Caenorhabditis elegans, which has asymmetric odor sensory neurons and taste perception neurons. In this worm left/right asymmetries are responsible for the sensing of a substantial number of salt ions, and lateralized responses to salt allow the worm to discriminate between distinct salt ions. The second case is the fruit fly Drosophila melanogaster, where the presence of asymmetry in a particular structure of the brain is important in the formation or retrieval of long-term memory. Moreover, I distinguish two distinct patterns of lateralization that occur in both vertebrates and invertebrates: individual-level and population-level lateralization. Theoretical models on the evolution of lateralization suggest that the alignment of lateralization at the population level may have evolved as an evolutionary stable strategy in which individually asymmetrical organisms must coordinate their behavior with that of other asymmetrical organisms. This implies that lateralization at the population-level is more likely to have evolved in social rather than in solitary species. I evaluate this new hypothesis with a specific focus on insects showing different level of sociality. In particular, I present a series of studies on antennal asymmetries in honeybees and other related species of bees, showing how insects may be extremely useful to test the evolutionary hypothesis.Traditionally, only humans were thought to exhibit brain and behavioral asymmetries, but several studies have revealed that most vertebrates are also lateralized. Recently, evidence of left–right asymmetries in invertebrates has begun to emerge, suggesting that lateralization of the nervous system may be a feature of simpler brains as well as more complex ones. Here I present some examples in invertebrates of sensory and motor asymmetries, as well as asymmetries in the nervous system. I illustrate two cases where an asymmetric brain is crucial for the development of some cognitive abilities. The first case is the nematode  Until some decades ago, it was widely and incorrectly assumed that lateralization of structure and behavior was unique to the human brain, and having a lateralized brain was a mark of the cognitive superiority of humans. Now it is well known that most vertebrates have strong left–right asymmetries in their brain and in their behavior and lateralization is widespread in the vertebrate subphylum  and spiders (Araneae) were found to be lateralized  uses its left anterior legs considerably more frequently than the right anterior legs during prey handling. Twelve ant species of Lasius niger kept mainly to the right on their foraging “streets,” whereas there was only one species that kept to the left  has been investigated to determine whether lateralization is evident in a bias to turn left or right , confirmed the existence of a population-level bias. To find out whether or not visual perception plays a role in determining the direction of turning, cuttlefish were tested either inside the empty apparatus or with attractive visual stimuli (sand and shadow) on either sides of the T-maze. The authors  deceive rival males by displaying male courtship patterns to receptive females on one side of the body, and simultaneously displaying female patterns to a single rival male on the other . The dextral and sinistral stocks are genetically DD or SS, respectively. On mating virgin sinistral and dextral types, offspring (F1 generation) that are genetically dextral (genotype = DS) but with a shell coil that is either sinistral  or dextral  are produced (F1 generation). By allowing the sinistral F1 mother to self-fertilize, offspring that have a dextral coil, but are genetically DD, DS, or SS are produced (F2 generation). Dextral SS individuals were identified by virtue of their producing sinistral young. A behavioral asymmetry in mating behavior, due to an anatomical asymmetry dependent on a maternal effect gene, has been observed in the pond snail tagnalis . The ponl effect , i.e., tL. stagnalis has a ring of nine ganglia that form a central nervous system around the esophagus, with two more distant buccal ganglia on the buccal mass. In all dextral individuals, the right parietal ganglion is fused with the visceral ganglion and the left visceral ganglion is unpaired. By contrast, in all sinistral individuals the reverse is observed; the left parietal ganglion is formed by fusion with a visceral ganglion. The central nervous system in sinistral pond snails, therefore, has an asymmetry that is the reverse of that of dextral snails. As the coil of the shell is determined by the maternal chirality genotype and the asymmetry of the behavior is in accordance with this, it is likely that the same genetic locus, or a closely linked gene, determines the behavior. These findings suggest that the lateralized behavior of the snails is established early in development and is a direct consequence of the asymmetry of the body.Chirality in mating behavior is matched by an asymmetry in the brain. Irrespectively of the kind of asymmetry, having an asymmetrical nervous system seems to give the individual some advantages. Lateralized animals have been shown to outperform non-lateralized in many circumstances , suggestD. melanogaster and the nematode Caenorhabditis elegans.Interestingly, not only in vertebrates but also in invertebrates an asymmetric brain is crucial for the development of some cognitive abilities. Two examples of invertebrate species where brain asymmetry at the individual level can confer advantages to the individual and, moreover, is necessary for the animal to have some cognitive abilities, are provided by the fruit fly Drosophila to establish short-term memory but it is important in the formation or retrieval of long-term memory.In the fruit fly, a structure located near the fan-shaped body connects the right and the left hemispheres . This stC. elegans. With its 302 neurons, the nematode offers a unique opportunity to address the manner in which symmetrical neuronal assemblies deviate to create functional lateralization. C. elegans. In this species, 2/3 of the neurons  are present as bilaterally symmetrical pairs. Particularly intriguing components of L-R asymmetry in the C. elegans nervous systems are neuron pairs (or neuroblasts) that are bilaterally symmetrical in terms of their post-morphogenetic position, morphology and lineage, but at some point during embryogenesis, after bilaterality has been established, undergo L-R-specific sub-differentiation programs. This is the case of the Amphid Single-ciliated Endings, ASEL (left)/ASER (right) neurons that are the main taste receptors of C. elegans. ASEL and ASER are bilaterally symmetrical with regard to cell position, axon morphology, outgrowth and placement, dendritic morphology, and qualitative aspects of synaptic connectivity patterns. However, three putative sensory receptors of the guanylyl cyclase class, gcy-5, gcy-6, and gcy-7, are expressed asymmetrically in ASEL  and ASER (gcy-5), two to left and one to the right  left-right asymmetries usually occur not only in single individuals but also in the same direction in most individuals. In this case, where most individuals show a similar direction of bias the group or population is biased, and so we speak of population-level lateralization. Individual brain efficiency does not require a definite proportion of left- and right-lateralized individuals. Thus, the arguments about the fact that brain lateralization increases individual efficiency do not explain population-level lateralization. Moreover, lateralization at the population level can also present ecological disadvantages, because it makes individual behavior more predictable to other organisms, such as predators. Theoretical models on the evolution of lateralization  suggest Invertebrates and in particular insects have been excellent models to test the hypothesis predicted by the theoretical models on the evolution of lateralization that directional (population-level) asymmetry should be found only in cooperative, social species . In factApis mellifera  display laterality in learning to associate an odor with a sugar reward. The researchers used the proboscis extension reflex (PER) paradigm (Apis mellifera ligustica Spin.) and checked for morphological differences in the number of sensilla between the right and the left antenna. Results showed that putative olfactory sensilla  were significantly more abundant on the right antenna surface than on the left antenna surface (mean difference of 3%), whereas sensilla not involved in olfaction  were more abundant on the left than on the right antenna surface (mean difference of 7%). However, it seems unlikely that this can account for the functional asymmetry.paradigm , in whicA strong odor dependence of the lateralization of short-term memory recall of odors has been reported in honeybees . After tThe asymmetry observed in the retrieval of olfactory learning in honeybee is much more complex than a difference in learning ability of the right and left antennae and the difference in number of olfactory sensilla is unlikely to explain entirely the behavioral laterality. Up to now, however, search for anatomical correlates of the asymmetry in higher centers of the bee brain has not revealed clear anatomical asymmetries .Apis mellifera L. (Fam. Apidae), and the solitary mason bee, Osmia cornuta . Unlike honeybees, mason bees are solitary: every female is fertile and makes its own separate nest, they don’t produce honey or wax and there are no workers .  workers . Lateral workers . Hence, Bombus terrestris , an annual social species of bees. trichodea type A, was shown to be more abundant on the surface of the right antenna than on the left one, and a slight tendency towards asymmetry was shown for a second class, i.e., sensilla coeloconica. Since electroantennographic responses represent the sum of responses of all ORNs housed in the sensilla of a single antenna  out of four species examined the majority of bumble bees circled in the same direction. Interestingly, the researchers did not observe any lateralization in B. terrestris. Since two species circled anticlockwise and one clockwise, it is unlikely that the asymmetry is a function of the structure of the florets.Bombus wurflenii and Bombus lucorum) feeding on Rhinanthus minor, a flower that can be robbed from either the right-hand side or the left-hand side and they looked at a possible effect of social learning on handedness. Numerous patches of R. minor spread across an alpine landscape were studied and each patch was found to be robbed on either the right or the left. The intensity of side bias increased through the season and was strongest in the most heavily robbed patches. Bees within patches seemed to learn robbing strategies (including handedness) from one another, either by direct observation or from experience with the location of holes, leading to rapid frequency-dependent selection for a common strategy, i.e., adopting the same handedness within particular flower patches.Bumblebees observe and copy the behavior of others with regard to floral choices  and, morFigure 1) to shed light on the possible evolutionary origins of the left-right antennal asymmetry. Three species of Australian native, stingless bees  were trained to discriminate two odors, lemon(+)/vanilla(-), using the PER paradigm. Recall of the olfactory memory at 1 h after training was better when the odor was