Abstract: Emotional and behavioral dysregulation during early childhood are associated with severe psychiatric, behavioral, and cognitive disorders through adulthood. Identifying the earliest antecedents of persisting emotional and behavioral dysregulation can inform risk detection practices and targeted interventions to promote adaptive developmental trajectories among at-risk children. Objective To characterize children’s emotional and behavioral regulation trajectories and examine risk factors associated with persisting dysregulation across early childhood. Design, Setting, and Participants This cohort study examined data from 20 United States cohorts participating in Environmental influences on Child Health Outcomes, which included 3934 mother-child pairs (singleton births) from 1990 to 2019. Statistical analysis was performed from January to August 2022. Exposures Standardized self-reports and medical data ascertained maternal, child, and environmental characteristics, including prenatal substance exposures, preterm birth, and multiple psychosocial adversities. Main Outcomes and Measures Child Behavior Checklist caregiver reports at 18 to 72 months of age, with Dysregulation Profile (CBCL-DP = sum of anxiety/depression, attention, and aggression). Results The sample included 3934 mother-child pairs studied at 18 to 72 months. Among the mothers, 718 (18.7%) were Hispanic, 275 (7.2%) were non-Hispanic Asian, 1220 (31.8%) were non-Hispanic Black, 1412 (36.9%) were non-Hispanic White; 3501 (89.7%) were at least 21 years of age at delivery. Among the children, 2093 (53.2%) were male, 1178 of 2143 with Psychosocial Adversity Index [PAI] data (55.0%) experienced multiple psychosocial adversities, 1148 (29.2%) were exposed prenatally to at least 1 psychoactive substance, and 3066 (80.2%) were term-born (≥37 weeks’ gestation). Growth mixture modeling characterized a 3-class CBCL-DP trajectory model: high and increasing (2.3% [n = 89] ), borderline and stable (12.3% [n = 479]), and low and decreasing (85.6% [n = 3366] ). Children in high and borderline dysregulation trajectories had more prevalent maternal psychological challenges (29.4%-50.0%). Multinomial logistic regression analyses indicated that children born preterm were more likely to be in the high dysregulation trajectory (adjusted odds ratio [aOR], 2.76; 95% CI, 2.08-3.65; P   & amp;lt; .001) or borderline dysregulation trajectory (aOR, 1.36; 95% CI, 1.06-1.76; P  = .02) vs low dysregulation trajectory. High vs low dysregulation trajectories were less prevalent for girls compared with boys (aOR, 0.60; 95% CI, 0.36-1.01; P  = .05) and children with lower PAI (aOR, 1.94; 95% CI, 1.51-2.49; P   & amp;lt; .001). Combined increases in PAI and prenatal substance exposures were associated with increased odds of high vs borderline dysregulation (aOR, 1.28; 95% CI, 1.08-1.53; P  = .006) and decreased odds of low vs high dysregulation (aOR, 0.77; 95% CI, 0.64-0.92; P  = .005). Conclusions and Relevance In this cohort study of behavioral dysregulation trajectories, associations were found with early risk factors. These findings may inform screening and diagnostic practices for addressing observed precursors of persisting dysregulation as they emerge among at-risk children.

