Using Light Polarization to Identify Fiber Orientation in Carbon Fiber Components: Metrological Analysis.

In this work, a method for measuring tow angles in carbon fiber components, based on the use of a polarized camera, is analyzed from a metrological point of view. Carbon fibers alter the direction of the reflected light's electrical field, so that in each point of the surface of a composite piece, the angle of polarization of reflected light matches the fiber orientation. A statistical analysis of the angle of linear polarization (AoLP) in each pixel of each examined area allows to evaluate the average winding angle. An evaluation of the measurement uncertainty of the method on a cylinder obtained by a filament winding process is carried out, and the result appears adequate for the study of the distribution of angles along the surface of the piece, in order to optimize the process.

Influence of Fiber Orientation on Mechanical Response of Jute Fiber-Reinforced Polymer Composites.

The influence of fiber orientation on the mechanical behavior of a polymer matrix composite reinforced with natural jute fibers is investigated in this study. Two fiber orientation configurations are examined: the first involves woven fibers aligned in the direction of testing, while the second considers a 45° orientation. The research involves manufacturing composite plates using jute fabric with the mentioned orientations, followed by cutting rectangular specimens for tensile testing to determine which orientation yields superior properties. Displacement fields are measured using a digital image correlation technique, synchronized with load data obtained from a universal testing machine equipped with a load cell to obtain stress-strain curves for each configuration. Results indicate that 0° specimens achieve higher stress but lower strain compared to 45° specimens. This research contributes to understanding the optimal fiber alignment for enhancing the mechanical performance of fiber-reinforced polymer composites.

Updated Static Influential Factor Analysis for Unidirectional Carbon-Based Composites.

The orientation of carbon fibers significantly affects the dynamic properties of unidirectional carbon-based composites (UCBCs), with variations under different static loads. A previous study analyzed changes in the modal parameters of UCBC structures by using the static load influential factor (SLIF). This study introduces a revised SLIF, derived from a simplified formulation that accounts for shifts in resonance frequency and the in-phase relationship between static load and modal response. The revised SLIF is theoretically linked to the modal participation factor in UCBC structures. The dynamic behavior of UCBCs was studied across six modes-four bending and two torsional-using specimens with five carbon fiber orientations, from 0 to 90 degrees. The revised SLIF showed significant effects in two robust specimens, #1 and #2, and an isotropic SUS304 specimen subjected to uniaxial pre-static load, with resonance frequency variations under 0.16%. In contrast, the original SLIF gave negligible results in the fifth mode due to a damping term, which, when multiplied by the resonance frequency, led to an undetectable indicator. Therefore, the revised SLIF more effectively captures the static load's impact on UCBC dynamic behavior compared with the original method.

Effect of Process Parameters on the Appearance of Defects of Flake-Pigmented Metallic Polymer.

This study investigates the influence of the main process parameters of injection molding(mold temperature, melt temperature, and injection rate) on the appearance of defects of flake-pigmented metallic polymer parts. To understand the influence of process parameters, an appearance defects index (ADI) is proposed to quantify the appearance defects. In this process, we propose a criterion for judging the appearance of defects based on the results of fiber orientation and tensor distribution analyses of the skin layer, which is then verified analytically by simulating experiments from the literature. Using the Taguchi experimental method, we designed an L25 orthogonal array to systematically evaluate the influence of process parameters. For each experimental condition, the signal-to-noise ratio (S/N ratio) was calculated to determine the optimal level of each factor and its influence on the appearance of defects. According to the results, mold temperature has the greatest influence on the appearance of defects, with an influence of 48.7%, followed by injection rate with an influence of 40.8%, and melt temperature with an influence of 10.5%. The optimal process parameters were found to be a mold temperature of 40 °C, a melt temperature of 250 °C, and an injection rate of 10 cm3/s, which resulted in a 12.6% improvement in the Appearance defects index (ADI) compared to the standard injection molding condition of ABS materials. This study confirmed that it is possible to improve the appearance of defects by adjusting the process parameters of injection molding.

