bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impurity modes in two-dimensional strongly coupled complex plasma crystals
Authors: WU 吴 Shijie 诗杰 , XU 徐 Yunqiao 韵乔 , XIAN 冼 Shunyu 顺宇 , HE 何 Xianglei 向磊 , YANG 杨 Xiaoyi 肖易 
Published: April 2024

Abstract
Complex plasma fluctuation processes have been extensively studied in many aspects, especially lattice waves in strongly coupled plasma crystals, which are of great significance for understanding fundamental physical phenomena. A challenge of experimental investigations in two-dimensional strongly coupled complex plasma crystals is to keep the main body and foreign particles of different masses on the same horizontal plane. To solve the problem, we have proposed a potential well formed by two negatively biased grids to bind the negatively charged particles in a two-dimensional (2D) plane, thus achieving a 2D plasma crystal in the microgravity environment. The study of such phenomena in complex plasma crystals under microgravity environment then becomes possible. In this paper, we focus on the continuum spectrum, including both phonon and optic branches of the impurity mode in a 2D system in microgravity environments. The results show the dispersion relation of the longitudinal and transverse impurity oscillation modes and their properties. Considering the macroscopic visibility of complex mesoscopic particle lattices, theoretical and experimental studies on this kind of complex plasma systems will help us further understand the physical nature of a wide range of condensed matters.
An application of the graph approach to life-cycle optimisation of vehicle electrification
Authors: Tokito Shohei , Nakamoto Yuya , Hanaka Tesshu 
Published: April 2024

Abstract
Although durable goods with low energy consumption are being promoted to achieve a decarbonised society, from the perspective of life-cycle assessment, the choice of new durable goods may increase CO2 emissions. To address this problem, research has been conducted on product replacement based on life-cycle optimisation (LCO), a method for identifying a replacement life span that minimises life-cycle CO2 emissions. However, several additional assumptions complicate the analysis of replacement patterns of products and conditional formulas because cumulative emissions do not increase linearly when considering energy mix and technology improvement, and it is difficult to extend the model to optimisation methods in previous LCO studies. This study developed a new LCO approach by applying the shortest path problem to graph theory. Our methodology can contribute to the following: (i) it is computationally inexpensive; (ii) it is intuitively easy to add complex conditions, such as various policy scenarios and parameter changes; and (iii) once the graph of replacement patterns is defined, the optimal solution can be derived using existing solution methods, such as the Dijkstra algorithm. As a case study, we focused on vehicle replacement, which is a major source of CO2 emissions and is being electrified. In particular, we identified vehicle switching paths that minimise life-cycle CO2 emissions by considering changes in Japan’s energy mix and alternative fuel vehicle (AFV) characteristics. We determined that the optimal vehicle replacement path method to reduce CO2 emissions is to switch first to plug-in hybrid electric vehicles (PHEVs) and then to battery electric vehicles (BEVs). Thus, we suggest that the transition to electric vehicles requires a step-by-step process. This methodology is not only conducive to AFV deployment for decarbonisation but can also be applied to other products, such as air conditioners and lighting. Thus, various transition policies could be formulated using our methodology.
Feature selection for high-dimensional neural network potentials with the adaptive group lasso
Authors: Sandberg Johannes , Voigtmann Thomas , Devijver Emilie , Jakse Noel 
Published: April 2024

Abstract
Neural network potentials are a powerful tool for atomistic simulations, allowing to accurately reproduce ab initio potential energy surfaces with computational performance approaching classical force fields. A central component of such potentials is the transformation of atomic positions into a set of atomic features in a most efficient and informative way. In this work, a feature selection method is introduced for high dimensional neural network potentials, based on the adaptive group lasso (AGL) approach. It is shown that the use of an embedded method, taking into account the interplay between features and their action in the estimator, is necessary to optimize the number of features. The method’s efficiency is tested on three different monoatomic systems, including Lennard–Jones as a simple test case, Aluminium as a system characterized by predominantly radial interactions, and Boron as representative of a system with strongly directional components in the interactions. The AGL is compared with unsupervised filter methods and found to perform consistently better in reducing the number of features needed to reproduce the reference simulation data at a similar level of accuracy as the starting feature set. In particular, our results show the importance of taking into account model predictions in feature selection for interatomic potentials.
Physics-informed neural networks for an optimal counterdiabatic quantum computation
Authors: Ferrer-Sánchez Antonio , Flores-Garrigos Carlos , Hernani-Morales Carlos , Orquín-Marqués José J , Hegade Narendra N , Gomez Cadavid Alejandro , Montalban Iraitz , Solano Enrique , Vives-Gilabert Yolanda , Martín-Guerrero José D 
Published: April 2024

