pwc-archive/papers-with-abstracts huggingface dataset:
A mechanism for sarcomere breathing: volume change and advective flow within the myofilament lattice
Authors: Julie A Cass, C. Dave Williams, Tom C Irving, Eric Lauga, Sage Malingen, Tom L. Daniel, Simon N. Sponberg
Date: 2019-04-29 00:00:00

During muscle contraction, myosin motors anchored to thick filaments bind to and slide actin thin filaments. These motors rely on energy derived from ATP, supplied, in part, by diffusion from the sarcoplasm to the interior of the lattice of actin and myosin filaments. The radial spacing of filaments in this lattice may change or remain constant during contraction. If the lattice is isovolumetric, it must expand when the muscle shortens. If, however, the spacing is constant or has a different pattern of axial and radial motion, then the lattice changes volume during contraction, driving fluid motion and assisting in the transport of molecules between the contractile lattice and the surrounding intracellular space. We first create an advective-diffusive-reaction flow model and show that the flow into and out of the sarcomere lattice would be significant in the absence of lattice expansion. Advective transport coupled to diffusion has the potential to substantially enhance metabolite exchange within the crowded sarcomere. Using time-resolved x-ray diffraction of contracting muscle, we next show that the contractile lattice is neither isovolumetric nor constant in spacing. Instead, lattice spacing is time-varying, depends on activation, and can manifest as an effective time-varying Poisson ratio. The resulting fluid flow in the sarcomere lattice of synchronous insect flight muscles is greater than expected for constant lattice spacing conditions. Lattice spacing depends on a variety of factors that produce radial force, including crossbridges, titin-like molecules, and other structural proteins. Volume change and advective transport varies with the phase of muscle stimulation but remains significant at all conditions. Akin to "breathing," advective-diffusive transport in sarcomeres is sufficient to promote metabolite exchange and may play a role in the regulation of contraction itself.
Efficiency in Truthful Auctions via a Social Network
Date: 2019-04-29 00:00:00

In this paper, we study efficiency in truthful auctions via a social network,
where a seller can only spread the information of an auction to the buyers
through the buyers' network. In single-item auctions, we show that no mechanism
is strategy-proof, individually rational, efficient, and weakly budget
balanced. In addition, we propose $\alpha$-APG mechanisms, a class of
mechanisms which operate a trade-off between efficiency and weakly budget
balancedness. In multi-item auctions, there already exists a strategy-proof
mechanism when all buyers need only one item. However, we indicate a
counter-example to strategy-proofness in this mechanism, and to the best of our
knowledge, the question of finding a strategy-proof mechanism remains open. We
assume that all buyers have decreasing marginal utility and propose a
generalized APG mechanism that is strategy-proof and individually rational but
not efficient. Importantly, we show that this mechanism achieves the largest
efficiency measure among all strategy-proof mechanisms.
Mean-variance portfolio selection under Volterra Heston model
Authors: Bingyan Han, Hoi Ying Wong
Date: 2019-04-29 00:00:00
Tasks: model

Motivated by empirical evidence for rough volatility models, this paper investigates continuous-time mean-variance (MV) portfolio selection under the Volterra Heston model. Due to the non-Markovian and non-semimartingale nature of the model, classic stochastic optimal control frameworks are not directly applicable to the associated optimization problem. By constructing an auxiliary stochastic process, we obtain the optimal investment strategy, which depends on the solution to a Riccati-Volterra equation. The MV efficient frontier is shown to maintain a quadratic curve. Numerical studies show that both roughness and volatility of volatility materially affect the optimal strategy.
Towards a Passive BCI to Induce Lucid Dream
Date: 2019-04-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Lucid dreaming (LD) is a phenomenon during which the person is aware that
he/she dreaming and is able to control the dream content. Studies have shown
that only 20% of people can experience lucid dreams on a regular basis.
However, LD frequency can be increased through induction techniques. External
stimulation technique relies on the ability to integrate external information
into the dream content. The aim is to remind the sleeper that she/he is
dreaming. If this type of protocol is not fully efficient, it demonstrates how
sensorial stimuli can be easily incorporated into people's dreams. The
objective of our project was to replicate this induction technique using
material less expensive and more portable. This material could simplify
experimental procedures. Participants could bring the material home, then have
a more ecological setting. First, we used the OpenBCI Cyton, a low-cost EEG
signal acquisition board in order to record and manually live-score sleep.
Then, we designed a mask containing two LEDs, connected to a microcontroller to
flash visual stimulation during sleep. We asked two volunteers to sleep for 2
hours in a dedicated room. One of the participants declared having a dream
during which the blue lights diffused by the mask were embedded into the dream
environment. The other participant woke up during the visual stimulation. These
results are congruent with previous studies. This work marked the first step of
a larger project. Our ongoing research includes the use of an online sleep
stage scoring tool and the possibility to automatically send stimuli according
to the sleep stage. We will also investigate other types of stimulus induction
in the future such as vibro-tactile stimulation that showed great potentials.
Empirical facts characterizing banking crises: an analysis via binary time series
Date: 2019-04-29 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

