Distinct Depth-Discrete Profiles of Microbial Communities and Geochemical Insights in the Subsurface Critical Zone.

Microbial assembly and metabolic potential in the subsurface critical zone (SCZ) are substantially impacted by subsurface geochemistry and hydrogeology, selecting for microbes distinct from those in surficial soils. In this study, we integrated metagenomics and geochemistry to elucidate how microbial composition and metabolic potential are shaped and impacted by vertical variations in geochemistry and hydrogeology in terrestrial subsurface sediment. A sediment core from an uncontaminated, pristine well at Oak Ridge Field Research Center in Oak Ridge, Tennessee, including the shallow subsurface, vadose zone, capillary fringe, and saturated zone, was used in this study. Our results showed that subsurface microbes were highly localized and that communities were rarely interconnected. Microbial community composition as well as metabolic potential in carbon and nitrogen cycling varied even over short vertical distances. Further analyses indicated a strong depth-related covariation of community composition with a subset of 12 environmental variables. An analysis of dissolved organic carbon (DOC) quality via ultrahigh resolution mass spectrometry suggested that the SCZ was generally a low-carbon environment, with the relative portion of labile DOC decreasing and that of recalcitrant DOC increasing along the depth, selecting microbes from copiotrophs to oligotrophs and also impacting the microbial metabolic potential in the carbon cycle. Our study demonstrates that sediment geochemistry and hydrogeology are vital in the selection of distinct microbial populations and metabolism in the SCZ. IMPORTANCE In this study, we explored the links between geochemical parameters, microbial community structure and metabolic potential across the depth of sediment, including the shallow subsurface, vadose zone, capillary fringe, and saturated zone. Our results revealed that microbes in the terrestrial subsurface can be highly localized, with communities rarely being interconnected along the depth. Overall, our research demonstrates that sediment geochemistry and hydrogeology are vital in the selection of distinct microbial populations and metabolic potential in different depths of subsurface terrestrial sediment. Such studies correlating microbial community analyses and geochemistry analyses, including high resolution mass spectrometry analyses of natural organic carbon, will further the fundamental understanding of microbial ecology and biogeochemistry in subsurface terrestrial ecosystems and will benefit the future development of predictive models on nutrient turnover in these environments.

Smartphone-Tracked Digital Markers of Momentary Subjective Stress in College Students: Idiographic Machine Learning Analysis.

BACKGROUND: Stress is an important predictor of mental health problems such as burnout and depression. Acute stress is considered adaptive, whereas chronic stress is viewed as detrimental to well-being. To aid in the early detection of chronic stress, machine learning models are increasingly trained to learn the quantitative relation from digital footprints to self-reported stress. Prior studies have investigated general principles in population-wide studies, but the extent to which the findings apply to individuals is understudied. OBJECTIVE: We aimed to explore to what extent machine learning models can leverage features of smartphone app use log data to recognize momentary subjective stress in individuals, which of these features are most important for predicting stress and represent potential digital markers of stress, the nature of the relations between these digital markers and stress, and the degree to which these relations differ across people. METHODS: Student participants (N=224) self-reported momentary subjective stress 5 times per day up to 60 days in total (44,381 observations); in parallel, dedicated smartphone software continuously logged their smartphone app use. We extracted features from the log data (eg, time spent on app categories such as messenger apps and proxies for sleep duration and onset) and trained machine learning models to predict momentary subjective stress from these features using 2 approaches: modeling general relations at the group level (nomothetic approach) and modeling relations for each person separately (idiographic approach). To identify potential digital markers of momentary subjective stress, we applied explainable artificial intelligence methodology (ie, Shapley additive explanations). We evaluated model accuracy on a person-to-person basis in out-of-sample observations. RESULTS: We identified prolonged use of messenger and social network site apps and proxies for sleep duration and onset as the most important features across modeling approaches (nomothetic vs idiographic). The relations of these digital markers with momentary subjective stress differed from person to person, as did model accuracy. Sleep proxies, messenger, and social network use were heterogeneously related to stress (ie, negative in some and positive or zero in others). Model predictions correlated positively and statistically significantly with self-reported stress in most individuals (median person-specific correlation=0.15-0.19 for nomothetic models and median person-specific correlation=0.00-0.09 for idiographic models). CONCLUSIONS: Our findings indicate that smartphone log data can be used for identifying digital markers of stress and also show that the relation between specific digital markers and stress differs from person to person. These findings warrant follow-up studies in other populations (eg, professionals and clinical populations) and pave the way for similar research using physiological measures of stress.