Abstract
A novel methodology that leverages physics-informed neural networks to optimize quantum circuits in systems with

N

Q

qubits by addressing the counterdiabatic (CD) protocol is introduced. The primary purpose is to employ physics-inspired deep learning techniques for accurately modeling the time evolution of various physical observables within quantum systems. To achieve this, we integrate essential physical information into an underlying neural network to effectively tackle the problem. Specifically, the imposition of the solution to meet the principle of least action, along with the hermiticity condition on all physical observables, among others, ensuring the acquisition of appropriate CD terms based on underlying physics. This approach provides a reliable alternative to previous methodologies relying on classical numerical approximations, eliminating their inherent constraints. The proposed method offers a versatile framework for optimizing physical observables relevant to the problem, such as the scheduling function, gauge potential, temporal evolution of energy levels, among others. This methodology has been successfully applied to 2-qubit representing

H

2

molecule using the STO-3G basis, demonstrating the derivation of a desirable decomposition for non-adiabatic terms through a linear combination of Pauli operators. This attribute confers significant advantages for practical implementation within quantum computing algorithms.
Militant Democracy
Authors: Schupmann Benjamin A 
Published: April 2024

Abstract
This chapter critically examines ‘militant democracy’, defined as the use of measures of constitutional entrenchment to defend democracy against actors intent on revolutionizing it through legal democratic means. It surveys existing normative theories to highlight the limitations and challenges in justifying and implementing those measures. One limitation is key to this chapter’s argument: the paradox of democracy’s self-defence. This paradox arises from concerns that measures of militant democracy may undermine the very democracy they aim to protect. Along with other normative challenges, this paradox casts doubts on the legitimacy of using militant democracy at all. This chapter concludes by arguing that these concerns could be more effectively addressed by developing an alternative normative theory of militant democracy.
Political Rights Restrictions
Authors: Schupmann Benjamin A 
Published: April 2024

Abstract
This chapter analyses political rights restrictions as the second principal mechanism of militant democracy, focusing in particular on the party ban. These restrictions are crucial for constitutional entrenchment, as antidemocrats can employ many indirect methods to undermine constitutional democracy, which do not require amending the constitution. Such methods include constitutional derogation and capturing the judiciary and government ministries. For that reason, a multifaceted approach is necessary, one that defends democracy against both antidemocratic actors and actions. This chapter demonstrates the compatibility of political rights restrictions with liberal normative theory and investigates their different forms beyond an outright party ban. It illustrates the significance of political rights restrictions in preventing antidemocratic organizations from legally obtaining and abusing state power.
‘This is the fate I choose’: Elsinore and thoughtful choice in Shakespeare games
Authors: Wold Julia 
Published: April 2024

Abstract
Interactivity is one of, if not the most, crucial factor in video game adaptations of literature. The 2019 point-and-click adventure game Elsinore makes the most of its interactivity by building its narrative and gameplay around structural and thematically relevant aspects of the source text, Hamlet. Specifically, the combination of knowledge and motivation, central to the philosophy of prohairesis or thoughtful choice, is also an essential element of a fulfilling game experience. Additionally, it is meaningful that Elsinore leverages its focus on choice to make Ophelia their player-character. In short, this article argues that interactive genres, specifically video games, are a natural fit for the Early Modern fixation on thoughtful choice, especially representations of characters infamous for their lack of choice. Furthermore, these adaptations continue to represent an encouraging future for the commercial success of such works and future scholarship in adaptation theory.
Can the potential benefit of individualizing treatment be assessed using trial summary statistics alone?
Authors: Galanter Nina , Carone Marco , Kessler Ronald C , Luedtke Alex 
Published: April 2024

Abstract
Individualizing treatment assignment can improve outcomes for diseases with patient-to-patient variability in comparative treatment effects. When a clinical trial demonstrates that some patients improve on treatment while others do not, it is tempting to assume that treatment effect heterogeneity exists. However, if outcome variability is mainly driven by factors other than variability in the treatment effect, investigating the extent to which covariate data can predict differential treatment response is a potential waste of resources. Motivated by recent meta-analyses assessing the potential of individualizing treatment for major depressive disorder using only summary statistics, we provide a method that uses summary statistics widely available in published clinical trial results to bound the benefit of optimally assigning treatment to each patient. We also offer alternate bounds for settings in which trial results are stratified by another covariate. Our upper bounds can be especially informative when they are small, as there is then little benefit to collecting additional covariate data. We demonstrate our approach using summary statistics from a depression treatment trial. Our methods are implemented in the rct2otrbounds R package.
BN-BacArena: Bayesian network extension of BacArena for the dynamic simulation of microbial communities
Authors: Blasco Telmo , Balzerani Francesco , Valcárcel Luis V , Larrañaga Pedro , Bielza Concha , Francino María Pilar , Rufián-Henares José Ángel , Planes Francisco J , Pérez-Burillo Sergio 
Published: April 2024

Abstract

Motivation
Simulating gut microbial dynamics is extremely challenging. Several computational tools, notably the widely used BacArena, enable modeling of dynamic changes in the microbial environment. These methods, however, do not comprehensively account for microbe–microbe stimulant or inhibitory effects or for nutrient–microbe inhibitory effects, typically observed in different compounds present in the daily diet.

