bluuebunny/crossref_metadata_2025_split huggingface dataset:
Remote effect of tropical south Atlantic sea surface temperature anomalies on April–June accumulated cyclone energy over the western North Pacific
Authors: Song Jinjie , Klotzbach Philip J , Wei Na , Duan Yihong 
Published: July 2024

Abstract
Global processes and their teleconnections, such as the El Niño-Southern Oscillation (ENSO), have been shown to be a large driver of interannual changes in accumulated cyclone energy (ACE) of western North Pacific (WNP) tropical cyclones (TCs), with higher ACE during El Niño and lower ACE during La Niña. However, it remains uncertain whether interannual changes in WNP TC ACE are modulated by sea surface temperature anomalies (SSTAs) in other oceans. This study finds a significant negative correlation between WNP TC ACE during the early season (April–June) and simultaneous SSTAs over the tropical south Atlantic (TSA) in 1970–2021. On average, in warm TSA years, basinwide April–June ACE is significantly lower, with significant ACE decreases mainly occurring over the region spanning 5°–30°N, 115°–150°E. This is a result of reduced TC frequency, intensity and duration, due to a remote modulation of WNP environmental conditions by TSA SSTAs. In warm TSA years, there are significant decreases in 700–500-hPa relative humidity, 850-hPa relative vorticity and 200-hPa divergence and significant increases in 850–200-hPa vertical wind shear over the portion of the WNP with significant ACE reductions. These environmental changes can be linked to an anomalous Walker circulation induced by TSA SSTAs.
Research on the flexural property of composite laminates with embedded PZT
Authors: Li Xining , Zhang Yaomao , Liu Yuzhi , Song Shiqi 
Published: July 2024

Abstract
The composite laminate embedded with piezoelectric lead zirconate titanate (PZT-5A) synthesizes the functional characteristics of a sensor such as damage monitoring and actuation. However, for composite laminates, PZT components belong to foreign impurities, which may destroy the integrity of the structure and damage the mechanical properties of the structure to some extent. In order to ensure the safety and reliability of the structure, this study revealed the influence of composite laminates embedded with PZT components on the flexural property through experiments and simulations. In the simulation, the shape of the resin enrichment zone was considered in combination with experimental observation. The results show that the error in the numerical prediction was less than 5%. Based on the particle swarm optimization (PSO) algorithm, the approximate symmetric layout of PZT in the laminate is obtained, which can reduce the influence of embedding on the bending performance of the structure. These results can provide reference and research ideas for the mechanical properties analysis of piezoelectric ceramic embedded structures.
Predictive modeling of specific fuel consumption in compression ignition engines using neural networks: a comparative analysis across diesel and polymer-based fuels
Authors: Modi Maulik A , Patel Tushar M 
Published: July 2024

Abstract
The study utilized neural network modeling to forecast the fuel consumption in compression ignition engines fueled by diesel, HDPE PO, PP PO, and LDPE PO. Using empirical data, a neural network model was constructed and used to estimate specific fuel consumption (SFC). Employing orthogonal arrays and parameter adjustments ensured accurate prediction of SFC, which was validated through experimentation. The multilayer perception network, coupled with traditional backpropagation, facilitates the nonlinear mapping of inputs to outcomes. In the LM10TP architecture, the key metrics from the training set included an impressive R-squared value of 1, indicating a perfect fit with a root mean square error (RMSE) of 0.0012 and a mean square error (MSE) of 1.5143E-06. Similarly, the validation set exhibited robust performance metrics with an R-squared value of 0.9999, an RMSE of 0.0011, and an MSE of 1.2185E-06. These metrics underscore the efficacy of neural networks in both the training and validation phases, affirming their utility as reliable predictive tools for SFC. Overall, this study highlights the effectiveness of neural network modeling for accurately predicting fuel consumption in compression ignition engines across diesel and polymer-based fuels. By leveraging empirical data and sophisticated modeling techniques, this study contributes to advancing predictive capabilities in the field, offering valuable insights for optimizing engine performance and fuel efficiency.
Experimental investigations and finite element simulation for predicting wear life of overrunning clutches
Authors: Dutt Karan A , Joshi Shashikant J , Shah Dhaval B , Prajapati Dipak 
Published: July 2024

