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Showing papers by "University of California published in 2021"


Journal ArticleDOI
TL;DR: In this paper, the authors determined factors associated with COVID-19-related death in people with rheumatic diseases, including age, sex, smoking status, comorbidities, diagnosis, disease activity and medications.
Abstract: OBJECTIVES: To determine factors associated with COVID-19-related death in people with rheumatic diseases. METHODS: Physician-reported registry of adults with rheumatic disease and confirmed or presumptive COVID-19 (from 24 March to 1 July 2020). The primary outcome was COVID-19-related death. Age, sex, smoking status, comorbidities, rheumatic disease diagnosis, disease activity and medications were included as covariates in multivariable logistic regression models. Analyses were further stratified according to rheumatic disease category. RESULTS: Of 3729 patients (mean age 57 years, 68% female), 390 (10.5%) died. Independent factors associated with COVID-19-related death were age (66-75 years: OR 3.00, 95% CI 2.13 to 4.22; >75 years: 6.18, 4.47 to 8.53; both vs ≤65 years), male sex (1.46, 1.11 to 1.91), hypertension combined with cardiovascular disease (1.89, 1.31 to 2.73), chronic lung disease (1.68, 1.26 to 2.25) and prednisolone-equivalent dosage >10 mg/day (1.69, 1.18 to 2.41; vs no glucocorticoid intake). Moderate/high disease activity (vs remission/low disease activity) was associated with higher odds of death (1.87, 1.27 to 2.77). Rituximab (4.04, 2.32 to 7.03), sulfasalazine (3.60, 1.66 to 7.78), immunosuppressants (azathioprine, cyclophosphamide, ciclosporin, mycophenolate or tacrolimus: 2.22, 1.43 to 3.46) and not receiving any disease-modifying anti-rheumatic drug (DMARD) (2.11, 1.48 to 3.01) were associated with higher odds of death, compared with methotrexate monotherapy. Other synthetic/biological DMARDs were not associated with COVID-19-related death. CONCLUSION: Among people with rheumatic disease, COVID-19-related death was associated with known general factors (older age, male sex and specific comorbidities) and disease-specific factors (disease activity and specific medications). The association with moderate/high disease activity highlights the importance of adequate disease control with DMARDs, preferably without increasing glucocorticoid dosages. Caution may be required with rituximab, sulfasalazine and some immunosuppressants.

405 citations


Journal ArticleDOI
TL;DR: Among patients with heart failure and a reduced ejection, patients who received omecamtiv mecarbil had a lower incidence of a composite of a heart-failure event or death from cardiovascular causes than those who received placebo.
Abstract: Background The selective cardiac myosin activator omecamtiv mecarbil has been shown to improve cardiac function in patients with heart failure with a reduced ejection fraction. Its effect ...

341 citations


Journal ArticleDOI
TL;DR: In particular, this article showed that simple gradient methods easily find near-optimal solutions to non-convex optimization problems, and despite giving a near-perfect fit to training data without any explicit effort to control model complexity, these methods exhibit excellent predictive accuracy.
Abstract: The remarkable practical success of deep learning has revealed some major surprises from a theoretical perspective. In particular, simple gradient methods easily find near-optimal solutions to non-convex optimization problems, and despite giving a near-perfect fit to training data without any explicit effort to control model complexity, these methods exhibit excellent predictive accuracy. We conjecture that specific principles underlie these phenomena: that overparametrization allows gradient methods to find interpolating solutions, that these methods implicitly impose regularization, and that overparametrization leads to benign overfitting, that is, accurate predictions despite overfitting training data. In this article, we survey recent progress in statistical learning theory that provides examples illustrating these principles in simpler settings. We first review classical uniform convergence results and why they fall short of explaining aspects of the behaviour of deep learning methods. We give examples of implicit regularization in simple settings, where gradient methods lead to minimal norm functions that perfectly fit the training data. Then we review prediction methods that exhibit benign overfitting, focusing on regression problems with quadratic loss. For these methods, we can decompose the prediction rule into a simple component that is useful for prediction and a spiky component that is useful for overfitting but, in a favourable setting, does not harm prediction accuracy. We focus specifically on the linear regime for neural networks, where the network can be approximated by a linear model. In this regime, we demonstrate the success of gradient flow, and we consider benign overfitting with two-layer networks, giving an exact asymptotic analysis that precisely demonstrates the impact of overparametrization. We conclude by highlighting the key challenges that arise in extending these insights to realistic deep learning settings.

