Reduction In Standard Deviation Research Articles

Quantifying Predictive Uncertainty and Feature Selection in River Bed Load Estimation: A Multi-Model Machine Learning Approach with Particle Swarm Optimization

This study presents a comprehensive multi-model machine learning (ML) approach to predict river bed load, addressing the challenge of quantifying predictive uncertainty in fluvial geomorphology. Six ML models—random forest (RF), categorical boosting (CAT), extra tree regression (ETR), gradient boosting machine (GBM), Bayesian regression model (BRM), and K-nearest neighbors (KNNs)—were thoroughly evaluated across several performance metrics like root mean square error (RMSE), and correlation coefficient (R). To enhance model training and optimize performance, particle swarm optimization (PSO) was employed for hyperparameter tuning across all the models, leveraging its capability to efficiently explore complex hyperparameter spaces. Our findings indicated that RF, GBM, CAT, and ETR demonstrate superior predictive performance (R score > 0.936), benefiting significantly from PSO. In contrast, BRM displayed lower performance (0.838), indicating challenges with Bayesian approaches. The feature importance analysis, including permutation feature and SHAP values, highlighted the non-linear interdependencies between the variables, with river discharge (Q), bed slope (S), and flow width (W) being the most influential. This study also examined the specific impact of individual variables on model performance by adding and excluding individual variables, which is particularly meaningful when choosing input variables for the model, especially in limited data conditions. Uncertainty quantification through Monte Carlo simulations highlighted the enhanced predictability and reliability of models with larger datasets. The correlation between increased training data and improved model precision was evident in the consistent rise in mean R scores and reduction in standard deviations as the sample size increased. This research underscored the potential of advanced ensemble methods and PSO to mitigate the limitations of single-predictor models and exploit collective model strengths, thereby improving the reliability of predictions in river bed load estimation. The insights from this study provide a valuable framework for future research directions focused on optimizing ensemble configurations for hydro-dynamic modeling.

Effect of Ultrasonic Cleaning Combined with Antibacterial Polypeptide Periodontal Gel on Inflammatory Reaction and Incidence of Adverse Reactions in Patients with Chronic Gingivitis.

The purpose of this investigation was to evaluate the efficacy of ultrasonic subgingival curettage in conjunction with antibacterial polypeptide periodontal gel in the management of chronic periodontitis of moderate to severe severity. Methods included dividing 500 hospitalised patients with moderate to severe chronic periodontitis evenly between an observation group and a control group. Subgingival ultrasonic curettage was performed on the placebo group. The non-treatment group received ultrasonic subgingival curettage and a periodontal gel rinse containing polypeptides. Results were compared before and after treatment in terms of the periodontal index, inflammation in the gingival crevicular fluid, and occlusal and masticatory efficiency. Both groups saw significant reductions in occlusal duration and occlusal force balance after treatment compared to pre-treatment levels, though the observation group saw a more dramatic decrease in these indices than the control group with P ≤ 0.05. The treatment and observation groups both saw significant reductions in the masticatory efficiency standard deviation afterward, but the index in the observation group was significantly lower than that of the control group with P ≤ 0.05.The authors claim that moderate to severe chronic periodontitis can be effectively treated with a combination of polypeptide periodontal gel and ultrasonic subgingival curettage. Substantial decreases from pre-treatment levels for both groups, with the Observation Group's index being significantly lower than the Control Group's index (P ≤ 0.05). It is possible that this treatment will help reduce inflammation and improve your periodontal health. Biting strength and occlusion stability can both be improved at the same time to help patients improve their chewing efficiency. Therefore, this method can be used securely in real-world patient care settings.

Global phase accuracy enhancement of structured light system calibration and 3D reconstruction by overcoming inevitable unsatisfactory intensity modulation

Mainstream fringe projection profilometry (FPP) faces challenges in measuring low signal-to-noise ratio intensity modulation areas, especially in shadow areas on objects and black areas on calibration plates, causing pseudo phases and obvious nonlinear calibration errors in 3D measurement. To address these issues concurrently, we proposed a cyclic phase-shifts difference (CPSD) module based on phase-shifting gratings. CPSD uses double-layer circulation difference to retain the pixels with higher light intensity eliminating the influence of ambient light and finally creates a mask removing and correcting black and shadow areas. Without further post-processing and projection operation, we achieved the global phase accuracy enhancement and obtained high-precision 3D point clouds without pseudo points. The method’s efficacy was proven through experiments on three kinds of calibration planes, achieving a significant reduction in mean absolute error (MAE) and standard deviation (STD) in plane reconstruction (up to 50.9% and 56.1% respectively). We believe that CPSD has enormous potential for wide application across various FPP-related fields. (Code and data are avaliable at: https://github.com/LiYiMingM/CPSD_shadow_and_black_area_phase_correction_in_SL3D)

