Common Parameters Research Articles

Stability, Vertex Stability, and Unfrozenness for Special Graph Classes

Frei et al. (J. Comput. Syst. Sci. 123, 103–121, 2022) show that the stability, vertex stability, and unfrozenness problems with respect to certain graph parameters are complete for Θ2P\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{\\Theta _{2}^{\ extrm{P}}}$$\\end{document}, the class of problems solvable in polynomial time by parallel access to an NP oracle. They studied the common graph parameters α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{\\alpha }$$\\end{document} (the independence number), β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{\\beta }$$\\end{document} (the vertex cover number), ω\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{\\omega }$$\\end{document} (the clique number), and χ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{\\chi }$$\\end{document} (the chromatic number). We complement their approach by providing polynomial-time algorithms solving these problems for special graph classes, namely for graphs with bounded tree-width or bounded clique-width. In order to improve these general time bounds even further, we then focus on trees, forests, bipartite graphs, and co-graphs.

Parental stress parameter difference after caring for congenital adrenal hyperplasia children with genital ambiguity

Abstract Introduction: Congenital adrenal hyperplasia (CAH) disrupts cortisol biosynthesis, which may cause genital ambiguity in females. Caring for children with genital ambiguity creates parental distress. Common stress parameters include biological (e.g., serotonin cortisol) and psychological (e.g., Depression, Anxiety, and Stress Scale [DASS] scores). This study elaborated parental stress parameter differences among parents of CAH children with and without genital ambiguity. Methods: This cross-sectional study included 34 fathers and 44 mothers of children with CAH. All participants agreed to fill in the DASS-42 and socioeconomic questionnaire. Morning serum serotonin and cortisol samples were examined using the enzyme-linked immune-absorbent assay technique. Results: The median (minimum–maximum) value of serotonin concentration was lower in fathers compared to mothers (281.18 [78.44–122.91] ng/ml vs. 399.78 [122.91–1050.0] ng/ml, P = 0.11); in parents of children with genital ambiguity (292.22 [78.44–1050.00] ng/ml vs. 426.71 [150.78–1050.00] ng/ml, P = 0.01); and in parents of children with a diagnosis time <1 year (225.00 [93.41–570.33] ng/ml vs. 371.815 [78.44–1050.00] ng/ml, P = 0.046). Higher cortisol concentration was observed in parents of children with diagnosis time <1 year (78.65 [50.89–126.89] ng/ml vs. 63.33 [6.14–283.67] ng/ml, P = 0.046). The proportion of depression was 24.4%, anxiety 45.1%, and stress 21.8%. Conclusion: Fathers, parents of children with genital ambiguity, and being recently diagnosed had lower serum serotonin levels. Parents of recently diagnosed children had higher cortisol levels. Anxiety was more common than depression and stress.

Machine Learning-Facilitated Cross-Panel Automatic Classification at Both Sample and Cell Level in Acute Myeloid Leukemia

