bluuebunny/crossref_metadata_2025_split huggingface dataset:
High fidelity two-qubit gates on fluxoniums using a tunable coupler
Authors: Moskalenko Ilya N. , Simakov Ilya A. , Abramov Nikolay N. , Grigorev Alexander A. , Moskalev Dmitry O. , Pishchimova Anastasiya A. , Smirnov Nikita S. , Zikiy Evgeniy V. , Rodionov Ilya A. , Besedin Ilya S. 
Published: November 2022

AbstractSuperconducting fluxonium qubits provide a promising alternative to transmons on the path toward large-scale superconductor-based quantum computing due to their better coherence and larger anharmonicity. A major challenge for multi-qubit fluxonium devices is the experimental demonstration of a scalable crosstalk-free multi-qubit architecture with high-fidelity single-qubit and two-qubit gates, single-shot readout, and state initialization. Here, we present a two-qubit fluxonium-based quantum processor with a tunable coupler element. We experimentally demonstrate fSim-type and controlled-Z-gates with 99.55 and 99.23% fidelities, respectively. The residual ZZ interaction is suppressed down to the few kHz levels. Using a galvanically coupled flux control line, we implement high-fidelity single-qubit gates and ground state initialization with a single arbitrary waveform generator channel per qubit.
Disrupted local functional connectivity in schizophrenia: An updated and extended meta-analysis
Authors: Cai Mengjing , Wang Rui , Liu Mengge , Du Xiaotong , Xue Kaizhong , Ji Yuan , Wang Zirui , Zhang Yijing , Guo Lining , Qin Wen , Zhu Wenshuang , Fu Jilian , Liu Feng 
Published: November 2022

AbstractNeuroimaging studies have shown that schizophrenia is associated with disruption of resting-state local functional connectivity. However, these findings vary considerably, which hampers our understanding of the underlying pathophysiological mechanisms of schizophrenia. Here, we performed an updated and extended meta-analysis to identify the most consistent changes of local functional connectivity measured by regional homogeneity (ReHo) in schizophrenia. Specifically, a systematic search of ReHo studies in patients with schizophrenia in PubMed, Embase, and Web of Science identified 18 studies (20 datasets), including 652 patients and 596 healthy controls. In addition, we included three whole-brain statistical maps of ReHo differences calculated based on independent datasets (163 patients and 194 controls). A voxel-wise meta-analysis was then conducted to investigate ReHo alterations and their relationship with clinical characteristics using the newly developed seed-based d mapping with permutation of subject images (SDM-PSI) meta-analytic approach. Compared with healthy controls, patients with schizophrenia showed significantly higher ReHo in the bilateral medial superior frontal gyrus, while lower ReHo in the bilateral postcentral gyrus, right precentral gyrus, and right middle occipital gyrus. The following sensitivity analyses including jackknife analysis, subgroup analysis, heterogeneity test, and publication bias test demonstrated that our results were robust and highly reliable. Meta-regression analysis revealed that illness duration was negatively correlated with ReHo abnormalities in the right precentral/postcentral gyrus. This comprehensive meta-analysis not only identified consistent and reliably aberrant local functional connectivity in schizophrenia but also helped to further deepen our understanding of its pathophysiology.
Denitrification mechanism in oxygen-rich aquatic environments through long-distance electron transfer
Authors: Wei Ming-Zhi , Liu Jin-Wei , Yang Qin-Zheng , Xue An , Wu Hao , Ni Jin-Ren , Winter Lea R. , Elimelech Menachem , Zhao Hua-Zhang 
Published: November 2022

AbstractThe lack of electron donors in oxygen-rich aquatic environments limits the ability of natural denitrification to remove excess nitrate, leading to eutrophication of aquatic ecosystems. Herein, we demonstrate that electron-rich substances in river or lake sediments could participate in long-distance electron rebalancing to reduce nitrate in the overlying water. A microstructure containing Dechloromonas and consisting of an inner layer of green rust and an outer layer of lepidocrocite forms in the sediment-water system through synergetic evolution and self-assembly. The microstructure enables long-distance electron transfer from the sediment to dilute nitrate in the overlying water. Specifically, the inner green rust adsorbs nitrate and reduces the kinetic barrier for denitrification via an Fe(II)/Fe(III) redox mediator. Our study reveals the mechanism of spontaneous electron transfer between distant and dilute electron donors and acceptors to achieve denitrification in electron-deficient aquatic systems.
A tensor-based formulation of hetero-functional graph theory
Authors: Farid Amro M. , Thompson Dakota J. , Schoonenberg Wester 
Published: November 2022

