An overview of the current genetic and phenotypical selection strategies to reduce the prevalence of feline hypertrophic cardiomyopathy = Een overzicht van de huidige genetische en fenotypische selectiestrategieën tegen hypertrofe cardiomyopathie bij de kat

Hypertrophic cardiomyopathy (HCM) is a common and potentially lethal heart disease in cats. To reduce its prevalence, breeding cats are frequently screened on the basis of their phenotype or genotype. Although echocardiography is the most reliable phenotypical method, its efficacy is limited by the incomplete penetrance of HCM and by difficulties in distinguishing primary HCM from other causes of left ventricular hypertrophy. On the other hand, genetic testing is hampered by the genetic heterogeneity of the disease. Genetic tests are currently only available for Maine Coons and Ragdolls. Because of the high prevalence of HCM, stringent selection may have a negative impact on the genetic diversity of a breed. A more optimal selection would therefore be a slow and careful exclusion of phenotypically and/or genetically positive cats

Legal Limitations of the Anthropological Notion of the Gift in Roman Law

AbstractThe gift from an anthropological perspective differs from the rigid civil notion of donation, which presupposes an unilateral, gratuitous transfer from one person to another. The anthropological notion of the gift includes all gratuitous transfers, either material or non-material. Therefore, the relation between the anthropological notion of the gift and the legal notion of donation is one of inclusion. However, limitations imposed on donations in Roman law follow the same logic of the legal limitations on other gratuitous transfers such as the ones prohibited by leges sumptuaria. In this article I will analyze the reasons behind the legal interdictions in Roman law on some gratuitous spending, including donations between spouses, the ones prohibited by Lex cincia de donis et muneribus and by leges sumptuaria, interdictions that deal with the anthropological function of the gift in the greco-roman world

Tricuspid valve dysplasia in dogs

A general overview of tricuspid valve dysplasia in dogs is presented in this review. This congenital disease has been described in numerous large dog breeds but especially the Labrador retriever is predisposed. The condition is relatively uncommon, with a prevalence of approximately seven percent of all congenital heart diseases in dogs. The asymptomatic phase may last for several years and depends on the severity of the valve malformation. In the clinical phase, exercise intolerance, fatigue, anorexia, cardiac cachexia, dyspnea and signs of right-sided congestive heart failure can be present. Echocardiography including Doppler imaging is warranted to confirm the diagnosis. Curative treatment involves surgical valve replacement but is technically challenging and still in its experimental phase in dogs. As such, treatment in dogs involves the administration of supportive medication once the dogs develop symptoms of congestive heart failure and consists of diuretics, ace-inhibitors and positive inotropic drugs

Fostering self-endorsed motivation to change in patients with an eating disorder: the role of perceived autonomy support and psychological need satisfaction

Objective: Although several studies have established the beneficial effects of self-endorsed forms of motivation for lasting therapeutic change, the way patients with an eating disorder can be encouraged to volitionally pursue change has received less attention. On the basis of Self-Determination Theory, this longitudinal study addressed the role of an autonomy-supportive environment and psychological need satisfaction in fostering self-endorsed motivation for change and subsequent weight gain. Method: Female inpatients (n = 84) with mainly anorexia nervosa and bulimia nervosa filled out questionnaires at the onset of, during, and at the end of treatment regarding their perceived autonomy support from parents, staff members, and fellow patients, their psychological need satisfaction, and their reasons for undertaking change. Furthermore, the Body Mass Index (BMI) of the patients at the onset and end of treatment was assessed by the staff. Path analyses were used to investigate the relations between these constructs. Results: At the start of treatment, perceived parental autonomy support related positively to self-endorsed motivation through psychological need satisfaction. Perceived staff and fellow patients autonomy support related to changes in self-endorsed motivation over the course of treatment through fostering change in psychological need satisfaction. Finally, relative increases in self-endorsed motivation related to relative increases in BMI throughout treatment in a subgroup of patients with anorexia nervosa. Discussion: These results point to the importance of an autonomy-supportive context for facilitating self-endorsed motivation

