Tool For Estimation Research Articles

Seismic Performance Prediction of RC, BRB and SDOF Structures Using Deep Learning and the Intensity Measure INp

The motivation for using artificial neural networks in this study stems from their computational efficiency and ability to model complex, high-level abstractions. Deep learning models were utilized to predict the structural responses of reinforced concrete (RC) buildings subjected to earthquakes. For this aim, the dataset for training and evaluation was derived from complex computational dynamic analyses, which involved scaling real ground motion records at different intensity levels (spectral acceleration Sa(T1) and the recently proposed INp). The results, specifically the maximum interstory drifts, were characterized for the output neurons in terms of their corresponding statistical parameters: mean, median, and standard deviation; while two input variables (fundamental period and earthquake intensity) were used in the neural networks to represent buildings and seismic risk. To validate deep learning as a robust tool for seismic predesign and rapid estimation, a prediction model was developed to assess the seismic performance of a complex RC building with buckling restrained braces (RC-BRBs). Additionally, other deep learning models were explored to predict ductility and hysteretic energy in nonlinear single degree of freedom (SDOF) systems. The findings demonstrated that increasing the number of hidden layers generally reduces prediction error, although an excessive number can lead to overfitting.

Multifaceted natural resources and green energy transformation for sustainable industrial development

Climate change is the most phenomenal challenge to humanity, and its roots are intervened with unsustainable industrialization, exercising overexploitation of natural resources. Therefore, the departure from non-renewable to renewables has become inevitable, though thought-provoking. In this respect, we explore how green energy transformation moderates the impacts of multifaceted natural resources on sustainable industrial development in the presence of other covariates involving technological progress, financial development, and economic progress. We compiled data from Group of Seven (G-7) members over the 1995−2018 period and applied panel quantile regression (PQREG) to capture the effects across varying levels of quantiles of sustainable industrial development. Results revealed a positive role of natural gas rents, while coal, forest, and total natural resource rents contributed adverse implications for sustainable industrial development. However, the green energy transformation proved to be the game changer because it not only directly induced sustainable industrial development improvement but also turned the unfavorable effects of coal, forest, and total natural resources into favorable ones by interacting with those multifaceted natural resources. Technological, financial, and economic progress supported sustainable industrial development in G-7 nations, particularly in members with existing middle and upper scales of sustainable industrial development. These findings are robust enough when subjected to different estimation tools. In light of these outcomes, the interaction between green energy transformation and natural resource policy is inevitably critical to attaining natural resource efficiency for sustainable industrial development. Therefore, it is imperative to establish a close policy coordination between advancing green energy technology and allocating natural resource revenue to achieve sustainable development goals (SDGs), with a particular emphasis on SDG-7 and SDG-13.

Estimating optimal decision trees for treatment assignment: The case of K > 2 treatment alternatives.

For many problems in clinical practice, multiple treatment alternatives are available. Given data from a randomized controlled trial or an observational study, an important challenge is to estimate an optimal decision rule that specifies for each client the most effective treatment alternative, given his or her pattern of pretreatment characteristics. In the present paper we will look for such a rule within the insightful family of classification trees. Unfortunately, however, there is dearth of readily accessible software tools for optimal decision tree estimation in the case of more than two treatment alternatives. Moreover, this primary tree estimation problem is also cursed with two secondary problems: a structural missingness in typical studies on treatment evaluation (because every individual is assigned to a single treatment alternative only), and a major issue of replicability. In this paper we propose solutions for both the primary and the secondary problems at stake. We evaluate the proposed solution in a simulation study, and illustrate with an application on the search for an optimal tree-based treatment regime in a randomized controlled trial on K = 3 different types of aftercare for younger women with early-stage breast cancer. We conclude by arguing that the proposed solutions may have relevance for several other classification problems inside and outside the domain of optimal treatment assignment.

