Microbial DNA sample preservation and possible artifacts for field-based research in remote tropical peatlands.

Surveying bacterial and archaeal microbial communities in host and environmental studies requires the collection and storage of samples. Many studies are conducted in distant locations challenging these prerequisites. The use of preserving buffers is an important alternative when lacking access to cryopreservation, however, its effectivity for samples with challenging chemistry or samples that provide opportunities for fast bacterial or archaeal growth upon exposure to an aerobic environment, like peat samples, requires methodological assessment. Here, in combination with an identified optimal DNA extraction kit for peat soil samples, we test the application of several commercial and a homemade preservation buffer and make recommendations on the method that can most effectively preserve a microbiome reflective of the original state. In treatments with a non-optimal buffer or in the absence, we observed notable community shifts beginning as early as three days post-preservation lowering diversity and community evenness, with growth-driven artifacts from a few specific phyla. However other buffers retain a very close composition relative to the original state, and we described several metrics to understand some variation across them. Due to the chemical effects of preservation buffers, it is critical to test their compatibility and reliability to preserve the original bacterial and archaeal community in different environments.

New insights into the signal transduction mechanism of O2-sensing FixL and other biological heme-based sensor proteins.

Recent structural and biophysical studies of O2-sensing FixL, NO-sensing soluble guanylate cyclase, and other biological heme-based sensing proteins have begun to reveal the details of their molecular mechanisms and shed light on how nature regulates important biological processes such as nitrogen fixation, blood pressure, neurotransmission, photosynthesis and circadian rhythm. The O2-sensing FixL protein from S. meliloti, the eukaryotic NO-sensing protein sGC, and the CO-sensing CooA protein from R. rubrum transmit their biological signals through gas-binding to the heme domain of these proteins, which inhibits or activates the regulatory, enzymatic domain. These proteins appear to propagate their signal by specific structural changes in the heme sensor domain initiated by the appropriate gas binding to the heme, which is then propagated through a coiled-coil linker or other domain to the regulatory, enzymatic domain that sends out the biological signal. The current understanding of the signal transduction mechanisms of O2-sensing FixL, NO-sensing sGC, CO-sensing CooA and other biological heme-based gas sensing proteins and their mechanistic themes are discussed, with recommendations for future work to further understand this rapidly growing area of biological heme-based gas sensors.

The Safety of Outpatient Health Care : Review of Electronic Health Records.

BACKGROUND: Despite considerable emphasis on delivering safe care, substantial patient harm occurs. Although most care occurs in the outpatient setting, knowledge of outpatient adverse events (AEs) remains limited. OBJECTIVE: To measure AEs in the outpatient setting. DESIGN: Retrospective review of the electronic health record (EHR). SETTING: 11 outpatient sites in Massachusetts in 2018. PATIENTS: 3103 patients who received outpatient care. MEASUREMENTS: Using a trigger method, nurse reviewers identified possible AEs and physicians adjudicated them, ranked severity, and assessed preventability. Generalized estimating equations were used to assess the association of having at least 1 AE with age, sex, race, and primary insurance. Variation in AE rates was analyzed across sites. RESULTS: The 3103 patients (mean age, 52 years) were more often female (59.8%), White (75.1%), English speakers (90.8%), and privately insured (70.4%) and had a mean of 4 outpatient encounters in 2018. Overall, 7.0% (95% CI, 4.6% to 9.3%) of patients had at least 1 AE (8.6 events per 100 patients annually). Adverse drug events were the most common AE (63.8%), followed by health care-associated infections (14.8%) and surgical or procedural events (14.2%). Severity was serious in 17.4% of AEs, life-threatening in 2.1%, and never fatal. Overall, 23.2% of AEs were preventable. Having at least 1 AE was less often associated with ages 18 to 44 years than with ages 65 to 84 years (standardized risk difference, -0.05 [CI, -0.09 to -0.02]) and more often associated with Black race than with Asian race (standardized risk difference, 0.09 [CI, 0.01 to 0.17]). Across study sites, 1.8% to 23.6% of patients had at least 1 AE and clinical category of AEs varied substantially. LIMITATION: Retrospective EHR review may miss AEs. CONCLUSION: Outpatient harm was relatively common and often serious. Adverse drug events were most frequent. Rates were higher among older adults. Interventions to curtail outpatient harm are urgently needed. PRIMARY FUNDING SOURCE: Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions.

