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The Tol-Pal system spans the cell envelope of Gram-negative bacteria, transducing the potential energy of the proton motive force (PMF) into dissociation of the TolB-Pal complex at the outer membrane (OM), freeing the lipoprotein Pal to bind the cell wall. The primary physiological role of Tol-Pal is to maintain OM integrity during cell division through accumulation of Pal molecules at division septa. How the protein complex couples the PMF at the inner membrane into work at the OM is unknown. The effectiveness of this trans-envelope energy transduction system is underscored by the fact that bacteriocins and bacteriophages co-opt Tol-Pal as part of their import/infection mechanisms. Mechanistic understanding of this process has been hindered by a lack of structural data for the inner membrane TolQ-TolR stator, of its complexes with peptidoglycan (PG) and TolA, and of how these elements combined power events at the OM. Recent studies on the homologous stators of Ton and Mot provide a starting point for understanding how Tol-Pal works. Here, we combine ab initio protein modeling with previous structural data on sub-complexes of Tol-Pal as well as mutagenesis, crosslinking, co-conservation analysis and functional data. Through this composite pooling of in silico, in vitro, and in vivo data, we propose a mechanism for force generation in which PMF-driven rotary motion within the stator drives conformational transitions within a long TolA helical hairpin domain, enabling it to reach the TolB-Pal complex at the OM.

The professional identity of scientists has historically been cultivated to value research over teaching, which can undermine initiatives that aim to reform science education. Course-Based Research Experiences (CRE) and the inclusive Research and Education Communities (iREC) are two successful and impactful reform efforts that integrate research and teaching. The aim of this study is to explicate the professional identity of instructors who implement a CRE within an established iREC and to explore how this identity contributes to the success of these programs. 97 CRE instructors from the Science Education Alliance (SEA) iREC participated in a 2-year, multi-stage, qualitative research project that involved weekly reflective journaling, autoethnographic description, small group evaluation and writing, and large-scale community checking. The resulting description of professional identity consisted of shared values (inclusivity, student success, community membership, ownership/agency, science, overcoming failure, and persistence), specified roles (mentor, advocate, scientist, educator, motivator, collaborator, community builder, learner, evaluator and project manager) and a stated sense of self (dedicated, resilient, pride in students, multiskilled, valued, community member, responsible and overworked). Analysis of individual reflective diary entries revealed how a professional identity underpinned and facilitated the ways in which faculty addressed challenges that arose and worked toward the success of every student. It is the self-concept of the professional identity of the instructor in the context of the CRE classroom that directed the extended commitment and effort that these instructors evidently put into their work with students, which facilitated student engagement, student persistence, and their collective scientific output. The study concludes that a professional identity of STEM faculty in the context of a CRE and iREC combines being a researcher and educator, and that this integrated identity is central for current initiatives aimed at transforming undergraduate STEM education.

Over the last two decades, there have been numerous initiatives to improve undergraduate student outcomes in STEM. One model for scalable reform is the inclusive Research Education Community (iREC). In an iREC, STEM faculty from colleges and universities across the nation are supported to adopt and sustainably implement course-based research – a form of science pedagogy that enhances student learning and persistence in science. In this study, we used pathway modeling to develop a qualitative description that explicates the HHMI Science Education Alliance (SEA) iREC as a model for facilitating the successful adoption and continued advancement of new curricular content and pedagogy. In particular, outcomes that faculty realize through their participation in the SEA iREC were identified, organized by time, and functionally linked. The resulting pathway model was then revised and refined based on several rounds of feedback from over 100 faculty members in the SEA iREC who participated in the study. Our results show that in an iREC, STEM faculty organized as a long-standing community of practice leverage one another, outside expertise, and data to adopt, implement, and iteratively advance their pedagogy. The opportunity to collaborate in this manner and, additionally, to be recognized for pedagogical contributions sustainably engages STEM faculty in the advancement of their pedagogy. Here, we present a detailed pathway model of SEA that, together with underpinning features of an iREC identified in this study, offers a framework to facilitate transformations in undergraduate science education.

