pwc-archive/papers-with-abstracts huggingface dataset:
Detecting functional decline from normal ageing to dementia: development and validation of a short version of the Amsterdam IADL Questionnaire
Date: 2017-03-21 00:00:00
Tasks: Diagnostic

INTRODUCTION: Detecting functional decline from normal ageing to dementia is
relevant for diagnostic and prognostic purposes. Therefore, the Amsterdam IADL
Questionnaire (A-IADL-Q) was developed: a 70-item proxy-based tool with good
psychometric properties. We aimed to design a short version whilst preserving
its psychometric quality. METHODS: Study partners of subjects (n=1355), ranging
from cognitively normal to dementia subjects, completed the original A-IADL-Q.
We selected the short version items using a stepwise procedure combining
missing data, Item Response Theory and input from respondents and experts. We
investigated internal consistency of the short version as well as concordance
with the original version. To assess its construct validity, we additionally
investigated concordance between the short version and the Mini-Mental State
Examination (MMSE) and Disability Assessment for Dementia (DAD). Lastly, we
investigated differences in IADL scores between diagnostic groups across the
dementia spectrum. RESULTS: We selected 30 items covering the entire spectrum
of IADL functioning. Internal consistency (.98) and concordance with the
original version (.97) were very high. Concordance with the MMSE (.72) and DAD
(.87) scores was high. IADL impairment scores increased across the spectrum
from normal cognition to dementia. DISCUSSION: The A-IADL-Q Short Version
(A-IADL-Q-SV) consists of 30 items. The A-IADL-Q-SV has maintained the
psychometric quality of the original A-IADL-Q. As such, it is a concise measure
of functional decline.
Robust Markowitz mean-variance portfolio selection under ambiguous covariance matrix *
Date: 2017-03-13 00:00:00

This paper studies a robust continuous-time Markowitz portfolio selection
pro\-blem where the model uncertainty carries on the covariance matrix of
multiple risky assets. This problem is formulated into a min-max mean-variance
problem over a set of non-dominated probability measures that is solved by a
McKean-Vlasov dynamic programming approach, which allows us to characterize the
solution in terms of a Bellman-Isaacs equation in the Wasserstein space of
probability measures. We provide explicit solutions for the optimal robust
portfolio strategies and illustrate our results in the case of uncertain
volatilities and ambiguous correlation between two risky assets. We then derive
the robust efficient frontier in closed-form, and obtain a lower bound for the
Sharpe ratio of any robust efficient portfolio strategy. Finally, we compare
the performance of Sharpe ratios for a robust investor and for an investor with
a misspecified model. MSC Classification: 91G10, 91G80, 60H30
Evaluating the stoichiometric and energetic constraints of cyanobacterial diurnal growth
Date: 2016-10-24 00:00:00
Tasks: Rhythm

Cyanobacteria are an integral part of the Earth's biogeochemical cycles and a
promising resource for the synthesis of renewable bioproducts from atmospheric
CO2 . Growth and metabolism of cyanobacteria are inherently tied to the diurnal
rhythm of light availability. As yet, however, insight into the stoichiometric
and energetic constraints of cyanobacterial diurnal growth is limited. Here, we
develop a computational platform to evaluate the optimality of diurnal
phototrophic growth using a high-quality genome-scale metabolic reconstruction
of the cyanobacterium Synechococcus elongatus PCC 7942. We formulate
phototrophic growth as a self-consistent autocatalytic process and evaluate the
resulting time-dependent resource allocation problem using constraint-based
analysis. Based on a narrow and well defined set of parameters, our approach
results in an ab initio prediction of growth properties over a full diurnal
cycle. In particular, our approach allows us to study the optimality of
metabolite partitioning during diurnal growth. The cyclic pattern of glycogen
accumulation, an emergent property of the model, has timing characteristics
that are shown to be a trade-off between conflicting cellular objectives. The
approach presented here provides insight into the time-dependent resource
allocation problem of phototrophic diurnal growth and may serve as a general
framework to evaluate the optimality of metabolic strategies that evolved in
photosynthetic organisms under diurnal conditions.
Noise-Robust Modes of the Retinal Population Code have the Geometry of "Ridges" and Correspond with Neuronal Communities
Date: 2017-03-12 00:00:00

