pwc-archive/papers-with-abstracts huggingface dataset:
Cellular tango: How extracellular matrix adhesion choreographs Rac-Rho signaling and cell movement
Authors: Elisabeth G. Rens, Leah Edelstein-Keshet
Date: 2021-04-19 00:00:00

The small GTPases Rac and Rho are known to regulate eukaryotic cell shape, promoting front protrusion (Rac) or rear retraction (Rho) of the cell edge. Such cell deformation changes the contact and adhesion of cell to the extracellular matrix (ECM), while ECM signaling through integrin receptors also affects GTPase activity. We develop and investigate a model for this three-way feedback loop in 1D and 2D spatial domains, as well as in a fully deforming 2D cell shape. The model consists of reaction-diffusion equations solved numerically with open-source software, Morpheus, and with custom-built cellular Potts model simulations. We find a variety of patterns and cell behaviors, including persistent polarity, flipped front-back cell polarity oscillations, and random protrusion-retraction. We show that the observed spatial patterns depend on the cell shape, and vice versa. The cell stiffness and biophysical properties also affect patterning, overall cell migration phenotypes.
Ordering results between the largest claims arising from two general heterogeneous portfolios
Authors: Sangita Das, Suchandan Kayal
Date: 2021-04-17 00:00:00

This work is entirely devoted to compare the largest claims from two heterogeneous portfolios. It is assumed that the claim amounts in an insurance portfolio are nonnegative absolutely continuous random variables and belong to a general family of distributions. The largest claims have been compared based on various stochastic orderings. The established sufficient conditions are associated with the matrices and vectors of model parameters. Applications of the results are provided for the purpose of illustration.
The American put with finite-time maturity and stochastic interest rate
Authors: Cheng Cai, Tiziano De Angelis, Jan Palczewski
Date: 2021-04-17 00:00:00

In this paper we study pricing of American put options on the Black and Scholes market with a stochastic interest rate and finite-time maturity. We prove that the option value is a $C^1$ function of the initial time, interest rate and stock price. By means of Ito calculus we rigorously derive the option value's early exercise premium formula and the associated hedging portfolio. We prove the existence of an optimal exercise boundary splitting the state space into continuation and stopping region. The boundary has a parametrisation as a jointly continuous function of time and stock price, and it is the unique solution to an integral equation which we compute numerically. Our results hold for a large class of interest rate models including CIR and Vasicek models. We show a numerical study of the option price and the optimal exercise boundary for Vasicek model.
Metabolically Stable Neurotensin Analogs Exert Potent and Long-Acting Analgesia Without Hypothermia
Authors: Mélanie Vivancos, Roberto Fanelli, Élie Besserer-Offroy, Sabrina Beaulieu, Magali Chartier, Martin Resua-Rojas, Christine E. Mona, Santo Previti, Emmanuelle Rémond, Jean-Michel Longpré, Florine Cavelier, Philippe Sarret
Date: 2021-04-16 00:00:00
Tasks: Management

The endogenous tridecapeptide neurotensin (NT) has emerged as an important inhibitory modulator of pain transmission, exerting its analgesic action through the activation of the G protein-coupled receptors, NTS1 and NTS2. Whereas both NT receptors mediate the analgesic effects of NT, NTS1 activation also produces hypotension and hypothermia, which may represent obstacles for the development of new pain medications. In the present study, we implemented various chemical strategies to improve the metabolic stability of the biologically active fragment NT(8-13) and assessed their NTS1/NTS2 relative binding affinities. We then determined their ability to reduce the nociceptive behaviors in acute, tonic, and chronic pain models and to modulate blood pressure and body temperature. To this end, we synthesized a series of NT(8-13) analogs carrying a reduced amide bond at Lys8-Lys9 and harboring site-selective modifications with unnatural amino acids, such as silaproline (Sip) and trimethylsilylalanine (TMSAla). Incorporation of Sip and TMSAla respectively in positions 10 and 13 of NT(8-13) combined with the Lys8-Lys9 reduced amine bond (JMV5296) greatly prolonged the plasma half-life time over 20 hours. These modifications also led to a 25-fold peptide selectivity toward NTS2. More importantly, central delivery of JMV5296 was able to induce a strong antinociceptive effect in acute (tail-flick), tonic (formalin), and chronic inflammatory (CFA) pain models without inducing hypothermia. Altogether, these results demonstrate that the chemically-modified NT(8-13) analog JMV5296 exhibits a better therapeutic profile and may thus represent a promising avenue to guide the development of new stable NT agonists and improve pain management.
Utilization of the simulated flue gas on the cultivation of Chlorella protothecoides
Authors: Cem Özel, Muharrem Erdem Boğoçlu, Ceren Keçeciler, Ecem Kaplan, Sevil Yücel
Date: 2021-04-16 00:00:00
Tasks: FLUE