Lipid-lowering drug targets and lung related diseases: a Mendelian randomization study

ABSTRACT OBJECTIVES This genetics-based study aimed to evaluate the relationships of long-term lipid-lowering agents on lung-related diseases (LRD) outcomes. METHODS We extracted genetic variations of six drug target genes from a major genome-wide association study of low-density lipoprotein cholesterol (LDL-C) in individuals predominantly of European ancestry to represent the effects of LDL-C-lowering treatment. We conducted drug-targeted Mendelian randomization and utilized a range of outcomes to capture evidence of adverse side effects related to the inhibition of the gene. RESULTS Our study did not find any significant association between genetically proxied APOB, CETP, HMGCR, NPCIL, PCSK9, and LDLR inhibition (equivalent to a one standard deviation reduction in LDL-C) with the risk of Small Cell Lung Cancer, Non-Small Cell Lung Cancer, idiopathic pulmonary fibrosis, and Pneumonia (P > 0.05). However, long-term inhibition of NPC1L that mimics the use of statin drugs may have contradictory effects on pulmonary edema (OR = 0.508, 95%CI = 0.328–0.786,P = 0.002) and chronic obstructive pulmonary disease (OR = 1.524, 95%CI = 1.099–2.115, P = 0.012). A series of sensitivity analyses and positive control analyses have been conducted to confirm the reliability of the results. CONCLUSIONS In conclusion, this study reveals inconsistent associations between genetic proxy inhibition of APOB, CETP, HMGCR, NPCIL, PCSK9 and LDLR with LRD in specific populations.

Nonergodic Ground-Motion Models for Subduction Zone and Crustal Earthquakes in Japan

ABSTRACT We investigate the nonergodic behavior of ground motions from subduction zone earthquakes and crustal earthquakes in Japan using the National Research Institute for Earth Science and Disaster Resilience strong-motion flatfile together with various reference ergodic ground-motion models (GMMs). For the nonergodic path effects, our nonergodic GMM has two path effects terms: a cell-specific linear-distance scaling, denoted as δP2PQ, that mimics the effects of a 3D Q structure, and a source- and site-specific term related to the effects of the 3D velocity structure, denoted δP2PV. The resulting model provides spatially varying nonergodic source, site, and path terms. The GMM smoothly interpolates and extrapolates the nonergodic terms in space so that the GMM can be applied to any combination of source and site locations in the region. In regions where data are sparse, the median nonergodic terms will approach zero but with large epistemic uncertainty. Over the period range of 0–10 s, the ranges of the standard deviations of the nonergodic source, path, and site terms are 0.2–0.65, 0.25–0.40, and 0.15–0.40 natural log units, respectively. The fully nonergodic models lead to a 40%–55% decrease in the aleatory standard deviation compared to the reference ergodic GMMs. This large reduction in the aleatory standard deviation combined with the change in the median given by the nonergodic terms can exert a significant impact on the computed seismic hazard for the Japan region.

Modulate the laser phase to improve the ns-LIBS spectrum signal based on orbital angular momentum.

Aiming to enhance the ns-LIBS signal, in this work, we introduced orbital angular momentum to modulate the laser phase of the Gaussian beam into the vortex beam. Under similar incident laser energy, the vortex beam promoted more uniform ablation and more ablation mass compared to the Gaussian beam, leading to elevated temperature and electron density in the laser-induced plasma. Consequently, the intensity of the ns-LIBS signal was improved. The enhancement effects based on the laser phase modulation were investigated on both metallic and non-metallic samples. The results showed that laser phase modulation resulted in a maximum 1.26-times increase in the peak intensities and a maximum 1.25-times increase in the signal-to-background ratio (SBR) of the Cu spectral lines of pure copper for a laser energy of 10 mJ. The peak intensities of Si atomic spectral lines were enhanced by 1.58-1.94 times using the vortex beam. Throughout the plasma evolution process, the plasma induced by the vortex beam exhibited prolonged duration and a longer continuous background, accompanied by a noticeable reduction in the relative standard deviation (RSD). The experimental results demonstrated that modulation the laser phase based on orbital angular momentum is a promising approach to enhancing the ns-LIBS signal.

