Subtle temperature-induced changes in small molecule conformer dynamics-observed and quantified by NOE spectroscopy

NOE-distance relationships are shown to be sufficiently accurate to monitor very small changes in conformer populations in response to temperature (<0.5%/10 degrees C) - in good agreement with Boltzmann-predictions, illustrating the effectiveness of accurate NOE-distance measurements in obtaining high quality dynamics as well as structural information for small molecules

IMPRESSION – prediction of NMR parameters for 3-dimensional chemical structures using machine learning with near quantum chemical accuracy

The IMPRESSION (Intelligent Machine PREdiction of Shift and Scalar Information Of Nuclei) machine learning system provides an efficient and accurate route to the prediction of NMR parameters from 3-dimensional chemical structures. Here we demonstrate that machine learning predictions, trained on quantum chemical computed values for NMR parameters, are essentially as accurate but computationally much more efficient (tens of milliseconds per molecule) than quantum chemical calculations (hours/days per molecule). Training the machine learning systems on quantum chemical, rather than experimental, data circumvents the need for existence of large, structurally diverse, error-free experimental databases and makes IMPRESSION applicable to solving 3-dimensional problems such as molecular conformation and isomeris

Limits to Sympathetic Evaporative Cooling of a Two-Component Fermi Gas

We find a limit cycle in a quasi-equilibrium model of evaporative cooling of a two-component fermion gas. The existence of such a limit cycle represents an obstruction to reaching the quantum ground state evaporatively. We show that evaporatively the \beta\mu ~ 1. We speculate that one may be able to cool an atomic fermi gas further by photoassociating dimers near the bottom of the fermi sea.Comment: Submitted to Phys. Rev

Accounting for data heterogeneity in integrative analysis and prediction methods: An application to Chronic Obstructive Pulmonary Disease

Epidemiologic and genetic studies in chronic obstructive pulmonary disease (COPD) and many complex diseases suggest subgroup disparities (e.g., by sex). We consider this problem from the standpoint of integrative analysis where we combine information from different views (e.g., genomics, proteomics, clinical data). Existing integrative analysis methods ignore the heterogeneity in subgroups, and stacking the views and accounting for subgroup heterogeneity does not model the association among the views. To address analytical challenges in the problem of our interest, we propose a statistical approach for joint association and prediction that leverages the strengths in each view to identify molecular signatures that are shared by and specific to males and females and that contribute to the variation in COPD, measured by airway wall thickness. HIP (Heterogeneity in Integration and Prediction) accounts for subgroup heterogeneity, allows for sparsity in variable selection, is applicable to multi-class and to univariate or multivariate continuous outcomes, and incorporates covariate adjustment. We develop efficient algorithms in PyTorch. Our COPD findings have identified several proteins, genes, and pathways that are common and specific to males and females, some of which have been implicated in COPD, while others could lead to new insights into sex differences in COPD mechanisms

Coherent Dynamics of Vortex Formation in Trapped Bose-Einstein Condensates

Simulations of a rotationally stirred condensate show that a regime of simple behaviour occurs in which a single vortex cycles in and out of the condensate. We present a simple quantitative model of this behaviour, which accurately describes the full vortex dynamics, including a critical angular speed of stirring for vortex formation. A method for experimentally preparing a condensate in a central vortex state is suggested.Comment: 4 pages, 4 figures, REVTeX 3.1; Submitted to Physical Review Letters (5 February 1999); See http://www.physics.otago.ac.nz/research/bec/vortex for MPEG movies and further information; Accepted for Physical Review Letters (24 June 1999); Changes: updated Figs 1 and 2 (new style), minor typos fixed, more discussion at en

Effect of quantum group invariance on trapped Fermi gases

We study the properties of a thermodynamic system having the symmetry of a quantum group and interacting with a harmonic potential. We calculate the dependence of the chemical potential, heat capacity and spatial distribution of the gas on the quantum group parameter $q$ and the number of spatial dimensions $D$. In addition, we consider a fourth-order interaction in the quantum group fields $\Psi$, and calculate the ground state energy up to first order.Comment: LaTeX file, 20 pages, four figures, uses epsf.sty, packaged as a single tar.gz uuencoded fil

