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While cancer has traditionally been considered a genetic disease, mounting evidence indicates an important role for non-genetic (epigenetic) mechanisms. Common anti-cancer drugs have recently been observed to induce the adoption of reversible drug-tolerant cell states, thereby accelerating the evolution of drug resistance. Determining how to optimally balance the competing goals of killing the tumor bulk and delaying resistance evolution in this scenario is a nontrivial question of high clinical importance. In this work, we use a combined mathematical and computational approach to study optimal dosing of anti-cancer drug treatment under drug-induced cell plasticity. Our results show that the optimal treatment steers the tumor into a fixed equilibrium composition while balancing the trade-off between cell kill and tolerance induction in a precisely quantifiable way. Under linear induction of tolerance, a low-dose constant strategy is optimal in equilibrium, while under uniform induction of tolerance, alternating between a large dose and no dose is best. The directionality of drug induction, whether the drug elevates transitions from sensitivity to tolerance or inhibits transitions back, significantly affects optimal dosing. To demonstrate the applicability of our approach, we use it to identify an optimal low-dose strategy for colorectal cancer using publicly available in vitro data., Comment: 46 pages, 10 figures

Image monitoring and guidance during medical examinations can aid both diagnosis and treatment. However, the sampling frequency is often too low, which creates a need to estimate the missing images. We present a probabilistic motion model for sequential medical images, with the ability to both estimate motion between acquired images and forecast the motion ahead of time. The core is a low-dimensional temporal process based on a linear Gaussian state-space model with analytically tractable solutions for forecasting, simulation, and imputation of missing samples. The results, from two experiments on publicly available cardiac datasets, show reliable motion estimates and an improved forecasting performance using patient-specific adaptation by online learning., Comment: Medical Image Computing and Computer Assisted Intervention (MICCAI) 2024

Resistance to therapy remains a significant challenge in cancer treatment, often due to the presence of a stem-like cell population that drives tumor recurrence post-treatment. Moreover, many anticancer therapies induce plasticity, converting initially drug-sensitive cells to a more resistant state, e.g. through epigenetic processes and de-differentiation programs. Understanding the balance between therapeutic anti-tumor effects and induced resistance is critical for identifying treatment strategies. In this study, we introduce a robust statistical framework, based on multi-type branching process models of the evolutionary dynamics of tumor cell populations, to detect and quantify therapy-induced resistance phenomena from high throughput drug screening data. Through comprehensive in silico experiments, we show the efficacy of our framework in estimating parameters governing population dynamics and drug responses in a heterogeneous tumor population where cell state transitions are influenced by the drug. Finally, using recent in vitro data from multiple sources, we demonstrate that our framework is effective for analyzing real-world data and generating meaningful predictions., Comment: We have updated our manuscript to reflect the latest version submitted to the journal

In scanning tunneling microscopy of molecules, an insulating buffer layer is often introduced to reduce interactions between adsorbed molecules and the substrate. Focusing on tunneling through the molecule's electronic transport gap, we demonstrate that the buffer itself strongly influences the wave function of the tunneling electron at the molecule. This is exemplified for an adsorbed platinum phthalocyanine molecule by varying the composition and thickness of the buffer. We find that, in particular, the buffer's lattice parameter is crucial. By expanding the wave function of the tunneling electron in molecular orbitals (MOs), we illustrate how one can strongly vary the relative weights of different MOs, such as the highest occupied MO versus some low-lying MOs with few nodal surfaces. The set of MOs with significant weight are important for processes used to manipulate the state of the molecule by a tunneling electron, such as molecular luminescence. The choice of buffer therefore provides an important tool for manipulating these processes., Comment: 30 pages, 5 figures