Risk Factors for Hypoglycemia During Treatment of Hyperglycemic Crises.

Objective: Diabetic ketoacidosis and hyperosmolar hyperglycemic state are life-threatening hyperglycemic crises often requiring intensive care unit (ICU) management. Treatment includes intravenous (IV) insulin with a transition to subcutaneous (SC) insulin upon resolution. Hypoglycemia is a common complication associated with treatment of hyperglycemic crises, but risk factors have not been well established. This study aimed to assess risk factors associated with hypoglycemia during treatment for hyperglycemic crises. Methods: This case-control study included ICU patients admitted with hyperglycemic crises at a single Veterans Affairs health system from 1 January 2013 to 31 March 2020. Patients who developed hypoglycemia during insulin treatment were compared with a control group. Odds of hypoglycemia were assessed based on risk factors, including BMI, comorbidities, and type of SC insulin used. Results: Of the 216 cases of hyperglycemic crises included, hypoglycemia occurred in 61 cases (44 on SC insulin, 11 on IV insulin, and 6 on both). Odds for hypoglycemia were significantly higher for underweight patients (odds ratio 4.52 [95% CI 1.05-19.55]), type 1 diabetes (4.02 [2.09-7.73]), chronic kidney disease (1.94 [1.05-3.57]), those resumed on the exact chronic SC insulin regimen following resolution (2.91 [1.06-7.95]), and patients who received NPH versus glargine insulin (5.13 [1.54-17.06]). No significant differences were seen in the other evaluated variables. Conclusion: This study found several factors associated with hypoglycemia during hyperglycemic crises treatment, many of which are not addressed in consensus statement recommendations. These findings may help ICU clinicians prevent complications related to hyperglycemic crisis management and generate hypotheses for future studies.

Coronavirus Disease 2019-Related Extensive Thrombosis in a Patient Receiving Therapeutic Anticoagulation With Dabigatran.

Introduction and Objective: Coronavirus disease 2019 (COVID-19) is associated with respiratory failure and a hypercoagulable state. Studies have shown the use of oral anticoagulants, specifically dabigatran, can significantly decrease mortality from COVID-19. Dabigatran is an oral direct thrombin inhibitor commonly used for nonvalvular atrial fibrillation and for the treatment or prevention of venous thromboembolism. The association of COVID-19-related extensive thrombosis while receiving full therapeutic anticoagulation with dabigatran has not been well-established in current literature. Case Report: We present a 73-year-old male patient with a history of persistent atrial fibrillation anticoagulated with dabigatran presenting with an active COVID-19 infection admitted to the intensive care unit. On hospital day 7, he developed extensive arterial and venous thromboembolisms. To our knowledge, this is the first published case of COVID-19-related extensive thrombosis while receiving full therapeutic anticoagulation with dabigatran. Discussion: Guidelines recommend prophylactic or therapeutic-dose anticoagulation with unfractionated heparin or low-molecular weight heparin for all patients if no contraindications exist; however, recommendations for the use of therapeutic oral anticoagulants have not been well established. Further studies are warranted to establish appropriate use of oral anticoagulants in the setting of COVID-19. Conclusion: Evidence from this report suggests clinicians should closely monitor patients at risk for hypercoagulability regardless of the anticoagulation therapy the patient may be receiving. Additionally, evidence from this case suggests a possible inferiority in the anticoagulation ability of dabigatran in patients with active COVID-19.

Coupling of metataxonomics and culturing improves bacterial diversity characterization and identifies a novel Rhizorhapis sp. with metal resistance potential in a multi-contaminated waste sediment.