Various works have already showed that common shocks and cross-country
financial linkages caused the banking systems of several countries to be highly
interconnected with the result that during bad times, banking crises may arise
simultaneously in different countries. Our aim is to provide further evidence
on the topic using a dataset made by dichotomous banking crises time series for
66 countries from 1800 to 2014. Via the use of heatmap matrices we show that
several countries exhibit pairwise correlation, which means that banking crises
tend to occur in the same year. Clustering analysis suggests that developed
countries (for the most European ones) are highly similar in terms of the path
of events. An analysis of the events that followed the Great Depression and the
Great Recession shows that after the crisis of 2008, banking crises tend to
characterize countries tied by financial links whereas before 2008 contagion
seems to affect countries in the same geographical area. Clustering analysis
shows also that after financial liberalization, crises affected countries with
similar economic structures and growth. Further researches should enlighten the
origin of these linkages investigating how the process of contagion eventually
happens.
11,700 km Transmission at 4.8 bit/4D-sym via Four-dimensional Geometrically-shaped Polarization-Ring-Switching Modulation
Date: 2019-04-29 00:00:00

Using a novel geometrically-shaped four-dimensional modulation format, we
transmitted 11x200 Gbit/s DWDM at 4.8 bit/4D-sym over 7,925 km and 11,700 km
using EDFA-only and hybrid amplification, respectively. A reach increase of 16%
is achieved over PM-8QAM.
Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics
Date: 2019-04-29 00:00:00

Background: Many genome-wide association studies have detected genomic
regions associated with traits, yet understanding the functional causes of
association often remains elusive. Utilizing systems approaches and focusing on
intermediate molecular phenotypes might facilitate biologic understanding.
Results: The availability of exome sequencing of two populations of
African-Americans and European-Americans from the Atherosclerosis Risk in
Communities study allowed us to investigate the effects of annotated
loss-of-function (LoF) mutations on 122 serum metabolites. To assess the
findings, we built metabolomic causal networks for each population separately
and utilized structural equation modeling. We then validated our findings with
a set of independent samples. By use of methods based on concepts of Mendelian
randomization of genetic variants, we showed that some of the affected
metabolites are risk predictors in the causal pathway of disease. For example,
LoF mutations in the gene KIAA1755 were identified to elevate the levels of
eicosapentaenoate (p-value=5E-14), an essential fatty acid clinically
identified to increase essential hypertension. We showed that this gene is in
the pathway to triglycerides, where both triglycerides and essential
hypertension are risk factors of metabolomic disorder and heart attack. We also
identified that the gene CLDN17, harboring loss-of-function mutations, had
pleiotropic actions on metabolites from amino acid and lipid pathways.
Conclusion: Using systems biology approaches for the analysis of metabolomics
and genetic data, we integrated several biological processes, which lead to
findings that may functionally connect genetic variants with complex diseases.
Human Spermbots for Cancer-Relevant Drug Delivery
Authors: Haifeng Xu, Mariana Medina-Sanchez, Daniel R. Brison, Richard J. Edmondson, Stephen S. Taylor, Louisa Nelson, Kang Zeng, Steven Bagley, Carla Ribeiro, Lina P. Restrepo, Elkin Lucena, Christine K. Schmidt, Oliver G. Schmidt
Date: 2019-04-29 00:00:00