Diffusion Model-based FOD Restoration from High Distortion in dMRI.

Fiber orientation distributions (FODs) is a popular model to represent the diffusion MRI (dMRI) data. However, imaging artifacts such as susceptibility-induced distortion in dMRI can cause signal loss and lead to the corrupted reconstruction of FODs, which prohibits successful fiber tracking and connectivity analysis in affected brain regions such as the brain stem. Generative models, such as the diffusion models, have been successfully applied in various image restoration tasks. However, their application on FOD images poses unique challenges since FODs are 4-dimensional data represented by spherical harmonics (SPHARM) with the 4-th dimension exhibiting order-related dependency. In this paper, we propose a novel diffusion model for FOD restoration that can recover the signal loss caused by distortion artifacts. We use volume-order encoding to enhance the ability of the diffusion model to generate individual FOD volumes at all SPHARM orders. Moreover, we add cross-attention features extracted across all SPHARM orders in generating every individual FOD volume to capture the order-related dependency across FOD volumes. We also condition the diffusion model with low-distortion FODs surrounding high-distortion areas to maintain the geometric coherence of the generated FODs. We trained and tested our model using data from the UK Biobank (n = 1315). On a test set with ground truth (n = 43), we demonstrate the high accuracy of the generated FODs in terms of root mean square errors of FOD volumes and angular errors of FOD peaks. We also apply our method to a test set with large distortion in the brain stem area (n = 1172) and demonstrate the efficacy of our method in restoring the FOD integrity and, hence, greatly improving tractography performance in affected brain regions.

Evaluation of the Modal Parameters of a Unidirectional Carbon-Based Composite Structure Using the Influential Factor of Static Loading.

Static loading can significantly alter the dynamics of unidirectional carbon-based composites (UCBCs), with modal parameters varying depending on the orientation of the carbon fibers. In this study, the sensitivity of modal parameters of UCBC structures under uniaxial static loading was investigated. The theoretical static load influential factor was derived from a linearized UCBC model and corresponded to the transformed decoupled response over the mass-normalized static load. Three rectangular UCBC specimens (carbon fiber orientation of 0°, 45°, and 90°) were prepared under fixed-fixed boundary conditions using a jig fixture. Uniaxial static loads between 0 N and 1000 N were applied, and the first three modes of the UCBC specimens were analyzed. An isotropic SUS304 specimen was used as a reference. The linearization assumption about the UCBC structure was preliminarily validated with the Modal Assurance Criterion (MAC). A high influential factor was found for the UCBC specimen when carbon fibers were aligned with the static load direction at the first two resonance frequencies. Therefore, the proposed influential factor is an efficient indicator for determining the sensitivity of the dynamic response of a UCBC structure over a static load case. The variations in the influential factors for the UCBC specimens were more pronounced than for the isotropic specimens.

Roles of collagen cross-links and osteon collagen/lamellar morphotypes in equine third metacarpals in tension and compression tests.

Many bones experience bending, placing one side in net compression and the other in net tension. Because bone mechanical properties are relatively reduced in tension compared with compression, adaptations are needed to reduce fracture risk. Several toughening mechanisms exist in bone, yet little is known of the influences of secondary osteon collagen/lamellar 'morphotypes' and potential interplay with intermolecular collagen cross-links (CCLs) in prevalent/predominant tension- and compression-loaded regions. Paired third metacarpals (MC3s) from 10 adult horses were prepared for mechanical testing. From one MC3/pair, 5 mm cubes were tested in compression at several mid-shaft locations. From contralateral bones, dumbbell-shaped specimens were tested in tension. Hence, habitual/natural tension- and compression-loaded regions were tested in both modes. Data included: elastic modulus, yield and ultimate strength, and energy absorption (toughness). Fragments of tested specimens were examined for predominant collagen fiber orientation (CFO; representing osteonal and non-osteonal bone), osteon morphotype score (MTS, representing osteonal CFO), mineralization, porosity and other histological characteristics. As a consequence of insufficient material from tension-tested specimens, CCLs were only examined in compression-tested specimens (HP, hydroxylysylpyridinoline; LP, lysylpyridinoline; PE, pentosidine). Among CCLs, only LP and HP/LP correlated significantly with mechanical parameters: LP with energy absorption, HP/LP with elastic modulus (both r=0.4). HP/LP showed a trend with energy absorption (r=-0.3, P=0.08). HP/LP more strongly correlated with osteon density and mineralization than CFO or MTS. Predominant CFO more strongly correlated with energy absorption than MTS in both testing modes. In general, CFO was found to be relatively prominent in affecting regional toughness in these equine MC3s in compression and tension.