Transformer CycleGAN with uncertainty estimation for CBCT based synthetic CT in adaptive radiotherapy.

Objective. Clinical implementation of synthetic CT (sCT) from cone-beam CT (CBCT) for adaptive radiotherapy necessitates a high degree of anatomical integrity, Hounsfield unit (HU) accuracy, and image quality. To achieve these goals, a vision-transformer and anatomically sensitive loss functions are described. Better quantification of image quality is achieved using the alignment-invariant Fréchet inception distance (FID), and uncertainty estimation for sCT risk prediction is implemented in a scalable plug-and-play manner.Approach. Baseline U-Net, generative adversarial network (GAN), and CycleGAN models were trained to identify shortcomings in each approach. The proposed CycleGAN-Best model was empirically optimized based on a large ablation study and evaluated using classical image quality metrics, FID, gamma index, and a segmentation analysis. Two uncertainty estimation methods, Monte-Carlo Dropout (MCD) and test-time augmentation (TTA), were introduced to model epistemic and aleatoric uncertainty.Main results. FID was correlated to blind observer image quality scores with a Correlation Coefficient of -0.83, validating the metric as an accurate quantifier of perceived image quality. The FID and mean absolute error (MAE) of CycleGAN-Best was 42.11 ± 5.99 and 25.00 ± 1.97 HU, compared to 63.42 ± 15.45 and 31.80 HU for CycleGAN-Baseline, and 144.32 ± 20.91 and 68.00 ± 5.06 HU for the CBCT, respectively. Gamma 1%/1 mm pass rates were 98.66 ± 0.54% for CycleGAN-Best, compared to 86.72 ± 2.55% for the CBCT. TTA and MCD-based uncertainty maps were well spatially correlated with poor synthesis outputs.Significance. Anatomical accuracy was achieved by suppressing CycleGAN-related artefacts. FID better discriminated image quality, where alignment-based metrics such as MAE erroneously suggest poorer outputs perform better. Uncertainty estimation for sCT was shown to correlate with poor outputs and has clinical relevancy toward model risk assessment and quality assurance. The proposed model and accompanying evaluation and risk assessment tools are necessary additions to achieve clinically robust sCT generation models.

Photoacoustic Calorimetry Studies of O2-Sensing FixL and (R200, I209) Variants from Sinorhizobium meliloti Reveal Conformational Changes Coupled to Ligand Photodissociation from the Heme-PAS Domain.

FixL is an oxygen-sensing heme-PAS protein that regulates nitrogen fixation in the root nodules of plants. In this paper, we present the first photothermal studies of the full-length wild-type FixL protein from Sinorhizobium meliloti and the first thermodynamic profile of a full-length heme-PAS protein. Photoacoustic calorimetry studies reveal a quadriphasic relaxation for SmFixL*WT and the five variant proteins (SmFixL*R200H, SmFixL*R200Q, SmFixL*R200E, SmFixL*R200A, and SmFixL*I209M) with four intermediates from <20 ns to ∼1.5 µs associated with the photodissociation of CO from the heme. The altered thermodynamic profiles of the full-length SmFixL* variant proteins confirm that the conserved heme domain residues R200 and I209 are important for signal transduction. In contrast, the truncated heme domain, SmFixLH128-264, shows only a single, fast monophasic relaxation at <50 ns associated with the fast disruption of a salt bridge and release of CO to the solvent, suggesting that the full-length protein is necessary to observe the conformational changes that propagate the signal from the heme domain to the kinase domain.

Developing machine learning systems worthy of trust for infection science: a requirement for future implementation into clinical practice.

Infection science is a discipline of healthcare which includes clinical microbiology, public health microbiology, mechanisms of microbial disease, and antimicrobial countermeasures. The importance of infection science has become more apparent in recent years during the SARS-CoV-2 (COVID-19) pandemic and subsequent highlighting of critical operational domains within infection science including the hospital, clinical laboratory, and public health environments to prevent, manage, and treat infectious diseases. However, as the global community transitions beyond the pandemic, the importance of infection science remains, with emerging infectious diseases, bloodstream infections, sepsis, and antimicrobial resistance becoming increasingly significant contributions to the burden of global disease. Machine learning (ML) is frequently applied in healthcare and medical domains, with growing interest in the application of ML techniques to problems in infection science. This has the potential to address several key aspects including improving patient outcomes, optimising workflows in the clinical laboratory, and supporting the management of public health. However, despite promising results, the implementation of ML into clinical practice and workflows is limited. Enabling the migration of ML models from the research to real world environment requires the development of trustworthy ML systems that support the requirements of users, stakeholders, and regulatory agencies. This paper will provide readers with a brief introduction to infection science, outline the principles of trustworthy ML systems, provide examples of the application of these principles in infection science, and propose future directions for moving towards the development of trustworthy ML systems in infection science.

