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Economics is in a state of \"creative ferment,\" according to lead authors Suresh Naidu, Dani Rodrik, and Gabriel Zucman. A decade after the Great Recession, they argue for a new brand of economics, one divorced from market fundamentalism and focused instead on a more inclusive society. Responses to their ideas--which come from economists, philosophers, political scientists, and policymakers across the political spectrum--showcase just how passionate the debate over the future of economics has become. -- Publishers website

To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional relevance of expressed microbial genes or proteins. This can be accomplished through metaproteomics, which characterizes the protein expression of microbiomes. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, to enable researchers to make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental caries plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN5, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the NCBI non-redundant database revealed that the sensitivity and specificity of functional annotation varied between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated more accurate GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform.

Background Emerging literature suggests that LGBTQ+ cancer survivors are more likely to experience financial burden than non‐LGBTQ+ survivors. However, LGBTQ+ cancer survivors experience with cost‐coping behaviors such as crowdfunding is understudied. Methods We aimed to assess LGBTQ+ inequity in cancer crowdfunding by combining community‐engaged and technology‐based methods. Crowdfunding campaigns were web‐scraped from GoFundMe and classified as cancer‐related and LGBTQ+ or non‐LGBTQ+ using term dictionaries. Bivariate analyses and generalized linear models were used to assess differential effects in total goal amount raised by LGBTQ+ status. Stratified models were run by online reach and LGBTQ+ inclusivity of state policy. Results A total of N = 188,342 active cancer‐related crowdfunding campaigns were web‐scraped from GoFundMe in November 2022, of which N = 535 were LGBTQ+ and ranged from 2014 to 2022. In multivariable models of recent campaigns (2019–2022), LGBTQ+ campaigns raised$1608 (95% CI: −2139, −1077) less than non‐LGBTQ+ campaigns. LGBTQ+ campaigns with low (26–45 donors), moderate (46–87 donors), and high (88–240 donors) online reach raised on average $ 1152 (95% CI: − $1589, −$ 716),$1050 (95% CI: −$ 1737, − $364), and $ 2655 (95% CI: − $4312, −$ 998) less than non‐LGBTQ+ campaigns respectively. When stratified by LGBTQ+ inclusivity of state level policy states with anti‐LGBTQ+ policy/lacking equitable policy raised on average $1910 (95% CI: −2640, −1182) less than non‐LGBTQ+ campaigns from the same states. Conclusions and Relevance Our findings revealed LGBTQ+ inequity in cancer‐related crowdfunding, suggesting that LGBTQ+ cancer survivors may be less able to address financial burden via crowdfunding in comparison to non‐LGBTQ+ cancer survivors—potentially widening existing economic inequities.

Cancer survivors frequently experience cancer-related financial burdens. The extent to which Lesbian, Gay, Bisexual, Transgender, Queer, Plus (LGBTQ+) populations experience cancer-related cost-coping behaviors such as crowdfunding is largely unknown, owing to a lack of sexual orientation and gender identity data collection and social stigma. Web-scraping has previously been used to evaluate inequities in online crowdfunding, but these methods alone do not adequately engage populations facing inequities. We describe the methodological process of integrating technology-based and community-engaged methods to explore the financial burden of cancer among LGBTQ+ individuals via online crowdfunding. To center the LGBTQ+ community, we followed community engagement guidelines by forming a study advisory board (SAB) of LGBTQ+ cancer survivors, caregivers, and professionals who were involved in every step of the research. SAB member engagement was tracked through quarterly SAB meeting attendance and an engagement survey. We then used web-scraping methods to extract a data set of online crowdfunding campaigns. The study team followed an integrated technology-based and community-engaged process to develop and refine term dictionaries for analyses. Term dictionaries were developed and refined in order to identify crowdfunding campaigns that were cancer- and LGBTQ+-related. Advisory board engagement was high according to metrics of meeting attendance, meeting participation, and anonymous board feedback. In collaboration with the SAB, the term dictionaries were iteratively edited and refined. The LGBTQ+ term dictionary was developed by the study team, while the cancer term dictionary was refined from an existing dictionary. The advisory board and analytic team members manually coded against the term dictionary and performed quality checks until high confidence in correct classification was achieved using pairwise agreement. Through each phase of manual coding and quality checks, the advisory board identified more misclassified campaigns than the analytic team alone. When refining the LGBTQ+ term dictionary, the analytic team identified 11.8% misclassification while the SAB identified 20.7% misclassification. Once each term dictionary was finalized, the LGBTQ+ term dictionary resulted in a 95% pairwise agreement, while the cancer term dictionary resulted in an 89.2% pairwise agreement. The classification tools developed by integrating community-engaged and technology-based methods were more accurate because of the equity-based approach of centering LGBTQ+ voices and their lived experiences. This exemplar suggests integrating community-engaged and technology-based methods to study inequities is highly feasible and has applications beyond LGBTQ+ financial burden research.

