bluuebunny/crossref_metadata_2025_split huggingface dataset:
A mutation in CCDC91, Homo sapiens coiled-coil domain containing 91 protein, cause autosomal-dominant acrokeratoelastoidosis
Authors: Zhu Yunlu , Bai Yun , Yan Wannian , Li Ming , Wu Fei , Xu Mingyuan , Wu Nanhui , Ge HongSong , Liu Yeqiang 
Published: April 2024

AbstractAcrokeratoelastoidosis (AKE) is a rare autosomal dominant hereditary skin disease characterized by small, round-oval, flat-topped keratotic papules on the palms, soles and dorsal aspect of hands or feet. The causative gene for AKE remains unidentified. This study aims to identify the causative gene of AKE and explore the underlying biological mechanisms. A large, three-generation Chinese family exhibiting classic AKE symptoms was identified. A genome-wide linkage analysis and whole-exome sequencing were employed to determine the causative gene. shRNA knockdown in human skin fibroblasts and CRISPR/Cas9 knockout in HEK293T cells were utilized to assess gene functions in the progression of elastic fiber biosynthesis. The linkage analysis identified a susceptibility region between rs7296765 to rs10784618 on chromosome 12. Whole-exome sequencing confirmed a splicing mutation of 1101 + 1 G > A in the CCDC91 gene, resulting in exon 11 skipping and a subsequent 59-amino-acid-residue loss (residues L309-Q367del). Further functional analysis revealed distended Golgi cisternae, cytoplasmic vesicle accumulation, and lysosome presence. Immnunostaining of si-CCDC91-HSF cells demonstrated tropoelastin accumulation in the Golgi and abnormal extracellular aggregates. There are no significant changes in Fibrillin-1 microfibril assembly and lysyl oxidase activity. The findings strongly suggest that the protein product of the CCDC91 gene plays a crucial role in elastin transport. This discovery enhances our understanding of CCDC91’s function and broadens the known pathogenic mechanisms of AKE.
Corneal neuropathic pain: a review to inform clinical practice
Authors: Watson Stephanie L. , Le Damien Tuan-Man 
Published: April 2024

AbstractCorneal neuropathic pain (CNP) is a poorly defined disease entity characterised by an aberrant pain response to normally non-painful stimuli and categorised into having peripheral and central mechanisms, with the former responding to instillation of topical anaesthetic. CNP is a challenging condition to diagnose due to numerous aetiologies, an absence of clinical signs and ancillary tests (in vivo confocal microscopy and esthesiometry), lacking the ability to confirm the diagnosis and having limited availability. Symptomatology maybe mirrored by severe and chronic forms of dry eye disease (DED), often leading to misdiagnosis and inadequate treatment. In practice, patients with suspected CNP can be assessed with questionnaires to elicit symptoms. A thorough ocular assessment is also performed to exclude any co-existent ocular conditions. A medical and mental health history should be sought due to associations with autoimmune disease, chronic pain syndromes, anxiety and depression. Management begins with communicating to the patient the nature of their condition. Ophthalmologists can prescribe topical therapies such as autologous serum eyedrops to optimise the ocular surface and promote neural regeneration. However, a multi-disciplinary treatment approach is often required, including mental health support, particularly when there are central mechanisms. General practitioners, pain specialists, neurologists and psychologists may be needed to assist with oral and behavioural therapies. Less data is available to support the safety and efficacy of adjuvant and surgical therapies and the long-term natural history remains to be determined. Hence clinical trials and registry studies are urgently needed to fill these data gaps with the aim to improve patient care.
Unlocking enhanced thermal conductivity in polymer blends through active learning
Authors: Xu Jiaxin , Luo Tengfei 
Published: April 2024

