pwc-archive/papers-with-abstracts huggingface dataset:
A Bayesian approach for extracting free energy profiles from cryo-electron microscopy experiments using a path collective variable
Authors: Julian Giraldo-Barreto, Sebastian Ortiz, Erik H. Thiede, Karen Palacio-Rodriguez, Bob Carpenter, Alex H. Barnett, Pilar Cossio
Date: 2021-02-03 00:00:00
Tasks: 3D Reconstruction, Bayesian Inference

Cryo-electron microscopy (cryo-EM) extracts single-particle density projections of individual biomolecules. Although cryo-EM is widely used for 3D reconstruction, due to its single-particle nature, it has the potential to provide information about the biomolecule's conformational variability and underlying free energy landscape. However, treating cryo-EM as a single-molecule technique is challenging because of the low signal-to-noise ratio (SNR) in the individual particles. In this work, we developed the cryo-BIFE method, cryo-EM Bayesian Inference of Free Energy profiles, that uses a path collective variable to extract free energy profiles and their uncertainties from cryo-EM images. We tested the framework over several synthetic systems, where we controlled the imaging parameters and conditions. We found that for realistic cryo-EM environments and relevant biomolecular systems, it is possible to recover the underlying free energy, with the pose accuracy and SNR as crucial determinants. Then, we used the method to study the conformational transitions of a calcium-activated channel with real cryo-EM particles. Interestingly, we recover the most probable conformation (used to generate a high resolution reconstruction of the calcium-bound state), and we find two additional meta-stable states, one which corresponds to the calcium-unbound conformation. As expected for turnover transitions within the same sample, the activation barriers are of the order of a couple $k_BT$. Extracting free energy profiles from cryo-EM will enable a more complete characterization of the thermodynamic ensemble of biomolecules.
Local sequence-structure relationships in proteins
Authors: Tatjana Škrbić, Amos Maritan, Achille Giacometti, Jayanth R. Banavar
Date: 2021-01-27 00:00:00
Tasks: Clustering, Specificity

We seek to understand the interplay between amino acid sequence and local structure in proteins. Are some amino acids unique in their ability to fit harmoniously into certain local structures? What is the role of sequence in sculpting the putative native state folds from myriad possible conformations? In order to address these questions, we represent the local structure of each C-alpha atom of a protein by just two angles, theta and mu, and we analyze a set of more than 4000 protein structures from the PDB. We use a hierarchical clustering scheme to divide the 20 amino acids into six distinct groups based on their similarity to each other in fitting local structural space. We present the results of a detailed analysis of patterns of amino acid specificity in adopting local structural conformations and show that the sequence-structure correlation is not very strong compared to a random assignment of sequence to structure. Yet, our analysis may be useful to determine an effective scoring rubric for quantifying the match of an amino acid to its putative local structure.
Research on the Power Transport Theorem Based Decoupling Mode Theory for Transceiving Systems
Authors: Renzun Lian
Date: 2021-02-11 00:00:00

The physical pictures of eigen-mode theory (EMT) and the conventional characteristic mode theory (CMT) reveal a fact that: the EMT and CMT are the modal theories for electromagnetic wave-guiding and scattering (for details, please see the Appendices E, F, G and H) systems respectively, rather than for electromagnetic transceiving systems. This Postdoctoral Research Report is devoted to establishing a novel modal theory - decoupling mode theory (DMT) - for transceiving systems, and constructing the energy-decoupled modes (DMs) of objective transceiving system. This Postdoctoral Research Report is a companion volume of the author's Doctoral Dissertation "Research on the Work-Energy Principle Based Characteristic Mode Theory for Scattering Systems" (arXiv:1907.11787).
Deep neural network-based automatic metasurface design with a wide frequency range
Authors: Fardin Ghorbani, Sina Beyraghi, Javad Shabanpour, Homayoon Oraizi, Hossein Soleimani, Mohammad Soleimani
Date: 2021-01-22 00:00:00