For mass spectrometry-based peptide and protein quantification, label-free quantification (LFQ) based on precursor mass peak (MS1) intensities is considered reliable due to its dynamic range, reproducibility, and accuracy. LFQ enables peptide-level quantitation, which is useful in proteomics (analyzing peptides carrying post-translational modifications) and multi-omics studies such as metaproteomics (analyzing taxon-specific microbial peptides) and proteogenomics (analyzing non-canonical sequences). Bioinformatics workflows accessible via the Galaxy platform have proven useful for analysis of such complex multi-omic studies. However, workflows within the Galaxy platform have lacked well-tested LFQ tools. In this study, we have evaluated moFF and FlashLFQ, two open-source LFQ tools, and implemented them within the Galaxy platform to offer access and use via established workflows. Through rigorous testing and communication with the tool developers, we have optimized the performance of each tool. Software features evaluated include: (a) match-between-runs (MBR); (b) using multiple file-formats as input for improved quantification; (c) use of containers and/or conda packages; (d) parameters needed for analyzing large datasets; and (e) optimization and validation of software performance. This work establishes a process for software implementation, optimization, and validation, and offers access to two robust software tools for LFQ-based analysis within the Galaxy platform.

Galaxy provides an accessible platform where multi-step data analysis workflows integrating disparate software can be run, even by researchers with limited programming expertise.  Applications of such sophisticated workflows are many, including those which integrate software from different 'omic domains (e.g. genomics, proteomics, metabolomics). In these complex workflows, intermediate outputs are often generated as tabular text files, which must be transformed into customized formats which are compatible with the next software tools in the pipeline.  Consequently, many text manipulation steps are added to an already complex workflow, overly complicating the process and decreasing usability, especially for non-expert bench researchers focused on obtaining results.  In some cases, limitations to existing text manipulation are such that desired analyses can only be carried out using highly sophisticated processing steps beyond the reach of most users.  As a solution, we have developed the Query Tabular Galaxy tool, which leverages a SQLite database generated from tabular input data.  This database can be queried and manipulated to produce transformed and customized tabular outputs compatible with downstream processing steps.  Regular expressions can also be utilized for even more sophisticated manipulations, such as find and replace and other filtering actions.  Using several Galaxy-based multi-omic workflows as an example, we demonstrate how the Query Tabular tool dramatically streamlines and simplifies the creation of multi-step analyses, efficiently enabling complicated textual manipulations and processing.  This tool should find broad utility for users of the Galaxy platform seeking to develop and use sophisticated workflows involving text manipulation on tabular outputs.

