pwc-archive/papers-with-abstracts huggingface dataset:
Development and evaluation of copper-containing mesoporous bioactive glasses for bone defects therapy
Authors: J Jimenez-Holguin, S Sanchez-Salcedo, M Vallet-Regi, A J Salinas
Date: 2021-03-21 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Mesoporous bioactive glasses, MBG, are gaining increasing interest in the design of new biomaterials for bone defects treatment. An important research trend to enhance their biological behavior is the inclusion of moderate amounts of oxides with therapeutical action such as CuO. In this paper, MBG with composition were synthesized, investigating the influence of the CuO content and some synthesis parameters in their properties. Two batch were developed; first one using HCl as catalyst and chlorides as CaO and CuO precursors, second one, using HNO3 and nitrates. MBG of chlorides batch exhibited calcium/copper phosphate nanoparticles, between 10 and 20 nm. Nevertheless, CuO- containing MBG of nitrates batch showed nuclei of metallic copper nanoparticles larger than 50 nm and quicker in vitro bioactive responses. Thus, they were coated by an apatite-like layer after 24 h soaked in simulated body fluid, a remarkably short period for MBG containing up to 5 % of CuO. A model, focused in the copper location in the glass network, was proposed to relate nanostructure and in vitro behaviour. Moreover, after 24 h in MEM or THB culture media, all the MBG released therapeutic amounts of Ca2+ and Cu2+ ions. Because the quick bioactive response in SBF, the capacity to host biomolecules in their pores and to release therapeutic concentrations of Ca2+ and Cu2+ ions, MBG of the nitrate batch are considered as excellent biomaterials for bone regeneration.
Sleep quality, chronotype and social jet lag of adolescents from a population with a very late chronotype
Authors: Andrés H. Calderón, Romina A. Capellino, Damián Dellavale, D. Lorena Franco, Pablo M. Gleiser, Sergio Lindenbaum, Mara López-Wortzman, Sabrina C. Riva, Fernanda R. Román, Sebastián Risau-Gusman
Date: 2021-03-19 00:00:00
Tasks: Sleep Quality

Sleep disorders can be a negative factor both for learning as for the mental and physical development of adolescents. It has been shown that, in many populations, adolescents tend to have a poor sleep quality, and a very late chronotype. Furthermore, these features peak at adolescence, in the sense that adults tend to sleep better and have an earlier chronotype. But what happens when we consider adolescents in a population where already adults have poor sleep quality and a very late chronotype? We have conducted two non-clinical studies in the city of Bariloche, Argentina aimed at measuring sleep quality, chronotype, and social jet lag, using the Pittsburgh and Munich questionnaires. These were administered individually to groups of high school students, as well as to smaller samples of adults and preadolescents, in order to study differences between adolescents and these groups. The results show that in this population sleep quality is much poorer than in most other healthy populations recorded elsewhere. Furthermore, sleep quality is consistently worse for adolescents than for the other groups. The difference with adults seems to be due mainly to increased daytime sleepiness and sleep latency, whereas the difference with preadolescents seems to be due mainly to shorter sleep duration. We also found that the chronotypes of all the groups are very late, with a peak at an age between 18 and 24 ys. Social jet lag and sleep onset latency are also large, and they peak at adolescence, which suggests that they might be closely related to the large prevalence of poor sleep quality that we find in adolescents.
Quantitative predictions of neoadjuvant chemotherapy effects in breast cancer by individual patient data assimililation
Authors: P. Castorina, D. Carco', C. Colarossi, M. Mare, L. Memeo, M. Pace, I. Puliafito, D. Giuffrida
Date: 2021-03-18 00:00:00

Neoadjuvant chemotherapy has been used for breast cancer aiming at downgrading before surgery. In this article we propose a new quantitative analysis of the effects of the neoadjuvant therapy to obtain numerical, personalized, predictions on the shrinkage of the tumor size after the drug doses, by data assimilation of the individual patient. The algorithm has been validated by a sample of 37 patients with histological diagnosis of locally advanced primary breast carcinoma. The biopsy specimen, the initial tumor size and its reduction after each treatment were known for all patients. We find that: a) the measure of tumor size at the diagnosis and after the first dose permits to predict the size reduction for the follow up; b) the results are in agreement with our data sample, within 10-20 %, for about 90% of the patients. The quantitative indications suggest the best time for surgery. The analysis is patient oriented, weakly model dependent and can be applied to other cancer phenotypes.
Density- and elongation speed-dependent error correction in RNA polymerization
Authors: Xinzhe Zuo, Tom Chou
Date: 2021-03-09 00:00:00

