bluuebunny/crossref_metadata_2025_split huggingface dataset:
Plasma metabolomics identifies differing endotypes of recurrent wheezing in preschool children differentiated by symptoms and social disadvantage
Authors: Fitzpatrick Anne M. , Grunwell Jocelyn R. , Gaur Hina , Kobara Seibi , Kamaleswaran Rishikesan 
Published: July 2024

AbstractPreschool children with recurrent wheezing are a heterogeneous population with many underlying biological pathways that contribute to clinical presentations. Although the morbidity of recurrent wheezing in preschool children is significant, biological studies in this population remain quite limited. To address this gap, this study performed untargeted plasma metabolomic analyses in 68 preschool children with recurrent wheezing to identify metabolomic endotypes of wheezing. K-means cluster analysis was performed on metabolomic dataset including a total of 1382 named and unnamed metabolites. We identified three metabolomic clusters which differed in symptom severity, exacerbation occurrence, and variables associated with social disadvantage. Metabolites that distinguished the clusters included those involved in fatty acid metabolism, fatty acids (long chain monounsaturated fatty acids, long chain polyunsaturated fatty acids, and long chain saturated fatty acids), lysophospholipids, phosphatidylcholines, and phosphatidylethanolamines. Pathway analyses identified pathways of interest in each cluster, including steroid metabolism, histidine metabolism, sphingomyelins, and sphingosines, among others. This study highlights the biologic complexity of recurrent wheezing in preschool children and offers novel metabolites and pathways that may be amenable to future study and intervention.
Single-cell RNA sequencing reveals special basal cells and fibroblasts in idiopathic pulmonary fibrosis
Authors: Jin Chengji , Chen Yahong , Wang Yujie , Li Jia , Liang Jin , Zheng Shaomao , Zhang Lipeng , Li Qiaoyu , Wang Yongchao , Ling Fayu , Li Yongjie , Zheng Yu , Nie Qiuli , Feng Qiong , Wang Jing , Yang Huiling 
Published: July 2024

AbstractIdiopathic pulmonary fibrosis (IPF) is the most predominant type of idiopathic interstitial pneumonia and has an increasing incidence, poor prognosis, and unclear pathogenesis. In order to investigate the molecular mechanisms underlying IPF further, we performed single-cell RNA sequencing analysis on three healthy controls and five IPF lung tissue samples. The results revealed a significant shift in epithelial cells (ECs) phenotypes in IPF, which may be attributed to the differentiation of alveolar type 2 cells to basal cells. In addition, several previously unrecognized basal cell subtypes were preliminarily identified, including extracellular matrix basal cells, which were increased in the IPF group. We identified a special population of fibroblasts that highly expressed extracellular matrix-related genes, POSTN, CTHRC1, COL3A1, COL5A2, and COL12A1. We propose that the close interaction between ECs and fibroblasts through ligand–receptor pairs may have a critical function in IPF development. Collectively, these outcomes provide innovative perspectives on the complexity and diversity of basal cells and fibroblasts in IPF and contribute to the understanding of possible mechanisms in pathological lung fibrosis.
Identification of an H-Ras nanocluster disrupting peptide
Authors: Steffen Candy Laura , Manoharan Ganesh babu , Pavic Karolina , Yeste-Vázquez Alejandro , Knuuttila Matias , Arora Neha , Zhou Yong , Härmä Harri , Gaigneaux Anthoula , Grossmann Tom N. , Abankwa Daniel Kwaku 
Published: July 2024

AbstractHyperactive Ras signalling is found in most cancers. Ras proteins are only active in membrane nanoclusters, which are therefore potential drug targets. We previously showed that the nanocluster scaffold galectin-1 (Gal1) enhances H-Ras nanoclustering via direct interaction with the Ras binding domain (RBD) of Raf. Here, we establish that the B-Raf preference of Gal1 emerges from the divergence of the Raf RBDs at their proposed Gal1-binding interface. We then identify the L5UR peptide, which disrupts this interaction by binding with low micromolar affinity to the B- and C-Raf-RBDs. Its 23-mer core fragment is sufficient to interfere with H-Ras nanoclustering, modulate Ras-signalling and moderately reduce cell viability. These latter two phenotypic effects may also emerge from the ability of L5UR to broadly engage with several RBD- and RA-domain containing Ras interactors. The L5UR-peptide core fragment is a starting point for the development of more specific reagents against Ras-nanoclustering and -interactors.
Structural constraints limit the regime of optimal flux in autocatalytic reaction networks
Authors: Despons Armand , De Decker Yannick , Lacoste David 
Published: July 2024

