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5,805 results for “Data model”
Data from: Convergence analysis of a finite element skull model of Herpestes javanicus (Carnivora, Mammalia): implications for robust comparative inferences of biomechanical function
Predictions of skull biomechanical capability based on virtual models constitute a valuable data source for testing hypotheses about craniodental form and feeding behavior. Such comparative analyses also inform dietary reconstruction in extinct species. 3D modeling using Finite Element (FE) methods is a common technique applied to the comparative analysis of craniodental function in extinct and extant vertebrates. However, taxonomically diverse skull models in the literature often are not directly comparable to each other, in part because of distinctions in how boundary conditions are defined, but also because of substantial differences in the number of FEs composing the models. In this study, we test whether a conventional convergence test is adequate in identifying the minimum number of FEs needed to achieve internally stable results for a single species. We constructed a series of skull models of Herpestes javanicus, and simulated unilateral biting across the dentition; the models differed in the number of FEs, degrees of freedom at the joint and bite point constraints, and type of tetrahedral FEs used. We found that convergence patterns differed across constraint types, FE quantities, and bite position simulated. Four-noded tetrahedral (tet-4) FE models with relaxed constraints produced the most stable measurements compared to over-constrained tet-4 models and to relaxed tet-10 models. In absence of an optimal FE quantity from convergence testing, we propose a broadly applicable sub-sampling protocol, whereby average measurement values across multiple models per specimen are used for among-species comparisons. A regime of sampling three low FE quantity models produced the closest estimates of mean measurement values relative to larger model sets, being within the 95% bootstrap estimated confidence intervals. Future studies should focus on identifying sources of variation associated with other FE modeling protocols, so that they can be accounted for before biomechanical attributes from these simulations are used to infer form–function linkage.
Data from: The relative importance of modeling site pattern heterogeneity versus partition-wise heterotachy in phylogenomic inference
Large taxa-rich genome-scale data sets are often necessary for resolving ancient phylogenetic relationships. But accurate phylogenetic inference requires that they are analyzed with realistic models that account for the heterogeneity in substitution patterns amongst the sites, genes and lineages. Two kinds of adjustments are frequently used: models that account for heterogeneity in amino acid frequencies at sites in proteins, and partitioned models that accommodate the heterogeneity in rates (branch lengths) among different proteins in different lineages (protein-wise heterotachy). Although partitioned and site-heterogeneous models are both widely used in isolation, their relative importance to the inference of correct phylogenies has not been carefully evaluated. We conducted several empirical analyses and a large set of simulations to compare the relative performances of partitioned models, site-heterogeneous models and combined partitioned site heterogeneous models. In general, site-homogeneous models (partitioned or not) performed worse than site heterogeneous, except in simulations with extreme protein-wise heterotachy. Furthermore, simulations using empirically-derived realistic parameter settings showed a marked long-branch attraction (LBA) problem for analyses employing protein-wise partitioning even when the generating model included partitioning. This LBA problem results from a small sample bias compounded over many single protein alignments. In some cases, this problem was ameliorated by clustering similarly-evolving proteins together into larger partitions using the PartitionFinder method. Similar results were obtained under simulations with larger numbers of taxa or heterogeneity in simulating topologies over genes. For an empirical Microsporidia test data set, all but one tested site-heterogeneous models (with or without partitioning) obtain the correct Microsporidia+Fungi grouping, whereas site-homogenous models (with or without partitioning) did not. The single exception was the fully partitioned site-heterogeneous analysis that succumbed to the compounded small sample LBA bias. In general unless protein-wise heterotachy effects are extreme, it is more important to model site-heterogeneity than protein-wise heterotachy in phylogenomic analyses. Complete protein-wise partitioning should be avoided as it can lead to a serious LBA bias. In cases of extreme protein-wise heterotachy, approaches that cluster similarly-evolving proteins together and coupled with site-heterogeneous models work well for phylogenetic estimation.
