Fast and accurate lacunarity calculation for large 3D micro-CT datasets

A robot-rodent interaction arena with adjustable spatial complexity for ethologically relevant behavioral studies

Outside of the laboratory, animals behave in spaces where they can transition between open areas and coverage as they interact with others. Replicating these conditions in the laboratory can be difficult to control and record. This has led to a dominance of relatively simple, static behavioral paradigms that reduce the ethological relevance of behaviors and may alter the engagement of cognitive processes such as planning and decision-making. Therefore, we developed a method for controllable, repeatable interactions with others in a reconfigurable space. Mice navigate a large honeycomb lattice of adjustable obstacles as they interact with an autonomous robot coupled to their actions. We illustrate the system using the robot as a pseudo-predator, delivering airpuffs to the mice. The combination of obstacles and a mobile threat elicits a diverse set of behaviors, such as increased path diversity, peeking, and baiting, providing a method to explore ethologically relevant behaviors in the laboratory.

Effects of solvent conditions on the self-assembly of heterotrimeric collagen-like peptide (CLP) triple helices: a coarse-grained simulation study

We perform coarse-grained (CG) molecular dynamics (MD) simulations to investigate the self-assembly of collagen-like peptide (CLP) triple helices into fibrillar structures and percolated networks as a function of solvent quality. The focus of this study is on CLP triple helices whose strands are different lengths (i.e., heterotrimers), leading to dangling 'sticky ends'. These 'sticky ends' are segments of the CLP strands that have unbonded hydrogen-bonding donor/acceptor sites that drive heterotrimeric CLP triple helices to physically associate with one another, leading to assembly into higher-order structures. We use a validated CG model for CLP in implicit solvent and capture varying solvent quality through changing strength of attraction between CG beads representing the amino acids in the CLP strands. Our CG MD simulations show that, at lower CLP concentrations, CLP heterotrimers assemble into fibrils and, at higher CLP concentrations, into percolated networks. At higher concentrations, decreasing solvent quality causes (i) the formation of heterogeneous network structures with a lower degree of branching at network junctions and (ii) increases in the diameter of network strands and pore sizes. We also observe a nonmonotonic effect of solvent quality on distances between network junctions due to the balance between heterotrimer end-end associations driven by hydrogen bonding and side-side associations driven by worsening solvent quality. Below the percolation threshold, we observe that decreasing solvent quality leads to the formation of fibrils composed of multiple aligned CLP triple helices, while the number of 'sticky ends' governs the spatial extent (radius of gyration) of the assembled fibrils.

The Fractal Viewpoint of Tumors and Nanoparticles

Even though the promising therapies against cancer are rapidly improved, the oncology patients population has seen exponential growth, placing cancer in 5th place among the ten deadliest diseases. Efficient drug delivery systems must overcome multiple barriers and maximize drug delivery to the target tumors, simultaneously limiting side effects. Since the first observation of the quantum tunneling phenomenon, many multidisciplinary studies have offered quantum-inspired solutions to optimized tumor mapping and efficient nanodrug design. The property of a wave function to propagate through a potential barrier offer the capability of obtaining 3D surface profiles using imaging of individual atoms on the surface of a material. The application of quantum tunneling on a scanning tunneling microscope offers an exact surface roughness mapping of tumors and pharmaceutical particles. Critical elements to cancer nanotherapeutics apply the fractal theory and calculate the fractal dimension for efficient tumor surface imaging at the atomic level. This review study presents the latest biological approaches to cancer management based on fractal geometry.

Application of Lacunarity for Quantification of Single Molecule Localization Microscopy Images

The quantitative analysis of datasets achieved by single molecule localization microscopy is vital for studying the structure of subcellular organizations. Cluster analysis has emerged as a multi-faceted tool in the structural analysis of localization datasets. However, the results it produces greatly depend on the set parameters, and the process can be computationally intensive. Here we present a new approach for structural analysis using lacunarity. Unlike cluster analysis, lacunarity can be calculated quickly while providing definitive information about the structure of the localizations. Using simulated data, we demonstrate how lacunarity results can be interpreted. We use these interpretations to compare our lacunarity analysis with our previous cluster analysis-based results in the field of DNA repair, showing the new algorithm’s efficiency.