Showing papers by "SRI International published in 2021"
TL;DR: In this paper, the authors show that the choice of pathway database used in enrichment analyses can have a much stronger effect on the enrichment results than the statistical corrections used in these analyses, and they demonstrate that to attain a given enrichment p-value, KEGG-based enrichment analyses require 1.3-2.0 times as many significantly expressed genes as does EcoCyc-based analyses.
Abstract: Enrichment or over-representation analysis is a common method used in bioinformatics studies of transcriptomics, metabolomics, and microbiome datasets. The key idea behind enrichment analysis is: given a set of significantly expressed genes (or metabolites), use that set to infer a smaller set of perturbed biological pathways or processes, in which those genes (or metabolites) play a role. Enrichment computations rely on collections of defined biological pathways and/or processes, which are usually drawn from pathway databases. Although practitioners of enrichment analysis take great care to employ statistical corrections (e.g., for multiple testing), they appear unaware that enrichment results are quite sensitive to the pathway definitions that the calculation uses. We show that alternative pathway definitions can alter enrichment p-values by up to nine orders of magnitude, whereas statistical corrections typically alter enrichment p-values by only two orders of magnitude. We present multiple examples where the smaller pathway definitions used in the EcoCyc database produces stronger enrichment p-values than the much larger pathway definitions used in the KEGG database; we demonstrate that to attain a given enrichment p-value, KEGG-based enrichment analyses require 1.3–2.0 times as many significantly expressed genes as does EcoCyc-based enrichment analyses. The large pathways in KEGG are problematic for another reason: they blur together multiple (as many as 21) biological processes. When such a KEGG pathway receives a high enrichment p-value, which of its component processes is perturbed is unclear, and thus the biological conclusions drawn from enrichment of large pathways are also in question. The choice of pathway database used in enrichment analyses can have a much stronger effect on the enrichment results than the statistical corrections used in these analyses.
102 citations
TL;DR: In this paper, a new and improved version (V4.0) of the NASA standard NO 2 product from the Ozone Monitoring Instrument (OMI) on the Paula satellite is presented, which enhances the NO 2 data quality through improvements to the air mass factors used in the retrieval algorithm.
Abstract: . We present a new and improved version (V4.0) of the NASA standard nitrogen
dioxide (NO 2 ) product from the Ozone Monitoring Instrument (OMI) on the
Aura satellite. This version incorporates the most salient improvements for
OMI NO 2 products suggested by expert users and enhances the NO 2
data quality in several ways through improvements to the air mass factors
(AMFs) used in the retrieval algorithm. The algorithm is based on
the geometry-dependent surface Lambertian equivalent reflectivity (GLER)
operational product that is available on an OMI pixel basis. GLER is
calculated using the vector linearized discrete ordinate radiative transfer
(VLIDORT) model, which uses as input high-resolution bidirectional
reflectance distribution function (BRDF) information from NASA's Aqua
Moderate Resolution Imaging Spectroradiometer (MODIS) instruments over land
and the wind-dependent Cox–Munk wave-facet slope distribution over water,
the latter with a contribution from the water-leaving radiance. The GLER
combined with consistently retrieved oxygen dimer (O 2 –O 2 )
absorption-based effective cloud fraction (ECF) and optical centroid
pressure (OCP) provide improved information to the new NO 2 AMF
calculations. The new AMFs increase the retrieved tropospheric NO 2 by
up to 50 % in highly polluted areas; these differences arise from both
cloud and surface BRDF effects as well as biases between the new MODIS-based
and previously used OMI-based climatological surface reflectance data sets.
We quantitatively evaluate the new NO 2 product using independent
observations from ground-based and airborne instruments. The new V4.0 data
and relevant explanatory documentation are publicly available from the NASA
Goddard Earth Sciences Data and Information Services Center ( https://disc.gsfc.nasa.gov/datasets/OMNO2_V003/summary/ , last access: 8 November 2020),
and we encourage their use over previous versions of OMI NO 2 products.
78 citations
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TL;DR: The Risks Forum addresses problems relating to software, hardware, people, and other circumstances relating to computer systems.
Abstract: Edited by PGN (Risks Forum Moderator, with contribu- tions by others as indicated. Opinions are individual rather than organizational, with usual disclaimers implied. We ad- dress problems relating to software, hardware, people, and other circumstances relevant to computer systems. Ref- erences (R i j) to the online Risks Forum denote RISKS vol i number j. Cited RISKS items generally identify contributors and sources, together with URLs. Official RISKS archives are available at www.risks.org, with nice html formatting and search engine courtesy of Lindsay Mar- shall at Newcastle:; http://catless.ncl.ac.uk/Risks/i.j.html (also ftp://www.sri.com/risks). CACM Inside Risks: http://www.csl.sri.com/neumann/insiderisks.html
54 citations
28 Jun 2021
TL;DR: The MadDE algorithm as discussed by the authors leverages the power of the multiple adaptation strategy with respect to the control parameters and search mechanisms and is tested on the benchmark functions taken from the CEC 2021 special session & competition on single-objective bound-constrained optimization.
