Prediction Equations Research Articles

Bayesian maximum entropy interpolation analysis for rapid assessment of seismic intensity using station and ground motion prediction equations

In this paper, we explored the combination of seismic station data and ground motion prediction equations (GMPE) to predict seismic intensity results by using Bayesian Maximum Entropy (BME) method. The results indicate that: 1) In earthquake analysis in Japan, soft data has predicted higher values of intensity in disaster areas. BME corrected this phenomenon, especially near the epicenter. Meanwhile, for earthquakes in the United States, BME corrected the erroneous prediction of rupture direction using soft data. 2) Compared with other spatial interpolation methods, the profile results of Japan earthquake and Turkey earthquake show that BME is more consistent with ShakeMap results than IDW and Kriging. Moreover, IDW has a low intensity anomaly zone. 3) The BME method overcomes the phenomenon that the strength evaluation results do not match the actual failure situation when the moment magnitude is small. It more accurately delineates the scope of the disaster area and enriches the post-earthquake processing of disaster area information and data. BME has a wide range of applicability, and it can still be effectively used for interpolation analysis when there is only soft data or few sites with data available.

Crop Phenology Estimation in Rice Fields Using Sentinel-1 GRD SAR Data and Machine Learning-Aided Particle Filtering Approach

Abstract. Monitoring crop phenology is essential for managing field disasters, protecting the environment, and making decisions about agricultural productivity. Because of its high timeliness, high resolution, great penetration, and sensitivity to specific structural elements, synthetic aperture radar (SAR) is a valuable technique for crop phenology estimation. Particle filtering (PF) belongs to the family of dynamical approach and has the ability to predict crop phenology with SAR data in real time. The observation equation is a key factor affecting the accuracy of particle filtering estimation and depends on fitting. Compared to the common polynomial fitting (POLY), machine learning methods can automatically learn features and handle complex data structures, offering greater flexibility and generalization capabilities. Therefore, incorporating two ensemble learning algorithms consisting of support vector machine regression (SVR), random forest regression (RFR), respectively, we proposed two machine learning-aided particle filtering approaches (PF-SVR, PF-RFR) to estimate crop phenology. One year of time-series Sentinel-1 GRD SAR data in 2017 covering rice fields in Sevilla region in Spain was used for establishing the observation and prediction equations, and the other year of data in 2018 was used for validating the prediction accuracy of PF methods. Four polarization features (VV, VH, VH/VV and Radar Vegetation Index (RVI)) were exploited as the observations in modeling. Experimental results reveals that the machine learning-aided methods are superior than the PF-POLY method. The PF-SVR exhibited better performance than the PF-RFR and PF-POLY methods. The optimal outcome from PF-SVR yielded a root-mean-square error (RMSE) of 7.79, compared to 7.94 for PF-RFR and 9.1 for PF-POLY. Moreover, the results suggest that the RVI is generally more sensitive than other features to crop phenology and the performance of polarization features presented consistent among all methods, i.e., RVI＞VV＞VH＞VH/VV. Our findings offer valuable references for real-time crop phenology monitoring with SAR data.

Evaluation of Rotation Capacity and Bauschinger Effect Coefficient of I-Shaped Beams Considering Loading Protocol Influences

Recent catastrophic earthquake events have reinforced the necessity of evaluating the seismic performance of buildings. Notably, the buildings can go into the plastic phase during a striking earthquake disaster. Under this condition, the current design codes assume seismic response reduction by virtue of the energy dissipation capacity of the structural members. In the strong-column–weak-beam design, which involves I-shaped beams and boxed columns, the mechanism is defined as a standard design scheme to prevent the building from collapsing. Therefore, energy dissipation relies highly on the I-shaped beam performance. However, the I-shaped beam performance can differ depending on the loading history experienced, whereas this effect is untouched in the prevailing evaluation equation. Hence, this study first performs cyclic loading tests of 11 specimens using different loading protocols. The experimental results clarify the fluctuation in the structural performance of I-shaped beams depending on the applied loading hysteresis, proving the necessity of considering the stress history for proper assessment. Furthermore, the database of experimental results is constructed based on the previous experimental studies. Ultimately, the novel evaluation equation is proposed to reflect the influences of the loading protocol. This equation is demonstrated to effectively assess the member performance retrieved from the experiment of 65 specimens, comprising 11 specimens from this investigation and 54 specimens from the database. The width–thickness ratio, shear span-to-depth ratio, and loading protocols are utilized as the evaluation parameters. Moreover, the prediction equation of the Bauschinger effect coefficient is newly established to convert the energy dissipation capacity under monotonically applied force into hysteretic energy dissipation under the cyclic forces.

