New Caledonian crows learn the functional properties of novel tool types

New Caledonian crows were presented with Bird and Emery's (2009a) Aesop's fable paradigm, which requires stones to be dropped into a water-filled tube to bring floating food within reach. The crows did not spontaneously use stones as tools, but quickly learned to do so, and to choose objects and materials with functional properties. Some crows discarded both inefficient and non-functional objects before observing their effects on the water level. Interestingly, the crows did not learn to discriminate between functional and non-functional objects and materials when there was an arbitrary, rather than causal, link between object and reward. This finding suggests that the crows' performances were not based on associative learning alone. That is, learning was not guided solely by the covariation rate between stimuli and outcomes or the conditioned reinforcement properties acquired by functional objects. Our results, therefore, show that New Caledonian crows can process causal information not only when it is linked to sticks and stick-like tools but also when it concerns the functional properties of novel types of tool.     

Mind the gap: investigating toddlers' sensitivity to contact relations in predictive events

Toddlers readily learn predictive relations between events (e.g., that event A predicts event B). However, they intervene on A to try to cause B only in a few contexts: When a dispositional agent initiates the event or when the event is described with causal language. The current studies look at whether toddlers' failures are due merely to the difficulty of initiating interventions or to more general constraints on the kinds of events they represent as causal. Toddlers saw a block slide towards a base, but an occluder prevented them from seeing whether the block contacted the base; after the block disappeared behind the occluder, a toy connected to the base did or did not activate. We hypothesized that if toddlers construed the events as causal, they would be sensitive to the contact relations between the participants in the predictive event. In Experiment 1, the block either moved spontaneously (no dispositional agent) or emerged already in motion (a dispositional agent was potentially present). Toddlers were sensitive to the contact relations only when a dispositional agent was potentially present. Experiment 2 confirmed that toddlers inferred a hidden agent was present when the block emerged in motion. In Experiment 3, the block moved spontaneously, but the events were described either with non-causal ("here's my block") or causal ("the block can make it go") language. Toddlers were sensitive to the contact relations only when given causal language. These findings suggest that dispositional agency and causal language facilitate toddlers' ability to represent causal relationships.     

Exploring causal networks of bovine milk fatty acids in a multivariate mixed model context

Background

Knowledge regarding causal relationships among traits is important to understand complex biological systems. Structural equation models (SEM) can be used to quantify the causal relations between traits, which allow prediction of outcomes to interventions applied to such a network. Such models are fitted conditionally on a causal structure among traits, represented by a directed acyclic graph and an Inductive Causation (IC) algorithm can be used to search for causal structures. The aim of this study was to explore the space of causal structures involving bovine milk fatty acids and to select a network supported by data as the structure of a SEM.

Results

The IC algorithm adapted to mixed models settings was applied to study 14 correlated bovine milk fatty acids, resulting in an undirected network. The undirected pathway from C4:0 to C12:0 resembled the de novo synthesis pathway of short and medium chain saturated fatty acids. By using prior knowledge, directions were assigned to that part of the network and the resulting structure was used to fit a SEM that led to structural coefficients ranging from 0.85 to 1.05. The deviance information criterion indicated that the SEM was more plausible than the multi-trait model.

Conclusions

The IC algorithm output pointed towards causal relations between the studied traits. This changed the focus from marginal associations between traits to direct relationships, thus towards relationships that may result in changes when external interventions are applied. The causal structure can give more insight into underlying mechanisms and the SEM can predict conditional changes due to such interventions.     

What Can Causal Networks Tell Us about Metabolic Pathways?

Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: “What can causal networks tell us about metabolic pathways?”. Using data from an Arabidopsis BaySha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies.     