The impact of microbial communities, also known as the microbiome, on human health and the environment is receiving increased attention. Studying translated gene products (proteins) and comparing metaproteomic profiles may elucidate how microbiomes respond to specific environmental stimuli, and interact with host organisms. Characterizing proteins expressed by a complex microbiome and interpreting their functional signature requires sophisticated informatics tools and workflows tailored to metaproteomics. Additionally, there is a need to disseminate these informatics resources to researchers undertaking metaproteomic studies, who could use them to make new and important discoveries in microbiome research. The Galaxy for proteomics platform (Galaxy-P) offers an open source, web-based bioinformatics platform for disseminating metaproteomics software and workflows. Within this platform, we have developed easily-accessible and documented metaproteomic software tools and workflows aimed at training researchers in their operation and disseminating the tools for more widespread use. The modular workflows encompass the core requirements of metaproteomic informatics: (a) database generation; (b) peptide spectral matching; (c) taxonomic analysis and (d) functional analysis. Much of the software available via the Galaxy-P platform was selected, packaged and deployed through an online metaproteomics “Contribution Fest“ undertaken by a unique consortium of expert software developers and users from the metaproteomics research community, who have co-authored this manuscript. These resources are documented on GitHub and freely available through the Galaxy Toolshed, as well as a publicly accessible metaproteomics gateway Galaxy instance. These documented workflows are well suited for the training of novice metaproteomics researchers, through online resources such as the Galaxy Training Network, as well as hands-on training workshops. Here, we describe the metaproteomics tools available within these Galaxy-based resources, as well as the process by which they were selected and implemented in our community-based work. We hope this description will increase access to and utilization of metaproteomics tools, as well as offer a framework for continued community-based development and dissemination of cutting edge metaproteomics software.

Galaxy provides an accessible platform where multi-step data analysis workflows integrating disparate software can be run, even by researchers with limited programming expertise. Applications of such sophisticated workflows are many, including those which integrate software from different 'omic domains (e.g. genomics, proteomics, metabolomics). In these complex workflows, intermediate outputs are often generated as tabular text files, which must be transformed into customized formats which are compatible with the next software tools in the pipeline. Consequently, many text manipulation steps are added to an already complex workflow, overly complicating the process. In some cases, limitations to existing text manipulation are such that desired analyses can only be carried out using highly sophisticated processing steps beyond the reach of even advanced users and developers. For users with some SQL knowledge, these text operations could be combined into single, concise query on a relational database. As a solution, we have developed the Query Tabular Galaxy tool, which leverages a SQLite database generated from tabular input data. This database can be queried and manipulated to produce transformed and customized tabular outputs compatible with downstream processing steps. Regular expressions can also be utilized for even more sophisticated manipulations, such as find and replace and other filtering actions. Using several Galaxy-based multi-omic workflows as an example, we demonstrate how the Query Tabular tool dramatically streamlines and simplifies the creation of multi-step analyses, efficiently enabling complicated textual manipulations and processing. This tool should find broad utility for users of the Galaxy platform seeking to develop and use sophisticated workflows involving text manipulation on tabular outputs.

The prevalence of child disability in the United States has sharply increased over the past 60 years. Caring for a child with a disability can be rewarding and can also lead to emotional and financial consequences for caregivers. This dissertation examined the economic and social effects of raising a child with a disability. The first paper examined how child disability affected mothers' labor market outcomes. I found that the onset of child disability led to decreases in employment, work hours, and earnings among mothers of children with disabilities compared to mothers of children without disabilities. Effects resolved by 4-6 years after child disability onset and were largest among mothers of children with infant-onset disabilities and Black mothers. The second paper assessed how child disability affected gender role attitudes of mothers and the interaction with employment. I hypothesized that child disability would lead to less egalitarian gender role attitudes among mothers of children with disabilities compared to mothers of children without disabilities. Child disability was not associated with changes in gender role attitudes but was associated with decreased full-time employment. Taken together, mothers of children with disabilities had gender role attitudes that were less aligned with their employment behaviors than mothers of children without disabilities. The third study evaluated the effects of state and local paid family leave (PFL) and paid sick leave (PSL) mandates on a comprehensive set of labor market outcomes of parents of children with disabilities. The analysis found that neither PFL nor PSL policies led to changes in labor market outcomes for either mothers or fathers of children with disabilities. This dissertation contributes to existing research on the economic impacts of child disability, finding that mothers of children with disabilities may not be able to maintain previous levels of employment, they do not revise gender beliefs to match reduced levels of employment, and current paid leave mandates do not increase employment. Other policies (for example, more generous Supplemental Security Income, caregiver tax credits, or expanded access to direct payments to family caregivers) may be necessary to support the economic well-being of families of children with disabilities.