Search Results for « Stem Noise »

Statistically weighted principal component analysis (wPCA) is widely used to reduce the noise of scanning transmission electron microscopy-energy-dispersive X-ray (STEM-EDX) spectroscopy data. It is beneficial to retain the spatial resolution of observation in each step of the analysis, but the direct application of wPCA without preprocessing, such as spatial averaging, often fails against low count STEM-EDX data. To enhance the applicability of wPCA while retaining spatial resolution, a step-by-step noise reduction method is considered in this study. Specifically, a numerical optimization is developed to simultaneously characterize the smoothness of EDX spectra and the low-rankness of the data. In the presented approach, a low count data is first spectrally smoothed by solving this optimization problem, and then further denoised by using wPCA to project onto a subspace rigorously spanned by a small number of components. A challenging example is provided, and the improved noise reduction performance is demonstrated and compared to using existing spectral smoothing techniques.

Statistically weighted principal component analysis (wPCA) is widely used to reduce the noise of scanning transmission electron microscopy-energy-dispersive X-ray (STEM-EDX) spectroscopy data. It is beneficial to retain the spatial resolution of observation in each step of the analysis, but the direct application of wPCA without preprocessing, such as spatial averaging, often fails against low count STEM-EDX data. To enhance the applicability of wPCA while retaining spatial resolution, a step-by-step noise reduction method is considered in this study. Specifically, a numerical optimization is developed to simultaneously characterize the smoothness of EDX spectra and the low-rankness of the data. In the presented approach, a low count data is first spectrally smoothed by solving this optimization problem, and then further denoised by using wPCA to project onto a subspace rigorously spanned by a small number of components. A challenging example is provided, and the improved noise reduction performance is demonstrated and compared to using existing spectral smoothing techniques.

Objective: This study aimed to investigate the effects of comprehensive ward noise management measures on anxiety, depression, and sleep quality in hematological patients after hematopoietic stem cell transplantation. Methods: From October 2023 to October 2024, 130 hematopoietic stem cell transplant patients admitted to Shanghai Changzheng Hospital were divided into an observation group (65 cases) and a control group (65 cases) based on whether they received ward noise management. Measures for managing noise in patient wards include environmental noise reduction modifications, the establishment of quiet protocols, the optimization of treatment processes, and health education. Ward noise levels, Hamilton Anxiety Rating Scale (HAMA) and Hamilton Depression Rating Scale (HAMD) scores, Pittsburgh Sleep Quality Index (PSQI) scores, and polysomnography (PSG) parameters were compared between the two groups before and after treatment. Independent samples t-tests were used to compare differences across groups, while chi-square tests were applied to count data. Multiple comparisons were corrected using the Holm–Bonferroni method. Results: After treatment, the daytime and nighttime ward noise levels in the observation group were significantly lower than those in the control group ( P < 0.05). The HAMA, HAMD, and PSQI scores in the observation group were all lower than those in the control group ( P < 0.05). PSG showed that the wake-after-sleep-onset time and sleep-onset latency in the observation group were significantly shorter than those in the control group after treatment, while the total sleep time was significantly longer ( P < 0.05). Conclusion: Patients who received noise management in our hospital wards experienced lower noise levels, reduced anxiety and depression, and improved both subjective and objective sleep quality.

Abstract Background Stochastic modelling plays a crucial role in comprehending the dynamics of intracellular events in various biochemical systems, including gene-expression models. Cell-to-cell variability arises from the stochasticity or noise in the levels of gene products such as messenger RNA (mRNA) and protein. The sources of noise can stem from different factors, including structural elements. Recent studies have revealed that the mRNA structure can be more intricate than previously assumed. Results Here, we focus on the formation of stem-loops and present a reinterpretation of previous data, offering new insights. Our analysis demonstrates that stem-loops that restrict translation have the potential to reduce noise. Conclusions In conclusion, we investigate a structured/generalised version of a stochastic gene-expression model, wherein mRNA molecules can be found in one of their finite number of different states and transition between them. By characterising and deriving non-trivial analytical expressions for the steady-state protein distribution, we provide two specific examples which can be readily obtained from the structured/generalised model, showcasing the model’s practical applicability.