In recent years, fossil-based fuels have been used to supply the energy needs of the world. Fossil-based fuels induce accumulation of the atmospheric CO2 which causes global warming. One of CO2 source is flue gas emission from the power plant. The microalgae have been considered excellent biological materials for reduction CO2 with ability to photosynthesis. In this study, air, 10% CO2, 15% CO2 and simulated flue gas (containing 15% CO2) feed were used to observe effect CO2 concentration and flue gas on cell growth and lipid content of Chlorella protothecoides. The highest dry cell weight (1,5 g/L) and lipid content (45%) values were obtained with 15% CO2 feed while the highest growth rate (1,10), biomass productivity (0,125 g/L/day), and lipid weight (0,63 g/g) were observed in 10% CO2 feed. Cultures fed with flue gas did not inhibited C. protothecoides growth and showed similar results with those fed with 15% CO2 gas in terms of growth rate, dry cell weight, biomass productivity and lipid content. These results showed that C. protothecoides has great potential for reducing CO2 emission from flue gas.
Activity stabilization in a population model of working memory by sinusoidal and noisy inputs
Authors: Nikita Novikov, Denis Zakharov, Victoria Moiseeva, Boris Gutkin
Date: 2021-04-19 00:00:00

According to mechanistic theories of working memory (WM), information is retained as persistent spiking activity of cortical neural networks. Yet, how this activity is related to changes in the oscillatory profile observed during WM tasks remains an open issue. We explore joint effects of input gamma-band oscillations and noise on the dynamics of several firing rate models of WM. The considered models have a metastable active regime, i.e. they demonstrate long-lasting transient post-stimulus firing rate elevation. We start from a single excitatory-inhibitory circuit and demonstrate that either gamma-band or noise input could stabilize the active regime, thus supporting WM retention. We then consider a system of two circuits with excitatory intercoupling. We find that fast coupling allows for better stabilization by common noise compared to independent noise and stronger amplification of this effect by in-phase gamma inputs compared to anti-phase inputs. Finally, we consider a multi-circuit system comprised of two clusters, each containing a group of circuits receiving a common noise input and a group of circuits receiving independent noise. Each cluster is associated with its own local gamma generator, so all its circuits receive gamma-band input in the same phase. We find that gamma-band input differentially stabilizes the activity of the "common-noise" groups compared to the "independent-noise" groups. If the inter-cluster connections are fast, this effect is more pronounced when the gamma-band input is delivered to the clusters in the same phase rather than in the anti-phase. Assuming that the common noise comes from a large-scale distributed WM representation, our results demonstrate that local gamma oscillations can stabilize the activity of the corresponding parts of this representation, with stronger effect for fast long-range connections and synchronized gamma oscillations.
Modeling the Nervous System as An Open Quantum System
Authors: Yu-Juan Sun, Wei-Min Zhang
Date: 2021-03-18 00:00:00

