Ground State of the Hydrogen Atom via Dirac Equation in a Minimal Length Scenario

In this work we calculate the correction to the ground state energy of the hydrogen atom due to contributions arising from the presence of a minimal length. The minimal length scenario is introduced by means of modifying the Dirac equation through a deformed Heisenberg algebra (kempf algebra). With the introduction of the Coulomb potential in the new Dirac energy operator, we calculate the energy shift of the ground state of the hydrogen atom in first order of the parameter related to the minimal length via perturbation theory.Comment: 11 page

Discovery of large genomic inversions using long range information.

BackgroundAlthough many algorithms are now available that aim to characterize different classes of structural variation, discovery of balanced rearrangements such as inversions remains an open problem. This is mainly due to the fact that breakpoints of such events typically lie within segmental duplications or common repeats, which reduces the mappability of short reads. The algorithms developed within the 1000 Genomes Project to identify inversions are limited to relatively short inversions, and there are currently no available algorithms to discover large inversions using high throughput sequencing technologies.ResultsHere we propose a novel algorithm, VALOR, to discover large inversions using new sequencing methods that provide long range information such as 10X Genomics linked-read sequencing, pooled clone sequencing, or other similar technologies that we commonly refer to as long range sequencing. We demonstrate the utility of VALOR using both pooled clone sequencing and 10X Genomics linked-read sequencing generated from the genome of an individual from the HapMap project (NA12878). We also provide a comprehensive comparison of VALOR against several state-of-the-art structural variation discovery algorithms that use whole genome shotgun sequencing data.ConclusionsIn this paper, we show that VALOR is able to accurately discover all previously identified and experimentally validated large inversions in the same genome with a low false discovery rate. Using VALOR, we also predicted a novel inversion, which we validated using fluorescent in situ hybridization. VALOR is available at https://github.com/BilkentCompGen/VALOR

Always on my mind: Cross-brain associations of mental health symptoms during simultaneous parent-child scanning.

How parents manifest symptoms of anxiety or depression may affect how children learn to modulate their own distress, thereby influencing the children's risk for developing an anxiety or mood disorder. Conversely, children's mental health symptoms may impact parents' experiences of negative emotions. Therefore, mental health symptoms can have bidirectional effects in parent-child relationships, particularly during moments of distress or frustration (e.g., when a parent or child makes a costly mistake). The present study used simultaneous functional magnetic resonance imaging (fMRI) of parent-adolescent dyads to examine how brain activity when responding to each other's costly errors (i.e., dyadic error processing) may be associated with symptoms of anxiety and depression. While undergoing simultaneous fMRI scans, healthy dyads completed a task involving feigned errors that indicated their family member made a costly mistake. Inter-brain, random-effects multivariate modeling revealed that parents who exhibited decreased medial prefrontal cortex and posterior cingulate cortex activation when viewing their child's costly error response had children with more symptoms of depression and anxiety. Adolescents with increased anterior insula activation when viewing a costly error made by their parent had more anxious parents. These results reveal cross-brain associations between mental health symptomatology and brain activity during parent-child dyadic error processing

MRI analysis for Hippocampus segmentation on a distributed infrastructure

Medical image computing raises new challenges due to the scale and the complexity of the required analyses. Medical image databases are currently available to supply clinical diagnosis. For instance, it is possible to provide diagnostic information based on an imaging biomarker comparing a single case to the reference group (controls or patients with disease). At the same time many sophisticated and computationally intensive algorithms have been implemented to extract useful information from medical images. Many applications would take great advantage by using scientific workflow technology due to its design, rapid implementation and reuse. However this technology requires a distributed computing infrastructure (such as Grid or Cloud) to be executed efficiently. One of the most used workflow manager for medical image processing is the LONI pipeline (LP), a graphical workbench developed by the Laboratory of Neuro Imaging (http://pipeline.loni.usc.edu). In this article we present a general approach to submit and monitor workflows on distributed infrastructures using LONI Pipeline, including European Grid Infrastructure (EGI) and Torque-based batch farm. In this paper we implemented a complete segmentation pipeline in brain magnetic resonance imaging (MRI). It requires time-consuming and data-intensive processing and for which reducing the computing time is crucial to meet clinical practice constraints. The developed approach is based on web services and can be used for any medical imaging application

