bluuebunny/crossref_metadata_2025_split huggingface dataset:
Placental cell type deconvolution reveals that cell proportions drive preeclampsia gene expression differences
Authors: Campbell Kyle A. , Colacino Justin A. , Puttabyatappa Muraly , Dou John F. , Elkin Elana R. , Hammoud Saher S. , Domino Steven E. , Dolinoy Dana C. , Goodrich Jaclyn M. , Loch-Caruso Rita , Padmanabhan Vasantha , Bakulski Kelly M. 
Published: March 2023

AbstractThe placenta mediates adverse pregnancy outcomes, including preeclampsia, which is characterized by gestational hypertension and proteinuria. Placental cell type heterogeneity in preeclampsia is not well-understood and limits mechanistic interpretation of bulk gene expression measures. We generated single-cell RNA-sequencing samples for integration with existing data to create the largest deconvolution reference of 19 fetal and 8 maternal cell types from placental villous tissue (n = 9 biological replicates) at term (n = 40,494 cells). We deconvoluted eight published microarray case–control studies of preeclampsia (n = 173 controls, 157 cases). Preeclampsia was associated with excess extravillous trophoblasts and fewer mesenchymal and Hofbauer cells. Adjustment for cellular composition reduced preeclampsia-associated differentially expressed genes (log2 fold-change cutoff = 0.1, FDR < 0.05) from 1154 to 0, whereas downregulation of mitochondrial biogenesis, aerobic respiration, and ribosome biogenesis were robust to cell type adjustment, suggesting direct changes to these pathways. Cellular composition mediated a substantial proportion of the association between preeclampsia and FLT1 (37.8%, 95% CI [27.5%, 48.8%]), LEP (34.5%, 95% CI [26.0%, 44.9%]), and ENG (34.5%, 95% CI [25.0%, 45.3%]) overexpression. Our findings indicate substantial placental cellular heterogeneity in preeclampsia contributes to previously observed bulk gene expression differences. This deconvolution reference lays the groundwork for cellular heterogeneity-aware investigation into placental dysfunction and adverse birth outcomes.
Quantitative proteomic landscape of unstable atherosclerosis identifies molecular signatures and therapeutic targets for plaque stabilization
Authors: Chen Yung-Chih , Smith Meaghan , Ying Ya-Lan , Makridakis Manousos , Noonan Jonathan , Kanellakis Peter , Rai Alin , Salim Agus , Murphy Andrew , Bobik Alex , Vlahou Antonia , Greening David W. , Peter Karlheinz 
Published: March 2023

AbstractAtherosclerotic plaque rupture leading to myocardial infarction is a major global health burden. Applying the tandem stenosis (TS) mouse model, which distinctively exhibits the characteristics of human plaque instability/rupture, we use quantitative proteomics to understand and directly compare unstable and stable atherosclerosis. Our data highlight the disparate natures and define unique protein signatures of unstable and stable atherosclerosis. Key proteins and pathway networks are identified such as the innate immune system, and neutrophil degranulation. The latter includes calprotectin S100A8/A9, which we validate in mouse and human unstable plaques, and we demonstrate the plaque-stabilizing effects of its inhibition. Overall, we provide critical insights into the unique proteomic landscape of unstable atherosclerosis (as distinct from stable atherosclerosis and vascular tissue). We further establish the TS model as a reliable preclinical tool for the discovery and testing of plaque-stabilizing drugs. Finally, we provide a knowledge resource defining unstable atherosclerosis that will facilitate the identification and validation of long-sought-after therapeutic targets and drugs for plaque stabilization.
The angiopoietin receptor Tie2 is atheroprotective in arterial endothelium
Authors: Anisimov Andrey , Fang Shentong , Hemanthakumar Karthik Amudhala , Örd Tiit , van Avondt Kristof , Chevre Raphael , Toropainen Anu , Singha Prosanta , Gilani Huda , Nguyen Su D. , Karaman Sinem , Korhonen Emilia A. , Adams Ralf H. , Augustin Hellmut G. , Öörni Katariina , Soehnlein Oliver , Kaikkonen Minna U. , Alitalo Kari 
Published: March 2023

