bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3)
Authors: Ma Jiayu , Fu Haiyang , Huba J. D. , Jin Yaqiu 
Published: April 2024

AbstractPurely data‐driven ionospheric modeling fails to adequately obey fundamental physical laws. To overcome this shortcoming, we propose a novel ionospheric inversion model, Physics‐Informed Neural Network based on fully physical models SAMI3 (PINN‐SAMI3). The model incorporates the governing equations of the ionospheric physical model SAMI3 into the neural network to reconstruct the temporal‐spatial distribution of ionospheric plasma parameters. The objective of this study is to investigate the feasibility of integrating physical models with machine learning for ionospheric modeling. The PINN‐SAMI3 framework enforces physical laws through the multiple ion species of continuity, momentum, temperature equations in the magnetic dipole coordinate system. The simulation results show that if sparse ion densities are used as training data, it is possible to retrieve ionospheric electron densities, ion velocities and ion temperatures, respectively. The optimal physical constraints have been also investigated for different inversion quantities. Furthermore, the impact of incorporating E × B velocity terms on inversion results during the periods of ionospheric calm and geomagnetic storm is analyzed. The PINN‐SAMI3 achieves good inversion results even using sparse data in comparison to the traditional artificial neural networks (ANN). The framework will contribute to advance the future space weather prediction capability with artificial intelligence (AI).
A Mass‐Conserving‐Perceptron for Machine‐Learning‐Based Modeling of Geoscientific Systems
Authors: Wang Yuan‐Heng , Gupta Hoshin V. 
Published: April 2024

AbstractAlthough decades of effort have been devoted to building Physical‐Conceptual (PC) models for predicting the time‐series evolution of geoscientific systems, recent work shows that Machine Learning (ML) based Gated Recurrent Neural Network technology can be used to develop models that are much more accurate. However, the difficulty of extracting physical understanding from ML‐based models complicates their utility for enhancing scientific knowledge regarding system structure and function. Here, we propose a physically interpretable Mass‐Conserving‐Perceptron (MCP) as a way to bridge the gap between PC‐based and ML‐based modeling approaches. The MCP exploits the inherent isomorphism between the directed graph structures underlying both PC models and GRNNs to explicitly represent the mass‐conserving nature of physical processes while enabling the functional nature of such processes to be directly learned (in an interpretable manner) from available data using off‐the‐shelf ML technology. As a proof of concept, we investigate the functional expressivity (capacity) of the MCP, explore its ability to parsimoniously represent the rainfall‐runoff (RR) dynamics of the Leaf River Basin, and demonstrate its utility for scientific hypothesis testing. To conclude, we discuss extensions of the concept to enable ML‐based physical‐conceptual representation of the coupled nature of mass‐energy‐information flows through geoscientific systems.
Blockade of TGF-β and PD-L1 by bintrafusp alfa promotes survival in preclinical ovarian cancer models by promoting T effector and NK cell responses
Authors: Kment Jacob , Newsted Daniel , Young Stephanie , Vermeulen Michael C. , Laight Brian J. , Greer Peter A. , Lan Yan , Craig Andrew W. 
Published: April 2024

Abstract
Background
Failure of immunotherapy in high-grade serous ovarian cancer (HGSC) may be due to high levels of transforming growth factor-β (TGF-β) in ascites or tumour immune microenvironment (TIME). Here, we test whether coordinated blockade of TGF-β and PD-L1 with bintrafusp alfa (BA) can provoke anti-tumour immune responses in preclinical HGSC models.

Methods
BA is a first-in-class bifunctional inhibitor of TGF-β and PD-L1, and was tested for effects on overall survival and altered TIME in syngeneic HGSC models.

Results
Using a mouse ID8-derived HGSC syngeneic model with IFNγ-inducible PD-L1 expression, BA treatments significantly reduced ascites development and tumour burden. BA treatments depleted TGF-β and VEGF in ascites, and skewed the TIME towards cytotoxicity compared to control. In the BR5 HGSC syngeneic model, BA treatments increased tumour-infiltrating CD8 T cells with effector memory and cytotoxic markers, as well as cytolytic NK cells. Extended BA treatments in the BR5 model produced ∼50% BA-cured mice that were protected from re-challenge. These BA-cured mice had increased peritoneal T-effector memory and NK cells compared to controls.

