pwc-archive/papers-with-abstracts huggingface dataset:
Environment driven oscillation in an off-lattice May--Leonard model
Authors: D. Bazeia, M. J. B. Ferreira, B. F. de Oliveira, A. Szolnoki
Date: 2021-06-03 00:00:00

Cyclic dominance of competing species is an intensively used working hypothesis to explain biodiversity in certain living systems, where the evolutionary selection principle would dictate a single victor otherwise. Technically the May--Leonard models offer a mathematical framework to describe the mentioned non-transitive interaction of competing species when individual movement is also considered in a spatial system. Emerging rotating spirals composed by the competing species are frequently observed character of the resulting patterns. But how do these spiraling patterns change when we vary the external environment which affects the general vitality of individuals? Motivated by this question we suggest an off-lattice version of the tradition May--Leonard model which allows us to change the actual state of the environment gradually. This can be done by introducing a local carrying capacity parameter which value can be varied gently in an off-lattice environment. Our results support a previous analysis obtained in a more intricate metapopulation model and we show that the well-known rotating spirals become evident in a benign environment when the general density of the population is high. The accompanying time-dependent oscillation of competing species can also be detected where the amplitude and the frequency show a scaling law of the parameter that characterizes the state of the environment. These observations highlight that the assumed non-transitive interaction alone is insufficient condition to maintain biodiversity safely, but the actual state of the environment, which characterizes the general living conditions, also plays a decisive role on the evolution of related systems.
Highly Accelerated EPI with Wave Encoding and Multi-shot Simultaneous Multi-Slice Imaging
Authors: Jaejin Cho, Congyu Liao, Qiyuan Tian, Zijing Zhang, Jinmin Xu, Wei-Ching Lo, Benedikt A. Poser, V. Andrew Stenger, Jason Stockmann, Kawin Setsompop, Berkin Bilgic
Date: 2021-06-03 00:00:00

We introduce wave encoded acquisition and reconstruction techniques for highly accelerated echo planar imaging (EPI) with reduced g-factor penalty and image artifacts. Wave-EPI involves playing sinusoidal gradients during the EPI readout while employing interslice shifts as in blipped-CAIPI acquisitions. This spreads the aliasing in all spatial directions, thereby taking better advantage of 3D coil sensitivity profiles. The amount of voxel spreading that can be achieved by the wave gradients during the short EPI readout period is constrained by the slew rate of the gradient coils and peripheral nerve stimulation (PNS) monitor. We propose to use a half-cycle sinusoidal gradient to increase the amount of voxel spreading that can be achieved while respecting the slew and stimulation constraints. Extending wave-EPI to multi-shot acquisition minimizes geometric distortion and voxel blurring at high in-plane resolution, while structured low-rank regularization mitigates shot-to-shot phase variations without additional navigators. We propose to use different point spread functions (PSFs) for the k-space lines with positive and negative polarities, which are calibrated with a FLEET-based reference scan and allow for addressing gradient imperfections. Wave-EPI provided whole-brain single-shot gradient echo (GE) and multi-shot spin echo (SE) EPI acquisitions at high acceleration factors and was combined with g-Slider slab encoding to boost the SNR level in 1mm isotropic diffusion imaging. Relative to blipped-CAIPI, wave-EPI reduced average and maximum g-factors by up to 1.21- and 1.37-fold, respectively. In conclusion, wave-EPI allows highly accelerated single- and multi-shot EPI with reduced g-factor and artifacts and may facilitate clinical and neuroscientific applications of EPI by improving the spatial and temporal resolution in functional and diffusion imaging.
Enrichment of gut microbiome strains for cultivation-free genome sequencing using droplet microfluidics
Authors: Anna Pryszlak, Tobias Wenzel, Kiley West Seitz, Falk Hildebrand, Ece Kartal, Marco Raffaele Cosenza, Vladimir Benes, Peer Bork, Christoph Merten
Date: 2021-06-02 00:00:00

