Delirium in older hospitalized patients—A prospective analysis of the detailed course of delirium in geriatric inpatients

Background: Delirium in older hospitalized patients (> 65) is a common clinical syndrome, which is frequently unrecognized. Aims: We aimed to describe the detailed clinical course of delirium and related cognitive functioning in geriatric patients in a mainly non-postoperative setting in association with demographic and clinical parameters and additionally to identify risk factors for delirium in this common setting. Methods: Inpatients of a geriatric ward were screened for delirium and in the case of presence of delirium included into the study. Patients received three assessments including Mini-Mental-Status-Examination (MMSE) and the Delirium Rating Scale Revised 98 (DRS-R-98). We conducted correlation and linear mixed-effects model analyses to detect associations. Results: Overall 31 patients (82 years (mean)) met the criteria for delirium and were included in the prospective observational study. Within one week of treatment, mean delirium symptom severity fell below the predefined cut-off. While overall cognitive functioning improved over time, short- and long-term memory deficits remained. Neuroradiological conspicuities were associated with cognitive deficits, but not with delirium severity. Discussion: The temporal stability of some delirium symptoms (short-/long-term memory, language) on the one hand and on the other hand decrease in others (hallucinations, orientation) shown in our study visualizes the heterogeneity of symptoms attributed to delirium and their different courses, which complicates the differentiation between delirium and a preexisting cognitive decline. The recovery from delirium seems to be independent of preclinical cognitive status. Conclusion: Treatment of the acute medical condition is associated with a fast decrease in delirium severity. Given the high incidence and prevalence of delirium in hospitalized older patients and its detrimental impact on cognition, abilities and personal independence further research needs to be done

MACSE: Multiple Alignment of Coding SEquences Accounting for Frameshifts and Stop Codons

Until now the most efficient solution to align nucleotide sequences containing open reading frames was to use indirect procedures that align amino acid translation before reporting the inferred gap positions at the codon level. There are two important pitfalls with this approach. Firstly, any premature stop codon impedes using such a strategy. Secondly, each sequence is translated with the same reading frame from beginning to end, so that the presence of a single additional nucleotide leads to both aberrant translation and alignment

Self-consistent Coronal Heating and Solar Wind Acceleration from Anisotropic Magnetohydrodynamic Turbulence

We present a series of models for the plasma properties along open magnetic flux tubes rooted in solar coronal holes, streamers, and active regions. These models represent the first self-consistent solutions that combine: (1) chromospheric heating driven by an empirically guided acoustic wave spectrum, (2) coronal heating from Alfven waves that have been partially reflected, then damped by anisotropic turbulent cascade, and (3) solar wind acceleration from gradients of gas pressure, acoustic wave pressure, and Alfven wave pressure. The only input parameters are the photospheric lower boundary conditions for the waves and the radial dependence of the background magnetic field along the flux tube. For a single choice for the photospheric wave properties, our models produce a realistic range of slow and fast solar wind conditions by varying only the coronal magnetic field. Specifically, a 2D model of coronal holes and streamers at solar minimum reproduces the latitudinal bifurcation of slow and fast streams seen by Ulysses. The radial gradient of the Alfven speed affects where the waves are reflected and damped, and thus whether energy is deposited below or above the Parker critical point. As predicted by earlier studies, a larger coronal ``expansion factor'' gives rise to a slower and denser wind, higher temperature at the coronal base, less intense Alfven waves at 1 AU, and correlative trends for commonly measured ratios of ion charge states and FIP-sensitive abundances that are in general agreement with observations. These models offer supporting evidence for the idea that coronal heating and solar wind acceleration (in open magnetic flux tubes) can occur as a result of wave dissipation and turbulent cascade. (abridged abstract)Comment: 32 pages (emulateapj style), 18 figures, ApJ Supplement, in press (v. 171, August 2007

The importance of the altricial – precocial spectrum for social complexity in mammals and birds:A review