Results
Here, we present BN-BacArena, an extension of BacArena consisting on the incorporation within the native computational framework of a Bayesian network model that accounts for microbe–microbe and nutrient–microbe interactions. Using in vitro experiments, 16S rRNA gene sequencing data and nutritional composition of 55 foods, the output Bayesian network showed 23 significant nutrient–bacteria interactions, suggesting the importance of compounds such as polyols, ascorbic acid, polyphenols and other phytochemicals, and 40 bacteria–bacteria significant relationships. With test data, BN-BacArena demonstrates a statistically significant improvement over BacArena to predict the time-dependent relative abundance of bacterial species involved in the gut microbiota upon different nutritional interventions. As a result, BN-BacArena opens new avenues for the dynamic modeling and simulation of the human gut microbiota metabolism.

Availability and implementation
MATLAB and R code are available in https://github.com/PlanesLab/BN-BacArena
scTPC: a novel semisupervised deep clustering model for scRNA-seq data
Authors: Qiu Yushan , Yang Lingfei , Jiang Hao , Zou Quan 
Published: April 2024

AbstractMotivationContinuous advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled researchers to further explore the study of cell heterogeneity, trajectory inference, identification of rare cell types, and neurology. Accurate scRNA-seq data clustering is crucial in single-cell sequencing data analysis. However, the high dimensionality, sparsity, and presence of “false” zero values in the data can pose challenges to clustering. Furthermore, current unsupervised clustering algorithms have not effectively leveraged prior biological knowledge, making cell clustering even more challenging.ResultsThis study investigates a semisupervised clustering model called scTPC, which integrates the triplet constraint, pairwise constraint, and cross-entropy constraint based on deep learning. Specifically, the model begins by pretraining a denoising autoencoder based on a zero-inflated negative binomial distribution. Deep clustering is then performed in the learned latent feature space using triplet constraints and pairwise constraints generated from partial labeled cells. Finally, to address imbalanced cell-type datasets, a weighted cross-entropy loss is introduced to optimize the model. A series of experimental results on 10 real scRNA-seq datasets and five simulated datasets demonstrate that scTPC achieves accurate clustering with a well-designed framework.Availability and implementationscTPC is a Python-based algorithm, and the code is available from https://github.com/LF-Yang/Code or https://zenodo.org/records/10951780.
Topical hidden genome: discovering latent cancer mutational topics using a Bayesian multilevel context-learning approach
Authors: Chakraborty Saptarshi , Guan Zoe , Begg Colin B , Shen Ronglai 
Published: April 2024

ABSTRACT
Inferring the cancer-type specificities of ultra-rare, genome-wide somatic mutations is an open problem. Traditional statistical methods cannot handle such data due to their ultra-high dimensionality and extreme data sparsity. To harness information in rare mutations, we have recently proposed a formal multilevel multilogistic “hidden genome” model. Through its hierarchical layers, the model condenses information in ultra-rare mutations through meta-features embodying mutation contexts to characterize cancer types. Consistent, scalable point estimation of the model can incorporate 10s of millions of variants across thousands of tumors and permit impressive prediction and attribution. However, principled statistical inference is infeasible due to the volume, correlation, and noninterpretability of mutation contexts. In this paper, we propose a novel framework that leverages topic models from computational linguistics to effectuate dimension reduction of mutation contexts producing interpretable, decorrelated meta-feature topics. We propose an efficient MCMC algorithm for implementation that permits rigorous full Bayesian inference at a scale that is orders of magnitude beyond the capability of existing out-of-the-box inferential high-dimensional multi-class regression methods and software. Applying our model to the Pan Cancer Analysis of Whole Genomes dataset reveals interesting biological insights including somatic mutational topics associated with UV exposure in skin cancer, aging in colorectal cancer, and strong influence of epigenome organization in liver cancer. Under cross-validation, our model demonstrates highly competitive predictive performance against blackbox methods of random forest and deep learning.
High-dimensional covariate-augmented overdispersed poisson factor model
Authors: Liu Wei , Zhong Qingzhi 
Published: April 2024

