bluuebunny/crossref_metadata_2025_split huggingface dataset:
A first-in-class leucyl-tRNA synthetase inhibitor, ganfeborole, for rifampicin-susceptible tuberculosis: a phase 2a open-label, randomized trial
Authors: Diacon Andreas H. , Barry Clifton E. , Carlton Alex , Chen Ray Y. , Davies Matt , de Jager Veronique , Fletcher Kim , Koh Gavin C. K. W. , Kontsevaya Irina , Heyckendorf Jan , Lange Christoph , Reimann Maja , Penman Sophie L. , Scott Rhona , Maher-Edwards Gareth , Tiberi Simon , Vlasakakis Georgios , Upton Caryn M. , Aguirre David Barros 
Published: February 2024

AbstractNew tuberculosis treatments are needed to address drug resistance, lengthy treatment duration and adverse reactions of available agents. GSK3036656 (ganfeborole) is a first-in-class benzoxaborole inhibiting the Mycobacterium tuberculosis leucyl-tRNA synthetase. Here, in this phase 2a, single-center, open-label, randomized trial, we assessed early bactericidal activity (primary objective) and safety and pharmacokinetics (secondary objectives) of ganfeborole in participants with untreated, rifampicin-susceptible pulmonary tuberculosis. Overall, 75 males were treated with ganfeborole (1/5/15/30 mg) or standard of care (Rifafour e-275 or generic alternative) once daily for 14 days. We observed numerical reductions in daily sputum-derived colony-forming units from baseline in participants receiving 5, 15 and 30 mg once daily but not those receiving 1 mg ganfeborole. Adverse event rates were comparable across groups; all events were grade 1 or 2. In a participant subset, post hoc exploratory computational analysis of 18F-fluorodeoxyglucose positron emission tomography/computed tomography findings showed measurable treatment responses across several lesion types in those receiving ganfeborole 30 mg at day 14. Analysis of whole-blood transcriptional treatment response to ganfeborole 30 mg at day 14 revealed a strong association with neutrophil-dominated transcriptional modules. The demonstrated bactericidal activity and acceptable safety profile suggest that ganfeborole is a potential candidate for combination treatment of pulmonary tuberculosis.ClinicalTrials.gov identifier: NCT03557281.
Analysis of clinical factors impacting recurrence in myxofibrosarcoma
Authors: Chen Wenlin , Ye Ming , Sun Ye , Wei Yongzhong , Huang Yumin 
Published: February 2024

AbstractMyxofibrosarcoma (MFS) is a malignant fibroblastic/myofibroblastic neoplasm with a prominent myxoid area. It has the clinical features of frequent local recurrence (LR) and occasional distant metastasis. Robust epidemiological data on MFS in China are lacking. The aim of this retrospective analysis was to determine the natural history of MFS, identify prognostic factors for recurrence and describe the real-life outcomes of MFS. We reviewed 52 patients with primary MFS from the First Affiliated Hospital of Nanjing Medical University diagnosed between 2016 and 2020. All tumors were subjected to retrospective univariate analysis for prognostic factors of the disease, including tumor size, grade, location and sex; patient age; planned operation; surgical margin; and laboratory results. The significant factors identified by univariate analysis were subsequently analyzed via multivariate analysis. Overall survival (OS), post-treatment LR and metastatic-free survival were assessed as outcomes. The median age was 61 years (range, 13–93). Fourteen (26.92%) patients exhibited low grade disease, and 38 (73.08%) exhibited high grade disease. Among the 29 males, and 23 females, 15 (28.85%) had tumors in the trunk, 37 (71.15%) had tumors in the extremities, 26 had undergone planned surgery, and 26 had unexpected unplanned operation. The margin was negative in 39 (75%) patients and positive in 13 patients (25%). The serum creatine kinase (CK) concentration was high level in 33 (63.46%) patients and low level in 19 (36.54%) patients. The serum lactate dehydrogenase (LDH) levels were low in 23 (44.23%) patients and high in 29 (55.77%) patients. LR was observed in 25 patients (48.08%), and 4 patients developed metastasis. A worse LR rate was found for patients with a low CK level (84.21%) than for those with a high CK level (27.27%) at 5 years (p < 0.05). The LR rate of patients who underwent planned surgery was lower than that of patients who underwent unplanned surgery (p < 0.05). There were significantly more patients with positive margins than patients with negative margins (92.30%, and 33.33%, respectively; p < 0.05). Moreover, superficial tumors were also associated with greater recurrence rate (2/20 [10%]) than deep tumors, (23/32 [71.86%]) [p < 0.05]. The probability of LR in patients with MFS was significantly greater in association with unplanned operations, positive margins, low serum CK levels or superficial tumor depth. These data could help identify high-risk patients; thus, more careful follow-up should be performed for higher-risk patients. Diagnosis and treatment at qualified regular medical centers can reduce the local recurrence rate of MFS.
Ability of a dynamical climate sensitive disease model to reproduce historical Rift Valley Fever outbreaks over Africa
Authors: Chemison Alizée , Ramstein Gilles , Jones Anne , Morse Andy , Caminade Cyril 
Published: February 2024