Using scanning tunneling microscopy (STM), we experimentally and theoretically investigate isolated platinum phthalocyanine (PtPc) molecules adsorbed on atomically thin NaCl(100) vapor deposited on Au(111). We obtain good agreement between theory and constant-height STM topography. We examine why strong distortions of STM images occur as a function of distance between molecule and STM tip. The images of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) exhibit, for increasing distance, significant radial expansion due to electron propagation in the vacuum. Additionally, the imaged angular dependence is substantially distorted. The LUMO image has substantial intensity along the molecular diagonals where PtPc has no atoms. In the electronic transport gap the image differs drastically from HOMO and LUMO, even at energies very close to these orbitals. As the tunneling becomes increasingly off-resonant, the eight angular lobes of the HOMO or of the degenerate LUMOs diminish and reveal four lobes with maxima along the molecular axes, where both, HOMO and LUMO have little or no weight. These images are strongly influenced by low-lying PtPc orbitals that have simple angular structures.

The alkali halides are ionic compounds. Each alkali atom donates an electron to a halogen atom, leading to ions with full shells. The valence band is mainly located on halogen atoms, while, in a traditional picture, the conduction band is mainly located on alkali atoms. Scanning tunnelling microscopy of NaCl at 4 K actually shows that the conduction band is located on Cl$^-$ because the strong Madelung potential reverses the order of the Na$^+$ 3s and Cl$^-$ 4s levels. We verify this reversal is true for both atomically thin and bulk NaCl, and discuss implications for II-VI and I-VII compounds.

We report on scanning tunneling microscopy (STM) topographs of individual metal phthalocyanines (MPc) on a thin salt (NaCl) film on a gold substrate, at tunneling energies within the molecule's electronic transport gap. Theoretical models of increasing complexity are discussed. The calculations for MPcs adsorbed on a thin NaCl layer on Au(111) demonstrate that the STM pattern rotates with the molecule's orientations - in excellent agreement with the experimental data. Thus, even the STM topography obtained for energies in the transport gap represent the structure of a one atom thick molecule. It is shown that the electronic states inside the transport gap can be rather accurately approximated by linear combinations of bound molecular orbitals (MOs). The gap states include not only the frontier orbitals but also surprisingly large contributions from energetically much lower MOs. These results will be essential for understanding processes, such as exciton creation, which can be induced by electrons tunneling through the transport gap of a molecule.

The site frequency spectrum (SFS) is a widely used summary statistic of genomic data. Motivated by recent evidence for the role of neutral evolution in cancer, we investigate the SFS of neutral mutations in an exponentially growing population. Using branching process techniques, we establish (first-order) almost sure convergence results for the SFS of a Galton-Watson process, evaluated either at a fixed time or at the stochastic time at which the population first reaches a certain size. We finally use our results to construct consistent estimators for the extinction probability and the effective mutation rate of a birth-death process., Comment: 35 pages. Main changes from v1: (1) Fixed-size result improved to almost sure convergence, (2) error bound in Lemma 1 and handling of error terms in its proof improved

Recent evidence suggests that nongenetic (epigenetic) mechanisms play an important role at all stages of cancer evolution. In many cancers, these mechanisms have been observed to induce dynamic switching between two or more cell states, which commonly show differential responses to drug treatments. To understand how these cancers evolve over time, and how they respond to treatment, we need to understand the state-dependent rates of cell proliferation and phenotypic switching. In this work, we propose a rigorous statistical framework for estimating these parameters, using data from commonly performed cell line experiments, where phenotypes are sorted and expanded in culture. The framework explicitly models the stochastic dynamics of cell division, cell death and phenotypic switching, and it provides likelihood-based confidence intervals for the model parameters. The input data can be either the fraction of cells or the number of cells in each state at one or more time points. Through a combination of theoretical analysis and numerical simulations, we show that when cell fraction data is used, the rates of switching may be the only parameters that can be estimated accurately. On the other hand, using cell number data enables accurate estimation of the net division rate for each phenotype, and it can even enable estimation of the state-dependent rates of cell division and cell death. We conclude by applying our framework to a publicly available dataset., Comment: 45 pages, 11 figures, accepted for publication in Journal of Theoretical Biology

Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages., Comment: 36 pages, 14 figures. v2: 1. Rearranged paper and figures. 2. Modified the figures to make them easier to access; results unchanged. 3. Revised the argument in section 3 and section 4; results unchanged. 4. Revised the abstract

The automotive and railway industries are rapidly transforming with a strong drive towards automation and digitalization, with the goal of increased convenience, safety, efficiency, and sustainability. Since assisted and fully automated automotive and train transport services increasingly rely on vehicle-to-everything communications, and high-accuracy real-time positioning, it is necessary to continuously maintain high-accuracy localization, even in occlusion scenes such as tunnels, urban canyons, or areas covered by dense foliage. In this paper, we review the 5G positioning framework of the 3rd Generation Partnership Project in terms of methods and architecture and propose enhancements to meet the stringent requirements imposed by the transport industry. In particular, we highlight the benefit of fusing cellular and sensor measurements and discuss required architecture and protocol support for achieving this at the network side. We also propose a positioning framework to fuse cellular network measurements with measurements by onboard sensors. We illustrate the viability of the proposed fusion-based positioning approach using a numerical example., Comment: 7 pages, 5 figures, accepted for publication in IEEE Communications Standards Magazine

One of the major challenges in communication, radar, and electronic warfare receivers arises from nearby device interference. The paper presents a 2-6 GHz GaN LNA front-end with onboard sensing, processing, and feedback utilizing microcontroller-based controls to achieve adaptation to a variety of interference scenarios through power and linearity regulations. The utilization of GaN LNA provides high power handling capability (30 dBm) and high linearity (OIP3= 30 dBm) for radar and EW applications. The system permits an LNA power consumption to tune from 500 mW to 2 W (4X increase) in order to adjust the linearity from P\textsubscript{1dB,IN}=-10.5 dBm to 0.5 dBm (>10X increase). Across the tuning range, the noise figure increases by approximately 0.4 dB. Feedback control methods are presented with backgrounds from control theory. The rest of the controls consume $\leq$10$\%$ (100 mW) of nominal LNA power (1 W) to achieve an adaptation time <1 ms., Comment: 16 Pages, 22 Figures. Accepted in TMTT, to be published

The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event., Comment: 27 pages, 7 figures

On-chip demagnetization refrigeration has recently emerged as a powerful tool for reaching microkelvin electron temperatures in nanoscale structures. The relative importance of cooling on-chip and off-chip components and the thermal subsystem dynamics are yet to be analyzed. We study a Coulomb blockade thermometer with on-chip copper refrigerant both experimentally and numerically, showing that dynamics in this device are captured by a first-principles model. Our work shows how to simulate thermal dynamics in devices down to microkelvin temperatures, and outlines a recipe for a low-investment platform for quantum technologies and fundamental nanoscience in this novel temperature range., Comment: 11 pages, 10 figures

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.

Access to lower temperatures has consistently enabled scientific breakthroughs. Pushing the limits of \emph{on-chip} temperatures deep into the microkelvin regime would open the door to unprecedented quantum coherence, novel quantum states of matter, and also the discovery of unexpected phenomena. Adiabatic demagnetization is the workhorse of microkelvin cooling, requiring a dilution refrigerator precooling stage. Pulse-tube dilution refrigerators have grown enormously in popularity due to their vast experimental space and independence of helium, but their unavoidable vibrations are making microkelvin cooling very difficult. On-chip thermometry in this unexplored territory is also not a trivial task due to extreme sensitivity to noise. Here, we present a pulse-tube compatible microkelvin sample holder with on-board cooling and microwave filtering and introduce a new type of temperature sensor, the gate Coulomb blockade thermometer (gCBT), working deep into the microkelvin regime. Using on- and off-chip cooling, we demonstrate electronic temperatures as low as 224$\pm$7$\mu$K, remaining below 300$\mu$K for 27 hours, thus providing sufficient time for measurements. Finally, we give an outlook for cooling below 50$\mu$K for a new generation of microkelvin transport experiments., Comment: 7 pages incl. 5 Figures plus supplementary materials with 6 Figures

