Genome graphs are emerging as an important novel approach to the analysis of high-throughput human sequencing data. By explicitly representing genetic variants and alternative haplotypes in a mappable data structure, they can enable the improved analysis of structurally variable and hyperpolymorphic regions of the genome. In most existing approaches, graphs are constructed from variant call sets derived from short-read sequencing. As long-read sequencing becomes more cost-effective and enables de novo assembly for increasing numbers of whole genomes, a method for the direct construction of a genome graph from sets of assembled human genomes would be desirable. Such assembly-based genome graphs would encompass the wide spectrum of genetic variation accessible to long-read-based de novo assembly, including large structural variants and divergent haplotypes. Here we present NovoGraph, a method for the construction of a human genome graph directly from a set of de novo assemblies. NovoGraph constructs a genome-wide multiple sequence alignment of all input contigs and creates a graph by merging the input sequences at positions that are both homologous and sequence-identical. NovoGraph outputs resulting graphs in VCF format that can be loaded into third-party genome graph toolkits. To demonstrate NovoGraph, we construct a genome graph with 23,478,835 variant sites and 30,582,795 variant alleles from de novo assemblies of seven ethnically diverse human genomes (AK1, CHM1, CHM13, HG003, HG004, HX1, NA19240). Initial evaluations show that mapping against the constructed graph reduces the average mismatch rate of reads from sample NA12878 by approximately 0.2%, albeit at a slightly increased rate of reads that remain unmapped.

Genome graphs are emerging as an important novel approach to the analysis of high-throughput human sequencing data. By explicitly representing genetic variants and alternative haplotypes in a mappable data structure, they can enable the improved analysis of structurally variable and hyperpolymorphic regions of the genome. In most existing approaches, graphs are constructed from variant call sets derived from short-read sequencing. As long-read sequencing becomes more cost-effective and enables de novo assembly for increasing numbers of whole genomes, a method for the direct construction of a genome graph from sets of assembled human genomes would be desirable. Such assembly-based genome graphs would encompass the wide spectrum of genetic variation accessible to long-read-based de novo assembly, including large structural variants and divergent haplotypes. Here we present NovoGraph, a method for the construction of a human genome graph directly from a set of de novo assemblies. NovoGraph constructs a genome-wide multiple sequence alignment of all input contigs and creates a graph by merging the input sequences at positions that are both homologous and sequence-identical. NovoGraph outputs resulting graphs in VCF format that can be loaded into third-party genome graph toolkits. To demonstrate NovoGraph, we construct a genome graph with 23,478,835 variant sites and 30,582,795 variant alleles from de novo assemblies of seven ethnically diverse human genomes (AK1, CHM1, CHM13, HG003, HG004, HX1, NA19240). Initial evaluations show that mapping against the constructed graph reduces the average mismatch rate of reads from sample NA12878 by approximately 0.2%, albeit at a slightly increased rate of reads that remain unmapped.

Genome graphs are emerging as an important novel approach to the analysis of high-throughput human sequencing data. By explicitly representing genetic variants and alternative haplotypes in a mappable data structure, they can enable the improved analysis of structurally variable and hyperpolymorphic regions of the genome. In most existing approaches, graphs are constructed from variant call sets derived from short-read sequencing. As long-read sequencing becomes more cost-effective and enables de novo assembly for increasing numbers of whole genomes, a method for the direct construction of a genome graph from sets of assembled human genomes would be desirable. Such assembly-based genome graphs would encompass the wide spectrum of genetic variation accessible to long-read-based de novo assembly, including large structural variants and divergent haplotypes. Here we present NovoGraph, a method for the construction of a human genome graph directly from a set of de novo assemblies. NovoGraph constructs a genome-wide multiple sequence alignment of all input contigs and creates a graph by merging the input sequences at positions that are both homologous and sequence-identical. NovoGraph outputs resulting graphs in VCF format that can be loaded into third-party genome graph toolkits. To demonstrate NovoGraph, we construct a genome graph with 23,478,835 variant sites and 30,582,795 variant alleles from de novo assemblies of seven ethnically diverse human genomes (AK1, CHM1, CHM13, HG003, HG004, HX1, NA19240). Initial evaluations show that mapping against the constructed graph reduces the average mismatch rate of reads from sample NA12878 by approximately 0.2%, albeit at a slightly increased rate of reads that remain unmapped.

