bluuebunny/crossref_metadata_2025_split huggingface dataset:
The error of estimated GFR in predialysis care
Authors: Escamilla-Cabrera Beatriz , Luis-Lima Sergio , Gallego-Valcarce Eduardo , Sánchez-Dorta Nuria Victoria , Negrín-Mena Natalia , Díaz-Martín Laura , Cruz-Perera Coriolano , Hernández-Valles Ana Monserrat , González-Rinne Federico , Rodríguez-Gamboa María José , Estupiñán-Torres Sara , Miquel-Rodríguez Rosa , Cobo-Caso María Ángeles , Delgado-Mallén Patricia , Fernández-Suárez Gema , González-Rinne Ana , Hernández-Barroso Grimanesa , González-Delgado Alejandra , Torres-Ramírez Armando , Jiménez-Sosa Alejandro , Ortiz Alberto , Gaspari Flavio , Hernández-Marrero Domingo , Porrini Esteban Luis 
Published: March 2024

AbstractThe error of estimated glomerular filtration rate (eGFR) and its consequences in predialysis are unknown. In this prospective multicentre study, 315 predialysis patients underwent measured GFR (mGFR) by the clearance of iohexol and eGFR by 52 formulas. Agreement between eGFR and mGFR was evaluated by concordance correlation coefficient (CCC), total deviation index (TDI) and coverage probability (CP). In a sub-analysis we assessed the impact of eGFR error on decision-making as (i) initiating dialysis, (ii) preparation for renal replacement therapy (RRT) and (iii) continuing clinical follow-up. For this sub-analysis, patients who started RRT due to clinical indications (uremia, fluid overload, etc.) were excluded. eGFR had scarce precision and accuracy in reflecting mGFR (average CCC 0.6, TDI 70% and cp 22%) both in creatinine- and cystatin-based formulas. Variations -larger than 10 ml/min- between mGFR and eGFR were frequent. The error of formulas would have suggested (a) premature preparation for RTT in 14% of stable patients evaluated by mGFR; (b) to continue clinical follow-up in 59% of subjects with indication for RTT preparation due to low GFRm and (c) to delay dialysis in all asymptomatic patients (n = 6) in whom RRT was indicated based on very low mGFR. The error of formulas in predialysis was frequent and large and may have consequences in clinical care.
With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots
Authors: Steiner Moriz , Huettmann F. , Bryans N. , Barker B. 
Published: March 2024

AbstractSpecies-habitat associations are correlative, can be quantified, and used for powerful inference. Nowadays, Species Distribution Models (SDMs) play a big role, e.g. using Machine Learning and AI algorithms, but their best-available technical opportunities remain still not used for their potential e.g. in the policy sector. Here we present Super SDMs that invoke ML, OA Big Data, and the Cloud with a workflow for the best-possible inference for the 300 + global squirrel species. Such global Big Data models are especially important for the many marginalized squirrel species and the high number of endangered and data-deficient species in the world, specifically in tropical regions. While our work shows common issues with SDMs and the maxent algorithm (‘Shallow Learning'), here we present a multi-species Big Data SDM template for subsequent ensemble models and generic progress to tackle global species hotspot and coldspot assessments for a more inclusive and holistic inference.
Sounding out maerl sediment thickness: an integrated data approach
Authors: Sheehy Jack , Bates Richard , Bell Michael , Porter Jo 
Published: March 2024

AbstractMaerl beds are listed as a priority marine feature in Scotland. They are noted for creating suitable benthic habitat for diverse communities of fauna and flora and in supporting a wide array of ecosystem services. Within the context of climate change, they are also recognised as a potential blue carbon habitat through sequestration of carbon in living biomass and underlying sediment. There are, however, significant data gaps on the potential of maerl carbon sequestration which impede inclusion in blue carbon policy frameworks. Key data gaps include sediment thickness, from which carbon content is extrapolated. There are additional logistical and financial barriers associated with quantification methods that aim to address these data gaps. This study investigates the use of sub-bottom profiling (SBP) to lessen financial and logistical constraints of maerl bed sediment thickness estimation and regional blue carbon quantification. SBP data were cross validated with cores, other SBP data on blue carbon sediments, and analysed with expert input. Combining SBP data with estimates of habitat health (as % cover) from drop-down video (DDV) data, and regional abiotic data, this study also elucidates links between abiotic and biotic factors in determining maerl habitat health and maerl sediment thickness through pathway analysis in structural equation modelling (SEM). SBP data were proved to be sufficiently robust for identification of maerl sediments when corroborated with core data. SBP and DDV data of maerl bed habitats in Orkney exhibited some positive correlations of sediment thickness with maerl % cover. The average maerl bed sediment thickness was 1.08 m across all ranges of habitat health. SEM analysis revealed maerl bed habitat health was strongly determined by abiotic factors. Maerl habitat health had a separate positive effect on maerl bed sediment thickness.
Significant reduction in creep life of P91 steam pipe elbow caused by an aberrant microstructure after short-term service
Authors: Zhou Hongyu , Li Jian , Liu Jie , Yu Peichen , Liu Xinyang , Fan Zhiyang , Hu Anqing , He Yinsheng 
Published: March 2024

