non-positive examples, respectively, when only considering random walks of length r. Specifically, in asymmetric 3Prop, i-th element of the vector C is the sample covariance matrix of C using positives and non-positives from the remaining 1/3 of the nodes in the training set.3Prop uses linear discriminant analysis (LDA) . The positives in this example are genes involved in “Transcriptional Initiation”. In The other panels in In our experiments we use a diverse collection of networks including: two types of molecular networks, protein-protein interaction (PI) and genetic interaction (GI) (downloaded from BioGRID We compare the performance of symmetric 3Prop with that of symmetric GLP (SLP) which has been shown to perform well in gene function prediction problems We evaluate GLP and 3Prop using two standard measures: area under the ROC curve (AUROC) and average precision (AUP).The ROC curve is a graphical plot of recall  as a function of false positive rate  for a binary classifier as we vary the discrimination threshold. The area under this curve (AUROC) can achieve a maximum value of 1 and a minimum of 0; a random classifier will result in AUROC of 0.5. AUROC can also be interpreted as the probability that a randomly chosen positive is assigned a discriminant score that is higher than a randomly chosen negative example. Precision at a given recall is defined as the fraction of predictions that are true positives and is given by TP/(TP+FP) where TP is the number of true positives and FP is the number of false positives at the given recall rate. A classifier that performs better in terms of AUROC is not guaranteed to perform better in terms of average precision, or vice versa. In general, average precision is a more suitable measure where there are many more non-positive compared to positive examples convergence rate of random walks on real-world networks (see the Discussion Section). In addition, we also observed that versions of 3Prop that consider longer random walks than three do not result in significant performance improvements. In particular, the area under the precision-recall curve of NProp peaks at 3Prop though there are some small gains in area under the ROC curve for 4, 5, and 6Prop   . Note thIn contrast, the 3Prop weights for the different patent categories vary considerably . Howevere.g.,Despite its limitations, label propagation has become the algorithm of choice for many node labeling problems. It is easy to implement, resists overfitting because it has only a single free parameter but it nonetheless performs as well as or better than much more complex algorithms on benchmark problems 3Prop retains all of the advantages of GLP but is faster and more accurate. If provided with Surprisingly, we have found that for many networks, the third iteration of label propagation receives a negative weight. Assigning these negative weights gives 3Prop the flexibility to exploit “over-counting” of random walks in real-world networks, where short random walk probabilities could have a large contribution to longer random walk probabilities.e.g.,e.g.,convergence rate of random walks on real-world networks. In particular, all non-bipartite, connected networks have an associated stationary distribution over the nodes, r, the rows of c. As such, once convergence is reached, adding r for the constant values of 3Prop only considers random walks of length up to three; a natural question is, “Why three and not more (or fewer)? ”. For example, even in assortatively mixed networks, having many paths of length two between two nodes is evidence that they are members of the same network module or “community” el e.g.,. Howeverve e.g.,. This maodes see  [46] or Figure S1r for the five Facebook networks and two molecular networks. Each grey line was generated by starting a random walk from a random node i and assessing the total variation distance between the distribution i. There are 100 grey lines, corresponding to 100 random selections of i. The red line shows the median. To obtain the convergence, we only consider the largest connected component for each network.Total variation distance between random walks of increasing length as a function of walk length (TIF)Click here for additional data file.Figure S2r in the Blogs network.Total variation distance between random walks of increasing length as a function of walk length (TIF)Click here for additional data file.Text S1Supplementary methods.(PDF)Click here for additional data file.


E-utilities and Python were used in combination with a programmatic web service access to obtain data from the NCBI Nucleotide Database. Results indicate that the American continent, and more specifically the USA, is the top contributor, while Africa and Antarctica are less well represented. This highlights the imbalance of exploration within these areas rather than any reduction in biodiversity. This study describes a novel approach to generating global scale patterns of bacterial biodiversity and biogeography and indicates that the Proteobacteria are the most abundant and widely distributed phylum within both databases.Databases are an essential tool and resource within the field of bioinformatics. The primary aim of this study was to generate an overview of global bacterial biodiversity and biogeography using available data from the two largest public online databases, NCBI Nucleotide and GBIF. The secondary aim was to highlight the contribution each geographic area has to each database. The basis for data analysis of this study was the metadata provided by both databases, mainly, the taxonomy and the geographical area origin of isolation of the microorganism (record). These were directly obtained from GBIF through the online interface, while  Biogeography aims to explain spatial patterns of diversity in the context of evolutionary events such as speciation, dispersal, extinction, and species interactions . MacroecData from many of these biodiversity studies are stored in databases, a structured and organized collection of information where the storage of and the access to information are facilitated to users. In biosciences, the