A defective viral genome strategy elicits broad protective immunity against respiratory viruses.

RNA viruses generate defective viral genomes (DVGs) that can interfere with replication of the parental wild-type virus. To examine their therapeutic potential, we created a DVG by deleting the capsid-coding region of poliovirus. Strikingly, intraperitoneal or intranasal administration of this genome, which we termed eTIP1, elicits an antiviral response, inhibits replication, and protects mice from several RNA viruses, including enteroviruses, influenza, and SARS-CoV-2. While eTIP1 replication following intranasal administration is limited to the nasal cavity, its antiviral action extends non-cell-autonomously to the lungs. eTIP1 broad-spectrum antiviral effects are mediated by both local and distal type I interferon responses. Importantly, while a single eTIP1 dose protects animals from SARS-CoV-2 infection, it also stimulates production of SARS-CoV-2 neutralizing antibodies that afford long-lasting protection from SARS-CoV-2 reinfection. Thus, eTIP1 is a safe and effective broad-spectrum antiviral generating short- and long-term protection against SARS-CoV-2 and other respiratory infections in animal models.

Experimental and mathematical insights on the interactions between poliovirus and a defective interfering genome.

During replication, RNA viruses accumulate genome alterations, such as mutations and deletions. The interactions between individual variants can determine the fitness of the virus population and, thus, the outcome of infection. To investigate the effects of defective interfering genomes (DI) on wild-type (WT) poliovirus replication, we developed an ordinary differential equation model, which enables exploring the parameter space of the WT and DI competition. We also experimentally examined virus and DI replication kinetics during co-infection, and used these data to infer model parameters. Our model identifies, and our experimental measurements confirm, that the efficiencies of DI genome replication and encapsidation are two most critical parameters determining the outcome of WT replication. However, an equilibrium can be established which enables WT to replicate, albeit to reduced levels.

Designing an AI-Based Virtual Try-On Web Application.

In the last few years, Augmented Reality, Virtual Reality, and Artificial Intelligence (AI) have been increasingly employed in different application domains. Among them, the retail market presents the opportunity to allow people to check the appearance of accessories, makeup, hairstyle, hair color, and clothes on themselves, exploiting virtual try-on applications. In this paper, we propose an eyewear virtual try-on experience based on a framework that leverages advanced deep learning-based computer vision techniques. The virtual try-on is performed on a 3D face reconstructed from a single input image. In designing our system, we started by studying the underlying architecture, components, and their interactions. Then, we assessed and compared existing face reconstruction approaches. To this end, we performed an extensive analysis and experiments for evaluating their design, complexity, geometry reconstruction errors, and reconstructed texture quality. The experiments allowed us to select the most suitable approach for our proposed try-on framework. Our system considers actual glasses and face sizes to provide a realistic fit estimation using a markerless approach. The user interacts with the system by using a web application optimized for desktop and mobile devices. Finally, we performed a usability study that showed an above-average score of our eyewear virtual try-on application.

Quanti.us: a tool for rapid, flexible, crowd-based annotation of images.

We describe Quanti.us , a crowd-based image-annotation platform that provides an accurate alternative to computational algorithms for difficult image-analysis problems. We used Quanti.us for a variety of medium-throughput image-analysis tasks and achieved 10-50× savings in analysis time compared with that required for the same task by a single expert annotator. We show equivalent deep learning performance for Quanti.us-derived and expert-derived annotations, which should allow scalable integration with tailored machine learning algorithms.

AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations.

We present a novel technique to predict binding affinity trends between two molecules from atomistic molecular dynamics simulations. The technique uses a neural network algorithm applied to a series of images encoding the distance between two molecules in time. We demonstrate that our algorithm is capable of separating with high accuracy non-hydrophobic mutations with low binding affinity from those with high binding affinity. Moreover, we show high accuracy in prediction using a small subset of the simulation, therefore requiring a much shorter simulation time. We apply our algorithm to the binding between several variants of the SARS-CoV-2 spike protein and the human receptor ACE2.

A Smart Mirror for Emotion Monitoring in Home Environments.

