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  1. Abstract Background

    Direct-sequencing technologies, such as Oxford Nanopore’s, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data.


    Here, we present Sequoia, a visual analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-downmore »onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization.


    Sequoia’s interactive features complement existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available at

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  2. Abstract Background

    Recent research has demonstrated the importance of entrepreneurship education programs (EEPs) in the professional development of engineering students. Numerous universities have adopted various forms of EEPs which are typically offered as elective programs. To create suitable programs that will encourage students to seek out EEPs, it is critical to understand the factors that influence student participation in EEPs. Using qualitative research methods, we examined the question “What influences engineering students’ participation in entrepreneurship education programs?” The purpose of our work is to identify and understand the factors impacting engineering student participation in EEPs.


    Analysis of 20 semi-structured interviews of undergraduatemore »engineering students was conducted using the first and second cycle coding methods to determine key factors that inform students’ participation in EEPs. We found that student decisions to participate in EEPs are influenced by several factors: entrepreneurial self-efficacy, entrepreneurial intent, attitude, subjective norm, goals, academic transitions, information and resources, social capital, opportunities and challenges, and past participation in EEPs.


    Findings demonstrate that students’ non-compulsory participation is not a result of a single act, but is regulated by multiple factors. Explication of these factors using our qualitative results provides actionable guidance for EEPs to encourage engineering students’ participation and offers directions for future research.

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  3. Abstract Background

    Transparent and accessible reporting of COVID-19 data is critical for public health efforts. Each Indian state has its own mechanism for reporting COVID-19 data, and the quality of their reporting has not been systematically evaluated. We present a comprehensive assessment of the quality of COVID-19 data reporting done by the Indian state governments between 19 May and 1 June, 2020.


    We designed a semi-quantitative framework with 45 indicators to assess the quality of COVID-19 data reporting. The framework captures four key aspects of public health data reporting – availability, accessibility, granularity, and privacy. We used this framework to calculatemore »a COVID-19 Data Reporting Score (CDRS, ranging from 0–1) for each state.


    Our results indicate a large disparity in the quality of COVID-19 data reporting across India. CDRS varies from 0.61 (good) in Karnataka to 0.0 (poor) in Bihar and Uttar Pradesh, with a median value of 0.26. Ten states do not report data stratified by age, gender, comorbidities or districts. Only ten states provide trend graphics for COVID-19 data. In addition, we identify that Punjab and Chandigarh compromised the privacy of individuals under quarantine by publicly releasing their personally identifiable information. The CDRS is positively associated with the state’s sustainable development index for good health and well-being (Pearson correlation:r=0.630,p=0.0003).


    Our assessment informs the public health efforts in India and serves as a guideline for pandemic data reporting. The disparity in CDRS highlights three important findings at the national, state, and individual level. At the national level, it shows the lack of a unified framework for reporting COVID-19 data in India, and highlights the need for a central agency to monitor or audit the quality of data reporting done by the states. Without a unified framework, it is difficult to aggregate the data from different states, gain insights, and coordinate an effective nationwide response to the pandemic. Moreover, it reflects the inadequacy in coordination or sharing of resources among the states. The disparate reporting score also reflects inequality in individual access to public health information and privacy protection based on the state of residence.

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  4. Abstract Background

    Biomolecular condensates are non-stoichiometric assemblies that are characterized by their capacity to spatially concentrate biomolecules and play a key role in cellular organization. Proteins that drive the formation of biomolecular condensates frequently contain oligomerization domains and intrinsically disordered regions (IDRs), both of which can contribute multivalent interactions that drive higher-order assembly. Our understanding of the relative and temporal contribution of oligomerization domains and IDRs to the material properties of in vivo biomolecular condensates is limited. Similarly, the spatial and temporal dependence of protein oligomeric state inside condensates has been largely unexplored in vivo.


    In this study, we combined quantitativemore »microscopy with number and brightness analysis to investigate the aging, material properties, and protein oligomeric state of biomolecular condensates in vivo. Our work is focused on condensates formed by AUXIN RESPONSE FACTOR 19 (ARF19), a transcription factor integral to the auxin signaling pathway in plants. ARF19 contains a large central glutamine-rich IDR and a C-terminal Phox Bem1 (PB1) oligomerization domain and forms cytoplasmic condensates.


    Our results reveal that the IDR amino acid composition can influence the morphology and material properties of ARF19 condensates. In contrast the distribution of oligomeric species within condensates appears insensitive to the IDR composition. In addition, we identified a relationship between the abundance of higher- and lower-order oligomers within individual condensates and their apparent fluidity.


