bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automated vs manual cardiac MRI planning: a single-center prospective evaluation of reliability and scan times
Authors: Glessgen Carl , Crowe Lindsey A. , Wetzl Jens , Schmidt Michaela , Yoon Seung Su , Vallée Jean-Paul , Deux Jean-François 
Published: January 2025

Abstract

Objectives
Evaluating the impact of an AI-based automated cardiac MRI (CMR) planning software on procedure errors and scan times compared to manual planning alone.

Material and methods
Consecutive patients undergoing non-stress CMR were prospectively enrolled at a single center (August 2023–February 2024) and randomized into manual, or automated scan execution using prototype software. Patients with pacemakers, targeted indications, or inability to consent were excluded. All patients underwent the same CMR protocol with contrast, in breath-hold (BH) or free breathing (FB). Supervising radiologists recorded procedure errors (plane prescription, forgotten views, incorrect propagation of cardiac planes, and field-of-view mismanagement). Scan times and idle phase (non-acquisition portion) were computed from scanner logs. Most data were non-normally distributed and compared using non-parametric tests.

Results
Eighty-two patients (mean age, 51.6 years ± 17.5; 56 men) were included. Forty-four patients underwent automated and 38 manual CMRs. The mean rate of procedure errors was significantly (p = 0.01) lower in the automated (0.45) than in the manual group (1.13). The rate of error-free examinations was higher (p = 0.03) in the automated (31/44; 70.5%) than in the manual group (17/38; 44.7%). Automated studies were shorter than manual studies in FB (30.3 vs 36.5 min, p < 0.001) but had similar durations in BH (42.0 vs 43.5 min, p = 0.42). The idle phase was lower in automated studies for FB and BH strategies (both p < 0.001).

Conclusion
An AI-based automated software performed CMR at a clinical level with fewer planning errors and improved efficiency compared to manual planning.

Key Points

Question

What is the impact of an AI-based automated CMR planning software on procedure errors and scan times compared to manual planning alone?

Findings

Software-driven examinations were more reliable (71% error-free) than human-planned ones (45% error-free) and showed improved efficiency with reduced idle time.

Clinical relevance

CMR examinations require extensive technologist training, and continuous attention, and involve many planning steps. A fully automated software reliably acquired non-stress CMR potentially reducing mistake risk and increasing data homogeneity.
Computational analysis of musical elements across twenty-two European countries
Authors: Mihelač Lorena , Povh Janez 
Published: January 2025

Abstract
This work examines the similarities and differences between twenty-two European countries by using the computational model Information Dynamics of Music (IDyOM) to analyze various musical elements in folk songs, children’s folk songs, and children’s songs. The examination of the (dis)similarities between 22 European countries tests two hypotheses. First, it examines whether there are significant differences in the use of musical elements between European countries that are considered to have a common musical style. Secondly, it explores whether the musical elements used in the representative music of a particular country are more similar in countries with similar cultural, political, historical and economic backgrounds and geographical proximity. The results of the research, which compared the three genres across 22 European countries, revealed significant differences that highlight the unique ways in which these genres manifest themselves and how musical elements are integrated into the musical structure, suggesting that European countries do not possess a single musical style. Furthermore, some geographically distant countries have exhibited similarities, while other geographically close countries showed dissimilarities. This implies that either there is no shared musical foundation across different countries, or that the unique variations in musical expression within certain countries have had a significant influence on the overall population.
Automated Detection of Cancer-Suspicious Findings in Japanese Radiology Reports with Natural Language Processing: A Multicenter Study
Authors: Sugimoto Kento , Wada Shoya , Konishi Shozo , Sato Junya , Okada Katsuki , Kido Shoji , Tomiyama Noriyuki , Matsumura Yasushi , Takeda Toshihiro 
Published: January 2025