Backtracking of RNA polymerase (RNAP) is an important pausing mechanism during DNA transcription that is part of the error correction process that enhances transcription fidelity. We model the backtracking mechanism of RNA polymerase, which usually happens when the polymerase tries to incorporate a mismatched nucleotide triphosphate. Previous models have made simplifying assumptions such as neglecting the trailing polymerase behind the backtracking polymerase or assuming that the trailing polymerase is stationary. We derive exact analytic solutions of a stochastic model that includes locally interacting RNAPs by explicitly showing how a trailing RNAP influences the probability that an error is corrected or incorporated by the leading backtracking RNAP. We also provide two related methods for computing the mean times to error correction or incorporation given an initial local RNAP configuration.
Amino acid frequency and domain features serve well for random forest based classification of thermophilic and mesophilic protein; a case study on serine proteases
Authors: Jithin S. Sunny, Lilly M. Saleena
Date: 2021-03-05 00:00:00
Tasks: Classification, feature selection, General Classification

Thermostability is an important prerequisite for enzymes employed for industrial applications. Several machine learning based models have thus been formulated for protein classification based on this particular trait. These models have employed features derived from sequences, structures or both resulting in a >93% accuracy based on a 10-fold cross-validation. Besides using various proteins from a wide range of organisms, such studies also rely on hundreds of features. In the present study, an enzyme specific classification model was created using significantly less number of features that provides a similar accuracy of classification for thermophilic and non-thermophilic enzyme serine proteases. For building the classifier, 219 thermophilic and 200 mesophilic bacterial genomes were mined for their respective serine protease sequences. Features were extracted for 800 sequences followed by feature selection. We deployed a random forest based classifier that identified thermophilic and non-thermophilic serine proteases with an accuracy of 95.71%. Knowledge of thermostability along with amino acid positional shifts can be vital for downstream protein engineering techniques. Thus, to emphasize the real time application of the enzyme specific classification model, a web platform has been designed. Combining the sequence data and the classification model, this prototype can allow users to align their query serine protease sequence against the custom database and identify its thermophilic nature.
Identification and Development of Therapeutics for COVID-19
Authors: Halie M. Rando, Nils Wellhausen, Soumita Ghosh, Alexandra J. Lee, Anna Ada Dattoli, Fengling Hu, James Brian Byrd, Diane N. Rafizadeh, Ronan Lordan, Yanjun Qi, Yuchen Sun, Christian Brueffer, Jeffrey M. Field, Marouen Ben Guebila, Nafisa M. Jadavji, Ashwin N. Skelly, Bharath Ramsundar, Jinhui Wang, Rishi Raj Goel, YoSon Park, the COVID-19 Review Consortium, Simina M. Boca, Anthony Gitter, Casey S. Greene
Date: 2021-03-03 00:00:00

After emerging in China in late 2019, the novel Severe acute respiratory syndrome-like coronavirus 2 (SARS-CoV-2) spread worldwide and as of early 2021, continues to significantly impact most countries. Only a small number of coronaviruses are known to infect humans, and only two are associated with the severe outcomes associated with SARS-CoV-2: Severe acute respiratory syndrome-related coronavirus, a closely related species of SARS-CoV-2 that emerged in 2002, and Middle East respiratory syndrome-related coronavirus, which emerged in 2012. Both of these previous epidemics were controlled fairly rapidly through public health measures, and no vaccines or robust therapeutic interventions were identified. However, previous insights into the immune response to coronaviruses gained during the outbreaks of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) have proved beneficial to identifying approaches to the treatment and prophylaxis of novel coronavirus disease 2019 (COVID-19). A number of potential therapeutics against SARS-CoV-2 and the resultant COVID-19 illness were rapidly identified, leading to a large number of clinical trials investigating a variety of possible therapeutic approaches being initiated early on in the pandemic. As a result, a small number of therapeutics have already been authorized by regulatory agencies such as the Food and Drug Administration (FDA) in the United States, and many other therapeutics remain under investigation. Here, we describe a range of approaches for the treatment of COVID-19, along with their proposed mechanisms of action and the current status of clinical investigation into each candidate. The status of these investigations will continue to evolve, and this review will be updated as progress is made.
A single-shot measurement of time-dependent diffusion over sub-millisecond timescales using static field gradient NMR
Authors: Teddy X. Cai, Nathan H. Williamson, Velencia J. Witherspoon, Rea Ravin, Peter J. Basser
Date: 2020-12-23 00:00:00