AbstractRecently, hetero-functional graph theory (HFGT) has developed as a means to mathematically model the structure of large-scale complex flexible engineering systems. It does so by fusing concepts from network science and model-based systems engineering (MBSE). For the former, it utilizes multiple graph-based data structures to support a matrix-based quantitative analysis. For the latter, HFGT inherits the heterogeneity of conceptual and ontological constructs found in model-based systems engineering including system form, system function, and system concept. These diverse conceptual constructs indicate multi-dimensional rather than two-dimensional relationships. This paper provides the first tensor-based treatment of hetero-functional graph theory. In particular, it addresses the “system concept” and the hetero-functional adjacency matrix from the perspective of tensors and introduces the hetero-functional incidence tensor as a new data structure. The tensor-based formulation described in this work makes a stronger tie between HFGT and its ontological foundations in MBSE. Finally, the tensor-based formulation facilitates several analytical results that provide an understanding of the relationships between HFGT and multi-layer networks.
Prevalence, incidence and mortality of hypertrophic cardiomyopathy based on a population cohort of 21.9 million in China
Authors: Bai Ying , Zheng Jian-Peng , Lu Feng , Zhang Xi-Lin , Sun Chang-Ping , Guo Wei-Hua , Zou Yi-Xi , Lip Gregory Y. H. , Shi Xu-Bo 
Published: November 2022

AbstractThere are limited studies on the prevalence and incidence of clinically diagnosed hypertrophic myocardiopathy (HCM) and its mortality in the Chinese population, and the projected population burden of HCM over the next decades. We collected data on HCM and its mortality from the Beijing Municipal Health Commission Information Center (BMHCIC) database and estimated the prevalence and incidence based on the whole Beijing population. Calculation of population trends was performed using annual percentage change (APC) and average annual percentage change (AAPC). Finally, future HCM incidence was built by modelling projection of HCM to the next decades using Poisson regression analysis and Gray Model 1,1(GM [1,1]). The prevalence of HCM was 0.0069% (95%CI, 0.0065–0.0072%; N = 1343) in 2010, rising to 0.076% (95% CI, 0.074–0.077%; N = 16,616) in 2019, and the incidence of HCM was 6.85 per 100 000 person-year in 2010, rising to 11.76 per 100 000 person-year in 2019. Males had higher prevalence and incidence of HCM than females. The APPC for the rising incidence of HCM was 5.8% and the expected numbers will double increase in 2029 by assuming the same increase trend as the last decades. HCM had increased annual incidence of HF (APPC: 8.4, 4.4–12.6, p < 0.05), and relatively stable annual incidence of mortality (APPC: 1.2%, − 2.3% to 4.8%, p > 0.05) during the studied period. Males had lower mortality (2.70% vs. 4.20%, p < 0.001) than females. The calculated HCM prevalence was much lower compared to prior screening studies from 2004, although the predicted HCM incidence would double over the next decades. HCM was associated with a stable risk of mortality during the studied period.
Efficient parameterisation of non-collinear energy landscapes in itinerant magnets
Authors: Jacobsson A. , Johansson G. , Gorbatov O. I. , Ležaić M. , Sanyal B. , Blügel S. , Etz C. 
Published: November 2022

AbstractMagnetic exchange interactions determine the magnetic groundstate, as well as magnetic excitations of materials and are thus essential to the emerging and fast evolving fields of spintronics and magnonics. The magnetic force theorem has been used extensively for studying magnetic exchange interactions. However, short-ranged interactions in itinerant magnetic systems are poorly described by this method and numerous strategies have been developed over the years to overcome this deficiency. The present study supplies a fully self-consistent method for systematic investigations of exchange interactions beyond the standard Heisenberg model. In order to better describe finite deviations from the magnetic ground state, an extended Heisenberg model, including multi-spin interactions, is suggested. Using cross-validation analysis, we show that this extended Heisenberg model gives a superior description for non-collinear magnetic configurations. This parameterisation method allows us to describe many different itinerant magnetic systems and can be useful for high-throughput calculations.
Prognostic prediction of novel risk scores (AML-DRG and AML-HCT-CR) in acute myeloid leukemia patients with allogeneic hematopoietic stem cell transplantation
Authors: Cao Weijie , Li Xiaoning , Zhang Ran , Bian Zhilei , Zhang Suping , Li Li , Xing Haizhou , Liu Changfeng , Xie Xinsheng , Jiang Zhongxing , Fang Xiaosheng , Wan Dingming , Yu Jifeng 
Published: November 2022

