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Although individual-difference studies have been invaluable in several domains of psychology, there has been less success in cognitive domains using experimental tasks. The problem is often called one of reliability: Individual differences in cognitive tasks, especially cognitive-control tasks, seem too unreliable. In this article, we use the language of hierarchical models to define a novel reliability measure—a signal-to-noise ratio—that reflects the nature of tasks alone without recourse to sample sizes. Signal-to-noise reliability may be used to plan appropriately powered studies as well as understand the cause of low correlations across tasks should they occur. Although signal-to-noise reliability is motivated by hierarchical models, it may be estimated from a simple calculation using straightforward summary statistics.

 

Science identity, or one's sense of recognition and competence as a scientist, is an invaluable tool for predicting student persistence and success, but is understudied among undergraduates completing preparatory work for later studies in medicine, nursing, and allied health (“pre‐health career students”). In the United States, pre‐health career students make up approximately half of all biology students and, as professionals, play important roles in caring for an aging, increasingly diverse population, managing the ongoing effects of a pandemic, and navigating socio‐political shifts in public attitudes toward science and evidence‐based medicine. Pre‐health career students are also often members of groups marginalized and minoritized in STEM education, and generally complete their degrees in community college settings, which are chronically under‐resourced and understudied. Understanding these students' science identities is thus a matter of social justice and increasingly important to public health in the United States. We examined science identity and engagement among community college biology students using two scales established and validated for use with STEM students attending four‐year institutions. Exploratory and confirmatory factor analysis were used on two sub‐samples drawn from the pool of 846 participants to confirm that the factor structures functioned as planned among the new population. Science identity values were then compared between pre‐health career students (pre‐nursing and pre‐allied health) and other groups. Pre‐health career students generally reported interest and performance/competence on par with their traditional STEM, pre‐med, and pre‐dentistry peers, challenging popular assumptions about these students' interests and abilities. However, they also reported significantly lower recognition than traditional STEM and pre‐med/dentistry students. The implications for public health, researchers, and faculty are discussed.

 

Opening and reclosing pyridines enables selective halogenation at the 3-position of the ring. 

Abstract Inference of clinical phenotypes is a fundamental task in precision medicine, and has therefore been heavily investigated in recent years in the context of electronic health records (EHR) using a large arsenal of machine learning techniques, as well as in the context of genetics using polygenic risk scores (PRS). In this work, we considered the epigenetic analog of PRS, methylation risk scores (MRS), a linear combination of methylation states. We measured methylation across a large cohort ( n  = 831) of diverse samples in the UCLA Health biobank, for which both genetic and complete EHR data are available. We constructed MRS for 607 phenotypes spanning diagnoses, clinical lab tests, and medication prescriptions. When added to a baseline set of predictive features, MRS significantly improved the imputation of 139 outcomes, whereas the PRS improved only 22 (median improvement for methylation 10.74%, 141.52%, and 15.46% in medications, labs, and diagnosis codes, respectively, whereas genotypes only improved the labs at a median increase of 18.42%). We added significant MRS to state-of-the-art EHR imputation methods that leverage the entire set of medical records, and found that including MRS as a medical feature in the algorithm significantly improves EHR imputation in 37% of lab tests examined (median R 2 increase 47.6%). Finally, we replicated several MRS in multiple external studies of methylation (minimum p -value of 2.72 × 10 −7 ) and replicated 22 of 30 tested MRS internally in two separate cohorts of different ethnicity. Our publicly available results and weights show promise for methylation risk scores as clinical and scientific tools. 

In an effort to increase community college (CC) biology education research (BER), an NSF-funded network called CC Bio INSITES (Community College Biology Instructor Network to Support Inquiry into Teaching and Education Scholarship; INSITES for short) was developed to provide intellectual, resource, and social support for CC faculty (CCF) to conduct BER. To investigate the efficacy of this network, we asked about the barriers and supports INSITES CCF have experienced when conducting BER and how specific INSITES supports have mitigated barriers and provided support for network members to engage in BER. We conducted interviews and focus groups with 17 network participants, representing 15 different CCs. Qualitative thematic analysis revealed six main barriers that INSITES CCF experience when conducting BER: time constraints, knowledge, incentives or rewards, administrative or peer support, infrastructure, and stigma or misconceptions associated with being CCF. Participants indicated how the supports provided by INSITES helped to mitigate each barrier. Social support was especially critical for CCF to develop a sense of belonging to the CC BER community, though that did not extend to the broader BER community. We describe how these supports function to support BER and recommend four actions for future support of CCF conducting BER. 

Combining different observational probes, such as galaxy clustering and weak lensing, is a promising technique for unveiling the physics of the Universe with upcoming dark energy experiments. The galaxy redshift sample from the Dark Energy Spectroscopic Instrument (DESI) will have a significant overlap with major ongoing imaging surveys specifically designed for weak lensing measurements: the Kilo-Degree Survey (KiDS), the Dark Energy Survey (DES), and the Hyper Suprime-Cam (HSC) survey. In this work, we analyse simulated redshift and lensing catalogues to establish a new strategy for combining high-quality cosmological imaging and spectroscopic data, in view of the first-year data assembly analysis of DESI. In a test case fitting for a reduced parameter set, we employ an optimal data compression scheme able to identify those aspects of the data that are most sensitive to cosmological information and amplify them with respect to other aspects of the data. We find this optimal compression approach is able to preserve all the information related to the growth of structures.

 

We present a measurement of the cross-correlation between theMagLimgalaxies selected from the Dark Energy Survey (DES) first three years of observations (Y3) and cosmic microwave background (CMB) lensing from the Atacama Cosmology Telescope (ACT) Data Release 4 (DR4), reconstructed over ∼ 436 sq. deg of the sky. Our galaxy sample, which covers ∼ 4143 sq. deg, is divided into six redshift bins spanning the redshift range of 0.20 < z < 1.05. We adopt a blinding procedure until passing all consistency and systematics tests. After imposing scale cuts for the cross-power spectrum measurement, we reject the null hypothesis of no correlation at 9.1σ. We constrain cosmological parameters from a joint analysis of galaxy and CMB lensing-galaxy power spectra considering a flat ΛCDM model, marginalized over 23 astrophysical and systematic nuisance parameters. We find the clustering amplitude S_8 ≡ σ_8(Ω_m/0.3)^0.5 = 0.75+0.04-0.05. In addition, we constrain the linear growth of cosmic structure as a function of redshift. Our results are consistent with recent DES Y3 analyses and suggest a preference for a lower S_8 compared to results from measurements of CMB anisotropies by the Planck satellite, although at a mild level (< 2σ) of statistical significance.