Time-dependent diffusion behavior is probed over sub-millisecond timescales in a single shot using an NMR static gradient, time-incremented echo train acquisition (SG-TIETA) framework. The method extends the Carr-Purcell-Meiboom-Gill (CPMG) cycle under a static field gradient by discretely incrementing the $\pi$-pulse spacings to simultaneously avoid off-resonance effects and probe a range of timescales ($50 - 500$ microseconds). Pulse spacings are optimized based on a derived ruleset. The remaining effects of pulse inaccuracy are examined and found to be consistent across pure liquids of different diffusivities: water, decane, and octanol-1. A pulse accuracy correction is developed. Instantaneous diffusivity, $D_{\mathrm{inst}}(t)$, curves (i.e., half of the time derivative of the mean-squared displacement in the gradient direction), are recovered from pulse accuracy-corrected SG-TIETA decays using a model-free, log-linear least squares inversion method validated by Monte Carlo simulations. A signal-averaged, 1-minute experiment is described. A flat $D_{\mathrm{inst}}(t)$ is measured on pure dodecamethylcyclohexasiloxane whereas decreasing $D_{\mathrm{inst}}(t)$ are measured on yeast suspensions, consistent with the expected short-time $D_{\mathrm{inst}}(t)$ behavior for confining microstructural barriers on the order of microns.
A Systematic Review of the Efforts and Hindrances of Modeling and Simulation of CAR T-cell Therapy
Authors: Ujwani Nukala, Marisabel Rodriguez Messan, Osman N. Yogurtcu, Xiaofei Wang, Hong Yang
Date: 2021-01-13 00:00:00

Chimeric Antigen Receptor (CAR) T-cell therapy is an immunotherapy that has recently become highly instrumental in the fight against life-threatening diseases. A variety of modeling and computational simulation efforts have addressed different aspects of CAR T therapy, including T-cell activation, T- and malignant cell population dynamics, therapeutic cost-effectiveness strategies, and patient survival analyses. In this article, we present a systematic review of those efforts, including mathematical, statistical, and stochastic models employing a wide range of algorithms, from differential equations to machine learning. To the best of our knowledge, this is the first review of all such models studying CAR T therapy. In this review, we provide a detailed summary of the strengths, limitations, methodology, data used, and data lacking in current published models. This information may help in designing and building better models for enhanced prediction and assessment of the benefit-risk balance associated with novel CAR T therapies, as well as with the data collection essential for building such models.
Betti Curves of Rank One Symmetric Matrices
Authors: Carina Curto, Joshua Paik, Igor Rivin
Date: 2021-03-01 00:00:00

Betti curves of symmetric matrices were introduced in (Giusti et. al., 2015) as a new class of matrix invariants that depend only on the relative ordering of matrix entries. These invariants are computed using persistent homology, and can be used to detect underlying structure in biological data that may otherwise be obscured by monotone nonlinearities. Here we prove three theorems that fully characterize the Betti curves of rank 1 symmetric matrices. We then illustrate how these Betti curve signatures arise in natural data obtained from calcium imaging of neural activity in zebrafish.
Absolute quantification of real-time PCR data with stage signal difference analysis
Authors: Chuanbo Liu, Jin Wang
Date: 2021-02-28 00:00:00

