The "Artificial Mathematician" Objection: Exploring the (Im)possibility of Automating Mathematical Understanding

Reuben Hersh confided to us that, about forty years ago, the late Paul Cohen predicted to him that at some unspecified point in the future, mathematicians would be replaced by computers. Rather than focus on computers replacing mathematicians, however, our aim is to consider the (im)possibility of human mathematicians being joined by “artificial mathematicians” in the proving practice—not just as a method of inquiry but as a fellow inquirer

Sub-chronic ketamine administration increases dopamine synthesis capacity in the mouse midbrain: a preclinical in vivo PET study

PURPOSE: There is robust evidence that people with schizophrenia show elevated dopamine (DA) synthesis capacity in the striatum. This finding comes from positron emission tomography (PET) studies using radiolabelled l-3,4-dihydroxyphenylalanine (18F-DOPA). DA synthesis capacity also appears to be elevated in the midbrain of people with schizophrenia compared to healthy controls. We therefore aimed to optimise a method to quantify 18F-DOPA uptake in the midbrain of mice, and to utilise this method to quantify DA synthesis capacity in the midbrain of the sub-chronic ketamine model of schizophrenia-relevant hyperdopaminergia. PROCEDURES: Adult male C57Bl6 mice were treated daily with either ketamine (30 mg/kg, i.p.) or vehicle (saline) for 5 days. On day 7, animals were administered 18F-DOPA (i.p.) and scanned in an Inveon PET/CT scanner. Data from the saline-treated group were used to optimise an atlas-based template to position the midbrain region of interest and to determine the analysis parameters which resulted in the greatest intra-group consistency. These parameters were then used to compare midbrain DA synthesis capacity (KiMod) between ketamine- and saline-treated animals. RESULTS: Using an atlas-based template to position the 3.7 mm3 midbrain ROI with a T*-Tend window of 15-140 min to estimate KiMod resulted in the lowest intra-group variability and moderate test-retest agreement. Using these parameters, we found that KiMod was elevated in the midbrain of ketamine-treated animals in comparison to saline-treated animals (t(22) = 2.19, p = 0.048). A positive correlation between DA synthesis capacity in the striatum and the midbrain was also evident in the saline-treated animals (r2 = 0.59, p = 0.005) but was absent in ketamine-treated animals (r2 = 0.004, p = 0.83). CONCLUSIONS: Using this optimised method for quantifying 18F-DOPA uptake in the midbrain, we found that elevated striatal DA synthesis capacity in the sub-chronic ketamine model extends to the midbrain. Interestingly, the dysconnectivity between the midbrain and striatum seen in this model is also evident in the clinical population. This model may therefore be ideal for assessing novel compounds which are designed to modulate pre-synaptic DA synthesis capacity

How predation and landscape fragmentation affect vole population dynamics

Background: Microtine species in Fennoscandia display a distinct north-south gradient from regular cycles to stable populations. The gradient has often been attributed to changes in the interactions between microtines and their predators. Although the spatial structure of the environment is known to influence predator-prey dynamics of a wide range of species, it has scarcely been considered in relation to the Fennoscandian gradient. Furthermore, the length of microtine breeding season also displays a north-south gradient. However, little consideration has been given to its role in shaping or generating population cycles. Because these factors covary along the gradient it is difficult to distinguish their effects experimentally in the field. The distinction is here attempted using realistic agent-based modelling. Methodology/Principal Findings: By using a spatially explicit computer simulation model based on behavioural and ecological data from the field vole (Microtus agrestis), we generated a number of repeated time series of vole densities whose mean population size and amplitude were measured. Subsequently, these time series were subjected to statistical autoregressive modelling, to investigate the effects on vole population dynamics of making predators more specialised, of altering the breeding season, and increasing the level of habitat fragmentation. We found that fragmentation as well as the presence of specialist predators are necessary for the occurrence of population cycles. Habitat fragmentation and predator assembly jointly determined cycle length and amplitude. Length of vole breeding season had little impact on the oscillations. Significance: There is good agreement between our results and the experimental work from Fennoscandia, but our results allow distinction of causation that is hard to unravel in field experiments. We hope our results will help understand the reasons for cycle gradients observed in other areas. Our results clearly demonstrate the importance of landscape fragmentation for population cycling and we recommend that the degree of fragmentation be more fully considered in future analyses of vole dynamics

The impact of viral mutations on recognition by SARS-CoV-2 specific T cells.

