Residual Mean Research Articles

Processes Controlling the Southern Ocean Temperature Change

Abstract Observations show that since the 1950s, the subsurface Southern Ocean has been rapidly warming in the south and cooling in the north. However, what drives these opposing latitudinal patterns of change is not well understood. By analyzing the outputs of atmosphere–ocean general circulation models, we examine and quantify the processes controlling the temperature change in the Southern Ocean. We find that subsurface cooling in the north results from the combined effect of weak warming driven by greenhouse gases and cooling forced by aerosols. Contrary to previous work, we find that the influence of stratospheric ozone forcing is small and unnecessary to explain the observed patterns. Analysis of heat budget diagnostics shows that ocean heat content change between 400 and 1200 m in the Southern Ocean is primarily driven by slight imbalances between the large and opposing changes in residual mean advection and isopycnal diffusion. We show that while surface heat flux anomalies are the main contributor to the warming in the Southern Ocean, changes in wind stress are also required to produce the observed pattern of temperature change. These results highlight the importance of ocean dynamics in subsurface ocean heat content change. The counterbalancing effects of aerosols and greenhouse gases on ocean heat content change in the Southern Ocean will change with aerosols declining in the future, while our results suggest that changes in stratospheric ozone forcing will have only a minor effect on future patterns of Southern Ocean thermohaline structure.

Calibration and time fading characterization of a new optically stimulated luminescence film dosimeter.

Optically stimulated luminescence (OSL) dosimeters produce a signal linear to the dose, which fades with time due to the spontaneous recombination of energetically unstable electron/hole traps. When used for radiotherapy (RT) applications, fading affects the signal-to-dose conversion and causes an error in the final dose measurement. Moreover, the signal fading depends to some extent on treatment-specific irradiation conditions such as irradiation times. In this work, a dose calibration function for a novel OSL film dosimeter was derived accounting for signal fading. The proposed calibration allows to perform dosimetry evaluation for different RT treatment regimes. A novel BaFBr:Eu2+ -based OSL film (Zeff , 6MV =4.7) was irradiated on a TrueBeam STx using a 6MV beam with setup: 0° gantry angle, 90cm SSD, 10cm depth, 10×10cm2 field. A total of 86 measurements were acquired for dose-rates ( ) of 600, 300, and 200MU/min for irradiation times (tir ) of 0.2, 1, 2, 4.5, 12, and 23min and various readout times (tscan ) between 4 and 1440min from the start of the exposure (beam-on time). The OSL signal, , was modeled via robust nonlinear regression, and two different power-law fading models were tested, respectively, independent (linear model) and dependent on the specific (delivery-dependent model). After 1 day from the exposure, the error on the dose measurement can be as high as 48% if a fading correction is not considered. The fading contribution was characterized by two accurate models with adjusted-R2 of 0.99. The difference between the two models is <4.75% for all and . For different beam-on times, 3, 10.5, and 20min, the optimum was calculated in order to achieve a signal-to-dose conversion with a model-related error <1%. In the case of a 3min irradiation, this condition is already met when the OSL-film is scanned immediately after the end of the irradiation. For an irradiation of 10.5 and 20min, the minimum scanning time to achieve this model-related error increases, respectively, to 30 and 90min. Under these conditions, the linear model can be used for the signal-to-dose conversion as an approximation of the delivery-dependent model. The signal-to-dose function, D(Mi , j , ), has a residual mean error of 0.016, which gives a residual dose uncertainty of 0.5mGy in the region of steep signal fading (i.e., =4min). The function of two variables is representable as a dose surface depending on the signal (Mi , j ) measured for each i,j-pixel and the time of scan ( ). The calibration of a novel OSL-film usable for dosimetry in different RT treatments was corrected for its signal fading with two different models. A linear calibration model independent from the treatment-specific irradiation condition results in a model-related error <1% if a proper scanning time is used for each irradiation length. This model is more practical than the delivery-dependent model because it does not need a pixel-to-pixel fading correction for different .