Real-time PCR, or Real-time Quantitative PCR (qPCR) is an effective approach to quantify nucleic acid samples. Given the complicated reaction system along with thermal cycles, there has been long-term confusion on accurately calculating the initial nucleic acid amounts from the fluorescence signals. Although many improved algorithms had been proposed, the classical threshold method is still the primary choice in the routine application. In this study, we will first illustrate the origin of the linear relationship between the threshold value and logarithm of the initial nucleic acid amount by reconstructing the PCR reaction process with stochastic simulations. We then develop a new method for the absolute quantification of nucleic acid samples with qPCR. By monitoring the fluorescence signal changes in every stage of the thermal cycle, we are able to calculate a representation of the step-wise efficiency change. This is the first work calculated PCR efficiency change directly from the fluorescence signal, without fitting or sophisticated analysis. Our results revealed that the efficiency change during the PCR process is complicated and can not be modeled simply by monotone function model. Based on the calculated efficiency, we illustrate a new absolute qPCR analysis method for accurately determining nucleic acid amount. The efficiency problem is completely avoided in this new method.
Verification of an agent-based disease model of human mycobacterium tuberculosis infection
Authors: Cristina Curreli, Francesco Pappalardo, Giulia Russo, Marzio Pennisi, Dimitrios Kiagias, Miguel Juarez, Marco Viceconti, .
Date: 2021-02-27 00:00:00

Agent-Based Models are a powerful class of computational models widely used to simulate complex phenomena in many different application areas. However, one of the most critical aspects, poorly investigated in the literature, regards an important step of the model credibility assessment: solution verification. This study overcomes this limitation by proposing a general verification framework for Agent-Based Models that aims at evaluating the numerical errors associated with the model. A step-by-step procedure, which consists of two main verification studies (deterministic and stochastic model verification), is described in detail and applied to a specific mission critical scenario: the quantification of the numerical approximation error for UISS-TB, an ABM of the human immune system developed to predict the progression of pulmonary tuberculosis. Results provide indications on the possibility to use the proposed model verification workflow to systematically identify and quantify numerical approximation errors associated with UISS-TB and, in general, with any other ABMs.
Deep learning can differentiate IDH-mutant from IDH-wild type GBM
Authors: Luca Pasquini, Antonio Napolitano, Emanuela Tagliente, Francesco Dellepiane, Martina Lucignani, Antonello Vidiri, Giulio Ranazzi, Antonella Stoppacciaro, Giulia Moltoni, Matteo Nicolai, Andrea Romano, Alberto Di Napoli, Alessandro Bozzao
Date: 2021-02-24 00:00:00
Tasks: Deep Learning, Vocal Bursts Type Prediction

Background: Distinction of IDH mutant and wildtype GBMs is challenging on MRI, since conventional imaging shows considerable overlap. While few studies employed deep-learning in a mixed low/high grade glioma population, a GBM-specific model is still lacking in the literature. Our objective was to develop a deep-learning model for IDH prediction in GBM by using Convoluted Neural Networks (CNN) on multiparametric MRI. Methods: We included 100 adult patients with pathologically proven GBM and IDH testing. MRI data included: morphologic sequences, rCBV and ADC maps. Tumor area was obtained by a bounding box function on the axial slice with widest tumor extension on T2 images and was projected on every sequence. Data was split into training and test (80:20) sets. A 4 block 2D - CNN architecture was implemented for IDH prediction on every MRI sequence. IDH mutation probability was calculated with softmax activation function from the last dense layer. Highest performance was calculated accounting for model accuracy and categorical cross-entropy loss (CCEL) in the test cohort. Results: Our model achieved the following performance: T1 (accuracy 77%, CCEL 1.4), T2 (accuracy 67%, CCEL 2.41), FLAIR (accuracy 77%, CCEL 1.98), MPRAGE (accuracy 66%, CCEL 2.55), rCBV (accuracy 83%, CCEL 0.64). ADC achieved lower performance. Conclusion: We built a GBM-tailored deep-learning model for IDH mutation prediction, achieving accuracy of 83% with rCBV maps. High predictivity of perfusion images may reflect the known correlation between IDH, hypoxia inducible factor (HIF) and neoangiogenesis. This model may set a path for non-invasive evaluation of IDH mutation in GBM.
Bayesian uncertainty quantification for data-driven equation learning
Authors: Simon Martina-Perez, Matthew J. Simpson, Ruth E. Baker
Date: 2021-02-23 00:00:00
Tasks: Uncertainty Quantification

