Morphological phylogenetics evaluated using novel evolutionary simulations

Evolutionary inferences require reliable phylogenies. Morphological data has traditionally been analysed using maximum parsimony, but recent simulation studies have suggested that Bayesian analyses yield more accurate trees. This debate is ongoing, in part, because of ambiguity over modes of morphological evolution and a lack of appropriate models. Here we investigate phylogenetic methods using two novel simulation models - one in which morphological characters evolve stochastically along lineages and another in which individuals undergo selection. Both models generate character data and lineage splitting simultaneously: the resulting trees are an emergent property, rather than a fixed parameter. Standard consensus methods for Bayesian searches (Mki) yield fewer incorrect nodes and quartets than the standard consensus trees recovered using equal weighting and implied weighting parsimony searches. Distances between the pool of derived trees (most parsimonious or posterior distribution) and the true trees - measured using Robinson-Foulds (RF), subtree prune and regraft (SPR), and tree bisection reconnection (TBR) metrics - demonstrate that this is related to the search strategy and consensus method of each technique. The amount and structure of homoplasy in character data differs between models. Morphological coherence, which has previously not been considered in this context, proves to be a more important factor for phylogenetic accuracy than homoplasy. Selection-based models exhibit relatively lower homoplasy, lower morphological coherence, and higher inaccuracy in inferred trees. Selection is a dominant driver of morphological evolution, but we demonstrate that it has a confounding effect on numerous character properties which are fundamental to phylogenetic inference. We suggest that the current debate should move beyond considerations of parsimony versus Bayesian, towards identifying modes of morphological evolution and using these to build models for probabilistic search methods

Inorganic caesium lead iodide perovskite solar cells

The vast majority of perovskite solar cell research has focused on organic–inorganic lead trihalide perovskites. Herein, we present working inorganic CsPbI3 perovskite solar cells for the first time. CsPbI3 normally resides in a yellow non-perovskite phase at room temperature, but by careful processing control and development of a low-temperature phase transition route we have stabilised the material in the black perovskite phase at room temperature. As such, we have fabricated solar cell devices in a variety of architectures, with current–voltage curve measured efficiency up to 2.9% for a planar heterojunction architecture, and stabilised power conversion efficiency of 1.7%. The well-functioning planar junction devices demonstrate long-range electron and hole transport in this material. Importantly, this work identifies that the organic cation is not essential, but simply a convenience for forming lead triiodide perovskites with good photovoltaic properties. We additionally observe significant rate-dependent current–voltage hysteresis in CsPbI3 devices, despite the absence of the organic polar molecule previously thought to be a candidate for inducing hysteresis via ferroelectric polarisation. Due to its space group, CsPbI3 cannot be a ferroelectric material, and thus we can conclude that ferroelectricity is not required to explain current–voltage hysteresis in perovskite solar cells. Our report of working inorganic perovskite solar cells paves the way for further developments likely to lead to much more thermally stable perovskite solar cells and other optoelectronic devices

Pseudorehearsal in value function approximation

Catastrophic forgetting is of special importance in reinforcement learning, as the data distribution is generally non-stationary over time. We study and compare several pseudorehearsal approaches for Q-learning with function approximation in a pole balancing task. We have found that pseudorehearsal seems to assist learning even in such very simple problems, given proper initialization of the rehearsal parameters

Productivity, niche availability, species richness, and extinction risk: Untangling relationships using individual-based simulations

It has often been suggested that the productivity of an ecosystem affects the number of species that it can support. Despite decades of study, the nature, extent, and underlying mechanisms of this relationship are unclear. One suggested mechanism is the “more individuals” hypothesis (MIH). This proposes that productivity controls the number of individuals in the ecosystem, and that more individuals can be divided into a greater number of species before their population size is sufficiently small for each to be at substantial risk of extinction. Here, we test this hypothesis using REvoSim: an individual-based eco-evolutionary system that simulates the evolution and speciation of populations over geological time, allowing phenomena occurring over timescales that cannot be easily observed in the real world to be evaluated. The individual-based nature of this system allows us to remove assumptions about the nature of speciation and extinction that previous models have had to make. Many of the predictions of the MIH are supported in our simulations: Rare species are more likely to undergo extinction than common species, and species richness scales with productivity. However, we also find support for relationships that contradict the predictions of the strict MIH: species population size scales with productivity, and species extinction risk is better predicted by relative than absolute species population size, apparently due to increased competition when total community abundance is higher. Furthermore, we show that the scaling of species richness with productivity depends upon the ability of species to partition niche space. Consequently, we suggest that the MIH is applicable only to ecosystems in which niche partitioning has not been halted by species saturation. Some hypotheses regarding patterns of biodiversity implicitly or explicitly overlook niche theory in favor of neutral explanations, as has historically been the case with the MIH. Our simulations demonstrate that niche theory exerts a control on the applicability of the MIH and thus needs to be accounted for in macroecology

Sustained positive behaviour change of wounded, injured and sick UK military following an adaptive adventure sports and health coaching recovery course