Introduction Flow cytometry (FC) immunophenotyping is essential for accurate and prompt diagnosis of acute myeloid leukemia (AML), with up to 95% of AML patients showing detectable aberrant immunophenotype (IP) by FC. Current manual and subjective analysis of clinical FC data, however, lacks reproducibility and leads to interpretation variation. In previous studies, we showed that supervised machine learning (ML) approaches can detect IP abnormalities and identify the acute leukemia subclassification accurately at the specimen level. We also found that comparable classification performance can be achieved using only a subset of markers of the reagent panel (Ko et al., 2018; Monaghan et al., 2022). To interpret the ML classification results, we have developed data clustering and discriminative learning methods to identify and visualize the diagnostic cell populations in a 2D space for hematopathology review (Ji et al., 2020; Lee et al., 2018). Built upon these approaches, this study reports our findings on developing an interpretable cross-panel ML classification model to support timely and accurate diagnosis and subclassification of AML. Materials and Method The AML diagnosis model at the sample level utilized FC data obtained from 53 bone marrow (BM) aspirate samples from Roswell Park Comprehensive Cancer Center (RPCCC) and 50 BM aspirate samples from UPMC. Data from RPCCC were acquired using a Beckman Coulter Navios EX and measured with the ClearLLab10C panel; while data from UPMC were acquired using a BD FACSCantoII and measured with a UPMC-developed diagnostic panel. We used only the common parameters collected within these 103 FC data sets and applied a previously published Gaussian Mixture Model-Support Vector Machine (GMM-SVM) based approach to train a cross panel sample-level classification model for AML diagnosis (Ko et al., 2018; Monaghan et al., 2022). Three-fold cross-validation was conducted to assess the model performance, using sensitivity, specificity, classification accuracy, and area under the receiver operating characteristic curve (AUC). For the cell-level visualization and analysis, all samples in the training set were pooled together, before a uniform manifold approximation and projection (UMAP) process was applied to transform and visualize the cell populations within the samples. Specifically, 26 non-neoplastic and 27 AML samples from the M2 panel of the ClearLLab10C from the RPCCC cohort were combined for the UMAP visualization. Then the FlowSOM unsupervised clustering method and the DAFI semi-supervised clustering method (Lee et al., 2018) were applied to identify all of the cell populations that have significant differences between the AML samples and the non-neoplastic controls. Results The cross-panel AML versus non-neoplastic classifier, combining FC data from UPMC and RPCCC achieved exceptional performance. The AML detection model using all parameters on the ClearLLab10C panel achieved an AUC of 100%. When using only the overlapping parameters between the ClearLLab10C and the UPMC panels, the classification model achieved an AUC of 99.5%, while the cross-panel classifier achieved an AUC of 99.1% (Table 1). Specimen-level visualization confirmed the separation of AML versus non-neoplastic controls. The UMAP plots, created by pooling non-neoplastic and AML samples, exhibited distinct patterns for different AML subtypes (M1 to M5) and a genetic mutation (e.g., AML IDH1), which were distinct from control samples. Using the automated gating analysis, cell populations identified as different between AML and control samples were characterized and visualized in 2D-by-2D scatter plots for straightforward interpretation and assessment based on their immunophenotype. Conclusion Our study demonstrated the feasibility to develop and assess a ML classification model for AML detection and diagnosis using overlapping markers in FC data across reagent panels and sites. Advanced visualization and automated gating analysis enabled the interpretation of the ML classification by identifying and characterizing the diagnostic cell populations and their phenotypic heterogeneity, providing insights into AML heterogeneity at both the specimen and the single cell level. These findings highlight the potential of ML capability for improving patient care by timely and accurate diagnosis and subclassification of AML.

Few-Shot Fault Diagnosis Method of Rotating Machinery Using Novel MCGM Based CNN

The existing fault diagnosis methods can achieve good results when various status fault data are available. However, the construction of the diagnosis model is often unachievable in the actual application because only normal data are available, which is actually a few-shot fault diagnosis problem. Therefore, a novel intelligent few-shot fault diagnosis method of rotating machinery based on the convolutional neural network (CNN) using virtual samples generated by the mechanism character generative model (MCGM) integrating the generative adversarial network (GAN) is proposed. The distribution pattern of common parameters that reflect the fault category is learned using the GAN and source domain fault data. Then, the normal state data of the target domain is combined with the distribution common parameters to generate virtual samples in target domain based on the MCGM. Moreover, the fault diagnosis model is trained by virtual samples based on the CNN. Finally, the proposed fault diagnosis method is validated using the laboratory bearing data, the industrial data and the public data of the rotating machinery, respectively. The results show that the proposed method achieves an average accuracy of 93.38% in the diagnostic task, exhibiting at least 4.56% better performance than other comparison methods.