introduction of computer processing and computer databases has opened up the potential for further investigation of combined existing data sets . These iIn our study, we used the concept of species occurrence data, mainly, observational data, and environmental survey data. In general, the data are what we term “point based,” although line , polygon , and grid data (observations or survey records from a regular grid) are also included. The majority of point-based data used here are georeferenced; that is, records with geographic references tie them to a particular place in space—whether with a georeferenced coordinate  or not —and time . Often, the data are also tied to a taxonomic name, but unidentified collections may also be included . We retrOne personal computer was used having a Dual Core CPU E5800 @ 3.20 GHz processor and 2 GB RAM. Internet connection was tested as 1.36 Mbps download and 5.55 Mbps upload .database query→data subset retrieval  in standardized response format for each geographical area→ analyze data and save the information summary for each geographical area.The approach used in this study for both databases is divided into three parts:GBIF Database. The Global Biodiversity Information Facility (GBIF) was established as a global megascience initiative to address one of the great challenges of the 21st century—harnessing knowledge of the Earth's biological diversity. GBIF envisions a world in which biodiversity information is freely and universally available for science, society, and a sustainable future. GBIF's mission is to be the foremost global resource for biodiversity information and engender smart solutions for environmental and human well-being. At the time of writing, the GBIF Database include 396,026,747 records, 345,561,101 of which have associated georeference data  (Version 1.2.6) .bacteria main groups = GBIF Database. The number of records with geographic coordinates from the GBIF Database is displayed through the GBIF species portal  as applied for other search qualifiers, for example: OrganismName[Organism] for organisms. The methodology used in this study would also retrieve the diversity in particular regions within a geographical area of interest either by declaring it as previously described or adding it as a subcondition after the search. While the new qualifier “/lat_lon available as 2005”, which indicates the GPS coordinates for the location at which a specimen, from which the sequence was obtained, was collected, it would be very useful and more accurate to determine the strain origin. This biogeography search for a particular region is much easier in GBIF, simply by either using bounding box or introducing coordinates  in the occurrence webpage as filters. It would be also possible and interesting to associate this biogeography study to ecological keywords which would highly be recommended to be completed by users. This association would be used in biodiversity informatics which surely generates worthwhile knowledge not only about the presence of the microorganism but also about its probable involvement in the ecosystem function and its different interactions.Synergistetes, Caldiserica, Elusimicrobia, Armatimonadetes, Ignavibacteria, Tenericutes, Thermomicrobia, and the newly established Nitrospinae phylum are considered either different or completely absent in one or another database used in this study. All of these points and others are more and more being discussed worldwide by the scientific community [One big challenge to the comparison of different databases is to cope with many different standards: for the registration and the retrieval system, data structure, and even the differences on fundamental aspects such as in taxonomical classification which was one example encountered in our study; where phyla: ommunity , 41.While the web interface is easier to deal with databases, the programmatic access seems to be more interesting, more flexible, offers more choices, and returns more personalized results; however, it needs some basic knowledge on the database structure, its database management system, and computer languages.Finally, while the study gives a preliminary overview of the world's bacterial biogeography, reflecting a part of the real biodiversity, other more upcoming efforts to determine Earth microbial biogeography and biodiversity are indeed in progress, we could mention “Earth Microbiome Project”. The project already processed over 200,000 samples from across the globe for these microbial communities using metagenomics, metatranscriptomics, and amplicon sequencing and started to generate huge amount of data to produce a global Gene Atlas describing protein space, environmental metabolic models for each biome, approximately 500,000 reconstructed microbial genomes, a global metabolic model, and a data-analysis portal for visualization of all information .The zipped file may be extracted in any directory in the computer but all files and the two folders should be extracted in the same directory so the scripts would function correctly. The scripts had been tested on Windows environment. Python 2.7 and Biopython 1.6 should be installed before running the scripts. To extract data from NCBI Nucleotide you should run “NCBI_Nucleotide_Tracker.py” for countries exception handling, run “NCBI_Nucleotide_Exception.py”. For the GBIF Database, only three countries are provided as an example in the folder “GBIF_Plus” for additional countries you may download them from GBIF website, the only data that should be extracted are these of taxonomy, no need for other metadata  as described in the paper. Once all countries of interest are downloaded you may run “GBIF_Filter.py” to filter data. The results of this study are presented in: “country_all.txt” for NCBI Nucleotide Database, “gbif_Classification_2000_Plus.txt”, “absent_taxa_Classification_2000_Plus.txt”, and absent_taxa_Classification_2000_Plus_ex_All.txt” for GBIF Database. The final results are presented in “NCBI_GBIF_overall_data.xlsx”. Some errors that may occur during the script execution are presented in “error.txt” file.Click here for additional data file.