Abstract: The COVID-19 pandemic led to widespread lockdowns and school closures that may have affected screen time among children. Although restrictions were strongest early in the pandemic, it is unclear how screen time changed as the pandemic progressed. Objective To evaluate change in children’s screen time from before the pandemic to during the pandemic, from July 2019 through August 2021. Design, Setting, and Participants This is a longitudinal cohort study with repeated measures of screen time collected before the pandemic and during 2 pandemic periods. Children aged 4 to 12 years and their parent were enrolled in 3 pediatric cohorts across 3 states in the US participating in the Environmental Influences of Child Health Outcomes (ECHO) Program. Data analysis was performed from November 2021 to July 2022. Exposures COVID-19 pandemic period: prepandemic (July 2019 to March 2020), pandemic period 1 (December 2020 to April 2021), and pandemic period 2 (May 2021 to August 2021). Main Outcomes and Measures The primary outcomes were total, educational (not including remote school), and recreational screen time assessed via the ECHO Child Media Use questionnaire. Linear mixed-effects models were used for screen time adjusted for child’s age, number of siblings, sex, race, ethnicity, and maternal education. Results The cohort included 228 children (prepandemic mean [SD] age, 7.0 [2.7] years; 100 female [43.9%]) with screen time measured during the prepandemic period and at least once during the pandemic period. Prepandemic mean (SD) total screen time was 4.4 (3.9) hours per day and increased 1.75 hours per day (95% CI, 1.18-2.31 hours per day) in the first pandemic period and 1.11 hours per day (95% CI, 0.49-1.72 hours per day) in the second pandemic period, in adjusted models. Prepandemic mean (SD) recreational screen time was 4.0 (3.5) hours per day and increased 0.89 hours per day (95% CI, 0.39-1.39 hours per day) in the first pandemic period and 0.70 hours per day (95% CI, 0.16-1.25 hours per day) in the second pandemic period. Prepandemic mean (SD) educational screen time was 0.5 (1.2) hours per day (median [IQR] , 0.0 [0.0-0.4] hours per day) and increased 0.93 hours per day (95% CI, 0.67-1.19 hours per day) in the first pandemic period and 0.46 hours per day (95% CI, 0.18-0.74 hours per day) in the second pandemic period. Conclusions and Relevance These findings suggest that screen time among children increased during the COVID-19 pandemic and remained elevated even after many public health precautions were lifted. The long-term association of increased screen time during the COVID-19 pandemic with children’s health needs to be determined.

Abstract: Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830] ). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables.

Abstract: We describe the calibration and imaging heuristics developed and deployed in the Atacama Large Millimeter/submillimeter Array (ALMA) interferometric data processing pipeline, as of ALMA Cycle 9 operations. The pipeline software framework is written in Python, with each data reduction stage layered on top of tasks and toolkit functions provided by the Common Astronomy Software Applications package. This framework supports a variety of tasks for observatory operations, including science data quality assurance, observing mode commissioning, and user reprocessing. It supports ALMA and Very Large Array interferometric data along with ALMA and NRO 45 m single dish data, via different stages and heuristics. In addition to producing calibration tables, calibrated measurement sets, and cleaned images, the pipeline creates a WebLog which serves as the primary interface for verifying the quality assurance of the data by the observatory and for examining the contents of the data by the user. Following the adoption of the pipeline by ALMA Operations in 2014, the heuristics have been refined through annual prioritized development cycles, culminating in a new pipeline release aligned with the start of each ALMA Cycle of observations. Initial development focused on basic calibration and flagging heuristics (Cycles 2–3), followed by imaging heuristics (Cycles 4–5). Further refinement of the flagging and imaging heuristics, including the introduction of parallel processing, proceeded for Cycles 6–7. In the 2020 release, the algorithm to identify channels to use for continuum subtraction and imaging was substantially improved by the addition of a moment difference analysis. A spectral renormalization stage was added for the 2021 release (Cycle 8) to correct high spectral resolution visibility data acquired on targets exhibiting strong celestial line emission in their autocorrelation spectra. The calibration heuristics used in the low signal-to-noise regime were improved for the 2022 release (Cycle 9). In the two most recent Cycles, 97% of ALMA data sets were calibrated and imaged with the pipeline, ensuring long-term automated reproducibility of results. We conclude with a brief description of plans for future additions, including a self-calibration stage, support for multi-configuration imaging, and complete calibration and imaging of full polarization data.

Abstract: While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients does not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involves assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post-challenge analysis identified a novel expression-based risk marker, PHF19 , which has recently been found to have an important biological role in multiple myeloma. Lastly, we show that a simple four feature predictor composed of age, ISS, and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.