Conclusions
Our preclinical studies of BA in advanced ovarian cancer models support further testing of BA as an improved immunotherapy option for patients with advanced ovarian cancer.
Inhibition of the galactosyltransferase C1GALT1 reduces osteosarcoma cell proliferation by interfering with ERK signaling and cell cycle progression
Authors: Watanabe Kentaro , Tasaka Keiji , Ogata Hideto , Kato Shota , Ueno Hiroo , Umeda Katsutsugu , Isobe Tomoya , Kubota Yasuo , Sekiguchi Masahiro , Kimura Shunsuke , Sato-Otsubo Aiko , Hiwatari Mitsuteru , Ushiku Tetsuo , Kato Motohiro , Oka Akira , Miyano Satoru , Ogawa Seishi , Takita Junko 
Published: April 2024

AbstractNovel therapeutic strategies are urgently required for osteosarcoma, given the early age at onset and persistently high mortality rate. Modern transcriptomics techniques can identify differentially expressed genes (DEGs) that may serve as biomarkers and therapeutic targets, so we screened for DEGs in osteosarcoma. We found that osteosarcoma cases could be divided into fair and poor survival groups based on gene expression profiles. Among the genes upregulated in the poor survival group, siRNA-mediated knockdown of the glycosylation-related gene C1GALT1 suppressed osteosarcoma cell proliferation in culture. Gene expression, phosphorylation, and glycome array analyses also demonstrated that C1GALT1 is required to maintain ERK signaling and cell cycle progression. Moreover, the C1GALT1 inhibitor itraconazole suppressed osteosarcoma cell proliferation in culture, while doxycycline-induced shRNA-mediated knockdown reduced xenograft osteosarcoma growth in mice. Elevated C1GALT1 expression is a potential early predictor of poor prognosis, while pharmacological inhibition may be a feasible treatment strategy for osteosarcoma.
Age-specific nasal epithelial responses to SARS-CoV-2 infection
Authors: Woodall Maximillian N. J. , Cujba Ana-Maria , Worlock Kaylee B. , Case Katie-Marie , Masonou Tereza , Yoshida Masahiro , Polanski Krzysztof , Huang Ni , Lindeboom Rik G. H. , Mamanova Lira , Bolt Liam , Richardson Laura , Cakir Batuhan , Ellis Samuel , Palor Machaela , Burgoyne Thomas , Pinto Andreia , Moulding Dale , McHugh Timothy D. , Saleh Aarash , Kilich Eliz , Mehta Puja , O’Callaghan Chris , Zhou Jie , Barclay Wendy , De Coppi Paolo , Butler Colin R. , Cortina-Borja Mario , Vinette Heloise , Roy Sunando , Breuer Judith , Chambers Rachel C. , Heywood Wendy E. , Mills Kevin , Hynds Robert E. , Teichmann Sarah A. , Meyer Kerstin B. , Nikolić Marko Z. , Smith Claire M. 
Published: April 2024

AbstractChildren infected with SARS-CoV-2 rarely progress to respiratory failure. However, the risk of mortality in infected people over 85 years of age remains high. Here we investigate differences in the cellular landscape and function of paediatric (<12 years), adult (30–50 years) and older adult (>70 years) ex vivo cultured nasal epithelial cells in response to infection with SARS-CoV-2. We show that cell tropism of SARS-CoV-2, and expression of ACE2 and TMPRSS2 in nasal epithelial cell subtypes, differ between age groups. While ciliated cells are viral replication centres across all age groups, a distinct goblet inflammatory subtype emerges in infected paediatric cultures and shows high expression of interferon-stimulated genes and incomplete viral replication. In contrast, older adult cultures infected with SARS-CoV-2 show a proportional increase in basaloid-like cells, which facilitate viral spread and are associated with altered epithelial repair pathways. We confirm age-specific induction of these cell types by integrating data from in vivo COVID-19 studies and validate that our in vitro model recapitulates early epithelial responses to SARS-CoV-2 infection.
Improving microbial phylogeny with citizen science within a mass-market video game
Authors: Sarrazin-Gendron Roman , Ghasemloo Gheidari Parham , Butyaev Alexander , Keding Timothy , Cai Eddie , Zheng Jiayue , Mutalova Renata , Mounthanyvong Julien , Zhu Yuxue , Nazarova Elena , Drogaris Chrisostomos , Erhart Kornél ,   Borderlands Science Development Team, Bélanger David , Bouffard Michael , Davidson Joshua , Falaise Mathieu , Fiset Vincent , Hebert Steven , Hewitt Dan , Huot Jonathan , Kim Seung , Moreau-Genest Jonathan , Najjab David , Prince Steve , Saintélien Ludger ,   Borderlands Science players, Brouillette Amélie , Richard Gabriel , Pitchford Randy , Caisse Sébastien , Blanchette Mathieu , McDonald Daniel , Knight Rob , Szantner Attila , Waldispühl Jérôme 
Published: April 2024