Cellular micromotors are attractive for locally delivering high concentrations of drug and targeting hard-to-reach disease sites such as cervical cancer and early ovarian cancer lesions by non-invasive means. Spermatozoa are highly efficient micromotors perfectly adapted to traveling up the female reproductive system. Indeed, bovine sperm-based micromotors have recently been reported as a potential candidate for the drug delivery toward gynecological cancers of clinical unmet need. However, due to major differences in the molecular make-up of bovine and human sperm, a key translational bottleneck for bringing this technology closer to the clinic is to transfer this concept to human sperm. Here, we successfully load human sperm with a chemotherapeutic drug and perform treatment of relevant 3D cervical cancer and patient-representative 3D ovarian cancer cell cultures, resulting in strong anti-cancer effects. Additionally, we show the subcellular localization of the chemotherapeutic drug within human sperm heads and assess drug effects on sperm motility and viability over time. Finally, we demonstrate guidance and release of human drug-loaded sperm onto cancer cell cultures by using streamlined microcap designs capable of simultaneously carrying multiple human sperm towards controlled drug dosing by transporting known numbers of sperm loaded with defined amounts of chemotherapeutic drug.
Mathematical studies of the dynamics of finite-size binary neural networks: A review of recent progress
Date: 2019-04-29 00:00:00

Traditional mathematical approaches to studying analytically the dynamics of
neural networks rely on the mean-field approximation, which is rigorously
applicable only to networks of infinite size. However, all existing real
biological networks have finite size, and many of them, such as microscopic
circuits in invertebrates, are composed only of a few tens of neurons. Thus, it
is important to be able to extend to small-size networks our ability to study
analytically neural dynamics. Analytical solutions of the dynamics of
finite-size neural networks have remained elusive for many decades, because the
powerful methods of statistical analysis, such as the central limit theorem and
the law of large numbers, do not apply to small networks. In this article, we
critically review recent progress on the study of the dynamics of small
networks composed of binary neurons. In particular, we review the mathematical
techniques we developed for studying the bifurcations of the network dynamics,
the dualism between neural activity and membrane potentials, cross-neuron
correlations, and pattern storage in stochastic networks. Finally, we highlight
key challenges that remain open, future directions for further progress, and
possible implications of our results for neuroscience.
Energy Balance in Cell Phone Radiofrequency Radiation Exposed Mice and Rats
Date: 2019-04-29 00:00:00

The National Toxicology Program exposed mice and rats to cell phone
radiofrequency radiation. They observer cancers in male rats but not in male
mice and different increases in body temperature between mice and rats using
identical frequency and intensity exposures. Using the Faraday's Law model,
thermoregulation considerations and an energy balance equation, we estimate
that the increase in temperature of neural circuits of mice exposed to
radiofrequency radiation is only 0.28 that of rats. This decrease in neural
circuit temperatures is consistent with the absence of cancer in mice. This
calculation also qualitatively explains the different increases in body
temperature in mice and rats under different radiofrequency intensities and
frequencies.
A minimal model for microbial biodiversity can reproduce experimentally observed ecological patterns
Date: 2020-01-23 00:00:00

