bluuebunny/crossref_metadata_2025_split huggingface dataset:
The impact of PA/I38 substitutions and PA polymorphisms on the susceptibility of zoonotic influenza A viruses to baloxavir
Authors: Taniguchi Keiichi , Noshi Takeshi , Omoto Shinya , Sato Akihiko , Shishido Takao , Matsuno Keita , Okamatsu Masatoshi , Krauss Scott , Webby Richard J , Sakoda Yoshihiro , Kida Hiroshi 
Published: January 2024

AbstractGenetic reassortment of avian, swine, and human influenza A viruses (IAVs) poses potential pandemic risks. Surveillance is important for influenza pandemic preparedness, but the susceptibility of zoonotic IAVs to the cap-dependent endonuclease inhibitor baloxavir acid (BXA) has not been thoroughly researched. Although an amino acid substitution at position 38 in the polymerase acidic protein (PA/I38) in seasonal IAVs reduces BXA susceptibility, PA polymorphisms at position 38 are rarely seen in zoonotic IAVs. Here, we examined the impact of PA/I38 substitutions on the BXA susceptibility of recombinant A(H5N1) viruses. PA mutants that harbored I38T, F, and M were 48.2-, 24.0-, and 15.5-fold less susceptible, respectively, to BXA than wild-type A(H5N1) but were susceptible to the neuraminidase inhibitor oseltamivir acid and the RNA polymerase inhibitor favipiravir. PA mutants exhibited significantly impaired replicative fitness in Madin-Darby canine kidney cells at 24 h postinfection. In addition, in order to investigate new genetic markers for BXA susceptibility, we screened geographically and temporally distinct IAVs isolated worldwide from birds and pigs. The results showed that BXA exhibited antiviral activity against avian and swine viruses with similar levels to seasonal isolates. All viruses tested in the study lacked the PA/I38 substitution and were susceptible to BXA. Isolates harboring amino acid polymorphisms at positions 20, 24, and 37, which have been implicated in the binding of BXA to the PA endonuclease domain, were also susceptible to BXA. These results suggest that monitoring of the PA/I38 substitution in animal-derived influenza viruses is important for preparedness against zoonotic influenza virus outbreaks.
A short review of the effect of external pressure on the batteries
Authors: Šedina Martin , Šimek Antonín , Báňa Jiří , Kazda Tomáš 
Published: January 2024

AbstractThe research of the batteries is still going forward and there are lots of challenges which should be solved. This text examines the effect of external pressure on different types of batteries and explores their potential for improving performance and lifetime. The studies reviewed in the text show interesting results where external pressure affects capacity, internal resistance, stability or other parameters of modern battery systems as Li-ion, solid-state, or Li–S batteries. Despite the challenges, the benefits for next-generation batteries seem promising and show its role in battery development and manufacturing.
Graphical abstract
Input/output-style approach to standardized traditional amortization plans
Authors: Pressacco Flavio , Ziani Laura 
Published: January 2024

AbstractWe apply an Input/Output approach to the analysis of the so-called traditional standardized amortization plans (STAP). The approach is based on a clear distinction between Input/trigger and Output of a STAP. The Output is a set of four vectors obtained by applying a set of transformation matrices to an appropriate Input/trigger vector. The main result of the paper is that, by this approach, any STAP reveals a dual nature through the introduction of the twin trigger theory.
Natural Language Processing Algorithm Used for Staging Pulmonary Oncology from Free-Text Radiological Reports: “Including PET-CT and Validation Towards Clinical Use”
Authors: Nobel J. Martijn , Puts Sander , Krdzalic Jasenko , Zegers Karen M. L. , Lobbes Marc B. I. , F. Robben Simon G. , Dekker André L. A. J. 
Published: January 2024

