pwc-archive/papers-with-abstracts huggingface dataset:
Risk assessment for airborne disease transmission by poly-pathogen aerosols
Authors: Freja Nordsiek, Eberhard Bodenschatz, Gholamhossein Bagheri
Date: 2020-11-28 00:00:00

In the case of airborne diseases, pathogen copies are transmitted by droplets of respiratory tract fluid that are exhaled by the infectious and, after partial or full drying, inhaled as aerosols by the susceptible. The risk of infection in indoor environments is typically modelled using the Wells-Riley model or a Wells-Riley-like formulation, usually assuming the pathogen dose follows a Poisson distribution (mono-pathogen assumption). Aerosols that hold more than one pathogen copy, i.e. poly-pathogen aerosols, break this assumption even if the aerosol dose itself follows a Poisson distribution. For the largest aerosols where the number of pathogen in each aerosol can sometimes be several hundred or several thousand, the effect is non-negligible, especially in diseases where the risk of infection per pathogen is high. Here we report on a generalization of the Wells-Riley model and dose-response models for poly-pathogen aerosols by separately modeling each number of pathogen copies per aerosol, while the aerosol dose itself follows a Poisson distribution. This results in a model for computational risk assessment suitable for mono-/poly-pathogen aerosols. We show that the mono-pathogen assumption significantly overestimates the risk of infection for high pathogen concentrations in the respiratory tract fluid. The model also includes the aerosol removal due to filtering by the individuals which becomes significant for poorly ventilated environments with a high density of individuals, and systematically includes the effects of facemasks in the infectious aerosol source and sink terms and dose calculations.
Feasibility Study of Microsecond Pulsed Microwave Ablation using a Minimally Invasive Antenna
Authors: Audrey L. Evans, James F. Sawicki, Hung Luyen, Yahya Mohtashami, Nader Behdad, Susan C. Hagness
Date: 2021-02-12 00:00:00

In this study we established the feasibility of producing localized ablation zones using microsecond pulsed microwave ablation (MWA) as an alternative to conventional continuous wave (CW) MWA. We verified that a thin floating-sleeve dipole ablation probe can withstand pulsed power delivery with peak powers as high as 25 kW, with pulse widths on the order of 1 us. We conducted MWA experiments in egg white using CW and pulsed modes of operation and found that ablation zones achieved via pulsed MWA are comparable in dimension to those created via CW MWA when the average power and procedure duration are equivalent. Finally, we performed pulsed MWA experiments in bovine liver and confirmed that pulsed MWA consistently produces large, localized ablation zones and temperatures that exceed 100{\deg}C. Establishing the feasibility of pulsed MWA opens the opportunity for developing a coupled MWA treatment and imaging system using pulsed MWA and microwave-induced thermoacoustic signals for real-time monitoring of MWA.
A comparison of three heart rate detection algorithms over ballistocardiogram signals
Authors: Ibrahim Sadek, Bessam Abdulrazak
Date: 2021-01-22 00:00:00
Tasks: Position, Template Matching

Heart rate (HR) detection from ballistocardiogram (BCG) signals is challenging because the signal morphology can vary between and within-subjects. Also, it differs from one sensor to another. Hence, it is essential to evaluate HR detection algorithms across several datasets and under different experimental setups. In this paper, we studied the potential of three HR detection algorithms across four independent BCG datasets. The three algorithms are as follows: the multiresolution analysis of the maximal overlap discrete wavelet transform (MODWT-MRA), continuous wavelet transform (CWT), and template matching (TM). The four datasets were obtained using a microbend fiber optic sensor, a fiber Bragg grating sensor, electromechanical films, and load cells, respectively. The datasets were gathered from: a) 10 patients during a polysomnography study, b) 50 subjects in a sitting position, c) 10 subjects in a sleeping position, and d) 40 subjects in a sleeping position. Overall, CWT with derivative of Gaussian provided superior results compared with the MODWT-MRA, CWT (frequency B-spline), and CWT (Shannon). That said, a BCG template was constructed from DataSet1. Then, it was used for HR detection in the other datasets. The TM method achieved satisfactory results for DataSet2 and DataSet3, but it did not detect the HR of two subjects in DataSet4. The proposed methods were implemented on a Raspberry Pi. As a result, the average time required to analyze a 30-second BCG signal was less than one second for all methods. Yet, the MODWT-MRA had the highest performance with an average time of 0.04 seconds.
High-throughput fast full-color digital pathology based on Fourier ptychographic microscopy via color transfer
Authors: Yuting Gao, Jiurun Chen, Aiye Wang, An Pan, Caiwen Ma, Baoli Yao
Date: 2021-01-19 00:00:00
Tasks: Colorization