presented to the right than to the left side of the bees. In contrast, recall at 5 h after training was better when the odor was presented to the left than to the right side of the bees. Hence, stingless bees (Meliponini) have the same laterality as honeybees (Apini), which may suggest that olfactory lateralization is likely to evolved prior to the evolutionary divergence of these species. The distributional pattern and fossil records are indicative of greater antiquity for the Meliponini compared to Apidi, Bombini, and Euglossini, and suggestive of an independent origin or an early divergence from a proto-other Apidae branch . In honeybees, recall of short-term memory is possible through the right side, whereas recall of long-term memory is possible through the left side (see The Honeybee Apis mellifera). A shift of recall access from one to the other side of the brain has been observed previously in birds (All the evidence about differences in the specializations of the left and right sides of the nervous system and behavior in invertebrates suggests that invertebrates share the attribute of lateralization with many vertebrates. This strengthens the conclusion that lateralization provides substantial advantages, since it has persisted, or evolved many times, in such diverse groups of animals. Asymmetries in invertebrates and vertebrates sometimes also show similarities in their appearance. One example is the processes of memory formation in parallel on the right and left sides of the brain and the interaction between the right and left memory traces during memory formation. In fruitflies, the transition from short- to long-term records of conditioning depends on an asymmetric body normally only present on the right side of the brain. When there is also a counterpart on the left, only short-term memory is formed (see The Fruit Fly in birds . This suThe difficult and complex issue is whether homologous genes in invertebrates and vertebrates determined lateralization or whether there has been analogous evolution of lateralized function in the two taxa. It is probable that the common ancestor of metazoan animals specified the right-left axis . Since iC. elegans and in the zebrafish Danio rerio (C. elegans are the result of gap-junctional communication and calcium influxes, whereas the neuroanatomical left-right differences in the epithalamus of D. rerio are the result of morphogenic changes regulated by secreted signaling molecules. Thus, the invertebrate and vertebrate species considered share some commonalities in the mechanisms involved in asymmetrical neural development, i.e., the interaction of neurons across the midline during formation of the asymmetrical nervous system, and the inherently stochastic nature of some developmental pathways. However, results need to be interpreted with caution since the evolutionary gap between the 302 neurons of the worm and the estimated 78,000 neurons of the larval fish (The recent comparison between the cellular and molecular mechanisms leading to neuronal asymmetries in the nematode io rerio  may alsoval fish  is consiThe ESS theory predicts that lateralization at the population-level is more likely to have evolved in “social” rather than in “solitary” species. Studies conducted in different species of insects seem to be in alignment with this prediction. Shoaling and not-shoaling fishes have also provided evidence in support of this hypothesis. In 20 species of teleost fishes, The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


TDP-43/TDP43/TARDBP gene disease-associated mutations identified to date. A web server interface has been developed to query the database and to provide tools for the analysis of already reported or novel TDP-43 gene mutations. As is usually the case with genetic association studies, assessing the potential impact of identified mutations is of crucial importance, and in order to avoid prediction biases it is essential to compare the prediction results. However, in most cases mutations have to be submitted separately to various prediction tools and the individual results manually merged together afterwards. The implemented web server aims to overcome the problem by providing simultaneous access to several prediction tools and by displaying the results into a single output. Furthermore, the results are displayed together in a comprehensive output for a more convenient analysis and are enriched with additional information about mutations. In addition, our web server can also display the mutation(s) of interest within an alignment of annotated TDP-43 protein sequences from different vertebrate species. In this way, the degree of sequence conservation where the mutation(s) occur can be easily tracked and visualized. The web server is freely available to researchers and can be accessed at http://bioinfo.hr/pro-mine. © 2010 Wiley-Liss, Inc.TDP-43 is a multifunctional RNA-binding protein found to be a major protein component of intracellular inclusions found in neurodegenerative disorders such as Fronto Temporal Lobar Degeneration, Amyotrophic Lateral Sclerosis, and Alzheimer Disease. PRO-MINE (PROtein Mutations In NEurodegeneration) is a database populated with manually curated data from the literature regarding all  Non-synonymous single nucleotide polymorphisms are a type of SNPs that result in a codon which codes for a different amino acid. Some of these polymorphisms are responsible for many diseases as they may lead to appreciable changes in protein structure and function. Annotating and distinguishing the polymorphisms that render the protein non-functional from those that do not is essential for the study of disease-associated mutations. However, screening the pathogenicity for each identified SNP experimentally is often a very laborious task that requires a considerable amount of time. To overcome this challenge, several computational approaches for  Vihinen .TARDBP mutation carriers in the aforementioned diseases the reader is referred to the Alzheimer Disease and Frontotemporal Dementia Mutation Database (http://www.molgen.ua.ac.be/ftdmutations) and to the Amyotrophic Lateral Sclerosis Online genetic Database .Nuclear factor TDP-43  is an example of a protein with high SNP count. TDP-43 is a multifunctional RNA binding protein that plays a role in several cellular processes such as transcription, pre-mRNA splicing, mRNA stability and mRNA transport . RecentlIn the beginning, the question was still open regarding the relative importance of this protein: should TDP-43 protein inclusions be considered as a simple pathological curiosity of these diseases or did it really play a role in their origin and progression ? The recMus musculus , a manually curated mutation repository providing detailed and updated information from the literature focusing on the potential functional consequences of the We designed and implemented a user-friendly web interface aimed to facilitate the access to the database and to integrate multiple prediction tools for the analysis of mutations. The results from the prediction tools are automatically retrieved into a single output, greatly reducing the amount of time required when studying the effects of mutations on protein. Furthermore, based on the fact that knowledge regarding the functional consequences of a mutation can also be gained from sequence comparison, PRO-MINE also offers the possibility to display an aligned set of protein sequences from several vertebrate species with the mutations of interest distinctly annotated.PRO-MINE represents a novel approach and an advantage upon other existing tools and provides the proof of concept for the need to generally integrate data with computational analytical tools in the study of mutations.TARDBP gene.PRO-MINE is a relational database of published disease-associated mutations in human neurodegenerative diseases, in particular focusing on the mutations, genes and exons. The entity relationships of the three tables are depicted in TARDBP gene but also protein and genomic data associated with the gene itself.The database was created using MySQL and consists of the following tables: TARDBP gene mutations was performed. The result was a curated set of 43 mutations representing the most exhaustive to date status of identified and characterized TDP-43 gene mutations, further referred to as ‘TDP-43 mutations core set’. Relevant information concerning the mutations was, wherever available, extracted from the literature and included in the mutations table. The information provided about a given mutation for each entry of the mutations table includes: (1) the exon number where the mutation occurs; (2) clinical information related to the mutation such as the number of patients where it was identified (in case of mutations found in different affected family members only one patient for family was computed), the phenotype presented by the patients or the onset site of the disease; (3) a brief description of observed experimental evidences on the functional consequences of the mutation on a protein; and (4) references to original papers that reported the mutation as well as later studies. The mutation nomenclature complies with the accepted guidelines proposed by the Human Genome Variation Society (http://www.hgvs.org/mutnomen) and the variants numbering system is given on the protein level relative to the amino acid reference sequence.An exhaustive manual review of the literature and public repository databases regarding genes and exons tables were retrieved from Ensembl (release 57 – Mar 2010) (TARDBP gene (RefSeq NM_007375.3) and its orthologs from six vertebrates: Pan troglodytes, Mus musculus, Rattus norvegicus, Gallus gallus, Xenopus tropicalis and Danio rerio.Protein sequences and genomic data stored in the ar 2010)  through http://www.perl.com). The scripting programs run on Apache web server and use MySQL (http://www.mysql.com) to retrieve and manage information stored in the database.The PRO-MINE web server is intended to facilitate the access to the data stored in the database and to provide means for its analysis. The web server interface consists of dynamically generated HTML pages and was developed using Perl CGI scripts  into neural networks. The reason for the use of complementary approaches lies with different strengths and weaknesses of various prediction algorithms, and in the absence of experimental evidence, the best strategy is to apply a consensus decision among as many prediction tools as possible.The four software tools use different models and features to predict the functional effect of mutations on protein. NetPhos uses sequence and structure-based neural networks for predicting phosphorylation sites at serine, threonine or tyrosine residues in protein sequences based on the premise that creation of new phosphoylation sites or disruption of existing ones can alter the functionality of a protein. Although in normal conditions l region . TherefoBesides these analysis tools available for user selection, PRO-MINE uses the multiple alignment software for protein and nucleotide sequences – MUSCLE  – in theBioinformatics methods are of great help in elucidating the potential functional impact of mutations on protein since they provide quick and accurate predictions. Nonetheless, each of the methods applies different features and algorithms in the prediction analysis and, as a consequence, different results might arise. This poses a challenge for