Point estimation following a two-stage group sequential trial.

Repeated testing in a group sequential trial can result in bias in the maximum likelihood estimate of the unknown parameter of interest. Many authors have therefore proposed adjusted point estimation procedures, which attempt to reduce such bias. Here, we describe nine possible point estimators within a common general framework for a two-stage group sequential trial. We then contrast their performance in five example trial settings, examining their conditional and marginal biases and residual mean square error. By focusing on the case of a trial with a single interim analysis, additional new results aiding the determination of the estimators are given. Our findings demonstrate that the uniform minimum variance unbiased estimator, whilst being marginally unbiased, often has large conditional bias and residual mean square error. If one is concerned solely about inference on progression to the second trial stage, the conditional uniform minimum variance unbiased estimator may be preferred. Two estimators, termed mean adjusted estimators, which attempt to reduce the marginal bias, arguably perform best in terms of the marginal residual mean square error. In all, one should choose an estimator accounting for its conditional and marginal biases and residual mean square error; the most suitable estimator will depend on relative desires to minimise each of these factors. If one cares solely about the conditional and marginal biases, the conditional maximum likelihood estimate may be preferred provided lower and upper stopping boundaries are included. If the conditional and marginal residual mean square error are also of concern, two mean adjusted estimators perform well.

Use of COMSOL software for modeling and simulation of copper removal in a dynamic mode on a new biowaste.

The main purpose of this investigation is to model the results related to biosorption using COMSOL (Multiphysics 4.3a), and to solve the advection-dispersion equation by using both linear and Langmuir models. A bidimensional model was then proposed to study the mass transfer in the process of copper ions sorption in a dynamic mode on cider vinegar residues. Sorption tests were realized by evaluating the influence of flow rate (0.75, 1, and 2.65 ml min-1 ), bed height (3.5, 7 and 8.5cm), and copper initial concentration (169 and 300 mg L-1 ). For all cases, the mathematical formulation was solved by assuming that the column is homogeneous and the sorption is instantaneous. The corresponding results were exploited through breakthrough curve profiles, where it was shown that the solutions obtained by the "Langmuir COMSOL" model coincide with the experimental values. In contrast, the linear model has been unable to fit them. The optimal results were analyzed by Thomas, Adam-Bohart Yoon Nelson, and Ogata-Bank models, which proves that the Thomas method is well adapted with a satisfactory correlation coefficient (0.93). Further, the model validation was performed by determining the residual root mean square error, which was found less than 0.3, thereby indicating a reasonable concordance between the estimated and experimental points. The high sorption capacity obtained was around of 41.37 mg g-1 , which suggests that the cider vinegar residues can be exploited as a low-cost, available, and effective sorbent biomass in the field of the treatment of industrial effluents. PRACTITIONER POINTS: Cider vinegar residues (CVR) as low cost biosorbent were studied for continuous biosorption. A successful COMSOL model was proposed and validated. CVR is an effective biosorbent for copper fixed bed biosorption. High sorption capacity was around of 41.37 mg g-1 under optimal conditions.

Donor-site Morbidity following Microvascular Fibular Transfer for Head and Neck Reconstruction