AbstractAutocatalytic chemical networks play a predominant role in a large number of natural systems such as in metabolic pathways and in ecological networks. Despite recent efforts, the precise impact of thermodynamic constraints on these networks remains elusive. In this work, we present a theoretical framework that allows specific bounds on the thermodynamic affinity and on the concentrations of autocatalysts in mass-action autocatalytic networks. These bounds can be obtained solely from the stoichiometry of the underlying chemical reaction network, and are independent from the numerical values of kinetic parameters. This property holds in the specific regime where all the fluxes of the network are tightly coupled and maximal. Our method is applicable to large networks, and can be used to complement constraints-based modeling methods of metabolic networks, which typically do not provide predictions about thermodynamic properties or concentration ranges of metabolites.
Solutions for decarbonising urban bus transport: a life cycle case study in Saudi Arabia
Authors: Zhao Chengcheng , Kobayashi Leiliang Zheng , Alquaity Awad Bin Saud , Monfort Jean-Christophe , Cenker Emre , Miralles Noliner , Sarathy S. Mani 
Published: July 2024

AbstractWith heavy reliance on fossil fuels, countries like Saudi Arabia face challenges in reducing carbon emissions from urban bus transportation. Herein, we address the gaps in evaluating proton-exchange membrane fuel cell buses and develop a globally relevant life-cycle assessment model using Saudi Arabia as a case study. We consider various bus propulsion technologies, including fuel cell buses powered by grey and blue hydrogen, battery electric buses, and diesel engines, and include the shipping phase, air conditioning load, and refuelling infrastructure. The assessment illustrates fuel cell buses using blue hydrogen can reduce emissions by 53.6% compared to diesel buses, despite a 19.5% increase in energy use from carbon capture and storage systems. Battery electric buses are affected by the energy mix and battery manufacturing, so only cut emissions by 16.9%. Sensitivity analysis shows climate benefits depend on energy sources and efficiencies of carbon capture and hydrogen production. By 2030, grey and blue hydrogen-powered fuel cell buses and battery electric buses are projected to reduce carbon emissions by 19.3%, 33.4%, and 51% respectively, compared to their 2022 levels. Fully renewable-powered battery electric buses potentially achieve up to 89.6% reduction. However, fuel cell buses consistently exhibit lower environmental burdens compared to battery electric buses.
The chemical ordering and local pressure dependence for truncated octahedron PdAuRh nanoalloys
Authors: Garip Ali Kemal , Göcen Tuğba , Ünlü Nihan 
Published: July 2024

Abstract
The optimal chemical ordering configurations of 38-atom trimetallic PdnAu(32−n)Rh6 nanoalloys with truncated octahedron (TO) geometry were determined through Monte Carlo Basin-Hopping algorithm employing the Gupta potential, followed by local relaxations. Variations in mixing energy were assessed to compare relative stability, revealing the lowest excess energy at Pd16Au16Rh6 composition at the Gupta level. Furthermore, a comprehensive analysis of local atomic pressure was conducted, exploring the factors influencing these pressures.
Shear-free inhomogeneous energy density in 4D Einstein-Gauss-Bonnet spherical systems
Authors: Yousaf Z , Almutairi Bander , Bhatti M Z , Farhat A , Khan A S 
Published: July 2024