Abstract: We propose a novel Evolutionary Algorithm (EA) based on the Differential Evolution algorithm for solving global numerical optimization problem in real-valued continuous parameter space. The proposed MadDE algorithm leverages the power of the multiple adaptation strategy with respect to the control parameters and search mechanisms, and is tested on the benchmark functions taken from the CEC 2021 special session & competition on single-objective bound-constrained optimization. Experimental results indicate that MadDE is able to achieve superior performance on global numerical optimization problems when compared against state-of-the-art real-parameter optimizers. We also provide a hyperparameter optimization algorithm SUBHO for improving the search performance of any EA by finding an optimal set of control parameters, and demonstrate its efficacy in enhancing MadDE’s performance on the same benchmark. The source code of our implementation is publicly available at https://github.com/subhodipbiswas/MadDE.
53 citations
TL;DR: In this paper, a genome-scale metabolic pathway database of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants, was generated and evaluated using a semi-automated validation pipeline.
Abstract: To understand and engineer plant metabolism, we need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we generated genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants (https://plantcyc.org). Of these, 104 have not been reported before. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell-type specific metabolic pathways. This work shows the quality and quantity of our resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology. This article is protected by copyright. All rights reserved.
53 citations
TL;DR: In the Adolescent Brain Cognitive Development (ABCD) study as discussed by the authors, the authors report activation patterns from functional MRI (fMRI) tasks completed at baseline, which were designed to measure cognitive impulse control with a stop signal task (SST; N = 5,547), reward anticipation and receipt with a monetary incentive delay (MID) task (N = 6,657), and working memory and emotion reactivity with an emotional N-back (EN-back) task.
Abstract: The Adolescent Brain Cognitive Development (ABCD) Study® is a 10-year longitudinal study of children recruited at ages 9 and 10. A battery of neuroimaging tasks are administered biennially to track neurodevelopment and identify individual differences in brain function. This study reports activation patterns from functional MRI (fMRI) tasks completed at baseline, which were designed to measure cognitive impulse control with a stop signal task (SST; N = 5,547), reward anticipation and receipt with a monetary incentive delay (MID) task (N = 6,657) and working memory and emotion reactivity with an emotional N-back (EN-back) task (N = 6,009). Further, we report the spatial reproducibility of activation patterns by assessing between-group vertex/voxelwise correlations of blood oxygen level-dependent (BOLD) activation. Analyses reveal robust brain activations that are consistent with the published literature, vary across fMRI tasks/contrasts and slightly correlate with individual behavioral performance on the tasks. These results establish the preadolescent brain function baseline, guide interpretation of cross-sectional analyses and will enable the investigation of longitudinal changes during adolescent development. This paper reports activation patterns for fMRI tasks assessing response inhibition, working memory and reward processing obtained at baseline in the longitudinal ABCD Study, providing a reference for research into adolescent brain development.
41 citations
10 Mar 2021
TL;DR: In this paper, the authors used microscale thermophoresis to test the binding of these molecules to the spike protein, and they found that tilorone and pyronaridine inhibited the virus replication in A549-ACE2 cells with IC50 values of 180 nM and IC50 198 nM, respectively.
Abstract: Severe acute respiratory coronavirus 2 (SARS-CoV-2) is a newly identified virus that has resulted in over 2.5 million deaths globally and over 116 million cases globally in March, 2021. Small-molecule inhibitors that reverse disease severity have proven difficult to discover. One of the key approaches that has been widely applied in an effort to speed up the translation of drugs is drug repurposing. A few drugs have shown in vitro activity against Ebola viruses and demonstrated activity against SARS-CoV-2 in vivo. Most notably, the RNA polymerase targeting remdesivir demonstrated activity in vitro and efficacy in the early stage of the disease in humans. Testing other small-molecule drugs that are active against Ebola viruses (EBOVs) would appear a reasonable strategy to evaluate their potential for SARS-CoV-2. We have previously repurposed pyronaridine, tilorone, and quinacrine (from malaria, influenza, and antiprotozoal uses, respectively) as inhibitors of Ebola and Marburg viruses in vitro in HeLa cells and mouse-adapted EBOV in mice in vivo. We have now tested these three drugs in various cell lines (VeroE6, Vero76, Caco-2, Calu-3, A549-ACE2, HUH-7, and monocytes) infected with SARS-CoV-2 as well as other viruses (including MHV and HCoV 229E). The compilation of these results indicated considerable variability in antiviral activity observed across cell lines. We found that tilorone and pyronaridine inhibited the virus replication in A549-ACE2 cells with IC50 values of 180 nM and IC50 198 nM, respectively. We used microscale thermophoresis to test the binding of these molecules to the spike protein, and tilorone and pyronaridine bind to the spike receptor binding domain protein with K d values of 339 and 647 nM, respectively. Human Cmax for pyronaridine and quinacrine is greater than the IC50 observed in A549-ACE2 cells. We also provide novel insights into the mechanism of these compounds which is likely lysosomotropic.
