pwc-archive/papers-with-abstracts huggingface dataset:
The signaling signature of the neurotensin type 1 receptor with endogenous ligands
Date: 2018-03-14 00:00:00

The human neurotensin 1 receptor (hNTS1) is a G protein-coupled receptor
involved in many physiological functions, including analgesia, hypothermia, and
hypotension. To gain a better understanding of which signaling pathways or
combination of pathways are linked to NTS1 activation and function, we
investigated the ability of activated hNTS1, which was stably expressed by
CHO-K1 cells, to directly engage G proteins, activate second messenger cascades
and recruit \b{eta}-arrestins. Using BRET-based biosensors, we found that
neurotensin (NT), NT(8-13) and neuromedin N (NN) activated the G{\alpha}q-,
G{\alpha}i1-, G{\alpha}oA-, and G{\alpha}13-protein signaling pathways as well
as the recruitment of \b{eta}-arrestins 1 and 2. Using pharmacological
inhibitors, we further demonstrated that all three ligands stimulated the
production of inositol phosphate and modulation of cAMP accumulation along with
ERK1/2 activation. Interestingly, despite the functional coupling to
G{\alpha}i1 and G{\alpha}oA, NT was found to produce higher levels of cAMP in
the presence of pertussis toxin, supporting that hNTS1 activation leads to cAMP
accumulation in a G{\alpha}s-dependent manner. Additionally, we demonstrated
that the full activation of ERK1/2 required signaling through both a
PTX-sensitive Gi/o-c-Src signaling pathway and PLCb-DAG-PKC-Raf-1- dependent
pathway downstream of Gq. Finally, the whole-cell integrated signatures
monitored by the cell-based surface plasmon resonance and changes in the
electrical impedance of a confluent cell monolayer led to identical phenotypic
responses between the three ligands. The characterization of the hNTS1-mediated
cellular signaling network will be helpful to accelerate the validation of
potential NTS1 biased ligands with an improved therapeutic/adverse effect
profile.
The hypotensive effect of activated apelin receptor is correlated with \b{eta}-arrestin recruitment
Date: 2018-03-14 00:00:00

The apelinergic system is an important player in the regulation of both
vascular tone and cardiovascular function, making this physiological system an
attractive target for drug development for hypertension, heart failure and
ischemic heart disease. Indeed, apelin exerts a positive inotropic effect in
humans whilst reducing peripheral vascular resistance. In this study, we
investigated the signaling pathways through which apelin exerts its hypotensive
action. We synthesized a series of apelin-13 analogs whereby the C-terminal
Phe13 residue was replaced by natural or unnatural amino acids. In HEK293 cells
expressing APJ, we evaluated the relative efficacy of these compounds to
activate G{\alpha}i1 and G{\alpha}oA G-proteins, recruit \b{eta}-arrestins 1
and 2 (\b{eta}arrs), and inhibit cAMP production. Calculating the transduction
ratio for each pathway allowed us to identify several analogs with distinct
signaling profiles. Furthermore, we found that these analogs delivered i.v. to
Sprague-Dawley rats exerted a wide range of hypotensive responses. Indeed, two
compounds lost their ability to lower blood pressure, while other analogs
significantly reduced blood pressure as apelin-13. Interestingly, analogs that
did not lower blood pressure were less effective at recruiting \b{eta}arrs.
Finally, using Spearman correlations, we established that the hypotensive
response was significantly correlated with \b{eta}arr recruitment but not with
G protein- dependent signaling. In conclusion, our results demonstrated that
the \b{eta}arr recruitment potency is involved in the hypotensive efficacy of
activated APJ.
Similar but Different: Exploiting Users' Congruity for Recommendation Systems
Authors: Beigi Ghazaleh, Liu Huan
Date: 2018-03-16 00:00:00
Tasks: Recommendation Systems, Sociology

