pwc-archive/papers-with-abstracts huggingface dataset:
Tomographic docking suggests the mechanism of auxin receptor TIR1 selectivity
Date: 2016-10-18 00:00:00

We study the binding of plant hormone IAA on its receptor TIR1 introducing a
novel computational method that we call tomographic docking and that accounts
for interactions occurring along the depth of the binding pocket. Our results
suggest that selectivity is related to constraints that potential ligands
encounter on their way from the surface of the protein to their final position
at the pocket bottom. Tomographic docking helps develop specific hypotheses
about ligand binding, distinguishing binders from non-binders, and suggests
that binding is a three-step mechanism, consisting of engagement with a niche
in the back wall of the pocket, interaction with a molecular filter which
allows or precludes further descent of ligands, and binding on the pocket base.
Only molecules that are able to descend the pocket and bind at its base allow
the co-receptor IAA7 to bind on the complex, thus behaving as active auxins.
Analyzing the interactions at different depths, our new method helps in
identifying critical residues that constitute preferred future study targets
and in the quest for safe and effective herbicides. Also, it has the potential
to extend the utility of docking from ligand searches to the study of processes
contributing to selectivity.
How to fold intricately: using theory and experiments to unravel the properties of knotted proteins
Date: 2016-10-18 00:00:00

Over the years, advances in experimental and computational methods have
helped us to understand the role of thermodynamic, kinetic and active
(chaperone-aided) effects in coordinating the folding steps required to
achieving a knotted native state. Here, we review such developments by paying
particular attention to the complementarity of experimental and computational
studies. Key open issues that could be tackled with either or both approaches
are finally pointed out.
phylodyn: an R package for phylodynamic simulation and inference
Date: 2016-10-18 00:00:00

We introduce phylodyn, an R package for phylodynamic analysis based on gene
genealogies. The package main functionality is Bayesian nonparametric
estimation of effective population size fluctuations over time. Our
implementation includes several Markov chain Monte Carlo-based methods and an
integrated nested Laplace approximation-based approach for phylodynamic
inference that have been developed in recent years. Genealogical data describe
the timed ancestral relationships of individuals sampled from a population of
interest. Here, individuals are assumed to be sampled at the same point in time
(isochronous sampling) or at different points in time (heterochronous
sampling); in addition, sampling events can be modeled with preferential
sampling, which means that the intensity of sampling events is allowed to
depend on the effective population size trajectory. We assume the coalescent
and the sequentially Markov coalescent processes as generative models of
genealogies. We include several coalescent simulation functions that are useful
for testing our phylodynamics methods via simulation studies. We compare the
performance and outputs of various methods implemented in phylodyn and outline
their strengths and weaknesses. R package phylodyn is available at
https://github.com/mdkarcher/phylodyn.
Finding Representative Points in Multivariate Data Using PCA
Authors: Ganesan Ashwinkumar, Oates Tim, Schmill Matt
Date: 2016-10-18 00:00:00

The idea of representation has been used in various fields of study from data
analysis to political science. In this paper, we define representativeness and
describe a method to isolate data points that can represent the entire data
set. Also, we show how the minimum set of representative data points can be
generated. We use data from GLOBE (a project to study the effects on Land
Change based on a set of parameters that include temperature, forest cover,
human population, atmospheric parameters and many other variables) to test &
validate the algorithm. Principal Component Analysis (PCA) is used to reduce
the dimensions of the multivariate data set, so that the representative points
can be generated efficiently and its Representativeness has been compared
against Random Sampling of points from the data set.
The temporal evolution of the central fixation bias in scene viewing
Date: 2017-06-21 00:00:00

When watching the image of a natural scene on a computer screen, observers
initially move their eyes towards the center of the image --- a reliable
experimental finding termed central fixation bias. This systematic tendency in
eye guidance likely masks attentional selection driven by image properties and
top-down cognitive processes. Here we show that the central fixation bias can
be reduced by delaying the initial saccade relative to image onset. In four
scene-viewing experiments we manipulated observers' initial gaze position and
delayed their first saccade by a specific time interval relative to the onset
of an image. We analyzed the distance to image center over time and show that
the central fixation bias of initial fixations was significantly reduced after
delayed saccade onsets. We additionally show that selection of the initial
saccade target strongly depended on the first saccade latency. Processes
influencing the time course of the central fixation bias were investigated by
comparing simulations of several dynamic and statistical models. Model
comparisons suggest that the central fixation bias is generated by a default
activation as a response to the sudden image onset and that this default
activation pattern decreases over time. Our results suggest that it may often
be preferable to use a modified version of the scene viewing paradigm that
decouples image onset from the start signal for scene exploration and
explicitly controls the central fixation bias. In general, the initial fixation
location and the latency of the first saccade need to be taken into
consideration when investigating eye movements during scene viewing.
Comparative Salt Tolerance Study of Some Acacia Species at Seed Germination Stage
Date: 2016-10-19 00:00:00