Beyond the scope of conventional metasurface which necessitates plenty of computational resources and time, an inverse design approach using machine learning algorithms promises an effective way for metasurfaces design. In this paper, benefiting from Deep Neural Network (DNN), an inverse design procedure of a metasurface in an ultra-wide working frequency band is presented where the output unit cell structure can be directly computed by a specified design target. To reach the highest working frequency, for training the DNN, we consider 8 ring-shaped patterns to generate resonant notches at a wide range of working frequencies from 4 to 45 GHz. We propose two network architectures. In one architecture, we restricted the output of the DNN, so the network can only generate the metasurface structure from the input of 8 ring-shaped patterns. This approach drastically reduces the computational time, while keeping the network's accuracy above 91\%. We show that our model based on DNN can satisfactorily generate the output metasurface structure with an average accuracy of over 90\% in both network architectures. Determination of the metasurface structure directly without time-consuming optimization procedures, having an ultra-wide working frequency, and high average accuracy equip an inspiring platform for engineering projects without the need for complex electromagnetic theory.
Bayesian Dynamical System Identification With Unified Sparsity Priors And Model Uncertainty
Authors: Prem Ratan Mohan Ram, Ulrich Römer, Richard Semaan
Date: 2021-03-08 00:00:00
Tasks: regression, Uncertainty Quantification

This work is concerned with uncertainty quantification in reduced-order dynamical system identification. Reduced-order models for system dynamics are ubiquitous in design and control applications and recent efforts focus on their data-driven construction. Our starting point is the sparse-identification of nonlinear dynamics (SINDy) framework, which reformulates system identification as a regression problem, where unknown functions are approximated from a sparse subset of an underlying library. In this manuscript, we formulate this system identification method in a Bayesian framework to handle parameter and structural model uncertainties. We present a general approach to enforce sparsity, which builds on the recently introduced class of neuronized priors. We perform comparisons between different variants such as Lasso, horseshoe, and spike and slab priors, which are all obtained by modifying a single activation function. We also outline how state observation noise can be incorporated with a probabilistic state-space model. The resulting Bayesian regression framework is robust and simple to implement. We apply the method to two generic numerical applications, the pendulum and the Lorenz system, and one aerodynamic application using experimental measurements.
Territorial differences in the spread of COVID-19 in European regions and US counties
Authors: Fabrizio Natale, Stefano Maria Iacus, Alessandra Conte, Spyridon Spyratos, Francesco Sermi
Date: 2021-03-15 00:00:00

This article explores the territorial differences in the onset and spread of COVID-19 and the excess mortality associated with the pandemic, across the European NUTS3 regions and US counties. Both in Europe and in the US, the pandemic arrived earlier and recorded higher Rt values in urban regions than in intermediate and rural ones. A similar gap is also found in the data on excess mortality. In the weeks during the first phase of the pandemic, urban regions in EU countries experienced excess mortality of up to 68pp more than rural ones. We show that, during the initial days of the pandemic, territorial differences in Rt by the degree of urbanisation can be largely explained by the level of internal, inbound and outbound mobility. The differences in the spread of COVID-19 by rural-urban typology and the role of mobility are less clear during the second wave. This could be linked to the fact that the infection is widespread across territories, to changes in mobility patterns during the summer period as well as to the different containment measures which reverse the causality between mobility and Rt.
A downsampling strategy to assess the predictive value of radiomic features
Authors: AS Dirand, F Frouin, I Buvat
Date: 2020-12-28 00:00:00

