bluuebunny/crossref_metadata_2025_split huggingface dataset:
The role of computer-assisted radiographer reporting in lung cancer screening programmes
Authors: Hall Helen , Ruparel Mamta , Quaife Samantha L. , Dickson Jennifer L. , Horst Carolyn , Tisi Sophie , Batty James , Woznitza Nicholas , Ahmed Asia , Burke Stephen , Shaw Penny , Soo May Jan , Taylor Magali , Navani Neal , Bhowmik Angshu , Baldwin David R. , Duffy Stephen W. , Devaraj Anand , Nair Arjun , Janes Sam M. 
Published: May 2022

Abstract
Objectives
Successful lung cancer screening delivery requires sensitive, timely reporting of low-dose computed tomography (LDCT) scans, placing a demand on radiology resources. Trained non-radiologist readers and computer-assisted detection (CADe) software may offer strategies to optimise the use of radiology resources without loss of sensitivity. This report examines the accuracy of trained reporting radiographers using CADe support to report LDCT scans performed as part of the Lung Screen Uptake Trial (LSUT).

Methods
In this observational cohort study, two radiographers independently read all LDCT performed within LSUT and reported on the presence of clinically significant nodules and common incidental findings (IFs), including recommendations for management. Reports were compared against a ‘reference standard’ (RS) derived from nodules identified by study radiologists without CADe, plus consensus radiologist review of any additional nodules identified by the radiographers.

Results
A total of 716 scans were included, 158 of which had one or more clinically significant pulmonary nodules as per our RS. Radiographer sensitivity against the RS was 68–73.7%, with specificity of 92.1–92.7%. Sensitivity for detection of proven cancers diagnosed from the baseline scan was 83.3–100%. The spectrum of IFs exceeded what could reasonably be covered in radiographer training.

Conclusion
Our findings highlight the complexity of LDCT reporting requirements, including the limitations of CADe and the breadth of IFs. We are unable to recommend CADe-supported radiographers as a sole reader of LDCT scans, but propose potential avenues for further research including initial triage of abnormal LDCT or reporting of follow-up surveillance scans.

Key Points
• Successful roll-out of mass screening programmes for lung cancer depends on timely, accurate CT scan reporting, placing a demand on existing radiology resources.
• This observational cohort study examines the accuracy of trained radiographers using computer-assisted detection (CADe) software to report lung cancer screening CT scans, as a potential means of supporting reporting workflows in LCS programmes.
• CADe-supported radiographers were less sensitive than radiologists at identifying clinically significant pulmonary nodules, but had a low false-positive rate and good sensitivity for detection of confirmed cancers.
A Causality-guided Statistical Approach for Modeling Extreme Mei-yu Rainfall Based on Known Large-scale Modes—A Pilot Study
Authors: Ng Kelvin S. , Leckebusch Gregor C. , Hodges Kevin I. 
Published: May 2022

AbstractExtreme Mei-yu rainfall (MYR) can cause catastrophic impacts to the economic development and societal welfare in China. While significant improvements have been made in climate models, they often struggle to simulate local-to-regional extreme rainfall (e.g., MYR). Yet, large-scale climate modes (LSCMs) are relatively well represented in climate models. Since there exists a close relationship between MYR and various LSCMs, it might be possible to develop causality-guided statistical models for MYR prediction based on LSCMs. These statistical models could then be applied to climate model simulations to improve the representation of MYR in climate models.In this pilot study, it is demonstrated that skillful causality-guided statistical models for MYR can be constructed based on known LSCMs. The relevancy of the selected predictors for statistical models are found to be consistent with the literature. The importance of temporal resolution in constructing statistical models for MYR is also shown and is in good agreement with the literature. The results demonstrate the reliability of the causality-guided approach in studying complex circulation systems such as the East Asian summer monsoon (EASM). Some limitations and possible improvements of the current approach are discussed. The application of the causality-guided approach opens up a new possibility to uncover the complex interactions in the EASM in future studies.
Operative versus conservative treatment of acute Achilles tendon ruptures: preliminary results of clinical outcome, kinematic MRI and contrast-enhanced ultrasound
Authors: Kosiol Juana , Keiler Alexander , Loizides Alexander , Gruber Hannes , Henninger Benjamin , Bölderl Andreas , Gruber Leonhard 
Published: May 2022

Abstract
Introduction
There is no uniform consensus on the gold standard therapy for acute Achilles tendon rupture. The aim of this pilot study was to compare operative and conservative treatment regarding imaging findings and clinical outcome.

Materials and methods
Surgically or conservatively treated patients with acute Achilles tendon rupture were retrospectively evaluated. Differences in tendon length and diameter with and without load were analysed using kinematic MRI, tendon perfusion, structural alterations, movement and scar tissue by means of grey-scale and contrast-enhanced ultrasound (CEUS). Intra- and interobserver agreement were recorded.