Machine learning pipeline for blood culture outcome prediction using Sysmex XN-2000 blood sample results in Western Australia.

BACKGROUND: Bloodstream infections (BSIs) are a significant burden on the global population and represent a key area of focus in the hospital environment. Blood culture (BC) testing is the standard diagnostic test utilised to confirm the presence of a BSI. However, current BC testing practices result in low positive yields and overuse of the diagnostic test. Diagnostic stewardship research regarding BC testing is increasing, and becoming more important to reduce unnecessary resource expenditure and antimicrobial use, especially as antimicrobial resistance continues to rise. This study aims to establish a machine learning (ML) pipeline for BC outcome prediction using data obtained from routinely analysed blood samples, including complete blood count (CBC), white blood cell differential (DIFF), and cell population data (CPD) produced by Sysmex XN-2000 analysers. METHODS: ML models were trained using retrospective data produced between 2018 and 2019, from patients at Sir Charles Gairdner hospital, Nedlands, Western Australia, and processed at Pathwest Laboratory Medicine, Nedlands. Trained ML models were evaluated using stratified 10-fold cross validation. RESULTS: Two ML models, an XGBoost model using CBC/DIFF/CPD features with boruta feature selection (BFS) , and a random forest model trained using CBC/DIFF features with BFS were selected for further validation after obtaining AUC scores of [Formula: see text] and [Formula: see text] respectively using stratified 10-fold cross validation. The XGBoost model obtained an AUC score of 0.76 on a internal validation set. The random forest model obtained AUC scores of 0.82 and 0.76 on internal and external validation datasets respectively. CONCLUSIONS: We have demonstrated the utility of using an ML pipeline combined with CBC/DIFF, and CBC/DIFF/CPD feature spaces for BC outcome prediction. This builds on the growing body of research in the area of BC outcome prediction, and provides opportunity for further research.

The Safety of Inpatient Health Care.

BACKGROUND: Adverse events during hospitalization are a major cause of patient harm, as documented in the 1991 Harvard Medical Practice Study. Patient safety has changed substantially in the decades since that study was conducted, and a more current assessment of harm during hospitalization is warranted. METHODS: We conducted a retrospective cohort study to assess the frequency, preventability, and severity of patient harm in a random sample of admissions from 11 Massachusetts hospitals during the 2018 calendar year. The occurrence of adverse events was assessed with the use of a trigger method (identification of information in a medical record that was previously shown to be associated with adverse events) and from review of medical records. Trained nurses reviewed records and identified admissions with possible adverse events that were then adjudicated by physicians, who confirmed the presence and characteristics of the adverse events. RESULTS: In a random sample of 2809 admissions, we identified at least one adverse event in 23.6%. Among 978 adverse events, 222 (22.7%) were judged to be preventable and 316 (32.3%) had a severity level of serious (i.e., caused harm that resulted in substantial intervention or prolonged recovery) or higher. A preventable adverse event occurred in 191 (6.8%) of all admissions, and a preventable adverse event with a severity level of serious or higher occurred in 29 (1.0%). There were seven deaths, one of which was deemed to be preventable. Adverse drug events were the most common adverse events (accounting for 39.0% of all events), followed by surgical or other procedural events (30.4%), patient-care events (which were defined as events associated with nursing care, including falls and pressure ulcers) (15.0%), and health care-associated infections (11.9%). CONCLUSIONS: Adverse events were identified in nearly one in four admissions, and approximately one fourth of the events were preventable. These findings underscore the importance of patient safety and the need for continuing improvement. (Funded by the Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions.).

Trend of Incidence of Ocular Injury in Service Members of the U.S. Armed Forces in 2016-2019.

