Oscillatory Coupling Between Neural and Cardiac Rhythms.

Oscillations serve a critical role in organizing biological systems. In the brain, oscillatory coupling is a fundamental mechanism of communication. The possibility that neural oscillations interact directly with slower physiological rhythms (e.g., heart rate, respiration) is largely unexplored and may have important implications for psychological functioning. Oscillations in heart rate, an aspect of heart rate variability (HRV), show remarkably robust associations with psychological health. Mather and Thayer proposed coupling between high-frequency HRV (HF-HRV) and neural oscillations as a mechanism that partially accounts for such relationships. We tested this hypothesis by measuring phase-amplitude coupling between HF-HRV and neural oscillations in 37 healthy adults at rest. Robust coupling was detected in all frequency bands. Granger causality analyses indicated stronger heart-to-brain than brain-to-heart effects in all frequency bands except gamma. These findings suggest that cardiac rhythms play a causal role in modulating neural oscillations, which may have important implications for mental health.

Effect of Far-Red Light on Biomass Accumulation, Plant Morphology, and Phytonutrient Composition of Ruby Streaks Mustard at Microgreen, Baby Leaf, and Flowering Stages.

Far-red (FR) light influences plant development significantly through shade avoidance response and photosynthetic modulation, but there is limited knowledge on how FR treatments influence the growth and nutrition of vegetables at different maturity stages in controlled environment agriculture (CEA). Here, we comprehensively investigated the impacts of FR on the yield, morphology, and phytonutrients of ruby streaks mustard (RS) at microgreen, baby leaf, and flowering stages. Treatments including white control, white with supplementary FR, white followed by singularly applied FR, and enhanced white (WE) matching the extended daily light integral (eDLI) of FR were designed for separating the effects of light intensity and quality. Results showed that singular and supplemental FR affected plant development and nutrition similarly throughout the growth cycle, with light intensity and quality playing varying roles at different stages. Specifically, FR did not affect the fresh and dry weight of microgreens but increased those values for baby leaves, although not as effectively as WE. Meanwhile, FR caused significant morphological change and accelerated the development of leaves, flowers, and seedpods more dramatically than WE. With regard to phytonutrients, light treatments affected the metabolomic profiles for baby leaves more dramatically than microgreens and flowers. FR decreased the glucosinolate and anthocyanin contents in microgreens and baby leaves, while WE increased the contents of those compounds in baby leaves. This study illustrates the complex impacts of FR on RS and provides valuable information for selecting optimal lighting conditions in CEA.

Assessing the post hoc effectiveness of tixagevimab-cilgavimab for prevention of SARS-CoV-2 infections in solid organ transplant recipients.

BACKGROUND: Tixagevimab-cilgavimab (Tix-Cil) was authorized for prophylaxis against COVID-19 in immunocompromised patients from December 2021 through January 2023. Real-world effectiveness for solid organ transplant (SOT) recipients has been unclear. METHODS: We enrolled 911 SOT recipients into a longitudinal COVID-19 serology study, of whom 381 (42%) received ≥1 dose of Tix-Cil. We collected and analyzed data on incident SARS-CoV-2 infections and antibody kinetics for all patients from January 2022 to March 2023, including periods dominated by Omicron BA and BQ subvariants. RESULTS: Over 253 ± 131 days of follow-up, there were 324 new-onset SARS-CoV-2 infections: 117 (31%) in Tix-Cil treated and 207 (39%) in Tix-Cil untreated patients (p = .012). In analyses adjusting for demographic, clinical, and COVID-19 exposure factors, any Tix-Cil treatment was associated with lower infection risk (OR 0.52, 95% CI 0.27-0.96, p = .039) throughout the surveillance period including when more resistant BQ.1 and BQ.1.1 subvariants had emerged (12/1/2022 onwards). Among treated patients, receiving a Tix-Cil dose was associated with substantial and sustained increase in anti-spike IgG antibody and angiotensin-converting enzyme 2 binding inhibition levels (Abbott Architect assay) that together also demonstrated association with lower infection risk (p = .042). During the full surveillance period, the frequency of infections requiring hospitalization was low overall (N = 26, 2.9% of the total cohort) and not significantly different between Tix-Cil recipients (N = 12, 3.2% of treated patients) and non-Tix-Cil recipients (N = 14, 2.6% of untreated patients) with unadjusted p = .31 for between-group difference. CONCLUSION: In a large cohort of SOT recipients, we found that Tix-Cil reduced infection risk even amidst emergent Omicron subvariants. Additionally, the extent of measurable humoral response to Tix-Cil may indicate relative effectiveness. Pre-exposure monoclonal antibody therapy may represent a strategy that will continue to offer clinical benefit for immunocompromised persons who are known to derive limited protection from vaccinations.