AbstractWe aimed to validate and prove the novel risk score models of acute myeloid leukemia (AML)-specific disease risk group (AML-DRG) and AML-Hematopoietic Cell Transplant-composite risk (AML-HCT-CR) in patients with acute myeloid leukemia (AML) after allogeneic hematopoietic stem cell transplantation (AHCT). Among the 172 AML patients analysed, 48.3% (n = 83) were females. Median age was 31.5 years (range 14 to 62 years), two patients was more than 60 years old (1.2%). Median follow-up was 44 months (range 1 to 94 months). According to the AML-DRG model, 109, 49 and 14 patients were in low-, intermediate- and high-risk group, respectively. According to the AML-HCT-CR model, 108, 30, 20 and 14 patients were in low-, intermediate-, high- and very high-risk group, respectively. Our results showed that the AML-DRG and AML-HCT-CR models significantly predicted cumulative incidence of relapse (p < 0.001; p < 0.001). But AML-DRG model was not associated with NRM (p = 0.072). Univariate analysis showed that the AML-DRG model could better stratify AML patients into different risk groups compared to the AML-HCT-CR model. Multivariate analysis confirmed that prognostic impact of AML-DRG and AML-HCT-CR models on post-transplant OS was independent to age, sex, conditioning type, transplant modality, and stem cell source (p < 0.001; p < 0.001). AML-DRG and AML-HCT-CR models can be used to effectively predict post-transplant survival in patients with AML receiving AHCT. Compared to AML-HCT-CR score, the AML-DRG score allows better stratification and improved survival prediction of AML patients post-transplant.
Application of shear stress for enhanced osteogenic differentiation of mouse induced pluripotent stem cells
Authors: Limraksasin Phoonsuk , Nattasit Praphawi , Manokawinchoke Jeeranan , Tiskratok Watcharaphol , Vinaikosol Naruephorn , Okawa Hiroko , Limjeerajarus Chalida Nakalekha , Limjeerajarus Nuttapol , Pavasant Prasit , Osathanon Thanaphum , Egusa Hiroshi 
Published: November 2022

AbstractThe self-organizing potential of induced pluripotent stem cells (iPSCs) represents a promising tool for bone tissue engineering. Shear stress promotes the osteogenic differentiation of mesenchymal stem cells, leading us to hypothesize that specific shear stress could enhance the osteogenic differentiation of iPSCs. For osteogenesis, embryoid bodies were formed for two days and then maintained in medium supplemented with retinoic acid for three days, followed by adherent culture in osteogenic induction medium for one day. The cells were then subjected to shear loading (0.15, 0.5, or 1.5 Pa) for two days. Among different magnitudes tested, 0.5 Pa induced the highest levels of osteogenic gene expression and greatest mineral deposition, corresponding to upregulated connexin 43 (Cx43) and phosphorylated Erk1/2 expression. Erk1/2 inhibition during shear loading resulted in decreased osteogenic gene expression and the suppression of mineral deposition. These results suggest that shear stress (0.5 Pa) enhances the osteogenic differentiation of iPSCs, partly through Cx43 and Erk1/2 signaling. Our findings shed light on the application of shear-stress technology to improve iPSC-based tissue-engineered bone for regenerative bone therapy.
Towards a knowledge graph for pre-/probiotics and microbiota–gut–brain axis diseases
Authors: Liu Ting , Lan Gongjin , Feenstra K. Anton , Huang Zhisheng , Heringa Jaap 
Published: November 2022