AbstractPolymers play an integral role in various applications, from everyday use to advanced technologies. In the era of machine learning (ML), polymer informatics has become a vital field for efficiently designing and developing polymeric materials. However, the focus of polymer informatics has predominantly centered on single-component polymers, leaving the vast chemical space of polymer blends relatively unexplored. This study employs a high-throughput molecular dynamics (MD) simulation combined with active learning (AL) to uncover polymer blends with enhanced thermal conductivity (TC) compared to the constituent single-component polymers. Initially, the TC of about 600 amorphous single-component polymers and 200 amorphous polymer blends with varying blending ratios are determined through MD simulations. The optimal representation method for polymer blends is identified, which involves a weighted sum approach that extends existing polymer representation from single-component polymers to polymer blends. An AL framework, combining MD simulation and ML, is employed to explore the TC of approximately 550,000 unlabeled polymer blends. The AL framework proves highly effective in accelerating the discovery of high-performance polymer blends for thermal transport. Additionally, we delve into the relationship between TC, radius of gyration (Rg), and hydrogen bonding, highlighting the roles of inter- and intra-chain interactions in thermal transport in amorphous polymer blends. A significant positive association between TC and Rg improvement and an indirect contribution from H-bond interaction to TC enhancement are revealed through a log-linear model and an odds ratio calculation, emphasizing the impact of increasing Rg and H-bond interactions on enhancing polymer blend TC.
Error-mitigated fermionic classical shadows on noisy quantum devices
Authors: Wu Bujiao , Koh Dax Enshan 
Published: April 2024

AbstractEfficiently estimating fermionic Hamiltonian expectation values is vital for simulating various physical systems. Classical shadow (CS) algorithms offer a solution by reducing the number of quantum state copies needed, but noise in quantum devices poses challenges. We propose an error-mitigated CS algorithm assuming gate-independent, time-stationary, and Markovian (GTM) noise. Forn-qubit systems, our algorithm, which employs the easily prepared initial state$$\left\vert {0}^{n}\right\rangle \,\left\langle {0}^{n}\right\vert$$0n0nassumed to be noiseless, efficiently estimatesk-RDMs with$$\widetilde{{{{\mathcal{O}}}}}(k{n}^{k})$$Õ(knk)state copies and$$\widetilde{{{{\mathcal{O}}}}}(\sqrt{n})$$Õ(n)calibration measurements for GTM noise with constant fidelities. We show that our algorithm is robust against noise types like depolarizing, damping, andX-rotation noise with constant strengths, showing scalings akin to prior CS algorithms for fermions but with better noise resilience. Numerical simulations confirm our algorithm’s efficacy in noisy settings, suggesting its viability for near-term quantum devices.
Giant nanomechanical energy storage capacity in twisted single-walled carbon nanotube ropes
Authors: Utsumi Shigenori , Ujjain Sanjeev Kumar , Takahashi Satoshi , Shimodomae Ryo , Yamaura Tae , Okuda Ryosuke , Kobayashi Ryuichiro , Takahashi Oga , Miyazono Satoshi , Kato Naoki , Aburamoto Keiichi , Hosoi Yuta , Ahuja Preety , Furuse Ayumi , Kawamata Yuma , Otsuka Hayato , Fujisawa Kazunori , Hayashi Takuya , Tománek David , Kaneko Katsumi 
Published: April 2024

AbstractA sustainable society requires high-energy storage devices characterized by lightness, compactness, a long life and superior safety, surpassing current battery and supercapacitor technologies. Single-walled carbon nanotubes (SWCNTs), which typically exhibit great toughness, have emerged as promising candidates for innovative energy storage solutions. Here we produced SWCNT ropes wrapped in thermoplastic polyurethane elastomers, and demonstrated experimentally that a twisted rope composed of these SWCNTs possesses the remarkable ability to reversibly store nanomechanical energy. Notably, the gravimetric energy density of these twisted ropes reaches up to 2.1 MJ kg−1, exceeding the energy storage capacity of mechanical steel springs by over four orders of magnitude and surpassing advanced lithium-ion batteries by a factor of three. In contrast to chemical and electrochemical energy carriers, the nanomechanical energy stored in a twisted SWCNT rope is safe even in hostile environments. This energy does not deplete over time and is accessible at temperatures ranging from −60 to +100 °C.
High-quality chromosome-level genome assembly of Nicotiana benthamiana
Authors: Ko Seo-Rin , Lee Sanghee , Koo Hyunjin , Seo Hyojeong , Yu Jaewoong , Kim Yong-Min , Kwon Suk-Yoon , Shin Ah-Young 
Published: April 2024