Abstract
Citizen science video games are designed primarily for users already inclined to contribute to science, which severely limits their accessibility for an estimated community of 3 billion gamers worldwide. We created Borderlands Science (BLS), a citizen science activity that is seamlessly integrated within a popular commercial video game played by tens of millions of gamers. This integration is facilitated by a novel game-first design of citizen science games, in which the game design aspect has the highest priority, and a suitable task is then mapped to the game design. BLS crowdsources a multiple alignment task of 1 million 16S ribosomal RNA sequences obtained from human microbiome studies. Since its initial release on 7 April 2020, over 4 million players have solved more than 135 million science puzzles, a task unsolvable by a single individual. Leveraging these results, we show that our multiple sequence alignment simultaneously improves microbial phylogeny estimations and UniFrac effect sizes compared to state-of-the-art computational methods. This achievement demonstrates that hyper-gamified scientific tasks attract massive crowds of contributors and offers invaluable resources to the scientific community.
Simultaneous single-cell three-dimensional genome and gene expression profiling uncovers dynamic enhancer connectivity underlying olfactory receptor choice
Authors: Wu Honggui , Zhang Jiankun , Jian Fanchong , Chen Jinxin Phaedo , Zheng Yinghui , Tan Longzhi , Sunney Xie X. 
Published: April 2024

AbstractThe simultaneous measurement of three-dimensional (3D) genome structure and gene expression of individual cells is critical for understanding a genome’s structure–function relationship, yet this is challenging for existing methods. Here we present ‘Linking mRNA to Chromatin Architecture (LiMCA)’, which jointly profiles the 3D genome and transcriptome with exceptional sensitivity and from low-input materials. Combining LiMCA and our high-resolution scATAC-seq assay, METATAC, we successfully characterized chromatin accessibility, as well as paired 3D genome structures and gene expression information, of individual developing olfactory sensory neurons. We expanded the repertoire of known olfactory receptor (OR) enhancers and discovered unexpected rules of their dynamics: OR genes and their enhancers are most accessible during early differentiation. Furthermore, we revealed the dynamic spatial relationship between ORs and enhancers behind stepwise OR expression. These findings offer valuable insights into how 3D connectivity of ORs and enhancers dynamically orchestrate the ‘one neuron–one receptor’ selection process.
Modeling of ionic liquids viscosity via advanced white-box machine learning
Authors: Kiani Sajad , Hadavimoghaddam Fahimeh , Atashrouz Saeid , Nedeljkovic Dragutin , Hemmati-Sarapardeh Abdolhossein , Mohaddespour Ahmad 
Published: April 2024

AbstractIonic liquids (ILs) are more widely used within the industry than ever before, and accurate models of their physicochemical characteristics are becoming increasingly important during the process optimization. It is especially challenging to simulate the viscosity of ILs since there is no widely agreed explanation of how viscosity is determined in liquids. In this research, genetic programming (GP) and group method of data handling (GMDH) models were used as white-box machine learning approaches to predict the viscosity of pure ILs. These methods were developed based on a large open literature database of 2813 experimental viscosity values from 45 various ILs at different pressures (0.06–298.9 MPa) and temperatures (253.15–573 K). The models were developed based on five, six, and seven inputs, and it was found that all the models with seven inputs provided more accurate results, while the models with five and six inputs had acceptable accuracy and simpler formulas. Based on GMDH and GP proposed approaches, the suggested GMDH model with seven inputs gave the most exact results with an average absolute relative deviation (AARD) of 8.14% and a coefficient of determination (R2) of 0.98. The proposed techniques were compared with theoretical and empirical models available in the literature, and it was displayed that the GMDH model with seven inputs strongly outperforms the existing approaches. The leverage statistical analysis revealed that most of the experimental data were located within the applicability domains of both GMDH and GP models and were of high quality. Trend analysis also illustrated that the GMDH and GP models could follow the expected trends of viscosity with variations in pressure and temperature. In addition, the relevancy factor portrayed that the temperature had the greatest impact on the ILs viscosity. The findings of this study illustrated that the proposed models represented strong alternatives to time-consuming and costly experimental methods of ILs viscosity measurement.
Long-term impact of late pulmonary hypertension requiring medication in extremely preterm infants with severe bronchopulmonary dysplasia
Authors: Kim Chan , Kim Sumin , Kim Hanna , Hwang Jieun , Kim Seung Hyun , Yang Misun , Ahn So Yoon , Sung Se In , Chang Yun Sil 
Published: April 2024

