Center for Advanced Systems Understanding

Quantifying animal movements is necessary for answering a wide array of research questions in ecology and conservation biology. Consequently, ecologists have made considerable efforts to identify the best way to estimate an animal's home range, and many methods of estimating home ranges have arisen over the past half a century. Most of these methods fall into two distinct categories of estimators that have only recently been described in statistical detail: those that measure range distributions (methods such as kernel density estimation that quantify the long-run behavior of a movement process that features restricted space use) and those that measure occurrence distributions (methods such as Brownian bridge movement models and the Correlated Random Walk Library that quantify uncertainty in an animal movement path during a specific period of observation). In this paper, we use theory, simulations, and empirical analysis to demonstrate the importance of appropriately using these two categories of distributions and their estimators. Conflating range and occurrence distributions can have serious consequences for ecological inference and conservation practice. For example, in most situations, home ranges estimated using estimators of occurrence distributions are too small, and this problem is exacerbated by ongoing improvements in tracking technology that enable more frequent and more accurate data on animal movements. We encourage researchers to use estimators of range distributions to quantify home ranges and estimators of occurrence distributions to answer other questions in movement ecology, such as when and where an animal crossed a linear feature, visited a location of interest, or interacted with other animals.

While defects are generally considered to be unavoidable in experiments, engineering them is a way of manipulating the physical properties of materials. Aiming to find a link between the location of single-vacancy (SV) defects and the resulting electronic band structure, we studied periodically arranged SV defects in graphene using the tight-binding method. Results show that SV defects can exhibit predictable electronic behavior when they reside on the same sublattice, following the electronic band structure of the armchair graphene nanoribbons (AGNRs) depending on the spacing between SVs, meaning the linear bands of graphene can be restored. AGNRs are known for their tunable electronic bandgap. However, when SVs are located on different sublattices, the interaction between the defect-induced states becomes strong and can introduce anisotropy into the electronic band structure, demonstrating that the relative position of the SVs can also act as an additional degree of freedom for tuning the electronic properties. Interestingly, the behavior is independent of the density of SVs; for a system fully defected with SVs, the electronic properties depend heavily on the sublattices involved.

Defect-induced magnetism in two-dimensional (2D) materials holds enormous potential for next-generation spintronic and quantum devices, yet its realization in multilayer systems remains elusive. In this work, we investigate the emergence and modulation of magnetism in multilayer PtSe₂ via a combined theoretical–experimental approach, integrating hybrid density functional theory (DFT) calculations with aberration-corrected scanning transmission electron microscopy (AC-STEM). In multilayer PtSe₂ with the presence of Pt vacancies, magnetism is typically quenched due to interlayer interactions, but it can be restored by complex defect structures comprising, in addition to a Pt vacancy, a PtSe antisite. These configurations induce magnetic moments of up to 3.16 μB and give rise to a two-dimensional half-metallic state in bilayer PtSe₂, which is highly desirable for spin-polarized transport. Furthermore, the electronic and magnetic properties can be tuned by nearby Se vacancies, which drive transitions between different types of magnetic states depending on the number of vacancies. When embedded in the middle layer of trilayer PtSe₂, this combination of defects extends the magnetic moments beyond the defect-carrying layer. Overall, these findings demonstrate that defect engineering enables robust magnetic phase control and spin-filtering behavior without external doping or strain, establishing PtSe₂ as a tunable 2D magnetic material platform for scalable, room-temperature spintronic and valleytronic applications.

The effect of the presence of the BAF-binding LEM-domain and LaminA Ig-fold on the stability of the BAF dimer was studied qualitatively using non-equilibrium pull simulations and quantitatively through the calculation of the potential of mean force profile along BAF-BAF separation distance. We find that hydrophobicity plays a significant role in stabilizing the BAF dimer when LEM-domain and LaminA are bound. The role of LEM-domain and LaminA in stabilizing the BAF dimer is explored by quantifying the strength of interaction between them, which are critical components of the nuclear lamina.