Rapid response of nonstructural carbohydrate allocation and photosynthesis to short photoperiod, low temperature, or elevated CO2 in Pinus strobus.

During autumn, decreasing photoperiod and temperature temporarily perturb the balance between carbon uptake and carbon demand in overwintering plants, requiring coordinated adjustments in photosynthesis and carbon allocation to re-establish homeostasis. Here we examined adjustments of photosynthesis and allocation of nonstructural carbohydrates (NSCs) following a sudden shift to short photoperiod, low temperature, and/or elevated CO2 in Pinus strobus seedlings. Seedlings were initially acclimated to 14 h photoperiod (22/15°C day/night) and ambient CO2 (400 ppm) or elevated CO2 (800 ppm). Seedlings were then shifted to 8 h photoperiod for one of three treatments: no temperature change at ambient CO2 (22/15°C, 400 ppm), low temperature at ambient CO2 (12/5°C, 400 ppm), or no temperature change at elevated CO2 (22/15°C, 800 ppm). Short photoperiod caused all seedlings to exhibit partial nighttime depletion of starch. Short photoperiod alone did not affect photosynthesis. Short photoperiod combined with low temperature caused hexose accumulation and repression of photosynthesis within 24 h, followed by a transient increase in nonphotochemical quenching (NPQ). Under long photoperiod, plants grown under elevated CO2 exhibited significantly higher NSCs and photosynthesis compared to ambient CO2 plants, but carbon uptake exceeded sink capacity, leading to elevated NPQ; carbon sink capacity was restored and NPQ relaxed within 24 h after shift to short photoperiod. Our findings indicate that P. strobus rapidly adjusts NSC allocation, not photosynthesis, to accommodate short photoperiod. However, the combination of short photoperiod and low temperature, or long photoperiod and elevated CO2 disrupts the balance between photosynthesis and carbon sink capacity, resulting in increased NPQ to alleviate excess energy.

Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care.

Importance: Health care algorithms are used for diagnosis, treatment, prognosis, risk stratification, and allocation of resources. Bias in the development and use of algorithms can lead to worse outcomes for racial and ethnic minoritized groups and other historically marginalized populations such as individuals with lower income. Objective: To provide a conceptual framework and guiding principles for mitigating and preventing bias in health care algorithms to promote health and health care equity. Evidence Review: The Agency for Healthcare Research and Quality and the National Institute for Minority Health and Health Disparities convened a diverse panel of experts to review evidence, hear from stakeholders, and receive community feedback. Findings: The panel developed a conceptual framework to apply guiding principles across an algorithm's life cycle, centering health and health care equity for patients and communities as the goal, within the wider context of structural racism and discrimination. Multiple stakeholders can mitigate and prevent bias at each phase of the algorithm life cycle, including problem formulation (phase 1); data selection, assessment, and management (phase 2); algorithm development, training, and validation (phase 3); deployment and integration of algorithms in intended settings (phase 4); and algorithm monitoring, maintenance, updating, or deimplementation (phase 5). Five principles should guide these efforts: (1) promote health and health care equity during all phases of the health care algorithm life cycle; (2) ensure health care algorithms and their use are transparent and explainable; (3) authentically engage patients and communities during all phases of the health care algorithm life cycle and earn trustworthiness; (4) explicitly identify health care algorithmic fairness issues and trade-offs; and (5) establish accountability for equity and fairness in outcomes from health care algorithms. Conclusions and Relevance: Multiple stakeholders must partner to create systems, processes, regulations, incentives, standards, and policies to mitigate and prevent algorithmic bias. Reforms should implement guiding principles that support promotion of health and health care equity in all phases of the algorithm life cycle as well as transparency and explainability, authentic community engagement and ethical partnerships, explicit identification of fairness issues and trade-offs, and accountability for equity and fairness.

One size doesn't fit all: Attitudes towards work modify the relation between parental leave length and postpartum depression.