The Ground-Motion Characterization Model for the 2022 New Zealand National Seismic Hazard Model

ABSTRACT This article summarizes the ground-motion characterization (GMC) model component of the 2022 New Zealand National Seismic Hazard Model (2022 NZ NSHM). The model development process included establishing a NZ-specific context through the creation of a new ground-motion database, and consideration of alternative ground-motion models (GMMs) that have been historically used in NZ or have been recently developed for global application with or without NZ-specific regionalizations. Explicit attention was given to models employing state-of-the-art approaches in terms of their ability to provide robust predictions when extrapolated beyond the predictor variable scenarios that are well constrained by empirical data alone. We adopted a “hybrid” logic tree that combined both a “weights-on-models” approach along with backbone models (i.e., metamodels), the former being the conventional approach to GMC logic tree modeling for NSHM applications using published models, and the latter being increasingly used in research literature and site-specific studies. In this vein, two NZ-specific GMMs were developed employing the backbone model construct. All of the adopted subduction GMMs in the logic tree were further modified from their published versions to include the effects of increased attenuation in the back-arc region; and, all but one model was modified to account for the reduction in ground-motion standard deviations as a result of nonlinear surficial site response. As well as being based on theoretical arguments, these adjustments were implemented as a result of hazard sensitivity analyses using models without these effects, which we consider gave unrealistically high hazard estimates.

Pediatric evaluations for deep learning CT denoising.

Deep learning (DL) CT denoising models have the potential to improve image quality for lower radiation dose exams. These models are generally trained with large quantities of adult patient image data. However, CT, and increasingly DL denoising methods, are used in both adult and pediatric populations. Pediatric body habitus and size can differ significantly from adults and vary dramatically from newborns to adolescents. Ensuring that pediatric subgroups of different body sizes are not disadvantaged by DL methods requires evaluations capable of assessing performance in each subgroup. To assess DL CT denoising in pediatric and adult-sized patients, we built a framework of computer simulated image quality (IQ) control phantoms and evaluation methodology. The computer simulated IQ phantoms in the framework featured pediatric-sized versions of standard CatPhan 600 and MITA-LCD phantoms with a range of diameters matching the mean effective diameters of pediatric patients ranging from newborns to 18 years old. These phantoms were used in simulating CT images that were then inputs for a DL denoiser to evaluate performance in different sized patients. Adult CT test images were simulated using standard-sized phantoms scanned with adult scan protocols. Pediatric CT test images were simulated with pediatric-sized phantoms and adjusted pediatric protocols. The framework's evaluation methodology consisted of denoising both adult and pediatric test images then assessing changes in image quality, including noise, image sharpness, CT number accuracy, and low contrast detectability. To demonstrate the use of the framework, a REDCNN denoising model trained on adult patient images was evaluated. To validate that the DL model performance measured with the proposed pediatric IQ phantoms was representative of performance in more realistic patient anatomy, anthropomorphic pediatric XCAT phantoms of the same age range were also used to compare noise reduction performance. Using the proposed pediatric-sized IQ phantom framework, size differences between adult and pediatric-sized phantoms were observed to substantially influence the adult trained DL denoising model's performance. When applied to adult images, the DL model achieved a 60% reduction in noise standard deviation without substantial loss in sharpness in mid or high spatial frequencies. However, in smaller phantoms the denoising performance dropped due to different image noise textures resulting from the smaller field of view (FOV) between adult and pediatric protocols. In the validation study, noise reduction trends in the pediatric-sized IQ phantoms were found to be consistent with those found in anthropomorphic phantoms. We developed a framework of using pediatric-sized IQ phantoms for pediatric subgroup evaluation of DL denoising models. Using the framework, we found the performance of an adult trained DL denoiser did not generalize well in the smaller diameter phantoms corresponding to younger pediatric patient sizes. Our work suggests noise texture differences from FOV changes between adult and pediatric protocols can contribute to poor generalizability in DL denoising and that the proposed framework is an effective means to identify these performance disparities for a given model.