BACKGROUND: Continuous refinement in head and neck reconstruction requires a thorough evaluation of donor-site morbidity of established workhorse flaps. Regardless of the favoritism bestowed on the free fibular flap (FFF) for head and neck reconstruction by most microsurgeons, the donor-site morbidity associated with harvesting remains a controversial and concerning issue. In fact, the perioperative donor-site complication rate following FFF harvest ranges between 2% to 38%. The objective of this study is to analyze surgical and postoperative factors that contribute to donor-site morbidity in patients undergoing FFF transfer for mandibular and/or maxillary reconstruction of oncologic defects. METHODS: We conducted a prospective single-institutional investigation to analyze complications and assess their effect on the postoperative status of the ankle, great toe, and gait in 53 patients who underwent reconstruction of maxillary and/or mandibular defects with the FFF. We used the American Academy of Orthopaedic Surgeons foot and ankle questionnaire (AAOS-FAOQ), X-ray of ankles, and clinical examination (pain, paresthesia, claw toe deformity, and range of motion) to evaluate postoperative outcomes one, three, and six months after surgery. RESULTS: Forty-six males (86.8%) and seven females (13.2%) were finally included. The mean age was 50.18 years (range, 22-75). The mean tourniquet time was 74.25 minutes. The mean length of the segment of fibula harvested was 12.85 ± 1.1 cm, while the residual mean distal and proximal fibula segment length were 6.6 cm (range, 6-8) and 6.7 cm (range, 6-8) respectively. The mean size of the skin paddle harvested was 53.2 cm2 (range, 36-64 cm2). Flap failure secondary to venous congestion was seen in 2 cases. The most common acute donor site complications recorded were edema of the ipsilateral foot (11.30%) and delayed wound healing (9.4%). Additional complications reported included partial graft loss (7.5%) and wound infection (7.5%). Three of the four grafts that were partially lost, had signs of infection (75%). Spontaneous recovery was observed in the 3 partial graft loss cases (75%) whereas 1 patient (25%) required an additional STSG. Long-term claw toe deformity was observed in 1 patient (1.8%) who had required excision of the flexus hallucis longus. Nerve alterations were present in the form of paresthesia (39.6%) and superficial peroneal nerve sensory loss (18.9%). The overall rate of perioperative donor site complications was 35.8%. Significant improvement in the average AAOS-FAOQ score was observed at 6-months when compared to 1-month results (p<.001). Dorsiflexion (p=0.923) and plantarflexion (p=0.913) did not significantly improve at different time points. Complications did not affect range of motion for dorsiflexion (p=0.759) or plantarflexion (p=0.254) at 6-months. A larger residual fibular stump (≥8cm) did not significantly affect postoperative ROM for dorsiflexion (p=0.232) or plantarflexion at 6-months. A large skin paddle area resulted in an increase in incidence of complications. (p <.001). CONCLUSION: Although FFF is associated with a high complication rate and postoperative reduction in lower limb performance, these findings do not challenge the reliability of this versatile flap. A larger flap’s skin paddle increases the rate of donor-site complications. Future studies focusing on refinement of harvest technique are anticipated.

An efficient low-pass-filtering algorithm to de-noise global GRACE data

The global monthly equivalent water height (EWH) anomaly (ΔEWH), derived from the Gravity Recovery and Climate Experiment (GRACE) level-2 data, helps understand the terrestrial water storage variation. Unfortunately, the ΔEWH data consist of stripes or noise. Numerous de-noising approaches have been studied. However, the following issues exist, possible overly smoothed results, two-step (de-striping and Gaussian-based smoothing) procedures introducing additional uncertainty and error propagation, requiring multi-temporal datasets or prior knowledge, and possible computational inefficiency. Thus, after analyzing the spectrum of the ΔEWH data, a novel low-pass-filtering algorithm is proposed to remove the noise and resolve the issues. Furthermore, without the global in situ measurements, an alternative assessment method is studied based on the additive characteristics of the signal and noise in the GRACE data. The method consists of the residual analysis and root mean square (RMS) value of the de-noised signal. Then, the proposed algorithm was applied to de-noising the ΔEWH datasets between 2002 and 2015. De-noised results are satisfactory qualitatively and quantitatively. Compared with well-known two-step de-noising methods, data blurring does not occur after the proposed algorithm. The algorithm removes noise the most assessed by the residual analysis and preserves the signal the most evaluated by large RMS values.

