bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Ability for Teaching Self-Efficacy by Clinical Preceptors in Diagnostic Medical Sonography
Authors: Peak Katherine 
Published: October 2023

Objective: This study was designed to explore the teaching self-efficacy, by clinical preceptors instructing sonography students, in bachelor’s degree-accredited sonography programs. Materials and Methods: A survey was provided to sonography clinical preceptors, to gauge their level of teaching self-efficacy. The survey instrument, Sonography Preceptor Teaching Self-Efficacy Inventory (SPTSEI) was used. The intent of the survey was to gauge, holistically, sonography preceptors’ perceptions of influencing student learning, in a clinical setting. Results: The survey respondents suggested that they had a high level of confidence in their skill to transmit knowledge and abilities to students. However, it was found that preceptors who had received some type of professional development, as preceptor, had a statistically significant higher level of teaching self-efficacy than those who had not received any type of training. Conclusion: Clinical preceptors are skilled health care professionals in their chosen discipline but may have little to no teaching experience. If preceptors do not fully understand their role in the educational process, both they and their students may not attain educational goals. It is therefore critically important that proper training and education, to support clinical preceptors, be provided for them. This survey would suggest that clinical preceptors, with training, can better perform their educational roles and better handle any challenges that might arise, as a part of providing clinical education.
The Flow Volume and Resistance Index of Patent Arteriovenous Fistulas, With Proper Function, Compared to Unhealthy Arteriovenous Fistulas, Using Spectral Doppler
Authors: Heydari Negar , Akhoundi Neda , Shakeri-Bavil Abdolhassan , Jalili Javad , Heydari Elnaz , Siami Alireza , Etemadi Jalal , Sedghian Sonia , Naseri Zahra , Pourasghary Sajjad 
Published: October 2023

Objective: This study was designed to use sonography and evaluate arteriovenous fistulas (AVFs) in dialysis patients. The aim was to determine criteria for distinguishing between patent AVFs with proper function (healthy) and unhealthy AVFs. Materials and Methods: This study enrolled 106 dialysis patients with an AVF. All fistulas were evaluated using spectral Doppler and grayscale sonography, assessing parameters such as draining vein diameter, distance of the venous vein from the skin surface, feeding artery diameter, presence of thrombosis, and fistula stenosis. Subsequently, Doppler criteria such as flow volume (FV) and resistance index (RI) were measured and compared between healthy and unhealthy AVFs. Results: This cohort’s spectral Doppler findings demonstrated lower FV and higher RI, in unhealthy AVFs, compared to healthy ones (283.5 ± 101.26 mL/min vs 1383.43 ± 756.76 mL/min; P = .006) and (0.55 ± 0.33 vs 0.37 ± 0.14; P = .004) respectively. Conclusion: The spectral Doppler and grayscale sonographic imaging made it possible to evaluate AVF performance and determine its quality. In this cohort, a decreased FV, increased RI, and decreased draining vein diameter were observed in unhealthy AVFs. These results would suggest that sonographers and clinicians should pay close attention to these parameters when assessing the function of an AVF, for patients undergoing hemodialysis.
A Comparative Study on Kinematic Calibration for a 3-DOF Parallel Manipulator Using the Complete-Minimal, Inverse-Kinematic and Geometric-Constraint Error Models
Authors: Wu Haiyu , Kong Lingyu , Li Qinchuan , Wang Hao , Chen Genliang 
Published: October 2023

AbstractKinematic calibration is a reliable way to improve the accuracy of parallel manipulators, while the error model dramatically affects the accuracy, reliability, and stability of identification results. In this paper, a comparison study on kinematic calibration for a 3-DOF parallel manipulator with three error models is presented to investigate the relative merits of different error modeling methods. The study takes into consideration the inverse-kinematic error model, which ignores all passive joint errors, the geometric-constraint error model, which is derived by special geometric constraints of the studied RPR-equivalent parallel manipulator, and the complete-minimal error model, which meets the complete, minimal, and continuous criteria. This comparison focuses on aspects such as modeling complexity, identification accuracy, the impact of noise uncertainty, and parameter identifiability. To facilitate a more intuitive comparison, simulations are conducted to draw conclusions in certain aspects, including accuracy, the influence of the S joint, identification with noises, and sensitivity indices. The simulations indicate that the complete-minimal error model exhibits the lowest residual values, and all error models demonstrate stability considering noises. Hereafter, an experiment is conducted on a prototype using a laser tracker, providing further insights into the differences among the three error models. The results show that the residual errors of this machine tool are significantly improved according to the identified parameters, and the complete-minimal error model can approach the measurements by nearly 90% compared to the inverse-kinematic error model. The findings pertaining to the model process, complexity, and limitations are also instructive for other parallel manipulators.
Genomic prediction based on selective linkage disequilibrium pruning of low-coverage whole-genome sequence variants in a pure Duroc population
Authors: Zhu Di , Zhao Yiqiang , Zhang Ran , Wu Hanyu , Cai Gengyuan , Wu Zhenfang , Wang Yuzhe , Hu Xiaoxiang 
Published: October 2023