AbstractLeukocytes and resident cells in the arterial wall contribute to atherosclerosis, especially at sites of disturbed blood flow. Expression of endothelial Tie1 receptor tyrosine kinase is enhanced at these sites, and attenuation of its expression reduces atherosclerotic burden and decreases inflammation. However, Tie2 tyrosine kinase function in atherosclerosis is unknown. Here we provide genetic evidence from humans and from an atherosclerotic mouse model to show that TIE2 is associated with protection from coronary artery disease. We show that deletion of Tie2, or both Tie2 and Tie1, in the arterial endothelium promotes atherosclerosis by increasing Foxo1 nuclear localization, endothelial adhesion molecule expression and accumulation of immune cells. We also show that Tie2 is expressed in a subset of aortic fibroblasts, and its silencing in these cells increases expression of inflammation-related genes. Our findings indicate that unlike Tie1, the Tie2 receptor functions as the dominant endothelial angiopoietin receptor that protects from atherosclerosis.
Postbiotics from Pichia kudriavzevii promote intestinal health performance through regulation of Limosilactobacillus reuteri in weaned piglets
Authors: Zhang Zhenting , Guo Qiujin , Wang Jing , Tan Hongyan , Jin Xuexia , Fan Yurong , Liu Jiali , Zhao Shumiao , Zheng Jinshui , Peng Nan 
Published: March 2023

The Pichia kudriavzevii FZ12 are attractive as alternatives to antibiotics against post-weaning diarrhea. We identified cytochrome c from FZ12 postbiotics that presented a positive effect on the growth and colonization of Limosilactobacillus reuteri.
DeepOCL: A deep neural network for Object Constraint Language generation from unrestricted nature language
Authors: Yang Yilong , Liu Yibo , Bao Tianshu , Wang Weiru , Niu Nan , Yin Yongfeng 
Published: March 2023

AbstractObject Constraint Language (OCL) is one kind of lightweight formal specification, which is widely used for software verification and validation in NASA and Object Management Group projects. Although OCL provides a simple expressive syntax, it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first‐order logic, which is approximately half accurate at the first stage of development. A deep neural network named DeepOCL is proposed, which takes the unrestricted natural language as inputs and automatically outputs the best‐scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule‐based generation approaches. To demonstrate the validity of our proposed approach, ablation experiments were conducted on a new sentence‐aligned dataset named OCLPairs. The experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation, scored 74.30 on BLEU, and greatly outperformed experienced developers by 35.19%. The proposed approach is the first deep learning approach to generate the OCL expression from the natural language. It can be further developed as a CASE tool for the software industry.
Stable data‐driven Koopman predictive control: Concentrated solar collector field case study
Authors: Gholaminejad Tahereh , Khaki‐Sedigh Ali 
Published: March 2023

AbstractNon‐linearity is an inherent feature of practical systems. Although there have been significant advances in the control of nonlinear systems, the proposed methods often require considerable computational resources or rely on local linearization around equilibrium points. The Koopman operator is an infinite‐dimensional linear operator that fully captures a system's non‐linear dynamics. However, one of the major problems is identifying a Koopman finite dimensional linear model for a nonlinear system. Initiated by the Willems’ fundamental Lemma, a class of data‐driven control methods has been developed for linear systems without the need to identify the system's matrices. Motivated by these two ideas, a data‐driven Koopman‐based predictive control scheme for non‐linear systems is proposed for unknown disturbed non‐linear systems utilising a finite‐length dataset. Then, considering the uncertainty in the Koopman state variables, a robust data‐driven Koopman predictive control structure is presented. Also, the results led to the design of a data‐driven Koopman predictive control strategy with terminal components to ensure the closed‐loop stability of nonlinear systems. The proposed scheme is tested on the distributed‐parameter model of the ACUREX solar collector field (located at Almería, Spain) to regulate the field outlet temperature around a desired value. Finally, simulation results show the effectiveness of the proposed approach.
Guide clinique des défauts primaires d’éruption
Authors: Choukroune Chloé 
Published: March 2023

