Turning on gravity with the Higgs mechanism

We investigate how a Higgs mechanism could be responsible for the emergence of gravity in extensions of Einstein theory, with a suitable low energy limit. In this scenario, at high energies, symmetry restoration could 'turn off' gravity, with dramatic implications for cosmology and quantum gravity. The sense in which gravity is muted depends on the details of the implementation. In the most extreme case gravity's dynamical degrees of freedom would only be unleashed after the Higgs field acquires a non-trivial vacuum expectation value, with gravity reduced to a topological field theory in the symmetric phase. We might also identify the Higgs and the Brans-Dicke fields in such a way that in the unbroken phase Newton's constant vanishes, decoupling matter and gravity. We discuss the broad implications of these scenarios

'The last channel': vision at the temporal margin of the field.

The human visual field, on the temporal side, extends to at least 90° from the line of sight. Using a two-alternative forced-choice procedure in which observers are asked to report the direction of motion of a Gabor patch, and taking precautions to exclude unconscious eye movements in the direction of the stimulus, we show that the limiting eccentricity of image-forming vision can be established with precision. There are large, but reliable, individual differences in the limiting eccentricity. The limiting eccentricity exhibits a dependence on log contrast; but it is not reduced when the modulation visible to the rods is attenuated, a result compatible with the histological evidence that the outermost part of the retina exhibits a high density of cones. Our working hypothesis is that only one type of neural channel is present in the far periphery of the retina, a channel that responds to temporally modulated stimuli of low spatial frequency and that is directionally selective.Evelyn Trus

Eyetracking metrics reveal impaired spatial anticipation in behavioural variant frontotemporal dementia.

Eyetracking technology has had limited application in the dementia field to date, with most studies attempting to discriminate syndrome subgroups on the basis of basic oculomotor functions rather than higher-order cognitive abilities. Eyetracking-based tasks may also offer opportunities to reduce or ameliorate problems associated with standard paper-and-pencil cognitive tests such as the complexity and linguistic demands of verbal test instructions, and the problems of tiredness and attention associated with lengthy tasks that generate few data points at a slow rate. In the present paper we adapted the Brixton spatial anticipation test to a computerized instruction-less version where oculomotor metrics, rather than overt verbal responses, were taken into account as indicators of high level cognitive functions. Twelve bvFTD (in whom spatial anticipation deficits were expected), six SD patients (in whom deficits were predicted to be less frequent) and 38 healthy controls were presented with a 10×7 matrix of white circles. During each trial (N=24) a black dot moved across seven positions on the screen, following 12 different patterns. Participants' eye movements were recorded. Frequentist statistical analysis of standard eye movement metrics were complemented by a Bayesian machine learning (ML) approach in which raw eyetracking time series datasets were examined to explore the ability to discriminate diagnostic group performance not only on the overall performance but also on individual trials. The original pen and paper Brixton test identified a spatial anticipation deficit in 7/12 (58%) of bvFTD and in 2/6 (33%) of SD patients. The eyetracking frequentist approach reported the deficit in 11/12 (92%) of bvFTD and in none (0%) of the SD patients. The machine learning approach had the main advantage of identifying significant differences from controls in 24/24 individual trials for bvFTD patients and in only 12/24 for SD patients. Results indicate that the fine grained rich datasets obtained from eyetacking metrics can inform us about high level cognitive functions in dementia, such as spatial anticipation. The ML approach can help identify conditions where subtle deficits are present and, potentially, contribute to test optimisation and the reduction of testing times. The absence of instructions also favoured a better distinction between different clinical groups of patients and can help provide valuable disease-specific markers

Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer's Disease

Model-based investigations of transneuronal spreading mechanisms in neurodegenerative diseases relate the pattern of pathology severity to the brain’s connectivity matrix, which reveals information about how pathology propagates through the connectivity network. Such network models typically use networks based on functional or structural connectivity in young and healthy individuals, and only end-stage patterns of pathology, thereby ignoring/excluding the effects of normal aging and disease progression. Here, we examine the sequence of changes in the elderly brain’s anatomical connectivity over the course of a neurodegenerative disease. We do this in a data-driven manner that is not dependent upon clinical disease stage, by using event-based disease progression modeling. Using data from the Alzheimer’s Disease Neuroimaging Initiative dataset, we sequence the progressive decline of anatomical connectivity, as quantified by graph-theory metrics, in the Alzheimer’s disease brain. Ours is the first single model to contribute to understanding all three of the nature, the location, and the sequence of changes to anatomical connectivity in the human brain due to Alzheimer’s disease. Our experimental results reveal new insights into Alzheimer’s disease: that degeneration of anatomical connectivity in the brain may be a viable, even early, biomarker and should be considered when studying such neurodegenerative diseases

