597 research outputs found
Hybridizing two-step growth mixture model and exploratory factor analysis to examine heterogeneity in nonlinear trajectories
Empirical researchers are usually interested in investigating the impacts of
baseline covariates have when uncovering sample heterogeneity and separating
samples into more homogeneous groups. However, a considerable number of studies
in the structural equation modeling (SEM) framework usually start with vague
hypotheses in terms of heterogeneity and possible reasons. It suggests that (1)
the determination and specification of a proper model with covariates is not
straightforward, and (2) the exploration process may be computational intensive
given that a model in the SEM framework is usually complicated and the pool of
candidate covariates is usually huge in the psychological and educational
domain where the SEM framework is widely employed. Following
\citet{Bakk2017two}, this article presents a two-step growth mixture model
(GMM) that examines the relationship between latent classes of nonlinear
trajectories and baseline characteristics. Our simulation studies demonstrate
that the proposed model is capable of clustering the nonlinear change patterns,
and estimating the parameters of interest unbiasedly, precisely, as well as
exhibiting appropriate confidence interval coverage. Considering the pool of
candidate covariates is usually huge and highly correlated, this study also
proposes implementing exploratory factor analysis (EFA) to reduce the dimension
of covariate space. We illustrate how to use the hybrid method, the two-step
GMM and EFA, to efficiently explore the heterogeneity of nonlinear trajectories
of longitudinal mathematics achievement data.Comment: Draft version 1.6, 08/08/2020. This paper has not been peer reviewed.
Please do not copy or cite without author's permissio
DNA methylation associated with postpartum depressive symptoms overlaps findings from a genome-wide association meta-analysis of depression
Background Perinatal depressive symptoms have been linked to adverse maternal and infant health outcomes. The etiology associated with perinatal depressive psychopathology is poorly understood, but accumulating evidence suggests that understanding inter-individual differences in DNA methylation (DNAm) patterning may provide insight regarding the genomic regions salient to the risk liability of perinatal depressive psychopathology.
Results Genome-wide DNAm was measured in maternal peripheral blood using the Infinium MethylationEPIC microarray. Ninety-two participants (46% African-American) had DNAm samples that passed all quality control metrics, and all participants were within 7 months of delivery. Linear models were constructed to identify differentially methylated sites and regions, and permutation testing was utilized to assess significance. Differentially methylated regions (DMRs) were defined as genomic regions of consistent DNAm change with at least two probes within 1 kb of each other. Maternal age, current smoking status, estimated cell-type proportions, ancestry-relevant principal components, days since delivery, and chip position served as covariates to adjust for technical and biological factors. Current postpartum depressive symptoms were measured using the Edinburgh Postnatal Depression Scale. Ninety-eight DMRs were significant (false discovery rate \u3c 5%) and overlapped 92 genes. Three of the regions overlap loci from the latest Psychiatric Genomics Consortium meta-analysis of depression.
Conclusions Many of the genes identified in this analysis corroborate previous allelic, transcriptomic, and DNAm association results related to depressive phenotypes. Future work should integrate data from multi-omic platforms to understand the functional relevance of these DMRs and refine DNAm association results by limiting phenotypic heterogeneity and clarifying if DNAm differences relate to the timing of onset, severity, duration of perinatal mental health outcomes of the current pregnancy or to previous history of depressive psychopathology
Discovery of the brightest T dwarf in the northern hemisphere
We report the discovery of a bright (H=12.77) brown dwarf designated SIMP
J013656.5+093347. The discovery was made as part of a near-infrared proper
motion survey, SIMP (Sondage Infrarouge de Mouvement Propre), which uses proper
motion and near-infrared/optical photometry to identify brown dwarf candidates.