AbstractNicotiana benthamiana is a fundamental model organism in plant research. Recent advancements in genomic sequencing have revealed significant intraspecific genetic variations. This study addresses the pressing need for a precise genome sequence specific to its geographic origin by presenting a comprehensive genome assembly of the N. benthamiana LAB strain from the Republic of Korea (NbKLAB). We compare this assembly with the widely used NbLAB360 strain, shedding light on essential genomic differences between them. The outcome is a high-quality, chromosome-level genome assembly comprising 19 chromosomes, spanning 2,762 Mb, with an N50 of 142.6 Mb. Comparative analyses revealed notable variations, including 46,215 protein-coding genes, with an impressive 99.5% BUSCO completeness score. Furthermore, the NbKLAB assembly substantially improved the QV from 33% for NbLAB360 to 49%. This refined chromosomal genome assembly for N. benthamiana, in conjunction with comparative insights, provides a valuable resource for genomics research and molecular biology. This accomplishment forms a strong foundation for in-depth exploration into the intricacies of plant genetics and genomics, improved precision, and a comparative framework.
Spatiotemporal atmospheric in-situ carbon dioxide data over the Indian sites-data perspective
Authors: Pathakoti Mahesh , D.V. Mahalakshmi , G. Sreenivas , Suryavanshi Arun Shamrao , Taori Alok , Kant Yogesh , P. Raja , Bothale Rajashree Vinod , Chauhan Prakash , K.S. Rajan , Sinha P. R. , Chandra Naveen , Dadhwal Vinay Kumar 
Published: April 2024

AbstractIn the current study, atmospheric carbon dioxide (CO2) data covering multiple locations in the Indian subcontinent are reported. This data was collected using a dedicated ground-based in-situ network established as part of the Geosphere-Biosphere Programme (CAP-IGBP) of the Climate and Atmospheric Processes of the Indian Space Research Organisation (ISRO). Data are collected over Ponmudi, Ooty, Sriharikota, Gadanki, Shadnagar, Nagpur, and Dehradun during 2014-2015, 2017–2020, 2012, 2011–2015, 2014–2017, 2017 and 2008–2011, respectively. The atmospheric CO2 generated as part of the CAP−IGBP network would enhance the understanding of CO2 variability in different time scales ranging from diurnal, seasonal, and annual over the Indian region. Data available under this network may be interesting to other research communities for modeling studies and spatiotemporal variability of atmospheric CO2 across the study locations. The work also evaluated the CO2 observations against the Model for Interdisciplinary Research on Climate version 4 atmospheric chemistry-transport model (MIROC4-ACTM) concentrations.
Quantization of linear acoustic and elastic wave models in characterizations of isomorphism
Authors: Yang Chen 
Published: April 2024

AbstractFrom the macroscopic to the microscopic world, quantum mechanical effects in acoustics and elastic waves have become increasingly important. Observations on the quantum effects of acoustic and elastic waves using experimental methods have been reported in the literature. However, the conventional formulations of acoustic and elastic waves are still mainly governed by classical models. In this study, we investigated the quantization of acoustic and elastic waves using generalized Lorenz gauges. The potential variables of acoustic and elastic waves can be quantized in a manner similar to that of electrodynamics. The results include the Schrödinger equation with minimal coupling between the field and particles. The quantization of field variables is established as a consequence of the gauge symmetry property of the Schrödinger equation. Later, we explored the connections between the parallel formulations of mechanics and waves through an algebraic aspect. This highlights the isomorphism pattern from the theoretical characterization within the parallel formulations. To support the results, the derivations of potential formulations based on Lorenz gauges and functional mapping between field variables are presented.
New treatment alternatives for primary and metastatic colorectal cancer by an integrated transcriptome and network analyses
Authors: Karaca Caner , Demir Karaman Ezgi , Leblebici Asim , Kurter Hasan , Ellidokuz Hulya , Koc Altug , Ellidokuz Ender Berat , Isik Zerrin , Basbinar Yasemin 
Published: April 2024