Estimating Full Longwave and Shortwave Radiative Transfer with Neural Networks of Varying Complexity

Abstract Radiative transfer (RT) is a crucial but computationally expensive process in numerical weather/climate prediction. We develop neural networks (NN) to emulate a common RT parameterization called the Rapid Radiative Transfer Model (RRTM), with the goal of creating a faster parameterization for the Global Forecast System (GFS) v16. In previous work we emulated a highly simplified version of the shortwave RRTM only—excluding many predictor variables, driven by Rapid Refresh forecasts interpolated to a consistent height grid, using only 30 sites in the Northern Hemisphere. In this work we emulate the full shortwave and longwave RRTM—with all predictor variables, driven by GFSv16 forecasts on the native pressure–sigma grid, using data from around the globe. We experiment with NNs of widely varying complexity, including the U-net++ and U-net3+ architectures and deeply supervised training, designed to ensure realistic and accurate structure in gridded predictions. We evaluate the optimal shortwave NN and optimal longwave NN in great detail—as a function of geographic location, cloud regime, and other weather types. Both NNs produce extremely reliable heating rates and fluxes. The shortwave NN has an overall RMSE/MAE/bias of 0.14/0.08/−0.002 K day−1 for heating rate and 6.3/4.3/−0.1 W m−2 for net flux. Analogous numbers for the longwave NN are 0.22/0.12/−0.0006 K day−1 and 1.07/0.76/+0.01 W m−2. Both NNs perform well in nearly all situations, and the shortwave (longwave) NN is 7510 (90) times faster than the RRTM. Both will soon be tested online in the GFSv16. Significance Statement Radiative transfer is an important process for weather and climate. Accurate radiative transfer models exist, such as the RRTM, but these models are computationally slow. We develop neural networks (NNs), a type of machine learning model that is often computationally fast after training, to mimic the RRTM. We wish to accelerate the RRTM by orders of magnitude without sacrificing much accuracy. We drive both the NNs and RRTM with data from the GFSv16, an operational weather model, using locations around the globe during all seasons. We show that the NNs are highly accurate and much faster than the RRTM, which suggests that the NNs could be used to solve radiative transfer inside the GFSv16.

Reference Intervals of Haematological Parameters for Apparently Healthy Adults in Northeast Ethiopia.

Clinical laboratory reference intervals play a vital role in evaluating overall well-being, tracking the progression of diseases, and detecting potential harmful effects and complications. Despite evidence revealing disparities, many African nations currently rely on reference intervals for blood analysis obtained mainly from Western populations. This practice increases the risk of misidentifying and misdiagnosing healthy individuals. The aim of this study was to establish common hematological parameters reference intervals for healthy adults in Northeast Ethiopia. This community-based cross-sectional study consisted of 328 individuals who were presumed to be in good health. To assess their blood-related characteristics, blood samples were collected and analyzed using the advanced Dirui BF-6500 analyzer, along with serological testing. In accordance with guidelines provided by the Clinical and Laboratory Standards Institute, the study employed a non-parametric approach to calculate the medians and 95% confidence intervals. To explore potential variations between males and females, a statistical test known as the Mann-Whitney U-test was used to compare the reference intervals. The established reference intervals were: white blood cells 3.5-11.3×109/L; red blood cells 4.0-6.1×1012/L; hemoglobin 11.2-17.5g/dL; hematocrit 35.4-52.0%; MCV 77.9-93.8fl; MCH 24.7-32.0pg; MCHC 306-349g/L; RDW-CV 12.1-13.8% and platelet 131-391×109/L. The reference values of monocytes, eosinophils, red blood cells, hemoglobin, hematocrit and RDW-CV in males were higher than females, while females had significantly higher platelet counts compared to males. The reference intervals discovered differed from the reference intervals now in use, those mentioned in earlier research in Ethiopia or other African nations, as well as those conducted on Western populations. In the adult demographic of Northeast Ethiopia, specific reference intervals for commonly observed hematological parameters were established, tailored to the local community. Consequently, these reference intervals hold the potential to enhance informed decision-making within this population, by providing valuable guidance when interpreting laboratory test outcomes.

