pwc-archive/papers-with-abstracts huggingface dataset:
Membrane tubulation by elongated and patchy nanoparticles
Date: 2016-09-15 00:00:00

Advances in nanotechnology lead to an increasing interest in how
nanoparticles interact with biomembranes. Nanoparticles are wrapped
spontaneously by biomembranes if the adhesive interactions between the
particles and membranes compensate for the cost of membrane bending. In the
last years, the cooperative wrapping of spherical nanoparticles in membrane
tubules has been observed in experiments and simulations. For spherical
nanoparticles, the stability of the particle-filled membrane tubules strongly
depends on the range of the adhesive particle-membrane interactions. In this
article, we show via modeling and energy minimization that elongated and patchy
particles are wrapped cooperatively in membrane tubules that are highly stable
for all ranges of the particle-membrane interactions, compared to individual
wrapping of the particles. The cooperative wrapping of linear chains of
elongated or patchy particles in membrane tubules may thus provide an efficient
route to induce membrane tubulation, or to store such particles in membranes.
Resource Selection for Federated Search on the Web
Authors: Nguyen Dong, Demeester Thomas, Trieschnigg Dolf, Hiemstra Djoerd
Date: 2016-09-15 00:00:00

A publicly available dataset for federated search reflecting a real web
environment has long been absent, making it difficult for researchers to test
the validity of their federated search algorithms for the web setting. We
present several experiments and analyses on resource selection on the web using
a recently released test collection containing the results from more than a
hundred real search engines, ranging from large general web search engines such
as Google, Bing and Yahoo to small domain-specific engines. First, we
experiment with estimating the size of uncooperative search engines on the web
using query based sampling and propose a new method using the ClueWeb09
dataset. We find the size estimates to be highly effective in resource
selection. Second, we show that an optimized federated search system based on
smaller web search engines can be an alternative to a system using large web
search engines. Third, we provide an empirical comparison of several popular
resource selection methods and find that these methods are not readily suitable
for resource selection on the web. Challenges include the sparse resource
descriptions and extremely skewed sizes of collections.
Price impact without order book: A study of the OTC credit index market
Date: 2016-09-15 00:00:00
Tasks: Attribute

We present a study of price impact in the over-the-counter credit index
market, where no limit order book is used. Contracts are traded via dealers,
that compete for the orders of clients. Despite this distinct microstructure,
we successfully apply the propagator technique to estimate the price impact of
individual transactions. Because orders are typically split less than in
multilateral markets, impact is observed to be mainly permanent, in line with
theoretical expectations. A simple method is presented to correct for errors in
our classification of trades between buying and selling. We find a very
significant, temporary increase in order flow correlations during late 2015 and
early 2016, which we attribute to increased order splitting or herding among
investors. We also find indications that orders advertised to less dealers may
have lower price impact. Quantitative results are compatible with earlier
findings in other more classical markets, further supporting the argument that
price impact is a universal phenomenon, to a large degree independent of market
microstructure.
Institutionalization in Efficient Markets: The Case of Price Bubbles
Date: 2016-08-23 00:00:00

We seek to deepen understanding of the micro-foundations of
institutionalization while contributing to a sociological theory of markets by
investigating the puzzle of price bubbles in financial markets. We find that
such markets, despite textbook conditions of high efficiency -- perfect
information, atomistic agents, no uncertainty -- quickly develop patterns
consistent with institutionalization processes.
Human brain parcellation using time courses of instantaneous connectivity
Date: 2016-11-21 00:00:00
Tasks: valid

Functional neuroimaging studies have lead to understanding the brain as a
collection of spatially segregated functional networks. It is thought that each
of these networks is in turn composed of a set of distinct sub-regions that
together support each network's function. Considering the sub-regions to be an
essential part of the brain's functional architecture, several strategies have
been put forward that aim at identifying the functional sub-units of the brain
by means of functional parcellations. Current parcellation strategies typically
employ a bottom-up strategy, creating a parcellation by clustering smaller
units. We propose a novel top-down parcellation strategy, using time courses of
instantaneous connectivity to subdivide an initial region of interest into
sub-regions. We use split-half reproducibility to choose the optimal number of
sub-regions. We apply our Instantaneous Connectivity Parcellation (ICP)
strategy on high-quality resting-state FMRI data, and demonstrate the ability
to generate parcellations for thalamus, entorhinal cortex, motor cortex, and
subcortex including brainstem and striatum. We evaluate the subdivisions
against available cytoarchitecture maps to show that the our parcellation
strategy recovers biologically valid subdivisions that adhere to known
cytoarchitectural features.
Quantitative assessment of increasing complexity
Date: 2017-01-12 00:00:00
Tasks: Attribute