AbstractMetastatic colorectal cancer (CRC) is still in need of effective treatments. This study applies a holistic approach to propose new targets for treatment of primary and liver metastatic CRC and investigates their therapeutic potential in-vitro. An integrative analysis of primary and metastatic CRC samples was implemented for alternative target and treatment proposals. Integrated microarray samples were grouped based on a co-expression network analysis. Significant gene modules correlated with primary CRC and metastatic phenotypes were identified. Network clustering and pathway enrichments were applied to gene modules to prioritize potential targets, which were shortlisted by independent validation. Finally, drug-target interaction search led to three agents for primary and liver metastatic CRC phenotypes. Hesperadin and BAY-1217389 suppress colony formation over a 14-day period, with Hesperadin showing additional efficacy in reducing cell viability within 48 h. As both candidates target the G2/M phase proteins NEK2 or TTK, we confirmed their anti-proliferative properties by Ki-67 staining. Hesperadinin particular arrested the cell cycle at the G2/M phase. IL-29A treatment reduced migration and invasion capacities of TGF-β induced metastatic cell lines. In addition, this anti-metastatic treatment attenuated TGF-β dependent mesenchymal transition. Network analysis suggests IL-29A induces the JAK/STAT pathway in a preventive manner.
Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
Authors: Zhou Feiyan , Fang Duanshu 
Published: April 2024

AbstractArrhythmias are irregular heartbeat rhythms caused by various conditions. Automated ECG signal classification aids in diagnosing and predicting arrhythmias. Current studies mostly focus on 1D ECG signals, overlooking the fusion of multiple ECG modalities for enhanced analysis. We converted ECG signals into modal images using RP, GAF, and MTF, inputting them into our classification model. To optimize detail retention, we introduced a CNN-based model with FCA for multimodal ECG tasks. Achieving 99.6% accuracy on the MIT-BIH arrhythmia database for five arrhythmias, our method outperforms prior models. Experimental results confirm its reliability for ECG classification tasks.
Young adults from disadvantaged groups experience more stress and deterioration in mental health associated with polycrisis
Authors: Kałwak Weronika , Weziak-Bialowolska Dorota , Wendołowska Anna , Bonarska Karolina , Sitnik-Warchulska Katarzyna , Bańbura Anna , Czyżowska Dorota , Gruszka Aleksandra , Opoczyńska-Morasiewicz Małgorzata , Izydorczyk Bernadetta 
Published: April 2024

AbstractThe recent polycrisis (COVID-19, Ukraine war, climate change, economic crisis) has been associated with mental health through cumulative stress, with young people being particularly vulnerable. We surveyed 403 college students from Poland to examine their psychological responses to the experienced crises. The results showed that polycrisis was associated with worse mental health of college students from disadvantaged groups (based on gender, sexual orientation, and financial situation) compared to other college students, in four areas: sense of proximity to the crises, stress caused by the crises, sense of responsibility for mitigating the crises, and experiencing everyday moral dilemmas regarding the crises. These young adults also suffered more in terms of negative affectivity, depressive symptoms, and subjective physical and mental health. Our findings suggest that when discussing public mental health perspectives, it is important to consider consequences of cumulative stress and its greater impact on young people from disadvantaged groups.
FIT calculator: a multi-risk prediction framework for medical outcomes using cardiorespiratory fitness data
Authors: Elshawi Radwa , Sakr Sherif , Al-Mallah Mouaz H. , Keteyian Steven J. , Brawner Clinton A. , Ehrman Jonathan K. 
Published: April 2024