A personal touch to demand response: An occupant-centric control strategy for HVAC systems using personalized comfort models

HAVC systems account for the majority of energy use in buildings. Therefore, research efforts have been made to develop control strategies to improve energy efficiency during the day and peak time. Studies have traditionally emphasized energy optimization while treating occupant experience using temperature constraints or standard generic metrics of comfort. A well-known strategy, in this category, is the use of a penalizing term when the temperature in an environment is deviated from a pre-defined setpoint. However, in reality, individual differences lead to diverse preferences and sensitivities to indoor thermal environments. Prior studies have not systematically evaluated the impact of such differences on user experience when using advanced control strategies for demand response. Accordingly, in this study, we have proposed a novel occupant-centric control framework (PICO: Personalization-Integrated Co-Optimization) that seeks to minimize energy cost (using dynamic pricing) with a penalizing term that is informed by probabilistic personalized comfort models of the occupants. We hypothesized that such integration results in increased efficiency (during the day and peak time) and improved user experience. Through a comprehensive uncertainty quantification analysis (to account for diversity in occupants' preferences, sensitivities, and number of occupants), we evaluated this framework by comparing it against three commonly used control strategies with varying levels of emphasis on user experience. Our analysis using numerous realizations of the framework operation for different combinations of simulated occupants showed that the proposed framework can adapt to different scenarios and improve the efficiency of operations. Summarizing the energy saving and user comfort experience metrics in an energy productivity measure (that quantifies the comfort gain per unit of energy use) we demonstrated that PICO increases peak time productivity up to 18.37% across various realizations. Moreover, the framework was demonstrated to be more consistent in providing an improved user experience reflected in a considerable reduction of standard deviations for thermal comfort experience, specifically for one occupant scenarios.

Time-Series InSAR with Deep-Learning-Based Topography-Dependent Atmospheric Delay Correction for Potential Landslide Detection

Synthetic aperture radar interferometry (InSAR) has emerged as an effective technique for monitoring potentially unstable landslides and has found widespread application. Nevertheless, in mountainous reservoir regions, the precision of time-series InSAR outcomes is often constrained by topography-dependent atmospheric delay (TDAD) effects. To address this limitation, we propose a novel InSAR time-series method that integrates TDAD correction. This approach employs advanced deep learning algorithms to individually model and mitigate TDAD for each interferogram, thereby enhancing the accuracy of small baseline subset InSAR (SBAS-InSAR) and stacking InSAR time-series analyses. Utilizing Sentinel-1 data, we apply this method to identify potential landslides in the Baihetan reservoir area, located in southwestern China, where we successfully identified 26 potential landslide sites. Comparative experimental results demonstrate a significant reduction (averaging 70% and reaching up to 90%) in phase standard deviation (StdDev) in the corrected interferograms, indicating a marked decrease in phase–topography correlation. Furthermore, the corrected time-series InSAR results effectively remove TDAD signals, leading to clearer displacement boundaries and a remarkable reduction in other spurious displacement signals. Overall, this method efficiently addresses TDAD in time-series InSAR, enabling precise identification of potentially unstable landslides influenced by TDAD, and providing essential technical support for early landslide hazard detection using time-series InSAR.

Complement C3 as a potential drug target in periodontitis: Evidence from the cis-Mendelian randomization approach.

Evidence from a Phase IIa trial showed that a complement C3-targeted drug reduced gingival inflammation in patients with gingivitis. Using drug-target Mendelian randomization (MR), we investigated whether genetically proxied C3 inhibition alters the risk of periodontitis. We used multiple 'cis' instruments from the vicinity of the encoding loci of C3. Instrument selection was restricted to the drug target encoding loci (chromosome 19; 6,677,715-6,730,573 (GRCh37/hg19)). We selected three uncorrelated single-nucleotide polymorphisms (rs141552034, rs145406915, rs11569479) that were associated with serum C3 levels (p value <1 × 10-4 ) from a genome-wide association study (GWAS) of 5368 European descent individuals. We extracted association statistics from a GWAS of 17,353 clinical periodontitis cases and 28,210 European controls. Wald ratios were combined using inverse-variance weighted meta-analysis to estimate the odds ratio (OR) of the genetically proxied inhibition of C3 in relation to periodontitis. MR analysis revealed that the inhibition of C3 reduces the odds of periodontitis (OR 0.91 per 1 standard deviation reduction in C3; 95% confidence interval 0.87-0.96, p value = .0003). Findings from our MR analysis suggest a potential protective effect of C3 blockade against periodontitis.