the choice of the best method to utilize. As intuitively obvious, the best strategy to overcome this problem is to use several prediction methods at the same time and to take into account all the results with the purpose of obtaining a more reliable evaluation. However, manual inspection and analysis of the results from several bioinformatics tools is a time-consuming procedure that requires parallel submission of the mutations of interest to each prediction server separately and manual combining of the resulting predictions afterwards. Usually, these tools require different input formatting and structure their output in different ways, and it is often impractical and time-consuming for the user to collect the data and integrate it in the proper context. Also, knowledge discovery process requires the researcher to use additional information sources and analysis tools.http://bioinfo.hr/pro-mine.In the PRO-MINE database and analysis server, the data and analysis tools are integrated in a user-friendly interface in order to shift the focus of the researcher towards the problem and away from manual tasks of data manipulation. Besides the manually curated database of known TDP-43 mutations, the analysis functions implemented in PRO-MINE include: (a) prediction of mutational effects on protein function and (b) protein sequence annotation with mutations. Details on the usage and currently available options are described below. The developed web tool is freely available at: XposY, where X and Y are the two amino acid variants  and pos is the position of the substitution in the sequence . The server also offers the possibility of using the TDP-43 mutations core set by simply selecting the corresponding checkbox. Additionally, both fields can be used in combination – an option that can be especially useful to compare new mutations with previously identified ones.The web server accepts as input a list of mutations which can be typed directly into the query input box or uploaded as a simple text file . MutatioDatabase and Analysis Tools section.By providing simultaneous access to several prediction tools, PRO-MINE renders the ability to obtain predictions more efficiently and in comparatively less time than manually combining analysis results from different sources. PRO-MINE integrates results from four separately selectable prediction tools , previouDatabase and Analysis Tools). An example of the results output for five mutations is shown in The results of the selected prediction tools are formatted and displayed in a single table, facilitating the comparison of the different predictions. Predictions regarding the TDP-43 mutations core set are pre-computed to avoid waiting time. Query results can also be viewed in plain text format or downloaded for further use. Additionally to the predictions, for the TDP-43 mutations core set, selecting the mutation identifier opens a new window displaying associated information such as clinical data about the patients presenting the mutation, a brief description of observed experimental effects of the selected mutation on protein function and the literature references reporting the mutation  and displays the TDP-43 protein sequences encoded by the TDP-43 gene from different vertebrate species with the submitted mutations annotated was implemented.Multiple alignment of protein sequences from different species provides insight into the level of conservation of an amino acid in a sequence position. Conserved positions are commonly associated with functionally important sites and disease-related mutations frequently occur in those positions. Based on these considerations, an analytical tool that aligns  with the TDP-43 mutations core set annotated is shown as an example in The described functionality offers a layout for a clear visualisation and analysis of protein sequence changes among organisms in the positions where mutations occur and the distribution of the mutations along the protein sequence. The alignment of the TDP-43 protein sequences from 3 species  (SOD1 genes (MIM# 147450)  , and in  138945)  and SOD1 147450) . Future Incorporating links into the results output to supplementary biological sources of relevance in the research of mutations is part of the ongoing work. We also plan, having in view the flexibility enhancement of PRO-MINE, to provide the possibility to submit not only mutations but also protein sequences as queries.Finally, it will also be possible in future updates to expand the list of prediction tools available in PRO-MINE as soon as they become publicly available.TARDBP gene mutations and a user-friendly web tool for the automated analysis of known and novel mutations.PRO-MINE is a high-quality manually curated database of The developed web tool simplifies and enhances the time-consuming task of assessing the potential effects of mutations on protein function by providing simultaneous retrieval and display of predictions from separate web tools for a set of submitted mutations. Moreover, up-to-date information as well as visualisation tools are available to complement and improve data evaluation.PRO-MINE is presented here as a proof of concept for derived databases that combine curated datasets with analytical tools in a single resource. Therefore, in future installations it is intended to broaden the use of the web server by expanding the database to other genes involved in neurodegeneration.We expect PRO-MINE to become a valuable tool in the study of disease-associated mutations as there is a notable and current increasing interest in the mutation research.


Cancer borealis. In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of the central pattern generator up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission. Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities.Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. Usually, these algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of a small central pattern generating circuit in the stomatogastric ganglion of the crab  To appreciate how neural circuits control behaviors, we must understand two things. First, how the neurons comprising the circuit are connected, and second, how neurons and their connections change after learning or in response to neuromodulators. Neuronal connectivity is difficult to determine experimentally, whereas neuronal activity can often be readily measured. We describe a statistical model to estimate circuit connectivity directly from measured activity patterns. We use the timing relationships between observed spikes to predict synaptic interactions between simultaneously observed neurons. The model estimate provides each predicted connection with a curve that represents how strongly, and at which temporal delays, one circuit element effectively influences another. These curves are analogous to synaptic interactions of the level of the membrane potential of biological neurons and share some of their features such as being inhibitory or excitatory. We test our method on recordings from the pyloric circuit in the crab stomatogastric ganglion, a small circuit whose connectivity is completely known beforehand, and find that the predicted circuit matches the biological one — a result other techniques failed to achieve. In addition, we show that drug manipulations impacting the circuit are revealed by this technique. These results illustrate the utility of our analysis approach for inferring connections from neural spiking activity. Nervous systems show highly complex dynamics. This complexity originates from the intrinsic dynamics of each neuron, from its synaptic connections, and modulation state There are many ways to build effective networks based on observed spiking activity. A commonly used network inference algorithm is Granger causality analysis In a recent study, Kispersky et al. In this paper, we will continue the analysis of the spike train data with the goal of inferring network consistent with known connectivity. Generalized linear models take into account the point process nature of spike trains and have been used to infer connectivity in other biological neuronal networks To obtain this result, it is important to consider the functional shape and magnitude of the interactions in the model rather than statistical significance as it is classically quantified by Granger causality analysis. In the second part of the study, we show that both a nonlinear point process model and our measure of coupling strength are necessary to successfully infer the connectivity.Finally, we show that inference using the point process model is robust to parameter changes, can be reproduced across several independent biological data sets, and can be used to predict how altered connectivity affects network function, i.e., the generation of the triphasic burst pattern. We demonstrate the ability of the method to track changes in the effective network connectivity structure caused by partial blocking of individual membrane currents or synaptic transmission. Our results add to the evidence in favor of applying point process statistical models to capture the statistics of spike trains. They constitute the first step toward the analysis of the relationship between structure and activity of larger neural circuits.Extracellular recordings were obtained from three units of the crab stomatogastric ganglion (STG), which produce the pyloric rhythm The physiological connections between the three units of the stomatogastric nervous system responsible for the pyloric rhythm are well understood A central question is: Given the spike trains, can we infer the connectivity of the circuit? Kispersky et al. demonstrated that in the presence of the strong oscillatory components, Granger causality analysis based on a linear firing rate model is unable to deduce the physiological connectivity pattern Our results show that two modifications to the approach of In point process models, the spiking activity of a neuron is conditionally explained by the previous firing activity of the neuron and activity of the recorded population . The coupling filters used by our model can be interpreted as synaptic-like interaction filters . Here, nThe magnitude and time scales of these features can be mapped to biological findings. For example, the positive peaks at lags between 20 ms (LP) and 50–60 ms (PD and PY) directly correspond to the typical inter-spike intervals within the bursts. The spike-triggered depolarization of the membrane potential is on the same time scale and can be measured with intracellular recordings The six cross-history kernels  can be separated into two groups: couplings in the direction of the firing order during the pyloric rhythm  and couplings counter to the order of a pyloric cycle  . The fira priori knowledge of the approximate expected density of the network , a connectivity diagram can be obtained, matching the known circuit connectivity  considered here, the LP-to-PY coupling is considered weak, while all other connections are considered strong To characterize how well our model fits the observed spiking pattern, we used the model to generate simulated spiking activity following a period of observed spikes. We find that stochastic simulations from the model generally produce a spiking pattern qualitatively similar to the pyloric rhythm observed in the real data set . In spitAlthough the model assigns a nonzero value to the PY-to-PD coupling, it is not essential to produce