AbstractRift Valley Fever (RVF) is a zoonosis transmitted by Aedes and Culex mosquitoes, and is considered a priority pathogen by the WHO. RVF epidemics mostly occur in Africa and can decimate livestock herds, causing significant economic losses and posing health risks for humans. RVF transmission is associated with the occurrence of El Niño events that cause floods in eastern Africa and favour the emergence of mosquitoes in wetlands. Different risk models have been developed to forecast RVF transmission risk but very few studies have validated models at pan-African scale. This study aims to validate the skill of the Liverpool Rift Valley Fever model (LRVF) in reproducing RVF epidemics over Africa and to explore the relationship between simulated climatic suitability for RVF transmission and large-scale climate modes of variability such as the El Niño Southern Oscillation (ENSO) and the Dipole Mode Index (DMI). Our results show that the LRVF model correctly simulates RVF transmission hotspots and reproduces large epidemics that affected African countries. LRVF was able to correctly reproduce major RVF epidemics in Somalia, Kenya, Zambia and to a lesser extent for Mauritania and Senegal. The positive phases of ENSO and DMI are associated with an increased risk of RVF over the Horn of Africa, with important time lags. Following research activities should focus on the development of predictive modelling systems at different time scales.
Predictors, management and prognosis of initial hyperemia of free flap
Authors: Bae Juyoung , Lee Kyeong-Tae 
Published: February 2024

AbstractIn free flap operation, temporary hyperemia of the transferred flaps can often be encountered in the early postoperative period, appearing reddish and rapid capillary refilling time, which mimics venous congestion. This study aimed to investigate the factors associated with the development of hyperemia and evaluate clinical course. Consecutive patients who underwent free flap-based reconstruction between December 2019 and October 2021 were reviewed. Independent risk factors associated with its development were assessed. Flap showing initial hyperemic features were assessed using flap blood glucose measurement (BGM). If it showed over 60 mg/dL, they were closely observed without management. Their clinical outcomes were evaluated. In total, 204 cases were analyzed, of which 35 (17.2%) showed initial hyperemia. Multivariable analyses showed that using thoracodorsal artery perforator flaps and muscle containing flaps (musculocutaneous/muscle-chimeric flaps) and conducting end-to-end arterial anastomosis (vs. end-to-side) were independent predictors. All cases with initial hyperemia showed over 60 mg/dL in BGM. The phenomenon resolved spontaneously within 6.9 h averagely. Overall perfusion-related complications developed in 10 (4.9%) cases, which rate did not differ between the two groups. Several factors might be associated with the development of initial hyperemia after free flap surgery. With proper assessment, this condition can be successfully managed without unnecessary intervention.
Evolutionary analysis of rainstorm momentum and non-stationary variating patterns in response to climatic changes across diverse terrains
Authors: Huang Chien-Lin , Hsu Nien-Sheng 
Published: February 2024