Results
No significant difference was detected regarding clinical outcome, B mode ultrasonography, contrast-enhanced sonography or MRI findings, although alterations in MRI-based measurements of tendon elasticity were found for both groups. Considerable elongation and thickening of the injured tendon were detected in both groups.

Conclusion
Both, conservative and surgical treatment showed comparable outcomes in our preliminary results and may suggest non-inferiority of a conservative approach.
Effect of intra-nasal nitrilotriacetic acid trisodium salt in lowering elevated calcium cations and improving olfactory dysfunction in COVID-19 patients
Authors: Abdelazim Mohamed H. , Abdelazim Ahmed H. , Ismaiel Wael F. , Alsobky Mahmoud E. , Younes Ahmed , Hadeya Abdelgawad M. , Ramzy Sherif , Shahin Mohammed 
Published: May 2022

Abstract
Purpose
An association between COVID-19 and olfactory dysfunction has been noted in many patients worldwide. The olfactory adaptation process leads to an increase in intracellular calcium cation levels. Nitrilotriacetic acid trisodium salt has high selective chelation for calcium cations from olfactory mucus. The aim of this work is to test the effect of an intranasal nitrilotriacetic acid trisodium salt to lower the elevated calcium cations in COVID-19 patients with relevant symptoms of olfactory dysfunction.

Methods
Fifty-eight COVID-19 adult patients with relevant symptoms of olfactory dysfunction were enrolled in a prospective randomized controlled trial. They received a nasal spray containing either 0.9% sodium chloride or 2% nitrilotriacetic acid trisodium salt. Olfactory function was assessed before and after treatment using the Sniffin’ Sticks test. Quantitative analysis of calcium cation concentration in nasal secretions was performed using a carbon paste ion-selective electrode.

Results
After the application of nitrilotriacetic acid trisodium salt compared to sodium chloride, a significant improvement from functional anosmia to healthy normosmia with significant decrease in calcium cation concentration was observed.

Conclusions
Further collaborative research is needed to fully investigate the effect of an intranasal nitrilotriacetic acid trisodium salt in the treatment of olfactory disorders.
Predicting clinical scores in Huntington’s disease: a lightweight speech test
Authors: Riad Rachid , Lunven Marine , Titeux Hadrien , Cao Xuan-Nga , Hamet Bagnou Jennifer , Lemoine Laurie , Montillot Justine , Sliwinski Agnes , Youssov Katia , Cleret de Langavant Laurent , Dupoux Emmanuel , Bachoud-Lévi Anne-Catherine 
Published: May 2022

Abstract
Objectives
Using brief samples of speech recordings, we aimed at predicting, through machine learning, the clinical performance in Huntington’s Disease (HD), an inherited Neurodegenerative disease (NDD).

Methods
We collected and analyzed 126 samples of audio recordings of both forward and backward counting from 103 Huntington’s disease gene carriers [87 manifest and 16 premanifest; mean age 50.6 (SD 11.2), range (27–88) years] from three multicenter prospective studies in France and Belgium (MIG-HD (ClinicalTrials.gov NCT00190450); BIO-HD (ClinicalTrials.gov NCT00190450) and Repair-HD (ClinicalTrials.gov NCT00190450). We pre-registered all of our methods before running any analyses, in order to avoid inflated results. We automatically extracted 60 speech features from blindly annotated samples. We used machine learning models to combine multiple speech features in order to make predictions at individual levels of the clinical markers. We trained machine learning models on 86% of the samples, the remaining 14% constituted the independent test set. We combined speech features with demographics variables (age, sex, CAG repeats, and burden score) to predict cognitive, motor, and functional scores of the Unified Huntington’s disease rating scale. We provided correlation between speech variables and striatal volumes.

Results
Speech features combined with demographics allowed the prediction of the individual cognitive, motor, and functional scores with a relative error from 12.7 to 20.0% which is better than predictions using demographics and genetic information. Both mean and standard deviation of pause durations during backward recitation and clinical scores correlated with striatal atrophy (Spearman 0.6 and 0.5–0.6, respectively).

Interpretation
Brief and examiner-free speech recording and analysis may become in the future an efficient method for remote evaluation of the individual condition in HD and likely in other NDD.
Validation of an autonomous artificial intelligence–based diagnostic system for holistic maculopathy screening in a routine occupational health checkup context
Authors: Font Octavi , Torrents-Barrena Jordina , Royo Dídac , García Sandra Banderas , Zarranz-Ventura Javier , Bures Anniken , Salinas Cecilia , Zapata Miguel Ángel 
Published: May 2022

Abstract

Purpose
This study aims to evaluate the ability of an autonomous artificial intelligence (AI) system for detection of the most common central retinal pathologies in fundus photography.