Male pelvic multi-organ segmentation using token-based transformer Vnet

Objective. This work aims to develop an automated segmentation method for the prostate and its surrounding organs-at-risk in pelvic computed tomography to facilitate prostate radiation treatment planning. Approach. In this work, we propose a novel deep learning algorithm combining a U-shaped convolutional neural network (CNN) and vision transformer (VIT) for multi-organ (i.e. bladder, prostate, rectum, left and right femoral heads) segmentation in male pelvic CT images. The U-shaped model consists of three components: a CNN-based encoder for local feature extraction, a token-based VIT for capturing global dependencies from the CNN features, and a CNN-based decoder for predicting the segmentation outcome from the VIT’s output. The novelty of our network is a token-based multi-head self-attention mechanism used in the transformer, which encourages long-range dependencies and forwards informative high-resolution feature maps from the encoder to the decoder. In addition, a knowledge distillation strategy is deployed to further enhance the learning capability of the proposed network. Main results. We evaluated the network using: (1) a dataset collected from 94 patients with prostate cancer; (2) and a public dataset CT-ORG. A quantitative evaluation of the proposed network’s performance was performed on each organ based on (1) volume similarity between the segmented contours and ground truth using Dice score, segmentation sensitivity, and precision, (2) surface similarity evaluated by Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMS), (3) and percentage volume difference (PVD). The performance was then compared against other state-of-art methods. Average volume similarity measures obtained by the network overall organs were Dice score = 0.91, sensitivity = 0.90, precision = 0.92, average surface similarities were HD = 3.78 mm, MSD = 1.24 mm, RMS = 2.03 mm; average percentage volume difference was PVD = 9.9% on the first dataset. The network also obtained Dice score = 0.93, sensitivity = 0.93, precision = 0.93, average surface similarities were HD = 5.82 mm, MSD = 1.16 mm, RMS = 1.24 mm; average percentage volume difference was PVD = 6.6% on the CT-ORG dataset. Significance. In summary, we propose a token-based transformer network with knowledge distillation for multi-organ segmentation using CT images. This method provides accurate and reliable segmentation results for each organ using CT imaging, facilitating the prostate radiation clinical workflow.

Automated lung tumor delineation on positron emission tomography/computed tomography via a hybrid regional network.

Multimodality positron emission tomography/computed tomography (PET/CT) imaging combines the anatomical information of CT with the functional information of PET. In the diagnosis and treatment of many cancers, such as non-small cell lung cancer (NSCLC), PET/CT imaging allows more accurate delineation of tumor or involved lymph nodes for radiation planning. In this paper, we propose a hybrid regional network method of automatically segmenting lung tumors from PET/CT images. The hybrid regional network architecture synthesizes the functional and anatomical information from the two image modalities, whereas the mask regional convolutional neural network (R-CNN) and scoring fine-tune the regional location and quality of the output segmentation. This model consists of five major subnetworks, that is, a dual feature representation network (DFRN), a regional proposal network (RPN), a specific tumor-wise R-CNN, a mask-Net, and a score head. Given a PET/CT image as inputs, the DFRN extracts feature maps from the PET and CT images. Then, the RPN and R-CNN work together to localize lung tumors and reduce the image size and feature map size by removing irrelevant regions. The mask-Net is used to segment tumor within a volume-of-interest (VOI) with a score head evaluating the segmentation performed by the mask-Net. Finally, the segmented tumor within the VOI was mapped back to the volumetric coordinate system based on the location information derived via the RPN and R-CNN. We trained, validated, and tested the proposed neural network using 100 PET/CT images of patients with NSCLC. A fivefold cross-validation study was performed. The segmentation was evaluated with two indicators: (1) multiple metrics, including the Dice similarity coefficient, Jacard, 95th percentile Hausdorff distance, mean surface distance (MSD), residual mean square distance, and center-of-mass distance; (2) Bland-Altman analysis and volumetric Pearson correlation analysis. In fivefold cross-validation, this method achieved Dice and MSD of 0.84±0.15 and 1.38±2.2mm, respectively. A new PET/CT can be segmented in 1s by this model. External validation on The Cancer Imaging Archive dataset (63 PET/CT images) indicates that the proposed model has superior performance compared to other methods. The proposed method shows great promise to automatically delineate NSCLC tumors on PET/CT images, thereby allowing for a more streamlined clinical workflow that is faster and reduces physician effort.