141 citations


Journal ArticleDOI
TL;DR: In this paper, the effect of neutralizing monoclonal antibody (YL-CoV555) on patients with Coronavirus disease 2019 (Covid-19) was investigated.
Abstract: Background LY-CoV555, a neutralizing monoclonal antibody, has been associated with a decrease in viral load and the frequency of hospitalizations or emergency department visits among outpatients with coronavirus disease 2019 (Covid-19). Data are needed on the effect of this antibody in patients who are hospitalized with Covid-19. Methods In this platform trial of therapeutic agents, we randomly assigned hospitalized patients who had Covid-19 without end-organ failure in a 1:1 ratio to receive either LY-CoV555 or matching placebo. In addition, all the patients received high-quality supportive care as background therapy, including the antiviral drug remdesivir and, when indicated, supplemental oxygen and glucocorticoids. LY-CoV555 (at a dose of 7000 mg) or placebo was administered as a single intravenous infusion over a 1-hour period. The primary outcome was a sustained recovery during a 90-day period, as assessed in a time-to-event analysis. An interim futility assessment was performed on the basis of a seven-category ordinal scale for pulmonary function on day 5. Results On October 26, 2020, the data and safety monitoring board recommended stopping enrollment for futility after 314 patients (163 in the LY-CoV555 group and 151 in the placebo group) had undergone randomization and infusion. The median interval since the onset of symptoms was 7 days (interquartile range, 5 to 9). At day 5, a total of 81 patients (50%) in the LY-CoV555 group and 81 (54%) in the placebo group were in one of the two most favorable categories of the pulmonary outcome. Across the seven categories, the odds ratio of being in a more favorable category in the LY-CoV555 group than in the placebo group was 0.85 (95% confidence interval [CI], 0.56 to 1.29; P = 0.45). The percentage of patients with the primary safety outcome (a composite of death, serious adverse events, or clinical grade 3 or 4 adverse events through day 5) was similar in the LY-CoV555 group and the placebo group (19% and 14%, respectively; odds ratio, 1.56; 95% CI, 0.78 to 3.10; P = 0.20). The rate ratio for a sustained recovery was 1.06 (95% CI, 0.77 to 1.47). Conclusions Monoclonal antibody LY-CoV555, when coadministered with remdesivir, did not demonstrate efficacy among hospitalized patients who had Covid-19 without end-organ failure. (Funded by Operation Warp Speed and others; TICO ClinicalTrials.gov number, NCT04501978.).

139 citations


Journal ArticleDOI
TL;DR: In this paper, an adversarial RL procedure automatically selects which motion to perform, dynamically interpolating and generalizing from the dataset, without requiring a high-level motion planner or other task-specific annotations of the motion clips.
Abstract: Synthesizing graceful and life-like behaviors for physically simulated characters has been a fundamental challenge in computer animation. Data-driven methods that leverage motion tracking are a prominent class of techniques for producing high fidelity motions for a wide range of behaviors. However, the effectiveness of these tracking-based methods often hinges on carefully designed objective functions, and when applied to large and diverse motion datasets, these methods require significant additional machinery to select the appropriate motion for the character to track in a given scenario. In this work, we propose to obviate the need to manually design imitation objectives and mechanisms for motion selection by utilizing a fully automated approach based on adversarial imitation learning. High-level task objectives that the character should perform can be specified by relatively simple reward functions, while the low-level style of the character's behaviors can be specified by a dataset of unstructured motion clips, without any explicit clip selection or sequencing. These motion clips are used to train an adversarial motion prior, which specifies style-rewards for training the character through reinforcement learning (RL). The adversarial RL procedure automatically selects which motion to perform, dynamically interpolating and generalizing from the dataset. Our system produces high-quality motions that are comparable to those achieved by state-of-the-art tracking-based techniques, while also being able to easily accommodate large datasets of unstructured motion clips. Composition of disparate skills emerges automatically from the motion prior, without requiring a high-level motion planner or other task-specific annotations of the motion clips. We demonstrate the effectiveness of our framework on a diverse cast of complex simulated characters and a challenging suite of motor control tasks.

129 citations


Journal ArticleDOI
TL;DR: In this paper, an adversarial RL procedure automatically selects which motion to perform, dynamically interpolating and generalizing from a dataset of unstructured motion clips, without any explicit clip selection or sequencing.
Abstract: Synthesizing graceful and life-like behaviors for physically simulated characters has been a fundamental challenge in computer animation. Data-driven methods that leverage motion tracking are a prominent class of techniques for producing high fidelity motions for a wide range of behaviors. However, the effectiveness of these tracking-based methods often hinges on carefully designed objective functions, and when applied to large and diverse motion datasets, these methods require significant additional machinery to select the appropriate motion for the character to track in a given scenario. In this work, we propose to obviate the need to manually design imitation objectives and mechanisms for motion selection by utilizing a fully automated approach based on adversarial imitation learning. High-level task objectives that the character should perform can be specified by relatively simple reward functions, while the low-level style of the character's behaviors can be specified by a dataset of unstructured motion clips, without any explicit clip selection or sequencing. For example, a character traversing an obstacle course might utilize a task-reward that only considers forward progress, while the dataset contains clips of relevant behaviors such as running, jumping, and rolling. These motion clips are used to train an adversarial motion prior, which specifies style-rewards for training the character through reinforcement learning (RL). The adversarial RL procedure automatically selects which motion to perform, dynamically interpolating and generalizing from the dataset. Our system produces high-quality motions that are comparable to those achieved by state-of-the-art tracking-based techniques, while also being able to easily accommodate large datasets of unstructured motion clips. Composition of disparate skills emerges automatically from the motion prior, without requiring a high-level motion planner or other task-specific annotations of the motion clips. We demonstrate the effectiveness of our framework on a diverse cast of complex simulated characters and a challenging suite of motor control tasks.

