Numerical integration of mechanical forces in center-based models for biological cell populations

Center-based models are used to simulate the mechanical behavior of biological cells during embryonic development or cancer growth. To allow for the simulation of biological populations potentially growing from a few individual cells to many thousands or more, these models have to be numerically efficient, while being reasonably accurate on the level of individual cell trajectories. In this work, we increase the robustness, accuracy, and efficiency of the simulation of center-based models by choosing the time steps adaptively in the numerical method and comparing five different integration methods. We investigate the gain in using single rate time stepping based on local estimates of the numerical errors for the forward and backward Euler methods of first order accuracy and a Runge-Kutta method and the trapezoidal method of second order accuracy. Properties of the analytical solution such as convergence to steady state and conservation of the center of gravity are inherited by the numerical solutions. Furthermore, we propose a multirate time stepping scheme that simulates regions with high local force gradients (e.g. as they happen after cell division) with multiple smaller time steps within a larger single time step for regions with smoother forces. These methods are compared for a model system in numerical experiments. We conclude, for example, that the multirate forward Euler method performs better than the Runge-Kutta method for low accuracy requirements but for higher accuracy the latter method is preferred. Only with frequent cell divisions the method with a fixed time step is the best choice

Improving burn diagnosis in medical image retrieval from grafting burn samples using B-coefficients and the CLAHE algorithm

This study focuses on the vital difficulty of burn assessment in medical image retrieval from grafted burn specimens particularly in resource-constrained contexts where speedy and precise diagnoses are required. Our solution combines sophisticated machine learning techniques, namely an Artificial Neural Network (ANN), with the Contrast Limited Adaptive Histogram Equalisation (CLAHE) algorithm in an Image Reclamation system. The statistical assessments of kurtosis value (KCLAHE=144.83) compared to the query image (Kquery=131.17) indicate a distribution with more pronounced tails in the CLAHE image, enhancing specific image features. Additionally, increased skewness in the CLAHE image (SCLAHE=5.92) suggests a shift toward higher intensity levels compared to the query image (Squery=4.47), further enhancing discernible image features. Through this incorporation, we carefully retain picture boundaries, boost local contrast, and minimize noise, hence enhancing burn diagnostic accuracy. Statistical analyses, such as kurtosis and skewness analysis, verify the improvements in visible picture aspects, offering significant insights into fundamental texture properties. We increase picture retrieval efficiency by using Bhattacharya coefficients and unique bin analysis, resulting in substantial enhancements in the retrieving score of matched images The ANN successfully differentiates between photos that require grafts and those that do not, providing a speedy and accurate diagnosis for acute burn injuries. This comprehensive technique greatly improves burn diagnosis, especially during emergencies, and shows promise for improving medical procedures. Our study helps to raise patient care standards in difficult medical situations by combining automated evaluation tools, powerful methods for image processing, and machine learning

On the Digital Front-Line : Far-Right Memory Work in Baltic, Central, and East European Online Spaces

This anthology explores the memory work performed by Baltic and Central and East European far-right actors in the online space. Situated at the crossroads between memory studies, far-right studies, and media studies, the volume’s seven chapters show how a wide range of far-right actors, from small movements to major parties, have exploited digital communication technologies in order to establish their plays with the past in the mainstream discourses of their respective national contexts. With a focus on the online memory work of the far right in Austria, Belarus, Czechia, Lithuania, Romania, Sweden, and Ukraine, the anthology dissects the nexus between politics, media, and memory to show how digital communication have empowered the memory work of marginal but dangerous societal actors. As the different contributions show, the online space has raised the visibility and success of organised intolerant groups and, consequently, it has magni ed the societal impact of their memory work. Thanks to digital media, the memory work of the far right can compete on an equal footing with state-endorsed memory politics.  rough manipulation of the historical narrative and thereby the perception and understanding of the past in civil societies, on websites, blogs, and social media, the far right has succeeded in overcoming its marginality and in normalising its messages of intolerance on a continental scale.Åke Wibergs Stiftelse, H20-005

Deep heterogeneous joint architecture : A temporal frequency surrogate model for fuel codes

Fuel performance codes, such as Transuranus, predict fuel behavior and are used to ensure the safe operation of nuclear reactors. These codes are moderately time-consuming and affordable in many applications but may be limited in others, primarily when many fuel rods must be evaluated simultaneously. This work presents how the temporal neural network techniques, Temporal Convolutional Networks, and a Fourier Neural Operator can be combined to form a deep heterogeneous joint architecture as a surrogate model for fuel performance modeling in time-critical situations. We train the model using realistic power histories and corresponding outputs generated using the fuel performance code Transuranus. The ultimate result is a surrogate model for use in time-critical situations that take milliseconds to evaluate for thousands of fuel rods and have a mean test error of unseen data around a few percent

The role of ecological networks of interactions on shaping evolvability

One of the main challenges still confronting biologists is unravelling the mechanismsunderlying the evolution of evolvability — the ability to produce heritable and adaptive phenotypic variation. The impact of ecological factors on evolvability remains largely unstudied. Ecological interactions among populations are a relevant ecological factor shaping biodiversity through coevolution, i.e. the reciprocal adaptation resulting from these interactions. This study adopts a community-wide approach to investigate how the complexity of interaction networks and degree (the number of interacting partners of each species) affect evolvability. Quantifying these metrics represents a monumental practical challenge, which is overcome by harnessing a digital life platform that simulates the coevolutionary process of hosts and their parasites. I found that more evolvable communities are those embedded in a more complex network of interactions. However, within each community, a wide range of evolvability values coexist; an observation not related to specific differences in degree. These results emphasise the role of ecological networks of interaction in shaping evolvability