An appealing new principle for neural population codes is that correlations
among neurons organize neural activity patterns into a discrete set of
clusters, which can each be viewed as a noise-robust population "codeword".
Previous studies assumed that these codewords corresponded geometrically with
local peaks in the probability landscape of neural population responses. Here,
we analyze multiple datasets of the responses of ~150 retinal ganglion cells
and show that local probability peaks are absent under broad, non-repeated
stimulus ensembles, which are characteristic of natural behavior. However, we
find that neural activity still forms noise-robust clusters in this regime,
albeit clusters with a different geometry. We start by defining a soft local
maximum, which is a local probability maximum when constrained to a fixed spike
count. Next, we show that soft local maxima are robustly present, and can
moreover be linked across different spike count levels in the probability
landscape to form a "ridge". We found that these ridges are comprised of
combinations of spiking and silence in the neural population such that all of
the spiking neurons are members of the same neuronal community, a notion from
network theory. We argue that a neuronal community shares many of the
properties of Donald Hebb's classic cell assembly, and show that a simple,
biologically plausible decoding algorithm can recognize the presence of a
specific neuronal community.
Decreased aneurysmal subarachnoid hemorrhage incidence rate in elderly population than in middle aged population: a retrospective analysis of 8,144 cases in Mainland China
Date: 2016-10-19 00:00:00

Purpose: Rupture of an intracranial aneurysm is the most common cause of
subarachnoid haemorrhage (SAH), which is a life-threatening acute
cerebrovascular event that typically affects working-age people. This study
aims to investigate the aneurysmal SAH incidence rate in elderly population
than in middle aged population in China. Materials and methods: Aneurysmal SAH
cases were collected retrospectively from the archives of 21 hospitals in
Mainland China. All the cases collected were from September 2016 and backward
consecutively for a period of time up to 8 years. SAH was initially diagnosed
by brain computed tomography, and CT angiography (CTA) or digital subtraction
angiography (DSA) was followed and SAH was confirmed to be due to cerebral
aneurysm. When for cases multiple bleeding occurred, the age of the first SAH
was used in this study. The toltal incidence from all hospital at each age were
summed together for females and males; then adjusted by the total population
number at each age for females and males. The total population data was from
the 2010 population census of the People's Republic of China. Results: In total
there were 8,144 cases, with 4,861 females and 3,283 males. Our analysis shows
for both females and males the relative aneurysmal SAH rate started to decrease
after around 65 years old. The males the relative aneurysmal SAH rate might
have started to decrease after around 55 years old. Conclusion: In contrast to
previous reports, our data demonstrated a decreased aneurysmal subarachnoid
hemorrhage incidence rate in elderly population than in middle aged population.
Our data therefore support the hypothesis that aneurysms do not grow
progressively once they form but probably either rupture or stabilize and that
very elderly patients are at a reduced risk of rupture compared with atients
who are younger with the same-sized aneurysms.
Estimation and simulation of foraging trips in land-based marine predators
Date: 2017-04-25 00:00:00

The behaviour of colony-based marine predators is the focus of much research
globally. Large telemetry and tracking data sets have been collected for this
group of animals, and are accompanied by many theoretical studies of optimal
foraging strategies. However, relatively few studies have detailed statistical
methods for inferring behaviours in central place foraging trips. In this paper
we describe an approach based on hidden Markov models, which splits foraging
trips into segments labelled as "outbound", "search", "forage", and "inbound".
By structuring the hidden Markov model transition matrix appropriately, the
model naturally handles the sequence of behaviours within a foraging trip.
Additionally, by structuring the model in this way, we are able to develop
realistic simulations from the fitted model. We demonstrate our approach on
data from southern elephant seals (Mirounga leonina) tagged on Kerguelen Island
in the Southern Ocean. We discuss the differences between our 4-state model and
the widely used 2-state model, and the advantages and disadvantages of
employing a more complex model.
Techniques for multifractal spectrum estimation in financial time series
Date: 2016-10-22 00:00:00
Tasks: Time Series, Time Series Analysis

Multifractal analysis is one of the important approaches that enables us to
measure the complexity of various data via the scaling properties. We compare
the most common techniques used for multifractal exponents estimation from both
theoretical and practical point of view. Particularly, we discuss the methods
based on estimation of R\'enyi entropy, which provide a powerful tool
especially in presence of heavy-tailed data. To put some flesh on bare bones,
all methods are compared on various real financial datasets, including daily
and high-frequency data.
The limits of weak selection and large population size in evolutionary game theory
Date: 2016-10-22 00:00:00

Evolutionary game theory is a mathematical approach to studying how social
behaviors evolve. In many recent works, evolutionary competition between
strategies is modeled as a stochastic process in a finite population. In this
context, two limits are both mathematically convenient and biologically
relevant: weak selection and large population size. These limits can be
combined in different ways, leading to potentially different results. We
consider two orderings: the wN limit, in which weak selection is applied before
the large population limit, and the Nw limit, in which the order is reversed.
Formal mathematical definitions of the Nw and wN limits are provided. Applying
these definitions to the Moran process of evolutionary game theory, we obtain
asymptotic expressions for fixation probability and conditions for success in
these limits. We find that the asymptotic expressions for fixation probability,
and the conditions for a strategy to be favored over a neutral mutation, are
different in the Nw and wN limits. However, the ordering of limits does not
affect the conditions for one strategy to be favored over another.
Death and rebirth of neural activity in sparse inhibitory networks
Date: 2017-03-12 00:00:00