Le défaut primaire d’éruption (DPE) est un trouble de l’éruption d’origine génétique. Il a été décrit pour la première fois en 1981 par William Proffit et Katherine Vig. Il peut être mis en évidence par l’association d’un interrogatoire précis, de la présence de signes cliniques et d’un examen radiologique. Toutefois, son diagnostic reste complexe, notamment en l’absence de test génétique destiné à l’usage quotidien des professionnels de santé.
La prise en charge n’est pas aisée, puisque les dents tendent à s’ankyloser. Elle requiert systématiquement une réfléxion pluridisciplinaire.
Cet article propose un guide clinique afin d’identifier les signes de ce trouble de l’éruption pour faciliter son diagnostic et sa prise en charge.
Our Voyage from Catalytic Cross-Hydroalkenylation to Transfer-Dehydroaromatization of Cyclic π-Systems: Reactivity and Selectivity Changes Enabled by NHC-Ni and NHC-Pd Hydride Equivalents
Authors: Chen Yang , Chen Weihao , Ho Chun-Yu , Gu Xiao 
Published: March 2023

AbstractN-Heterocyclic carbene ligated transition-metal catalysts often show interesting properties and reactivity as compared to conventional ligand systems. In (NHC)Ni and (NHC)Pd hydrides, a dramatic reactivity changed from cross-hydroalkenylation to transfer-dehydroaromatization was observed under optimized conditions. This account summarizes our recent efforts and stories behind this serendipitous discovery. The mechanistic studies revealed that the keys to divert the desired reactivity are the differences in the olefin insertion selectivity and the hydrometallated species reactivity.
An algorithm for predicting job vacancies using online job postings in Australia
Authors: Evans David , Mason Claire , Chen Haohui , Reeson Andrew 
Published: March 2023

AbstractTimely and accurate statistics on the labour market enable policymakers to rapidly respond to changing economic conditions. Estimates of job vacancies by national statistical agencies are highly accurate but reported infrequently and with time lags. In contrast, online job postings provide a high-frequency indicator of vacancies with less accuracy. In this study we develop a robust signal averaging algorithm to measure job vacancies using online job postings data. We apply the algorithm using data on Australian job postings and show that it accurately predicts changes in job vacancies over a 4.5-year period. We also show that the algorithm is significantly more accurate than using raw counts of job postings to predict vacancies. The algorithm therefore offers a promising approach to the timely and reliable measurement of changes in vacancies.
Performance of a chirped-pulse Fourier transform millimeter wave spectrometer in the range of 75–110 GHz
Authors: Hermanns M. , Wehres N. , Heyne B. , Honingh C. E. , Graf U. U. , Schlemmer S. 
Published: March 2023

We present a home-built chirped-pulse Fourier transform millimeter wave (CP-FTMMW) spectrometer. The setup is devoted to the sensitive recording of high-resolution molecular spectroscopy in the W band between 75 and 110 GHz. We describe the experimental setup in detail, including a characterization of the chirp excitation source, the optical beam path, and the receiver. The receiver is a further development of our 100 GHz emission spectrometer. The spectrometer is equipped with a pulsed jet expansion and a DC discharge. Spectra of methyl cyanide as well as hydrogen cyanide (HCN) and hydrogen isocyanide (HNC) products from the DC discharge of this molecule are recorded to characterize the performance of the CP-FTMMW instrument. The formation of the HCN isomer is favored by a factor of 63 with respect to HNC. Hot/cold calibration measurements enable a direct comparison of the signal and noise levels of the CP-FTMMW spectra to those of the emission spectrometer. For the CP-FTMMW instrument, we find many orders of magnitude of signal enhancement and a much stronger noise reduction due to the coherent detection scheme.
Vacuum-dual static perfect fluid obeying
p=−(n−3)ρ/(n+1)
in
n(⩾4)
dimensions
Authors: Maeda Hideki 
Published: March 2023

Abstract
We obtain the general

n
(
⩾
4
)

-dimensional static solution with an

(
n
−
2
)

-dimensional Einstein base manifold for a perfect fluid obeying a linear equation of state

p
=
−
(
n
−
3
)
ρ

/

(
n
+
1
)

. It is a generalization of Semiz’s four-dimensional general solution with spherical symmetry and consists of two different classes. Through the Buchdahl transformation, the class-I and class-II solutions are dual to the topological Schwarzschild–Tangherlini-(A)dS solution and one of the Λ-vacuum direct-product solutions, respectively. While the metric of the spherically symmetric class-I solution is

