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Assessments with a large amount of small, similar, or often repetitive tasks are being used in educational, neurocognitive, and psychological contexts. For example, respondents are asked to recognize numbers or letters from a large pool of those and the number of correct answers is a count variable. In 1960, George Rasch developed the Rasch Poisson counts model (RPCM) to handle that type of assessment. This article extends the RPCM into the world of diagnostic classification models (DCMs) where a Poisson distribution is applied to traditional DCMs. A framework of Poisson DCMs is proposed and demonstrated through an operational dataset. This study aims to be exploratory with recommendations for future research given in the end.Setting cutoff scores is one of the most common practices when using scales to aid in classification purposes. This process is usually done univariately where each optimal cutoff value is decided sequentially, subscale by subscale. While it is widely known that this process necessarily reduces the probability of "passing" such a test, what is not properly recognized is that such a test loses power to meaningfully discriminate between target groups with each new subscale that is introduced. We quantify and describe this property via an analytical exposition highlighting the counterintuitive geometry implied by marginal threshold-setting in multiple dimensions. Recommendations are presented that encourage applied researchers to think jointly, rather than marginally, when setting cutoff scores to ensure an informative test.Multilevel structural equation modeling (MSEM) allows researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the results of MSEM models remains unknown. This article explores how variation in the sampling ratio in MSEM affects the measurement of Level 2 (L2) latent constructs. Specifically, we investigated whether the sampling ratio is related to bias and variability in aggregated L2 construct measurement and estimation in the context of doubly latent MSEM models utilizing a two-step Monte Carlo simulation study. Findings suggest that while lower sampling ratios were related to increased bias, standard errors, and root mean square error, the overall size of these errors was negligible, making the doubly latent model an appealing choice for researchers. An applied example using empirical survey data is further provided to illustrate the application and interpretation of the model. We conclude by considering the implications of various sampling ratios on the design of MSEM studies, with a particular focus on educational research.Determining the number of factors in exploratory factor analysis is arguably the most crucial decision a researcher faces when conducting the analysis. While several simulation studies exist that compare various so-called factor retention criteria under different data conditions, little is known about the impact of missing data on this process. Hence, in this study, we evaluated the performance of different factor retention criteria-the Factor Forest, parallel analysis based on a principal component analysis as well as parallel analysis based on the common factor model and the comparison data approach-in combination with different missing data methods, namely an expectation-maximization algorithm called Amelia, predictive mean matching, and random forest imputation within the multiple imputations by chained equations (MICE) framework as well as pairwise deletion with regard to their accuracy in determining the number of factors when data are missing. Data were simulated for different sample sizes, numbers of factors, numbers of manifest variables (indicators), between-factor correlations, missing data mechanisms and proportions of missing values. In the majority of conditions and for all factor retention criteria except the comparison data approach, the missing data mechanism had little impact on the accuracy and pairwise deletion performed comparably well as the more sophisticated imputation methods. In some conditions, especially small-sample cases and when comparison data were used to determine the number of factors, random forest imputation was preferable to other missing data methods, though. Accordingly, depending on data characteristics and the selected factor retention criterion, choosing an appropriate missing data method is crucial to obtain a valid estimate of the number of factors to extract.Complex research questions often need large samples to obtain accurate estimates of parameters and adequate power. Combining extant data sets into a large, pooled data set is one way this can be accomplished without expending resources. Measurement invariance (MI) modeling is an established approach to ensure participant scores are on the same scale. There are two major problems when combining independent data sets through MI. First, sample sizes will often be large leading to small differences becoming noninvariant. Second, not all data sets may include the same combination of measures. In this article, we present a method that can deal with both these problems and is user friendly. It is a combination of generating random normal deviates for variables missing completely in combination with assessing model fit using the root mean square error of approximation good enough principle, based on the hypothesis that the difference between groups is not zero but small. We demonstrate the method by examining MI across eight independent data sets and compare the MI decisions of the traditional and good enough approach. Our results show the approach has potential in combining educational data.
We analyzed the mortality risk and its predictors in patients hospitalized for heart failure (HF).

Patients discharged from hospitalization for acute decompensation of HF in 2010-2020 and younger than 86 years were followed (n=4097). We assessed the incidence and trends of all-cause death, its main predictors, and the pharmacotherapy recommended at discharge from the hospital.