Abstract
Missed critical imaging findings, particularly those indicating cancer, are a common issue that can result in delays in patient follow-up and treatment. To address this, we developed a rule-based natural language processing (NLP) algorithm to detect cancer-suspicious findings from Japanese radiology reports. The dataset used consisted of chest and abdomen CT reports from six institutions. Reports from our institution were used for algorithm development and internal evaluation, while reports from the other five institutions were used for external evaluation. To create the gold standard, reports were annotated by two experienced physicians. Data were statistically analyzed using precision, recall and F1 score with 1000 bootstrap iterations. BERT was used as a baseline deep learning model, and its performance was compared with the proposed rule-based method. At the report level of detection, the overall precision, recall, and F-1 score were 0.886, 0.886, and 0.883, respectively, for the rule-based algorithm, which were higher than those of the deep learning algorithm (0.851, 0.679, and 0.733). The overall results include both internal and external validation data. For the internal validation set, the precision, recall, and F-1 score were 0.929, 0.929, and 0.927, respectively. For the external validation set, the precision, recall, and F-1 score were 0.875, 0.879, and 0.873, demonstrating generalizability. In conclusion, we show the rule-based NLP algorithm exhibited a high performance in detecting cancer-suspicious findings from multi-institutional CT reports.
Skewness Issues in Quantifying Efficiency: Insights from Stochastic Frontier Panel Models Based on Closed Skew Normal Approximations
Authors: Haschka Rouven E. , Wied Dominik 
Published: January 2025

Abstract
Typically, the inefficiency term in stochastic frontier models is assumed to be positively skewed; however, efficiency scores are biased if this assumption is violated. This paper considers the case in which negative skewness is also allowed in the model. The paper discusses estimation of a stochastic frontier panel model with unobserved fixed effects without having to identify additional parameters that determine skewness of inefficiency. On the one hand, the parameters can be estimated via integrating out nuisance parameters by means of marginal maximum likelihood. On the other hand, we propose an approximation based on closed skew normal distributions, which turns out to be sufficiently accurate for maximum likelihood estimation. Simulations assess the finite sample performance of estimators and show that all model parameters and efficiency scores can be estimated consistently regardless of positive or negative inefficiency skewness. An empirical analysis to unravel inefficiencies in the German healthcare system demonstrates the practical relevance of the model.
Missing value replacement in strings and applications
Authors: Bernardini Giulia , Liu Chang , Loukides Grigorios , Marchetti-Spaccamela Alberto , Pissis Solon P. , Stougie Leen , Sweering Michelle 
Published: January 2025

Abstract
Missing values arise routinely in real-world sequential (string) datasets due to: (1) imprecise data measurements; (2) flexible sequence modeling, such as binding profiles of molecular sequences; or (3) the existence of confidential information in a dataset which has been deleted deliberately for privacy protection. In order to analyze such datasets, it is often important to replace each missing value, with one or more valid letters, in an efficient and effective way. Here we formalize this task as a combinatorial optimization problem: the set of constraints includes the context of the missing value (i.e., its vicinity) as well as a finite set of user-defined forbidden patterns, modeling, for instance, implausible or confidential patterns; and the objective function seeks to minimize the number of new letters we introduce. Algorithmically, our problem translates to finding shortest paths in special graphs that contain forbidden edges representing the forbidden patterns. Our work makes the following contributions: (1) we design a linear-time algorithm to solve this problem for strings over constant-sized alphabets; (2) we show how our algorithm can be effortlessly applied to fully sanitize a private string in the presence of a set of fixed-length forbidden patterns [Bernardini et al. 2021a]; (3) we propose a methodology for sanitizing and clustering a collection of private strings that utilizes our algorithm and an effective and efficiently computable distance measure; and (4) we present extensive experimental results showing that our methodology can efficiently sanitize a collection of private strings while preserving clustering quality, outperforming the state of the art and baselines. To arrive at our theoretical results, we employ techniques from formal languages and combinatorial pattern matching.
Differential coding of the occurrence of negative eventualities and the nonoccurrence of eventualities in Japanese
Authors: Oshima David Y. 
Published: January 2025

