bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mass–radius relation of neutron stars and massive pulsars with realistic equation of state
Authors: Seif W M , Hashem A S , Abualhamd H A 
Published: April 2024

Abstract
We use up-to-date constraints on the mass and radius of 15 neutron star objects and pulsars, from electromagnetic and gravitational wave observables and different theoretical schemes, to extend the nuclear equation of state (EOS) based on realistic Michigan-three-Yukawa (M3Y) nucleon–nucleon interaction, which truly accounts for the low-density EOS of nuclear matter (NM), to describe dense NM. The considered EOSs are employed to map the mass–radius profiles using the Tolman–Oppenheimer–Volkoff equations of hydrostatic equilibrium. We found that the EOSs from CDM3Y-230 to CDM3Y-270, with saturation incompressibility K
0 = 230–270 MeV, successfully reproduce most of the recent constraints on the NS masses and radii. Based on both M3Y-Paris and M3Y-Reid NN interactions, these EOSs indicate radius of 11.67 ± 0.34 km for the NS of 1.4 M
ʘ mass, and the expected maximum NS mass (M
max) to be 1.93 ± 0.21 M
ʘ. The upper limits of constraints indicated stiffer EOSs of K
0 = 300–330 MeV, which have estimated 1arger radii of 12.29 ± 0.14 km for NS (1.4 M
ʘ) and heavier M
max of 2.31 ± 0.14 M
ʘ. Increasing the stiffness of the employed EOS is found to increase the indicated maximum mass of NS, its radius and maximum compactness, the core–crust transition density, the speed of sound in its interior, and slightly the transition proton-fraction, but to decrease the abundance of the proton, muon, and electron over npeμ core matter of NS, as well as the estimated central density.
Electron power absorption in CF4 capacitively coupled RF plasmas operated in the striation mode
Authors: Masheyeva Ranna , Vass Mate , Wang Xiao-Kun , Liu Yong-Xin , Derzsi Aranka , Hartmann Peter , Schulze Julian , Donkó Zoltán 
Published: April 2024

Abstract
The electron power absorption mechanisms in electronegative capacitively coupled plasmas in CF4 are investigated using particle-in-cell/Monte Carlo collisions simulations at a pressure of

p
=

60 Pa, a driving frequency of

f
=

13.56 MHz for voltage amplitudes in the interval of

ϕ
0

=

100

−

300 V, where pronounced self-organized density variations, i.e. striations, develop. The calculations are based on the Boltzmann term analysis, a computational diagnostic method capable of providing a complete spatio-temporal description of electron power absorption. The discharge undergoes an electron power absorption mode transition from the drift-ambipolar- to the striation-mode at φ
0 = 180 V. Although Ohmic power absorption is found to be the dominant electron power absorption mechanism in the parameter range considered, the electron power absorption mode transition can be inferred from the behaviour of the spatio-temporally averaged ambipolar power absorption as a function of the voltage amplitude. Furthermore, it is shown, that as a consequence of the presence of striations, the temporal modulation of the electron density leads to a temporal modulation of the ambipolar electric field, which is responsible for the striated structures of various physical quantities related to electrons, such as the electron temperature and the ionization source function.
Dynamic modeling and optimization of mechanical drift for compliant parallel micromanipulation driven by V-shape linear ultrasonic motors
Authors: Li Xinjian , Yao Zhiyuan , Xu Hao , Dai Shichao 
Published: April 2024

Abstract
Designing micro-mechanisms that satisfy large strokes, high precision, and multiple degrees of freedom has consistently been a research focus and challenge in the field of micromanipulation systems. Linear ultrasonic motors (LUMs), with advantages such as millimeter-level travel, nanometer-level motion resolution, and power-off self-locking, significantly contribute to the advancement in the mechanism design of micromanipulation systems. However, the challenge of mechanical drift lacks a comprehensive theoretical analysis to explain its cause and limits its application. This paper presents a mechanical drift modeling method for a compliant parallel micromanipulation (CPM) driven by a LUM. The model primarily focuses on the creep characteristics of the flexure clamp element in the LUM. Based on the stiffness matrix analysis method of the compliant parallel mechanism kinematics, the mechanical drift dynamics model of the CPM is established. The influence of the main structural parameters on the CPM mechanical drift is investigated. The research indicates that the creep of flexure clamps in the CPM is the main factor leading to the mechanical drift. Optimization of mechanical drift is achieved by choosing flexure clamps with higher tangential stiffness for LUM and flexure hinge joints with greater flexibility for CPM. Finally, experimental studies reveal that under equivalent conditions, the mechanical drift of the optimized CPM was significantly reduced than before, proving the rationality of the theoretical model and optimization method.
Spectroscopy of flux-driven Kerr parametric oscillators by reflection coefficient measurement
Authors: Yamaguchi Aiko , Masuda Shumpei , Matsuzaki Yuichiro , Yamaji Tomohiro , Satoh Tetsuro , Morioka Ayuka , Kawakami Yohei , Igarashi Yuichi , Shirane Masayuki , Yamamoto Tsuyoshi 
Published: April 2024