This paper presents LuMaMi28, a real-time 28 GHz massive multiple-input multiple-output (MIMO) testbed. In this testbed, the base station has 16 transceiver chains with a fully-digital beamforming architecture (with different pre-coding algorithms) and simultaneously supports multiple user equipments (UEs) with spatial multiplexing. The UEs are equipped with a beam-switchable antenna array for real-time antenna selection where the one with the highest channel magnitude, out of four pre-defined beams, is selected. For the beam-switchable antenna array, we consider two kinds of UE antennas, with different beam-width and different peak-gain. Based on this testbed, we provide measurement results for millimeter-wave (mmWave) massive MIMO performance in different real-life scenarios with static and mobile UEs. We explore the potential benefit of the mmWave massive MIMO systems with antenna selection based on measured channel data, and discuss the performance results through real-time measurements., Comment: 14 pages, 17 figures

Wireless-based activity sensing has gained significant attention due to its wide range of applications. We investigate radio-based multi-class classification of human activities using massive multiple-input multiple-output (MIMO) channel measurements in line-of-sight and non line-of-sight scenarios. We propose a tensor decomposition-based algorithm to extract features by exploiting the complex correlation characteristics across time, frequency, and space from channel tensors formed from the measurements, followed by a neural network that learns the relationship between the input features and output target labels. Through evaluations of real measurement data, it is demonstrated that the classification accuracy using a massive MIMO array achieves significantly better results compared to the state-of-the-art even for a smaller experimental data set., Comment: accepted for publication in IEEE Wireless Communications Letters

Due to the increase of Internet-of-Things (IoT) devices, IoT networks are getting overcrowded. Networks can be extended with more gateways, increasing the number of supported devices. However, as investigated in this work, massive MIMO has the potential to increase the number of simultaneous connections, while also lowering the energy expenditure of these devices. We present a study of the channel characteristics of massive MIMO in the unlicensed sub-GHz band. The goal is to support IoT applications with strict requirements in terms of number of devices, power consumption, and reliability. The assessment is based on experimental measurements using both a uniform linear and a rectangular array. Our study demonstrates and validates the advantages of deploying massive MIMO gateways to serve IoT nodes. While the results are general, here we specifically focus on static nodes. The array gain and channel hardening effect yield opportunities to lower the transmit-power of IoT nodes while also increasing reliability. The exploration confirms that exploiting large arrays brings great opportunities to connect a massive number of IoT devices by separating the nodes in the spatial domain. In addition, we give an outlook on how static IoT nodes could be scheduled based on partial channel state information.

Indoor positioning is currently recognized as one of the important features in emergency, commercial and industrial applications. The 5G network enhances mobility, flexibility, reliability, and security to new higher levels which greatly benefit the IoT and industrial applications. Industrial IoT (IIoT) use-cases are characterized by ambitious system requirements for positioning accuracy in many verticals. For example, on the factory floor, it is important to locate assets and moving objects such as forklifts. The deployment design for different IIoT environments has a significant impact on the positioning per-performance in terms of both accuracy and availability of the service. Indoor factory (InF) and indoor open office (IOO) are two available and standardized Third Generation Partnership Project (3GPP) scenarios for evaluation of indoor channel models and positioning performance in IIoT use cases. This paper aims to evaluate the positioning performance in terms of accuracy and availability while considering different deployment strategies. Our simulation-based evaluation shows that deployment plays a vital role when it comes to achieving high accuracy positioning performance. It is for example favorable to deploy the 5G Transmission and Reception Points (TRPs) on the walls of the factory halls than deploying them attached to the ceiling.