Local Granger causality

Granger causality (GC) is a statistical notion of causal influence based on prediction via linear vector autoregression. For Gaussian variables it is equivalent to transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes. We exploit such equivalence and calculate exactly the local Granger causality, i.e., the profile of the information transferred from the driver to the target process at each discrete time point; in this frame, GC is the average of its local version. We show that the variability of the local GC around its mean relates to the interplay between driver and innovation (autoregressive noise) processes, and it may reveal transient instances of information transfer not detectable from its average values. Our approach offers a robust and computationally fast method to follow the information transfer along the time history of linear stochastic processes, as well as of nonlinear complex systems studied in the Gaussian approximation

An open-source, low-cost NMR spectrometer operating in the mT field regime

In recent years, low field and ultra-low field NMR spectrometers have gained interest due to their portability, lower cost, and reduced subject-induced magnetic field inhomogeneities. Here, we describe the design of a low-cost multinuclear NMR spectrometer operating in the ultra-low field regime (ULF), which possesses high spectral resolution and enables arbitrary pulse programming. An inexpensive multifunction input/output (I/O) device is used to handle waveform generation and digitization in the kHz operating range. A home-built radio frequency (RF) mixing circuit is used to down-mix the NMR signals, allowing for the slower sampling rates and lower memory requirements needed to enable minute-long acquisitions using a standard Windows PC. The LabVIEW code, along with a bill of materials for all components used in the spectrometer, is included. As proof of concept, 1H relaxation measurements and the simultaneous detection of 1H with gas phase and dissolved 129Xe frequencies using the described low field NMR spectrometer are demonstrated

Characterization of a K+-induced conformational switch in a human telomeric DNA oligonucleotide using 2-aminopurine fluorescence

Human telomeric DNA consists of tandem repeats of the DNA sequence d(GGGTTA). Oligodeoxynucletotide telomere models such as d[A(GGGTTA)(3)GGG] (Tel22) fold in a cation-dependent manner into quadruplex structures consisting of stacked G-quartets linked by d(TTA) loops. NMR has shown that in Na(+) solutions Tel22 forms a ‘basket’ topology of four antiparallel strands; in contrast, Tel22 in K(+) solutions consists of a mixture of unknown topologies. Our previous studies on the mechanism of folding of Tel22 and similar telomere analogs utilized changes in UV absorption between 270 and 325 nm that report primarily on G-quartet formation and stacking showed that quadruplex formation occurs within milliseconds upon mixing with an appropriate cation. In the current study, we assessed the dynamics and equilibria of folding of specific loops by using Tel22 derivatives in which the dA residues were serially substituted with the fluorescent reporter base, 2-aminopurine (2-AP). Tel22 folding induced by Na(+) or K(+) assessed by changes in 2-AP fluorescence consists of at least three kinetic steps with time constants spanning a range of ms to several hundred seconds. Na(+)-dependent equilibrium titrations of Tel22 folding could be approximated as a cooperative two-state process. In contrast, K(+)-dependent folding curves were biphasic, revealing that different conformational ensembles are present in 1 mM and 30 mM K(+). This conclusion was confirmed by (1)H NMR. Molecular dynamics simulations revealed a K(+) binding pocket in Tel22 located near dA1 that is specific for the so-called hybrid-1 conformation in which strand 1 is in a parallel arrangement. The possible presence of this topologically specific binding site suggests that K(+) may play an allosteric role in regulating telomere conformation and function by modulating quadruplex tertiary structure

Gradients of O-information: Low-order descriptors of high-order dependencies

O-information is an information-theoretic metric that captures the overall balance between redundant and synergistic information shared by groups of three or more variables. To complement the global assessment provided by this metric, here we propose the gradients of the O-information as low-order descriptors that can characterize how high-order effects are localized across a system of interest. We illustrate the capabilities of the proposed framework by revealing the role of specific spins in Ising models with frustration, in Ising models with three-spin interactions, and in a linear vectorial autoregressive process. We also provide an example of practical data analysis on U.S. macroeconomic data. Our theoretical and empirical analyses demonstrate the potential of these gradients to highlight the contribution of variables in forming high-order informational circuits