The present study aimed to investigate the relationship between parental leave length and maternal depressive symptoms at six- and twelve-months postpartum and whether this relation was influenced by women's attitudes towards leave, whether leave was paid or unpaid, and the reason they returned to work. The sample included 115 working women recruited during pregnancy as part of a larger longitudinal study. Analyses revealed that maternal attitudes toward leave influenced the association between leave length and depressive symptoms. Specifically, longer leaves were associated with increased depressive symptoms for women who missed their previous activities at work. Furthermore, women who missed work and had leave for 16 weeks or more, exhibited higher depressive symptoms at six- and twelve-months. Last, results also indicated that women who returned to work solely for monetary reasons exhibited more depressive symptoms at six-months postpartum than those who returned to work for other reasons. This study is among the first to show that women's attitudes towards parental leave and their individual reasons for returning to work are important factors to consider that may have potential implications for parental leave policies.

Protocol for the development of guidance for collaborator and partner engagement in health care evidence syntheses.

BACKGROUND: Involving collaborators and partners in research may increase relevance and uptake, while reducing health and social inequities. Collaborators and partners include people and groups interested in health research: health care providers, patients and caregivers, payers of health research, payers of health services, publishers, policymakers, researchers, product makers, program managers, and the public. Evidence syntheses inform decisions about health care services, treatments, and practice, which ultimately affect health outcomes. Our objectives are to: A. Identify, map, and synthesize qualitative and quantitative findings related to engagement in evidence syntheses B. Explore how engagement in evidence synthesis promotes health equity C. Develop equity-oriented guidance on methods for conducting, evaluating, and reporting engagement in evidence syntheses METHODS: Our diverse, international team will develop guidance for engagement with collaborators and partners throughout multiple sequential steps using an integrated knowledge translation approach: 1. Reviews. We will co-produce 1 scoping review, 3 systematic reviews and 1 evidence map focusing on (a) methods, (b) barriers and facilitators, (c) conflict of interest considerations, (d) impacts, and (e) equity considerations of engagement in evidence synthesis. 2. Methods study, interviews, and survey. We will contextualise the findings of step 1 by assessing a sample of evidence syntheses reporting on engagement with collaborators and partners and through conducting interviews with collaborators and partners who have been involved in producing evidence syntheses. We will use these findings to develop draft guidance checklists and will assess agreement with each item through an international survey. 3. CONSENSUS: The guidance checklists will be co-produced and finalised at a consensus meeting with collaborators and partners. 4. DISSEMINATION: We will develop a dissemination plan with our collaborators and partners and work collaboratively to improve adoption of our guidance by key organizations. CONCLUSION: Our international team will develop guidance for collaborator and partner engagement in health care evidence syntheses. Incorporating partnership values and expectations may result in better uptake, potentially reducing health inequities.

Awareness of Racial and Ethnic Bias and Potential Solutions to Address Bias With Use of Health Care Algorithms.

Importance: Algorithms are commonly incorporated into health care decision tools used by health systems and payers and thus affect quality of care, access, and health outcomes. Some algorithms include a patient's race or ethnicity among their inputs and can lead clinicians and decision-makers to make choices that vary by race and potentially affect inequities. Objective: To inform an evidence review on the use of race- and ethnicity-based algorithms in health care by gathering public and stakeholder perspectives about the repercussions of and efforts to address algorithm-related bias. Design, Setting, and Participants: Qualitative methods were used to analyze responses. Responses were initially open coded and then consolidated to create a codebook, with themes and subthemes identified and finalized by consensus. This qualitative study was conducted from May 4, 2021, through December 7, 2022. Forty-two organization representatives (eg, clinical professional societies, universities, government agencies, payers, and health technology organizations) and individuals responded to the request for information. Main Outcomes and Measures: Identification of algorithms with the potential for race- and ethnicity-based biases and qualitative themes. Results: Forty-two respondents identified 18 algorithms currently in use with the potential for bias, including, for example, the Simple Calculated Osteoporosis Risk Estimation risk prediction tool and the risk calculator for vaginal birth after cesarean section. The 7 qualitative themes, with 31 subthemes, included the following: (1) algorithms are in widespread use and have significant repercussions, (2) bias can result from algorithms whether or not they explicitly include race, (3) clinicians and patients are often unaware of the use of algorithms and potential for bias, (4) race is a social construct used as a proxy for clinical variables, (5) there is a lack of standardization in how race and social determinants of health are collected and defined, (6) bias can be introduced at all stages of algorithm development, and (7) algorithms should be discussed as part of shared decision-making between the patient and clinician. Conclusions and Relevance: This qualitative study found that participants perceived widespread and increasing use of algorithms in health care and lack of oversight, potentially exacerbating racial and ethnic inequities. Increasing awareness for clinicians and patients and standardized, transparent approaches for algorithm development and implementation may be needed to address racial and ethnic biases related to algorithms.

