Publications by authors named "Simone Bianco"

46 Publications

Semi-Supervised Pipeline for Autonomous Annotation of SARS-CoV-2 Genomes.

Viruses 2021 12 3;13(12). Epub 2021 Dec 3.

AI and Cognitive Software, IBM Almaden Research Center, San Jose, CA 95120, USA.

SARS-CoV-2 genomic sequencing efforts have scaled dramatically to address the current global pandemic and aid public health. However, autonomous genome annotation of SARS-CoV-2 genes, proteins, and domains is not readily accomplished by existing methods and results in missing or incorrect sequences. To overcome this limitation, we developed a novel semi-supervised pipeline for automated gene, protein, and functional domain annotation of SARS-CoV-2 genomes that differentiates itself by not relying on the use of a single reference genome and by overcoming atypical genomic traits that challenge traditional bioinformatic methods. We analyzed an initial corpus of 66,000 SARS-CoV-2 genome sequences collected from labs across the world using our method and identified the comprehensive set of known proteins with 98.5% set membership accuracy and 99.1% accuracy in length prediction, compared to proteome references, including Replicase polyprotein 1ab (with its transcriptional slippage site). Compared to other published tools, such as Prokka (base) and VAPiD, we yielded a 6.4- and 1.8-fold increase in protein annotations. Our method generated 13,000,000 gene, protein, and domain sequences-some conserved across time and geography and others representing emerging variants. We observed 3362 non-redundant sequences per protein on average within this corpus and described key D614G and N501Y variants spatiotemporally in the initial genome corpus. For spike glycoprotein domains, we achieved greater than 97.9% sequence identity to references and characterized receptor binding domain variants. We further demonstrated the robustness and extensibility of our method on an additional 4000 variant diverse genomes containing all named variants of concern and interest as of August 2021. In this cohort, we successfully identified all keystone spike glycoprotein mutations in our predicted protein sequences with greater than 99% accuracy as well as demonstrating high accuracy of the protein and domain annotations. This work comprehensively presents the molecular targets to refine biomedical interventions for SARS-CoV-2 with a scalable, high-accuracy method to analyze newly sequenced infections as they arise.
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http://dx.doi.org/10.3390/v13122426DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8706859PMC
December 2021

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

Cell 2021 12 18;184(25):6037-6051.e14. Epub 2021 Nov 18.

Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94158, USA. Electronic address:

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.
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http://dx.doi.org/10.1016/j.cell.2021.11.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8598942PMC
December 2021

A Smart Mirror for Emotion Monitoring in Home Environments.

Sensors (Basel) 2021 Nov 9;21(22). Epub 2021 Nov 9.

Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy.

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.
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http://dx.doi.org/10.3390/s21227453DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8625889PMC
November 2021

Globally local: Hyper-local modeling for accurate forecast of COVID-19.

Epidemics 2021 12 15;37:100510. Epub 2021 Oct 15.

IBM Research, Almaden Research Center, 650 Harry Rd, San Jose, CA, 95120, USA. Electronic address:

Importance: Assumption of a well-mixed population during modeling is often erroneously made without due analysis of its validity. Ignoring the importance of the geo-spatial granularity at which the data is collected could have significant implications on the quality of forecasts and the actionable clinical recommendations that are based on it.

Objective: This paper's primary objective is to test the hypothesis that the characteristic dynamics defining the trajectory of the pandemic in a region is lost when the data is aggregated and modeled at higher geo-spatial levels.

Design: We use publicly available confirmed SARS-CoV-2 cases and deaths from January 1st, 2020 to August 3rd, 2020 in the United States at different geo-spatial granularities to conduct our experiments. To understand the impact of this hypothesis, the output of this study was implemented in Tampa General Hospital (TGH) to provide resource demand forecast.

Results: The Mean Absolute Percentage Error (MAPE) in the forecast confirmed cases can be 30% higher for modeling at the state-level than aggregating model results at the scale of counties or clusters of counties. Similarly, modeling at a state-level and crafting policy decisions based on them may not be effective - county-level forecasts made by partitioning state-level forecasts are 3x worse for confirmed cases and 20x worse for deaths relative to the same model at the county level. By leveraging these results, TGH was able to accurately allocate clinical resources to tackle COVID-19 cases, continue elective surgical procedures largely uninterrupted and avoid costly construction of overflow capacity in the first two epidemic waves.