122 citations


Proceedings ArticleDOI
19 Jun 2021
TL;DR: In this article, the authors propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch, which is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms.
Abstract: Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often difficult to re-implement, evaluate and port across different settings, where even results on standard benchmarks are hard to reproduce. In this work, we propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch. Avalanche is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms.

97 citations


Journal ArticleDOI
TL;DR: The second-order effects of remote working have been explored in this article, where the authors explore three of the most important secondorder effects likely to shape the trajectory of work for several decades: (1) Remote work creates vast amounts of digital exhaust, (2) Digital exhaust is used to turn employees into data representations, and (3) Artificial intelligence (AI) uses those data representations to predict (and shape) employee behaviour.
Abstract: Companies have flirted with remote work since the 1970s. Estimates from late 2019 suggested that slightly more than 5 per cent of employees worked remotely with regularity. But as COVID-19 has spread across the globe over the past few months, and shelterin-place orders were issued by governments, many companies have initiated a rapid and wholesale shift to remote work arrangements, at least for knowledge-intensive work. This shift is enabled by digital technologies that allow workers to communicate via text, audio, and video and to share and edit data and documents in real-time. As examples of the dramatic and swift increase in remote work enabled by digital technologies, Zoom’s daily active user base grew by 67 per cent in March 2020, the number of daily active users of Microsoft Teams grew from 20 million in November 2019 to 44 million in March 2020, and Slack added 7,000 new paid customers in February and March, 2020 – roughly 40 per cent more than in each of its previous two quarters. Google announced it will continue remote work until at least summer 2021 and Twitter extended the opportunity for all employees to work remotely indefinitely. Most discussions around this shift to remote work have focused on exploring first-order effects – areas of organizational behaviour that are likely to change directly following a transition to working exclusively through digital technologies. Although first-order effects are important, it is the second-order effects of remote working that have the potential to be the most profound, but are the most understudied. In this paper, I explore three of the most important second-order effects likely to shape the trajectory of work for several decades: (1) Remote work creates vast amounts of digital exhaust, (2) Digital exhaust is used to turn employees into data representations, and (3) Artificial intelligence (AI) uses those data representations to predict (and shape) employee behaviour. Journal of Management Studies 58:1 January 2021 doi:10.1111/joms.12648

93 citations


Posted ContentDOI
07 Feb 2021-medRxiv
TL;DR: For example, Washington et al. as discussed by the authors investigated the prevalence and growth dynamics of the SARS-CoV-2 variant in the United States (U.S.), tracking it back to its early emergence and onward local transmission.
Abstract: Author(s): Washington, Nicole L; Gangavarapu, Karthik; Zeller, Mark; Bolze, Alexandre; Cirulli, Elizabeth T; Schiabor Barrett, Kelly M; Larsen, Brendan B; Anderson, Catelyn; White, Simon; Cassens, Tyler; Jacobs, Sharoni; Levan, Geraint; Nguyen, Jason; Ramirez, Jimmy M; Rivera-Garcia, Charlotte; Sandoval, Efren; Wang, Xueqing; Wong, David; Spencer, Emily; Robles-Sikisaka, Refugio; Kurzban, Ezra; Hughes, Laura D; Deng, Xianding; Wang, Candace; Servellita, Venice; Valentine, Holly; De Hoff, Peter; Seaver, Phoebe; Sathe, Shashank; Gietzen, Kimberly; Sickler, Brad; Antico, Jay; Hoon, Kelly; Liu, Jingtao; Harding, Aaron; Bakhtar, Omid; Basler, Tracy; Austin, Brett; Isaksson, Magnus; Febbo, Phil; Becker, David; Laurent, Marc; McDonald, Eric; Yeo, Gene W; Knight, Rob; Laurent, Louise C; de Feo, Eileen; Worobey, Michael; Chiu, Charles; Suchard, Marc A; Lu, James T; Lee, William; Andersen, Kristian G | Abstract: As of January of 2021, the highly transmissible B.1.1.7 variant of SARS-CoV-2, which was first identified in the United Kingdom (U.K.), has gained a strong foothold across the world. Because of the sudden and rapid rise of B.1.1.7, we investigated the prevalence and growth dynamics of this variant in the United States (U.S.), tracking it back to its early emergence and onward local transmission. We found that the RT-qPCR testing anomaly of S gene target failure (SGTF), first observed in the U.K., was a reliable proxy for B.1.1.7 detection. We sequenced 212 B.1.1.7 SARS-CoV-2 genomes collected from testing facilities in the U.S. from December 2020 to January 2021. We found that while the fraction of B.1.1.7 among SGTF samples varied by state, detection of the variant increased at a logistic rate similar to those observed elsewhere, with a doubling rate of a little over a week and an increased transmission rate of 35-45%. By performing time-aware Bayesian phylodynamic analyses, we revealed several independent introductions of B.1.1.7 into the U.S. as early as late November 2020, with onward community transmission enabling the variant to spread to at least 30 states as of January 2021. Our study shows that the U.S. is on a similar trajectory as other countries where B.1.1.7 rapidly became the dominant SARS-CoV-2 variant, requiring immediate and decisive action to minimize COVID-19 morbidity and mortality.