Characterization of add-on testing before and after automation at a core laboratory

ObjectivesAdd-on testing refers to the process that occurs in clinical laboratories when clinicians request that additional tests be performed on a previously analysed specimen. This is a common but inefficient procedure, highly time-consuming, especially at core laboratories and could be optimised by automating these procedures. The aims of this study are: 1) To describe patterns of add-on testing at a core laboratory at a tertiary hospital, 2) To evaluate turnaround time (TAT) before and after automation of the pre-, post- and analytical phases. MethodsRetrospective, observational study conducted at the biochemistry area of a core laboratory of all add-on orders received in two different months (pre-automation and post-automation). ResultsA total of 2464 add-on orders were analysed, representing around 5 % of total requests. Most orders were for either one (>50 %) or two (≈20 %) tests. Most orders were received during the week (from Monday to Friday), particularly during the morning shift (>50 %). More than 50 % of requests were made by the Emergency Department. The two most common add-on parameters were C-reactive protein and N-terminal pro-brain natriuretic peptide. After automation, the median TAT decreased by 42.3 % (from 52 to 22 min). The largest decreases in TAT were observed for routine samples (58.89 %) and fully automated analyses (56.86 %). ConclusionsAutomation of our core laboratory substantially reduced turnaround time for add-on testing, indicating an increase in efficiency. Automation eliminated several manual steps in the process, leading to a mean reduction of 15 work hours per day (more than 2 full-time equivalents).

Two commodity queueing inventory system with random common lifetime, two demand classes and pool of customers

This paper analyzes a two-commodity perishable queueing inventory system with random common lifetimes commodity-wise, positive service time, and individual ordering policy. We have considered two types of customers: priority customers and non-priority customers. There is a buffer of finite capacity for the priority customers and a pool with infinite capacity made available for the non-priority customers. The arrival of these customers is occurred by two independent Poisson processes. The lifetimes of each of the commodities and service times follow independent exponential distributions. The matrix geometric approach is used to analyze the model's steady-state behavior and we computed various system performance measures. This article examines the effects of the common lifetime parameters on the measures of performance, such as average cycle time, the average number of reorders due to the realization of the common lifetime of commodities, etc. Also, a sensitivity analysis of the system parameters on the performance measures is carried out. Further, a cost function is constructed based on the performance measures, and its convexity is examined numerically.

Influence of Taper surface topographies on contact deformation and stresses

The role of bore and trunnion surface topography on the failure rate of total hip joint replacements due to trunnionosis is not clear despite significant variations in the design of taper components between manufacturers. Taper surface topography, along with other taper design parameters such as clearance, diameter, and assembly force, determine the initial interlock of the contacting surfaces after assembly; this has been related to relative motions that can cause fretting and corrosion at the taper interface. However, in most in-silico parametrical taper studies associated with taper micromotions, the bore and trunnion surfaces have been simplified using a flat surface and/or sinusoidal functions to mimic the surface roughness. The current study tests the hypothesis that the use of simple geometrical functions for the taper surface topography can predict the surface mechanics developed in assembled tapers. Measured and simulated surfaces of bores and trunnions were characterised using common roughness parameters and spectral density estimations. Using the same characterised surface profiles, 2D Finite Element (FE) models of CoCr alloy femoral heads and Ti alloy trunnions were developed. Models simulated assembly conditions at different resultant forces ranging from 0.5 to 4.0 kN, contact conditions were determined and associated with their topographical characteristics. Measured surfaces of bore and trunnion components comprise up to seven dominant spatial frequencies. Flattening of the trunnion microgrooved peaks was observed during the assembly of the taper. When the femoral head bore and trunnion topography were both considered a reduced number of microgrooved peaks were in contact, from 51 in an idealised taper surfaces to 35 in measured surfaces using an assembly reaction force of 4 kN. The contact points in the models developed high plastic strains, which were greater than that associated with failure of the material. Results showed that line and sine wave functions over estimate contact points at the taper interface compared to those surfaces that consider roughness and peak variation. These findings highlight the important role of modelling the full surface topography on the taper contact mechanics, as surface variations in the roughness and waviness change the performance of tapers.