Understanding NF-κB signaling via mathematical modeling

Mammalian inflammatory signaling, for which NF-κB is a principal transcription factor, is an exquisite example of how cellular signaling pathways can be regulated to produce different yet specific responses to different inflammatory insults. Mathematical models, tightly linked to experiment, have been instrumental in unraveling the forms of regulation in NF-κB signaling and their underlying molecular mechanisms. Our initial model of the IκB–NF-κB signaling module highlighted the role of negative feedback in the control of NF-κB temporal dynamics and gene expression. Subsequent studies sparked by this work have helped to characterize additional feedback loops, the input–output behavior of the module, crosstalk between multiple NF-κB-activating pathways, and NF-κB oscillations. We anticipate that computational techniques will enable further progress in the NF-κB field, and the signal transduction field in general, and we discuss potential upcoming developments

Linear approaches to intramolecular Förster Resonance Energy Transfer probe measurements for quantitative modeling

Numerous unimolecular, genetically-encoded Forster Resonance Energy Transfer (FRET) probes for monitoring biochemical activities in live cells have been developed over the past decade. As these probes allow for collection of high frequency, spatially resolved data on signaling events in live cells and tissues, they are an attractive technology for obtaining data to develop quantitative, mathematical models of spatiotemporal signaling dynamics. However, to be useful for such purposes the observed FRET from such probes should be related to a biological quantity of interest through a defined mathematical relationship, which is straightforward when this relationship is linear, and can be difficult otherwise. First, we show that only in rare circumstances is the observed FRET linearly proportional to a biochemical activity. Therefore in most cases FRET measurements should only be compared either to explicitly modeled probes or to concentrations of products of the biochemical activity, but not to activities themselves. Importantly, we find that FRET measured by standard intensity-based, ratiometric methods is inherently non-linear with respect to the fraction of probes undergoing FRET. Alternatively, we find that quantifying FRET either via (1) fluorescence lifetime imaging (FLIM) or (2) ratiometric methods where the donor emission intensity is divided by the directly-excited acceptor emission intensity (denoted R<sub>alt</sub>) is linear with respect to the fraction of probes undergoing FRET. This linearity property allows one to calculate the fraction of active probes based on the FRET measurement. Thus, our results suggest that either FLIM or ratiometric methods based on R<sub>alt</sub> are the preferred techniques for obtaining quantitative data from FRET probe experiments for mathematical modeling purpose

Metacarpophalangeal joint loads during bonobo locomotion: model predictions vs. proxies

The analysis of internal trabecular and cortical bone has been an informative tool for drawing inferences about behaviour in extant and fossil primate taxa. Within the hand, metacarpal bone architecture has been shown to correlate well with primate locomotion; however, the extent of morphological differences across taxa is unexpectedly small given the variability in hand use. One explanation for this observation is that the activity-related differences in the joint loads acting on the bone are simply smaller than estimated based on commonly used proxies (i.e. external loading and joint posture), which neglect the influence of muscle forces. In this study, experimental data and a musculoskeletal finger model are used to test this hypothesis by comparing differences between climbing and knuckle-walking locomotion of captive bonobos (Pan paniscus) based on (i) joint load magnitude and direction predicted by the models and (ii) proxy estimations. The results showed that the activity-related differences in predicted joint loads are indeed much smaller than the proxies would suggest, with joint load magnitudes being almost identical between the two locomotor modes. Differences in joint load directions were smaller but still evident, indicating that joint load directions might be a more robust indicator of variation in hand use than joint load magnitudes. Overall, this study emphasizes the importance of including muscular forces in the interpretation of skeletal remains and promotes the use of musculoskeletal models for correct functional interpretations