The Student Voice: Perceptions of Durable Learning.

BACKGROUND: Durable learning is the teaching/learning methods that result in retained knowledge that can be transferred to practice. There is limited research on durable learning in nursing education (NE). PURPOSE: The purpose of this study was to understand the perceptions of how learners acquire and retain knowledge. METHODS: Nursing students and recent graduates were recruited from 2 large universities. Focus groups (n = 7) were audio-recorded, and transcripts were analyzed. RESULTS: Thematic analysis of learners' perceptions revealed (1) effective learner-initiated techniques, (2) effective instructor-initiated techniques, (3) learner-initiated techniques that were not effective, and (4) instructor-initiated techniques in the classroom, clinical practice, and simulation that were not effective. CONCLUSION: Additional research is needed to understand faculty perspectives on durable learning techniques and their effectiveness. Findings from student, graduate, and faculty perspectives will guide the development of a validated, reliable tool to evaluate durable learning in NE.

Durable Learning Strategies in Nursing Education: State-of-the-Evidence Review.

BACKGROUND: Health professions (HP) students must achieve durable learning (DL) to transfer and apply knowledge from the classroom to the clinical setting. This review examines the state of the science of classroom-based DL in HP. METHOD: The Joanna Briggs Systematic Review Methodology was used. MEDLINE, CINAHL, PsycINFO, and ERIC databases were searched for articles published from 2006 to 2022. A total of 2,000 titles were identified for review, with 51 studies being selected for inclusion. RESULTS: Multiple classroom-based learning strategies generally reported as being effective were identified, including flipped classroom, educational technology, spaced learning, team-based learning, concept mapping and schema, testing, and case study and problem-based learning. CONCLUSION: Although DL has been proven to be effective in the classroom setting for HP, no one type has been shown to be more effective than others. Additional research is needed within the context of transferring knowledge to clinical settings and in nursing education. [J Nurs Educ. 2024;63(1):24-31.].

The C-SHIFT Algorithm for Normalizing Covariances.

Omics technologies are powerful tools for analyzing patterns in gene expression data for thousands of genes. Due to a number of systematic variations in experiments, the raw gene expression data is often obfuscated by undesirable technical noises. Various normalization techniques were designed in an attempt to remove these non-biological errors prior to any statistical analysis. One of the reasons for normalizing data is the need for recovering the covariance matrix used in gene network analysis. In this paper, we introduce a novel normalization technique, called the covariance shift (C-SHIFT) method. This normalization algorithm uses optimization techniques together with the blessing of dimensionality philosophy and energy minimization hypothesis for covariance matrix recovery under additive noise (in biology, known as the bias). Thus, it is perfectly suited for the analysis of logarithmic gene expression data. Numerical experiments on synthetic data demonstrate the method's advantage over the classical normalization techniques. Namely, the comparison is made with Rank, Quantile, cyclic LOESS (locally estimated scatterplot smoothing), and MAD (median absolute deviation) normalization methods. We also evaluate the performance of C-SHIFT algorithm on real biological data.

What's the norm in normalization? A frightening note on the use of RT-qPCR in the livestock science.

Reverse-Transcription quantitative PCR (RT-qPCR) provides a valuable tool to study gene expression with exquisite sensitivity. To retain its inferential power, user-introduced technical variability must be reduced and accounted for. Selecting a set of stably expressed internal control genes (ICG), validated for each experimental condition/sample set, is widely accepted as a reliable way to normalize RT-qPCR data and account for said variability. Despite significant efforts in establishing standardized and resource-efficient normalization approaches, numerous recent reports have underlined deficiencies in the state of RT-qPCR normalization. Livestock science has benefitted tremendously from the use of RT-qPCR; however, the issue of lack of proper normalization likely affects this discipline as well. We thus decided to determine whether this is true, and to which extent. We conducted an in-depth analysis of all (225) RT-qPCR articles published in the six most prominent livestock journals in the field from 2013 to 2017. A quantitative scale was constructed, and values were assigned to each article based on the number of ICG used, the use of a publicly available algorithm to assess the reliability of ICG, and the reporting of pertinent information related to ICG (ranges from 0 = total noncompliance - to 100 = total compliance). Out of the surveyed group, only 10.7% of the publications obtained a score of 100, while the largest group (n = 158) was represented by articles that scored 0. Subdividing articles based on whether an algorithm to validate ICG was used (YAL) or not (NAL) revealed the use of a larger number of ICG to normalize RT-qPCR in the YAL group compared to NAL (1.4-fold more, 95% C.I.: 1.11-1.84) and was closer to the "gold standard" of three ICG. Using an algorithm also increased the diversity of ICG and significantly reduced the use of RNA18S, whose suitability as ICG has been thoroughly debated. These remarkably low normalization standards are likely to generate questionable results that can severely hinder the advance of transcriptomic studies in livestock science and related fields.

Band structure of Si/Ge core-shell nanowires along the [110] direction modulated by external uniaxial strain.

Strain modulated electronic properties of Si/Ge core-shell nanowires along the [110] direction were reported, on the basis of first principles density-functional theory calculations. In particular, the energy dispersion relationship of the conduction/valence band was explored in detail. At the Γ point, the energy levels of both bands are significantly altered by applied uniaxial strain, which results in an evident change of the band gap. In contrast, for the K vectors far away from Γ, the variation of the conduction/valence band with strain is much reduced. In addition, with a sufficient tensile strain (∼1%), the valence band edge shifts away from Γ, which indicates that the band gap of the Si/Ge core-shell nanowires experiences a transition from direct to indirect. Our studies further showed that effective masses of charge carriers can also be tuned using the external uniaxial strain. The effective mass of the hole increases dramatically with tensile strain, while strain shows a minimal effect on tuning the effective mass of the electron. Finally, the relation between strain and the conduction/valence band edge is discussed thoroughly in terms of site-projected wavefunction characters.