Enhancing UHPC Tensile Performance Using Polystyrene Beads: Significant Improvements and Mechanisms.

This study investigates utilizing spherical polystyrene (PS) beads as artificial flaws to improve ultrahigh-performance concrete (UHPC) tensile performance using a uniaxial tensile test and explains the corresponding mechanisms by analyzing the internal material structure of UHPC specimens with X-ray CT scanning. With a hooked steel fiber volume fraction of 2%, three PS bead dosages were employed to study tensile behavior changes in dog-bone UHPC specimens. A 33.4% increase in ultimate tensile strength and 174.8% increase in ultimate tensile strain were recorded after adding PS beads with a volume fraction of 2%. To explain this improvement, X-ray CT scanning was utilized to investigate the post-test internal material structures of the dog-bone specimens. AVIZO software was used to analyze the CT information. The CT results revealed that PS beads could not only serve as the artificial flaws to increase the cracking behavior of the matrix of UHPC but also significantly optimize the fiber orientation. The PS beads could serve as stirrers during the mixing process to distribute fiber more uniformly. The test results indicate a relationship between fiber orientation and UHPC tensile strength.

Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study.

Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results than standard methods such as Constrained Spherical Deconvolution and two state-of-the-art deep learning methods. For voxels with one and two fibers, respectively, our method shows an agreement rate in terms of the number of fibers of 77.5% and 22.2%, which is 3% and 5.4% higher than other deep learning methods, and an angular error of 10° and 20°, which is 6° and 5° lower than other deep learning methods. To determine baselines for assessing the performance of our method, we compute agreement metrics using densely sampled newborn data. Moreover, we demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical external datasets of newborns and fetuses. We validate fetal FODs, successfully estimated for the first time with deep learning, using post-mortem histological data. Our results show the advantage of deep learning in computing the fiber orientation density for the developing brain from in-vivo dMRI measurements that are often very limited due to constrained acquisition times. Our findings also highlight the intrinsic limitations of dMRI for probing the developing brain microstructure.

Improved algorithm for generating evenly-spaced streamlines from an orientation field on a triangulated surface.

BACKGROUND: Vector fields such as cardiac fiber orientation can be visualized on a surface using streamlines. The application of evenly-spaced streamline generation to the construction of interconnected cable structure for cardiac propagation models has more stringent requirements imperfectly fulfilled by current algorithms. METHOD: We developed an open-source C++/python package for the placement of evenly-spaced streamlines on a triangulated surface. The new algorithm improves upon previous works by more accurately handling streamline extremities, U-turns and limit cycles, by providing stronger geometrical guarantees on inter-streamline minimal distance, particularly when a high density of streamlines (up to 10µm spacing) is desired, and by making a more efficient parallel implementation available. The approach requires finding intersections between geometrical capsules and triangles to update an occupancy mask defined on the triangles. This enables fast streamline integration from thousands of seed points to identify optimal streamline placement. RESULTS: The algorithm was assessed qualitatively on different left atrial models of fiber orientation with varying mesh resolutions (up to 375k triangles) and quantitatively by measuring streamline lengths and distribution of inter-streamline minimal distance. The complexity and the computational performance of the algorithm were studied as a function of streamline spacing in relation to triangular mesh resolution. CONCLUSION: More accurate geometrical computations, attention to details and fine-tuning led to an algorithm more amenable to applications that require precise positioning of streamlines.

