Revealing structure-function relationships in functional flow networks via persistent homology

Complex networks encountered in biology are often characterized by significant structural diversity. Whether it be differences in the three-dimensional structure of allosteric proteins, or the variation among the micro-scale structures of organisms' cerebral vasculature systems, identifying relationships between structure and function often poses a difficult challenge. Here we showcase an approach to characterizing structure-function relationships in complex networks applied in the context of flow networks tuned to perform specific functions. Using persistent homology, we analyze flow networks tuned to perform complex multifunctional tasks, answering the question of how local changes in the network structure coordinate to create functionality at at the scale of the entire network. We find that the response of such networks encodes hidden topological features - sectors of uniform pressure - that are not apparent in the underlying network architectures, Regardless of differences in local connectivity, these features provide a universal topological description for all networks that perform these types of functions. We show that these features correlate strongly with the tuned response, providing a clear topological relationship between structure and function and structural insight into the limits of multifunctionality.Comment: 22 pages (double column), 12 figure

Theory of Non-equilibrium Single Electron Dynamics in STM Imaging of Dangling Bonds on a Hydrogenated Silicon Surface

During fabrication and scanning-tunneling-microscope (STM) imaging of dangling bonds (DBs) on a hydrogenated silicon surface, we consistently observed halo-like features around isolated DBs for specific imaging conditions. These surround individual or small groups of DBs, have abnormally sharp edges, and cannot be explained by conventional STM theory. Here we investigate the nature of these features by a comprehensive 3-dimensional model of elastic and inelastic charge transfer in the vicinity of a DB. Our essential finding is that non-equilibrium current through the localized electronic state of a DB determines the charging state of the DB. This localized charge distorts the electronic bands of the silicon sample, which in turn affects the STM current in that vicinity causing the halo effect. The influence of various imaging conditions and characteristics of the sample on STM images of DBs is also investigated.Comment: 33 pages, 9 figure

Bacteriophages of wastewater foaming-associated filamentous Gordonia reduce host levels in raw activated sludge

Filamentous bacteria are a normal and necessary component of the activated sludge wastewater treatment process, but the overgrowth of filamentous bacteria results in foaming and bulking associated disruptions. Bacteriophages, or phages, were investigated for their potential to reduce the titer of foaming bacteria in a mixed-microbial activated sludge matrix. Foaming-associated filamentous bacteria were isolated from activated sludge of a commercial wastewater treatment plan and identified as Gordonia species by 16S rDNA sequencing. Four representative phages were isolated that target G. malaquae and two un-named Gordonia species isolates. Electron microscopy revealed the phages to be siphophages with long tails. Three of the phages - GordTnk2, Gmala1, and GordDuk1 - had very similar ~76 kb genomes, with >93% DNA identity. These genomes shared limited synteny with Rhodococcus equi phage ReqiDocB7 and Gordonia phage GTE7. In contrast, the genome of phage Gsput1 was smaller (43 kb) and was not similar enough to any known phage to be placed within an established phage type. Application of these four phages at MOIs of 5–15 significantly reduced Gordonia host levels in a wastewater sludge model by approximately 10-fold as compared to non-phage treated reactors. Phage control was observed for nine days after treatment

Differential Effects of Lipid-lowering Drugs in Modulating Morphology of Cholesterol Particles.

Treatment of dyslipidemia patients with lipid-lowering drugs leads to a significant reduction in low-density lipoproteins (LDL) level and a low to moderate level of increase in high-density lipoprotein (HDL) cholesterol in plasma. However, a possible role of these drugs in altering morphology and distribution of cholesterol particles is poorly understood. Here, we describe the in vitro evaluation of lipid-lowering drug effects in modulating morphological features of cholesterol particles using the plaque array method in combination with imaging flow cytometry. Image analyses of the cholesterol particles indicated that lovastatin, simvastatin, ezetimibe, and atorvastatin induce the formation of both globular and linear strand-shaped particles, whereas niacin, fibrates, fluvastatin, and rosuvastatin induce the formation of only globular-shaped particles. Next, purified very low-density lipoprotein (VLDL) and LDL particles incubated with these drugs showed changes in the morphology and image texture of cholesterol particles subpopulations. Furthermore, screening of 50 serum samples revealed the presence of a higher level of linear shaped HDL cholesterol particles in subjects with dyslipidemia (mean of 18.3%) compared to the age-matched normal (mean of 11.1%) samples. We also observed considerable variations in lipid-lowering drug effects on reducing linear shaped LDL and HDL cholesterol particles formation in serum samples. These findings indicate that lipid-lowering drugs, in addition to their cell-mediated hypolipidemic effects, may directly modulate morphology of cholesterol particles by a non-enzymatic mechanism of action. The outcomes of these results have potential to inform diagnosis of atherosclerosis and predict optimal lipid-lowering therapy

Two-period linear mixed effects models to analyze clinical trials with run-in data when the primary outcome is continuous: Applications to Alzheimer\u27s disease.

Introduction: Study outcomes can be measured repeatedly based on the clinical trial protocol before randomization during what is known as the run-in period. However, it has not been established how best to incorporate run-in data into the primary analysis of the trial. Methods: We proposed two-period (run-in period and randomization period) linear mixed effects models to simultaneously model the run-in data and the postrandomization data. Results: Compared with the traditional models, the two-period linear mixed effects models can increase the power up to 15% and yield similar power for both unequal randomization and equal randomization. Discussion: Given that analysis of run-in data using the two-period linear mixed effects models allows more participants (unequal randomization) to be on the active treatment with similar power to that of the equal-randomization trials, it may reduce the dropout by assigning more participants to the active treatment and thus improve the efficiency of AD clinical trials