AbstractScientific publications present biological relationships but are structured for human reading, making it difficult to use this resource for semantic integration and querying. Existing databases, on the other hand, are well structured for automated analysis, but do not contain comprehensive biological knowledge. We devised an approach for constructing comprehensive knowledge graphs from these two types of resources and applied it to investigate relationships between pre-/probiotics and microbiota–gut–brain axis diseases. To this end, we created (i) a knowledge base, dubbed ppstatement, containing manually curated detailed annotations, and (ii) a knowledge base, called ppconcept, containing automatically annotated concepts. The resulting Pre-/Probiotics Knowledge Graph (PPKG) combines these two knowledge bases with three other public databases (i.e. MeSH, UMLS and SNOMED CT). To validate the performance of PPKG and to demonstrate the added value of integrating two knowledge bases, we created four biological query cases. The query cases demonstrate that we can retrieve co-occurring concepts of interest, and also that combining the two knowledge bases leads to more comprehensive query results than utilizing them separately. The PPKG enables users to pose research queries such as “which pre-/probiotics combinations may benefit depression?”, potentially leading to novel biological insights.
Inverse design of core-shell particles with discrete material classes using neural networks
Authors: Kuhn Lina , Repän Taavi , Rockstuhl Carsten 
Published: November 2022

AbstractThe design of scatterers on demand is a challenging task that requires the investigation and development of novel and flexible approaches. In this paper, we propose a machine learning-assisted optimization framework to design multi-layered core-shell particles that provide a scattering response on demand. Artificial neural networks can learn to predict the scattering spectrum of core-shell particles with high accuracy and can act as fully differentiable surrogate models for a gradient-based design approach. To enable the fabrication of the particles, we consider existing materials and introduce a novel two-step optimization to treat continuous geometric parameters and discrete feasible materials simultaneously. Moreover, we overcome the non-uniqueness of the problem and expand the design space to particles of varying numbers of shells, i.e., different number of optimization parameters, with a classification network. Our method is 1–2 orders of magnitudes faster than conventional approaches in both forward prediction and inverse design and is potentially scalable to even larger and more complex scatterers.
Shrinkage estimators of large covariance matrices with Toeplitz targets in array signal processing
Authors: Zhang Bin , Yuan Shoucheng 
Published: November 2022

AbstractThe problem of estimating a large covariance matrix arises in various statistical applications. This paper develops new covariance matrix estimators based on shrinkage regularization. Individually, we consider two kinds of Toeplitz-structured target matrices as the data come from the complex Gaussian distribution. We derive the optimal tuning parameter under the mean squared error criterion in closed form by discovering the mathematical properties of the two target matrices. We get some vital moment properties of the complex Wishart distribution, then simplify the optimal tuning parameter. By unbiasedly estimating the unknown scalar quantities involved in the optimal tuning parameter, we propose two shrinkage estimators available in the large-dimensional setting. For verifying the performance of the proposed covariance matrix estimators, we provide some numerical simulations and applications to array signal processing compared to some existing estimators.
Electrical resistivity of the Fe–Si–S ternary system: implications for timing of thermal convection shutdown in the lunar core
Authors: Littleton Joshua A. H. , Yong Wenjun , Secco Richard A. 
Published: November 2022

AbstractThe composition of the lunar core has been suggested to be Fe-rich with varying amounts of lighter elements, such as Si and S. Presence of Si and S affects electrical and thermal transport properties and thus influences core thermal processes and evolution. Paleomagnetic observations constrain a high intensity magnetic field that ceases shortly after formation of the moon (~ 3.5–4.2 Ga year ago), and thermal convection in the core may contribute to generation of this field. In this study, the electrical resistivity of Fe-14 wt% Si-3 wt% S was measured in both solid and molten states at pressures up to 5 GPa and thermal conductivity was calculated via the Wiedemann–Franz Law from the electrical measurements. The results were used to estimate the adiabatic conductive heat flux of a molten Fe-14 wt% Si-3 wt% S lunar core and compared to a Fe-2-17 wt% Si lunar core, which showed that thermal convection of either core composition shuts down within the duration of the high intensity magnetic field: (1) 3.17–3.72 Ga year ago for a Fe-14 wt% Si-3 wt% S core; and (ii) 3.38–3.86 Ga years ago for a Fe-2-17 wt% Si core. Results favouring compatibility of these core compositions with paleomagnetic observations are strongly dependent on the temperature of the core-mantle boundary and time-dependent mantle-side heat flux.
Distributed lag inspired machine learning for predicting vaccine-induced changes in COVID-19 hospitalization and intensive care unit admission
Authors: Khan Atikur R. , Hasan Khandaker Tabin , Abedin Sumaiya , Khan Saleheen 
Published: November 2022