Many studies are devoted to the design of radiomic models for a prediction task. When no effective model is found, it is often difficult to know whether the radiomic features do not include information relevant to the task or because of insufficient data. We propose a downsampling method to answer that question when considering a classification task into two groups. Using two large patient cohorts, several experimental configurations involving different numbers of patients were created. Univariate or multivariate radiomic models were designed from each configuration. Their performance as reflected by the Youden index (YI) and Area Under the receiver operating characteristic Curve (AUC) was compared to the stable performance obtained with the highest number of patients. A downsampling method is described to predict the YI and AUC achievable with a large number of patients. Using the multivariate models involving machine learning, YI and AUC increased with the number of patients while they decreased for univariate models. The downsampling method better estimated YI and AUC obtained with the largest number of patients than the YI and AUC obtained using the number of available patients and identifies the lack of information relevant to the classification task when no such information exists.
The Elephant in the Room: Why Transformative Education Must Address the Problem of Endless Exponential Economic Growth
Authors: Chirag Dhara, Vandana Singh
Date: 2021-01-19 00:00:00

A transformative approach to addressing complex social-environmental problems warrants reexamining our most fundamental assumptions about sustainability and progress, including the entrenched imperative for limitless economic growth. Our global resource footprint has grown in lock-step with GDP since the industrial revolution, spawning the climate and ecological crises. Faith that technology will eventually decouple resource use from GDP growth is pervasive, despite there being practically no empirical evidence of decoupling in any country. We argue that complete long-term decoupling is, in fact, well-nigh impossible for fundamental physical, mathematical, logical, pragmatic and behavioural reasons. We suggest that a crucial first step toward a transformative education is to acknowledge this incompatibility, and provide examples of where and how our arguments may be incorporated in education. More broadly, we propose that foregrounding SDG 12 with a functional definition of sustainability, and educating and upskilling students to this end, must be a necessary minimum goal of any transformative approach to sustainability education. Our aim is to provide a conceptual scaffolding around which learning frameworks may be developed to make room for diverse alternative paths to truly sustainable social-ecological cultures.
OUTCOMES: Rapid Under-sampling Optimization achieves up to 50% improvements in reconstruction accuracy for multi-contrast MRI sequences
Authors: Ke Wang, Enhao Gong, Yuxin Zhang, Suchadrima Banerjee, Greg Zaharchuk, John Pauly
Date: 2021-03-08 00:00:00
Tasks: compressed sensing, GPU

Multi-contrast Magnetic Resonance Imaging (MRI) acquisitions from a single scan have tremendous potential to streamline exams and reduce imaging time. However, maintaining clinically feasible scan time necessitates significant undersampling, pushing the limits on compressed sensing and other low-dimensional techniques. During MRI scanning, one of the possible solutions is by using undersampling designs which can effectively improve the acquisition and achieve higher reconstruction accuracy. However, existing undersampling optimization methods are time-consuming and the limited performance prevents their clinical applications. In this paper, we proposed an improved undersampling trajectory optimization scheme to generate an optimized trajectory within seconds and apply it to subsequent multi-contrast MRI datasets on a per-subject basis, where we named it OUTCOMES. By using a data-driven method combined with improved algorithm design, GPU acceleration, and more efficient computation, the proposed method can optimize a trajectory within 5-10 seconds and achieve 30%-50% reconstruction improvement with the same acquisition cost, which makes real-time under-sampling optimization possible for clinical applications.
TransCT: Dual-path Transformer for Low Dose Computed Tomography
Authors: Zhicheng Zhang, Lequan Yu, Xiaokun Liang, Wei Zhao, Lei Xing
Date: 2021-02-28 00:00:00

