bluuebunny/crossref_metadata_2025_split huggingface dataset:
Obstructive sleep apnea is position dependent in young infants
Authors: Kukkola Hanna-Leena , Kirjavainen Turkka 
Published: August 2022

Abstract
Background
Obstructive sleep apnea in infants with Pierre Robin sequence is sleep-position dependent. The influence of sleep position on obstructive events is not established in other infants.

Methods
We re-evaluated ten-year pediatric sleep center data in infants aged less than six months, with polysomnography performed in different sleep positions. We excluded infants with syndromes, genetic defects, or structural anomalies.

Results
Comparison of breathing between supine and side sleeping positions was performed for 72 infants at the median corrected age of 4 weeks (interquartile range (IQR) 2-8 weeks). Of the infants, 74% were male, 35% were born prematurely, and 35% underwent study because of a life-threatening event or for being a SIDS sibling. Upper airway obstruction was more frequent (obstructive apnea-hypopnea index (OAHI), p < 0.001), 95th-percentile end-tidal carbon dioxide levels were higher (p = 0.004), and the work of breathing was heavier (p = 0.002) in the supine than in the side position. Median OAHI in the supine position was 8 h−1 (IQR 4–20 h−1), and in the side position was 4 h−1 (IQR 0-10 h−1).

Conclusions
Obstructive upper airway events in young infants are more frequent when supine than when sleeping on the side.

Impact

The effect of sleep position on obstructive sleep apnea is not well established in infants other than in those with Pierre Robin sequence.

A tendency for upper airway obstruction is position dependent in most infants aged less than 6 months. Upper airway obstruction is more common, end-tidal carbon dioxide 95th-percentile values higher, and breathing more laborious in the supine than in the side-sleeping position.

Upper airway obstruction and obstructive events have high REM sleep predominance.

As part of obstructive sleep apnea treatment in young infants, side-sleeping positioning may prove useful.
ABCA1 is an extracellular phospholipid translocase
Authors: Segrest Jere P. , Tang Chongren , Song Hyun D. , Jones Martin K. , Davidson W. Sean , Aller Stephen G. , Heinecke Jay W. 
Published: August 2022

AbstractProduction of high density lipoprotein (HDL) requires ATP-binding cassette transporter A1 (ABCA1) to drive phospholipid (PL) from the plasma membrane into extracellular apolipoprotein A-I. Here, we use simulations to show that domains of ABCA1 within the plasma membrane remove PL from the membrane’s outer leaflet. In our simulations, after the lipid diffuses into the interior of ABCA1’s outward-open cavity, PL extracted by the gateway passes through a ring-shaped domain, the annulus orifice, which forms the base of an elongated hydrophobic tunnel in the transporter’s extracellular domain. Engineered mutations in the gateway and annulus strongly inhibit lipid export by ABCA1 without affecting cell-surface expression levels. Our finding that ABCA1 extracts lipid from the outer face of the plasma membrane and forces it through its gateway and annulus into an elongated hydrophobic tunnel contrasts with the alternating access model, which proposes that ABCA1 flops PL substrate from the inner leaflet to the outer leaflet of the membrane. Consistent with our model, ABCA1 lacks the charged amino acid residues in the transmembrane domain found in the floppase members of the ABC transporter family.
Tuning moiré excitons and correlated electronic states through layer degree of freedom
Authors: Chen Dongxue , Lian Zhen , Huang Xiong , Su Ying , Rashetnia Mina , Yan Li , Blei Mark , Taniguchi Takashi , Watanabe Kenji , Tongay Sefaattin , Wang Zenghui , Zhang Chuanwei , Cui Yong-Tao , Shi Su-Fei 
Published: August 2022