Radiology outpatients’ awareness of their out-of-pocket estimates and hospital price estimator tools in the United States

ObjectivePrice transparency mandates by the Centers for Medicare and Medicaid Services (CMS) were implemented in 2021 for hospitals and 2023 for health plans. We assessed U.S. patients’ awareness of their estimated out-of-pocket cost (OOPC) and hospital price-estimator tool among outpatients undergoing advanced imaging. MethodsEnglish-, Spanish-, and Vietnamese-speaking adults receiving a CT, PET/CT, or MRI at a tertiary academic center between 11/2022 and 03/2023 completed a 15-minute survey. We assessed awareness of OOPC estimates prior to imaging and the hospital price-estimator tool, and factors associated with such awareness using multivariable regression analysis. Results423 patients were included (mean age: 57.1 ± 15.6; 57.4 % female; 55 % White; 21.8 % Hispanic; 18.9 % Asian). Only 10.7 % and 16.8 % were aware of their OOPC before receipt of imaging and center'shospital price-estimator tool, respectively. No patient used the hospital price estimator tool to obtain their OOPC estimate. Annual household income of $50,000 or more (OR: 30.25, 95 % CI: 1.78, 513.79), and having at least one comorbidity (OR: 4.59, 95 % CI: 1.42, 14.79) were associated with a higher probability of knowing OOPC prior to imaging. Patients who experienced financial hardship were less likely to be aware of their OOPC (OR: 0.29, CI: 0.10, 0.86). No significant factors were associated with awareness of hospital price estimator tool in full regression model. ConclusionOur study highlights limited OOPC and price estimator tool awareness, two years post-CMS mandate, with specific demographics more likely to know their OOPC. Our findings emphasize the urgency for targeted interventions to increase price awareness and enhance healthcare decision-making.

A Validated Estimate of Visceral Adipose Tissue Volume in Relation to Cancer Risk.

Despite the recognized role of visceral adipose tissue (VAT) in carcinogenesis, its independent association with cancer risk beyond traditional obesity measures remains unknown due to limited availability of imaging data. We developed an estimation equation for VAT volume (L) using Elastic Net Regression based on demographic and anthropometric data in a subcohort of participants in the UK Biobank (UKB; N = 23,148) with abdominal MRI scans. This equation was externally validated in 2,713 participants from the 2017-2018 National Health and Nutrition Examination Survey (NHANES) according to sex, age, and race groups. We then applied the equation to the overall UKB cohort of 461,665 participants to evaluate the prospective association between estimated VAT (eVAT) and cancer risk using Cox proportional hazards models. We also calculated the population attributable risk (PAR) of cancer associated with eVAT and BMI. eVAT showed a high correlation with measured VAT in internal and external validations (r = 0.81-0.86). During a median 12-year follow-up in the UKB, we documented 37,397 incident cancer cases; eVAT was significantly associated with elevated risk of obesity-related and individual cancers, independent of BMI and waist circumference. PAR for total cancer associated with high (quartiles 2-4 vs 1) eVAT (9.0-11.6%) was higher than high BMI (Q2-4 vs 1; 5.0-8.2%). eVAT showed robust performance in both UKB and NHANES and was associated with cancer risk independent of BMI and waist circumference. This study provides a potential clinical tool for VAT estimation and underscores that VAT can be an important target for cancer prevention.

Estimating wheat evapotranspiration through remote sensing utilizing GeeSEBAL and comparing with lysimetric data

Accurate estimation of ET is vital for water resource management. In recent decades, researchers have focused on utilizing satellite imagery for this purpose. The use of RS data has enabled the development of new models that provide detailed spatial assessments. GeeSEBAL, an automated ET estimation tool, employs the SEBAL algorithm via GEE. The current version of GeeSEBAL utilizes Landsat images and ERA5 global reanalysis data to produce time series estimates. Landsat 8 images were processed into a 16-day time series spanning 2013–2022, specifically during the wheat growing season. To validate the GeeSEBAL model for 2013–2014, results were compared against lysimeter data. Subsequently, ET was calculated for the years 2015–2022. The evaluation of GeeSEBAL against lysimetric data, by metrics such as R2, RMSE, MAE, NSE, and NRMSE, yielded values of 0.94, 0.98, 0.07, 0.86, and 0.62, respectively. Those findings underscore the importance of GeeSEBAL for estimating wheat ET in regions with limited data availability.