Abstract
Background
Although the accumulation of whole-genome sequencing (WGS) data has accelerated the identification of mutations underlying complex traits, its impact on the accuracy of genomic predictions is limited. Reliable genotyping data and pre-selected beneficial loci can be used to improve prediction accuracy. Previously, we reported a low-coverage sequencing genotyping method that yielded 11.3 million highly accurate single-nucleotide polymorphisms (SNPs) in pigs. Here, we introduce a method termed selective linkage disequilibrium pruning (SLDP), which refines the set of SNPs that show a large gain during prediction of complex traits using whole-genome SNP data.

Results
We used the SLDP method to identify and select markers among millions of SNPs based on genome-wide association study (GWAS) prior information. We evaluated the performance of SLDP with respect to three real traits and six simulated traits with varying genetic architectures using two representative models (genomic best linear unbiased prediction and BayesR) on samples from 3579 Duroc boars. SLDP was determined by testing 180 combinations of two core parameters (GWAS P-value thresholds and linkage disequilibrium r2). The parameters for each trait were optimized in the training population by five fold cross-validation and then tested in the validation population. Similar to previous GWAS prior-based methods, the performance of SLDP was mainly affected by the genetic architecture of the traits analyzed. Specifically, SLDP performed better for traits controlled by major quantitative trait loci (QTL) or a small number of quantitative trait nucleotides (QTN). Compared with two commercial SNP chips, genotyping-by-sequencing data, and an unselected whole-genome SNP panel, the SLDP strategy led to significant improvements in prediction accuracy, which ranged from 0.84 to 3.22% for real traits controlled by major or moderate QTL and from 1.23 to 11.47% for simulated traits controlled by a small number of QTN.

Conclusions
The SLDP marker selection method can be incorporated into mainstream prediction models to yield accuracy improvements for traits with a relatively simple genetic architecture, however, it has no significant advantage for traits not controlled by major QTL. The main factors that affect its performance are the genetic architecture of traits and the reliability of GWAS prior information. Our findings can facilitate the application of WGS-based genomic selection.
Artificial Intelligence based wrapper for high dimensional feature selection
Authors: Jain Rahi , Xu Wei 
Published: October 2023

AbstractBackgroundFeature selection is important in high dimensional data analysis. The wrapper approach is one of the ways to perform feature selection, but it is computationally intensive as it builds and evaluates models of multiple subsets of features. The existing wrapper algorithm primarily focuses on shortening the path to find an optimal feature set. However, it underutilizes the capability of feature subset models, which impacts feature selection and its predictive performance.Method and ResultsThis study proposes a novel Artificial Intelligence based Wrapper (AIWrap) algorithm that integrates Artificial Intelligence (AI) with the existing wrapper algorithm. The algorithm develops a Performance Prediction Model using AI which predicts the model performance of any feature set and allows the wrapper algorithm to evaluate the feature subset performance in a model without building the model. The algorithm can make the wrapper algorithm more relevant for high-dimensional data. We evaluate the performance of this algorithm using simulated studies and real research studies. AIWrap shows better or at par feature selection and model prediction performance than standard penalized feature selection algorithms and wrapper algorithms.ConclusionAIWrap approach provides an alternative algorithm to the existing algorithms for feature selection. The current study focuses on AIWrap application in continuous cross-sectional data. However, it could be applied to other datasets like longitudinal, categorical and time-to-event biological data.
Asterics: a simple tool for the ExploRation and Integration of omiCS data
Authors: Maigné Élise , Noirot Céline , Henry Julien , Adu Kesewaah Yaa , Badin Ludovic , Déjean Sébastien , Guilmineau Camille , Krebs Arielle , Mathevet Fanny , Segalini Audrey , Thomassin Laurent , Colongo David , Gaspin Christine , Liaubet Laurence , Vialaneix Nathalie 
Published: October 2023

AbstractBackgroundThe rapid development of omics acquisition techniques has induced the production of a large volume of heterogeneous and multi-level omics datasets, which require specific and sometimes complex analyses to obtain relevant biological information. Here, we present ASTERICS (version 2.5), a publicly available web interface for the analyses of omics datasets.ResultsASTERICS is designed to make both standard and complex exploratory and integration analysis workflows easily available to biologists and to provide high quality interactive plots. Special care has been taken to provide a comprehensive documentation of the implemented analyses and to guide users toward sound analysis choices regarding some specific omics data. Data and analyses are organized in a comprehensive graphical workflow within ASTERICS workspace to facilitate the understanding of successive data editions and analyses leading to a given result.ConclusionASTERICS provides an easy to use platform for omics data exploration and integration. The modular organization of its open source code makes it easy to incorporate new workflows and analyses by external contributors. ASTERICS is available athttps://asterics.miat.inrae.frand can also be deployed using provided docker images.
Exogenous auxin regulates the growth and development of peach fruit at the expansion stage by mediating multiple-hormone signaling
Authors: Zhang Yanping , Su Ziwen , Luo Linjia , Wang Pengkai , Zhu Xudong , Liu Jiecai , Wang Chen 
Published: October 2023

