Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of SARS-CoV-2-specific immune alterations in acutely ill patients

Dysregulated immune profiles have been described in symptomatic SARS-CoV-2-infected patients. Whether the reported immune alterations are specific to SARS-CoV-2 infection or also triggered by other acute illnesses remains unclear. We performed flow cytometry analysis on fresh peripheral blood from a consecutive cohort of i) patients hospitalized with acute SARS-CoV-2 infection; ii) patients of comparable age/sex hospitalized for other acute disease (SARS-CoV-2 negative); and iii) healthy controls. Using both data-driven and hypothesis-driven analyses, we found several dysregulations in immune cell subsets (e.g. decreased proportion of T cells) that are similarly associated with acute SARS-CoV-2 infection and non-COVID-19 related acute illnesses. In contrast, we identified specific differences in myeloid and lymphocyte subsets that are associated with SARS-CoV-2 status (e.g. elevated proportion of ICAM-1+ mature/activated neutrophils, ALCAM+ monocytes, and CD38+CD8+ T cells). A subset of SARS-CoV-2-specific immune alterations correlated with disease severity, disease outcome at 30 days and mortality. Our data provides novel understanding of the immune dysregulation that are specifically associated with SARS-CoV-2 infection among acute care hospitalized patients. Our study lays the foundation for the development of specific biomarkers to stratify SARS-CoV-2+ patients at risk of unfavorable outcome and uncover novel candidate molecules to investigate from a therapeutic perspective.
Early empirical assessment of the N501Y mutant strains of SARS-CoV-2 in the United Kingdom, October to November 2020

Two new SARS-CoV-2 lineages with the N501Y mutation in the receptor binding domain of the spike protein have rapidly become prevalent in the UK. We estimated that the earlier 501Y lineage without amino acid deletion {Delta}69/{Delta}70 circulating mainly between early September to mid-November was 10% (6-13%) more transmissible than the 501N lineage, and the currently dominant 501Y lineage with amino acid deletion {Delta}69/{Delta}70 circulating since late September was 75% (70-80%) more transmissible than the 501N lineage.
Optimal strategies for combining vaccine prioritization and social distancing to reduce hospitalizations and mitigate COVID19 progression

Social distancing is an effective population-level mitigation strategy to prevent COVID19 propagation but it does not reduce the number of susceptible individuals and bears severe economic and psychological consequences. A vaccine has recently been developed but its deployment will be limited and not immediate. Designing an optimal combination of these two intervention strategies is a priority, but a mechanistic understanding of the interplay between these strategies is missing. To tackle this challenge we developed an age-structured deterministic model in which vaccines are deployed during the pandemic to individuals who, in the eye of public health, are susceptible (do not show symptoms). The model allows for flexible and dynamic prioritization strategies with shifts between target groups. We find that social distancing applied uniformly to all ages and with vaccination targeted towards adults (20-59) or elderly (60+) work in synergism but up to a threshold beyond which vaccination is not efficient. The inefficiency threshold can be eliminated by targeting social distancing at the age groups that are not vaccinated and the optimal strategy is to prioritize vaccines to elderly. Nevertheless, while vaccination reduces hospitalizations, it does not affect the time it takes to eliminate the virus from the population, which is affected only by social distancing. Finally, the same reduction in hospitalization can be achieved via different combination of strategies, giving decision makers flexibility in choosing public health policies. Our study provides insights into the factors that affect vaccination success and provides methodology to test different intervention strategies in a way that will align with ethical guidelines.
COVID-19: An analysis of social media and research publication activity during the early stages of the pandemic

Background/Objectives: The COVID-19 pandemic has highlighted the importance of rapid dissemination of scientific and medical discovery. Social media (SoMe) has become an invaluable platform in science and medicine. This study analyzed activity of SoMe (Twitter), preprints, and publications related to COVID-19 and gastroenterology (GI) during the COVID-19 pandemic. Methods: Data from Twitter, preprint servers and PubMed was collected and analyzed from December 2019 through May 2020. Global and regional geographic and gastrointestinal organ specific social media trends were compared to preprint and publication activity; any associations were identified. Results: Over the 6-month period, there were 73,079 tweets from 44,609 users, 7,164 publications, and 4,702 preprints. Twitter activity peaked during March while preprints and publications peaked in April 2020. Strong correlations were identified between Twitter and both preprints and publications activity (p<0.001 for both). While COVID-19 data across the 3 platforms concentrated on pulmonology/critical care, the majority of GI tweets pertained to pancreatology, most publications focused on hepatology, and most preprints covered hepatology and luminal GI (LGI). There were significant associations between Twitter activity and research for all GI subfields (p=0.009 for LGI, p=0.006 for hepatology and IBD, p=0.007 for endoscopy), except pancreatology (p=0.2). Twitter activity was highest in the US (7,331 tweets) whereas PubMed activity was highest in China (1,768 publications). Conclusions: The COVID-19 pandemic has highlighted the utility of SoMe as a vehicle for disseminating scientific information during a public health crisis. Scientists and clinicians should consider the use of SoMe in augmenting public awareness of their scholarly pursuits.
Deep Multi-attribute Graph Representation Learning on Protein Structures