Abstract
We explore the inhomogeneity factors for the initially regular relativistic spheres in 4D-Einstein-Gauss-Bonnet (EGB) theory. The corresponding equations of motion are derived once the generic expressions for the kinematical variables are obtained for spherically symmetric self-gravitating system. By using the non-zero divergence of the stress-energy tensor, the independent components of Bianchi identities are constructed. To enable a thorough explanation of the inhomogeneity of the particular shear free matter distribution, we computed two distinct components of evolution equations employing the Weyl tensor. We then investigate the requisite variables for the irregularity by looking at particular scenarios in both the adiabatic and non-adiabatic domains. These instances demonstrate how, in addition to other factors, the Gauss-Bonnet terms contribute to the regularity requirements of the collapsing fluid.
Dirac fermions collimation in heterostructures based on tilted Dirac cone materials
Authors: Choubabi E B , Lemaalem B , Raggui M , Belouad A , Houça R , Kamal A , Monkade M 
Published: July 2024

Abstract
This paper aims to theoretically analyze the behavior of Dirac fermions in a tilted Dirac cone material, particularly those interacting with a barrier potential. Our results show that the degree of tilt in the y-direction can lead to different collimations of Dirac fermion beams relative to the Fermi and confinement surfaces. We have highlighted a range of results, including the conical geometry by illustrating the active surfaces and their geometric parameters in reciprocal space. To study the transmission probability, we have conducted numerical analyses, considering various system configurations and different external and internal physical parameters to characterize the fermionic transport behavior in a proposed heterostructure. Additionally, we examined the transmission of Dirac fermions in relation to the refractive indices and refraction between the different media constituting the system, discussing the tunneling effect and the Klein paradox in relation to various physical parameters. Our findings lay the groundwork for the development of controllable electronic devices using Dirac fermion collimation, governed by the tilt parameter, enabling precise manipulation and enhanced functionality.
Homogeneous spacetime with shear viscosity
Authors: Cho Inyong , Shaikh Rajibul 
Published: July 2024

Abstract
We study the homogeneous and anisotropic evolution of Bianchi type-I
spacetime driven by perfect fluid with shear viscosity. We obtain exact
solutions by considering the simplest form of the equation of state wherein
the pressure and the shear stress are proportional to the energy density
individually. A special case of our general solutions represent Bianchi
type-VII cosmology. We analyse the singularity structure of the solutions
and its connection with various energy conditions. We find that the
initial singularity can be removed only for the Bianchi type-VII. We also
analyse the late-time behaviour of the solutions and find that, compared
to the usual Friedmann universe, the spacetime expands less rapidly and
the energy density drops faster.
Assessing expert reliability in determining intracranial EEG channel quality and introducing the automated bad channel detection algorithm
Authors: Hattab Tariq , König Seth D , Carlson Danielle C , Hayes Rebecca F , Sha Zhiyi , Park Michael C , Kahn Lora , Patel Sima , McGovern Robert A , Henry Thomas , Khan Fawad , Herman Alexander B , Darrow David P 
Published: July 2024

Abstract

Objective. To evaluate the inter- and intra-rater reliability for the identification of bad channels among neurologists, EEG Technologists, and naïve research personnel, and to compare their performance with the automated bad channel detection (ABCD) algorithm for detecting bad channels. Approach. Six Neurologists, ten EEG Technologists, and six naïve research personnel (22 raters in total) were asked to rate 1440 real intracranial EEG channels as good or bad. Intra- and interrater kappa statistics were calculated for each group. We then compared each group to the ABCD algorithm which uses spectral and temporal domain features to classify channels as good or bad. Main results. Analysis of channel ratings from our participants revealed variable intra-rater reliability within each group, with no significant differences across groups. Inter-rater reliability was moderate among neurologists and EEG Technologists but minimal among naïve participants. Neurologists demonstrated a slightly higher consistency in ratings than EEG Technologists. Both groups occasionally misclassified flat channels as good, and participants generally focused on low-frequency content for their assessments. The ABCD algorithm, in contrast, relied more on high-frequency content. A logistic regression model showed a linear relationship between the algorithm’s ratings and user responses for predominantly good channels, but less so for channels rated as bad. Sensitivity and specificity analyses further highlighted differences in rating patterns among the groups, with neurologists showing higher sensitivity and naïve personnel higher specificity. Significance. Our study reveals the bias in human assessments of intracranial electroencephalography (iEEG) data quality and the tendency of even experienced professionals to overlook certain bad channels, highlighting the need for standardized, unbiased methods. The ABCD algorithm, outperforming human raters, suggests the potential of automated solutions for more reliable iEEG interpretation and seizure characterization, offering a reliable approach free from human biases.
Magnetic nanomaterials mediate precise magnetic therapy
Authors: Liu Sha , Sun Jianfei 
Published: July 2024