Data from: Space-use behavior of woodland caribou based on a cognitive movement model
1. Movement patterns offer a rich source of information on animal behaviour and the ecological significance of landscape attributes. This is especially useful for species occupying remote landscapes where direct behavioural observations are limited. In this study, we fit a mechanistic model of animal cognition and movement to GPS positional data of woodland caribou (Rangifer tarandus caribou; Gmelin 1788) collected over a wide range of ecological conditions. 2. The model explicitly tracks individual animal informational state over space and time, with resulting parameter estimates that have direct cognitive and ecological meaning. Three biotic landscape attributes were hypothesized to motivate caribou movement: forage abundance (dietary digestible biomass), wolf (Canis lupus; Linnaeus, 1758) density and moose (Alces alces; Linnaeus, 1758) habitat. Wolves are the main predator of caribou in this system and moose are their primary prey. 3. Resulting parameter estimates clearly indicated that forage abundance is an important driver of caribou movement patterns, with predator and moose avoidance often having a strong effect, but not for all individuals. From the cognitive perspective, our results support the notion that caribou rely on limited sensory inputs from their surroundings, as well as on long-term spatial memory, to make informed movement decisions. Our study demonstrates how sensory, memory and motion capacities may interact with ecological fitness covariates to influence movement decisions by free-ranging animals.
Data from: Spatially explicit models of divergence and genome hitchhiking
Strong barriers to genetic exchange can exist at divergently selected loci, whereas alleles at neutral loci flow more readily between populations, thus impeding divergence and speciation in the face of gene flow. However, 'divergence hitchhiking' theory posits that divergent selection can generate large regions of differentiation around selected loci. 'Genome hitchhiking' theory suggests that selection can also cause reductions in average genome-wide rates of gene flow, resulting in widespread genomic divergence (rather than divergence only around specific selected loci). Spatial heterogeneity is ubiquitous in nature, yet previous models of genetic barriers to gene flow have explored limited combinations of spatial and selective scenarios. Using simulations of secondary contact of populations, we explore barriers to gene flow in various selective and spatial contexts in continuous, two-dimensional, spatially explicit environments. In general, effects of hitchhiking are strongest in environments with regular spatial patterning of starkly divergent habitat types. When divergent selection is very strong, the absence of intermediate habitat types increases the effects of hitchhiking. However, when selection is moderate or weak, regular (versus random) spatial arrangement of habitat types becomes more important than the presence of intermediate habitats per se. We also document counterintuitive processes arising from the stochastic interplay of selection, gene flow, and drift. Our results indicate that generalization of results from two-deme models requires caution and increase understanding of the genomic and geographic basis of population divergence.
Data from: Magnetic resonance imaging and tensor-based morphometry in the MPTP non-human primate model of Parkinson's disease
Parkinson's disease (PD) is the second most common neurodegenerative disorder producing a variety of motor and cognitive deficits with the causes remaining largely unknown. The gradual loss of the nigrostriatal pathway is currently considered the pivotal pathological event. To better understand the progression of PD and improve treatment management, defining the disease on a structural basis and expanding brain analysis to extra-nigral structures is indispensable. The anatomical complexity and the presence of neuromelanin, make the use of non-human primates an essential element in developing putative imaging biomarkers of PD. To this end, ex vivo T2-weighted magnetic resonance images were acquired from control and 1-methyl-4 phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated marmosets. Volume measurements of the caudate, putamen, and substantia nigra indicated significant atrophy and cortical thinning. Tensor-based morphometry provided a more extensive and hypothesis free assessment of widespread changes caused by the toxin insult to the brain, especially highlighting regional cortical atrophy. The results highlight the importance of developing imaging biomarkers of PD in non-human primate models considering their distinct neuroanatomy. It is essential to further develop these biomarkers in vivo to provide non-invasive tools to detect pre-symptomatic PD and to monitor potential disease altering therapeutics.
