Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

A Comparison of Relaxations of Multiset Cannonical Correlation Analysis and Applications

Canonical correlation analysis is a statistical technique that is used to find relations between two sets of variables. An important extension in pattern analysis is to consider more than two sets of variables. This problem can be expressed as a quadratically constrained quadratic program (QCQP), commonly referred to Multi-set Canonical Correlation Analysis (MCCA). This is a non-convex problem and so greedy algorithms converge to local optima without any guarantees on global optimality. In this paper, we show that despite being highly structured, finding the optimal solution is NP-Hard. This motivates our relaxation of the QCQP to a semidefinite program (SDP). The SDP is convex, can be solved reasonably efficiently and comes with both absolute and output-sensitive approximation quality. In addition to theoretical guarantees, we do an extensive comparison of the QCQP method and the SDP relaxation on a variety of synthetic and real world data. Finally, we present two useful extensions: we incorporate kernel methods and computing multiple sets of canonical vectors

Explaining heatwaves with machine learning

Heatwaves are known to arise from the interplay between large-scale climate variability, synoptic weather patterns and regional to local scale surface processes. While recent research has made important progress for each individual contributing factor, ways to properly incorporate multiple or all of them in a unified analysis are still lacking. In this study, we consider a wide range of possible predictor variables from the ERA5 reanalysis, and ask, how much information on heatwave occurrence in Europe can be learned from each of them. To simplify the problem, we first adapt the recently developed logistic principal component analysis to the task of compressing large binary heatwave fields to a small number of interpretable principal components. The relationships between heatwaves and various climate variables can then be learned by a neural network. Starting from the simple notion that the importance of a variable is given by its impact on the performance of our statistical model, we arrive naturally at the definition of Shapley values. Classic results of game theory show that this is the only fair way of distributing the overall success of a model among its inputs. With this approach, we find a non-linear model that explains 70% of reduced heatwave variability, 27% of which are due to upper level geopotential while top level soil moisture contributes 15% of the overall score. In addition, Shapley interaction values enable us to quantify overlapping information and positive synergies between all pairs of predictors

Bone preserving level of osteotomy in short-stem total hip arthroplasty does not influence stress shielding dimensions – a comparing finite elements analysis

Background The main objective of every new development in total hip arthroplasty (THA) is the longest possible survival of the implant. Periprosthetic stress shielding is a scientifically proven phenomenon which leads to inadvertent bone loss. So far, many studies have analysed whether implanting different hip stem prostheses result in significant preservation of bone stock. The aim of this preclinical study was to investigate design- depended differences of the stress shielding effect after implantation of a selection of short-stem THA-prostheses that are currently available. Methods Based on computerised tomography (CT), a finite elements (FE) model was generated and a virtual THA was performed with different stem designs of the implant. Stems were chosen by osteotomy level at the femoral neck (collum, partial collum, trochanter sparing, trochanter harming). Analyses were performed with previously validated FE models to identify changes in the strain energy density (SED). Results In the trochanteric region, only the collum-type stem demonstrated a biomechanical behaviour similar to the native femur. In contrast, no difference in biomechanical behaviour was found between partial collum, trochanter harming and trochanter sparing models. All of the short stem-prostheses showed lower stress-shielding than a standard stem. Conclusion Based on the results of this study, we cannot confirm that the design of current short stem THA-implants leads to a different stress shielding effect with regard to the level of osteotomy. Somehow unexpected, we found a bone stock protection in metadiaphyseal bone by simulating a more distal approach for osteotomy. Further clinical and biomechanical research including long-term results is needed to understand the influence of short- stem THA on bone remodelling and to find the optimal stem-design for a reduction of the stress shielding effect

Maastricht 2.0: Eine neue Finanzregel für Europa = Maastricht 2.0: A new financial rule for Europe

No abstract

Empowering antimicrobial photodynamic therapy of Staphylococcus aureus infections with potassium iodide