AbstractThis study aims to analyze time-series measurements encompassing rainstorm events with over a century of datasets to identify rainstorm evolution and dimensional transitions in non-stationarity. Rainstorm events are identified using partial duration series (PDS) to extract changes in rainstorm characteristics, namely maximum intensity (MAXI), duration (D), total rainfall (TR), and average rainfall intensity (ARI), in response to climate change. Ensemble empirical mode decomposition is used for trend filtering and non-stationary identification to explore spatiotemporal insight patterns. Trend models for the first–second-order moments of rainstorm characteristics are used to formulate the identified mean–variance trends using combined multi-dimensional linear-parabolic regression. Best-fitting combinations of various distributions (probability density functions) and trend models for multiple characteristic series are identified based on the Akaike information criterion. We analyze the dimensional transition in rainfall non-stationarity based on sensitivity analysis using PDS to determine its natural geophysical causes. The integrated methodology was applied to the data retrieved from nine weather stations in Taiwan. Our findings reveal rainstorm characteristics of “short D but high rainfall intensity” or “lower MAXI but high TR” across multiple stations. The parabolic trend of the first-order moment (i.e., mean) of ARI, D, and TR appears at the endpoint of the mountain ranges. Areas receiving monsoons and those on the windward plain show a rising parabolic trend in the first- and second-order moments (i.e., mean–variance) characterizing MAXI, implying that the occurrence frequency and magnitude of extreme MAXI increases. Non-stationary transitions in MAXI appear for mountain ranges exposed to the monsoon co-movement effect on both windward and leeward sides. Stations in the plains and rift valleys show upgraded and downgraded transitions in the non-stationary dimensions for D, respectively.
A comparative study of federated learning methods for COVID-19 detection
Authors: Darzi Erfan , Sijtsema Nanna M. , van Ooijen P. M. A. 
Published: February 2024

AbstractDeep learning has proven to be highly effective in diagnosing COVID-19; however, its efficacy is contingent upon the availability of extensive data for model training. The data sharing among hospitals, which is crucial for training robust models, is often restricted by privacy regulations. Federated learning (FL) emerges as a solution by enabling model training across multiple hospitals while preserving data privacy. However, the deployment of FL can be resource-intensive, necessitating efficient utilization of computational and network resources. In this study, we evaluate the performance and resource efficiency of five FL algorithms in the context of COVID-19 detection using Convolutional Neural Networks (CNNs) in a decentralized setting. The evaluation involves varying the number of participating entities, the number of federated rounds, and the selection algorithms. Our findings indicate that the Cyclic Weight Transfer algorithm exhibits superior performance, particularly when the number of participating hospitals is limited. These insights hold practical implications for the deployment of FL algorithms in COVID-19 detection and broader medical image analysis.
Applying AVWEWM to ethical decision-making during autonomous vehicle crashes
Authors: Liu Guoman , Luo Yufeng , Sheng Jing 
Published: February 2024

AbstractAt present, a few scholars studied influencing factors, rules and mechanisms of decision-making in ethical dilemmas. Many factors have been identified, and a few rules and mechanisms have been proposed. However, due to the inability to evaluate the weight and role of each factor in decision-making, it is difficult to establish a computational decision-making model to solve ethical dilemmas. Therefore, entropy weighted method (EWM) and Attribute Value Weighted EWM (AVWEWM) are used to process 84 dilemmas respectively to evaluate the weight and role of each factor in decision-making, then decision-making models based on EWM and AVWEWM are constructed to make decisions during autonomous vehicle (AV) crashes respectively. Lastly, 40 dilemmas are designed to test both decision-making models. The test results indicate that both can make clear decision-making in 40 dilemmas. However, the decision-making by AVWEWM is more consistent with public opinion than EWM. In addition, according to the weight and role of each factor in decision-making, it can also provide a few references for traffic management and legal departments to formulate traffic laws and regulations for AV in the future.
Fast quasi-null-filling of radiation patterns for multiple solutions generation
Authors: López-Álvarez Cibrán , López-Martín María Elena , Rodríguez-González Juan Antonio , Ares-Pena Francisco José 
Published: February 2024

AbstractHere we present an improved, rapid method for filling quasi-nulls in symmetrical radiation patterns synthesized by equispaced linear arrays, leading to the generation of multiple solutions. Considering the polynomial representation of the pattern, this null-filling is achieved by displacing the roots radially off the unit circle, keeping a constant displacement. This allows analyzing how the potential solutions vary with the quasi-uniform filling and the associated directivity loss. This method is based on the Cardano-Vieta relations, which link the coefficients of a complex Schelkunoff polynomial with its roots. As examples of application, we have considered a 20/100 element Dolph-Chebyshev pattern, with a spacing between the elements $$\lambda / 2$$

λ
/
2

, side lobe level of − 20/− 28 dB and three inner sidelobes at − 40/− 50 dB.
Assessment of metal extraction from e-waste using supported IL membrane with reliable comparison between RSM regression and ANN framework
Authors: Hemmati Alireza , Asadollahzadeh Mehdi , Torkaman Rezvan 
Published: February 2024