Evaluating correlation-based metric for surrogate marker qualification within a causal correlation framework

Biomarkers play an increasing role in the clinical development of new therapeutics. Earlier clinical decisions facilitated by biomarkers can lead to reduced costs and duration of drug development. Associations between biomarkers and clinical endpoints are often viewed as initial evidence supporting the intended purpose. As a result, even though it is widely understood that correlation is not proof of a causal relationship, correlation continues to be used as a metric for biomarker qualification in practice. In this article, we introduce a causal correlation framework where two different types of correlations are defined at the individual level. We show that the correlation estimate is a composite of different components, and needs to be interpreted with caution when used for biomarker qualification to avoid misleading conclusions. Otherwise, a significant correlation can be concluded even in the absence of a true underlying association. We also show how the causal quantities of interest are testable in a crossover design and provide discussion on the challenges that exist in a parallel group setting.     

Getting rid of interventions

According to James Woodward’s influential interventionist account of causation, X is a cause of Y iff, roughly, there is a possible intervention on X that changes Y. Woodward requires that interventions be merely logically possible. I will argue for two claims against this modal character of interventions: First, merely logically possible interventions are dispensable for the semantic project of providing an account of the meaning of causal statements. If interventions are indeed dispensable, the interventionist theory collapses into (some sort of) a counterfactual theory of causation. Thus, the interventionist theory is not tenable as a theory of causation in its own right. Second, if one maintains that merely logically possible interventions are indispensable, then interventions with this modal character lead to the fatal result that interventionist counterfactuals are evaluated inadequately. Consequently, interventionists offer an inadequate theory of causation. I suggest that if we are concerned with explicating causal concepts and stating the truth-conditions of causal claims we best get rid of Woodwardian interventions.     

Biomechanical imaging of cell stiffness and prestress with subcellular resolution

Knowledge of cell mechanical properties, such as elastic modulus, is essential to understanding the mechanisms by which cells carry out many integrated functions in health and disease. Cellular stiffness is regulated by the composition, structural organization, and indigenous mechanical stress (or prestress) borne by the cytoskeleton. Current methods for measuring stiffness and cytoskeletal prestress of living cells necessitate either limited spatial resolution but with high speed, or spatial maps of the entire cell at the expense of long imaging times. We have developed a novel technique, called biomechanical imaging, for generating maps of both cellular stiffness and prestress that requires less than 30 s of interrogation time, but which provides subcellular spatial resolution. The technique is based on the ability to measure tractions applied to the cell while simultaneously observing cell deformation, combined with capability to solve an elastic inverse problem to find cell stiffness and prestress distributions. We demonstrated the application of this technique by carrying out detailed mapping of the shear modulus and cytoskeletal prestress distributions of 3T3 fibroblasts, making no assumptions regarding those distributions or the correlation between them. We also showed that on the whole cell level, the average shear modulus is closely associated with the average prestress, which is consistent with the data from the literature. Data collection is a straightforward procedure that lends itself to other biochemical/biomechanical interventions. Biomechanical imaging thus offers a new tool that can be used in studies of cell biomechanics and mechanobiology where fast imaging of cell properties and prestress is desired at subcellular resolution.     

Reconstruction of gene networks using Bayesian learning and manipulation experiments

MOTIVATION: The analysis of high-throughput experimental data, for example from microarray experiments, is currently seen as a promising way of finding regulatory relationships between genes. Bayesian networks have been suggested for learning gene regulatory networks from observational data. Not all causal relationships can be inferred from correlation data alone. Often several equivalent but different directed graphs explain the data equally well. Intervention experiments where genes are manipulated can help to narrow down the range of possible networks. RESULTS: We describe an active learning algorithm that suggests an optimized sequence of intervention experiments. Simulation experiments show that our selection scheme is better than an unguided choice of interventions in learning the correct network and compares favorably in running time and results with methods based on value of information calculations.     

The Central Dogma as a thesis of causal specificity

I present a reconstruction of F.H.C. Crick's two 1957 hypotheses 'Sequence Hypothesis' and 'Central Dogma' in terms of a contemporary philosophical theory of causation. Analyzing in particular the experimental evidence that Crick cited, I argue that these hypotheses can be understood as claims about the actual difference-making cause in protein synthesis. As these hypotheses are only true if restricted to certain nucleic acids in certain organisms, I then examine the concept of causal specificity and its potential to counter claims about causal parity of DNA and other cellular components. I first show that causal specificity is a special kind of invariance under interventions, namely invariance of generalizations that range over finite sets of discrete variables. Then, I show that this notion allows the articulation of a middle ground in the debate over causal parity.     