AbstractThis study investigated whether late pulmonary hypertension (LPH) independently increases the risk of long-term mortality or neurodevelopmental delay (NDD) in extremely preterm infants (EPIs) with severe bronchopulmonary dysplasia (BPD). Using prospectively collected data from the Korean Neonatal Network, we included EPIs with severe BPD born at 22–27 weeks’ gestation between 2013 and 2021. EPIs having severe BPD with LPH (LPH, n = 124) were matched 1:3 with those without pulmonary hypertension (PH) as controls (CON, n = 372), via propensity score matching. LPH was defined as PH with the initiation of medication after 36 weeks’ corrected age (CA). Long-term mortality after 36 weeks’ CA or NDD at 18–24 months’ CA was analyzed. NDD was assessed using composite scores based on various neurodevelopmental assessment modalities. LPH had significantly higher long-term mortality or NDD (45.2% vs. 23.1%, P < 0.001), mortality (24.2% vs. 4.84%, P < 0.001), and NDD (68.4% vs. 37.8%, P = 0.001), respectively than CON, even after adjusting for different demographic factors. Multivariable regression demonstrated that LPH independently increased the risk of mortality or NDD (adjusted odds ratio, 1.95; 95% confidence intervals, 1.17–3.25). When LPH occurs in EPIs with severe BPD, special monitoring and meticulous care for long-term survival and neurodevelopment are continuously needed.
Dynamic mechanical response and crack evolution law of raw coal loaded by dynamic-static coupling under three-dimensional constraints
Authors: Zhao ShunKun , Wei ShanYang , Zhang Lin , Tian Xianggui , Yang XingZhuan , Wang Xing 
Published: April 2024

AbstractThis paper presents the investigation of the dynamic mechanical properties of coal rock under complex stress conditions at depth, based on the improved Separate Hopkinson Pressure Bar Test System. A total of 15 groups of coal samples were used to perform dynamic impact tests under different conditions. The changing rules of dynamic strength, crushing, fractal dimension and damage modes of coal under different stress conditions were analyzed. A total of nine groups of coal samples were selected for numerical simulation using ANSYS/LS-DYNA. The results show that: (1) The stress–strain curves of coal specimens under different strain rates, different confining pressures and axial pressures have basically the same trend and the curves show a certain jump forward. (2) The peak dynamic stress of the coal specimens increased linearly with the increase of strain rate and confining pressure, and the ambient pressure limited the expansion of internal cracks of the coal specimens under impact loading. Based on the experimental and simulated data, the maximum relative errors between the experimental and simulated data were determined to be 2.9578% for Group A, 6.177% for Group B, and 6.382% for Group C, respectively. (3) The damage modes of the coal samples under the three-dimensional dynamic-static combined loading were mainly “X” type and “conical” shear damage. The fractal dimension increases with the increase of strain rate, decreases with the increase of confining pressure, and first decreases and then increases with the increase of axial pressure. This research achievement can provide theoretical support for the prevention of dynamic disasters in deep coal mine engineering.
Effects of sleep disturbance, cancer-related fatigue, and psychological distress on breast cancer patients’ quality of life: a prospective longitudinal observational study
Authors: Tao Lin , Lv Jieying , Zhong Ting , Zeng Xiaohong , Han Manxia , Fu Lan , Chen Hong 
Published: April 2024

AbstractMore attention has gone to researching the cancer-related fatigue (CRF)–sleep disturbance (SD)–psychological distress (PD) symptom cluster in breast cancer patients during the chemotherapy period, but the change trend and heterogeneous development track in the whole treatment stage remain unclear, and it is also unclear whether the appearance of and changes in one symptom cause changes in other symptoms and quality of life (QoL). This study, using breast cancer patients’ data collected through a validated questionnaire, examined the relationships between SD, CRF, PD, and QoL using latent growth modeling analyses. CRF developmental trajectories showed an upward trend over five surveys (slope = 0.649, P < 0.001); PD showed a significant weakening trend (slope = − 0.583, P < 0.001); SD showed an increasing trend (slope = 0.345, P < 0.001), and QoL showed a statistically significant weakening trend (slope = − 0.373, P < 0.001). The initial CRF (coefficient = − 0.233, P < 0.01), PD (coefficient = − 0.296, P < 0.01), and SD (coefficient = − 0.388, P < 0.001) levels had a statistically significant negative effect on initial QoL level. The linear development rate of PD was statistically significant and negatively affected that of QoL (coefficient = − 0.305, P < 0.05), whereas the quadratic development rate of SD negatively affected that of QoL (coefficient = − 0.391, P < 0.05). Medical staff should identify the change characteristics of different variables based on SD, CRF, PD, and QoL change trajectories, and advance the intervention time, as changes in variables affect other variables’ subsequent changes.
Average log change rate of pretreatment squamous cell carcinoma antigen after concurrent chemoradiotherapy in stage IIIC1 cervical squamous cell carcinoma
Authors: Cho Oyeon , Chun Mison , Chang Suk-Joon 
Published: April 2024