Long-term contaminated environments have been recognized as potential hotspots for bacterial discovery in taxonomic and functional terms for bioremediation purposes. Here, bacterial diversity in waste sediment collected from a former industrial dumpsite and contaminated with petroleum hydrocarbon and heavy metals was investigated through the parallel application of culture-independent (16S rRNA gene amplicon sequencing) and -dependent (plate culturing followed by colony picking and identification of isolates by 16S rRNA gene Sanger sequencing) approaches. The bacterial diversities retrieved by both approaches greatly differed. Bacteroidetes and Proteobacteria were dominant in the culture-independent community, while Firmicutes and Actinobacteria were the main culturable groups. Only 2.7% of OTUs (operational taxonomic units) in the culture-independent dataset were cultured. Most of the culturable OTUs were absent or in very low abundances in the culture-independent dataset, revealing that culturing is a useful tool to study the rare bacterial biosphere. One culturable OTUs (comprising only the isolate SPR117) was identified as a potential new species in the genus Rhizorhapis (class Alphaproteobacteria) and was selected for further characterization. Phytopathogenicity tests showed that Rhizorhapis sp. strain SPR117 (ATCC TSD-228) is not pathogenic to lettuce, despite the only described species in this genus, Rhizorhapis suberifaciens, is causal agent of the lettuce corky root disease. The genome of the strain SPR117 was sequenced, assembled in 256 contigs, with a length of 4,419,522 bp and a GC content of 59.9%, and its further annotation revealed the presence of genes related to the resistance to arsenic, copper, iron, and mercury, among other metals. Therefore, the coupling of metataxonomics and culturing is a useful tool to obtain not only an improved description of bacterial communities in contaminated environments, but also to isolate microorganisms with bioremediation potential.

A Defined Medium for Cultivation and Exometabolite Profiling of Soil Bacteria.

Exometabolomics is an approach to assess how microorganisms alter, or react to their environments through the depletion and production of metabolites. It allows the examination of how soil microbes transform the small molecule metabolites within their environment, which can be used to study resource competition and cross-feeding. This approach is most powerful when used with defined media that enable tracking of all metabolites. However, microbial growth media have traditionally been developed for the isolation and growth of microorganisms but not metabolite utilization profiling through Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS). Here, we describe the construction of a defined medium, the Northen Lab Defined Medium (NLDM), that not only supports the growth of diverse soil bacteria but also is defined and therefore suited for exometabolomic experiments. Metabolites included in NLDM were selected based on their presence in R2A medium and soil, elemental stoichiometry requirements, as well as knowledge of metabolite usage by different bacteria. We found that NLDM supported the growth of 108 of the 110 phylogenetically diverse (spanning 36 different families) soil bacterial isolates tested and all of its metabolites were trackable through LC-MS/MS analysis. These results demonstrate the viability and utility of the constructed NLDM medium for growing and characterizing diverse microbial isolates and communities.

Genotype to ecotype in niche environments: adaptation of Arthrobacter to carbon availability and environmental conditions.

Niche environmental conditions influence both the structure and function of microbial communities and the cellular function of individual strains. The terrestrial subsurface is a dynamic and diverse environment that exhibits specific biogeochemical conditions associated with depth, resulting in distinct environmental niches. Here, we present the characterization of seven distinct strains belonging to the genus Arthrobacter isolated from varying depths of a single sediment core and associated groundwater from an adjacent well. We characterized genotype and phenotype of each isolate to connect specific cellular functions and metabolisms to ecotype. Arthrobacter isolates from each ecotype demonstrated functional and genomic capacities specific to their biogeochemical conditions of origin, including laboratory-demonstrated characterization of salinity tolerance and optimal pH, and genes for utilization of carbohydrates and other carbon substrates. Analysis of the Arthrobacter pangenome revealed that it is notably open with a volatile accessory genome compared to previous pangenome studies on other genera, suggesting a high potential for adaptability to environmental niches.

Do data from mechanical Turk subjects replicate accuracy, response time, and diffusion modeling results?