39 citations
University of Southern California1, University of California, Irvine2, University of California, San Diego3, SRI International4, Johns Hopkins University5, Washington University in St. Louis6, Icahn School of Medicine at Mount Sinai7, University of Colorado Boulder8, Yale University9, McGovern Institute for Brain Research10, University of Vermont11, University of Maryland, Baltimore12, University of Pittsburgh13, University of Florida14, National Institutes of Health15, University of California, Los Angeles16, Florida International University17, National Institute on Drug Abuse18, University of Minnesota19, University of Rhode Island20, University of Rochester21, Virginia Commonwealth University22, University of San Diego23, Medical University of South Carolina24, University of Michigan25, University of Utah26, Thomas Jefferson University27, University of Wisconsin–Milwaukee28, University of Tennessee29, Oklahoma State University–Stillwater30, Oregon Health & Science University31, University of Missouri32
TL;DR: Herting et al. as mentioned in this paper examined individual variability between perceived physical features and hormones of pubertal maturation in 9-10-year-old children as a function of sociodemographic characteristics.
Abstract: Author(s): Herting, Megan M; Uban, Kristina A; Gonzalez, Marybel Robledo; Baker, Fiona C; Kan, Eric C; Thompson, Wesley K; Granger, Douglas A; Albaugh, Matthew D; Anokhin, Andrey P; Bagot, Kara S; Banich, Marie T; Barch, Deanna M; Baskin-Sommers, Arielle; Breslin, Florence J; Casey, BJ; Chaarani, Bader; Chang, Linda; Clark, Duncan B; Cloak, Christine C; Constable, R Todd; Cottler, Linda B; Dagher, Rada K; Dapretto, Mirella; Dick, Anthony S; Dosenbach, Nico; Dowling, Gayathri J; Dumas, Julie A; Edwards, Sarah; Ernst, Thomas; Fair, Damien A; Feldstein-Ewing, Sarah W; Freedman, Edward G; Fuemmeler, Bernard F; Garavan, Hugh; Gee, Dylan G; Giedd, Jay N; Glaser, Paul EA; Goldstone, Aimee; Gray, Kevin M; Hawes, Samuel W; Heath, Andrew C; Heitzeg, Mary M; Hewitt, John K; Heyser, Charles J; Hoffman, Elizabeth A; Huber, Rebekah S; Huestis, Marilyn A; Hyde, Luke W; Infante, M Alejandra; Ivanova, Masha Y; Jacobus, Joanna; Jernigan, Terry L; Karcher, Nicole R; Laird, Angela R; LeBlanc, Kimberly H; Lisdahl, Krista; Luciana, Monica; Luna, Beatriz; Maes, Hermine H; Marshall, Andrew T; Mason, Michael J; McGlade, Erin C; Morris, Amanda S; Nagel, Bonnie J; Neigh, Gretchen N; Palmer, Clare E; Paulus, Martin P; Potter, Alexandra S; Puttler, Leon I; Rajapakse, Nishadi; Rapuano, Kristina; Reeves, Gloria; Renshaw, Perry F; Schirda, Claudiu; Sher, Kenneth J; Sheth, Chandni; Shilling, Paul D; Squeglia, Lindsay M; Sutherland, Matthew T; Tapert, Susan F; Tomko, Rachel L; Yurgelun-Todd, Deborah; Wade, Natasha E; Weiss, Susan RB; Zucker, Robert A | Abstract: AimTo examine individual variability between perceived physical features and hormones of pubertal maturation in 9-10-year-old children as a function of sociodemographic characteristics.MethodsCross-sectional metrics of puberty were utilized from the baseline assessment of the Adolescent Brain Cognitive Development (ABCD) Study-a multi-site sample of 9-10 year-olds (n = 11,875)-and included perceived physical features via the pubertal development scale (PDS) and child salivary hormone levels (dehydroepiandrosterone and testosterone in all, and estradiol in females). Multi-level models examined the relationships among sociodemographic measures, physical features, and hormone levels. A group factor analysis (GFA) was implemented to extract latent variables of pubertal maturation that integrated both measures of perceived physical features and hormone levels.ResultsPDS summary scores indicated more males (70%) than females (31%) were prepubertal. Perceived physical features and hormone levels were significantly associated with child's weight status and income, such that more mature scores were observed among children that were overweight/obese or from households with low-income. Results from the GFA identified two latent factors that described individual differences in pubertal maturation among both females and males, with factor 1 driven by higher hormone levels, and factor 2 driven by perceived physical maturation. The correspondence between latent factor 1 scores (hormones) and latent factor 2 scores (perceived physical maturation) revealed synchronous and asynchronous relationships between hormones and concomitant physical features in this large young adolescent sample.ConclusionsSociodemographic measures were associated with both objective hormone and self-report physical measures of pubertal maturation in a large, diverse sample of 9-10 year-olds. The latent variables of pubertal maturation described a complex interplay between perceived physical changes and hormone levels that hallmark sexual maturation, which future studies can examine in relation to trajectories of brain maturation, risk/resilience to substance use, and other mental health outcomes.
