Promising hyperspectral remote sensing exhibits substantial potential in monitoring soil heavy metal (SHM) contamination. Nevertheless, the local spatial perturbation effects induced by environmental factors introduce considerable variability in SHM distribution. This engenders non-stationary relationship between SHM concentrations and spectral reflectance, posing challenges for accurate inversion of SHM globally. Addressing this gap, a novel Hierarchical Residual Correction-based Hyperspectral Inversion Method (HRCHIM) is proposed for SHM, considering their spatial heterogeneity. Initially, a global model is constructed using ground hyperspectral data to predict SHM concentration, capturing overarching contamination trends. Subsequently, four hierarchical levels, segmented by residual standard deviation (SD) intervals, identify critical environmental factors via Geodetector. These factors inform local residual correction models, refining global model predictions. HRCHIM aims to synergize global trends and local stochasticity to enhance prediction accuracy and interpretation of SHM spatial heterogeneity. Validated through a case study of a Cadmium(Cd)-contaminated mine area, six critical environmental factors were identified, exhibiting significant differences across hierarchical levels. By incorporating hierarchical correction models, HRCHIM demonstrated superior inversion performance compared to other conventional methods, achieving optimal prediction accuracies (Rv2 = 0.94, RMSEv = 0.21, and RPDv = 4.11). This innovative method can facilitate more precise and targeted strategies for preventing and controlling SHM contamination.

This paper aimed to monitor the exhaust gas temperature (EGT) from the end of the low-pressure turbine (LPT) of a gas turbine for period of 6 months. To achieve this, 16 thermocouples were strategically placed to gather data at different points in the exhaust system. This comprehensive approach allowed for a detailed analysis of the exhaust gas temperature, which is a critical factor in the health of hot section of gas turbines. The results of this study provide valuable insights that can be used to optimize the periodic inspections of gas turbines and improve their decisions. The investigation of thermal fluctuations that can cause damage to hot components has been carried out using two statistical methods - Standard deviation and Skewness. By analyzing the standard deviation, the degree to which the temperature values vary from the mean and relative normal condition of each unit can be determined. Skewness helps to identify whether the temperature data is skewed towards the high or low values, indicating the presence of potential anomalies. The application of these statistical methods is aimed at understanding the impact of temperature fluctuations on hot components and developing maintenance strategies to mitigate their effects. In order to verify the accuracy of the statistical results, a thorough borescope inspection of the gas turbine is carried out in accordance with the maintenance manual. These inspections were conducted at three distinct intervals to ensure a comprehensive evaluation of the gas turbine condition performance and condition. The results of this inspection serve as a critical component in determining the optimal maintenance and repair plan for the gas turbine.

UNASSIGNED: To explore the impact of the level of differentiation in a minimum of two follicles with a diameter of ≥18 mm on the outcome of controlled ovarian hyperstimulation on the day of human chorionic gonadotropin (hCG) administration.
UNASSIGNED: Single-center data from January 2018 to December 2021 was retrospectively analyzed for 1,199 patients with fresh embryo transfer for assisted reproduction. The absolute value of the standard deviation of the follicle size of at least 2 follicles ≥18 mm in diameter in both ovaries on the day of hCG was taken as the degree of differentiation of the dominant follicle after ovulation induction, based on the standard deviation response to the degree of dispersion of the data. The degree of follicular differentiation was divided into 3 groups according to the size of the value, and the general clinical conditions, laboratory indexes, and clinical outcomes of the patients in the 3 groups were compared.
UNASSIGNED: Among the three groups, the body mass index (BMI) of the ≤1s group was lower than that of the other two groups (P< 0.05), while the follicle-stimulating hormone (FSH) and Anti-Mullerian hormone (AMH) were higher (P< 0.05), and the implantation rate and clinical pregnancy rate were significantly higher than those of the other two groups (P< 0.01). After multifactorial logistic regression to correct for confounding factors, with the ≤1s group as the reference, the implantation rate, hCG-positive rate, clinical pregnancy rate and live birth rate of embryo transfer in the ≥2S group were significantly lower (P< 0.01). The results of curve fitting analysis showed that the live birth rate decreased gradually with the increase of the absolute standard deviation (P=0.0079).
UNASSIGNED: Differences in follicle diameters ≥18 mm on the day of hCG injection did not have an impact on embryo quality, but had an impact on pregnancy outcomes. The less the variation in follicle size, the more homogeneous the follicle development and the higher the likelihood of live births.