Blepharoptosis and cognitive performance: a population-based study of 1.4 million adolescents.

The purpose of this study is to examine the association between blepharoptosis and cognitive performance in late adolescence. This population-based, retrospective, cross-sectional study included 1,411,570 Israeli-born adolescents (620,107 women, 43.9%) aged 16-19 years who were medically examined before compulsory military service between 1993 and 2017. The diagnosis of blepharoptosis was verified by an ophthalmologist. Cognitive performance was assessed by a validated intelligence-quotient-equivalent test, comprising four domains (problem-solving, verbal abstraction and categorization, verbal comprehension, and mathematical abilities). Cognitive Z-scores were calculated and categorized as high (≥ 1 standard deviation (SD)), medium (- 1 to < 1 SD), and low (less than - 1 SD). Relationships were analyzed using regression models adjusted for sociodemographic variables including sex, year of birth, residential socioeconomic status, education level, body mass index, and familial country of origin. A total of 577 (41 per 100,000, 32.2% women) adolescents were diagnosed with blepharoptosis. The proportions of unilateral and bilateral visual impairment among adolescents with blepharoptosis were 13.0% and 3.5%, respectively. In a multivariable analysis, blepharoptosis was associated with a 0.18 SD reduction in cognitive Z-score (p < 0.001). The adjusted odds ratios for low and high cognitive Z-scores in adolescents with blepharoptosis were 1.54 (1.25-1.89) and 0.80 (0.62-1.04), respectively. This relationship persisted when adolescents with normal best-corrected visual acuity or unimpaired health status were analyzed separately. Conclusions: Blepharoptosis is associated with reduced cognitive performance determined in late adolescence. Future prospective studies should investigate the causes of this link and their underlying mechanisms. What is Known: •While earlier investigations have examined the effects of blepharoptosis on vision and quality of life, the association between blepharoptosis and cognitive outcomes in youth has remained unexplored. What is New: •This nationwide study involving 1.4 million Israeli adolescents found a correlation between blepharoptosis and reduced cognitive performance. •Our findings suggest a potential interplay between blepharoptosis and cognitive development in the pediatric population, calling for increased focus on the educational needs of affected individuals.

Analysis of the precision of determination of aircraft coordinates using EGNOS+SDCM solution

This paper presents an algorithm for determining the precision parameter for aircraft position coordinates based on a combined GPS/EGNOS and GPS/SDCM solution. The proposed algorithm uses a weighted average model that com-bines a single GPS/EGNOS and GPS/SDCM position navigation solution to determine the resulting aircraft coordi-nates. The weighted mean model include the linear coefficients as a function of: the inverse of the number of tracked GPS satellites for which EGNOS and SDCM corrections have been generated, and the inverse of the geometric coeffi-cient of the PDOP (Position Dilution of Precision). The corrections between the single GPS/EGNOS and GPS/SDCM solution to the aircraft's resultant coordinates are then calculated on this basis. Finally, the standard deviation for the aircraft resultant BLh (B-Latitude, L-Longitude, h- ellipsoidal height) coordinates is calculated as a measure of preci-sion. The research experiment used recorded on-board GPS+SBAS data from two GNSS receivers mounted on a Dia-mond DA 20-C1 aircraft. The test flight was carried out on the Olsztyn-Suwaki-Olsztyn route. The calculations of aircraft position based on GPS/EGNOS and GPS/SDCM solution were performed in the RTKLIB v.2.4.3 program in the RTKPOST module. Next, aircraft resultant coordinates and standard deviations were computed in Scilab v.6.0.0 soft-ware package. Based on the tests performed, it was found that for the Trimble Alloy receiver, the standard deviation values for the ellipsoidal coordinates BLh of the aircraft do not exceed 1.77 m. However, for the Septentrio AsterRx2i receiver, the values of standard deviations for the aircraft's ellipsoidal BLh coordinates do not exceed 5.04 m. The use of linear coefficients as the inverse of the number of tracked GPS satellites with SBAS corrections in the GPS/EGNOS+GPS/SDCM positioning model resulted in a reduction in standard deviations of approximately 50-51% relative to the solution with linear coefficients calculated as the inverse of the PDOP parameter. In paper, the standard deviation was also obtained using arithmetic mean model. However the values of standard deviation from weighted mean model are lower than arithmetic mean model.