Full-color imaging is significant in digital pathology. Compared with a grayscale image or a pseudo-color image that only contains the contrast information, it can identify and detect the target object better with color texture information. Fourier ptychographic microscopy (FPM) is a high-throughput computational imaging technique that breaks the tradeoff between high resolution (HR) and large field-of-view (FOV), which eliminates the artifacts of scanning and stitching in digital pathology and improves its imaging efficiency. However, the conventional full-color digital pathology based on FPM is still time-consuming due to the repeated experiments with tri-wavelengths. A color transfer FPM approach, termed CFPM was reported. The color texture information of a low resolution (LR) full-color pathologic image is directly transferred to the HR grayscale FPM image captured by only a single wavelength. The color space of FPM based on the standard CIE-XYZ color model and display based on the standard RGB (sRGB) color space were established. Different FPM colorization schemes were analyzed and compared with thirty different biological samples. The average root-mean-square error (RMSE) of the conventional method and CFPM compared with the ground truth is 5.3% and 5.7%, respectively. Therefore, the acquisition time is significantly reduced by 2/3 with the sacrifice of precision of only 0.4%. And CFPM method is also compatible with advanced fast FPM approaches to reduce computation time further.
CT Super Resolution via Zero Shot Learning
Authors: Zhicheng Zhang, Shaode Yu, Wenjian Qin, Xiaokun Liang, Yaoqin Xie, Guohua Cao
Date: 2020-12-16 00:00:00
Tasks: Computed Tomography (CT), Super-Resolution, Zero-Shot Learning

Computed Tomography (CT) is an advanced imaging technology used in many important applications. Here we present a deep-learning (DL) based CT super-resolution (SR) method that can reconstruct low-resolution (LR) sinograms into high resolution (HR) CT images. The method synergistically combines a SR model in sinogram domain, a deblur model in image domain, and the iterative framework into a CT SR algorithm super resolution and deblur based iterative reconstruction (SADIR). We incorporated the CT domain knowledge into the SADIR and unrolled it into a DL network (SADIR Net). The SADIR Net is a zero shot learning (ZSL) network, which can be trained and tested with a single sinogram in the test time. The SADIR was evaluated via SR CT imaging of a Catphan700 physical phantom and a biological ham, and its performance was compared to the other state of the art (SotA) DL-based methods. The results show that the zero-shot SADIR-Net can indeed provide a performance comparable to the other SotA methods for CT SR reconstruction, especially in situations where training data is limited. The SADIR method can find use in improving CT resolution beyond hardware limits or lowering requirement on CT hardware.
PyPhase -- a Python package for X-ray phase imaging
Authors: Max Langer, Yuhe Zhang, Diogo Figueirinhas, Jean-Baptiste Forien, Claire Mouton, Rajmund Mokso, Pablo Villanueva-Perez
Date: 2020-12-14 00:00:00
Tasks: Retrieval

X-ray propagation-based imaging techniques are well-established at synchrotron radiation and laboratory sources. However, most reconstruction algorithms for such image modalities, also known as phase retrieval algorithms, have been developed specifically for one instrument by and for experts, making the development and spreading of the use of such techniques difficult. Here, we present PyPhase, a free and open-source package for propagation-based near-field phase reconstructions, which is distributed under the CeCILL license. PyPhase implements some of the most popular phase-retrieval algorithms in a highly-modular framework supporting the deployment on large-scale computing facilities. This makes the integration, the development of new phase-retrieval algorithms, and the deployment on different computing infrastructures straight-forward. To demonstrate its capabilities and simplicity, we present its application to data acquired at synchrotron MAX~IV (Lund, Sweden).
Single-photon imaging over 200 km
Authors: Zheng-Ping Li, Jun-Tian Ye, Xin Huang, Peng-Yu Jiang, Yuan Cao, Yu Hong, Chao Yu, Jun Zhang, Qiang Zhang, Cheng-Zhi Peng, Feihu Xu, Jian-Wei Pan
Date: 2021-03-10 00:00:00

