We classify the self-dual (or holomorphic) vertex operator superalgebras (SVOAs) of central charge 24, or in physics parlance the purely left-moving, spin 2-dimensional conformal field theories with just one primary field.

There are exactly 969 such SVOAs under suitable regularity assumptions and the assumption that the shorter moonshine module VB^# is the unique self-dual SVOA of central charge 23.5 whose weight-1/2 and weight-1 spaces vanish. Additionally, there might be self-dual SVOAs arising as "fake copies" of VB^# tensored with a free fermion F.

We construct and classify the self-dual SVOAs by determining the 2-neighbourhood graph of the self-dual (purely bosonic) VOAs of central charge 24 and also by realising them as simple-current extensions of a dual pair containing a certain maximal lattice VOA. We show that all SVOAs besides VB^# x F and potential fake copies thereof stem from elements of the Conway group Co_0, the automorphism group of the Leech lattice.

By splitting off free fermions F, if possible, we obtain the classification for all central charges less than or equal to 24.

This is based on joint work with Gerald Höhn (arXiv:2303.17190)

There is much current interest in macroecology to make predictions of future biodiversity patterns in order to inform both regional and global priorities for conservation and sustainability of ecosystem functions and services. Species distribution models use data on species' occurrence records, environmental predictor variables, and sometimes other data sources to estimate niche relationships and distribution extents—these models can also be combined to make biodiversity estimates. As the field of species distribution modeling has grown considerably over the past two decades, many approaches now exist to build models, evaluate their performance, and use them to make predictions for unsampled areas and times. I will provide an overview of current techniques to predict future distributions of species and biodiversity, detail some issues with these techniques concerning uncertainty and realism of predictions, and contribute my humble thoughts on where the field should go from here.

Our Material DX research project (http://pixy.polym.kyoto-u.ac.jp/ku_numata/index.html) is dedicated to addressing challenges in the design and synthesis of polymeric materials. Our primary objective is to establish a comprehensive material research and technical platform built upon a polymer database. Our efforts center on the creation and advancement of bioadaptive materials featuring biological functionalities and physical properties.1,2 Within the domain of polymer science, the integration of material informatics (MI) for establishing correlations between material structure and properties, along with the utilization of extensive databases, has not witnessed substantial advancement in recent times.

Structural protein such as spider silk is an eco- and bio-friendly polymer as well as one of the key factors to realize the unique properties and functions of natural tissues and organisms.3,4 However, use of structural proteins as structural materials in human life is still challenging. Spider silks are among the toughest known materials and thus provide models for renewable, biodegradable and sustainable biopolymers. However, the entirety of their diversity still remains elusive, and silks that exceed the performance limits of industrial fibers are constantly being discovered. We obtained transcriptome assemblies from 1,098 species of spiders to comprehensively catalog silk gene sequences and measured the mechanical, thermal, structural, and hydration properties of the dragline silks of 446 species.5 The combination of these silk protein genotype-phenotype data revealed essential contributions of multicomponent structures in high-performance dragline silks as well as numerous amino acid motifs contributing to each of the measured properties. We hope that our global sampling, comprehensive testing, integrated analysis and open data will provide a solid starting point for future biomaterial designs.

We present a new way to study cosmic inflation with gravitational waves. The gravitational signal is generated thanks to nonlinear structures in the inflaton field, called oscillons. This novel probe allows us to test models of inflation which are challenging to test with CMB experiments.

Presented is a systematic, global study of Galactic supernova remnants (SNRs) hosting Central Compact Objects (CCOs) aimed at addressing their explosion properties and supernova progenitors. With the Chandra and XMM-Newton telescopes, a spatially resolved X-ray spectroscopy study is performed on seven SNRs that show evidence of shock-heated ejecta. Using an algorithm, we segmented each SNR in the sample into regions of similar surface brightness. These regions were fit with one- or two-component plasma shock model(s) in order to separate the forward-shocked interstellar medium from the reverse shock-heated ejecta which peak in the X-ray bands for elements including O, Ne, Mg, Si, S, Ar, Ca, and Fe. We subsequently derived the explosion properties for each SNR in the sample and found overall low explosion energies (<10^51 erg). To address their progenitor mass, we compare the measured abundances from our spectroscopic modelling to five of the most widely used explosion models and a relatively new electron-capture supernova model. Additionally, we explore degeneracy in the explosion energy and its effects on the progenitor mass estimates. However, no explosion models match all of the measured ejecta abundances for any of the SNRs in our sample. Therefore, we present our best progenitor mass estimates and find overall low progenitor masses (<=25 solar masses) and we highlight the discrepancies between the observed data and the theoretical explosion models.

Inhalational anthrax, caused by the bacterium Bacillus anthracis, is a disease with very high fatality rates. Due to the significant risk posed if the bacterium was to be intentionally used as a bioweapon, it is important to be able to defend against such an attack and to make optimal decisions about treatment strategies. Mechanistic mathematical models can help to quantify and improve understanding of the underlying mechanisms of the infection. In this talk, I will present a multi-scale mathematical model for the infection dynamics of inhalational anthrax. This approach involves constructing individual models for the intracellular, within-host, and population-level infection dynamics, to define key quantities characterising infection at each level, which can be used to link dynamics across scales. I will begin by introducing a model for the intracellular infection dynamics of B. anthracis, which describes the interaction between B. anthracis spores and host cells. The model can be used to predict the distribution of outcomes from this host-pathogen interaction. For example, it can be used to estimate the number of bacteria released upon rupture of an infected phagocyte, as well as the timing of phagocyte rupture and bacterial release. Next, I will show how these key outputs can be used to connect the intracellular model to a model of the infection at the within-host scale. The within-host model aims to provide an overall understanding of the early progression of the infection, and is parametrised with infection data from studies of rabbits and guinea pigs. Furthermore, this model allows the probability of infection and the time to infection to be calculated. Building a model that offers a realistic mechanistic description of these different animal responses to the inhalation of B. anthracis spores is an important step towards eventually extrapolating the model to describe the dynamics of human infection. This would enable predictions of how many individuals would become infected in different exposure scenarios and also on what timescale this would occur.

Cosmological observations have led to an extremely precise understanding of the large-scale structure of the Universe. A common assumption is to extrapolate large-scale properties to smaller scales; however, whether this is correct or not is unknown and many well-motivated early Universe scenarios predict substantially different structure formation histories. In this seminar I will discuss two scenarios where nonlinear structures form much earlier than is typically assumed. In the first case, the initial fluctuations are enhanced on small scales leading to either primordial black holes clusters or WIMP minihalos right after matter-radiation equality. In the second, I will show that an additional attractive dark force leads to structure formation even in the radiation dominated Universe. I will furthermore discuss possible observations of such early structure formation including changes to the cosmic microwave background, dark matter annihilation, and when the first galaxies form.