We report a droplet microfluidic method to target and sort individual cells directly from complex microbiome samples, and to prepare these cells for bulk whole genome sequencing without cultivation. We characterize this approach by recovering bacteria spiked into human stool samples at a ratio as low as 1:250 and by successfully enriching endogenous Bacteroides vulgatus to the level required for de-novo assembly of high-quality genomes. While microbiome strains are increasingly demanded for biomedical applications, the vast majority of species and strains are uncultivated and without reference genomes. We address this shortcoming by encapsulating complex microbiome samples directly into microfluidic droplets and amplify a target-specific genomic fragment using a custom molecular TaqMan probe. We separate those positive droplets by droplet sorting, selectively enriching single target strain cells. Finally, we present a protocol to purify the genomic DNA while specifically removing amplicons and cell debris for high-quality genome sequencing.
Personalized Communication Strategies: Towards A New Debtor Typology Framework
Authors: Minou Ghaffari, Maxime Kaniewicz, Stephan Stricker
Date: 2021-06-03 00:00:00

Based on debt collection agency (PAIR Finance) data, we developed a novel debtor typology framework by expanding previous approaches to 4 behavioral dimensions. The 4 dimensions we identified were willingness to pay, ability to pay, financial organization, and rational behavior. Using these dimensions, debtors could be classified into 16 different typologies. We identified 5 main typologies, which account for 63% of the debtors in our data set. Further, we observed that each debtor typology reacted differently to the content and timing of reminder messages, allowing us to define an optimal debt collection strategy for each typology. For example, sending a reciprocity message at 8 p.m. in the evening is the most successful strategy to get a reaction from a debtor who is willing to pay their debt, able to pay their debt, chaotic in terms of their financial organization, and emotional when communicating and handling their finances. In sum, our findings suggest that each debtor type should be approached in a personalized way using different tonalities and timing schedules.
Adding fuel to human capital: Exploring the educational effects of cooking fuel choice from rural India
Authors: Shreya Biswas, Upasak Das
Date: 2021-06-03 00:00:00

The study examines the effect of cooking fuel choice on educational outcomes of adolescent children in rural India. Using multiple large-scale nationally representative datasets, we observe household solid fuel usage to adversely impact school attendance, years of schooling and age-appropriate grade progression among children. This inference is robust to alternative ways of measuring educational outcomes, other datasets, specifications and estimation techniques. Importantly, the effect is found to be more pronounced for females in comparison to the males highlighting the gendered nature of the impact. On exploring possible pathways, we find that the direct time substitution on account of solid fuel collection and preparation can explain the detrimental educational outcomes that include learning outcomes as well, even though we are unable to reject the health channel. In the light of the micro and macro level vulnerabilities posed by the COVID-19 outbreak, the paper recommends interventions that have the potential to fasten the household energy transition towards clean fuel in the post-covid world.
Theoretical and methodological approaches to the study of the problem of corruption
Authors: Valeri Lipunov, Vladislav Shirshikov, Jonathan Lewis
Date: 2021-06-03 00:00:00

The article reveals the main theoretical approaches to the analysis and study of the phenomenon of corruption. Special attention is paid to the consideration of the index approach to the analysis of corruption.
Transitional Dynamics of the Saving Rate and Economic Growth
Authors: Markus Brueckner, Tomoo Kikuchi, George Vachadze
Date: 2020-12-31 00:00:00
Tasks: Time Series, Time Series Analysis

We estimate the relationship between GDP per capita growth and the growth rate of the national savings rate using a panel of 130 countries over the period 1960-2017. We find that GDP per capita growth increases (decreases) the growth rate of the national savings rate in poor countries (rich countries), and a higher credit-to-GDP ratio decreases the national savings rate as well as the income elasticity of the national savings rate. We develop a model with a credit constraint to explain the growth-saving relationship by the saving behavior of entrepreneurs at both the intensive and extensive margins. We further present supporting evidence for our theoretical findings by utilizing cross-country time series data of the number of new businesses registered and the corporate savings rate.
The Origin of Corporate Control Power
Authors: Jie He, Min Wang
Date: 2021-06-03 00:00:00