31 citations
TL;DR: The authors showed that deletion of CTLA-4 from B-1a cells results in mice that spontaneously develop autoantibodies, T follicular helper (Tfh) cells and germinal centers (GCs) in the spleen, and autoimmune pathology later in life.
Abstract: CTLA-4 is an important regulator of T-cell function. Here, we report that expression of this immune-regulator in mouse B-1a cells has a critical function in maintaining self-tolerance by regulating these early-developing B cells that express a repertoire enriched for auto-reactivity. Selective deletion of CTLA-4 from B cells results in mice that spontaneously develop autoantibodies, T follicular helper (Tfh) cells and germinal centers (GCs) in the spleen, and autoimmune pathology later in life. This impaired immune homeostasis results from B-1a cell dysfunction upon loss of CTLA-4. Therefore, CTLA-4-deficient B-1a cells up-regulate epigenetic and transcriptional activation programs and show increased self-replenishment. These activated cells further internalize surface IgM, differentiate into antigen-presenting cells and, when reconstituted in normal IgH-allotype congenic recipient mice, induce GCs and Tfh cells expressing a highly selected repertoire. These findings show that CTLA-4 regulation of B-1a cells is a crucial immune-regulatory mechanism.
28 citations
TL;DR: In this article, the authors designed the optimal properties for a well-tolerated M1-agonist with the potential to alleviate cognitive loss by taking a stepwise translational approach from atomic structure, cell/tissuebased assays, evaluation in preclinical species, clinical safety testing, and finally establishing activity in memory centers in humans.
27 citations
01 Jun 2021
TL;DR: Zhang et al. as discussed by the authors proposed a depth-aware mirror segmentation method that leverages depth estimates from ToF-based cameras as an additional cue to disambiguate challenging cases where the contrast or relation in RGB colors between the mirror reflection and the surrounding scene is subtle.
Abstract: We present a novel mirror segmentation method that leverages depth estimates from ToF-based cameras as an additional cue to disambiguate challenging cases where the contrast or relation in RGB colors between the mirror reflection and the surrounding scene is subtle. A key observation is that ToF depth estimates do not report the true depth of the mirror surface, but instead return the total length of the reflected light paths, thereby creating obvious depth dis-continuities at the mirror boundaries. To exploit depth information in mirror segmentation, we first construct a large-scale RGB-D mirror segmentation dataset, which we subsequently employ to train a novel depth-aware mirror segmentation framework. Our mirror segmentation framework first locates the mirrors based on color and depth discontinuities and correlations. Next, our model further refines the mirror boundaries through contextual contrast taking into account both color and depth information. We extensively validate our depth-aware mirror segmentation method and demonstrate that our model outperforms state-of-the-art RGB and RGB-D based methods for mirror segmentation. Experimental results also show that depth is a powerful cue for mirror segmentation.
University of Michigan1, Sandia National Laboratories2, University of Wisconsin-Madison3, University of Ulsan4, SRI International5, National University of Singapore6, ETH Zurich7, University of Florence8, University of California, Irvine9, Princeton University10, Federal University of Rio de Janeiro11, University for Foreigners Perugia12, Arizona State University13, University of Washington14, Pacific Northwest National Laboratory15, Georgia Institute of Technology16
TL;DR: This work presents a framework for real‐time flood modeling and uncertainty quantification that combines the physics of fluid motion with advances in probabilistic methods and has the potential to improve flood prediction and analysis and can be extended to other hazard assessments requiring intense high‐fidelity computations in real-time.
28 Jun 2021
TL;DR: In this paper, a margin loss was proposed to regularize the similarity in relationships of the representations across subjects and modalities, and a conditional convolution was used to design a single model for encoding images of all modalities.
Abstract: Multi-modal MRIs are widely used in neuroimaging applications since different MR sequences provide complementary information about brain structures. Recent works have suggested that multi-modal deep learning analysis can benefit from explicitly disentangling anatomical (shape) and modality (appearance) information into separate image presentations. In this work, we challenge mainstream strategies by showing that they do not naturally lead to representation disentanglement both in theory and in practice. To address this issue, we propose a margin loss that regularizes the similarity in relationships of the representations across subjects and modalities. To enable robust training, we further use a conditional convolution to design a single model for encoding images of all modalities. Lastly, we propose a fusion function to combine the disentangled anatomical representations as a set of modality-invariant features for downstream tasks. We evaluate the proposed method on three multi-modal neuroimaging datasets. Experiments show that our proposed method can achieve superior disentangled representations compared to existing disentanglement strategies. Results also indicate that the fused anatomical representation has potential in the downstream task of zero-dose PET reconstruction and brain tumor segmentation. The code is available at https://github.com/ouyangjiahong/representation-disentanglement.