Wastewater-based epidemiology (WBE) has become a valuable tool for monitoring the prevalence of SARS-CoV-2 on university campuses. However, concerns about effectiveness of raw sewage as a COVID-19 early warning system still exist, and it’s not clear how useful normalization by simultaneous comparison of Pepper Mild Mottle Virus (PMMoV) is in addressing variations resulting from fecal discharge dilution. This study aims to contribute insights into these aspects by conducting an academic-year field trial at the student residences on the University of Tennessee, Knoxville campus, raw sewage. This was done to investigate the correlations between SARS-CoV-2 RNA load, both with and without PMMoV normalization, and various parameters, including active COVID-19 cases, self-isolations, and their combination among all student residents. Significant positive correlations between SARS-CoV-2 RNA load a week prior, during the monitoring week, and the subsequent week with active cases. Despite these correlations, normalization by PMMoV does not enhance these associations. These findings suggest the potential utility of SARS-CoV-2 RNA load as an early warning indicator and provide valuable insights into the application and limitations of WBE for COVID-19 surveillance specifically within the context of raw sewage on university campuses.

BackgroundThere is a gross shortage of sleep specialist providers within the military health system. Telehealth and mobile health represent promising approaches to increase access to high quality, cost-effective care in the U.S military.ObjectivesThis paper reports findings from a mixed-methods clinical implementation study of a novel sleep telehealth platform at two military treatment facilities in the National Capitol Region. The platform includes a mobile app and integrated wearable sensors (i.e., a commercial off-the-shelf sleep tracker [Fitbit]). The primary purpose was to evaluate the implementation of a 10-day remote monitoring assessment and provision of evidence-based sleep treatment recommendations to patients and providers. In addition, we sought to observe, in an exploratory manner, subsequent engagement with the app during 5 days of personalized sleep education and training.MethodsPatients with sleep problems completed an intensive 10-day remote monitoring assessment that included a baseline intake questionnaire, daily sleep diaries, twice daily symptom surveys, and Fitbit. Based on this assessment, patients received personalized assessment results. Concurrently, a provider report was generated that included provisional diagnoses and evidence-based treatment recommendations. Next, participants gained access to personalized sleep education and trainings within the mobile app. Within an established implementation science framework, outcomes were assessed via behavioral adherence (engagement with the app) and separate questionnaires for patients and providers. Last, we conducted four focus groups with patients and 12 key informant interviews with primary care managers (PCMs) and economic stakeholders to seek feedback and recommendations for future directions.ResultsTwo hundred and seventy patients participated in the study. Using validated research questionnaires, participants reported high-risk for obstructive sleep apnea (65.6%), moderate to severe insomnia (38.2%), and moderate to severe daytime sleepiness (38.5%), and moderate to severe anxiety (14.1%) and depressive (20.4%) symptoms. Total sleep time was 6.6 (SD = 1.8) h based on sleep diaries and 6.1 (SD = 1.8) h based on Fitbit. Regarding implementation, reach, effectiveness, adoption, implementation, and maintenance were all notably high, based on quantitative and qualitative data from participants and PCMs.ConclusionsSleep telehealth and mobile health represent promising approaches to increase access to cost-effective, evidence-based care for sleep disorders in the U.S. military.

Understanding the composition, texture, and morphology of volcanic rocks that have erupted at the surface better constrains the eruption style and is vital to infer subsurface processes, the development of magma upon ascent, and the potential for future eruptions. The reflectance and emission spectroscopy of these rocks, collected from the near-infrared (NIR) through the thermal infrared (TIR) portion of the electromagnetic (EM) spectrum, provides the data necessary to retrieve composition, micron-scale surface roughness, and particle size. Remote imaging systems enable the analyses of active volcanoes in remote regions, where sample collection for laboratory analysis poses a significant challenge. Laboratory hyperspectral data of samples acquired at volcanic deposits are easily resampled to the spectral resolution of any infrared sensor and provide a means of estimating the composition of volcanoes and their products worldwide, as well as those on other planetary bodies such as the Moon and Mars. In this review paper, we provide an overview of the current use of infrared reflectance and emission spectroscopy as an analysis tool in volcanology, including ground-based imaging systems that acquire unprecedented detail and serve as testbeds for new orbital concepts. We also discuss the potential impact that future satellite missions will have on volcano science as spectral, spatial, and temporal resolutions improve.