AbstractAccurately predicting patients' risk for specific medical outcomes is paramount for effective healthcare management and personalized medicine. While a substantial body of literature addresses the prediction of diverse medical conditions, existing models predominantly focus on singular outcomes, limiting their scope to one disease at a time. However, clinical reality often entails patients concurrently facing multiple health risks across various medical domains. In response to this gap, our study proposes a novel multi-risk framework adept at simultaneous risk prediction for multiple clinical outcomes, including diabetes, mortality, and hypertension. Leveraging a concise set of features extracted from patients' cardiorespiratory fitness data, our framework minimizes computational complexity while maximizing predictive accuracy. Moreover, we integrate a state-of-the-art instance-based interpretability technique into our framework, providing users with comprehensive explanations for each prediction. These explanations afford medical practitioners invaluable insights into the primary health factors influencing individual predictions, fostering greater trust and utility in the underlying prediction models. Our approach thus stands to significantly enhance healthcare decision-making processes, facilitating more targeted interventions and improving patient outcomes in clinical practice. Our prediction framework utilizes an automated machine learning framework, Auto-Weka, to optimize machine learning models and hyper-parameter configurations for the simultaneous prediction of three medical outcomes: diabetes, mortality, and hypertension. Additionally, we employ a local interpretability technique to elucidate predictions generated by our framework. These explanations manifest visually, highlighting key attributes contributing to each instance's prediction for enhanced interpretability. Using automated machine learning techniques, the models simultaneously predict hypertension, mortality, and diabetes risks, utilizing only nine patient features. They achieved an average AUC of 0.90 ± 0.001 on the hypertension dataset, 0.90 ± 0.002 on the mortality dataset, and 0.89 ± 0.001 on the diabetes dataset through tenfold cross-validation. Additionally, the models demonstrated strong performance with an average AUC of 0.89 ± 0.001 on the hypertension dataset, 0.90 ± 0.001 on the mortality dataset, and 0.89 ± 0.001 on the diabetes dataset using bootstrap evaluation with 1000 resamples.
Research on depth measurement calibration of light field camera based on Gaussian fitting
Authors: Yang Miao 
Published: April 2024

AbstractOptical field imaging technology does not require a complicated optical path layout and thus reduces hardware costs. Given that only one single exposure of a single camera can obtain three-dimensional information, this paper proposes an improved calibration method for depth measurement based on the theoretical model of optical field imaging. Specifically, the calibration time can be reduced since the Gaussian fitting can reduce the number of refocused images used to obtain the optimal refocusing coefficient calibration. Moreover, the proposed method achieves the same effect as the multiple refocusing calibration strategy but requires less image processing time during calibration. At the same time, this method's depth resolution is analyzed in detail.
Optimizing an electromagnetic wave absorber for bi-anisotropic metasurfaces based on toroidal modes
Authors: Aghdasinia Sina , Allahverdizadeh Hossein , Afkari Ehsan , Ahmadpour Behrouz , Bemani Mohammad 
Published: April 2024

AbstractThe design and optimization of an electromagnetic wave absorber for far-field wireless power transmission (WPT) is the subject of this research study. The goal of the research is to effectively absorb energy from ambient RF electromagnetic waves without the usage of a ground plane by employing metasurfaces with chiral components.By integrating trioidal moments into the design theory, the objective is to create a metasurface that functions in two frequency bands and produces high-quality resonance. The study also explores the dual non-homogeneity property of structures, polarization tensor coefficients, and the electromagnetic response of non-homogeneous metasurfaces. Based on the relative orientation of induced fields and moments, it delves deeper into the two basic possibilities for dual non-homogeneous elements. The development of chiral metasurfaces and the notion of electromagnetic chirality and its implications for polarization properties are introduced.
Nonlinear dynamic separation characteristics of friction pair and experimental analysis
Authors: Zhao Yongqiang , Chen Huajun , Pan Wenlong , Cai Wenqing , Zhou Yuhan , Zhai Baoyu , Ni Xiangdong 
Published: April 2024

AbstractThis paper aims to reduce friction pair erosion of the clutch in the case of continuous shift; the dynamic separation process of the friction pair is investigated. The temperature of the friction pair, friction torque, and separation speed in the separation process are taken as the research objects, and the dynamics simulation model and finite element thermal coupling simulation model of the clutch separation process are established. The nonlinear dynamic separation characteristics of the friction pair are investigated by comparing and analyzing the effects of control parameters such as rotational speed difference, damping ratio, and lubricant viscosity on the friction torque, friction pair separation speed, separation gap, and contact stress during the separation process. The gap recovery coefficient is proposed as a response indicator for observing the separation process in response to the inability to observe the nonlinear dynamic motion of the friction pair during the separation process and to measure the end time of the separation. Finally, the clutch was subjected to a separation test. The results show that the proposed gap recovery coefficient accurately describes the separation process. The simulation model can simulate the clutch's separation and predict the trend of separation parameters.