University of California, San Francisco1, University of California, San Diego2, Stanford University3, Washington University in St. Louis4, Georgia State University5, Anschutz Medical Campus6, University of Southern California7, University of Missouri8, University of Michigan9, Oregon Health & Science University10, University of Colorado Boulder11, Medical University of South Carolina12, Virginia Commonwealth University13, University of Utah14, University of Maryland, Baltimore15, Texas A&M University16, National Institutes of Health17, Yale University18, Florida International University19, SRI International20, Icahn School of Medicine at Mount Sinai21, McGovern Institute for Brain Research22, University of Chicago23, University of California, Los Angeles24, University of California, Irvine25, University of Florida26, University of Minnesota27, University of Pittsburgh28, University of Rochester29, University of Tennessee30, University of Vermont31
TL;DR: In the case of brain magnetic resonance imaging (MRI), reliable data about the prevalence and significance of incidental findings in the general population are limited, making it difficult to anticipate, communicate, and manage these findings as mentioned in this paper.
Abstract: Importance Incidental findings (IFs) are unexpected abnormalities discovered during imaging and can range from normal anatomic variants to findings requiring urgent medical intervention. In the case of brain magnetic resonance imaging (MRI), reliable data about the prevalence and significance of IFs in the general population are limited, making it difficult to anticipate, communicate, and manage these findings. Objectives To determine the overall prevalence of IFs in brain MRI in the nonclinical pediatric population as well as the rates of specific findings and findings for which clinical referral is recommended. Design, setting, and participants This cohort study was based on the April 2019 release of baseline data from 11 810 children aged 9 to 10 years who were enrolled and completed baseline neuroimaging in the Adolescent Brain Cognitive Development (ABCD) study, the largest US population-based longitudinal observational study of brain development and child health, between September 1, 2016, and November 15, 2018. Participants were enrolled at 21 sites across the US designed to mirror the demographic characteristics of the US population. Baseline structural MRIs were centrally reviewed for IFs by board-certified neuroradiologists and findings were described and categorized (category 1, no abnormal findings; 2, no referral recommended; 3; consider referral; and 4, consider immediate referral). Children were enrolled through a broad school-based recruitment process in which all children of eligible age at selected schools were invited to participate. Exclusion criteria were severe sensory, intellectual, medical, or neurologic disorders that would preclude or interfere with study participation. During the enrollment process, demographic data were monitored to ensure that the study met targets for sex, socioeconomic, ethnic, and racial diversity. Data were analyzed from March 15, 2018, to November 20, 2020. Main outcomes and measures Percentage of children with IFs in each category and prevalence of specific IFs. Results A total of 11 679 children (52.1% boys, mean [SD] age, 9.9 [0.62] years) had interpretable baseline structural MRI results. Of these, 2464 participants (21.1%) had IFs, including 2013 children (17.2%) assigned to category 2, 431 (3.7%) assigned to category 3, and 20 (0.2%) assigned to category 4. Overall rates of IFs did not differ significantly between singleton and twin gestations or between monozygotic and dizygotic twins, but heritability analysis showed heritability for the presence or absence of IFs (h2 = 0.260; 95% CI, 0.135-0.387). Conclusions and relevance Incidental findings in brain MRI and findings with potential clinical significance are both common in the general pediatric population. By assessing IFs and concurrent developmental and health measures and following these findings over the longitudinal study course, the ABCD study has the potential to determine the significance of many common IFs.
TL;DR: In this article, a Bayesian machine learning model was used to predict the best compounds for SARS-CoV-2 in vitro inhibition data and used them to prioritize additional FDA-approved compounds for in vitro testing selected from our in-house compound library.
Abstract: With the rapidly evolving SARS-CoV-2 variants of concern, there is an urgent need for the discovery of further treatments for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need, and numerous compounds have already been selected for in vitro testing by several groups. These have led to a growing database of molecules with in vitro activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein, we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2 in vitro inhibition data and used them to prioritize additional FDA-approved compounds for in vitro testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, lumefantrine, an antimalarial was selected for testing and showed limited antiviral activity in cell-based assays while demonstrating binding (Kd 259 nM) to the spike protein using microscale thermophoresis. Several other compounds which we prioritized have since been tested by others and were also found to be active in vitro. This combined machine learning and in vitro testing approach can be expanded to virtually screen available molecules with predicted activity against SARS-CoV-2 reference WIV04 strain and circulating variants of concern. In the process of this work, we have created multiple iterations of machine learning models that can be used as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 with over 500 compounds is now freely available at www.assaycentral.org.
TL;DR: Li et al. as mentioned in this paper proposed Longitudinal Self-Supervised Learning (LSSL) to identify changes and consistencies across the multiple MRIs acquired of each individual over time by formulating a multivariate mapping between factors (e.g., brain age) associated with an MRI and a latent image representation.
01 Jan 2021
TL;DR: Although ALchemist does not require instrumentation, it is highly effective in partitioning execution to autonomous tasks (in order to avoid bogus dependencies) and deriving precise attack provenance graphs, with very small overhead.