How does the control power of corporate shareholder arise? On the fundamental principles of economics, we discover that the probability of top1 shareholder possessing optimal control power evolves in Fibonacci series pattern and emerges as the wave between 1/2 and 2/3 along with time in period of 12h (h is the time distance between the state and state of the evolution). This novel feature suggests the efficiency of the allocation of corporate shareholders' right and power. Data on the Chinese stock market support this prediction.
Sparse Inpainting with Smoothed Particle Hydrodynamics
Authors: Viktor Daropoulos, Matthias Augustin, Joachim Weickert
Date: 2020-11-23 00:00:00
Tasks: Image Inpainting

Digital image inpainting refers to techniques used to reconstruct a damaged or incomplete image by exploiting available image information. The main goal of this work is to perform the image inpainting process from a set of sparsely distributed image samples with the Smoothed Particle Hydrodynamics (SPH) technique. As, in its naive formulation, the SPH technique is not even capable of reproducing constant functions, we modify the approach to obtain an approximation which can reproduce constant and linear functions. Furthermore, we examine the use of Voronoi tessellation for defining the necessary parameters in the SPH method as well as selecting optimally located image samples. In addition to this spatial optimization, optimization of data values is also implemented in order to further improve the results. Apart from a traditional Gaussian smoothing kernel, we assess the performance of other kernels on both random and spatially optimized masks. Since the use of isotropic smoothing kernels is not optimal in the presence of objects with a clear preferred orientation in the image, we also examine anisotropic smoothing kernels. Our final algorithm can compete with well-performing sparse inpainting techniques based on homogeneous or anisotropic diffusion processes as well as with exemplar-based approaches.
Three-dimensional Epanechnikov mixture regression in image coding
Authors: Boning Liu, Yan Zhao, Xiaomeng Jiang, Shigang Wang
Date: 2021-06-03 00:00:00
Tasks: regression

Kernel methods have been studied extensively in recent years. We propose a three-dimensional (3-D) Epanechnikov Mixture Regression (EMR) based on our Epanechnikov Kernel (EK) and realize a complete framework for image coding. In our research, we deduce the covariance-matrix form of 3-D Epanechnikov kernels and their correlated statistics to obtain the Epanechnikov mixture models. To apply our theories to image coding, we propose the 3-D EMR which can better model an image in smaller blocks compared with the conventional Gaussian Mixture Regression (GMR). The regressions are all based on our improved Expectation-Maximization (EM) algorithm with mean square error optimization. Finally, we design an Adaptive Mode Selection (AMS) algorithm to realize the best model pattern combination for coding. Our recovered image has clear outlines and superior coding efficiency compared to JPEG below 0.25bpp. Our work realizes an unprecedented theory application by: (1) enriching the theory of Epanechnikov kernel,(2) improving the EM algorithm using MSE optimization, (3) exploiting the EMR and its application in image coding, and (4) AMS optimal modeling combined with Gaussian and Epanechnikov kernel.
Waveform Design for Joint Sensing and Communications in Millimeter-Wave and Low Terahertz Bands
Authors: Tianqi Mao, Jiaxuan Chen, Qi Wang, Chong Han, Zhaocheng Wang, George K. Karagiannidis
Date: 2021-06-03 00:00:00

The convergence of sensing and communication in the millimeter-wave (mmWave) and low terahertz (THz) bands has been envisioned as a promising technology, since it incorporates high-rate data transmission of hundreds of Gbps and mm-level radar sensing in a spectrum- and cost-efficient manner, by sharing both the frequency and hardware resources. However, the joint radar sensing and communication (JRC) system faces considerable challenges in the mmWave and low-THz scale, due to the peculiarities of the propagation channel and radio-frequency (RF) front ends. To this end, the waveform design for the JRC systems in mmWave and low-THz bands with ultra-broad bandwidth is investigated in this paper. Firstly, by considering the JRC design based on the co-existence concept, where both functions operate in a time-domain duplex (TDD) manner, a novel multi-subband quasi-perfect (MS-QP) sequence, composed of multiple perfect subsequences on different subbands, is proposed for target sensing, which achieves accurate target ranging and velocity estimation, whilst only requiring cost-efficient low-rate analog-to-digital converters (A/Ds) for sequence detection. Furthermore, the root index of each perfect subsequence is designed to eliminate the influence of strong Doppler shift on radar sensing. Finally, a data-embedded MS-QP (DE-MS-QP) waveform is constructed through time-domain extension of the MS-QP sequence, generating null frequency points on each subband for data transmission. Unlike the co-existence-based JRC system in TDD manner, the proposed DE-MS-QP waveform enables simultaneous interference-free sensing and communication, whilst inheriting all the merits from MS-QP sequences. Numerical results validate the superiority of the proposed waveforms regarding the communication and sensing performances, hardware cost as well as flexibility of the resource allocation between the dual functions.
Accurate computational evolution of proteins and its dependence on deep learning
Authors: Prabha Sankara Narayanan, Ashish Runthala
Date: 2021-06-03 00:00:00
Tasks: Deep Learning, Specificity