Methods
Retrospective diagnostic test evaluation on a raw dataset of 5918 images (2839 individuals) evaluated with non-mydriatic cameras during routine occupational health checkups. Three camera models were employed: Optomed Aurora (field of view — FOV 50º, 88% of the dataset), ZEISS VISUSCOUT 100 (FOV 40º, 9%), and Optomed SmartScope M5 (FOV 40º, 3%). Image acquisition took 2 min per patient. Ground truth for each image of the dataset was determined by 2 masked retina specialists, and disagreements were resolved by a 3rd retina specialist. The specific pathologies considered for evaluation were “diabetic retinopathy” (DR), “Age-related macular degeneration” (AMD), “glaucomatous optic neuropathy” (GON), and “Nevus.” Images with maculopathy signs that did not match the described taxonomy were classified as “Other.”

Results
The combination of algorithms to detect any abnormalities had an area under the curve (AUC) of 0.963 with a sensitivity of 92.9% and a specificity of 86.8%. The algorithms individually obtained are as follows: AMD AUC 0.980 (sensitivity 93.8%; specificity 95.7%), DR AUC 0.950 (sensitivity 81.1%; specificity 94.8%), GON AUC 0.889 (sensitivity 53.6% specificity 95.7%), Nevus AUC 0.931 (sensitivity 86.7%; specificity 90.7%).

Conclusion
Our holistic AI approach reaches high diagnostic accuracy at simultaneous detection of DR, AMD, and Nevus. The integration of pathology-specific algorithms permits higher sensitivities with minimal impact on its specificity. It also reduces the risk of missing incidental findings. Deep learning may facilitate wider screenings of eye diseases.
Price of robustness optimization through demand forecasting with an application to waste management
Authors: Gentile Claudio , Pinto Diego Maria , Stecca Giuseppe 
Published: May 2022

AbstractRobust optimization can be effectively used to protect production plans against uncertainties. This is particularly important in sectors where variability is inherent the process to be planned. The drawback of robust optimization is the chance of producing over-conservative solutions with respect to the real occurrences of the stochastic parameters. Information can be added in order to better control the extra cost resulting from considering the parameter variability. This work investigates how demand forecasting can be used in conjunction with robust optimization in order to achieve an optimal planning while considering demand uncertainties. In the proposed procedure, forecast is used to update uncertain parameters of the robust model. Moreover, the robustness budget is optimized at each planned stage in a rolling planning horizon. In this way, the parameters of the robust model can be dynamically updated tacking information from the data. The study is applied to a reverse logistics case, where the planning of sorting for material recycling is affected by uncertainties in the demand, consisting of waste material to be sorted and recycled. Results are compared with a standard robust optimization approach, using real case instances, showing potentialities of the proposed method.
Assessing cloud QoS predictions using OWA in neural network methods
Authors: Hussain Walayat , Gao Honghao , Raza Muhammad Raheel , Rabhi Fethi A. , Merigó Jose M. 
Published: May 2022

AbstractQuality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods—Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy.
Development of root resorption during orthodontic tooth movement after cleft repair using different grafting materials in rats
Authors: Möhlhenrich Stephan Christian , Kniha Kristian , Magnuska Zuzanna , Chhatwani Sachin , Hermanns-Sachweh Benita , Gremse Felix , Hölzle Frank , Danesh Gholamreza , Modabber Ali 
Published: May 2022

Abstract
Objective
The aim of the present study was to investigate the influence of three grafting materials for cleft repair on orthodontic tooth movement in rats.

Materials and methods
Artificial alveolar clefts were created in 21 Wistar rats and were repaired 4 weeks later using autografts, human xenografts and synthetic bone substitute (beta-tricalcium phosphate/hydroxyapatite [β-TCP/HA]). A further 4 weeks later, the first molar was moved into the reconstructed maxilla. Microfocus computed tomography (μCT) was performed six times (T0–T5) to assess the tooth movement and root resorption. After 8 weeks, the affected reconstructed jaw was resected for histopathological investigation.

Results
Total distances reached ranged from 0.82 ± 0.72 mm (β-TCP/HA) to 0.67 ± 0.27 mm (autograft). The resorption was particularly determined at the mesiobuccal root. Descriptive tooth movement slowed and root resorption increased slightly. However, neither the radiological changes during tooth movement (µCT T1 vs. µCT T5: autograft 1.85 ± 0.39 mm3 vs. 2.38 ± 0.35 mm3, p = 0.30; human xenograft 1.75 ± 0.45 mm3 vs. 2.17 ± 0.26 mm3, p = 0.54; β-TCP/HA: 1.52 ± 0.42 mm3 vs. 1.88 ± 0.41 mm3, p = 0.60) nor the histological differences after tooth movement (human xenograft: 0.078 ± 0.05 mm2; β-TCP/HA: 0.067 ± 0.049 mm2; autograft: 0.048 ± 0.015 mm2) were statistically significant.