AbstractMoiré coupling in transition metal dichalcogenides (TMDCs) superlattices introduces flat minibands that enable strong electronic correlation and fascinating correlated states, and it also modifies the strong Coulomb-interaction-driven excitons and gives rise to moiré excitons. Here, we introduce the layer degree of freedom to the WSe2/WS2 moiré superlattice by changing WSe2 from monolayer to bilayer and trilayer. We observe systematic changes of optical spectra of the moiré excitons, which directly confirm the highly interfacial nature of moiré coupling at the WSe2/WS2 interface. In addition, the energy resonances of moiré excitons are strongly modified, with their separation significantly increased in multilayer WSe2/monolayer WS2 moiré superlattice. The additional WSe2 layers also modulate the strong electronic correlation strength, evidenced by the reduced Mott transition temperature with added WSe2 layer(s). The layer dependence of both moiré excitons and correlated electronic states can be well described by our theoretical model. Our study presents a new method to tune the strong electronic correlation and moiré exciton bands in the TMDCs moiré superlattices, ushering in an exciting platform to engineer quantum phenomena stemming from strong correlation and Coulomb interaction.
Skyrmions in synthetic antiferromagnets and their nucleation via electrical current and ultra-fast laser illumination
Authors: Juge Roméo , Sisodia Naveen , Larrañaga Joseba Urrestarazu , Zhang Qiang , Pham Van Tuong , Rana Kumari Gaurav , Sarpi Brice , Mille Nicolas , Stanescu Stefan , Belkhou Rachid , Mawass Mohamad-Assaad , Novakovic-Marinkovic Nina , Kronast Florian , Weigand Markus , Gräfe Joachim , Wintz Sebastian , Finizio Simone , Raabe Jörg , Aballe Lucia , Foerster Michael , Belmeguenai Mohamed , Buda-Prejbeanu Liliana D. , Pelloux-Prayer Johan , Shaw Justin M. , Nembach Hans T. , Ranno Laurent , Gaudin Gilles , Boulle Olivier 
Published: August 2022

AbstractMagnetic skyrmions are topological spin textures that hold great promise as nanoscale information carriers in non-volatile memory and logic devices. While room-temperature magnetic skyrmions and their current-induced motion were recently demonstrated, the stray field resulting from their finite magnetisation and their topological charge limit their minimum size and reliable motion. Antiferromagnetic skyrmions allow to lift these limitations owing to their vanishing magnetisation and net zero topological charge, promising ultra-small and ultra-fast skyrmions. Here, we report on the observation of isolated skyrmions in compensated synthetic antiferromagnets at zero field and room temperature using X-ray magnetic microscopy. Micromagnetic simulations and an analytical model confirm the chiral antiferromagnetic nature of these skyrmions and allow the identification of the physical mechanisms controlling their size and stability. Finally, we demonstrate the nucleation of synthetic antiferromagnetic skyrmions via local current injection and ultra-fast laser excitation.
A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues
Authors: Saul Dominik , Kosinsky Robyn Laura , Atkinson Elizabeth J. , Doolittle Madison L. , Zhang Xu , LeBrasseur Nathan K. , Pignolo Robert J. , Robbins Paul D. , Niedernhofer Laura J. , Ikeno Yuji , Jurk Diana , Passos João F. , Hickson LaTonya J. , Xue Ailing , Monroe David G. , Tchkonia Tamara , Kirkland James L. , Farr Joshua N. , Khosla Sundeep 
Published: August 2022

AbstractAlthough cellular senescence drives multiple age-related co-morbidities through the senescence-associated secretory phenotype, in vivo senescent cell identification remains challenging. Here, we generate a gene set (SenMayo) and validate its enrichment in bone biopsies from two aged human cohorts. We further demonstrate reductions in SenMayo in bone following genetic clearance of senescent cells in mice and in adipose tissue from humans following pharmacological senescent cell clearance. We next use SenMayo to identify senescent hematopoietic or mesenchymal cells at the single cell level from human and murine bone marrow/bone scRNA-seq data. Thus, SenMayo identifies senescent cells across tissues and species with high fidelity. Using this senescence panel, we are able to characterize senescent cells at the single cell level and identify key intercellular signaling pathways. SenMayo also represents a potentially clinically applicable panel for monitoring senescent cell burden with aging and other conditions as well as in studies of senolytic drugs.
An Artificial Intelligence Dataset for Solar Energy Locations in India
Authors: Ortiz Anthony , Negandhi Dhaval , Mysorekar Sagar R. , Nagaraju Shivaprakash K. , Kiesecker Joseph , Robinson Caleb , Bhatia Priyal , Khurana Aditi , Wang Jane , Oviedo Felipe , Ferres Juan Lavista 
Published: August 2022

AbstractRapid development of renewable energy sources, particularly solar photovoltaics (PV), is critical to mitigate climate change. As a result, India has set ambitious goals to install 500 gigawatts of solar energy capacity by 2030. Given the large footprint projected to meet renewables energy targets, the potential for land use conflicts over environmental values is high. To expedite development of solar energy, land use planners will need access to up-to-date and accurate geo-spatial information of PV infrastructure. In this work, we developed a spatially explicit machine learning model to map utility-scale solar projects across India using freely available satellite imagery with a mean accuracy of 92%. Our model predictions were validated by human experts to obtain a dataset of 1363 solar PV farms. Using this dataset, we measure the solar footprint across India and quantified the degree of landcover modification associated with the development of PV infrastructure. Our analysis indicates that over 74% of solar development In India was built on landcover types that have natural ecosystem preservation, or agricultural value.
Risk factors and prediction model for nonalcoholic fatty liver disease in northwest China
Authors: Li Danting , Zhang Meiyu , Wu Shengli , Tan Huiwen , Li Nong 
Published: August 2022

