"It doesn't matter if you are in charge of the trees, you always miss the trees for the forest": Power and the illusion of explanatory depth.

Power can increase overconfidence and illusory thinking. We investigated whether power is also related to the illusion of explanatory depth (IOED), people's tendency to think they understand the world in more detail, coherence, and depth than they actually do. Abstract thinking was reported as a reason for the IOED, and according to the social distance theory of power, power increases abstract thinking. We linked these literatures and tested construal style as a mediator. Further, predispositions can moderate effects of power and we considered narcissism as a candidate because narcissism leads to overconfidence and may thus increase the IOED especially in combination with high power. In three preregistered studies (total N = 607), we manipulated power or measured feelings of power. We found evidence for the IOED (regarding explanatory knowledge about devices). Power led to general overconfidence but had only a small impact on the IOED. Power and narcissism had a small interactive effect on the IOED. Meta-analytical techniques suggest that previous findings on the construal-style-IOED link show only weak evidential value. Implications refer to research on management, power, and overconfidence.

Examining the links between self-tracking and perfectionism dimensions.

BACKGROUND: Self-tracking - the collection, storage, analysis, and evaluation of self-related data (e.g., on one's diet, fitness activities, sports performance, or finances) - is a recent and widespread trend. Less is known about who engages in self-tracking. We expected perfectionism to be linked to self-tracking because performance optimization is central to this activity. PARTICIPANTS AND PROCEDURE: A German convenience sample (N = 145; 64% women, mean age = 32 years) was recruited for this cross-sectional study. The sample comprised a mix of students and community participants. Participants completed an online questionnaire with scales on self-tracking (Self Quantification Scale), perfectionism (Multidimensional Perfectionism Scale with subscales striving for achievement and evaluative concerns), and personality (Big Five Inventory-10). RESULTS: Using a two-dimensional conceptualization of perfectionism and controlling for the Big Five, we found that striving for achievement was strongly positively related to self-tracking, whereas evaluative concerns was not significantly linked. Apparently, people who set high goals and want to meet high standards are more likely than others to engage in self-tracking. However, people's engagement in self-tracking was independent of their personality. CONCLUSIONS: The results point to the importance of distinguishing between different perfectionism dimensions in relation to self-tracking. Future research could explore additional performance-related traits (e.g., grit) to expand the understanding of self-tracking.

Atopy and Elevation of IgE, IgG3, and IgG4 May Be Risk Factors for Post COVID-19 Condition in Children and Adolescents.

SARS-CoV-2 infection causes transient cardiorespiratory and neurological disorders, and severe acute illness is rare among children. Post COVID-19 condition (PCC) may cause profound, persistent phenotypes with increasing prevalence. Its manifestation and risk factors remain elusive. In this monocentric study, we hypothesized that atopy, the tendency to produce an exaggerated immunoglobulin E (IgE) immune response, is a risk factor for the manifestation of pediatric PCC. We present a patient cohort (n = 28) from an early pandemic period (2021-2022) with comprehensive evaluations of phenotypes, pulmonary function, and molecular investigations. PCC predominantly affected adolescents and presented with fatigue, dyspnea, and post-exertional malaise. Sensitizations to aeroallergens were found in 93% of cases. We observed elevated IgE levels (mean 174.2 kU/L, reference < 100 kU/L) regardless of disease severity. Concurrent Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) was found in 29% of patients that also faced challenges in school attendance. ME/CFS manifestation was significantly associated with elevated immunoglobulin G subclasses IgG3 (p < 0.05) and IgG4 (p < 0.05). A total of 57% of patients showed self-limiting disease courses with mean recovery at 12.7 months (range 5-25 months), 29% at 19.2 months (range 12-30 months), and the rest demonstrated overall improvement. These findings offer additional insights into immune dysregulation as a risk factor for pediatric PCC.

Dominance and Prestige as Self-Concept Facets.