A low resolution (lambda/dlambda~40) spectrum of this brown dwarf covering the
0.88-2.35 microns wavelength interval is presented. Analysis of the spectrum
indicates a spectral type of T2.5+/-0.5. A photometric distance of 6.4+/-0.3 pc
is estimated assuming it is a single object. Current observations rule out a
binary of mass ratio ~1 and separation >5 AU. SIMP 0136 is the brightest T
dwarf in the northern hemisphere and is surpassed only by Eps Indi Bab over the
whole sky. It is thus an excellent candidate for detailed studies and should
become a benchmark object for the early-T spectral class.Comment: 4 pages, 3 figures, To be published in November 1, 2006 issue of
ApJL. Following IAU recommendation, the survey acronym (IBIS) was changed to
SIM
Obtaining interpretable parameters from reparameterizing longitudinal models: transformation matrices between growth factors in two parameter-spaces
The linear spline growth model (LSGM), which approximates complex patterns
using at least two linear segments, is a popular tool for examining nonlinear
change patterns. Among such models, the linear-linear piecewise change pattern
is the most straightforward one. An earlier study has proved that other than
the intercept and slopes, the knot (or change-point), at which two linear
segments join together, can be estimated as a growth factor in a
reparameterized longitudinal model in the latent growth curve modeling
framework. However, the reparameterized coefficients were no longer directly
related to the underlying developmental process and therefore lacked
meaningful, substantive interpretation, although they were simple functions of
the original parameters. This study proposes transformation matrices between
parameters in the original and reparameterized models so that the interpretable
coefficients directly related to the underlying change pattern can be derived
from reparameterized ones. Additionally, the study extends the existing
linear-linear piecewise model to allow for individual measurement occasions,
and investigates predictors for the individual-differences in change patterns.
We present the proposed methods with simulation studies and a real-world data
analysis. Our simulation studies demonstrate that the proposed method can
generally provide an unbiased and consistent estimation of model parameters of
interest and confidence intervals with satisfactory coverage probabilities. An
empirical example using longitudinal mathematics achievement scores shows that
the model can estimate the growth factor coefficients and path coefficients
directly related to the underlying developmental process, thereby providing
meaningful interpretation. For easier implementation, we also provide the
corresponding code for the proposed models.Comment: Draft version 1.6, 07/28/2020. This paper has not been peer reviewed.
Please do not copy or cite without author's permissio
Notes on Captive Sea Otters
Notes on the behaviour of three yearlings kept two and a half months in 1954 in a dry environment at Amchitka in the Aleutians. Their sleeping, preening, reaction to man and feeding habits, drinking, locomotion, handling, food and sociability voice, etc., are discussed in detail. Their anatomy and environment in captivity are also discussed: water for swimming was found desirable, if not necessary. Results of physiological investigations are reported by D.E. Stullken and C.M. Kirkpatrick, q.v
Clustering of solutions in the random satisfiability problem
Using elementary rigorous methods we prove the existence of a clustered phase
in the random -SAT problem, for . In this phase the solutions are
grouped into clusters which are far away from each other. The results are in
agreement with previous predictions of the cavity method and give a rigorous
confirmation to one of its main building blocks. It can be generalized to other
systems of both physical and computational interest.Comment: 4 pages, 1 figur
FIRE Spectroscopy of Five Late-type T Dwarfs Discovered with the Wide-field Infrared Survey Explorer
We present the discovery of five late-type T dwarfs identified with the
Wide-field Infrared Survey Explorer (WISE). Low-resolution near-infrared
spectroscopy obtained with the Magellan Folded-port InfraRed Echellette (FIRE)
reveal strong water and methane absorption in all five sources, and spectral
indices and comparison to spectral templates indicate classifications ranging
from T5.5 to T8.5:. The spectrum of the latest-type source, WISE J1812+2721, is
an excellent match to that of the T8.5 companion brown dwarf Wolf 940B.