C
∞

at the Killing horizon for n = 4 and 5, it is C
1 for

n
⩾
6

and then the Killing horizon turns to be a parallelly propagated curvature singularity. For n = 4 and 5, the spherically symmetric class-I solution can be attached to the Schwarzschild–Tangherlini vacuum black hole with the same value of the mass parameter at the Killing horizon in a regular manner, namely without a lightlike massive thin-shell. This construction allows new configurations of an asymptotically (locally) flat black hole to emerge. If a static perfect fluid hovers outside a vacuum black hole, its energy density is negative. In contrast, if the dynamical region inside the event horizon of a vacuum black hole is replaced by the class-I solution, the corresponding matter field is an anisotropic fluid and may satisfy the null and strong energy conditions. While the latter configuration always involves a spacelike singularity inside the horizon for n = 4, it becomes a non-singular black hole of the big-bounce type for n = 5 if the ADM mass is larger than a critical value.
Superconductivity in Mo4Ga20As with endohedral gallium clusters
Authors: Ruan Bin-Bin , Chen Le-Wei , Shi Yun-Qing , Yi Jun-Kun , Yang Qing-Song , Zhou Meng-Hu , Ma Ming-Wei , Chen Gen-Fu , Ren Zhi-An 
Published: March 2023

Abstract
We report the discovery and detailed investigation of superconductivity in Mo4Ga20As. Mo4Ga20As crystallizes in a space group of I4/m (No. 87), with the lattice parameters a = 12.86352 Å and c = 5.30031 Å. The resistivity, magnetization, and specific heat data reveal Mo4Ga20As to be a type-II superconductor with T
c = 5.6 K. The upper and lower critical fields are estimated to be 2.78 T and 22.0 mT, respectively. In addition, electron-phonon coupling in Mo4Ga20As is possibly stronger than the BCS weak-coupling limit. First-principles calculations suggest the Fermi level being dominated by the Mo-4d and Ga-4p orbitals.
Remote monitoring method for human body electrostatic potential based on symbolic regression machine learning
Authors: Man Menghua , Wei Ming 
Published: March 2023

Abstract
Remote real-time monitoring of the human body electrostatic potential is of great value to the investigation, analysis, and prevention of electrostatic hazard accidents. The non-contact measurement method inverses the body electrostatic potential by detecting the surrounding electrostatic field. The distribution of electrostatic fields around the human body is easily influenced by the placement of metal equipment and the architectural structure in the application scenario. Therefore, physical modeling-based inversion lacks generality. Field-measured electrostatic signals and symbolic regression machine learning are used to remotely monitor body electrostatic potential. In a 25 m2 laboratory, four non-contact electrostatic sensors, a contact-type body voltage measuring system, and an ultra-wideband positioning system were used to establish the experiment setting. Sixty sets of on-site test data from three participants were used for model training and performance evaluation. The results indicate that the normalized root-mean-square errors of the body electrostatic potential ranged from 0.01 to 0.22. The optimal results satisfy the IEC 61340-4-5:2018 criteria for the precision of the body potential measuring system.
Intelligent fault diagnosis method for rolling bearing using WMNRS and LSSVM
Authors: Bai Xuezong , Zeng Shilong , Ma Qiang , Feng Zihao , An Zongwen 
Published: March 2023

Abstract
A weighted multi-neighborhood rough set (WMNRS) algorithm is designed to resolve the issue in which the neighborhood radius must be adjusted iteratively and cannot be automatically determined in the neighborhood rough set. This algorithm combined with the least squares support vector machines (LSSVM) is used for analyzing rolling bearing condition monitoring data; consequently, an intelligent fault diagnosis method is proposed. Specifically, the time-domain and frequency-domain features are extracted from the collected vibration signals to construct an original feature set. The WMNRS algorithm is then applied to screen the primary sensitive components from the constructed feature set. Finally, an optimized LSSVM is utilized to recognize the fault types. The developed method is validated on a public dataset and a measured rolling bearing dataset. The results demonstrate that the method achieves excellent diagnostic performance. Furthermore, the proposed method has some supremacy regarding running time.
Impacts of respiratory fluctuations on cerebral circulation: a machine-learning-integrated 0–1D multiscale hemodynamic model
Authors: Li Ruichen , Sughimoto Koichi , Zhang Xiancheng , Wang Sirui , Liu Hao 
Published: March 2023