The 30 days all-cause mortality was in discharged patients 3.2%, while 1-year 20.4% and 5-years 55.4%. We observed a modest trend to decreased 1-year mortality risk over time. Any increase of year of hospitalization by one was associated with about 5% lower risk in the fully adjusted model. Regarding predictors of 1-year mortality risk, a positive association was found for age over 65, history of malignancy, and peak brain natriuretic peptide during hospitalization ≥10times higher than normal concentration. In contrast, as protective factors, we identified LDL ≥1.8 mmol/L, treatment with beta-blockers, renin-angiotensin axis blockers, statins, and implanted cardioverter in the same regression model. The ejection fraction category and primary etiology of HF (coronary artery disease vs. others) did not significantly affect the mortality risk in a fully adjusted model.

Despite advances in cardiovascular disease management over the last two decades, the prognosis of patients hospitalized for heart failure remained highly unfavorable.
Despite advances in cardiovascular disease management over the last two decades, the prognosis of patients hospitalized for heart failure remained highly unfavorable.RNA G-quadruplex (rG4)-SELEX is a method that generates L-RNA aptamers to target an rG4 structure of interest, which can be applied to inhibit G-quadruplex-mediated interactions that have important roles in gene regulation and function. Here we present a Protocol Extension substantially modifying an existing SELEX protocol to describe in detail the procedures involved in performing rG4-SELEX to identify rG4-specific binders that can effectively suppress rG4-peptide and rG4-protein associations. This Protocol Extension improves the speed of aptamer discovery and identification, offering a suite of techniques to characterize the aptamer secondary structure and monitor binding affinity and specificity, and demonstrating the utility of the L-RNA aptamer. The previous protocol mainly describes the identification of RNA aptamers against proteins of interest, whereas in this Protocol Extension we present the development of an unnatural RNA aptamer against an RNA structure of interest, with the potential to be applicable to other nucleic acid motifs or biomolecules. rG4-SELEX starts with a random D-RNA library incubated with the L-rG4 target of interest, followed by binding, washing and elution of the library. Enriched D-aptamer candidates are sequenced and structurally characterized. Then, the L-aptamer is synthesized and used for different applications. rG4-SELEX can be carried out by an experienced molecular biologist with a basic understanding of nucleic acids. The development of rG4-targeting L-RNA aptamers expands the current rG4 toolkit to explore innovative rG4-related applications, and opens new doors to discovering novel rG4 biology in the near future. The duration of each selection cycle as outlined in the protocol is ~2 d.Plate tectonics requires a low-viscosity layer beneath the lithosphere-asthenosphere boundary (LAB), yet the origin of this ductile transition remains debated1,2. Explanations include the weakening effects of increasing temperature3,4, mineral hydration5 or partial melt6. Electrical resistivity is sensitive to all three effects7, including melt volatile content8, but previous LAB constraints from magnetotelluric soundings did not simultaneously consider the thermodynamic stability of the inferred amount of melt and the effect of uncertainty in the estimated resistivity8-14. Here we couple an experimentally constrained parameterization of mantle melting in the presence of volatiles15,16 with Bayesian resistivity inversion17 and apply this to magnetotelluric data sensitive to a LAB channel beneath the Cocos Plate9. Paradoxically, we find that the conductive channel requires either anomalously large melt fractions with moderate volatile contents or moderate melt fractions with anomalously large volatile contents, depending on the assumed mantle temperature. Large melt fractions are unlikely to be mechanically stable and conflict with melt-migration models18. As large volatile contents require a highly enriched mantle source inconsistent with mid-ocean-ridge estimates19, our results indicate that a mantle plume emplaced volatile-rich melts in the LAB channel. This requires the presence of a previously undetected nearby plume or the influence of the distant Galápagos hotspot. Plumes that feed thin, hydrous melt channels9,14,20 may be an unrecognized source of LAB anomalies globally.Precisely engineered mechanical oscillators keep time, filter signals and sense motion, making them an indispensable part of the technological landscape of today. selleck products These unique capabilities motivate bringing mechanical devices into the quantum domain by interfacing them with engineered quantum circuits. Proposals to combine microwave-frequency mechanical resonators with superconducting devices suggest the possibility of powerful quantum acoustic processors1-3. Meanwhile, experiments in several mechanical systems have demonstrated quantum state control and readout4,5, phonon number resolution6,7 and phonon-mediated qubit-qubit interactions8,9. At present, these acoustic platforms lack processors capable of controlling the quantum states of several mechanical oscillators with a single qubit and the rapid quantum non-demolition measurements of mechanical states needed for error correction. Here we use a superconducting qubit to control and read out the quantum state of a pair of nanomechanical resonators. Our device is capable of fast qubit-mechanics swap operations, which we use to deterministically manipulate the mechanical states.
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