Objective: The purpose of this study was to assess and compare the seed
germination response of six Acacia species under different NaCl concentrations
in order to explore opportunities for selection and breeding salt tolerant
genotypes. Methodology: Germination of seeds was evaluated under salt stresses
using 5 treatment levels: 0, 100, 200, 300 and 400 mM of NaCl. Corrected
germination rate (GC), germination rate index (GRI) and mean germination time
(MGT) were recorded during 10 days. Results: The results indicated that
germination was significantly reduced in all species with the increase in NaCl
concentrations. However, significant interspecific variation for salt tolerance
was observed. The greatest variability in tolerance was observed at moderate
salt stress (200 mM of NaCl) and the decrease in germination appeared to be
more accentuated in A. cyanophylla and A. cyclops. Although, A. raddiana,
remains the most interesting, it preserved the highest percentage (GC = 80%)
and velocity of germination in all species studied in this study, even in the
high salt levels. This species exhibited a particular adaptability to salt
environment, at least at this stage in the life cycle and could be recommended
for plantation establishment in salt affected areas. On the other hand, when
ungerminated seeds were transferred from NaCl treatments to distilled water,
they recovered largely their germination without a lag period and with high
speed. This indicated that the germination inhibition was related to a
reversible osmotic stress that induced dormancy rather than specific ion
toxicity. Conclusion: This ability to germinate after exposure to higher
concentrations of NaCl suggests that studied species, especially the most
tolerant could be able to germinate under the salt affected soils and could be
utilized for the rehabilitation of damaged arid zones.
Puzzles in modern biology. III. Two kinds of causality in age-related disease
Date: 2016-10-19 00:00:00

The two primary causal dimensions of age-related disease are rate and
function. Change in rate of disease development shifts the age of onset. Change
in physiological function provides necessary steps in disease progression. A
causal factor may alter the rate of physiological change, but that causal
factor itself may have no direct physiological role. Alternatively, a causal
factor may provide a necessary physiological function, but that causal factor
itself may not alter the rate of disease onset. The rate-function duality
provides the basis for solving puzzles of age-related disease. Causal factors
of cancer illustrate the duality between rate processes of discovery, such as
somatic mutation, and necessary physiological functions, such as invasive
penetration across tissue barriers. Examples from cancer suggest general
principles of age-related disease.
Puzzles in modern biology. IV. Neurodegeneration, localized origin and widespread decay
Date: 2016-10-19 00:00:00

The motor neuron disease amyotrophic lateral sclerosis (ALS) typically begins
with localized muscle weakness. Progressive, widespread paralysis often follows
over a few years. Does the disease begin with local changes in a small piece of
neural tissue and then spread? Or does neural decay happen independently across
diverse spatial locations? The distinction matters, because local initiation
may arise by local changes in a tissue microenvironment, by somatic mutation,
or by various epigenetic or regulatory fluctuations in a few cells. A local
trigger must be coupled with a mechanism for spread. By contrast, independent
decay across spatial locations cannot begin by a local change, but must depend
on some global predisposition or spatially distributed change that leads to
approximately synchronous decay. This article outlines the conceptual frame by
which one contrasts local triggers and spread versus parallel spatially
distributed decay. Various neurodegenerative diseases differ in their
mechanistic details, but all can usefully be understood as falling along a
continuum of interacting local and global processes. Cancer provides an example
of disease progression by local triggers and spatial spread, setting a
conceptual basis for clarifying puzzles in neurodegeneration. Heart disease
also has crucial interactions between global processes, such as circulating
lipid levels, and local processes in the development of atherosclerotic
plaques. The distinction between local and global processes helps to understand
these various age-related diseases.
On the effects of alternative optima in context-specific metabolic model predictions
Date: 2017-06-15 00:00:00
Tasks: Data Integration, Specificity

Recent methodological developments have facilitated the integration of
high-throughput data into genome-scale models to obtain context-specific
metabolic reconstructions. A unique solution to this data integration problem
often may not be guaranteed, leading to a multitude of context-specific
predictions equally concordant with the integrated data. Yet, little attention
has been paid to the alternative optima resulting from the integration of
context-specific data. Here we present computational approaches to analyze
alternative optima for different context-specific data integration instances.
By using these approaches on metabolic reconstructions for the leaf of
Arabidopsis thaliana and the human liver, we show that the analysis of
alternative optima is key to adequately evaluating the specificity of the
predictions in particular cellular contexts. While we provide several ways to
reduce the ambiguity in the context-specific predictions, our findings indicate
that the existence of alternative optimal solutions warrant caution in detailed
context-specific analyses of metabolism.
Theory on the mechanisms of combinatorial binding of transcription factors with DNA
Date: 2016-10-20 00:00:00