A Novel Multi-Robot Task Assignment Scheme Based on a Multi-Angle K-Means Clustering Algorithm and a Two-Stage Load-Balancing Strategy

A practical task assignment is one of the core issues of a multi-robot system. In this paper, a multi-robot task assignment strategy based on load balancing is proposed to effectively balance and plan out the execution cost of each robot when it has a large number of working task points. Considering the variability of the execution task cost in practical situations with different task point categories, the multi-robot task assignment (MRTA) problem is transformed into a multiple traveling salesman problem (MTSP) using a multi-angle K-means clustering algorithm. To solve the problem of unbalanced cost consumed by each robot after clustering assignment, which leads to low efficiency of system completion, a two-stage load-balancing strategy is presented. The first stage of this strategy makes a large adjustment to the unbalanced task set, and the second stage achieves a small fine-tuning to the unbalanced task set. The experimental results show that the standard deviation of the cost ratio of each set decreases when four robots perform the task between 100 and 550 work points using the load-balancing strategy. The reduction in standard deviation is between 3.53% and 83.44%. The maximum cost of individual robots decreases between 0.18% and 14.27%. The proposed method can effectively solve the uneven execution cost of each robot in the task assignment process and improve the efficiency of the system in completing tasks.

Indoor air pollution and child development in Indonesia: Stunted by the smoke?

AbstractMotivationAir pollution in general, and indoor air pollution in particular, have been linked to severe, acute ill health. Less attention has been paid to long‐term outcomes such as stunting (height for age).PurposeDoes reduced air pollution inside the home resulting from burning cleaner fuels affect child development?Methods and approachWe compare stunting of persons aged under 20 years of age in households with and without clean stoves between 1997 and 2000. Data come from the Indonesian Family Life Survey, a repeat survey that includes over 30,000 respondents. We model relations by regression; correcting for endogeneity (wealth affects both health and choice of stove) and confounders by using instrumental variables.FindingsUsing a clean stove leads to a 0.28 standard deviation reduction in stunting. The result is significant and consistent across specifications; the estimate is double the size of estimates that do not address the endogeneity of wealth.Policy implicationsPolicy‐makers should encourage households to adopt clean stoves and to use clean fuels. Public intervention is especially indicated in rural areas, where many households prefer to use cheaper, but polluting, stoves and fuels.

Structure Optimization and Cooling Performance of a Heat Sink With Discontinuous Triangular Ribs Impacted by Hybrid Nanofluid Confined Slot Jet Impingement

Abstract Due to the increased use of high heat flux electronic equipment, improving the heat transfer capacity and surface temperature uniformity of heat sinks have become a major concern. In this study, a novel confined slot jet impingement heat sink with discontinuous triangular ribs (CSJIHS-TR) in the wall impingement zone is proposed. The upper cover plate is angled to improve the ability of the heat sink to dissipate heat. A Cu-Al2O3/water hybrid nanofluid is chosen as coolant. The main structural parameters of CSJIHS-TR are optimized using multi-objective. The optimized CSJIHS-TR is investigated using the Re and φ of the hybrid nanofluid. The results show that the optimized CSJIHS-TR exhibits an improved heat transfer capacity. The optimized CSJIHS-TR achieves a 27.8% improvement in performance evaluation criterion (PEC) and a 91.0% reduction in temperature standard deviation (Tstd) compared to the confined slot jet impingement flat-plate heat sink (CSJIFPHS). As Re of the jet impingement and φ of the hybrid nanofluid increase, the heat transfer capacity and temperature uniformity of the optimized CSJIHS-TR increase; however, the flow resistance of CSJIHS-TR also increases. In addition, compared with the mononanofluid, the temperature uniformity and the heat transfer capacity of the CSJIHS-TR with hybrid nanofluid are significantly improved.

Constrained one-step material decomposition reconstruction of head CT data from a silicon photon-counting prototype.

