Showing papers by "University of Rochester published in 2022"

Characterizing the Fast Radio Burst Host Galaxy Population and its Connection to Transients in the Local and Extragalactic Universe

Abstract: We present the localization and host galaxies of one repeating and two apparently non-repeating Fast Radio Bursts. FRB20180301A was detected and localized with the Karl G. Jansky Very Large Array to a star-forming galaxy at $z=0.3304$. FRB20191228A, and FRB20200906A were detected and localized by the Australian Square Kilometre Array Pathfinder to host galaxies at $z=0.2430$ and $z=0.3688$, respectively. We combine these with 13 other well-localized FRBs in the literature, and analyze the host galaxy properties. We find no significant differences in the host properties of repeating and apparently non-repeating FRBs. FRB hosts are moderately star-forming, with masses slightly offset from the star-forming main-sequence. Star formation and low-ionization nuclear emission-line region (LINER) emission are major sources of ionization in FRB host galaxies, with the former dominant in repeating FRB hosts. FRB hosts do not track stellar mass and star formation as seen in field galaxies (more than 95% confidence). FRBs are rare in massive red galaxies, suggesting that progenitor formation channels are not solely dominated by delayed channels which lag star formation by Gigayears. The global properties of FRB hosts are indistinguishable from core-collapse supernovae (CCSNe) and short gamma-ray bursts (SGRBs) hosts, and the spatial offset (from galaxy centers) of FRBs is mostly inconsistent with that of the Galactic neutron star population (95% confidence). The spatial offsets of FRBs (normalized to the galaxy effective radius) also differ from those of globular clusters (GCs) in late- and early-type galaxies with 95% confidence.

Social media study of public opinions on potential COVID-19 vaccines: informing dissent, disparities, and dissemination

Abstract: Background The current development of vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unprecedented. Little is known, however, about the nuanced public opinions on the vaccines on social media. Methods We adopted a human-guided machine learning framework using more than six million tweets from almost two million unique Twitter users to capture public opinions on the vaccines for SARS-CoV-2, classifying them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine. After feature inference and opinion mining, 10,945 unique Twitter users were included in the study population. Multinomial logistic regression and counterfactual analysis were conducted. Results Socioeconomically disadvantaged groups were more likely to hold polarized opinions on coronavirus disease 2019 (COVID-19) vaccines, either pro-vaccine ( B=0.40,SE=0.08,P<0.001,OR=1.49;95%CI=1.26--1.75 ) or anti-vaccine ( B=0.52,SE=0.06,P<0.001,OR=1.69;95%CI=1.49--1.91 ). People who have the worst personal pandemic experience were more likely to hold the anti-vaccine opinion ( B=-0.18,SE=0.04,P<0.001,OR=0.84;95%CI=0.77--0.90 ). The United States public is most concerned about the safety, effectiveness, and political issues regarding vaccines for COVID-19, and improving personal pandemic experience increases the vaccine acceptance level. Conclusion Opinion on COVID-19 vaccine uptake varies across people of different characteristics.

Opportunistic experiments to constrain aerosol effective radiative forcing

Abstract: Aerosol-cloud interactions (ACIs) are considered to be the most uncertain driver of present-day radiative forcing due to human activities. The nonlinearity of cloud-state changes to aerosol perturbations make it challenging to attribute causality in observed relationships of aerosol radiative forcing. Using correlations to infer causality can be challenging when meteorological variability also drives both aerosol and cloud changes independently. Natural and anthropogenic aerosol perturbations from well-defined sources provide "opportunistic experiments" (also known as natural experiments) to investigate ACI in cases where causality may be more confidently inferred. These perturbations cover a wide range of locations and spatiotemporal scales, including point sources such as volcanic eruptions or industrial sources, plumes from biomass burning or forest fires, and tracks from individual ships or shipping corridors. We review the different experimental conditions and conduct a synthesis of the available satellite datasets and field campaigns to place these opportunistic experiments on a common footing, facilitating new insights and a clearer understanding of key uncertainties in aerosol radiative forcing. Cloud albedo perturbations are strongly sensitive to background meteorological conditions. Strong liquid water path increases due to aerosol perturbations are largely ruled out by averaging across experiments. Opportunistic experiments have significantly improved process-level understanding of ACI, but it remains unclear how reliably the relationships found can be scaled to the global level, thus demonstrating a need for deeper investigation in order to improve assessments of aerosol radiative forcing and climate change.

Secondary structure prediction for RNA sequences including N6-methyladenosine

Abstract: There is increasing interest in the roles of covalently modified nucleotides in RNA. There has been, however, an inability to account for modifications in secondary structure prediction because of a lack of software and thermodynamic parameters. We report the solution for these issues for N6-methyladenosine (m6A), allowing secondary structure prediction for an alphabet of A, C, G, U, and m6A. The RNAstructure software now works with user-defined nucleotide alphabets of any size. We also report a set of nearest neighbor parameters for helices and loops containing m6A, using experiments. Interestingly, N6-methylation decreases folding stability for adenosines in the middle of a helix, has little effect on folding stability for adenosines at the ends of helices, and increases folding stability for unpaired adenosines stacked on a helix. We demonstrate predictions for an N6-methylation-activated protein recognition site from MALAT1 and human transcriptome-wide effects of N6-methylation on the probability of adenosine being buried in a helix.