Conclusions And Relevance: Accurate forecasting at the county level requires hyper-local modeling with county resolution. State-level modeling does not accurately predict community spread in smaller sub-regions because state populations are not well mixed, resulting in large prediction errors. Actionable decisions such as deciding whether to cancel planned surgeries or construct overflow capacity require models with local specificity.
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http://dx.doi.org/10.1016/j.epidem.2021.100510DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518202PMC
December 2021

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

Eur Phys J E Soft Matter 2021 Oct 6;44(10):123. Epub 2021 Oct 6.

IBM Almaden Research Center, 650 Harry Rd, San Jose, CA, 95120, USA.

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.
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http://dx.doi.org/10.1140/epje/s10189-021-00119-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8493367PMC
October 2021

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

PLoS Pathog 2021 09 27;17(9):e1009277. Epub 2021 Sep 27.

Department of Microbiology and Immunology, University of California, San Francisco, California, United States of America.

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.
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http://dx.doi.org/10.1371/journal.ppat.1009277DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8496841PMC
September 2021

Examining the interplay between face mask usage, asymptomatic transmission, and social distancing on the spread of COVID-19.

Sci Rep 2021 08 6;11(1):15998. Epub 2021 Aug 6.

Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, 94158, USA.

COVID-19's high virus transmission rates have caused a pandemic that is exacerbated by the high rates of asymptomatic and presymptomatic infections. These factors suggest that face masks and social distance could be paramount in containing the pandemic. We examined the efficacy of each measure and the combination of both measures using an agent-based model within a closed space that approximated real-life interactions. By explicitly considering different fractions of asymptomatic individuals, as well as a realistic hypothesis of face masks protection during inhaling and exhaling, our simulations demonstrate that a synergistic use of face masks and social distancing is the most effective intervention to curb the infection spread. To control the pandemic, our models suggest that high adherence to social distance is necessary to curb the spread of the disease, and that wearing face masks provides optimal protection even if only a small portion of the population comply with social distance. Finally, the face mask effectiveness in curbing the viral spread is not reduced if a large fraction of population is asymptomatic. Our findings have important implications for policies that dictate the reopening of social gatherings.
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http://dx.doi.org/10.1038/s41598-021-94960-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8346500PMC
August 2021

Optimal periodic closure for minimizing risk in emerging disease outbreaks.

PLoS One 2021 6;16(1):e0244706. Epub 2021 Jan 6.

U.S. Naval Research Laboratory, Washington, DC, United States of America.

Without vaccines and treatments, societies must rely on non-pharmaceutical intervention strategies to control the spread of emerging diseases such as COVID-19. Though complete lockdown is epidemiologically effective, because it eliminates infectious contacts, it comes with significant costs. Several recent studies have suggested that a plausible compromise strategy for minimizing epidemic risk is periodic closure, in which populations oscillate between wide-spread social restrictions and relaxation. However, no underlying theory has been proposed to predict and explain optimal closure periods as a function of epidemiological and social parameters. In this work we develop such an analytical theory for SEIR-like model diseases, showing how characteristic closure periods emerge that minimize the total outbreak, and increase predictably with the reproductive number and incubation periods of a disease- as long as both are within predictable limits. Using our approach we demonstrate a sweet-spot effect in which optimal periodic closure is maximally effective for diseases with similar incubation and recovery periods. Our results compare well to numerical simulations, including in COVID-19 models where infectivity and recovery show significant variation.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244706PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787468PMC
January 2021

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

J Opt Soc Am A Opt Image Sci Vis 2020 Nov;37(11):1721-1730

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.
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http://dx.doi.org/10.1364/JOSAA.398692DOI Listing
November 2020

Non-random distribution of vacuoles in Schizosaccharomyces pombe.

Phys Biol 2020 10 9;17(6):065004. Epub 2020 Oct 9.

Department of Biology, San Francisco State University, San Francisco, CA, United States of America. Center for Cellular Construction, San Francisco Bay Area, CA, United States of America.

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.
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http://dx.doi.org/10.1088/1478-3975/aba510DOI Listing
October 2020

Mapping Attenuation Determinants in Enterovirus-D68.

