What is the identity of the true electrocatalytic species?
This fundamental question has plagued the molecular electrocatalysis community during its decades-long search for selective and efficient ...transition-metal based electrocatalysts for fuel forming reactions. Identifying when the added species is a precatalyst that transforms into the active catalyst
in situ
is an extraordinarily complex endeavor. Thankfully, the last decade has witnessed a resurgence of interest in understanding and controlling these transformations, leading to an expansion of the experimental toolkit available to probe catalyst identity. In this Tutorial Review, researchers will learn how the nature of the active catalyst can be uncovered using state-of-the-art electrochemical and spectroscopic methods. Analysis of catalytic voltammograms can quickly furnish qualitative evidence of precatalyst transformation and a library of these tell-tale signs is discussed, along with the chemical phenomena underpinning each feature. Complementary electrochemical and spectroscopic methods for identifying
in situ
generation of heterogeneous catalysts are also presented, outlining the conditions required for correct application with special emphasis on potential pitfalls when studying weakly-adsorbed material. Case studies are presented to showcase how these different probes can be integrated to develop a comprehensive picture of precatalyst transformation.
What is the identity of the true electrocatalytic species?
Objectives To determine the associations of breast milk intake after birth with neurological outcomes at term equivalent and 7 years of age in very preterm infants Study design We studied 180 infants ...born at <30 weeks' gestation or <1250 grams birth weight enrolled in the Victorian Infant Brain Studies cohort from 2001-2003. We calculated the number of days on which infants received >50% of enteral intake as breast milk from 0-28 days of life. Outcomes included brain volumes measured by magnetic resonance imaging at term equivalent and 7 years of age, and cognitive (IQ, reading, mathematics, attention, working memory, language, visual perception) and motor testing at 7 years of age. We adjusted for age, sex, social risk, and neonatal illness in linear regression. Results A greater number of days on which infants received >50% breast milk was associated with greater deep nuclear gray matter volume at term equivalent age (0.15 cc/d; 95% CI, 0.05-0.25); and with better performance at age 7 years of age on IQ (0.5 points/d; 95% CI, 0.2-0.8), mathematics (0.5; 95% CI, 0.1-0.9), working memory (0.5; 95% CI, 0.1-0.9), and motor function (0.1; 95% CI, 0.0-0.2) tests. No differences in regional brain volumes at 7 years of age in relation to breast milk intake were observed. Conclusion Predominant breast milk feeding in the first 28 days of life was associated with a greater deep nuclear gray matter volume at term equivalent age and better IQ, academic achievement, working memory, and motor function at 7 years of age in very preterm infants.
Missing data are common in medical research, which can lead to a loss in statistical power and potentially biased results if not handled appropriately. Multiple imputation (MI) is a statistical ...method, widely adopted in practice, for dealing with missing data. Many academic journals now emphasise the importance of reporting information regarding missing data and proposed guidelines for documenting the application of MI have been published. This review evaluated the reporting of missing data, the application of MI including the details provided regarding the imputation model, and the frequency of sensitivity analyses within the MI framework in medical research articles.
A systematic review of articles published in the Lancet and New England Journal of Medicine between January 2008 and December 2013 in which MI was implemented was carried out.
We identified 103 papers that used MI, with the number of papers increasing from 11 in 2008 to 26 in 2013. Nearly half of the papers specified the proportion of complete cases or the proportion with missing data by each variable. In the majority of the articles (86%) the imputed variables were specified. Of the 38 papers (37%) that stated the method of imputation, 20 used chained equations, 8 used multivariate normal imputation, and 10 used alternative methods. Very few articles (9%) detailed how they handled non-normally distributed variables during imputation. Thirty-nine papers (38%) stated the variables included in the imputation model. Less than half of the papers (46%) reported the number of imputations, and only two papers compared the distribution of imputed and observed data. Sixty-six papers presented the results from MI as a secondary analysis. Only three articles carried out a sensitivity analysis following MI to assess departures from the missing at random assumption, with details of the sensitivity analyses only provided by one article.
This review outlined deficiencies in the documenting of missing data and the details provided about imputation. Furthermore, only a few articles performed sensitivity analyses following MI even though this is strongly recommended in guidelines. Authors are encouraged to follow the available guidelines and provide information on missing data and the imputation process.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in ...observational research. Importantly, the lack of transparency around methodological decisions is threatening the validity and reproducibility of modern research. We present a practical framework for handling and reporting the analysis of incomplete data in observational studies, which we illustrate using a case study from the Avon Longitudinal Study of Parents and Children. The framework consists of three steps: 1) Develop an analysis plan specifying the analysis model and how missing data are going to be addressed. An important consideration is whether a complete records’ analysis is likely to be valid, whether multiple imputation or an alternative approach is likely to offer benefits and whether a sensitivity analysis regarding the missingness mechanism is required; 2) Examine the data, checking the methods outlined in the analysis plan are appropriate, and conduct the preplanned analysis; and 3) Report the results, including a description of the missing data, details on how the missing data were addressed, and the results from all analyses, interpreted in light of the missing data and the clinical relevance. This framework seeks to support researchers in thinking systematically about missing data and transparently reporting the potential effect on the study results, therefore increasing the confidence in and reproducibility of research findings.