Various types of long-term stable relationships that individuals uphold, including cooperation and competition between group members, define social complexity in vertebrates. Numerous life history, physiological and cognitive traits have been shown to affect, or to be affected by, such social relationships. As such, differences in developmental modes, i.e. the ‘altricial-precocial’ spectrum, may play an important role in understanding the interspecific variation in occurrence of social interactions, but to what extent this is the case is unclear because the role of the developmental mode has not been studied directly in across-species studies of sociality. In other words, although there are studies on the effects of developmental mode on brain size, on the effects of brain size on cognition, and on the effects of cognition on social complexity, there are no studies directly investigating the link between developmental mode and social complexity. This is surprising because developmental differences play a significant role in the evolution of, for example, brain size, which is in turn considered an essential building block with respect to social complexity. Here, we compiled an overview of studies on various aspects of the complexity of social systems in altricial and precocial mammals and birds. Although systematic studies are scarce and do not allow for a quantitative comparison, we show that several forms of social relationships and cognitive abilities occur in species along the entire developmental spectrum. Based on the existing evidence it seems that differences in developmental modes play a minor role in whether or not individuals or species are able to meet the cognitive capabilities and requirements for maintaining complex social relationships. Given the scarcity of comparative studies and potential subtle differences, however, we suggest that future studies should consider developmental differences to determine whether our finding is general or whether some of the vast variation in social complexity across species can be explained by developmental mode. This would allow a more detailed assessment of the relative importance of developmental mode in the evolution of vertebrate social systems

Gennetzwerkrekonstruktion mit effizienter Bayes'scher Selektion von gruppierten Variablen

Die Gesamtheit der Gene eines Organismus ist verwoben in einem ausgeklügelten Netzwerk von Interaktionen. Viele dieser Interaktionen sind unbekannt, aber das Wissen um die genaue Gennetzwerkstruktur ist unter anderem wichtig für medizinische Anwendungen. Das unterstreicht die Dringlichkeit, aus experimentellen Genexpressionsdaten das zugrundeliegende Gennetzwerk zu rekonstruieren, auch für sehr große Netzwerke mit vielen Genen. Gennetzwerkrekonstruktion kann als ein Problem von Variablenselektion in linearer Regression aufgefasst werden. Wir nehmen als zusätzliche Information über das Netzwerk (wie z.B. das gemeinsame Binden von Transkriptionsfaktoren) eine Gruppierung der Variablen hinzu. Die bisher verfügbaren Methoden für Variablenselektion mit Gruppierung haben verschiedene Nachteile: "Lasso" und seine Abwandlungen setzen die Regressionskoeffizienten zu gering an und nutzen die Gruppierungsinformation nicht voll aus, Bayes'sche Ansätze benutzen meist das langsame Gibbs-Sampling, um Parameter zu bestimmen, dies verhindert ihren Einsatz für die Gennetzwerkrekonstruktion. Wir präsentieren hier eine Bayes'sche Methode für Variablenselektion mit Gruppierungsinformation, die Spärlichkeit in den Koeffizienten zwischen und innerhalb von Gruppen durchsetzt, und außerdem die Parameter mit einem deterministischen und schnellen Algorithmus bestimmt ("Expectation Propagation"). Wir wenden unsere neue Methode für die Gennetzwerkrekonstruktion an und erweitern sie auch auf das vektorautoregressive Modell für Zeitreihendaten. Wir zeigen auf simulierten und experimentellen Daten, dass aus drei Gründen der Bayes'sche Ansatz die beste Wahl für Netzwerkrekonstruktion ist: die höchste Zahl an korrekt identifizierten Variablen, beste Voraussagekraft auf neuen Daten und eine angemessene Rechendauer. Weiterhin zeigen wir, dass auch auf Zeitreihendaten der Bayes'sche Ansatz den Lasso-Methoden überlegen ist, wobei die Resultate mit einem linearen Modell auf experimentellen Zeitreihendaten generell weniger belastbar sind. Darüber hinaus ist unsere neue Methode nicht nur auf die Rekonstruktion von Gennetzwerken beschränkt, sondern kann auf jedes Variablenselektionsproblem angewendet werden, bei dem eine Gruppierung der Variablen vorliegt.All the genes of an organism's genome build up an intricate network of connections between them. Many of these connections are unknown, but knowing about the structure of the network is important for e.g. medical applications. This leads to the problem of reverse engineering the (large-scale) gene regulatory network from gene expression data. Gene network reconstruction can be formulated as a problem of feature selection in a linear regression framework, and we include additional information (like co-binding of transcription factors) about the network with a grouping of features. Available methods for feature selection in the presence of grouping information have different short-comings: Lasso methods underestimate the regression coefficients and do not make good use of the grouping information, and Bayesian approaches often rely on the stochastic and slow Gibbs sampling procedure to recover the parameters, which makes them infeasible for gene network reconstruction. Here we present a Bayesian method for feature selection with grouping information (with sparsity on the between- and within group level), where the parameters are recovered by a deterministic algorithm (expectation propagation). This sparse-group framework is applied to (large-scale) gene network reconstruction from gene expression data and extended to the vector autoregressive model for time series data. We prove (on simulated and experimental data) that the Bayesian approach is the best choice for network reconstruction for three reasons: Highest number of correctly selected features, best prediction on new data and reasonable computing time. We show that a Bayesian approach to feature selection is superior to lasso methods on time series data. Results on experimental temporal data are inconclusive for the linear model. Finally we note that the presented method is very fundamental and not restricted to the reconstruction of gene regulatory networks, but can be applied to any feature selection problem with grouped features

