OBJECTIVES/GOALS: This study will collect multimodal and longitudinal data in adults with obsessive-compulsive disorder and healthy controls. A mixed effects random forest machine learning approach will be taken to develop a model that can predict individualized longitudinal OCD symptom burden. METHODS/STUDY POPULATION: Baseline resting state functional MRI (rsfMRI) and measures of symptom burden will be collected in adults with OCD and healthy controls. Longitudinal measures of behavior and physiology–such as heart rate, activity, and sleep metrics - will be collected using Fitbit Charge 5 tracker. Daily assessments of symptom burden and functional status will be collected through a smartphone app. Individuals with OCD will start pharmacotherapy during the study period and all participants will be followed for a total of 10 weeks. Repeat rsfMRI imaging will occur at study conclusion. Data will be analyzed using a mixed effects random forest machine learning algorithm with assessment of model performance. RESULTS/ANTICIPATED RESULTS: Prior studies of symptom severity in psychiatric illness and affect in non-clinical populations have found longitudinal features - such as lexical and acoustic measures, participant context, heart rate, and sleep metrics–that were predictive of these states over time. It is anticipated that the present study will extend these results to individuals with OCD and identify physiologic and behavioral features that track personalized symptom burden longitudinally in this patient population. A model able to predict when symptoms are elevated could allow for provision of additional treatment or interventions targeted to times of high symptom burden. DISCUSSION/SIGNIFICANCE: This study will be the first to collect and analyze longitudinal measures of behavior, symptoms, and physiology in patients with OCD with a goal of predicting symptom burden. Identification of elevated symptom burden would allow for implementation of just-in-time treatment, during these periods.

Whole-genome bisulfite sequencing (WGBS) is the standard method for profiling DNA methylation at single-nucleotide resolution. Different tools have been developed to extract differentially methylated regions (DMRs), often built upon assumptions from mammalian data. Here, we present MethylScore, a pipeline to analyse WGBS data and to account for the substantially more complex and variable nature of plant DNA methylation. MethylScore uses an unsupervised machine learning approach to segment the genome by classification into states of high and low methylation. It processes data from genomic alignments to DMR output and is designed to be usable by novice and expert users alike. We show how MethylScore can identify DMRs from hundreds of samples and how its data-driven approach can stratify associated samples without prior information. We identify DMRs in the A. thaliana 1,001 Genomes dataset to unveil known and unknown genotype–epigenotype associations .

OBJECTIVES/GOALS: Osteoarthritis (OA) is a cartilage destroying disease. We are investigating abaloparatide (ABL) activation of parathyroid hormone receptor type 1 (PTH1R), which is expressed by articular chondrocytes in OA. We propose ABL treatment is chondroprotective in murine PTOA via stimulation of matrix production and inhibition of chondrocyte maturation. METHODS/STUDY POPULATION: 16-week-old C57BL/6 male mice received destabilization of the medial meniscus (DMM) surgery to induce knee PTOA. Beginning 2 weeks post-DMM, 40 μg/kg of ABL (or saline) was administered daily via subcutaneous injection and tissues were harvested after 6 weeks of daily injections and 8 weeks after DMM surgery. Harvested joint tissues were used for histological and molecular assessment of OA using three 5 μm thick sagittal sections from each joint, 50 μm apart, cut from the medial compartment of injured knees. Safranin O/Fast Green tissue staining and immunohistochemistry-based detection of type 10 collagen (Col10) and lubricin (Prg4) was performed using standard methods. Histomorphometric quantification of tibial cartilage area and larger hypertrophic-like cells was performed using the Osteomeasure system. RESULTS/ANTICIPATED RESULTS: Safranin O/Fast Green stained sections showed a decreased cartilage loss in DMM joints from ABL-treated versus saline-treated mice. Histomorphometric analysis of total tibial cartilage area revealed preservation of cartilage tissue on the tibial surface. Immunohistochemical analyses showed that upregulation of Col10 in DMM joints was mitigated in the cartilage of ABL-treated mice, and chondrocyte expression of Prg4 was increased in uncalcified cartilage areas in ABL-treated group. The Prg4 finding suggests a matrix anabolic effect that may counter OA cartilage loss. Quantification of chondrocytes in uncalcified and calcified tibial cartilage areas revealed a reduction in the number of larger hypertrophic-like cells in ABL treated mice, suggesting deceleration of hypertrophic differentiation. DISCUSSION/SIGNIFICANCE: Cartilage preservation/regeneration therapies would fill a critical unmet need. We demonstrate that an osteoporosis drug targeting PTH1R decelerates PTOA in mice. ABL treatment was associated with preservation of cartilage, decreased Col10, increased Prg4, and decreased number of large hypertrophic-like chondrocytes in the tibial cartilage.

