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Disturbances in trait emotions are a predominant feature in schizophrenia. However, less is known about (a) differences in trait emotion across phases of the illness such as the clinical high-risk (CHR) phase and (b) whether abnormalities in trait emotion that are associated with negative symptoms are driven by primary (i.e. idiopathic) or secondary (e.g. depression, anxiety) factors.
To examine profiles of trait affective disturbance and their clinical correlates in individuals with schizophrenia and individuals at CHR for psychosis.
In two studies (sample 1: 56 out-patients diagnosed with schizophrenia and 34 demographically matched individuals without schizophrenia (controls); sample 2: 50 individuals at CHR and 56 individuals not at CHR (controls)), participants completed self-report trait positive affect and negative affect questionnaires, clinical symptom interviews (positive, negative, disorganised, depression, anxiety) and community-based functional outcome measures.
Both clinical groups reported lower levels of positive affect (specific to joy among individuals with schizophrenia) and higher levels of negative affect compared with controls. For individuals with schizophrenia, links were found between positive affect and negative symptoms (which remained after controlling for secondary factors) and between negative affect and positive symptoms. For individuals at CHR, links were found between both affect dimensions and both types of symptom (which were largely accounted for by secondary factors).
Both clinical groups showed some evidence of reduced trait positive affect and elevated trait negative affect, suggesting that increasing trait positive affect and reducing trait negative affect is an important treatment goal across both populations. Clinical correlates of these emotional abnormalities were more integrally linked to clinical symptoms in individuals with schizophrenia and more closely linked to secondary influences such as depression and anxiety in individuals at CHR.
The use of cover crops in soybean production systems has increased in recent years. There are many questions surrounding cover crops—specifically about benefits to crop production and most effective herbicides for spring termination. No studies evaluating cover crop termination have been conducted across a wide geographic area, to our knowledge. Therefore, field experiments were conducted in 2016 and 2017 in Arkansas, Indiana, Mississippi, Missouri, and Wisconsin for spring termination of regionally specific cover crops. Glyphosate-, glufosinate-, and paraquat-containing treatments were applied between April 15 and April 29 in 2016 and April 10 and April 20 in 2017. Visible control of cover crops was determined 28 days after treatment. Glyphosate-containing herbicide treatments were more effective than paraquat- and glufosinate-containing treatments, providing 71% to 97% control across all site years. Specifically, glyphosate at 1.12 kg ha−1 applied alone or with 2,4-D at 0.56 kg ha−1, saflufenacil at 0.025 kg ha−1, or clethodim at 0.56 kg ha−1 provided the most effective control on all grass cover crop species. Glyphosate-, paraquat-, or glufosinate-containing treatments were generally most effective on broadleaf cover crop species when applied with 2,4-D or dicamba. Results from this research indicate that proper herbicide selection is crucial to successfully terminate cover crops in the spring.
Medical residents are an important group for antimicrobial stewardship programs (ASPs) to target with interventions aimed at improving antibiotic prescribing. In this study, we compared antimicrobial prescribing practices of 2 academic medical teams receiving different ASP training approaches along with a hospitalist control group.
Retrospective cohort study comparing guideline-concordant antibiotic prescribing for 3 common infections among a family medicine (FM) resident service, an internal medicine (IM) resident service, and hospitalists.
Community teaching hospital.
Adult patients admitted between July 1, 2016, and June 30, 2017, with a discharge diagnosis of pneumonia, cellulitis, and urinary tract infections were reviewed.
All 3 medical teams received identical baseline ASP education and daily antibiotic prescribing audit with feedback via clinical pharmacists. The FM resident service received an additional layer of targeted ASP intervention that included biweekly stewardship-focused rounds with an ASP physician and clinical pharmacist leadership. Guideline-concordant prescribing was assessed based on the institution’s ASP guidelines.
Of 1,572 patients, 295 (18.8%) were eligible for inclusion (FM, 96; IM, 69; hospitalist, 130). The percentage of patients receiving guideline-concordant antibiotic selection empirically was similar between groups for all diagnoses (FM, 87.5%; IM, 87%; hospitalist, 83.8%; P = .702). No differences were observed in appropriate definitive antibiotic selection among groups (FM, 92.4%; IM, 89.1%; hospitalist, 89.9%; P = .746). The FM resident service was more likely to prescribe a guideline-concordant duration of therapy across all diagnoses (FM, 74%; IM, 56.5%; hospitalist, 44.6%; P < .001).
Adding dedicated stewardship-focused rounds into the graduate medical curriculum demonstrated increased guideline adherence specifically to duration of therapy recommendations.