PURPOSE: Ocular injuries pose a significant threat to performance of military functions by the U.S. service members. To estimate the burden of ocular injuries in service members, administrative health records from the Military Health System were collected and analyzed, inclusive of ocular injuries incurred during deployment, military training, or outside of duty hours. METHODS: Patient encounters which matched the predesignated ICD10 codes for ocular injury and complications were extracted and were longitudinally analyzed to categorize patients into complicated (documentation of surgical procedure performed or ocular condition requiring further treatment) or uncomplicated (documentation of injury only). Comparison of incidence rates between groups was made, and geospatial analysis of the number of patients with ocular injury was conducted. RESULTS: A total of 61,680 incidences of ocular injuries were identified from 2016 to 2019. The incidence rates for complicated and uncomplicated injuries were 21.3 and 82.3 per 10,000, respectively. The incidence rate of uncomplicated injury declined from 2016 to 2019, while that of complicated injury was relatively stable. For complicated ocular injury, the relative risk of males was 62% higher than that of females. The incidence rates of the Army and the Marines were significantly higher than those of the Air Force. California, Texas, and Virginia ranked top three in the numbers of complicated ocular injury patients. Unintentional injuries and struck were the most frequently coded intention and mechanism. CONCLUSION: Our results provide the critical information on trends of ocular injuries in relation to demographics, service branches, and occupations. Categorization of the severity of ocular injuries is important to inform health services operations analysis across the Military Health System to enhance medical readiness and improve outcomes.

Incidence of Optic Neuritis and the Associated Risk of Multiple Sclerosis for Service Members of U.S. Armed Forces.

INTRODUCTION: Optic neuritis (ON), an acute inflammation of the optic nerve resulting in eye pain and temporary vision loss, is one of the leading causes of vision-related hospital bed days in the U.S. Military and may be a harbinger of multiple sclerosis (MS). We developed a case identification algorithm to estimate incidence rates of ON and the conversion rate to MS based on a retrospective assessment of medical records of service members (SMs) of the U.S. Armed Force. MATERIALS AND METHODS: Electronic medical records (EMRs) from 2006 to 2018 in the Defense Medical Surveillance System were screened using the case identification algorithms for ON and MS diagnosis. The incidences rates of ON were calculated. The rates of conversion to MS was modeled using the Kaplan-Meier survival analysis. RESULTS: The overall incidence rate of ON was 8.1 per 100,000 from 2006 to 2018. Females had a rate (16.9 per 100,000) three times higher than males. Most (68%) of subsequent diagnoses of MS were made within 1 year after diagnosis of ON. The overall 5-year risk of progression to MS was 15% (11%-16% for 95% CI). The risk of conversion to MS in females was significantly higher than in males. CONCLUSIONS: We developed an efficient tool to explore the EMR database to estimate the burden of ON in the U.S. Military and the MS conversion based on a dynamic cohort. The estimated conversion rates to MS feeds into inform retention and fitness-for-duty policy in these SMs.

Association of Chlorhexidine Use and Scaling and Root Planing With Birth Outcomes in Pregnant Individuals With Periodontitis: A Systematic Review and Meta-analysis.

Importance: Chlorhexidine mouthwash enhances treatment effects of conventional periodontal treatment, but data on chlorhexidine as a source of heterogeneity in meta-analyses assessing the treatment of maternal periodontitis in association with birth outcomes are lacking. Objective: To assess possible heterogeneity by chlorhexidine use in randomized clinical trials (RCTs) evaluating the effect of periodontal treatment (ie, scaling and root planing [SRP]) vs no treatment on birth outcomes. Data Sources: Cochrane Oral Health's Trials Register, Cochrane Pregnancy and Childbirth's Trials Register, Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE Ovid, Embase Ovid, LILACS BIREME Virtual Health Library (Latin American and Caribbean Health Science Information database), US National Institutes of Health Ongoing Trials Register (ClinicalTrials.gov), and the WHO International Clinical Trials Registry Platform were searched through March 2022. Study Selection: RCTs were included if they were conducted among pregnant individuals with periodontitis, used interventions consisting of SRP vs no periodontal treatment, and assessed birth outcomes. Data Extraction and Synthesis: Data were abstracted with consensus of 2 reviewers using Rayyan and assessed for bias with the Cochrane Risk of Bias 2 tool before random effects subgroup meta-analyses. Analyses were conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline. Main Outcomes and Measures: Outcomes of interest were preterm birth (ie, <37 weeks' gestation) and low birth weight (ie, <2500 g). Results: There were 12 studies with a total of 5735 participants evaluating preterm birth. Control group participants did not receive any treatment or use chlorhexidine during pregnancy. All intervention group participants received SRP; in 5 of these studies (with 2570 participants), pregnant participants in the treatment group either received chlorhexidine mouthwash or advice to use it, but participants in the remaining 7 studies (with 3183 participants) did not. There were 8 studies with a total of 3510 participants evaluating low birth weight, including 3 studies with SRP plus chlorhexidine (with 594 participants) and 6 studies with SRP only (with 2916 participants). The SRP plus chlorhexidine groups had lower risk of preterm birth (relative risk [RR], 0.56; 95% CI, 0.34-0.93) and low birth weight (RR, 0.47; 95% CI, 0.32-0.68) but not the SRP-only groups (preterm birth: RR, 1.03; 95% CI, 0.82-1.29; low birth weight: RR, 0.82; 95% CI, 0.62-1.08). Conclusions and Relevance: These findings suggest that treating maternal periodontitis with chlorhexidine mouthwash plus SRP was associated with reduced risk of preterm and low birth weight. Well-conducted RCTs are needed to test this hypothesis.