Infections caused by the Gram-positive bacterium Staphylococcus aureus, especially methicillin-resistant S. aureus (MRSA), impose a great burden on global healthcare systems. Thus, there is an urgent need for alternative approaches to fight staphylococcal infections, such as targeted antimicrobial photodynamic therapy (aPDT). We recently reported that targeted aPDT with the S. aureus-specific immunoconjugate 1D9-700DX can be effectively applied to eradicate MRSA. Nonetheless, the efficacy of aPDT in the human body may be diminished by powerful antioxidant activities. In particular, we observed that the efficacy of aPDT with 1D9-700DX towards MRSA was reduced in human plasma. Here we show that this antagonistic effect can be attributed to human serum albumin, which represents the largest pool of free thiols in plasma for trapping reactive oxygen species. Importantly, we also show that our targeted aPDT approach with 1D9-700DX can be empowered by the non-toxic inorganic salt potassium iodide (KI), which reacts with the singlet oxygen produced upon aPDT, resulting in the formation of free iodine. The targeted iodine formation allows full eradication of MRSA (more than 6-log reduction) without negatively affecting other non-targeted bacterial species or human cells. Altogether, we show that the addition of KI allows a drastic reduction of both the amount of the immunoconjugate 1D9-700DX and the irradiation time needed for effective elimination of MRSA by aPDT in the presence of human serum albumin

Posture Care Management in School Based Settings for Children with Complex Physical Disabilities

Posture care management (PCM) is an intervention that helps protect a person’s body through different positions within different contexts. PCM is not a singular direct intervention as each person is different and has different needs. This process is especially important for those who cannot reposition themselves such as children with complex physical disabilities. These children rely mainly for caregivers and school staff this process since home and school are the main places they spend the beginning years of their lives. Therefore, it is important to educate caregivers and school staff on the guidelines of proper PCM to promote health, well-being, and occupational engagement in these children.https://soar.usa.edu/otdcapstonessummer2023/1050/thumbnail.jp

Mechanistic description of spatial processes using integrative modelling of noise-corrupted imaging data

Spatial patterns are ubiquitous on the subcellular, cellular and tissue level, and can be studied using imaging techniques such as light and fluorescence microscopy. Imaging data provide quantitative information about biological systems; however, mechanisms causing spatial patterning often remain elusive. In recent years, spatio-temporal mathematical modelling has helped to overcome this problem. Yet, outliers and structured noise limit modelling of whole imaging data, and models often consider spatial summary statistics. Here, we introduce an integrated data-driven modelling approach that can cope with measurement artefacts and whole imaging data. Our approach combines mechanistic models of the biological processes with robust statistical models of the measurement process. The parameters of the integrated model are calibrated using a maximum-likelihood approach. We used this integrated modelling approach to study in vivo gradients of the chemokine (C-C motif) ligand 21 (CCL21). CCL21 gradients guide dendritic cells and are important in the adaptive immune response. Using artificial data, we verified that the integrated modelling approach provides reliable parameter estimates in the presence of measurement noise and that bias and variance of these estimates are reduced compared to conventional approaches. The application to experimental data allowed the parametrization and subsequent refinement of the model using additional mechanisms. Among other results, model-based hypothesis testing predicted lymphatic vessel-dependent concentration of heparan sulfate, the binding partner of CCL21. The selected model provided an accurate description of the experimental data and was partially validated using published data. Our findings demonstrate that integrated statistical modelling of whole imaging data is computationally feasible and can provide novel biological insights

Framework and guidelines for implementing the proposed IUCN Environmental Impact Classification for Alien Taxa (EICAT)

Recently, Blackburn et al. (2014) developed a simple, objective and transparent method for classifying alien taxa in terms of the magnitude of their detrimental environmental impacts in recipient areas. Here, we present a comprehensive framework and guidelines for implementing this method, which we term the Environmental Impact Classification for Alien Taxa, or EICAT. We detail criteria for applying the EICAT scheme in a consistent and comparable fashion, prescribe the supporting information that should be supplied along with classifications, and describe the process for implementing the method. This comment aims to draw the attention of interested parties to the framework and guidelines, and to present them in their entirety in a location where they are freely accessible to any potential users