Encapsulation of W/O/W Acerola Emulsion by Spray Drying: Optimization, Release Kinetics, and Storage Stability

Acerola (Malpighia emarginata DC.) is a sub-tropical and tropical fruit renowned for its high levels of vitamin C and phenolic compounds, which offer health benefits. This study aimed to optimize the spray drying process by determining the inlet and outlet temperatures using response surface methodology (RSM) with the central composite design. Additionally, it aimed to evaluate the release kinetics in the hydrophilic food simulation environment and the stability of the resulting powder under various storage temperatures. The RSM method determined the optimal inlet and outlet temperatures as 157 °C and 91 °C, respectively. High-accuracy prediction equations (R2 ≥ 0.88) were developed for moisture content (3.02%), process yield (91.15%), and the encapsulation yield of total polyphenol content (61.44%), total flavonoid content (37.42%), and vitamin C (27.19%), with a predicted monolayer moisture content below 4.01%, according to the BET equation. The powder exhibited good dissolution characteristics in the acidic hydrophilic food simulation environment and showed greater stability when stored at 10 °C for 30 days, compared to storage at 35 °C and 45 °C.

UHPC modulus of elasticity: Assessment and new developments using companion materials and structural data

The determination of the concrete modulus of elasticity (MOE) plays a crucial role in the analysis and design of structural reinforced and prestressed concrete elements. Various empirical MOE prediction equations have been developed for UHPC. However, most of the existing models have been typically calibrated or developed based on data from one type of UHPC and material tests only. As such, no previous work looked at the MOE prediction equations validity at a large-scale structural level and for the different range of UHPC mixtures that span from economic or non-proprietary lower-end mixtures to robust carbon nanofiber (CNF)-enhanced and high-end mixtures. This research fills this knowledge gap by revisiting the topic of UHPC MOE prediction equations using exclusive companion datasets of materials and full-scale structural tests that also incorporate, for the first time, four different types of UHPC mixtures. In this study, MOE values are estimated from experimentally determined axial stiffness of 22 full-scale UHPC columns with varying cross-sections in conjunction with hundreds of cylinders. The study first compared cylinders and columns-based MOE values, then used the extensive experimental datasets to assess recent and relevant UHPC MOE prediction equations, including what has been proposed for the AASHTO UHPC Structural Design Guide. The results show that while some of the existing prediction models are accurate for traditional UHPC mixtures, all models consistently underestimate MOE for CNF-enhanced UHPC and overestimate it for economic UHPC mixtures with local sand and cement. The paper concludes with a recommendation to account for UHPC mixture type and proposes new modifications for adopting MOE prediction equations for all types of UHPC.

Personalized lung care: Bronchopulmonary dysplasia risk prediction tool tailored for neonates born in resource-limited settings.

Predicting bronchopulmonary dysplasia (BPD) to assess the risk-benefit of therapy is necessary considering the side effects of medications. We developed and validated an instrument for predicting BPD and compared it with an instrument currently used for neonates born in a Brazilian hospital. This was a retrospective cohort study of patients born between 2016 and 2020 with a gestational age (GA) between 23 and 30 weeks. Predictive equations were elaborated using methods of component variable selection collected on the 14th day of life; 70% of the sample was randomly selected for the construction of risk prediction equations and the remaining 30% for their validation, application, and comparison with the National Institute of Child Health and Human Development (NICHD) instrument. The sensitivity, specificity, and predictive values of the equations were calculated. The equation that used variables with p < 5% in Fisher's exact test presented the best results: specificity of 98% and positive predictive value of 93% and could be used for BPD prediction of all small-for-gestational-age (SGA) infants. The NICHD calculator applied to our population had a specificity of 93% and a positive predictive value of 75% and could not be applied to extremely SGA infants. Our tool can predict the risk of BPD on the 14th day of life, has higher specificity and positive predictive value to our population than the NICHD instrument, and can be suitable for SGA infants. The results must be confirmed by applying it to other populations to validate our tool.