Abstract
We report the spectroscopic characterization of a Kerr parametric oscillator (KPO) based on the measurement of its reflection coefficient under a two-photon drive induced by flux modulation. The measured reflection spectra show good agreement with numerical simulations in terms of their dependence on the two-photon drive amplitude. The spectra can be interpreted as changes in system’s eigenenergies, transition matrix elements, and the population of the eigenstates, although the linewidth of the resonance structure is not fully explained. We also show that the drive-amplitude dependence of the spectra can be explained analytically by using the concepts of Rabi splitting and the Stark shift. By comparing the experimentally obtained spectra with theory, we show that the two-photon drive amplitude at the device can be precisely determined, which is important for the application of KPOs in quantum information processing.
Tuning the lattice thermal conductivity of Janus SnSSe by interlayer twisting: a machine-learning-based study
Authors: Luo Yufeng , Cao Haibin , Li Mengke , Yuan Hongmei , Liu Huijun 
Published: April 2024

Abstract
Twisted two-dimensional materials have recently attracted tremendous interest owing to their unique structures and fantastic electronic properties. However, the effect of interlayer twisting on the phonon transport properties is less known, especially for the twist-angle-dependent lattice thermal conductivity (

κ

L

). Using the emerging Janus SnSSe bilayer as a prototypical example, we develop an accurate machine learning potential, which is adopted to efficiently predict the

κ

L

at a series of twist angles via iterative solution of the Boltzmann transport equation. It is found that the

κ

L

exhibits a distinct non-monotonous dependence on the twist angle, which can be traced back to the bonding heterogeneity between high-symmetry stacking regions inside the moiré unit cell. In contrast to the general belief, the optical phonons make a major contribution toward the

κ

L

of the twisted structures. Moreover, we demonstrate that four-phonon scattering can significantly reduce the

κ

L

of SnSSe bilayer at higher temperatures, which becomes more pronounced by interlayer twisting. Our work not only highlights the strong predictive power of machine learning potential, but also offers new insights into the design of thermal smart materials with tunable

κ

L

.
Bayesian analysis of a generalized Starobinsky model with reheating constraints
Authors: Linares Cedeño Francisco X. , Germán Gabriel , Hidalgo Juan Carlos 
Published: April 2024

Abstract
We study a generalization of the Starobinsky model adding a term of the form R
2p
to the Einstien-Hilbert action. we take the power p as a parameter of the model and explore the constraints from CMB plus BAO data through a Bayesian analysis, thus exploring a range of values for the exponent parameter. We incorporate a reheating phase to the model through the background matter content (equation of state) and the duration of this period (number of e-foldings of reheating). We find that incorporating information from reheating imposes constraints on cosmological quantities, more stringent than the case of no reheating when tested with the Planck+BAO data. The inferred value of the exponent parameter is statistically consitent with p = 1, favoring the original Starobinsky potential. Moreover, we report tighter constraints on p and the number of e-folds in comparison with previous works. The obtained values for other inflationary observational parameters, such as the scalar spectral index ns
and the scalar amplitude of perturbations As
, are consistent with prior measurements. Finally we present the alternative use of consistency relations in order to simplify the parameter space and test the generalized Starobinsky potential even more efficiently.
Sovereignty, Territoriality, and Private International Law in Classical Muslim International Law
Authors: Fadel Mohammad 
Published: April 2024

Abstract
Scholars in recent years have shown interest in challenging the historical origins of international law and its normative claims to universality. This Article challenges the prevailing conceptions of Islamic international law (al-siyar), first set out in English-language scholarship by Majid Khadduri, as primarily an ad-hoc response to the failed aspiration of a universal Muslim commonwealth. It shows that Islamic international law, in its classical phase (eighth–thirteenth centuries), as first formulated by Iraqi, and later, Central Asian, scholars (who together later came to be known as Ḥanafīs), understood all legal order as being rooted in sovereignty and territoriality, with shared religion a secondary concern. This theory of legal order arose out of an understanding of political order as emerging from a natural and universal condition of war that is incidental to the individual’s natural sovereignty. I trace the genealogy of this conception to the founding moment of the Muslim commonwealth and describe its manifestation in classical Ḥanafī solutions to a series of cases in “private international law.”
Side scatter ratio of the CD105-positive and CD105-negative red blood cell fractions is useful for the detection of low-grade myelodysplastic neoplasms by flow cytometry
Authors: Wang Zijan , Ciarlini Pedro C , Oduro Kwadwo A , Gadde Ramya , O’Neill Stacey , Zhao Chen , Meyerson Howard J 
Published: April 2024

Abstract

Objectives
We assessed the utility of red blood cell (RBC) CD105 and side scatter (SSC) parameters by flow cytometry for the detection of low-grade myelodysplastic neoplasms (MDS) in bone marrow specimens.