Kinetics and Thermodynamics of β-Carotene Adsorption onto Acid-Activated Clays Modified by Zero Valent Iron

The adsorption of β-carotene from crude palm oil onto acid-activated clay and clay modified by zero valent iron (ZVI) was investigated in this work. Spectroscopic studies including FTIR, XRD, and SEM were used for its characterization. The adsorption characteristics such as kinetics, mechanism, isotherms, and thermodynamics of β-carotene were studied. The kinetic data were analyzed using the pseudo-first-order kinetic equation, pseudo-second-order kinetic equation, and intraparticle diffusion model. The pseudo-second-order kinetic model is the only one that describes the experimental data well (R2 ≥ 0.969). The chemical analysis of bulk clay showed that the predominant oxides are Al2O3 (57.91 wt%), Fe2O3 (32.54 wt%), SiO2 (3.09 wt%), K2O (2.37 wt%), and CaO2 (1.73 wt%). The adsorption capacity increases with an increase in temperature. The equilibrium data were described better by the Freundlich model for all clays. To determine the best fit kinetic model for each system, three error analysis methods, namely, chi-square (χ2), residual mean squared error (RMSE), and mean percent error (%APE) were used to evaluate the data. A thermodynamic study demonstrated that β-carotene adsorption is spontaneous, endothermic, and an entropy driven process for both forms of clay.

Machine Learning Sequential Methodology for Robot Inverse Kinematic Modelling

The application of robots is growing in most countries, occupying a relevant place in everyday environments. Robots are still affected by errors due to their limitations, which may compromise the final performance. Accurate trajectories and positionings are strict requirements that robots have to satisfy and may be studied by the inverse kinematic (IK) formulation. The IK conventional numerical techniques are computationally intensive procedures, focusing on the robot joint values simultaneously and increasing the complexity of the solution identification. In this scenario, Machine Learning strategies may be adopted to achieve effective and robust manipulator’s IK formulation due to their computational efficiency and learning ability. This work proposes a machine learning (ML) sequential methodology for robot inverse kinematics modeling, iterating the model prediction at each joint. The method implements an automatic Denavit-Hartenberg (D-H) parameters formulation code to obtain the forward kinematic (FK) equations required to produce the robot dataset. Moreover, the artificial neural network (ANN) architecture is selected as a structure and the number of layers in combination with the hidden neurons per layer is defined by an offline optimization phase based on the genetic algorithm (GA) technique for each joint. The ANN is implemented with the following settings: scaled conjugate gradient as training function and the mean squared error as the loss function. Different network architectures are examined to validate the IK procedure, ranging from global to sequential and considering the computation direction (from end-effector or from basement). The method is validated in the simulated and experimental laboratory environment, considering articulated robots. The sequential method exhibits a reduction of the mean squared error index of 42.7–56.7%, compared to the global scheme. Results show the outstanding performance of the IK model in robot joint space prediction, with a residual mean absolute error of 0.370–0.699 mm in trajectory following 150.0–200.0 mm paths applied to a real robot.

Dermoscopic image segmentation based on Pyramid Residual Attention Module.