Microtubule condensate interactions are fundamental for cell division, vesicle transport and cellular locomotion. Accordingly, they represent a large number of attractive drug targets. Due to the size of microtubuli and the slow timescale of condensate structural relaxation, there has not been a systematic investigation at the molecular level as to what binding patterns (molecular grammar) enable condensate binding to microtubuli. We provide a protocol that is able to predict whether any given disordered protein sequence will bind to microtubuli. This protocol is suitable for high-throughput screening. Our pattern analysis allows us to establish two categories of strongly interacting subsequences that enable binding to microtubuli: positively charged hydrophobic clusters and alternating charge sequences. Their overall optimal balance is analyzed and preferential regions of interaction on microtubuli are identified and validated with known experimental results. Our results enable rapid prototyping of proteins that target the microtubule surface, i.e. they predict whether unstructured proteins will wet the microtubule interface.

Abstract Many fields of physics use quantum Monte Carlo techniques, but struggle to estimate dynamic spectra via the analytic continuation of imaginary-time quantum Monte Carlo data. One of the most ubiquitous approaches to analytic continuation is the maximum entropy method (MEM). We supply a dual Newton optimization algorithm to be used within the MEM and provide analytic bounds for the algorithm's error. The optimization algorithm is freely availible on github (https://github.com/MPF-Optimization-Laboratory/DualPerspective.jl). The MEM is typically used with Bryan's controversial algorithm [Rothkopf, "Bryan’s Maximum Entropy Method" Data 5.3 (2020)]. We present new theoretical issues that are not yet in the literature. Our algorithm has all the theoretical benefits of Bryan's algorithm without these theoretical issues the implementation of the dual Newton optimizer within the MEM is freely available on github (https://github.com/chunatho/InverseProblemSolvers). We compare the MEM with Bryan's optimization to the MEM with our dual Newton optimization on test problems from lattice quantum chromodynamics and plasma physics. These comparisons show that in the presence of noise the dual Newton algorithm produces better estimates and error bars; this indicates the limits of Bryan's algorithm's applicability. We use the MEM to investigate authentic quantum Monte Carlo data for the uniform electron gas at warm dense matter conditions and further substantiate the roton-type feature in the dispersion relation.

Abstract Environmental flows shape spatial patterns and population dynamics; however, we do not fully understand how they influence the onset of pattern formation and population abundance. By combining numerical simulations and analytical approximations, we show that the spatial structure of the flow’s velocity field determines the pattern formation instability in two nonlocal models of population dynamics. For a simple shear flow, where one of the primary axes of the population pattern can align with the flow, the onset of pattern formation remains unaffected. In contrast, a vortex flow delays the pattern instability relative to the no-flow case. The velocity field, therefore, interacts with the spatial feedbacks driving pattern formation in complex ways, which also leads to different oscillatory time series of population abundance. Depending on the flow, population abundance may exhibit regular oscillations with a characteristic frequency or long, erratic transients before settling into a more stable regime.

Cities are the growth engines responsible for shaping the global economy, major contributors to climate change, and are significantly affected by it. However, the United Nations adopted the Sustainable Development Goals (SDGs) to make these cities and human settlements inclusive, safe, resilient, and sustainable. Yet, the rapid and unplanned urban expansion exacerbates various environmental challenges and reduces green cover in urban areas. To address these issues and meet the SDGs, stakeholders need to emphasise and optimise urban spaces. This study investigates the borough-level analysis of green spaces and human exposure to green spaces across London using satellite-derived datasets on vegetation and socio-economic factors to examine the variations in urban vegetation cover and urban population exposure to vegetation cover between 2017 and 2024. This study highlights the spatial disparity in green space coverage and exposure to green space between the inner and outer boroughs of London. The methodology used here suggests an average loss of approximately 11 and 9 percent in green space coverage and green space exposure to population, respectively, between 2017 and 2024 across London boroughs.