Online data collection is being used more and more, especially in the face of the COVID crisis. To examine the quality of such data, we chose to replicate lexical decision and item recognition paradigms from Ratcliff et al. (Cognitive Psychology, 60, 127-157, 2010) and numerosity discrimination paradigms from Ratcliff and McKoon (Psychological Review, 125, 183-217, 2018) with subjects recruited from Amazon Mechanical Turk (AMT). Along with these tasks, we collected data from either an IQ test or a math computation test. Subjects in the lexical decision and item recognition tasks were relatively well-behaved, with only a few giving a significant number of responses with response times (RTs) under 300 ms at chance accuracy, i.e., fast guesses, and a few with unstable RTs across a session. But in the numerosity discrimination tasks, almost half of the subjects gave a significant number of fast guesses and/or unstable RTs across the session. Diffusion model parameters were largely consistent with the earlier studies as were correlations across tasks and correlations with IQ and age. One surprising result was that eliminating fast outliers from subjects with highly variable RTs (those eliminated from the main analyses) produced diffusion model analyses that showed patterns of correlations similar to the subjects with stable performance. Methods for displaying data to examine stability, eliminating subjects, and implementing RT data collection on AMT including checks on timing are also discussed.

Are Parents Less Responsive to Young Children When They Are on Their Phones? A Systematic Naturalistic Observation Study.

This study examined whether parents are less responsive to their young children (0-5) when they use a phone. We systematically observed 53 parent-child dyads in consultation bureau waiting rooms and playgrounds. Twenty-three parents used their phone at least once during the observation. Across the dyads, we observed parent and child behavior during a total of 1,038 ten-second intervals. Of these intervals, 641 contained a bid for attention from the child. Accounting for the nested nature of the data, we found that the odds of parents responding to their child's bid for attention were five times lower when using a phone than when not using one. Moreover, parents' responses were less timely, weaker, showed less affect, and were less likely to prioritize the child over other activities. While being fully absorbed in one's phone significantly decreased the odds of responding compared to when not using a phone, occasionally glancing at the phone did not, suggesting that parents may have developed a "mode" of phone use for managing dual attention over the phone and the child. In addition, while a higher intensity of phone use does seem to matter, it did not differ from intense engagement in other nonchild directed activities. The incidence of fully absorbed phone use, however, is greater. Finally, the results show that asking for consent for the observation beforehand leads to a decrease in the odds of phone use, suggesting a social desirability bias. Overall, the findings support concerns over the impact of parental phone use on child development.

Sample size, number of categories and sampling assumptions: Exploring some differences between categorization and generalization.

Categorization and generalization are fundamentally related inference problems. Yet leading computational models of categorization (as exemplified by, e.g., Nosofsky, 1986) and generalization (as exemplified by, e.g., Tenenbaum and Griffiths, 2001) make qualitatively different predictions about how inference should change as a function of the number of items. Assuming all else is equal, categorization models predict that increasing the number of items in a category increases the chance of assigning a new item to that category; generalization models predict a decrease, or category tightening with additional exemplars. This paper investigates this discrepancy, showing that people do indeed perform qualitatively differently in categorization and generalization tasks even when all superficial elements of the task are kept constant. Furthermore, the effect of category frequency on generalization is moderated by assumptions about how the items are sampled. We show that neither model naturally accounts for the pattern of behavior across both categorization and generalization tasks, and discuss theoretical extensions of these frameworks to account for the importance of category frequency and sampling assumptions.

Do Additional Features Help or Hurt Category Learning? The Curse of Dimensionality in Human Learners.

The curse of dimensionality, which has been widely studied in statistics and machine learning, occurs when additional features cause the size of the feature space to grow so quickly that learning classification rules becomes increasingly difficult. How do people overcome the curse of dimensionality when acquiring real-world categories that have many different features? Here we investigate the possibility that the structure of categories can help. We show that when categories follow a family resemblance structure, people are unaffected by the presence of additional features in learning. However, when categories are based on a single feature, they fall prey to the curse, and having additional irrelevant features hurts performance. We compare and contrast these results to three different computational models to show that a model with limited computational capacity best captures human performance across almost all of the conditions in both experiments.

Cross-situational learning in a Zipfian environment.

Both adults and children have shown impressive cross-situational word learning in which they leverage the statistics of word usage across many different scenes in order to isolate specific word meanings (e.g., Yu & Smith, 2007). However, relatively little is known about how this learning scales to real language. Some theoretical analyses suggest that when words follow a Zipfian distribution, as they do in natural language, it should be more difficult to learn a lexicon because of the many low-frequency words that are only observed a few times (Blythe, Smith, & Smith, 2010; Vogt, 2012). Although this effect can be mitigated somewhat by assuming mutual exclusivity (Reisenauer, Smith, & Blythe, 2013), no mathematical analyses suggest that learning in a Zipfian environment should be easier. In this work, we show the opposite of the predicted effect using cross-situational learning experiments with adults: when the distribution of words and meanings is Zipfian, learning is not impaired and is usually improved. Over a series of experiments, we provide evidence that this is because Zipfian distributions help people to disambiguate the meanings of the other words in the situation.