Stochastic modeling of the receiver clock parameter in Galileo-only and multi-GNSS PPP solutions

In Precise Point Positioning (PPP), the receiver clock parameter is typically estimated independently in each observation epoch, which increases the noise of the estimated station coordinates and troposphere parameters due to correlations. Applying stochastic modeling to the receiver clock parameter stabilizes PPP solutions and reduces clock noise for the time transfer. However, the receiver clock modeling is possible only for the GNSS receivers connected to the utmost stable atomic clocks. We propose receiver clock modeling that involves the Markov stochastic process in the form of a random walk. We test different levels of random walk constraints for GNSS stations equipped with different types of clocks for Galileo-only and multi-GNSS solutions in kinematic and static PPP. In multi-GNSS solutions, the common clock parameter is derived with inter-system biases (ISBs). This raises the question of the constraints that should be imposed on the common clock only or also on the ISBs. We found that similar results can be achieved by imposing constraints on the common clock parameter and estimating ISB as a constant parameter and when constraining the common clock parameter and ISBs with a ratio of 1:100. Other ratios of clock-to-ISB constraints, such as 1:1 and 1:10, give inferior results. In the kinematic PPP, stochastic clock modeling has a marginal impact on the North and East coordinate components, whereas the Up component is substantially improved for GNSS receivers equipped with hydrogen masers. In the static PPP, the clock modeling improves the time transfer, due to the reduced noise of the clocks.

Bayesian nonparametric Erlang mixture modeling for survival analysis

Development of a flexible Erlang mixture model for survival analysis is introduced. The model for the survival density is built from a structured mixture of Erlang densities, mixing on the integer shape parameter with a common scale parameter. The mixture weights are constructed through increments of a distribution function on the positive real line, which is assigned a Dirichlet process prior. The model has a relatively simple structure, balancing flexibility with efficient posterior computation. Moreover, it implies a mixture representation for the hazard function that involves time-dependent mixture weights, thus offering a general approach to hazard estimation. Extension of the model is made to accommodate survival responses corresponding to multiple experimental groups, using a dependent Dirichlet process prior for the group-specific distributions that define the mixture weights. Model properties, prior specification, and posterior simulation are discussed, and the methodology is illustrated with synthetic and real data examples.

Diet-induced animal model anti-obesity, phytochemical profiling, and in silico analysis of culinary plant gokhru (Pedalium murex L.) mucilage

Plant-based diets (PBDs) are renowned for managing and developing bioactive chemical inhibitors to combat obesity, a well-known global public health concern. There are currently no published research studies examining the effects of food plant mucilage dietary supplements on animal models of obesity induced by high-fat diets (HFD). The present research investigated the anti-obesity properties of the culinary plant Pedalium murex L. mucilage (PMM) in obese albino male rats models fed HFD. PMM's HR-LCMS phytochemical profiling and in silico evaluation of anti-obesity and drug-likeness using Schrodinger’s Glide, QikProp, and GROMACS modules were also investigated. In vivo, anti-obesity model animal rat’s daily dietary intake, common blood biochemical parameters, and histological examination of the liver and kidney tissues for the development of macrovesicular and microvesicular steatosis were all performed. Among the 46 Phytochemicals profiled, 7(14)-Bisabolene-2, 3, 10,11tetrol, Moschamine, and N-Feruloyltyramine show prominent anti-obesity activity and drug-like characteristics in silico. Rats given PMM showed significantly lower serum levels of total cholesterol (TC), low-density lipoprotein (LDL), and triglycerides (TGs), increased levels of high-density lipoprotein (HDL), as well as macro-and microvesicular steatosis, lobular inflammation of the liver and kidney tissues. This suggests that PMM is an effective natural anti-obesity therapeutic ingredient or dietary supplement with a high concentration of anti-obesity phytochemicals that mainly satisfies the needs for such natural anti-obesity medicine or a supplement. Communicated by Ramaswamy H. Sarma