Data from: A stochastic model for annual reproductive success
Demographic stochasticity can have large effects on the dynamics of small populations as well as on the persistence of rare genotypes and lineages. Survival is sensibly modeled as a binomial process, but annual reproductive success (ARS) is more complex and general models for demographic stochasticity do not exist. Here we introduce a stochastic model framework for ARS and illustrate some of its properties. We model a sequence of stochastic events: nest completion, the number of eggs or neonates produced, nest predation, and the survival of individual offspring to independence. We also allow multiple nesting attempts within a breeding season. Most of these components can be described by Bernoulli or binomial processes; the exception is the distribution of offspring number. Using clutch and litter size distributions from 53 vertebrate species, we demonstrate that among‐individual variability in offspring number can usually be described by the generalized Poisson distribution. Our model framework allows the demographic variance to be calculated from underlying biological processes and can easily be linked to models of environmental stochasticity or selection because of its parametric structure. In addition, it reveals that the distributions of ARS are often multimodal and skewed, with implications for extinction risk and evolution in small populations.
Data from: Assessing parameter identifiability in phylogenetic models using Data Cloning
The success of model-based methods in phylogenetics has motivated much research aimed at generating new, biologically informative models. This new computer-intensive approaches to phylogenetics demands validation studies and sound measures of performance. To date such work has consisted only of simulation studies, estimation of known phylogenies and difficult mathematical analyses assessing the estimability of parameters. Little practical guidance has been available to practitioners and theoreticians alike as to when and why the parameters in a particular model can be identified reliably. Here, we illustrate how Data Cloning (DC), a recently developed methodology to compute the Maximum Likelihood estimates along with their asymptotic variance, can be used to diagnose structural parameter non-identifiability (NI) and distinguish it from other parameter estimability problems including the case where parameters are structurally identifiable, but are not estimable in given data set (INE), and the case where parameters are identifiable, and estimable, but only weakly so (WE). The application of the DC theorem uses well-known and widely used Bayesian computational techniques. With the DC approach, practitioners can use any Bayesian phylogenetics software to be able to diagnose non-identifiability. Theoreticians and practitioners alike now have a powerful tool to detect non-identifiability while investigating complex modeling scenarios, where getting closed-form expressions in a probabilistic study is complicated. Furthermore, here we also show how DC can be used as a tool to examine and eliminate the influence of the priors, in particular if the process of prior elicitation is not straightforward. Finally, when applied to phylogenetic inference, DC can be used to study at least two important statistical questions: assessing identifiability of discrete parameters, like the tree topology, and developing efficient sampling methods for computationally expensive posterior densities.
Data from: We happy few: using structured population models to identify the decisive events in the lives of exceptional individuals
In any population, some individuals make it big: they are among the few that produce many offspring, grow to large size, and so on. What distinguishes the lives of these happy few? We present three approaches for identifying what factors distinguish those "lucky" individuals who come to dominate reproduction in a population without fixed differences between individuals (genotype, site quality, etc.): comparing life-history trajectories for lucky and unlucky individuals and calculating the elasticity of the probability of becoming lucky to perturbations in demographic rates at a given size or a given age. As examples we consider published size-structured integral projection models for the tropical tree Dacrydium elatum and the semiarid shrub Artemisia ordosica and an age-size-structured matrix model for the tropical tree Cedrela ordosica. We find that good fortune (e.g., rapid growth) when small and young matters much more than good fortune when older and larger. Becoming lucky is primarily a matter of surviving while others die. For species with more variable growth (such as Cedrela and Ordosica), it is also a matter of growing fast. We focus on reproductive skew, but our methods are broadly applicable and can be used to investigate how individuals come to be exceptional in any aspect.