On-Site Earthquake Early Warning Model for Selected Records in the NGA-West2 Dataset Using S- and P-Wave Spectral Ratios

ABSTRACT On-site earthquake early warning (EEW) requires the best estimates of earthquake magnitude and distance parameters within a few seconds after the P-wave arrival for estimating the subsequent S-wave parameters. The errors in the estimates of the earthquake P-wave parameters will propagate into the estimates of the S-wave parameters. To solve this problem, we used the methodology by Zhao and Zhao (2019), which uses the spectral ratio R3P between the response spectral values of the first 3 s of the S wave and that of the first 3 s of the P wave, referred to as the R3P model. The modeling presented here was based on strong-motion records from the Next Generation Attenuation (NGA)-West2 dataset. We also used the spectral ratio RFP between the response spectral values from the full records from the S-wave arrival time to the end of the record and that of the first 3 s P wave to develop a second EEW model (the RFP model). An advantage of these two models is that the magnitude and hypocentral distance are not required in considerable magnitude and distance ranges. This means that the errors in the estimated source and path parameters from the first 3 s of the P wave will not affect the model predictions. A theoretical justification for these results is that the magnitude and the distance scaling rates for the first 3 s of the P wave are similar to those of the first 3 s of the S wave. This may also apply to the full S-wave window within useful EEW magnitude and distance ranges. We also found that when the estimated magnitude and distance for a record are necessary, the effect of the corresponding errors would be smaller than using a ground-motion prediction equation (GMPE), because the magnitude and distance scaling rates from this study are smaller than those of many GMPEs.

Radiation Safety Assessment in Prostate Cancer Treatment: A Predictive Approach for I-125 Brachytherapy

This study uses Monte Carlo simulation and experimental measurements to develop a predictive model for estimating the external dose rate associated with permanent radioactive source implantation in prostate cancer patients. The objective is to estimate the accuracy of the patient’s external dose rate measurement. First, I-125 radioactive sources were implanted into Mylar window water phantoms to simulate the permanent implantation of these sources in patients. Water phantom experimental measurement was combined with Monte Carlo simulation to develop predictive equations, whose performance was verified against external clinical data. The model’s accuracy in predicting the external dose rate in patients with permanently implanted I-125 radioactive sources was high (R2 = 0.999). A comparative analysis of the experimental measurements and the Monte Carlo simulations revealed that the maximum discrepancy between the measured and calculated values for the water phantom was less than 5.00%. The model is practical for radiation safety assessments, enabling the evaluation of radiation exposure risks to individuals around patients with permanently implanted I-125 radioactive sources.

Cooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with SHAP approach

Since the cooling systems used in buildings in hot climates account for a significant portion of the energy consumption, it is very important for both economy and environment to accurately predict the cooling load and consider it in building designs. This study aimed to maximize energy efficiency by appropriately selecting the features of a building that affect its cooling load. To this end, data-driven, accurate, and accessible tools were developed that enable the prediction of the cooling load of a building by practitioners. The study involves simulating the energy consumption of a mid-rise, double-story terrace house in Malaysia using building information modeling (BIM) and estimating the cooling load using ensemble machine learning models and genetic programming. Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) models have been developed and made available as an online interactive graphical user interface on the Streamlit platform. Furthermore, the symbolic regression technique has been utilized to obtain a closed-form equation that predicts the cooling load. The dataset used for training the predictive models comprised 94,310 data points with 10 input variables and the cooling load as the output variable. Performance metrics such as the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were used to measure the predictive model performances. The results of the machine learning models indicated successful prediction, with the CatBoost model achieving the highest score (R2 = 0.9990) among the four ensemble models and the predictive equation. The SHAP analysis determined the aspect ratio of the building as the most impactful feature of the building.