or maintain the pyloric rhythm expressed by the model circuit. To demonstrate this, we set that particular cross-coupling filter to zero in the maximum-likelihood fit and left all other parameters unchanged. When we used this modified model to simulate new spike trains, it displayed a triphasic rhythm  qualitatFirst, we show robustness to the amount of data used for fitting. Specifically, we fit a sequence of models with increasing amounts of data used to train the model and observed the evolution of coupling strengths over time . We founModel parameters arameter . TherefoIt is known that the maximal time period to consider history effects can have a profound effect on the inferred networks, for both linear and point process models. For the point process model considered here, the maximal time lags for the self- and cross-coupling filters were not chosen arbitrarily, but based on a model selection procedure that selected an optimal time scale based on a penalized likelihood criterion .To investigate whether the difference between the weakest (PY-to-PD) and the remaining connections was significant, we computed the uncertainties associated with the coupling strengths based on the maximum-likelihood estimate of the model and its covariance structure see . The staWe also performed a goodness-of-fit test tailored to the point process model based on the multivariate time-rescaling theorem Finally, we repeated the model selection and fitting procedure for three additional independent preparations from different animals, each with spike train recordings of variable length. All recordings qualitatively showed a stable pyloric rhythm, although the temporal scales, like the burst cycle period and the exact temporal phase relationships between units, varied considerably across data sets.For all four data sets, we found qualitatively similar results regarding the inferred connection strengths . NotablyKim et al. and others used a measure based on Granger causality to quantify the effective coupling between spike trains We used this Granger causality measure using the same point process model as above and parameters determined by the model selection procedure and failed to obtain couplings consistent with known physiology  nor by vOne might wonder whether a linear rate model as (implicitly) used in The analysis yielded nine couplings (self-couplings included) between the three neurons . Visual To test whether the linear model provided a good fit to the data, we used the estimated model to simulate activity after a period of observed activity. If the model were appropriate, we would expect it to produce qualitatively similar spiking activity consistent with the observed data. Instead, we found the linear model is unable to maintain the pyloric rhythm, and activity values start to diverge after only two seconds of simulated activity . While tA further exploration of the parameter space, similar to the previous section, shows that no parameter choice, such as the amount of data used and how far the coupling filters extend in time, leads to a network that would be consistent with physiology . OverallWhen we analyzed all four data sets, the physiologically nonexistent connection corresponds to the weakest one in only two out of the four cases. In addition, coupling strengths grouped by connection across all four networks did not show a consistent pattern . MoreoveFor completeness, we reproduced the original analysis of To this point, we have considered the standard pyloric rhythm in its default configuration. A useful method of network inference should also detect and track changes that occur to the coupling strengths. To this end, we applied the point process model to two data sets where the isolated pyloric circuit is perturbed by pharmacological agents.In the first data set, CsCl was applied to a preparation of the pyloric circuit of the STG. CsCl is known to block an intrinsic current, the h-current . PTX is known to block inhibitory synaptic transmission in the STG In the STG, most synapses of the LP and PY cells are inhibitory and mediated by glutamate Indeed, when we fit the point process model to the data sets before and after application of PTX, we find cross-couplings are decreased toward zero, i.e., become weaker . Notablyt link , .To find out which couplings in the network are crucial for the presence of the stable pyloric rhythm, we simulated spike trains from four different models estimated from the PTX condition. The models differed in the constraints placed on the allowed network pattern. As before  experimeOverall, these results illustrate the utility of the point process model in inference of effective connectivity. Bath application of two pharmacological agents alter the expected circuit connectivity by changing either the intrinsic currents of each neuron (CsCl) or the synaptic interactions between neurons (PTX). In both cases, the point process method detected the anticipated changes.In this work we considered the application of a point process model to infer connections of a three-neuron circuit. To the best of our knowledge, these results provide the first successful application of a network inference algorithm to spike train data recorded from identified neurons within a circuit whose underlying synaptic architecture has been fully characterized. Typically, such inference algorithms have only been validated using simulation studies The crab stomatogastric nervous system is well suited to study network inference algorithms like the point process model. The circuit consists of a small number of elements whose synaptic interactions are well studied and whose monosynaptic connectivity is established Most of our analysis can be readily applied to other small neural circuits, e.g. central pattern generators in the respiratory system in vertebrates a priori that a model that treats the time of spikes as the sole input, i.e., does not have access to the membrane potential, can correctly perform connectivity inference. For the circuits considered here, this did not seem to pose a problem because, at least in the STG, prolonged membrane depolarizations always appear simultaneously with spiking activity. Therefore, spikes are proxy measurements to determine the state of the membrane potential. Furthermore, the time scales of the graded synaptic interactions are similar to the ones observed from spike-triggered transmitter release and the ones estimated in our model Synaptic transmission in the STG occurs as a graded  release of neurotransmitters and is thus mediated by sub-threshold depolarizations as well as spikes A model of the central pattern generator for the pyloric rhythm can be evaluated using at least two criteria: One criterion is how close the model reproduces a given, observed set of spike trains and their statistics, e.g., the number of spikes per burst and the average inter-burst duration. For understanding the functional behavior of the circuit, a broader criterion can be applied: A model would match the data if it qualitatively reproduces the stable, triphasic burst pattern, regardless of the exact spike train statistics. It is evident that many models will fulfill either one or both criteria with the first criterion being an additional constraint on the second. This explains why deviations from the best-fitting model (according to the first criterion) can still generate spike patterns that may be equally functionally valid  synaptic effects or unobserved common input To elucidate the reasons why Granger causality analysis using a linear model failed to recover the true connectivity in Analysis with an underlying linear rate model is based on the assumption that neural firing rates are linearly interacting. Even the inclusion of very long time scales in the linear model did not lead to a correct inference using any of the proposed connectivity measures. This observation points to a general limitation of the simple linear autoregressive models. Further, the physical mechanism for the LP and PY neurons to initiate spiking is a release from inhibition A firing rate model includes a smoothing preprocessing step on the input spike trains. When applied to data from the STG this preprocessing preserves the qualitative phase relationship between the neurons during the pyloric rhythm, but temporal information about the spike timings is lost. In a system that relies heavily on graded synaptic transmission, like the STG We have observed that the amount of available data strongly influenced the statistical significance of a directed coupling. Typically, for our data sets, 15 seconds of data or more are enough to yield highly significant Granger causality (GC) scores for all six connections, for both the linear and point process models. Hence, statistical significance alone is not useful in this case to determine the presence of effective coupling between two neurons. Because the magnitude of the GC score varies as well with the amount of data, its value cannot be used for inference of coupling strength beyond relative comparisons see also . Moreovemodel-free procedures Granger causality analysis and related approaches are sometimes called The point process model was primarily used as an inference tool to deduce the connectivity between a set of observed neurons. Although this constitutes a statistical and phenomenological model , we have shown its potential as a generative model . The couCoupling filters interact in a multiplicative way, i.e., they modulate an underlying baseline firing rate instead of increasing or decreasing the firing rate by a fixed amount. The coupling strength (the integrated area under the interaction kernel) is related to the number of spikes that, depending on the instantaneous postsynaptic firing rate, are generated or suppressed on average due to a single presynaptic spike From a biological perspective, the sum of contributions of past neural activity in a point process model can be interpreted as the influence on the neurons' membrane potential. The coupling filters correspond to synaptic interactions, e.g., in the spike-response model The relationship between the effective coupling filters and biological postsynaptic potentials is not unique. This is especially true for inhibitory connections in the STG: Consider presynaptic spiking activity that always occurs at a fixed relative phase of the postsynaptic burst cycle in which the postsynaptic neuron is already hyperpolarized. The observation of the absence of any postsynaptic spike does not contain any information about the amplitude of the synaptic conductance beyond a minimal value that prevents the postsynaptic neuron from firing. In these cases, estimates of coupling filters diverge and we cap them at an arbitrary value that does not affect the qualitative results of the analysis. This so-called phase response saturation has been shown in experiments and detailed neuron models of the pyloric rhythm We note that while using the net integral of the coupling filters as a measure of coupling strength has led to a good correlation between inferred coupling strengths and the presence of real couplings, other measures of coupling strengths might be useful to consider as well. These could include other features of the kernel (such as its peak amplitude) or be limited to certain temporal scales .statistical significance alone would result in the inference of a fully connected network. Yet, we have observed that setting the