Reimagining Hair Science: A New Approach to Classify Curly Hair Phenotypes via New Quantitative Geometric and Structural Mechanical Parameters.

Hair is a natural polymeric composite primarily composed of tight macrobundles of keratin proteins, which are highly responsive to external stimuli, similarly to the hydrogels and other natural fibrous gel systems like collagen and fibrin.Hair and its appearance play a significant role in human society. As a highly complex biocomposite system, it has been traditionally challenging to characterize and thus develop personal care products. Over the last few decades, a significant societal paradigm shift occurred among those with curly hair, accepting the natural morphological shape of their curls and styling their hair according to its innate, distinct, and unique material properties, which has given rise to the development of new hair classification systems, beyond the traditional and highly limited race-based distinction (Caucasian, Mongolian, and African). L'Oréal developed a hair typing taxonomy based on quantitative geometric parameters among the four key patterns─straight, wavy, curly, and kinky, but it fails to capture the complex diversity of curly and kinky hair. Acclaimed celebrity hair stylist Andre Walker developed a classification system that is the existing gold standard for classifying curly and kinky hair, but it relies upon qualitative classification measures, making the system vague and ambiguous of phenotypic differences. The goal of this research is to use quantitative methods to identify new geometric parameters more representative of curly and kinky hair curl patterns, therefore providing more information on the kinds of personal care products that will resonate best with them and thus maximize desired appearance and health, and to correlate these new parameters with its mechanical properties. This was accomplished by identifying new geometric and mechanical parameters from several types of human hair samples.Geometric properties were measured using scanning electron microscopy (SEM), photogrammetry, and optical microscopy. Mechanical properties were measured under tensile extension using a texture analyzer (TA) and a dynamic mechanical analyzer (DMA), which bears similarity to the common act of brushing or combing. Both instruments measure force as a function of applied displacement, thus allowing the relationship between stress and applied stretch ratio to be measured as a hair strand uncurls and stretches to the point of fracture. From the resulting data, correlations were made between fiber geometry and mechanical performance. This data will be used to draw more conclusions on the contribution that fiber morphology has on hair fiber mechanics and will promote cultural inclusion among researchers and consumers possessing curly and kinky hair.

3D visualization processes for recreating and studying organismal form.

The study of biological form is a vital goal of evolutionary biology and functional morphology. We review an emerging set of methods that allow scientists to create and study accurate 3D models of living organisms and animate those models for biomechanical and fluid dynamic analyses. The methods for creating such models include 3D photogrammetry, laser and CT scanning, and 3D software. New multi-camera devices can be used to create accurate 3D models of living animals in the wild and captivity. New websites and virtual reality/augmented reality devices now enable the visualization and sharing of these data. We provide examples of these approaches for animals ranging from large whales to lizards and show applications for several areas: Natural history collections; body condition/scaling, bioinspired robotics, computational fluids dynamics (CFD), machine learning, and education. We provide two datasets to demonstrate the efficacy of CFD and machine learning approaches and conclude with a prospectus.

A rapid and cost-effective pipeline for digitization of museum specimens with 3D photogrammetry.

Natural history collections are yielding more information as digitization brings specimen data to researchers, connects specimens across museums, and as new technologies allow for more large-scale data collection. Therefore, a key goal in specimen digitization is developing methods that both increase access and allow for the highest yield of phenomic data. 3D digitization is increasingly popular because it has the potential to meet both aspects of that key goal. However, current methods overlook or do not prioritize some of the most sought-after phenotypic traits, those involving the external appearance of specimens, especially color. Here, we introduce an efficient and cost-effective pipeline for 3D photogrammetry to capture the external appearance of natural history specimens and other museum objects. 3D photogrammetry aligns and compares sets of dozens, hundreds, or even thousands of photos to create 3D models. The hardware set-up requires little physical space and around $3,000 in initial investment, while the software pipeline requires $1,400/year for proprietary software subscriptions (with open-source alternatives). The creation of each 3D model takes 1-2 hours/specimen and much of the software pipeline is automated with minimal supervision required, including the onerous step of mesh processing. We showcase the method by creating 3D models for most of the type specimens in the Moore Laboratory of Zoology bird collection and show that digital bill measurements are comparable to hand-taken measurements. Color data, while not included as part of this pipeline, is easily extractable from the models and one of the most promising areas of data collection. Future advances can adapt the method for ultraviolet reflectance capture and increased efficiency and model quality. Combined with genomic data, phenomic data from 3D models including photogrammetry will open new doors to understanding organismal evolution.