From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II-Harnessing data.

Although our observing capabilities of solar-induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in-situ SIF observing capability especially in "data desert" regions, improving cross-instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.

Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding.

Motivation: The vast expansion of sequence data generated from single organisms and microbiomes has precipitated the need for faster and more sensitive methods to assess evolutionary and functional relationships between proteins. Representing proteins as sets of short peptide sequences (kmers) has been used for rapid, accurate classification of proteins into functional categories; however, this approach employs an exact-match methodology and thus may be limited in terms of sensitivity and coverage. We have previously used similarity groupings, based on the chemical properties of amino acids, to form reduced character sets and recode proteins. This amino acid recoding (AAR) approach simplifies the construction of protein representations in the form of kmer vectors, which can link sequences with distant sequence similarity and provide accurate classification of problematic protein families. Results: Here, we describe Snekmer, a software tool for recoding proteins into AAR kmer vectors and performing either (i) construction of supervised classification models trained on input protein families or (ii) clustering for de novo determination of protein families. We provide examples of the operation of the tool against a set of nitrogen cycling families originally collected using both standard hidden Markov models and a larger set of proteins from Uniprot and demonstrate that our method accurately differentiates these sequences in both operation modes. Availability and implementation: Snekmer is written in Python using Snakemake. Code and data used in this article, along with tutorial notebooks, are available at http://github.com/PNNL-CompBio/Snekmer under an open-source BSD-3 license. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I-Harnessing theory.

Solar-induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability of SIF data at increasingly higher spatial and temporal resolutions, sparking applications in diverse research sectors (e.g., ecology, agriculture, hydrology, climate, and socioeconomics). These applications must deal with complexities caused by tremendous variations in scale and the impacts of interacting and superimposing plant physiology and three-dimensional vegetation structure on the emission and scattering of SIF. At present, these complexities have not been overcome. To advance future research, the two companion reviews aim to (1) develop an analytical framework for inferring terrestrial vegetation structures and function that are tied to SIF emission, (2) synthesize progress and identify challenges in SIF research via the lens of multi-sector applications, and (3) map out actionable solutions to tackle these challenges and offer our vision for research priorities over the next 5-10 years based on the proposed analytical framework. This paper is the first of the two companion reviews, and theory oriented. It introduces a theoretically rigorous yet practically applicable analytical framework. Guided by this framework, we offer theoretical perspectives on three overarching questions: (1) The forward (mechanism) question-How are the dynamics of SIF affected by terrestrial ecosystem structure and function? (2) The inference question: What aspects of terrestrial ecosystem structure, function, and service can be reliably inferred from remotely sensed SIF and how? (3) The innovation question: What innovations are needed to realize the full potential of SIF remote sensing for real-world applications under climate change? The analytical framework elucidates that process complexity must be appreciated in inferring ecosystem structure and function from the observed SIF; this framework can serve as a diagnosis and inference tool for versatile applications across diverse spatial and temporal scales.

Methanogens and Hydrogen Sulfide Producing Bacteria Guide Distinct Gut Microbe Profiles and Irritable Bowel Syndrome Subtypes.