Low dose computed tomography (LDCT) has attracted more and more attention in routine clinical diagnosis assessment, therapy planning, etc., which can reduce the dose of X-ray radiation to patients. However, the noise caused by low X-ray exposure degrades the CT image quality and then affects clinical diagnosis accuracy. In this paper, we train a transformer-based neural network to enhance the final CT image quality. To be specific, we first decompose the noisy LDCT image into two parts: high-frequency (HF) and low-frequency (LF) compositions. Then, we extract content features (X_{L_c}) and latent texture features (X_{L_t}) from the LF part, as well as HF embeddings (X_{H_f}) from the HF part. Further, we feed X_{L_t} and X_{H_f} into a modified transformer with three encoders and decoders to obtain well-refined HF texture features. After that, we combine these well-refined HF texture features with the pre-extracted X_{L_c} to encourage the restoration of high-quality LDCT images with the assistance of piecewise reconstruction. Extensive experiments on Mayo LDCT dataset show that our method produces superior results and outperforms other methods.
Diffusion-weighted MRI-guided needle biopsies permit quantitative tumor heterogeneity assessment and cell load estimation
Authors: Yi Yin, Kai Breuhahn, Hans-Ulrich Kauczor, Oliver Sedlaczek, Irene E. Vignon-Clementel, Dirk Drasdo
Date: 2021-03-01 00:00:00
Tasks: Relation

Quantitative information on tumor heterogeneity and cell load could assist in designing effective and refined personalized treatment strategies. It was recently shown by us that such information can be inferred from the diffusion parameter D derived from the diffusion-weighted MRI (DWI) if a relation between D and cell density can be established. However, such relation cannot a priori be assumed to be constant for all patients and tumor types. Hence to assist in clinical decisions in palliative settings, the relation needs to be established without tumor resection. It is here demonstrated that biopsies may contain sufficient information for this purpose if the localization of biopsies is chosen as systematically elaborated in this paper. A superpixel-based method for automated optimal localization of biopsies from the DWI D-map is proposed. The performance of the DWI-guided procedure is evaluated by extensive simulations of biopsies. Needle biopsies yield sufficient histological information to establish a quantitative relationship between D-value and cell density, provided they are taken from regions with high, intermediate, and low D-value in DWI. The automated localization of the biopsy regions is demonstrated from a NSCLC patient tumor. In this case, even two or three biopsies give a reasonable estimate. Simulations of needle biopsies under different conditions indicate that the DWI-guidance highly improves the estimation results. Tumor cellularity and heterogeneity in solid tumors may be reliably investigated from DWI and a few needle biopsies that are sampled in regions of well-separated D-values, excluding adipose tissue. This procedure could provide a way of embedding in the clinical workflow assistance in cancer diagnosis and treatment based on personalized information.
Non-integer (or fractional) power model of a viral spreading: application to the COVID-19
Authors: Alain Oustaloup, François Levron, Stéphane Victor, Luc Dugard
Date: 2021-02-24 00:00:00

This paper proposes a very simple deterministic mathematical model, which, by using a power-law, is a \emph{non-integer power model} (or \emph{fractional power model (FPM)}). Such a model, in non-integer power of time, namely $t^m$ up to constants, enables representing at each day, with a good precision, the totality of the contaminated individuals. Despite being enriched with knowledge through an internal structure based on a geometric sequence "with variable ratio", the model (in its non-integer representation) has only three parameters, among which the non-integer power, $m$, that determines on its own, according to its value, an aggravation or an improvement of the viral spreading. Its simplicity comes from the power-law, $t^m$, which simply expresses the singular dynamics of the operator of non-integer differentiation or integration, of high parametric compactness, that governs diffusion phenomena and, as shown in this paper, the spreading phenomena by contamination. The proposed model is indeed validated with the official data of Ministry of Health on the COVID-19 spreading. Used in prediction, it well enables justifying the choice of a lockdown, without which the spreading would have highly worsened. The comparison of this model in $t^m$ with two known models having the same number of parameters, well shows that its representativity of the real data is better or more general. Finally, in a more fundamental context and particularly in terms of complexity and simplicity, a self-filtering action enables showing the compatibility between the \emph{internal complexity} that the internal structure and its stochastic behavior present, and the \emph{global simplicity} that the model in $t^m$ offers in a deterministic manner: it is true that the non-integer power of a power-law is well a marker of complexity.
Stochastic social behavior coupled to COVID-19 dynamics leads to waves, plateaus and an endemic state
Authors: Alexei V. Tkachenko, Sergei Maslov, Tong Wang, Ahmed Elbanna, George N. Wong, Nigel Goldenfeld
Date: 2021-01-28 00:00:00