AbstractRecently, efficient techniques to remove indium ions from e-waste have been described due to their critical application. This paper illustrates the recovery of indium ions from an aqueous solution using a liquid membrane. CyphosIL 104 described the excellent potential for the extraction of indium ions. Evaluation of the five process parameters, such as indium concentration (10–100 mg/L), carrier concentration (0.05–0.2 mol/L), feed phase acidity (0.01–3 mol/L), chloride ion concentration (0.5–4 mol/L) and the stripping agent concentration (0.1–5 mol/L) were conducted. The interactive impacts of the various parameters on the extraction efficiency were investigated. The response surface methodology (RSM) and artificial neural network (ANN) were employed to model and compare the FS-SLM process results. RSM model with a quadratic equation (R2 = 0.9589) was the most suitable model for describing the efficiency. ANN model with six neurons showed a prediction of extraction efficiency with R2 = 0.9860. The best-optimized data were: 73.92 mg/L, 0.157 mol/L, 1.386 mol/L, 2.99 mol/L, and 3.06 mol/L for indium concentration, carrier concentration, feed phase acidity, chloride ion concentration, and stripping agent concentration. The results achieved by RSM and ANN led to an experimentally determined extraction efficiency of 93.91%, and 94.85%, respectively. It was close to the experimental data in the optimization condition (95.77%). Also, the evaluation shows that the ANN model has a better prediction and fitting ability to reach outcomes than the RSM model.
Comparative evaluation of real-time PCR and ELISA for the detection of human fascioliasis
Authors: Bakhshipour Fatemeh , Zibaei Mohammad , Rokni Mohammad Bagher , Miahipour Abolfazl , Firoozeh Farzaneh , Beheshti Masoud , Beikzadeh Leila , Alizadeh Gita , Aryaeipour Mojgan , Raissi Vahid 
Published: February 2024

AbstractFascioliasis is a zoonotic parasitic infection caused by Fasciola species in humans and animals. Despite significant advances in vaccination and new therapeutic agents, little attention has been paid to validating methods for the diagnosis of fascioliasis in humans. Serological techniques are convenient assays that significantly improves the diagnosis of Fasciola infection. However, a more sensitive method is required. The aim of this study was to compare the Real-Time PCR technique with the indirect-ELISA for the detection of Fasciola hepatica in human. Using a panel of sera from patients infected with Fasciola hepatica (n = 51), other parasitic infections (n = 7), and uninfected controls (n = 12), we optimized an ELISA which employs an excretory–secretory antigens from F. hepatica for the detection of human fascioliasis. After DNA extraction from the samples, molecular analysis was done using Real-Time PCR technique based on the Fasciola ribosomal ITS1 sequence. Of 70 patient serum samples, 44 (62.86%) samples were identified as positive F. hepatica infection using ELISA and Real-Time PCR assays. There was no cross-reaction with other parasitic diseases such as toxoplasmosis, leishmaniasis, taeniasis, hydatidosis, trichinosis, toxocariasis, and strongyloidiasis. The significant difference between the agreement and similarity of the results of patients with indirect ELISA and Real-Time PCR was 94.4% and 99.2%, respectively (Cohen’s kappa ≥ 0.7; P = 0.02). Based on the Kappa agreement findings, the significant agreement between the results of ELISA and Real-Time PCR indicates the accuracy and reliability of these tests in the diagnosis of F. hepatica in humans.
Biorenewable vegetable oil based nonisocyanate polyurethanes and nanocomposites; formulation, characterisation, biodegradation, anticorrosion and antifouling coatings
Authors: Sulthana Yasmin R. , Chelike Dinesh K. , Gurusamy Thangavelu Senthil A. 
Published: February 2024

Castor oil cyclic carbonate chosen as sustainable precursor to afford biodegradable NIPU via polyaddition of aliphatic diamine and NIPU nanocomposites by functionalized Fe3O4 via covalent incorporation, used as anticorrosion and antifouling coating.
Designing a Module on Stuttering and Cluttering: A Guide for Speech-Language Pathology Educators
Authors: Connery Amy , Shé Caitríona Ní 
Published: February 2024

Purpose:
Due to the scope of practice of speech-language pathology (SLP) expanding considerably in recent times, there is reduced emphasis on certain communication conditions within the curricula of SLP university programs. Stuttering and cluttering are neglected components of such curricula, despite the complex clinical skill set required to work with these client groups. Evaluation of the content and quality of modules on stuttering and cluttering is warranted to ensure that SLP students are graduating with adequate competence and confidence for supporting people with these conditions. This tutorial, based on a review of the literature, aims to provide guidance to educators who are designing or revising such modules.

