bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stability of the retinal image under normal viewing conditions and the implications for neural adaptation
Authors: Manzanera Silvestre , Artal Pablo 
Published: January 2024

AbstractPrevious studies have demonstrated that the visual system adapts to the specific aberration pattern of an individual’s eye. Alterations to this pattern can lead to reduced visual performance, even when the Root Mean Square (RMS) of the wavefront error remains constant. However, it is well-established that ocular aberrations are dynamic and can change with factors such as pupil size and accommodation. This raises an intriguing question: can the neural system adapt to continuously changing aberration patterns? To address this question, we measured the ocular aberrations in four subjects under various natural viewing conditions, which included changes in accommodative state and pupil size. We subsequently computed the associated Point Spread Functions (PSFs). For each subject, we examined the stability in the orientation of the PSFs and analyzed the cross-correlation between different PSFs. These findings were then compared to the characteristics of a distribution featuring PSF shapes akin to random variations. Our results indicate that the changes observed in the PSFs are not substantial enough to produce a PSF shape distribution resembling random variations. This lends support to the notion that neural adaptation is indeed a viable mechanism even in response to continuously changing aberration patterns.
Urinary vanin-1, tubular injury, and graft failure in kidney transplant recipients
Authors: Alkaff Firas F. , Kremer Daan , Niekolaas Tessa M. , van den Born Jacob , Rimbach Gerald , Tseng Tzu-Ling , Berger Stefan P. , Bakker Stephan J. L. , de Borst Martin H. 
Published: January 2024

AbstractWe investigated whether urinary vascular non-inflammatory molecule-1 (vanin-1), a promising early-onset tubular injury marker, correlates with other established tubular injury markers and is associated with graft failure in kidney transplant recipients (KTR). We measured 24 h urinary vanin-1 excretion in 656 KTR (age 53 ± 13 years, 43% female, estimated glomerular filtration rate (eGFR) 53 ± 21 mL/min/1.73 m2) who had undergone kidney transplantation ≥ 1 year. The median 24 h urinary vanin-1 excretion was 145 [51–331] pmol/24 h. 24 h urinary vanin-1 excretion correlated weakly but significantly with other tubular injury markers (ρ = 0.14, p < 0.001 with urinary liver-type fatty acid binding protein, ρ = 0.13, p = 0.001 with urinary post-translationally modified fetuin-A protein, and ρ = 0.10, p = 0.011 with plasma neutrophil gelatinase-associated lipocalin) and with eGFR (ρ = − 0.13, p = 0.001). During a median follow-up of 7.4 [4.9–8.0] years, 94 (14%) KTR developed death-censored graft failure. In multivariable Cox regression analyses, 24 h urinary vanin-1 excretion was not associated with an increased risk of death-censored graft failure (adjusted hazard ratio [95% confidence interval] = 0.96 [0.86–1.07], p = 0.5). In conclusion, our findings do not support the role of urinary vanin-1 as a biomarker of graft failure after kidney transplantation.
A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data
Authors: Ahmadi Najia , Nguyen Quang Vu , Sedlmayr Martin , Wolfien Markus 
Published: January 2024

