Naked mole-rats (NMRs;Heterocephalus glaber) are highly adapted, eusocial rodents renowned for their extreme longevity and resistance to cancer. Because cancer has not been formally described in this species, NMRs have been increasingly utilized as an animal model in aging and cancer research. We previously reported the occurrence of several age-related diseases, including putative pre-neoplastic lesions, in zoo-housed NMR colonies. Here, we report for the first time 2 cases of cancer in zoo-housed NMRs. In Case No. 1, we observed a subcutaneous mass in the axillary region of a 22-year-old male NMR, with histologic, immunohistochemical (pancytokeratin positive, rare p63 immunolabeling, and smooth muscle actin negative), and ultrastructural characteristics of an adenocarcinoma possibly of mammary or salivary origin. In Case No. 2, we observed a densely cellular, poorly demarcated gastric mass of polygonal cells arranged in nests with positive immunolabeling for synaptophysin and chromogranin indicative of a neuroendocrine carcinoma in an approximately 20-year-old male NMR. We also include a brief discussion of other proliferative growths and pre-cancerous lesions diagnosed in 1 zoo colony. Although these case reports do not alter the longstanding observation of cancer resistance, they do raise questions about the scope of cancer resistance and the interpretation of biomedical studies in this model. These reports also highlight the benefit of long-term disease investigations in zoo-housed populations to better understand naturally occurring disease processes in species used as models in biomedical research.

This paper presents the formulation and implementation of an Error in Constitutive Equations (ECE) method suitable for large-scale inverse identification of linear elastic material properties in the context of steady-state elastodynamics. In ECE-based methods, the inverse problem is postulated as an optimization problem in which the cost functional measures the discrepancy in the constitutive equations that connect kinematically admissible strains and dynamically admissible stresses. Furthermore, in a more recent modality of this methodology introduced by Feissel and Allix (2007), referred to as the Modified ECE (MECE), the measured data is incorporated into the formulation as a quadratic penalty term. We show that a simple and efficient continuation scheme for the penalty term, suggested by the theory of quadratic penalty methods, can significantly accelerate the convergence of the MECE algorithm. Furthermore, a (block) successive over-relaxation (SOR) technique is introduced, enabling the use of existing parallel finite element codes with minimal modification to solve the coupled system of equations that arises from the optimality conditions in MECE methods. Our numerical results demonstrate that the proposed methodology can successfully reconstruct the spatial distribution of elastic material parameters from partial and noisy measurements in as few as ten iterations in a 2D example and fifty in a 3D example. We show (through numerical experiments) that the proposed continuation scheme can improve the rate of convergence of MECE methods by at least an order of magnitude versus the alternative of using a fixed penalty parameter. Furthermore, the proposed block SOR strategy coupled with existing parallel solvers produces a computationally efficient MECE method that can be used for large scale materials identification problems, as demonstrated on a 3D example involving about 400,000 unknown moduli. Finally, our numerical results suggest that the proposed MECE approach can be significantly faster than the conventional approach of L2 minimization using quasi-Newton methods.

Despite extensive investigations of the neocortex in the domestic cat, little is known about neuronal morphology in larger felids. To this end, the present study characterized and quantified the somatodendritic morphology of neocortical neurons in prefrontal, motor, and visual cortices of the Siberian tiger (Panthera tigris altaica) and clouded leopard (Neofelis nebulosa). After neurons were stained with a modified Golgi technique (N = 194), dendritic branching and spine distributions were analyzed using computer-assisted morphometry. Qualitatively, aspiny and spiny neurons in both species appeared morphologically similar to those observed in the domestic cat. Although the morphology of spiny neurons was diverse, with the presence of extraverted, inverted, horizontal, and multiapical pyramidal neurons, the most common variant was the typical pyramidal neuron. Gigantopyramidal neurons in the motor cortex were extremely large, confirming the observation of Brodmann ([1909] Vergleichende Lokalisationlehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues. Leipzig, Germany: J.A. Barth), who found large somata for these neurons in carnivores in general, and felids in particular. Quantitatively, a MARSplines analysis of dendritic measures differentiated typical pyramidal neurons between the Siberian tiger and the clouded leopard with 93% accuracy. In general, the dendrites of typical pyramidal neurons were more complex in the tiger than in the leopards. Moreover, dendritic measures in tiger pyramidal neurons were disproportionally large relative to body/brain size insofar as they were nearly as extensive as those observed in much larger mammals (e.g., African elephant). Comparison of neuronal morphology in a more diverse collection of larger felids may elucidate the comparative context for the relatively large size of the pyramidal neurons observed in the present study. J. Comp. Neurol. 524:3641-3665, 2016. © 2016 Wiley Periodicals, Inc.