Abstract
In Japanese, the use of a negative preterite (past-perfective) clause (“…V-nakatta”) is pragmatically constrained, and oftentimes a negative nonpast-nonperfective clause (“…V-te
inai”) is used where a preterite clause is expected. This work argues that the Japanese negative preterite invariably represents the occurrence of a “negative eventuality”—something whose existence and ontological nature have been extensively debated—rather than the “nonoccurrence” of eventualities. This has significant implications for the general-linguistic understanding of negation, (i) lending support to the supposition that negation comes in two varieties: “propositional” (or Boolean) negation, which expresses the nonoccurrence of eventualities, and “eventive” negation, which expresses the occurrence of a negative eventuality, and also (ii) implying that some negative predicate forms specifically express the latter. It will furthermore be argued that, while in general Japanese nonpast-tensed clauses specify that the topic time is the present or a future time, this is not necessarily the case with nonpast-nonperfective clauses, making it possible for a negative nonpast-nonperfective clause to represent the nonoccurrence of eventualities in a past topic time.
What is ‘teacher awareness’ and can teachers use it to overcome their expectation bias?– a thematic analysis of research
Authors: Lomholt Rune 
Published: January 2025

Abstract
In many teacher expectation studies, it is suggested that teachers can overcome expectation bias through enhanced awareness. In this exploratory paper, I extrapolate and map the conceptual demarcations of ‘teacher awareness’ based on a thematic analysis of twenty-six teacher expectation studies identified in a systematic literature search (2018–2023). I identify three analytical themes in the literature, and my analysis suggests that there is often a fundamental assumption of cognitive control attached to teacher awareness. In many teacher expectations studies, the formation of teacher expectations is characterised as an active process in which teachers integrate, assimilate and interpret student information using critical reasoning and analytical thinking. Furthermore, the analysis suggests that the conceptualisations of teacher awareness and the teacher expectation construct are interdependent. With reference to recent research which shows that teachers unconsciously process student information and have implicit bias when they form academic expectations, I argue that different conceptualisations of teacher awareness of expectation bias do not have equally practical applicability for teachers. By drawing on Dual Process Theory and educational sociological research, I argue that greater theoretical explication of the teacher expectation construct is necessary in future research to develop a concept of teacher awareness that is applicable to overcome expectation bias in teacher practice.
Mutual Relations Between Arbuscular Mycorrhizal Fungi and Ralstonia solanacearum in Potato Brown Rot Disease
Authors: El-Abeid S. E. , Balabel N. M. , Messiha N. A. S. 
Published: January 2025

Abstract
Potato brown rot disease can devastate potato production. Arbuscular mycorrhizal fungi (AMFs) are known to boost crop output. This study examines the interaction between AMFs and Ralstonia solanacearum, focusing on their effects on tuber yield and brown rot incidence. AMF increased the tuber yield by 68% (P = 0.03) in sandy soil and 30% (P = 0.047) in clay soil, along with an increase in HCO₃⁻ in sandy soil by 56% (P = 0.001) and a 24% increase in clay soil (P = 0.002). A 12% decrease in soil organic matter from 1.37 to 1.20% (P = 0.044) was recorded in sandy soil. AMF increased the pathogen populations in the rhizosphere from 3.2 to 7.8 (log CFU/g, P = 0.02) in sandy soil without a significant increase in Area Under the Disease Progression Curve. The pathogen decreased AMF colonization by 47% in sandy soil and 48% in clay soil, as well as sporulation by 63% in sandy and 57% in clay soils respectively (P < 0.05 for all). It decreased the densities of phosphate-solubilizing bacteria from 6.19 up to 5.39 (P = 0.001) in sandy soil and from 6.04 to 5.16 (P = 0.008) in clay soil. Additionally, the pathogen decreased the silicate-solubilizing bacteria from 6.94 to 5.32 (P < 0.001) in sandy and from 6.82 up to 6.17 (P = 0.04) in clay soils respectively. In conclusion, while AMF significantly increased potato yield, it is not recommended for soils infested with R. solanacearum due to the potential increase in disease risk.