INTRODUCTION: Irritable bowel syndrome (IBS) includes diarrhea-predominant (IBS-D) and constipation-predominant (IBS-C) subtypes. We combined breath testing and stool microbiome sequencing to identify potential microbial drivers of IBS subtypes. METHODS: IBS-C and IBS-D subjects from 2 randomized controlled trials (NCT03763175 and NCT04557215) were included. Baseline breath carbon dioxide, hydrogen (H 2 ), methane (CH 4 ), and hydrogen sulfide (H 2 S) levels were measured by gas chromatography, and baseline stool microbiome composition was analyzed by 16S rRNA sequencing. Microbial metabolic pathways were analyzed using Kyoto Encyclopedia of Genes and Genomes collection databases. RESULTS: IBS-C subjects had higher breath CH 4 that correlated with higher gut microbial diversity and higher relative abundance (RA) of stool methanogens, predominantly Methanobrevibacter , as well as higher absolute abundance of Methanobrevibacter smithii in stool. IBS-D subjects had higher breath H 2 that correlated with lower microbial diversity and higher breath H 2 S that correlated with higher RA of H 2 S-producing bacteria, including Fusobacterium and Desulfovibrio spp. The predominant H 2 producers were different in these distinct microtypes, with higher RA of Ruminococcaceae and Christensenellaceae in IBS-C/CH 4 + (which correlated with Methanobacteriaceae RA) and higher Enterobacteriaceae RA in IBS-D. Finally, microbial metabolic pathway analysis revealed enrichment of Kyoto Encyclopedia of Genes and Genomes modules associated with methanogenesis and biosynthesis of methanogenesis cofactor F420 in IBS-C/CH 4 + subjects, whereas modules associated with H 2 S production, including sulfate reduction pathways, were enriched in IBS-D. DISCUSSION: Our findings identify distinct gut microtypes linked to breath gas patterns in IBS-C and IBS-D subjects, driven by methanogens such as M. smithii and H 2 S producers such as Fusobacterium and Desulfovibrio spp, respectively.

DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data.

We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface (API) and command-line tool for high-dimensional mass spectrometry data analysis workflows that offers ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, CCS, tandem mass spectra, and isotopic signature. Notably, DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation; algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity, (ii) increase alignment/feature matching confidence among data sets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS metabolomics data to illustrate the advantages of a multidimensional approach in each data processing step.

A Smartphone Application Using Artificial Intelligence Is Superior To Subject Self-Reporting When Assessing Stool Form.

INTRODUCTION: Stool form assessment relies on subjective patient reports using the Bristol Stool Scale (BSS). In a novel smartphone application (app), trained artificial intelligence (AI) characterizes digital images of users' stool. In this study, we evaluate this AI for accuracy in assessing stool characteristics. METHODS: Subjects with diarrhea-predominant irritable bowel syndrome image-captured every stool for 2 weeks using the app, which assessed images for 5 visual characteristics (BSS, consistency, fragmentation, edge fuzziness, and volume). In the validation phase, using 2 expert gastroenterologists as a gold standard, sensitivity, specificity, accuracy, and diagnostic odds ratios of subject-reported vs AI-graded BSS scores were compared. In the implementation phase, agreements between AI-graded and subject-reported daily average BSS scores were determined, and subject BSS and AI stool characteristics scores were correlated with diarrhea-predominant irritable bowel syndrome symptom severity scores. RESULTS: In the validation phase (n = 14), there was good agreement between the 2 experts and AI characterizations for BSS (intraclass correlation coefficients [ICC] = 0.782-0.852), stool consistency (ICC = 0.873-0.890), edge fuzziness (ICC = 0.836-0.839), fragmentation (ICC = 0.837-0.863), and volume (ICC = 0.725-0.851). AI outperformed subjects' self-reports in categorizing daily average BSS scores as constipation, normal, or diarrhea. In the implementation phase (n = 25), the agreement between AI and self-reported BSS scores was moderate (ICC = 0.61). AI stool characterization also correlated better than subject reports with diarrhea severity scores. DISCUSSION: A novel smartphone application can determine BSS and other visual stool characteristics with high accuracy compared with the 2 expert gastroenterologists. Moreover, trained AI was superior to subject self-reporting of BSS. AI assessments could provide more objective outcome measures for stool characterization in gastroenterology.

Duodenal microbiome changes in postmenopausal women: effects of hormone therapy and implications for cardiovascular risk.