Conventionally, intelligence is seen as a property of individuals. However, it is also known to be a property of collectives. Here, we broaden the idea of intelligence as a collective property and extend it to the planetary scale. We consider the ways in which the appearance of technological intelligence may represent a kind of planetary scale transition, and thus might be seen not as something which happens on a planet but to a planet, much as some models propose the origin of life itself was a planetary phenomenon. Our approach follows the recognition among researchers that the correct scale to understand key aspects of life and its evolution is planetary, as opposed to the more traditional focus on individual species. We explore ways in which the concept may prove useful for three distinct domains: Earth Systems and Exoplanet studies; Anthropocene and Sustainability studies; and the study of Technosignatures and the Search for Extraterrestrial Intelligence (SETI). We argue that explorations of planetary intelligence, defined as the acquisition and application of collective knowledge operating at a planetary scale and integrated into the function of coupled planetary systems, can prove a useful framework for understanding possible paths of the long-term evolution of inhabited planets including future trajectories for life on Earth and predicting features of intelligentially steered planetary evolution on other worlds.

Background: In November 2020, bamlanivimab received emergency use authorization (EUA) to treat patients with early, mild-to-moderate COVID-19 who are at high risk of progression. Montefiore Medical Center serves an economically underserved community of >1.4 million residents in the Bronx, New York. Montefiore’s antimicrobial stewardship team (AST) developed a multidisciplinary treatment pathway for patients meeting EUA criteria: (1) outpatients and hospital associates and (2) acute-care patients (EDs or inpatient). Methods: The Montefiore AST established a centralized process for screening high-risk COVID-19 patients 7 days a week. Referrals were sent by e-mail from occupational health, primary care practices, specialty practices, emergency departments, and urgent care centers. Patients were screened in real time and were treated in the ED or a newly established infusion center within 24 hours. After infusion, all patients received phone calls from nurses and had an infectious diseases televisit. Demographics, clinical symptoms, subsequent ED visit or hospital admission, and timing from infusion to ED or hospitalization were obtained from the electronic health record. Results: In total, 281 high-risk patients (median age, 62 years; 57% female) received bamlanivimab at the infusion center or in the acute-care setting between December 2, 2020, and January 27, 2021 (Table 1). The number of treated patients increased weekly (Figure 1). Also, 62% were Hispanic or black, and 96% met EUA criteria. Furthermore, 51 (18%) were referred from occupational health, 205 (73%) were referred from the community, and 25 (9%) were inpatients (https://www.fda.gov/media/143605/download). All patients were successfully infused without adverse reactions. In addition, 23 patients (8.2%) were hospitalized and 6 (2.1%) visited EDs within 30 days of treatment. The average number of days between symptom onset and infusion was 4.9. The median age of admitted versus nonadmitted patients was 68 years versus 61.5 years (P = .07). Conclusions: An AST-coordinated bamlanivimab treatment program successfully treated multiple high-risk COVID-19 patients and potentially reduced hospitalizations. However, the effort, personnel, and resources required are significant. Dedicated hospital investment is necessary for maximal success.

Funding: No

Disclosures: None

Table 1.

Figure 1.

In 2018, the Neurodevelopmental and Psychosocial Interventions Working Group of the Cardiac Neurodevelopmental Outcome Collaborative convened through support from an R13 grant from the National Heart, Lung, and Blood Institute to survey the state of neurodevelopmental and psychosocial intervention research in CHD and to propose a slate of critical questions and investigations required to improve outcomes for this growing population of survivors and their families. Prior research, although limited, suggests that individualised developmental care interventions delivered early in life are beneficial for improving a range of outcomes including feeding, motor and cognitive development, and physiological regulation. Interventions to address self-regulatory, cognitive, and social-emotional challenges have shown promise in other medical populations, yet their applicability and effectiveness for use in individuals with CHD have not been examined. To move this field of research forward, we must strive to better understand the impact of neurodevelopmental and psychosocial intervention within the CHD population including adapting existing interventions for individuals with CHD. We must examine the ways in which dedicated cardiac neurodevelopmental follow-up programmes bolster resilience and support children and families through the myriad transitions inherent to the experience of living with CHD. And, we must ensure that interventions are person-/family-centred, inclusive of individuals from diverse cultural backgrounds as well as those with genetic/medical comorbidities, and proactive in their efforts to include individuals who are at highest risk but who may be traditionally less likely to participate in intervention trials.