System identification and force estimation of robotic manipulator using semirecursive multibody formulation

AbstractForce estimation in multibody dynamics relies heavily on knowing the system model with a high level of accuracy. However, in complex mechatronic systems, such as robots or mobile machinery, the values of model parameters may be only roughly estimated based on design information, such as CAD data. The errors in model parameters consequently have a direct effect on force estimation accuracy because the estimator compensates the erroneous inertia, friction, and applied forces by changing the value of estimated external force. The objective of this study is to present the workflow of system identification and state/force estimation of an open-loop multibody structure. The system identification utilizes a linear regression identification method used in robotics adapted to the multibody framework. The semirecursive multibody formulation, in particular, is studied as a formulation for both system identification and force estimation. The multibody state/force estimator is constructed using extended Kalman filter. The specific aim of this paper is to demonstrate the utilization of these per se known modeling, identification, and estimation tools to address their current lack of integration as a complete toolchain in virtual sensing of multibody systems. The methodology of the study is tested with both artificial and experimental data of Stäubli TX40 robotic manipulator. In the experimental analysis, an openly available benchmark data set was used. Artificial data were created by running an inverse dynamics analysis with inertia and friction parameters taken from literature. The results show that the multibody inertia and friction parameters can be accurately identified and the identified model can be used to produce decent estimates of external forces. The proposed multibody system identification method itself opens new opportunities in tuning the multibody models used in product development. Moreover, effective use of system identification together with state estimation helps to build more accurate estimators. When the system model is accurately identified, the capability of state estimator to observe unknown inputs, such as external forces, is significantly enhanced.

Wearable high-density EMG sleeve for complex hand gesture classification and continuous joint angle estimation

High-density electromyography (HD-EMG) can provide a natural interface to enhance human–computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with 97.3±0.3%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$97.3 \\pm 0.3\\%$$\\end{document} accuracy calculated on a 100 ms bin-by-bin basis. In a separate mixed dataset consisting of 19 movements randomly interspersed, decoding performance achieved an average bin-wise accuracy of 92.8±0.8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$92.8 \\pm 0.8\\%$$\\end{document}. When evaluating decoders for use in real-time scenarios, we found that decoders can reliably decode both movements and movement transitions, achieving an average accuracy of 93.3±0.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$93.3 \\pm 0.9\\%$$\\end{document} on the sequential set and 88.5±0.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$88.5 \\pm 0.9\\%$$\\end{document} on the mixed set. Furthermore, we estimated continuous joint angles from the EMG sleeve data, achieving a R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} of 0.884±0.003\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.884 \\pm 0.003$$\\end{document} in the sequential set and 0.750±0.008\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.750 \\pm 0.008$$\\end{document} in the mixed set. Median absolute error (MAE) was kept below 10° across all joints, with a grand average MAE of 1.8±0.04∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.8 \\pm 0.04^\\circ$$\\end{document} and 3.4±0.07∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.4 \\pm 0.07^\\circ$$\\end{document} for the sequential and mixed datasets, respectively. We also assessed two algorithm modifications to address specific challenges for EMG-driven HCI applications. To minimize decoder latency, we used a method that accounts for reaction time by dynamically shifting cue labels in time. To reduce training requirements, we show that pretraining models with historical data provided an increase in decoding performance compared with models that were not pretrained when reducing the in-session training data to only one attempt of each movement. The HD-EMG sleeve, combined with sophisticated machine learning algorithms, can be a powerful tool for hand gesture recognition and joint angle estimation. This technology holds significant promise for applications in HCI, such as prosthetics, assistive technology, rehabilitation, and human–robot collaboration.

Predicting ERCP procedure time - the SWedish Estimation of ERCP Time (SWEET) tool.