Data from: Anthracycline drugs on modified surface of quercetin-loaded polymer nanoparticles: a dual drug delivery model for cancer treatment
Polymer nanoparticles are vehicles used for delivery of hydrophobic anti-cancer drugs, like doxorubicin, paclitaxel or chemopreventors like quercetin (Q). The present study deals with the synthesis and characterisation of nano formulations (NFs) from Q loaded PLGA (poly lactic-co-glycolic acid) nano particles (NPs) by surface modification. The surface of Q-loaded (NPs) is modified by coating with biopolymers like bovine serum albumin (BSA) or histones (His). Conventional chemotherapeutic drugs adriamycin (ADR) and mitoxantrone (MTX) are bound to BSA and His respectively before being coated on Q-loaded NPs to nano formulate NF1 and NF2 respectively. The sizes of these NFs are in the range 400-500 nm as ascertained by SEM and DLS measurements. Encapsulation of Q in polymer NPs is confirmed from shifts in FT-IR, TGA and DSC traces of Q-loaded NPs compared to native PLGA and Q. Surface modification in NFs is evidenced by three distinct regions in their TEM images; the core, polymer capsule and the coated surface. Negative zeta potential of Q-loaded NPs shifted to positive potential on surface modification in NF1 and NF2. In vitro release of Q from the NFs lasted up to twenty days with an early burst release. NF2 is better formulation than NF1 as loading of MTX is 85% compared to 23% loading of ADR. Such NFs are expected to overcome multi-drug resistance (MDR) by reaching and treating the target cancerous cells by virtue of size, charge and retention.
Data from: A new global palaeobiogeographical model for the late Mesozoic and early Tertiary
Late Mesozoic palaeobiogeography has been characterized by a distinction between the northern territories of Laurasia and the southern landmasses of Gondwana. The repeated discovery of Gondwanan lineages in Laurasia has led to the proposal of alternative scenarios to explain these anomalous occurrences. A new biogeographical model for late Mesozoic terrestrial ecosystems is here proposed, in which Europe and 'Gondwanan' territories possessed a common Eurogondwanan fauna during the earliest Cretaceous. Subsequently, following the Hauterivian, Europe severed from Africa and connected to Asiamerica resulting in a faunal interchange. This model explains the presence of 'Gondwanan' taxa in Laurasia and the absence of Laurasian forms in the southern territories during the Cretaceous. In order to test this new palaeobiogeographical model, tree reconciliation analyses (TRAs) were performed based on biogeographical signals provided by a supertree of late Mesozoic archosaurs. The TRAs found significant evidence for the presence of an earliest Cretaceous Eurogondwanan fauna, followed by a relatively short-term Gondwana-Laurasia dichotomy. The analysis recovered evidence for a biogeographical re-connection of the European territories with Africa and South America-Antarctica during the Campanian to Maastrichtian time-slice. This biogeographical scenario appears to continue through the early Tertiary and sheds light on the trans-Atlantic disjunct distributions of several extant plant and animal groups.
Data from: Quantifying and modelling decay in forecast proficiency indicates the limits of transferability in land-cover classification
1. The ability to provide reliable projections for the current and future distribution patterns of land-covers is fundamental if we wish to protect and manage our diminishing natural resources. Two inter-related revolutions made map productions feasible at unprecedented resolutions- the availability of high-resolution remotely-sensed data and the development of machine-learning algorithms. However, the ground-truth data needed for training models is in most cases spatially and temporally clustered. Therefore, map production requires extrapolation of models from one place to another and the uncertainty cost of such extrapolation is rarely explored. In other words, we focus mainly on projections, and less on quantifying how reliable they are. 2. Following the concept of 'forecast horizon', we suggest that the predictability of land-cover classification models should be methodologically explored with quantitative tools as a continuum against distances measured along multiple dimensions. Focusing on ten agricultural sites from England and using models specifically designed to predict multivariate decay-curves we ask: how does a model's predictive performance decay with distance? More specifically, we explored if we could predict the proficiency (kappa statistics) of a model trained in one site when making predictions in another site based on the spatial, temporal, spectral and environmental distances between sites. 3. We found that model proficiency decays with spatial, temporal, spectral and environmental distance between sites. More importantly, we found for the first time that it is possible to predict the performance a model transferred to or from a novel site will have, based on its distances from known sites. The spatial distance variables where the most important when predicting model transferability. 4. Exploring model transferability as a continuum may have multiple usages including predicting uncertainty values in space and time, prioritization of strategies for ground-truth data collection, and optimizing model characteristics for defined tasks.
