Scope: Dysbiosis of gut microbiota is involved in metabolic syndrome (MetS) development, which has a different incidence between men (M) and women (W). The differences in gut microbiota in MetS patients are explored according to gender, and whether consuming two healthy diets, Mediterranean (MED) and low-fat (LF), may, over time, differentially shape the gut microbiota dysbiosis according to gender is evaluated. Materials and Methods: All the women from the CORDIOPREV study whose feces samples were available and a similar number of men, matched by the main metabolic variables (N = 246, 123 women and 123 men), and categorized according to the presence or not of MetS are included. Gut microbiota is analyzed at baseline and after 3 years of dietary intervention. Results: Higher abundance of Collinsella, Alistipes, Anaerotruncus, and Phascolarctobacterium genera is observed in MetS-W than in MetS-M, whereas the abundance of Faecalibacterium and Prevotella genera is higher in MetS-M than in MetS-W. Moreover, higher levels of Desulfovibrio, Roseburia, and Holdemania are observed in men than in women after the consumption of the LF diet. Conclusion: The results suggest the potential involvement of differences in gut microbiota in the unequal incidence of metabolic diseases between genders, and a sex-dependent effect on shaping the gut microbiota according to diet.

Circulating microRNAs (miRNAs) have been proposed as type 2 diabetes biomarkers, and they may be a more sensitive way to predict development of the disease than the currently used tools. Our aim was to identify whether circulating miRNAs, added to clinical and biochemical markers, yielded better potential for predicting type 2 diabetes. The study included 462 non-diabetic patients at baseline in the CORDIOPREV study. After a median follow-up of 60 months, 107 of them developed type 2 diabetes. Plasma levels of 24 miRNAs were measured at baseline by qRT-PCR, and other strong biomarkers to predict diabetes were determined. The ROC analysis identified 9 miRNAs, which, added to HbA1c, have a greater predictive value in early diagnosis of type 2 diabetes (AUC = 0.8342) than HbA1c alone (AUC = 0.6950). The miRNA and HbA1c-based model did not improve when the FINDRISC was included (AUC = 0.8293). Cox regression analyses showed that patients with low miR-103, miR-28-3p, miR-29a, and miR-9 and high miR-30a-5p and miR-150 circulating levels have a higher risk of disease (HR = 11.27; 95% CI = 2.61–48.65). Our results suggest that circulating miRNAs could potentially be used as a new tool for predicting the development of type 2 diabetes in clinical practice.

Graphical abstract

A 2D topology-based digital image processing framework is presented here. This framework consists of the computation of a flexible geometric graphbased structure, starting from a raster representation of a digital image I. This structure is called Homological Spanning Forest (HSF for short), and it is built on a cell complex associated to I. The HSF framework allows an efficient and accurate topological analysis of regions of interest (ROIs) by using a four-level architecture. By topological analysis, we mean not only the computation of Euler characteristic, genus or Betti numbers, but also advanced computational algebraic topological information derived from homological classification of cycles. An initial HSF representation can be modified to obtain a different one, in which ROIs are almost isolated and ready to be topologically analyzed. The HSF framework is susceptible of being parallelized and generalized to higher dimensions. Keywords

a b s t r a c tMorse theory is a fundamental tool for analyzing the geometry and topology of smooth manifolds. This tool was translated by Forman to discrete structures such as cell complexes, by using discrete Morse functions or equivalently gradient vector fields. Once a discrete gradient vector field has been defined on a finite cell complex, information about its homology can be directly deduced from it. In this paper we introduce the foundations of a homology-based heuristic for finding optimal discrete gradient vector fields on a general finite cell complex K. The method is based on a computational homological algebra representation (called homological spanning forest or HSF, for short) that is an useful framework to design fast and efficient algorithms for computing advanced algebraic-topological information (classification of cycles, cohomology algebra, homology A(1)-coalgebra, cohomology operations, homotopy groups, . . .). Our approach is to consider the optimality problem as a homology computation process for a chain complex endowed with an extra chain homotopy operator.

No abstract

Starting from an nD geometrical object, a cellular subdivision of such an object provides an algebraic counterpart from which homology information can be computed. In this paper, we develop a process to drastically reduce the amount of data that represent the original object, with the purpose of a subsequent homology computation. The technique applied is based on the construction of a sequence of elementary chain homotopies (integral operators) which algebraically connect the initial object with a simplified one with the same homological information than the former.

Abstract. Given a 3D binary voxel-based digital object V , an algorithm for computing homological information for V via a polyhedral cell complex is designed. By homological information we understand not only Betti numbers, representative cycles of homology classes and homological classification of cycles but also the computation of homology numbers related additional algebraic structures defined on homology (coproduct in homology, product in cohomology, (co)homology operations,...). The algorithm is mainly based on the following facts: a) a local 3D-polyhedrization of any 2×2×2 configuration of mutually 26-adjacent black voxels providing a coherent cell complex at global level; b) a description of the homology of a digital volume as an algebraic-gradient vector field on the cell complex (see Discrete Morse Theory [5],AT-model method [7,5]) . Saving this vector field, we go further obtaining homological information at no extra time processing cost.