AbstractP91 steel is an important steam pipe for ultra-supercritical power plants due to its excellent creep strength, which generally has a design life of 100,000 h. Here, we found a significant aberrant decrease in the creep rupture life of a main steam pipe elbow after only 20,000 h of service. The microstructure in the aberrant piece exhibited a decomposition of martensitic lath into blocky ferrite due to recrystallization and accumulation of M23C6 as well as formation of the Laves phase along the prior austenitic grain boundaries, resulting in the decrease of hardness that no long meet ASME standard requirement. The creep testing of the P91 piece at 550–600 °C and 85–140 MPa shows that the influence of temperature on the cavity formation and cracking is greater than that of the applied stress. The rupture life is nearly two orders of magnitude shorter than the normal P91, attributing to the creep damage of the subgrain growth, M23C6 and Laves phase coarsening (aggregation approaching 3.4 μm). The residual life of the aberrant piece was evaluated to be 53,353 h based on the Larson–Miller parameter, which is much shorter than the design life, suggesting the safety operation of the elbow area should be paid more attention during the afterward service periods. P91 steel, main steam pipe elbow, aberrant microstructure, service degradation, creep life prediction
Bearing fault detection by using graph autoencoder and ensemble learning
Authors: Wang Meng , Yu Jiong , Leng Hongyong , Du Xusheng , Liu Yiran 
Published: March 2024

AbstractThe research and application of bearing fault diagnosis techniques are crucial for enhancing equipment reliability, extending bearing lifespan, and reducing maintenance expenses. Nevertheless, most existing methods encounter challenges in discriminating between signals from machines operating under normal and faulty conditions, leading to unstable detection results. To tackle this issue, the present study proposes a novel approach for bearing fault detection based on graph neural networks and ensemble learning. Our key contribution is a novel stochasticity-based compositional method that transforms Euclidean-structured data into a graph format for processing by graph neural networks, with feature fusion and a newly proposed ensemble learning strategy for outlier detection specifically designed for bearing fault diagnosis. This approach marks a significant advancement in accurately identifying bearing faults, highlighting our study's pivotal role in enhancing diagnostic methodologies.
Effects of background doping, interdiffusion and layer thickness fluctuation on the transport characteristics of THz quantum cascade lasers
Authors: Stanojević Novak , Demić Aleksandar , Vuković Nikola , Dean Paul , Ikonić Zoran , Indjin Dragan , Radovanović Jelena 
Published: March 2024

AbstractIn this work, we investigate the effects of n and p-type background doping, interface composition diffusion (interdiffusion) of the barrier material and layer thickness variation during molecular beam epitaxy (MBE) growth on transport characteristics of terahertz-frequency quantum cascade lasers (THz QCLs). We analysed four exemplary structures: a bound-to-continuum design, hybrid design, LO-phonon design and a two-well high-temperature performance LO-phonon design. The exemplary bound-to-continuum design has shown to be the most sensitive to the background doping as it stops lasing for concentrations around $$1.0\cdot 10^{15}$$

1.0
·

10
15

–$$2.0\cdot 10^{15}$$

2.0
·

10
15

cm$$^{-3}$$

-
3

. The LO-phonon design is the most sensitive to growth fluctuations during MBE and this is critical for novel LO-phonon structures optimised for high-temperature performance. We predict that interdiffusion mostly affects current density for designs with narrow barrier layers and higher $$\textrm{Al}$$
Al
composition. We show that layer thickness variation leads to significant changes in material gain and current density, and in some cases to the growth of nonfunctional devices. These effects serve as a beacon of fundamental calibration steps required for successful realisation of THz QCLs.
Occurrence and mechanisms of tigecycline resistance in carbapenem- and colistin-resistant Klebsiella pneumoniae in Thailand
Authors: Chirabhundhu Nachat , Luk-In Sirirat , Phuadraksa Thanawat , Wichit Sineewanlaya , Chatsuwan Tanittha , Wannigama Dhammika Leshan , Yainoy Sakda 
Published: March 2024