Long-range active imaging has widespread applications in remote sensing and target recognition. Single-photon light detection and ranging (lidar) has been shown to have high sensitivity and temporal resolution. On the application front, however, the operating range of practical single-photon lidar systems is limited to about tens of kilometers over the Earth's atmosphere, mainly due to the weak echo signal mixed with high background noise. Here, we present a compact coaxial single-photon lidar system capable of realizing 3D imaging at up to 201.5 km. It is achieved by using high-efficiency optical devices for collection and detection, and what we believe is a new noise-suppression technique that is efficient for long-range applications. We show that photon-efficient computational algorithms enable accurate 3D imaging over hundreds of kilometers with as few as 0.44 signal photons per pixel. The results represent a significant step toward practical, low-power lidar over extra-long ranges.
The Cathedral and the Starship: Learning from the Middle Ages for Future Long-Duration Projects
Authors: Andreas M. Hein
Date: 2020-07-07 00:00:00
Tasks: Management

A popular analogue used in the space domain is that of historical building projects, notably cathedrals that took decades and in some cases centuries to complete. Cathedrals are often taken as archetypes for long-term projects. In this article, I will explore the cathedral from the point of view of project management and systems architecting and draw implications for long-term projects in the space domain, notably developing a starship. I will show that the popular image of a cathedral as a continuous long-term project is in contradiction to the current state of research. More specifically, I will show that for the following propositions: The cathedrals were built based on an initial detailed master plan; Building was a continuous process that adhered to the master plan; Investments were continuously provided for the building process. Although initial plans might have existed, the construction process took often place in multiple campaigns, sometimes separated by decades. Such interruptions made knowledge-preservation very challenging. The reason for the long stretches of inactivity was mostly due to a lack of funding. Hence, the availability of funding coincided with construction activity. These findings paint a much more relevant picture of cathedral building for long-duration projects today: How can a project be completed despite a range of uncertainties regarding loss in skills, shortage in funding, and interruptions? It is concluded that long-term projects such as an interstellar exploration program can take inspiration from cathedrals by developing a modular architecture, allowing for extensibility and flexibility, thinking about value delivery at an early point, and establishing mechanisms and an organization for stable funding.
When to end a lock down? How fast must vaccination campaigns proceed in order to keep health costs in check?
Authors: Claudius Gros, Thomas Czypionka, Daniel Gros
Date: 2021-03-29 00:00:00

We propose a simple rule of thumb for countries which have embarked on a vaccination campaign while still facing the need to keep non-pharmaceutical interventions (NPI) in place because of the ongoing spread of SARS-CoV-2. If the aim is to keep the death rate from increasing, NPIs can be loosened when it is possible to vaccinate more than twice the growth rate of new cases. If the aim is to keep the pressure on hospitals under control, the vaccination rate has to be about four times higher. These simple rules can be derived from the observation that the risk of death or a severe course requiring hospitalization from a COVID-19 infection increases exponentially with age and that the sizes of age cohorts decrease linearly at the top of the population pyramid. Protecting the over 60-year-olds, which constitute approximately one-quarter of the population in Europe (and most OECD countries), reduces the potential loss of life by 95 percent.
Epidemic spreading under mutually independent intra- and inter-host pathogen evolution
Authors: Xiyun Zhang, Zhongyuan Ruan, Muhua Zheng, Jie zhou, Stefano Boccaletti, Baruch Barzel
Date: 2021-02-19 00:00:00
Tasks: Unity

