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Analysis of muscle architecture, traditionally conducted via gross dissection, has been used to evaluate adaptive relationships between anatomical form and behavioral function. However, gross dissection cannot preserve three‐dimensional relationships between myological structures for analysis. To analyze such data, we employ diffusible, iodine‐based contrast‐enhanced computed tomography (DiceCT) to explore the relationships between feeding ecology and masticatory muscle microanatomy in eight dietarily diverse strepsirrhines: allowing, for the first time, preservation of three‐dimensional fascicle orientation and tortuosity across a functional comparative sample. We find that fascicle properties derived from these digital analyses generally agree with those measured from gross‐dissected conspecifics. Physiological cross‐sectional area was greatest in species with mechanically challenging diets. Frugivorous taxa and the wood‐gouging species all exhibit long jaw adductor fascicles, while more folivorous species show the shortest relative jaw adductor fascicle lengths. Fascicle orientation in the parasagittal plane also seems to have a clear dietary association: most folivorous taxa have masseter and temporalis muscle vectors that intersect acutely while these vectors intersect obliquely in more frugivorous species. Finally, we observed notably greater magnitudes of fascicle tortuosity, as well as greater interspecific variation in tortuosity, within the jaw adductor musculature than in the jaw abductors. While the use of a single specimen per species precludes analysis of intraspecific variation, our data highlight the diversity of microanatomical variation that exists within the strepsirrhine feeding system and suggest that muscle architectural configurations are evolutionarily labile in response to dietary ecology—an observation to be explored across larger samples in the future.

 

Stomatal density and length were measured on leaves of sugar maple (Acer sacharrum Marsh.) and yellow birch (Betula alleghaniensis Britton.) trees in New Hampshire at the Bartlett Experimental Forest, Hubbard Brook Experimental Forest, and Jeffers Brook as part of the Multiple Elementation Limitation in Northern Hardwood Ecosystems (MELNHE) study. Leaves were collected in late July and early August in 2021 and 2022 from the tops of dominant and codominant trees using a shotgun. These measurements were made on 3 leaves from each tree. These data correspond with other foliar trait data collected from the same trees in 2021 and 2022. That EDI package is as follows: Hong, S.D., K.E. Gonzales, C.R. See, and R.D. Yanai. 2021. MELNHE: Foliar Chemistry 2008-2016 in Bartlett, Hubbard Brook, and Jeffers Brook (12 stands) ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/b23deb8e1ccf1c1413382bf911c6be19 This data package contains the raw images underlying the data reported in a separate data package on stomatal density and length: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=372 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. 

Stomatal density and length were measured on leaves of sugar maple (Acer sacharrum Marsh.) and yellow birch (Betula alleghaniensis Britton.) trees in New Hampshire at the Bartlett Experimental Forest, Hubbard Brook Experimental Forest, and Jeffers Brook as part of the Multiple Elementation Limitation in Northern Hardwood Ecosystems (MELNHE) study. Leaves were collected in late July and early August in 2021 and 2022 from the tops of dominant and codominant trees using a shotgun. These measurements were made on 3 leaves from each tree. These data correspond with other foliar trait data collected from the same trees in 2021 and 2022. That EDI package is as follows: Hong, S.D., K.E. Gonzales, C.R. See, and R.D. Yanai. 2021. MELNHE: Foliar Chemistry 2008-2016 in Bartlett, Hubbard Brook, and Jeffers Brook (12 stands) ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/b23deb8e1ccf1c1413382bf911c6be19 This data package contains the stomatal density and length derived from the raw images in a separate EDI data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=321 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. 

Abstract Protamine proteins dramatically condense DNA in sperm to almost crystalline packing levels. Here, we measure the first step in the in vitro pathway, the folding of DNA into a single loop. Current models for DNA loop formation are one-step, all-or-nothing models with a looped state and an unlooped state. However, when we use a Tethered Particle Motion (TPM) assay to measure the dynamic, real-time looping of DNA by protamine, we observe the presence of multiple folded states that are long-lived (∼100 s) and reversible. In addition, we measure folding on DNA molecules that are too short to form loops. This suggests that protamine is using a multi-step process to loop the DNA rather than a one-step process. To visualize the DNA structures, we used an Atomic Force Microscopy (AFM) assay. We see that some folded DNA molecules are loops with a ∼10-nm radius and some of the folded molecules are partial loops—c-shapes or s-shapes—that have a radius of curvature of ∼10 nm. Further analysis of these structures suggest that protamine is bending the DNA to achieve this curvature rather than increasing the flexibility of the DNA. We therefore conclude that protamine loops DNA in multiple steps, bending it into a loop. 

Overhauling a laboratory experiment, course, or curriculum is a daunting process. Here, I describe a four-step process our department used to overhaul our laboratory curriculum and courses. This four-step process includes: 1) identifying learning goals, 2) describing current practices, 3) making changes, and 4) planning for assessment. In addition, I describe how we updated experiments in the courses to be “3D”. These “3D” experiments are designed to meet three different types of goals: theoretical goals, experimental goals, and computational goals.