Epigenetic inheritance. Concepts, mechanisms and perspectives

Parents' stressful experiences can influence an offspring's vulnerability to many pathological conditions, including psychopathologies, and their effects may even endure for several generations. Nevertheless, the cause of this phenomenon has not been determined, and only recently have scientists turned to epigenetics to answer this question. There is extensive literature on epigenetics, but no consensus exists with regard to how and what can (and must) be considered to study and define epigenetics processes and their inheritance. In this work, we aimed to clarify and systematize these concepts. To this end, we analyzed the dynamics of epigenetic changes over time in detail and defined three types of epigenetics: a direct form of epigenetics (DE) and two indirect epigenetic processes-within (WIE) and across (AIE). DE refers to changes that occur in the lifespan of an individual, due to direct experiences with his environment. WIE concerns changes that occur inside of the womb, due to events during gestation. Finally, AIE defines changes that affect the individual's predecessors (parents, grandparents, etc.), due to events that occur even long before conception and that are somehow (e.g., through gametes, the intrauterine environment setting) transmitted across generations. This distinction allows us to organize the main body of epigenetic evidence according to these categories and then focus on the latter (AIE), referring to it as a faster route of informational transmission across generations-compared with genetic inheritance-that guides human evolution in a Lamarckian (i.e., experience-dependent) manner. Of the molecular processes that are implicated in this phenomenon, well-known (methylation) and novel (non-coding RNA, ncRNA) regulatory mechanisms are converging. Our discussion of the chief methods that are used to study epigenetic inheritance highlights the most compelling technical and theoretical problems of this discipline. Experimental suggestions to expand this field are provided, and their practical and ethical implications are discussed extensivel

Functional data analysis in shape analysis

Mid-level processes on images often return outputs in functional form. In this context the use of functional data analysis (FDA) in image analysis is considered. In particular, attention is focussed on shape analysis, where the use of FDA in the functional approach (contour functions) shows its superiority over other approaches, such as the landmark based approach or the set theory approach, on two different problems (principal component analysis and discriminant analysis) in a well-known database of bone outlines. Furthermore, a problem that has hardly ever been considered in the literature is dealt with: multivariate functional discrimination. A discriminant function based on independent component analysis for indicating where the differences between groups are and what their level of discrimination is, is proposed. The classification results obtained with the methodology are very promising. Finally, an analysis of hippocampal differences in Alzheimer’s disease is carried out

Hippocampal shape analysis in Alzheimer’s disease using Functional Data Analysis

The hippocampus is one of the first affected regions in Alzheimer's disease. The left hippocampi of control subjects, patients with mild cognitive impairment and patients with Alzheimer's disease are represented by spherical harmonics. Functional data analysis is used in the hippocampal shape analysis. Functional principal component analysis and functional independent component analysis are defined for multivariate functions with two arguments. A functional linear discriminant function is also defined. Comparisons with other approaches are carried out. Our functional approach gives promising results, especially in shape classification. Copyright © 2013 John Wiley & Sons, Ltd

Ising model on a Galton-Watson tree with a sparse random external field

We consider the Ising model on a supercritical Galton-Watson tree $\mathbf{T}_n$ of depth $n$ with a sparse random external field, given by a collection of i.i.d. Bernouilli random variables with vanishing parameter $p_n$. This may me viewed as a toy model for the Ising model on a configuration model with a few interfering external vertices carrying a plus spin: the question is to know how many (or how few) interfering vertices are enough to influence the whole graph. Our main result consists in providing a necessary and sufficient condition on the parameters $(p_n)_{n\geq 0}$ for the root of $\mathbf{T}_n$ to remain magnetized in the large $n$ limit. Our model is closely related to the Ising model on a (random) pruned sub-tree $\mathbf{T}_n^*$ with plus boundary condition; one key result is that this pruned tree turns out to be an inhomogeneous, $n$-dependent, Branching Process. We then use standard tools such as tree recursions and non-linear capacities to study the Ising model on this sequence of Galton-Watson trees; one difficulty is that the offspring distributions of $\mathbf{T}_n^*$, in addition to vary along the generations $0\leq k \leq n-1$, also depend on~$n$.Comment: 54 pages, comments welcom