Efficient suppression of endogenous CFTR nonsense mutations using anticodon-engineered transfer RNAs

Abstract: Nonsense mutations or premature termination codons (PTCs) comprise ∼11% of all genetic lesions, which result in over 7,000 distinct genetic diseases. Due to their outsized impact on human health, considerable effort has been made to find therapies for nonsense-associated diseases. Suppressor tRNAs have long been identified as a possible therapeutic for nonsense-associated diseases; however, their ability to inhibit nonsense-mediated mRNA decay (NMD) and support significant protein translation from endogenous transcripts has not been determined in mammalian cells. Here, we investigated the ability of anticodon edited (ACE)-tRNAs to suppress cystic fibrosis (CF) causing PTCs in the cystic fibrosis transmembrane regulator (CFTR) gene in gene-edited immortalized human bronchial epithelial (16HBEge) cells. Delivery of ACE-tRNAs to 16HBEge cells harboring three common CF mutations G542XUGA-, R1162XUGA-, and W1282XUGA-CFTR PTCs significantly inhibited NMD and rescued endogenous mRNA expression. Furthermore, delivery of our highly active leucine-encoding ACE-tRNA resulted in rescue of W1282X-CFTR channel function to levels that significantly exceed the necessary CFTR channel function for therapeutic relevance. This study establishes the ACE-tRNA approach as a potential standalone therapeutic for nonsense-associated diseases due to its ability to rescue both mRNA and full-length protein expression from PTC-containing endogenous genes.

A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations

Abstract: Lending decisions are usually made with proprietary models that provide minimally acceptable explanations to users. In a future world without such secrecy, what decision support tools would one want to use for justified lending decisions? This question is timely, since the economy has dramatically shifted due to a pandemic, and a massive number of new loans will be necessary in the short term. We propose a framework for such decisions, including a globally interpretable machine learning model, an interactive visualization of it, and several types of summaries and explanations for any given decision. The machine learning model is a two-layer additive risk model, which resembles a two-layer neural network, but is decomposable into subscales. In this model, each node in the first (hidden) layer represents a meaningful subscale model, and all of the nonlinearities are transparent. Our online visualization tool allows exploration of this model, showing precisely how it came to its conclusion. We provide three types of explanations that are simpler than, but consistent with, the global model: case-based reasoning explanations that use neighboring past cases, a set of features that were the most important for the model's prediction, and summary-explanations that provide a customized sparse explanation for any particular lending decision made by the model. Our framework earned the FICO recognition award for the Explainable Machine Learning Challenge, which was the first public challenge in the domain of explainable machine learning. 1

Mechanical Properties and Strain Transfer Behavior of Molybdenum Ditelluride (MoTe2) Thin Films

Abstract: Transition metal dichalcogenides (TMDs) offer superior properties over conventional materials in many areas such as in electronic devices. In recent years, TMDs have been shown to display a phase switching mechanism under the application of external mechanical strain, making them exciting candidates for phase change transistors. Molybdenum ditelluride (MoTe2) is one such material that has been engineered as a strain-based phase change transistor. In this work, we explore various aspects of the mechanical properties of this material by a suite of computational and experimental approaches. First, we present parameterization of an interatomic potential for modeling monolayer as well as multilayered MoTe2 films. For generating the empirical potential parameter set, we fit results from density functional theory calculations using a random search algorithm known as particle swarm optimization. The potential closely predicts structural properties, elastic constants, and vibrational frequencies of MoTe2 indicating a reliable fit. Our simulated mechanical response matches earlier larger scale experimental nanoindentation results with excellent prediction of fracture points. Simulation of uniaxial tensile deformation by molecular dynamics shows the complete non-linear stress-strain response up to failure. Mechanical behavior, including failure properties, exhibits directional anisotropy due to the variation of bond alignments with crystal orientation. Furthermore, we show the deterioration of mechanical properties with increasing temperature. Finally, we present computational and experimental evidence of an extended c-axis strain transfer length in MoTe2 compared to TMDs with smaller chalcogen atoms.

DS-ADMM++: A Novel Distributed Quantized ADMM to Speed up Differentially Private Matrix Factorization

Abstract: Matrix factorization is a powerful method to implement collaborative filtering recommender systems. This article addresses two major challenges, privacy and efficiency, which matrix factorization is facing. We based our work on DS-ADMM, a distributed matrix factorization algorithm with decent efficiency, to achieve the following two pieces of work: (1) Integrated local differential privacy paradigm into DS-ADMM to provide the privacy-preserving property; (2) Introduced a stochastic quantized function to reduce transmission overheads in ADMM to further improve efficiency. We named our work DS-ADMM++, in which one ’+’ refers to differential privacy, and the other ’+’ refers to quantized techniques. DS-ADMM++ is the first to perform efficient and private matrix factorization under the scenarios of differential privacy and DS-ADMM. We conducted experiments with benchmark data sets to demonstrate that our approach provides differential privacy and excellent scalability with a decent loss of accuracy.