Smart mirrors are devices that can display any kind of information and can interact with the user using touch and voice commands. Different kinds of smart mirrors exist: general purpose, medical, fashion, and other task specific ones. General purpose smart mirrors are suitable for home environments but the exiting ones offer similar, limited functionalities. In this paper, we present a general-purpose smart mirror that integrates several functionalities, standard and advanced, to support users in their everyday life. Among the advanced functionalities are the capabilities of detecting a person's emotions, the short- and long-term monitoring and analysis of the emotions, a double authentication protocol to preserve the privacy, and the integration of Alexa Skills to extend the applications of the smart mirrors. We exploit a deep learning technique to develop most of the smart functionalities. The effectiveness of the device is demonstrated by the performances of the implemented functionalities, and the evaluation in terms of its usability with real users.

Insular Microbiogeography: Three Pathogens as Exemplars.

Traditional taxonomy in biology assumes that life is organized in a simple tree. Attempts to classify microorganisms in this way in the genomics era led microbiologists to look for finite sets of 'core' genes that uniquely group taxa as clades in the tree. However, the diversity revealed by large-scale whole genome sequencing is calling into question the long-held model of a hierarchical tree of life, which leads to questioning of the definition of a species. Large-scale studies of microbial genome diversity reveal that the cumulative number of new genes discovered increases with the number of genomes studied as a power law and subsequently leads to the lack of evidence for a unique core genome within closely related organisms. Sampling 'enough' new genomes leads to the discovery of a replacement or alternative to any gene. This power law behaviour points to an underlying self-organizing critical process that may be guided by mutation and niche selection. Microbes in any particular niche exist within a local web of organism interdependence known as the microbiome. The same mechanism that underpins the macro-ecological scaling first observed by MacArthur and Wilson also applies to microbial communities. Recent metagenomic studies of a food microbiome demonstrate the diverse distribution of community members, but also genotypes for a single species within a more complex community. Collectively, these results suggest that traditional taxonomic classification of bacteria could be replaced with a quasispecies model. This model is commonly accepted in virology and better describes the diversity and dynamic exchange of genes that also hold true for bacteria. This model will enable microbiologists to conduct population-scale studies to describe microbial behaviour, as opposed to a single isolate as a representative.

Non-random distribution of vacuoles in Schizosaccharomyces pombe.

A central question in eukaryotic cell biology asks, during cell division, how is the growth and distribution of organelles regulated to ensure each daughter cell receives an appropriate amount. For vacuoles in budding yeast, there are well described organelle-to-cell size scaling trends as well as inheritance mechanisms involving highly coordinated movements. It is unclear whether such mechanisms are necessary in the symmetrically dividing fission yeast, Schizosaccharomyces pombe, in which random partitioning may be utilized to distribute vacuoles to daughter cells. To address the increasing need for high-throughput analysis, we are augmenting existing semi-automated image processing by developing fully automated machine learning methods for locating vacuoles and segmenting fission yeast cells from brightfield and fluorescence micrographs. All strains studied show qualitative correlations in vacuole-to-cell size scaling trends, i.e. vacuole volume, surface area, and number all increase with cell size. Furthermore, increasing vacuole number was found to be a consistent mechanism for the increase in total vacuole size in the cell. Vacuoles are not distributed evenly throughout the cell with respect to available cytoplasm. Rather, vacuoles show distinct peaks in distribution close to the nucleus, and this preferential localization was confirmed in mutants in which nucleus position is perturbed. Disruption of microtubules leads to quantitative changes in both vacuole size scaling trends and distribution patterns, indicating the microtubule cytoskeleton is a key mechanism for maintaining vacuole structure.

Towards computer-aided design of cellular structure.

Cells are complex machines with tremendous potential for applications in medicine and biotechnology. Although much effort has been devoted to engineering the metabolic, genetic, and signaling pathways of cells, methods for systematically engineering the physical structure of cells are less developed. Here we consider how coarse-grained models for cellular geometry at the organelle level can be used to build computer-aided design (CAD) tools for cellular structure.

ARC: Angle-Retaining Chromaticity diagram for color constancy error analysis.