Study on the Influence of Runner and Overflow Area Design on Flow-Fiber Coupling in a Multi-Cavity System.

Fiber-reinforced composites (FRPs) are characterized by their lightweight nature and superior mechanical characteristics, rendering them extensively utilized across various sectors such as aerospace and automotive industries. Nevertheless, the precise mechanisms governing the interaction between the fibers present in FRPs and the polymer melt during industrial processing, particularly the manipulation of the flow-fiber coupling effect, remain incompletely elucidated. Hence, this study introduces a geometrically symmetrical 1 × 4 multi-cavity mold system, where each cavity conforms to the ASTM D638 Type V standard specimen. The research utilizes theoretical simulation analysis and experimental validation to investigate the influence of runner and overflow design on the flow-fiber coupling effect. The findings indicate that the polymer melt, directed by a geometrically symmetrical runner, results in consistent fiber orientation within each mold cavity. Furthermore, in the context of simulation analysis, the inclusion of the flow-fiber coupling effect within the system results in elevated sprue pressure levels and an expanded core layer region in comparison to systems lacking this coupling effect. This observation aligns well with the existing literature on the subject. Moreover, analysis of fiber orientation in different flow field areas reveals that the addition of an overflow area alters the flow field, leading to a significant delay in the flow-fiber coupling effect. To demonstrate the impact of overflow area design on the flow-fiber effect, the integration of fiber orientation distribution analysis highlights a transformation in fiber arrangement from the flow direction to cross-flow and thickness directions near the end-of-fill region in the injected part. Additionally, examination of the geometric dimensions of the injected part reveals asymmetrical geometric shrinkage between upstream and downstream areas in the end-of-fill region, consistent with microscopic fiber orientation changes influenced by the delayed flow-fiber coupling effect guided by the overflow area. In brief, the introduction of the overflow area extends the duration in which the polymer melt exerts control in the flow direction, consequently prolonging the period in which the fiber orientation governs in the flow direction (A11). This leads to the impact of fiber orientation on the flow of the polymer melt, with the flow reciprocally affecting the fibers. Subsequently, the interaction between these two elements persists until a state of equilibrium is achieved, known as the flow-fiber coupling effect, which is delayed.

Bioprinting-Assisted Tissue Assembly for Structural and Functional Modulation of Engineered Heart Tissue Mimicking Left Ventricular Myocardial Fiber Orientation.

Left ventricular twist is influenced by the unique oriented structure of myocardial fibers. Replicating this intricate structural-functional relationship in an in vitro heart model remains challenging, mainly due to the difficulties in achieving a complex structure with synchrony between layers. This study introduces a novel approach through the utilization of bioprinting-assisted tissue assembly (BATA)-a synergistic integration of bioprinting and tissue assembly strategies. By flexibly manufacturing tissue modules and assembly platforms, BATA can create structures that traditional methods find difficult to achieve. This approach integrates engineered heart tissue (EHT) modules, each with intrinsic functional and structural characteristics, into a layered, multi-oriented tissue in a controlled manner. EHTs assembled in different orientations exhibit various contractile forces and electrical signal patterns. The BATA is capable of constructing complex myocardial fiber orientations within a chamber-like structure (MoCha). MoCha replicates the native cardiac architecture by exhibiting three layers and three alignment directions, and it reproduces the left ventricular twist by exhibiting synchronized contraction between layers and mimicking the native cardiac architecture. The potential of BATA extends to engineering tissues capable of constructing and functioning as complete organs on a large scale. This advancement holds the promise of realizing future organ-on-demand technology.

NVAM-Net: deep learning networks for reconstructing high-quality fiber orientation distributions.