Spectral CT material decomposition provides quantitative information but is challenged by the instability of the inversion into basis materials. We have previously proposed the constrained One-Step Spectral CT Image Reconstruction (cOSSCIR) algorithm to stabilize the material decomposition inversion by directly estimating basis material images from spectral CT data. cOSSCIR was previously investigated on phantomdata. This study investigates the performance of cOSSCIR using head CT datasets acquired on a clinical photon-counting CT (PCCT) prototype. This is the first investigation of cOSSCIR for large-scale, anatomically complex, clinical PCCT data. The cOSSCIR decomposition is preceded by a spectrum estimation and nonlinear counts correction calibration step to address nonideal detectoreffects. Head CT data were acquired on an early prototype clinical PCCT system using an edge-on silicon detector with eight energy bins. Calibration data of a step wedge phantom were also acquired and used to train a spectral model to account for the source spectrum and detector spectral response, and also to train a nonlinear counts correction model to account for pulse pileup effects. The cOSSCIR algorithm optimized the bone and adipose basis images directly from the photon counts data, while placing a grouped total variation (TV) constraint on the basis images. For comparison, basis images were also reconstructed by a two-step projection-domain approach of Maximum Likelihood Estimation (MLE) for decomposing basis sinograms, followed by filtered backprojection (MLE + FBP) or a TV minimization algorithm (MLE + TVmin ) to reconstruct basis images. We hypothesize that the cOSSCIR approach will provide a more stable inversion into basis images compared to two-step approaches. To investigate this hypothesis, the noise standard deviation in bone and soft-tissue regions of interest (ROIs) in the reconstructed images were compared between cOSSCIR and the two-step methods for a range of regularization constraintsettings. cOSSCIR reduced the noise standard deviation in the basis images by a factor of two to six compared to that of MLE + TVmin , when both algorithms were constrained to produce images with the same TV. The cOSSCIR images demonstrated qualitatively improved spatial resolution and depiction of fine anatomical detail. The MLE + TVmin algorithm resulted in lower noise standard deviation than cOSSCIR for the virtual monoenergetic images (VMIs) at higher energy levels and constraint settings, while the cOSSCIR VMIs resulted in lower noise standard deviation at lower energy levels and overall higher qualitative spatial resolution. There were no statistically significant differences in the mean values within the bone region of images reconstructed by the studied algorithms. There were statistically significant differences in the mean values within the soft-tissue region of the reconstructed images, with cOSSCIR producing mean values closer to the expectedvalues. The cOSSCIR algorithm, combined with our previously proposed spectral model estimation and nonlinear counts correction method, successfully estimated bone and adipose basis images from high resolution, large-scale patient data from a clinical PCCT prototype. The cOSSCIR basis images were able to depict fine anatomical details with a factor of two to six reduction in noise standard deviation compared to that of the MLE + TVmin two-stepapproach.

Genetically mimicked effects of ASGR1 inhibitors on all-cause mortality and health outcomes: a drug-target Mendelian randomization study and a phenome-wide association study

BackgroundAsialoglycoprotein receptor 1 (ASGR1) is emerging as a potential drug target to reduce low-density lipoprotein (LDL)-cholesterol and coronary artery disease (CAD) risk. Here, we investigated genetically mimicked ASGR1 inhibitors on all-cause mortality and any possible adverse effects.MethodsWe conducted a drug-target Mendelian randomization study to assess genetically mimicked effects of ASGR1 inhibitors on all-cause mortality and 25 a priori outcomes relevant to lipid traits, CAD, and possible adverse effects, i.e. liver function, cholelithiasis, adiposity and type 2 diabetes. We also performed a phenome-wide association study of 1951 health-related phenotypes to identify any novel effects. Associations found were compared with those for currently used lipid modifiers, assessed using colocalization, and replicated where possible.ResultsGenetically mimicked ASGR1 inhibitors were associated with a longer lifespan (3.31 years per standard deviation reduction in LDL-cholesterol, 95% confidence interval 1.01 to 5.62). Genetically mimicked ASGR1 inhibitors were inversely associated with apolipoprotein B (apoB), triglycerides (TG) and CAD risk. Genetically mimicked ASGR1 inhibitors were positively associated with alkaline phosphatase, gamma glutamyltransferase, erythrocyte traits, insulin-like growth factor 1 (IGF-1) and C-reactive protein (CRP), but were inversely associated with albumin and calcium. Genetically mimicked ASGR1 inhibitors were not associated with cholelithiasis, adiposity or type 2 diabetes. Associations with apoB and TG were stronger for ASGR1 inhibitors compared with currently used lipid modifiers, and most non-lipid effects were specific to ASGR1 inhibitors. The probabilities for colocalization were > 0.80 for most of these associations, but were 0.42 for lifespan and 0.30 for CAD. These associations were replicated using alternative genetic instruments and other publicly available genetic summary statistics.ConclusionsGenetically mimicked ASGR1 inhibitors reduced all-cause mortality. Beyond lipid-lowering, genetically mimicked ASGR1 inhibitors increased liver enzymes, erythrocyte traits, IGF-1 and CRP, but decreased albumin and calcium.