We identify amino acid variants within dominant SARS-CoV-2 T cell epitopes by interrogating global sequence data. Several variants within nucleocapsid and ORF3a epitopes have arisen independently in multiple lineages and result in loss of recognition by epitope-specific T cells assessed by IFN-γ and cytotoxic killing assays. Complete loss of T cell responsiveness was seen due to Q213K in the A∗01:01-restricted CD8+ ORF3a epitope FTSDYYQLY207-215; due to P13L, P13S, and P13T in the B∗27:05-restricted CD8+ nucleocapsid epitope QRNAPRITF9-17; and due to T362I and P365S in the A∗03:01/A∗11:01-restricted CD8+ nucleocapsid epitope KTFPPTEPK361-369. CD8+ T cell lines unable to recognize variant epitopes have diverse T cell receptor repertoires. These data demonstrate the potential for T cell evasion and highlight the need for ongoing surveillance for variants capable of escaping T cell as well as humoral immunity.This work is supported by the UK Medical Research Council (MRC); Chinese Academy of Medical Sciences(CAMS) Innovation Fund for Medical Sciences (CIFMS), China; National Institute for Health Research (NIHR)Oxford Biomedical Research Centre, and UK Researchand Innovation (UKRI)/NIHR through the UK Coro-navirus Immunology Consortium (UK-CIC). Sequencing of SARS-CoV-2 samples and collation of data wasundertaken by the COG-UK CONSORTIUM. COG-UK is supported by funding from the Medical ResearchCouncil (MRC) part of UK Research & Innovation (UKRI),the National Institute of Health Research (NIHR),and Genome Research Limited, operating as the Wellcome Sanger Institute. T.I.d.S. is supported by a Well-come Trust Intermediate Clinical Fellowship (110058/Z/15/Z). L.T. is supported by the Wellcome Trust(grant number 205228/Z/16/Z) and by theUniversity of Liverpool Centre for Excellence in Infectious DiseaseResearch (CEIDR). S.D. is funded by an NIHR GlobalResearch Professorship (NIHR300791). L.T. and S.C.M.are also supported by the U.S. Food and Drug Administration Medical Countermeasures Initiative contract75F40120C00085 and the National Institute for Health Research Health Protection Research Unit (HPRU) inEmerging and Zoonotic Infections (NIHR200907) at University of Liverpool inpartnership with Public HealthEngland (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford.L.T. is based at the University of Liverpool. M.D.P. is funded by the NIHR Sheffield Biomedical ResearchCentre (BRC – IS-BRC-1215-20017). ISARIC4C is supported by the MRC (grant no MC_PC_19059). J.C.K.is a Wellcome Investigator (WT204969/Z/16/Z) and supported by NIHR Oxford Biomedical Research Centreand CIFMS. The views expressed are those of the authors and not necessarily those of the NIHR or MRC

Trait plasticity in species interactions: a driving force of community dynamics.