    IDR amino acid composition affects condensate morphology and material properties. In ARF condensates, altering the amino acid composition of the IDR did not greatly affect the oligomeric state of proteins within the condensate.

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  5. Abstract Background

    Future expansion of corn-derived ethanol raises concerns of sustainability and competition with the food industry. Therefore, cellulosic biofuels derived from agricultural waste and dedicated energy crops are necessary. To date, slow and incomplete saccharification as well as high enzyme costs have hindered the economic viability of cellulosic biofuels, and while approaches like simultaneous saccharification and fermentation (SSF) and the use of thermotolerant microorganisms can enhance production, further improvements are needed. Cellulosic emulsions have been shown to enhance saccharification by increasing enzyme contact with cellulose fibers. In this study, we use these emulsions to develop an emulsified SSF (eSSF) processmore »for rapid and efficient cellulosic biofuel production and make a direct three-way comparison of ethanol production betweenS. cerevisiae,O. polymorpha, andK. marxianusin glucose and cellulosic media at different temperatures.


    In this work, we show that cellulosic emulsions hydrolyze rapidly at temperatures tolerable to yeast, reaching up to 40-fold higher conversion in the first hour compared to microcrystalline cellulose (MCC). To evaluate suitable conditions for the eSSF process, we explored the upper temperature limits for the thermotolerant yeastsKluyveromyces marxianusandOgataea polymorpha, as well asSaccharomyces cerevisiae, and observed robust fermentation at up to 46, 50, and 42 °C for each yeast, respectively. We show that the eSSF process reaches high ethanol titers in short processing times, and produces close to theoretical yields at temperatures as low as 30 °C. Finally, we demonstrate the transferability of the eSSF technology to other products by producing the advanced biofuel isobutanol in a light-controlled eSSF using optogenetic regulators, resulting in up to fourfold higher titers relative to MCC SSF.


    The eSSF process addresses the main challenges of cellulosic biofuel production by increasing saccharification rate at temperatures tolerable to yeast. The rapid hydrolysis of these emulsions at low temperatures permits fermentation using non-thermotolerant yeasts, short processing times, low enzyme loads, and makes it possible to extend the process to chemicals other than ethanol, such as isobutanol. This transferability establishes the eSSF process as a platform for the sustainable production of biofuels and chemicals as a whole.

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  6. Abstract Background

    The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR.

    more »Results

    We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification.


    SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

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  7. Abstract Key message

    A population of lettuce that segregated for photoperiod sensitivity was planted under long-day and short-day conditions. Genetic mapping revealed two distinct sets of QTLs controlling daylength-independent and photoperiod-sensitive flowering time.


    The molecular mechanism of flowering time regulation in lettuce is of interest to both geneticists and breeders because of the extensive impact of this trait on agricultural production. Lettuce is a facultative long-day plant which changes in flowering time in response to photoperiod. Variations exist in both flowering time and the degree of photoperiod sensitivity among accessions of wild (Lactuca serriola) and cultivated (L. sativa) lettuce. An F6populationmore »of 236 recombinant inbred lines (RILs) was previously developed from a cross between a late-flowering, photoperiod-sensitiveL. serriolaaccession and an early-flowering, photoperiod-insensitiveL. sativaaccession. This population was planted under long-day (LD) and short-day (SD) conditions in a total of four field and screenhouse trials; the developmental phenotype was scored weekly in each trial. Using genotyping-by-sequencing (GBS) data of the RILs, quantitative trait loci (QTL) mapping revealed five flowering time QTLs that together explained more than 20% of the variation in flowering time under LD conditions. Using two independent statistical models to extract the photoperiod sensitivity phenotype from the LD and SD flowering time data, we identified an additional five QTLs that together explained more than 30% of the variation in photoperiod sensitivity in the population. Orthology and sequence analysis of genes within the nine QTLs revealed potential functional equivalents in the lettuce genome to the key regulators of flowering time and photoperiodism,FDandCONSTANS, respectively, in Arabidopsis.

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  8. Abstract Background

    β-Caryophyllene is a plant terpenoid with therapeutic and biofuel properties. Production of terpenoids through microbial cells is a potentially sustainable alternative for production. Adaptive laboratory evolution is a complementary technique to metabolic engineering for strain improvement, if the product-of-interest is coupled with growth. Here we use a combination of pathway engineering and adaptive laboratory evolution to improve the production of β-caryophyllene, an extracellular product, by leveraging the antioxidant potential of the compound.