AbstractNatural language processing (NLP) can be used to process and structure free text, such as (free text) radiological reports. In radiology, it is important that reports are complete and accurate for clinical staging of, for instance, pulmonary oncology. A computed tomography (CT) or positron emission tomography (PET)-CT scan is of great importance in tumor staging, and NLP may be of additional value to the radiological report when used in the staging process as it may be able to extract the T and N stage of the 8th tumor–node–metastasis (TNM) classification system. The purpose of this study is to evaluate a new TN algorithm (TN-PET-CT) by adding a layer of metabolic activity to an already existing rule-based NLP algorithm (TN-CT). This new TN-PET-CT algorithm is capable of staging chest CT examinations as well as PET-CT scans. The study design made it possible to perform a subgroup analysis to test the external validation of the prior TN-CT algorithm. For information extraction and matching, pyContextNLP, SpaCy, and regular expressions were used. Overall TN accuracy score of the TN-PET-CT algorithm was 0.73 and 0.62 in the training and validation set (N = 63, N = 100). The external validation of the TN-CT classifier (N = 65) was 0.72. Overall, it is possible to adjust the TN-CT algorithm into a TN-PET-CT algorithm. However, outcomes highly depend on the accuracy of the report, the used vocabulary, and its context to express, for example, uncertainty. This is true for both the adjusted PET-CT algorithm and for the CT algorithm when applied in another hospital.
Development of End-to-End AI–Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation
Authors: Santinha João , Katsaros Vasileios , Stranjalis George , Liouta Evangelia , Boskos Christos , Matos Celso , Viegas Catarina , Papanikolaou Nickolas 
Published: January 2024

AbstractRadiogenomics has shown potential to predict genomic phenotypes from medical images. The development of models using standard-of-care pre-operative MRI images, as opposed to advanced MRI images, enables a broader reach of such models. In this work, a radiogenomics model for IDH mutation status prediction from standard-of-care MRIs in patients with glioma was developed and validated using multicentric data. A cohort of 142 (wild-type: 32.4%) patients with glioma retrieved from the TCIA/TCGA was used to train a logistic regression model to predict the IDH mutation status. The model was evaluated using retrospective data collected in two distinct hospitals, comprising 36 (wild-type: 63.9%) and 53 (wild-type: 75.5%) patients. Model development utilized ROC analysis. Model discrimination and calibration were used for validation. The model yielded an AUC of 0.741 vs. 0.716 vs. 0.938, a sensitivity of 0.784 vs. 0.739 vs. 0.875, and a specificity of 0.657 vs. 0.692 vs. 1.000 on the training, test cohort 1, and test cohort 2, respectively. The assessment of model fairness suggested an unbiased model for age and sex, and calibration tests showed a p < 0.05. These results indicate that the developed model allows the prediction of the IDH mutation status in gliomas using standard-of-care MRI images and does not appear to hold sex and age biases.
Hybrid Topological Data Analysis and Deep Learning for Basal Cell Carcinoma Diagnosis
Authors: Maurya Akanksha , Stanley R. Joe , Lama Norsang , Nambisan Anand K. , Patel Gehana , Saeed Daniyal , Swinfard Samantha , Smith Colin , Jagannathan Sadhika , Hagerty Jason R. , Stoecker William V. 
Published: January 2024

AbstractA critical clinical indicator for basal cell carcinoma (BCC) is the presence of telangiectasia (narrow, arborizing blood vessels) within the skin lesions. Many skin cancer imaging processes today exploit deep learning (DL) models for diagnosis, segmentation of features, and feature analysis. To extend automated diagnosis, recent computational intelligence research has also explored the field of Topological Data Analysis (TDA), a branch of mathematics that uses topology to extract meaningful information from highly complex data. This study combines TDA and DL with ensemble learning to create a hybrid TDA-DL BCC diagnostic model. Persistence homology (a TDA technique) is implemented to extract topological features from automatically segmented telangiectasia as well as skin lesions, and DL features are generated by fine-tuning a pre-trained EfficientNet-B5 model. The final hybrid TDA-DL model achieves state-of-the-art accuracy of 97.4% and an AUC of 0.995 on a holdout test of 395 skin lesions for BCC diagnosis. This study demonstrates that telangiectasia features improve BCC diagnosis, and TDA techniques hold the potential to improve DL performance.
Evaluation of Spectral X-Ray Imaging for Panoramic Dental Images Based on a Simulation Framework
Authors: Berthe Daniel , Kolb Anna , Rabi Abdulrahman , Sellerer Thorsten , Somerkivi Villseveri , Feuerriegel Georg Constantin , Sauter Andreas Philipp , Meurer Felix , Hämisch York , Pantsar Tuomas , Lohman Henrik , Pfeiffer Daniela , Pfeiffer Franz 
Published: January 2024