OBJECTIVE: To investigate the association between total sleep duration variability and stroke in the middle-aged and elderly population in China.
METHODS: Data were collected from the 2011, 2013, 2015, and 2018 surveys of the China Health and Retirement Longitudinal Study (CHARLS). A total of 3485 participants, who had not experienced a stroke until 2015 and completed the follow-up in 2018, were enrolled to analyze the relationship between total sleep duration variability and new stroke. Total sleep duration was calculated by summing self-reported nocturnal sleep duration and daytime napping. The variability was determined by calculating the standard deviation (SD) of total sleep duration across the first three waves. A binary logistic regression model was utilized to analyze this association.
RESULTS: Of the 3485 participants, 183 (5.25%) sustained a stroke event. A dose-response relationship was observed, indicating an increased stroke risk of 0.2 per unit (hours) increase in total sleep duration variability [OR (95% CI): 1.20 (1.01-1.42)]. Upon stratification by sex groups, this increased risk was significant only in men [OR (95% CI): 1.44 (1.12-1.83)].
CONCLUSIONS: Increased total sleep duration variability was associated with an increased risk of stroke in the middle-aged and elderly, independent of factors such as age, nocturnal sleep duration, napping habits, region of residence, hypertension, diabetes mellitus, dyslipidemia, BMI, smoking, drinking habits, and marital status. However, a more notable correlation was observed in males.

Despite frequent claims regarding radical extensions of human lifespan in the near future, many pragmatic scientists caution against excessive and baseless optimism on this front. In this study, we examine the compensation effect of mortality (CEM) as a potential challenge to substantial lifespan extension. The CEM is an empirical mortality regularity, often depicted as relative mortality convergence at advanced ages. Analysis of mortality data from 44 human populations, available in the Human Mortality Database, demonstrated that CEM can be represented as a continuous decline in relative mortality variation (assessed through the coefficient of variation and the standard deviation of the logarithm of mortality) with age, reaching a minimum corresponding to the species-specific lifespan. Through this method, the species-specific lifespan is determined to be 96-97 years, closely aligning with estimates derived from correlations between Gompertz parameters (95-98 years). Importantly, this representation of CEM can be achieved non-parametrically, eliminating the need for estimating Gompertz parameters. CEM is a challenge to lifespan extension, because it suggests that the true aging rate in humans (based on loss of vital elements, e.g., functional cells) remains stable at approximately 1% per year in the majority of human populations and is not affected by environmental or familial longevity factors. Given this rate of functional cell loss, one might anticipate that the total pool of functional cells could be entirely depleted by the age of 115-120 years creating physiological limit to human lifespan. Mortality pattern of supercentenarians (110 + years) aligns with this prediction.

OBJECTIVE: To investigate whether the quantitative index of signal intensity (SI) heterogeneity on T2-weighted (T2W) magnetic resonance images can predict the difficulty and efficacy of high-intensity focused ultrasound (HIFU) ablation for uterine fibroids.
METHODS: The standard deviation (SD) of T2W image (T2WI) SI was used to quantify SI heterogeneity. The correlation between SD and the non-perfused volume ratio (NPVR) in 575 patients undergoing HIFU treatment was retrospectively analyzed, and the efficacy of SD in predicting NPVR was discussed. Three classifications were made based on the SD, and the ablation difficulty and ablation effect of different grades were compared. A total of 65 cases from another center were used as an external validation set to verify the classification performance of SD.
RESULTS: The SD of SI was negatively correlated with NPVR (r = -0.460, p < 0.001). The predictive efficiency of SD for the ablation effect was higher than that of the scaled signal intensity (0.767 vs. 0.701, p = 0.006). Univariate and multivariate logistic regression analyses showed that SD was an independent predictor of ablation effect. Based on SD, the three classifications were divided into SD I: SD < 101.0, SD II: 101.0 ≤ SD < 138.7, and SD III: SD≥ 138.7. The treatment time, sonication time, treatment intensity, and total energy of SD I were lower than those of SD II and III (p < 0.05).
CONCLUSIONS: The heterogeneity of T2WI SI of uterine fibroids is negatively correlated with NPVR. The SD of SI can be used to predict the ablation difficulty and ablation effect of HIFU.

UNASSIGNED: Calculation of the uncertainty of the individual time-integrated activity coefficient (TIACs) is desirable in molecular radiotherapy. However, the calculation of TIAC\'s uncertainty in single-time-point (STP) method has never been reported in the literature. This study presents a method based on the Bayesian fitting (BF) to calculate the standard deviation (SD) of individual TIACs in the STP dosimetry.
UNASSIGNED: Biokinetic data of 177Lu-DOTATATE in kidneys were obtained from PMID33443063. BF methods with extended objective function, which optimize the fitting using prior knowledge of the function\'s parameters, were used. Reference TIACs (rTIACs) were calculated by fitting a mono-exponential function to the all-time-point data. The goodness of fit was checked based on the visual inspection and the coefficient of variations (CV) of the fitted parameters < 0.5. BF with relative (BFr) and absolute-based (BFa) variance methods were used to obtain the calculated TIACs (cTIACs) from the STP dosimetry. Performance of the STP method was obtained by calculating the relative deviation (RD) between cTIACs and rTIACs.
UNASSIGNED: Visual inspection showed a good fit for all patients with CV of fitted parameters less than 50%. The mean ± SD of cTIAC\'s %RD were 7.0 ± 25.2 for BFr and 2.6 ± 8.9 for BFa. The range of %CV of the individual cTIAC\'s SD for BFr and BFa methods was 36-78% and 22-33%, respectively, while the %CV of the rTIAC SD was 0.8-49%.
UNASSIGNED: We introduce the BF method to calculate the SD of individual TIACs in STP dosimetry. The presented method might be used as an alternative method for uncertainty analysis in STP dosimetry.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s13139-024-00851-8.

