Carbon Free Boston: Technical Summary

Part of a series of reports that includes: Carbon Free Boston: Summary Report; Carbon Free Boston: Social Equity Report; Carbon Free Boston: Buildings Technical Report; Carbon Free Boston: Transportation Technical Report; Carbon Free Boston: Waste Technical Report; Carbon Free Boston: Energy Technical Report; Carbon Free Boston: Offsets Technical Report; Available at http://sites.bu.edu/cfb/OVERVIEW: This technical summary is intended to argument the rest of the Carbon Free Boston technical reports that seek to achieve this goal of deep mitigation. This document provides below: a rationale for carbon neutrality, a high level description of Carbon Free Boston’s analytical approach; a summary of crosssector strategies; a high level analysis of air quality impacts; and, a brief analysis of off-road and street light emissions.Published versio

Qualitative study of system-level factors related to genomic implementation

PURPOSE: Research on genomic medicine integration has focused on applications at the individual level, with less attention paid to implementation within clinical settings. Therefore, we conducted a qualitative study using the Consolidated Framework for Implementation Research (CFIR) to identify system-level factors that played a role in implementation of genomic medicine within Implementing GeNomics In PracTicE (IGNITE) Network projects. METHODS: Up to four study personnel, including principal investigators and study coordinators from each of six IGNITE projects, were interviewed using a semistructured interview guide that asked interviewees to describe study site(s), progress at each site, and factors facilitating or impeding project implementation. Interviews were coded following CFIR inner-setting constructs. RESULTS: Key barriers included (1) limitations in integrating genomic data and clinical decision support tools into electronic health records, (2) physician reluctance toward genomic research participation and clinical implementation due to a limited evidence base, (3) inadequate reimbursement for genomic medicine, (4) communication among and between investigators and clinicians, and (5) lack of clinical and leadership engagement. CONCLUSION: Implementation of genomic medicine is hindered by several system-level barriers to both research and practice. Addressing these barriers may serve as important facilitators for studying and implementing genomics in practice

The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2

Understanding the circumstances that lead to pandemics is important for their prevention. Here, we analyze the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted A and B. Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October–8 December), while the separate introduction of lineage A likely occurred within weeks of this event. These findings indicate that it is unlikely that SARS-CoV-2 circulated widely in humans prior to November 2019 and define the narrow window between when SARS-CoV-2 first jumped into humans and when the first cases of COVID-19 were reported. As with other coronaviruses, SARS-CoV-2 emergence likely resulted from multiple zoonotic events

The White Mountain Polarimeter Telescope and an Upper Limit on CMB Polarization

The White Mountain Polarimeter (WMPol) is a dedicated ground-based microwave telescope and receiver system for observing polarization of the Cosmic Microwave Background. WMPol is located at an altitude of 3880 meters on a plateau in the White Mountains of Eastern California, USA, at the Barcroft Facility of the University of California White Mountain Research Station. Presented here is a description of the instrument and the data collected during April through October 2004. We set an upper limit on $E$-mode polarization of 14 $\mu\mathrm{K}$ (95% confidence limit) in the multipole range $170<\ell<240$. This result was obtained with 422 hours of observations of a 3 $\mathrm{deg}^2$ sky area about the North Celestial Pole, using a 42 GHz polarimeter. This upper limit is consistent with $EE$ polarization predicted from a standard $\Lambda$-CDM concordance model.Comment: 35 pages. 12 figures. To appear in ApJ

Best Practices and Joint Calling of the HumanExome BeadChip: The CHARGE Consortium

Genotyping arrays are a cost effective approach when typing previously-identified genetic polymorphisms in large numbers of samples. One limitation of genotyping arrays with rare variants (e.g., minor allele frequency [MAF] <0.01) is the difficulty that automated clustering algorithms have to accurately detect and assign genotype calls. Combining intensity data from large numbers of samples may increase the ability to accurately call the genotypes of rare variants. Approximately 62,000 ethnically diverse samples from eleven Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium cohorts were genotyped with the Illumina HumanExome BeadChip across seven genotyping centers. The raw data files for the samples were assembled into a single project for joint calling. To assess the quality of the joint calling, concordance of genotypes in a subset of individuals having both exome chip and exome sequence data was analyzed. After exclusion of low performing SNPs on the exome chip and non-overlap of SNPs derived from sequence data, genotypes of 185,119 variants (11,356 were monomorphic) were compared in 530 individuals that had whole exome sequence data. A total of 98,113,070 pairs of genotypes were tested and 99.77% were concordant, 0.14% had missing data, and 0.09% were discordant. We report that joint calling allows the ability to accurately genotype rare variation using array technology when large sample sizes are available and best practices are followed. The cluster file from this experiment is available at www.chargeconsortium.com/main/exomechip