The dynamics of epidemic spreading is often reduced to the single control parameter $R_0$, whose value, above or below unity, determines the state of the contagion. If, however, the pathogen evolves as it spreads, $R_0$ may change over time, potentially leading to a mutation-driven spread, in which an initially sub-pandemic pathogen undergoes a breakthrough mutation. To predict the boundaries of this pandemic phase, we introduce here a modeling framework to couple the network spreading patterns with the intra-host evolutionary dynamics. For many pathogens these two processes, intra- and inter-host, are driven by different selection forces. And yet here we show that even in the extreme case when these two forces are mutually independent, mutations can still fundamentally alter the pandemic phase-diagram, whose transitions are now shaped, not just by $R_0$, but also by the balance between the epidemic and the evolutionary timescales. If mutations are too slow, the pathogen prevalence decays prior to the appearance of a critical mutation. On the other hand, if mutations are too rapid, the pathogen evolution becomes volatile and, once again, it fails to spread. Between these two extremes, however, we identify a broad range of conditions in which an initially sub-pandemic pathogen can break through to gain widespread prevalence.
Inferring the main drivers of SARS-CoV-2 global transmissibility by feature selection methods
Authors: Marko Djordjevic, Igor Salom, Sofija Markovic, Andjela Rodic, Ognjen Milicevic, Magdalena Djordjevic
Date: 2021-03-28 00:00:00
Tasks: feature selection

Identifying the main environmental drivers of SARS-CoV-2 transmissibility in the population is crucial for understanding current and potential future outbursts of COVID-19 and other infectious diseases. To address this problem, we concentrate on the basic reproduction number $R_0$, which is not sensitive to testing coverage and represents transmissibility in an absence of social distancing and in a completely susceptible population. While many variables may potentially influence $R_0$, a high correlation between these variables may obscure the result interpretation. Consequently, we combine Principal Component Analysis with feature selection methods from several regression-based approaches to identify the main demographic and meteorological drivers behind $R_0$. We robustly obtain that country's wealth/development (GDP per capita or Human Development Index) is the most important $R_0$ predictor at the global level, probably being a good proxy for the overall contact frequency in a population. This main effect is modulated by built-up area per capita (crowdedness in indoor space), onset of infection (likely related to increased awareness of infection risks), net migration, unhealthy living lifestyle/conditions including pollution, seasonality, and possibly BCG vaccination prevalence. Also, we argue that several variables that significantly correlate with transmissibility do not directly influence $R_0$ or affect it differently than suggested by naive analysis.
Understanding infection progression under strong control measures through universal COVID-19 growth signatures
Authors: Magdalena Djordjevic, Marko Djordjevic, Bojana Ilic, Stefan Stojku, Igor Salom
Date: 2020-12-20 00:00:00

Widespread growth signatures in COVID-19 confirmed case counts are reported, with sharp transitions between three distinct dynamical regimes (exponential, superlinear and sublinear). Through analytical and numerical analysis, a novel framework is developed that exploits information in these signatures. An approach well known to physics is applied, where one looks for common dynamical features, independently from differences in other factors. These features and associated scaling laws are used as a powerful tool to pinpoint regions where analytical derivations are effective, get an insight into qualitative changes of the disease progression, and infer the key infection parameters. The developed framework for joint analytical and numerical analysis of empirically observed COVID-19 growth patterns can lead to a fundamental understanding of infection progression under strong control measures, applicable to outbursts of both COVID-19 and other infectious diseases.
An epidemic model for COVID-19 transmission in Argentina: Exploration of the alternating quarantine and massive testing strategies
Authors: Lautaro Vassallo, Ignacio A. Perez, Lucila G. Alvarez-Zuzek, Julián Amaya, Marcos F. Torres, Lucas D. Valdez, Cristian E. La Rocca, Lidia A. Braunstein
Date: 2020-12-04 00:00:00