PURPOSE: Diffusion magnetic resonance imaging (dMRI) is a widely used non-invasive method for investigating brain anatomical structures. Conventional techniques for estimating fiber orientation distribution (FOD) from dMRI data often neglect voxel-level spatial relationships, leading to ambiguous associations between target voxels and their neighbors, which, in turn, adversely impacts FOD accuracy. This study aims to address this issue by introducing a novel neural network, the neighboring voxel attention mechanism network (NVAM-Net), designed to reconstruct high-quality FOD images. METHODS: The NVAM-Net leverages a Transformer architecture and incorporates two innovative attention mechanisms: voxel attention and surface attention. These mechanisms are specifically designed to capture overlooked features among neighboring voxels. The processed features are subsequently passed through two fully connected layers, further enhancing FOD estimation accuracy by separately estimating spherical harmonics (SH) coefficients of varying orders. RESULTS: The experimental findings, based on the Human Connectome Project (HCP) dataset, reveal that the reconstructed super-resolution FOD images achieve results comparable to those obtained through more advanced dMRI acquisition protocols. These results underscore the NVAM-Net's robust performance in reconstructing multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD). CONCLUSION: In summary, this research underscores the NVAM-Net's advantages and practical feasibility in reconstructing high-quality FOD images. It provides a reliable reference point for clinical applications in the field of diffusion magnetic resonance imaging.

Voxel-Wise Fusion of 3T and 7T Diffusion MRI Data to Extract more Accurate Fiber Orientations.

While 7T diffusion magnetic resonance imaging (dMRI) has high spatial resolution, its diffusion imaging quality is usually affected by signal loss due to B1 inhomogeneity, T2 decay, susceptibility, and chemical shift. In contrast, 3T dMRI has relative higher diffusion angular resolution, but lower spatial resolution. Combination of 3T and 7T dMRI, thus, may provide more detailed and accurate information about the voxel-wise fiber orientations to better understand the structural brain connectivity. However, this topic has not yet been thoroughly explored until now. In this study, we explored the feasibility of fusing 3T and 7T dMRI data to extract voxel-wise quantitative parameters at higher spatial resolution. After 3T and 7T dMRI data was preprocessed, respectively, 3T dMRI volumes were coregistered into 7T dMRI space. Then, 7T dMRI data was harmonized to the coregistered 3T dMRI B0 (b = 0) images. Last, harmonized 7T dMRI data was fused with 3T dMRI data according to four fusion rules proposed in this study. We employed high-quality 3T and 7T dMRI datasets (N = 24) from the Human Connectome Project to test our algorithms. The diffusion tensors (DTs) and orientation distribution functions (ODFs) estimated from the 3T-7T fused dMRI volumes were statistically analyzed. More voxels containing multiple fiber populations were found from the fused dMRI data than from 7T dMRI data set. Moreover, extra fiber directions were extracted in temporal brain regions from the fused dMRI data at Otsu's thresholds of quantitative anisotropy, but could not be extracted from 7T dMRI dataset. This study provides novel algorithms to fuse intra-subject 3T and 7T dMRI data for extracting more detailed information of voxel-wise quantitative parameters, and a new perspective to build more accurate structural brain networks.

Anisotropy of the R1/T2* value dependent on white matter fiber orientation with respect to the B0 field.

The R1 (1/T1) map divided by the T2* map (R1/T2* map) draws attention as a high-resolution myelin-related map. However, both R1 and R2* (1/T2*) values demonstrate anisotropy dependent on the white matter (WM) fiber orientation with respect to the static magnetic (B0) field. Therefore, this study primarily aimed to investigate the comprehensive impact of these angular-dependent anisotropies on the R1/T2* value. This study enrolled 10 healthy human volunteers (age = 25 ± 1.3) on the 3.0 T MRI system. For R1/T2* map calculation, whole brain R1 and T2* maps were repeatedly obtained in three head tilt positions by magnetization-prepared two rapid gradient echoes and multiple spoiled gradient echo sequences, respectively. Afterward, all maps were spatially normalized and registered to the Johns Hopkins University WM atlas. R1/T2*, R1, and R2* values were binned for fiber orientation related to the B0 field, which was estimated from diffusion-weighted echo-planar imaging data with 3° intervals, to investigate angular-dependent anisotropies in vivo. A larger change in the R1/T2* value in the global WM region as a function of fiber orientation with respect to the B0 field was observed compared to the R1 and R2* values alone. The minimum R1/T2* value at the near magic-angle range was 18.86% lower than the maximum value at the perpendicular angle range. Furthermore, R1/T2* values in the corpus callosum tract and the right and left cingulum cingulate gyrus tracts changed among the three head tilt positions due to fiber orientation changes. In conclusion, the R1/T2* value demonstrates distinctive and complicated angular-dependent anisotropy indicating the trends of both R1 and R2* values and may provide supplemental information for detecting slight changes in the microstructure of myelin and axons.