It is well recognized that population heterogeneity plays an important role in the spread of epidemics. While individual variations in social activity are often assumed to be persistent, i.e. constant in time, here we discuss the consequences of dynamic heterogeneity. By integrating the stochastic dynamics of social activity into traditional epidemiological models we demonstrate the emergence of a new long timescale governing the epidemic in broad agreement with empirical data. Our model captures multiple features of real-life epidemics such as COVID-19, including prolonged plateaus and multiple waves, which are transiently suppressed due to the dynamic nature of social activity. The existence of the long timescale due to the interplay between epidemic and social dynamics provides a unifying picture of how a fast-paced epidemic typically will transition to the endemic state.
Joint multi-field T$_1$ quantification for fast field-cycling MRI
Authors: Markus Bödenler, Oliver Maier, Rudolf Stollberger, Lionel M. Broche, P. James Ross, Mary-Joan MacLeod, Hermann Scharfetter
Date: 2021-02-19 00:00:00

Purpose: Recent developments in hardware design enable the use of Fast Field-Cycling (FFC) techniques in MRI to exploit the different relaxation rates at very low field strength, achieving novel contrast. The method opens new avenues for in vivo characterisations of pathologies but at the expense of longer acquisition times. To mitigate this we propose a model-based reconstruction method that fully exploits the high information redundancy offered by FFC methods. Methods: The proposed model-based approach utilizes joint spatial information from all fields by means of a Frobenius - total generalized variation regularization. The algorithm was tested on brain stroke images, both simulated and acquired from FFC patients scans using an FFC spin echo sequences. The results are compared to three non-linear least squares fits with progressively increasing complexity. Results: The proposed method shows excellent abilities to remove noise while maintaining sharp image features with large signal-to-noise ratio gains at low-field images, clearly outperforming the reference approach. Especially patient data shows huge improvements in visual appearance over all fields. Conclusion: The proposed reconstruction technique largely improves FFC image quality, further pushing this new technology towards clinical standards.
Self-organization of oscillation in an epidemic model for COVID-19
Authors: Takashi Odagaki
Date: 2021-01-27 00:00:00

On the basis of a compartment model, the epidemic curve is investigated when the net rate $\lambda$ of change of the number of infected individuals $I$ is given by an ellipse in the $\lambda$-$I$ plane which is supported in $[I_{\ell}, I_h]$. With $a \equiv (I_h - I_{\ell})/(I_h + I_{\ell})$, it is shown that (1) when $a < 1$ or $I_{\ell} >0$, oscillation of the infection curve is self-organized and the period of the oscillation is in proportion to the ratio of the difference $ (I_h - I_{\ell})$ and the geometric mean $\sqrt{I_h I_{\ell}}$ of $I_h$ and $I_{\ell}$, (2) when $a = 1$, the infection curve shows a critical behavior where it decays obeying a power law function with exponent $-2$ in the long time limit after a peak, and (3) when $a > 1$, the infection curve decays exponentially in the long time limit after a peak. The present result indicates that the pandemic can be controlled by a measure which makes $I_{\ell} < 0$.
Beyond multi-view deconvolution for inherently-aligned fluorescence tomography
Authors: Daniele Ancora, Gianluca Valentini, Antonio Pifferi, Andrea Bassi
Date: 2021-02-15 00:00:00

In multi-view fluorescence microscopy, each angular acquisition needs to be aligned with care to obtain an optimal volumetric reconstruction. Here, instead, we propose a neat protocol based on auto-correlation inversion, that leads directly to the formation of inherently aligned tomographies. Our method generates sharp reconstructions, with the same accuracy reachable after sub-pixel alignment but with improved point-spread-function. The procedure can be performed simultaneously with deconvolution further increasing the reconstruction resolution.