Is Structural Equation Modeling Advantageous for the Genetic Improvement of Multiple Traits?

Structural equation models (SEMs) are multivariate specifications capable of conveying causal relationships among traits. Although these models offer insights into how phenotypic traits relate to each other, it is unclear whether and how they can improve multiple-trait selection. Here, we explored concepts involved in SEMs, seeking for benefits that could be brought to breeding programs, relative to the standard multitrait model (MTM) commonly used. Genetic effects pertaining to SEMs and MTMs have distinct meanings. In SEMs, they represent genetic effects acting directly on each trait, without mediation by other traits in the model; in MTMs they express overall genetic effects on each trait, equivalent to lumping together direct and indirect genetic effects discriminated by SEMs. However, in breeding programs the goal is selecting candidates that produce offspring with best phenotypes, regardless of how traits are causally associated, so overall additive genetic effects are the matter. Thus, no information is lost in standard settings by using MTM-based predictions, even if traits are indeed causally associated. Nonetheless, causal information allows predicting effects of external interventions. One may be interested in predictions for scenarios where interventions are performed, e.g., artificially defining the value of a trait, blocking causal associations, or modifying their magnitudes. We demonstrate that with information provided by SEMs, predictions for these scenarios are possible from data recorded under no interventions. Contrariwise, MTMs do not provide information for such predictions. As livestock and crop production involves interventions such as management practices, SEMs may be advantageous in many settings.     

Social cognition

Social cognition concerns the various psychological processes that enable individuals to take advantage of being part of a social group. Of major importance to social cognition are the various social signals that enable us to learn about the world. Such signals include facial expressions, such as fear and disgust, which warn us of danger, and eye gaze direction, which indicate where interesting things can be found. Such signals are particularly important in infant development. Social referencing, for example, refers to the phenomenon in which infants refer to their mothers' facial expressions to determine whether or not to approach a novel object. We can learn a great deal simply by observing others. Much of this signalling seems to happen automatically and unconsciously on the part of both the sender and the receiver. We can learn to fear a stimulus by observing the response of another, in the absence of awareness of that stimulus. By contrast, learning by instruction, rather than observation, does seem to depend upon awareness of the stimulus, since such learning does not generalize to situations where the stimulus is presented subliminally. Learning by instruction depends upon a meta-cognitive process through which both the sender and the receiver recognize that signals are intended to be signals. An example would be the 'ostensive' signals that indicate that what follows are intentional communications. Infants learn more from signals that they recognize to be instructive. I speculate that it is this ability to recognize and learn from instructions rather than mere observation which permitted that advanced ability to benefit from cultural learning that seems to be unique to the human race.     

Using Multivariable Mendelian Randomization to Disentangle the Causal Effects of Lipid Fractions

Background

Previous Mendelian randomization studies have suggested that, while low-density lipoprotein cholesterol (LDL-c) and triglycerides are causally implicated in coronary artery disease (CAD) risk, high-density lipoprotein cholesterol (HDL-c) may not be, with causal effect estimates compatible with the null.

Principal Findings

The causal effects of these three lipid fractions can be better identified using the extended methods of ‘multivariable Mendelian randomization’. We employ this approach using published data on 185 lipid-related genetic variants and their associations with lipid fractions in 188,578 participants, and with CAD risk in 22,233 cases and 64,762 controls. Our results suggest that HDL-c may be causally protective of CAD risk, independently of the effects of LDL-c and triglycerides. Estimated causal odds ratios per standard deviation increase, based on 162 variants not having pleiotropic associations with either blood pressure or body mass index, are 1.57 (95% credible interval 1.45 to 1.70) for LDL-c, 0.91 (0.83 to 0.99, p-value  = 0.028) for HDL-c, and 1.29 (1.16 to 1.43) for triglycerides.

Significance

Some interventions on HDL-c concentrations may influence risk of CAD, but to a lesser extent than interventions on LDL-c. A causal interpretation of these estimates relies on the assumption that the genetic variants do not have pleiotropic associations with risk factors on other pathways to CAD. If they do, a weaker conclusion is that genetic predictors of LDL-c, HDL-c and triglycerides each have independent associations with CAD risk.     

