Embryogenesis of egg-laying animals is a biological example of a thermodynamically quasi-isolated system. Additional to the genetic code, most energy and matter necessary for embryo development is stored inside the egg from the time point of fertilization up to the time point of hatching. It remains controversial how energy is stored and transferred, especially in quiescent periods between large-scale deformations. Here, we study extra-embryonic tissue dynamics in two key processes during embryo development, gastrulation and dorsal closure, in the serosa membrane of the red flour beetle Tribolium castaneum. Is serosa membrane retraction a passive process enabled by prestress from serosa window closure? We argue that energy in the serosa tissue is close-to-fully conserved between serosa window closure during gastrulation and dorsal closure. We identify active and passive processes with sub-cellular resolution using 3D non-invasive velocity and stress estimation techniques. We localize the point of origin of forces driving serosa tissue rupture and retraction. This study of extra-embryonic tissue dynamics underpins the importance of studying both mechanics and thermodynamics in embryogenesis.

The movement patterns of cell organelles within plant cells can be important Indicators of their functional interactions. We analyse the patterns of plastids in root cells projected in two-dimensional space, which captures about 90% of the movement variability.

The resulting planar movement patterns often appear piecewise unidirectional with piecewise constant speed. For their description we propose a parsimonious stochastic model with a piecewise constant movement direction and stepsize and independent and normally distributed errors. Maximum likelihood estimators can be obtained locally.

Within this model assuming we know the change points in the stepsize the changes in the movement direction can be estimated using a successive moving window approach and vice versa due to the connection between the two features. For the joint change point detection in direction and speed we use a reparameterization of the model and proceed with a bivariate MOSUM technique inspired by (Messer 2021) to detect changes in the expectation of movement increments. Under the null hypothesis the resulting bivariate process is asymptotically Gaussian with a covariance structure determined by the weighted increments used for the parameter estimation. Finally, we classify the detected changes into changes in the biologically interpretable parameters of direction and speed. To this end, we propose a graphical approach which illustrates the relationship between the change points detected in the reparameterization and the local estimations of stepsize and direction. We discuss advantages and restrictions of the proposed approach and show its application to a dataset of recorded organelle tracks.

Relativistic collision of isobaric systems is found to be valuable in differentiating the nucleon distributions for nuclei with the same mass number. In recent contrast experiment of

$^{96}_{44}\text{Ru}+^{96}_{44}\text{Ru}$ versus

$^{96}_{40}\text{Zr}+^{96}_{40}\text{Zr}$ collisions at

$\sqrt{s_\text{NN}} = 200~\text{GeV}$, the ratios of multiplicity

distribution, elliptic flow, triangular flow, and radial flow are precisely measured and found to be significantly different from unity, indicating the difference in the shapes of the isobar pair. In this work, we investigate the feasibility of nuclear structure reconstruction from heavy-ion collision observables.

We perform Bayesian Inference with employing the Monte-Carlo Glauber model as an estimator of the mapping from nuclear structure to the final state observables and to provide the mock data for reconstruction. By varying combination of observables included in the mock data, we find it plausible to infer Woods--Saxon parameters from the observables. We also observe that single-system multiplicity distribution for the isobar system, rather than their ratio, is crucial to simultaneously determine the nuclear structure for the isobar system.

The COVID-19 pandemic has shown the intricacies of modelling biological processes when human interactions are governing the dynamics. Specifically, the complex interplay of individual reactions and policy responses make predictions for more than a few weeks infeasible. In addition, the emergence of new variants and the unknown seasonality of the virus changed the dynamics repeatedly. By augmenting classical epidemiological mean-field models with universal function approximators we can use single time series to fit time-dependent infection-rates. The time dependency includes, among others, seasonality, new variants, and policy. This work will enable the detection of other time-dependent parameters such as hospitalization or death rates over the course of the whole pandemic. These techniques can also be used to find the hidden dynamics of inter-regional mobility during an epidemic. Starting from a complex mechanistic model with explicit mobility terms, we use population and geographic data to learn the mobility terms from incidence data.

The world community is on a crossroads. While Western societies are recognizing, tackling and preparing for climate change, a majority of the developing countries keeps on denying and rejecting its responsibilities. However, there is only one planet, with only one atmosphere, and the state of global climate is tightly connected to all of humankind, especially its way to economize and power the world. The presented work picks up this issue, examining the relationship between the post-war population trends, the general rise in living standards, as well as the release of greenhouse gases. For the European example it is discussed, which efforts and sacrifices have to be made to actually achieve climate neutrality, how the whole energy system has to be transformed from conventional energy carriers to renewable ones, and how advantage has to be taken of cross-sector couplings among electricity, heating, transport and the industry. As a first step towards an optimization of this complex energy network within the PyPSA-Eur modeling framework, an analysis of the energy relevant meteorological variables and their corresponding power potentials is presented, each time with respect to the EURO-CORDEX climate ensemble projecting distinct climate futures.