We propose a neural network model of multi-neuron interacting system that simulates neurons to interact each other through the surroundings of neuronal cell bodies. We physically model the neuronal cell surroundings, include the dendrites, the axons and the synapses as well as the surrounding glial cells, as a collection of all kinds of oscillating modes arisen from the electric circuital environment of neuronal action potentials. By analyzing the dynamics of this neural model through the master equation approach of open quantum systems, we investigate the collective behavior of neurons. After applying stimulations to the neural network, the neuronal collective state is activated and shows the action potential behavior. We find that this model can generate random neuron-neuron interactions and is proper to describe the process of information transmission in the nervous system physically, which may pave a potential route toward understanding the dynamics of nervous system.
PM$_{2.5}$ as a major predictor of COVID-19 basic reproduction number in the USA
Authors: Ognjen Milicevic, Igor Salom, Andjela Rodic, Sofija Markovic, Marko Tumbas, Dusan Zigic, Magdalena Djordjevic, Marko Djordjevic
Date: 2021-04-19 00:00:00
Tasks: feature selection

Many studies have proposed a relationship between COVID-19 transmissibility and ambient pollution levels. However, a major limitation in establishing such associations is to adequately account for complex disease dynamics, influenced by e.g. significant differences in control measures and testing policies. Another difficulty is appropriately controlling the effects of other potentially important factors, due to both their mutual correlations and a limited dataset. To overcome these difficulties, we will here use the basic reproduction number ($R_0$) that we estimate for USA states using non-linear dynamics methods. To account for a large number of predictors (many of which are mutually strongly correlated), combined with a limited dataset, we employ machine-learning methods. Specifically, to reduce dimensionality without complicating the variable interpretation, we employ Principal Component Analysis on subsets of mutually related (and correlated) predictors. Methods that allow feature (predictor) selection, and ranking their importance, are then used, including both linear regressions with regularization and feature selection (Lasso and Elastic Net) and non-parametric methods based on ensembles of weak-learners (Random Forest and Gradient Boost). Through these substantially different approaches, we robustly obtain that PM$_{2.5}$ is a major predictor of $R_0$ in USA states, with corrections from factors such as other pollutants, prosperity measures, population density, chronic disease levels, and possibly racial composition. As a rough magnitude estimate, we obtain that a relative change in $R_0$, with variations in pollution levels observed in the USA, is typically ~30%, which further underscores the importance of pollution in COVID-19 transmissibility.
Phage-antibiotic synergy inhibited by temperate and chronic virus competition
Authors: Kylie J Landa, Lauren M Mossman, Rachel J Whitaker, Zoi Rapti, Sara M Clifton
Date: 2021-04-19 00:00:00

As antibiotic resistance grows more frequent for common bacterial infections, alternative treatment strategies such as phage therapy have become more widely studied in the medical field. While many studies have explored the efficacy of antibiotics, phage therapy, or synergistic combinations of phages and antibiotics, the impact of virus competition on the efficacy of antibiotic treatment has not yet been considered. Here, we model the synergy between antibiotics and two viral types, temperate and chronic, in controlling bacterial infections. We demonstrate that while combinations of antibiotic and temperate viruses exhibit synergy, competition between temperate and chronic viruses inhibits bacterial control with antibiotics. In fact, our model reveals that antibiotic treatment may counterintuitively increase the bacterial load when a large fraction of the bacteria develop antibiotic-resistance.
Monitoring urban ecosystem service value using dynamic multi-level grids
Authors: Zhenfeng Shao, Yong Li, Xiao Huang, Bowen Cai, Lin Ding, Wenkang Pan, Ya zhang
Date: 2021-04-15 00:00:00
Tasks: valid