Inadequate sampling approaches to wastewater analyses can introduce biases, leading to inaccurate results such as false negatives and significant over- or underestimation of average daily viral concentrations, due to the sporadic nature of viral input. To address this challenge, we conducted a field trial within the University of Tennessee residence halls, employing different composite sampling modes that encompassed different time intervals (1 h, 2 h, 4 h, 6 h, and 24 h) across various time windows (morning, afternoon, evening, and late-night). Our primary objective was to identify the optimal approach for generating representative composite samples of SARS-CoV-2 from raw wastewater. Utilizing reverse transcription-quantitative polymerase chain reaction, we quantified the levels of SARS-CoV-2 RNA and pepper mild mottle virus (PMMoV) RNA in raw sewage. Our findings consistently demonstrated that PMMoV RNA, an indicator virus of human fecal contamination in water environment, exhibited higher abundance and lower variability compared to pathogenic SARS-CoV-2 RNA. Significantly, both SARS-CoV-2 and PMMoV RNA exhibited greater variability in 1 h individual composite samples throughout the entire sampling period, contrasting with the stability observed in other time-based composite samples. Through a comprehensive analysis of various composite sampling modes using the Quade Nonparametric ANCOVA test with date, PMMoV concentration and site as covariates, we concluded that employing a composite sampler during a focused 6 h morning window for pathogenic SARS-CoV-2 RNA is a pragmatic and cost-effective strategy for achieving representative composite samples within a single day in wastewater-based epidemiology applications. This method has the potential to significantly enhance the accuracy and reliability of data collected at the community level, thereby contributing to more informed public health decision-making during a pandemic.

IntroductionAlthough severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) RNA has been frequently detected in sewage from many university dormitories to inform public health decisions during the COVID-19 pandemic, a clear understanding of SARS-CoV-2 RNA persistence in site-specific raw sewage is still lacking. To investigate the SARS-CoV-2 RNA persistence, a field trial was conducted in the University of Tennessee dormitories raw sewage, similar to municipal wastewater.MethodsThe decay of enveloped SARS-CoV-2 RNA and non-enveloped Pepper mild mottle virus (PMMoV) RNA was investigated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in raw sewage at 4°C and 20°C.ResultsTemperature, followed by the concentration level of SARS-CoV-2 RNA, was the most significant factors that influenced the first-order decay rate constants (k) of SARS-CoV-2 RNA. The mean k values of SARS-CoV-2 RNA were 0.094 day−1 at 4°C and 0.261 day−1 at 20°C. At high-, medium-, and low-concentration levels of SARS-CoV-2 RNA, the mean k values were 0.367, 0.169, and 0.091 day−1, respectively. Furthermore, there was a statistical difference between the decay of enveloped SARS-CoV-2 and non-enveloped PMMoV RNA at different temperature conditions.DiscussionThe first decay rates for both temperatures were statistically comparable for SARS-CoV-2 RNA, which showed sensitivity to elevated temperatures but not for PMMoV RNA. This study provides evidence for the persistence of viral RNA in site-specific raw sewage at different temperature conditions and concentration levels.