AbstractWe aimed to determine whether pretreatment squamous cell carcinoma antigen (SCC-Ag) levels and the average logarithmic change in SCC-Ag levels ($$\frac{{\Delta \log \left( {{\text{SCC-Ag}}} \right)}}{{\Delta {\text{time}}}}$$

Δ
log

SCC-Ag

Δ
time

) after concurrent chemoradiotherapy (CCRT) could predict treatment outcomes in patients with stage IIIC1 cervical squamous cell carcinoma (SCC). We analyzed 168 patients with stage IIIC1 cervical SCC who underwent primary CCRT and collected data on age, local extension, treatment details, hematological parameters, and tumor markers such as SCC-Ag and carcinoembryonic antigen 21-1 (Cyfra). Predictive performances of pretreatment SCC-Ag levels and $$\frac{{\Delta \log \left( {{\text{SCC-Ag}}} \right)}}{{\Delta {\text{time}}}}$$

Δ
log

SCC-Ag

Δ
time

were assessed using receiver operating characteristic curves. Survival analysis was performed using the Cox regression model and Kaplan–Meier plots. The combination of pretreatment SCC-Ag levels and $$\frac{{\Delta \log \left( {{\text{SCC-Ag}}} \right)}}{{\Delta {\text{time}}}}$$

Δ
log

SCC-Ag

Δ
time

showed higher area under the curve values than pretreatment SCC-Ag levels alone (area under the curve; 95% confidence interval [CI] 0.708 [0.581–0.836] vs. 0.666 [0.528–0.804], respectively). Pretreatment SCC-Ag (≥ 5 ng/ml and Cyfra levels (≥ 3.15 ng/ml) and $$\frac{{\Delta \log \left( {{\text{SCC-Ag}}} \right)}}{{\Delta {\text{time}}}}$$

Δ
log

SCC-Ag

Δ
time

(≥ − 1.575) were significant predictors of disease-specific survival. The 5-year disease-specific survival rates significantly differed among the low-, intermediate-, and high-risk groups. Risk stratification using both pretreatment SCC-Ag levels and $$\frac{{\Delta \log \left( {{\text{SCC-Ag}}} \right)}}{{\Delta {\text{time}}}}$$

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log

SCC-Ag

Δ
time

may predict treatment outcomes of patients with stage IIIC1 SCC.
A balanced quantum Hall resistor
Authors: Fijalkowski Kajetan M. , Liu Nan , Klement Martin , Schreyeck Steffen , Brunner Karl , Gould Charles , Molenkamp Laurens W. 
Published: April 2024

AbstractThe quantum anomalous Hall effect in magnetic topological insulators has potential for use in quantum resistance metrology applications. Electronic conductance is quantized to e2/h (where e is the elementary charge and h is the Planck constant) due to the effect, which persists down to zero external magnetic field and is compatible with the quantum standard of voltage. However, metrological applications of the quantum anomalous Hall effect are currently restricted by the need for low measurement currents and low temperatures. Here we report a measurement scheme that increases the robustness of a zero-magnetic-field quantum anomalous Hall resistor and extends its operating range to higher currents. In the scheme, we simultaneously inject current into two disconnected perimeters of a multi-terminal Corbino device, which is based on V0.1(Bi0.2Sb0.8)1.9Te3, to balance the electrochemical potential between the edges. This screens the electric field that drives backscattering through the bulk and thus improves the stability of the quantization at increased currents. Our approach could also be applied to existing quantum resistance standards that rely on the integer quantum Hall effect.
Tutorial: a beginner’s guide to building a representative model of dynamical systems using the adjoint method
Authors: Lettermann Leon , Jurado Alejandro , Betz Timo , Wörgötter Florentin , Herzog Sebastian 
Published: April 2024

AbstractBuilding a representative model of a complex dynamical system from empirical evidence remains a highly challenging problem. Classically, these models are described by systems of differential equations that depend on parameters that need to be optimized by comparison with data. In this tutorial, we introduce the most common multi-parameter estimation techniques, highlighting their successes and limitations. We demonstrate how to use the adjoint method, which allows efficient handling of large systems with many unknown parameters, and present prototypical examples across several fields of physics. Our primary objective is to provide a practical introduction to adjoint optimization, catering for a broad audience of scientists and engineers.