Morpho-Functional Evaluation of Full-Thickness Macular Holes by the Integration of Optical Coherence Tomography Angiography and Microperimetry

(1) Objective: To use optical coherence tomography angiography (OCTA) and microperimetry (MP) to evaluate the correlation between retinal structure and function in patients with idiopathic, full-thickness macular holes (FTMHs) (2) Methods: This prospective, observational study included 11 eyes of 10 patients with FTMHs evaluated before surgery using OCTA and MP. MP sensitivity maps were superimposed and registered on slabs corresponding to superficial capillary plexus (SCP) and deep capillary plexus (DCP) on OCTA, and on the outer plexiform layer (OPL) and the Henle fiber layer (HFL) complex in en face OCT. On these maps, mean retinal sensitivity was calculated at 2 degrees and 4 degrees, all centered on the FTMH. Cystic cavity extension was assessed on the slab corresponding to the OPL + HFL complex in en face OCT and DCP in OCTA using the Image J software (Version 1.49v; National Institutes of Health, Bethesda, MD, USA); (3) Results: Absolute scotomas were observed corresponding to the FTMH. Additionally, rings of relative scotoma in the perilesional area were detected and correlated to the cystic spaces on en face OCT and OCTA. There was a significant correlation between reduced retinal sensitivity at 2 degrees and 4 degrees diameters around the FTMH and the extension of cystic areas (p < 0.01). There was a significant correlation between the extension of cystic cavities and BCVA (p < 0.01). (4) Conclusions: Morpho-functional analysis of FTMH using OCTA and MP, and the correlation between vascular abnormalities and impaired retinal sensitivity, may provide new, useful information. This integrated evaluation of FTMH may be useful to determine the function-structure correlation before and after vitreoretinal surgery, in order to gain a better understanding of the functional consequences induced by the morphological alterations, assessing outcomes in a more objective way, and potentially adding new surgical prognostic factors

Holographic Geometry and Noise in Matrix Theory

Using Matrix Theory as a concrete example of a fundamental holographic theory, we show that the emergent macroscopic spacetime displays a new macroscopic quantum structure, holographic geometry, and a new observable phenomenon, holographic noise, with phenomenology similar to that previously derived on the basis of a quasi-monochromatic wave theory. Traces of matrix operators on a light sheet with a compact dimension of size $R$ are interpreted as transverse position operators for macroscopic bodies. An effective quantum wave equation for spacetime is derived from the Matrix Hamiltonian. Its solutions display eigenmodes that connect longitudinal separation and transverse position operators on macroscopic scales. Measurements of transverse relative positions of macroscopically separated bodies, such as signals in Michelson interferometers, are shown to display holographic nonlocality, indeterminacy and noise, whose properties can be predicted with no parameters except $R$. Similar results are derived using a detailed scattering calculation of the matrix wavefunction. Current experimental technology will allow a definitive and precise test or validation of this interpretation of holographic fundamental theories. In the latter case, they will yield a direct measurement of $R$ independent of the gravitational definition of the Planck length, and a direct measurement of the total number of degrees of freedom.Comment: 19 pages, 2 figures; v2: factors of Planck mass written explicitly, typos correcte

Ordering and fluctuations in the ground state of the one-dimensional and two-dimensional S = 1/2 XXZ antiferromagnets: A study of dynamical properties based on the recursion method

The recursion method is applied to the T = 0 dynamics of the S = 1/2 XXZ model on a linear chain and a square lattice. By means of new calculational techniques for the analysis of the continued-fraction coefficients pertaining to specific dynamical quantities, we obtain reliable information on the type of ordering in the ground state, on the size of gaps in the dynamically relevant excitation spectrum, on the bandwidths of dominant structures in spectral densities, on the exponents of infrared singularities, and on the detailed shape of spectral-weight distributions. We investigate some characteristic properties of the dynamic structure factors Sμμ(q, ω) and the spin autocorrelation functions Sμμ(ω) = N-1tsumqSμμ(q, ω), specifically their dependence on the uniaxial anisotropy, i.e., on the parameter which controls the type of ordering and the amount of quantum fluctuations in the ground state. We find, for example, that the different degrees of ordering in the planar regime of the one-dimensional and two-dimensional systems (criticality versus antiferromagnetic long-range order) have characteristic signatures in the dynamical properties which are conspicuously displayed in our results