The pervasive use of social media provides massive data about individuals'
online social activities and their social relations. The building block of most
existing recommendation systems is the similarity between users with social
relations, i.e., friends. While friendship ensures some homophily, the
similarity of a user with her friends can vary as the number of friends
increases. Research from sociology suggests that friends are more similar than
strangers, but friends can have different interests. Exogenous information such
as comments and ratings may help discern different degrees of agreement (i.e.,
congruity) among similar users. In this paper, we investigate if users'
congruity can be incorporated into recommendation systems to improve it's
performance. Experimental results demonstrate the effectiveness of embedding
congruity related information into recommendation systems.
Ergodicity in Stationary Graph Processes: A Weak Law of Large Numbers
Date: 2019-03-29 00:00:00

For stationary signals in time the weak law of large numbers (WLLN) states
that ensemble and realization averages are within e of each other with a
probability of order O(1/Ne^2) when considering N signal components. The graph
WLLN introduced in this paper shows that the same is essentially true for
signals supported on graphs. However, the notions of stationarity, ensemble
mean, and realization mean are different. Recent papers have defined graph
stationary signals as those that satisfy a form of invariance with respect to
graph diffusion. The ensemble mean of a graph stationary signal is not a
constant but a node-varying signal whose structure depends on the spectral
properties of the graph. The realization average of a graph signal is defined
here as an average of successive weighted averages of local signal values with
signal values of neighboring nodes. The graph WLLN shows that this two
node-varying signals are within e of each other with probability of order
O(1/Ne^2) in at least some nodes. In stationary time signals, the realization
average is not only a consistent estimator of the ensemble mean but also
optimal in terms of mean squared error (MSE). This is not true of graph
signals. Optimal MSE graph filter designs are also presented. An example
problem concerning the estimation of the mean of a Gaussian random field is
presented.
Identification of GSM and LTE Signals Using Their Second-order Cyclostationarity
Date: 2018-03-12 00:00:00

Automatic signal identification (ASI) has various millitary and commercial
applications, such as spectrum surveillance and cognitive radio. In this paper,
a novel ASI algorithm is proposed for the identification of GSM and LTE
signals, which is based on the pilot-induced second-order cyclostationarity.
The proposed algorithm provides a very good performance at low signal-to-noise
ratios and short observation times, with no need for channel estimation, and
timing and frequency synchronization. Simulations and off-the-air signals
acquired with the ThinkRF WSA4000 receiver are used to confirm the findings.
A Generalization of the Robust Positive Expectation Theorem for Stock Trading via Feedback Control
Date: 2018-03-13 00:00:00

The starting point of this paper is the so-called Robust Positive Expectation
(RPE) Theorem, a result which appears in literature in the context of
Simultaneous Long-Short stock trading. This theorem states that using a
combination of two specially-constructed linear feedback trading controllers,
one long and one short, the expected value of the resulting gain-loss function
is guaranteed to be robustly positive with respect to a large class of
stochastic processes for the stock price. The main result of this paper is a
generalization of this theorem. Whereas previous work applies to a single
stock, in this paper, we consider a pair of stocks. To this end, we make two
assumptions on their expected returns. The first assumption involves price
correlation between the two stocks and the second involves a bounded non-zero
momentum condition. With known uncertainty bounds on the parameters associated
with these assumptions, our new version of the RPE Theorem provides necessary
and sufficient conditions on the positive feedback parameter K of the
controller under which robust positive expectation is assured. We also
demonstrate that our result generalizes the one existing for the single-stock
case. Finally, it is noted that our results also can be interpreted in the
context of pairs trading.
How Smart Are `Water Smart Landscapes'?
Date: 2018-03-13 00:00:00