INTRODUCTION: A rising trend has occurred in the physical and mental health challenges faced by recovering UK service personnel. To support these individuals, bespoke inclusive multiactivity and adventurous training courses (MAC) have been developed. This study investigated the MAC's influence on participants' ability to sustain day-to-day changes that facilitate positive mental health and psychological need satisfaction. METHODS: The 146 UK service personnel who participated in this study attended a five-day MAC 12 months ago. To investigate how the supportive experience influenced participants' lives, quantitative and qualitative data were collected via an online survey. Open-ended questioning and abductive analysis were conducted to understand mechanisms, influential aspects of the course and positive behaviour change. RESULTS: Positive behaviour changes were reported by 74% of the respondents. These changes align with positive psychological well-being (98%). Impactful elements of the course experienced by participants mostly aligned with the three basic psychological needs of autonomy (34%), competence (36%) and relatedness (61%). CONCLUSIONS: Recovery support programmes that encompass health coaching adventurous activities, such as the MAC, can initiate long-term positive behaviour change for recovering military personnel. In this specific context, the concurrence of the self-determination theory concepts that underpin the course delivery and participant outcomes is a powerful endorsement of implementation fidelity

Providing recovery support to wounded, injured, and sick UK military personnel throughout the COVID-19 pandemic

Health precautions implemented by the United Kingdom (UK) government to limit the spread of the Coronavirus Disease 2019 (COVID-19) led to the closure of many well-being support services in 2020. This created a need to re-think how impactful recovery support courses can be provided. One such service was that of the five-day Multi Activity Course (MAC) which was redesigned in accordance with national health guidelines to allow continued access for Wounded, Injured and Sick (WIS) military personnel to the service; the positive impacts of which are well established. This study investigated the influence of the newly developed Reduced numbers MAC (R-MAC) on the WIS participants lives during and for 12 months after attending. The R-MAC led to comparable impacts for participants well-being, at a time in which people’s mental well-being was often being adversely affected. The positive mental well-being of the 261 participants improved by 33% throughout the course and remained 14% higher for the 37 participants who provided data six months after attending. Key facets of the experience that were most impactful for the participants were (i) shared experience with other veterans, (ii) discussing issues in a safe environment while receiving support from the staff and (iii) developing knowledge around self-help/personal development. Adapting to the challenging circumstances and developing the R-MAC mitigated against the already adverse impact of the COVID-19 pandemic for the WIS participants

Optimality-based Analysis of XCSF Compaction in Discrete Reinforcement Learning

Learning classifier systems (LCSs) are population-based predictive systems that were originally envisioned as agents to act in reinforcement learning (RL) environments. These systems can suffer from population bloat and so are amenable to compaction techniques that try to strike a balance between population size and performance. A well-studied LCS architecture is XCSF, which in the RL setting acts as a Q-function approximator. We apply XCSF to a deterministic and stochastic variant of the FrozenLake8x8 environment from OpenAI Gym, with its performance compared in terms of function approximation error and policy accuracy to the optimal Q-functions and policies produced by solving the environments via dynamic programming. We then introduce a novel compaction algorithm (Greedy Niche Mass Compaction - GNMC) and study its operation on XCSF's trained populations. Results show that given a suitable parametrisation, GNMC preserves or even slightly improves function approximation error while yielding a significant reduction in population size. Reasonable preservation of policy accuracy also occurs, and we link this metric to the commonly used steps-to-goal metric in maze-like environments, illustrating how the metrics are complementary rather than competitive

Adherence and persistence to direct oral anticoagulants in atrial fibrillation: a population-based study

Background Despite simpler regimens than vitamin K antagonists (VKAs) for stroke prevention in atrial fibrillation (AF), adherence (taking drugs as prescribed) and persistence (continuation of drugs) to direct oral anticoagulants are suboptimal, yet understudied in electronic health records (EHRs). Objective We investigated (1) time trends at individual and system levels, and (2) the risk factors for and associations between adherence and persistence. Methods In UK primary care EHR (The Health Information Network 2011–2016), we investigated adherence and persistence at 1 year for oral anticoagulants (OACs) in adults with incident AF. Baseline characteristics were analysed by OAC and adherence/persistence status. Risk factors for non-adherence and non-persistence were assessed using Cox and logistic regression. Patterns of adherence and persistence were analysed. Results Among 36 652 individuals with incident AF, cardiovascular comorbidities (median CHA2DS2VASc[Congestive heart failure, Hypertension, Age≥75 years, Diabetes mellitus, Stroke, Vascular disease, Age 65-74 years, Sex category] 3) and polypharmacy (median number of drugs 6) were common. Adherence was 55.2% (95% CI 54.6 to 55.7), 51.2% (95% CI 50.6 to 51.8), 66.5% (95% CI 63.7 to 69.2), 63.1% (95% CI 61.8 to 64.4) and 64.7% (95% CI 63.2 to 66.1) for all OACs, VKA, dabigatran, rivaroxaban and apixaban. One-year persistence was 65.9% (95% CI 65.4 to 66.5), 63.4% (95% CI 62.8 to 64.0), 61.4% (95% CI 58.3 to 64.2), 72.3% (95% CI 70.9 to 73.7) and 78.7% (95% CI 77.1 to 80.1) for all OACs, VKA, dabigatran, rivaroxaban and apixaban. Risk of non-adherence and non-persistence increased over time at individual and system levels. Increasing comorbidity was associated with reduced risk of non-adherence and non-persistence across all OACs. Overall rates of ‘primary non-adherence’ (stopping after first prescription), ‘non-adherent non-persistence’ and ‘persistent adherence’ were 3.5%, 26.5% and 40.2%, differing across OACs. Conclusions Adherence and persistence to OACs are low at 1 year with heterogeneity across drugs and over time at individual and system levels. Better understanding of contributory factors will inform interventions to improve adherence and persistence across OACs in individuals and populations