We develop a theoretical framework on the mechanism of combinatorial binding
of transcription factors (TFs) with their specific binding sites on DNA. We
consider three possible mechanisms viz. monomer, hetero-oligomer and
coordinated recruitment pathways. In the monomer pathway, combinatorial TFs
search for their targets in an independent manner and the protein-protein
interactions among them will be insignificant. The protein-protein interactions
are very strong so that the hetero-oligomer complex of TFs as a whole searches
for the cognate sites in case of hetero-oligomer pathway. The TF which arrived
first will recruit the adjacent TFs in a sequential manner in the recruitment
pathway. The free energy released from the protein-protein interactions among
TFs will be in turn utilized to stabilize the TFs-DNA complex. Such coordinated
binding of TFs in fact emerges as the cooperative effect. Monomer and
hetero-oligomer pathways are efficient only when few TFs are involved in the
combinatorial regulation. Detailed random walk simulations suggest that when
the number of TFs in a combination increases then the searching efficiency of
TFs in these pathways decreases with the increasing number of TFs in a power
law manner. The power law exponent associated with the monomer pathway seems to
be strongly dependent on the number of TFs, distance between the initial
position of TFs from their specific binding sites and the hop size associated
with the dynamics of TFs on DNA.
Accounting for the Complex Hierarchical Topology of EEG Phase-Based Functional Connectivity in Network Binarisation
Date: 2017-09-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity

Research into binary network analysis of brain function faces a
methodological challenge in selecting an appropriate threshold to binarise edge
weights. For EEG phase-based functional connectivity, we test the hypothesis
that such binarisation should take into account the complex hierarchical
structure found in functional connectivity. We explore the density range
suitable for such structure and provide a comparison of state-of-the-art
binarisation techniques, the recently proposed Cluster-Span Threshold (CST),
minimum spanning trees, efficiency-cost optimisation and union of shortest path
graphs, with arbitrary proportional thresholds and weighted networks. We test
these techniques on weighted complex hierarchy models by contrasting model
realisations with small parametric differences. We also test the robustness of
these techniques to random and targeted topological attacks.We find that the
CST performs consistenty well in state-of-the-art modelling of EEG network
topology, robustness to topological network attacks, and in three real
datasets, agreeing with our hypothesis of hierarchical complexity. This
provides interesting new evidence into the relevance of considering a large
number of edges in EEG functional connectivity research to provide
informational density in the topology.
Anfrage-getriebener Wissenstransfer zur Unterstuetzung von Datenanalysten
Authors: Wahl Andreas M., Endler Gregor, Schwab Peter K., Herbst Sebastian, Lenz Richard
Date: 2016-10-20 00:00:00
Tasks: Data Integration, Management

In larger organizations, multiple teams of data scientists have to integrate
data from heterogeneous data sources as preparation for data analysis tasks.
Writing effective analytical queries requires data scientists to have in-depth
knowledge of the existence, semantics, and usage context of data sources. Once
gathered, such knowledge is informally shared within a specific team of data
scientists, but usually is neither formalized nor shared with other teams.
Potential synergies remain unused. We therefore introduce a novel approach
which extends data management systems with additional knowledge-sharing
capabilities to facilitate user collaboration without altering established data
analysis workflows. Relevant collective knowledge from the query log is
extracted to support data source discovery and incremental data integration.
Extracted knowledge is formalized and provided at query time.
Detecting and Summarizing Emergent Events in Microblogs and Social Media Streams by Dynamic Centralities
Authors: Avudaiappan Neela, Herzog Alexander, Kadam Sneha, Du Yuheng, Thatcher Jason, Safro Ilya
Date: 2016-10-20 00:00:00
Tasks: Decision Making

Methods for detecting and summarizing emergent keywords have been extensively
studied since social media and microblogging activities have started to play an
important role in data analysis and decision making. We present a system for
monitoring emergent keywords and summarizing a document stream based on the
dynamic semantic graphs of streaming documents. We introduce the notion of
dynamic eigenvector centrality for ranking emergent keywords, and present an
algorithm for summarizing emergent events that is based on the minimum weight
set cover. We demonstrate our system with an analysis of streaming Twitter data
related to public security events.
Ten Blue Links on Mars
Authors: Clarke Charles L. A., Cormack Gordon V., Lin Jimmy, Roegiest Adam
Date: 2016-10-20 00:00:00

