8 research outputs found
Global variability in leaf respiration in relation to climate, plant functional types and leaf traits
• Leaf dark respiration (Rdark) is an important yet poorly quantified component of the global carbon cycle. Given this, we analyzed a new global database of Rdark and associated leaf traits.
• Data for 899 species were compiled from 100 sites (from the Arctic to the tropics). Several woody and nonwoody plant functional types (PFTs) were represented. Mixed-effects models were used to disentangle sources of variation in Rdark.
• Area-based Rdark at the prevailing average daily growth temperature (T) of each site increased only twofold from the Arctic to the tropics, despite a 20°C increase in growing T (8–28°C). By contrast, Rdark at a standard T (25°C, Rdark25) was threefold higher in the Arctic than in the tropics, and twofold higher at arid than at mesic sites. Species and PFTs at cold sites exhibited higher Rdark25 at a given photosynthetic capacity (Vcmax25) or leaf nitrogen concentration ([N]) than species at warmer sites. Rdark25 values at any given Vcmax25 or [N] were higher in herbs than in woody plants.
• The results highlight variation in Rdark among species and across global gradients in T and aridity. In addition to their ecological significance, the results provide a framework for improving representation of Rdark in terrestrial biosphere models (TBMs) and associated land-surface components of Earth system models (ESMs)
Vaccination with partial transmission and social distancing on contact networks
We study the impact of vaccination on the risk of epidemics spreading through
structured networks using the cavity method of statistical physics. We relax
the assumption that vaccination prevents all transmission of a disease used in
previous studies, such that vaccinated nodes have a small probability of
transmission. To do so we extend the cavity method to study networks where
nodes have heterogeneous transmissibility. We find that vaccination with
partial transmission still provides herd immunity and show how the herd
immunity threshold depends upon the assortativity between nodes of different
transmissibility. In addition, we study the impact of social distancing via
bond percolation and show that percolation targeting links between nodes of
high transmissibility can reduce the risk of an epidemic greater than targeting
links between nodes of high degree. Finally, we extend recent methods to
compute the distributional equations of risk in populations with heterogeneous
transmissibility and show how targeted social distancing measures may reduce
overall risk greater than untargeted vaccination campaigns, by comparing the
effect of random and targeted strategies of node and link deletion on the risk
distribution.Comment: 35 pages, 9 figure
Modelling the interplay between the CD4 <sup>+</sup> /CD8 <sup>+</sup> T-cell ratio and the expression of MHC-I in tumours
Describing the anti-tumour immune response as a series of cellular kinetic reactions from known immunological mechanisms, we create a mathematical model that shows the CD4[Formula: see text] /CD8[Formula: see text] T-cell ratio, T-cell infiltration and the expression of MHC-I to be interacting factors in tumour elimination. Methods from dynamical systems theory and non-equilibrium statistical mechanics are used to model the T-cell dependent anti-tumour immune response. Our model predicts a critical level of MHC-I expression which determines whether or not the tumour escapes the immune response. This critical level of MHC-I depends on the helper/cytotoxic T-cell ratio. However, our model also suggests that the immune system is robust against small changes in this ratio. We also find that T-cell infiltration and the specificity of the intra-tumour TCR repertoire will affect the critical MHC-I expression. Our work suggests that the functional form of the time evolution of MHC-I expression may explain the qualitative behaviour of tumour growth seen in patients
Dynamics of sparse Boolean networks with multi-node and self-interactions
We analyse the equilibrium behaviour and non-equilibrium dynamics of sparse
Boolean networks with self-interactions that evolve according to synchronous
Glauber dynamics. Equilibrium analysis is achieved via a novel application of
the cavity method to the temperature-dependent pseudo-Hamiltonian that
characterises the equilibrium state of systems with parallel dynamics.
Similarly, the non-equilibrium dynamics can be analysed by using the dynamical
version of the cavity method. It is well known, however, that when
self-interactions are present, direct application of the dynamical cavity
method is cumbersome, due to the presence of strong memory effects, which
prevent explicit analysis of the dynamics beyond a few time steps. To overcome
this difficulty, we show that it is possible to map a system of variables
to an equivalent bipartite system of variables, for which the dynamical
cavity method can be used under the usual one time approximation scheme. This
substantial technical advancement allows for the study of transient and
long-time behaviour of systems with self-interactions. Finally, we study the
dynamics of systems with multi-node interactions, recently used to model gene
regulatory networks, by mapping this to a bipartite system of Boolean variables
with 2-body interactions. We show that when interactions have a degree of
bidirectionality such systems are able to support a multiplicity of diverse
attractors, an important requirement for a gene-regulatory network to sustain
multi-cellular life.Comment: 39 pages, 11 figures