The duration of an endoscopic retrograde cholangiopancreatography (ERCP) is influenced by a multitude of factors. The aim of this study was to describe the factors influencing ERCP time and to create a tool for preintervention estimation of ERCP time. Data from 74 248 ERCPs performed from 2010 to 2019 were extracted from the Swedish National Quality Registry (GallRiks) to identify variables predictive for ERCP time using linear regression analyses and root mean squared error (RMSE) as a loss function. Ten variables were combined to create an estimation tool for ERCP duration. The tool was externally validated using 9472 ERCPs from 2020 to 2021. Mean (SD) ERCP time was 36.8 (25.3) minutes. Indications with the strongest influence on ERCP time were primary sclerosing cholangitis and chronic pancreatitis. Hilar and intrahepatic biliary strictures and interventions on the pancreatic duct were the anatomic features that most strongly affected ERCP time. The procedure steps with most influence were intraductal endoscopy, lithotripsy, dilation, and papillectomy. Based on these results, we built and validated the SW: edish E: stimation of E: RCP T: ime (SWEET) tool, which is based on a 10-factor scoring system (e.g. 5 minutes for bile duct cannulation and 15 minutes for pancreatic duct cannulation) and predicted ERCP time with an average difference between actual and predicted duration of 17.5 minutes during external validation. Based on new insights into the factors affecting ERCP time, we created the SWEET tool, the first specific tool for preintervention estimation of ERCP time, which is easy-to-apply in everyday clinical practice, to guide efficient ERCP scheduling.

Using the Family Planning Estimation Tool (FPET) to assess national-level family planning trends and future projections for contraceptive prevalence and associated demand for HIV-infected women in sub-Saharan Africa.

The combination of low uptake of modern contraceptives, high rates of unintended pregnancies, and the pervasive HIV epidemic in Sub-Saharan Africa (SSA) poses a threat to maternal, newborn, and child health in the region. This study examined the prevalence, need, and demand satisfied by modern contraceptive methods for women who tested positive for HIV (both unmarried and married) in 10 countries in SSA. We used the Family Planning Estimation Tool (FPET) to generate national-level trends and projections from 1983 through 2030. Individual-level data from 30 DHS surveys conducted between 2004 and 2018 in 10 sub-Saharan Africa (SSA) countries were used to produce projections for 1) all women and 2) unmarried and married women who tested positive for HIV. Throughout the period, Ethiopian and Guinean women who tested positive for HIV had a higher %mCPR (utilization of modern family planning methods) vis-à-vis all women. Among women who tested positive for HIV, the highest percentage of family planning demand satisfied by modern methods was observed in Zimbabwe (85.27, CI: 76.32-91.69), Lesotho (82.75, CI: 71.80-89.86), Rwanda (80.17, CI: 70.01-87.62), Malawi (73.11, CI: 61.50-82.63), and Zambia (72.63, CI: 64.49-80.09). The highest unmet need for modern contraceptives was found in Senegal (25.38, CI:18.36-33.72), followed by Cameroon (23.59, CI:19.30-28.59) and Sierra Leone (23.16, CI:16.64-32.05). Zimbabwe had the lowest unmet need (10.61, CI:6.36-16.13) and achieved the highest change in %mCPR (49.28, SE:6.80). Among married women who tested positive for HIV, their unmet need for modern contraception will remain higher in 2030. Continuing existing policies until 2030 would result in significant coverage gain among married vis-à-vis unmarried women who tested positive for HIV. Our projections emphasize the importance of country-specific strengthening initiatives, programs, and services targeting unmarried women.

Assessing CNN Architectures for Estimating Correct Posture in Cruise Machinists

Cruise machinists operate in dynamic and physically demanding environments where improper posture can lead to musculoskeletal disorders, adversely affecting their health and work efficiency. Current ergonomic assessments in such settings are often generic and not tailored to the unique challenges of maritime operations. This paper presents a novel application of artificial intelligence tools for real-time posture estimation specifically designed for cruise machinists. The primary aim is to enhance occupational health and safety by providing precise, real-time feedback on ergonomic practices. We developed a dataset by capturing video recordings of cruise machinists at work, which were processed to extract skeletal outlines using advanced computer vision techniques. This dataset was used to train deep neural networks, optimizing them for accuracy in diverse and constrained shipboard environments. The networks were tested across various computational platforms to ensure robustness and adaptability. The AI model demonstrated high efficacy in recognizing both correct and incorrect postures under real-world conditions aboard ships. The system significantly outperformed traditional ergonomic assessment tools in terms of speed, accuracy, and the ability to provide instant feedback. The findings suggest that AI-enhanced ergonomic assessments could be a transformative approach for occupational health across various industries.