Patient Embeddings From Diagnosis Codes for Health Care Prediction Tasks: Pat2Vec Machine Learning Framework

BackgroundIn health care, diagnosis codes in claims data and electronic health records (EHRs) play an important role in data-driven decision making. Any analysis that uses a patient’s diagnosis codes to predict future outcomes or describe morbidity requires a numerical representation of this diagnosis profile made up of string-based diagnosis codes. These numerical representations are especially important for machine learning models. Most commonly, binary-encoded representations have been used, usually for a subset of diagnoses. In real-world health care applications, several issues arise: patient profiles show high variability even when the underlying diseases are the same, they may have gaps and not contain all available information, and a large number of appropriate diagnoses must be considered. ObjectiveWe herein present Pat2Vec, a self-supervised machine learning framework inspired by neural network–based natural language processing that embeds complete diagnosis profiles into a small real-valued numerical vector. MethodsBased on German outpatient claims data with diagnosis codes according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10), we discovered an optimal vectorization embedding model for patient diagnosis profiles with Bayesian optimization for the hyperparameters. The calibration process ensured a robust embedding model for health care–relevant tasks by aggregating the metrics of different regression and classification tasks using different machine learning algorithms (linear and logistic regression as well as gradient-boosted trees). The models were tested against a baseline model that binary encodes the most common diagnoses. The study used diagnosis profiles and supplementary data from more than 10 million patients from 2016 to 2019 and was based on the largest German ambulatory claims data set. To describe subpopulations in health care, we identified clusters (via density-based clustering) and visualized patient vectors in 2D (via dimensionality reduction with uniform manifold approximation). Furthermore, we applied our vectorization model to predict prospective drug prescription costs based on patients’ diagnoses. ResultsOur final models outperform the baseline model (binary encoding) with equal dimensions. They are more robust to missing data and show large performance gains, particularly in lower dimensions, demonstrating the embedding model’s compression of nonlinear information. In the future, other sources of health care data can be integrated into the current diagnosis-based framework. Other researchers can apply our publicly shared embedding model to their own diagnosis data. ConclusionsWe envision a wide range of applications for Pat2Vec that will improve health care quality, including personalized prevention and signal detection in patient surveillance as well as health care resource planning based on subcohorts identified by our data-driven machine learning framework

Causal graph analysis of COVID-19 observational data in German districts reveals effects of determining factors on reported case numbers.