AbstractThe emergence of collaborations, which standardize and combine multiple clinical databases across different regions, provide a wealthy source of data, which is fundamental for clinical prediction models, such as patient-level predictions. With the aid of such large data pools, researchers are able to develop clinical prediction models for improved disease classification, risk assessment, and beyond. To fully utilize this potential, Machine Learning (ML) methods are commonly required to process these large amounts of data on disease-specific patient cohorts. As a consequence, the Observational Health Data Sciences and Informatics (OHDSI) collaborative develops a framework to facilitate the application of ML models for these standardized patient datasets by using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). In this study, we compare the feasibility of current web-based OHDSI approaches, namely ATLAS and “Patient-level Prediction” (PLP), against a native solution (R based) to conduct such ML-based patient-level prediction analyses in OMOP. This will enable potential users to select the most suitable approach for their investigation. Each of the applied ML solutions was individually utilized to solve the same patient-level prediction task. Both approaches went through an exemplary benchmarking analysis to assess the weaknesses and strengths of the PLP R-Package. In this work, the performance of this package was subsequently compared versus the commonly used native R-package calledMachine Learning in R 3(mlr3), and its sub-packages. The approaches were evaluated on performance, execution time, and ease of model implementation. The results show that the PLP package has shorter execution times, which indicates great scalability, as well as intuitive code implementation, and numerous possibilities for visualization. However, limitations in comparison to native packages were depicted in the implementation of specific ML classifiers (e.g., Lasso), which may result in a decreased performance for real-world prediction problems. The findings here contribute to the overall effort of developing ML-based prediction models on a clinical scale and provide a snapshot for future studies that explicitly aim to develop patient-level prediction models in OMOP CDM.
Biosurfactant and biopolymer producing microorganisms from West Kazakhstan oilfield
Authors: Shaimerdenova Ulzhan , Kaiyrmanova Gulzhan , Lewandowska Wioleta , Bartoszewicz Marek , Swiecicka Izabela , Yernazarova Aliya 
Published: January 2024

AbstractMicrobiological enhanced oil recovery (MEOR) uses indigenous or exogenous microorganisms and nutrients to enhance oil production through synthesis of metabolites reducing oil viscosity and surface tension. In order to find bacteria suitable for MEOR, we studied 26 isolates from wells in the Akingen oilfield in West Kazakhstan. Six of them were selected for further analysis based on their ability to reduce surface tension to less than 40 mN/m, with the A9 isolate exhibiting tension reduction values of 32.76 ± 0.3 mN/m. Based on the morphological features, biochemical activities, and the 16S rRNA gene, the isolates were classified to the Bacillus subtilis group. In the phylogenetic analysis the isolates grouped into two main clusters. Genes encoding the surfactin synthetase subunits were found in A2, A8, A9, A12, PW2, only the PW2 strain had lchAA encoding lichenysin, while sacB encoding levan was noted in A2, A8, A9, and A12. The expression of srfAB, srfAC, and sacB tested with qPCR varied among strains. Nevertheless, whereas temperature moderately affects the expression level, with the highest level recorded at 40 °C, salinity significantly impacts the expression of the genes encoding biosurfactants. B. subtilis strains isolated in the study, especially A9, are promising for microbial-enhanced oil recovery.
A neutrophil extracellular trap-related risk score predicts prognosis and characterizes the tumor microenvironment in multiple myeloma
Authors: Zhao Zhijia , Huo Yuan , Du Yufeng , Huang Yanan , Liu Hongchen , Zhang Chengtao , Yan Jinsong 
Published: January 2024

AbstractMultiple myeloma (MM) is a distinguished hematologic malignancy, with existing studies elucidating its interaction with neutrophil extracellular traps (NETs), which may potentially facilitate tumor growth. However, systematic investigations into the role of NETs in MM remain limited. Utilizing the single-cell dataset GSE223060, we discerned active NET cell subgroups, namely neutrophils, monocytes, and macrophages. A transcriptional trajectory was subsequently constructed to comprehend the progression of MM. Following this, an analysis of cellular communication in MM was conducted with a particular emphasis on neutrophils, revealing an augmentation in interactions albeit with diminished strength, alongside abnormal communication links between neutrophils and NK cells within MM samples. Through the intersection of differentially expressed genes (DEGs) between NET active/inactive cells and MM versus healthy samples, a total of 316 genes were identified. This led to the development of a 13-gene risk model for prognostic prediction based on overall survival, utilizing transcriptomics dataset GSE136337. The high-risk group manifested altered immune infiltration and heightened sensitivity to chemotherapy. A constructed nomogram for predicting survival probabilities demonstrated encouraging AUCs for 1, 3, and 5-year survival predictions. Collectively, our findings unveil a novel NET-related prognostic signature for MM, thereby providing a potential avenue for therapeutic exploration.
Diagnostic performance of deep learning in ultrasound diagnosis of breast cancer: a systematic review
Authors: Dan Qing , Xu Ziting , Burrows Hannah , Bissram Jennifer , Stringer Jeffrey S. A. , Li Yingjia 
Published: January 2024