Abstract
An overrunning clutch, generally known as a freewheel clutch, is a direction dependent torque transmitting device that works on the principle of wedge friction. The overrunning wear characteristics of freewheels are studied using pin-on-disc tribometry. The wear experiments for freewheels are performed at accelerated loads to promote wear in a short period. The overrunning wear life of the clutch under operating conditions is predicted using an appropriate load-life relationship. A finite element-based Archard’s wear model is implemented as a numerical strategy to evaluate the wear profile. The maximum local wear for various loads is computed using experimentally obtained wear and friction coefficients. The numerical simulation is performed with an adaptive mesh technique utilizing incremental nodal displacements to predict surface wear. The experimental and numerical results are compared in terms of wear characteristics. The numerical wear results are almost 11% higher than the experimental results. The wear life of an overrunning clutch is predicted in terms of overrunning speed based on the wear amount.
Dispersion frequency technique to explain the charge transfer process involved in cadmium detection using aniline, N-phenylglycine and graphene oxide based electrochemical sensor
Authors: Dutta Kusumita , Panda Siddhartha 
Published: July 2024

Abstract
A new technique, termed as dispersion frequency (DF) technique, based on Maxwell Wagner dispersion frequency, was proposed for the first time in an electrochemical sensor to explain the charge transfer mechanism involved, behind the sensing process. Dispersion frequency can be characterized by the maximum dispersion in capacitance, which is an interfacial relaxation effect, and occurs in systems where electric current passes through the electrode electrolyte interface across the double layer. In this work, this technique was used to explain the reason behind the improvement in the selectivity by varying the film thickness and amount of graphene oxide (GO), in composite (from aniline, N-phenylglycine and GO) films. For that purpose, electrochemical impedance spectroscopy (EIS) experiments were conducted, and the DF values were evaluated. It was found that the DF values decrease with film thickness and increase with the GO content when detecting Cd2+ in buffer. Also, the DF value changed (decreased) in presence of interfering species. Thus, it was demonstrated that the DF values could be used to predict and explain the interference effect. The behavior of the DF values was opposite to that of the barrier width (BW) values as studied in a previous work which too were a function of the film thickness and the GO content. Both explain independent physical phenomena which are related to the same charge transfer process. In this work, a correlation coefficient relating the barrier width and dispersion frequency values with respect to film thicknesses at any specific amount of GO content was evaluated. This correlation coefficient can be used to evaluate one parameter if the other is known for a series of film thicknesses by doing lesser number of EIS experiments.
Unveiling the robustness of machine learning families
Authors: Fabra-Boluda R , Ferri C , Ramírez-Quintana M J , Martínez-Plumed F 
Published: July 2024

Abstract
The evaluation of machine learning systems has typically been limited to performance measures on clean and curated datasets, which may not accurately reflect their robustness in real-world situations where data distribution can vary from learning to deployment, and where truthfully predict some instances could be more difficult than others. Therefore, a key aspect in understanding robustness is instance difficulty, which refers to the level of unexpectedness of system failure on a specific instance. We present a framework that evaluates the robustness of different ML models using item response theory-based estimates of instance difficulty for supervised tasks. This framework evaluates performance deviations by applying perturbation methods that simulate noise and variability in deployment conditions. Our findings result in the development of a comprehensive taxonomy of ML techniques, based on both the robustness of the models and the difficulty of the instances, providing a deeper understanding of the strengths and limitations of specific families of ML models. This study is a significant step towards exposing vulnerabilities of particular families of ML models.
Ethnic differences in the effects of apolipoprotein E ɛ4 and vascular risk factors on accelerated brain aging
Authors: Im Yanghee , Kang Sung Hoon , Park Gilsoon , Yoo Heejin , Chun Min Young , Kim Chi-Hun , Park Chae Jung , Kim Jun Pyo , Jang Hyemin , Kim Hee Jin , Oh Kyungmi , Koh Seong-Beom , Lee Jong-Min , Na Duk L , Seo Sang Won , Kim Hosung 
Published: July 2024