Several determinants are suspected to be causal drivers for new cases of COVID-19 infection. Correcting for possible confounders, we estimated the effects of the most prominent determining factors on reported case numbers. To this end, we used a directed acyclic graph (DAG) as a graphical representation of the hypothesized causal effects of the determinants on new reported cases of COVID-19. Based on this, we computed valid adjustment sets of the possible confounding factors. We collected data for Germany from publicly available sources (e.g. Robert Koch Institute, Germany's National Meteorological Service, Google) for 401 German districts over the period of 15 February to 8 July 2020, and estimated total causal effects based on our DAG analysis by negative binomial regression. Our analysis revealed favorable effects of increasing temperature, increased public mobility for essential shopping (grocery and pharmacy) or within residential areas, and awareness measured by COVID-19 burden, all of them reducing the outcome of newly reported COVID-19 cases. Conversely, we saw adverse effects leading to an increase in new COVID-19 cases for public mobility in retail and recreational areas or workplaces, awareness measured by searches for "corona" in Google, higher rainfall, and some socio-demographic factors. Non-pharmaceutical interventions were found to be effective in reducing case numbers. This comprehensive causal graph analysis of a variety of determinants affecting COVID-19 progression gives strong evidence for the driving forces of mobility, public awareness, and temperature, whose implications need to be taken into account for future decisions regarding pandemic management

Between carbonatite and lamproite—Diamondiferous Torngat ultramafic lamprophyres formed by carbonate-fluxed melting of cratonic MARID-type metasomes

New U–Pb perovskite ages reveal that diamondiferous ultramafic lamprophyre magmas erupted through the Archean crust of northern Labrador and Quebec (eastern Canada) between ca. 610 and 565 Ma, a period of strong rifting activity throughout contiguous Laurentia and Baltica. The observed Torngat carbonate-rich aillikite/carbonatite and carbonate-poor mela-aillikite dyke varieties show a large spread in Sr–Nd–Hf–Pb isotope ratios with pronounced correlations between isotope systems. An isotopically depleted component is identified solely within aillikites (87Sr/86Sri = 0.70323–0.70377; εNdi = +1.2–+1.8; εHfi = +1.4–+3.5; 206Pb/204Pbi = 18.2–18.5), whereas some aillikites and all mela-aillikites range to more enriched isotope signatures (87Sr/86Sri = 0.70388–0.70523; εNdi = −0.5 to −3.9; εHfi = −0.6 to −6.0; 206Pb/204Pbi = 17.8–18.2). These contrasting isotopic characteristics of aillikites/carbonatites and mela-aillikites, along with subtle differences in their modal carbonate, SiO2, Al2O3, Na2O, Cs–Rb, and Zr–Hf contents, are consistent with two distinctive metasomatic assemblages of different age in the mantle magma source region.Integration of petrologic, geochemical, and isotopic information leads us to propose that the isotopically enriched component originated from a reduced phlogopite-richterite-Ti-oxide dominated source assemblage that is reminiscent of MARID suite xenoliths. In contrast, the isotopically depleted component was derived from a more oxidized phlogopite-carbonate dominated source assemblage. We argue that low-degree CO2-rich potassic silicate melts from the convective upper mantle were preferentially channelled into an older, pre-existing MARID-type vein network at the base of the North Atlantic craton lithosphere, where they froze to form new phlogopite-carbonate dominated veins. Continued stretching and thinning of the cratonic lithosphere during the Late Neoproterozoic remobilized the carbonate-rich vein material and induced volatile-fluxed fusion of the MARID-type veins and the cold peridotite substrate. Isotopic modelling suggests that only 5–12% trace element contribution from such geochemically extreme MARID-type material is required to produce the observed compositional shift from the isotopically most depleted aillikites/carbonatites towards enriched mela-aillikites. We conclude that cold cratonic mantle lithosphere can host several generations of contrasting vein assemblages, and that each may have formed during past tectonic and magmatic events under distinctively different physicochemical conditions. Although cratonic MARID-type and carbonate-bearing veins in peridotite can be the respective sources for lamproite and carbonatite magmas when present as the sole metasome, their concomitant fusion in a complex source region may give rise to a whole new variety of deep volatile-rich magmas and we suggest that orangeites (formerly Group 2 kimberlites), kamafugites, and certain types of ultramafic lamprophyre are formed in this manner