Surveys of microbial biodiversity such as the Earth Microbiome Project (EMP)
and the Human Microbiome Project (HMP) have revealed robust ecological patterns
across different environments. A major goal in ecology is to leverage these
patterns to identify the ecological processes shaping microbial ecosystems. One
promising approach is to use minimal models that can relate mechanistic
assumptions at the microbe scale to community-level patterns. Here, we
demonstrate the utility of this approach by showing that the Microbial Consumer
Resource Model (MiCRM) -- a minimal model for microbial communities with
resource competition, metabolic crossfeeding and stochastic colonization -- can
qualitatively reproduce patterns found in survey data including compositional
gradients, dissimilarity/overlap correlations, richness/harshness correlations,
and nestedness of community composition. By using the MiCRM to generate
synthetic data with different environmental and taxonomical structure, we show
that large scale patterns in the EMP can be reproduced by considering the
energetic cost of surviving in harsh environments and HMP patterns may reflect
the importance of environmental filtering in shaping competition. We also show
that recently discovered dissimilarity-overlap correlations in the HMP likely
arise from communities that share similar environments rather than reflecting
universal dynamics. We identify ecologically meaningful changes in parameters
that alter or destroy each one of these patterns, suggesting new mechanistic
hypotheses for further investigation. These findings highlight the promise of
minimal models for microbial ecology.
Vaccination with Leishmania mexicana LPG induces PD-1 in CD8+ and PD-L2 in macrophages thereby suppressing the immune response: A model to assess vaccine efficacy
Authors: M. B. Martínez Salazar, J. Delgado Domínguez, J. Silva Estrada, C. González Bonilla, I. Becker
Date: 2019-04-29 00:00:00

Leishmania lipophosphoglycan is a molecule that has been used as a vaccine candidate, with contradictory results. Since unsuccessful protection could be related to suppressed T cell responses, we analyzed the expression of inhibitory receptor PD-1 in CD8+ and CD4+ lymphocytes and it is ligand PD-L2 in macrophages of BALB/c mice immunized with various doses of Leishmania mexicana LPG and re-stimulated in vitro with different concentrations of LPG. Vaccination with LPG enhanced the expression of PD-1 in CD8+ cells. Activation molecules CD137 were reduced in CD8+ cells from vaccinated mice. In vitro re-stimulation enhanced PD-L2 expression in macrophages of healthy mice in a dose-dependent fashion. The expression of PD-1, PD-L2, and CD137 is modulated according to the amount of LPG used during immunization and in vitro re-stimulation. We analyzed the expression of these molecules in mice infected with 1 x 10'4 or 1 x 10'5 L. mexicana promastigotes and re-stimulated in vitro with LPG. Infection with 1 x 10'5 parasites increased the PD-1 expression in CD8+ and diminished PD-L2 in macrophages. When these CD8+ cells were re-stimulated in vitro with LPG, simulating a second exposure to parasite antigens, PD-1 expression increased significantly more, in a dose-dependent fashion. We conclude that CD8+ T lymphocytes and macrophages express inhibition molecules according to the concentrations of Leishmania LPG and to the parasite load. Vaccination with increased amounts of LPG or infections with higher parasite numbers induces enhanced expression of PD-1 and functional inactivation of CD8+ cells, which can have critical consequences in leishmaniasis since these cells are crucial for disease control.
Hyperspectral Unmixing via Deep Autoencoder Networks for a Generalized Linear-Mixture/Nonlinear-Fluctuation Model
Authors: Min Zhao, Mou Wang, Jie Chen, Susanto Rahardja
Date: 2019-04-30 00:00:00
Tasks: Hyperspectral Unmixing

Spectral unmixing is an important task in hyperspectral image processing for separating the mixed spectral data pertaining to various materials observed individual pixels. Recently, nonlinear spectral unmixing has received particular attention because a linear mixture is not appropriate under many conditions. However, existing nonlinear unmixing approaches are often based on specific assumptions regarding the inherent nonlinearity, and they can be ineffective when applied to conditions deviating from the original assumptions. Therefore, these approaches are not well suited to scenes with unknown nonlinearity characteristics. This paper presents an unsupervised nonlinear spectral unmixing method based on a deep autoencoder network that applies to a generalized linear-mixture/nonlinear fluctuation model, consisting of a linear mixture component and an additive nonlinear mixture component that depends on both endmembers and abundances. The proposed approach benefits from the universal modeling ability of deep neural networks to learn the inherent nonlinearity of the nonlinear mixture component from the data itself via the autoencoder network, rather than relying on an assumed form. Extensive experiments with numerically synthetic, labeled laboratory-created data and real airborne data, illustrate the generality and effectiveness of this approach compared with state-of-the-art methods.
Antiepileptic drugs induce subcritical dynamics in human cortical networks
Date: 2019-04-30 00:00:00