Genetic confounding of the relationship between father absence and age at menarche

Research in evolutionary psychology, and life history theory in particular, has yielded important insights into the developmental processes that underpin variation in growth, psychological functioning, and behavioral outcomes across individuals. Yet, there are methodological concerns that limit the ability to draw causal inferences about human development and psychological functioning within a life history framework. The current study used a simulation-based modeling approach to estimate the degree of genetic confounding in tests of a well-researched life history hypothesis: that father absence (X) is associated with earlier age at menarche (Y). The results demonstrate that the genetic correlation between X and Y can confound the phenotypic association between the two variables, even if the genetic correlation is small—suggesting that failure to control for the genetic correlation between X and Y could produce a spurious phenotypic correlation. We discuss the implications of these results for research on human life history, and highlight the utility of incorporating genetically sensitive tests into future life history research.     

The scope and limits of overimitation in the transmission of artefact culture

Children are generally masterful imitators, both rational and flexible in their reproduction of others'' actions. After observing an adult operating an unfamiliar object, however, young children will frequently overimitate, reproducing not only the actions that were causally necessary but also those that were clearly superfluous. Why does overimitation occur? We argue that when children observe an adult intentionally acting on a novel object, they may automatically encode all of the adult''s actions as causally meaningful. This process of automatic causal encoding (ACE) would generally guide children to accurate beliefs about even highly opaque objects. In situations where some of an adult''s intentional actions were unnecessary, however, it would also lead to persistent overimitation. Here, we undertake a thorough examination of the ACE hypothesis, reviewing prior evidence and offering three new experiments to further test the theory. We show that children will persist in overimitating even when doing so is costly (underscoring the involuntary nature of the effect), but also that the effect is constrained by intentionality in a manner consistent with its posited learning function. Overimitation may illuminate not only the structure of children''s causal understanding, but also the social learning processes that support our species'' artefact-centric culture.     

A novel regulatory facet for hypertriglyceridemia: The role of microRNAs in the regulation of triglyceride-rich lipoprotein biosynthesis

Atherosclerotic cardiovascular disease (ASCVD) is one of the major leading global causes of death. Genetic and epidemiological studies strongly support the causal association between triacylglycerol-rich lipoproteins (TAGRL) and atherogenesis, even in statin-treated patients. Recent genetic evidence has clarified that variants in several key genes implicated in TAGRL metabolism are strongly linked to the increased ASCVD risk. There are several triacylglycerol-lowering agents; however, new therapeutic options are in development, among which are miRNA-based therapeutic approaches. MicroRNAs (miRNAs) are small non-coding RNAs (18–25 nucleotides) that negatively modulate gene expression through translational repression or degradation of target mRNAs, thereby reducing the levels of functional genes. MiRNAs play a crucial role in the development of hypertriglyceridemia as several miRNAs are dysregulated in both synthesis and clearance of TAGRL particles. MiRNA-based therapies in ASCVD have not yet been applied in human trials but are attractive. This review provides a concise overview of current interventions for hypertriglyceridemia and the development of novel miRNA and siRNA-based drugs. We summarize the miRNAs involved in the regulation of key genes in the TAGRLs synthesis pathway, which has gained attention as a novel target for therapeutic applications in CVD.     

History influences signal recognition: neural network models of túngara frogs

Animals often attend to only a few of the cues provided by the complex displays of conspecifics. We suggest that these perceptual biases are influenced by mechanisms of signal recognition inherited from antecedent species. We tested this hypothesis by manipulating the evolutionary history of artificial neural networks, observing how the resulting networks respond to many novel stimuli and comparing these responses to the behaviour of females in phonotaxis experiments. Networks with different evolutionary histories proved equally capable of evolving to recognize the call of the túngara frog, Physalaemus pustulosus, but exhibited distinct responses to novel stimuli. History influenced the ability of networks to predict known responses of túngara frogs; network accuracy was determined by how closely the network history approximated the hypothesized history of the túngara frog. Our findings emphasize the influence of past selection pressures on current perceptual mechanisms, and demonstrate how neural network models can be used to address behavioural questions that are intractable through traditional methods.     

