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This study investigates how the course format change caused by covid-19 pandemic affected learning behaviors and performance of college students enrolled in a large introductory history course. Clickstream log files capturing how students were interacting with online learning contents were analyzed to identify the learning behaviors of students before and after the mid-semester course format change. The non-parametric regression model was developed to examine the relationship between learning performance of students and course format change. Although the frequency of accessing learning resources decreased during the first three weeks after the course format change, it had a relatively small effect on the learning performance of students. The quantile regression model indicates the mid-semester course format change is associated with about 3.3% decrease in the learning performance of students. These results suggest that students were quite resilient and their learning during the pandemic was not as bad as we feared.

In low-dose cone beam computed tomography (CT), the insufficient number of photons inevitably results in noise, which reduces the accuracy of disease diagnosis. One approach to improving the image quality of CT images acquired using a low-dose protocol involves the utilization of a reconstruction algorithm that efficiently reduces noise. In this study, we modeled the Feldkamp–Davis–Kress (FDK) algorithm using various filters and projection angles and applied it to the reconstruction process using CT simulation. To quantitatively evaluate the quality of the reconstruction images, we measured the coefficient of variation (COV), and signal-to-noise ratio (SNR) in the air, brain, and bone regions to evaluate the noise level. Furthermore, we calculated root mean square error (RMSE), universal image quality index (UQI), and blind/referenceless image spatial quality evaluator (BRISQUE) as similarity and no-reference evaluation. The Hann filter of the FDK algorithm showed superior performance in terms of COV, SNR, RMSE, and UQI compared to the other filters. In addition, when analyzing the COV and SNR results, we observed that image quality increased significantly at projection angles smaller than approximately 2.8°. Moreover, based on BRISQUE results, we confirm that the Shepp–Logan filter exhibited the most superior performance. In conclusion, we believe that the application of the Hann filter in the FDK reconstruction process offers significant advantages in improving the image quality acquired under a low-dose protocol, and we expect that our study will be a preliminary study of no-reference evaluation of CT reconstruction images. [ABSTRACT FROM AUTHOR]

Noise in computed tomography (CT) is inevitably generated, which lowers the accuracy of disease diagnosis. The non-local means approach, a software technique for reducing noise, is widely used in medical imaging. In this study, we propose a noise reduction algorithm based on fast non-local means (FNLMs) and apply it to CT images of a phantom created using 3D printing technology. The self-produced phantom was manufactured using filaments with similar density to human brain tissues. To quantitatively evaluate image quality, the contrast-to-noise ratio (CNR), coefficient of variation (COV), and normalized noise power spectrum (NNPS) were calculated. The results demonstrate that the optimized smoothing factors of FNLMs are 0.08, 0.16, 0.22, 0.25, and 0.32 at 0.001, 0.005, 0.01, 0.05, and 0.1 of noise intensities, respectively. In addition, we compared the optimized FNLMs with noisy, local filters and total variation algorithms. As a result, FNLMs showed superior performance compared to various denoising techniques. Particularly, comparing the optimized FNLMs to the noisy images, the CNR improved by 6.53 to 16.34 times, COV improved by 6.55 to 18.28 times, and the NNPS improved by 10−2 mm2 on average. In conclusion, our approach shows significant potential in enhancing CT image quality with anthropomorphic phantoms, thus addressing the noise issue and improving diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Multiple bacterial genera take advantage of the multifunctional autoprocessing repeats-in-toxin (MARTX) toxin to invade host cells. Secretion of the MARTX toxin by Vibrio vulnificus, a deadly opportunistic pathogen that causes primary septicemia, the precursor of sepsis, is a major driver of infection; however, the molecular mechanism via which the toxin contributes to septicemia remains unclear. Here, we report the crystal and cryo-electron microscopy (EM) structures of a toxin effector duet comprising the domain of unknown function in the first position (DUF1)/Rho inactivation domain (RID) complexed with human targets. These structures reveal how the duet is used by bacteria as a potent weapon. The data show that DUF1 acts as a RID-dependent transforming NADase domain (RDTND) that disrupts NAD+ homeostasis by hijacking calmodulin. The cryo-EM structure of the RDTND-RID duet complexed with calmodulin and Rac1, together with immunological analyses in vitro and in mice, provide mechanistic insight into how V. vulnificus uses the duet to suppress ROS generation by depleting NAD(P)+ and modifying Rac1 in a mutually-reinforcing manner that ultimately paralyzes first line immune responses, promotes dissemination of invaders, and induces sepsis. These data may allow development of tools or strategies to combat MARTX toxin-related human diseases. The MARTX toxins secreted by clinical V. vulnificus strains release the DUF1-RID effector duet in infected host cells. Here, the authors show that the duet hijacks calmodulin and Rac1, respectively, and transforms into a potent weapon to promote sepsis. [ABSTRACT FROM AUTHOR]