91 citations


Journal ArticleDOI
TL;DR: This article explored the effect of explicitly racial and inflammatory speech by political elites on mass citizens in a societal context where equality norms are widespread and generally heeded yet a subset of citizens still possesses deeply ingrained racial prejudices.
Abstract: This article explores the effect of explicitly racial and inflammatory speech by political elites on mass citizens in a societal context where equality norms are widespread and generally heeded yet a subset of citizens nonetheless possesses deeply ingrained racial prejudices. The authors argue that such speech should have an ‘emboldening effect’ among the prejudiced, particularly where it is not clearly and strongly condemned by other elite political actors. To test this argument, the study focuses on the case of the Trump campaign for president in the United States, and utilizes a survey experiment embedded within an online panel study. The results demonstrate that in the absence of prejudiced elite speech, prejudiced citizens constrain the expression of their prejudice. However, in the presence of prejudiced elite speech – particularly when it is tacitly condoned by other elites – the study finds that the prejudiced are emboldened to both express and act upon their prejudices.

77 citations


Posted ContentDOI
Estee Y Cramer1, Evan L. Ray1, Velma K. Lopez2, Johannes Bracher3  +281 moreInstitutions (53)
05 Feb 2021-medRxiv
TL;DR: In this paper, the authors systematically evaluated 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level at the CDC.
Abstract: Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies In 2020, the COVID-19 Forecast Hub (https://covid19forecasthuborg/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level One of these models was a multi-model ensemble that combined all available forecasts each week The performance of individual models showed high variability across time, geospatial units, and forecast horizons Half of the models evaluated showed better accuracy than a naive baseline model In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

Journal ArticleDOI
TL;DR: The World Scientists' Warning to Humanity, issued by the Alliance of World Scientists, by exploring opportunities for sustaining ILK systems on behalf of the future stewardship of our planet as discussed by the authors raises the alarm about the pervasive and ubiquitous erosion of knowledge and practice and the social and ecological consequences of this erosion.
Abstract: The knowledge systems and practices of Indigenous Peoples and local communities play critical roles in safeguarding the biological and cultural diversity of our planet. Globalization, government policies, capitalism, colonialism, and other rapid social-ecological changes threaten the relationships between Indigenous Peoples and local communities and their environments, thereby challenging the continuity and dynamism of Indigenous and Local Knowledge (ILK). In this article, we contribute to the “World Scientists' Warning to Humanity,” issued by the Alliance of World Scientists, by exploring opportunities for sustaining ILK systems on behalf of the future stewardship of our planet. Our warning raises the alarm about the pervasive and ubiquitous erosion of knowledge and practice and the social and ecological consequences of this erosion. While ILK systems can be adaptable and resilient, the foundations of these knowledge systems are compromised by ongoing suppression, misrepresentation, appropriation, assimilation, disconnection, and destruction of biocultural heritage. Three case studies illustrate these processes and how protecting ILK is central to biocultural conservation. We conclude with 15 recommendations that call for the recognition and support of Indigenous Peoples and local communities and their knowledge systems. Enacting these recommendations will entail a transformative and sustained shift in how ILK systems, their knowledge holders, and their multiple expressions in lands and waters are recognized, affirmed, and valued. We appeal for urgent action to support the efforts of Indigenous Peoples and local communities around the world to maintain their knowledge systems, languages, stewardship rights, ties to lands and waters, and the biocultural integrity of their territories—on which we all depend.

Journal ArticleDOI
TL;DR: In this article, a model-to-crop translational work was conducted in which two AtDMR6 orthologs in tomato, SlDMR 6-1 and SlD MR 6-2, were characterized and shown to be upregulated by pathogen infection.
Abstract: Plant diseases are among the major causes of crop yield losses around the world. To confer disease resistance, conventional breeding relies on the deployment of single resistance (R) genes. However, this strategy has been easily overcome by constantly evolving pathogens. Disabling susceptibility (S) genes is a promising alternative to R genes in breeding programs, as it usually offers durable and broad-spectrum disease resistance. In Arabidopsis, the S gene DMR6 (AtDMR6) encodes an enzyme identified as a susceptibility factor to bacterial and oomycete pathogens. Here, we present a model-to-crop translational work in which we characterize two AtDMR6 orthologs in tomato, SlDMR6-1 and SlDMR6-2. We show that SlDMR6-1, but not SlDMR6-2, is up-regulated by pathogen infection. In agreement, Sldmr6-1 mutants display enhanced resistance against different classes of pathogens, such as bacteria, oomycete, and fungi. Notably, disease resistance correlates with increased salicylic acid (SA) levels and transcriptional activation of immune responses. Furthermore, we demonstrate that SlDMR6-1 and SlDMR6-2 display SA-5 hydroxylase activity, thus contributing to the elucidation of the enzymatic function of DMR6. We then propose that SlDMR6 duplication in tomato resulted in subsequent subfunctionalization, in which SlDMR6-2 specialized in balancing SA levels in flowers/fruits, while SlDMR6-1 conserved the ability to fine-tune SA levels during pathogen infection of the plant vegetative tissues. Overall, this work not only corroborates a mechanism underlying SA homeostasis in plants, but also presents a promising strategy for engineering broad-spectrum and durable disease resistance in crops.