known missing physiological connection to zero did not change the functional behavior of the modeled circuit suggesting that statistical significance is not an appropriate metric for determining functional interactions in this data set. It is known that networks with different neuron parameters can express very similar pyloric-like rhythmic activity physiological significance is desirable.For the point process model presented here, all available data were used and free parameters were chosen with a straightforward, but rigorous model selection procedure. Because a nonzero coupling strength is recovered for each possible connection, different binary connectomes can be obtained by varying a threshold that determines whether a connection is substantial. Using a threshold to determine a binary circuit diagram based on Finally, an advantage of point process models is the availability of goodness-of-fit tests that are not always assessed in practice in Granger causality analysis. When we applied model adequacy tests to the linear rate model, we could identify its shortcomings in capturing the structure of the data. The results hinted at the necessary modifications to construct a model whose network inference could match the physiology. Because any model-based assessment of connectivity is expected to show model misspecifications given enough data, we suggest methods that explicitly consider the structure of the data in building the model and use interpretable measures of connectivity rather than statistical significance levels. A series of goodness-of-fit tests, tailored to the point process nature of the model, strengthened our confidence in the model's inferred network structure and demonstrated the robustness of our results.In general, effective connectivity will not necessarily be equal to physiological or structural connectivity Although we have shown that linear models do not recover the physiological network architecture in the pyloric circuit, they may be more applicable to large networks where measurements reflect averaged population activity and nonlinearities may potentially average out in vivo functional imaging using two-photon calcium imaging with subsequent paired patch-clamp recordings of the same individual cells in slices Experimental protocols necessary to obtain both signals and structural information of neural circuits are being actively developed: A recent study combined A growing scientific community is interested in multi-neuron models and connectomics. As these data become more widely available, principled methods that incorporate known statistical structure in the data — such as the one proposed here — will be of fundamental importance.Full experimental details for the four data sets can be found elsewhere Vaseline mixed with mineral oil was used to build waterproof wells around identified nerves to record action potentials from stomatogastric ganglion (STG) neurons. Steel electrodes were placed into these wells with reference electrodes in the bath to record electrical signals. These signals were recorded with an AM Systems Model 1700 AC Amplifier and digitized with an Axon Instruments Digidata 1440A . pClamp software , running on a PC computer, was used to record extracellular signals continuously.During recordings, saline was continuously perfused and recording temperature was kept as close to Recordings were obtained from four preparations for recording periods ranging between 140 and 300 seconds. For predicting changes of coupling strength by pharmacological conditions, data were acquired in a similar way as described above. Specifically, CsCl at 5 mM concentration was applied to the preparation to block h-currents. Recordings include 300 s of data before the application (control) and 300 s after application of CsCl (condition). Visible spike sorting artifacts were removed by visual inspection. The model selection procedure selected a maximal lag of 1 s for the self-history filters and 350 ms for the cross-coupling filters where maximal lags were jointly optimized for both data sets using the BIC-penalized likelihood criterion.For the application of picrotoxin (PTX), the control condition consists of 360 s of recordings before the application and 120 s of stationary activity 6 minutes after application of PTX  at A multivariate point process model of the spiking activity was constructed using the conditional intensity framework ults see .We determined the maximal lags for the self- and cross-coupling filters separately by a BIC criterion  fitting  was corrOnce we determined the maximal lag for the self-coupling filter, we fitted full models including the cross-coupling filters and varied their maximal lag up to 1.2 times the burst cycle period of the data set. The maximal range of tested values was chosen so that a U-shaped curve could be obtained in all cases. The lag that corresponded to the minimal BIC value was then chosen as the maximal lag for all six cross-coupling filters.Model parameters Statistical significance of single parameter values can be (approximately) established using the Wald statistic Spike train activity was simulated from the model by drawing stochastic samples according to We applied the previously described analysis steps to all four data sets. Specifically, the model selection procedure (using BIC-corrected log likelihoods) is performed separately for each data set. We report and visualize the results only for the first data set, unless otherwise noted. For all data sets, we used the complete recording periods unless otherwise noted.Firing rates for the three neurons X, Y and Z are obtained from the spike train recordings by first convolving the spike trains with a half-Gaussian  filter with standard deviation A multivariate linear model is then constructed for the  firing rate at time To generate stochastic samples from the model, multivariate models were first fit to each neuron. For the first second, the time series For the point process model, we define the directed coupling strength between two neurons as the net integral of the corresponding cross-coupling filter:was used .The type of the directed interaction between X and Y is completely specified by the filter coefficients. The reduction of the  interaction into a single quantity like CS is not unique. For the point process model, We use the absolute value of the integral in Granger causality analysis attempts to assess the strength of a causal  interaction between two time series X and Y in the presence of other explanatory variables, e.g. a third time-series Z. We briefly describe the framework here, more details may be found elsewhere  was chosen (blue dot). B, Following the choice of the extent of the self-coupling filter, we added cross-coupling terms to the models of varying lengths and again selected for the optimal time scale according to the model selection criterion (blue dot). The model selection produced self-coupling filters slightly shorter than an average burst cycle period and cross-coupling filters substantially shorter than the burst cycle period so that interactions were deemed important only for time lags on the order of 100 ms. C, Variability of parameter estimates. Height of the bars indicate the inferred coupling strength for each of the six possible directed interactions. Error bars denote standard deviation and are obtained analytically from the maximum-likelihood parameter estimates and estimated covariance matrix. D–E, D). In no case is the physiologically absent connection  estimated as the weakest. Similar results are obtained when measuring coupling strength as the area under the filter (data not shown). For the point process model, using a pair-wise model versus the full multivariate model has little effect on the inferred coupling strengths (E). This is because of the different phases in which the three neurons fire in the pyloric rhythm and the short extent of the coupling filters that lead to little to no overlap between coupling filters. For both versions of the model, the physiologically absent connection is inferred as the weakest . F, G, BIC-corrected log likelihoods are shown for different choices of the polynomial degrees of the kernel representation. Lower values indicate a better fit. The choice of quadratic spline basis functions (asterisk) is justified from a model selection criterion based on the log-likelihood criterion.(TIF)Click here for additional data file.Figure S2Multivariate goodness-of-fit analysis for the point process model increases confidence of the inference.A, Observed spike train and instantaneous firing rate estimates. Spike trains (top) and estimated firing rates B–D, Multivariate goodness-of-fit analysis. We employed the multivariate time-rescaling test as a goodness-of-fit test for point process models. Residuals are calculated based on the model fit and the observed spike trains and should form a homogeneous Poisson process. Normalized residuals (solid lines) should lie completely within the 95% confidence intervals (dashed lines) to pass the goodness-of-fit test (B). Residuals are normalized by the sample size to allow for global confidence intervals. A necessary condition to pass the multivariate time-rescaling test is that the superposition of all spike trains forms a Poisson process with an independent mark sequence. The mark sequence is the sequence of neuron identities which correspond to the spikes in the superimposed process. Independence between consecutive marks in the sequence is tested with a C, D). A  regions , middle.(TIF)Click here for additional data file.Figure S3Goodness-of-fit analysis for the linear rate model can reveal its inadequacy.A, Comparison of original and fitted firing rates. The spike trains are smoothed with a half-Gaussian kernel of fixed bandwidth to obtain a smooth estimate of the firing rate (top). The fitted signals of the linear rate models (middle) and the residuals (bottom), the difference between original and fitted signals, is plotted for 150 consecutive time bins. Qualitatively, the observed and fitted activity traces seem to match; however, closer inspection of the residuals reveals that they are neither Gaussian nor white. For a sufficient model fit, residuals should form a sequence of independently distributed Gaussian variables. B–C, Residual analysis shows the linear rate model is an inadequate model. The histogram of residuals is shown for the PD neuron (B). Lilliefors' procedure rejected the null hypothesis that the residuals are samples of a Gaussian distribution  illustrates that residuals are not independent of each other (histogram-based (TIF)Click here for additional data file.Figure S4A linear rate model using the Granger causality criterion to define coupling does not accurately reproduce the known physiological connectivity for a wide range of parameter choices.A, B, C, Linear Granger causality fails to recover the known physiological connectivity for all four data sets. Horizontal scatter is for visualization only. The GC scores are not the same as in D, Granger causality scores for the nonlinear point process model  and the linear rate model  are correlated ((TIF)Click here for additional data file.Text S1Additional methods. We present detailed information about methods on evaluating goodness-of-fit for point process models and linear rate models as well as how to quantify the uncertainty in the coupling strength estimates.(PDF)Click here for additional data file.