Viruses 2020 08 8;12(8). Epub 2020 Aug 8.

Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94158, USA.

Enterovirus (EV)-D68 has been associated with epidemics in the United Sates in 2014, 2016 and 2018. This study aims to identify potential viral virulence determinants. We found that neonatal type I interferon receptor knockout mice are susceptible to EV-D68 infection via intraperitoneal inoculation and were able to recapitulate the paralysis process observed in human disease. Among the EV-D68 strains tested, strain US/MO-14-18949 caused no observable disease in this mouse model, whereas the other strains caused paralysis and death. Sequence analysis revealed several conserved genetic changes among these virus strains: nucleotide positions 107 and 648 in the 5'-untranslated region (UTR); amino acid position 88 in VP3; 1, 148, 282 and 283 in VP1; 22 in 2A; 47 in 3A. A series of chimeric and point-mutated infectious clones were constructed to identify viral elements responsible for the distinct virulence. A single amino acid change from isoleucine to valine at position 88 in VP3 attenuated neurovirulence by reducing virus replication in the brain and spinal cord of infected mice.
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http://dx.doi.org/10.3390/v12080867DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472100PMC
August 2020

Annotation-free learning of plankton for classification and anomaly detection.

Sci Rep 2020 07 22;10(1):12142. Epub 2020 Jul 22.

Industrial and Applied Genomics, AI and Cognitive Software, IBM Research - Almaden, San Jose, CA, USA.

The acquisition of increasingly large plankton digital image datasets requires automatic methods of recognition and classification. As data size and collection speed increases, manual annotation and database representation are often bottlenecks for utilization of machine learning algorithms for taxonomic classification of plankton species in field studies. In this paper we present a novel set of algorithms to perform accurate detection and classification of plankton species with minimal supervision. Our algorithms approach the performance of existing supervised machine learning algorithms when tested on a plankton dataset generated from a custom-built lensless digital device. Similar results are obtained on a larger image dataset obtained from the Woods Hole Oceanographic Institution. Additionally, we introduce a new algorithm to perform anomaly detection on unclassified samples. Here an anomaly is defined as a significant deviation from the established classification. Our algorithms are designed to provide a new way to monitor the environment with a class of rapid online intelligent detectors.
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http://dx.doi.org/10.1038/s41598-020-68662-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7376023PMC
July 2020

Non-random distribution of vacuoles in.

Phys Biol 2020 Jul 10. Epub 2020 Jul 10.

Biology, San Francisco State University, San Francisco, California, UNITED STATES.

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 regulation is necessary in the symmetrically dividing fission yeast, Schizosaccharomyces pombe, in which random partitioning may be utilized to distribute vacuoles to daughter cells. To test this idea, we utilized machine learning to develop automated methods for segmenting fission yeast cells and locating vacuoles in live fission yeast cells from brightfield and fluorescence micrographs. We have found that the scaling trends of vacuole number, volume and surface area-to-cell size is consistent across a variety of strains, and that vacuole proliferation is a key mechanism for vacuole growth. We also show that 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 clustering was confirmed in mutants in which nucleus position is perturbed. Future work will establish the molecular mechanism for this clustering.
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http://dx.doi.org/10.1088/1478-3975/aba510DOI Listing
July 2020

Personalized Image Enhancement Using Neural Spline Color Transforms.

IEEE Trans Image Process 2020 May 1. Epub 2020 May 1.

In this work we present SpliNet, a novel CNNbased method that estimates a global color transform for the enhancement of raw images. The method is designed to improve the perceived quality of the images by reproducing the ability of an expert in the field of photo editing. The transformation applied to the input image is found by a convolutional neural network specifically trained for this purpose. More precisely, the network takes as input a raw image and produces as output one set of control points for each of the three color channels. Then, the control points are interpolated with natural cubic splines and the resulting functions are globally applied to the values of the input pixels to produce the output image. Experimental results compare favorably against recent methods in the state of the art on the MIT-Adobe FiveK dataset. Furthermore, we also propose an extension of the SpliNet in which a single neural network is used to model the style of multiple reference retouchers by embedding them into a user space. The style of new users can be reproduced without retraining the network, after a quick modeling stage in which they are positioned in the user space on the basis of their preferences on a very small set of retouched images.
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http://dx.doi.org/10.1109/TIP.2020.2989584DOI Listing
May 2020

IVL-SYNTHSFM-v2: A synthetic dataset with exact ground truth for the evaluation of 3D reconstruction pipelines.

