Bose-Einstein condensation of trapped atoms with dipole interactions

The path integral Monte Carlo method is used to simulate dilute trapped Bose gases and to investigate the equilibrium properties at finite temperatures. The quantum particles have a long-range dipole-dipole interaction and a short-range s-wave interaction. Using an anisotropic pseudopotential for the long-range dipolar interaction and a hard-sphere potential for the short-range s-wave interaction, we calculate the energetics and structural properties as a function of temperature and the number of particles. Also, in order to determine the effects of dipole-dipole forces and the influence of the trapping field on the dipolar condensate, we use two cylindrically symmetric harmonic confinements (a cigar-shaped trap and a disk-shaped trap). We find that the net effect of dipole-dipole interactions is governed by the trapping geometry. For a cigar-shaped trap, the net contribution of dipolar interactions is attractive and the shrinking of the density profiles is observed. For a disk-shaped trap, the net effect of long-range dipolar forces is repulsive and the density profiles expand

MildInt: Deep Learning-Based Multimodal Longitudinal Data Integration Framework

As large amounts of heterogeneous biomedical data become available, numerous methods for integrating such datasets have been developed to extract complementary knowledge from multiple domains of sources. Recently, a deep learning approach has shown promising results in a variety of research areas. However, applying the deep learning approach requires expertise for constructing a deep architecture that can take multimodal longitudinal data. Thus, in this paper, a deep learning-based python package for data integration is developed. The python package deep learning-based multimodal longitudinal data integration framework (MildInt) provides the preconstructed deep learning architecture for a classification task. MildInt contains two learning phases: learning feature representation from each modality of data and training a classifier for the final decision. Adopting deep architecture in the first phase leads to learning more task-relevant feature representation than a linear model. In the second phase, linear regression classifier is used for detecting and investigating biomarkers from multimodal data. Thus, by combining the linear model and the deep learning model, higher accuracy and better interpretability can be achieved. We validated the performance of our package using simulation data and real data. For the real data, as a pilot study, we used clinical and multimodal neuroimaging datasets in Alzheimer's disease to predict the disease progression. MildInt is capable of integrating multiple forms of numerical data including time series and non-time series data for extracting complementary features from the multimodal dataset

Hippocampal Sclerosis of Aging, a Common Alzheimer's Disease 'Mimic': Risk Genotypes are Associated with Brain Atrophy Outside the Temporal Lobe

Hippocampal sclerosis of aging (HS-Aging) is a common brain disease in older adults with a clinical course that is similar to Alzheimer's disease. Four single-nucleotide polymorphisms (SNPs) have previously shown association with HS-Aging. The present study investigated structural brain changes associated with these SNPs using surface-based analysis. Participants from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI; n = 1,239), with both MRI scans and genotype data, were used to assess the association between brain atrophy and previously identified HS-Aging risk SNPs in the following genes: GRN, TMEM106B, ABCC9, and KCNMB2 (minor allele frequency for each is >30%). A fifth SNP (near the ABCC9 gene) was evaluated in post-hoc analysis. The GRN risk SNP (rs5848_T) was associated with a pattern of atrophy in the dorsomedial frontal lobes bilaterally, remarkable since GRN is a risk factor for frontotemporal dementia. The ABCC9 risk SNP (rs704180_A) was associated with multifocal atrophy whereas a SNP (rs7488080_A) nearby (∼50 kb upstream) ABCC9 was associated with atrophy in the right entorhinal cortex. Neither TMEM106B (rs1990622_T), KCNMB2 (rs9637454_A), nor any of the non-risk alleles were associated with brain atrophy. When all four previously identified HS-Aging risk SNPs were summed into a polygenic risk score, there was a pattern of associated multifocal brain atrophy in a predominately frontal pattern. We conclude that common SNPs previously linked to HS-Aging pathology were associated with a distinct pattern of anterior cortical atrophy. Genetic variation associated with HS-Aging pathology may represent a non-Alzheimer's disease contribution to atrophy outside of the hippocampus in older adults

Bose-Einstein Condensation Temperature of a Homogeneous Weakly Interacting Bose Gas : PIMC study

Using a finite-temperature Path Integral Monte Carlo simulation (PIMC) method and finite-size scaling, we have investigated the interaction-induced shift of the phase transition temperature for Bose-Einstein condensation of homogeneous weakly interacting Bose gases in three dimensions, which is given by a proposed analytical expression $T_{c} = T_{c}^{0}\{1 + c_{1}an^{1/3}+[c'_{2}\ln(an^{1/3})+c''_{2}]a^{2}n^{2/3} +O(a^{3}n)\}$, where $T_{c}^{0}$ is the critical temperature for an ideal gas, $a$ is the s-wave scattering length, and $n$ is the number density. We have used smaller number densities and more time slices than in the previous PIMC simulations [Gruter {\it et al.}, Phys. Rev. Lett. {\bf 79}, 3549 (1997)] in order to understand the difference in the value of the coefficient $c_{1}$ between their results and the (apparently) other reliable results in the literature. Our results show that $\{(T_{c}-T_{c}^{0})/T_{c}^{0}\}/(an^{1/3})$ depends strongly on the interaction strength $an^{1/3}$ while the previous PIMC results are considerably flatter and smaller than our results. We obtain $c_{1}$ = 1.32 $\pm$ 0.14, in agreement with results from recent Monte Carlo methods of three-dimensional O(2) scalar $\phi^{4}$ field theory and variational perturbation theory

Progress in Polygenic Composite Scores in Alzheimer’s and other Complex Diseases

Advances in high-throughput genotyping and next-generation sequencing (NGS) coupled with larger sample sizes brings the realization of precision medicine closer than ever. Polygenic approaches incorporating the aggregate influence of multiple genetic variants can contribute to a better understanding of the genetic architecture of many complex diseases and facilitate patient stratification. This review addresses polygenic concepts, methodological developments, hypotheses, and key issues in study design. Polygenic risk scores (PRSs) have been applied to many complex diseases and here we focus on Alzheimer's disease (AD) as a primary exemplar. This review was designed to serve as a starting point for investigators wishing to use PRSs in their research and those interested in enhancing clinical study designs through enrichment strategies