Extracting causal rules from spatio-temporal data

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-23374-1_2This paper is concerned with the problem of detecting causality in spatiotemporal data. In contrast to most previous work on causality, we adopt a logical rather than a probabilistic approach. By defining the logical form of the desired causal rules, the algorithm developed in this paper searches for instances of rules of that form that explain as fully as possible the observations found in a data set. Experiments with synthetic data, where the underlying causal rules are known, show that in many cases the algorithm is able to retrieve close approximations to the rules that generated the data. However, experiments with real data concerning the movement of fish in a large Australian river system reveal significant practical limitations, primarily as a consequence of the coarse granularity of such movement data. In response, instead of focusing on strict causation (where an environmental event initiates a movement event), further experiments focused on perpetuation (where environmental conditions are the drivers of ongoing processes of movement). After retasking to search for a different logical form of rules compatible with perpetuation, our algorithm was able to identify perpetuation rules that explain a significant proportion of the fish movements. For example, approximately one fifth of the detected long-range movements of fish over a period of six years were accounted for by 26 rules taking account of variations in water-level alone.EPSRCAustralian Research Council (ARC) under the Discovery Projects Schem

Towards causal benchmarking of bias in face analysis algorithms

Measuring algorithmic bias is crucial both to assess algorithmic fairness, and to guide the improvement of algorithms. Current methods to measure algorithmic bias in computer vision, which are based on observational datasets, are inadequate for this task because they conflate algorithmic bias with dataset bias. To address this problem we develop an experimental method for measuring algorithmic bias of face analysis algorithms, which manipulates directly the attributes of interest, e.g., gender and skin tone, in order to reveal causal links between attribute variation and performance change. Our proposed method is based on generating synthetic ``transects'' of matched sample images that are designed to differ along specific attributes while leaving other attributes constant. A crucial aspect of our approach is relying on the perception of human observers, both to guide manipulations, and to measure algorithmic bias. Besides allowing the measurement of algorithmic bias, synthetic transects have other advantages with respect to observational datasets: they sample attributes more evenly allowing for more straightforward bias analysis on minority and intersectional groups, they enable prediction of bias in new scenarios, they greatly reduce ethical and legal challenges, and they are economical and fast to obtain, helping make bias testing affordable and widely available. We validate our method by comparing it to a study that employs the traditional observational method for analyzing bias in gender classification algorithms. The two methods reach different conclusions. While the observational method reports gender and skin color biases, the experimental method reveals biases due to gender, hair length, age, and facial hair

The use of biomedicine, complementary and alternative medicine, and ethnomedicine for the treatment of epilepsy among people of South Asian origin in the UK

Studies have shown that a significant proportion of people with epilepsy use complementary and alternative medicine (CAM). CAM use is known to vary between different ethnic groups and cultural contexts; however, little attention has been devoted to inter-ethnic differences within the UK population. We studied the use of biomedicine, complementary and alternative medicine, and ethnomedicine in a sample of people with epilepsy of South Asian origin living in the north of England. Interviews were conducted with 30 people of South Asian origin and 16 carers drawn from a sampling frame of patients over 18 years old with epilepsy, compiled from epilepsy registers and hospital databases. All interviews were tape-recorded, translated if required and transcribed. A framework approach was adopted to analyse the data. All those interviewed were taking conventional anti-epileptic drugs. Most had also sought help from traditional South Asian practitioners, but only two people had tried conventional CAM. Decisions to consult a traditional healer were taken by families rather than by individuals with epilepsy. Those who made the decision to consult a traditional healer were usually older family members and their motivations and perceptions of safety and efficacy often differed from those of the recipients of the treatment. No-one had discussed the use of traditional therapies with their doctor. The patterns observed in the UK mirrored those reported among people with epilepsy in India and Pakistan. The health care-seeking behaviour of study participants, although mainly confined within the ethnomedicine sector, shared much in common with that of people who use global CAM. The appeal of traditional therapies lay in their religious and moral legitimacy within the South Asian community, especially to the older generation who were disproportionately influential in the determination of treatment choices. As a second generation made up of people of Pakistani origin born in the UK reach the age when they are the influential decision makers in their families, resort to traditional therapies may decline. People had long experience of navigating plural systems of health care and avoided potential conflict by maintaining strict separation between different sectors. Health care practitioners need to approach these issues with sensitivity and to regard traditional healers as potential allies, rather than competitors or quacks