Stable water isotopic composition and evaporation to inflow ratios for high-mountain tropical Ecuadorian lakes

ABSTRACT In this study, we explore the utility of stable isotopes of water (δ2H and δ18O) as integrative proxies for evaporative condition in high-mountain tropical lakes using a set of small Ecuadorian lakes in the Cajas National Park. Specifically, we (1) characterize the variability of stable water isotopic composition across several lakes of varying size and depth and (2) determine the evaporation as a fraction of inflow using stable isotopes of water for these lakes. We have used the Hydrocalculator tool for estimation of the evaporation to inflow (E/I) ratio. Results indicated that larger lakes have lower seasonal variability in isotopic composition across their depth compared with the smaller lakes. Further, there was clear seasonal variation across the monthly E/I ratios estimated in several lakes, indicating that evaporation likely plays some (albeit limited) role in the water level variability experienced within a year.

A Gate-Level Power Estimation Approach with a Comprehensive Definition of Thresholds for Classification and Filtering of Inertial Glitch Pulses

Estimation of power consumption in digital circuits is performed at gate-level simulation. Its accuracy depends on the models of gate delays that capture the effects of spurious signal transitions, called “glitches”. Electronic Design Automation (EDA) software considers inertial gate delays and represses a glitch in the cell’s output if its width is below a threshold. Selecting threshold values for the inertial glitch classification and filtering is crucial for precise power estimations. In this direction, we explore the effectiveness of automatically adjusting such thresholds on a cell-specific basis according to the local cell’s information. We used a commercial industry-standard gate-level power estimation tool and a 32 nm CMOS standard cell library. Via power measurements in circuit simulations, we created customized lookup tables for each library cell employed in the benchmark circuits. We compared the proposed approach’s performance with other methods for glitch threshold definition. Our method demonstrated good power estimation accuracy while presenting the lowest mean absolute error among all the cells of the circuits under test and the smallest standard deviation. The latter suggests that the proposed method achieves better cell-specific accuracy, which is expected to allow for more precise circuit-level power estimations in complex circuits with a large number of combinational cells.

Estimating the vulnerability of industrial network infrastructure in Central and Eastern Europe

Industrial infrastructure has suffered an unprecedented number of attacks in Central and Eastern Europe (CEE). This situation can be attributed to many geopolitical factors, including hybrid military conflicts and criminal activity. Industrial networks belonging to the countries that were once under Soviet influence suffer from an elevated risk of cyberattacks. The goal of this work is to propose an easy way to estimate the vulnerabilities of industrial networks to cyber threats on a national level. Since analysis of the industrial vulnerability landscape is difficult, this study proposes an assessment based on the popularity of vulnerable technologies—VPc. This metric is composed of search volume data on keywords related to industrial network technologies and reported security vulnerabilities associated with these words. Data on 116 keywords was analysed and a country-specific VPc index was calculated for twenty states in CEE. The analysis of the popularity of industrial technologies and vendors in CEE reveals interesting information about the industrial security and vulnerability landscape. The results show that some countries (e.g. Estonia) have more resilient industrial infrastructure than others (e.g. Belarus). The results presented in this study are not in conflict with other data and estimation attempts, including the National Cyber Security Index (NCSI). As new vulnerabilities are noted every day, the industrial security landscape changes rapidly. Therefore, a new easy-to-use metric (VPc) can be successfully used for general estimations. This work shows that the VPc score agrees with other estimates and analyses, but as with any other general estimation tool, it must be used with caution.

Analyzing the effect of hydrodynamic damping on the wake behavior in circular blade cascades: numerical study on a global mode

Abstract In this study, a numerical investigation has been performed to extract and compare the hydrodynamic damping of a non-rotating system of eight planar hydrofoils, arranged in a radial configuration, with the related wake behaviour downstream the structure. A modal acoustic analysis has been performed on the structure in order to extract the global modes corresponding to Nodal Diameter 0. Using the deformation related to the global mode, the hydrodynamic damping has been extracted for different velocities, ranging from 2 to 14 m/s, by analyzing the work done on the structure from the fluid, following the so-called “flutter” method developed by ANSYS®. The related wake behaviour has been analyzed for each specific velocity and hydrodynamic damping conditions. This numerical study has been performed in ANSYS Mechanical® and CFX®. The results for the present geometry suggest that the primary factor governing vortex shedding frequency is the motion of the body itself, in the present case locked to the excitation frequency of the global structural mode (ND0). The study identifies a lock-in region around 10 m/s, consistent with empirical formulas. After the region of lock-in, the fluctuations of velocity and pressure values tend to converge, indicating a stabilization of the wake dynamics. The observed hydrodynamic damping trend exhibits a linear pattern preceding the lock-in region, succeeded by a steep rise thereafter. This pattern aligns with established literature, affirming its consistency with prior research findings. Overall, this numerical approach offers valuable insights into wake behavior and hydrodynamic damping value serving as a promising tool for preliminary estimation and analysis of oscillating systems in water flowing environments. Its low computational complexity and rapid completion time make it a viable alternative to more complex FSI simulations, facilitating comprehensive exploration of multiple flow scenarios within a reasonable time frame of weeks.