We propose a stacked convolutional neural network incorporating a novel and efficient pyramid residual attention (PRA) module for the task of automatic segmentation of dermoscopic images. Precise segmentation is a significant and challenging step for computer-aided diagnosis technology in skin lesion diagnosis and treatment. The proposed PRA has the following characteristics: First, we concentrate on three widely used modules in the PRA. The purpose of the pyramid structure is to extract the feature information of the lesion area at different scales, the residual means is aimed to ensure the efficiency of model training, and the attention mechanism is used to screen effective features maps. Thanks to the PRA, our network can still obtain precise boundary information that distinguishes healthy skin from diseased areas for the blurred lesion areas. Secondly, the proposed PRA can increase the segmentation ability of a single module for lesion regions through efficient stacking. The third, we incorporate the idea of encoder-decoder into the architecture of the overall network. Compared with the traditional networks, we divide the segmentation procedure into three levels and construct the pyramid residual attention network (PRAN). The shallow layer mainly processes spatial information, the middle layer refines both spatial and semantic information, and the deep layer intensively learns semantic information. The basic module of PRAN is PRA, which is enough to ensure the efficiency of the three-layer architecture network. We extensively evaluate our method on ISIC2017 and ISIC2018 datasets. The experimental results demonstrate that PRAN can obtain better segmentation performance comparable to state-of-the-art deep learning models under the same experiment environment conditions.

Anthropogenic Influence on the Antarctic Mesospheric Cooling Observed during the Southern Hemisphere Minor Sudden Stratospheric Warming

Processes behind Sudden Stratospheric Warming (SSW), which occurs more frequently in the northern hemispheric polar latitudes and its influence from the stratosphere to the upper atmosphere are well documented. However, physical processes associated with SSW, although it ensues rarely in the southern hemisphere (SH), have a strong influence on the background atmosphere from the stratosphere to the mesosphere and are poorly understood. Using a ground-based meteor radar, satellite-borne Microwave-Limb sounder, and Modern-Era Retrospective Analysis for Research and Applications observations, we identified cooling of Antarctic mesopause by 26 K in response to a 66 K warming in the polar stratosphere during the 2019 minor SSW in the SH. The observed cooling is attributed to the interplay between planetary waves, CO2 infrared cooling, and O3 depletion, rather than adiabatic cooling due to gravity waves alone during SSW. It is proposed that anthropogenic and other sources generating chemical tracers in the lower atmosphere have caused mesospheric cooling and could be transported from the lower atmosphere both vertically and meridionally through residual mean meridional circulation from the tropics. Therefore, our study for the first time demonstrates the effect of lower atmosphere chemistry on the polar mesosphere thermal structure during the 2019 SSW.

Alpha Power Exponentiated New Weibull-Pareto Distribution: Its Properties and Applications

In this paper, a new five-parameter model called alpha power exponentiated new Weibull-Pareto distribution is introduced based on a new developing technique. We derived some properties relating to the proposed distribution, including moments, moment generating function, quantile function, mean residual life and mean waiting time, and order statistics of the new model. The model parameters are estimated using the maximum likelihood method. Some simulation studies are performed to investigate the effectiveness of the estimates. Finally, we used three real-life data sets to show the flexibility of the introduced distribution.

Some new Mathematical Properties for Kumaraswamy Fréchet distribution

In this research, some mathematical properties for Kumaraswamy Fréchet distribution was presented, include entropy, the Shannon entropy, probability weighted moments, moments of residual life and mean of residual life. the properties were concluded for the Kumaraswamy Fréchet distribution using the probability density function (pdf) and cumulative distribution function according to linear representations.

Jackknife empirical likelihood ratio test for testing mean residual life and mean past life ordering

We propose nonparametric tests for testing mean residual life and mean past life ordering. Asymptotic properties of the proposed test statistics are studied. We also propose Jackknife Empirical Likelihood (JEL) ratio tests for testing mean residual life and mean past life ordering. Some numerical results are presented to evaluate the performance of the proposed tests by comparing them with few competing tests available in the literature. Finally, we illustrate the proposed test procedures using real data sets.