Visual Causality: Investigating Graph Layouts for Understanding Causal Processes

Causal diagrams provide a graphical formalism indicating how statistical models can be used to study causal processes. Despite the extensive research on the efficacy of aesthetic graphic layouts, the causal inference domain has not benefited from the results of this research. In this paper, we investigate the performance of graph visualisations for supporting users’ understanding of causal graphs. Two studies were conducted to compare graph visualisations for understanding causation and identifying confounding variables in a causal graph. The first study results suggest that while adjacency matrix layouts are better for understanding direct causation, node-link diagrams are better for understanding mediated causation along causal paths. The second study revealed that node-link layouts, and in particular layouts created by a radial algorithm, are more effective for identifying confounder and collider variables

Self-Organization of Anastral Spindles by Synergy of Dynamic Instability, Autocatalytic Microtubule Production, and a Spatial Signaling Gradient

Assembly of the mitotic spindle is a classic example of macromolecular self-organization. During spindle assembly, microtubules (MTs) accumulate around chromatin. In centrosomal spindles, centrosomes at the spindle poles are the dominating source of MT production. However, many systems assemble anastral spindles, i.e., spindles without centrosomes at the poles. How anastral spindles produce and maintain a high concentration of MTs in the absence of centrosome-catalyzed MT production is unknown. With a combined biochemistry-computer simulation approach, we show that the concerted activity of three components can efficiently concentrate microtubules (MTs) at chromatin: (1) an external stimulus in form of a RanGTP gradient centered on chromatin, (2) a feed-back loop where MTs induce production of new MTs, and (3) continuous re-organization of MT structures by dynamic instability. The mechanism proposed here can generate and maintain a dissipative MT super-structure within a RanGTP gradient

A core outcome set for aphasia treatment research: the ROMA consensus statement

Background: A core outcome set (COS; an agreed, minimum set of outcomes) was needed to address the heterogeneous measurement of outcomes in aphasia treatment research and to facilitate the production of transparent, meaningful and efficient outcome data. Objective: The Research Outcome Measurement in Aphasia (ROMA) consensus statement provides evidence-based recommendations for the measurement of outcomes for adults with post-stroke aphasia within phase I-IV aphasia treatment studies. Methods: This statement was informed by a four-year program of research which comprised investigation of stakeholder-important outcomes using consensus processes, a scoping review of aphasia outcome measurement instruments, and an international consensus meeting. This paper provides an overview of this process and presents the results and recommendations arising from the international consensus meeting. Results: Five essential outcome constructs were identified: Language, communication, patient-reported satisfaction with treatment and impact of treatment, emotional wellbeing, and quality of life. Consensus was reached for the following measurement instruments: Language: The Western Aphasia Battery Revised (WAB-R) (74% consensus); emotional well-being: General Health Questionnaire (GHQ)-12 (83% consensus); quality of life: Stroke and Aphasia Quality of Life Scale (SAQOL-39) (96% consensus). Consensus was unable to be reached for measures of communication (where multiple measures exist) or patient-reported satisfaction with treatment or impact of treatment (where no measures exist). Discussion: Harmonisation of the ROMA COS with other core outcome initiatives in stroke rehabilitation is discussed. Ongoing research and consensus processes are outlined. Conclusion: The WAB-R, GHQ, and SAQOL-39 are recommended to be routinely included within phase I-IV aphasia treatment studies. This consensus statement has been endorsed by the Collaboration of Aphasia Trialists, the British Aphasiology Society, the German Society for Aphasia Research and Therapy, and the Royal College of Speech Language Therapists

Using Stochastic Causal Trees to Augment Bayesian Networks for Modeling eQTL Datasets