Ecosystem services are the direct and indirect contributions of an ecosystem to human well-being and survival. Ecosystem valuation is a method of assigning a monetary value to an ecosystem with its goods and services,often referred to as ecosystem service value (ESV). With the rapid expansion of cities, a mismatch occurs between urban development and ecological development, and it is increasingly urgent to establish a valid ecological assessment method. In this study, we propose an ecological evaluation standard framework by designing an ESV monitoring workflow based on the establishment of multi-level grids. The proposed method is able to capture multi-scale features, facilitates multi-level spatial expression, and can effectively reveal the spatial heterogeneity of ESV. Taking Haian city in the Jiangsu province as the study case, we implemented the proposed dynamic multi-level grids-based (DMLG) to calculate its urban ESV in 2016 and 2019. We found that the ESV of Haian city showed considerable growth (increased by 24.54 million RMB). Negative ESVs are concentrated in the central city, which presented a rapid trend of outward expansion. The results illustrated that the ongoing urban expanse does not reduce the ecological value in the study area. The proposed unified grid framework can be applied to other geographical regions and is expected to benefit future studies in ecosystem service evaluation in terms of capture multi-level spatial heterogeneity.
A cross-study analysis of drug response prediction in cancer cell lines
Authors: Fangfang Xia, Jonathan Allen, Prasanna Balaprakash, Thomas Brettin, Cristina Garcia-Cardona, Austin Clyde, Judith Cohn, James Doroshow, Xiaotian Duan, Veronika Dubinkina, Yvonne Evrard, Ya Ju Fan, Jason Gans, Stewart He, Pinyi Lu, Sergei Maslov, Alexander Partin, Maulik Shukla, Eric Stahlberg, Justin M. Wozniak, Hyunseung Yoo, George Zaki, Yitan Zhu, Rick Stevens
Date: 2021-04-18 00:00:00
Tasks: BIG-bench Machine Learning, Diversity, Drug Response Prediction

To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross validation within a single study to assess model accuracy. While an essential first step, cross validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: NCI60, CTRP, GDSC, CCLE and gCSI. Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies, and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.
Multifunctional pH sensitive 3D scaffolds for treatment and prevention of bone infection
Authors: Cicuendez M, Doadrio JC, Hernandez A, Portoles MT, Izquierdo-Barba I, Vallet-Regi M
Date: 2021-04-19 00:00:00

Multifunctional-therapeutic 3D scaffolds have been prepared. These biomaterials are able to destroy the S. aureus bacteria biofilm and to allow bone regeneration at the same time. The present study is focused on the design of pH sensitive 3D hierarchical meso-macroporous scaffolds based on MGHA nanocomposite formed by a mesostructured glassy network with embedded hydroxyapatite nanoparticles, whose mesopores have been loaded with levofloxacin as antibacterial agent. These 3D platforms exhibit controlled and pH-dependent levofloxacin release, sustained over time at physiological pH (7.4) and notably increased at infection pH (6.7 and 5.5), which is due to the different interaction rate between diverse levofloxacin species and the silica matrix. These 3D systems are able to inhibit the S. aureus growth and to destroy the bacterial biofilm without cytotoxic effects on human osteoblasts and allowing an adequate colonization and differentiation of preosteoblastic cells on their surface. These findings suggest promising applications of these hierarchical MGHA nanocomposite scaffolds for the treatment and prevention of bone infection.
Mixed-charge pseudo-zwitterionic mesoporous silica nanoparticles with lowfouling and reduced cell uptake properties
Authors: Noemi Encinas, Mercedes Angulo, Carlos Astorga, Montserrat Colilla, Isabel Izquierdo-Barba, Maria Vallet-Regi
Date: 2021-04-19 00:00:00

The design of drug delivery systems needs to consider biocompatibility and host body recognition for an adequate actuation. In this work, mesoporous silica nanoparticles (MSNs) surfaces were successfully modified with two silane molecules to provide mixed-charge brushes (-NH3/-PO3) and well evaluated in terms of surface properties, low-fouling capability and cell uptake in comparison to PEGylated MSNs. The modification process consists in the simultaneous direct-grafting of hydrolysable short chain amino (aminopropyl silanetriol, APST) and phosphonate-based (trihydroxy-silyl-propyl-methyl-phosphonate, THSPMP) silane molecules able to provide a pseudo-zwitterionic nature under physiological pH conditions. Results confirmed that both mixed-charged pseudo-zwitterionic MSNs (ZMSN) and PEG-MSN display a significant reduction of serum protein adhesion and macrophages uptake with respect to pristine MSNs. In the case of ZMSNs, his reduction is up to a 70-90% for protein adsorption and c.a. 60% for cellular uptake. This pseudo-zwitterionic modification has been focused on the aim of local treatment of bacterial infections through the synergistic effect between the capability of the nanocarriers to transport and deliver specific drugs and the electrostatic interactions between the mixed-charge systems and bacteria. These findings open promising future expectations for the effective treatment of bacterial infections through the use mixed-charge pseudo-zwitterionic MSNs furtive to macrophages and with antimicrobial properties.
Mathematical Modeling of Chemotaxis Guided Amoeboid Cell Swimming
Authors: Qixuan Wang, Hao Wu
Date: 2021-04-07 00:00:00