Journal ArticleDOI
TL;DR: In this paper, it has been assumed that battery thermal management systems should be designed to maintain the battery temperature around room temperature, but that is not always true as lithium-ion battery (LIB) RD the battery heat needs to be retained or dissipated to elevate or avoid temperature rise.

Journal ArticleDOI
TL;DR: In this paper, a genome-wide chromatin accessibility profile of 18 Arabidopsis mutants that are deficient in CG, CHG, or CHH DNA methylation was presented.
Abstract: DNA methylation is a major epigenetic modification found across species and has a profound impact on many biological processes. However, its influence on chromatin accessibility and higher-order genome organization remains unclear, particularly in plants. Here, we present genome-wide chromatin accessibility profiles of 18 Arabidopsis mutants that are deficient in CG, CHG, or CHH DNA methylation. We find that DNA methylation in all three sequence contexts impacts chromatin accessibility in heterochromatin. Many chromatin regions maintain inaccessibility when DNA methylation is lost in only one or two sequence contexts, and signatures of accessibility are particularly affected when DNA methylation is reduced in all contexts, suggesting an interplay between different types of DNA methylation. In addition, we found that increased chromatin accessibility was not always accompanied by increased transcription, suggesting that DNA methylation can directly impact chromatin structure by other mechanisms. We also observed that an increase in chromatin accessibility was accompanied by enhanced long-range chromatin interactions. Together, these results provide a valuable resource for chromatin architecture and DNA methylation analyses and uncover a pivotal role for methylation in the maintenance of heterochromatin inaccessibility.

Journal ArticleDOI
TL;DR: This paper examined the relationship between democratization and education provision empirically, leveraging new datasets covering 109 countries and 200 years, and found that, on average, democratization had no or little impact on primary school enrollment rates.
Abstract: Because primary education is often conceptualized as a pro-poor redistributive policy, a common argument is that democratization increases its provision. But primary education can also serve the goals of autocrats, including redistribution, promoting loyalty, nation-building, and/or industrialization. To examine the relationship between democratization and education provision empirically, I leverage new datasets covering 109 countries and 200 years. Difference-in-differences and interrupted time series estimates find that, on average, democratization had no or little impact on primary school enrollment rates. When unpacking this average null result, I find that, consistent with median voter theories, democratization can lead to an expansion of primary schooling, but the key condition under which it does—when a majority lacked access to primary schooling before democratization—rarely holds. Around the world, state-controlled primary schooling emerged a century before democratization, and in three-fourths of countries that democratized, a majority already had access to primary education before democratization.

Journal ArticleDOI
TL;DR: In this paper, the authors review existing Earth observation datasets, models, and algorithms used for irrigation mapping and quantification from the field to the global scale and identify current shortcomings of irrigation monitoring capabilities from space and phrase guidelines for potential future satellite missions and observation strategies.
Abstract: Irrigation represents one of the most impactful human interventions in the terrestrial water cycle. Knowing the distribution and extent of irrigated areas as well as the amount of water used for irrigation plays a central role in modeling irrigation water requirements and quantifying the impact of irrigation on regional climate, river discharge, and groundwater depletion. Obtaining high-quality global information about irrigation is challenging, especially in terms of quantification of the water actually used for irrigation. Here, we review existing Earth observation datasets, models, and algorithms used for irrigation mapping and quantification from the field to the global scale. The current observation capacities are confronted with the results of a survey on user requirements on satellite-observed irrigation for agricultural water resources’ management. Based on this information, we identify current shortcomings of irrigation monitoring capabilities from space and phrase guidelines for potential future satellite missions and observation strategies.