Access to information is essential to effective and efficient clinical practice. On one hand, clinicians must keep track of new scientific evidence relevant to their field of practice. On the other hand, encounters with patients generate questions requiring answers in order to provide adequate clinical care. The main challenge is to access truly useful information that is: 1) relevant to clinical practice, 2) scientifically valid, and 3) requires minimal efforts to find, to appraise and to decide to integrate or not into practice. [e.g.,, amc.ca, Journal Watch), e-mails alerts  and RSS  . There is, however, a risk of being rapidly overloaded with useless information, either irrelevant to practice, invalid, and/or needing extensive work to appraise, if pushed information is not judiciously selected.Three main strategies can be used to take advantage of Internet resources: “push”, “pull” and “exchange.” The “push” strategy is similar to subscribing to a paper-based medical journal. The main purpose of this strategy is to keep aware of new knowledge relevant to practice. Many organizations offer to “push” clinical information to health care providers through Web portals  and systematic reviews  to virtual libraries , repositories of clinical practice guidelines , critical summaries, synopsis of original articles  and evidence-based e-textbooks , a huge amount of clinical information can be accessed on the Web or on a personal digital assistant (PDA). Federated search engines searching simultaneous resources such as Infoclinique, TripDatabase and MacPlus can ease the process of finding answers to clinical questions in the maze of information. Most questions can be answered by a judicious combination of just a few of these resources.Information can also be actively searched for (“pull” strategy), usually for the purpose of answering clinical questions. Most often, electronic tools offer a much faster and broader access to information than searching through piles of medical journals accumulating on shelves over the years. From databases of original studies . Website such as Asklepios from the Canadian Medical Association is an example of the multiple initiatives now available to foster communities of practice among health care providers who are sharing similar interests. However, we are still at the “early adapters” phase of implementation and the effectiveness of these new e-tools to improve healthcare has yet to be demonstrated. By combining selected “push” and “pull” resources, the internet can provide access to useful information to facilitate evidence-based clinical care.


Now scientists are pointing to a different class of warming agents they say also must be targeted to keep global temperatures in check. Dubbed “short-lived climate forcings” (SLCFs), these other emissions—namely, black carbon particles, methane, hydrofluorocarbons, and tropospheric ozone—are even more powerful than CO2 in terms of their warming potential. But they persist in the atmosphere for much shorter durations than CO2, which can linger airborne for hundreds to thousands of years.1For decades, efforts to slow global warming have mostly aimed to limit heat-trapping emissions of carbon dioxide  called attention to SLCFs, claiming their emissions must be cut together with COAmong the SLCFs, black carbon garners the most attention because its climate and health effects are greater than those of the others, says Mark Jacobson, a professor in the Stanford University Department of Energy Resources Engineering. Evidence on black carbon’s climate impacts has been building since at least the mid-1990s, when Ramanathan and colleague Paul Cruzan, a Nobel prize–winning atmospheric chemist from the Max Planck Institute for Chemistry, first speculated that “brown clouds” laden with the dark particles influence weather patterns over South Asia, a hypothesis that was supported by future research.4But the way black carbon affects the climate is nuanced and hard to study, and it’s only recently that the science has begun to mature to the degree that policies to limit emissions can be proposed on climatic grounds, says Drew Shindell, a scientist with the Goddard Institute for Space Studies at the National Aeronautics and Space Administration (NASA), who led the panel that produced the new report by UNEP. “What we’re seeing now with the UNEP document and other more recent papers are attempts to generate the first cohesive picture of black carbon’s effects on the climate and ways to address it,” Seidel says.Spewed into the air by diesel engines, dirty cookstoves, and open burning, black carbon is the material that burns in an orange flame, explains Tami Bond, an affiliate professor of atmospheric sciences at the University of Illinois at Urbana–Champaign. “What you see in fire is black carbon glowing,” she says. What escapes to the air from fire, Bond adds, are agglomerated particles of nearly pure carbon, each several thousand times smaller than the width of a human hair.2,6Those particles absorb sunlight in all its wavelengths and transfer its warmth to the atmosphere. With roughly a million times the heat-trapping power of COBut black carbon is also co-emitted with other particles that reflect more sunlight than they absorb. And these other specks of ash and organic materials have a net cooling effect, such that combustion emissions will warm the air only as much as their black carbon content allows. With a roughly 1:1 ratio of organic2 they put into the air, he says.Emissions from solid fuel combustion—namely, from cookstoves that burn animal dung, wood, and other types of biomass—follow with a ratio of organic to black carbon particles of 4:1. Open fires tend to smolder and eject a lot of ash particles that reflect sunlight, but even so, they exert a net warming effect on the atmosphere, Jacobson says. On the other hand, emissions from forest fires, with an 8:1 organic to black carbon particle ratio, cool the atmosphere in the short run but lead to warming later because of the massive amounts of COAbout 77% of the estimated 8,000 kilotons of black carbon emitted globally every year come from the developing world, discharged mainly from cookstoves, open burning, and old diesel engines,8North Amerian emissions dominate when it comes to the black carbon falling on ice in Greenland, Shindell says, while European emissions dominate what reaches the rest of the Arctic. “The largest black carbon source in both North America and Europe is diesel, so I think it’s safe to say that’s the biggest [contributor from these countries],” he says.As for additional contributions from northern industrialized countries—and Arctic ice sheets are known to be most vulnerable to black carbon emissions from locales north of the 40th parallel9“What we can say is that black carbon from northern countries is the dominant contributor to darkening of Arctic snow, which is at least partly responsible for melting,” he says. “It’s hard to be more definitive as black