Two basic strategies can be applied to navigate hierarchies: (a) dominance, which involves the induction of fear, intimidation, or coercion to obtain status, or (b) prestige, which involves using one's skills, knowledge, or expertise to pursue status. In the present research, we refined the original dominance and prestige account and the respective self-report scale and conceptualized and assessed both variables as stable self-concept facets. By doing so, we extended the explanatory power of the model. Four studies (total N = 1,993) showed good psychometric properties for the newly developed dominance and prestige questionnaire (DPQ). Both dominance and prestige showed high temporal stability. In testing associations with 72 personality variables and 14 objective criteria, nomological and criterion validity were supported. For the first time, the concepts were shown to predict friendship satisfaction. Further, in testing a truth and bias model, we found high self-other agreement for both self-concept facets. Thus, self-perceptions of dominance and prestige proved to be stable, valid, accurate, and relevant in contexts beyond leadership. Future research concerning the self-perception of these concepts could test the relevance of dominance and prestige in additional spheres of life (e.g. families, academia).

Commentary on Elkjær et al.'s (2020) Meta-Analysis on Expansive Versus Contractive Nonverbal Displays.

We offer a critical perspective on the meta-analysis by Elkjær et al. (2020) by pointing out three constraints: The first refers to open-science practices, the second addresses the selection of studies, and the third offers a broader theoretical perspective. We argue that preregistration and adherence to the highest standards of conducting meta-analyses is important. Further, we identified several missing studies. Regarding the theoretical perspective, we suggest that it may be useful to tie body positions into the dominance-prestige framework and, on that basis, to distinguish two types of body positions. Such an approach has the potential to account for discrepancies in previous meta-analytical evidence regarding the effects of expansive versus contractive nonverbal displays. Future research may thus be able to provide not only methodological but also theoretical innovations to the field of body positions.

Prevalence of COVID-19 Among Children and Adolescents While Easing Lockdown Restrictions in Cologne, North Rhine-Westphalia, Germany.

BACKGROUND: In Germany, widespread full closures of schools and day care facilities were part of lockdown measures to control the spread of coronavirus disease 2019 (COVID-19). In the state of North Rhine-Westphalia closures took place on March 16, 2020 and were gradually eased from end of April 2020 until beginning of June 2020. OBJECTIVE: This study aims to evaluate the prevalence of COVID-19 among children and adolescents during the reopening period of schools and day care facilities in Cologne, North Rhine-Westphalia, Germany. It further depicts medical history and results of physical examinations of pediatric patients undergoing a test for severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2). METHODS: Testing for SARS-CoV-2 was carried out by a naso- and / or oropharyngeal swab by local pediatricians at the time of presentation. Samples were analyzed by real-time reverse transcription polymerase chain reaction (RT-PCR). Medical history and physical examination results were retrospectively analyzed. RESULTS: 525 children and adolescents presented mainly with mild upper respiratory tract infections. Three patients were diagnosed with COVID-19. Their medical history and examination results did not stand out from the other patients. CONCLUSION: A precautious stepwise opening of schools and day care facilities was not associated with the occurrence of a relevant prevalence of COVID-19 among children and adolescents. However, a low general prevalence of COVID-19 at the end of the observation period has to be taken into account. Systematic testing might enable adjusted regulations in favor of full closures, especially in the light of increasing evidence for pediatric patients constituting a low-risk group for COVID-19.

Of Mice and Men: The Coronavirus MHV and Mouse Models as a Translational Approach to Understand SARS-CoV-2.

The fatal acute respiratory coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since COVID-19 was declared a pandemic by the World Health Organization in March 2020, infection and mortality rates have been rising steadily worldwide. The lack of a vaccine, as well as preventive and therapeutic strategies, emphasize the need to develop new strategies to mitigate SARS-CoV-2 transmission and pathogenesis. Since mouse hepatitis virus (MHV), severe acute respiratory syndrome coronavirus (SARS-CoV), and SARS-CoV-2 share a common genus, lessons learnt from MHV and SARS-CoV could offer mechanistic insights into SARS-CoV-2. This review provides a comprehensive review of MHV in mice and SARS-CoV-2 in humans, thereby highlighting further translational avenues in the development of innovative strategies in controlling the detrimental course of SARS-CoV-2. Specifically, we have focused on various aspects, including host species, organotropism, transmission, clinical disease, pathogenesis, control and therapy, MHV as a model for SARS-CoV and SARS-CoV-2 as well as mouse models for infection with SARS-CoV and SARS-CoV-2. While MHV in mice and SARS-CoV-2 in humans share various similarities, there are also differences that need to be addressed when studying murine models. Translational approaches, such as humanized mouse models are pivotal in studying the clinical course and pathology observed in COVID-19 patients. Lessons from prior murine studies on coronavirus, coupled with novel murine models could offer new promising avenues for treatment of COVID-19.