AbstractDeep learning (DL) has been widely investigated in breast ultrasound (US) for distinguishing between benign and malignant breast masses. This systematic review of test diagnosis aims to examine the accuracy of DL, compared to human readers, for the diagnosis of breast cancer in the US under clinical settings. Our literature search included records from databases including PubMed, Embase, Scopus, and Cochrane Library. Test accuracy outcomes were synthesized to compare the diagnostic performance of DL and human readers as well as to evaluate the assistive role of DL to human readers. A total of 16 studies involving 9238 female participants were included. There were no prospective studies comparing the test accuracy of DL versus human readers in clinical workflows. Diagnostic test results varied across the included studies. In 14 studies employing standalone DL systems, DL showed significantly lower sensitivities in 5 studies with comparable specificities and outperformed human readers at higher specificities in another 4 studies; in the remaining studies, DL models and human readers showed equivalent test outcomes. In 12 studies that assessed assistive DL systems, no studies proved the assistive role of DL in the overall diagnostic performance of human readers. Current evidence is insufficient to conclude that DL outperforms human readers or enhances the accuracy of diagnostic breast US in a clinical setting. Standardization of study methodologies is required to improve the reproducibility and generalizability of DL research, which will aid in clinical translation and application.
Spin-order-dependent magneto-elastic coupling in two dimensional antiferromagnetic MnPSe3 observed through Raman spectroscopy
Authors: Gillard Daniel J. , Wolverson Daniel , Hutchings Oscar M. , Tartakovskii Alexander I. 
Published: January 2024

AbstractLayered antiferromagnetic materials have recently emerged as an intriguing subset of the two-dimensional family providing a highly accessible regime with prospects for layer-number-dependent magnetism. Furthermore, transition metal phosphorus trichalcogenides, MPX3 (M = transition metal; X = chalcogen) provide a platform on which to investigate fundamental interactions between magnetic and lattice degrees of freedom and further explore the developing fields of spintronics and magnonics. Here, we use a combination of temperature dependent Raman spectroscopy and density functional theory to explore magnetic-ordering-dependent interactions between the manganese spin degree of freedom and lattice vibrations of the non-magnetic sub-lattice via a Kramers-Anderson super-exchange pathway in both bulk, and few-layer, manganese phosphorus triselenide (MnPSe3). We observe a nonlinear temperature-dependent shift of phonon modes predominantly associated with the non-magnetic sub-lattice, revealing their non-trivial spin-phonon coupling below the Néel temperature at 74 K, allowing us to extract mode-specific spin-phonon coupling constants.
Defined microbial communities and their soluble products protect mice from Clostridioides difficile infection
Authors: Douchant Katya , He Shu-Mei , Noordhof Curtis , Greenlaw Jill , Vancuren Sarah , Schroeter Kathleen , Allen-Vercoe Emma , Sjaarda Calvin , Vanner Stephen J. , Petrof Elaine O. , Sheth Prameet M. , Guzman Mabel 
Published: January 2024

AbstractClostridioides difficileis the leading cause of antibiotic-associated infectious diarrhea. The development ofC.difficileinfection is tied to perturbations of the bacterial community in the gastrointestinal tract, called the gastrointestinal microbiota. Repairing the gastrointestinal microbiota by introducing lab-designed bacterial communities, or defined microbial communities, has recently shown promise as therapeutics againstC.difficileinfection, however, the mechanisms of action of defined microbial communities remain unclear. Using an antibiotic-C.difficilemouse model, we report the ability of an 18-member community and a refined 4-member community to protect mice from two ribotypes ofC.difficile(CD027, CD078;p < 0.05). Furthermore, bacteria-free supernatant delivered orally to mice from the 4-member community proteolyzedC.difficiletoxins in vitro and protected mice fromC.difficileinfection in vivo (p < 0.05). This study demonstrates that bacteria-free supernatant is sufficient to protect mice fromC.difficile; and could be further explored as a therapeutic strategy againstC.difficileinfection.
Enhanced future vegetation growth with elevated carbon dioxide concentrations could increase fire activity
Authors: Allen Robert J. , Gomez James , Horowitz Larry W. , Shevliakova Elena 
Published: January 2024