In this paper, we clarify the mechanisms underlying a general phenomenon
present in pulse-coupled heterogeneous inhibitory networks: inhibition can
induce not only suppression of the neural activity, as expected, but it can
also promote neural reactivation. In particular, for globally coupled systems,
the number of firing neurons monotonically reduces upon increasing the strength
of inhibition (neurons' death). However, the random pruning of the connections
is able to reverse the action of inhibition, i.e. in a sparse network a
sufficiently strong synaptic strength can surprisingly promote, rather than
depress, the activity of the neurons (neurons' rebirth). Thus the number of
firing neurons reveals a minimum at some intermediate synaptic strength. We
show that this minimum signals a transition from a regime dominated by the
neurons with higher firing activity to a phase where all neurons are
effectively sub-threshold and their irregular firing is driven by current
fluctuations. We explain the origin of the transition by deriving an analytic
mean field formulation of the problem able to provide the fraction of active
neurons as well as the first two moments of their firing statistics. The
introduction of a synaptic time scale does not modify the main aspects of the
reported phenomenon. However, for sufficiently slow synapses the transition
becomes dramatic, the system passes from a perfectly regular evolution to an
irregular bursting dynamics. In this latter regime the model provides
predictions consistent with experimental findings for a specific class of
neurons, namely the medium spiny neurons in the striatum.
Positive steady state in multi-species models of real food webs
Date: 2016-10-23 00:00:00

Real food web data available in the literature presents us with the relations
between various species, sizes of these species, metabolic types of the species
and other useful information, which allows us to define parameters for the
mathematical dynamical models of these food webs, and perform theoretical
studies of these models. Unfortunately, the researches often face the problem
of the extinction of the species in such situations, which could be an
important limiting factor. In this paper, we propose a simple algorithm of
parameterisation that leads to the existence of positive steady state and
improves persistence of the species in multi-species models of real food webs.
Cohort aggregation modelling for complex forest stands: Spruce-aspen mixtures in British Columbia
Date: 2016-10-25 00:00:00
Tasks: Management

Mixed-species growth models are needed as a synthesis of ecological knowledge
and for guiding forest management. Individual-tree models have been commonly
used, but the difficulties of reliably scaling from the individual to the stand
level are often underestimated. Emergent properties and statistical issues
limit their effectiveness. A more holistic modelling of aggregates at the whole
stand level is a potentially attractive alternative. This work explores
methodology for developing biologically consistent dynamic mixture models where
the state is described by aggregate stand-level variables for species or
age/size cohorts. The methods are demonstrated and tested with a two-cohort
model for spruce-aspen mixtures named SAM. The models combine single-species
submodels and submodels for resource partitioning among the cohorts. The
partitioning allows for differences in competitive strength among species and
size classes, and for complementarity effects. Height growth reduction in
suppressed cohorts is also modelled. SAM fits well the available data, and
exhibits behaviors consistent with current ecological knowledge. The general
framework can be applied to any number of cohorts, and should be useful as a
basis for modelling other mixed-species or uneven-aged stands.
The asset price bubbles in emerging financial markets: a new statistical approach
Date: 2016-10-24 00:00:00

The bubble is a controversial and important issue. Many methods which based
on the rational expectation have been proposed to detect the bubble. However,
for some developing countries, epically China, the asset markets are so young
that for many companies, there are no dividends and fundamental value, making
it difficult (if not impossible) to measure the bubbles by existing methods.
Therefore, we proposed a simple but effective statistical method and three
statistics (that is, C, U, V) to capture and quantify asset price bubbles,
especially in immature emerging markets. To present a clear example of the
application of this method to real world problems, we also applied our method
to re-examine empirically the asset price bubble in some stock markets. Our
main contributions to current literature are as follows: firstly, this method
does not rely on fundamental value, the discount rates and dividends, therefore
is applicable to the immature markets without the sufficient data of such
kinds, secondly, this new method allows us to examine different influences
(herding behavior, abnormal fluctuation and composite influence) of bubble. Our
new statistical approach is, to the best of our knowledge, the only robust way
in existing literature to to quantify the asset price bubble especially in
emerging markets.
Population growth, interest rate, and housing tax in the transitional China
Date: 2016-10-24 00:00:00