Early Hemodynamic Outcomes in Self-Expandable Valves: Comparison of ACURATE Neo Versus ACURATE Neo2

BackgroundComparisons of hemodynamic results between ACURATE Neo and ACURATE Neo 2, which have updated outer sealing skirts, are limited. This retrospective study aimed to demonstrate the differences in hemodynamic outcomes between the two transcatheter heart valves (THVs). MethodsWe included 449 patients who underwent transfemoral transcatheter aortic valve replacement (TAVR) with either ACURATE Neo2 (n = 100) or ACURATE Neo (n = 348) between January 2016 and November 2021. The primary endpoint was the incidence of moderate or severe paravalvular leakage (PVL). The mean aortic pressure gradient (APG), peak aortic velocity (AV), and early clinical outcomes were assessed as secondary outcomes. ResultsIn the propensity-score matching comparison (94 pairs), there were no significant differences in the incidence of moderate or severe PVL (4.3 % in Neo2 group vs. 8.5 % in Neo group, p = 0.233), and peak AV and mean APG were significantly higher in Neo2 group (Peak AV: 2.1 ± 0.4 vs. 1.9 ± 0.5 m/s, p = 0.003; mean APG: 10.6 ± 4.8 vs. 9.0 ± 6.1 mmHg, p = 0.045). In the multivariate logistic regression analysis, the THV type (ACURATE Neo2) was not an independent predictor of moderate or severe PVL. ConclusionThe incidence of moderate or severe PVL did not significantly differ between TAVR with ACURATE Neo2 and ACURATE Neo; however, it was numerically lower in ACURATE Neo2. In contrast, the Neo2 group had a higher residual mean APG and peak AV. A larger study with long-term follow-up is warranted to assess the clinical relevance of these findings.

Comparison of nonlinear models for describing the pre-weaning growth of Timahdite lambs and genetic and non-genetic effects for curve parameters

The objectives of this study were to determine the appropriate nonlinear model for describing the pre-weaning growth curve of Timahdite lambs and to study the genetic and non-genetic effects of curve parameters with the best fit model. Nonlinear models of Brody, Gompertz, von Bertalanffy and Logistic were fitted to 46372 body weight-age records belonging to 11,620 lambs, ranging from birth to weaning age. The statistics used to compare the growth models were adjusted determination coefficient (Radj2), residual mean square (MSE), Akaike’s information criterion (AIC) and Bayesian information criterion (BIC). Among the four nonlinear models, the von Bertalanffy function had the high Radj2 and the smallest MSE, AIC and BIC values, indicating the best fit for Timahdite lambs. Parameters A, B and k of the best model were estimated to be 33.3 ± 0.70, 0.50 ± 0.01 and 0.01 ± 0.01, respectively. The flock and period of birth had significant effects on parameters A, B and k, the age of dam and birth type on parameters B and k, and the sex on parameter A only. Heritability estimates for growth curve parameters A, B and k were 0.01 ± 0.01, 0.02 ± 0.01 and 0.02 ± 0.01, respectively. Estimates of genetic correlation between A-B, A-k and B-k were 0.79 ± 0.11, −0.71 ± 0.12 and −0.23 ± 0.18, respectively. Their corresponding phenotypic correlations were 0.77, −0.81 and −0.54, respectively. It was concluded that among the nonlinear models, the von Bertalanffy model presented the best fit in describing the growth curve in Timahdite lambs and much of the improvement on growth curve parameters could be obtained by improving associated managerial and environmental conditions.

On the Residual Lifetime and Inactivity Time in Mixtures

In this paper we study the aging characteristics in mixtures of distributions, providing characterizations for their derivatives that explain the smooth behavior of the mixture. The classical preservation results for the reversed hazard rate, mean residual life and mean inactivity time are derived under a different approach than in previous studies. We focus on the variance of both the residual life and inactivity time in mixtures, obtaining some preservation properties. We also state conditions for weak and strong bending properties for the variance of the residual life and the inactivity time in mixtures.