Data Brief 2020 Apr 23;29:105041. Epub 2019 Dec 23.

DISCo-Department of Informatics, Systems and Communication, University of Milano - Bicocca, viale Sarca 336, 20126, Milano, Italy.

This article presents a dataset with 4000 synthetic images portraying five 3D models from different viewpoints under varying lighting conditions. Depth of field and motion blur have also been used to generate realistic images. For each object, 8 scenes with different combinations of lighting, depth of field and motion blur are created and images are taken from 100 points of view. Data also includes information about camera intrinsic and extrinsic calibration parameters for each image as well as the ground truth geometry of the 3D models. The images were rendered using Blender. The aim of this dataset is to allow evaluation and comparison of different solutions for 3D reconstruction of objects starting from a set of images taken under different realistic acquisition setups.
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http://dx.doi.org/10.1016/j.dib.2019.105041DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6971370PMC
April 2020

Towards computer-aided design of cellular structure.

Phys Biol 2020 02 25;17(2):023001. Epub 2020 Feb 25.

Center for Cellular Construction, San Francisco, CA, United States of America. IBM Almaden Research Center, San Jose, CA, United States of America.

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.
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http://dx.doi.org/10.1088/1478-3975/ab6d43DOI Listing
February 2020

Insular Microbiogeography: Three Pathogens as Exemplars.

Curr Issues Mol Biol 2020 9;36:89-108. Epub 2019 Oct 9.

University of California Davis, School of Veterinary Medicine, 100K Pathogen Genome Project, Davis, CA, USA.

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.
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http://dx.doi.org/10.21775/cimb.036.089DOI Listing
September 2020

STEM: An Open Source Tool for Disease Modeling.

Health Secur 2019 Jul/Aug;17(4):291-306

Judith V. Douglas, MHS, was Lead Technical Writer, Science to Solutions; Simone Bianco, PhD, is a Research Staff Member, Industrial and Applied Genomics, Science to Solutions; Stefan Edlund, MS, is a Research Software Engineer, Industrial and Applied Genomics, Science to Solutions; Kun (Maggie) Hu, PhD, is Research Manager, Public Health and Food Safety; and James H. Kaufman, PhD, is Chief Scientist, Science to Solutions; all at IBM Research-Almaden, San Jose, CA.

The Spatiotemporal Epidemiologic Modeler (STEM) is an open source software project supported by the Eclipse Foundation and used by a global community of researchers and public health officials working to track and, when possible, control outbreaks of infectious disease in human and animal populations. STEM is not a model or a tool designed for a specific disease; it is a flexible, modular framework supporting exchange and integration of community models, reusable plug-in components, and denominator data, available to researchers worldwide at www.eclipse.org/stem. A review of multiple projects illustrates its capabilities. STEM has been used to study variations in transmission of seasonal influenza in Israel by strains; evaluate social distancing measures taken to curb the H1N1 epidemic in Mexico City; study measles outbreaks in part of London and inform local policy on immunization; and gain insights into H7N9 avian influenza transmission in China. A multistrain dengue fever model explored the roles of the mosquito vector, cross-strain immunity, and antibody response in the frequency of dengue outbreaks. STEM has also been used to study the impact of variations in climate on malaria incidence. During the Ebola epidemic, a weekly conference call supported the global modeling community; subsequent work modeled the impact of behavioral change and tested disease reintroduction via animal reservoirs. Work in Germany tracked salmonella in pork from farm to fork; and a recent doctoral dissertation used the air travel feature to compare the potential threats posed by weaponizing infectious diseases. Current projects include work in Great Britain to evaluate control strategies for parasitic disease in sheep, and in Germany and Hungary, to validate the model and inform policy decisions for African swine fever. STEM Version 4.0.0, released in early 2019, includes tools used in these projects and updates technical aspects of the framework to ease its use and re-use.
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http://dx.doi.org/10.1089/hs.2019.0018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6708268PMC
May 2020

Providing a Single Ground-Truth for Illuminant Estimation for the ColorChecker Dataset.