AbstractModern photon counting detectors allow the calculation of virtual monoenergetic or material decomposed X-ray images but are not yet used for dental panoramic radiography systems. To assess the diagnostic potential and image quality of photon counting detectors in dental panoramic radiography, ethics approval from the local ethics committee was obtained for this retrospective study. Conventional CT scans of the head and neck region were segmented into bone and soft tissue. The resulting datasets were used to calculate panoramic equivalent thickness bone and soft tissue images by forward projection, using a geometry like that of conventional panoramic radiographic systems. The panoramic equivalent thickness images were utilized to generate synthetic conventional panoramic radiographs and panoramic virtual monoenergetic radiographs at various energies. The conventional, two virtual monoenergetic images at 40 keV and 60 keV, and material-separated bone and soft tissue panoramic equivalent thickness X-ray images simulated from 17 head CTs were evaluated in a reader study involving three experienced radiologists regarding their diagnostic value and image quality. Compared to conventional panoramic radiographs, the material-separated bone panoramic equivalent thickness image exhibits a higher image quality and diagnostic value in assessing the bone structure $$\left(p<.001\right)$$

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001

. Panoramic virtual monoenergetic radiographs do not show a significant advantage over conventional panoramic radiographs. The conducted reader study shows the potential of spectral X-ray imaging for dental panoramic imaging to improve the diagnostic value and image quality.
Using the Textual Content of Radiological Reports to Detect Emerging Diseases: A Proof-of-Concept Study of COVID-19
Authors: Crombé Amandine , Lecomte Jean-Christophe , Seux Mylène , Banaste Nathan , Gorincour Guillaume 
Published: January 2024

AbstractChanges in the content of radiological reports at population level could detect emerging diseases. Herein, we developed a method to quantify similarities in consecutive temporal groupings of radiological reports using natural language processing, and we investigated whether appearance of dissimilarities between consecutive periods correlated with the beginning of the COVID-19 pandemic in France. CT reports from 67,368 consecutive adults across 62 emergency departments throughout France between October 2019 and March 2020 were collected. Reports were vectorized using time frequency–inverse document frequency (TF-IDF) analysis on one-grams. For each successive 2-week period, we performed unsupervised clustering of the reports based on TF-IDF values and partition-around-medoids. Next, we assessed the similarities between this clustering and a clustering from two weeks before according to the average adjusted Rand index (AARI). Statistical analyses included (1) cross-correlation functions (CCFs) with the number of positive SARS-CoV-2 tests and advanced sanitary index for flu syndromes (ASI-flu, from open-source dataset), and (2) linear regressions of time series at different lags to understand the variations of AARI over time. Overall, 13,235 chest CT reports were analyzed. AARI was correlated with ASI-flu at lag = + 1, + 5, and + 6 weeks (P = 0.0454, 0.0121, and 0.0042, respectively) and with SARS-CoV-2 positive tests at lag = − 1 and 0 week (P = 0.0057 and 0.0001, respectively). In the best fit, AARI correlated with the ASI-flu with a lag of 2 weeks (P = 0.0026), SARS-CoV-2-positive tests in the same week (P < 0.0001) and their interaction (P < 0.0001) (adjusted R2 = 0.921). Thus, our method enables the automatic monitoring of changes in radiological reports and could help capturing disease emergence.
MRI-Based Machine Learning Fusion Models to Distinguish Encephalitis and Gliomas
Authors: Zheng Fei , Yin Ping , Yang Li , Wang Yujian , Hao Wenhan , Hao Qi , Chen Xuzhu , Hong Nan 
Published: January 2024