Abstract

Objective. This study aims to accurately identify the effects of respiration on the hemodynamics of the human cardiovascular system, especially the cerebral circulation. Approach: we have developed a machine learning (ML)-integrated zero–one-dimensional (0–1D) multiscale hemodynamic model combining a lumped-parameter 0D model for the peripheral vascular bed and a one-dimensional (1D) hemodynamic model for the vascular network. In vivo measurement data of 21 patients were retrieved and partitioned into 8000 data samples in which respiratory fluctuation (RF) of intrathoracic pressure (ITP) was fitted by the Fourier series. ML-based classification and regression algorithms were used to examine the influencing factors and variation trends of the key parameters in the ITP equations and the mean arterial pressure. These parameters were employed as the initial conditions of the 0–1D model to calculate the radial artery blood pressure and the vertebral artery blood flow volume (VAFV). Main results: during stable spontaneous respiration, the VAFV can be augmented at the inhalation endpoints by approximately 0.1 ml s−1 for infants and 0.5 ml s−1 for adolescents or adults, compared to those without RF effects. It is verified that deep respiration can further increase the ranges up to 0.25 ml s−1 and 1 ml s−1, respectively. Significance. This study reveals that reasonable adjustment of respiratory patterns, i.e. in deep breathing, enhances the VAFV and promotes cerebral circulation.
Optical tunable multifunctional applications based on graphene metasurface in terahertz
Authors: Xu Hui , Li Ming , Chen Zhiquan , He Longhui , Dong Yulan , Li Xuelei , Wang Xiaojing , Nie Guozheng , He Zhihui , Zeng Biao 
Published: March 2023

Abstract
Due to the superior properties of graphene and the application potential of surface plasmons, the research of graphene surface plasmons has become a hot research direction. Based on the surface plasmons of graphene, this paper has done some researches on the plasma induced transparency, absorption, and slow light effect. The main work and results of this paper are as follows: we have designed a graphene-based metamaterial structure that can realize a dual plasma induced transparency (PIT) effect. The specific structure is formed by the periodic arrangement of graphene bands (as bright mode) and band edge microchips (as bright mode). We use the finite-difference time-domain (FDTD) method to study the dual PIT effect from the aspect of numerical simulation, and then further study the phenomenon of this device from the theoretical fitting of the coupled mode theory (CMT). The CMT model explores the physical mechanism of dual PIT spectral line and obtains a good fitting result. By studying the formation mechanism of the dual PIT effect, we have found that the graphene band as a bright mode interacts with the band edge microchip as a dark mode, and then the dual PIT is formed by destructive interference of the bright and dark modes. In order to better external modulation, the structure only studies the modulation effect caused by the change of Fermi level affected by the external voltage of graphene. Moreover, we also have studied the slow light performance of this structure, and the slow light coefficient reached 0.236 picoseconds (ps). This proposed coupling system of dual PIT effect has important research significance in optical switches, optical loop, and slow light devices.
Observation of improved confinement by non-axisymmetric magnetic field in KSTAR
Authors: Kim Kimin , Kang Jisung , Kim Hyunseok , Yi Sumin , Yoo Jeongwon , Choi Minjun , Rhee Tongnyeol , Kim Junghee , Lee Myungwon , Ko Won-Ha , Kwon Jaemin 
Published: March 2023

Abstract
We report the observation of improved confinement discharge in a magnetic braking experiment in the KSTAR tokamak. The improved confinement is achieved with reduced toroidal plasma rotation by non-axisymmetric magnetic field induced toroidal rotation braking along with significant reduction of edge localized modes (ELMs). Modifications in multi-channel transport raise fast ion slowing-down time and improve neutral beam deposition, leading to improved fast ion confinement. We show that modifications of radial electric field and E × B shear flow by magnetic braking provoke an enhanced pedestal to sustain thermal confinement against degradation in the typical 3D field experiment.