skip to main content

Search for: All records

Creators/Authors contains: "Cowen, Lenore"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract

    The degree of polarization in many societies has become a pressing concern in media studies. Typically, it is argued that the internet and social media have created more media producers than ever before, allowing individual, biased media consumers to expose themselves only to what already confirms their beliefs, leading to polarized echo-chambers that further deepen polarization. This work introduces extensions to the recent Cognitive Cascades model of Rabb et al. to study this dynamic, allowing for simulation of information spread between media and networks of variably biased citizens. Our results partially confirm the above polarization logic, but also reveal several important enabling conditions for polarization to occur: (1) the distribution of media belief must be more polarized than the population; (2) the population must be at least somewhat persuadable to changing their belief according to new messages they hear; and finally, (3) the media must statically continue to broadcast more polarized messages rather than, say, adjust to appeal more to the beliefs of their current subscribers. Moreover, and somewhat counter-intuitively, under these conditions we find that polarization is more likely to occur when media consumers are exposed to more diverse messages, and that polarization occurred most often when there were low levels of echo-chambers and fragmentation. These results suggest that polarization is not simply due to biased individuals responding to an influx of media sources in the digital age, but also a consequence of polarized media conditions within an information ecosystem that supports more diverse exposure than is typically thought.

    more » « less
  2. Abstract Motivation

    High-quality computational structural models are now precomputed and available for nearly every protein in UniProt. However, the best way to leverage these models to predict which pairs of proteins interact in a high-throughput manner is not immediately clear. The recent Foldseek method of van Kempen et al. encodes the structural information of distances and angles along the protein backbone into a linear string of the same length as the protein string, using tokens from a 21-letter discretized structural alphabet (3Di).


    We show that using both the amino acid sequence and the 3Di sequence generated by Foldseek as inputs to our recent deep-learning method, Topsy-Turvy, substantially improves the performance of predicting protein–protein interactions cross-species. Thus TT3D (Topsy-Turvy 3D) presents a way to reuse all the computational effort going into producing high-quality structural models from sequence, while being sufficiently lightweight so that high-quality binary protein–protein interaction predictions across all protein pairs can be made genome-wide.

    Availability and Implementation

    TT3D is available at An archived version of the code at time of submission can be found at

    more » « less
  3. Various algorithmic and statistical approaches have been proposed to uncover functionally coherent network motifs consisting of sets of genes that may occur as compensatory pathways (called Between Pathway Modules, or BPMs) in a high-throughput S. Cerevisiae genetic interaction network. We extend our previous Local-Cut/Genecentric method to also make use of a spectral clustering of the physical interaction network, and uncover some interesting new fault-tolerant modules. 
    more » « less
  4. Coral reefs are home to over two million species and provide habitat for roughly 25% of all marine animals, but they are being severely threatened by pollution and climate change. A large amount of genomic, transcriptomic, and other omics data is becoming increasingly available from different species of reef-building corals, the unicellular dinoflagellates, and the coral microbiome (bacteria, archaea, viruses, fungi, etc.). Such new data present an opportunity for bioinformatics researchers and computational biologists to contribute to a timely, compelling, and urgent investigation of critical factors that influence reef health and resilience. 
    more » « less
  5. Remote scientific collaborations have been pivotal in generating scientific discoveries and breakthroughs that accelerate research in many fields. Emerging VR applications for remote work, which utilize commercially available head-mounted displays (HMDs), offer the promise to enhance collaboration, through spatial and embodied experiences. However, there is little evidence on how professionals in general, and scientists in particular, could use existing commercial VR applications to support their cognitive and creative collaborative processes while exploring real-world data as part of day-to-day collaborative work. In this paper, we present findings from an empirical study with 14 coral reef scientists, examining how they chose to utilize available resources in existing virtual environments for their ongoing data-driven collaborative research. We shed light on scientists’ data organization practices, identify affordances unique to VR for supporting cognition in a collaborative setting, and highlight design requirements for supporting cognitive and creative collaboration processes in future tools. 
    more » « less
  6. Abstract