Abstract: Cyber-attacks are becoming more persistent and complex. Most state-of-the-art attack forensics techniques either require annotating and instrumenting software applications or rely on high quality execution profiling to serve as the basis for anomaly detection. We propose a novel attack forensics technique ALchemist. It is based on the observations that builtin application logs provide critical high-level semantics and audit logs provide low-level fine-grained information; and the two share a lot of common elements. ALchemist is hence a log fusion technique that couples application logs and audit logs to derive critical attack information invisible in either log. It is based on a relational reasoning engine Datalog and features the capabilities of inferring new relations such as the task structure of execution (e.g., tabs in firefox), especially in the presence of complex asynchronous execution models, and high-level dependencies between log events. Our evaluation on 15 popular applications including firefox, Chromium, and OpenOffice, and 14 APT attacks from the literature demonstrates that although ALchemist does not require instrumentation, it is highly effective in partitioning execution to autonomous tasks (in order to avoid bogus dependencies) and deriving precise attack provenance graphs, with very small overhead. It also outperforms NoDoze and OmegaLog, two stateof-the-art techniques that do not require instrumentation.
TL;DR: While animal models have greatly informed understanding of this fascinating disorder and the role of the hypocretin/orexin system in sleep/wake control, the question of why these neurons degenerate in human narcolepsy is only beginning to be understood.
Abstract: Animal models have advanced not only our understanding of the etiology and phenotype of the sleep disorder narcolepsy but have also informed sleep/wake regulation more generally. The identification of an inheritable narcolepsy phenotype in dogs in the 1970s allowed the establishment of a breeding colony at Stanford University, resulting in studies that provided the first insights into the genetics and neurotransmitter systems that underlie cataplexy and rapid-eye movement sleep atonia. Although the discovery of the hypocretin/orexin neuropeptides in 1998 initially seemed unrelated to sleep/wake control, the description of the phenotype of the prepro-orexin knockout (KO) mouse as strongly resembling cataplexy, the pathognomonic symptom of narcolepsy, along with identification of a mutation in hypocretin receptor-2 gene as the source of canine narcolepsy, unequivocally established the relationship between this system and narcolepsy. The subsequent discovery of hypocretin neuron degeneration in human narcolepsy demystified a disorder whose etiology had been unknown since its initial description 120 years earlier. These breakthroughs prompted the development of numerous other animal models that have allowed manipulation of the hypocretin/orexin system, thereby advancing our understanding of sleep/wake circuitry. While animal models have greatly informed understanding of this fascinating disorder and the role of the hypocretin/orexin system in sleep/wake control, the question of why these neurons degenerate in human narcolepsy is only beginning to be understood. The development of new immune-mediated narcolepsy models are likely to further inform the etiology of this sleep disorder and animal models will undoubtedly play a critical role in the development of novel narcolepsy therapeutics.
30 May 2021
TL;DR: In this article, a top-down egocentric map representation is proposed to encode vital scene semantics such as traversable paths, unexplored areas, and observed scene objects along with raw visual streams such as RGB, depth, and semantic segmentation masks.
Abstract: Visual navigation for autonomous agents is a core task in the fields of computer vision and robotics. Learning-based methods, such as deep reinforcement learning, have the potential to outperform the classical solutions developed for this task; however, they come at a significantly increased computational load. Through this work, we design a novel approach that focuses on performing better or comparable to the existing learning-based solutions but under a clear time/computational budget. To this end, we propose a method to encode vital scene semantics such as traversable paths, unexplored areas, and observed scene objects–alongside raw visual streams such as RGB, depth, and semantic segmentation masks—into a semantically informed, top-down egocentric map representation. Further, to enable the effective use of this information, we introduce a novel 2-D map attention mechanism, based on the successful multi-layer Transformer networks. We conduct experiments on 3-D reconstructed indoor PointGoal visual navigation and demonstrate the effectiveness of our approach. We show that by using our novel attention schema and auxiliary rewards to better utilize scene semantics, we outperform multiple baselines trained with only raw inputs or implicit semantic information while operating with an 80% decrease in the agent’s experience.
30 May 2021
TL;DR: In this article, the authors propose a framework for high-precision surgical subtasks by learning local, sample-efficient, accurate, closed-loop policies that use visual feedback instead of robot encoder estimates.
Abstract: Assisting surgeons with automation of surgical subtasks is challenging due to backlash, hysteresis, and variable tensioning in cable-driven robots. These issues are exacerbated as surgical instruments are changed during an operation. In this work, we propose a framework for automation of high- precision surgical subtasks by learning local, sample-efficient, accurate, closed-loop policies that use visual feedback instead of robot encoder estimates. This framework, which we call deep Intermittent Visual Servoing (IVS), switches to a learned visual servo policy for high-precision segments of repetitive surgical tasks while relying on a coarse open-loop policy for the segments where precision is not necessary. We train the policy using only 180 human demonstrations that are roughly 2 seconds each. Results on a da Vinci Research Kit suggest that combining the coarse policy with half a second of corrections from the learned policy during each high-precision segment improves the success rate on the Fundamentals of Laparoscopic Surgery peg transfer task from 72.9% to 99.2%, 31.3% to 99.2%, and 47.2% to 100.0% for 3 instruments with differing cable properties. In the contexts we studied, IVS attains the highest published success rates for automated surgical peg transfer and is significantly more reliable than previous techniques when instruments are changed. Supplementary material is available at https://tinyurl.com/ivs-icra.