carbon trends during the last few decades, when melting has accelerated greatly, seem not to be large—roughly flat, really—but we only have data for the Western Hemisphere, and even that is fairly sparse.”Unlike greenhouse gases, which float around the planet on long time scales, black carbon travels in the air for only a week or 10 days before it washes out of the atmosphere.5Daniel Rosenfeld, a professor of atmospheric sciences at the Hebrew University of Jerusalem, says much about black carbon’s influence on weather remains unknown, however. Ordinarily, airborne particulates seed clouds, he explains, but black carbon particles can get hot enough to vaporize water and prevent clouds from forming at all. Cloud losses result in more heating of the ground, Jacobson adds. And that reduces air pressure over land, which draws air currents from areas of higher pressure, resulting in higher windspeeds.But depending on a range of conditions, including the particulate makeup of the pollution and topographical features of the land, particle emissions can also seed clouds made up of unusually small droplets. These clouds don’t coalesce into denser forms that would otherwise fall as rain, Rosenfeld explains. The result is more clouds but less rain than usual, with commensurate impacts on water supplies and agriculture.10The implications of these impacts are a focus of intense research, but in the meantime, Erika Rosenthal, a staff attorney at Earth Justice, says that South Asian monsoons now come roughly two to three weeks earlier than usual, perhaps because of the region’s heavily polluted air.Still, Rosenfeld cautions that the science in this area is an evolving story. “It’s very difficult for the scientific community to tease out these effects,” he says. “We’re trying to distinguish radiative effects from how particles absorb solar rays apart from air pollution’s effects on clouds, precipitation, and evaporative forces. This is a very big challenge in the field.”Just how climate-related concerns about black carbon will drive policy remains to be seen. Policy momentum on SLCFs is picking up on certain fronts. UNEP’s 2011 report presents 16 strategies to stanch the flow of SLCFs into the atmosphere, among them capping fugitive methane emissions from industry and agriculture, banning open-field burning of agricultural waste, taking old diesel vehicles off the road, and substituting traditional biomass cookstoves in the developing world with cleaner models. If achieved within the next 20 years, those measures could halve the rate of climate change expected by mid-century while avoiding some 0.7–4.6 million premature deaths that would have resulted from poor air quality, UNEP asserts.2Meanwhile, a task force convened by the Arctic Council, an intergovernmental forum of circumpolar nations, is investigating ways to lower SLCF emissions with an eye toward limiting rates of ice sheet melting in the near term.According to Seidel, the United Nations Framework Convention on Climate Change isn’t well suited for negotiations on black carbon; “You’re more likely to see this move forward under regional frameworks focused on air quality,” he says. As an example, he cites the Montréal Protocol, which successfully phased out the chlorofluorocarbons that degrade the ozone layer.In the United States, black carbon reductions apply mainly to diesel standards, which have already been tightening since the 1970s in response to health needs. The California Air Resources Board (CARB) led the charge, issuing the first statewide regulation on diesel emissions from heavy trucks in the late 1980s. Since then, regulations have steadily tightened in California,14In 2006 the EPA adopted an ultra-low-sulfur diesel requirement for on-road vehicles that dropped allowable concentrations from 500 to 15 ppm, and the agency is now expanding that rule to cover more transportation sources, including off-road vehicles, railroads, and ships. Ultra-low-sulfur diesel fuels end up reducing black carbon emissions because they allow for the use of particulate exhaust filters, which would have been “poisoned”  by sulfates. Since 2007, the EPA has mandated that all new on-road vehicles be equipped with advanced emission controls that require the new cleaner diesel fuels to run properly.Ramanathan’s group recently published a study showing that California’s black carbon emissions dropped 50% over the period 1989–2008.But Bart Croes, chief of the CARB Research Division, says there’s no plan to tighten the state’s diesel regulations further in response to climate concerns. “Public health is the major driver behind these regulations, and they appear to have also reduced climate impacts,” he says. “So we see no need to modify our regulations [specifically] to address the climate. What we’re doing for public health is also exactly what we should be doing for the climate.”California now mandates retrofits to bring all pre-2007 on-road diesel truck and buses in line with current particle emissions regulations. According to CARB calculations, these older vehicles accounted for 95% of all diesel particulate emitted from on-road trucks and buses in California in 2010. The estimated cost to retrofit trucks and buses in the state will be $2.2 billion from 2012 to 2025.5That’s a lot of money. But considering that 90% of U.S. black carbon emissions come from the transportation sector, mainly diesel vehicles, it’s also just part of what the nation would have to pay in order to meet UNEP’s aim to install diesel particle filters for on- and off-road vehicles and to eliminate high-emitting on- and off-road vehicles, which are 2 of the 16 strategies identified in its report.2Meanwhile, looking for budget-slashing opportunities, President Obama recently cut 2012 funding for the Diesel Emissions Reduction Program, which gives EPA grant and loan authority to fund the retrofitting or replacement of existing diesel vehicles. The alternative, of course, is to refrain from mandatory retrofitting and take the vehicles off the road through attrition.But that leads to an intriguing question: If—as is the case in California—the United States is unwilling or unlikely to impose further tightening of diesel regulations in response to climate concerns, how does the emerging evidence on black carbon influence environmental policy here? Seidel says there is no evidence that cleaning up diesels in the United States will have the biggest, let alone the most cost-effective, impacts on slowing warming in the Arctic. Yet Rosenthal argues that U.S. contributions to Arctic black carbon pollution constitute an imperative for the country to clean up its diesel emissions faster.But most of the opportunity to reduce emissions are found in the developing world, she adds, where diesel standards aren’t as stringent, and where cookstoves and open burning pose major environmental problems. “The science and policy dilemmas are complicated,” Rosenthal says. “But we need to make decisions about this now.”