Proceedings ArticleDOI
09 Sep 2021
TL;DR: In this article, an ultra-low-power tag that can be localized at high accuracy over extended distances is proposed. But the authors focus on the free space path loss problem experienced by signals from the tag at mmWave bands by building upon Van Atta Arrays that retroreflect incident energy back towards the transmitting radar with minimal loss and low power consumption.
Abstract: This paper presents Millimetro, an ultra-low-power tag that can be localized at high accuracy over extended distances. We develop Millimetro in the context of autonomous driving to efficiently localize roadside infrastructure such as lane markers and road signs, even if obscured from view, where visual sensing fails. While RF-based localization offers a natural solution, current ultra-low-power localization systems struggle to operate accurately at extended ranges under strict latency requirements. Millimetro addresses this challenge by re-using existing automotive radars that operate at mmWave frequency where plentiful bandwidth is available to ensure high accuracy and low latency. We address the crucial free space path loss problem experienced by signals from the tag at mmWave bands by building upon Van Atta Arrays that retro-reflect incident energy back towards the transmitting radar with minimal loss and low power consumption. Our experimental results indoors and outdoors demonstrate a scalable system that operates at a desirable range (over 100 m), accuracy (centimeter-level), and ultra-low-power (

Journal ArticleDOI
TL;DR: The authors examined the role of household income, bread-winning responsibilities, and household composition in women's political ambition and found that bread-consuming mothers are more likely to run for office.
Abstract: Women’s underrepresentation in American politics is often attributed to relatively low levels of political ambition. Yet scholarship still grapples with a major leak in the pipeline to power: that many qualified and politically ambitious women decide against candidacy. Focusing on women with political ambition, we theorize that at the final stage of candidate emergence, household income, breadwinning responsibilities, and household composition are interlocking obstacles to women’s candidacies. We examine these dynamics through a multimethod design that includes an original survey of women most likely to run for office: alumnae of the largest Democratic campaign training organization in the United States. Although we do not find income effects, we provide evidence that breadwinning—responsibility for a majority of household income—negatively affects women’s ambition, especially for mothers. These findings have important implications for understanding how the political economy of the household affects candidate emergence and descriptive representation in the United States.

Journal ArticleDOI
TL;DR: In this article, a suite of 151 genes were found to show parallel signatures of positive selection associated with alpine colonization, involved in response to cold, high radiation, short season, herbivores, and pathogens.
Abstract: Parallel adaptation provides valuable insight into the predictability of evolutionary change through replicated natural experiments. A steadily increasing number of studies have demonstrated genomic parallelism, yet the magnitude of this parallelism varies depending on whether populations, species, or genera are compared. This led us to hypothesize that the magnitude of genomic parallelism scales with genetic divergence between lineages, but whether this is the case and the underlying evolutionary processes remain unknown. Here, we resequenced seven parallel lineages of two Arabidopsis species, which repeatedly adapted to challenging alpine environments. By combining genome-wide divergence scans with model-based approaches, we detected a suite of 151 genes that show parallel signatures of positive selection associated with alpine colonization, involved in response to cold, high radiation, short season, herbivores, and pathogens. We complemented these parallel candidates with published gene lists from five additional alpine Brassicaceae and tested our hypothesis on a broad scale spanning ∼0.02 to 18 My of divergence. Indeed, we found quantitatively variable genomic parallelism whose extent significantly decreased with increasing divergence between the compared lineages. We further modeled parallel evolution over the Arabidopsis candidate genes and showed that a decreasing probability of repeated selection on the same standing or introgressed alleles drives the observed pattern of divergence-dependent parallelism. We therefore conclude that genetic divergence between populations, species, and genera, affecting the pool of shared variants, is an important factor in the predictability of genome evolution.

Journal ArticleDOI
TL;DR: In this article, a tandem activity-based sensing and labeling strategy for H2O2 imaging was proposed that enables capture and permanent recording of localized H 2O2 fluxes.
Abstract: Reactive oxygen species (ROS) like hydrogen peroxide (H2O2) are transient species that have broad actions in signaling and stress, but spatioanatomical understanding of their biology remains insufficient. Here, we report a tandem activity-based sensing and labeling strategy for H2O2 imaging that enables capture and permanent recording of localized H2O2 fluxes. Peroxy Green-1 Fluoromethyl (PG1-FM) is a diffusible small-molecule probe that senses H2O2 by a boronate oxidation reaction to trigger dual release and covalent labeling of a fluorescent product, thus preserving spatial information on local H2O2 changes. This unique reagent enables visualization of transcellular redox signaling in a microglia-neuron coculture cell model, where selective activation of microglia for ROS production increases H2O2 in nearby neurons. In addition to identifying ROS-mediated cell-to-cell communication, this work provides a starting point for the design of chemical probes that can achieve high spatial fidelity by combining activity-based sensing and labeling strategies.