AbstractThis paper aims to compare the performance of the classical machine learning (CML) model and the deep learning (DL) model, and to assess the effectiveness of utilizing fusion radiomics from both CML and DL in distinguishing encephalitis from glioma in atypical cases. We analysed the axial FLAIR images of preoperative MRI in 116 patients pathologically confirmed as gliomas and clinically diagnosed with encephalitis. The 3 CML models (logistic regression (LR), support vector machine (SVM) and multi-layer perceptron (MLP)), 3 DL models (DenseNet 121, ResNet 50 and ResNet 18) and a deep learning radiomic (DLR) model were established, respectively. The area under the receiver operating curve (AUC) and sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV) were calculated for the training and validation sets. In addition, a deep learning radiomic nomogram (DLRN) and a web calculator were designed as a tool to aid clinical decision-making. The best DL model (ResNet50) consistently outperformed the best CML model (LR). The DLR model had the best predictive performance, with AUC, sensitivity, specificity, accuracy, NPV and PPV of 0.879, 0.929, 0.800, 0.875, 0.867 and 0.889 in the validation sets, respectively. Calibration curve of DLR model shows good agreement between prediction and observation, and the decision curve analysis (DCA) indicated that the DLR model had higher overall net benefit than the other two models (ResNet50 and LR). Meanwhile, the DLRN and web calculator can provide dynamic assessments. Machine learning (ML) models have the potential to non-invasively differentiate between encephalitis and glioma in atypical cases. Furthermore, combining DL and CML techniques could enhance the performance of the ML models.
Masticatory Muscles Activation and TMJ Space During Asymmetrically Loaded Jaw Closing
Authors: Angst Lea , Koolstra Jan Harm , Wiedemeier Daniel , Van Sluijs Rachel M. , Pulfer Anina M. , Gallo Luigi M. , Colombo Vera 
Published: January 2024

AbstractMasticatory muscle activation and temporomandibular joint (TMJ) load generated during asymmetrically loaded jaw closing are largely unknown. Two different strategies were developed to explain how the central nervous system (CNS) generates muscle activation patterns during motion: minimization of joint load (MJL) vs. minimization of muscle effort (MME). The aim of the present study was to investigate, experimentally, the neuromuscular strategy selected by the CNS to coordinate jaw closing in reaction to the application of an external asymmetric load. Masticatory muscle activation was measured with electromyography (EMG) and the minimum intra-articular distance (MID) was assessed by dynamic stereometry to infer joint loading. Ten healthy subjects performed jaw-closing movements against an asymmetric mandibular load set from 0.0 to 2.0 kg in 0.5-kg steps. Recordings were analyzed by exploratory and graphical statistical tools. Moreover, the observed differences in MID and EMG among the various mandibular loads were tested using non-parametric tests for repeated measures data. The ipsilateral-contralateral differences in MID and EMG of the anterior temporalis showed a significant increase (p < 0.001, p = 0.01) with increasing asymmetrical load with both joints being most heavily loaded at 1 kg. EMG signals of the masseter did not change significantly with increasing load. This study is the first to have analyzed the changes in the TMJ intra-articular space during asymmetrically loaded jaw-closing movements, not only three dimensionally and dynamically, but also combined with EMG. Asymmetrical load affected the TMJ space and masticatory muscle activation patterns, primarily resulting in an increased activation of the anterior temporalis muscle. This might suggest the involvement of a control mechanism to protect the joints from overloading. However, the results do not fully support the hypothesis of MJL nor the MME strategy.
Explainable text-based features in predictive models of crowdfunding campaigns
Authors: Pekar Viktor , Candi Marina , Beltagui Ahmad , Stylos Nikolaos , Liu Wei 
Published: January 2024

AbstractReward-Based Crowdfunding offers an opportunity for innovative ventures that would not be supported through traditional financing. A key problem for those seeking funding is understanding which features of a crowdfunding campaign will sway the decisions of a sufficient number of funders. Predictive models of fund-raising campaigns used in combination with Explainable AI methods promise to provide such insights. However, previous work on Explainable AI has largely focused on quantitative structured data. In this study, our aim is to construct explainable models of human decisions based on analysis of natural language text, thus contributing to a fast-growing body of research on the use of Explainable AI for text analytics. We propose a novel method to construct predictions based on text via semantic clustering of sentences, which, compared with traditional methods using individual words and phrases, allows complex meaning contained in the text to be operationalised. Using experimental evaluation, we compare our proposed method to keyword extraction and topic modelling, which have traditionally been used in similar applications. Our results demonstrate that the sentence clustering method produces features with significant predictive power, compared to keyword-based methods and topic models, but which are much easier to interpret for human raters. We furthermore conduct a SHAP analysis of the models incorporating sentence clusters, demonstrating concrete insights into the types of natural language content that influence the outcome of crowdfunding campaigns.
A Study of Intermittent Turbulence in Stable Arctic Boundary Layers
Authors: Butterworth Brian J. , de Boer Gijs , Lawrence Dale 
Published: January 2024