We examine the joint distribution of levels of income per capita, life expectancy, and years of schooling across countries in 1960 and in 2000. In 1960 countries were clustered in two groups; a rich, highly educated, high longevity “developed” group and a poor, less educated, high mortality, “underdeveloped” group. By 2000 however we see the emergence of three groups; one underdeveloped group remaining near 1960 levels, a developed group with higher levels of education, income, and health than in 1960, and an intermediate group lying between these two. This finding is consistent with both the ideas of a new “middle income trap” that countries face even if they escape the “low income trap”, as well as the notion that countries which escaped the poverty trap form a temporary “transition regime” along their path to the “developed” group. There is strong evidence that countries are clustered into at least two groups in terms of income per capita. Quah  finds evSimilarly, Mayer-Folkes joint distribution of income per capita, life expectancy and schooling. We focus on these three variables, adding schooling to the established focus on the distribution of income and health, because they have been identified as fundamental determinants of human welfare , 27 countries remained in the lowest group, and the other 31 are part of the middle group in 2000. This middle group consists largely of Latin American countries, emerging East Asian economies, and a range of countries from the Middle East.Those who moved up already started on a higher level of all three indicators in 1960. When examining the development of indicators of this group, one notes a remarkably steady rate of progress in health and education indicators in this group with life expectancy advancing by about 5 years per decade, and education by 1 year per decade. In contrast, GDP growth varies much more . Those that remained in the poor group developed quite differently over time. After some modest progress in all indicators in the 1960 s and 70 s, income stagnated, and health improvements also slowed down dramatically since then; only education years continued to rise largely unabated. This suggests that these two groups of countries were really on different trajectories leading them to separate into two components. It also suggests that the linkages between the three indicators are not as close as one might surmise. In particular, education improvements seem possible without much income growth and the relation between health and income improvements is also not as close with income fluctuating much more.We document the emergence of a third development club. In 1960 countries were clustered in two groups; a rich, highly educated, high longevity “developed” club and a poor, less educated, high mortality “underdeveloped” club. By 2000 we see the emergence of three clubs; one underdeveloped group remaining near 1960 levels, a developed group with higher levels of education, income, and health than in 1960, and an intermediate group lying between these two.This sheds some light on the issue of convergence in development. There is a group of poor countries that are stagnating, and a group of rich countries that are forging ahead, leading to increasing worldwide income inequality. However about half the countries that were poor in 1960 have been very successful, and have seen substantial improvements in income, health and schooling. These countries were already better off in 1960 but were able to steadily enhance income, education and health levels that allowed them to escape from the low development group.Our results raise the issue of what lies behind the move from a simple “poverty trap” setting in 1960 of two clusters to the three clusters we see in 2000. They emphasize the disparate experience of the underdeveloped countries with one sub-group having done remarkably well while another has largely failed. The emergence of a middle group is consistent with two fundamentally different interpretations. One interpretation could be the idea of a new “middle income trap” that countries face even if they escape the “low income trap” Griffith ; evidencAnother interpretation could be the idea that a large number of countries, which escaped the poverty trap, form a temporary “transition regime” along their path to the “developed club” Galor . If such


In a recent article I reviewed an influential theory of sleep function, the “synaptic homeostasis hypothesis (SHY.)” According to SHY, sleep renormalizes synapses that are potentiated during prior wakefulness. I concluded that while SHY is a seminal theory with important implications about sleep function and the brain, its underlying mechanisms are poorly defined. In an accompanying article, the authors of SHY responded at length. Their reply is thoughtful and provocative, but unfortunately many of the points I raised were not accurately represented or addressed. In this brief commentary, I attempt to clarify some points of confusion. I also explain why any theory of sleep function is incomplete without an understanding of the underlying cellular mechanisms. In their companion article, Tononi and Cirelli argue that I have missed the big picture by conflating questions of sleep function with the underlying mechanisms . As I ha The underlying message from Tononi and Cirelli is that what really matters is the “…the end result” , page 4  Tononi and Cirelli suggest that I am simply confused about SHY. Indeed, I do find SHY confusing because the proposed mechanisms (to date) seem inconsistent with what is actually known about synaptic plasticity. Moreover, their imprecise terminology fogs the issue. They claim that they never meant that the potentiation in wakefulness is Hebbian , yet it is difficult to draw any other conclusion based on their own words. Their liberal and interchangeable use of the words “long-term potentiation”, “LTP,” or “LTP-like” , 5 leave They further imply that I have conflated accepted meanings of the terms “synaptic scaling” or “synaptic homeostasis” with what they now call “synaptic renormalization.” But I am not the guilty party here. The concepts and terminology of synaptic homeostasis and