AbstractTigecycline has been regarded as one of the most important last-resort antibiotics for the treatment of infections caused by extensively drug-resistant (XDR) bacteria, particularly carbapenem- and colistin-resistant Klebsiella pneumoniae (C-C-RKP). However, reports on tigecycline resistance have been growing. Overall, ~ 4000 K. pneumoniae clinical isolates were collected over a five-year period (2017–2021), in which 240 isolates of C-C-RKP were investigated. Most of these isolates (91.7%) were resistant to tigecycline. Notably, a high-risk clone of ST16 was predominantly identified, which was associated with the co-harboring of blaNDM-1 and blaOXA-232 genes. Their major mechanism of tigecycline resistance was the overexpression of efflux pump acrB gene and its regulator RamA, which was caused by mutations in RamR (M184V, Y59C, I141T, A28T, C99/C100 insertion), in RamR binding site (PI) of ramA gene (C139T), in MarR (S82G), and/or in AcrR (L154R, R13Q). Interestingly, four isolates of ST147 carried the mutated tet(A) efflux pump gene. To our knowledge, this is the first report on the prevalence and mechanisms of tigecycline resistance in C-C-RKP isolated from Thailand. The high incidence of tigecycline resistance observed among C-C-RKP in this study reflects an ongoing evolution of XDR bacteria against the last-resort antibiotics, which demands urgent action.
AMPK stimulation inhibits YAP/TAZ signaling to ameliorate hepatic fibrosis
Authors: Shihan Mahbubul H. , Sharma Sachin , Cable Carson , Prathigudupu Vijaya , Chen Alina , Mattis Aras N. , Chen Jennifer Y. 
Published: March 2024

AbstractHepatic fibrosis is driven by the activation of hepatic stellate cells (HSCs). The Hippo pathway and its effectors, YAP and TAZ, are key regulators of HSC activation and fibrosis. However, there is a lack of mechanistic understanding of YAP/TAZ regulation in HSCs. Here we show that AMPK activation leads to YAP/TAZ inhibition and HSC inactivation in vitro, while the expression of a kinase-inactive mutant reversed these effects compared to wild type AMPKɑ1. Notably, the depletion of LATS1/2, an upstream kinase of YAP/TAZ signaling, rescues YAP/TAZ activation, suggesting that AMPK may be mediating YAP/TAZ inhibition via LATS1/2. In the carbon tetrachloride mouse model of fibrosis, pharmacologic activation of AMPK in HSCs inhibits YAP/TAZ signaling and reduces fibrosis. The findings implicate AMPK as a critical regulator of YAP/TAZ signaling and HSC inactivation and highlight AMPK activation as a therapeutic target for the treatment of hepatic fibrosis.
Real-world testing of an artificial intelligence algorithm for the analysis of chest X-rays in primary care settings
Authors: Miró Catalina Queralt , Vidal-Alaball Josep , Fuster-Casanovas Aïna , Escalé-Besa Anna , Ruiz Comellas Anna , Solé-Casals Jordi 
Published: March 2024

AbstractInterpreting chest X-rays is a complex task, and artificial intelligence algorithms for this purpose are currently being developed. It is important to perform external validations of these algorithms in order to implement them. This study therefore aims to externally validate an AI algorithm’s diagnoses in real clinical practice, comparing them to a radiologist’s diagnoses. The aim is also to identify diagnoses the algorithm may not have been trained for. A prospective observational study for the external validation of the AI algorithm in a region of Catalonia, comparing the AI algorithm’s diagnosis with that of the reference radiologist, considered the gold standard. The external validation was performed with a sample of 278 images and reports, 51.8% of which showed no radiological abnormalities according to the radiologist's report. Analysing the validity of the AI algorithm, the average accuracy was 0.95 (95% CI 0.92; 0.98), the sensitivity was 0.48 (95% CI 0.30; 0.66) and the specificity was 0.98 (95% CI 0.97; 0.99). The conditions where the algorithm was most sensitive were external, upper abdominal and cardiac and/or valvular implants. On the other hand, the conditions where the algorithm was less sensitive were in the mediastinum, vessels and bone. The algorithm has been validated in the primary care setting and has proven to be useful when identifying images with or without conditions. However, in order to be a valuable tool to help and support experts, it requires additional real-world training to enhance its diagnostic capabilities for some of the conditions analysed. Our study emphasizes the need for continuous improvement to ensure the algorithm’s effectiveness in primary care.
WGCNA analysis of the effect of exogenous BR on leaf angle of maize mutant lpa1
Authors: Ji Xiangzhuo , Gao Qiaohong , Zhuang Zelong , Chang Fangguo , Peng Yunling 
Published: March 2024