The current study aimed to examine the psychometric properties of two geriatric anxiety measures: the Geriatric Anxiety Inventory (GAI) and the Geriatric Anxiety Scale (GAS). This study also aimed to determine the relationships of these measures with two measures of functional ability and impairment: the Barkley Functional Impairment Scale (BFIS) and the Everyday Cognition Scale (E-Cog).
Confirmatory factor analyses (CFA) were used to analyze the factor structures of the GAI and GAS in older adults. Tests for dependent correlations were used to examine the relationship between anxiety scales and functioning.
Amazon’s Mechanical Turk
348 participants (aged 55–85, M= 62.75 (4.8), 66.5% female) with no history of psychosis or traumatic brain injury.
CFAs supported the previously demonstrated bifactor solution for the GAI. For the GAS, the previously demonstrated three-factor model demonstrated a good-to-excellent fit. Given the high correlation between the cognitive and affective factors (r =.89), a bifactor solution was also tested. The bifactor model of the GAS was found to be primarily unidimensional. Tests for dependent correlations revealed that the GAS demonstrated stronger relationships with measures of self-reported functional impairment than the GAI.
The current study provides further psychometric validation of the factor structure of two geriatric anxiety measures in an older adult sample. The results support previous work completed on the GAI and the GAS. The GAS was more strongly correlated with self-reported functional impairment than the GAI, which may reflect differences in content in the two measures.
Cognitive impairment is strongly linked with persistent disability in people with mood disorders, but the factors that explain cognitive impairment in this population are unclear.
To estimate the total effect of (a) bipolar disorder and (b) major depression on cognitive function, and the magnitude of the effect that is explained by potentially modifiable intermediate factors.
Cross-sectional study using baseline data from the UK Biobank cohort. Participants were categorised as having bipolar disorder (n = 2709), major depression (n = 50 975) or no mood disorder (n = 102 931 and n = 105 284). The outcomes were computerised tests of reasoning, reaction time and memory. The potential mediators were cardiometabolic disease and psychotropic medication. Analyses were informed by graphical methods and controlled for confounding using regression, propensity score-based methods and G-computation.
Group differences of small magnitude were found on a visuospatial memory test. Z-score differences for the bipolar disorder group were in the range −0.23 to −0.17 (95% CI −0.39 to −0.03) across different estimation methods, and for the major depression group they were approximately −0.07 (95% CI −0.10 to −0.03). One-quarter of the effect was mediated via psychotropic medication in the bipolar disorder group (−0.05; 95% CI −0.09 to −0.01). No evidence was found for mediation via cardiometabolic disease.
In a large community-based sample in middle to early old age, bipolar disorder and depression were associated with lower visuospatial memory performance, in part potentially due to psychotropic medication use. Mood disorders and their treatments will have increasing importance for population cognitive health as the proportion of older adults continues to grow.
Declaration of interest
I.J.D. is a UK Biobank participant. J.P.P. is a member of the UK Biobank Steering Committee.
Isothermal titration calorimetry combined with surface complexation modeling is an ideal technique to provide further characterization of microbial surface reactivity towards protons and metal ions. This technique can produce enthalpies of protonation and metal ion coordination of acidic functional groups on microbial surfaces. This information is critical for understanding the thermodynamic driving force of surface complexation and provides key information for the indirect identification of surface ligands. Topics covered in this chapter include how this technique complements traditional methods of microbial surface reactivity, necessary system characterization prior to performing calorimetric experiments, how to prepare biomass and solutions for calorimetric titrations, difficult aspects of this technique, and data analysis and interpretation.
Scanning probe microscopy (SPM) is a suite of related imaging methods, in which variations in the interaction force between a probe and a sample surface are used to generate image contrast. These instruments are incredibly sensitive; they can measure forces on the order of those required to break physical and chemical bonds, and under the most optimal conditions, atomic-scale resolution can be achieved. Although SPM is still primarily used for imaging, it is increasingly being used to measure nanoscale properties and interaction forces. This chapter serves as an introduction to the fundamentals of SPM and to the most prevalent methods needed for the investigation of mineral–microbe interactions.
X-ray diffraction techniques provide information regarding the formation and alteration of mineral phases that is critical for assessing geomicrobial processes. Of particular interest is the use of powder X-ray diffraction (pXRD) to identify unknown solid-state materials, determine the particle size of nanoscale mineral phases, and refine structure characteristics, such as unit cell parameters and atomic positions. The goal of this chapter is to provide practical knowledge for the successful preparation of solid mineral samples, optimal data collection strategies, and analysis of diffractograms collected from pXRD experiments. Specific uses of pXRD techniques in geomicrobiology are discussed to demonstrate the importance of diffraction in advancing our understanding of microbial communities in geologic systems.