PSI-21 Optimizing sample size for accurate measurements and prediction equations for intestinal morphology in broiler chickens

Abstract Histological measurements of intestinal morphology, including villi height (VH) and crypt depth (CD), are routinely used to evaluate intestinal health and the nutrient absorptive capacity in monogastric animals. These metrics suffer from significant natural variation, and there is no established standard for the number of unique measurements required to robustly estimate these variables for a given animal. Furthermore, the potential utility of additional variables, including villus width (VW), perimeter (VP), or villus area (VA), has not been explored. Therefore, the objective of the current experiment was to estimate the minimum number of measurements required to control the intrasample variance in each variable and determine if more complex or time-consuming metrics, such as VP and/or VA, can be accurately estimated from linear measurements. A set of 1283 intestinal morphology data, including VH, CD, VW, VP, and VA, measured from 68 intestinal tissues (46 ileum and 22 jejunum) of 4 experiments were used in the current study (Figure 1). Retrospective calculation of the CV for all combinations of N measurements per sample was used to determine the impact of measurement number on the quality of data. Data for the jejunum and ileum were pooled together, and the NLIN procedure of SAS was used to estimate the optimal measurement for intestinal morphology measurements using a quadratic broken line. The VA was estimated using geometric calculation with the assumption that VA equals 0.67 × base width × VH. A paired t-test was conducted to compare measured and calculated VA. The optimal number of measurements to minimize within-sample CV for VH, CD, VW, VP, and VA ranged from 10.6 to 10.8. The VH was highly correlated (P &amp;lt; 0.05) with VP (r = 0.95 and 0.87) and VA (r = 0.77 and 0.63) in the jejunum and ileum, respectively (Table 1 and 2). Additionally, there was a significant difference (P &amp;lt; 0.05) between measured (0.110 µm2) and calculated VA (0.086 µm2). In conclusion, this study shows that measuring 11 villi per sample is required for minimizing variations within the jejunal or ileal tissue in broiler chickens. Also, estimating VA based on idealized geometry was found to be inaccurate, necessitating either direct measurement or more complex prediction equations.

111 Current and future usage of soybean meal in swine diets in China

Abstract The objective of this presentation was to introduce the utilization tendency and research progress of soybean meal (SBM) in swine diets in China. As for the utilization tendency of SBM in China, it not only brings great pressure to the food security of China, but also directly pushes the high production cost of animal products. In 2022, 117 million tons of protein feed was consumed by the animal husbandry in China, accounting for nearly 1/3 of the total protein feed consumption in the world. Thus, the application of low-protein diet has been promoted with the strong support of the Ministry of Agriculture and Rural Affairs in China since 2018. With the related techniques, including popularizing low protein diet, developing new protein feed resources, promoting precise nutrition, and feeding technology, even though the compound feed output has increased by more than 84 million tons in 2022 in China, the SBM consumption in feed has reduced 6%. Recently, the geopolitical challenges, regional unrest and climate change tendency have increased the great uncertainty of protein feed supply, which requires us to deeply understand the significance of SBM reduction and substitution in animal feed. As for the research progress, the available energy contents of SBM were determined on growing pigs. Barrows [n = 24; initial body weight (BW) = 36 ± 1.4 kg] were randomly allotted to 4 diets containing SBM with either corn basal or corn-soybean meal basal, and total feces and urine collection method and indirect calorimetry method were used to determine the available energy of SBM. The results showed that the average digestible energy (DE), metabolizable energy (ME), net energy (NE), and retained energy (RE) values in SBM [17.36, 16.52, 10.62, and 5.06 MJ/kg dry matter (DM), respectively] determined using the corn-SBM-basal diet were not different from the those determined using the corn-basal diet. Moreover, the best fitted prediction equations developed based on 22 SBM samples produced from soybeans from China (n = 6), USA (n = 6), Brazil (n = 7) or Argentina (n = 3) were: DE = 38.44 - 0.43 CF - 0.98 GE + 0.11 ADF (R2 = 0.67, P &amp;lt; 0.01) and ME = 2.74 + 0.97 DE - 0.06 CP (R2 = 0.79, P &amp;lt; 0.01). The in vitro digestion kinetics of SBM from different sources showed that the release of amino acids in the 0-10 min time interval was significantly negatively correlated with the NE of SBM (R2 = -0.34).