Enzyme is the major workhorse to carry out the diverse cellular functions. It catalyzes the biological reactions with a high specificity, with its topology playing a crucial role. For ecologically safe production of numerous bioproducts including drugs and chemicals, we have been striving to design the industrially useful enzyme molecules with highly improved catalytic capability. As the sequence space is enormous for an enzyme, its quick and effective exploration is quite improbable for the mutagenesis studies whose accuracy is greatly reliant on the prior information of the mutated sites and the extent of rigorous screening of the mutant libraries. Although directed evolution methods significantly aid the construction of a functionally improved molecule, their credibility depends on the successful excavation of the functionally similar sequence space in the available databases, encompassing billions of proteins. As deep learning methods aid us to extensively uncover the underlying network of all the key catalytic positions without any experimental data, their implementation has reliably increased the accuracy of directed evolution. The chapter comprehensively explains data mining and deep learning methods to further showcase their importance in enzyme engineering methods. The key biological and algorithmic limitations of these deep learning methodologies are lastly highlighted.
Solvatochromism of (-)-Epigallocatechin 3-O-Gallate (EGCG) Fluorescence: Dependence on Solvent Protonicity
Authors: Vladislav Snitsarev, Elena Petroff, David P. Rotella
Date: 2021-06-03 00:00:00

Health benefits of EGCG are well established; however, the mechanisms of EGCG action are not completely understood. In our previous study we discovered solvatochromism of EGCG fluorescence and described the dependence of fluorescence maxima on solvent polarity. We also noted that fluorescence intensity (FI) depends on solvent. The goal of this study was to gain insights into how the protonic properties of the environment affect FI. We demonstrate that 1) FI of EGCG inversely correlates with the autoprotolysis constant (Kap) of the solvents, 2) HCl decreases, and NaOH increases FI of EGCG in water, and 3) NaOH evokes slow (~10 min time scale) transient changes in FI of EGCG. We conclude that EGCG fluorescence depends on Kap, i.e. protonicity of the environment, that is useful for differentiating EGCG in protic aqueous environment at physiological pH~7.0{\div}7.4, where EGCG fluorescence is substantially quenched, from EGCG in aprotic environments, where EGCG fluorescence significantly increases. Thus, this property of EGCG fluorescence is useful for investigation of specific EGCG interactions within more aprotic protein binding sites.
There are no valid points of criticism in Tyshkovskiy and Panchin's response (10.1002/bies.202000325) to our paper "The genetic structure of SARS-CoV-2 does not rule out a laboratory origin" (DOI: 10.1002/bies.202000240)
Authors: Yuri Deigin, Rossana Segreto
Date: 2021-06-03 00:00:00
Tasks: valid

Tyshkovskiy and Panchin have recently published a commentary on our paper in which they outline several "points of disagreement with the Segreto/Deigin hypothesis". As our paper is titled "The genetic structure of SARS-CoV-2 does not rule out a laboratory origin", points of disagreement should provide evidence that rules out a laboratory origin. However, Tyshkovskiy and Panchin provide no such evidence and instead attempt to criticize our arguments that highlight aspects of SARS-CoV-2 that could be consistent with the lab leak hypothesis. Strikingly, Tyshkovskiy and Panchin's main point of criticism is based on a false premise that we have claimed RaTG13 to be a direct progenitor of SARS-CoV-2, and their other points of criticism are either incorrect or irrelevant to our hypotheses. Thus, the genetic structure of SARS-CoV-2 remains consistent with both natural or laboratory origin, which means that both the zoonotic and the lab leak hypothesis need to be investigated equally thoroughly.
The SIR model towards he data. One year of Covid-19 pandemic in Italy case study and plausible "real" numbers
Authors: Ignazio Lazzizzera
Date: 2021-06-03 00:00:00