Cortical network functioning critically depends on finely tuned interactions
to afford neuronal activity propagation over long distances while avoiding
runaway excitation. This importance is highlighted by the pathological
consequences and impaired performance resulting from aberrant network
excitability in psychiatric and neurological diseases, such as epilepsy. Theory
and experiment suggest that the control of activity propagation by network
interactions can be adequately described by a branching process. This
hypothesis is partially supported by strong evidence for balanced
spatiotemporal dynamics observed in the cerebral cortex, however, evidence of a
causal relationship between network interactions and cortex activity, as
predicted by a branching process, is missing in humans. Here we test this
cause-effect relationship by monitoring cortex activity under systematic
pharmacological reduction of cortical network interactions with antiepileptic
drugs. We report that cortical activity cascades, presented by the propagating
patterns of epileptic spikes, as well as temporal correlations decline
precisely as predicted for a branching process. Our results provide the missing
link to the branching process theory of cortical network function with
implications for understanding the foundations of cortical excitability and its
monitoring in conditions like epilepsy.
Open-Source Matlab-Based Graphical User Interface (GUI) For Computer Control of Microscopes Using Micro-Manager
Date: 2019-06-18 00:00:00

Live time-lapse microscopy is essential for a wide range of biological
applications. Software-based automation is the gold standard for the operation
of hardware accessories necessary for image acquisition. Given that current
software packages are neither affordable nor open to complex structured
programming, we have developed a Matlab-based graphical user interface (GUI)
that is fundamentally accessible while providing limitless avenues for further
customization. The GUI simultaneously communicates with the open-source,
cross-functional platform micro-Manager for controlling hardware for time-lapse
image acquisition and with other software for image processing. The use of the
GUI is demonstrated through an 18-hour cell migration experiment. The results
suggest that the GUI can generate high-quality, high-throughput time-lapse
images. The core code behind the GUI is open-source so that it can be modified
and upgraded. Therefore, it benefits the researchers worldwide by providing
them a fundamental yet functional template to add in advanced features specific
to their needs, such as additional non-microscopy hardware parts and software
packages supported by the Matlab software.
Supervised Machine Learning for Eliciting Individual Demand
Authors: John A. Clithero, Jae Joon Lee, Joshua Tasoff
Date: 2019-04-30 00:00:00
Tasks: BIG-bench Machine Learning

Direct elicitation, guided by theory, is the standard method for eliciting latent preferences. The canonical direct-elicitation approach for measuring individuals' valuations for goods is the Becker-DeGroot-Marschak procedure, which generates willingness-to-pay (WTP) values that are imprecise and systematically biased by understating valuations. We show that enhancing elicited WTP values with supervised machine learning (SML) can substantially improve estimates of peoples' out-of-sample purchase behavior. Furthermore, swapping WTP data with choice data generated from a simple task, two-alternative forced choice, leads to comparable performance. Combining all the data with the best-performing SML methods yields large improvements in predicting out-of-sample purchases. We quantify the benefit of using various SML methods in conjunction with using different types of data. Our results suggest that prices set by SML would increase revenue by 28% over using the stated WTP, with the same data.
Three-dimensional imaging with single-frame jigsaw-puzzle-reorganized sinusoidal fringe using multi-pixel axial flat brush scanning
Date: 2019-04-30 00:00:00

Structured-light three-dimensional (3D) imaging can achieve 3D shape of a
stationary object via one or more pixelated array cameras with phase-shifting
illumination. In order to extend 3D imaging to moving scenarios, we propose a
3D imaging method with double projection of a single-frame modulated light
pattern and a sampling pattern. It can continuously image the moving 3D scene
by making multi-pixel detector axial flat brush scan along the motion axis.
Utilizing spatial multiplexing for multiple single-pixel imaging, each
single-pixel does not need to keep staring at some part of the object, avoiding
motion blur problem. The performance of our method has been demonstrated by
numerical simulations. Given this, we believe that the technique paves the way
to practical applications including product line 3D monitoring.