AbstractMany regions of the planet have experienced an increase in fire activity in recent decades. Although such increases are consistent with warming and drying under continued climate change, the driving mechanisms remain uncertain. Here, we investigate the effects of increasing atmospheric carbon dioxide concentrations on future fire activity using seven Earth system models. Centered on the time of carbon dioxide doubling, the multi-model mean percent change in fire carbon emissions is 66.4 ± 38.8% (versus 1850 carbon dioxide concentrations, under fixed 1850 land-use conditions). A substantial increase is associated with enhanced vegetation growth due to carbon dioxide biogeochemical impacts at 60.1 ± 46.9%. In contrast, carbon dioxide radiative impacts, including warming and drying, yield a negligible response of fire carbon emissions at 1.7 ± 9.4%. Although model representation of fire processes remains uncertain, our results show the importance of vegetation dynamics to future increases in fire activity under increasing carbon dioxide, with potentially important policy implications.
Deep learning-based approach for high spatial resolution fibre shape sensing
Authors: Manavi Roodsari Samaneh , Freund Sara , Angelmahr Martin , Seppi Carlo , Rauter Georg , Schade Wolfgang , Cattin Philippe C. 
Published: January 2024

AbstractFiber optic shape sensing is an innovative technology that has enabled remarkable advances in various navigation and tracking applications. Although the state-of-the-art fiber optic shape sensing mechanisms can provide sub-millimeter spatial resolution for off-axis strain measurement and reconstruct the sensor’s shape with high tip accuracy, their overall cost is very high. The major challenge in more cost-effective fiber sensor alternatives for providing accurate shape measurement is the limited sensing resolution in detecting shape deformations. Here, we present a data-driven technique to overcome this limitation by removing strain measurement, curvature estimation, and shape reconstruction steps. We designed an end-to-end convolutional neural network that is trained to directly predict the sensor’s shape based on its spectrum. Our fiber sensor is based on easy-to-fabricate eccentric fiber Bragg gratings and can be interrogated with a simple and cost-effective readout unit in the spectral domain. We demonstrate that our deep-learning model benefits from undesired bending-induced effects (e.g., cladding mode coupling and polarization), which contain high-resolution shape deformation information. These findings are the preliminary steps toward a low-cost yet accurate fiber shape sensing solution for detecting complex multi-bend deformations.
Photocatalyst-free H2O-regulated and regiodivergent multicomponent hydrogenation/bifunctional sulfonylation of alkynes
Authors: Sun Jie , Wang Chaodong , Wu Chunlei , Wang Wenjian , Zeng Yue , Song Shengjie , Chen Zhiwei , Li Jianjun 
Published: January 2024

An efficient multicomponent reaction for the preparation of vinyl sulfone was achieved by the hydrogenation/bifunctional sulfonylation of alkynyl esters with N-allyl bromodifluoroacetamide, DABSO, and H2O under visible-light-only conditions.
The application of distributed autonomous organization governance mechanisms to civic medical data management
Authors: Cunningham James , Davies Nigel , Devaney Sarah , Holm Søren , Harding Mike , Neumann Victoria , Ainsworth John 
Published: January 2024

AbstractDecentralized autonomous organizations (DAOs) have emerged as a novel governance mechanism that operates through distributed ledgers and smart contracts, enabling members to direct an organization's actions. The widespread adoption of DAOs has occurred in response to their utility in managing emergent semi‐structured projects and has led to the development of various innovative governance mechanisms. The mechanisms employed by DAOs has the potential to be generalized beyond their core financial domain to a wide range of use cases. In the medical field the use of blockchain and DAOs can provide secure and transparent access to medical data, while ensuring patient privacy. Civic access to medical data is a growing area of interest, where individuals have control over their own medical data and can share it with healthcare providers, researchers, and other stakeholders. DAOs can facilitate this civic access, enabling individuals to share their data securely and selectively with authorized parties for research and other purposes. This paper explores the use of DAOs to medical data sharing, with a focus on ownership, governance, and transaction models. An application framework and API that enables the deployment of DAO‐like organizations is derived and this approach is applied to the patient‐centric management of medical data.
A bitcoin service community classification method based on Random Forest and improved KNN algorithm
Authors: Gao Muyun , Lin Shenwen , Tian Xin , He Xi , He Ketai , Chen Shifeng 
Published: January 2024