Graphical Abstract
Investigative Interviewers’ Peer Feedback On a Child Forensic Interview
Authors: Sharman Stefanie J. , Danby Meaghan C. 
Published: January 2025

Abstract
Ongoing feedback following training helps support child forensic interviewers’ adherence to best practice techniques in the field. Given the challenges in employing experts to deliver this feedback, having trained interviewers provide peer feedback may be more feasible. Across two studies, we examined the accuracy and quality of trained child interviewers’ peer reviews. Two samples of police officers recently trained in child forensic interviewing were recruited (Study 1 n = 60; Study 2 n = 63). Participants reviewed a transcript of a fictional child interview. They rated how well the interviewer adhered to best practice in each phase of the interview, gave comments to justify each rating, and provided two paragraphs of feedback for the interviewer. Compared to experts’ ratings, participants rated the interviewer as more closely adhering to best practice in all interview phases (except the episodic memory training phase in Study 2) and identified significantly fewer elements of best practice in their explanations and feedback paragraphs. Overall, participants identified more negative than positive elements of the interviewer’s practice, but participants given instructions on how to write quality feedback provided more positive comments and used a ‘feedback sandwich’ structure. Participants in Study 2 (who had less training in child interviewing) found the task more difficult than those in Study 1. These studies demonstrate that recently trained child interviewers provided somewhat useful peer feedback, but it was more limited—and more positive—than experts’ feedback. Being informed about providing feedback increased the quality of participants’ feedback, although the improvement was small.
Prevalence of metabolic syndrome among pregnant women: a systematic review and meta-analysis
Authors: Mohebi A. , Pathirana MM , Khoja A. , Wittwer MR , Lowe K. , Fisher D. , Kharwadkar S. , Gomes C. , Gamage T. , Toyer E. , Young S. , Arstall MA , Andraweera PH 
Published: January 2025

Abstract

Purpose
Metabolic syndrome (MetS) is a cluster of risk factors that increase the risk of cardiometabolic diseases. The prevalence of MetS and individual components across pregnancy has not been reviewed in the literature. This research was conducted to identify the prevalence of MetS and its components among pregnant women.

Methods
The PubMed, EMBASE, CINAHL, Web of Science and Scopus databases were searched. The review protocol is registered in PROSPERO (CRD42023460729). Quality assessment was performed using the JBI critical appraisal checklist. The study selection, data extraction and data analyses were performed in accordance with the MOOSE guidelines.

Results
The prevalence of MetS among pregnant women was 16.3%, (n = 3946). The prevalences for individual MetS components were: low HDL, 12.3% (n = 1108); high fasting glucose, 16.2% (n = 2333); high triglycerides, 48.5% (n = 2880); obesity, 42.7% (n = 5162) and high blood pressure 37.7% (n = 828). According to the definitions used to diagnose MetS, the prevalences were 18.2% according to the World Health Organization, 15.0% according to the International Diabetes Federation and 17.2% according to the National Cholesterol Education Program Adult Treatment Panel III. When stratified by gestational age at assessment, the prevalence of MetS was 9.9% before 16 weeks’ and 24.1% after 20 weeks’ of gestation.

Conclusion
This review demonstrates that MetS is detected in approximately one-fifth of pregnant women. Screening for MetS and its components during pregnancy may help identify young women at risk for future cardiovascular disease.
Monitoring the rehabilitation process of the windthrow area using UAS images and performance comparison of Sentinel-2A based different vegetation indexes
Authors: Çinar Tunahan , Uslu Ayşegül , Aydin Abdurrahim 
Published: January 2025