Data from: Markovian language model of the DNA and its information content
This work proposes a Markovian memoryless model for the DNA that simplifies enormously the complexity of it. We encode nucleotide sequences into symbolic sequences, called words, from which we establish meaningful length of words and groups of words that share symbolic similarities. Interpreting a node to represent a group of similar words and edges to represent their functional connectivity allows us to construct a network of the grammatical rules governing the appearance of groups of words in the DNA. Our model allows us to predict the transition between groups of words in the DNA with unprecedented accuracy, and to easily calculate many informational quantities to better characterize the DNA. In addition, we reduce the DNA of known bacteria to a network of only tens of nodes, show how our model can be used to detect similar (or dissimilar) genes in different organisms, and which sequences of symbols are responsible for most of the information content of the DNA. Therefore, the DNA can indeed be treated as a language, a Markovian language, where a 'word' is an element of a group, and its grammar represents the rules behind the probability of transitions between any two groups.
Data from: Achieving a step change in the tuberculosis epidemic through comprehensive community-wide intervention: A model-based analysis
<p><span><span><span><span><span><span><span><span><span><span><span><b>Background:</b> Global progress towards reducing tuberculosis (TB) incidence and mortality has consistently lagged behind World Health Organization targets leading to a perception that large reductions in TB burden cannot be achieved. However, several recent and historical trials suggest that intervention efforts that are comprehensive and focused can have substantial epidemiological impact. We aimed to quantify the potential epidemiological impact of an intensive but realistic, community-wide campaign utilizing existing tools, and designed to achieve a "step change" in TB burden.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Methods:</b> We developed a compartmental model of tuberculosis transmission in a mid-sized city in India, the country with the greatest absolute burden of TB worldwide. We modeled the impact of a campaign comprising one-time community-wide screening with treatment for TB disease and preventive therapy for latent TB infection (LTBI). This one-time intervention was followed by strengthening of tuberculosis-related health system achieved by leveraging the one-time campaign. We estimated the tuberculosis cases and deaths that could be averted over 10 years using this comprehensive approach and assessed the contributions of individual components of the intervention.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results: </b>A campaign that successfully screened 70% of the adult population for active and latent tuberculosis and subsequently reduced diagnostic and treatment delays and unsuccessful treatment outcomes by 50% was projected to avert 7,800 (95% range: 5,450 – 10,200) cases and 1,710 (1,290 – 2,180) tuberculosis-related deaths per 1 million population over 10 years. Of the total averted deaths, 33.5% (28.2 – 38.3) were attributable to inclusion of preventive therapy and 52.9% (48.4 - 56.9) to health system strengthening. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusions:</b> A one-time, community-wide mass campaign, comprehensively designed to detect, treat, and prevent tuberculosis with currently existing tools can have meaningful and long-lasting epidemiological impact. Successful treatment of LTBI is critical to achieving this result. Health system strengthening is essential to any effort to transform the TB response.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Heterogeneous models place the root of the placental mammal phylogeny
Heterogeneity among life traits in mammals has resulted in considerable phylogenetic conflict, particularly concerning the position of the placental root. Layered upon this are gene- and lineage-specific variation in amino acid substitution rates and compositional biases. Life trait variations that may impact upon mutational rates are longevity, metabolic rate, body size and germ line generation time. Over the past 12 years, three main conflicting hypotheses have emerged for the placement of the placental root. These hypotheses place: the Atlantogenata (common ancestor of Xenarthra plus Afrotheria), the Afrotheria, or the Xenarthra as the sister group to all other placental mammals. Model adequacy is critical for accurate tree reconstruction and by failing to account for these compositional and character exchange heterogeneities across the tree and dataset, previous studies have not provided a strongly supported hypothesis for the placental root. For the first time, models that accommodate both tree and dataset heterogeneity have been applied to mammal data. Here we show the impact of accurate model assignment and the importance of datasets in accommodating model parameters while maintaining the power to reject competing hypotheses. Through these sophisticated methods, we demonstrate the importance of model adequacy, dataset power and provide strong support for the Atlantogenata over other competing hypotheses for the position of the placental root.
