Human life expectancy has been increasing steadily over the last century but this has resulted in an increasing incidence of age-related chronic diseases. Over 60% of people over the age of 65 will suffer from more than one disease at the same time (multimorbidity) and 25-50% of those over 80 years old develop frailty, defined as an accumulation of deficits and loss of reserve. Multimorbidity and frailty have complex medical needs and are strongly associated with disability and hospitalization. However, current treatments are suboptimal with problems of polypharmacy due to the fact that each disease is treated individually. Geroprotectors target fundamental mechanisms of ageing common to multiple age-related diseases and shows promise in delaying the onset of multimorbidity and frailty in animal models. However, their clinical testing in patients has been challenging due to the high level of complexity in the mode of action of geroprotectors and in the way multimorbidity and frailty develop.

 The talk will give an overview of these problems and make the case for the use of AI approaches to solve some of those complex issues with a view of designing appropriate clinical trials with geroprotectors to prevent age-related multimorbidity and frailty and extend healthspan.

Our current approach to cancer treatment has been largely driven by finding molecular targets, those patients fortunate enough to have a targetable mutation will receive a fixed treatment schedule designed to deliver the maximum tolerated dose (MTD). These therapies generally achieve impressive short-term responses, that unfortunately give way to treatment resistance and tumor relapse. The importance of evolution during both tumor progression, metastasis and treatment response is becoming more widely accepted. However, MTD treatment strategies continue to dominate the precision oncology landscape and ignore the fact that treatments drive the evolution of resistance. Here we present an integrated theoretical, experimental and clinical approach to develop treatment strategies that specifically embrace cancer evolution. We will consider the importance of using treatment response as a critical driver of subsequent treatment decisions, rather than fixed strategies that ignore it. Through the integrated application of drug treatments and drug holidays we will illustrate that, evolutionary therapy can drive either tumor control or extinction. Our results strongly indicate that the future of precision medicine shouldn’t be in the development of new drugs but rather in the smarter evolutionary application of preexisting ones.

Bacteria use intercellular signalling, or quorum sensing (QS), to share information and respond collectively to aspects of their surroundings. The autoinducers that carry this information are exposed to the external environment. Consequently, they are affected by factors such as removal through fluid flow, a ubiquitous feature of bacterial habitats ranging from the gut and lungs to lakes and oceans.

We develop and apply a general theory that identifies and quantifies the conditions required for QS activation in fluid flow by systematically linking cell- and population-level genetic and physical processes. We predict that cell-level positive feedback promotes a robust collective response, and can act as a low-pass filter at the population level in oscillatory flow, responding only to changes over slow enough timescales. Moreover, we use our model to hypothesize how bacterial populations can discern between increases in cell density and decreases in flow rate.

A major challenge in the study of biological systems is that of model discovery: turning data into reduced order models that are not just predictive, but provide insight into the nature of the underlying system that generated the data. We introduce a number of data-driven strategies for discovering nonlinear multiscale dynamical systems and their embeddings from data.  Such data-driven methods can be used in the biological sciences where rich data streams are affording new possibilities for the understanding and characterization of complex, networked systems.  In neuroscience, for instance, the integration of these various concepts (reduced-order modeling, equation-free, machine learning, sparsity, networks, multi-scale physics and adaptive control) are critical to formulating successful modeling strategies that perhaps can say something meaningful about experiments.   These methods will be demonstrated on a number of neural systems.  I will also highlight how such methods can be used to quantify cognitive and decision-making deficits arising from neurodegenerative diseases and/or traumatic brain injuries (concussions).

A graded unipotent group U is a unipotent group with a 1PS of automorphisms C^* -- > Aut(U), such that the this 1PS acts on the Lie(U) with all weights positive. Let \hat U be the semi-direct product of U with this 1PS. Let \hat U act linearly on (X,L), a projective variety with a very ample line bundle. With the condition `semistability coincides with stability', and after suitable twist of rational characters, the \hat U-linearisation has a projective geometric quotient, and the invariants are finitely generated. This is a result from \emph{Geometric invariant theory for graded unipotent groups and applications} by G Bérczi, B Doran, T Hawes, F Kirwan, 2018.

Link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_NzU0ODY5MTUtMzUz…

Ollivier Ricci curvature is a notion originated from Riemannian Geometry and suitable for applying on different settings from smooth manifolds to discrete structures such as (directed) hypergraphs. In the past few years, alongside Forman Ricci curvature, this curvature as an edge based measure, has become a popular and powerful tool for network analysis. This notion is defined based on optimal transport problem (Wasserstein distance) between sets of probability measures supported on data points and can nicely detect some important features such as clustering and sparsity in their structures. After introducing this notion for (directed) hypergraphs and mentioning some of its properties, as one of the main recent applications, I will present the result of implementation of this tool for the analysis of chemical reaction networks. 

Homophily can put minority groups at a disadvantage by restricting their ability to establish connections with majority groups or to access novel information. In this talk, I show how this phenomenon is manifested in a variety of online and face-to-face social networks and what societal consequences it has on the visibility and ranking of minorities. I propose a network model with tunable homophily and group sizes and demonstrate how the ranking of nodes is affected by homophilic
behavior. I will discuss the implications of this research on algorithms and perception biases.

 An important property of a Gromov hyperbolic space is that every path that is locally a quasi-geodesic is globally a quasi-geodesic. A theorem of Gromov states that this is a characterization of hyperbolicity, which means that all the properties of hyperbolic spaces and groups can be traced back to this simple fact. In this talk we generalize this property by considering only Morse quasi-geodesics.

We show that not only does this allow us to consider a much larger class of examples, such as CAT(0) spaces, hierarchically hyperbolic spaces and fundamental groups of 3-manifolds, but also we can effortlessly generalize several results from the theory of hyperbolic groups that were previously unknown in this generality.