Locally Self-Adjustive Smoothing for Measurement Noise Reduction with Application to Automated Peak Detection.

Smoothing is a widely used approach for measurement noise reduction in spectral analysis. However, it suffers from signal distortion caused by peak suppression. A locally self-adjustive smoothing method is developed that retains sharp peaks and less distorted signals. The proposed method uses only one parameter that determines the global smoothness of data, while balancing the local smoothness using the data itself. Simulation and real experiments in comparison with existing convolution-based smoothing methods indicate both qualitatively and quantitatively improved noise reduction performance in practical scenarios. We also discuss parameter selection and demonstrate an application for the automated smoothing and detection of a given number of peaks from noisy measurement data.

Fast computational approach with prior dimension reduction for three-dimensional chemical component analysis using CT data of spectral imaging.

Spectral image (SI) measurement techniques, such as X-ray absorption fine structure (XAFS) imaging and scanning transmission electron microscopy (STEM) with energy-dispersive X-ray spectroscopy (EDS) or electron energy loss spectroscopy (EELS), are useful for identifying chemical structures in composite materials. Machine-learning techniques have been developed for automatic analysis of SI data, and their usefulness has been proven. Recently, an extended measurement technique combining SI with a computed tomography (CT) technique (CT-SI), such as CT-XAFS and STEM-EDS/EELS tomography, was developed to identify the three-dimensional (3D) structures of chemical components. CT-SI analysis can be conducted by combining CT reconstruction algorithms and chemical component analysis based on machine learning techniques. However, this analysis incurs high computational costs owing to the size of the CT-SI datasets. To address this problem, this study proposed a fast computational approach for 3D chemical component analysis in an unsupervised learning setting. The primary idea for reducing the computational cost involved compressing the CT-SI data prior to CT computation and performing 3D reconstruction and chemical component analysis on the compressed data. The proposed approach significantly reduced the computational cost without losing information about the 3D structure and chemical components. We experimentally evaluated the proposed approach using synthetic and real CT-XAFS data, which demonstrated that our approach achieved a significantly faster computational speed than the conventional approach while maintaining analysis performance. As the proposed procedure can be implemented with any CT algorithm, it is expected to accelerate 3D analyses with sparse regularized CT algorithms in noisy and sparse CT-SI datasets.

Procalcitonin, brain natriuretic peptide and albumin as markers to predict prognosis in hospitalized older Japanese patients with a risk of infection.

AIM: Whether serum concentration of procalcitonin (PCT), brain natriuretic peptide (BNP) and albumin (Alb) have an association with the outcome of hospitalized older patients is unclear. We investigated clinical outcomes and any predictive factors in hospitalized Japanese older patients with a risk of infection. METHODS: In the retrospective study, 820 Japanese patients were followed up for 30 days or until death. During the observation period, 656 patients survived and 164 patients died. The predictive factors of death were analyzed according to demographic and clinical variables. RESULTS: The survival rate was decreased as the serum PCT increased from <0.5 to ≥10 ng/mL, as was also the case with BNP from <300 to ≥300 pg./mL, whereas low Alb (<2.5 g/dL) showed a lower survival rate than high Alb (≥2.5 g/dL; P < 0.01). Using the Cox regression model, the multivariable-adjusted hazard ratios (95% confidence interval) were as follows: PCT 0.5-2 versus <0.5 ng/mL: 1.61(1.04-2.49), PCT 2-10 versus <0.5 ng/mL: 1.91(1.15-3.16), PCT ≥10 versus <0.5 ng/mL: 2.90(1.84-4.59), high BNP 1.26 (0.89-1.76) and low Alb 0.68 (0.52-0.87). The mortality rate increased as the number of scores (PCT + BNP + Alb) increased. CONCLUSIONS: Concentration-dependent high PCT, high BNP and low Alb were positive risk factors associated with poor prognosis in hospitalized older patients with a risk of infection. Geriatr Gerontol Int 2024; 24: 571-576.