Understanding the effectiveness of alternative approaches to water
conservation is crucially important for ensuring the security and reliability
of water services for urban residents. We analyze data from one of the
longest-running "cash for grass" policies - the Southern Nevada Water
Authority's Water Smart Landscapes program, where homeowners are paid to
replace grass with xeric landscaping. We use a twelve year long panel dataset
of monthly water consumption records for 300,000 households in Las Vegas,
Nevada. Utilizing a panel difference-in-differences approach, we estimate the
average water savings per square meter of turf removed. We find that
participation in this program reduced the average treated household's
consumption by 18 percent. We find no evidence that water savings degrade as
the landscape ages, or that water savings per unit area are influenced by the
value of the rebate. Depending on the assumed time horizon of benefits from
turf removal, we find that the WSL program cost the water authority about $1.62
per thousand gallons of water saved, which compares favorably to alternative
means of water conservation or supply augmentation.
On the Variability of Chaos Indices in Sleep EEG Signals
Date: 2018-03-13 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Previous researches revealed the chaotic and nonlinear nature of EEG signal.
In this paper we inspected the variability of chaotic indices of the sleep EEG
signal such as largest Lyapunov exponent, mutual information, correlation
dimension and minimum embedding dimension among different subjects, conditions
and sleep stages. Empirical histograms of these indices are obtained from sleep
EEG of 31 subjects, showing that, with a good accuracy, these indices in each
stage of sleep vary from healthy human subjects to subjects suspected to have
sleep-disordered breathing.
A Perceptual Based Motion Compensation Technique for Video Coding
Date: 2018-03-13 00:00:00
Tasks: Motion Compensation, Motion Estimation, SSIM

Motion estimation is one of the important procedures in the all video
encoders. Most of the complexity of the video coder depends on the complexity
of the motion estimation step. The original motion estimation algorithm has a
remarkable complexity and therefore many improvements were proposed to enhance
the crude version of the motion estimation. The basic idea of many of these
works were to optimize some distortion function for mean squared error (MSE) or
sum of absolute difference (SAD) in block matching But it is shown that these
metrics do not conclude the quality as it is, on the other hand, they are not
compatible with the human visual system (HVS). In this paper we explored the
usage of the image quality metrics in the video coding and more specific in the
motion estimation. We have utilized the perceptual image quality metrics
instead of MSE or SAD in the block based motion estimation. Three different
metrics have used: structural similarity or SSIM, complex wavelet structural
similarity or CW-SSIM, visual information fidelity or VIF. Experimental results
showed that usage of the quality criterions can improve the compression rate
while the quality remains fix and thus better quality in coded video at the
same bit budget.
Exploring the Distributed Video Coding in a Quality Assessment Context
Date: 2018-03-13 00:00:00
Tasks: Decoder, Motion Compensation, Motion Estimation, Video Compression

In the popular video coding trend, the encoder has the task to exploit both
spatial and temporal redundancies present in the video sequence, which is a
complex procedure. As a result almost all video encoders have five to ten times
more complexity than their decoders. In a video compression process, one of the
main tasks at the encoder side is motion estimation which is to extract the
temporal correlation between frames. Distributed video coding (DVC) proposed
the idea that can lead to low complexity encoders and higher complexity
decoders. DVC is a new paradigm in video compression based on the information
theoretic ideas of Slepian-Wolf and Wyner-Ziv theorems. Wyner-Ziv coding is
naturally robust against transmission errors and can be used for joint source
and channel coding. Side Information is one of the key components of the
Wyner-Ziv decoder. Better side information generation will result in better
functionality of Wyner-Ziv coder. In this paper we proposed a new method that
can generate side information with a better quality and thus better
compression. We have used HVS (human visual system) based image quality metrics
as our quality criterion. The motion estimation we used in the decoder is
modified due to these metrics such that we could obtain finer side information.
The motion compensation is optimized for perceptual quality metrics and leads
to better side information generation compared to con- ventional MSE (mean
squared error) or SAD (sum of absolute difference) based motion compensation
currently used in the literature. Better motion compensation means better
compression.
Cubic Spline Interpolation Segmenting over Conventional Segmentation Procedures: Application and Advantages
Date: 2018-03-13 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

To design a novel method for segmenting the image using Cubic Spline
Interpolation and compare it with different techniques to determine which gives
an efficient data to segment an image. This paper compares polynomial least
square interpolation and the conventional Otsu thresholding with spline
interpolation technique for image segmentation. The threshold value is
determined using the above-mentioned techniques which are then used to segment
an image into the binary image. The results of the proposed technique are also
compared with the conventional algorithms after applying image equalizations.
The better technique is determined based on the deviation and mean square error
when compared with an accurately segmented image. The image with least amount
of deviation and mean square error is declared as the better technique.
A Human Visual System-Based 3D Video Quality Metric
Date: 2018-03-13 00:00:00