<p>Abstract</p> <p>Background</p> <p>The combination of genotypic and genome-wide expression data arising from segregating populations offers an unprecedented opportunity to model and dissect complex phenotypes. The immense potential offered by these data derives from the fact that genotypic variation is the sole source of perturbation and can therefore be used to reconcile changes in gene expression programs with the parental genotypes. To date, several methodologies have been developed for modeling eQTL data. These methods generally leverage genotypic data to resolve causal relationships among gene pairs implicated as associates in the expression data. In particular, leading studies have augmented Bayesian networks with genotypic data, providing a powerful framework for learning and modeling causal relationships. While these initial efforts have provided promising results, one major drawback associated with these methods is that they are generally limited to resolving causal orderings for transcripts most proximal to the genomic loci. In this manuscript, we present a probabilistic method capable of learning the causal relationships between transcripts at all levels in the network. We use the information provided by our method as a prior for Bayesian network structure learning, resulting in enhanced performance for gene network reconstruction.</p> <p>Results</p> <p>Using established protocols to synthesize eQTL networks and corresponding data, we show that our method achieves improved performance over existing leading methods. For the goal of gene network reconstruction, our method achieves improvements in recall ranging from 20% to 90% across a broad range of precision levels and for datasets of varying sample sizes. Additionally, we show that the learned networks can be utilized for expression quantitative trait loci mapping, resulting in upwards of 10-fold increases in recall over traditional univariate mapping.</p> <p>Conclusions</p> <p>Using the information from our method as a prior for Bayesian network structure learning yields large improvements in accuracy for the tasks of gene network reconstruction and expression quantitative trait loci mapping. In particular, our method is effective for establishing causal relationships between transcripts located both proximally and distally from genomic loci.</p

What the ‘Moonwalk’ Illusion Reveals about the Perception of Relative Depth from Motion

When one visual object moves behind another, the object farther from the viewer is progressively occluded and/or disoccluded by the nearer object. For nearly half a century, this dynamic occlusion cue has beenthought to be sufficient by itself for determining the relative depth of the two objects. This view is consistent with the self-evident geometric fact that the surface undergoing dynamic occlusion is always farther from the viewer than the occluding surface. Here we use a contextual manipulation ofa previously known motion illusion, which we refer to as the‘Moonwalk’ illusion, to demonstrate that the visual system cannot determine relative depth from dynamic occlusion alone. Indeed, in the Moonwalk illusion, human observers perceive a relative depth contrary to the dynamic occlusion cue. However, the perception of the expected relative depth is restored by contextual manipulations unrelated to dynamic occlusion. On the other hand, we show that an Ideal Observer can determine using dynamic occlusion alone in the same Moonwalk stimuli, indicating that the dynamic occlusion cue is, in principle, sufficient for determining relative depth. Our results indicate that in order to correctly perceive relative depth from dynamic occlusion, the human brain, unlike the Ideal Observer, needs additionalsegmentation information that delineate the occluder from the occluded object. Thus, neural mechanisms of object segmentation must, in addition to motion mechanisms that extract information about relative depth, play a crucial role in the perception of relative depth from motion

Noisy-threshold control of cell death

<p>Abstract</p> <p>Background</p> <p>Cellular responses to death-promoting stimuli typically proceed through a differentiated multistage process, involving a lag phase, extensive death, and potential adaptation. Deregulation of this chain of events is at the root of many diseases. Improper adaptation is particularly important because it allows cell sub-populations to survive even in the continuous presence of death conditions, which results, among others, in the eventual failure of many targeted anticancer therapies.</p> <p>Results</p> <p>Here, I show that these typical responses arise naturally from the interplay of intracellular variability with a threshold-based control mechanism that detects cellular changes in addition to just the cellular state itself. Implementation of this mechanism in a quantitative model for T-cell apoptosis, a prototypical example of programmed cell death, captures with exceptional accuracy experimental observations for different expression levels of the oncogene Bcl-x_Land directly links adaptation with noise in an ATP threshold below which cells die.</p> <p>Conclusions</p> <p>These results indicate that oncogenes like Bcl-x_L, besides regulating absolute death values, can have a novel role as active controllers of cell-cell variability and the extent of adaptation.</p