AbstractDistributed lags play important roles in explaining the short-run dynamic and long-run cumulative effects of features on a response variable. Unlike the usual lag length selection, important lags with significant weights are selected in a distributed lag model (DLM). Inspired by the importance of distributed lags, this research focuses on the construction of distributed lag inspired machine learning (DLIML) for predicting vaccine-induced changes in COVID-19 hospitalization and intensive care unit (ICU) admission rates. Importance of a lagged feature in DLM is examined by hypothesis testing and a subset of important features are selected by evaluating an information criterion. Akin to the DLM, we demonstrate the selection of distributed lags in machine learning by evaluating importance scores and objective functions. Finally, we apply the DLIML with supervised learning for forecasting daily changes in COVID-19 hospitalization and ICU admission rates in United Kingdom (UK) and United States of America (USA). A sharp decline in hospitalization and ICU admission rates are observed when around 40% people are vaccinated. For one percent more vaccination, daily changes in hospitalization and ICU admission rates are expected to reduce by 4.05 and 0.74 per million after 14 days in UK, and 5.98 and 1.04 per million after 20 days in USA, respectively. Long-run cumulative effects in the DLM demonstrate that the daily changes in hospitalization and ICU admission rates are expected to jitter around the zero line in a long-run. Application of the DLIML selects fewer lagged features but provides qualitatively better forecasting outcome for data-driven healthcare service planning.
Genetic insights into smoking behaviours in 10,558 men of African ancestry from continental Africa and the UK
Authors: Piga Noemi-Nicole , Boua Palwende Romuald , Soremekun Chisom , Shrine Nick , Coley Kayesha , Brandenburg Jean-Tristan , Tobin Martin D. , Ramsay Michèle , Fatumo Segun , Choudhury Ananyo , Batini Chiara 
Published: November 2022

AbstractSmoking is a leading risk factor for many of the top ten causes of death worldwide. Of the 1.3 billion smokers globally, 80% live in low- and middle-income countries, where the number of deaths due to tobacco use is expected to double in the next decade according to the World Health Organization. Genetic studies have helped to identify biological pathways for smoking behaviours, but have mostly focussed on individuals of European ancestry or living in either North America or Europe. We performed a genome-wide association study of two smoking behaviour traits in 10,558 men of African ancestry living in five African countries and the UK. Eight independent variants were associated with either smoking initiation or cessation at P-value < 5 × 10–6, four being monomorphic or rare in European populations. Gene prioritisation strategy highlighted five genes, including SEMA6D, previously described as associated with several smoking behaviour traits. These results confirm the importance of analysing underrepresented populations in genetic epidemiology, and the urgent need for larger genomic studies to boost discovery power to better understand smoking behaviours, as well as many other traits.
Long-term benefits of heart rate variability biofeedback training in older adults with different levels of social interaction: a pilot study
Authors: de Souza Perciliany Martins , de Cássia Souza Miriam , Diniz Luiza Araújo , Araújo Cássia Regina Vieira , Lopez Mariana , Volchan Eliane , Fernandes Orlando , Sanchez Tiago Arruda , Souza Gabriela Guerra Leal 
Published: November 2022

AbstractTo test whether heart rate variability (HRV) biofeedback training benefits older adults with different social interaction levels. Methods. 32 older adults (16 were institutionalized and 16 were not). Both groups received 14 sessions, 15 min, 3 times a week, with half of the individuals receiving HRV biofeedback training and the other half receiving control training. The following parameters were assessed immediately before and after training, and 4.5 weeks after the last session (follow-up period): aerobic conditioning, anthropometric data, emotional scores, and HRV components. Results. Before the training, the institutionalized individuals had higher scores of loneliness (p < 0.01) and depression (p < 0.0001) and lower social touches (p < 0.0001), body mass (p = 0.04), and body fat percentage (p = 0.002) than the non-institutionalized individuals. HRV biofeedback improved symptoms of depression in both groups. HRV improved only in the non-institutionalized group, and loneliness only in the institutionalized group. Lastly, all changes persisted after the follow-up period. Conclusions. HRV biofeedback training was effective in improving symptoms of depression in older adults. Improvement of HRV and loneliness was dependent on the level of social interaction.