Abstract: Multiple myeloma (MM) is a hematological malignancy of terminally differentiated plasma cells residing within the bone marrow with 25,000-30,000 patients diagnosed in the United States each year. The disease's clinical course depends on a complex interplay chromosomal abnormalities and mutations within plasma cells and patient socio-demographic factors. Novel treatments extended the time to disease progression and overall survival for the majority of patients. However, a subset of 15%-20% of MM patients exhibit an aggressive disease course with rapid disease progression and poor overall survival regardless of treatment. Accurately predicting which patients are at high-risk is critical to designing studies with a better understanding of myeloma progression and enabling the discovery of novel therapeutics that extend the progression free period of these patients. To date, most MM risk models use patient demographic data, clinical laboratory results and cytogenetic assays to predict clinical outcome. High-risk associated cytogenetic alterations include deletion of 17p or gain of 1q as well as t(14;16), t(14;20), and most commonly t(4,14), which leads to juxtaposition of MMSET with the immunoglobulin heavy chain locus promoter, resulting in overexpression of the MMSET oncogene. While cytogenetic assays, in particular fluorescence in situ hybridization (FISH), are widely available, their risk prediction is sub-optimal and recently developed gene expression based classifiers predict more accurately rapid progression. To investigate possible improvements to models of myeloma risk, we organized the Multiple Myeloma DREAM Challenge, focusing on predicting high-risk, defined as disease progression or death prior to 18 months from diagnosis. This effort combined 4 discovery datasets providing participants with clinical, cytogenetic, demographic and gene expression data to facilitate model development while retaining 4 additional datasets, whose clinical outcome was not publicly available, in order to benchmark submitted models. This crowd-sourced effort resulted in the unbiased assessment of 171 predictive algorithms on the validation dataset (N = 823 unique patient samples). Analysis of top performing methods identified high expression of PHF19, a histone methyltransferase, as the gene most strongly associated with disease progression, showing greater predictive power than the expression level of the putative high-risk gene MMSET. We show that a simple 4 feature model composed of age, stage and the gene expression of PHF19 and MMSET is as accurate as much larger published models composed of over 50 genes combined with ISS and age. Results from this work suggest that combination of gene expression and clinical data increases accuracy of high risk models which would improve patient selection in the clinic. Disclosures Towfic: Celgene Corporation: Employment, Equity Ownership. Dalton:MILLENNIUM PHARMACEUTICALS, INC.: Honoraria. Goldschmidt:Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Research Funding; Amgen: Consultancy, Research Funding; Chugai: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Molecular Partners: Research Funding; MSD: Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Research Funding; Dietmar-Hopp-Stiftung: Research Funding; John-Hopkins University: Research Funding. Avet-Loiseau:takeda: Consultancy, Other: travel fees, lecture fees, Research Funding; celgene: Consultancy, Other: travel fees, lecture fees, Research Funding. Ortiz:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Dervan:Celgene: Employment. Flynt:Celgene Corporation: Employment, Equity Ownership. Dai:M2Gen: Employment. Bassett:Celgene: Employment, Equity Ownership. Sonneveld:SkylineDx: Research Funding; Takeda: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Honoraria; Amgen: Honoraria, Research Funding. Shain:Amgen: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy. Munshi:Abbvie: Consultancy; Takeda: Consultancy; Oncopep: Consultancy; Celgene: Consultancy; Adaptive: Consultancy; Amgen: Consultancy; Janssen: Consultancy. Morgan:Bristol-Myers Squibb, Celgene Corporation, Takeda: Consultancy, Honoraria; Celgene Corporation, Janssen: Research Funding; Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses. Walker:Celgene: Research Funding. Thakurta:Celgene: Employment, Equity Ownership.