The COVID-19 pandemic has challenged authorities at different levels of government administration around the globe. When faced with diseases of this severity, it is useful for the authorities to have prediction tools to estimate in advance the impact on the health system and the human, material, and economic resources that will be necessary. In this paper, we construct an extended Susceptible-Exposed-Infected-Recovered model that incorporates the social structure of Mar del Plata, the $4^\circ$ most inhabited city in Argentina and head of the Municipality of General Pueyrred\'on. Moreover, we consider detailed partitions of infected individuals according to the illness severity, as well as data of local health resources, to bring these predictions closer to the local reality. Tuning the corresponding epidemic parameters for COVID-19, we study an alternating quarantine strategy, in which a part of the population can circulate without restrictions at any time, while the rest is equally divided into two groups and goes on successive periods of normal activity and lockdown, each one with a duration of $\tau$ days. Besides, we implement a random testing strategy over the population. We found that $\tau = 7$ is a good choice for the quarantine strategy since it matches with the weekly cycle as it reduces the infected population. Focusing on the health system, projecting from the situation as of September 30, we foresee a difficulty to avoid saturation of ICU, given the extremely low levels of mobility that would be required. In the worst case, our model estimates that four thousand deaths would occur, of which 30\% could be avoided with proper medical attention. Nonetheless, we found that aggressive testing would allow an increase in the percentage of people that can circulate without restrictions, being the equipment required to deal with the additional critical patients relatively low.
Evaluation of the number of undiagnosed infected in an outbreak using source of infection measurements
Authors: Akiva B. Melka, yoram louzoun
Date: 2020-06-09 00:00:00

In times of outbreaks, an essential requirement for better monitoring is the evaluation of the number of undiagnosed infected individuals. An accurate estimate of this fraction is crucial for the assessment of the situation and the establishment of protective measures. In most current studies using epidemics models, the total number of infected is either approximated by the number of diagnosed individuals or is dependent on the model parameters and assumptions, which are often debated. We here study the relationship between the fraction of diagnosed infected out of all infected, and the fraction of infected with known contaminator out of all diagnosed infected. We show that those two are approximately the same in exponential models and across most models currently used in the study of epidemics, independently of the model parameters. As an application, we compute an estimate of the effective number of infected by the SARS-CoV-2 virus in various countries.
Disease Momentum: Estimating the Reproduction Number in the Presence of Superspreading
Authors: Kory D. Johnson, Mathias Beiglböck, Manuel Eder, Annemarie Grass, Joachim Hermisson, Gudmund Pammer, Jitka Polechová, Daniel Toneian, Benjamin Wölfl
Date: 2020-12-16 00:00:00
Tasks: Prediction Intervals

A primary quantity of interest in the study of infectious diseases is the average number of new infections that an infected person produces. This so-called reproduction number has significant implications for the disease progression. There has been increasing literature suggesting that superspreading, the significant variability in number of new infections caused by individuals, plays an important role in the spread of SARS-CoV-2. In this paper, we consider the effect that such superspreading has on the estimation of the reproduction number and subsequent estimates of future cases. Accordingly, we employ a simple extension to models currently used in the literature to estimate the reproduction number and present a case-study of the progression of COVID-19 in Austria. Our models demonstrate that the estimation uncertainty of the reproduction number increases with superspreading and that this improves the performance of prediction intervals. Of independent interest is the derivation of a transparent formula that connects the extent of superspreading to the width of credible intervals for the reproduction number. This serves as a valuable heuristic for understanding the uncertainty surrounding diseases with superspreading.
Influence of the neighborhood on cyclic models of biodiversity
Authors: D. Bazeia, M. Bongestab, B. F. de Oliveira
Date: 2021-03-08 00:00:00

This work deals with the influence of the neighborhood in simple rock-paper-scissors models of biodiversity. We consider the case of three distinct species which evolve under the standard rules of mobility, reproduction and competition. The rule of competition follows the guidance of the rock-paper-scissors game, with the prey being annihilated, leaving an empty site in accordance with the May-Leonard proposal for the predator and prey competition. We use the von Neumann neighborhood, but we consider mobility under the presence of the first, second and third neighbors in three distinct environments, one with equal probability and the others with probability following the power law and exponential profiles. The results are different, but they all show that increasing the neighbourhood increases the characteristic length of the system in an important way. We have studied other possibilities, in particular the case where one modifies the manner a specific species competes, unveiling the interesting result in which the strongest individuals may constitute the less abundant population.