The current case report presents an account of a unique surgical procedure performed to remove an extremely rare occurrence of a transsacral abdominal mass from a 24-year-old female. The patient presented with subtle sacrococcygeal pain for two months and a presacral abdominal mass derived from the right ovary. The mass was misdiagnosed as a presacral tumor based on the results of magnetic resonance imaging (MRI) performed prior to the surgery. The patient also exhibited the symptoms commonly caused by a presacral mass, however, during the surgery, the mass was not initially located under the sacrum. An ultrasound examination and an analysis of an intraoperative frozen section indicated that the mass was a mature cystic teratoma (MCT) of the ovary, located in the peritoneal cavity between the rectum and uterus. The mass was successfully resected and removed from the affected ovary through the abdominal cavity via the sacral region. A pathological examination of the tumor section confirmed a diagnosis of a MCT of the ovary. Mature cystic teratomas (MCTs) are germ cell tumors that commonly occur in the ovaries, accounting for 10% of all ovarian tumors . TranssaA 24-year-old Mongolian female was referred to the outpatient clinic at Huashan Hospital  with mild sacrococcygeal pain that had been present for two months with no obvious causation. The individual had no fever, abdominal pain or changes in habitual normal bowel movement and urination. Magnetic resonance imaging (MRI) of the patient’s pelvis revealed a 53×40×33-mm pelvic mass, posterior to the sacrum and at the right side of the rectum . An enhaUpon admission to the hospital, no abdominal tenderness, rebound pain or palpable masses were identified by a physical examination. The initial rectal exam detected the inferior end of a non-mobile, non-tender and smooth-edged mass attached to the right posterior side of the rectum.The patient was scheduled for surgical treatment under general anesthesia two days after admission. Intraoperatively, the patient was placed in the prone jack-knife position. The surgery was performed with a 10-cm incision at the inferior margin of the fifth sacrum. The coccyx and the inferior margin of the fifth sacrum were removed. The right side of the wall of the rectum was dissociated from the presacral rectum by an incision into the gorge of the sacrum. No tumor mass was located by laparotomy or the initial examination during surgery. Next, an ultrasound examination was performed and the mass was visualized at the right side of rectum, posterior to the uterus. The right ovary was not located at its normal anatomical site. The peritoneum was opened and the enlarged right ovary was identified as a presacral mass with an irregular appearance. The mass had a diameter of 5 cm and its outer boundary was smooth with no feeding vessel or ligamentous connection to the surrounding organs, including the rectum, uterus, cervix and vagina. The sacrum and coccyx were unaffected . The lefThe right ovary tumor mass was resected and removed from the normal ovarian tissue by a gynecologist. The examination of an intraoperative frozen section indicated a possible diagnosis of a presacral MCT (dermoid cyst). A further pathological examination confirmed the diagnosis of a presacral MCT. The patient recovered uneventfully and was discharged 6 days after the surgery. Written informed consent was obtained from the patient for the publication of this case report.Teratomas arise mainly from totipotential gonadal cells, and while a number of them may have malignant potential, the majority are benign at the time of identification. The cause of teratomas remains unknown . These mAn accurate diagnosis of a presacral mass prior to surgical removal is extremely important. A number of standard diagnostic imaging methods, including ultrasonography , computed tomography (CT) and MRI, are readily available and must be used to generate a detailed image of the mass to facilitate pre-operative planning ,9. AlthoRadiological visualization techniques, including CT and MRI, enable us to generate a pre-operative diagnosis for presacral tumors ,12. CT sIn the current patient, MRI showed the fat-like and soft tissue components within the presacral mass. Since a diagnosis of a primary presacral MCT could not be ruled out, particularly due to the patient’s medical history and physical examination results, the transsacral approach was selected for the surgery.Surgical resection is the standard curative treatment for the majority of MCTs. For patients who are otherwise healthy and whose lesions appear resectable, the management of such lesions is almost always surgical resection. The approach depends on the nature and location of the lesion. Previous studies have reported that lesions which do not extend below S4 should be resected transabdominally, while lesions below S4 and <8 cm in diameter should be resected transsacrally and larger lesions or those in an intermediate position may require a combined abdominal and sacral surgical procedure ,16. AccoThe final diagnosis for this patient was a transsacral ovarian MCT in the presacral region. Ovarian MCTs mostly occur in the pelvic cavity and are commonly observed in the pouch of Douglas and occasionally in the uterosacral ligament . In rareIn addition, the most frequent symptom of MCT is lower abdominal pain , while tPerforming a laparotomy with an uncertain target and an unplanned surgical approach must be prevented to limit surgery-related trauma and complications. During transsacral surgery to remove a presacral mass, ultrasonography must be performed if the mass cannot be located initially.In the present case, no mass was located by the laparotomy or initial examination during the surgery. An ultrasound examination was performed, confirming that the mass was at the right side of rectum, posterior to the uterus and embedded in the predicted right ovary, which was not at its normal anatomical location. Since the tumor was covered by normal smooth ovarian tissue and had no adhesion to other organs and as the patient was in the prone jack-knife position, the likelihood that the tumor may have moved and descended into the abdominal cavity must be considered.We resected and dissociated the tumor mass from the ovary through the sacral region and returned the ovary to the normal anatomical location. The organs posterior to the bladder were exposed in a clear view to avoid injuring the ureters. As MCTs are curable in the majority of cases by a complete surgical resection of the tumor alone , and as Ovarian MCTs commonly occur in the pelvic cavity and are rarely identified in the presacral position. In cases where ultrasound and radiological examinations strongly indicate the presence of a teratoma, a diagnosis of a prescacral ovarian MCT must be taken into consideration.Pre-operative ultrasound and radiographic examinations must be performed in the supine and prone positions to assess the firmness and mobility of the presacral mass, as well as its derivation and relationship to the surroundings. Based on these efforts, the most efficient and least trauma-inflicting surgical approach must be determined prior to surgery.To dissect a transsacral abdominal mass, an ultrasound examination must be performed during the surgery to assist in the identification of the mass if it is not firmly attached, movable or is not able to be located directly during surgery initially. If the transsacral laparotomy fails to locate the mass, an additional surgical procedure, such as a transparietal laparotomy, should be performed. To assess a transsacral mass, it is feasible that, during transsacral abdominal surgery, the peritoneum is opened from the pelvic floor. By contrast, for a common MCT that may be curatively managed by complete surgical resection transabdomenally, the transsacral approach is unnecessary and may be relatively more traumatic.