OBJECTIVE: Hormone therapy (HT) is used to treat menopause-related conditions and symptoms. The small intestine plays key roles in metabolic and endocrine function, but the effects of HT on the small intestinal microbiome are unknown. Here, we characterize duodenal microbiome differences, and the effects of HT, in postmenopausal women. METHODS: Female participants undergoing esophagogastroduodenoscopy who were postmenopausal and taking HT (HT+), postmenopausal but not taking HT (HT-), or of reproductive age and not taking exogenous hormones (RA), were identified and matched for body mass index (±3 kg/m2). DNAs were isolated from duodenal aspirates obtained during upper endoscopy. V3 and V4 libraries were used for 16S rRNA sequencing. Serum hormone levels were analyzed by Luminex FlexMap. RESULTS: The core duodenal microbiome was different in HT- participants (n = 12) when compared with RA participants (n = 10), but more similar in HT+ (n = 13) and RA participants. HT- participants had increased Proteobacteria taxa, leading to greater microbial dysbiosis compared with HT+ participants, and had decreased prevalence of Bacteroidetes, which was associated with higher fasting glucose levels, lower duodenal microbial diversity, and lower testosterone levels. HT+ participants had significantly higher estradiol (P = 0.04) and progesterone (P = 0.04), and lower fasting glucose (P = 0.03), than HT- participants, and had increased relative abundance of Prevotella (P = 0.01), and decreased Escherichia (P = 1.12E-7), Klebsiella (P = 5.93E-7), and Lactobacillus (P = 0.02), all associated with lower cardiovascular disease risks. CONCLUSIONS: These findings support previous studies suggesting that HT may have beneficial effects following menopause, and although preliminary, may also support a beneficial effect of HT on the duodenal microbiome.

The physiological basis for estimating photosynthesis from Chla fluorescence.

Solar-induced Chl fluorescence (SIF) offers the potential to curb large uncertainties in the estimation of photosynthesis across biomes and climates, and at different spatiotemporal scales. However, it remains unclear how SIF should be used to mechanistically estimate photosynthesis. In this study, we built a quantitative framework for the estimation of photosynthesis, based on a mechanistic light reaction model with the Chla fluorescence of Photosystem II (SIFPSII ) as an input (MLR-SIF). Utilizing 29 C3 and C4 plant species that are representative of major plant biomes across the globe, we confirmed the validity of this framework at the leaf level. The MLR-SIF model is capable of accurately reproducing photosynthesis for all C3 and C4 species under diverse light, temperature, and CO2 conditions. We further tested the robustness of the MLR-SIF model using Monte Carlo simulations, and found that photosynthesis estimates were much less sensitive to parameter uncertainties relative to the conventional Farquhar, von Caemmerer, Berry (FvCB) model because of the additional independent information contained in SIFPSII . Once inferred from direct observables of SIF, SIFPSII provides 'parameter savings' to the MLR-SIF model, compared to the mechanistically equivalent FvCB model, and thus avoids the uncertainties arising as a result of imperfect model parameterization. Our findings set the stage for future efforts to employ SIF mechanistically to improve photosynthesis estimates across a variety of scales, functional groups, and environmental conditions.

A Single Fasting Exhaled Methane Level Correlates With Fecal Methanogen Load, Clinical Symptoms and Accurately Detects Intestinal Methanogen Overgrowth.

INTRODUCTION: A 2-hour breath test is the gold standard for diagnosing intestinal methanogen overgrowth (IMO). This method can be cumbersome especially if used repetitively to monitor treatment response. Therefore, we aimed to assess the reliability of a fasting single methane measurement (SMM) in diagnosing IMO and its utility as a biomarker to monitor treatment response in subjects with IMO. METHODS: First, we calculated the test characteristics of SMM compared with lactulose and glucose breath test in 2 large-scale retrospective cohorts. Second, the symptomology associated with SMM using various cutoffs was analyzed. Third, in a double-blind randomized control trial, the temporal stability of SMM levels in subjects taking placebo was analyzed. Fourth, stool Methanobrevibacter smithii loads were quantified using quantitative polymerase chain reaction and compared with SMM levels. Last, the change in SMM over time during antibiotic therapy was analyzed. RESULTS: Using the cutoff of SMM ≥10 ppm, SMM had a sensitivity of 86.4% and specificity of 100% for diagnosing IMO on the glucose and lactulose breath tests and was associated with constipation (5.65 ± 3.47 vs 4.32 ± 3.62, P = 0.008). SMM remained stable for 14 weeks without treatment (P = 0.45), and antibiotics lead to a decrease in SMM after 2 days (P < 0.0001). SMM was positively associate with stool M. smithii load (R = 0.65, P < 0.0001). DISCUSSION: Fasting SMM ≥10 ppm seems to accurately diagnose IMO, is associated with constipation, and correlates with stool M. smithii. SMM seems to be stable without treatment and decreases after antibiotics. SMM may be a useful test to diagnose IMO and monitor treatment response.