Abstract
Magnetic nanoparticle (MNP)-mediated precision magnet therapy plays a crucial role in treating various diseases. This therapeutic strategy compensates for the limitations of low spatial resolution and low focusing of magnetic stimulation, and realizes the goal of wireless teletherapy with precise targeting of focal areas. This paper summarizes the preparation methods of magnetic nanomaterials, the properties of magnetic nanoparticles, the biological effects, and the measurement methods for detecting magnetism; discusses the research progress of precision magnetotherapy in the treatment of psychiatric disorders, neurological injuries, metabolic disorders, and bone-related disorders, and looks forward to the future development trend of precision magnet therapy.
Sediment ballast accelerates sinking of Alaska North Slope crude oil measured ex situ with surface water from Cook Inlet
Authors: Ross Jesse , Kinner Nancy , Saupe Susan , Ziervogel Kai 
Published: July 2024

Abstract
Oil spilled into the ocean interacts with suspended matter forming aggregates that transport oil into subsurface layers and towards the bottom. We conducted a series of laboratory experiments to explore aggregation of oil with natural phytoplankton assemblages from Cook Inlet, Alaska at three times during a spring bloom. Oil and phytoplankton formed marine oil snow (MOS) that remained positively buoyant with a small fraction of MOS sinking to the bottom of our experimental bottles. Seawater treatments amended with suspended sediments formed oil-mineral aggregates (OMAs) with an oil capacity similar to MOS (∼20% of aggregate area was covered with oil). OMAs accelerated oil sedimentation in our bottles relative to MOS sedimentation underlining the significance of suspended matter as ballast for sinking oil. Our results reveal potential transport mechanisms of oil in Cook Inlet which apply to other coastal systems with high productivity and sediment loads.
Identification of end-milling chatter based on comprehensive feature fusion
Authors: Matthew Dialoke Ejiofor , Cao Hongrui , Shi Jianghai 
Published: July 2024

Abstract
The main barrier impeding the advancement of high-speed milling is chatter, which has a detrimental effect on the dimensional accuracy and quality of the finished workpiece. A reliable and precise chatter identification method is essential to improving the quality of machining. This paper presents a novel method for chatter identification using a comprehensive feature fusion of the Short-Time Fourier Transform (STFT) and the Fourier Synchrosqueezing Transform (FSST). The Wavelet Packet Transform (WPT) was used to pre-process the collected vibration and force signals. Wavelet packets with rich chatter information were then selected and reconstructed for further analysis. To reduce the effects of the rotating frequency and generate a hybrid spectrum with high resolution, a Gabor time-frequency filter is employed. As chatter indicators, standard deviation, skewness, and root mean square are computed. With a higher Time-Frequency Representation (TFR) resolution and a shorter computation time of 0.46 and 0.97 s across vibration and force signals, the proposed method outperforms conventional STFT and FSST. As a result, it can be used to reliably identify chatter from the onset, which is beneficial for machining monitoring.
Solving deep-learning density functional theory via variational autoencoders
Authors: Costa Emanuele , Scriva Giuseppe , Pilati Sebastiano 
Published: July 2024

Abstract
In recent years, machine learning models, chiefly deep neural networks, have revealed suited to learn accurate energy-density functionals from data. However, problematic instabilities have been shown to occur in the search of ground-state density profiles via energy minimization. Indeed, any small noise can lead astray from realistic profiles, causing the failure of the learned functional and, hence, strong violations of the variational property. In this article, we employ variational autoencoders (VAEs) to build a compressed, flexible, and regular representation of the ground-state density profiles of various quantum models. Performing energy minimization in this compressed space allows us to avoid both numerical instabilities and variational biases due to excessive constraints. Our tests are performed on one-dimensional single-particle models from the literature in the field and, notably, on a three-dimensional disordered potential. In all cases, the ground-state energies are estimated with errors below the chemical accuracy and the density profiles are accurately reproduced without numerical artifacts. Furthermore, we show that it is possible to perform transfer learning, applying pre-trained VAEs to different potentials.
Modular linear differential operators and generalized Rankin-Cohen brackets
Authors: Nagatomo Kiyokazu , Sakai Yuichi , Zagier Don 
Published: July 2024