    Using oxidative stress as selective pressure, we developed an adaptive laboratory evolution that worked to evolve an engineered β-caryophyllene producing yeast strain for improved production within amore »few generations. This strategy resulted in fourfold increase in production in isolated mutants. Further increasing the flux to β-caryophyllene in the best evolved mutant achieved a titer of 104.7 ± 6.2 mg/L product. Genomic analysis revealed a gain-of-function mutation in the a-factor exporterSTE6was identified to be involved in significantly increased production, likely as a result of increased product export.


    An optimized selection strategy based on oxidative stress was developed to improve the production of the extracellular product β-caryophyllene in an engineered yeast strain. Application of the selection strategy in adaptive laboratory evolution resulted in mutants with significantly increased production and identification of novel responsible mutations.

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  9. Background:

    Athletes, especially female athletes, experience high rates of tibial bone stress injuries (BSIs). Knowledge of tibial loads during walking and running is needed to understand injury mechanisms and design safe running progression programs.


    To examine tibial loads as a function of gait speed in male and female runners.

    Study Design:

    Controlled laboratory study.


    Kinematic and kinetic data were collected on 40 recreational runners (20 female, 20 male) during 4 instrumented gait speed conditions on a treadmill (walk, preferred run, slow run, fast run). Musculoskeletal modeling, using participant-specific magnetic resonance imaging and motion data, was used to estimate tibial stress. Peak tibialmore »stress and stress-time impulse were analyzed using 2-factor multivariate analyses of variance (speed*sex) and post hoc comparisons (α = .05). Bone geometry and tibial forces and moments were examined.


    Peak compression was influenced by speed ( P < .001); increasing speed generally increased tibial compression in both sexes. Women displayed greater increases in peak tension ( P = .001) and shear ( P < .001) than men when transitioning from walking to running. Further, women displayed greater peak tibial stress overall ( P < .001). Compressive and tensile stress-time impulse varied by speed ( P < .001) and sex ( P = .006); impulse was lower during running than walking and greater in women. A shear stress-time impulse interaction ( P < .001) indicated that women displayed greater impulse relative to men when changing from a walk to a run. Compared with men, women displayed smaller tibiae ( P < .001) and disproportionately lower tibial forces ( P≤ .001-.035).


    Peak tibial stress increased with gait speed, with a 2-fold increase in running relative to walking. Women displayed greater tibial stress than men and greater increases in stress when shifting from walking to running. Sex differences appear to be the result of smaller bone geometry in women and tibial forces that were not proportionately lower, given the womens’ smaller stature and lower mass relative to men.

    Clinical Relevance:

    These results may inform interventions to regulate running-related training loads and highlight a need to increase bone strength in women. Lower relative bone strength in women may contribute to a sex bias in tibial BSIs, and female runners may benefit from a slower progression when initiating a running program.

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    Free, publicly-accessible full text available June 2, 2022
  10. Abstract Aims

    Grassland-to-shrubland transition is a common form of land degradation in drylands worldwide. It is often attributed to changes in disturbance regimes, particularly overgrazing. A myriad of direct and indirect effects (e.g., accelerated soil erosion) of grazing may favor shrubs over grasses, but their relative importance is unclear. We tested the hypothesis that topsoil “winnowing” by wind erosion would differentially affect grass and shrub seedling establishment to promote shrub recruitment over that of grass.


    We monitored germination and seedling growth of contrasting perennial grass (Bouteloua eriopoda,Sporobolus airoides, andAristida purpurea) and shrub (Prosopis glandulosa,Atriplex canescens, andLarrea tridentata) functional groups on field-collectedmore »non-winnowed and winnowed soils under well-watered greenhouse conditions.


    Non-winnowed soils were finer-textured and had higher nutrient contents than winnowed soils, but based on desorption curves, winnowed soils had more plant-available moisture. Contrary to expectations, seed germination and seedling growth on winnowed and non-winnowed soils were comparable within a given species. The N2-fixing deciduous shrubP. glandulosawas first to emerge and complete germination, and had the greatest biomass accumulation of all species.


    Germination and early seedling growth of grasses and shrubs on winnowed soils were not adversely nor differentially affected comparing with that observed on non-winnowed soils under well-watered greenhouse conditions. Early germination and rapid growth may giveP. glandulosaa competitive advantage over grasses and other shrub species at the establishment stage in grazed grasslands. Field establishment experiments are needed to confirm our findings in these controlled environment trials.

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