Coupled abiotic-biotic cycling of nitrous oxide in tropical peatlands.

Atmospheric nitrous oxide (N2O) is a potent greenhouse gas thought to be mainly derived from microbial metabolism as part of the denitrification pathway. Here we report that in unexplored peat soils of Central and South America, N2O production can be driven by abiotic reactions (≤98%) highly competitive to their enzymatic counterparts. Extracted soil iron positively correlated with in situ abiotic N2O production determined by isotopic tracers. Moreover, we found that microbial N2O reduction accompanied abiotic production, essentially closing a coupled abiotic-biotic N2O cycle. Anaerobic N2O consumption occurred ubiquitously (pH 6.4-3.7), with proportions of diverse clade II N2O reducers increasing with consumption rates. Our findings show that denitrification in tropical peat soils is not a purely biological process but rather a 'mosaic' of abiotic and biotic reduction reactions. We predict that hydrological and temperature fluctuations differentially affect abiotic and biotic drivers and further contribute to the high N2O flux variation in the region.

Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.

The use of deep learning (DL) to improve cone-beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation therapy (ART) by utilizing up-to-date patient anatomy to modify treatment parameters before irradiation. Poor CBCT image quality has been an impediment to realizing ART due to the increased scatter conditions inherent to cone-beam acquisitions. Given the recent interest in DL applications in radiation oncology, and specifically DL for CBCT correction, we provide a systematic theoretical and literature review for future stakeholders. The review encompasses DL approaches for synthetic CT generation, as well as projection domain methods employed in the CBCT correction literature. We review trends pertaining to publications from January 2018 to April 2022 and condense their major findings-with emphasis on study design and DL techniques. Clinically relevant endpoints relating to image quality and dosimetric accuracy are summarized, highlighting gaps in the literature. Finally, we make recommendations for both clinicians and DL practitioners based on literature trends and the current DL state-of-the-art methods utilized in radiation oncology.

A new approach to categorization of ocular injury among U.S. Armed Forces.

This report describes a new approach to categorizing ocular injury using Military Health System data, the application of an algorithm to a dataset, and the verification of the results using an audit of clinical data. Based on health care encounter data, an algorithm was developed to systematically document the occurrence of specific complications and medical procedures in the 12 months following initial ocular injuries. The injuries were classified into 1 of 2 groups: "uncomplicated injury" with no complications or medical procedures and "complicated injury" with complications and/or medical procedures. Injuries in the latter group were further classified by severity into low, moderate, and high strata based on a ranking of complications and medical procedures. From 2016 through 2019, 12,664 complicated ocular injuries and 49,016 uncomplicated injuries were identified among active duty U.S. military members. The vast majority (84%) of complications were concurrent or occurred within 30 days following the injury. The 3 most common complications (orbital floor fracture, iridocyclitis and recurrent corneal erosion) accounted for 52% of complications. These findings underscore the importance of accurate classification of complex ocular injuries to inform studies in multiple areas including injury prevention, the development of clinical guidelines, and health services research.

Decreasing Tryptophan and Increasing Neopterin Plasma Levels During Pregnancy are Associated with High First Trimester Porphyromonas gingivalis K-Serotype IgG Serointensity in a Cohort of Hispanic Women.