Method:
The All Ireland Society for Higher Education (AISHE) model for module design provides a practical and theoretically underpinned guide to educators in higher education on the design of a new module or the review of an existing one. The model's seven key components are discussed, and their application to a module on stuttering and cluttering is outlined.

Results:
The AISHE model provides a systematic and user-friendly approach to module design in SLP university programs. It supports educators who are designing a new module or revising a module currently being taught on stuttering and cluttering.

Conclusions:
Educators are encouraged to familiarize themselves with the seven components of the AISHE model and to use it as a tool to design or revise modules on stuttering and cluttering. This will ensure that SLP students are graduating with increased competence and confidence in working with these client groups.
Physics‐informed surrogates for electromagnetic dynamics using Transformers and graph neural networks
Authors: Noakoasteen O. , Christodoulou C. , Peng Z. , Goudos S. K. 
Published: February 2024

AbstractA novel use case for two data‐driven models, namely, a Transformer and a convolutional graph neural network (CGNN) is proposed. The authors propose to use these models for emulating the dynamics of electromagnetic (EM) propagation and scattering. The Transformer translates a past sequence into a future sequence by constructing representations from the past and using it to predict the future, taking all of its own previous predictions as input at each step of prediction. The CGNN updates the current state of attribute vectors of each node by passing it information (messages) from all of its neighbouring nodes. We train these models with FDTD simulations of plane waves propagating and scattering from PEC objects. The authors demonstrate that, within the bounds of computational resources, the Transformer can be utilised as a surrogate for EM dynamics, providing 14× speed‐up, while the CGNN can be utilised as a next‐frame predictor, providing 9× speed‐up. When comparing the accuracy of these two models with the authors’ previously developed Encoder‐Recurrent‐Decoder (ERD) model, it is observed that the error for both the Transformer and the CGNN remains within the same bound for the ERD model. To the best of the authors’ knowledge, this work is the first to utilise the Transformer as a surrogate for EM dynamics.
Machine learning applications in studies of the physical properties of active galactic nuclei based on photometric observations
Authors: Mechbal Sarah , Ackermann Markus , Kowalski Marek 
Published: February 2024

Context. We investigate the physical nature of active galactic nuclei (AGNs) using machine learning (ML) tools.
Aims. We show that the redshift, z, bolometric luminosity, LBol, central mass of the supermassive black hole (SMBH), MBH, Eddington ratio, λEdd, and AGN class (obscured or unobscured) can be reconstructed through multi-wavelength photometric observations only.
Methods. We trained a random forest regressor (RFR) ML-model on 7616 spectroscopically observed AGNs from the SPIDERS-AGN survey, which had previously been cross-matched with soft X-ray observations (from ROSAT or XMM), WISE mid-infrared photometry, and optical photometry from SDSS ugriz filters. We built a catalog of 21 050 AGNs that were subsequently reconstructed with the trained RFR; for 9687 sources, we found archival redshift measurements. All AGNs were classified as either type 1 or type 2 using a random forest classifier (RFC) algorithm on a subset of known sources. All known photometric measurement uncertainties were incorporated via a simulation-based approach.
Results. We present the reconstructed catalog of 21 050 AGNs with redshifts ranging from 0 < z < 2.5. We determined z estimations for 11 363 new sources, with both accuracy and outlier rates within 2%. The distinction between type 1 or type 2 AGNs could be identified with respective efficiencies of 94% and 89%. The estimated obscuration level, a proxy for AGN classification, of all sources is given in the dataset. The LBol, MBH, and λEdd values are given for 21 050 new sources with their estimated error. These results have been made publicly available.
Conclusions. The release of this catalog will advance AGN studies by presenting key parameters of the accretion history of 6 dex in luminosity over a wide range of z. Similar applications of ML techniques using photometric data only will be essential in the future, with large datasets from eROSITA, JSWT, and the VRO poised to be released in the next decade.