Although several 2D quality metrics have been proposed for images and videos,
in the case of 3D efforts are only at the initial stages. In this paper, we
propose a new full-reference quality metric for 3D content. Our method is
modeled around the HVS, fusing the information of both left and right channels,
considering color components, the cyclopean views of the two videos and
disparity. Performance evaluations showed that our 3D quality metric
successfully monitors the degradation of quality caused by several
representative types of distortion and it has 86% correlation with the results
of subjective evaluations.
A Random Matrix Approach to Wide Band Spectrum Sensing: Unknown Noise Variance Case
Date: 2018-03-13 00:00:00

In this paper three different scenarios in wide band spectrum sensing have
been studied. While the signal and noise statistics are supposed to be
unspecified, random matrixes have been utilized in order to estimate the noise
variance. These scenarios are: 1- Number of subbands is specified and there is
enough information regarding being used or being unused for each of them. 2-
Number of subbands is known but there is no information about usage
distribution among them. 3- Number of subbands is unknown. Simulation results
showed the superior performance of the proposed scheme. Regarding the number of
samples, the proposed method requires less number of samples compared to the
cyclo-stationary spectrum sensing algorithms and more samples compared to the
energy detection based methods. But, regarding the detection probability, the
proposed method is superior compared to both other spectrum sensing methods.
3D Video Quality Metric for 3D Video Compression
Date: 2018-03-13 00:00:00
Tasks: MS-SSIM, SSIM, Video Compression

As the evolution of multiview display technology is bringing glasses-free
3DTV closer to reality, MPEG and VCEG are preparing an extension to HEVC to
encode multiview video content. View synthesis in the current version of the 3D
video codec is performed using PSNR as a quality metric measure. In this paper,
we propose a full- reference Human-Visual-System based 3D video quality metric
to be used in multiview encoding as an alternative to PSNR. Performance of our
metric is tested in a 2-view case scenario. The quality of the compressed
stereo pair, formed from a decoded view and a synthesized view, is evaluated at
the encoder side. The performance is verified through a series of subjective
tests and compared with that of PSNR, SSIM, MS-SSIM, VIFp, and VQM metrics.
Experimental results showed that our 3D quality metric has the highest
correlation with Mean Opinion Scores (MOS) compared to the other tested
metrics.
Bayesian Detection of Abnormal ADS in Mutant Caenorhabditis elegans Embryos
Date: 2018-03-13 00:00:00
Tasks: quantile regression, Vocal Bursts Type Prediction

Cell division timing is critical for cell fate specification and
morphogenesis during embryogenesis. How division timings are regulated among
cells during development is poorly understood. Here we focus on the comparison
of asynchrony of division between sister cells (ADS) between wild-type and
mutant individuals of Caenorhabditis elegans. Since the replicate number of
mutant individuals of each mutated gene, usually one, is far smaller than that
of wild-type, direct comparison of two distributions of ADS between wild-type
and mutant type, such as Kolmogorov- Smirnov test, is not feasible. On the
other hand, we find that sometimes ADS is correlated with the life span of
corresponding mother cell in wild-type. Hence, we apply a semiparametric
Bayesian quantile regression method to estimate the 95% confidence interval
curve of ADS with respect to life span of mother cell of wild-type individuals.
Then, mutant-type ADSs outside the corresponding confidence interval are
selected out as abnormal one with a significance level of 0.05. Simulation
study demonstrates the accuracy of our method and Gene Enrichment Analysis
validates the results of real data sets.
Effect of High Frame Rates on 3D Video Quality of Experience
Date: 2018-03-13 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper, we study the effect of 3D videos with increased frame rates on
the viewers quality of experience. We performed a series of subjective tests to
seek the subjects preferences among videos of the same scene at four different
frame rates: 24, 30, 48, and 60 frames per second (fps). Results revealed that
subjects clearly prefer higher frame rates. In particular, Mean Opinion Score
(MOS) values associated with the 60 fps 3D videos were 55% greater than MOS
values of the 24 fps 3D videos.
Protein Mutation Stability Ternary Classification using Neural Networks and Rigidity Analysis
Date: 2018-03-13 00:00:00
Tasks: Classification