This paper explores a simple question: How would we provide a high-quality
search experience on Mars, where the fundamental physical limit is
speed-of-light propagation delays on the order of tens of minutes? On Earth,
users are accustomed to nearly instantaneous response times from search
engines. Is it possible to overcome orders-of-magnitude longer latency to
provide a tolerable user experience on Mars? In this paper, we formulate the
searching from Mars problem as a tradeoff between "effort" (waiting for
responses from Earth) and "data transfer" (pre-fetching or caching data on
Mars). The contribution of our work is articulating this design space and
presenting two case studies that explore the effectiveness of baseline
techniques, using publicly available data from the TREC Total Recall and
Sessions Tracks. We intend for this research problem to be aspirational and
inspirational - even if one is not convinced by the premise of Mars
colonization, there are Earth-based scenarios such as searching from a rural
village in India that share similar constraints, thus making the problem worthy
of exploration and attention from researchers.
When do Correlations Increase with Firing Rate?
Date: 2017-01-26 00:00:00

A central question in neuroscience is to understand how noisy firing patterns
are used to transmit information. Because neural spiking is noisy, spiking
patterns are often quantified via pairwise correlations, or the probability
that two cells will spike coincidentally, above and beyond their baseline
firing rate. One observation frequently made in experiments, is that
correlations can increase systematically with firing rate. Theoretical studies
have determined that stimulus-dependent correlations that increase with firing
rate can have beneficial effects on information coding; however, we still have
an incomplete understanding of what circuit mechanisms do, or do not, produce
this correlation-firing rate relationship.
  Here, we study the relationship between pairwise correlations and firing
rates in recurrently coupled excitatory-inhibitory spiking networks with
conductance-based synapses. We found that with stronger excitatory coupling, a
positive relationship emerges between pairwise correlations and firing rates.
To explain these findings, we used linear response theory to predict the full
correlation matrix and to decompose correlations in terms of graph motifs. We
then used this decomposition to explain why covariation of correlations with
firing rate -- a relationship previously explained in feedforward networks
driven by correlated input -- emerges in some recurrent networks but not in
others. Furthermore, when correlations covary with firing rate, this
relationship is reflected in low-rank structure in the correlation matrix.
Lumbar degenerative spondylolisthesis epidemiology: a systemic review with a focus on gender-specific and age-specific prevalence
Date: 2016-10-19 00:00:00
Tasks: Epidemiology

The epidemiology of lumbar degenerative spondylolisthesis (DS) remains
controversial. We performed a systemic review with the aim to have a better
understanding of DS's prevalence in general population. The results showed the
prevalence of DS is very gender specific and age specific. Both women and men
have few DS before 50 years old, after 50 years old both women and men start to
develop DS, with women having a faster developing rate than men. For elderly
Chinese (>=65 yrs, mean age: 72.5 yrs), large population based studies
(MsOS(Hong Kong) and MrOS (Hong Kong), females n=2000 and males n=2000) showed
DS prevalence was for 25.0% for women and 19.1% for men, and the prevalence F:M
(women:men) ratio was 1.3:1. The published data (MsOS(USA) and MrOS(USA)
studies) seem to show elderly Caucasian American has a higher DS prevalence,
being approximately 60-70% higher than elderly Chinese; however the prevalence
F:M ratio was similar to elderly Chinese population. Patient data showed female
patients more often received treatment than men; and preliminary data show the
ratio of numbers of female patients received treatment compared with men did
not differ between Northeast Asians (Chinese, Japanese, and Korean) and
European and American Caucasians, being around 2:1 in elderly population. The
existing data also suggest that menopause may be a contributing factor for the
accelerated DS development in post-menopausal
Inverse Power Flow Problem
Authors: Ye Yuan, Steven Low, Omid Ardakanian, Claire Tomlin
Date: 2016-10-21 00:00:00

This paper formulates an inverse power flow problem which is to infer a nodal admittance matrix (hence the network structure of a power system) from voltage and current phasors measured at a number of buses. We show that the admittance matrix can be uniquely identified from a sequence of measurements corresponding to different steady states when every node in the system is equipped with a measurement device, and a Kron-reduced admittance matrix can be determined even if some nodes in the system are not monitored (hidden nodes). Furthermore, we propose effective algorithms based on graph theory to uncover the actual admittance matrix of radial systems with hidden nodes. We provide theoretical guarantees for the recovered admittance matrix and demonstrate that the actual admittance matrix can be fully recovered even from the Kron-reduced admittance matrix under some mild assumptions. Simulations on standard test systems confirm that these algorithms are capable of providing accurate estimates of the admittance matrix from noisy sensor data.