Cells and microorganisms adopt various strategies to migrate in response to different environmental stimuli. To date, many modeling research has focused on the crawling-based Dictyostelium discoideum (Dd) cells migration induced by chemotaxis, yet recent experimental results reveal that even without adhesion or contact to a substrate, Dd cells can still swim to follow chemoattractant signals. In this paper, we develop a modeling framework to investigate the chemotaxis induced amoeboid cell swimming dynamics. A minimal swimming system consists of one deformable Dd amoeboid cell and a dilute suspension of bacteria, and the bacteria produce chemoattractant signals that attract the Dd cell. We use the mathematical amoeba model to generate Dd cell deformation and solve the resulting low Reynolds number flows, and use a moving mesh based finite volume method to solve the reaction-diffusion-convection equation. Using the computational model, we show that chemotaxis guides a swimming Dd cell to follow and catch bacteria, while on the other hand, bacterial rheotaxis may help the bacteria to escape from the predator Dd cell.
A Black-Scholes user's guide to the Bachelier model
Authors: Jaehyuk Choi, Minsuk Kwak, Chyng Wen Tee, Yumeng Wang
Date: 2021-04-18 00:00:00
Tasks: Management

To cope with the negative oil futures price caused by the COVID-19 recession, global commodity futures exchanges temporarily switched the option model from Black--Scholes to Bachelier in 2020. This study reviews the literature on Bachelier's pioneering option pricing model and summarizes the practical results on volatility conversion, risk management, stochastic volatility, and barrier options pricing to facilitate the model transition. In particular, using the displaced Black-Scholes model as a model family with the Black-Scholes and Bachelier models as special cases, we not only connect the two models but also present a continuous spectrum of model choices.
On the Investment Strategies in Occupational Pension Plans
Authors: Frank Bosserhoff, An Chen, Nils Sorensen, Mitja Stadje
Date: 2021-04-18 00:00:00
Tasks: Stochastic Optimization

Demographic changes increase the necessity to base the pension system more and more on the second and the third pillar, namely the occupational and private pension plans; this paper deals with Target Date Funds (TDFs), which are a typical investment opportunity for occupational pension planners. TDFs are usually identified with a decreasing fraction of wealth invested in equity (a so-called glide path) as retirement comes closer, i.e., wealth is invested more risky the younger the saver is. We investigate whether this is actually optimal in the presence of non-tradable income risk in a stochastic volatility environment. The retirement planning procedure is formulated as a stochastic optimization problem. We find it is the (random) contributions that induce the optimal path exhibiting a glide path structure, both in the constant and stochastic volatility environment. Moreover, the initial wealth and the initial contribution made to a retirement account strongly influence the fractional amount of wealth to be invested in risky assets. The risk aversion of an individual mainly determines the steepness of the glide path.
Detecting cognitive decline using speech only: The ADReSSo Challenge
Authors: Saturnino Luz, Fasih Haider, Sofia de la Fuente, Davida Fromm, Brian MacWhinney
Date: 2021-03-23 00:00:00
Tasks: General Classification, Prediction, regression

Building on the success of the ADReSS Challenge at Interspeech 2020, which attracted the participation of 34 teams from across the world, the ADReSSo Challenge targets three difficult automatic prediction problems of societal and medical relevance, namely: detection of Alzheimer's Dementia, inference of cognitive testing scores, and prediction of cognitive decline. This paper presents these prediction tasks in detail, describes the datasets used, and reports the results of the baseline classification and regression models we developed for each task. A combination of acoustic and linguistic features extracted directly from audio recordings, without human intervention, yielded a baseline accuracy of 78.87% for the AD classification task, an MMSE prediction root mean squared (RMSE) error of 5.28, and 68.75% accuracy for the cognitive decline prediction task.