Legius syndrome: Case report and review of literature

A 8-month-old child was referred to our Dermatologic Unit for suspected Neurofibromatosis type 1 (NF 1), because of the appearance, since few days after birth, of numerous caf\ue9-au-lait spots (seven larger than 5 mm); no other sign evocative of NF 1 was found. Her family history was remarkable for the presence of multiple caf\ue9-au-lait spots in the mother, the grandfather and two aunts. The family had been already examined for NF 1, but no sign evocative of the disease was found. We then suspected Legius syndrome, a dominant disease characterized by a mild neurofibromatosis 1 phenotype. The diagnosis was confirmed by the finding of a mutation in SPRED1 gene, a feedback regulator of RAS/MAPK signaling. Here, we discuss the differential diagnosis of caf\ue8-au-lait spots and we briefly review the existing literature about Legius syndrome

The child’s self-perception about dental decay in the change of deciduous teeth

Abstract in proceedings of the Fourth International Congress of CiiEM: Health, Well-Being and Ageing in the 21st Century, held at Egas Moniz’ University Campus in Monte de Caparica, Almada, from 3–5 June 2019.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.info:eu-repo/semantics/publishedVersio

A neuroimaging data set on problem solving in the case of the reversal error: Putamen data

Structural Magnetic Resonance Images (sMRI) for a sample of university students were recorded. Out of magnetic resonance, students performed a test of algebra problem solving. As we are interested in reversal errors, the test was prepared to detect this kind of error. Depending on the number of mistakes made, students were divided into two groups: one group contains 15 students that responded erroneously to more than 60% of the 16 questions, and the other group contains 18 students that did not make any mistake. We are interested in the more relevant brain structures for this neuroeducation problem. The analysis of these data can be found in Ferrando et al. (2020) [1]. The results of the volumetric analysis showed differences between groups in the right and left putamen. Therefore, both putamens were pre-processed and segmented to use them in the shape analysis. The dataset contains the slices of the left and right putamen and the left putamen of each of 33 subjects, 20 females. It also contains a vector that indicates the group to each subject belongs to

Detecting and visualizing differences in brain structures with SPHARM and functional data analysis

A new procedure for classifying brain structures described by SPHARM is presented. We combine a dimension reduction technique (functional principal component analysis or functional independent component analysis) with stepwise variable selection for linear discriminant classification. This procedure is compared with many well-known methods in a novel classification problem in neuroeducation, where the reversal error (a common error in mathematical problem solving) is analyzed by using the left and right putamens of 33 participants. The comparison shows that our proposal not only provides outstanding performance in terms of predictive power, but it is also valuable in terms of interpretation, since it yields a linear discriminant function for 3D structures

Ordinal classification of 3D brain structures by functional data analysis

We introduce several ordinal classification methods for functional data, specificallymultiargument and multivariate functional data. Their performance is analyzed in fourreal data sets that belong to a neuroeducational problem and a neuropathologicalproblem.Funding for open access charge: CRUE-Universitat Jaume

The underlying neural bases of the reversal error while solving algebraic word problems

Problem solving is a core element in mathematical learning. The reversal error in problem solving occurs when students are able to recognize the information in the statement of comparison word problems, but they reverse the relationship between two variables when building the equations. Functional magnetic resonance images were acquired to identify for the first time the neural bases associated with the reversal error. The neuronal bases linked to this error have been used as inputs in 13 classifiers to discriminate between reversal error and non-reversal error groups. We found brain activation in bilateral fronto-parietal areas in the participants who committed reversal errors, and only left fronto-parietal activation in those who did not, suggesting that the reversal error group needed a greater cognitive demand. Instead, the non-reversal error group seems to show that they have developed solid algebraic knowledge. Additionally, the results showed brain activation in the right middle temporal gyrus when comparing the reversal error vs non-reversal error groups. This activation would be associated with the semantic processing which is required to understand the statement and build the equation. Finally, the classifier results show that the brain areas activated could be considered good biomarkers to help us identify competent solvers