BACKGROUND: Immune activation or high levels of stress may lead to increased metabolism of tryptophan during pregnancy. Porphyromonas gingivalis (Pg), the "keystone" periodontal pathogen, induces immune and indoleamine 2,3-dioxygenase (IDO) activation. Thus, we hypothesized that larger gestational decreases in tryptophan and elevations in neopterin and kynurenine would occur in pregnant women with elevated IgG antibodies to Pg capsular (K) serotypes. METHODS: Venous blood of 52 Hispanic pregnant women with a mean age (SD) of 31.8 (5.9) years was sampled once per trimester of pregnancy (V1, V2, V3), and plasma was obtained and stored. ELISAs were used to measure Pg capsular (K) serotype IgG serointensity (V1 only) and neopterin levels (V1-V3). Tryptophan and kynurenine (V1-V3) were measured with high-performance liquid chromatography. The participants having IgG serointensity for any of the seven Pg K serotypes in the highest quartile were defined as the "High PgK_IgG" group and those having IgG serointensity for all K serotypes in the lowest three quartiles were defined as the "Low PgK_IgG" group. Statistics included multivariable linear and nonparametric methods. RESULTS: Significant decreases in plasma tryptophan levels and increases in neopterin during gestation were found in "High PgK_IgG" women but not in "Low PgK_IgG" women. Kynurenine changes were not significantly different between the two groups. CONCLUSION: If replicated in larger studies and further characterized clinically, radiologically, and microbiologically, our results may potentially lead to novel interventional targets, as well as the development of more complete prognostic and predictive interactive biomarkers for adverse obstetrical outcomes and peripartum depression, and their prevention.

Delineating the Drivers and Functionality of Methanogenic Niches within an Arid Landfill.

Microbial communities mediate the transformation of organic matter within landfills into methane (CH4). Yet their ecological role in CH4 production is rarely evaluated. To characterize the microbiome associated with this biotransformation, the overall community and methanogenic Archaea were surveyed in an arid landfill using leachate collected from distinctly aged landfill cells (i.e., younger, intermediate, and older). We hypothesized that distinct methanogenic niches exist within an arid landfill, driven by geochemical gradients that developed under extended and age-dependent waste biodegradation stages. Using 16S rRNA and mcrA gene amplicon sequencing, we identified putative methanogenic niches as follows. The order Methanomicrobiales was the most abundant order in leachate from younger cells, where leachate temperature and propionate concentrations were measured at 41.8°C ± 1.7°C and 57.1 ± 10.7 mg L-1. In intermediate-aged cells, the family Methanocellaceae was identified as a putative specialist family under intermediate-temperature and -total dissolved solid (TDS) conditions, wherein samples had a higher alpha diversity index and near CH4 concentrations. In older-aged cells, accumulating metals and TDS supported Methanocorpusculaceae, "Candidatus Bathyarchaeota," and "Candidatus Verstraetearchaeota" operational taxonomic units (OTUs). Consistent with the mcrA data, we assayed methanogenic activity across the age gradient through stable isotopic measurements of δ13C of CH4 and δ13C of CO2. The majority (80%) of the samples' carbon fractionation was consistent with hydrogenotrophic methanogenesis. Together, we report age-dependent geochemical gradients detected through leachate in an arid landfill seemingly influencing CH4 production, niche partitioning, and methanogenic activity. IMPORTANCE Microbiome analysis is becoming common in select municipal and service ecosystems, including wastewater treatment and anaerobic digestion, but its potential as a microbial-status-informative tool to promote or mitigate CH4 production has not yet been evaluated in landfills. Methanogenesis mediated by Archaea is highly active in solid-waste microbiomes but is commonly neglected in studies employing next-generation sequencing techniques. Identifying methanogenic niches within a landfill offers detail into operations that positively or negatively impact the commercial production of methane known as biomethanation. We provide evidence that the geochemistry of leachate and its microbiome can be a variable accounting for ecosystem-level (coarse) variation of CH4 production, where we demonstrate through independent assessments of leachate and gas collection that the functional variability of an arid landfill is linked to the composition of methanogenic Archaea.

Oral Inflammatory Burden and Carotid Atherosclerosis Among Stroke Patients.