The spread of infectious diseases at the global scale is mediated by long-range human travel. Our ability to predict the impact of an outbreak on human health requires understanding the spatiotemporal signature of early-time spreading from a specific location. Here, we show that network topology, geography, traffic structure and individual mobility patterns are all essential for accurate predictions of disease spreading. Specifically, we study contagion dynamics through the air transportation network by means of a stochastic agent-tracking model that accounts for the spatial distribution of airports, detailed air traffic and the correlated nature of mobility patterns and waiting-time distributions of individual agents. From the simulation results and the empirical air-travel data, we formulate a metric of influential spreading––the geographic spreading centrality––which accounts for spatial organization and the hierarchical structure of the network traffic, and provides an accurate measure of the early-time spreading power of individual nodes. The study of complex systems as networks has revolutionized many disciplines in physics and the social and natural sciences Network models of epidemic spreading have rationalized our understanding of how diseases propagate through a mobile interactome like the human population. “Fermionic” models regard each node as an individual, or a perfectly homogeneous community. In these models, the epidemic threshold for disease spreading vanishes in (infinite-size) scale-free networks, owing to the broad degree distribution It has been shown recently that advection-driven transport, or bias, in complex networks exerts a fundamental control on agent spreading Given that epidemic spreading is mediated by human travel, and that individual human mobility is far from being random price of anarchy in transportation networks The impact of behavioral changes on the invasion threshold and global attack have recently been analyzed in the context of an SIR infection model Taken together, these previous results reflect an emphasis on the asymptotic late-time behavior of contagion processes, typically characterized by infection thresholds and the fraction of infected nodes for both “fermionic” a priori, the early-time spreading potential of individual nodes, as measured by the total square displacement of infected agents. We find that existing metrics of influential spreading––including connectivity In this article, we present a new metric to identify and rank influential spreaders of infectious diseases in human transportation networks. Our metapopulation model of contagion dynamics is based on a time-resolved stochastic description of individual agent mobility through the air transportation system. The model is traffic-driven, and agents traverse the network following empirical stochastic rules that reflect the patterns of individual human mobility www.faa.gov) that includes all flights from all domestic and international airlines with at least one origin or destination inside the US (including Alaska and Hawaii), for the period between January 2007 and July 2010. Note that we do not have traffic information about flights whose origin and destination is outside the US. The air transportation network is a space-embedded network with 1833 airports, or nodes, and approximately 50,000 connections, or directed links , and neglect the large heterogeneity in waiting times. We will see that resolving these spatio-temporal processes, while not critical for late-time measures of disease spreading, is essential in the early-time contagion dynamics.To study the dynamics of disease spreading through the air transportation network, we use the Susceptible–Infected–Recovered (SIR) contagion model. This model divides each subpopulation into a number of healthy  fraction of the population affected by the outbreak, as our measure of the incidence of the epidemic. We initialize the disease with a small number of infected individuals randomly chosen from the whole population. We obtain representative statistics by performing a Monte Carlo study and averaging over many realizations.We apply the SIR contagion model to the four reference models described above and to our empirical mobility model. We employ the delay in the epidemic threshold with respect to Models 3 and 4, indicating the nontrivial dependence of contagion dynamics on human mobility.We find that the global attack is quite sensitive to the degree of fidelity of the metapopulation mobility model, especially in the range of low reproductive numbers . NaturalFinding measures of power and centrality of individuals has been a primary interest of network science late-time behavior––which has been studied more extensively––and the early-time dynamics, for which much less is known. We show that the two behaviors are controlled by different mechanisms and, as a result, require different measures of spreading.Here we revisit what is meant by spreading, and make a crucial distinction between the asymptotic We perform numerical simulations of epidemic spreading in our model by initializing the SIR compartmental model with infectious individuals at one single subpopulation. We compare the asymptotic, late-time spreading ability of different subpopulations by means of the global attack of the SIR epidemic a. We stLate-time measures of spreading, such as the asymptotic global attack, cannot capture the details of early-time contagion dynamics. The vigor of initial spreading, however, is likely the crucial aspect in the assessment and implementation of remedial action for highly contagious diseases total square displacement (TSD) of the infected agents,The natural measure of physical spreading is the We compare the TSD for 40 major airports in the US, 10 days after the infection starts at each of those airports, and a reproductive number We rank all 40 airports according to their TSD at early times. The curve of ordinal ranking vs. normalized TSD is markedly concave, indicating that only a handful of airports are very good spreaders . The lisWe perform a sensitivity analysis with respect to the reproductive number, measured . Clearlyer  days b.both the largest degree and the largest traffic, yet it comes in 8th place, with an early-time spreading power as low as It is instructive to compare the TSD-ranking curve with the rankings provided by existing metrics of centrality and influential spreading, including the normalized degree spreader a,b. Betspreader c. For enetworks employ the same empirical model, but modify specific aspects of the network to test whether they have an important bearing on early-time spreading. Null network 1 has the same degree and traffic distributions as the original air transportation network, but changes the geographical information by randomizing the identity of the nodes. In null network 2, we eliminate the traffic quenched disorder by homogenizing outgoing probabilities across the nodes’ links, but preserving the position of the nodes. We apply the same mobility and epidemic models and we rank the same airports according to TSD. We find that these rankings are always, for each realization of the null networks, profoundly dissimilar to that of the original network . The choice of destination from a given origin is random, weighted by traffic from the origin-destination database. A constant time step pendence b, suggegeographic spreading centrality (GSC). We express the vector of airport spreading centralities spreading strengthsIn the light of these observations, we propose a new metric to characterize the ability of an airport to spread an infection spatially at early times, the local measure that accounts for the node’s traffic, degree, and spatial scale of influence. The overall spreading ability of a node, however, must reflect the spreading strength of its neighbors, its neighbors’ neighbors, and so on. This notion has led to the classical understanding of the centrality of a node as a generalized eigenvalue problem a priori metric of influential early-time spreaders.We compare the airport rankings predicted by GSC with those obtained from the model simulations, and find excellent quantitative agreement , suggestTo quantify the correlation between the ranking provided by the TSD and the centrality measures, we use the Kendall tau  would have the largest spreading ability. This is clearly not the case, as it ranks 8th in terms of spreading power. The reason is that much of that traffic is of regional nature, within North America, and that many of the connected airports are not, themselves, strong spreaders. The GSC metric allows for a rationalization of the surprising fact that an airport like Honolulu (HNL) ranks third in early-time spreading, very close to JFK and LAX. Despite having a relatively low connectivity a and toCharacterizing the early-time behavior of epidemic spreading is critical to inform decisions during public-health emergencies, and to design regulations aimed at mitigating global pandemics. Here, we show that subpopulations that act as powerful spreaders of infectious diseases at early times––identified by the TSD during the first 10 days of the contagion–– differ significantly from the central spreaders in terms of the late-time global attack.Simulating the infectious dynamics during the initial stages of spreading requires a modeling framework in which transport and contagion processes are time-resolved. We develop a stochastic-agent mobility model through the air transportation network that relies on 3 years of US-centric air travel data and four months of individual travel itineraries. We use this database to build empirical distributions of waiting times at connecting airports and final destinations, and train the model to reproduce the recurrent mobility patterns of individuals. Our analysis demonstrates that the detailed spatiotemporal signatures of individual mobility patterns collectively impact epidemic spreading, especially in the range of low reproductive numbers.Existing metrics of influential spreaders in networks were not designed to characterize the early-time spreading behavior. Here we propose a new metric, the geographic spreading centrality, which accounts for the local strength in terms of the node’s traffic, degree and spatial scale of influence, as well as its global role within the network by incorporating the strength of its neighbors. This metric is able to successfully rank influential spreaders at early times, as evidenced by the agreement between the metric’s prediction and detailed Monte Carlo simulations. The geographic spreading centrality opens the door to the quantitative understanding of spreading dynamics on other networks embedded in space, in which topology alone is insufficient to fully characterize the system