Dormancy is a physiological state that plants enter for winter hardiness. Environmental-induced dormancy onset and release in temperate perennials coordinate growth cessation and resumption, but how the entire process, especially chilling-dependent dormancy release and flowering, is regulated remains largely unclear. We utilized the transcriptome profiles of floral buds from fall to spring in apricot (Prunus armeniaca) genotypes with contrasting bloom dates and peach (Prunus persica) genotypes with contrasting chilling requirements (CR) to explore the genetic regulation of bud dormancy. We identified distinct gene expression programming patterns in endodormancy and ecodormancy that reproducibly occur between different genotypes and species. During the transition from endo- to eco-dormancy, 1,367 and 2,102 genes changed in expression in apricot and peach, respectively. Over 600 differentially expressed genes were shared in peach and apricot, including three DORMANCY ASSOCIATED MADS-box (DAM) genes (DAM4, DAM5, and DAM6). Of the shared genes, 99 are located within peach CR quantitative trait loci, suggesting these genes as candidates for dormancy regulation. Co-expression and functional analyses revealed that distinctive metabolic processes distinguish dormancy stages, with genes expressed during endodormancy involved in chromatin remodeling and reproduction, while the genes induced at ecodormancy were mainly related to pollen development and cell wall biosynthesis. Gene expression analyses between two Prunus species highlighted the conserved transcriptional control of physiological activities in endodormancy and ecodormancy and revealed genes that may be involved in the transition between the two stages.

Sorghum (Sorghum bicolor L. Moench) offers substantial potential as a feedstock for the production of sugar-derived biofuels and biochemical products from cell wall polysaccharides (i. e., cellulose and hemicelluloses) and water-extractable sugars (i.e., glucose, fructose, sucrose, and starch). A number of preprocessing schemes can be envisioned that involve processes such as sugar extraction, pretreatment, and densification that could be employed in decentralized, regional-scale biomass processing depots. In this work, an energy sorghum exhibiting a combination of high biomass productivity and high sugar accumulation was evaluated for its potential for integration into several potential biomass preprocessing schemes. This included counter-current extraction of water-soluble sugars followed by mild NaOH or liquid hot water pretreatment of the extracted bagasse. A novel processing scheme was investigated that could integrate with current diffuser-type extraction systems for sugar extraction. In this approach, mild NaOH pretreatment (i.e., 8%) at high metabolic yields without detoxification using a Saccharomyces cerevisiae strain metabolically engineered and evolved to ferment xylose.

Wildland research, management, and policy in western democracies have long relied on concepts of equilibrium: succession, sustained yield, stable age or species compositions, fire return intervals, and historical range of variability critically depend on equilibrium assumptions. Not surprisingly, these largely static concepts form the basis for societal expectations, dominant management paradigms, and environmental legislation. Knowledge generation has also assumed high levels of stasis, concentrating on correlational patterns with the expectation that these patterns would be reliably transferrable. Changes in climate, the introduction of large numbers of exotic organisms, and anthropogenic land conversion are leading to unprecedented changes in disturbance regimes and landscape composition. Importantly, these changes are largely non-reversable; once introduced exotic species are seldom eradicated, climates will continue to warm for the foreseeable future, and many types of land conversion cannot be easily undone. Due to their effects on extant infrastructure and expectations for ecosystem services, these changes are, and will be, viewed by western societies as overwhelmingly negative. The continued acceleration of change will generate increasingly novel systems for which the transferability of correlational relationships will prove unreliable. Our abilities to predict system trajectories will therefore necessarily decrease. In this environment, top-down, expert dominated approaches to environmental decision making are unlikely to produce results that meet broader societal expectations. To be successful we need to embrace a more inclusive paradigm of collaborative governance and multiple forms of knowledge for adapting to constant change, including indigenous epistemological systems. By increasing public and stakeholder participation, we can encourage collaborative social learning allowing all parties to more fully understand the complexities and tradeoffs associated with wildland management and the technical limits of models that seek to quantify those tradeoffs. System novelty will necessarily make forecasting more dependent on predictive modeling and will require better models. Data collection should therefore be strongly influenced by model input requirements and validation; research will need to focus on fundamental and causal relationships to a much greater degree than is done currently.