Discerning how a mutation affects the stability of a protein is central to
the study of a wide range of diseases. Machine learning and statistical
analysis techniques can inform how to allocate limited resources to the
considerable time and cost associated with wet lab mutagenesis experiments. In
this work we explore the effectiveness of using a neural network classifier to
predict the change in the stability of a protein due to a mutation. Assessing
the accuracy of our approach is dependent on the use of experimental data about
the effects of mutations performed in vitro. Because the experimental data is
prone to discrepancies when similar experiments have been performed by multiple
laboratories, the use of the data near the juncture of stabilizing and
destabilizing mutations is questionable. We address this later problem via a
systematic approach in which we explore the use of a three-way classification
scheme with stabilizing, destabilizing, and inconclusive labels. For a
systematic search of potential classification cutoff values our classifier
achieved 68 percent accuracy on ternary classification for cutoff values of
-0.6 and 0.7 with a low rate of classifying stabilizing as destabilizing and
vice versa.
Pharmacophore and ligand-guided screening of antibacterial leads targeting antibiotic resistance factor in Gram-negative bacteria
Date: 2018-03-13 00:00:00

Pathogenic Gram-negative bacteria have developed resistance to antibiotics
due to their ability in creating an envelope on the outer layer of
lipooligosaccharides (LOS). The cationic phosphoethanolamine (PEA) decoration
of LOS lipid A is regulated by lipid A-PEA transferase (EptA) which may serve
as a prominent target for developing new antibiotics. The structural
characterization of Neisserial EptA has provided a structural basis to its
catalytic mechanisms and ligand recognition that are crucial for inhibitor
development. In this study, a combination of pharmacophore- and ligand-based
approach has been employed to explore novel potent EptA inhibitors among
millions of commercially-available compounds and approved drugs. A total of
8166 hit molecules obtained from ZincPharmer pharmacophore-based screening and
PubMed ligand similarity search were further examined through individual
two-step semi-flexible docking simulation performed in MOE. Best hits were
therefore selected based on their docking score and consensus of the two
docking validations. Free energy of binding calculation suggests that the best
20 consensus compounds have a stronger binding affinity than EptA natural
substrate PEA. Further interaction analyses of selected eight ligands
demonstrate that these ligands have overall more effective interactions with
catalytically-essential residues and metal cofactors of EptA. Selected hits can
be further analyzed in vitro and examined through a pre-clinical trial. This
study provides an insight into drug repurposing which may serve as an initial
step to develop novel potent EptA inhibitors to combat the virulence of
multi-drug resistant Gram-negative bacteria.
Evaluating the Performance of Existing Full-Reference Quality Metrics on High Dynamic Range (HDR) Video Content
Date: 2018-03-13 00:00:00
Tasks: Video Compression

While there exists a wide variety of Low Dynamic Range (LDR) quality metrics,
only a limited number of metrics are designed specifically for the High Dynamic
Range (HDR) content. With the introduction of HDR video compression
standardization effort by international standardization bodies, the need for an
efficient video quality metric for HDR applications has become more pronounced.
The objective of this study is to compare the performance of the existing
full-reference LDR and HDR video quality metrics on HDR content and identify
the most effective one for HDR applications. To this end, a new HDR video
dataset is created, which consists of representative indoor and outdoor video
sequences with different brightness, motion levels and different representing
types of distortions. The quality of each distorted video in this dataset is
evaluated both subjectively and objectively. The correlation between the
subjective and objective results confirm that VIF quality metric outperforms
all to ther tested metrics in the presence of the tested types of distortions.