We study the build up of complexity on the example of 1 kg matter in
different forms. We start on the simplest example of ideal gases, and then
continue with more complex chemical, biological, life and social and technical
structures. We assess the complexity of these systems quantitatively, based on
their entropy. We present a method to attribute the same entropy to known
physical systems and to complex organic molecules up to a DNA. The important
steps in this program and the basic obstacles are discussed.
Statistically validated lead-lag networks and inventory prediction in the foreign exchange market
Date: 2018-07-26 00:00:00

We introduce a method to infer lead-lag networks of agents' actions in
complex systems. These networks open the way to both microscopic and
macroscopic states prediction in such systems. We apply this method to
trader-resolved data in the foreign exchange market. We show that these
networks are remarkably persistent, which explains why and how order flow
prediction is possible from trader-resolved data. In addition, if traders'
actions depend on past prices, the evolution of the average price paid by
traders may also be predictable. Using random forests, we verify that the
predictability of both the sign of order flow and the direction of average
transaction price is strong for retail investors at an hourly time scale, which
is of great relevance to brokers and order matching engines. Finally, we argue
that the existence of trader lead-lag networks explains in a self-referential
way why a given trader becomes active, which is in line with the fact that most
trading activity has an endogenous origin.
Mathematical Imaging Methods for Mitosis Analysis in Live-Cell Phase Contrast Microscopy
Date: 2017-02-10 00:00:00
Tasks: Mitosis Detection

In this paper we propose a workflow to detect and track mitotic cells in
time-lapse microscopy image sequences. In order to avoid the requirement for
cell lines expressing fluorescent markers and the associated phototoxicity,
phase contrast microscopy is often preferred over fluorescence microscopy in
live-cell imaging. However, common specific image characteristics complicate
image processing and impede use of standard methods. Nevertheless, automated
analysis is desirable due to manual analysis being subjective, biased and
extremely time-consuming for large data sets. Here, we present the following
workflow based on mathematical imaging methods. In the first step, mitosis
detection is performed by means of the circular Hough transform. The obtained
circular contour subsequently serves as an initialisation for the tracking
algorithm based on variational methods. It is sub-divided into two parts: in
order to determine the beginning of the whole mitosis cycle, a backwards
tracking procedure is performed. After that, the cell is tracked forwards in
time until the end of mitosis. As a result, the average of mitosis duration and
ratios of different cell fates (cell death, no division, division into two or
more daughter cells) can be measured and statistics on cell morphologies can be
obtained. All of the tools are featured in the user-friendly
MATLAB$^{\circledR}$ Graphical User Interface MitosisAnalyser.
Single variant bottleneck in the early dynamics of H. influenzae bacteremia in neonatal rats questions the theory of independent action
Date: 2016-09-15 00:00:00

There is an abundance of information about the genetic basis, physiological
and molecular mechanisms of bacterial pathogenesis. In contrast, relatively
little is known about population dynamic processes, by which bacteria colonize
hosts and invade tissues and cells and thereby cause disease. In an article
published in 1978, Moxon and Murphy presented evidence that, when inoculated
intranasally with a mixture streptomycin sensitive and resistant (Sm$^S$ and
Sm$^R$) and otherwise isogenic stains of Haemophilus influenzae type b (Hib),
neonatal rats develop a bacteremic infection that often is dominated by only
one strain, Sm$^S$ or Sm$^R$. After rulling out other possibilities through
years of related experiments, the field seems to have settled on a plausible
explanation for this phenomenon: the first bacterium to invade the host
activates the host immune response that `shuts the door' on the second invading
strain. To explore this hypothesis in a necessarily quantitative way, we
modeled this process with a set of mixed stochastic and deterministic
differential equations. Our analysis of the properties of this model with
realistic parameters suggests that this hypothesis cannot explain the
experimental results of Moxon and Murphy, and in particular the observed
relationship between the frequency of different types of blood infections
(bacteremias) and the inoculum size. We propose modifications to the model that
come closer to explaining these data. However, the modified and better fitting
model contradicts the common theory of independent action of individual
bacteria in establishing infections. We discuss the implications of these
results.
Microscopic Understanding of Cross-Responses between Stocks: a Two-Component Price Impact Model
Date: 2017-07-10 00:00:00