Abstract
The frequency of the apolipoprotein E ɛ4 allele and vascular risk factors differs among ethnic groups. We aimed to assess the combined effects of apolipoprotein E ɛ4 and vascular risk factors on brain age in Korean and UK cognitively unimpaired populations. We also aimed to determine the differences in the combined effects between the two populations. We enrolled 2314 cognitively unimpaired individuals aged ≥45 years from Korea and 6942 cognitively unimpaired individuals from the UK, who were matched using propensity scores. Brain age was defined using the brain age index. The apolipoprotein E genotype (ɛ4 carriers, ɛ2 carriers and ɛ3/ɛ3 homozygotes) and vascular risk factors (age, hypertension and diabetes) were considered predictors. Apolipoprotein E ɛ4 carriers in the Korean (β = 0.511, P = 0.012) and UK (β = 0.302, P = 0.006) groups had higher brain age index values. The adverse effects of the apolipoprotein E genotype on brain age index values increased with age in the Korean group alone (ɛ2 carriers × age, β = 0.085, P = 0.009; ɛ4 carriers × age, β = 0.100, P &lt; 0.001). The apolipoprotein E genotype, age and ethnicity showed a three-way interaction with the brain age index (ɛ2 carriers × age × ethnicity, β = 0.091, P = 0.022; ɛ4 carriers × age × ethnicity, β = 0.093, P = 0.003). The effects of apolipoprotein E on the brain age index values were more pronounced in individuals with hypertension in the Korean group alone (ɛ4 carriers × hypertension, β = 0.777, P = 0.038). The apolipoprotein E genotype, age and ethnicity showed a three-way interaction with the brain age index (ɛ4 carriers × hypertension × ethnicity, β=1.091, P = 0.014). We highlight the ethnic differences in the combined effects of the apolipoprotein E ɛ4 genotype and vascular risk factors on accelerated brain age. These findings emphasize the need for ethnicity-specific strategies to mitigate apolipoprotein E ɛ4-related brain aging in cognitively unimpaired individuals.
Speech and neuroimaging effects following HiCommunication: a randomized controlled group intervention trial in Parkinson’s disease
Authors: Steurer Hanna , Albrecht Franziska , Gustafsson Joakim Körner , Razi Adeel , Franzén Erika , Schalling Ellika 
Published: July 2024

Abstract
Speech, voice and communication changes are common in Parkinson's disease. HiCommunication is a novel group intervention for speech and communication in Parkinson’s disease based on principles driving neuroplasticity. In a randomized controlled trial, 95 participants with Parkinson’s disease were allocated to HiCommunication or an active control intervention. Acoustic analysis was performed pre-, post- and six months after intervention. Intention-to-treat analyses with missing values imputed in linear multilevel models and complimentary per-protocol analyses were performed. The proportion of participants with a clinically relevant increase in the primary outcome measure of voice sound level was calculated. Resting-state functional MRI was performed pre- and post-intervention. Spectral dynamic causal modelling and the parametric empirical Bayes methods were applied to resting-state functional MRI data to describe effective connectivity changes in a speech-motor-related network of brain regions. From pre- to post-intervention, there were significant group-by-time interaction effects for the measures voice sound level in text reading (unstandardized b = 2.3, P = 0.003), voice sound level in monologue (unstandardized b = 2.1, P = 0.009), Acoustic Voice Quality Index (unstandardized b = −0.5, P = 0.016) and Harmonics-to-Noise Ratio (unstandardized b = 1.3, P = 0.014) post-intervention. For 59% of the participants, the increase in voice sound level after HiCommunication was clinically relevant. There were no sustained effects at the six-month follow-up. In the effective connectivity analysis, there was a significant decrease in inhibitory self-connectivity in the left supplementary motor area and increased connectivity from the right supplementary motor area to the left paracentral gyrus after HiCommunication compared to after the active control intervention. In conclusion, the HiCommunication intervention showed promising effects on voice sound level and voice quality in people with Parkinson’s disease, motivating investigations of barriers and facilitators for implementation of the intervention in healthcare settings. Resting-state brain effective connectivity was altered following the intervention in areas implicated, possibly due to reorganization in brain networks.
Activation of the inflammasome and pyroptosis cascade in podocytes of patients with minimal change disease
Authors: Kajio Yuki , Suzuki Taihei , Kobayashi Kazuki , Kanazawa Nobuhiro , Iyoda Masayuki , Honda Hirokazu , Honda Kazuho 
Published: July 2024