Age estimates, typically determined by counting periodic growth increments in calcified structures of vertebrates, are the basis of population dynamics models used for managing exploited or threatened species. In fisheries research, the use of otolith growth rings as an indicator of fish age has increased considerably in recent decades. However, otolith readings include various sources of uncertainty. Current ageing methods, which converts an average count of rings into age, only provide periodic age estimates in which the range of uncertainty is fully ignored. In this study, we describe a hierarchical model for estimating individual ages from repeated otolith readings. The model was developed within a Bayesian framework to explicitly represent the sources of uncertainty associated with age estimation, to allow for individual variations and to include knowledge on parameters from expertise. The performance of the proposed model was examined through simulations, and then it was coupled to a two-stanza somatic growth model to evaluate the impact of the age estimation method on the age composition of commercial fisheries catches. We illustrate our approach using the saggital otoliths of yellowfin tuna of the Indian Ocean collected through large-scale mark-recapture experiments. The simulation performance suggested that the ageing error model was able to estimate the ageing biases and provide accurate age estimates, regardless of the age of the fish. Coupled with the growth model, this approach appeared suitable for modeling the growth of Indian Ocean yellowfin and is consistent with findings of previous studies. The simulations showed that the choice of the ageing method can strongly affect growth estimates with subsequent implications for age-structured data used as inputs for population models. Finally, our modeling approach revealed particularly useful to reflect uncertainty around age estimates into the process of growth estimation and it can be applied to any study relying on age estimation. Estimating individual age of living beings is fundamental in ecological studies. For some time, growth, survival and reproductive characteristics have been shown to be age-dependent as ageing generally conducts to decreasing fertility and increasing mortality with advancing age Age estimates are generally based on counts of periodic growth increments deposited in the hard and skeletal tissues of vertebrates that are capable of recording some biological phenomena, e.g. mammalian teeth, tortoise shell, bird feather, insect cuticle, bivalve shell, and coral skeleton However, otolith reading involves some interpretation by the reader which can lead to imprecision and bias in age estimation Multiple independent readings of the same otolith have often been used to estimate the consistency and reproducibility of reading method, to compare the readers skill and to assess the imprecision in age estimates Thunnus albacares, Bonnaterre 1788) is an epipelagic species that is widely distributed in the tropical and subtropical waters of the major oceans. It has been commercially harvested since the early 1950s Yellowfin tuna , a large scale mark-recapture program, and the West Sumatra Tuna Tagging Project (WSTTP), a simple capture program, that were carried out by the IOTC. The tagging operations of RTTP were conducted between 2005–2007 on two pole-and-line vessels that were chartered to operate in the Western Indian Ocean. Field operations consisted in catching tunas, tagging them on a vinyl-covered cradle, measuring their fork length . The WSTTP program was conducted in August 2007 on a pole-and-line vessel in the off western Indonesia. The fork length of fish measured either in a marked cradle or using a calipers, and the precisely date and geographic location were recorded for each fish caught.Sagittal otoliths were collected from 128 yellowfin recovered in the Western Indian Ocean through the RTTP, including 124 OTC-tagged fish, and measuring between 43 to 85 cm 46.6 cm  , Fig. S1Yellowfin have fragile, thin and elliptical otoliths that require particular care during their preparation and subsequent interpretation of microstructural features Otoliths collected from OTC-tagged fishes, an increment count was made for different otolith sections: (i) between the nucleus and the OTC mark , which allows for a smooth transition between two different growth rate coefficients  was used to compare the marginal posterior distributions of each parameter with its actual value and evaluate the accuracy of models fits. Accuracy is related to the similarity between the marginal modes and the true values, while precision refers to the uncertainty around the modal value, i.e. the discrepancies between the samples generated by the MCMC simulation. The MSE is a measure of the average of the square of errors and was calculated as follows:In addition, the mean relative error (MRE) was used to give information on the bias in parameter estimates:Finally, an age-length key was derived from both the coupled model and the classical model and was used to convert the catch-at-size into catch-at-age. The two conversions were then compared. For this example, we used 2010 IOTC catch data from French purse seiners in the Western Indian Ocean.The fit of the ageing model to the simulated data showed good agreement between the estimated and simulated age, as well as a strong ability to estimate the increment deposition rate ibutions . The modSimulations that focus on the number of readings showed that the ageing error model provided accurate age estimates, with accuracy increasing with the number of readings . The RMSRegardless of the number of readings, the ageing error model estimated age more accurately than the traditional method. The RMSE values obtained with the ageing error model were significantly lower than those obtained with the traditional method , it has recently been demonstrated that the assumption of a daily deposition rate is not met. As such, although the use of the traditional method for this species is internationally accepted, its use has resulted in a significant overestimation of fish age with important implications for estimates of key biological parameters The traditional ageing method, which converts an average count of increments into age, depends on the assumption that each increment corresponds to one day It is also necessary to assess the precision of the ageing technique The model evaluation by simulation suggested that it was capable of estimating both the ageing biases and an accurate age regardless of the age of the fish. The mean and variance of the deposit bias were consistently well-estimated. The model performed less well in estimating the bias linked to the misinterpretation of the marginal rings. However, edge level errors are partly estimated by the overall reading error. For yellowfin, expert judgement suggests that the nucleus and edge biases could be very low, i.e. less than five increments, resulting in error ranging from 4% for a six month-old fish to 0.4% for a five year-old fish. As such, these biases would become negligible when the age in days values are converted to age in year values. Thus, the intermediate method in which the average number of increments is corrected for the actual deposit frequency provided age estimates as accurately as the ageing error model. However, the range of uncertainty is fully ignored which may have serious implications on the final outcome of the analysis The method developed in this study appears suitable for modeling the growth of Indian Ocean yellowfin. The fit of the growth model appeared to be adequate in the first stanza. In this first phase, the expected first growth rate, 2.11 cm.monthEstimating the growth parameters with the growth model coupled with the ageing error model was more accurate, but less precise, than with the classical growth model. This latter used the traditional ageing method which underestimated the “actual” age of fish and resulted in a consistent overestimation in the growth curve. In contrast, taking into account uncertainties associated with age estimates led to a growth curve that fitted very closely to the simulated data in the first stanza. Such differences greatly influenced the age distributions of the fisheries catches. In addition, by disregarding the ageing errors, the classical model was overconfident in determining uncertainties of growth estimates. It is critical to represent the full range of uncertainty to adequately evaluate management alternatives and the expected consequences of decision-making However, there are some technical difficulties associated with the use of Bayesian inferences. One of the main criticisms is the need to specify prior distributions, which can have some influence on the parameter posterior distributions . Commonly, sensitivity analyses are performed to evaluate the effect of prior change on the posterior. As increasing data quality and quantity reduces the prior influence. Therefore, these sensitivity analyses are above all an assessment tool for appreciate the amount of information in the data The general approach developed in this study can be applied to other species and can be adapted to any structure that produces periodic growth increments in response to various ecological issues.Figure S1Tagging area (gray-colored) and points of tag recovery (circles) of RTTP program and sampling area of WSTTP program (square).(TIF)Click here for additional data file.Figure S2Simulation framework for testing the ageing error model. Different sources of uncertainty are added to simulated ages to randomly generate noisy increments that were then used as inputs in the model.(TIF)Click here for additional data file.Figure S3Marginal posterior distributions of the ageing error model parameters (black) compared with the simulated values (grey). Two alternatives were considered, with (right) and without (left) individual variability in increment formation; A, B and C represents the first, second and third simulated data set respectively.(TIFF)Click here for additional data file.Figure S4Boxplot of the RMSE values obtained with the ageing error model for different number of otolith readings. a, b and c represents the first, second and third simulated data set respectively.(TIFF)Click here for additional data file.Figure S5Boxplot of RMSE values obtained with the ageing error model, the traditional method and the intermediate method for different number of otolith readings. a, b and c represents the first, second and third simulated data set respectively.(TIFF)Click here for additional data file.Figure S6Marginal posterior distributions of the parameters of the growth model coupled with the ageing error model (black). The grey curves correspond to the prior distributions.(TIFF)Click here for additional data file.Table S1Summarize of data used in ageing error model. A: fish for which the time-at-liberty is known, B: fish for which the time-at-liberty is unknown, (DOC)Click here for additional data file.Table S2Comparison of RMSE values obtained with the ageing error model for different number of otolith readings using a Wilcoxon test. 2 L, 3 L, 4 L and 5 L correspond to the number of readings of the same otolith; a, b and c represents the first, second and third simulated data set respectively.(DOC)Click here for additional data file.Table S3Comparison of the RMSE values obtained with the ageing error model, the traditional method and the intermediate method for different number of otolith readings using a Wilcoxon test. 2 L, 3 L, 4 L and 5 L correspond to the number of readings of the same otolith; a, b and c represents the first, second and third simulated data set respectively.(DOC)Click here for additional data file.Table S4Correlation-covariance matrix of growth parameters. Numerals in bold represent the covariances.(DOC)Click here for additional data file.