The intra‐matrix and gastrointestinal parameters affecting the bioaccessibility of food biopeptides

SummaryThe discussions around biopeptides generated from foods are associated with the numerous physiologic functions and therapeutic interventions that they potentiate, especially toward cardiovascular and degenerative diseases. The recurring issue about these biopeptides, however, is the wide gap left between laboratory experimentations and human intervention studies, apart from the fact that the conditions of testing or validation are not the same in both instances. Therefore, the gastrointestinal processing of biopeptides must be on par with their bioaccessibility, which is a critical step toward their bioavailability. The checks and balances of bioaccessibility are associated with the effects of the gastrointestinal enzymes, the interplay of other micro‐ and macro‐nutrients in whole or part, as well as the disruption of the integrity of the intestinal mucosal cells. We herein discussed the interplay of biopeptides with food matrices with regard to metal‐, phenol‐, lipid‐, and sugar‐peptide complexes, including mucins and other gastrointestinal enzyme secretion. These most common parameters and mechanistic activities led to the proposition of novel techniques for ensuring peptides' bioaccessibility and physiologic functions, a backdrop for futuristic studies that would contribute to our understanding and actualisation of bioaccessible peptides well incorporated and maintained in desirable functional food products.

Stratification of hypertension and SARS-CoV-2 infection by quantitative NMR spectroscopy of human blood serum

BackgroundDiagnostic approaches like the nuclear magnetic resonance spectroscopy (NMR) based quantification of metabolites, lipoproteins, and inflammation markers has helped to identify typical alterations in the blood serum of COVID-19 patients. However, confounders such as sex, and comorbidities, which strongly influence the metabolome, were often not considered. Therefore, the aim of this NMR study was to consider sex, as well as arterial hypertension (AHT), when investigating COVID-19-positive serum samples in a large age-and sex matched cohort.MethodsNMR serum data from 329 COVID-19 patients were compared with 305 healthy controls. 134 COVID-19 patients were affected by AHT. These were analyzed together with NMR data from 58 hypertensives without COVID-19. In addition to metabolite, lipoprotein, and glycoprotein data from NMR, common laboratory parameters were considered. Sex was considered in detail for all comparisons.ResultsHere, we show that several differences emerge from previous NMR COVID-19 studies when AHT is considered. Especially, the previously described triglyceride-rich lipoprotein profile is no longer observed in COVID-19 patients, nor an increase in ketone bodies. Further alterations are a decrease in glutamine, leucine, isoleucine, and lysine, citric acid, HDL-4 particles, and total cholesterol. Additionally, hypertensive COVID-19 patients show higher inflammatory NMR parameters than normotensive patients.ConclusionsWe present a more precise picture of COVID-19 blood serum parameters. Accordingly, considering sex and comorbidities should be included in future metabolomics studies for improved and refined patient stratification. Due to metabolic similarities with other viral infections, these results can be applied to other respiratory diseases in the future.

A Cautionary Note on the Use of Linear Regression for Hypothesis Testing

This note draws researchers’ attention to the use of linear regression for the purpose of conducting a hypothesis testing. Even when multiple explanatory variables are included in a regression equation to preclude the hazard of a simple regression of omitting other factors, multicollinearity unfortunately is inherent in multiple regression simply because the included explanatory variables can share some common parameter domain; that is, they co-vary. Here we shall show that however one transforms the variance-covariance matrix of the least-squares estimation to reduce the estimation errors, the procedure amounts to affine transformations of the explanatory variables so that despite the transformation they remain to co-vary, rendering the coefficient as a partial derivative invalid. The root of this problem originates from the fact that one receives the explanatory variables’ values by observation rather than predetermining their values and then collecting the corresponding dependent variable’ values. This situation becomes especially disconcerting when a transformed explanatory variable has its estimated coefficient enjoying an exceptional degree of confidence but of no mathematical status as a partial derivative, misleading engineering or medical prescriptions and public policies.