CROSS-AGE AND CROSS-SITE DOMAIN SHIFT IMPACTS ON DEEP LEARNING-BASED WHITE MATTER FIBER ESTIMATION IN NEWBORN AND BABY BRAINS.

Deep learning models have shown great promise in estimating tissue microstructure from limited diffusion magnetic resonance imaging data. However, these models face domain shift challenges when test and train data are from different scanners and protocols, or when the models are applied to data with inherent variations such as the developing brains of infants and children scanned at various ages. Several techniques have been proposed to address some of these challenges, such as data harmonization or domain adaptation in the adult brain. However, those techniques remain unexplored for the estimation of fiber orientation distribution functions in the rapidly developing brains of infants. In this work, we extensively investigate the age effect and domain shift within and across two different cohorts of 201 newborns and 165 babies using the Method of Moments and fine-tuning strategies. Our results show that reduced variations in the microstructural development of babies in comparison to newborns directly impact the deep learning models' cross-age performance. We also demonstrate that a small number of target domain samples can significantly mitigate domain shift problems.

Prediction Models of Mechanical Properties of Jute/PLA Composite Based on X-ray Computed Tomography.

The tensile strength and modulus of elasticity of a jute/polylactic acid (PLA) composite were found to vary nonlinearly with the loading angle of the specimen through the tensile test. The variation in these properties was related to the fiber orientation distribution (FOD) and fiber length distribution (FLD). In order to study the effects of the FOD and FLD of short fibers on the mechanical properties and to better predict the mechanical properties of short-fiber composites, the true distribution of short fibers in the composite was accurately obtained using X-ray computed tomography (XCT), in which about 70% of the jute fibers were less than 300 µm in length and the fibers were mainly distributed along the direction of mold flow. The probability density functions of the FOD and FLD were obtained by further analyzing the XCT data. Strength and elastic modulus prediction models applicable to short-fiber-reinforced polymer (SFRP) composites were created by modifying the laminate theory and the rule of mixtures using the probability density functions of the FOD and FLD. The experimental measurements were in good agreement with the model predictions.

Multiphysics and multiscale modeling of uterine contractions: integrating electrical dynamics and soft tissue deformation with fiber orientation.

The development of a comprehensive uterine model that seamlessly integrates the intricate interactions between the electrical and mechanical aspects of uterine activity could potentially facilitate the prediction and management of labor complications. Such a model has the potential to enhance our understanding of the initiation and synchronization mechanisms involved in uterine contractions, providing a more profound comprehension of the factors associated with labor complications, including preterm labor. Consequently, it has the capacity to assist in more effective preparation and intervention strategies for managing such complications. In this study, we present a computational model that effectively integrates the electrical and mechanical components of uterine contractions. By combining a state-of-the-art electrical model with the Hyperelastic Mass-Spring Model (HyperMSM), we adopt a multiphysics and multiscale approach to capture the electrical and mechanical activities within the uterus. The electrical model incorporates the generation and propagation of action potentials, while the HyperMSM simulates the mechanical behavior and deformations of the uterine tissue. Notably, our model takes into account the orientation of muscle fibers, ensuring that the simulated contractions align with their inherent directional characteristics. One noteworthy aspect of our contraction model is its novel approach to scaling the rest state of the mesh elements, as opposed to the conventional method of applying mechanical loads. By doing so, we eliminate artificial strain energy resulting from the resistance of soft tissues' elastic properties during contractions. We validated our proposed model through test simulations, demonstrating its feasibility and its ability to reproduce expected contraction patterns across different mesh resolutions and configurations. Moving forward, future research efforts should prioritize the validation of our model using robust clinical data. Additionally, it is crucial to refine the model by incorporating a more realistic uterus model derived from medical imaging. Furthermore, applying the model to simulate the entire childbirth process holds immense potential for gaining deeper insights into the intricate dynamics of labor.