We construct a price impact model between stocks in a correlated market. For
the price change of a given stock induced by the short-run liquidity of this
stock itself and of the information about other stocks, we introduce a self-
and a cross-impact function of the time lag. We model the average
cross-response functions for individual stocks employing the impact functions
of the time lag, the impact functions of traded volumes and the trade-sign
correlators. To quantify the self- and cross-impacts, we propose a construction
to fix the parameters in the impact functions. These parameters are further
corroborated by a diffusion function that measures the correlated motion of
prices from different stocks. This construction is mainly ad hoc and
alternative ones are not excluded. It turns out that both the sign cross- and
self-correlators are connected with the cross-responses. The self- and
cross-impact functions are indispensable to compensate amplification effects
which are due to the sign correlators integrated over time. We further quantify
and interpret the price impacts of time lag in terms of temporary and permanent
components. To support our model, we also analyze empirical data, in particular
the memory properties of the sign self- and average cross-correlators. The
relation between the average cross-responses and the traded volumes which are
smaller than their average is of power-law form.
A Quadratically Regularized Functional Canonical Correlation Analysis for Identifying the Global Structure of Pleiotropy with NGS Data
Date: 2016-09-16 00:00:00

Investigating the pleiotropic effects of genetic variants can increase
statistical power, provide important information to achieve deep understanding
of the complex genetic structures of disease, and offer powerful tools for
designing effective treatments with fewer side effects. However, the current
multiple phenotype association analysis paradigm lacks breadth (number of
phenotypes and genetic variants jointly analyzed at the same time) and depth
(hierarchical structure of phenotype and genotypes). A key issue for high
dimensional pleiotropic analysis is to effectively extract informative internal
representation and features from high dimensional genotype and phenotype data.
To explore multiple levels of representations of genetic variants, learn their
internal patterns involved in the disease development, and overcome critical
barriers in advancing the development of novel statistical methods and
computational algorithms for genetic pleiotropic analysis, we proposed a new
framework referred to as a quadratically regularized functional CCA (QRFCCA)
for association analysis which combines three approaches: (1) quadratically
regularized matrix factorization, (2) functional data analysis and (3)
canonical correlation analysis (CCA). Large-scale simulations show that the
QRFCCA has a much higher power than that of the nine competing statistics while
retaining the appropriate type 1 errors. To further evaluate performance, the
QRFCCA and nine other statistics are applied to the whole genome sequencing
dataset from the TwinsUK study. We identify a total of 79 genes with rare
variants and 67 genes with common variants significantly associated with the 46
traits using QRFCCA. The results show that the QRFCCA substantially outperforms
the nine other statistics.
A Calvin bestiary
Date: 2016-09-16 00:00:00

This paper compares a number of mathematical models for the Calvin cycle of
photosynthesis and presents theorems on the existence and stability of steady
states of these models. Results on five-variable models in the literature are
surveyed. Next a number of larger models related to one introduced by
Pettersson and Ryde-Pettersson are discussed. The mathematical nature of this
model is clarified, showing that it is naturally defined as a system of
differential-algebraic equations. It is proved that there are choices of
parameters for which this model admits more than one positive steady state.
This is done by analysing the limit where the storage of sugars from the cycle
as starch is shut down. There is also a discussion of the minimal models for
the cycle due to Hahn.
Spatial firm competition in two dimensions with linear transportation costs: simulations and analytical results
Date: 2016-09-16 00:00:00

Models of spatial firm competition assume that customers are distributed in
space and transportation costs are associated with their purchases of products
from a small number of firms that are also placed at definite locations. It has
been long known that the competition equilibrium is not guaranteed to exist if
the most straightforward linear transportation costs are assumed. We show by
simulations and also analytically that if periodic boundary conditions in two
dimensions are assumed, the equilibrium exists for a pair of firms at any
distance. When a larger number of firms is considered, we find that their total
equilibrium profit is inversely proportional to the square root of the number
of firms. We end with a numerical investigation of the system's behavior for a
general transportation cost exponent.
Export dynamics as an optimal growth problem in the network of global economy
Date: 2016-09-16 00:00:00

We analyze export data aggregated at world global level of 219 classes of
products over a period of 39 years. Our main goal is to set up a dynamical
model to identify and quantify plausible mechanisms by which the evolutions of
the various exports affect each other. This is pursued through a stochastic
differential description, partly inspired by approaches used in population
dynamics or directed polymers in random media. We outline a complex network of
transfer rates which describes how resources are shifted between different
product classes, and determines how casual favorable conditions for one export
can spread to the other ones. A calibration procedure allows to fit four free
model-parameters such that the dynamical evolution becomes consistent with the
average growth, the fluctuations, and the ranking of the export values observed
in real data. Growth crucially depends on the balance between maintaining and
shifting resources to different exports, like in an explore-exploit problem.
Remarkably, the calibrated parameters warrant a close-to-maximum growth rate
under the transient conditions realized in the period covered by data, implying
an optimal self organization of the global export. According to the model,
major structural changes in the global economy take tens of years.
Generalized Plasma Skimming Model for Cells and Drug Carriers in the Microvasculature
Date: 2016-09-20 00:00:00