Dynamic Gait Stability and Spatiotemporal Gait Parameters During Overground Walking in Professional Ballet Dancers.

Introduction: It has been recognized that practicing ballet could strengthen the leg muscles, improve balance, and reduce fall risk. However, few studies have investigated how ballet practice alters a person's gait pattern, and this knowledge gap could present a barrier to designing ballet-based training programs. This study examined dynamic gait stability and spatiotemporal gait parameters among professional ballet dancers during normal level overground walking. Methods: Twenty young adults were recruited: 10 ballet dancers (24.5 ± 4.9 years) and 10 age- and sex-matched non-dancers (22.6 ± 3.4 years). Participants walked on a 10 m linear walkway at their self-selected speed. Dynamic gait stability and common gait parameters (step length, step width, gait speed, and cadence) were determined from the collected kinematic data and compared between groups with a significance level of .05. Results: The results showed that both groups displayed comparable dynamic gait stability at touchdown (P = .140) and liftoff (P = .638). However, ballet dancers walked with a longer (P = .054), narrower (P = .009), and faster step (P = .014) at a marginally quicker speed (P = .063) than non-dancers. Conclusion: Our study suggests that young professional ballet dancers have different gait patterns, but similar dynamic gait stability compared to non-dancers. These findings not only provide insight into the mechanisms of dynamic stability control among young ballet dancers during gait but expand our understanding of the control of dynamic gait balance of human locomotion across a wide variety of populations and walking conditions.

Criteria to Evaluate Tumor Enlargement During the Active Surveillance of High-Risk Thyroid Nodules: Which is Better, Diameter or Volume?

Tumor enlargement is the most common parameter identifying disease progression during active surveillance, but the value and significance of the changes in tumor diameter and volume in the evaluation of tumor growth have not been compared. This cohort study included 468 patients with high-risk thyroid nodule, in whom nodule size change was monitored using ultrasound, to compare the changes in tumor diameter and volume in assessing tumor growth. A total of 569 high-risk thyroid nodules were found in the 468 patients. A total of 14 nodules (2.5%) showed a diameter increase ≥ 3mm. The number of nodules with a peak volume change exceeding 50% and 100% was 185 (32.5%) and 86 (15.1%), respectively. Among the 555 stable nodules, the number of nodules with volume fluctuations exceeding 50% and 100% was 171 (30.8%) and 72 (13.0%), respectively. Among 212 stable nodules at the baseline and in the first three follow-up, the percentage of peak volume fluctuations exceeding 50% (48.5% vs. 28.5%, p = 0.004) and 100% (26.5% vs. 8.3%, p < 0.001) in the nodules with the sum of three diameters (SOTDs) ≤ 1cm was significantly higher than that of nodules with SOTDs > 1cm. A statistically significant difference was also found in the range distribution of SOTDs ≤ 1cm and SOTDs > 1cm (p = 0.007). Volume is not an appropriate method for determining tumor growth. Tumor diameter measurement alone serves as a better surrogate for disease progression in sonographically high-risk thyroid nodules than volume.

Development and Optimization of 11C-Labeled Radiotracers: A Review of the Modern Quality Control Design Process.