INTRODUCTION: This study aimed to test the hypothesis that oral inflammatory burden (OIB) is independently associated with the carotid atherosclerotic burden (CAB) among individuals with ischemic stroke (IS) or transient ischemic attack (TIA). METHODS: This cross-sectional observational study included 240 hospital patients with the diagnosis of IS or TIA. The main exposures were apical periodontitis (AP), root canal treatment (RCT), and crestal alveolar (periodontal) bone loss (BL), and the main outcome was the CAB. Exposure and outcome variables were measured through a head and neck multidetector computed tomography angiography and CAB was dichotomized in <50% and ≥50% vessel occlusion. OIB scored as a composite measure of the endodontic and periodontal disease exposure. Hospital health records provided information on sociodemographic and medical covariates. Prevalence ratios (PRs) were calculated through Poisson regression models, estimating the relationship between the oral exposures and CAB, with = 5%. RESULTS: Mean age was 62.15 ± 13.1 years, with 56.7% men. Univariate analyses showed that AP ≥2 (PR = 1.83; 95% confidence interval [CI], 1.05-3.17) and endodontic burden (EB) (AP and/or RCT ≥ 2) (PR = 1.98; 95% CI, 1.13-3.47) were associated with CAB ≥50%. Multivariate models, adjusted for sociodemographic and medical covariates, revealed that pooled periodontal and endodontic parameters (OIB = BL ≥ 5 mm and EB ≥ 2) were independently associated with CAB ≥ 50% (PR = 2.47; 95% CI, 1.04-5.87). CONCLUSION: A higher OIB was independently associated with increased levels of CAB among hospital patients with IS or TIA. The combination of endodontic and periodontal parameters strengthened the observed association and should be evaluated in future studies on the relationship between oral health and cardiovascular outcomes.

Both real-time and long-term environmental data perform well in predicting shorebird distributions in managed habitat.

Highly mobile species, such as migratory birds, respond to seasonal and interannual variability in resource availability by moving to better habitats. Despite the recognized importance of resource thresholds, species-distribution models typically rely on long-term average habitat conditions, mostly because large-extent, temporally resolved, environmental data are difficult to obtain. Recent advances in remote sensing make it possible to incorporate more frequent measurements of changing landscapes; however, there is often a cost in terms of model building and processing and the added value of such efforts is unknown. Our study tests whether incorporating real-time environmental data increases the predictive ability of distribution models, relative to using long-term average data. We developed and compared distribution models for shorebirds in California's Central Valley based on high temporal resolution (every 16 days), and 17-year long-term average surface water data. Using abundance-weighted boosted regression trees, we modeled monthly shorebird occurrence as a function of surface water availability, crop type, wetland type, road density, temperature, and bird data source. Although modeling with both real-time and long-term average data provided good fit to withheld validation data (the area under the receiver operating characteristic curve, or AUC, averaged between 0.79 and 0.89 for all taxa), there were small differences in model performance. The best models incorporated long-term average conditions and spatial pattern information for real-time flooding (e.g., perimeter-area ratio of real-time water bodies). There was not a substantial difference in the performance of real-time and long-term average data models within time periods when real-time surface water differed substantially from the long-term average (specifically during drought years 2013-2016) and in intermittently flooded months or locations. Spatial predictions resulting from the models differed most in the southern region of the study area where there is lower water availability, fewer birds, and lower sampling density. Prediction uncertainty in the southern region of the study area highlights the need for increased sampling in this area. Because both sets of data performed similarly, the choice of which data to use may depend on the management context. Real-time data may ultimately be best for guiding dynamic, adaptive conservation actions, whereas models based on long-term averages may be more helpful for guiding permanent wetland protection and restoration.

Reaching Herd Immunity During the SARS-CoV-2 Pandemic: What School Nurses Need to Know.

The National Association of School Nurses supports pandemic control efforts. School nurses are advocates for their students, caregivers, school staff, teachers, and school administrators. With a clear understanding of how the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus evolves over time and changes transmissibility through mutations, school nurses gain understanding in epidemiologic calculation of herd immunity. To understand why the estimates of herd immunity fluctuate, as often reported in the news, school nurses need to understand how epidemiologist calculate this number. Obtaining herd immunity will protect the most vulnerable in the population. If all countries have access to vaccines and populations choose to receive vaccinations, herd immunity is more likely to be obtained. Equipped with knowledge of how herd immunity is calculated, school nurses are in a position to educate and advocate for the use of vaccines.