106 Net energy of soybean meal

Abstract Our current net energy (NE) systems were developed based on work conducted around 40 yr ago. At that time, it was normal that pigs deposited no more than 40% of the protein they were fed. However, modern genetic lines are much leaner than older genetics and they deposit more protein than earlier. It is, therefore, not uncommon that protein deposition in modern genotypes exceeds 60% of dietary protein intake. This means that more of the amino acids in the protein-rich ingredients are used for protein deposition and less are being deaminated with a subsequent reduced energetic cost of deamination associated with feeding protein-rich ingredients. As a consequence, the theoretical energy value of protein rich ingredients such as soybean meal (SBM) is greater when fed to modern pigs than to older genotypes that were used when our current NE systems were developed. Indeed, in several recent experiments it was demonstrated that the ME and NE of SBM is greater than indicated in current feed ingredient tables. A greater NE than predicted has also been demonstrated for field peas, whereas corn does not seem to have increased energy value compared with previous estimates, which is consistent with the hypothesis that it is the increased protein efficiency of modern pigs that is the reason for the greater NE value in SBM. As a consequence of the greater NE value of SBM, less added fat need to be used to reach a certain NE level in the diet, which will reduce overall diet costs. However, because of the greater genetic capacity for protein deposition in modern pigs, there is a need to upgrade the NE system and to develop new equations to predict NE in diets and ingredients based on the protein deposition capacity of modern pigs. It can also be argued that because commercially fed pigs always are group housed and allowed ad libitum intake of feed, pigs fed to generate prediction equations for NE also need to be group housed and allowed ad libitum intake of feed because both animal behavior and level of feed intake may affect the NE value of diets. In conclusion, because of the increased ability of modern genotypes of pigs to retain dietary protein in the body, there is reduced deamination of amino acids and therefore reduced energy loss in modern pigs than in older genotypes, which results in greater NE of protein rich ingredients including SBM. There is, therefore, a need for developing a new NE system that preferably uses group-housed pigs that are allowed ad libitum access to feed.

Prediction of the carcass tissue composition of “Blackbelly” lambs using in vivo and postmortem measurements

Objective. Predict the tissue carcass composition of “Blackbelly” lambs using in vivo and postmortem measurements. Materials and methods. Twenty lambs with an average age and weight of seven months and 29.07±2.88 kg, respectively, were used. Before slaughter, the subcutaneous fat thickness, depth, width and Longissimus dorsi muscle area were measured with ultrasonography. After slaughter, the cold carcass and tissues: muscle, fat and bone weight, were recorded. In the carcass, the thoracic depth, length, perimeter, length and width of the leg, as well as the compactness index, were also measured and recorded. Correlation analysis and regression models were used to predict tissue carcass composition. Results. The carcass tissues were correlated with L. dorsi muscle depth (p≤0.05; r-values ranged from 0.67 to 0.80) and carcass compactness index (p≤0.05; r ranged from 0.54 to 0.75). The r2 for the prediction equations of the carcass tissue composition ranged from 0.71 to 0.78 for fat (p≤0.001). Conclusions. The use of in vivo and postmortem measurements allowed the prediction of tissue carcass composition of lambs, with moderate to high accuracy (r2 &gt;0.71≤ and ≤0.78). 

Whole-Body Sweat Rate Prediction: Outdoor Running and Cycling Exercise.