AbstractLeaf angle, as one of the important agronomic traits of maize, can directly affect the planting density of maize, thereby affecting its yield. Here we used the ZmLPA1 gene mutant lpa1 to study maize leaf angle and found that the lpa1 leaf angle changed significantly under exogenous brassinosteroid (BR) treatment compared with WT (inbred line B73). Transcriptome sequencing of WT and lpa1 treated with different concentrations of exogenous BR showed that the differentially expressed genes were upregulated with auxin, cytokinin and brassinosteroid; Genes associated with abscisic acid are down-regulated. The differentially expressed genes in WT and lpa1 by weighted gene co-expression network analysis (WGCNA) yielded two gene modules associated with maize leaf angle change under exogenous BR treatment. The results provide a new theory for the regulation of maize leaf angle by lpa1 and exogenous BR.
The clinical features, treatment and prognosis of neutropenic fever and Coronavirus disease 2019 results of the multicentre teos study
Authors: Başkol Elik Dilşah , Kaya Şafak , Alkan Sevil , Demirdal Tuna , Sener Alper , Kaya Selçuk , Güzel Tunçcan Özlem , Kayaaslan Bircan , Güner Rahmet , Eser Fatma , Kahraman Hasip , Birengel Serhat , Sarıcaoğlu Elif Mukime , Eroğlu Esma , Çölkesen Fatma , Öztürk Erman , Berk Cam Hande , Mermutluoğlu Çiğdem , Özer Balin Şafak , Sincan Gülden , Altın Nilgün , Sili Uluhan , Suntur Bedia Mutay , Arslan Gülen Tuğba , Deveci Burak , Saba Rabin , İncecik Şaban , Eser Karlıdağ Gülden , Hakko Elif , Akdağ Damla , Erdem Hüseyin Aytaç , Sipahi Hilal , Çicek Candan , Taşbakan Mehmet Sezai , Taşbakan Meltem , Pullukçu Hüsnü , Yamazhan Tansu , Arda Bilgin , Ulusoy Sercan , Sipahi Oguz Resat 
Published: March 2024

AbstractThis multicentre (22 centres in Turkey) retrospective cohort study aimed to assess the clinical outcomes of patients with neutropenic fever and SARS-CoV-2 positivity. Study period was 15 March 2020–15 August 2021. A total of 170 cases (58 female, aged 59 ± 15.5 years) that fulfilled the inclusion criteria were included in the study. One-month mortality rate (OMM) was 44.8%. The logistic regression analysis showed the following significant variables for the mentioned dependent variables: (i) achieving PCR negativity: receiving a maximum of 5 days of favipiravir (p = 0.005, OR 5.166, 95% CI 1.639–16.280); (ii) need for ICU: receiving glycopeptide therapy at any time during the COVID-19/FEN episode (p = 0.001, OR 6.566, 95% CI 2.137–20.172), the need for mechanical ventilation (p < 0.001, OR 62.042, 95% CI 9.528–404.011); (iii) need for mechanical ventilation: failure to recover from neutropenia (p < 0.001, OR 17.869, 95% CI 3.592–88.907), receiving tocilizumab therapy (p = 0.028, OR 32.227, 95% CI 1.469–707.053), septic shock (p = 0.001, OR 15.4 96% CI 3.164–75.897), and the need for ICU (p < 0.001, OR 91.818, 95% CI 15.360–548.873), (iv) OMM: [mechanical ventilation (p = 0.001, OR 19.041, 95% CI 3.229–112.286) and septic shock (p = 0.010, OR 5.589,95% CI 1.509–20.700)]. Although it includes a relatively limited number of patients, our findings suggest that COVID-19 and FEN are associated with significant mortality and morbidity.
A dynamic panel threshold model analysis on heterogeneous environmental regulation, R&D investment, and enterprise green total factor productivity
Authors: Liu Lu , Ren Rong , Cui Kaiyuan , Song Lei 
Published: March 2024