Equation learning aims to infer differential equation models from data. While a number of studies have shown that differential equation models can be successfully identified when the data are sufficiently detailed and corrupted with relatively small amounts of noise, the relationship between observation noise and uncertainty in the learned differential equation models remains unexplored. We demonstrate that for noisy data sets there exists great variation in both the structure of the learned differential equation models as well as the parameter values. We explore how to combine data sets to quantify uncertainty in the learned models, and at the same time draw mechanistic conclusions about the target differential equations. We generate noisy data using a stochastic agent-based model and combine equation learning methods with approximate Bayesian computation (ABC) to show that the correct differential equation model can be successfully learned from data, while a quantification of uncertainty is given by a posterior distribution in parameter space.
Describing the movement of molecules in reduced-dimension models
Authors: Natasha S. Savage
Date: 2020-11-02 00:00:00

When addressing spatial biological questions using mathematical models, symmetries within the system are often exploited to simplify the problem by reducing its physical dimension. In a reduced-dimension model molecular movement is restricted to the reduced dimension, changing the nature of molecular movement. This change in molecular movement can lead to quantitatively and even qualitatively different results in the full and reduced systems. Within this manuscript we discuss the condition under which restricted molecular movement in reduced-dimension models accurately approximates molecular movement in the full system. For those systems which do not satisfy the condition, we present a general method for approximating unrestricted molecular movement in reduced-dimension models. We will derive a mathematically robust, finite difference method for solving the 2D diffusion equation within a 1D reduced-dimension model. The methods described here can be used to improve the accuracy of many reduced-dimension models while retaining benefits of system simplification.
Prediction of Influenza A virus infections in humans using an Artificial Neural Network learning approach
Authors: Charalambos Chrysostomou, Harris Partaourides, Huseyin Seker
Date: 2021-02-12 00:00:00

The Influenza type A virus can be considered as one of the most severe viruses that can infect multiple species with often fatal consequences to the hosts. The Haemagglutinin (HA) gene of the virus has the potential to be a target for antiviral drug development realised through accurate identification of its sub-types and possible the targeted hosts. In this paper, to accurately predict if an Influenza type A virus has the capability to infect human hosts, by using only the HA gene, is therefore developed and tested. The predictive model follows three main steps; (i) decoding the protein sequences into numerical signals using EIIP amino acid scale, (ii) analysing these sequences by using Discrete Fourier Transform (DFT) and extracting DFT-based features, (iii) using a predictive model, based on Artificial Neural Networks and using the features generated by DFT. In this analysis, from the Influenza Research Database, 30724, 18236 and 8157 HA protein sequences were collected for Human, Avian and Swine, respectively. Given this set of the proteins, the proposed method yielded 97.36% (+- 0.04%), 97.26% (+- 0.26%), 0.978 (+- 0.004), 0.963 (+- 0.005) and 0.945 (+- 0.005) for the training accuracy validation accuracy, precision, recall and Mathews Correlation Coefficient (MCC) respectively, based on a 10-fold cross-validation. The classification model generated by using one of the largest dataset, if not the largest, yields promising results that could lead to early detection of such species and help develop precautionary measurements for possible human infections.
An overview of continuous and discrete phasor analysis of binned or time-gated periodic decays
Authors: Xavier Michalet
Date: 2021-02-06 00:00:00

Time-resolved analysis of periodically excited luminescence decays by the phasor method in the presence of time-gating or binning is revisited. Analytical expressions for discrete configurations of square gates are derived and the locus of the phasors of such modified periodic single-exponential decays is compared to the canonical universal semicircle. The effects of IRF offset, decay truncation and gate shape are also discussed. Finally, modified expressions for the phase and modulus lifetimes are provided for some simple cases. A discussion of a modified phasor calibration approach is presented.