Deep networks for system identification : A survey

Deep learning is a topic of considerable current interest. The availability of massive data collections and powerful software resources has led to an impressive amount of results in many application areas that reveal essential but hidden properties of the observations. System identification learns mathematical descriptions of dynamic systems from input-output data and can thus benefit from the advances of deep neural networks to enrich the possible range of models to choose from. For this reason, we provide a survey of deep learning from a system identification perspective. We cover a wide spectrum of topics to enable researchers to understand the methods, providing rigorous practical and theoretical insights into the benefits and challenges of using them. The main aim of the identified model is to predict new data from previous observations. This can be achieved with different deep learning-based modelling techniques and we discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks. Their parameters have to be estimated from past data to optimize the prediction performance. For this purpose, we discuss a specific set of first-order optimization tools that have emerged as efficient. The survey then draws connections to the well-studied area of kernel-based methods. They control the data fit by regularization terms that penalize models not in line with prior assumptions. We illustrate how to cast them in deep architectures to obtain deep kernel-based methods. The success of deep learning also resulted in surprising empirical observations, like the counter-intuitive behaviour of models with many parameters. We discuss the role of overparameterized models, including their connection to kernels, as well as implicit regularization mechanisms which affect generalization, specifically the interesting phenomena of benign overfitting and double-descent. Finally, we highlight numerical, computational and software aspects in the area with the help of applied examples

Facile one-step fabrication of Li4Ti5O12 coatings by suspension plasma spraying

Spinel Li4Ti5O12 (LTO) is a promising anode material for solid state thin film batteries (SSTB) due to its almost-zero volume change and promising Li-ion mobility. However, preparing LTO anodes for SSTB demands tedious vacuum-based processing steps that are not cost effective. In this context, the present study embarks on evaluating the versatile suspension plasma spraying (SPS) approach to fabricate LTO coatings without using any binder. The microstructure and stoichiometry of the fabricated LTO coatings developed through the SPS route reveals retention of ∼76 wt.% of the spinel LTO from the starting feedstock, with minor amounts of rutile and anatase TiO2. The SPS experiments yielded varying thickness build up rates of the LTO coatings depending on the processing parameters adopted. The electrochemical data of the produced LTO based electrode tested in a half-cell through galvanostatic cycling show reversible lithiation and delithiation at expected potential, thereby validating the promise of the SPS technique for potential fabrication of SSTB components once fully optimized

Invasive ventilation at the boundary of viability : A respiratory pathophysiology study of infants born between 22 and 24 weeks of gestation

Background: Invasive ventilation of infants born before 24 weeks of gestation is critical for survival and long-term respiratory outcomes, but currently there is a lack of evidence to guide respiratory management. We aimed to compare respiratory mechanics and gas exchange in ventilated extremely preterm infants born before and after 24 weeks of gestation. Methods: Secondary analysis of two prospective observational cohort studies, comparing respiratory mechanics and indices of gas exchange in ventilated infants born at 22-24 weeks of gestation (N=14) compared to infants born at 25-27 weeks (N=37). The ventilation/perfusion ratio (V-A/Q), intrapulmonary shunt, alveolar dead space (V-Dalv) and adjusted alveolar surface area (S-A) were measured in infants born at the Neonatal Unit of King's College Hospital NHS Foundation Trust, London, UK. Results: Compared to infants of 25-27 weeks, infants of 22-24 weeks had higher median (IQR) intrapulmonary shunt [18 (4 - 29) % vs 8 (2 - 12) %, p=0.044] and higher VDalv [0.9 (0.6 - 1.4) vs 0.6 (0.5 - 0.7) ml/kg, p=0.036], but did not differ in VA/Q. Compared to infants of 25-27 weeks, the infants of 22-24 weeks had a lower adjusted S-A [509 (322- 687) vs 706 (564 - 800) cm(2), p=0.044]. The infants in the two groups did not differ in any of the indices of respiratory mechanics. Conclusion: Ventilated infants born before 24 completed weeks of gestation exhibit abnormal gas exchange, with higher alveolar dead space and intrapulmonary shunt and a decreased alveolar surface area compared to extreme preterms born after 24 weeks of gestation

Stability estimates for radial basis function methods applied to linear scalar conservation laws

We derive stability estimates for three commonly used radial basis function (RBF) methods to solve hyperbolic time-dependent PDEs: the RBF generated finite difference (RBF-FD) method, the RBF partition of unity method (RBF-PUM) and Kansa's (global) RBF method. We give the estimates in the discrete l(2)-norm 2-norm intrinsic to each of the three methods. The results show that Kansa's method and RBF-PUM can be l(2)-stable 2-stable in time under a sufficiently large oversampling of the discretized system of equations. The RBF-FD method in addition requires stabilization of the spurious jump terms due to the discontinuous RBF-FD cardinal basis functions. Numerical experiments show an agreement with our theoretical observations