Graphs as a type of data structure have recently attracted significant attention. Representation learning of geometric graphs has achieved great success in many fields including molecular, social, and financial networks. It is natural to present proteins as graphs in which nodes represent the residues and edges represent the pairwise interactions between residues. However, 3D protein structures have rarely been studied as graphs directly. The challenges include: 1) Proteins are complex macromolecules composed of thousands of atoms making them much harder to model than micro-molecules. 2) Capturing the long-range pairwise relations for protein structure modeling remains under-explored. 3) Few studies have focused on learning the different attributes of proteins together. To address the above challenges, we propose a new graph neural network architecture to represent the proteins as 3D graphs and predict both distance geometric graph representation and dihedral geometric graph representation together. This gives a significant advantage because this network opens a new path from the sequence to structure. We conducted extensive experiments on four different datasets and demonstrated the effectiveness of the proposed method.
Estimates for commutators of fractional differential operators via harmonic extension

This master thesis is based on the paper"Sharp commutator estimates via harmonic extensions"by Lenzmann and Schikorra, in which they proposed a method to prove estimates for commutators involving Riesz transforms, fractional Laplacians and Riesz potentials, see arXiv:1609.08547. These proofs only involve harmonic extensions to the upper half-space and integration by parts next to some elementary transfromations, since the deeper theory is concentrated in a variety of trace characterization results which can be used as a blackbox. In the first half of this thesis, after collecting some elementary results for the s-harmonic extension by Caffarelli and Silvestre, we use this method to prove a variety of commutator estimates, closely following Lenzmann and Schikorra except for shortening some proofs. In the second half, we prove generalized versions of the blackbox estimates listed by Lenzmann and Schikorra and discuss the different building blocks which make up these blackbox estimates, including Triebel-Lizorkin and Besov-Lipschitz space characterizations as well as square function estimates.
Effect of additives on mass transfer in turbine aeration

Mass transfer coefficients and interfacial areas were determined for the aeration of aqueous solutions in a turbine agitated vessel. The mass transfer coefficients measured for water without additive and for sodium chloride solutions matched very well to measurements in the literature for air bubbles of the same diameter in free rise. Thus the only effect of agitation was to determine the bubble size which then in turn set the coefficient. Two surface active agents were studied: sodium dodecyl sulfate and Dow Corning Antifoam C. The rate of mass transfer increased with the former additive but decreased with the latter; however, the mass transfer coefficient was the exact same function of bubble diameter in both cases and the different rates are attributed to the quite different effects on interfacial area.
Explicitly Encouraging Low Fractional Dimensional Trajectories Via Reinforcement Learning

A key limitation in using various modern methods of machine learning in developing feedback control policies is the lack of appropriate methodologies to analyze their long-term dynamics, in terms of making any sort of guarantees (even statistically) about robustness. The central reasons for this are largely due to the so-called curse of dimensionality, combined with the black-box nature of the resulting control policies themselves. This paper aims at the first of these issues. Although the full state space of a system may be quite large in dimensionality, it is a common feature of most model-based control methods that the resulting closed-loop systems demonstrate dominant dynamics that are rapidly driven to some lower-dimensional sub-space within. In this work we argue that the dimensionality of this subspace is captured by tools from fractal geometry, namely various notions of a fractional dimension. We then show that the dimensionality of trajectories induced by model free reinforcement learning agents can be influenced adding a post processing function to the agents reward signal. We verify that the dimensionality reduction is robust to noise being added to the system and show that that the modified agents are more actually more robust to noise and push disturbances in general for the systems we examined.
Pluripotential-Theoretic Stability Thresholds

Given a compact polarized manifold $(X,L)$, we introduce two new stability thresholds in terms of singularity types of global quasi-plurisubharmonic functions on $X$. We prove that in the Fano setting, the new invariants can effectively detect K-stability of $X$. We study some functionals of geodesic rays in the space of Kähler potentials by means of the corresponding test curves. In particular, we introduce a new entropy functional of quasi-plurisubharmonic functions and relate the radial entropy functional to this new entropy functional.
Effects of impulsive harvesting and an evolving domain in a diffusive logistic model