Exploration of the Structural and Energetic Landscape of Glycol Nucleic Acids

Glycol nucleic acid (GNA) is a non-natural analog of DNA In place of the deoxyribose unit of DNA, GNA has an acyclic ethylene glycol unit (Fig. 1) The differences between DNA and GNA are evident in the duplex structure (Fig. 3) Instead of a major and minor groove, GNA has one large groove (Fig. 3) The base pairs of GNA wrap around the single groove like a ribbon on a spool (Fig. 3) GNA has primarily intra-strand base stacking, with each base stacking on top of a base of the opposite strand, as opposed to the inter-strand base stacking of DNA (Fig. 2) GNA has a higher stability than DNA, as shown by its melting point being, on average, 20 degrees Celsius higher than DNA The stability of GNA appears to be due to entropic factors, not enthalpic factors Due to its stability and unique shape, GNA is of interest for its use in place of DNA as a molecular scaffold Molecular dynamics (MD) uses classical laws of motion to follow the movement of atoms or molecules in computer simulations MD can be used to explore the properties of nucleic acids Studies comparing MD simulations to atomic force microscopy have found that the results of simulated pulling of nucleic acids are accurate and realisti

A happiness degree predictor using the conceptual data structure for deep learning architectures

[EN] Background and Objective: Happiness is a universal fundamental human goal. Since the emergence of Positive Psychology, a major focus in psychological research has been to study the role of certain factors in the prediction of happiness. The conventional methodologies are based on linear relationships, such as the commonly used Multivariate Linear Regression (MLR), which may suffer from the lack of representative capacity to the varied psychological features. Using Deep Neural Networks (DNN), we define a Happiness Degree Predictor (H-DP) based on the answers to five psychometric standardized questionnaires. Methods: A Data-Structure driven architecture for DNNs (D-SDNN) is proposed for defining an HDP in which the network architecture enables the conceptual interpretation of psychological factors associated with happiness. Four different neural network configurations have been tested, varying the number of neurons and the presence or absence of bias in the hidden layers. Two metrics for evaluating the influence of conceptual dimensions have been defined and computed: one quantifies the influence weight of the conceptual dimension in absolute terms and the other one pinpoints the direction (positive or negative) of the influence. Materials: A cross-sectional survey targeting the non-institutionalized adult population residing in Spain was completed by 823 cases. The total of 111 elements of the survey are grouped by socio-demographic data and by five psychometric scales (Brief COPE Inventory, EPQR-A, GHQ-28, MOS-SSS, and SDHS) measuring several psychological factors acting one as the outcome (SDHS) and the four others as predictors. Results: Our D-SDNN approach provided a better outcome (MSE: 1.46 · 10^-2 ) than MLR (MSE: 2.30 · 10^-2 ), hence improving by 37% the predictive accuracy, and allowing to simulate the conceptual structure. Conclusions: We observe a better performance of Deep Neural Networks (DNN) with respect to traditional methodologies. This demonstrates its capability to capture the conceptual structure for predicting happiness degrees through psychological variables assessed by standardized questionnaires. It also permits to estimate the influence of each factor on the outcome without assuming a linear relationship.Perez-Benito, FJ.; Villacampa-Fernandez, P.; Conejero, JA.; Garcia-Gomez, JM.; Navarro-Pardo, E. (2019). A happiness degree predictor using the conceptual data structure for deep learning architectures. Computer Methods and Programs in Biomedicine. 168:59-68. https://doi.org/10.1016/j.cmpb.2017.11.004S596816

On factorization of some permutation polynomials over finite fields

Factorization of polynomials over finite fields is a classical problem, going back to the 19th century. However, factorization of an important class, namely, of permutation polynomials was not studied previously. In this thesis we present results on factorization of permutation polynomials of Fq,q 2: In order to tackle this problem, we consider permutation polynomials Fn(x)2 Fq[x], n 0; which are defined recursively as compositions of monomials of degree d with gcd(d;q {u100000} 1) = 1, and linear polynomials. Extensions of Fq defined by using the recursive structure of Fn(x) satisfy particular properties that enable us to employ techniques from Galois theory. In consequence, we obtain a variety of results on degrees and number of irreducible factors of the polynomials Fn(x)