Methods
Ten RBC parameters incorporating CD105 or SSC combined with the Meyerson-Alayed scoring system (MASS) metrics were retrospectively evaluated by flow cytometry for utility in detecting low-grade MDS (n = 56) compared with cytopenic controls (n = 86).

Results
Myelodysplastic neoplasms were associated with 7 of the RBC parameters in univariate analysis. Multivariate analysis using cutoff values based on optimal and 95% specificity levels of the RBC metrics and the MASS parameters revealed the SSC ratio of CD105-positive and CD105-negative RBC fractions (CD105+/− SSC); the percentage and coefficient of variation of the CD105-positive fraction of RBCs (CD105%, CD105+CV) emerged as significant RBC variables. Two simple scoring schemes using these RBC values along with MASS parameters were identified: 1 using CD105+/− SSC, CD105%, and CD105+CV combined with the percentage of CD177-positive granulocytes (CD177%), myeloblast percentage (CD34%), and granulocyte SSC (GranSSC), and the other incorporating CD105+/− SSC, CD105+CV, CD177%, CD34%, GranSSC, and B-cell progenitor percentage. Both demonstrated a sensitivity of approximately 80%, with a specificity of roughly 90% for the detection of MDS compared with cytopenic controls.

Conclusions
The red blood cell parameter, CD105+/− SSC, appears to be beneficial in the evaluation of low-grade MDS by flow cytometry.
Pulmonary symptoms associated with heavy alcohol consumption among people living with HIV: an analysis of the NHANES 1999–2010
Authors: New-Aaron Moses , Kang Mohleen , Yeligar Samantha M 
Published: April 2024

Abstract

Aim
This matched case–control study aimed to provide epidemiologic evidence of increased burden of respiratory symptoms and pulmonary function decline among people living with human immunodeficiency virus (HIV) and a history of heavy alcohol consumption.

Methods
Cases were participants with HIV (PWH; n = 75, 33%), and controls were participants without HIV (PWoH; n = 150, 67%). PWH were matched to PWoH by age and sex in the ratio of 1:2. Eligible participants responded to the respiratory health National Health and Nutrition Examination Survey questionnaire [prolonged coughs (≥3 months), bringing up of phlegm (≥3 months), and a history of wheezing or whistling in the chest (past year)]. The effects of both alcohol and HIV on participants’ pulmonary function were determined using linear regression analysis.

Results
History of heavy alcohol consumption was more prevalent among PWH (40%) compared to PWoH (27%). PWH who had a history of heavy alcohol consumption had a higher prevalence of coughing most days (45% vs. 4%, P = .0010), bringing up phlegm most days (31% vs. 0%, P = .0012), and wheezing or whistling in the chest (40% vs. 20%, P = .058) compared to participants who did not heavily consume alcohol. Furthermore, a history of heavy alcohol consumption was associated with decreased forced expiratory volume (ml) in 1 s/forced vital capacity among PWH (β = − 0.098 95% C.I. −0.16, −0.04, P = .03) after adjusting for having smoked at least 100 cigarettes in life.

Conclusion
A history of heavy alcohol use increased respiratory symptoms and suppressed pulmonary function among people living with HIV. This study provides epidemiological evidence of the respiratory symptom burden of people living with HIV who have a history of heavy alcohol consumption.
A Novel Computerized Flexible Attention Test in Detecting Executive Dysfunction of Patients with Early-Onset Cognitive Impairment and Dementia
Authors: Tikkanen Veera , Krüger Johanna , Heikkinen Anna-Leena , Hänninen Tuomo , Hublin Christer , Koivisto Anne M , Virkkala Jussi , Saari Toni T , Remes Anne M , Paajanen Teemu I 
Published: April 2024

Abstract

Objective
The number of computer-based cognitive tests has increased in recent years, but there is a need for tests focusing on the assessment of executive function (EF), as it can be crucial for the identification of early-onset neurodegenerative disorders. This study aims to examine the ability of the Flexible Attention Test (FAT), a new computer-based test battery for detecting executive dysfunction of early-onset cognitive impairment and dementia patients.