Abstract: We present deep X-ray and radio observations of the fast blue optical transient (FBOT) AT 2020xnd/ZTF 20acigmel at z = 0.2433 from 13 days to 269 days after explosion. AT 2020xnd belongs to the category of optically luminous FBOTs with similarities to the archetypal event AT 2018cow. AT 2020xnd shows luminous radio emission reaching L ν ≈ 8 × 10 29 erg s −1 Hz −1 at 20 GHz and 75 days post-explosion, accompanied by luminous and rapidly fading soft X-ray emission peaking at L X ≈ 6 × 10 42 erg s −1 . Interpreting the radio emission in the context of synchrotron radiation from the explosion’s shock interaction with the environment, we find that AT 2020xnd launched a high-velocity outflow ( v ∼ 0.1 c –0.2 c ) propagating into a dense circumstellar medium (effective M ̇ ≈ 10 − 3 M ⊙ yr −1 for an assumed wind velocity of v w = 1000 km s −1 ). Similar to AT 2018cow, the detected X-ray emission is in excess compared to the extrapolated synchrotron spectrum and constitutes a different emission component, possibly powered by accretion onto a newly formed black hole or neutron star. These properties make AT 2020xnd a high-redshift analog to AT 2018cow, and establish AT 2020xnd as the fourth member of the class of optically luminous FBOTs with luminous multiwavelength counterparts.

Abstract: Few population-based studies in the US collected individual-level data from families during the COVID-19 pandemic. Objective To examine differences in COVID-19 pandemic–related experiences in a large sociodemographically diverse sample of children and caregivers. Design, Setting, and Participants The Environmental influences on Child Health Outcomes (ECHO) multi-cohort consortium is an ongoing study that brings together 64 individual cohorts with participants (24 757 children and 31 700 caregivers in this study) in all 50 US states and Puerto Rico. Participants who completed the ECHO COVID-19 survey between April 2020 and March 2022 were included in this cross-sectional analysis. Data were analyzed from July 2021 to September 2022. Main Outcomes and Measures Exposures of interest were caregiver education level, child life stage (infant, preschool, middle childhood, and adolescent), and urban or rural (population & amp;lt;50 000) residence. Dependent variables included COVID-19 infection status and testing; disruptions to school, child care, and health care; financial hardships; and remote work. Outcomes were examined separately in logistic regression models mutually adjusted for exposures of interest and race, ethnicity, US Census division, sex, and survey administration date. Results Analyses included 14 646 children (mean [SD] age, 7.1 [4.4] years; 7120 [49%] female) and 13 644 caregivers (mean [SD] age, 37.6 [7.2] years; 13 381 [98%] female). Caregivers were racially (3% Asian; 16% Black; 12% multiple race; 63% White) and ethnically (19% Hispanic) diverse and comparable with the US population. Less than high school education (vs master’s degree or more) was associated with more challenges accessing COVID-19 tests (adjusted odds ratio [aOR], 1.88; 95% CI, 1.06-1.58), lower odds of working remotely (aOR, 0.04; 95% CI, 0.03-0.07), and more food access concerns (aOR, 4.14; 95% CI, 3.20-5.36). Compared with other age groups, young children (age 1 to 5 years) were least likely to receive support from schools during school closures, and their caregivers were most likely to have challenges arranging childcare and concerns about work impacts. Rural caregivers were less likely to rank health concerns (aOR, 0.77; 95% CI, 0.69-0.86) and social distancing (aOR, 0.82; 95% CI, 0.73-0.91) as top stressors compared with urban caregivers. Conclusions Findings in this cohort study of US families highlighted pandemic-related burdens faced by families with lower socioeconomic status and young children. Populations more vulnerable to public health crises should be prioritized in recovery efforts and future planning.

Abstract: The FDA recently approved eight targeted therapies for acute myeloid leukemia (AML), including the BCL-2 inhibitor venetoclax. Maximizing efficacy of these treatments requires refining patient selection. To this end, we analyzed two recent AML studies profiling the gene expression and ex vivo drug response of primary patient samples. We find that ex vivo samples often exhibit a general sensitivity to (any) drug exposure, independent of drug target. We observe that this “general response across drugs” (GRD) is associated with FLT3 -ITD mutations, clinical response to standard induction chemotherapy, and overall survival. Further, incorporating GRD into expression-based regression models trained on one of the studies improved their performance in predicting ex vivo response in the second study, thus signifying its relevance to precision oncology efforts. We find that venetoclax response is independent of GRD but instead show that it is linked to expression of monocyte-associated genes by developing and applying a multi-source Bayesian regression approach. The method shares information across studies to robustly identify biomarkers of drug response and is broadly applicable in integrative analyses.