AbstractThere are service communities with different functions in the Bitcoin transactions system. Identifying community categories helps to further understand the Bitcoin transactions system and facilitates targeted regulation of anonymized Bitcoin transactions. To this end, a Bitcoin service community classification method based on Random Forest and improved K‐Nearest Neighbor (KNN) algorithm is proposed. First, the transaction characteristics of different types of communities are analyzed and summarized, and the corresponding transaction features are extracted from the address and entity levels; then multiple classification algorithms are compared, the optimal model to filter the effective features is selected, and the feature vector of entity addresses is constructed. Finally, a classification model is constructed based on Random Forest and improved KNN algorithm to classify the entities. By constructing different classification models for experimental comparison, the accuracy and stability advantages of the proposed method for classification in service community classification research are verified.
Intelligent algorithms and methodologies for low‐carbon smart manufacturing: Review on past research, recent developments and future research directions
Authors: Joshi Sudhanshu , Sharma Manu 
Published: January 2024

AbstractSignificant attention has been given to low‐carbon smart manufacturing (SM) as a strategy for promoting sustainability and carbon‐free emissions in the manufacturing industry. The implementation of intelligent algorithms and procedures enables the attainment and enhancement of low‐carbon clever manufacturing processes. These algorithms facilitate real‐time monitoring and predictive maintenance, ensuring efficient and sustainable operations and data‐driven decision‐making, increasing resource utilisation, waste reduction, and energy efficiency. The research examines the utilisation of algorithms in the context of low‐carbon smart manufacturing, encompassing machine learning, optimisation algorithms, and predictive analytics. A comprehensive literature evaluation spanning from 2011 to 2023 is conducted to assess the significance of low‐carbon approaches in the context of smart manufacturing. An integrated approach is used using content analysis, network data analysis, bibliometric analysis, and cluster analysis. Based on the published literature the leading contributors to low‐carbon manufacturing research are India, China, United States, United Kingdom, Singapore, and Italy. The results have shown five main themes—Low‐carbon smart manufacturing and applications of Algorithms; Industry 4.0 technologies for low‐carbon manufacturing; low carbon and green manufacturing; Low‐carbon Manufacturing, and Product design and control; Lean Systems and Smart Manufacturing. The purpose of this study is to provide policymakers and researchers with a guide for the academic development of low‐carbon manufacturing by evaluating research efforts in light of identified research deficits.
Partial discharge pattern recognition algorithm of overhead covered conductors based on feature optimization and bidirectional LSTM‐GRU
Authors: Zhang Chungfeng , Chen Mingli , Zhang Yongjun , Deng Wenyang , Gong Yu , Zhang Di 
Published: January 2024

AbstractRecognition of partial discharge (PD) patterns is essential for insulation diagnosis of covered conductors in overhead lines. Current research has not sufficiently addressed the complex background noise in real environments, and most detection methods depend primarily on feature engineering or deep learning, suggesting potential for improvement in accuracy and efficiency. This has led the authors to propose a PD pattern recognition algorithm that integrates feature selection and deep learning. This algorithm incorporates the design of a discrete wavelet denoising function specifically tailored to the characteristics of PD for data preprocessing. It employs Bayesian optimization algorithms and light gradient boosting machines for characterizing corona discharge features. Furthermore, it develops multi‐scale clustering features and phase‐resolved features for feature fusion, and constructs insightful features based on the light gradient boosting machine. Finally, a novel deep learning model is formulated, demonstrating exceptional detection performance for early faults in covered conductors. Experimental results show that this algorithm attains an Matthews correlation coefficient score of 0.814, a 13.2% improvement over the baseline algorithm's 0.719, and a speed increase of 39.18%. The final accuracy amounts to 97.85%. This algorithm demonstrates exceptional performance in detecting early insulation faults in conductors.