Color constancy algorithms are typically evaluated with a statistical analysis of the recovery angular error and the reproduction angular error between the estimated and ground truth illuminants. Such analysis provides information about only the magnitude of the errors, and not about their chromatic properties. We propose an Angle-Retaining Chromaticity diagram (ARC) for the visual analysis of the estimated illuminants and the corresponding errors. We provide both quantitative and qualitative proof of the superiority of ARC in preserving angular distances compared to other chromaticity diagrams, making it possible to quantify the reproduction and recovery errors in terms of Euclidean distances on a plane. We present two case studies for the application of the ARC diagram in the visualization of the ground truth illuminants of color constancy datasets, and the visual analysis of error distributions of color constancy algorithms.

Addressing Drug Resistance in Cancer with Macromolecular Chemotherapeutic Agents.

Drug resistance to chemotherapeutics is a recurrent issue plaguing many cancer treatment regimens. To circumvent resistance issues, we have designed a new class of macromolecules as self-contained chemotherapeutic agents. The macromolecular chemotherapeutic agents readily self-assemble into well-defined nanoparticles and show excellent activity in vitro against multiple cancer cell lines. These cationic polymers function by selectively binding and lysing cancer cell membranes. As a consequence of this mechanism, they exhibit significant potency against drug-resistant cancer cells and cancer stem cells, prevent cancer cell migration, and do not induce resistance onset following multiple treatment passages. Concurrent experiments with the small-molecule chemotherapeutic, doxorubicin, show aggressive resistance onset in cancer cells, a lack of efficacy against drug-resistant cancer cell lines, and a failure to prevent cancer cell migration. Additionally, the polymers showed anticancer efficacy in a hepatocellular carcinoma patient derived xenograft mouse model. Overall, these results demonstrate a new approach to designing anticancer therapeutics utilizing macromolecular compounds.

Fine-Grained Face Annotation Using Deep Multi-Task CNN.

We present a multi-task learning-based convolutional neural network (MTL-CNN) able to estimate multiple tags describing face images simultaneously. In total, the model is able to estimate up to 74 different face attributes belonging to three distinct recognition tasks: age group, gender and visual attributes (such as hair color, face shape and the presence of makeup). The proposed model shares all the CNN's parameters among tasks and deals with task-specific estimation through the introduction of two components: (i) a gating mechanism to control activations' sharing and to adaptively route them across different face attributes; (ii) a module to post-process the predictions in order to take into account the correlation among face attributes. The model is trained by fusing multiple databases for increasing the number of face attributes that can be estimated and using a center loss for disentangling representations among face attributes in the embedding space. Extensive experiments validate the effectiveness of the proposed approach.

Meaning making after a near-death experience: The relevance of intrapsychic and interpersonal dynamics.

This study aims to investigate the processes used by individuals to integrate a near-death experience (NDE) and to discuss the use of a meaning-making component to help people who have had such experiences. A psychotherapist interviewed six individuals who reported having had a NDE. Transcripts of the interviews were coded using an interpretative phenomenological analysis. The authors identified intrapsychic and interpersonal dynamics implicated in the individuals' meaning-making processes, and the problems encountered during their integration of the experience. Meaning-based approaches are a feasible theoretical framework for shedding light on the NDE and providing support for people who have lived through them.

A tradeoff between enterovirus A71 particle stability and cell entry.

A central role of viral capsids is to protect the viral genome from the harsh extracellular environment while facilitating initiation of infection when the virus encounters a target cell. Viruses are thought to have evolved an optimal equilibrium between particle stability and efficiency of cell entry. In this study, we genetically perturb this equilibrium in a non-enveloped virus, enterovirus A71 to determine its structural basis. We isolate a single-point mutation variant with increased particle thermotolerance and decreased efficiency of cell entry. Using cryo-electron microscopy and molecular dynamics simulations, we determine that the thermostable native particles have acquired an expanded conformation that results in a significant increase in protein dynamics. Examining the intermediate states of the thermostable variant reveals a potential pathway for uncoating. We propose a sequential release of the lipid pocket factor, followed by internal VP4 and ultimately the viral RNA.