AbstractPolar boundary layers are difficult to model due to the existence of intermittent turbulence within stable layers. Here we present a case study evaluation of coherent structures in a stable boundary layer observed during a series of flights with an uncrewed aircraft system (DataHawk2) on 19 October 2016 at Oliktok Point, Alaska as part of the ERASMUS (Evaluation of Routine Atmospheric Sounding Measurements using Unmanned Systems) field campaign. During a sequence of five flights over a nine-hour period, 57 profiles of atmospheric properties (0–400 m a.g.l) were collected. Turbulence was identified using derived Richardson Number, temperature structure function parameter, and turbulence kinetic energy dissipation. Throughout all flights on this strongly stable day, intermittent turbulence was observed. These turbulent layers showed well-mixed potential temperature profiles embedded within otherwise stable potential temperature profiles; often resulting in a characteristic staircase pattern. Turbulent layers ranged from 1 to 30 m deep, with most individual layers being 1–2 m deep. Vertical propagation velocities of layers in the lower atmosphere were on the order of a few cm s−1, typical of non-convective environments. In different regions of the profile, turbulence was driven by a different balance of buoyancy and shear forces, with turbulence in the near surface environment driven by strong shear forces overcoming strong resistance to buoyancy, while turbulence in elevated layers characterized by weaker shear forces overcoming weaker resistance to buoyancy. We discuss the potential of such datasets for improving subgrid parameterizations of small-scale turbulence embedded within stable boundary layers.
Effect of Small-Scale Topographical Variations and Fetch from Roughness Elements on the Stable Boundary Layer Turbulence Statistics
Authors: Bhimireddy Sudheer R. , Sun Jielun , Wang Junming , Kristovich David A. R. , Hiscox April L. 
Published: January 2024

AbstractUnderstanding the influence of roughness and terrain slope on stable boundary layer turbulence is challenging. This is investigated using observations collected from October to November of 2018 during the Stable Atmospheric Variability ANd Transport (SAVANT) field campaign conducted in a shallow sloping Midwestern field. We analyze the turbulence velocity scale and its variation with the mean wind speed using observations up to 10–20 m on four meteorological towers located along a shallow gully. The roughness length for momentum over this complex terrain varied with wind direction from 0.0049 m to a maximum of 0.12 m for winds coming through deciduous trees present in the field. The variation of the turbulence velocity with wind speed shows a transition from a weak wind regime to a stronger wind regime, as reported by past studies. This transition is not observed for winds coming from the tree area, where turbulence is enhanced even for weak wind speeds. For weak stratification and stronger winds, the turbulent velocity scale increased with an increase in roughness while the terrain slope is seen to have a weak influence. The sizes of the dominant turbulent eddies seen from the vertical velocity power spectra are observed to be larger for winds coming through the tree area. The turbulence enhancement by the trees is found to be strong within a fetch distance of 7 times the tree height and not observable at 16 times of the tree height.
Children’s Thoughts on Wonder in Fairy Tales
Authors: Kaliszewska-Henczel Magdalena 
Published: January 2024

AbstractFairy tales are a means to introduce the Polish language and social education exercises in Polish kindergartens and schools (early childhood literature education). In their methodological proposals, authors of school handbooks use fairy tales as a medium for teaching moral values. The Goldfish is an example of such an approach (Fałdzińska and Rączyńska, 2017). This article presents the thoughts of children aged six to nine (Polish kindergarten and primary school years one to three) constructed around the fairy tale in question, which, on the one hand, can be understood as a cautionary lesson about the importance of remaining satisfied with one’s station (Tatar 2014), and on the other serves as a meta-tale, exposing a rule of the genre’s poetics (Somoff, 2019). Assuming the role of observer-as-participant, intent on providing interpretations, the author of this study aims to identify children’s categories of wonder occurring in Aleksandr Afanasyev’s The Goldfish (Afanasyev, 1984). It was observed that children reflect on the rules of wonder in fairy tales (as culture-specific texts) and derive social meanings significant from the viewpoint of fairy tale categories. As such, they were not only limited to the lesson of condemning greed, which is often assigned to The Goldfish.