Hot money: Illuminating the financing of high-carbon infrastructure in the developing world

Major infrastructure financiers will have to significantly decarbonize their investments to meet mounting promises to cut carbon emissions to “net-zero” by mid-century. We provide new details about those needed shifts. Using two World Bank databases of infrastructure projects throughout the developing world, and applying a methodology for imputing the projects' likely future carbon output, we assess the emissions profile of power-plant projects executed from 2018 through 2020 — the three years immediately preceding the spate of net-zero pledges. We find that approximately half the generation executed in those years is too carbon-intensive to align with keeping Earth's average temperature from exceeding 1.5°C above pre-industrial levels, largely because of the prevalence of new natural-gas–fired power plants. We also find new evidence of host countries' agency in shaping carbon trajectories: Much of the climate-misaligned financing is not foreign but domestic. And we find different institutions are financing infrastructure portfolios with significantly differing carbon intensities.

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New data yields insights on climate impact of developing-world infrastructure finance

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52% of power generation added in developing world from 2018 through 2020 is climate-misaligned

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Climate benefits of falling coal financing eclipsed by surging natural-gas financing

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Much high-carbon developing-world finance comes from domestic, not foreign, investors

Nephele: a cloud platform for simplified, standardized and reproducible microbiome data analysis

Motivation

Widespread interest in the study of the microbiome has resulted in data proliferation and the development of powerful computational tools. However, many scientific researchers lack the time, training, or infrastructure to work with large datasets or to install and use command line tools.

Results

The National Institute of Allergy and Infectious Diseases (NIAID) has created Nephele, a cloud-based microbiome data analysis platform with standardized pipelines and a simple web interface for transforming raw data into biological insights. Nephele integrates common microbiome analysis tools as well as valuable reference datasets like the healthy human subjects cohort of the Human Microbiome Project (HMP). Nephele is built on the Amazon Web Services cloud, which provides centralized and automated storage and compute capacity, thereby reducing the burden on researchers and their institutions.

Availability and implementation

https://nephele.niaid.nih.gov and https://github.com/niaid/Nephele.

Contact

darrell.hurt@nih.gov.

The Papillomavirus Episteme: a major update to the papillomavirus sequence database

The Papillomavirus Episteme (PaVE) is a database of curated papillomavirus genomic sequences, accompanied by web-based sequence analysis tools. This update describes the addition of major new features. The papillomavirus genomes within PaVE have been further annotated, and now includes the major spliced mRNA transcripts. Viral genes and transcripts can be visualized on both linear and circular genome browsers. Evolutionary relationships among PaVE reference protein sequences can be analysed using multiple sequence alignments and phylogenetic trees. To assist in viral discovery, PaVE offers a typing tool; a simplified algorithm to determine whether a newly sequenced virus is novel. PaVE also now contains an image library containing gross clinical and histopathological images of papillomavirus infected lesions. Database URL: https://pave.niaid.nih.gov/.

NEP: web server for epitope prediction based on antibody neutralization of viral strains with diverse sequences

Delineation of the antigenic site, or epitope, recognized by an antibody can provide clues about functional vulnerabilities and resistance mechanisms, and can therefore guide antibody optimization and epitope-based vaccine design. Previously, we developed an algorithm for antibody-epitope prediction based on antibody neutralization of viral strains with diverse sequences and validated the algorithm on a set of broadly neutralizing HIV-1 antibodies. Here we describe the implementation of this algorithm, NEP (Neutralization-based Epitope Prediction), as a web-based server. The users must supply as input: (i) an alignment of antigen sequences of diverse viral strains; (ii) neutralization data for the antibody of interest against the same set of antigen sequences; and (iii) (optional) a structure of the unbound antigen, for enhanced prediction accuracy. The prediction results can be downloaded or viewed interactively on the antigen structure (if supplied) from the web browser using a JSmol applet. Since neutralization experiments are typically performed as one of the first steps in the characterization of an antibody to determine its breadth and potency, the NEP server can be used to predict antibody-epitope information at no additional experimental costs. NEP can be accessed on the internet at http://exon.niaid.nih.gov/nep.

Usefulness of psychosocial theory variables in explaining fat-related dietary behavior in Chinese Americans: association with degree of acculturation

OBJECTIVE

To determine the usefulness of variables from psychosocial models of health behavior in explaining fat-related dietary behavior among a sample of Chinese Americans.

DESIGN

A survey questionnaire was administered to a convenience sample of Chinese Americans and analyzed for descriptive statistics and relationships among variables.

SUBJECTS/SETTINGS

Participants were 600 healthy individuals, ranging from 25 to 70 years of age, living in New York City.

VARIABLES MEASURED

Demographic factors, degree of acculturation, food preferences, and 13 social psychological scales derived from the Theory of Planned Behavior, the Healthy Belief Model, and Social Cognitive Theory. Dependent measures assessed were intention to reduce dietary fat and behaviors related to the selection of reduced-fat diets.

STATISTICAL ANALYSES

Descriptive statistics, Pearsons' correlation coefficients, t-tests, one-way analyses of variance, and multiple regression analyses were used.

RESULTS

Attitude, overall health concern, and self-efficacy accounted for 58% of the variance in behavioral intention for the entire sample. Attitude, perceived barriers, and self-efficacy accounted for 19% of the variance in the prediction of dietary fat reduction behaviors. In general, a gradient was seen in the increased predictiveness of each regression model by degree of acculturation of the immigrants to American culture: predictiveness (R2) for behavior ranged from 15% for the least to 34% for the most acculturated. Acculturation was significantly related to declines in the influence of habit and of social norms. These effects were not seen by length of residency.

IMPLICATIONS

Nutrition educators should assess the degree of acculturation of groups with whom they work and recognize that the degree of acculturation impacts the relative importance of various psychosocial variables in fat reduction behaviors.