Health Coaching for Chronic Disease Management: A Program Evaluation Conducted at Denver Health Managed Care

The purpose of this study is to evaluate health coaching at Denver Health Managed Care Center. We evaluated patients with diabetes and obesity who received a telephonic, motivational-interviewing intervention. Our hypothesis was that patients who received health coaching would have improved health outcomes compared to patients who received standard medical care alone. This research benefits this specific program in clarifying effectiveness (i.e. improvement in health and efficiency, indicated by length of treatment). Health coaching is a growing area of study. This research also adds to a broader conversation about what professionals best serve in the role of health coaches, what theoretical approach can be most effective, and how patients of lower socioeconomic status respond to this type of program. Health coaching participants had significantly improved A1c from baseline (M = 8.16; SD = 2.33) to follow up (M = 7.80, SD = 1.91), t(76) = 2.062, p \u3c .05, but no improvements in other health outcomes . Conclusions and directions for future research are discussed

Computational Design and Characterisation of Gyroid Structures with Different Gradient Functions for Porosity Adjustment

Triply periodic minimal surface (TPMS) structures have a very good lightweight potential, due to their surface-to-volume ratio, and thus are contents of various applications and research areas, such as tissue engineering, crash structures, or heat exchangers. While TPMS structures with a uniform porosity or a linear gradient have been considered in the literature, this paper focuses on the investigation of the mechanical properties of gyroid structures with non-linear porosity gradients. For the realisation of the different porosity gradients, an algorithm is introduced that allows the porosity to be adjusted by definable functions. A parametric study is performed on the resulting gyroid structures by performing mechanical simulations in the linear deformation regime. The transformation into dimensionless parameters enables material-independent statements, which is possible due to linearity. Thus, the effective elastic behaviour depends only on the structure geometry. As a result, by introducing non-linear gradient functions and varying the density of the structure over the entire volume, specific strengths can be generated in certain areas of interest. A computational design of porosity enables an accelerated application-specific structure development in the field of engineering

Wicking in Porous Polymeric Membranes: Determination of an Effective Capillary Radius to Predict the Flow Behavior in Lateral Flow Assays

The working principle of lateral flow assays, such as the widely used COVID-19 rapid tests, is based on the capillary-driven liquid transport of a sample fluid to a test line using porous polymeric membranes as the conductive medium. In order to predict this wicking process by simplified analytical models, it is essential to determine an effective capillary radius for the highly porous and open-pored membranes. In this work, a parametric study is performed with selected simplified structures, representing the complex microstructure of the membrane. For this, a phase-field approach with a special wetting boundary condition to describe the meniscus formation and the corresponding mean surface curvature for each structure setup is used. As a main result, an analytical correlation between geometric structure parameters and an effective capillary radius, based on a correction factor, are obtained. The resulting correlation is verified by applying image analysis methods on reconstructed computer tomography scans of two different porous polymeric membranes and thus determining the geometric structure parameters. Subsequently, a macroscale flow model that includes the correlated effective pore size and geometrical capillary radius is applied, and the results are compared with wicking experiments. Based on the derived correction function, it is shown that the analytical prediction of the wicking process in highly porous polymeric membranes is possible without the fitting of experimental wicking data. Furthermore, it can be seen that the estimated effective pore radius of the two membranes is 8 to 10 times higher than their geometric mean pore radii

KadiStudio: FAIR Modelling of Scientific Research Processes

FAIR handling of scientific data plays a significant role in current efforts towards a more sustainable research culture and serves as a prerequisite for the fourth scientific paradigm, that is, data-driven research. To enforce the FAIR principles by ensuring the reproducibility of scientific data and tracking their provenance comprehensibly, the FAIR modelling of research processes in form of automatable workflows is necessary. By providing reusable procedures containing expert knowledge, such workflows contribute decisively to the quality and the acceleration of scientific research. In this work, the requirements for a system to be capable of modelling FAIR workflows are defined and a generic concept for modelling research processes as workflows is developed. For this, research processes are iteratively divided into impartible subprocesses at different detail levels using the input-process-output model. The concrete software implementation of the identified, universally applicable concept is finally presented in form of the workflow editor KadiStudio of the Karlsruhe Data Infrastructure for Materials Science (Kadi4Mat)

Characterization of porous membranes using artificial neural networks

Porous membranes have been utilized intensively in a wide range of fields due to their special characteristics and a rigorous characterization of their microstructures is crucial for understanding their properties and improving the performance for target applications. A promising method for the quantitative analysis of porous structures leverages the physics-based generation of porous structures at the pore scale, which can be validated against real experimental microstructures, followed by building the process–structure–property relationships with data-driven algorithms such as artificial neural networks. In this study, a Variational AutoEncoder (VAE) neural network model is used to characterize the 3D structural information of porous materials and to represent them with low-dimensional latent variables, which further model the structure–property relationship and solve the inverse problem of process–structure linkage combined with the Bayesian optimization method. Our methods provide a quantitative way to learn structural descriptors in an unsupervised manner which can characterize porous microstructures robustly

Identification of direct residue contacts in protein-protein interaction by message passing

Understanding the molecular determinants of specificity in protein-protein interaction is an outstanding challenge of postgenome biology. The availability of large protein databases generated from sequences of hundreds of bacterial genomes enables various statistical approaches to this problem. In this context covariance-based methods have been used to identify correlation between amino acid positions in interacting proteins. However, these methods have an important shortcoming, in that they cannot distinguish between directly and indirectly correlated residues. We developed a method that combines covariance analysis with global inference analysis, adopted from use in statistical physics. Applied to a set of >2,500 representatives of the bacterial two-component signal transduction system, the combination of covariance with global inference successfully and robustly identified residue pairs that are proximal in space without resorting to ad hoc tuning parameters, both for heterointeractions between sensor kinase (SK) and response regulator (RR) proteins and for homointeractions between RR proteins. The spectacular success of this approach illustrates the effectiveness of the global inference approach in identifying direct interaction based on sequence information alone. We expect this method to be applicable soon to interaction surfaces between proteins present in only 1 copy per genome as the number of sequenced genomes continues to expand. Use of this method could significantly increase the potential targets for therapeutic intervention, shed light on the mechanism of protein-protein interaction, and establish the foundation for the accurate prediction of interacting protein partners.Comment: Supplementary information available on http://www.pnas.org/content/106/1/67.abstrac

A U-Net-Based Self-Stitching Method for Generating Periodic Grain Structures

When modeling microstructures, the computational resource requirements increase rapidly as the simulation domain becomes larger. As a result, simulating a small representative fraction under periodic boundary conditions is often a necessary simplification. However, the truncated structures leave nonphysical boundaries, which are detrimental to numerical modeling. Here, we propose a self-stitching algorithm for generating periodic structures, demonstrated in a grain structure. The main idea of our algorithm is to artificially add structural information between mismatched boundary pairs, using the hierarchical spatial predictions of the U-Net. The algorithm provides an automatic and unbiased way to obtain periodic boundaries in grain structures and can be applied to porous microstructures in a similar way