scaling predate SHY  and therLike activity-dependent synaptic scaling (emphasis added), however, sleep-dependent downscaling would affect most or all of a neuron's synapses. In this respect, downscaling is conceptually different from long-term depression, which affects select groups of synapses, or depotentiation, which affects only recently potentiated ones” [ “ed ones” , page 54outcome of a homeostatic adjustment of synaptic weights in a network, as measured, for example, by mEPSCs [Therefore, their use of these terms, if in fact they mean something else, is perplexing. The distinctions they make between their version of downscaling and scaling proper are welcome , but quiy mEPSCs , 8. Thisy mEPSCs , 10. identical to synaptic scaling as currently understood in the field. I instead argued that since the concepts of scaling figure prominently in SHY, and because scaling is an accepted means of globally adjusting synaptic weight, it is logical to examine the claims of SHY based on what is known about synaptic scaling. I look forward to future efforts to distinguish “synaptic renormalization” from synaptic scaling, but this name change does not inoculate SHY against scrutiny. In the end, it really does not matter what new name they give to their process (or set of processes) that weakens synapses. The point I was making, and which they missed, is that any form of plasticity  should be examined through the prism of known facts about Hebbian and non-Hebbian plasticity. Case in point: I never stated that the “downscaling” of SHY is in vivo. These findings are consistent with my original discussion [ The veracity and validity of a scientific theory is not solely determined by the amount of data piled on one side of a scale. It is also determined by a careful examination of each piece of evidence, pro and con. Upon close examination, threads of supportive findings may unravel, and when contrary evidence is properly considered, an otherwise impressive mass of findings may collapse. A large part of their response is a long catalog of supportive data—some of which I included in my review. However, as I pointed out, careful examination reveals alternative explanations for some of their results. They also largely ignore evidence consistent with net increases in synaptic strength after sleep , or findscussion  and compcumulative effects of natural patterns in brain temperature  across the 24-hour day or after sleep deprivation. Tononi and Cirelli present a number of arguments against these ideas, but I have a better idea. Why not perform the simple experiments I proposed? For example, why not examine the effects of cooling or warming on Drosophila synaptic proteins ? Why not experimentally clamp corticosterone as done by Mongrain et al. [ In my original review, I considered biological processes other than sleep that might explain some findings ascribed to SHY. These included the n et al.  and thuscompletely wrong. The lesson from Ptolemy is that scientific theories are ultimately judged not only by their power to explain, but by the validity of their physical mechanisms.  In his discussion of sleep theories 50 years ago, Nathaniel Kleitman reminded his readers of the cautionary tale of Ptolemy of Alexandria . Ptolemyany species. Second, they have not experimentally excluded the role of other biological processes that coincide with sleep. A final consideration is that if SHY is a scientific hypothesis, then Tononi and Cirelli should propose an experimental outcome that would force a rejection of SHY. The ultimate validity of SHY requires that these points be addressed. Tononi and Cirelli argue that for SHY to be valid, it does not matter how synapses are weaker after a sleep period, just that they are . But Pto


The river’s two hydroelectric dams have become stars of a four-decade-long saga, culminating in the complete removal of the lower Elwha Dam this past summer, with the taller Glines Canyon Dam breached but not yet gone; it should be fully removed by summer 2013. Together they make up the biggest dam-removal project and the second biggest restoration project ever undertaken by the National Park Service (NPS), after the Everglades.3 of clay, silt, sand, gravel, and cobble that had built up behind the dams began to flow.,This summer Chinook and other salmon species spawned in tributaries that had been blocked for a century. Meanwhile, more than 24 million ydTearing down dams releases mass quantities of pent-up sediment and, in some cases, contaminants in the short term, but it restores ecosystems in the long term. As the fish begin returning to the Elwha River, the biggest dam removal in history is being touted as a model for future dam breaches.The dams originated in the mind of entrepreneur Thomas Aldwell, who envisioned power plants to fuel the local economy and run a paper mill. “When these dams were built, [they provided] the only power on the Olympic peninsula,” says NPS public information officer Barb Maynes. The 105-foot Elwha Dam was completed in 1914 at river mile 4.9, and the 210-foot Glines Canyon Dam followed in 1927 at mile 13.6.In the 1970s, Robert Elofson, the Lower Elwha Klallam Tribe’s river restoration director, hiked to the Elwha headwaters. “I thought, ‘This river is gorgeous. All it needs is salmon,’ ” he says. At the time, he says, he imagined getting salmon past the dams, but never dreamed he would see those dams removed in his lifetime.When Aldwell built the Elwha Dam, Native Americans were not considered U.S. citizens. “In the old days, we were physically defending our land,” says Elofson. “We still do, but in another way.” Now full citizens with enforceable tribal treaty rights to half the harvestable catch in their traditional fishing grounds, the tribe played a leading role in the decades-long battle to breach both dams. Environmental groups joined the fight to remove the dams in order to restore the river’s fisheries.The dam owners initially balked, and many local townspeople feared a federal land grab, job losses, fouled water, and the removal of dams that were an integral part of local culture. The struggle eventually led to the Elwha River Ecosystem and Fisheries Restoration Act of 1992,“Once the secretary [of the Department of the Interior (DOI)] made his decision, that’s when the public came out in opposition,” says Brian Winter, Elwha project team leader for the NPS, which now owns the dams. “Dam removal was not as much in front of people’s minds as it is today.” On the other hand, he says, people were anxious for work, and the project offered local employment: “I’d go to businesses, and [people] would say, ‘I’m really against the project, but if you’re going to do it, do it soon because we need jobs.’”