Data from: Secondary structure models of 18S and 28S rRNAs of the true bugs based on complete rDNA sequences of Eurydema maracandica Oshanin, 1871 (Heteroptera: Pentatomidae)
The sequences of 18S and 28S rDNAs have been used as molecular markers to resolve phylogenetic relationships of Heteroptera for two decades. The complete sequences of 18S rDNAs have been used in many studies, while in most studies only partial sequences of 28S rDNAs have been used due to technical difficulties of amplifying the complete lengths. In this study, we amplified the complete 18S and 28S rDNA sequences of Eurydema maracandica Oshanin, 1871, and reconstructed the secondary structure models of the corresponding rRNAs. In addition, and more importantly, all of the length variable regions of 18S rRNA were compared among 37 families of Heteroptera based on 140 sequences, and the D3 region of 28S rRNA was compared among 51 families based on 84 sequences. It was found that 8 length variable regions could potentially serve as molecular synapomorphies for some monophyletic groups. Therefore discoveries of more molecular synapomorphies for specific clades can be anticipated from amplification of complete 18S and 28S rDNAs of more representatives of Heteroptera.
Data from: Modelling the co-evolution of indirect genetic effects and inherited variability
When individuals interact, their phenotypes may be affected by genes in their social partners, a phenomenon known as Indirect Genetic Effects (IGEs). In aquaculture species and some plants, competition not only affects trait levels of individuals, but also inflates variation of trait values among individuals. Variability of trait values has been studied as a quantitative trait in itself, and is often referred to as inherited variability. Although the observed phenotypic relationship between competition and variability suggests an underlying genetic relationship, models of IGE and inherited variability do not allow for such relationship. Models of trait levels show IGEs may considerably change heritable variation in trait values. Currently, we lack the tools to investigate whether this result extends to inherited variability. Here we present a model that integrates IGEs and inherited variability. In this model, the target phenotype, say growth rate, is a function of genetic and environmental effects of the focal individual and of the difference in trait values between the social partner and the focal individual, multiplied by a regression coefficient. The regression coefficient is a genetic trait which is measure of cooperation; a negative value indicates competition, a positive value cooperation, and an increasing value due to selection indicates the evolution of cooperation. Our simulations show that the model results in increased variability of body weight with increase of competition. When competition decreases, variability becomes significantly smaller. Our findings suggest we may have been overlooking an entire level of genetic variation in variability, the one due to IGEs.
Data from: Diurnal variation in the production of vocal information about food supports a model of social adjustment in wild songbirds
Wintering songbirds have been widely shown to make economic foraging decisions to manage the changing balance of risks from predation and starvation over the course of the day. In this study, we ask whether the communication and use of information about food availability differ throughout the day. First, we assessed temporal variation in food-related vocal information produced in foraging flocks of tits (Paridae) using audio recordings at RFID-equipped feeding stations. Vocal activity was highest in the morning and decreased into the afternoon. This pattern was not explained by there being fewer birds present, as we found that group sizes increased over the course of the day. Next, we experimentally tested the underlying causes for this diurnal calling pattern. We set up bird feeders with or without playback of calls from tits, either in the morning or in the afternoon, and compared latency to feeder discovery, accumulation of flock members, and total number of birds visiting the feeder. Irrespective of time of day, playbacks had a strong effect on all three response measures when compared to silent control trials, demonstrating that tits will readily use vocal information to improve food detection throughout the day. Thus, the diurnal pattern of foraging behaviour did not appear to affect use and production of food-related vocalizations. Instead, we suggest that, as the day progresses and foraging group sizes increase, the costs of producing calls at the food source (e.g. competition and attraction of predators) outweigh the benefits of recruiting group members (i.e. adding individuals to large groups only marginally increases safety in numbers), causing the observed decrease in vocal activity into the afternoon. Our findings imply that individuals make economic social adjustments based on conditions of their social environment when deciding to vocally recruit group members.