The prevalence of comorbid chronic pain conditions among patients with temporomandibular disorders

Abstract: This systematic review was designed to evaluate the presence of comorbid conditions among patients with temporomandibular disorders (TMDs).The authors reviewed studies that reported the prevalence or incidence of chronic pain conditions or psychiatric disorders (anxiety, mood, personality disorders) among patients with any type of TMD. The authors calculated sample size-weighted prevalence estimates when data were reported in 2 or more studies for the same comorbid condition.A total of 9 prevalence studies and no incidence studies were eligible for review; 8 of the studies examined chronic pain comorbidities. Weighted estimates showed high prevalence of pain comorbidities across studies, including current chronic back pain (66%), myofascial syndrome (50%), chronic stomach pain (50%), chronic migraine headache (40%), irritable bowel syndrome (19%), and fibromyalgia (14%). A single study examined psychiatric disorders and found that current depression was the most prevalent disorder identified (17.5%).There is a high prevalence of comorbid chronic pain conditions among patients with TMDs, with more than 50% of patients reporting chronic back pain, myofascial syndrome, and chronic stomach pain. Psychiatric disorders among patients with different types of TMDs were studied less commonly in this pain population. Knowledge of the distribution of these and other comorbid disease conditions among patients with different types of TMDs can help dentists and other health care providers to identify personalized treatment strategies, including the coordination of care across medical specialties.

Nuclear Magnetic Resonance Spectra and AMBER OL3 and ROC-RNA Simulations of UCUCGU Reveal Force Field Strengths and Weaknesses for Single-Stranded RNA

Abstract: Single-stranded regions of RNA are important for folding of sequences into 3D structures and for design of therapeutics targeting RNA. Prediction of ensembles of 3D structures for single-stranded regions often involves classical mechanical approximations of interactions defined by quantum mechanical calculations on small model systems. Nuclear magnetic resonance (NMR) spectra and molecular dynamics (MD) simulations of short single strands provide tests for how well the approximations model many of the interactions. Here, the NMR spectra for UCUCGU at 2, 15, and 30 °C are compared to simulations with the AMBER force fields, OL3 and ROC-RNA. This is the first such comparison to an oligoribonucleotide containing an internal guanosine nucleotide (G). G is particularly interesting because of its many H-bonding groups, large dipole moment, and proclivity for both syn and anti conformations. Results reveal formation of a G amino to phosphate non-bridging oxygen H-bond. The results also demonstrate dramatic differences in details of the predicted structures. The variations emphasize the dependence of predictions on individual parameters and their balance with the rest of the force field. The NMR data can serve as a benchmark for future force fields.

Harmful practices prevail despite legal knowledge: a mixed-method study on the paradox of child marriage in Bangladesh

Abstract: Child marriage is a globally recognised human rights violation that disproportionately affects girls, especially in developing countries. It has serious negative consequences on girls' physical, mental, sexual, and reproductive health and rights. Although well-pronounced laws against child marriage were enacted in Bangladesh, the practice remains a significant challenge. Lack of law enforcement and persistent social norms ultimately allow child marriage to persist around the country. Social norms have an impact on the prevalent attitudes toward child marriage. Therefore, this mixed-method study aimed to explore the legal knowledge, perception, and practice of child marriage in Bangladesh. This study was part of a broader evaluation of a UNICEF media programme. Adolescent boys and girls aged between 10 and 19 years and their parents were interviewed in three Bangladeshi districts. All the respondents were aware of the legal age of marriage and knew that child marriage is punishable by law. This study illuminated the reasons, including early marriage among boys, poverty, dowry, and sexual harassment. Communities and policymakers need to be engaged to trigger larger structural and cultural changes to remedy the harmful social norm and its practice.

Sim2real Learning of Obstacle Avoidance for Robotic Manipulators in Uncertain Environments

Abstract: Obstacle avoidance for robotic manipulators can be challenging when they operate in unstructured environments. This problem is probed with the sim-to-real (sim2real) deep reinforcement learning, such that a moving policy of the robotic arm is learnt in a simulator and then adapted to the real world. However, the problem of sim2real adaptation is notoriously difficult. To this end, this work proposes (1) a unified representation of obstacles and targets to capture the underlying dynamics of the environment while allowing generalization to unseen goals and (2) a flexible end-to-end model combining the unified representation with the deep reinforcement learning control module that can be trained by interacting with the environment. Such a representation is agnostic to the shape and appearance of the underlying objects, which simplifies and unifies the scene representation in both simulated and real worlds. We implement this idea with a vision-based actor-critic framework by devising a bounding box predictor module. The predictor estimates the 3D bounding boxes of obstacles and targets from the RGB-D input. The features extracted by the predictor are fed into the policy network, and all the modules are jointly trained. This makes the policy learn object-aware scene representation, which leads to a data-efficient learning of the obstacle avoidance policy. Our experiments in simulated environment and the real-world show that the end-to-end model of the unified representation achieves better sim2real adaption and scene generalization than state-of-the-art techniques.