“Last year when we shut down the power plants, it was a big deal,” says Richard Bauman, manager of the Elwha project for the U.S. Bureau of Reclamation (USBR), which was tasked with dam removal and preparing the site for demolition. “It was kind of emotional, stepping from one age into a new age.”For the Elwha people, dam removal represents a return to a bygone age. The river once boasted famed runs of Pacific Northwest salmon species—Chinook, pink, chum, coho, and sockeye—as well as steelhead and cutthroat trout. People caught 100-pound Chinook here, and elders tell of walking across the backs of spawning salmon.Levels of PCBs measured in Elwha River fish in 1999 were relatively low—about 9–17 ppb total PCBs—but they exceeded the human health criterion of 5.3 ppb that Washington State uses for listing under 303(d), says Art Johnson, an environmental scientist with the Washington Department of Ecology (WADOE). Background levels of total PCBs in Washington freshwater fish fillets average around 5 ppb, while fish fillets from urban/industrial lakes and rivers in the state average around 60 ppb.“A lot of time when people find out a river is on the 303(d) list, they panic,” says WADOE environmental specialist Brandee Era-Miller. But risk, she says, is a function of consumption, and Native American populations whose diet relies heavily on fish are at greater risk of PCB-related health effects than the average person. The Washington Department of Health declares fish consumption advisories for PCBs at concentrations of 23 ppb and has set reference doses for subsistence fishers of 2.5 ppb for cancer and 9.83 ppb for noncancer end points;Washington is in the process of revising its fish consumption rate, a value that represents the average amount of fish consumed statewide and that’s used to estimate exposure to toxics found in fish. The rate gets plugged in to risk assessment formulas that determine acceptable levels of human exposure to various compounds, which in turn determines what levels of pollutants are allowed in industry effluent.“We feel the state fish consumption number is grossly inadequate,” says Matt Beirne, environmental coordinator for natural resources for the Lower Elwha Klallam Tribe. “It’s not protective of even the average fish consumer, let alone Native Americans.” The present state consumption rate is 6.5 g/day, but the Lower Elwha Klallam Tribe’s fish consumption falls in line with that of the Suquamish Tribe, the highest in the state at nearly 500 g/day, he adds.Washington is now revising its human health–based criteria water quality standards, which “are directly tied to pollution limits,” says Sandy Howard, WADOE communications manager. As the state revises its surface water quality standards for human health, even lower PCB levels will be required for a waterway to be declared clean.,In 2011 Oregon became a national leader by raising its fish consumption rate from 17.5 g/day to a more realistic 175 g/day, resulting in the most protective water quality regulations in the United States.The health benefits of eating fish should be considered along with the risks.The Elwha Act stipulated that municipal and industrial water must be protected in the event of dam removal. That resulted in a new water treatment plant for nearby Port Angeles, new wells for local water associations, and a new hatchery and wastewater collection system for the tribe. Once it was determined that the dams must go, plans were made through the EIS process to safeguard against increased flood risk with bolstered levees and to clean up lead, asbestos, polychlorinated biphenyls (PCBs), and petroleum hydrocarbons at the dam sites. Biologists would conduct pre- and postremoval studies of sediment transport to understand how it would affect both people and the environment, and the tribe would receive $4 million from the DOI for land acquisition and economic development for losses suffered from the dams.Yet federal funds are never guaranteed, especially in lean economic times. “We weren’t confident [removal] was going to happen until the first contract was awarded,” says Elofson. Fifteen years after the passage of the act, construction finally began on the Port Angeles water treatment facility.During the dam-removal planning process, the Washington State Department of Health determined that the city’s water source was not “groundwater” but “groundwater under the direct influence of surface water.” This meant the water was vulnerable to microbial contamination and needed more stringent protection to meet the EPA Surface Water Treatment Rule under the Safe Drinking Water Act.The city would have had to build a facility with or without dam removal, but “the timing of it meant that when the Park Service constructed the city’s municipal water treatment plant, it had to address that [additional concern],” explains Maynes. She says the city’s new state-of-the-art facility plus an industrial water treatment plant cost the project more than $100 million.Less than 10% of the project’s final $325 million budgetOnce the water treatment facilities were operational, dam removal began. Randle and others had planned long and hard how best to remove the dams: quickly or slowly? “People joked, ‘we could get the Navy out of BremertonBy May 2012 the Elwha Dam and its reservoir, Lake Aldwell, were no more. As of July, the Elwha River carried half a million tons of sediment, 50,000 standard dump-truck loads, according to Chris Magirl, a research hydrologist with the U.S. Geological Survey (USGS). “Only about five percent of the anticipated sediment load has been mobilized,” Magirl says. “So, in other words, the sediment party has just started.”