Biology subjects

Schillinger, D.

Publications and source records attributed to Schillinger, D..

3 recordsLinked to original sources

bioRxiv2026

Inferring scaling laws of branching networks from their geometry by global optimization

Optimality principles are widely used to understand branching networks, yet they are tested on individual branchings that cannot reliably distinguish competing scaling laws. We formulate the missing link between the laws and measurements by mapping each law to its predicted branch points through global optimization. Vascular branch points closely match weighted-length optima when each vessel is allowed its own fitted weight. Inferred scaling laws reveal type-specific geometric signatures hidden from local analysis. They beat all tested laws, including Murray's law in 1,679 of 1,744 held-out hepatic and pulmonary trees. For coronary arteries on the heart surface, an exact discrete optimization model extends the framework beyond convex geometry and supports area-preserving scaling. The framework also identifies distinct laws for airways and plant shoots while rejecting weighted-length optimality for neuronal axons. Global optimization turns network geometry into a quantitative test of the principles that govern branching networks.

biophysics↗

bioRxiv2025

In Vivo Network-Level Cerebrovascular Mapping Reveals the Impact of Flow Topology on Capillary Stalls After Stroke

Cerebral microvasculature is essential for brain function, but how flow and large-scale connectivity contribute to its resilience or failure remains poorly understood. To address this, we developed OMNIMap, a framework for mesoscale in vivo mapping of functional microvascular networks, capturing flow dynamics and connectivity across thousands of capillaries. OMNIMap integrates extended-focus optical coherence microscopy and learning-based segmentation with global vessel-graph optimization to resolve artery-vein classification and branching order, linking capillary flow and stalls to broader network context. Applied to over 40,000 capillaries in the mouse cortex before and after ischemic stroke, we observe heterogeneous vulnerability patterns: while most capillaries stall or reduce flow after arterial occlusion, some experience accelerated flow. Further analysis revealed that stall-prone flow topology subtypes were less prevalent than their robust counterparts. Notably, the overall distribution of these subtypes remains largely preserved after stroke, revealing a previously unrecognized, system-level organizing principle that alleviates the impact of individual capillary stalls to maintain network-level perfusion.

neuroscience↗

bioRxiv2018

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study

Limited health literacy can be a barrier to healthcare delivery, but widespread classification of patient health literacy is challenging. We applied natural language processing and machine learning on a large sample of 283,216 secure messages sent from 6,941 patients to their clinicians for this study to develop and validate literacy profiles as indicators of patients health literacy. All patients were participants in Kaiser Permanente Northern Californias DISTANCE Study. We created three literacy profiles, comparing performance of each literacy profile against a gold standard of patient self-report. We also analyzed associations between the literacy profiles and patient demographics, health outcomes and healthcare utilization. T-tests were used for numeric data such as A1C, Charlson comorbidity index and healthcare utilization rates, and chi-square tests for categorical data such as sex, race, continuous medication gaps and severe hypoglycemia. Literacy profiles varied in their test characteristics, with C-statistics ranging from 0.61-0.74. Relationships between literacy profiles and health outcomes revealed patterns consistent with previous health literacy research: patients identified via literacy profiles as having limited health literacy were older and more likely minority; had poorer medication adherence and glycemic control; and higher rates of hypoglycemia, comorbidities and healthcare utilization. This research represents the first successful attempt to use natural language processing and machine learning to measure health literacy. Literacy profiles offer an automated and economical way to identify patients with limited health literacy and a greater vulnerability to poor health outcomes.

bioinformatics↗