Integration of Around-the-Clock Clinical Pharmacy Specialists Into the Critical Care Team Can Increase Safety of Hyperglycemic Crisis Management.

IN BRIEF "Quality Improvement Success Stories" are published by the American Diabetes Association in collaboration with the American College of Physicians, Inc., and the National Diabetes Education Program. This series is intended to highlight best practices and strategies from programs and clinics that have successfully improved the quality of care for people with diabetes or related conditions. Each article in the series is reviewed and follows a standard format developed by the editors of Clinical Diabetes. The following article describes an effort to improve the safety of hyperglycemic crisis management at a Veterans Affairs Medical Center by making clinical pharmacy specialists available to the critical care team 24 hours/day.

Percutaneous ureteral biopsy: safety and diagnostic yield.

PURPOSE: To evaluate safety and diagnostic yield of percutaneous CT-guided biopsy of extrarenal upper urinary tract lesions. MATERIALS AND METHODS: Retrospective review of our institutional database of image-guided biopsies yielded 44 CT-guided percutaneous biopsies in 44 unique patients that targeted ureteral (30, 68%) or other non-renal upper urinary tract lesions (14, 32%) between January 1, 2000 and May 1, 2017. Indications, pre-biopsy imaging, biopsy technique, peri-procedural antithrombotic use, complications including bleeding defined by Society of Interventional Radiology criteria, pathology results, and subsequent imaging were reviewed up to 3 months after the procedure to evaluate safety and diagnostic yield. RESULTS: Mean patient age was 66 (range 27-88) and 23/44 patients were male. The majority (34/44) of lesions were sampled with an 18-gauge biopsy device via a 17-gauge introducer needle, and the remaining 10/44 lesions were sampled with a 19/20 gauge system. The mean number of core samples obtained was 4 (range 2-10). No major complications occurred. Specifically, no patient developed a urine leak or urinary obstruction. Minor complications occurred in 3/44 (7%) biopsies, all retroperitoneal hemorrhages that did not require transfusion or other intervention. Biopsy was adequate for pathologic examination in 41 of 44 (93%) cases. Among patients undergoing surgical resection, biopsy diagnosis was concordant with surgical pathology in 9/10 (90%) cases and discordant in 1/10 (10%). CONCLUSION: CT-guided percutaneous biopsy of upper urinary tract lesions can be performed safely, with high diagnostic yield, and with a high rate of concordance on subsequent surgical pathology.

A Bedside Computerized Decision-Support Tool for Intravenous Insulin Infusion Management in Critically Ill Patients.

Intravenous (IV) insulin infusions using a validated protocol are the recommended method for blood glucose control in critically ill patients. Computerized decision-support tools improve quality over manual paper-based protocols. However, nonproprietary computerized tools targeting the recommended blood glucose range of 140-180 mg/dL are not readily available. A bedside computerized decision-support tool was developed at a US Department of Veterans Affairs health system to assist the nursing staff with the management of patients requiring IV insulin infusion. Initial evaluation showed that the tool was useful in the safe and effective management of an IV insulin infusion protocol for blood glucose control targeting the updated blood glucose range.

Categorical perception.

Categorical perception (CP) is the phenomenon by which the categories possessed by an observer influences the observers' perception. Experimentally, CP is revealed when an observer's ability to make perceptual discriminations between things is better when those things belong to different categories rather than the same category, controlling for the physical difference between the things. We consider several core questions related to CP: Is it caused by innate and/or learned categories, how early in the information processing stream do categories influence perception, and what is the relation between ongoing linguistic processing and CP? CP for both speech and visual entities are surveyed, as are computational and mathematical models of CP. CP is an important phenomenon in cognitive science because it represents an essential adaptation of perception to support categorizations that an organism needs to make. Sensory signals that could be linearly related to physical qualities are warped in a nonlinear manner, transforming analog inputs into quasi-digital, quasi-symbolic encodings. Copyright © 2009 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.