ABSTRACT

Background
In contrast to childhood minimal change disease (MCD), adult-onset MCD frequently recurs and requires prolonged immunosuppressive therapy. Accordingly, an investigation of the pathogenesis of adult MCD is required. MCD is usually accompanied by severe dyslipidaemia. Oxidized low-density lipoprotein (ox-LDL) is known to function in a damage-associated molecular pattern (DAMP) through CD36, triggering the NOD-like receptor thermal protein domain-associated protein 3 (NLRP3) inflammasome and programmed cell death called pyroptosis. However, the relationship between MCD pathogenesis and NLRP3 inflammasome/pyroptosis activation via CD36 is not fully understood.

Methods
We conducted comprehensive histological and clinical evaluations by analysing renal biopsy (RBx) specimens and urine samples obtained from 26 patients with MCD. These samples were compared with control kidneys from 15 transplant donors and urine samples from 15 healthy volunteers.

Results
The number of podocytes was lower in the MCD group than in the control group. Urinary ox-LDL levels were higher in the MCD group than in the control group. Immunofluorescence staining revealed that NLRP3 and CD36 were upregulated in MCD podocytes. Urinary interleukin (IL)-18 levels increased in patients with MCD. Steroid therapy performed before RBx appeared to maintain the podocyte number and reduce urinary ox-LDL and IL-18 levels.

Conclusion
In MCD, the NLRP3 inflammasome and pyroptosis cascade seem to be activated via upregulation of CD36 in podocytes, associated with increased urinary ox-LDL. Elevated urinary IL-18 levels suggest that pyroptosis may occur in MCD. Further research is required to confirm the significance of the podocyte NLRP3 inflammasome/pyroptosis in MCD.
Towards discovery: an end-to-end system for uncovering novel biomedical relations
Authors: Almeida Tiago , Jonker Richard A A , Antunes Rui , Almeida João R , Matos Sérgio 
Published: July 2024

AbstractBiomedical relation extraction is an ongoing challenge within the natural language processing community. Its application is important for understanding scientific biomedical literature, with many use cases, such as drug discovery, precision medicine, disease diagnosis, treatment optimization and biomedical knowledge graph construction. Therefore, the development of a tool capable of effectively addressing this task holds the potential to improve knowledge discovery by automating the extraction of relations from research manuscripts. The first track in the BioCreative VIII competition extended the scope of this challenge by introducing the detection of novel relations within the literature. This paper describes that our participation system initially focused on jointly extracting and classifying novel relations between biomedical entities. We then describe our subsequent advancement to an end-to-end model. Specifically, we enhanced our initial system by incorporating it into a cascading pipeline that includes a tagger and linker module. This integration enables the comprehensive extraction of relations and classification of their novelty directly from raw text. Our experiments yielded promising results, and our tagger module managed to attain state-of-the-art named entity recognition performance, with a micro F1-score of 90.24, while our end-to-end system achieved a competitive novelty F1-score of 24.59. The code to run our system is publicly available at https://github.com/ieeta-pt/BioNExt.Database URL: https://github.com/ieeta-pt/BioNExt
Mushroom poisoning with cardiogenic shock caused by Russula subnigricans successfully treated with mechanical circulatory support devices: a case report
Authors: Iseki Shota , Yamaguchi Shogo , Ozaki Yuta , Uemura Yusuke 
Published: July 2024

Abstract

Background
Mushroom poisoning caused by the ingestion of Russula subnigricans has been reported to cause rhabdomyolysis and cardiac dysfunction, leading to death. There have been few reports of cardiogenic shock induced by mushroom poisoning that was successfully treated using mechanical circulatory support devices.