Evolutionary community ecology is an emerging field of study that includes evolutionary principles such as individual trait variation and plasticity of traits to provide a more mechanistic insight as to how species diversity is maintained and community processes are shaped across time and space. In this review we explore phenotypic plasticity in functional traits and its consequences at the community level. We argue that resource requirement and resource uptake are plastic traits that can alter fundamental and realised niches of species in the community if environmental conditions change. We conceptually add to niche models by including phenotypic plasticity in traits involved in resource allocation under stress. Two qualitative predictions that we derive are: (1) plasticity in resource requirement induced by availability of resources enlarges the fundamental niche of species and causes a reduction of vacant niches for other species and (2) plasticity in the proportional resource uptake results in expansion of the realized niche, causing a reduction in the possibility for coexistence with other species. We illustrate these predictions with data on the competitive impact of invasive species. Furthermore, we review the quickly increasing number of empirical studies on evolutionary community ecology and demonstrate the impact of phenotypic plasticity on community composition. Among others, we give examples that show that differences in the level of phenotypic plasticity can disrupt species interactions when environmental conditions change, due to effects on realized niches. Finally, we indicate several promising directions for future phenotypic plasticity research in a community context. We need an integrative, trait-based approach that has its roots in community and evolutionary ecology in order to face fast changing environmental conditions such as global warming and urbanization that pose ecological as well as evolutionary challenges. © Springer Science+Business Media B.V. 2010

Spatial growth rate of emerging SARS-CoV-2 lineages in England, September 2020-December 2021

This paper uses a robust method of spatial epidemiological analysis to assess the spatial growth rate of multiple lineages of SARS-CoV-2 in the local authority areas of England, September 2020–December 2021. Using the genomic surveillance records of the COVID-19 Genomics UK (COG-UK) Consortium, the analysis identifies a substantial (7.6-fold) difference in the average rate of spatial growth of 37 sample lineages, from the slowest (Delta AY.4.3) to the fastest (Omicron BA.1). Spatial growth of the Omicron (B.1.1.529 and BA) variant was found to be 2.81× faster than the Delta (B.1.617.2 and AY) variant and 3.76× faster than the Alpha (B.1.1.7 and Q) variant. In addition to AY.4.2 (a designated variant under investigation, VUI-21OCT-01), three Delta sublineages (AY.43, AY.98 and AY.120) were found to display a statistically faster rate of spatial growth than the parent lineage and would seem to merit further investigation. We suggest that the monitoring of spatial growth rates is a potentially valuable adjunct to outbreak response procedures for emerging SARS-CoV-2 variants in a defined population

SARS-CoV-2 lineage dynamics in England from September to November 2021: high diversity of Delta sub-lineages and increased transmissibility of AY.4.2

Background: Since the emergence of SARS-CoV-2, evolutionary pressure has driven large increases in the transmissibility of the virus. However, with increasing levels of immunity through vaccination and natural infection the evolutionary pressure will switch towards immune escape. Genomic surveillance in regions of high immunity is crucial in detecting emerging variants that can more successfully navigate the immune landscape. Methods: We present phylogenetic relationships and lineage dynamics within England (a country with high levels of immunity), as inferred from a random community sample of individuals who provided a self-administered throat and nose swab for rt-PCR testing as part of the REal-time Assessment of Community Transmission-1 (REACT-1) study. During round 14 (9 September–27 September 2021) and 15 (19 October–5 November 2021) lineages were determined for 1322 positive individuals, with 27.1% of those which reported their symptom status reporting no symptoms in the previous month. Results: We identified 44 unique lineages, all of which were Delta or Delta sub-lineages, and found a reduction in their mutation rate over the study period. The proportion of the Delta sub-lineage AY.4.2 was increasing, with a reproduction number 15% (95% CI 8–23%) greater than the most prevalent lineage, AY.4. Further, AY.4.2 was less associated with the most predictive COVID-19 symptoms (p = 0.029) and had a reduced mutation rate (p = 0.050). Both AY.4.2 and AY.4 were found to be geographically clustered in September but this was no longer the case by late October/early November, with only the lineage AY.6 exhibiting clustering towards the South of England. Conclusions: As SARS-CoV-2 moves towards endemicity and new variants emerge, genomic data obtained from random community samples can augment routine surveillance data without the potential biases introduced due to higher sampling rates of symptomatic individuals. © 2022, The Author(s)