Iron Loading and Overloading due to Ineffective Erythropoiesis

Erythropoiesis describes the hematopoietic process of cell proliferation and differentiation that results in the production of mature circulating erythrocytes. Adult humans produce 200 billion erythrocytes daily, and approximately 1 billion iron molecules are incorporated into the hemoglobin contained within each erythrocyte. Thus, iron usage for the hemoglobin production is a primary regulator of plasma iron supply and demand. In many anemias, additional sources of iron from diet and tissue stores are needed to meet the erythroid demand. Among a subset of anemias that arise from ineffective erythropoiesis, iron absorption and accumulation in the tissues increases to levels that are in excess of erythropoiesis demand even in the absence of transfusion. The mechanisms responsible for iron overloading due to ineffective erythropoiesis are not fully understood. Based upon data that is currently available, it is proposed in this review that loading and overloading of iron can be regulated by distinct or combined mechanisms associated with erythropoiesis. The concept of erythroid regulation of iron is broadened to include both physiological and pathological hepcidin suppression in cases of ineffective erythropoiesis

On the degrees of freedom of a semi-Riemannian metric

A semi-Riemannian metric in a n-manifold has n(n-1)/2 degrees of freedom, i.e. as many as the number of components of a differential 2-form. We prove that any semi-Riemannian metric can be obtained as a deformation of a constant curvature metric, this deformation being parametrized by a 2-for

Penrose type inequalities for asymptotically hyperbolic graphs

In this paper we study asymptotically hyperbolic manifolds given as graphs of asymptotically constant functions over hyperbolic space \bH^n. The graphs are considered as subsets of \bH^{n+1} and carry the induced metric. For such manifolds the scalar curvature appears in the divergence of a 1-form involving the integrand for the asymptotically hyperbolic mass. Integrating this divergence we estimate the mass by an integral over an inner boundary. In case the inner boundary satisfies a convexity condition this can in turn be estimated in terms of the area of the inner boundary. The resulting estimates are similar to the conjectured Penrose inequality for asymptotically hyperbolic manifolds. The work presented here is inspired by Lam's article concerning the asymptotically Euclidean case.Comment: 29 pages, no figure, includes a proof of the equality cas

A genotyping array of 3,400 Single Nucleotide Polymorphisms (SNPs) advances the genetic analysis of the iconic tree Araucaria angustifolia, showing that the natural populations ar e more differentiated than previously reported.

Edição especial dos resumos do IUFRO World Congress, 25., 2019, Curitiba

Disentangling human error from the ground truth in segmentation of medical images

Recent years have seen increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depends on the quality of labels. This problem is particularly pertinent in the medical image domain, where both the annotation cost and inter-observer variability are high. In a typical label acquisition process, different human experts provide their estimates of the "true'' segmentation labels under the influence of their own biases and competence levels. Treating these noisy labels blindly as the ground truth limits the performance that automatic segmentation algorithms can achieve. In this work, we present a method for jointly learning, from purely noisy observations alone, the reliability of individual annotators and the true segmentation label distributions, using two coupled CNNs. The separation of the two is achieved by encouraging the estimated annotators to be maximally unreliable while achieving high fidelity with the noisy training data. We first define a toy segmentation dataset based on MNIST and study the properties of the proposed algorithm. We then demonstrate the utility of the method on three public medical imaging segmentation datasets with simulated (when necessary) and real diverse annotations: 1) MSLSC (multiple-sclerosis lesions); 2) BraTS (brain tumours); 3) LIDC-IDRI (lung abnormalities). In all cases, our method outperforms competing methods and relevant baselines particularly in cases where the number of annotations is small and the amount of disagreement is large. The experiments also show strong ability to capture the complex spatial characteristics of annotators' mistakes. Our code is available at \url{https://github.com/moucheng2017/LearnNoisyLabelsMedicalImages}

(Re)constructing Dimensions

Compactifying a higher-dimensional theory defined in R^{1,3+n} on an n-dimensional manifold {\cal M} results in a spectrum of four-dimensional (bosonic) fields with masses m^2_i = \lambda_i, where - \lambda_i are the eigenvalues of the Laplacian on the compact manifold. The question we address in this paper is the inverse: given the masses of the Kaluza-Klein fields in four dimensions, what can we say about the size and shape (i.e. the topology and the metric) of the compact manifold? We present some examples of isospectral manifolds (i.e., different manifolds which give rise to the same Kaluza-Klein mass spectrum). Some of these examples are Ricci-flat, complex and K\"{a}hler and so they are isospectral backgrounds for string theory. Utilizing results from finite spectral geometry, we also discuss the accuracy of reconstructing the properties of the compact manifold (e.g., its dimension, volume, and curvature etc) from measuring the masses of only a finite number of Kaluza-Klein modes.Comment: 23 pages, 3 figures, 2 references adde

Let's Agree to Disagree: Learning Highly Debatable Multirater Labelling

Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time. In a radiological setting, objects commonly have high within-class appearance variability whilst sharing certain characteristics across different classes, making their distinction even more difficult. As an example, markers of cerebral small vessel disease, such as enlarged perivascular spaces (EPVS) and lacunes, can be very varied in their appearance while exhibiting high inter-class similarity, making this task highly challenging for human raters. In this work, we investigate joint models of individual rater behaviour and multi-rater consensus in a deep learning setting, and apply it to a brain lesion object-detection task. Results show that jointly modelling both individual and consensus estimates leads to significant improvements in performance when compared to directly predicting consensus labels, while also allowing the characterization of human-rater consistency

Perfectionism and self-conscious emotions in British and Japanese students: Predicting pride and embarrassment after success and failure

Regarding self-conscious emotions, studies have shown that different forms of perfectionism show different relationships with pride, shame, and embarrassment depending on success and failure. What is unknown is whether these relationships also show cultural variations. Therefore, we conducted a study investigating how self-oriented and socially prescribed perfectionism predicted pride and embarrassment after success and failure comparing 363 British and 352 Japanese students. Students were asked to respond to a set of scenarios where they imagined achieving either perfect (success) or flawed results (failure). In both British and Japanese students, self-oriented perfectionism positively predicted pride after success and embarrassment after failure whereas socially prescribed perfectionism predicted embarrassment after success and failure. Moreover, in Japanese students, socially prescribed perfectionism positively predicted pride after success and self-oriented perfectionism negatively predicted pride after failure. The findings have implications for our understanding of perfectionism indicating that the perfectionism–pride relationship not only varies between perfectionism dimensions, but may also show cultural variations

First and second variation formulae for the sub-Riemannian area in three-dimensional pseudo-hermitian manifolds

We calculate the first and the second variation formula for the sub-Riemannian area in three dimensional pseudo-hermitian manifolds. We consider general variations that can move the singular set of a C^2 surface and non-singular variation for C_H^2 surfaces. These formulas enable us to construct a stability operator for non-singular C^2 surfaces and another one for C2 (eventually singular) surfaces. Then we can obtain a necessary condition for the stability of a non-singular surface in a pseudo-hermitian 3-manifold in term of the pseudo-hermitian torsion and the Webster scalar curvature. Finally we classify complete stable surfaces in the roto-traslation group RT .Comment: 36 pages. Misprints corrected. Statement of Proposition 9.8 slightly changed and Remark 9.9 adde