In this work, the SIR epidemiological model is reformulated so to highlight the important {\em effective reproduction number}, as well as to account for the {\em generation time}, inverse of the {\em incidence rate}, and the {\em infectious period} (or {\em removal period}), inverse of the {\em removal rate}. The aim is to check whether the relationships the model poses among the various observables are actually found in the data. The study case of the second through the third wave of the Covid-19 pandemic in Italy is taken. Given its scale invariance, initially the model is tested with reference to the curve of swab-confirmed infectious individuals only. It is found to match the data if the given curve of the {\em removed} (that is healed or deceased) individuals is assumed underestimated by a factor of about 3 together with other related curves. Contextually, the {\em generation time} and the {\em removal period}, as well as the {\em effective reproduction number}, are obtained fitting the SIR equations to the data; the outcomes prove to be in good agreement with those of other works. Then, using knowledge of the proportion of Covid-19 transmissions likely occurring from individuals who didn't develop symptoms, thus mainly undetected, an estimate of the {\em "true numbers"} of the epidemic is obtained, looking also in good agreement with results from other, completely different works. The line of this work is new and the procedures are computationally really inexpensive.
Plasticity as a link between spatially explicit, distance-independent, and whole-stand forest growth models
Authors: Oscar García
Date: 2021-05-30 00:00:00
Tasks: Management

Models at various levels of resolution are commonly used, both for forest management and in ecological research. They all have comparative advantages and disadvantages, making desirable a better understanding of the relationships between the various approaches. It is found that accounting for crown and root plasticity creates more realistic links between spatial and non-spatial models than simply ignoring spatial structure. The article reviews also the connection between distance-independent models and size distributions, and how distributions evolve over time and relate to whole-stand descriptions. In addition, some ways in which stand-level knowledge feeds back into detailed individual-tree formulations are demonstrated. The presentation intends to be accessible to non-specialists. Study implications: Introducing plasticity improves the representation of physio-ecological processes in spatial modelling. Plasticity explains in part the practical success of distance-independent models. The nature of size distributions and their relationship to individual-tree and whole-stand models are discussed. I point out limitations of various approaches and questions for future research.
Limits of accuracy for parameter estimation and localisation in Single-Molecule Microscopy via sequential Monte Carlo methods
Authors: A. Marie d'Avigneau, S. S. Singh, R. J. Ober
Date: 2021-06-03 00:00:00
Tasks: parameter estimation

Assessing the quality of parameter estimates for models describing the motion of single molecules in cellular environments is an important problem in fluorescence microscopy. We consider the fundamental data model, where molecules emit photons at random times and the photons arrive at random locations on the detector according to complex point spread functions (PSFs). The random, non-Gaussian PSF of the detection process and random trajectory of the molecule make inference challenging. Moreover, the presence of other nearby molecules causes further uncertainty in the origin of the measurements, which impacts the statistical precision of estimates. We quantify the limits of accuracy of model parameter estimates and separation distance between closely spaced molecules (known as the resolution problem) by computing the Cramer-Rao lower bound (CRLB), or equivalently the inverse of the Fisher information matrix (FIM), for the variance of estimates. This fundamental CRLB is crucial, as it provides a lower bound for more practical scenarios. While analytic expressions for the FIM can be derived for static molecules, the analytical tools to evaluate it for molecules whose trajectories follow SDEs are still mostly missing. We address this by presenting a general SMC based methodology for both parameter inference and computing the desired accuracy limits for non-static molecules and a non-Gaussian fundamental detection model. For the first time, we are able to estimate the FIM for stochastically moving molecules observed through the Airy and Born & Wolf PSF. This is achieved by estimating the score and observed information matrix via SMC. We sum up the outcome of our numerical work by summarising the qualitative behaviours for the accuracy limits as functions of e.g. collected photon count, molecule diffusion, etc. We also verify that we can recover known results from the static molecule case.