•Missing data are ubiquitous in medical research.•Guidance is available, but missing data are still often not handled appropriately.•We present a framework for handling and reporting analyses of incomplete data.•This framework encourages researchers to think systematically about missing data.•Adoption of this framework will increase the reproducibility of research findings.•This article provides a much needed framework for handling and reporting the analysis of incomplete data in observational studies.•The framework puts a strong emphasis on preplanning the statistical analysis and encourages transparency when reporting the results of a study.•Adoption of this framework will increase the confidence in and reproducibility of research findings.
Multiple imputation (MI) is now widely used to handle missing data in longitudinal studies. Several MI techniques have been proposed to impute incomplete longitudinal covariates, including standard ...fully conditional specification (FCS-Standard) and joint multivariate normal imputation (JM-MVN), which treat repeated measurements as distinct variables, and various extensions based on generalized linear mixed models. Although these MI approaches have been implemented in various software packages, there has not been a comprehensive evaluation of the relative performance of these methods in the context of longitudinal data.
Using both empirical data and a simulation study based on data from the six waves of the Longitudinal Study of Australian Children (N = 4661), we investigated the performance of a wide range of MI methods available in standard software packages for investigating the association between child body mass index (BMI) and quality of life using both a linear regression and a linear mixed-effects model.
In this paper, we have identified and compared 12 different MI methods for imputing missing data in longitudinal studies. Analysis of simulated data under missing at random (MAR) mechanisms showed that the generally available MI methods provided less biased estimates with better coverage for the linear regression model and around half of these methods performed well for the estimation of regression parameters for a linear mixed model with random intercept. With the observed data, we observed an inverse association between child BMI and quality of life, with available data as well as multiple imputation.
Both FCS-Standard and JM-MVN performed well for the estimation of regression parameters in both analysis models. More complex methods that explicitly reflect the longitudinal structure for these analysis models may only be needed in specific circumstances such as irregularly spaced data.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
School climate is a leading factor in explaining student learning and achievement. Less work has explored the impact of both staff and student perceptions of school climate raising interesting ...questions about whether staff school climate experiences can add "value" to students' achievement. In the current research, multiple sources were integrated into a multilevel model, including staff self-reports, student self-reports, objective school records of academic achievement, and socio-economic demographics. Achievement was assessed using a national literacy and numeracy tests (
= 760 staff and 2,257 students from 17 secondary schools). In addition, guided by the "social identity approach," school identification is investigated as a possible psychological mechanism to explain the relationship between school climate and achievement. In line with predictions, results show that students' perceptions of school climate significantly explain writing and numeracy achievement and this effect is mediated by students' psychological identification with the school. Furthermore, staff perceptions of school climate explain students' achievement on numeracy, writing and reading tests (while accounting for students' responses). However, staff's school identification did not play a significant role. Implications of these findings for organizational, social, and educational research are discussed.