Development and validation of a machine learning–based, point-of-care risk calculator for post-ERCP pancreatitis and prophylaxis selection

A robust model of post-ERCP pancreatitis (PEP) risk is not currently available. We aimed to develop a machine learning-based tool for PEP risk prediction to aid in clinical decision making related to periprocedural prophylaxis selection and postprocedural monitoring. Feature selection, model training, and validation were performed using patient-level data from 12 randomized controlled trials. A gradient-boosted machine (GBM) model was trained to estimate PEP risk, and the performance of the resulting model was evaluated using the area under the receiver operating curve (AUC) with 5-fold cross-validation. A web-based clinical decision-making tool was created, and a prospective pilot study was performed using data from ERCPs performed at the Johns Hopkins Hospital over a 1-month period. A total of 7389 patients were included in the GBM with an 8.6% rate of PEP. The model was trained on 20 PEP risk factors and 5 prophylactic interventions (rectal nonsteroidal anti-inflammatory drugs [NSAIDs], aggressive hydration, combined rectal NSAIDs and aggressive hydration, pancreatic duct stenting, and combined rectal NSAIDs and pancreatic duct stenting). The resulting GBM model had an AUC of 0.70 (65% specificity, 65% sensitivity, 95% negative predictive value, and 15% positive predictive value). A total of 135 patients were included in the prospective pilot study, resulting in an AUC of 0.74. This study demonstrates the feasibility and utility of a novel machine learning-based PEP risk estimation tool with high negative predictive value to aid in prophylaxis selection and identify patients at low risk who may not require extended postprocedure monitoring.

Soil Organic Carbon Prediction Based on Vis-NIR Spectral Classification Data Using GWPCA-FCM Algorithm.

Soil visible and near-infrared reflectance spectroscopy is an effective tool for the rapid estimation of soil organic carbon (SOC). The development of spectroscopic technology has increased the application of spectral libraries for SOC research. However, the direct application of spectral libraries for SOC prediction remains challenging due to the high variability in soil types and soil-forming factors. This study aims to address this challenge by improving SOC prediction accuracy through spectral classification. We utilized the European Land Use and Cover Area frame Survey (LUCAS) large-scale spectral library and employed a geographically weighted principal component analysis (GWPCA) combined with a fuzzy c-means (FCM) clustering algorithm to classify the spectra. Subsequently, we used partial least squares regression (PLSR) and the Cubist model for SOC prediction. Additionally, we classified the soil data by land cover types and compared the classification prediction results with those obtained from spectral classification. The results showed that (1) the GWPCA-FCM-Cubist model yielded the best predictions, with an average accuracy of R2 = 0.83 and RPIQ = 2.95, representing improvements of 10.33% and 18.00% in R2 and RPIQ, respectively, compared to unclassified full sample modeling. (2) The accuracy of spectral classification modeling based on GWPCA-FCM was significantly superior to that of land cover type classification modeling. Specifically, there was a 7.64% and 14.22% improvement in R2 and RPIQ, respectively, under PLSR, and a 13.36% and 29.10% improvement in R2 and RPIQ, respectively, under Cubist. (3) Overall, the prediction accuracy of Cubist models was better than that of PLSR models. These findings indicate that the application of GWPCA and FCM clustering in conjunction with the Cubist modeling technique can significantly enhance the prediction accuracy of SOC from large-scale spectral libraries.

SSN2: The next generation of spatial stream network modeling in R.