PURPOSE: Competency-based medical education (CBME) represents a shift to a paradigm with shared definitions, explicit outcomes, and assessments of competence. The groundwork has been laid to ensure all learners achieve the desired outcomes along the medical education continuum using the principles of CBME. However, this continuum spans the major transition from undergraduate medical education (UME) to graduate medical education (GME) that is also evolving. This study explores the experiences of medical educators working to use CBME assessments in the context of the UME-GME transition and their perspectives on the existing challenges. METHOD: This study used a constructivist-oriented qualitative methodology. In-depth, semistructured interviews of UME and GME leaders in CBME were performed between February 2019 and January 2020 via Zoom. When possible, each interviewee was interviewed by 2 team members, one with UME and one with GME experience, which allowed follow-up questions to be pursued that reflected the perspectives of both UME and GME educators more fully. A multistep iterative process of thematic analysis was used to analyze the transcripts and identify patterns across interviews. RESULTS: The 9 interviewees represented a broad swath of UME and GME leadership positions, though most had an internal medicine training background. Analysis identified 4 overarching themes: mistrust (a trust chasm exists between UME and GME); misaligned goals (the residency selection process is antithetical to CBME); inadequate communication (communication regarding competence is infrequent, often unidirectional, and lacks a shared language); and inflexible timeframes (current training timeframes do not account for individual learners competency trajectories). CONCLUSIONS: Despite the mutual desire and commitment to move to CBME across the continuum, mistrust, misaligned goals, inadequate communication, and inflexible timeframes confound such efforts of individual schools and programs. If current efforts to improve the UME-GME transition address the themes identified, educators may be more successful implementing CBME along the continuum.

In August 2022 the Department of Health and Human Services (HHS) issued a notice of proposed rulemaking prohibiting covered entities, which include health care providers and health plans, from discriminating against individuals when using clinical algorithms in decision making. However, HHS did not provide specific guidelines on how covered entities should prevent discrimination. We conducted a scoping review of literature published during the period 2011-22 to identify health care applications, frameworks, reviews and perspectives, and assessment tools that identify and mitigate bias in clinical algorithms, with a specific focus on racial and ethnic bias. Our scoping review encompassed 109 articles comprising 45 empirical health care applications that included tools tested in health care settings, 16 frameworks, and 48 reviews and perspectives. We identified a wide range of technical, operational, and systemwide bias mitigation strategies for clinical algorithms, but there was no consensus in the literature on a single best practice that covered entities could employ to meet the HHS requirements. Future research should identify optimal bias mitigation methods for various scenarios, depending on factors such as patient population, clinical setting, algorithm design, and types of bias to be addressed.

Nanoscale stress-sensing can be used across fields ranging from detection of incipient cracks in structural mechanics to monitoring forces in biological tissues. We demonstrate how tetrapod quantum dots (tQDs) embedded in block copolymers act as sensors of tensile/compressive stress. Remarkably, tQDs can detect their own composite dispersion and mechanical properties with a switch in optomechanical response when tQDs are in direct contact. Using experimental characterizations, atomistic simulations and finite-element analyses, we show that under tensile stress, densely packed tQDs exhibit a photoluminescence peak shifted to higher energies ("blue-shift") due to volumetric compressive stress in their core; loosely packed tQDs exhibit a peak shifted to lower energies ("red-shift") from tensile stress in the core. The stress shifts result from the tQD's unique branched morphology in which the CdS arms act as antennas that amplify the stress in the CdSe core. Our nanocomposites exhibit excellent cyclability and scalability with no degraded properties of the host polymer. Colloidal tQDs allow sensing in many materials to potentially enable autoresponsive, smart structural nanocomposites that self-predict impending fracture.

Nitrogen is an essential macronutrient for plant growth and basic metabolic processes. The application of nitrogen-containing fertilizer increases yield, which has been a substantial factor in the green revolution¹. Ecologically, however, excessive application of fertilizer has disastrous effects such as eutrophication². A better understanding of how plants regulate nitrogen metabolism is critical to increase plant yield and reduce fertilizer overuse. Here we present a transcriptional regulatory network and twenty-one transcription factors that regulate the architecture of root and shoot systems in response to changes in nitrogen availability. Genetic perturbation of a subset of these transcription factors revealed coordinate transcriptional regulation of enzymes involved in nitrogen metabolism. Transcriptional regulators in the network are transcriptionally modified by feedback via genetic perturbation of nitrogen metabolism. The network, genes and gene-regulatory modules identified here will prove critical to increasing agricultural productivity.

Plant secondary cell walls constitute the majority of plant biomass. They are predominantly found in xylem cells, which are derived from vascular initials during vascularization. Little is known about these processes in grass species despite their emerging importance as biomass feedstocks. The targeted biofuel crop Sorghum bicolor has a sequenced and well-annotated genome, making it an ideal monocot model for addressing vascularization and biomass deposition. Here we generated tissue-specific transcriptome and DNA methylome data from sorghum shoots, roots and developing root vascular and nonvascular tissues. Many genes associated with vascular development in other species show enriched expression in developing vasculature. However, several transcription factor families varied in vascular expression in sorghum compared with Arabidopsis and maize. Furthermore, differential expression of genes associated with DNA methylation were identified between vascular and nonvascular tissues, implying that changes in DNA methylation are a feature of sorghum root vascularization, which we confirmed using tissue-specific DNA methylome data. Roots treated with a DNA methylation inhibitor also showed a significant decrease in root length. Tissues and organs can be discriminated based on their genomic methylation patterns and methylation context. Consequently, tissue-specific changes in DNA methylation are part of the normal developmental process.