Color space transformations for digital photography exploiting information about the illuminant estimation process.

The color reproduction accuracy is a key factor to the overall perceived image quality in digital photography. In this framework, both the illuminant estimation process and the color correction matrix concur in the formation of the overall perceived image quality. To the best of our knowledge, the two processes have always been studied separately, thus ignoring the interactions between them. We investigate here these interactions, showing how the color correction transform amplifies the illuminant estimation errors. We demonstrate that incorporating knowledge about the illuminant estimation behavior in the optimization of the color correction matrix makes it possible to alleviate the error amplification. Different strategies to improve color accuracy under both perfect and imperfect white point estimations are investigated, and the experimental results obtained with a digital camera simulator are reported.

Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world.

This editorial article aims to highlight advances in artificial intelligence (AI) technologies in five areas: Collaborative AI, Multimodal AI, Human-Centered AI, Equitable AI, and Ethical and Value-based AI in order to cope with future complex socioeconomic and public health issues.

Decoding CAR T cell phenotype using combinatorial signaling motif libraries and machine learning.

Chimeric antigen receptor (CAR) costimulatory domains derived from native immune receptors steer the phenotypic output of therapeutic T cells. We constructed a library of CARs containing ~2300 synthetic costimulatory domains, built from combinations of 13 signaling motifs. These CARs promoted diverse human T cell fates, which were sensitive to motif combinations and configurations. Neural networks trained to decode the combinatorial grammar of CAR signaling motifs allowed extraction of key design rules. For example, non-native combinations of motifs that bind tumor necrosis factor receptor-associated factors (TRAFs) and phospholipase C gamma 1 (PLCγ1) enhanced cytotoxicity and stemness associated with effective tumor killing. Thus, libraries built from minimal building blocks of signaling, combined with machine learning, can efficiently guide engineering of receptors with desired phenotypes.

Predicting Epitope Candidates for SARS-CoV-2.

Epitopes are short amino acid sequences that define the antigen signature to which an antibody or T cell receptor binds. In light of the current pandemic, epitope analysis and prediction are paramount to improving serological testing and developing vaccines. In this paper, known epitope sequences from SARS-CoV, SARS-CoV-2, and other Coronaviridae were leveraged to identify additional antigen regions in 62K SARS-CoV-2 genomes. Additionally, we present epitope distribution across SARS-CoV-2 genomes, locate the most commonly found epitopes, and discuss where epitopes are located on proteins and how epitopes can be grouped into classes. The mutation density of different protein regions is presented using a big data approach. It was observed that there are 112 B cell and 279 T cell conserved epitopes between SARS-CoV-2 and SARS-CoV, with more diverse sequences found in Nucleoprotein and Spike glycoprotein.

Asymmetry in the presence of migration stabilizes multistrain disease outbreaks.

We study the effect of migration between coupled populations, or patches, on the stability properties of multistrain disease dynamics. The epidemic model used in this work displays a Hopf bifurcation to oscillations in a single, well-mixed population. It is shown numerically that migration between two non-identical patches stabilizes the endemic steady state, delaying the onset of large amplitude outbreaks and reducing the total number of infections. This result is motivated by analyzing generic Hopf bifurcations with different frequencies and with diffusive coupling between them. Stabilization of the steady state is again seen, indicating that our observation in the full multistrain model is based on qualitative characteristics of the dynamics rather than on details of the disease model.

Maximal sensitive dependence and the optimal path to epidemic extinction.

Extinction of an epidemic or a species is a rare event that occurs due to a large, rare stochastic fluctuation. Although the extinction process is dynamically unstable, it follows an optimal path that maximizes the probability of extinction. We show that the optimal path is also directly related to the finite-time Lyapunov exponents of the underlying dynamical system in that the optimal path displays maximum sensitivity to initial conditions. We consider several stochastic epidemic models, and examine the extinction process in a dynamical systems framework. Using the dynamics of the finite-time Lyapunov exponents as a constructive tool, we demonstrate that the dynamical systems viewpoint of extinction evolves naturally toward the optimal path.