Case Summary
A 38-year-old man presented with gastrointestinal symptoms a day after consuming a curry made with forest-collected mushrooms and was admitted with a diagnosis of rhabdomyolysis. Despite appropriate fluid management for severe rhabdomyolysis, the patient experienced anuria and cardiogenic shock with a remarkably reduced left ventricular ejection function, followed by the development of ventricular fibrillation. Mechanical support using Impella CP, veno-arterial extracorporeal membranous oxygenation (VA-ECMO), and continuous haemodiafiltration were administered for cardiogenic shock and severe rhabdomyolysis. His cardiac and renal function gradually improved, and the patient was successfully weaned off VA-ECMO on day 4, Impella CP on day 5, and renal replacement therapy on day 23. The left ventricular ejection fraction returned to normal without any neurological, respiratory, or renal sequelae. The remaining mushroom samples were identified as R. subnigricans by polymerase chain reaction testing.

Discussion
This is the first reported case of cardiogenic shock caused by R. subnigricans poisoning, successfully treated with Impella CP and VA-ECMO. The optimal use of mechanical circulatory support devices plays an important role in the treatment of cardiogenic shock caused by mushroom toxicity.
Recent and speculative remote myocardial trauma: a case series
Authors: Lu Zhijuan , Zhang Xiaocong , Huang Yanling , Song Jia , Zhang Chaoqun , Wang Qiang , Wu Handong , Huang Xinsheng 
Published: July 2024

Abstract

Background
Cardiac blunt trauma clinically presents as a spectrum of injuries of varying severity. However, the diagnosis of complications of remote myocardial trauma is often challenging, especially if the patient forgets to mention a remote history of chest trauma.

Case summary
In this study, we present a patient who recently experienced traumatic myocardial dissection and interventricular septal rupture, alongside three patients exhibiting a mimic double-chambered left ventricle, indicative of prior remote myocardial trauma potentially associated with myocardial dissecting tear.

Discussion
Patients with recent severe myocardial injury are detectable through cardiac imaging. However, forgotten remote myocardial trauma can lead to adverse myocardial remodelling, heart failure, and arrhythmias. Long-term myocardial remodelling can obscure initial myocardial imaging characteristics, posing challenges in interpretation. Our case series suggests that remote myocardial trauma may be more prevalent than commonly thought of in clinical practice.
Effect of urban environment on cardiovascular health: a feasibility pilot study using machine learning to predict heart rate variability in patients with heart failure
Authors: van Es Valerie A A , De Lathauwer Ignace L J , Lopata Richard G P , Kemperman Astrid D A M , van Dongen Robert P , Brouwers Rutger W M , Funk Mathias , Kemps Hareld M C 
Published: July 2024

Abstract

Aims
Urbanization is related to non-communicable diseases such as congestive heart failure (CHF). Understanding the influence of diverse living environments on physiological variables such as heart rate variability (HRV) in patients with chronic cardiac disease may contribute to more effective lifestyle advice and telerehabilitation strategies. This study explores how machine learning (ML) models can predict HRV metrics, which measure autonomic nervous system responses to environmental attributes in uncontrolled real-world settings. The goal is to validate whether this approach can ascertain and quantify the connection between environmental attributes and cardiac autonomic response in patients with CHF.

Methods and results
A total of 20 participants (10 healthy individuals and 10 patients with CHF) wore smartwatches for 3 weeks, recording activities, locations, and heart rate (HR). Environmental attributes were extracted from Google Street View images. Machine learning models were trained and tested on the data to predict HRV metrics. The models were evaluated using Spearman’s correlation, root mean square error, prediction intervals, and Bland–Altman analysis. Machine learning models predicted HRV metrics related to vagal activity well (R &gt; 0.8 for HR; 0.8 &gt; R &gt; 0.5 for the root mean square of successive interbeat interval differences and the Poincaré plot standard deviation perpendicular to the line of identity; 0.5 &gt; R &gt; 0.4 for the high frequency power and the ratio of the absolute low- and high frequency power induced by environmental attributes. However, they struggled with metrics related to overall autonomic activity, due to the complex balance between sympathetic and parasympathetic modulation.

Conclusion
This study highlights the potential of ML-based models to discern vagal dynamics influenced by living environments in healthy individuals and patients diagnosed with CHF. Ultimately, this strategy could offer rehabilitation and tailored lifestyle advice, leading to improved prognosis and enhanced overall patient well-being in CHF.