Abstract
Background
Fruit expansion stage is crucial to fruit yield and quality formation, and auxin plays a significant role by mediating multi-hormone signals during fruit expansion. However, till now, it is still unclear of the molecular regulatory network during auxin-mediated peach fruit expansion.

Results
Here, exogenous NAA application markedly increased IAA content and drastically decreased ABA content at the fruit expansion stage. Correspondingly, NAA mainly induced the auxin biosynthesis gene (1 PpYUCCA) and early auxin-responsive genes (7PpIAA, 3 PpGH3, and 14 PpSAUR); while NAA down-regulated ABA biosynthesis genes (2 PpNCED, 1 PpABA3, and 1 PpAAO3). In addition, many DEGs involved in other plant hormone biosynthesis and signal transduction were significantly enriched after NAA treatment, including 7 JA, 7 CTK, 6 ETH, and 3 GA. Furthermore, we also found that NAA treatment down-regulated most of genes involved in the growth and development of peach fruit, including the cell wall metabolism-related genes (PpEG), sucrose metabolism-related genes (PpSPS), phenylalanine metabolism-related genes (PpPAL, Pp4CL, and PpHCT), and transcription factors (PpNAC, PpMADS-box, PpDof, PpSBP, and PpHB).

Conclusion
Overall, NAA treatment at the fruit expansion stage could inhibit some metabolism processes involved in the related genes in the growth and development of peach fruit by regulating multiple-hormone signaling networks. These results help reveal the short-term regulatory mechanism of auxin at the fruit expansion stage and provide new insights into the multi-hormone cascade regulatory network of fruit growth and development.
Detecting influential subjects in intensive longitudinal data using mixed-effects location scale models
Authors: Zhang Xingruo , Hedeker Donald 
Published: October 2023

AbstractBackgroundCollection of intensive longitudinal health outcomes allows joint modeling of their mean (location) and variability (scale). Focusing on the location of the outcome, measures to detect influential subjects in longitudinal data using standard mixed-effects regression models (MRMs) have been widely discussed. However, no existing approach enables the detection of subjects that heavily influence the scale of the outcome.MethodsWe propose applying mixed-effects location scale (MELS) modeling combined with commonly used influence measures such as Cook’s distance and DFBETAS to fill this gap. In this paper, we provide a framework for researchers to follow when trying to detect influential subjects for both the scale and location of the outcome. The framework allows detailed examination of each subject’s influence on model fit as well as point estimates and precision of coefficients in different components of a MELS model.ResultsWe simulated two common scenarios in longitudinal healthcare studies and found that influence measures in our framework successfully capture influential subjects over 99% of the time. We also re-analyzed data from a health behavior study and found 4 particularly influential subjects, among which two cannot be detected by influence analyses via regular MRMs.ConclusionThe proposed framework can help researchers detect influential subject(s) that will be otherwise overlooked by influential analysis using regular MRMs and analyze all data in one model despite influential subjects.
Comparing methods to predict baseline mortality for excess mortality calculations
Authors: Ferenci Tamás 
Published: October 2023

Abstract
Background
The World Health Organization (WHO)’s excess mortality estimates presented in May 2022 stirred controversy, due in part to the high estimate provided for Germany, which was later attributed to the spline model used. This paper aims to reproduce the problem using synthetic datasets, thus allowing the investigation of its sensitivity to parameters, both of the mortality curve and of the used method, thereby shedding light on the conditions that gave rise to this error and identifying possible remedies.

Methods
A negative binomial model was used accounting for long-term change, seasonality, flu seasons, and heat waves. Simulated mortality curves from this model were then analysed using simple methods (mean, linear trend), the WHO method, and the method of Acosta and Irizarry.

Results
The performance of the WHO’s method with its original parametrization was indeed very poor, however it can be profoundly improved by a better choice of parameters. The Acosta–Irizarry method outperformed the WHO method despite being also based on splines, but it was also dependent on its parameters. Linear extrapolation could produce very good results, but was highly dependent on the choice of the starting year, while the average was the worst in almost all cases.