In microvascular transport, where both blood and drug carriers are involved,
plasma skimming has a key role on changing hematocrit level and drug carrier
concentration in capillary beds after continuous vessel bifurcation in the
microvasculature. While there have been numerous studies on modeling the plasma
skimming of blood, previous works lacked in consideration of its interaction
with drug carriers. In this paper, a generalized plasma skimming model is
suggested to predict the redistributions of both the cells and drug carriers at
each bifurcation. In order to examine its applicability, this new model was
applied on a single bifurcation system to predict the redistribution of red
blood cells and drug carriers. Furthermore, this model was tested at
microvascular network level under different plasma skimming conditions for
predicting the concentration of drug carriers. Based on these results, the
applicability of this generalized plasma skimming model is fully discussed and
future works along with the model's limitations are summarized.
A Fractional Differential Transformation Solution Method for the Assessment, Monitoring, Control, and Evaluation of HIV/AIDS Confirmed Status with Vertical Transmission in Nigeria
Date: 2016-09-10 00:00:00
Tasks: Management

HIV is a deadly virus transmitted either through having of unprotected sex,
mother to child transmission, sharing of unsterilized objects that is capable
of making cut or wounds on the body, through blood or bodily fluid
transmission. AIDS has no permanent cure but remedies that help in suppressing
the power effect of the virus are available. Previous studies has shown that
the epidemic claimed more lives via vertical transmission, mother-to-child
transmission, blood transfusion, sexual intercourse and injection drug users.
The associated models were derived using diagnosis and treatments records of
confirmed status of HIV/AIDS clients. A new model analysis were proposed that
could handle cases of HIV/AIDS routes of transmission and treatments via
vertical, mother-to-child, blood transfusion, sexual intercourse, injection
drug users that were not considered in previous studies. The new model was
solved using Fractional Differential Transformation by Caputo sense algorithm.
The new model is a major contribution for optimal assessment, monitoring,
evaluation, control and management of HIV/AIDS, with 75% accuracy.
Knowledge-based machine learning methods for macromolecular 3D structure prediction
Date: 2016-12-13 00:00:00
Tasks: BIG-bench Machine Learning, Drug Design

Predicting the 3D structure of a macromolecule, such as a protein or an RNA
molecule, is ranked top among the most difficult and attractive problems in
bioinformatics and computational biology. Its importance comes from the
relationship between the 3D structure and the function of a given protein or
RNA. 3D structures also help to find the ligands of the protein, which are
usually small molecules, a key step in drug design. Unfortunately, there is no
shortcut to accurately obtain the 3D structure of a macromolecule. Many
physical measurements of macromolecular 3D structures cannot scale up, due to
their large labor costs and the requirements for lab conditions.
  In recent years, computational methods have made huge progress due to advance
in computation speed and machine learning methods. These methods only need the
sequence information to predict 3D structures by employing various mathematical
models and machine learning methods. The success of computational methods is
highly dependent on a large database of the proteins and RNA with known
structures.
  However, the performance of computational methods are always expected to be
improved. There are several reasons for this. First, we are facing, and will
continue to face sparseness of data.Secondly, the 3D structure space is too
large for our computational capability.
  The two obstacles can be removed by knowledge-based methods, which combine
knowledge learned from the known structures and biologists' knowledge of the
folding process of protein or RNA. In the dissertation, I will present my
results in building a knowledge-based method by using machine learning methods
to tackle this problem. My methods include the knowledge constraints on
intermediate states, which can highly reduce the solution space of a protein or
RNA, in turn increasing the efficiency of the structure folding method and
improving its accuracy.
Global Sensitivity Analysis in a Mathematical Model of the Renal Interstitium
Date: 2016-08-13 00:00:00
Tasks: Kidney Function, Sensitivity

The pressure in the renal interstitium is an important factor for normal
kidney function. Here we develop a computational model of the rat kidney and
use it to investigate the relationship between arterial blood pressure and
interstitial fluid pressure. In addition, we investigate how tissue flexibility
influences this relationship. Due to the complexity of the model, the large
number of parameters, and the inherent uncertainty of the experimental data, we
utilize Monte Carlo sampling to study the model's behavior under a wide range
of parameter values and to compute first- and total-order sensitivity indices.
Characteristically, at elevated arterial blood pressure, the model predicts
cases with increased or reduced interstitial pressure. The transition between
the two cases is controlled mostly by the compliance of the blood vessels
located before the afferent arterioles.