ToxCast EPA in Vitro to in Vivo Challenge: Insight into the Rank-I Model.

The ToxCast EPA challenge was managed by TopCoder in Spring 2014. The goal of the challenge was to develop a model to predict the lowest effect level (LEL) concentration based on in vitro measurements and calculated in silico descriptors. This article summarizes the computational steps used to develop the Rank-I model, which calculated the lowest prediction error for the secret test data set of the challenge. The model was developed using the publicly available Online CHEmical database and Modeling environment (OCHEM), and it is freely available at http://ochem.eu/article/68104 . Surprisingly, this model does not use any in vitro measurements. The logic of the decision steps used to develop the model and the reason to skip inclusion of in vitro measurements is described. We also show that inclusion of in vitro assays would not improve the accuracy of the model.

Using Online Tool (iPrior) for Modeling ToxCast™ Assays Towards Prioritization of Animal Toxicity Testing.

The use of long-term animal studies for human and environmental toxicity estimation is more discouraged than ever before. Alternative models for toxicity prediction, including QSAR studies, are gaining more ground. A recent approach is to combine in vitro chemical profiling and in silico chemical descriptors with the knowledge about toxicity pathways to derive a unique signature for toxicity endpoints. In this study we investigate the ToxCast™ Phase I data regarding their ability to predict long-term animal toxicity. We investigated thousands of models constructed in an effort to predict 61 toxicity endpoints using multiple descriptor packages and hundreds of in vitro assays. We investigated the use of in vitro assays and biochemical pathways on model performance. We identified 10 toxicity endpoints where biologically derived descriptors from in vitro assays or pathway perturbations improved the model prediction ability. In vivo toxicity endpoints proved generally challenging to model. Few models were possible to readily model with a balanced accuracy (BA) above 0.7. We also constructed in silico models to predict the outcome of 144 in vitro assays. This showed better statistical metrics with 79 out of 144 assays having median balanced accuracy above 0.7. This suggests that the in vitro datasets have a better modelability than in vivo animal toxicities for the given datasets. Moreover, we published an online platform (http://iprior.ochem.eu) that automates large-scale model building and analysis.

Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process.

BACKGROUND: QSAR is an established and powerful method for cheap in silico assessment of physicochemical properties and biological activities of chemical compounds. However, QSAR models are rather complex mathematical constructs that cannot easily be interpreted. Medicinal chemists would benefit from practical guidance regarding which molecules to synthesize. Another possible approach is analysis of pairs of very similar molecules, so-called matched molecular pairs (MMPs). Such an approach allows identification of molecular transformations that affect particular activities (e.g. toxicity). In contrast to QSAR, chemical interpretation of these transformations is straightforward. Furthermore, such transformations can give medicinal chemists useful hints for the hit-to-lead optimization process. RESULTS: The current study suggests a combination of QSAR and MMP approaches by finding MMP transformations based on QSAR predictions for large chemical datasets. The study shows that such an approach, referred to as prediction-driven MMP analysis, is a useful tool for medicinal chemists, allowing identification of large numbers of "interesting" transformations that can be used to drive the molecular optimization process. All the methodological developments have been implemented as software products available online as part of OCHEM (http://ochem.eu/). CONCLUSIONS: The prediction-driven MMPs methodology was exemplified by two use cases: modelling of aquatic toxicity and CYP3A4 inhibition. This approach helped us to interpret QSAR models and allowed identification of a number of "significant" molecular transformations that affect the desired properties. This can facilitate drug design as a part of molecular optimization process. Graphical AbstractMolecular matched pairs and transformation graphs facilitate interpretable molecular optimisation process.