Our aim was to develop and validate separate whole-body sweat rate prediction equations for moderate to high intensity outdoor cycling and running, using simple measured or estimated activity and environmental inputs. Across two collection sites in Australia, 182 outdoor running trials, and 158 outdoor cycling trials were completed at a wet-bulb globe temperature ranging from ~15 to ~29˚C, with ~60-min whole-body sweat rates measured in each trial. Data were randomly separated into model development (running: 120; cycling: 100 trials) and validation groups (running: 62; cycling: 58 trials), enabling proprietary prediction models to be developed and then validated. Running and cycling models were also developed and tested when locally measured environmental conditions were substituted with participant subjective ratings for black globe temperature, wind speed, and humidity. The mean absolute error for predicted sweating rate was 0.03 and 0.02 L·h-1 for running and cycling models, respectively. The 95% confidence intervals for running (+0.44 and -0.38 L·h-1) and cycling (+0.45 and -0.42 L·h-1) were within acceptable limits for an equivalent change in total body mass over 3 h of ±2%. The individual variance in observed sweating described by the predictive models was 77% and 60% for running and cycling, respectively. Substituting measured environmental variables with subjective assessments of climatic characteristics reduced the variation in observed sweating described by the running model by up to ~25%, but only by ~2% for the cycling model. These prediction models are publicly accessible (https://sweatratecalculator.com) and can guide individualized hydration management in advance of outdoor running and cycling.

Abstract PO3-19-04: The CROWN STUDY: A multicenter prospective cohort study of cardiovascular outcomes in premenopausal women with hormone receptor positive breast cancer treated with ovarian suppression and an aromatase inhibitor

Abstract Background: Treatment for premenopausal women with high or intermediate risk hormone receptor (HR)+ breast cancer (BC) includes the concurrent use of ovarian function suppression (OFS) and an aromatase inhibitor (AI) to induce near complete estrogen deprivation (NCED). The long-term cardiovascular (CV) sequela for women treated with NCED is unknown. Premature menopause in non-cancer populations is associated with CV disease. The CROWN study (CaRdiac Outcomes With Near complete estrogen deprivation) will use sophisticated imaging assessments of cardiac dysfunction to understand the evolution of CV injury, as well as biomarker and demographic correlates, with the goal of developing tools to assess and mitigate CV risk. Methods: This is a NIH funded prospective cohort study conducted at 3 regional NCI-supported Cancer Centers (Atrium Health Wake Forest Baptist, Virginia Commonwealth and Duke) that will include premenopausal women, age ≤ 55, with Stage I-III BC following completion of planned chemotherapy, surgery and radiation with an ECOG 0-1. HR+ BC patients will receive an AI and OFS. Women with HR- BC are included as comparators. CV imaging and biomarkers will be obtained at baseline, 1 year and 2 years (Table 1). These assessments will include serial cardiac magnetic resonance (CMR) and coronary computed tomography angiography (CCTA) imaging as well as laboratory measurements, including exploratory biomarkers. The primary objective is to determine the 24-month difference in stress myocardial blood flow as measured by adenosine CMR imaging in both groups. We will correlate CMR imaging with CCTA to provide complementary detail of coronary plaque changes. The study will also assess the relevance of pre-existing risk factors on study outcomes, including an emphasis on racial disparities, and dynamic change in modifiable and treatment related risk factors. We plan to enroll 90 women, 67 in the NCED group and 23 in the HR-group, allowing for a 10% drop out rate. There are two primary types of statistical analyses. The first includes testing hypotheses between group (NCED vs HR-) and within group (longitudinal changes within the NCED group). The second analyses, involve developing predictive equations utilizing a stepwise linear regression approach to determine if patient demographics, clinical parameters and serum biomarkers are associated with cardiovascular changes over time. A total of 17 patients are enrolled as of 07/2023. NCT05309655 Study Procedures XX- XX Citation Format: Susan Dent, Emily Douglas, Sarah Hatcher, Katherine Ansley, Carolyn Park, Nathaniel O'Connell, Mary Hackney, Ralph D'Agostino, Rani Bansal, Renee Avecilla, Monica Brown, Kacey Clayton-Stiglbauer, Igor Klem, Jennifer Jordan, Alexandra Thomas. The CROWN STUDY: A multicenter prospective cohort study of cardiovascular outcomes in premenopausal women with hormone receptor positive breast cancer treated with ovarian suppression and an aromatase inhibitor [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-19-04.