    The application of established cell viability assays such as the commonly used trypan blue staining method to coral cells is not straightforward due to different culture parameters and different cellular features specific to mammalian cells compared to marine invertebrates. UsingPocillopora damicornisas a model, we characterized the autofluorescence and tested different fluorescent dye pair combinations to identify alternative viability indicators. The cytotoxicity of different representative molecules, namely small organic molecules, proteins and nanoparticles (NP), was measured after 24 h of exposure using the fluorescent dye pair Hoechst 33342 and SYTOX orange. Our results show that this dye pair can be distinctly measured in the presence of fluorescent proteins plus chlorophyll.P. damicorniscells exposed for 24 h to Triton-X100, insulin or titanium dioxide (TiO2) NPs, respectively, at concentrations ranging from 0.5 to 100 µg/mL, revealed a LC50 of 0.46 µg/mL for Triton-X100, 6.21 µg/mL for TiO2NPs and 33.9 µg/mL for insulin. This work presents the approach used to customize dye pairs for membrane integrity-based cell viability assays considering the species- and genotype-specific autofluorescence of scleractinian corals, namely: endogenous fluorescence characterization followed by the selection of dyes that do not overlap with endogenous signals.

    more » « less
  7. Abstract Summary

    Computational methods to predict protein–protein interaction (PPI) typically segregate into sequence-based ‘bottom-up’ methods that infer properties from the characteristics of the individual protein sequences, or global ‘top-down’ methods that infer properties from the pattern of already known PPIs in the species of interest. However, a way to incorporate top-down insights into sequence-based bottom-up PPI prediction methods has been elusive. We thus introduce Topsy-Turvy, a method that newly synthesizes both views in a sequence-based, multi-scale, deep-learning model for PPI prediction. While Topsy-Turvy makes predictions using only sequence data, during the training phase it takes a transfer-learning approach by incorporating patterns from both global and molecular-level views of protein interaction. In a cross-species context, we show it achieves state-of-the-art performance, offering the ability to perform genome-scale, interpretable PPI prediction for non-model organisms with no existing experimental PPI data. In species with available experimental PPI data, we further present a Topsy-Turvy hybrid (TT-Hybrid) model which integrates Topsy-Turvy with a purely network-based model for link prediction that provides information about species-specific network rewiring. TT-Hybrid makes accurate predictions for both well- and sparsely-characterized proteins, outperforming both its constituent components as well as other state-of-the-art PPI prediction methods. Furthermore, running Topsy-Turvy and TT-Hybrid screens is feasible for whole genomes, and thus these methods scale to settings where other methods (e.g. AlphaFold-Multimer) might be infeasible. The generalizability, accuracy and genome-level scalability of Topsy-Turvy and TT-Hybrid unlocks a more comprehensive map of protein interaction and organization in both model and non-model organisms.

    Availability and implementation

    Supplementary information

    Supplementary data are available at Bioinformatics online.

    more » « less
  8. Cremonini, Marco (Ed.)
    Understanding the spread of false or dangerous beliefs—often called misinformation or disinformation—through a population has never seemed so urgent. Network science researchers have often taken a page from epidemiologists, and modeled the spread of false beliefs as similar to how a disease spreads through a social network. However, absent from those disease-inspired models is an internal model of an individual’s set of current beliefs, where cognitive science has increasingly documented how the interaction between mental models and incoming messages seems to be crucially important for their adoption or rejection. Some computational social science modelers analyze agent-based models where individuals do have simulated cognition, but they often lack the strengths of network science, namely in empirically-driven network structures. We introduce a cognitive cascade model that combines a network science belief cascade approach with an internal cognitive model of the individual agents as in opinion diffusion models as a public opinion diffusion (POD) model, adding media institutions as agents which begin opinion cascades. We show that the model, even with a very simplistic belief function to capture cognitive effects cited in disinformation study (dissonance and exposure), adds expressive power over existing cascade models. We conduct an analysis of the cognitive cascade model with our simple cognitive function across various graph topologies and institutional messaging patterns. We argue from our results that population-level aggregate outcomes of the model qualitatively match what has been reported in COVID-related public opinion polls, and that the model dynamics lend insights as to how to address the spread of problematic beliefs. The overall model sets up a framework with which social science misinformation researchers and computational opinion diffusion modelers can join forces to understand, and hopefully learn how to best counter, the spread of disinformation and “alternative facts.” 
    more » « less