“This is the largest-ever controlled sediment release in a dam-removal project anywhere in the world,” says Randle. Dozens of scientists will continue to study water quality and the ecosystem, wading in the river’s tributaries to measure turbidity, scuba diving to catalog riverine life, tracking salmon, and monitoring how turbidity affects the water treatment facility, which supplies not just the city’s drinking water but also the paper mill and the state and tribal hatcheries.Already, the turbidity has had unanticipated effects on the tribe’s hatchery. “[The hatchery] is doing its job, but the Elwha fish are even more sensitive than we thought they would be to sediment in the river,” Elofsen says. He says the salmon’s growth was impaired when the water hit 30 nephelometric turbidity units , peaking in May 2012, although the hatchery water turbidity has since declined.The Port Angeles facility was designed to process up to 40,000 mg/L total suspended solidsThe only stakeholder still awaiting funds is the tribe; although their new hatchery and wastewater treatment facility are complete, they have not seen the promised $4 million. Former U.S. senator Bill Bradley (D–NJ), one of the Elwha Act’s sponsors, wrote a letter to DOI secretary Ken Salazar in March 2012, stating, “[W]e did not consider removal of the dams and restoration of the fisheries to be, by themselves, commensurate with the fulfillment of the nation’s obligation to the tribe.”Cleaning up environmental contaminants at the two dam sites has received less attention than the sediment control aspect of the project. The structures associated with the dams were constructed when lead-based paint and asbestos were commonly used and when transformer oil contained PCBs. Testing prior to DOI acquisition of the dams had indicated that the transformer oil from both dams was PCB-free,3 of lead- and PCB-contaminated soil, which was disposed of at a facility in Oregon.Contractors removed 1,000 ydThe seemingly pristine river had long been on the EPA’s 303(d) list of waterways in violation of the Clean Water Act for PCBs—a site located between the two reservoirs tested positive for PCBs in the 1980s. Once a site is listed, it stays on that list until followup testing proves the condition no longer exists. So in 1999 researchers with the Washington Department of Ecology (WADOE) returned to test fish downstream of the lower dam.Despite the Elwha Act’s focus on fisheries and ecosystem restoration, the multimillion-dollar, multidecade project contained no provisions for addressing the 303(d) listing and ascertaining whether the dam-removal process might disturb PCBs in sediment. Instead, the 1996 draft EIS states simply that NPS policies require park managers to “take action to address existing hazardous waste problems such as . . . contamination of water, soil, and air.”Winter is satisfied with testing completed during the dam-removal process. “We [took] samples of the deltas because that’s the stuff that’s eroding, and we found no contaminants,” he says.18Alternatively, an unconfirmed rumor holds that a transformer fell in the river years ago. “A landowner who wanted to develop an RV park on private land within Olympic National Park installed a transformer as a part of his project,” says Winter. “Decades ago, a flood resulted in erosion of his land and loss of the transformer into the river, so the story goes.” The landowner’s site was located above Lake Aldwell.Although PCBs were found in the onsite soil and in operating components of both dams, fish contamination in the lower river could also be the result of atmospheric deposition or contaminated hatchery feed, a problem in the 1990s.Winter does not agree that the 303(d) listing is relevant to the Elwha project. “The responsibility for sampling, monitoring, and reporting to the public any possible health threat from PCBs rests with the water quality regulatory agencies, in this case WADOE,” he says. “Had there been any issue of PCBs and the restoration project by WADOE or the EPA, such a sampling requirement would have been included in the water quality certificate the NPS obtained.”The Fish Restoration Plan developed by the National Oceanic and Atmospheric Administration pursuant to the Elwha Act does mention the 303(d) listing and recommends regular water quality monitoring.Thus far, however, it is not. And many scientists working on the project were unaware of the listing. “There are so many people doing so many different things,” Pess says, “I think it’s a case of one hand doesn’t know what another is doing.”Just as dam removal was set in motion, controversy struck again. In February 2012 four conservation groups sued the Lower Elwha Klallam Tribe and several federal agencies to halt the release of non-native steelhead trout from the tribe’s hatchery and Chinook salmon from the state hatchery.“Things are happening on a daily basis,” says Pess. “Numerous fish have spawned above the dam now.” This year, Chinook, pink, and coho salmon plus steelhead have spawned in river reaches previously blocked. The river ecosystem is undergoing a dramatic transformation and is a living laboratory for scientists. The people of the Lower Elwha Klallam Tribe have voluntary relinquished their rights to fish for five years, but in a few years they expect to see increased salmon runs in their historic fishing ground, as will recreational fishers. Most of the environmental contaminants from the dams will have been remediated and removed by then as well. Yet with the biggest sediment slugs still to come, the salient question for the restoration project is to quantify the impacts of these heavy sediment loads on spawning and rearing salmon.,If the Elwha tells a story, perhaps it is that ultimately it costs less to remove a dam and restore a river than it does to maintain the old dam.“I do think most people now see this as something very positive. It’s bringing a lot of attention to the area, which is good in terms of tourism,” says Maynes. “The dams are gone, fish are being seen in the river, and new beaches are establishing down at the mouth of the river. We’re already seeing benefits.”