Fat-free mass predictive equation using multifrequency bioelectrical impedance data in adolescent soccer athletes: development and cross-validation

Objectives: This study aimed to develop and cross-validate a fat-free mass (FFM) predictive equation using multifrequency bioelectrical impedance data (MF-BIA) in adolescent soccer athletes. Methods: Male adolescent soccer athletes (n=149; 13 to 19 years old) were randomly sorted using Excel and independently selected for development group (n=100) or cross-validation group (n=49). The FFM reference values were determined using dual-energy X-ray absorptiometry (DXA). Single-frequency BIA was used to plot tolerance ellipses. Multifrequency-BIA raw data were used as independent variables in regression models. Student's independent t-test was used to compare development and cross-validation groups. Stepwise multiple regression was used to develop the FFM predictive equation. Bland-Altman plots, Lin's concordance correlation coefficient (CCC), according to McBride criteria, precision, accuracy, and standard error of estimation (SEE) were calculated to evaluate the concordance and reliability of estimates. Bioelectrical impedance vector analysis (BIVA) was plotted to evaluate hydration status. Results: No differences (P>0.05) were observed between development and validation groups in chronological age, anthropometric data, bioelectrical impedance data, and FFM values obtained using DXA. BIVA tolerance showed that all participants presented adequate hydration status when compared with the reference population. The new FFM predictive equation developed and validated: FFM (kg)= 7.064+ 0.592 * Age (years)+ 0.554* Weight(kg) + 0.365 * Height²/Resistance (cm²/Ω), presented R²= 0.95; SEE= 1.76 kg; CCC= 0.95, accuracy= 0.98, and strength of concordance= 0.99. Conclusions: The present study developed and cross-validated an FFM predictive equation based on multifrequency bioelectrical data providing substantial FFM accuracy for male adolescent soccer athletes.

Predictive Factors of Mortality in Pediatric Pleuropulmonary Blastoma: A Surveillance Epidemiology and End Results Database Analysis

ABSTRACT Purpose: Pleuropulmonary blastoma (PPB) is a rare malignancy associated with mutations in the DICER1 gene. Early-stage disease (PPB type I) mimics cystic lung malformations and develops in infants &lt;1 year of age, and PPB type II and III arises in older children. The objective of this study was to analyze predictive factors of mortality in pediatric patients aged 0–19 years diagnosed with PPB between 2000 and 2019 in the USA. Methods: A retrospective analysis of pediatric patients (0–19 years) in the Surveillance Epidemiology and End Results database was conducted from 2000 to 2019 with a diagnosis of PPB using International Classification of Disease for Oncology, third edition code 8973/3 and rare tumor code 45. Demographics, incidence, staging, treatment, and mortality were extracted. A mortality risk predictive equation was developed using logistic regression. Statistical analysis was conducted through Microsoft Excel Analysis ToolPak and Solver. Results: There were a total of 71 new cases of PPB during the study period, with 16 (22%) deaths. The demographic analysis demonstrated that 40/71 (56.3%) patients were female, 57/71 (80.3%) were White, and 64/71 (90.1%) resided in metropolitan areas. Regression analysis demonstrated a statistically significant correlation between mortality and stage (P = 0.029), need for chemotherapy (P = 0.047), and female sex (P = 0.019). There was no significant correlation between mortality and need for radiation, race, or age at diagnosis. Multiple logistic regression analysis generated a predictive equation of mortality dependent on the stage of PPB, need for chemotherapy, and sex. This equation has an 82% accuracy, 81% sensitivity, and an 18% false positive rate. Conclusion: PPB is a rare disease. Distinguishing PPB from benign cystic lung malformations in infancy is important to avoid progression to Type II and III PPB. Advanced stages of PPB have a greater need for systemic chemotherapy and radiation with a poor prognosis.