UNASSIGNED: Early detection of lung cancer through accurate diagnosis of malignant lung nodules using chest CT scans offers patients the highest chance of successful treatment and survival. Despite advancements in computer vision through deep learning algorithms, the detection of malignant nodules faces significant challenges due to insufficient training datasets.
UNASSIGNED: This study introduces a model based on collaborative deep learning (CDL) to differentiate between cancerous and non-cancerous nodules in chest CT scans with limited available data. The model dissects a nodule into its constituent parts using six characteristics, allowing it to learn detailed features of lung nodules. It utilizes a CDL submodel that incorporates six types of feature patches to fine-tune a network previously trained with ResNet-50. An adaptive weighting method learned through error backpropagation enhances the process of identifying lung nodules, incorporating these CDL submodels for improved accuracy.
UNASSIGNED: The CDL model demonstrated a high level of performance in classifying lung nodules, achieving an accuracy of 93.24%. This represents a significant improvement over current state-of-the-art methods, indicating the effectiveness of the proposed approach.
UNASSIGNED: The findings suggest that the CDL model, with its unique structure and adaptive weighting method, offers a promising solution to the challenge of accurately detecting malignant lung nodules with limited data. This approach not only improves diagnostic accuracy but also contributes to the early detection and treatment of lung cancer, potentially saving lives.

Experimental water research lacks clear methodology to estimate experimental error. Especially when natural waters are involved, the characterization tools bear method-specific artifacts while the varying environmental conditions prevent regular repeats. This tutorial review identifies common mistakes, and proposes a practical procedure to determine experimental errors at the example of membrane filtration. Statistical analysis is often applied to an insufficient number of repeated measurements, while not all error sources and contributions are considered. This results in an underestimation of the experimental error. Variations in relevant experimental parameters need to be investigated systematically, and the related errors are quantified as a half of the variation between the max and min values when standard deviation is not applicable. Error of calculated parameters (e.g. flux, pollutant removal and mass loss) is estimated by applying error propagation, where weighing contributions of the experimental parameters are considered. Appropriate judgment and five-time repetition of a selected experiment under identical conditions are proposed to validate the propagated experimental error. For validation, the five repeated data points should lie within the estimated error range of the error bar. The proposed error evaluation procedure is adaptable in experimental water research and intended for researchers to identify the contributing factors of an experimental error and carry out appropriate error quantification and validation. The most important aim is to raise awareness of the necessity to question error methodology and reproducibility of experimental data, to produce and publish high quality research.

BACKGROUND: Heterogeneous distribution in myocardial perfusion images (MPI) obtained by scintigraphy is often observed in cardiac diseases with normal myocardial perfusion. However, quantitative assessments of such heterogeneity have not been established. We hypothesized that the heterogeneity in MPI can be quantitatively evaluated through histogram analysis, calculating the standard deviation (SD), the 95% bandwidth (BW95%), and entropy.
METHODS: We examined resting 99mTc-MIBI images in 20 healthy subjects and 29 patients with cardiac disease who had none or very-mild reduced myocardial perfusion evaluated as a low summed rest score (0 to 4, the range of the studied healthy subjects). Two nuclear medicine specialists blindly divided them into two groups: non-heterogeneity or heterogeneity group, based solely on their visual assessments of heterogeneity on splash and polar maps generated from single-photon emission computed tomography (SPECT) images. The %uptake was determined by dividing the tracer count of each pixel by the tracer count of the pixel with the highest value in the LV myocardium. SD, BW95%, and entropy from histogram patterns were analyzed from the polar map data array of each %uptake. We investigated whether heterogeneity could be assessed using SD, BW95, and entropy in two groups classified by visual assessments. Additionally, we evaluated the area under the curve (AUC) to identify heterogeneity in the receiver operating characteristic curve analysis.
RESULTS: Based solely on visual assessments, 11 (22%) and 38 (78%) cases were classified into the non-heterogeneity and heterogeneity groups, respectively. The non-heterogeneity group consisted of only healthy subjects, and all patients with cardiac disease were classified into the heterogeneity group. The cases in the heterogeneity group had significantly higher values of heterogeneity indices (SD, BW95%, and entropy) in %uptake than those in the non-heterogeneity group (p < 0.05 for all). The AUCs of the heterogeneity indices were sufficiently high (AUCs > 0.90 for all) in distinguishing cases with visually heterogeneous distribution or patients with cardiac disease.
CONCLUSIONS: Heterogeneity in MPI can be evaluated using SD, BW95%, and entropy through histogram analysis. These novel indices may help identify patients with subtle myocardial changes, even in images that show preserved perfusion (345/350).