Journal ArticleDOI
TL;DR: In this article, the authors explore indoor ozone chemistry in a house in California with two adult inhabitants using space-and time-resolved measurements of ozone and volatile organic compounds (VOCs) acquired over an 8-week summer campaign.
Abstract: Outdoor ozone transported indoors initiates oxidative chemistry, forming volatile organic products The influence of ozone chemistry on indoor air composition has not been directly quantified in normally occupied residences Here, we explore indoor ozone chemistry in a house in California with two adult inhabitants We utilize space- and time-resolved measurements of ozone and volatile organic compounds (VOCs) acquired over an 8-wk summer campaign Despite overall low indoor ozone concentrations (mean value of 43 ppb) and a relatively low indoor ozone decay constant (13 h-1), we identified multiple VOCs exhibiting clear contributions from ozone-initiated chemistry indoors These chemicals include 6-methyl-5-hepten-2-one (6-MHO), 4-oxopentanal (4-OPA), nonenal, and C8-C12 saturated aldehydes, which are among the commonly reported products from laboratory studies of ozone interactions with indoor surfaces and with human skin lipids These VOCs together accounted for ≥12% molecular yield with respect to house-wide consumed ozone, with the highest net product yield for nonanal (≥35%), followed by 6-MHO (27%) and 4-OPA (26%) Although 6-MHO and 4-OPA are prominent ozonolysis products of skin lipids (specifically squalene), ozone reaction with the body envelopes of the two occupants in this house are insufficient to explain the observed yields Relatedly, we observed that ozone-driven chemistry continued to produce 6-MHO and 4-OPA even after the occupants had been away from the house for 5 d These observations provide evidence that skin lipids transferred to indoor surfaces made substantial contributions to ozone reactivity in the studied house

Posted ContentDOI
TL;DR: The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition as discussed by the authors.
Abstract: The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. Furthermore, the abrupt reduction in emissions during the lockdown periods led to clearly observable changes in atmospheric composition, which provide direct insight into feedbacks between the Earth system and human activity. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes, due in large part to their different lifetimes. Here, we discuss several key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed, in part due to decreased ocean uptake of CO2 and a likely increase in CH4 lifetime from reduced NO x emissions. Second, the response of O3 to decreased NO x emissions showed significant spatial and temporal variability, due to differing chemical regimes around the world. Finally, the overall response of atmospheric composition to emissions changes is heavily modulated by factors including carbon-cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality, such as wildfires and the ozone climate penalty.


Proceedings ArticleDOI
14 Jun 2021
TL;DR: In this article, the authors present an in-depth characterization of the micro-op cache, reverse-engineering many undocumented features, and further describe attacks that exploit the microop cache as a timing channel to transmit secret information.
Abstract: Modern Intel, AMD, and ARM processors translate complex instructions into simpler internal micro-ops that are then cached in a dedicated on-chip structure called the micro-op cache. This work presents an in-depth characterization study of the micro-op cache, reverse-engineering many undocumented features, and further describes attacks that exploit the micro-op cache as a timing channel to transmit secret information. In particular, this paper describes three attacks - (1) a same thread cross-domain attack that leaks secrets across the user-kernel boundary, (2) a cross-SMT thread attack that transmits secrets across two SMT threads via the micro-op cache, and (3) transient execution attacks that have the ability to leak an unauthorized secret accessed along a misspeculated path, even before the transient instruction is dispatched to execution, breaking several existing invisible speculation and fencing-based solutions that mitigate Spectre.

Journal ArticleDOI
TL;DR: The authors conducted a meta-analysis of 46 natural experiments that use difference-in-difference designs to estimate the causal effect of commodity price changes on armed civil conflict and found that price increases for labor-intensive agricultural commodities reduce conflict, while increases in the price of oil, a capital intensive commodity, provoke conflict.
Abstract: Scholars of the resource curse argue that reliance on primary commodities destabilizes governments: price fluctuations generate windfalls or periods of austerity that provoke or intensify civil conflict. Over 350 quantitative studies test this claim, but prominent results point in different directions, making it difficult to discern which results reliably hold across contexts. We conduct a meta-analysis of 46 natural experiments that use difference-in-difference designs to estimate the causal effect of commodity price changes on armed civil conflict. We show that commodity price changes, on average, do not change the likelihood of conflict. However, there are cross-cutting effects by commodity type. In line with theory, we find price increases for labor-intensive agricultural commodities reduce conflict, while increases in the price of oil, a capital-intensive commodity, provoke conflict. We also find that price increases for lootable artisanal minerals provoke conflict. Our meta-analysis consolidates existing evidence, but also highlights opportunities for future research.

Journal ArticleDOI
TL;DR: In this article, the authors used a combination of genomic and metagenomic approaches to identify a variety of nonrandom, parallel mutations associated with transplantation, including mutations in genes related to nutrient acquisition, stress response, and exopolysaccharide production.
Abstract: Microbial community responses to environmental change are largely associated with ecological processes; however, the potential for microbes to rapidly evolve and adapt remains relatively unexplored in natural environments. To assess how ecological and evolutionary processes simultaneously alter the genetic diversity of a microbiome, we conducted two concurrent experiments in the leaf litter layer of soil over 18 mo across a climate gradient in Southern California. In the first experiment, we reciprocally transplanted microbial communities from five sites to test whether ecological shifts in ecotypes of the abundant bacterium, Curtobacterium, corresponded to past adaptive differentiation. In the transplanted communities, ecotypes converged toward that of the native communities growing on a common litter substrate. Moreover, these shifts were correlated with community-weighted mean trait values of the Curtobacterium ecotypes, indicating that some of the trait variation among ecotypes could be explained by local adaptation to climate conditions. In the second experiment, we transplanted an isogenic Curtobacterium strain and tracked genomic mutations associated with the sites across the same climate gradient. Using a combination of genomic and metagenomic approaches, we identified a variety of nonrandom, parallel mutations associated with transplantation, including mutations in genes related to nutrient acquisition, stress response, and exopolysaccharide production. Together, the field experiments demonstrate how both demographic shifts of previously adapted ecotypes and contemporary evolution can alter the diversity of a soil microbiome on the same timescale.