Strain-mode-specific mechanical testing and the interpretation of bone adaptation in the deer calcaneus.

The artiodactyl (deer and sheep) calcaneus is a model that helps in understanding how many bones achieve anatomical optimization and functional adaptation. We consider how the dorsal and plantar cortices of these bones are optimized in quasi-isolation (the conventional view) versus in the context of load sharing along the calcaneal shaft by "tension members" (the plantar ligament and superficial digital flexor tendon). This load-sharing concept replaces the conventional view, as we have argued in a recent publication that employs an advanced analytical model of habitual loading and fracture risk factors of the deer calcaneus. Like deer and sheep calcanei, many mammalian limb bones also experience prevalent bending, which seems problematic because the bone is weaker and less fatigue-resistant in tension than compression. To understand how bones adapt to bending loads and counteract deleterious consequences of tension, it is important to examine both strain-mode-specific (S-M-S) testing (compression testing of bone habitually loaded in compression; tension testing of bone habitually loaded in tension) and non-S-M-S testing. Mechanical testing was performed on individually machined specimens from the dorsal "compression cortex" and plantar "tension cortex" of adult deer calcanei and were independently tested to failure in one of these two strain modes. We hypothesized that the mechanical properties of each cortex region would be optimized for its habitual strain mode when these regions are considered independently. Consistent with this hypothesis, energy absorption parameters were approximately three times greater in S-M-S compression testing in the dorsal/compression cortex when compared to non-S-M-S tension testing of the dorsal cortex. However, inconsistent with this hypothesis, S-M-S tension testing of the plantar/tension cortex did not show greater energy absorption compared to non-S-M-S compression testing of the plantar cortex. When compared to the dorsal cortex, the plantar cortex only had a higher elastic modulus (in S-M-S testing of both regions). Therefore, the greater strength and capacity for energy absorption of the dorsal cortex might "protect" the weaker plantar cortex during functional loading. However, this conventional interpretation (i.e., considering adaptation of each cortex in isolation) is rejected when critically considering the load-sharing influences of the ligament and tendon that course along the plantar cortex. This important finding/interpretation has general implications for a better understanding of how other similarly loaded bones achieve anatomical optimization and functional adaptation.

Comparing age- and bone-related differences in collagen fiber orientation: A case study of bats and laboratory mice using quantitative polarized light microscopy.

As bones age in most mammals, they typically become more fragile. This state of bone fragility is often associated with more homogenous collagen fiber orientations (CFO). Unlike most mammals, bats maintain mechanically competent bone throughout their lifespans, but little is known of positional and age-related changes in CFO within wing bones. This study tests the hypothesis that age-related changes in CFO in big brown bats (Eptesicus fuscus) differ from those of the standard mammalian model for skeletal aging, the C57BL/6 laboratory mouse. We used data from quantitative polarized light microscopy (qPLM) to compare CFO across the lifespan of long-lived big brown bats and age matched C57BL/6 mice. Eptesicus and C57BL/6 mice displayed idiosyncratic patterns of CFO. Consistent age-related changes were only apparent in the outer cortical bone of Eptesicus, where bone tissue is more longitudinally arranged and more anisotropic in older individuals. Both taxa displayed a ring of more transversely oriented bone tissue surrounding the medullary cavity. In Eptesicus, this tissue represents a greater proportion of the overall cross-section, and is more clearly helically aligned (arranged at 45° to the bone long axis) than similar bone tissue in mice. Bat wing bones displayed a proximodistal gradient in CFO anisotropy and longitudinal orientation in both outer and inner cortical bone compartments. This study lays a methodological foundation for the quantitative evaluation of bone tissue architecture in volant and non-volant mammals that may be expanded in the future.