AbstractIn recent years, nonalcoholic fatty liver disease (NAFLD) has become the most important chronic liver disease worldwide. The prevalence of NAFLD in China has also increased year by year. This study aimed to detect NAFLD early by developing a nomogram model in Chinese individuals. A total of 8861 subjects who underwent physical examination in Karamay and were 18 to 62 years old were enrolled. Clinical information, laboratory results and ultrasound findings were retrieved. The participants were randomly assigned to the development set (n = 6203) and the validation set (n = 2658). Significant variables independently associated with NAFLD were identified by least absolute shrinkage and selection operator (LASSO) regression and the multiple logistic regression model. Six variables were selected to construct the nomogram: age, sex, waist circumference (WC), body mass index (BMI), alanine aminotransferase (ALT), triglycerides and glucose index (TyG). The area under the receiver operating characteristic curve (AUROC) of the development set and validation set was 0.886 and 0.894, respectively. The calibration curves showed excellent accuracy of the nomogram model. This physical examination and laboratory test-based nomogram can predict the risk of NAFLD intuitively and individually.
Decontamination of actual radioactive wastewater containing 137Cs using bentonite as a natural adsorbent: equilibrium, kinetics, and thermodynamic studies
Authors: Muslim Wasan A. , Albayati Talib M. , Al-Nasri Salam K. 
Published: August 2022

AbstractBatch adsorption treatment using Iraqi bentonite as a natural adsorbent was adopted in this study to decontaminate actual 137Cs radioactive wastewater from the Al-Tuwaitha Nuclear Research Center, located south of Baghdad. The bentonite characterization was applied before and after treatment, using chemical compositions analyses, X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), Brunauer–Emmett–Teller (BET) surface area analysis and Fourier-transform infrared spectroscopy (FT-IR). The batch adsorption mode was applied with the initial radioactivity concentration (1440.5 Bq/L), solid/liquid ratio (1 g/L), pH (6–8), contact time (1.5 h), and temperature (298°K). The adsorption experiments showed a decontamination removal efficiency of about 95.66% of 137Cs. A Freundlich adsorption isotherm model was approved for the adsorption of 137Cs, with a coefficient of determination R2 = 0.998. A pseudo-second-order model fitted well with the adsorption of 137Cs, with R2 = 0.983. The positive value of ΔH° in the thermodynamic results indicated that the adsorption process was endothermic physisorption (ΔH° = 15.01 kJ mol−1), spontaneous and favorable (ΔG° = −7.66 kJ mol−1 K−1), with a very low degree of disorder (ΔS° = 0.076 kJ mol−1 K−1).
Deep learning predicts all-cause mortality from longitudinal total-body DXA imaging
Authors: Glaser Yannik , Shepherd John , Leong Lambert , Wolfgruber Thomas , Lui Li-Yung , Sadowski Peter , Cummings Steven R. 
Published: August 2022

Abstract
Background
Mortality research has identified biomarkers predictive of all-cause mortality risk. Most of these markers, such as body mass index, are predictive cross-sectionally, while for others the longitudinal change has been shown to be predictive, for instance greater-than-average muscle and weight loss in older adults. And while sometimes markers are derived from imaging modalities such as DXA, full scans are rarely used. This study builds on that knowledge and tests two hypotheses to improve all-cause mortality prediction. The first hypothesis is that features derived from raw total-body DXA imaging using deep learning are predictive of all-cause mortality with and without clinical risk factors, meanwhile, the second hypothesis states that sequential total-body DXA scans and recurrent neural network models outperform comparable models using only one observation with and without clinical risk factors.

Methods
Multiple deep neural network architectures were designed to test theses hypotheses. The models were trained and evaluated on data from the 16-year-long Health, Aging, and Body Composition Study including over 15,000 scans from over 3000 older, multi-race male and female adults. This study further used explainable AI techniques to interpret the predictions and evaluate the contribution of different inputs.

Results
The results demonstrate that longitudinal total-body DXA scans are predictive of all-cause mortality and improve performance of traditional mortality prediction models. On a held-out test set, the strongest model achieves an area under the receiver operator characteristic curve of 0.79.

