Developmental mechanisms underlying differential claw expression in the autopodia of geckos

BACKGROUND: The limb and autopodium are frequently employed to study pattern formation during embryonic development, providing insights into how cells give rise to complex anatomical structures. With regard to the differentiation of structures at the distal tips of digits, geckos constitute an attractive clade, because within their ranks they exhibit multiple independent occurrences of claw loss and reduction, these being linked to the development of adhesive pads. The developmental patterns that lead to claw loss, however, remain undescribed. Among geckos, Tarentola is a genus characterized by large claws on digits III and IV of the manus and pes, with digits I, II, and V bearing only vestigial claws, or lacking them entirely. The variable expression of claws on different digits provides the opportunity to investigate the processes leading to claw reduction and loss within a single species. RESULTS: Here, we document the embryonic developmental dynamics that lead to this intraspecifically variable pattern, focusing on the cellular processes of proliferation and cell death. We find that claws initially develop on all digits of all autopodia, but, later in development, those of digits I, II, and V regress, leading to the adult condition in which robust claws are evident only on digits III and IV. Early apoptotic activity at the digit tips, followed by apoptosis of the claw primordium, premature ossification of the terminal phalanges, and later differential proliferative activity are collectively responsible for claw regression in particular digits. CONCLUSIONS: Claw reduction and loss in Tarentola result from differential intensities of apoptosis and cellular proliferation in different digits, and these processes have already had some effect before visible signs of claw development are evident. The differential processes persist through later developmental stages. Variable expression of iteratively homologous structures between digits within autopodia makes claw reduction and loss in Tarentola an excellent vehicle for exploring the developmental mechanisms that lead to evolutionary reduction and loss of structures. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13227-015-0003-9) contains supplementary material, which is available to authorized users

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In this chapter several aspects of the electronic and phonon structure are considered for the design and engineering of advanced thermoelectric materials. For a given compound, its thermoelectric figure of merit, zT, is fully exploited only when the free carrier density is optimized. Achieving higher zT beyond this requires the improvement in the material quality factor B. Using experimental data on lead chalcogenides as well as examples of other good thermoelectric materials, we demonstrate how the fundamental material parameters: effective mass, band anisotropy, deformation potential, and band degeneracy, among others, impact the thermoelectric properties and lead to desirable thermoelectric materials. As the quality factor B is introduced under the assumption of acoustic phonon (deformation potential) scattering, a brief discussion about carrier scattering mechanisms is also included. This simple model with the use of an effective deformation potential coefficient fits the experimental properties of real materials with complex structures and multi-valley Fermi surfaces remarkably well—which is fortunate as these are features likely found in advanced thermoelectric materials

Using a Machine Learning Approach to Predict Outcomes after Radiosurgery for Cerebral Arteriovenous Malformations

Predictions of patient outcomes after a given therapy are fundamental to medical practice. We employ a machine learning approach towards predicting the outcomes after stereotactic radiosurgery for cerebral arteriovenous malformations (AVMs). Using three prospective databases, a machine learning approach of feature engineering and model optimization was implemented to create the most accurate predictor of AVM outcomes. Existing prognostic systems were scored for purposes of comparison. The final predictor was secondarily validated on an independent site’s dataset not utilized for initial construction. Out of 1,810 patients, 1,674 to 1,291 patients depending upon time threshold, with 23 features were included for analysis and divided into training and validation sets. The best predictor had an average area under the curve (AUC) of 0.71 compared to existing clinical systems of 0.63 across all time points. On the heldout dataset, the predictor had an accuracy of around 0.74 at across all time thresholds with a specificity and sensitivity of 62% and 85% respectively. This machine learning approach was able to provide the best possible predictions of AVM radiosurgery outcomes of any method to date, identify a novel radiobiological feature (3D surface dose), and demonstrate a paradigm for further development of prognostic tools in medical care