IMPORTANCE: Moderate and late preterm (MLPT) births comprise most preterm infants. Therefore, long-term developmental concerns in this population potentially have a large public health influence. ...While there are increasing reports of developmental problems in MLPT children, detail is lacking on the precise domains that are affected. OBJECTIVE: To compare neurodevelopment and social-emotional development between MLPT infants and term-born control infants at age 2 years. DESIGN, SETTING, AND PARTICIPANTS: This investigation was a prospective longitudinal cohort study at a single tertiary hospital. Participants were MLPT infants (32-36 weeks’ completed gestation) and healthy full-term controls (≥37 weeks’ gestation) recruited at birth. During a 3-year period between December 7, 2009, and November 7, 2012, MLPT infants were recruited at birth from the neonatal unit and postnatal wards of the Royal Women’s Hospital, Melbourne, Australia. The term control recruitment extended to March 26, 2014. The dates of the data developmental assessments were February 23, 2012, to April 8, 2016. EXPOSURE: Moderate and late preterm birth. MAIN OUTCOMES AND MEASURES: Cerebral palsy, blindness, and deafness assessed by a pediatrician; cognitive, language, and motor development assessed using the Bayley Scales of Infant Development–Third Edition (developmental delay was defined as less than −1 SD relative to the mean in controls in any domain of the scales); and social-emotional and behavioral problems assessed by a parent questionnaire (Infant Toddler Social Emotional Assessment). Outcomes were compared between birth groups using linear and logistic regression, adjusted for social risk. RESULTS: In total, 198 MLPT infants (98.5% of 201 recruited) and 183 term-born controls (91.0% of 201 recruited) were assessed at 2 years’ corrected age. Compared with controls, MLPT children had worse cognitive, language, and motor development at age 2 years, with adjusted composite score mean differences of −5.3 (95% CI, −8.2 to −2.4) for cognitive development, −11.4 (95% CI, −15.3 to −7.5) for language development, and −7.3 (95% CI, −10.6 to −3.9) for motor development. The odds of developmental delay were higher in the MLPT group compared with controls, with adjusted odds ratios of 1.8 (95% CI, 1.1-3.0) for cognitive delay, 3.1 (95% CI, 1.8-5.2) for language delay, and 2.4 (95% CI, 1.3-4.5) for motor delay. Overall social-emotional competence was worse in MLPT children compared with controls (t statistic mean difference, −3.6 (95% CI, −5.8 to −1.4), but other behavioral domains were similar. The odds of being at risk for social-emotional competence were 3.9 (95% CI, 1.4-10.9) for MLPT children compared with controls. CONCLUSIONS AND RELEVANCE: Moderate and late preterm children exhibited developmental delay compared with their term-born peers, most marked in the language domain. This knowledge of developmental needs in MLPT infants will assist in targeting surveillance and intervention.
A goal of HIV vaccine development is to elicit antibodies with neutralizing breadth. Broadly neutralizing antibodies (bNAbs) to HIV often have unusual sequences with long heavy-chain ...complementarity-determining region loops, high somatic mutation rates and polyreactivity. A subset of HIV-infected individuals develops such antibodies, but it is unclear whether this reflects systematic differences in their antibody repertoires or is a consequence of rare stochastic events involving individual clones. We sequenced antibody heavy-chain repertoires in a large cohort of HIV-infected individuals with bNAb responses or no neutralization breadth and uninfected controls, identifying consistent features of bNAb repertoires, encompassing thousands of B cell clones per individual, with correlated T cell phenotypes. These repertoire features were not observed during chronic cytomegalovirus infection in an independent cohort. Our data indicate that the development of numerous B cell lineages with antibody features associated with autoreactivity may be a key aspect in the development of HIV neutralizing antibody breadth.
The Stimulator of Interferon Genes (STING) pathway initiates potent immune responses upon recognition of DNA. To initiate signaling, serine 365 (S365) in the C-terminal tail (CTT) of STING is ...phosphorylated, leading to induction of type I interferons (IFNs). Additionally, evolutionary conserved responses such as autophagy also occur downstream of STING, but their relative importance during in vivo infections remains unclear. Here we report that mice harboring a serine 365-to-alanine (S365A) mutation in STING are unexpectedly resistant to Herpes Simplex Virus (HSV)-1, despite lacking STING-induced type I IFN responses. By contrast, resistance to HSV-1 is abolished in mice lacking the STING CTT, suggesting that the STING CTT initiates protective responses against HSV-1, independently of type I IFNs. Interestingly, we find that STING-induced autophagy is a CTT- and TBK1-dependent but IRF3-independent process that is conserved in the STING S365A mice. Thus, interferon-independent functions of STING mediate STING-dependent antiviral responses in vivo.
Statistical analysis in epidemiologic studies is often hindered by missing data, and multiple imputation is increasingly being used to handle this problem. In a simulation study, the authors compared ...2 methods for imputation that are widely available in standard software: fully conditional specification (FCS) or “chained equations” and multivariate normal imputation (MVNI). The authors created data sets of 1,000 observations to simulate a cohort study, and missing data were induced under 3 missing-data mechanisms. Imputations were performed using FCS (Royston's “ice”) and MVNI (Schafer's NORM) in Stata (Stata Corporation, College Station, Texas), with transformations or prediction matching being used to manage nonnormality in the continuous variables. Inferences for a set of regression parameters were compared between these approaches and a complete-case analysis. As expected, both FCS and MVNI were generally less biased than complete-case analysis, and both produced similar results despite the presence of binary and ordinal variables that clearly did not follow a normal distribution. Ignoring skewness in a continuous covariate led to large biases and poor coverage for the corresponding regression parameter under both approaches, although inferences for other parameters were largely unaffected. These results provide reassurance that similar results can be expected from FCS and MVNI in a standard regression analysis involving variously scaled variables.
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