Lipid biomarker analysis is a useful tool for characterizing microbial communities in geomicrobiology. Phospholipid fatty acids (PLFA) are major components of microbial membranes, and analysis of these markers provides insight into microbial biomass, community structure, and metabolic processes. This article reviews the methods for extraction, fractionation, derivatization, and quantification of PLFA, as well as the interpretation of PLFA patterns for microbial community analysis in natural environmental systems. The discussion centers on the development, the subsequent modifications, and the advantages and limitations of the methods. Two case studies are given to illustrate the applications of intact phospholipid profiling (IPP) and PLFA in geomicrobiology. The recent developments and future directions of microbial signature lipid analysis are also discussed.
Geomicrobiological investigations benefit from knowledge of geochemical and biological systems at different scales, including information about both the abiotic and the biotic components. Gathering this information requires analysis and characterization of both abiotic and biotic components of the target system. The techniques presented in this chapter were selected to cover a variety of needs in geomicrobiological studies, including general sample collection and storage, organic and inorganic compound quantification, and best practices for cultivation, observation, and analysis of microorganisms and microbial communities. In this chapter, introductions and discussions for common techniques provide the reader with a basic understanding of the technique itself, which samples can be analyzed using the technique, and how to prepare samples for analysis. Detailed methods are provided for select techniques, and citations to standard methods are provided for techniques whenever available. For techniques that are rapidly evolving, recent developments and applications are discussed.
Developing the ability to regulate one's emotions in accordance with
contextual demands (i.e., emotion regulation) is a central developmental task of
early childhood. These processes are supported by the engagement of the
autonomic nervous system (ANS), a physiological hub of a vast network tasked
with dynamically integrating real-time experiential inputs with internal
motivational and goal states. To date, much of what is known about the ANS and
emotion regulation has been based on measures of respiratory sinus arrhythmia, a
cardiac indicator of parasympathetic activity. In the present study, we draw
from dynamical systems models to introduce two nonlinear indices of cardiac
complexity (fractality and sample entropy) as potential indicators of these
broader ANS dynamics. Using data from a stratified sample of preschoolers living
in high- (i.e., emergency homeless shelter) and low-risk contexts
(N = 115), we show that, in conjunction with
respiratory sinus arrhythmia, these nonlinear indices may help to clarify
important differences in the behavioral manifestations of emotion regulation. In
particular, our results suggest that cardiac complexity may be especially useful
for discerning active, effortful emotion regulation from less effortful
regulation and dysregulation.
Geomicrobiology is the study of microbes and microbial processes and their role in driving environmental and geological processes at scales ranging from the nano, micron, to meter scale. This growing field has seen major advances in recent years, largely due to the development of new analytical tools and improvements to existing techniques, which allow us to better understand the complex interactions between microbes and their surroundings. In this comprehensive handbook, expert authors outline the state-of-the-art and emerging analytical techniques used in geomicrobiology. Readers are guided through each technique including background theory, sample preparation, standard methodology, data collection and analysis, best practices and common pitfalls, and examples of how and where the technique has been applied. The book provides a practical go-to reference for advanced students, researchers and professional scientists looking to employ techniques commonly used in geomicrobiology.
Field experiments were conducted in 2012 and 2013 across four locations for a total of 6 site-years in the midsouthern United States to determine the effect of growth stage at exposure on soybean sensitivity to sublethal rates of dicamba (8.8 g ae ha−1) and 2,4-D (140 g ae ha−1). Regression analysis revealed that soybean was most susceptible to injury from 2,4-D when exposed between 413 and 1,391 accumulated growing degree days (GDD) from planting, approximately between V1 and R2 growth stages. In terms of terminal plant height, soybean was most susceptible to 2,4-D between 448 and 1,719 GDD, or from V1 to R4. However, maximum susceptibility to 2,4-D was only between 624 and 1,001 GDD or from V3 to V5 for yield loss. As expected, soybean was sensitive to dicamba for longer spans of time, ranging from 0 to 1,162 GDD for visible injury or from emergence to R2. Likewise, soybean height was most affected when dicamba exposure occurred between 847 and 1,276 GDD or from V4 to R2. Regarding grain yield, soybean was most susceptible to dicamba between 820 and 1,339 GDD or from V4 to R2. Consequently, these data indicate that soybean response to 2,4-D and dicamba can be variable within vegetative or reproductive growth stages; therefore, specific growth stage at the time of exposure should be considered when evaluating injury from off-target movement. In addition, application of dicamba near susceptible soybean within the V4 to R2 growth stages should be avoided because this is the time of maximum susceptibility. Research regarding soybean sensitivity to 2,4-D and dicamba should focus on multiple exposure times and also avoid generalizing growth stages to vegetative or reproductive.