Conclusions
Splines are not inherently unsuitable for predicting baseline mortality, but caution should be taken. In particular, the results suggest that the key issue is that the splines should not be too flexible to avoid overfitting. Even after having investigated a limited number of scenarios, the results suggest that there is not a single method that outperforms the others in all situations. As the WHO method on the German data illustrates, whatever method is chosen, it remains important to visualize the data, the fit, and the predictions before trusting any result. It will be interesting to see whether further research including other scenarios will come to similar conclusions.
Lifestyle-based nomogram for identifying the Chaoshan inhabitants of China at high risk of Helicobacter pylori infection
Authors: Lin Yi-ting , Wang Pei-ru , Xue Wen-wen , Zhou Si-si , Huang Ze-yu , Li Yu-ting , Zheng Zhuo-na , Hou Wen-jing , Chen Qi-xian , Yu Jing 
Published: October 2023

Abstract
Background
Helicobacter pylori (HP) infection is associated with various diseases. Early detection can prevent the onset of illness. We constructed a nomogram to predict groups at high risk of HP infection.

Methods
Patients who underwent regular medical check-ups at hospital in Chaoshan, China from March to September 2022 were randomly allocated to the training and validation cohorts. Risk factors including basic characteristics and lifestyle habits associated with HP infection were analyzed by logistic regression analyses. The independent varieties were calculated and plotted into a nomogram. The nomogram was internally validated by receiver operating characteristic curve, calibration, and decision curve analyses (DCAs).

Results
Of the 945 patients, 680 were included in the training cohort and 265 in the validation cohort. 356 patients in training cohort with positive 13 C-UBT results served as the infected group, and 324 without infection were the control group. The multivariate regression analyses showed that the risk factors for HP infection included alcohol consumption (OR = 1.29, 95%CI = 0.78–2.13, P = 0.03), family history of gastric disease (OR = 4.35, 95%CI = 1.47–12.84, P = 0.01), living with an HP-positive individual (OR = 18.09, 95%CI = 10.29–31.82, P < 0.0001), drinking hot tea (OR = 1.58, 95%CI = 1.05–2.48, P = 0.04), and infection status of co-drinkers unknown (OR = 2.29, 95%CI = 1.04–5.06, P = 0.04). However, drinking tea > 3 times per day (OR = 0.56, 95%CI = 0.33–0.95, P = 0.03), using serving chopsticks (OR = 0.30, 95%CI = 0.12–0.49, P < 0.0001) were protective factors for HP infection. The nomogram had an area under the curve (AUC) of 0.85 in the training cohort. The DCA was above the reference line within a large threshold range, indicating that the model was better. The calibration analyses showed the actual occurrence rate was basically consistent with the predicted occurrence rate. The model was validated in the validation cohort, and had a good AUC (0.80), DCA and calibration curve results.

Conclusions
This nomogram, which incorporates basic characteristics and lifestyle habits, is an efficient model for predicting those at high risk of HP infection in the Chaoshan region.
Association between ICU admission (neutrophil + monocyte)/lymphocyte ratio and 30-day mortality in patients with sepsis: a retrospective cohort study
Authors: Guo Manliang , He Wanmei , Mao Xueyan , Luo Yuling , Zeng Mian 
Published: October 2023

Abstract
Background
Sepsis is an important public health issue, and it is urgent to develop valuable indicators to predict the prognosis of sepsis. Our study aims to assess the predictive value of ICU admission (Neutrophil + Monocyte)/lymphocyte ratio (NMLR) on the 30-day mortality of sepsis patients.

Methods
A retrospective analysis was conducted in septic patients, and the data were collected from Medical Information Mart for Intensive Care IV (MIMIC-IV). Univariate and multivariate Cox regression analyses were conducted to investigate the relation between ICU admission NMLR and 30-day mortality. Restricted cubic spline (RCS) was performed to determine the optimum cut-off value of ICU admission NMLR. Survival outcomes of the two groups with different ICU admission NMLR levels were estimated using the Kaplan-Meier method and compared by the log-rank test.

Results
Finally, 7292 patients were recruited in the study, of which 1601 died within 30 days of discharge. The non-survival group had higher ICU admission NMLR values than patients in the survival group (12.24 [6.44–23.67] vs. 8.71 [4.81–16.26], P < 0.001). Univariate and multivariate Cox regression analysis demonstrated that ICU admission NMLR was an independent prognostic predictor on 30-day mortality (Univariate: P < 0.001; multivariate: P = 0.011). The RCS model demonstrated the upturn and non-linear relationship between ICU admission NMLR and 30-day mortality (Nonlinearity: P = 0.0124). According to the KM curve analysis,30-day survival was worse in the higher ICU admission NMLR group than that in the lower ICU admission NMLR group (Log rank test, P < 0.0001).

Conclusion
The elevated ICU admission NMLR level is an independent risk factor for high 30-day mortality in patients with sepsis.