Body fat assessment in youth with overweight or obesity by an automated bioelectrical impedance analysis device, in comparison with the dual-energy x-ray absorptiometry: a cross sectional study

BackgroundBioelectrical impedance analysis (BIA) is a widely used method to assess total body fat (TBF) depots characterising obesity. Automated BIA devices provide an inexpensive and easy assessment of TBF, making them widely available to the general public and healthcare providers without specific qualification to assess body composition. The equations included in the automated BIA devices have been developed in very few specific populations, which means that they are not suitable to assess TBF for everyone and need to be validated before use in other populations. The aim of the present work is to evaluate the accuracy of the automated BIA device Tanita® BC-532 in youth of White European ethnicity, compared with the dual-energy x-ray absorptiometry (DEXA), gold standard measurement of TBF.MethodsTotal body fat percentage (TBF%) was measured with the BIA device Tanita® BC-532 and DEXA (Hologic® QDR4500W) in 197 youth of White European ethnicity (N = 104 girls), 7-17 years old, and visiting the Diabetes & Endocrinology Care Paediatrics Clinic, Centre Hospitalier de Luxembourg, for overweight or obesity management.ResultsTBF% evaluated with BIA was significantly correlated with TBF% measured with DEXA in both boys (r Pearson = 0.617) and girls (r Pearson = 0.648) (p < 10− 4). However, the residual mean between the assessment of TBF% by BIA and by DEXA [TBF BIA (%)-TBF DEXA (%)] is extremely high (mean ± standard deviation = 10.52% ± 5.22% in boys, respectively 9.96% ± 4.40% in girls). The maximal absolute residual value is also very high, about 24% in both genders.ConclusionsThe automated BIA device Tanita® BC-532 appears to be not accurate to assess total body fat in youth with overweight or obesity. There is a need to calibrate the BIA device before its use in the populations where it was not previously validated.

Identification and immunological characterization of cuproptosis-related molecular clusters in Alzheimer's disease.

IntroductionAlzheimer's disease is the most common dementia with clinical and pathological heterogeneity. Cuproptosis is a recently reported form of cell death, which appears to result in the progression of various diseases. Therefore, our study aimed to explore cuproptosis-related molecular clusters in Alzheimer's disease and construct a prediction model.MethodsBased on the GSE33000 dataset, we analyzed the expression profiles of cuproptosis regulators and immune characteristics in Alzheimer's disease. Using 310 Alzheimer's disease samples, we explored the molecular clusters based on cuproptosis-related genes, along with the related immune cell infiltration. Cluster-specific differentially expressed genes were identified using the WGCNA algorithm. Subsequently, the optimal machine model was chosen by comparing the performance of the random forest model, support vector machine model, generalized linear model, and eXtreme Gradient Boosting. Nomogram, calibration curve, decision curve analysis, and three external datasets were applied for validating the predictive efficiency.ResultsThe dysregulated cuproptosis-related genes and activated immune responses were determined between Alzheimer's disease and non-Alzheimer's disease controls. Two cuproptosis-related molecular clusters were defined in Alzheimer's disease. Analysis of immune infiltration suggested the significant heterogeneity of immunity between distinct clusters. Cluster2 was characterized by elevated immune scores and relatively higher levels of immune infiltration. Functional analysis showed that cluster-specific differentially expressed genes in Cluster2 were closely related to various immune responses. The Random forest machine model presented the best discriminative performance with relatively lower residual and root mean square error, and a higher area under the curve (AUC = 0.9829). A final 5-gene-based random forest model was constructed, exhibiting satisfactory performance in two external validation datasets (AUC = 0.8529 and 0.8333). The nomogram, calibration curve, and decision curve analysis also demonstrated the accuracy to predict Alzheimer's disease subtypes. Further analysis revealed that these five model-related genes were significantly associated with the Aβ-42 levels and β-secretase activity.ConclusionOur study systematically illustrated the complicated relationship between cuproptosis and Alzheimer's disease, and developed a promising prediction model to evaluate the risk of cuproptosis subtypes and the pathological outcome of Alzheimer's disease patients.