Data from: Spermatozoa scattering by a microchannel feature: an elastohydrodynamic model
Sperm traverse their microenvironment through viscous fluid by propagating flagellar waves; the waveform emerges as a consequence of elastic structure, internal active moments and low Reynolds number fluid dynamics. Engineered microchannels have recently been proposed as a method of sorting and manipulating motile cells; the interaction of cells with these artificial environments therefore warrants investigation. A numerical method is presented for large-amplitude elastohydrodynamic interaction of active swimmers with domain features. This method is employed to examine hydrodynamic scattering by a model microchannel backstep feature. Scattering is shown to depend on backstep height and the relative strength of viscous and elastic forces in the flagellum. In a 'high viscosity' parameter regime corresponding to human sperm in cervical mucus analogue, this hydrodynamic contribution to scattering is comparable in magnitude to recent data on contact effects, being of the order of 5°–10°. Scattering can be positive or negative depending on the relative strength of viscous and elastic effects, emphasizing the importance of viscosity on the interaction of sperm with their microenvironment. The modulation of scattering angle by viscosity is associated with variations in flagellar asymmetry induced by the elastohydrodynamic interaction with the boundary feature.
Data from: Probabilistic graphical model representation in phylogenetics
Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development. Clear communication and representation of the chosen model is crucial for: (1) reproducibility of an analysis, (2) model development and (3) software design. Moreover, a unified, clear and understandable framework for model representation lowers the barrier for beginners and non-specialists to grasp complex phylogenetic models, including their assumptions and parameter/variable dependencies. Graphical modeling is a unifying framework that has gained in popularity in the statistical literature in recent years. The core idea is to break complex models into conditionally independent distributions. The strength lies in the comprehensibility, flexibility, and adaptability of this formalism, and the large body of computational work based on it. Graphical models are well-suited to teach statistical models, to facilitate communication among phylogeneticists and in the development of generic software for simulation and statistical inference. Here, we provide an introduction to graphical models for phylogeneticists and extend the standard graphical model representation to the realm of phylogenetics. We introduce a new graphical model component, tree plates, to capture the changing structure of the subgraph corresponding to a phylogenetic tree. We describe a range of phylogenetic models using the graphical model framework and introduce modules to simplify the representation of standard components in large and complex models. Phylogenetic model graphs can be readily used in simulation, maximum likelihood inference, and Bayesian inference using, for example, Metropolis-Hastings or Gibbs sampling of the posterior distribution.
Data from: Sex-related online behaviors, perceived peer norms and adolescents' experience with sexual behavior: testing an integrative model
Research on the role of sex-related Internet use in adolescents' sexual development has often isolated the Internet and online behaviors from other, offline influencing factors in adolescents' lives, such as processes in the peer domain. The aim of this study was to test an integrative model explaining how receptive (i.e., use of sexually explicit Internet material [SEIM]) and interactive (i.e., use of social networking sites [SNS]) sex-related online behaviors interrelate with perceived peer norms in predicting adolescents' experience with sexual behavior. Structural equation modeling on longitudinal data from 1,132 Dutch adolescents (Mage T1 = 13.95; range 11-17; 52.7% boys) demonstrated concurrent, direct, and indirect effects between sex-related online behaviors, perceived peer norms, and experience with sexual behavior. SEIM use (among boys) and SNS use (among boys and girls) predicted increases in adolescents' perceptions of peer approval of sexual behavior and/or in their estimates of the numbers of sexually active peers. These perceptions, in turn, predicted increases in adolescents' level of experience with sexual behavior at the end of the study. Boys' SNS use also directly predicted increased levels of experience with sexual behavior. These findings highlight the need for multisystemic research and intervention development to promote adolescents' sexual health.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.