Posted ContentDOI
29 Jul 2021-bioRxiv
TL;DR: In this article, the authors conducted a longitudinal study of infection-naive and COVID-19 convalescent donors before vaccination and after their first and second vaccine doses, using a high-parameter CyTOF analysis to phenotype their SARS-CoV-2-specific T cells.
Abstract: While mRNA vaccines are proving highly efficacious against SARS-CoV-2, it is important to determine how booster doses and prior infection influence the immune defense they elicit, and whether they protect against variants. Focusing on the T cell response, we conducted a longitudinal study of infection-naive and COVID-19 convalescent donors before vaccination and after their first and second vaccine doses, using a high-parameter CyTOF analysis to phenotype their SARS-CoV-2-specific T cells. Vaccine-elicited spike-specific T cells responded similarly to stimulation by spike epitopes from the ancestral, B.1.1.7 and B.1.351 variant strains, both in terms of cell numbers and phenotypes. In infection-naive individuals, the second dose boosted the quantity and altered the phenotypic properties of SARS-CoV-2-specific T cells, while in convalescents the second dose changed neither. Spike-specific T cells from convalescent vaccinees differed strikingly from those of infection-naive vaccinees, with phenotypic features suggesting superior long-term persistence and ability to home to the respiratory tract including the nasopharynx. These results provide reassurance that vaccine-elicited T cells respond robustly to emerging viral variants, confirm that convalescents may not need a second vaccine dose, and suggest that vaccinated convalescents may have more persistent nasopharynx-homing SARS-CoV-2-specific T cells compared to their infection-naive counterparts.

Journal ArticleDOI
TL;DR: NeuMIP as mentioned in this paper generalizes traditional mipmap pyramids to pyramids of neural textures, combined with a fully connected network, and introduces neural offsets, a novel method which enables rendering materials with intricate parallax effects without any tessellation.
Abstract: We propose NeuMIP, a neural method for representing and rendering a variety of material appearances at different scales. Classical prefiltering (mipmapping) methods work well on simple material properties such as diffuse color, but fail to generalize to normals, self-shadowing, fibers or more complex microstructures and reflectances. In this work, we generalize traditional mipmap pyramids to pyramids of neural textures, combined with a fully connected network. We also introduce neural offsets, a novel method which enables rendering materials with intricate parallax effects without any tessellation. This generalizes classical parallax mapping, but is trained without supervision by any explicit heightfield. Neural materials within our system support a 7-dimensional query, including position, incoming and outgoing direction, and the desired filter kernel size. The materials have small storage (on the order of standard mipmapping except with more texture channels), and can be integrated within common Monte-Carlo path tracing systems. We demonstrate our method on a variety of materials, resulting in complex appearance across levels of detail, with accurate parallax, self-shadowing, and other effects.

Journal ArticleDOI
TL;DR: In this article, the behavioral response of an apex predator to changes in prey behavior and condition can dramatically alter the role and relative contribution of top-down forcing, depending on the spatial organization of ecosystem states.
Abstract: Consumer and predator foraging behavior can impart profound trait-mediated constraints on community regulation that scale up to influence the structure and stability of ecosystems. Here, we demonstrate how the behavioral response of an apex predator to changes in prey behavior and condition can dramatically alter the role and relative contribution of top-down forcing, depending on the spatial organization of ecosystem states. In 2014, a rapid and dramatic decline in the abundance of a mesopredator (Pycnopodia helianthoides) and primary producer (Macrocystis pyrifera) coincided with a fundamental change in purple sea urchin (Strongylocentrotus purpuratus) foraging behavior and condition, resulting in a spatial mosaic of kelp forests interspersed with patches of sea urchin barrens. We show that this mosaic of adjacent alternative ecosystem states led to an increase in the number of sea otters (Enhydra lutris nereis) specializing on urchin prey, a population-level increase in urchin consumption, and an increase in sea otter survivorship. We further show that the spatial distribution of sea otter foraging efforts for urchin prey was not directly linked to high prey density but rather was predicted by the distribution of energetically profitable prey. Therefore, we infer that spatially explicit sea otter foraging enhances the resistance of remnant forests to overgrazing but does not directly contribute to the resilience (recovery) of forests. These results highlight the role of consumer and predator trait-mediated responses to resource mosaics that are common throughout natural ecosystems and enhance understanding of reciprocal feedbacks between top-down and bottom-up forcing on the regional stability of ecosystems.