Carotid Baroreflex Control of Heart Rate is Enhanced during Whole-body Heat Stress

Whole-body heat stress (WBH) reduces orthostatic tolerance. While impaired carotid baroreflex (CBR) function during WBH has been reported, study design considerations may limit interpretation of previous findings. We sought to test the hypothesis that CBR function is unaltered during WBH. CBR function was assessed in ten subjects using 5-sec trials of neck pressure (45, 30 and 15 Torr) and neck suction (-20, -40, -60 and - 80 Torr) during normothermia (NT) and passive WBH (Δ core temp ~1 °C). Analysis of stimulus response curves (4-parameter logistic model) for CBR control of heart rate (CBR-HR) and mean arterial pressure (CBR-MAP), as well as separate 2-way ANOVA of the hypo- and hypertensive stimuli (factor 1: thermal condition, factor 2: chamber pressure) were performed. For CBR-HR, maximal gain was increased during WBH (-0.73±0.37) compared to NT (-0.39±0.11, p=0.03). In addition, the CBR-HR responding range was increased during WBH (32±15) compared to NT (18±8 bpm, p=0.03). Separate analysis of hypertensive stimulation revealed enhanced HR responses during WBH at -40, -60 and -80 Torr (condition*chamber pressure interaction, p=0.049) compared to NT. For CBR-MAP, both logistic analysis and separate 2-way ANOVA revealed no differences during WBH. Therefore, despite marked orthostatic intolerance observed during WBH, CBR control of heart rate (enhanced) and arterial pressure (no change) is well-preserved

Activity driven modeling of time varying networks

Network modeling plays a critical role in identifying statistical regularities and structural principles common to many systems. The large majority of recent modeling approaches are connectivity driven. The structural patterns of the network are at the basis of the mechanisms ruling the network formation. Connectivity driven models necessarily provide a time-aggregated representation that may fail to describe the instantaneous and fluctuating dynamics of many networks. We address this challenge by defining the activity potential, a time invariant function characterizing the agents' interactions and constructing an activity driven model capable of encoding the instantaneous time description of the network dynamics. The model provides an explanation of structural features such as the presence of hubs, which simply originate from the heterogeneous activity of agents. Within this framework, highly dynamical networks can be described analytically, allowing a quantitative discussion of the biases induced by the time-aggregated representations in the analysis of dynamical processes.Comment: 10 pages, 4 figure

Multiple-membership multiple-classification models for social network and group dependences

The social network literature on network dependences has largely ignored other sources of dependence, such as the school that a student attends, or the area in which an individual lives. The multilevel modelling literature on school and area dependences has, in turn, largely ignored social networks. To bridge this divide, a multiple-membership multiple-classification modelling approach for jointly investigating social network and group dependences is presented. This allows social network and group dependences on individual responses to be investigated and compared. The approach is used to analyse a subsample of the Adolescent Health Study data set from the USA, where the response variable of interest is individual level educational attainment, and the three individual level covariates are sex, ethnic group and age. Individual, network, school and area dependences are accounted for in the analysis. The network dependences can be accounted for by including the network as a classification in the model, using various network configurations, such as ego-nets and cliques. The results suggest that ignoring the network affects the estimates of variation for the classifications that are included in the random part of the model (school, area and individual), as well as having some influence on the point estimates and standard errors of the estimates of regression coefficients for covariates in the fixed part of the model. From a substantive perspective, this approach provides a flexible and practical way of investigating variation in an individual level response due to social network dependences, and estimating the share of variation of an individual response for network, school and area classifications

Spatial Heterogeneity Can Lead to Substantial Local Variations in COVID-19 Timing and Severity

Standard epidemiological models for COVID-19 employ variants of compartment (SIR) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 U.S cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly non-uniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform healthcare planning, predict community outcomes, or identify potential disparities