First contact with another civilization, or simply another intelligence of some kind, will likely be quite different depending on whether that intelligence is more or less advanced than ourselves. If we assume that the lifetime distribution of intelligences follows an approximately exponential distribution, one might naively assume that the pile-up of short-lived entities dominates any detection or contact scenario. However, it is argued here that the probability of contact is proportional to the age of said intelligence (or possibly stronger), which introduces a selection effect. We demonstrate that detected intelligences will have a mean age twice that of the underlying (detected + undetected) population, using the exponential model. We find that our first contact will most likely be with an older intelligence, provided that the maximum allowed mean lifetime of the intelligence population, τmax, is ≥ e times larger than our own. Older intelligences may be rare but they disproportionately contribute to first contacts, introducing what we call a ‘contact inequality’, analogous to wealth inequality. This reasoning formalizes intuitional arguments and highlights that first contact would likely be one-sided, with ramifications for how we approach SETI.

OBJECTIVES/GOALS: Rapid and accurate identification of primary malaria vector species from collected specimens is the most critical aspect of effective vector surveillance and control. This interdisciplinary team of engineers aims to automate identification using a deep learning computer vision algorithm. METHODS/STUDY POPULATION: The team spent August of 2019 observing and participating in control and surveillance activities in Zambia and Uganda. They conducted >65 interviews with key stakeholders across 9 malaria control and surveillance sites, ranging from field and community health workers, to malaria researchers and Ministry of Health employees. Stakeholder feedback validated the need for a more accurate and efficient method of vector identification in order to more effectively deploy targeted malaria interventions. The team set forth in designing and prototyping a portable, automated field tool that could speciate mosquito vectors to the complex level using artificial intelligence. RESULTS/ANTICIPATED RESULTS: The team’s research demonstrated that accuracy, cost effectiveness, and ease of use would be critical to the successful adoption of the tool. Results of initial prototyping, usability studies, and stakeholder surveys were used to determine the tool’s minimal user specifications: 1) the ability to distinguish between Anopheles Gambiae and Anopheles Funestus, the two principal malaria vectors in the countries visited, 2) achieving an identification accuracy of ≥90% to the complex level, and 3) accessibility to the speciation data 3-7 days following vector collection. Next steps include optimizing the tool to deploy a minimal viable product for testing in Kenya by the summer of 2020. DISCUSSION/SIGNIFICANCE OF IMPACT: The accurate, high-quality surveillance enabled by this device would allow malaria control programs to scale surveillance to remote regions where an entomologist may not be available, allowing malaria programs to deploy effective interventions, monitor results, and prevent disease.

The Fontan Outcomes Network was created to improve outcomes for children and adults with single ventricle CHD living with Fontan circulation. The network mission is to optimise longevity and quality of life by improving physical health, neurodevelopmental outcomes, resilience, and emotional health for these individuals and their families. This manuscript describes the systematic design of this new learning health network, including the initial steps in development of a national, lifespan registry, and pilot testing of data collection forms at 10 congenital heart centres.

Identifying early risk factors for the development of social anxiety symptoms has important translational implications. Accurately identifying which children are at the highest risk is of critical importance, especially if we can identify risk early in development. We examined continued risk for social anxiety symptoms at the transition to adolescence in a community sample of children (n = 112) that had been observed for high fearfulness at age 2 and tracked for social anxiety symptoms from preschool through age 6. In our previous studies, we found that a pattern of dysregulated fear (DF), characterized by high fear in low threat contexts, predicted social anxiety symptoms at ages 3, 4, 5, and 6 years across two samples. In the current study, we re-evaluated these children at 11–13 years of age by using parent and child reports of social anxiety symptoms, parental monitoring, and peer relationship quality. The scores for DF uniquely predicted adolescents’ social anxiety symptoms beyond the prediction that was made by more proximal measures of behavioral (e.g., kindergarten social withdrawal) and concurrent environmental risk factors (e.g., parental monitoring, peer relationships). Implications for early detection, prevention, and intervention are discussed.