IEEE Trans Pattern Anal Mach Intell 2020 May 1;42(5):1286-1287. Epub 2019 Jul 1.

The ColorChecker dataset is one of the most widely used image sets for evaluating and ranking illuminant estimation algorithms. However, this single set of images has at least 3 different sets of ground-truth (i.e., correct answers) associated with it. In the literature it is often asserted that one algorithm is better than another when the algorithms in question have been tuned and tested with the different ground-truths. In this short correspondence we present some of the background as to why the 3 existing ground-truths are different and go on to make a new single and recommended set of correct answers. Experiments reinforce the importance of this work in that we show that the total ordering of a set of algorithms may be reversed depending on whether we use the new or legacy ground-truth data.
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http://dx.doi.org/10.1109/TPAMI.2019.2919824DOI Listing
May 2020

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

Sensors (Basel) 2018 Aug 14;18(8). Epub 2018 Aug 14.

Department of Informatics, Systems and Communication, University of Milano-Bicocca, viale Sarca, 336 Milano, Italy.

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.
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http://dx.doi.org/10.3390/s18082666DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111573PMC
August 2018

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

Nat Methods 2018 08 31;15(8):587-590. Epub 2018 Jul 31.

Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.

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.
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http://dx.doi.org/10.1038/s41592-018-0069-0DOI Listing
August 2018

Addressing Drug Resistance in Cancer with Macromolecular Chemotherapeutic Agents.

J Am Chem Soc 2018 03 15;140(12):4244-4252. Epub 2018 Mar 15.

IBM Research-Almaden , 650 Harry Road , San Jose , California 95120 United States.

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.
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http://dx.doi.org/10.1021/jacs.7b11468DOI Listing
March 2018

A macromolecular approach to eradicate multidrug resistant bacterial infections while mitigating drug resistance onset.

Nat Commun 2018 03 2;9(1):917. Epub 2018 Mar 2.

Institute of Bioengineering and Nanotechnology, 31 Biopolis Way, Singapore, 138669, Singapore.

Polymyxins remain the last line treatment for multidrug-resistant (MDR) infections. As polymyxins resistance emerges, there is an urgent need to develop effective antimicrobial agents capable of mitigating MDR. Here, we report biodegradable guanidinium-functionalized polycarbonates with a distinctive mechanism that does not induce drug resistance. Unlike conventional antibiotics, repeated use of the polymers does not lead to drug resistance. Transcriptomic analysis of bacteria further supports development of resistance to antibiotics but not to the macromolecules after 30 treatments. Importantly, high in vivo treatment efficacy of the macromolecules is achieved in MDR A. baumannii-, E. coli-, K. pneumoniae-, methicillin-resistant S. aureus-, cecal ligation and puncture-induced polymicrobial peritonitis, and P. aeruginosa lung infection mouse models while remaining non-toxic (e.g., therapeutic index-ED/LD: 1473 for A. baumannii infection). These biodegradable synthetic macromolecules have been demonstrated to have broad spectrum in vivo antimicrobial activity, and have excellent potential as systemic antimicrobials against MDR infections.
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http://dx.doi.org/10.1038/s41467-018-03325-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5834525PMC
March 2018

Hamiltonian Analysis of Subcritical Stochastic Epidemic Dynamics.

Comput Math Methods Med 2017 28;2017:4253167. Epub 2017 Aug 28.

Francis I. Proctor Foundation, University of California, San Francisco, San Francisco, CA, USA.

We extend a technique of approximation of the long-term behavior of a supercritical stochastic epidemic model, using the WKB approximation and a Hamiltonian phase space, to the subcritical case. The limiting behavior of the model and approximation are qualitatively different in the subcritical case, requiring a novel analysis of the limiting behavior of the Hamiltonian system away from its deterministic subsystem. This yields a novel, general technique of approximation of the quasistationary distribution of stochastic epidemic and birth-death models and may lead to techniques for analysis of these models beyond the quasistationary distribution. For a classic SIS model, the approximation found for the quasistationary distribution is very similar to published approximations but not identical. For a birth-death process without depletion of susceptibles, the approximation is exact. Dynamics on the phase plane similar to those predicted by the Hamiltonian analysis are demonstrated in cross-sectional data from trachoma treatment trials in Ethiopia, in which declining prevalences are consistent with subcritical epidemic dynamics.
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http://dx.doi.org/10.1155/2017/4253167DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5592420PMC
October 2017

Single and Multiple Illuminant Estimation Using Convolutional Neural Networks.