967-P: Evaluation of CV and SD as Glucose Variability Metrics Based on Data from the GOLD and SILVER Trials

Objective: Coefficient of variation (CV) and standard deviation (SD) are key metrics of glucose variability (GV) in clinical care and research. We evaluated the adequacy of CV and SD as GV metrics over a wide range of mean blood glucose (MBG) levels in people with (T1D). Methods: Analyses were performed on data from the randomized clinical GOLD and SILVER trials (n=160). Mixed effects models were used to evaluate the SD in relation to MBG during stable therapy, accounting for subject-specific trends and correlations in repeated measures data. Results: The SD of blood glucose levels did not increase linearly with MBG level during CGM and blood glucose monitoring (BGM) treatments (both p&amp;lt;0.0001). The lack-of-fit of the constant CV model was most distinct at high glucose levels &amp;gt;12 mmol/L (Fig.). During BGM, a 33% reduction in MBG from 12 to 8 mmol/L was associated with a 20% (95% CI 14.9 to 24.9%) reduction in SD. The treatment effect on MBG-adjusted GV measured by CV was underestimated by 27%, compared to an adjusted analysis accounting for a quadratic relation between SD and MBG. Conclusion: CV is not an optimal GV measure since SD changes less than the MBG during stable glucose-lowering therapy. A quadratic model adjusting SD to MBG is instead suggested when evaluating glucose-lowering therapies in people with T1D. Fig. - Association between SD and MBG in the GOLD and SILVER trials during stable glucose-monitoring method CGM/SMBG. Disclosure P.J.Fatulla: None. H.Imberg: None. I.B.Hirsch: Consultant; Abbott Diabetes, Lifecare, Inc., Hagar, Research Support; Beta Bionics, Inc., Insulet Corporation, Dexcom, Inc. T.Heise: Advisory Panel; Novo Nordisk, Consultant; Gan &amp; Lee Pharmaceuticals, Research Support; Adocia, AstraZeneca, Biocon, Crinetics Pharmaceuticals, Inc., Eli Lilly and Company, Genova, Novo Nordisk, Sanofi, Zealand Pharma A/S, Speaker's Bureau; Eli Lilly and Company, Novo Nordisk. M.Lind: Consultant; AstraZeneca, Eli Lilly and Company, Novo Nordisk, Research Support; Eli Lilly and Company.

Performance evaluation of DFT based speckle reduction framework for synthetic aperture radar (SAR) images at different frequencies and image regions

Polarimetric Synthetic Aperture Radar (PolSAR) images are widely used for remote sensing and geoscience applications. However, the coherent processing of radar signals in PolSAR imaging leads to the presence of speckle noise, which can significantly degrade image quality and limit the accuracy of subsequent analyses. To address this issue, speckle reduction frameworks are often applied to PolSAR images to reduce the noise level and enhance image quality. In this paper, the performance of Discrete Fourier Transform (DFT) based speckle reduction framework is evaluated on different bands (L, C, and P band) against various evaluation metrics like CV, SD, SNR, ENL, SSI and SMPI. The proposed framework is evaluated by comparing filtered and unfiltered images across different parameters, such as mean, standard deviation, coefficient of variation, equivalent number of looks (ENL), variance, and signal-to-noise ratio (SNR) on all three bands for the diagonal elements (T11, T22, and T33) of T3 matrix. These metrics provide a comprehensive evaluation of the proposed framework’s ability to (i) smoothen homogeneous regions, (ii) preserve contours, and (iii) retain polarimetric information. The framework’s ability to reduce speckle noise and improve image quality is demonstrated through a significant reduction in standard deviation, coefficient of variation, and improvement in SNR and ENL values. The proposed framework successfully preserved polarimetric information while effectively suppressing speckle noise. These results suggest that the proposed framework could be a valuable tool for improving PolSAR image quality and enhancing subsequent processing of PolSAR data.