PDE-based Group Equivariant Convolutional Neural Networks

We present a PDE-based framework that generalizes Group equivariant Convolutional Neural Networks (G-CNNs). In this framework, a network layer is seen as a set of PDE-solvers where geometrically meaningful PDE-coefficients become the layer's trainable weights. Formulating our PDEs on homogeneous spaces allows these networks to be designed with built-in symmetries such as rotation in addition to the standard translation equivariance of CNNs. Having all the desired symmetries included in the design obviates the need to include them by means of costly techniques such as data augmentation. We will discuss our PDE-based G-CNNs (PDE-G-CNNs) in a general homogeneous space setting while also going into the specifics of our primary case of interest: roto-translation equivariance. We solve the PDE of interest by a combination of linear group convolutions and non-linear morphological group convolutions with analytic kernel approximations that we underpin with formal theorems. Our kernel approximations allow for fast GPU-implementation of the PDE-solvers, we release our implementation with this article in the form of the LieTorch extension to PyTorch, available at https://gitlab.com/bsmetsjr/lietorch . Just like for linear convolution a morphological convolution is specified by a kernel that we train in our PDE-G-CNNs. In PDE-G-CNNs we do not use non-linearities such as max/min-pooling and ReLUs as they are already subsumed by morphological convolutions. We present a set of experiments to demonstrate the strength of the proposed PDE-G-CNNs in increasing the performance of deep learning based imaging applications with far fewer parameters than traditional CNNs.Comment: 27 pages, 18 figures. v2 changes: - mentioned KerCNNs - added section Generalization of G-CNNs - clarification that the experiments utilized automatic differentiation and SGD. v3 changes: - streamlined theoretical framework - formulation and proof Thm.1 & 2 - expanded experiments. v4 changes: typos in Prop.5 and (20) v5/6 changes: minor revisio

Total Variation and Mean Curvature PDEs on Rd⋊Sd−1\mathbb{R}^d \rtimes S^{d-1}

Total variation regularization and total variation flows (TVF) have been widely applied for image enhancement and denoising. To include a generic preservation of crossing curvilinear structures in TVF we lift images to the homogeneous space $M = \mathbb{R}^d \rtimes S^{d-1}$ of positions and orientations as a Lie group quotient in SE(d). For d = 2 this is called 'total roto-translation variation' by Chambolle & Pock. We extend this to d = 3, by a PDE-approach with a limiting procedure for which we prove convergence. We also include a Mean Curvature Flow (MCF) in our PDE model on M. This was first proposed for d = 2 by Citti et al. and we extend this to d = 3. Furthermore, for d = 2 we take advantage of locally optimal differential frames in invertible orientation scores (OS). We apply our TVF and MCF in the denoising/enhancement of crossing fiber bundles in DW-MRI. In comparison to data-driven diffusions, we see a better preservation of bundle boundaries and angular sharpness in fiber orientation densities at crossings. We support this by error comparisons on a noisy DW-MRI phantom. We also apply our TVF and MCF in enhancement of crossing elongated structures in 2D images via OS, and compare the results to nonlinear diffusions (CED-OS) via OS.Comment: Submission to the Seventh International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2019). (v2) Typo correction in lemma 1. (v3) Typo correction last paragraph page

Analysis of (sub-)Riemannian PDE-G-CNNs

Group equivariant convolutional neural networks (G-CNNs) have been successfully applied in geometric deep learning. Typically, G-CNNs have the advantage over CNNs that they do not waste network capacity on training symmetries that should have been hard-coded in the network. The recently introduced framework of PDE-based G-CNNs (PDE-G-CNNs) generalizes G-CNNs. PDE-G-CNNs have the core advantages that they simultaneously (1) reduce network complexity, (2) increase classification performance, and (3) provide geometric interpretability. Their implementations primarily consist of linear and morphological convolutions with kernels. In this paper, we show that the previously suggested approximative morphological kernels do not always accurately approximate the exact kernels accurately. More specifically, depending on the spatial anisotropy of the Riemannian metric, we argue that one must resort to sub-Riemannian approximations. We solve this problem by providing a new approximative kernel that works regardless of the anisotropy. We provide new theorems with better error estimates of the approximative kernels, and prove that they all carry the same reflectional symmetries as the exact ones. We test the effectiveness of multiple approximative kernels within the PDE-G-CNN framework on two datasets, and observe an improvement with the new approximative kernels. We report that the PDE-G-CNNs again allow for a considerable reduction of network complexity while having comparable or better performance than G-CNNs and CNNs on the two datasets. Moreover, PDE-G-CNNs have the advantage of better geometric interpretability over G-CNNs, as the morphological kernels are related to association fields from neurogeometry