Mouse Adenovirus Type 1 Persistence Exacerbates Inflammation Induced by Allogeneic Bone Marrow Transplantation.

Bone marrow transplantation (BMT) recipients are at risk for substantial morbidity and mortality from human adenovirus infections, often in the setting of reactivation of persistent virus. Human adenovirus persistence in mucosal lymphocytes has been described, but specific cellular reservoirs of persistence and effects of persistence on host responses to unrelated stimuli are not completely understood. We used mouse adenovirus type 1 (MAV-1) to characterize persistence of an adenovirus in its natural host and test the hypothesis that persistence increases complications of BMT. Following intranasal infection of C57BL/6J mice, MAV-1 DNA was detected in lung, mediastinal lymph nodes, and liver during acute infection at 7 days postinfection (dpi), and at lower levels at 28 dpi that remained stable through 150 dpi. Expression of early and late viral transcripts was detected in those organs at 7 dpi but not at later time points. MAV-1 persistence was not affected by deficiency of IFN-γ. We detected no evidence of MAV-1 reactivation in vivo following allogeneic BMT of persistently infected mice. Persistent infection did not substantially affect mortality, weight loss, or pulmonary inflammation following BMT. However, T cell infiltration and increased expression of pro-inflammatory cytokines consistent with graft-versus-host disease (GVHD) were more pronounced in livers of persistently infected BMT mice than in uninfected BMT mice. These results suggest that MAV-1 persists in multiple sites without detectable evidence of ongoing replication. Our results indicate that MAV-1 persistence alters host responses to an unrelated challenge, even in the absence of detectable reactivation. IMPORTANCE Long-term persistence in an infected host is an essential step in the life cycle of DNA viruses. Adenoviruses persist in their host following acute infection, but the nature of adenovirus persistence remains incompletely understood. Following intranasal infection of mice, we found that MAV-1 persists for a prolonged period in multiple organs, although we did not detect evidence of ongoing replication. Because BMT recipients are at risk for substantial morbidity and mortality from human adenovirus infections, often in the setting of reactivation of persistent virus in the recipient, we extended our findings using MAV-1 infection in a mouse model of BMT. MAV-1 persistence exacerbated GVHD-like inflammation following allogeneic BMT, even in the absence of virus reactivation. This novel finding suggests that adenovirus persistence has consequences, and it highlights the potential for a persistent adenovirus to influence host responses to unrelated challenges.

ALT Levels in Treatment-Naive, Chronic Hepatitis B Patients with Concurrent Fatty Liver Disease: A US Nationwide Study.

INTRODUCTION: Treatment criteria for chronic hepatitis B (CHB) relies on ALT, which can be impacted by concurrent nonalcoholic fatty liver disease (NAFLD), but ALT data on patients with CHB and NAFLD are limited. We aimed to characterize ALT distribution in untreated CHB patients with NAFLD. METHODS: We retrospectively analyzed untreated US adults with CHB (533 with NAFLD, 3,172 without NAFLD) using the Clinformatics™ Data Mart Database (2003-2019). The main outcome was ALT elevation (>1× upper limit of normal, 35/25 U/L for men/women, respectively). Secondary outcomes were advanced fibrosis (via FIB-4 index) and factors associated with fibrosis. RESULTS: The majority of patients were Asian (61.0%) and hepatitis B e-antigen (HBeAg)-negative (90.4%). Patients with CHB and NAFLD were older (57.2 vs. 49.5 years, p < 0.001), more likely male (59.3% vs. 46.2%, p < 0.001), with higher percentages of advanced fibrosis (3.6% vs. 2.6%, p < 0.001) than those with CHB alone. CHB-NAFLD patients were more likely to have elevated ALT than those with CHB only, but this difference was only significant among those with low hepatitis B virus (HBV) DNA (38.1% vs. 25.6%, p < 0.001), not those with higher HBV DNA (>2,000 IU/mL). After adjusting for HBeAg, HBV DNA, and diabetes, NAFLD was not independently associated with advanced fibrosis (odds ratio 1.18, 95% confidence interval: 0.30-4.59, p = 0.81). DISCUSSION: CHB-NAFLD patients with HBV DNA below treatment threshold were more likely to have elevated ALT but not those with higher HBV DNA, suggesting that ALT threshold does not need to be raised for antiviral eligibility for CHB with NAFLD.