Thiamin status of premature infants assessed by measurement of thiamin diphosphate in whole blood.

Premature infants constitute a risk group for thiamin deficiency but only little is known about their thiamin status. The aim of the present study was to investigate the thiamin status of premature infants by determination of thiamin diphosphate (TDP) and to identify risk factors for low TDP concentrations. In a prospective, longitudinal study TDP was determined by HPLC in whole blood in the first days of life and approximately every 2 weeks. Demographical data, weight gain, type of nutrition and thiamin intake were recorded. A total of 111 premature infants were included at the Children's Hospital of the University of Cologne, Germany from May 2009 until December 2010 and 222 blood samples were analysed. TDP concentrations showed an age-dependent decline (age 0­10 d, mean TDP = 110.6 ng/ml; age 11­20 d, mean TDP = 95.4 ng/ml; age 21­103 d, mean TDP = 33.6 ng/ml). There was no significant difference between males and females. Young gestational age and low birth weight were associated with low TDP concentrations. No infant was diagnosed with thiamin deficiency. The current nutritional regimen in our hospital did not lead to thiamin deficiency in the study cohort. Further research is required to evaluate how TDP concentrations are regulated in premature infants.

Severe human bocavirus infection, Germany.

Human bocavirus (HBoV), discovered in 2005, can cause respiratory disease or no symptoms at all. We confirmed HBoV infection in an 8-month-old girl with hypoxia, respiratory distress, wheezing, cough, and fever. This case demonstrates that lower respiratory tract infection caused by HBoV can lead to severe and life-threatening disease.

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information.

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu.

A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition.

Prediction of CYP450 inhibition activity of small molecules poses an important task due to high risk of drug-drug interactions. CYP1A2 is an important member of CYP450 superfamily and accounts for 15% of total CYP450 presence in human liver. This article compares 80 in-silico QSAR models that were created by following the same procedure with different combinations of descriptors and machine learning methods. The training and test sets consist of 3745 and 3741 inhibitors and noninhibitors from PubChem BioAssay database. A heterogeneous external test set of 160 inhibitors was collected from literature. The studied descriptor sets involve E-state, Dragon and ISIDA SMF descriptors. Machine learning methods involve Associative Neural Networks (ASNN), K Nearest Neighbors (kNN), Random Tree (RT), C4.5 Tree (J48), and Support Vector Machines (SVM). The influence of descriptor selection on model accuracy was studied. The benefits of "bagging" modeling approach were shown. Applicability domain approach was successfully applied in this study and ways of increasing model accuracy through use of applicability domain measures were demonstrated as well as fragment-based model interpretation was performed. The most accurate models in this study achieved values of 83% and 68% correctly classified instances on the internal and external test sets, respectively. The applicability domain approach allowed increasing the prediction accuracy to 90% for 78% of the internal and 17% of the external test sets, respectively. The most accurate models are available online at http://ochem.eu/models/Q5747 .

Predicting the pKa of small molecule.

The biopharmaceutical profile of a compound depends directly on the dissociation constants of its acidic and basic groups, commonly expressed as the negative decadic logarithm pKa of the acid dissociation constant (Ka). We survey the literature on computational methods to predict the pKa of small molecules. In this, we address data availability (used data sets, data quality, proprietary versus public data), molecular representations (quantum mechanics, descriptors, structured representations), prediction methods (approaches, implementations), as well as pKa-specific issues such as mono- and multiprotic compounds. We discuss advantages, problems, recent progress, and challenges in the field.

Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set.

The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The developed parameter of "distance to model" (DM) is defined as a metric of similarity between the training and test set compounds that have been subjected to QSAR/QSPR modeling. In our previous work, we demonstrated the utility and optimal performance of DM metrics that have been based on the standard deviation within an ensemble of QSAR models. The current study applies such analysis to 30 QSAR models for the Ames mutagenicity data set that were previously reported within the 2009 QSAR challenge. We demonstrate that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs. The presented approach identifies 30-60% of compounds having an accuracy of prediction similar to the interlaboratory accuracy of the Ames test, which is estimated to be 90%. Thus, the in silico predictions can be used to halve the cost of experimental measurements by providing a similar prediction accuracy. The developed model has been made publicly available at http://ochem.eu/models/1 .