AbstractEnvironmental regulations are important means to influence manufacturing enterprise green development. However, there are two completely different conclusions both in theoretical and in empirical research, namely the “Follow Cost” theory and the “Porter Hypothesis”. The nonlinear mechanism needs to be considered. Therefore, this study aims to explain the threshold impact of heterogeneous environmental regulations on enterprise green total factor productivity. Environmental regulations are divided into different sub-categories, then based on the panel data of 1220 Chinese manufacturing listed companies from 2011 to 2020, this paper uses threshold regression model to examine the impact of heterogeneous environmental regulations on Chinese manufacturing enterprise Green Total Factor Productivity. The empirical results show that: (1) Command-controlled, market-incentive and voluntary-agreement environmental regulation all have a significant nonlinear impact on enterprise Green Total Factor Productivity. (2) Enterprise R&D investment plays a threshold role in the impact. (3) There are industry and equity type differences in the impact process. This study focuses on the micro level of enterprises and tests the threshold mechanism, which make some theoretical complement to previous researches. The research results are not only beneficial for the government to propose appropriate environmental regulatory policies, but also for enterprises to achieve green growth through heterogeneous R&D investment.
Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control
Authors: Punyavathi R. , Pandian A. , Singh Arvind R. , Bajaj Mohit , Tuka Milkias Berhanu , Blazek Vojtech 
Published: March 2024

AbstractThis paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. The proposed control algorithm orchestrates power sharing among the battery, supercapacitor, and PV sources, optimizing the utilization of available renewable energy and ensuring stringent voltage regulation of the DC bus. Notably, the ML-based control ensures precise torque and speed regulation, resulting in significantly reduced torque ripple and transient response times. In practical terms, the system maintains the DC bus voltage within a mere 2.7% deviation from the nominal value under various operating conditions, a substantial improvement over existing systems. Furthermore, the supercapacitor excels at managing rapid variations in load power, while the battery adjusts smoothly to meet the demands. Simulation results confirm the system's robust performance. The HESS effectively maintains voltage stability, even under the most challenging conditions. Additionally, its torque response is exceptionally robust, with negligible steady-state torque ripple and fast transient response times. The system also handles speed reversal commands efficiently, a vital feature for real-world applications. By showcasing these capabilities, the paper lays the groundwork for a more sustainable and efficient future for LEVs, suggesting pathways for scalable and advanced electric mobility solutions.
Automated detection of Alzheimer’s disease: a multi-modal approach with 3D MRI and amyloid PET
Authors: Castellano Giovanna , Esposito Andrea , Lella Eufemia , Montanaro Graziano , Vessio Gennaro 
Published: March 2024

AbstractRecent advances in deep learning and imaging technologies have revolutionized automated medical image analysis, especially in diagnosing Alzheimer’s disease through neuroimaging. Despite the availability of various imaging modalities for the same patient, the development of multi-modal models leveraging these modalities remains underexplored. This paper addresses this gap by proposing and evaluating classification models using 2D and 3D MRI images and amyloid PET scans in uni-modal and multi-modal frameworks. Our findings demonstrate that models using volumetric data learn more effective representations than those using only 2D images. Furthermore, integrating multiple modalities enhances model performance over single-modality approaches significantly. We achieved state-of-the-art performance on the OASIS-3 cohort. Additionally, explainability analyses with Grad-CAM indicate that our model focuses on crucial AD-related regions for its predictions, underscoring its potential to aid in understanding the disease’s causes.
A 10‐bit 13.3 µW single‐slope analog‐to‐digital converter with auto‐zero power‐down technique
Authors: Lee Youngwoo , Kim Suhwan , Jun Jaehoon 
Published: March 2024

AbstractThis letter presents a low‐power single‐slope analog‐to‐digital converter (ADC) for column‐parallel architectures. A simple and effective design technique is proposed to solve the input‐dependent power consumption problem of the conventional single‐slope ADCs. A decision‐feedback loop is implemented in the second stage of the comparator. Based on the negative‐feedback path, which is activated after the signal decision of the comparator, the input‐dependent dynamic current path in the amplifier is disabled. Furthermore, an additional low‐power design technique is proposed to save the power consumption of the ADC by optimizing the offset cancelling auto‐zero period. With the combination of the proposed techniques, the power consumption of the single‐slope ADC can be effectively saved while suppressing the dynamic current.