ABSTRACT
The current Poisson factor models often assume that the factors are unknown, which overlooks the explanatory potential of certain observable covariates. This study focuses on high dimensional settings, where the number of the count response variables and/or covariates can diverge as the sample size increases. A covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data. A group of identifiability conditions is provided to theoretically guarantee computational identifiability. We incorporate the interdependence of both response variables and covariates by imposing a low-rank constraint on the large coefficient matrix. To address the computation challenges posed by nonlinearity, two high-dimensional latent matrices, and the low-rank constraint, we propose a novel variational estimation scheme that combines Laplace and Taylor approximations. We also develop a criterion based on a singular value ratio to determine the number of factors and the rank of the coefficient matrix. Comprehensive simulation studies demonstrate that the proposed method outperforms the state-of-the-art methods in estimation accuracy and computational efficiency. The practical merit of our method is demonstrated by an application to the CITE-seq dataset. A flexible implementation of our proposed method is available in the R package COAP.
Cohort study on incidence of new-onset type 2 diabetes in patients after bariatric surgery and matched controls
Authors: Vahtera Viiko , Pajarinen Jukka , Kivimäki Mika , Ervasti Jenni , Pentti Jaana , Stenholm Sari , Vahtera Jussi , Salminen Paulina 
Published: April 2024

Abstract

Background
Metabolic bariatric surgery the reduces risk of new-onset type 2 diabetes in individuals with obesity, but it is unclear whether the benefit varies by sex, age, or socioeconomic status. The aim was to assess the risk of new-onset type 2 diabetes after metabolic bariatric surgery in these subgroups.

Methods
The Finnish Public Sector study, a follow-up study with matched controls nested in a large employee cohort, included patients without type 2 diabetes and with a diagnosis of obesity or self-reported BMI of at least 35 kg/m2. For each patient who had laparoscopic metabolic bariatric surgery (2008–2016), two propensity-score matched controls were selected. New-onset type 2 diabetes was ascertained from linked records from national health registries.

Results
The study included a total of 917 patients and 1811 matched controls with obesity. New-onset type 2 diabetes was diagnosed in 15 of the patients who had metabolic bariatric surgery (4.1 per 1000 person-years) and 164 controls (20.2 per 1000 person-years). The corresponding rate ratio (RR) was 0.20 (95% c.i. 0.12 to 0.35) and the rate difference (RD) was −16.1 (−19.8 to −12.3) per 1000 person-years. The risk reduction was more marked in individuals of low socioeconomic status (RR 0.10 (0.04 to 0.26) and RD −20.6 (−25.6 to −15.5) per 1000 person-years) than in those with higher socioeconomic status (RR 0.35 (0.18 to 0.66) and RD −11.5 (−16.9 to −6.0) per 1000 person-years) (Pinteraction = 0.017). No differences were observed between sexes or age groups.

Conclusion
Metabolic bariatric surgery was associated with a reduced risk of new-onset type 2 diabetes in men and women and in all age groups. The greatest benefit was observed in individuals of low socioeconomic status.
Quantification of neurofilament light and glial fibrillary acidic protein in finger-prick blood
Authors: Kolanko Magdalena A , Huber Hanna , David Michael C B , Montoliu-Gaya Laia , Simrén Joel , Blennow Kaj , Zetterberg Henrik , Nilforooshan Ramin , Malhotra Paresh , Sharp David J , Ashton Nicholas J , Graham Neil S N 
Published: April 2024

Abstract
An accurate diagnosis of neurodegenerative disease and traumatic brain injury is important for prognostication and treatment. Neurofilament light and glial fibrillary acidic protein (GFAP) are leading biomarkers for neurodegeneration and glial activation that are detectable in blood. Yet, current recommendations require rapid centrifugation and ultra-low temperature storage post-venepuncture. Here, we investigated if these markers can be accurately measured in finger-prick blood using dried plasma spot cards. Fifty patients (46 with dementia; 4 with traumatic brain injury) and 19 healthy volunteers underwent finger-prick and venous sampling using dried plasma spot cards and aligned plasma sampling. Neurofilament light and GFAP were quantified using a Single molecule array assay and correlations between plasma and dried plasma spot cards assessed. Biomarker concentrations in plasma and finger-prick dried plasma spot samples were significantly positively correlated (neurofilament light ρ = 0.57; GFAP ρ = 0.58, P &lt; 0.001). Finger-prick neurofilament light and GFAP were significantly elevated after acute traumatic brain injury with non-significant group-level increases in dementia (91% having Alzheimer’s disease dementia). In conclusion, we present preliminary evidence that quantifying GFAP and neurofilament light using finger-prick blood collection is viable, with samples stored at room temperature using dried plasma spot cards. This has potential to expand and promote equitable testing access, including in settings where trained personnel are unavailable to perform venepuncture.