Estimation of Acid Dissociation Constants Using Graph Kernels.

The biopharmaceutical profile of a compound depends directly on the dissociation constants of its acidic and basic groups. We estimate these constants using kernel ridge regression and graph kernels. The performance of our approach is similar to that of a semi-empirical model (Tehan et al, QSAR & Comb. Sci. 21(5): 457-472, 473-485) based on frontier electron theory, but uses only the annotated structure graph. In particular, no structure optimization is necessary. We discuss advantages and shortcomings of our approach.

Determination of thiamin diphosphate in whole blood samples by high-performance liquid chromatography--a method suitable for pediatric diagnostics.

An improved and easy to use method for the determination of thiamin diphosphate (TDP) in 100 microl of whole blood was developed. The small sample volume makes it possible to assess the nutritional status of vitamin B(1) in infants and even in preterm infants. Sample preparation comprises the extraction of TDP from whole blood by hemolysis, protein precipitation with trichloroacetic acid, and subsequent centrifugation. Potassium ferricyanide is used for pre-column derivatization of TDP to its fluorescent thiochrome derivative. Chromatographic separation was carried out using a reversed-phase column and an isocratic elution which consisted of a phosphate buffer and acetonitrile. TDP was detected fluorimetrically and quantified by external standardization. Method validation showed a high precision, almost complete recovery, and a high sensitivity. The lower limit of detection and the lower limit of quantification were 0.2 ng/ml and 4 ng/ml, respectively. Linearity was demonstrated over the expected concentration range of 4-400 ng/ml. In conclusion, we present a convenient HPLC method for the determination of TDP which is precise, sensitive and suitable for pediatric diagnostics.

Automatic determination of reaction mappings and reaction center information. 1. The imaginary transition state energy approach.

Chemical reactions transform the reactant molecules by deleting existing and forming new bonds. The identification of these so-called reacting bonds is important for studying the reaction mechanism and for applications in metabolomics, e.g. for interpreting substrate labeling experiments. Here, we introduce an approach which suggests the simplest possible reaction center at the heavy atom level, with high accuracy. In contrast to current methods the approach is motivated by a simple theoretical model based on a crude approximation of the reaction energetics, and takes the complete reacting system into account. Finally, it recovers all optimal solutions to the problem while removing all symmetry-related, redundant solutions. We apply the method on the complete KEGG database of biochemical reactions, and compare our approach with previous methods. The resulting reaction centers are represented as imaginary transition states, which are molecule-like representations of reaction mechanisms. We provide the statistics of the calculations on the KEGG database and discuss some examples for the different types of alternative solutions found.

Automatic determination of reaction mappings and reaction center information. 2. Validation on a biochemical reaction database.

The correct identification of the reacting bonds and atoms is a prerequisite for the analysis of the reaction mechanism. We have recently developed a method based on the Imaginary Transition State Energy Minimization approach for automatically determining the reaction center information and the atom-atom mapping numbers. We test here the accuracy of this ITSE approach by comparing the predictions of the method against more than 1500 manually annotated reactions from BioPath, a comprehensive database of biochemical reactions. The results show high agreement between manually annotated mappings and computational predictions (98.4%), with significant discrepancies in only 24 cases out of 1542 (1.6%). This result validates both the computational prediction and the database, at the same time, as the results of the former agree with expert knowledge and the latter appears largely self-consistent, and consistent with a simple principle. In 10 of the discrepant cases, simple chemical arguments or independent literature studies support the predicted reaction center. In five reaction instances the differences in the automatically and manually annotated mappings are described in detail. Finally, in approximately 200 cases the algorithm finds alternate reaction centers, which need to be studied on a case by case basis, as the exact choice of the alternative may depend on the enzyme catalyzing the reaction.