The aim of this paper is to give expressions for modular linear differential operators (MLDOs) of any order. In particular, we show that they can all be described in terms of Rankin-Cohen brackets and a modified Rankin-Cohen bracket found by Kaneko and Koike. We also give more uniform descriptions of MLDOs in terms of canonically defined higher Serre derivatives and an extension of Rankin-Cohen brackets, as well as in terms of quasimodular forms and almost holomorphic modular forms. The last of these descriptions involves the holomorphic projection map. The paper also includes some general results on the theory of quasimodular forms on both cocompact and non-cocompact subgroups of

S

L
2

(

R

)

SL_2(\mathbb {R})

, as well as a slight sharpening of a theorem of Martin and Royer on Rankin-Cohen brackets of quasimodular forms.
Social Psychological Processes Underlying Collective Action in Repressive Contexts: What We Know and Ways Forward for Future Research
Authors: Ayanian Arin H , Tausch Nicole , Saab Rim 
Published: July 2024

Abstract
Social psychological research recently gained momentum in studying collective action in more repressive and non-democratic contexts. The present chapter provides a detailed account of the social psychological processes underlying engagement in nonviolent collective action at a micro (individual) level when facing risks of repression, and how perceived repression shapes these processes. We draw on our quantitative findings from Egypt, Russia, Ukraine, Hong Kong, and Turkey. We locate our work within the broader interdisciplinary literature. We also propose possible directions research can take to further our understanding of collective action in repressive contexts. Among others, we highlight the need to explore in more detail the nature of repression (e.g., type, severity, duration) and identities (e.g., multiple identities, non-unified movement), compare authoritarian and democratic contexts, and consider different antecedents (e.g., affective loyalties, contextual and cultural factors) and consequences (e.g., well-being). We discuss challenges for future research and implications for protest leaders and organizers.
Social Psychological Dynamics of Repression and Resistance to Repression: Insights From the October 2019 Lebanese Uprising
Authors: Saab Rim , Ayanian Arin H , Adra Aya , Badaan Vivienne 
Published: July 2024

Abstract
Inspired by our lived experience of the October 17 Lebanese uprising in 2019, we propose a framework to advance our understanding of the social psychological dynamics of repression and counter-repression. The framework draws on the growing social psychological literature on antecedents of collective action (e.g., injustice, efficacy, identity, anger, social motive, reward motive), which we term “social psychological capital of collective action.” We argue that to the extent that repression aims to demobilize a social movement by undermining the social psychological capital of collective action, effective counter-repression would consist of (a) identifying the targeted social psychological mechanisms of demobilization, and (b) fighting repression through actions aimed at preserving and reinforcing the threatened social psychological capital of collective action. We illustrate these dynamics with examples from the Lebanese uprising pertaining to perceived injustice, efficacy, personal costs and fear, and identity. We discuss limitations, future research directions, and potential implications for activists.
Digital Interfaces of Repression and Resistance
Authors: Uyheng Joshua , Montiel Cristina Jayme 
Published: July 2024

Abstract
This chapter proposes a discursive view of how digital technologies feature in the social psychologies of repression and resistance in a repressive state. Contemporary authoritarian states increasingly assimilate digital technologies to misinform and repress publics. In counterforce, social resistance harnesses these tools to promote public truth and, cloaked by digital anonymity, protect oppositionists from state harassment. We conceptualize digital space as a political sphere for multi-layered practices of agency in authoritarian societies. Invoking the Philippine experience during the rollout of an anti-terrorism law, we highlight asymmetrical social psychological relations in the political clash, and the necessity for consolidating meso-level and micro-level technological forces against a powerful digitalized state. Finally, we suggest novel, interdisciplinary directions for the study of digital resistance in authoritarian states and beyond.