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BackgroundAdvanced cardiac life support (ACLS) skills are essential for nurses. During the COVID-19 pandemic, augmented reality (AR) technologies were incorporated into medical education to increase learning motivation and accessibility. ObjectiveThis study aims to determine whether AR for educational applications can significantly improve crash cart learning, learning motivation, cognitive load, and system usability. It focused on a subgroup of nurses with less than 2 years of experience. MethodsThis randomized controlled trial study was conducted in a medical center in southern Taiwan. An ACLS cart training course was developed using AR technologies in the first stage. Additionally, the efficacy of the developed ACLS training course was evaluated. The AR group used a crash cart learning system developed with AR technology, while the control group received traditional lecture-based instruction. Both groups were evaluated immediately after the course. Performance was assessed through learning outcomes related to overall ACLS and crash cart use. The Instructional Materials Motivation Survey, System Usability Scale, and Cognitive Load Theory Questionnaire were also used to assess secondary outcomes in the AR group. Subgroup analyses were performed for nurses with less than 2 years of experience. ResultsAll 102 nurses completed the course, with 43 nurses in the AR group and 59 nurses in the control group. The AR group outperformed the control group regarding overall ACLS outcomes and crash cart learning outcomes (P=.002; P=.01). The improvement rate was the largest for new staff regardless of the overall learning effect and the crash cart effect. Subgroup analysis revealed that nurses with less than 2 years of experience in the AR group showed more significant improvements in both overall learning (P

Abstract This paper proposes a metal artifact reduction method of using MV-CBCT images to correct metal artifacts in kV-CT images, especially for the complex metal artifacts caused by multi-metal interaction of patients with head and neck tumors. The different tissue regions are segmented in the MV-CBCT images to obtain template images and the metal region is segmented in the kV-CT images. Forward projection is performed to get sinogram of the template images, kV-CT images and metal region images. Artifact images can be reconstructed through those sonograms. Corrected images is generated by subtracting the artifact images from the original kV-CT images. After the first correction, the template images are generated again and brought into the previous step for iteration to get better correction result. CT data set of 7 patients are used in this study, compared with linear interpolation metal artifact (LIMAR) and normalized metal artifact reduction method, mean relative error of CT value is reduced by 50.5% and 63.3%, noise is reduced by 56.2% and 58.9%. The Identifiability Score of the tooth, upper/lower jaw, tongue, lips, masseter muscle and cavity in the corrected images by the proposed method have significantly improved (P

Wavelet transformation is a widely used method in high-frequency sequence stratigraphic analysis. However, the application is problematic since different wavelets always return the same sequence analysis results. To address this issue, we applied five commonly used wavelets to theoretical sequence models to document some application criteria. Five gradual scale-change sequence models were simplified from the glutenite succession deposition by gravity flows to form the fining-upwards cycle sequences (FUCS) and coarsening-upwards cycle sequences (CUCS). After conducting theoretical sequence model tests, the optimal wavelet (sym4) was selected and successfully used with actual data to identify the sequence boundaries. We also proposed a new method to optimize the scale of continuous wavelet transformation (CWT) for sequence boundary determination. We found that the balloon-like marks in scalograms of db4, sym4, and coif4 wavelet determine, respectively, the fourth-order sequence boundary, the thick succession sequence boundaries in FUCS, and the thick succession sequence in FUCS and CUCS. Comparing the sequence identification results shows that the asymmetric wavelets had an advantage in high-frequency sequence boundary determination and sedimentary cycle discrimination through the amplitude trend of the coefficient, in which the sym4 wavelet is the most effective. In conclusion, the asymmetry of wavelets is the first selection principle, of which asymmetric wavelets are more sensitive to sediment deposition by flood flows. The match of the wavelet between the sequence is the second selection principle, in which the correlation of time-frequency impacts the accuracy of sequence surface localization. However, the waveform of the wavelet is a visual and abstract parameter for sequence boundary detection. The appropriate wavelet for lacustrine sequence analysis is the asymmetric wavelet with a weak number of side lobes. The depositional flows, depositional process, and autogenic are three sedimentary factors that influence the sequence analysis results. [ABSTRACT FROM AUTHOR]

Background: The commitment of nursing preceptors to their role is an important driving force that supports their clinical teaching and affects teaching quality. Role commitment undergoes dynamic development and thus changes over time. Existing studies have utilized only cross-sectional study designs and have not analyzed the changes in commitment trajectories with related factors. Purpose: This study aimed to investigate the development trajectories of the commitment of preceptors and to examine the predictors between the trajectories of role commitment among nursing preceptors. Methods: A single-group, repeated-measures design was adopted, and 59 participants completed the Commitment to the Preceptor Role Scale and the Preceptor_s Perception of Support Scale. The latent class growth analysis method was used to estimate the trajectory class patterns. The Wilcoxon rank-sum test, a nonparametric method, was used to compare the differences in demographic characteristics between the trajectories of commitment among nursing preceptors. Predictorswere examined using binary logistic regression analysis. Results: The two-class model was the best-fitting model to describe the trajectories of nursing preceptor commitment. The two classes in this model were "low commitment," which accounted for 90.3% of all the participants, and "high commitment," which accounted for 9.7%. A significant difference was found between the two classes in terms of motivation for being a preceptor (p = .048). Neither demographic characteristics nor organizational support had a predictive effect on the trajectories of commitment development. Conclusions/Implications for Practice: This study found a low level of role commitment among new preceptors. Moreover, internal motivation was found to be a significant factor affecting the trajectories of this commitment. Therefore, institutions should foster an appropriate environment to enhance the role identity of preceptors as well as cultivate and stimulate their commitment to this role. [ABSTRACT FROM AUTHOR]

Apoptosis, which is usually accompanied by DNA degradation, is important not only for the homeostasis of metazoans but also for mammalian development. If DNA is not properly degraded in these processes, it can cause diverse diseases, such as anemia, cataracts, and some autoimmune diseases. A large effort has been made to identify these nucleases that are responsible for these effects. In contrast to Deoxyribonuclease I (DNase I), Deoxyribonuclease II (DNase II) has been less well characterized in these processes. Additionally, enzymes of DNase II family in Trichinella spiralis, which is an intracellular parasitic nematode, are also considered involved in the development of the nematode. We have compiled information from studies on DNase II from various organisms and found some nonclassic features in these enzymes of T. spiralis. Here we have reviewed the characterization and functions of DNase II in these processes and predicted the functions of these enzymes in T. spiralis during host invasion and development. [ABSTRACT FROM AUTHOR]

Let G be a planar graph without two triangles sharing a common vertex. We prove that (1) G is 4-choosable and (2) G is edge-(Δ(G)+1)-choosable when its maximum degree Δ(G) ≠ 5. [ABSTRACT FROM AUTHOR]