Solute flow and particle transport in aquatic ecosystems: A review on the effect of emergent and rigid vegetation

In-channel vegetation is ubiquitous in aquatic environments and plays a critical role in the fate and transport of solutes and particles in aquatic ecosystems. Recent studies have advanced our understanding of the role of vegetation in solute flow and particle transport in aquatic ecosystems. This review summarizes these papers and discusses the impacts of emergent and rigid vegetation on the surface flow, the advection and dispersion of solutes, suspended load transport, bedload transport, and hyporheic exchange. The two competing effects of emergent vegetation on the above transport processes are discussed. On the one hand, emergent vegetation reduces mean flow velocity at the same surface slope, which reduces mass transport. On the other hand, at the same mean flow velocity, vegetation generates turbulence, which enhances mass transport. Mechanistic understanding of these two competing effects and predictive equations derived from laboratory experiments are discussed. Predictive equations for the mean flow velocity and turbulent kinetic energy inside an emergent vegetation canopy are derived based on force and energy balance. The impacts of emergent vegetation on the advection-dispersion process, the suspended load and bedload transport, and the hyporheic exchange are summarized. The impacts of other vegetation-related factors, such as vegetation morphology, submergence, and flexibility, are briefly discussed. The role of vegetation in transporting other particles, such as micro- and macro-plastics, is also briefly discussed. Finally, suggestions for future research directions are proposed to advance the understanding of the dynamic interplays among natural vegetation, flow dynamics, and sedimentary processes.

Rapid determination of source parameters of the M6.2 Jishishan earthquake in Gansu Province and its application in emergency response

In this study, we swiftly determined the focal parameters (focal mechanism, seismic imaging process, magnitude) of the Jishishan earthquake, leveraging a solved fault model to promptly assess the intensity field and casualties. The investigation began by retrieving the source mechanism through the P-wave initial motion and W-phase method. This enabled us to chart the spatial and temporal distribution of energy release in the source area via the back-projection technique. Following this, we estimated the earthquake’s intensity field by merging the source inversion findings with the ground motion prediction equation. This analysis facilitated the evaluation of earthquake casualties, utilizing the theoretical intensity field and a casualty assessment model.Our findings indicate that the fault type is a thrust fault, characterized by a unilateral rupture in the direction of NW, with a rupture length spanning approximately 10-15 km and a duration ranging between 8-10 seconds. The earthquake’s magnitude varied from M 5.9 to M 6.2. The demarcated high-intensity areas, as per our intensity assessment, align closely with the actual survey results. Furthermore, the predicted total casualties and identified critical rescue zones closely match the real-world casualty figures. These insights offer crucial technical support for governmental emergency command and rescue operations.

Synthetic phantoms enable calibration between abattoir based dual energy X-ray absorptiometers used for prediction of lamb carcass composition

Dual energy x-ray absorptiometry (DXA) devices were installed at two Australian abattoirs to predict computed tomography (CT) determined fat % and lean % of lamb carcasses. This study tested three algorithms developed for these devices, termed β1, β2 and β3, and assessed their accuracy and precision in predicting CT composition. Algorithm β3 included the use of a plastic phantom calibration block scanned by both DXA devices to adjust prediction equations, resulting in superior accuracy to the algorithms without phantom calibration (β1 and β2). When compared to the gold-standard CT composition, the bias of the DXA predictions was lowest when using algorithm β3 at the two sites (−1.17%, −0.49% for fat %, 0.11%, −0.37% for lean %). The difference of DXA composition predictions between sites was lowest when using algorithm β3, which demonstrated between site differences of 0.59 CT fat %, and 0.46 CT lean%. In contrast, algorithm β1 and β2 produced differences of 23.7% and 30.8% for CT fat, and 17.3% and 21.9% for CT lean between the two DXA devices. There was a small difference of 0.78% between the fat predictions of the first DXA image compared to the second DXA image for each carcass. The precision of predictions improved slightly using algorithm β3. This work demonstrates that the in-line DXA systems can produce comparable results across sites.