IEEE Trans Image Process 2017 Sep 7;26(9):4347-4362. Epub 2017 Jun 7.

In this paper, we present a three-stage method for the estimation of the color of the illuminant in RAW images. The first stage uses a convolutional neural network that has been specially designed to produce multiple local estimates of the illuminant. The second stage, given the local estimates, determines the number of illuminants in the scene. Finally, local illuminant estimates are refined by non-linear local aggregation, resulting in a global estimate in case of single illuminant. An extensive comparison with both local and global illuminant estimation methods in the state of the art, on standard data sets with single and multiple illuminants, proves the effectiveness of our method.
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http://dx.doi.org/10.1109/TIP.2017.2713044DOI Listing
September 2017

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

Death Stud 2017 10 27;41(9):562-573. Epub 2017 Mar 27.

a Department of Philosophy, Sociology, Pedagogy and Applied Psychology (FISPPA) , University of Padova , Padova , Italy.

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.
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http://dx.doi.org/10.1080/07481187.2017.1310768DOI Listing
October 2017

Extinction pathways and outbreak vulnerability in a stochastic Ebola model.

J R Soc Interface 2017 02;14(127)

Department of Industrial and Applied Genomics, IBM Accelerated Discovery Laboratory, IBM Almaden Research Center, 650 Harry Road, San Jose, CA 95120, USA.

A zoonotic disease is a disease that can be passed from animals to humans. Zoonotic viruses may adapt to a human host eventually becoming endemic in humans, but before doing so punctuated outbreaks of the zoonotic virus may be observed. The Ebola virus disease (EVD) is an example of such a disease. The animal population in which the disease agent is able to reproduce in sufficient number to be able to transmit to a susceptible human host is called a reservoir. There is little work devoted to understanding stochastic population dynamics in the presence of a reservoir, specifically the phenomena of disease extinction and reintroduction. Here, we build a stochastic EVD model and explicitly consider the impacts of an animal reservoir on the disease persistence. Our modelling approach enables the analysis of invasion and fade-out dynamics, including the efficacy of possible intervention strategies. We investigate outbreak vulnerability and the probability of local extinction and quantify the effective basic reproduction number. We also consider the effects of dynamic population size. Our results provide an improved understanding of outbreak and extinction dynamics in zoonotic diseases, such as EVD.
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http://dx.doi.org/10.1098/rsif.2016.0847DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5332568PMC
February 2017

Traditional Meditation, Mindfulness and Psychodynamic Approach: An Integrative Perspective.

Front Psychol 2016 21;7:552. Epub 2016 Apr 21.

Department of Philosophy, Sociology, Education and Applied Psychology, University of PadovaPadova, Italy; Cognitive Neuroscience Center, University of PadovaPadova, Italy.

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http://dx.doi.org/10.3389/fpsyg.2016.00552DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4838602PMC
May 2016

RNA Recombination Enhances Adaptability and Is Required for Virus Spread and Virulence.

Cell Host Microbe 2016 Apr;19(4):493-503

Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94158, USA. Electronic address:

Mutation and recombination are central processes driving microbial evolution. A high mutation rate fuels adaptation but also generates deleterious mutations. Recombination between two different genomes may resolve this paradox, alleviating effects of clonal interference and purging deleterious mutations. Here we demonstrate that recombination significantly accelerates adaptation and evolution during acute virus infection. We identified a poliovirus recombination determinant within the virus polymerase, mutation of which reduces recombination rates without altering replication fidelity. By generating a panel of variants with distinct mutation rates and recombination ability, we demonstrate that recombination is essential to enrich the population in beneficial mutations and purge it from deleterious mutations. The concerted activities of mutation and recombination are key to virus spread and virulence in infected animals. These findings inform a mathematical model to demonstrate that poliovirus adapts most rapidly at an optimal mutation rate determined by the trade-off between selection and accumulation of detrimental mutations.
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http://dx.doi.org/10.1016/j.chom.2016.03.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4840895PMC
April 2016
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