This paper combines and develops the models in Lastrapes (2002) and Mankiw &
Weil (1989), which enables us to analyze the effects of interest rate and
population growth shocks on housing price in one integrated framework. Based on
this model, we carry out policy simulations to examine whether the housing
(stock or flow) tax reduces the housing price fluctuations caused by interest
rate or population growth shocks. Simulation results imply that the choice of
housing tax tools depends on the kind of shock that housing market faces. In
the situation where the housing price volatility is caused by the population
growth shock, the flow tax can reduce the volatility of housing price while the
stock tax makes no difference to it. If the shock is resulting from the
interest rate, the policy maker should not impose any kind of the housing
taxes. Furthermore, the effect of one kind of the housing tax can be
strengthened by that of the other type of housing tax.
Novel probabilistic models of spatial genetic ancestry with applications to stratification correction in genome-wide association studies
Date: 2016-10-25 00:00:00

Genetic variation in human populations is influenced by geographic ancestry
due to spatial locality in historical mating and migration patterns. Spatial
population structure in genetic datasets has been traditionally analyzed using
either model-free algorithms, such as principal components analysis (PCA) and
multidimensional scaling, or using explicit spatial probabilistic models of
allele frequency evolution. We develop a general probabilistic model and an
associated inference algorithm that unify the model-based and data-driven
approaches to visualizing and inferring population structure. Our algorithm,
Geographic Ancestry Positioning (GAP), relates local genetic distances between
samples to their spatial distances, and can be used for visually discerning
population structure as well as accurately inferring the spatial origin of
individuals on a two-dimensional continuum. On both simulated and several real
datasets from diverse human populations, GAP exhibits substantially lower error
in reconstructing spatial ancestry coordinates compared to PCA.
  Our spatial inference algorithm can also be effectively applied to the
problem of population stratification in genome-wide association studies (GWAS),
where hidden population structure can create fictitious associations when
population ancestry is correlated with both the genotype and the trait. We
develop an association test that uses the ancestry coordinates inferred by GAP
to accurately account for ancestry-induced correlations in GWAS. Based on
simulations and analysis of a dataset of 10 metabolic traits measured in a
Northern Finland cohort, which is known to exhibit significant population
structure, we find that our method has superior power to current approaches.
A single step protein assay that is both detergent and reducer compatible: The cydex blue assay
Date: 2016-10-24 00:00:00

Determination of protein concentration in often an absolute pre-requisite in
preparing samples for biochemical and proteomic analyses. However, current
protein assay methods are not compatible with both reducers and detergents,
which are however present simultaneously in most denaturing extraction buffers
used in proteomics and electrophoresis, and in particular in SDS
electrophoresis. We found that inclusion of cyclodextrins in a Coomassie
blue-based assay made it compatible with detergents, as cyclodextrins complex
detergents in a 1:1 molecular ratio. As this type of assay is intrinsically
resistant to reducers, we have thus developed a single step assay that is both
detergent and reducer compatible. Depending on the type and concentration of
detergents present in the sample buffer, either beta-cyclodextrin or
alpha-cyclodextrin can be used, the former being able to complex a wider range
of detergents and the latter being able to complex higher amounts of detergents
due to its greater solubility in water. Cyclodextrins are used at final
concentrations of 2-10 mg/mL in the assay mix. This typically allows to measure
samples containing as little as 0.1 mg/mL protein, in the presence of up to 2%
detergent and reducers such as 5 % mercaptoethanol or 50 mM DTT in a single
step with a simple spectrophotometric assay. This article is protected by
copyright. All rights reserved.
Fluid Dynamics in Heart Development: Effects of Hematocrit and Trabeculation
Date: 2016-10-24 00:00:00

Recent \emph{in vivo} experiments have illustrated the importance of
understanding the hemodynamics of heart morphogenesis. In particular,
ventricular trabeculation is governed by a delicate interaction between
hemodynamic forces, myocardial activity, and morphogen gradients, all of which
are coupled to genetic regulatory networks. The underlying hemodynamics at the
stage of development in which the trabeculae form is particularly complex,
given the balance between inertial and viscous forces. Small perturbations in
the geometry, scale, and steadiness of the flow can lead to changes in the
overall flow structures and chemical morphogen gradients, including the local
direction of flow, the transport of morphogens, and the formation of vortices.
The immersed boundary method was used to solve the fluid-structure interaction
problem of fluid flow moving through a two chambered heart of a zebrafish
(\emph{Danio rerio}), with a trabeculated ventricle, at $96\ hpf$ (hours post
fertilization). Trabeculae heights and hematocrit were varied, and simulations
were conducted for two orders of magnitude of Womersley number, extending
beyond the biologically relevant range ($0.2$ -- $12.0$). Both intracardial and
intertrabecular vortices formed in the ventricle for biologically relevant
parameter values. The bifurcation from smooth streaming flow to vortical flow
depends upon the trabeculae geometry, hematocrit, and $Wo$. This work shows the
importance of hematocrit and geometry in determining the bulk flow patterns in
the heart at this stage of development.