The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models

The term “effect” in additive genetic effect suggests a causal meaning. However, inferences of such quantities for selection purposes are typically viewed and conducted as a prediction task. Predictive ability as tested by cross-validation is currently the most acceptable criterion for comparing models and evaluating new methodologies. Nevertheless, it does not directly indicate if predictors reflect causal effects. Such evaluations would require causal inference methods that are not typical in genomic prediction for selection. This suggests that the usual approach to infer genetic effects contradicts the label of the quantity inferred. Here we investigate if genomic predictors for selection should be treated as standard predictors or if they must reflect a causal effect to be useful, requiring causal inference methods. Conducting the analysis as a prediction or as a causal inference task affects, for example, how covariates of the regression model are chosen, which may heavily affect the magnitude of genomic predictors and therefore selection decisions. We demonstrate that selection requires learning causal genetic effects. However, genomic predictors from some models might capture noncausal signal, providing good predictive ability but poorly representing true genetic effects. Simulated examples are used to show that aiming for predictive ability may lead to poor modeling decisions, while causal inference approaches may guide the construction of regression models that better infer the target genetic effect even when they underperform in cross-validation tests. In conclusion, genomic selection models should be constructed to aim primarily for identifiability of causal genetic effects, not for predictive ability.     

A computational method based on the integration of heterogeneous networks for predicting disease-gene associations

The identification of disease-causing genes is a fundamental challenge in human health and of great importance in improving medical care, and provides a better understanding of gene functions. Recent computational approaches based on the interactions among human proteins and disease similarities have shown their power in tackling the issue. In this paper, a novel systematic and global method that integrates two heterogeneous networks for prioritizing candidate disease-causing genes is provided, based on the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein interactions. In this method, the association score function between a query disease and a candidate gene is defined as the weighted sum of all the association scores between similar diseases and neighbouring genes. Moreover, the topological correlation of these two heterogeneous networks can be incorporated into the definition of the score function, and finally an iterative algorithm is designed for this issue. This method was tested with 10-fold cross-validation on all 1,126 diseases that have at least a known causal gene, and it ranked the correct gene as one of the top ten in 622 of all the 1,428 cases, significantly outperforming a state-of-the-art method called PRINCE. The results brought about by this method were applied to study three multi-factorial disorders: breast cancer, Alzheimer disease and diabetes mellitus type 2, and some suggestions of novel causal genes and candidate disease-causing subnetworks were provided for further investigation.     

Nonlinear time series analysis unravels underlying mechanisms of interspecific synchrony among foliage-feeding forest Lepidoptera species

Interspecific synchrony, that is, synchrony in population dynamics among sympatric populations of different species can arise via several possible mechanisms, including common environmental effects, direct interactions between species, and shared trophic interactions, so that distinguishing the relative importance of these causes can be challenging. In this study, to overcome this difficulty, we combine traditional correlation analysis with a novel framework of nonlinear time series analysis, empirical dynamic modeling (EDM). The EDM is an analytical framework to identify causal relationships and measure changing interaction strength from time series. We apply this approach to time series of sympatric foliage-feeding forest Lepidoptera species in the Slovak Republic and yearly mean temperature, precipitation and North Atlantic Oscillation Index. These Lepidoptera species include both free-feeding and leaf-roller larval life histories: the former are hypothesized to be more strongly affected by similar exogenous environments, while the latter are isolated from such pressures. Correlation analysis showed that interspecific synchrony is generally strongest between species within same feeding guild. In addition, the convergent cross mapping analysis detected causal effects of meteorological factors on most of the free-feeding species while such effects were not observed in the leaf-rolling species. However, there were fewer causal relationships among species. The multivariate S-map analysis showed that meteorological factors tend to affect similar free-feeding species that are synchronous with each other. These results indicate that shared meteorological factors are key drivers of interspecific synchrony among members of the free-feeding guild, but do not play the same role in synchronizing species within the leaf-roller guild.