The SSN2 R package provides tools for spatial statistical modeling, parameter estimation, and prediction on stream (river) networks. SSN2 is the successor to the SSN R package (Ver Hoef, Peterson, Clifford, & Shah, 2014), which was archived alongside broader changes in the R-spatial ecosystem (Nowosad, 2023) that included 1) the retirement of rgdal (Bivand, Keitt, & Rowlingson, 2021), rgeos (Bivand & Rundel, 2020), and maptools (Bivand & Lewin-Koh, 2021) and 2) the lack of active development of sp (Bivand, Pebesma, & Gómez-Rubio, 2013). SSN2 maintains compatibility with the input data file structures used by the SSN R package but leverages modern R-spatial tools like sf (Pebesma, 2018). SSN2 also provides many useful features that were not available in the SSN R package, including new modeling and helper functions, enhanced fitting algorithms, and simplified syntax consistent with other R generic functions.

Proposal of a novel cardiovascular risk prediction score in lupus nephritis.

Patients with systemic lupus erythematosus are prone to develop cardiovascular disease (CVD), and have increased morbidity and mortality. We conducted a retrospective analysis on lupus nephritis patients to assess the occurrence and predictors of major adverse cardiovascular events (MACE). Data were collected from patients who underwent kidney biopsy between 2005 and 2020. Statistical analysis was performed to unveil correlations. 91 patients were analyzed in this period, with a mean age of 37.3 ± 12.3 years and 86% being female. The mean follow-up time was 62 ± 48 months. 15.38% of the patients underwent at least one MACE. Two patients deceased of CVD. Increased age (35.81 ± 11.14 vs 45.5 ± 15.11 years, p=0.012) entailed a higher occurrence of MACEs. Neutrophil count (5.15 ± 2.83 vs 7.3 ± 2.99 Giga/L, p=0.001) was higher, whereas diastolic blood pressure (DBP) was lower (89.51 ± 10.96 vs 78.43 ± 6.9 mmHg, p<0.001) at the time of the biopsy in patients with MACE. Age, neutrophil count, and DBP proved to be independent predictors of MACEs. We propose a new model (CANDE - Cardiovascular risk based on Age, Neutrophil count, and Diastolic blood pressure Estimation score) calculated from these variables, which predicts the probability of MACE occurrence. This study underscores the importance of actively screening for cardiovascular risks in this vulnerable patient population. Age, neutrophil count, and diastolic blood pressure have been established as independent risk factors for MACE in lupus nephritis. The CANDE score derived from these parameters may serve as a prompt, cost-effective, and easily accessible estimation tool for assessing the likelihood of major adverse cardiovascular risk. These findings emphasize the necessity for comprehensive management strategies addressing both immune dysregulation and cardiovascular risk factors in systemic lupus erythematosus to mitigate adverse outcomes.

Ornaments for efficient allele-specific expression estimation with bias correction

Allele-specific expression plays a crucial role in unraveling various biological mechanisms, including genomic imprinting and gene expression controlled by cis-regulatory variants. However, existing methods for quantification from RNA-sequencing (RNA-seq) reads do not adequately and efficiently remove various allele-specific read mapping biases, such as reference bias arising from reads containing the alternative allele that do not map to the reference transcriptome or ambiguous mapping bias caused by reads containing the reference allele that map differently from reads containing the alternative allele. We present Ornaments, a computational tool for rapid and accurate estimation of allele-specific transcript expression at unphased heterozygous loci from RNA-seq reads while correcting for allele-specific read mapping biases. Ornaments removes reference bias by mapping reads to a personalized transcriptome and ambiguous mapping bias by probabilistically assigning reads to multiple transcripts and variant loci they map to. Ornaments is a lightweight extension of kallisto, a popular tool for fast RNA-seq quantification, that improves the efficiency and accuracy of WASP, a popular tool for bias correction in allele-specific read mapping. In experiments with simulated and human lymphoblastoid cell-line RNA-seq reads with the genomes of the 1000 Genomes Project, we demonstrate that Ornaments improves the accuracy of WASP and kallisto, is nearly as efficient as kallisto, and is an order of magnitude faster than WASP per sample, with the additional cost of constructing a personalized index for multiple samples. Additionally, we show that Ornaments finds imprinted transcripts with higher sensitivity than WASP, which detects imprinted signals only at gene level.