Conclusion
The autografts, human xenografts or synthetic bone substitute used for cleft repair seem to have a similar effect on the subsequent orthodontic tooth movement and the associated root resorptions.

Clinical relevance
Development of root resorptions seems to have a secondary role in choosing a suitable grafting material for cleft repair.
Data-driven decision management of urban underground infrastructure through groundwater-level time-series cluster analysis: the case of Milan (Italy)
Authors: Sartirana Davide , Rotiroti Marco , Bonomi Tullia , De Amicis Mattia , Nava Veronica , Fumagalli Letizia , Zanotti Chiara 
Published: May 2022

AbstractThe significant increase in urbanization has resulted in greater use of the subsurface in urban planning and, therefore, increased interaction between groundwater and underground infrastructure. Numerical models are the primary tool adopted to manage the resulting problems; however, their construction is time- and cost-consuming. Groundwater-level time-series analysis can be a complementary method, as this data-driven approach does not require an extensive understanding of the geological and boundary conditions, even if providing insights into the hydrogeologic behaviour. Thus, a data-driven approach was adopted to analyse groundwater time-series of the shallow aquifer, occupied by several underground structures, beneath Milan city (Northern Italy). Statistical (Mann-Kendall and Sen’s slope estimator, autocorrelation and cross-correlation, hierarchical cluster analysis) and geospatial techniques were used to detect the potential variables influencing the groundwater levels of 95 monitoring wells, covering the period 2005–2019. A general rising trend of the water table was identified, with local hydrogeologic differences in the western and southernmost areas. Based on time-series analysis results, four management areas have been identified. These areas could act as future geographic units with specific groundwater management strategies. In particular, subsurface public car parks can be classified with respect to groundwater flooding as (1) not submerged, (2) possibly critical, or (3) submerged at different groundwater conditions. According to these outcomes, targeted guidelines for constructing new car parks have been elaborated for each management area. The methodology proved to be efficient in improving the urban conceptual model and helping stakeholders design the planned underground development, considering groundwater aspects.
Integrated uncertainty quantification and sensitivity analysis of single-component dynamic column breakthrough experiments
Authors: Ward Adam , Pini Ronny 
Published: May 2022

AbstractWe have carried out the traditional analysis of a set of dynamic breakthrough experiments on the $$\hbox {CO}_2$$

CO
2

/He system adsorbing onto activated carbon by fitting a 1D dynamic column breakthrough model to the transient experimental profiles. We have quantified the uncertainties in the fitted model parameters using the techniques of Bayesian inference, and have propagated these parametric uncertainties through the dynamic model to assess the robustness of the modelling. We have found significant uncertainties in the outlet mole fraction profile, internal temperature profile and internal adsorption profiles of approximately $$\pm 15\%$$

±
15
%

. To assess routes to reduce these uncertainties we have applied a global variance-based sensitivity analysis to the dynamic model using the Sobol method. We have found that approximately $$70\%$$

70
%

of the observed variability in the modelling outputs can be attributed to uncertainties in the adsorption isotherm parameters that describe its temperature dependence. We also make various recommendations for practitioners, using the developed Bayesian statistical tools, regarding the choice of the isotherm model, the choice of the fitting data for the extraction of system specific parameters and the simplification of the wall energy balance.
Deformable image registration with attention-guided fusion of multi-scale deformation fields
Authors: He Zhiquan , He Yupeng , Cao Wenming 
Published: May 2022

AbstractDeformable medical image registration plays a crucial role in theoretical research and clinical application. Traditional methods suffer from low registration accuracy and efficiency. Recent deep learning-based methods have made significant progresses, especially those weakly supervised by anatomical segmentations. However, the performance still needs further improvement, especially for images with large deformations. This work proposes a novel deformable image registration method based on an attention-guided fusion of multi-scale deformation fields. Specifically, we adopt a separately trained segmentation network to segment the regions of interest to remove the interference from the uninterested areas. Then, we construct a novel dense registration network to predict the deformation fields of multiple scales and combine them for final registration through an attention-weighted field fusion process. The proposed contour loss and image structural similarity index (SSIM) based loss further enhance the model training through regularization. Compared to the state-of-the-art methods on three benchmark datasets, our method has achieved significant performance improvement in terms of the average Dice similarity score (DSC), Hausdorff distance (HD), Average symmetric surface distance (ASSD), and Jacobian coefficient (JAC). For example, the improvements on the SHEN dataset are 0.014, 5.134, 0.559, and 359.936, respectively.