TL;DR: In this article, a combination of high-throughput screening, machine learning, and docking was used to identify new inhibitors of Acetylcholinesterase (AChE).
Abstract: Acetylcholinesterase (AChE) is an important drug target in neurological disorders like Alzheimer's disease, Lewy body dementia, and Parkinson's disease dementia as well as for other conditions like myasthenia gravis and anticholinergic poisoning. In this study, we have used a combination of high-throughput screening, machine learning, and docking to identify new inhibitors of this enzyme. Bayesian machine learning models were generated with literature data from ChEMBL for eel and human AChE inhibitors as well as butyrylcholinesterase inhibitors (BuChE) and compared with other machine learning methods. High-throughput screens for the eel AChE inhibitor model identified several molecules including tilorone, an antiviral drug that is well-established outside of the United States, as a newly identified nanomolar AChE inhibitor. We have described how tilorone inhibits both eel and human AChE with IC50's of 14.4 nM and 64.4 nM, respectively, but does not inhibit the closely related BuChE IC50 > 50 μM. We have docked tilorone into the human AChE crystal structure and shown that this selectivity is likely due to the reliance on a specific interaction with a hydrophobic residue in the peripheral anionic site of AChE that is absent in BuChE. We also conducted a pharmacological safety profile (SafetyScreen44) and kinase selectivity screen (SelectScreen) that showed tilorone (1 μM) only inhibited AChE out of 44 toxicology target proteins evaluated and did not appreciably inhibit any of the 485 kinases tested. This study suggests there may be a potential role for repurposing tilorone or its derivatives in conditions that benefit from AChE inhibition.
TL;DR: This paper examines the problem of identifying technical inconsistencies that arise in the functional descriptions of open source malware threat reporting information, and introduces GapFinder, a new inconsistency checking system for identifying semantic inconsistencies within the cybersecurity domain.
Abstract: Textual data mining of open source intelligence on the Web has become an increasingly important topic across a wide range of domains such as business, law enforcement, military, and cybersecurity Text mining efforts utilize natural language processing to transform unstructured web content into structured forms that can drive various machine learning applications and data indexing services For example, applications for text mining in cybersecurity have produced a range of threat intelligence services that serve the IT industry However, a less studied problem is that of automating the identification of semantic inconsistencies among various text input sources In this paper, we introduce GapFinder, a new inconsistency checking system for identifying semantic inconsistencies within the cybersecurity domain Specifically, we examine the problem of identifying technical inconsistencies that arise in the functional descriptions of open source malware threat reporting information Our evaluation, using tens of thousands of relations derived from web-based malware threat reports, demonstrates the ability of GapFinder to identify the presence of inconsistencies
TL;DR: Results from assessment implementation indicate that the assessments worked as designed and reveal student challenges with CT concepts and practices, pointing to the utility of the assessment as a curricular tool and the need for emphasizing certain CT concept and practices in future curricular initiatives and teacher professional development.
Abstract: Background and Context: In today’s increasingly digital world, it is critical that all students learn to think computationally from an early age. Assessments of Computational Thinking (CT) are esse...
27 Sep 2021
TL;DR: Longitudinal Neighborhood Embedding (LNE) as mentioned in this paper is a self-supervised representation learning method for representation learning that explicitly models the similarity between trajectory vectors across different subjects.
Abstract: Longitudinal MRIs are often used to capture the gradual deterioration of brain structure and function caused by aging or neurological diseases. Analyzing this data via machine learning generally requires a large number of ground-truth labels, which are often missing or expensive to obtain. Reducing the need for labels, we propose a self-supervised strategy for representation learning named Longitudinal Neighborhood Embedding (LNE). Motivated by concepts in contrastive learning, LNE explicitly models the similarity between trajectory vectors across different subjects. We do so by building a graph in each training iteration defining neighborhoods in the latent space so that the progression direction of a subject follows the direction of its neighbors. This results in a smooth trajectory field that captures the global morphological change of the brain while maintaining the local continuity. We apply LNE to longitudinal T1w MRIs of two neuroimaging studies: a dataset composed of 274 healthy subjects, and Alzheimer’s Disease Neuroimaging Initiative (ADNI, \(N=632\)). The visualization of the smooth trajectory vector field and superior performance on downstream tasks demonstrate the strength of the proposed method over existing self-supervised methods in extracting information associated with normal aging and in revealing the impact of neurodegenerative disorders. The code is available at https://github.com/ouyangjiahong/longitudinal-neighbourhood-embedding.
TL;DR: Females have higher mortality than males at identical radiation doses, and blood transfusions increased survival of male animals at lower doses but not at high doses of radiation exposure, in the NHP-TBI model.