In order to understand how the combination of the evolution of a domain and impulsive harvesting affect the dynamics of a population, we investigate a diffusive logistic population model with impulsive harvesting on a periodically evolving domain. Initially the ecological reproduction index of the impulsive problem is introduced and given by an explicit formula, which depends on the domain evolution rate and the impulsive function. Then the threshold dynamics of the population subject to monotone or nonmonotone impulsive harvesting are established based on this index. Finally numerical simulations are carried out to illustrate our theoretical results, and these reveal that a large domain evolution rate can improve the populations ability to survive, no matter which impulsive harvesting takes place. On the contrary, impulsive harvesting has a negative effect on the survival of the population, and can even lead to its extinction.
C-Watcher: A Framework for Early Detection of High-Risk Neighborhoods Ahead of COVID-19 Outbreak

The novel coronavirus disease (COVID-19) has crushed daily routines and is still rampaging through the world. Existing solution for nonpharmaceutical interventions usually needs to timely and precisely select a subset of residential urban areas for containment or even quarantine, where the spatial distribution of confirmed cases has been considered as a key criterion for the subset selection. While such containment measure has successfully stopped or slowed down the spread of COVID-19 in some countries, it is criticized for being inefficient or ineffective, as the statistics of confirmed cases are usually time-delayed and coarse-grained. To tackle the issues, we propose C-Watcher, a novel data-driven framework that aims at screening every neighborhood in a target city and predicting infection risks, prior to the spread of COVID-19 from epicenters to the city. In terms of design, C-Watcher collects large-scale long-term human mobility data from Baidu Maps, then characterizes every residential neighborhood in the city using a set of features based on urban mobility patterns. Furthermore, to transfer the firsthand knowledge (witted in epicenters) to the target city before local outbreaks, we adopt a novel adversarial encoder framework to learn “city-invariant” representations from the mobility-related features for precise early detection of high-risk neighborhoods, even before any confirmed cases known, in the target city. We carried out extensive experiments on C-Watcher using the real-data records in the early stage of COVID-19 outbreaks, where the results demonstrate the efficiency and effectiveness of C-Watcher for early detection of high-risk neighborhoods from a large number of cities.
Topological and geometrical aspects of band theory

This paper provides a pedagogical introduction to recent developments in geometrical and topological band theory following the discovery of graphene and topological insulators. Amusingly, many of these developments have a connection to contributions in high-energy physics by Dirac. The review starts by a presentation of the Dirac magnetic monopole, goes on with the Berry phase in a two-level system and the geometrical/topological band theory for Bloch electrons in crystals. Next, specific examples of tight-binding models giving rise to lattice versions of the Dirac equation in various space dimension are presented: in 1D (Su–Schrieffer–Heeger (SSH) and Rice–Mele models), 2D (graphene, boron nitride, Haldane model) and 3D (Weyl semi-metals). The focus is on topological insulators and topological semi-metals. The latter have a Fermi surface that is characterized as a topological defect. For topological insulators, the two alternative view points of twisted fiber bundles and of topological textures are developed. The minimal mathematical background in topology (essentially on homotopy groups and fiber bundles) is provided when needed. Topics rarely reviewed include: periodic versus canonical Bloch Hamiltonian (basis I/II issue), Zak versus Berry phase, the vanishing electric polarization of the SSH model and Dirac insulators.
A COLREGs Compliant Motion Planner for Autonomous Maneuvering of Marine Vessels in Complex Environments

An enabling technology for future sea transports is safe and energy-efficient autonomous maritime navigation in narrow environments with other marine vessels present. This requires that the algorithm controlling the ship is able to account for the vessel’s dynamics, and obeys the rules specified in the international regulations for preventing collision at sea (COLREGs). To account for these properties, this work proposes a twostep optimization-based and COLREGs-compliant motion planner tailored for large vessels. In the first step, a lattice-based motion planner is used to compute a dynamically feasible but suboptimal trajectory based on a library of precomputed motion primitives. To comply with the rules specified in COLREGs, the lattice-based planner is augmented with discrete states representing what type of COLREGs situations that are active with respect to other nearby vessels. The trajectory from the lattice-based planner is then used as warm-start for the second recedinghorizon improvement step based on direct optimal control techniques. The aim of the second step is to solve the problem to local optimality with the discrete COLREGs states remained fixed. The applicability of the proposed motion planner is demonstrated in a simulation study, where it computes energy-efficient and COLREGs-compliant trajectories. Moreover, it is shown that the motion planner is able to prevent complex collision situations from occurring.
The Use of Micro-Fading Spectrometry to Evaluate the Light Fastness of Materials in Oxygen-Free Environments

ABSTRACT The use of inert gases for displaying sensitive objects and for the treatment of artifacts infected with bio-deteriorating agents is a current practice in many cultural institutions around the world. However, some artifacts may also experience color changes as a result of exposure to light in these reducing environments. Therefore, it becomes essential to evaluate the light stability of an object under anoxic conditions prior to its exhibition. This study dealt with the development of a procedure based on micro-fading spectroscopy to determine the light fastness of materials when exposed to an argon atmosphere. The efficacy of this method has been tested using turmeric/silk and Prussian blue/silk systems. Evaluation of color changes in real time have demonstrated that the approach provides an efficient way of detecting any potential variation occurring as a result of long-term anoxic display in a museum environment.
Chemical and Mechanical Wear of Cotton Fabric in Laundering