Introduction - Several 11C-tracers have demonstrated high potential in early diagnostic PET imaging applications of neurodegenerative diseases including Alzheimer's and Parkinson's disease. These radiotracers often track critical biomarkers in disease pathogenesis such as tau fibrils ([11C]PBB3) or β-amyloid plaques ([11C]PiB) associated with such diseases. Purpose - The short review aims to serve as a guideline in the future development of radiotracers for students, postdocs and/or new radiochemists who will be synthesizing clinical grade or novel research 11C-tracers, including knowledge of regulatory requirements. We aim to bridge the gap between novel and established 11C-tracer quality control (QC) processes through exploring the design process and regulatory requirements for 11C-pharmaceuticals. Methods - A literature survey was undertaken to identify articles with a detailed description of the QC methodology and characterization for each of the sections of the review. Overview - First a general summary of 11C-tracer production was presented; this was used to establish possible places for contamination or assurances for a sterile final product. The key mandated QC analyses for clinical use were then discussed. Further, we assessed the QC methods used for established 11C-tracers and then reviewed the routine QC tests for preclinical translational and validation studies. Therefore, both mandated QC methods for clinical and preclinical animal studies were reviewed. Last, some examples of optimization and automation were reviewed, and implications of the QC practices associated with such procedures were considered. Conclusion - All of the common QC parameters associated with 11C-tracers under clinical and preclinical settings (along with a few exceptions) were discussed in detail. While it is important to establish standard, peer-reviewed QC testing protocols for a novel 11C-tracer entering the clinical umbrella, equal importance is needed on preclinical applications to address credibility and repeatability for the study.

From Incremental Test to Continuous Running at Fixed Lactate Thresholds: Individual Responses on %VO2max, %HRmax, Lactate Accumulation, and RPE.

With Norway's successes in middle and long-distance running, lactate-guided threshold training has regained importance in recent years. Therefore, the aim of the present study was to investigate the individual responses on common monitoring parameters based on a lactate-guided conventional training method. In total, 15 trained runners (10 males, 5 females; 18.6 ± 3.3 years; VO2max: 59.3 ± 5.9 mL kg-1 min-1) completed a 40-min continuous running session at a fixed lactate threshold load of 2 mmol L-1. Lactate (La), oxygen uptake (VO2), heart rate (HR), and rating of perceived exertion (RPE) were recorded. The chosen workload led to lactate values of 2.85 ± 0.56 mmol L-1 (range: 1.90-3.80), a percentage of VO2max utilization (%VO2max) of 79.2 ± 2.5% (range: 74.9-83.8), a percentage of HRmax utilization (%HRmax) of 92.2 ± 2.5% (range: 88.1-95.3), and an RPE of 6.1 ± 1.9 (range: 3-10) at the end of the running session. Thereby, the individual responses differed considerably. These results indicate that a conventional continuous training method based on a fixed lactate threshold can lead to different individual responses, potentially resulting in various physiological impacts. Moreover, correlation analyses suggest that athletes with higher lactate threshold performance levels must choose their intensity in continuous training methods more conservatively (lower percentage intensity based on a fixed threshold) to avoid eliciting excessively strong metabolic responses.

Effective Sample Size with the Bivariate Gaussian Common Component Model

Effective sample size (ESS) consists of an equivalent number of sampling units of a georeferenced variable that would produce the same sampling error, as it considers the information that each georeferenced sampling unit contains about itself as well as in relation to its neighboring sampling units. This measure can provide useful information in the planning of future georeferenced sampling for spatial variability experiments. The objective of this article was to develop a bivariate methodology for ESS (ESSbi), considering the bivariate Gaussian common component model (BGCCM), which accounts both for the spatial correlation between the two variables and for the individual spatial association. All properties affecting the univariate methodology were verified for ESSbi using simulation studies or algebraic methods, including scenarios to verify the impact of the BGCCM common range parameter on the estimated ESSbi values. ESSbi was applied to real organic matter (OM) and sum of bases (SB) data from an agricultural area. The study found that 60% of the sample observations of the OM–SB pair contained spatially redundant information. The reduced sample configuration proved efficient by preserving spatial variability when comparing the original and reduced OM maps, using SB as a covariate. The Tau concordance index confirmed moderate accuracy between the maps.