Method
We analyzed the FAT subtask results in memory clinic patients with cognitive symptom onset at ≤65 years. The patients were divided into four groups: early onset dementia (EOD, n = 48), mild cognitive impairment due to neurological causes (MCI-n, n = 34), MCI due to other causes (MCI-o, n = 99), and subjective cognitive decline (SCD, n = 14). The test accuracy to distinguish EOD patients from other groups was examined, as well as correlations with pen-and-paper EF tests. We also reported the 12-months follow-up results.

Results
The EOD and MCI-n patients performed significantly poorer (p ≤ .002) than those in the MCI-o and SCD groups in most of the FAT subtasks. The accuracies of the FAT subtasks to detect EOD from other causes were mainly moderate (0.34 ≤ area under the curve &lt; 0.74). The FAT subtasks correlated logically with corresponding pen-and-paper EF tests (.15 ≤ r ≤ .75). No systematic learning effects were detected in the FAT performance at follow-up.

Conclusions
The FAT appears to be a promising method for the precise evaluation of EF and applicable distinguishing early-onset neurodegenerative disorders from patients with other causes of cognitive problems.
Search for protein kinase(s) related to cell growth or viability maintenance in the presence of ethanol in budding and fission yeasts
Authors: Ushiyama Yuto , Nishida Ikuhisa , Tomiyama Saki , Tanaka Hitomi , Kume Kazunori , Hirata Dai 
Published: April 2024

ABSTRACT
Alcohol fermentation comprises two phases: phase 1, alcohol fermentation occurs while yeast cells proliferate; phase 2, growth stops and alcohol fermentation continues. We categorized genes related to proliferation in low ethanol (phase 1) and viability in high ethanol (phase 2) as Alcohol Growth Ability (AGA) and Alcohol Viability (ALV), respectively. Although genes required for phase 1 are examined in budding yeast, those for phase 2 are unknown. We set conditions for ALV screening, searched for protein kinases (PKs) related to ALV in budding yeast, and expanded two screenings to fission yeast. Bub1 kinase was important for proliferation in low ethanol but not for viability in high ethanol, suggesting that the important PKs differ between the two phases. It was indeed the case. Further, 3 common PKs were identified as AGA in both yeasts, suggesting that the important cellular mechanism in phase 1 is conserved in both yeasts, at least partially.
From tradition to innovation: conventional and deep learning frameworks in genome annotation
Authors: Chen Zhaojia , Ain Noor ul , Zhao Qian , Zhang Xingtan 
Published: April 2024

Abstract
Following the milestone success of the Human Genome Project, the ‘Encyclopedia of DNA Elements (ENCODE)’ initiative was launched in 2003 to unearth information about the numerous functional elements within the genome. This endeavor coincided with the emergence of numerous novel technologies, accompanied by the provision of vast amounts of whole-genome sequences, high-throughput data such as ChIP-Seq and RNA-Seq. Extracting biologically meaningful information from this massive dataset has become a critical aspect of many recent studies, particularly in annotating and predicting the functions of unknown genes. The core idea behind genome annotation is to identify genes and various functional elements within the genome sequence and infer their biological functions. Traditional wet-lab experimental methods still rely on extensive efforts for functional verification. However, early bioinformatics algorithms and software primarily employed shallow learning techniques; thus, the ability to characterize data and features learning was limited. With the widespread adoption of RNA-Seq technology, scientists from the biological community began to harness the potential of machine learning and deep learning approaches for gene structure prediction and functional annotation. In this context, we reviewed both conventional methods and contemporary deep learning frameworks, and highlighted novel perspectives on the challenges arising during annotation underscoring the dynamic nature of this evolving scientific landscape.
Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes
Authors: Ma Guoxuan , Kang Jian , Yu Tianwei 
Published: April 2024

AbstractUntargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.
GLDM: hit molecule generation with constrained graph latent diffusion model
Authors: Wang Conghao , Ong Hiok Hian , Chiba Shunsuke , Rajapakse Jagath C 
Published: April 2024

Abstract
Discovering hit molecules with desired biological activity in a directed manner is a promising but profound task in computer-aided drug discovery. Inspired by recent generative AI approaches, particularly Diffusion Models (DM), we propose Graph Latent Diffusion Model (GLDM)—a latent DM that preserves both the effectiveness of autoencoders of compressing complex chemical data and the DM’s capabilities of generating novel molecules. Specifically, we first develop an autoencoder to encode the molecular data into low-dimensional latent representations and then train the DM on the latent space to generate molecules inducing targeted biological activity defined by gene expression profiles. Manipulating DM in the latent space rather than the input space avoids complicated operations to map molecule decomposition and reconstruction to diffusion processes, and thus improves training efficiency. Experiments show that GLDM not only achieves outstanding performances on molecular generation benchmarks, but also generates samples with optimal chemical properties and potentials to induce desired biological activity.