Abstract
Windthrows significantly disrupt forest ecosystems, impacting biotic community life cycles. To ensure the reformation of the ecosystem chain, it is essential to rehabilitate the windthrow area as soon as possible. Therefore, it is mandotory to determine the success of the rehabilitation processes. In this study, the rehabilitation process of windthrow that occurred in the Düzce Tatlıdere Forest District (DTFD) was identified using vegetation indices calculated from Unmanned Aircraft System (UAS) images and Sentinel-2A satellite images between 2017 and 2022. The Normalized Difference Red Edge Index (NDRE), Plant Senescence Reflectance Index (PSRI), and Normalized Difference Vegetation Index (NDVI) were calculated from Sentinel-2A satellite images, and the most successful index for detecting reforested areas was identified. UAS images were used to create training data, and this data was used to classify Sentinel-2A images with the Random Forest (RF) algorithm. The classification’s accuracy was assessed using the Kappa Coefficient and Overall Accuracy (%). Results showed that NDVI had the lowest accuracy in both years, whereas NDRE succesfully detected windthrow area borders. PSRI was most successful in monitoring rehabilitation progress and detecting reforested areas between 2017 and 2022. This study, he effectiveness and limitations of the NDRE, PSRI and NDVI indices in the rehabilitation process of the windthrow area have been detected, and the most important Sentinel-2A bands were determined based on the results of the RF classification. This study is pioneering in the use of NDRE and PSRI to detect reforested areas post-windthrow.
Convergence of nanotechnology and artificial intelligence in the fight against liver cancer: a comprehensive review
Authors: Bhange Manjusha , Telange Darshan 
Published: January 2025

Abstract
Liver cancer is one of the most challenging malignancies, often associated with poor prognosis and limited treatment options. Recent advancements in nanotechnology and artificial intelligence (AI) have opened new frontiers in the fight against this disease. Nanotechnology enables precise, targeted drug delivery, enhancing the efficacy of therapeutics while minimizing off-target effects. Simultaneously, AI contributes to improved diagnostic accuracy, predictive modeling, and the development of personalized treatment strategies. This review explores the convergence of nanotechnology and AI in liver cancer treatment, evaluating current progress, identifying existing research gaps, and discussing future directions. We highlight how AI-powered algorithms can optimize nanocarrier design, facilitate real-time monitoring of treatment efficacy, and enhance clinical decision-making. By integrating AI with nanotechnology, clinicians can achieve more accurate patient stratification and treatment personalization, ultimately improving patient outcomes. This convergence holds significant promise for transforming liver cancer therapy into a more precise, individualized, and efficient process. However, data privacy, regulatory hurdles, and the need for large-scale clinical validation remain. Addressing these issues will be essential to fully realizing the potential of these technologies in oncology.
Adsorptive removal of toxic methylene blue and crystal violet dyes from aqueous solutions using Prunus spinosa: isotherm, kinetic, thermodynamic, and error analysis
Authors: Onuk Elif Burcu , Isik Birol 
Published: January 2025

Abstract
In this study, it was aimed to use Prunus spinosa L. fruit pulp as an adsorbent zero-waste and low-cost for the removal of toxic methylene blue (MB) and crystal violet (CV) dyes from aqueous solutions. The adsorbent was characterized utilizing FTIR-ATR, SEM, and pHpzc tests. The pHpzc value of the adsorbent is 4.96. According to optimization experiments, the optimum adsorbent dosage was determined as 0.05 g/50 mL for MB and CV dyes, the optimum pH values were determined as approximately 7 for MB and CV dyes, and the optimum contact time was determined as 45 min for MB and 30 min for CV dyes. The Langmuir model has been used to calculate the maximum adsorption capacities of MB and CV dyes at a temperature of 298 K. The obtained values are 59.59 mg/g for MB and 53.19 mg/g for CV. The experimental data for Prunus spinosa L. for both dyes exhibited a pseudo-second-order kinetic model. According to error analyses, the reproducibility and applicability of isotherm and kinetic models were investigated. From thermodynamic results, the enthalpy values were calculated as − 42.04 kJ/mol for MB and − 24.08 kJ/mol for CV dyes, which indicates that the process is exothermic. Also, the Gibbs free energies of MB and CV dyes were determined as − 34.20 kJ/mol and − 32.33 kJ/mol at 298 K, which indicates the process is spontaneous. Research and comparisons with other adsorbents have demonstrated that Prunus spinosa L. is a cost-effective and appealing choice for removing MB and CV dyes from water solutions.

Graphical Abstract