Abstract: Harmonized animal models are an indispensable tool for the development of safe and effective medical countermeasures (MCMs) against radiation injury, and rhesus macaques (referred herein as NHPs) p...
University of California, San Diego1, University of Vermont2, University of Wisconsin–Milwaukee3, Medical University of South Carolina4, McGovern Institute for Brain Research5, SRI International6, Virginia Commonwealth University7, National Institutes of Health8, Children's Hospital Los Angeles9, University of Tennessee10, Icahn School of Medicine at Mount Sinai11
TL;DR: The most common types of apps used were for streaming (mean 1 hour 57 minutes per day, SD 1 hour 32 minutes), communication, gaming, and social media as discussed by the authors, while self-report and parent report were correlated with passive sensing data.
Abstract: Background: Concerns abound regarding childhood smartphone use, but studies to date have largely relied on self-reported screen use. Self-reporting of screen use is known to be misreported by pediatric samples and their parents, limiting the accurate determination of the impact of screen use on social, emotional, and cognitive development. Thus, a more passive, objective measurement of smartphone screen use among children is needed.
Objective: This study aims to passively sense smartphone screen use by time and types of apps used in a pilot sample of children and to assess the feasibility of passive sensing in a larger longitudinal sample.
Methods: The Adolescent Brain Cognitive Development (ABCD) study used passive, objective phone app methods for assessing smartphone screen use over 4 weeks in 2019-2020 in a subsample of 67 participants (aged 11-12 years; 31/67, 46% female; 23/67, 34% White). Children and their parents both reported average smartphone screen use before and after the study period, and they completed a questionnaire regarding the acceptability of the study protocol. Descriptive statistics for smartphone screen use, app use, and protocol feasibility and acceptability were reviewed. Analyses of variance were run to assess differences in categorical app use by demographics. Self-report and parent report were correlated with passive sensing data.
Results: Self-report of smartphone screen use was partly consistent with objective measurement (r=0.49), although objective data indicated that children used their phones more than they reported. Passive sensing revealed the most common types of apps used were for streaming (mean 1 hour 57 minutes per day, SD 1 hour 32 minutes), communication (mean 48 minutes per day, SD 1 hour 17 minutes), gaming (mean 41 minutes per day, SD 41 minutes), and social media (mean 36 minutes per day, SD 1 hour 7 minutes). Passive sensing of smartphone screen use was generally acceptable to children (43/62, 69%) and parents (53/62, 85%).
Conclusions: The results of passive, objective sensing suggest that children use their phones more than they self-report. Therefore, use of more robust methods for objective data collection is necessary and feasible in pediatric samples. These data may then more accurately reflect the impact of smartphone screen use on behavioral and emotional functioning. Accordingly, the ABCD study is implementing a passive sensing protocol in the full ABCD cohort. Taken together, passive assessment with a phone app provided objective, low-burden, novel, informative data about preteen smartphone screen use.
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TL;DR: In this paper, a micromercury trapped ion clock with high frequency stability has been demonstrated using a sealed 30-cc vacuum tube with one layer of magnetic shielding, light source, and detector assembly.
Abstract: Mercury trapped ion clocks have demonstrated great long-term frequency stability and robustness. In this paper, we report a demonstration of an integrated 100-cc physics package in an effort to develop a micromercury trapped ion clock with high frequency stability. The physics package consists of a sealed 30-cc vacuum tube with one layer of magnetic shielding, light source, and detector assembly. A field emitter array and a 194-nm microplasma lamp were employed together with a microtrap tube to reduce the size and power consumption for a mercury trapped ion clock. We show that the 100-cc physics package is capable of providing a fractional frequency stability of 1×10−11τ−1/2 down to 5×10−14 after a few hours of integration. We also show a set of environmental sensitivity evaluations as well as the clock frequency retrace.
Proceedings Article•
01 Jan 2021TL;DR: Zhang et al. as discussed by the authors proposed a multi-modal based approach to fuse visual cues from the frame and event domains to enhance the single object tracking performance, especially in degraded conditions (e.g., scenes with high dynamic range, low light, and fast-motion objects).
Abstract: Inspired by the complementarity between conventional frame-based and bio-inspired event-based cameras, we propose a multi-modal based approach to fuse visual cues from the frame- and event-domain to enhance the single object tracking performance, especially in degraded conditions (e.g., scenes with high dynamic range, low light, and fast-motion objects). The proposed approach can effectively and adaptively combine meaningful information from both domains. Our approach's effectiveness is enforced by a novel designed cross-domain attention schemes, which can effectively enhance features based on self- and cross-domain attention schemes; The adaptiveness is guarded by a specially designed weighting scheme, which can adaptively balance the contribution of the two domains. To exploit event-based visual cues in single-object tracking, we construct a large-scale frame-event-based dataset, which we subsequently employ to train a novel frame-event fusion based model. Extensive experiments show that the proposed approach outperforms state-of-the-art frame-based tracking methods by at least 10.4% and 11.9% in terms of representative success rate and precision rate, respectively. Besides, the effectiveness of each key component of our approach is evidenced by our thorough ablation study.