WISE-based spectrophotometric distance estimates place these T dwarfs at 12-13
pc from the Sun, assuming they are single. Preliminary fits of the spectral
data to the atmosphere models of Saumon & Marley indicate effective
temperatures ranging from 600 K to 930 K, both cloudy and cloud-free
atmospheres, and a broad range of ages and masses. In particular, two sources
show evidence of both low surface gravity and cloudy atmospheres, tentatively
supporting a trend noted in other young brown dwarfs and exoplanets. In
contrast, the high proper motion T dwarf WISE J2018-7423 exhibits a suppressed
K-band peak and blue spectrophotometric J-K colors indicative of an old,
massive brown dwarf; however, it lacks the broadened Y-band peak seen in
metal-poor counterparts. These results illustrate the broad diversity of
low-temperature brown dwarfs that will be uncovered with WISE.Comment: 19 pages, 13 figures; accepted for publication to Ap
The statistical mechanics of complex signaling networks : nerve growth factor signaling
It is becoming increasingly appreciated that the signal transduction systems
used by eukaryotic cells to achieve a variety of essential responses represent
highly complex networks rather than simple linear pathways. While significant
effort is being made to experimentally measure the rate constants for
individual steps in these signaling networks, many of the parameters required
to describe the behavior of these systems remain unknown, or at best,
estimates. With these goals and caveats in mind, we use methods of statistical
mechanics to extract useful predictions for complex cellular signaling
networks. To establish the usefulness of our approach, we have applied our
methods towards modeling the nerve growth factor (NGF)-induced differentiation
of neuronal cells. Using our approach, we are able to extract predictions that
are highly specific and accurate, thereby enabling us to predict the influence
of specific signaling modules in determining the integrated cellular response
to the two growth factors. We show that extracting biologically relevant
predictions from complex signaling models appears to be possible even in the
absence of measurements of all the individual rate constants. Our methods also
raise some interesting insights into the design and possible evolution of
cellular systems, highlighting an inherent property of these systems wherein
particular ''soft'' combinations of parameters can be varied over wide ranges
without impacting the final output and demonstrating that a few ''stiff''
parameter combinations center around the paramount regulatory steps of the
network. We refer to this property -- which is distinct from robustness -- as
''sloppiness.''Comment: 24 pages, 10 EPS figures, 1 GIF (makes 5 multi-panel figs + caption
for GIF), IOP style; supp. info/figs. included as brown_supp.pd
Synthesizing and tuning chemical reaction networks with specified behaviours
We consider how to generate chemical reaction networks (CRNs) from functional
specifications. We propose a two-stage approach that combines synthesis by
satisfiability modulo theories and Markov chain Monte Carlo based optimisation.
First, we identify candidate CRNs that have the possibility to produce correct
computations for a given finite set of inputs. We then optimise the reaction
rates of each CRN using a combination of stochastic search techniques applied
to the chemical master equation, simultaneously improving the of correct
behaviour and ruling out spurious solutions. In addition, we use techniques
from continuous time Markov chain theory to study the expected termination time
for each CRN. We illustrate our approach by identifying CRNs for majority
decision-making and division computation, which includes the identification of
both known and unknown networks.Comment: 17 pages, 6 figures, appeared the proceedings of the 21st conference
on DNA Computing and Molecular Programming, 201
Space-time Phase Transitions in Driven Kinetically Constrained Lattice Models
Kinetically constrained models (KCMs) have been used to study and understand
the origin of glassy dynamics. Despite having trivial thermodynamic properties,
their dynamics slows down dramatically at low temperatures while displaying
dynamical heterogeneity as seen in glass forming supercooled liquids. This
dynamics has its origin in an ergodic-nonergodic first-order phase transition
between phases of distinct dynamical "activity". This is a "space-time"
transition as it corresponds to a singular change in ensembles of trajectories
of the dynamics rather than ensembles of configurations. Here we extend these
ideas to driven glassy systems by considering KCMs driven into non-equilibrium
steady states through non-conservative forces. By classifying trajectories
through their entropy production we prove that driven KCMs also display an
analogous first-order space-time transition between dynamical phases of finite
and vanishing entropy production. We also discuss how trajectories with rare
values of entropy production can be realized as typical trajectories of a
mapped system with modified forces
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