International organisations are studying the standardisation of unsoiled cotton test pieces and methods of using them for the determination of chemical damage and mechanical wear in laundering. Of particular importance is the relation between chemical attack, as measured by viscosity or fluidity changes, and the corresponding strength loss. Study of the literature reveals that the available experimental data are far from complete and that published curves differ. In order to clarify the position, loads of test pieces of two different plain–weave cotton fabrics were laundered up to 35 times, without and with hypochlorite bleach, under different conditions, and an attempt was made to find the breaking strength loss corresponding to a given fluidity increase under identical mechanical conditions. It is concluded that, up to a 20 % strength loss, the relation between strength loss and fluidity increase is a linear one, provided that the severity of the chemical attack remains constant; above this value a curve similar to that of Clibbens and Ridge is found. In practice, however, test pieces are examined after a constant number of launderings, usually 25; consequently, low fluidity values correspond to a slow chemical attack, and high values to a rapid chemical attack. Under these conditions a curve is found that is similar to the Delft curve now under consideration for international standardisation, up toa 20% strength loss; above this value it remains similar to the curve of Clibbens and R idge.
Cloud Removal in Remote Sensing Images Using Generative Adversarial Networks and SAR-to-Optical Image Translation

Satellite images are often contaminated by clouds. Cloud removal has received special attention due to the wide range of satellite image applications. As the clouds thicken, the process of removing them becomes more challenging. In such cases, using auxiliary images, such as near-infrared or synthetic aperture radar (SAR), for reconstructing is common. In this study, we attempt to solve the problem using two generative adversarial networks (GANs): the first translates SAR images to optical images, and the second removes clouds using the translated images of prior GAN. Also, we propose dilated residual inception blocks (DRIBs) instead of vanilla U-net in the generator networks and use structural similarity index measure (SSIM) in addition to the L1 Loss function. Reducing the number of downsamplings and expanding receptive fields by dilated convolutions increased the quality of output images. We used the SEN1-2 dataset to train and test both GANs, and we made cloudy images by adding synthetic clouds to optical images. In addition, we used the SEN12MS-CR dataset to test network performance to remove real clouds. The restored images are evaluated using PSNR, SSIM, SAM, MAE, RMSE, and Q. We compared the proposed method with state-of-the-art deep learning models and achieved more accurate results in both SAR-to-optical image translation and cloud removal parts.
Model wave functions for an interface between lattice Laughlin and Moore-Read states

We use conformal field theory to construct model wavefunctions for an interface between lattice versions of a bosonic ν = 1/2 Laughlin state and a fermionic ν = 5/2 Moore-Read state. The properties of the resulting model state, such as particle density, correlation function and Rényi entanglement entropy are then studied using the Monte Carlo approach. Moreover, we construct the wavefunctions also for localized anyonic excitations (quasiparticles and quasiholes). We study their density profile, charge and statistics. We show that, similarly to the Laughlin-Laughlin case studied earlier, some anyons (the Laughlin Abelian ones) can cross the interface, while others (the non-Abelian ones) lose their anyonic character in such a process. Also, we argue that, under an assumption of local particle exchange, multiple interfaces give rise to a topological degeneracy, which can be interpreted as originating from Majorana zero modes.
Semiempirical Modeling of the Atmospheres of the M Dwarf Exoplanet Hosts GJ 832 and GJ 581

Stellar ultraviolet (UV) radiation drives photochemistry, and extreme-ultraviolet (EUV) radiation drives mass loss in exoplanet atmospheres. However, the UV flux is partly unobservable due to interstellar absorption, particularly in the EUV range (100–912 Å). It is therefore necessary to reconstruct the unobservable spectra in order to characterize the radiation environment of exoplanets. In the present work, we use a radiative transfer code SSRPM to build one-dimensional semiempirical models of two M dwarf exoplanet hosts, GJ 832 and GJ 581, and synthesize their spectra. SSRPM is equipped with an extensive atomic and molecular database and full-NLTE capabilities. We use observations in the visible, ultraviolet, and X-ray ranges to constrain atmospheric structures of the modeled stars. The synthesized integrated EUV fluxes are found to be in good agreement with other reconstruction techniques, but the spectral energy distributions disagree significantly across the EUV range. More than two-thirds of the EUV flux is formed above 105 K. We find that the far-ultraviolet (FUV) continuum contributes 42%–54% of the entire FUV flux between 1450 and 1700 Å. The comparison of stellar structures of GJ 832 and GJ 581 suggests that GJ 832 is a more magnetically active star, which is corroborated by other activity indicators.