Conclusion
This study demonstrates the efficacy of deep learning for the analysis of DXA medical imaging in a cross-sectional and longitudinal setting. By analyzing the trained deep learning models, this work also sheds light on what constitutes healthy aging in a diverse cohort.
Local binary pattern‐based reversible data hiding
Authors: Sahu Monalisa , Padhy Neelamadhab , Gantayat Sasanko Sekhar , Sahu Aditya Kumar 
Published: August 2022

AbstractA novel local binary pattern‐based reversible data hiding (LBP‐RDH) technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding capacity. During embedding, the image is divided into various 3×3 blocks. Then, using the LBP‐based image descriptor, the LBP codes for each block are computed. Next, the obtained LBP codes are XORed with the embedding bits and are concealed in the respective blocks using the proposed pixel readjustment process. Further, each cover image (CI) pixel produces two different stego‐image pixels. Likewise, during extraction, the CI pixels are restored without the loss of a single bit of information. The outcome of the proposed technique with respect to perceptual transparency measures, such as peak signal‐to‐noise ratio and structural similarity index, is found to be superior to that of some of the recent and state‐of‐the‐art techniques. In addition, the proposed technique has shown excellent resilience to various stego‐attacks, such as pixel difference histogram as well as regular and singular analysis. Besides, the out‐off boundary pixel problem, which endures in most of the contemporary data hiding techniques, has been successfully addressed.
Real‐time out‐of‐distribution detection in cyber‐physical systems with learning‐enabled components
Authors: Cai Feiyang , Koutsoukos Xenofon 
Published: August 2022

AbstractLearning‐enabled components (LECs) such as deep neural networks are used increasingly in cyber‐physical systems (CPS) since they can handle the uncertainty and variability of the environment and increase the level of autonomy. LECs, however, may compromise system safety since their predictions may have large errors, for example, when the data available at runtime are different than the data used for training. This study considers the problem of efficient and robust out‐of‐distribution detection for learning‐enabled CPS. Out‐of‐distribution detection using a single input example is typically not robust and may result in a large number of false alarms. The proposed approach utilises neural network architectures that are used to compute efficiently the nonconformity of new inputs relative to the training data. Specifically, variational autoencoder and deep support vector data description networks are used to learn models for the real‐time detection of out‐of‐distribution high‐dimensional inputs. Robustness can be improved by incorporating saliency maps that identify parts of the input contributing most to the LEC predictions. We demonstrate the approach using simulation case studies of an advanced emergency braking system and a self‐driving end‐to‐end controller, as well as a real‐world data set for autonomous driving. The experimental results show a small detection delay with a very small number of false alarms while the execution time is comparable to the execution time of the original LECs.
Distributed output agreement of PWM‐controlled double‐integrator multi‐agent systems with switching topologies
Authors: Xu Jingwei , Bao Adiya , Jin Zhenghong , Liu Tengfei 
Published: August 2022

AbstractThis paper studies the distributed pulse‐width‐modulation (PWM) control problem for double‐integrator multi‐agent systems with switching topologies. Usually, a PWM control signal updates only on discrete‐time sample instants and only takes three constant values. This means that the methods based on linear and continuous designs are not directly applicable. The major contribution of this paper is that a new class of distributed PWM controllers, which feature nested saturated loops, is proposed. With the new design, the outputs of the controlled agents asymptotically converge to each other, as long as a mild connectivity condition is satisfied. The validity of the proposed PWM controller is proved based on a new invariant‐set‐like method. This work first shows that the state of each PWM‐controlled agent stays within an appropriately chosen closed set if the interactions are bounded. Then, under a mild connectivity condition, the state boundedness and convergence of the closed‐loop multi‐agent system by analyzing the boundedness of the interactions are proved. A numerical example is employed to verify the effectiveness of the proposed design.
Short‐term load forecasting in smart grids using artificial intelligence methods: A survey
Authors: Salehimehr Sirus , Taheri Behrooz , Sedighizadeh Mostafa 
Published: August 2022

AbstractElectrical load forecasting is crucial to achieving better efficiency, reliability, and power quality in modern power systems. Applying short‐term load forecasting, a balance can be preserved between supply and demand; the cost of electricity production will also be decreased. Several methods are proposed for short‐term load forecasting in smart grids in recent years and each of them has its own advantages and weaknesses. Among these methods, popularity is increasing in machine learning techniques. This study is to review three common artificial intelligence load forecasting methods, including long short‐term memory, group method of data handling, and adaptive neuro‐fuzzy inference system that have been used to forecast load in a smart grid consisting of a photovoltaic, wind turbine, battery energy storage system, and electric vehicle charging stations. The performance of these methods is evaluated given accuracy and the system's hardware requirements. The noisy condition of the system is also investigated when these methods are used for load forecasting. The results show that the long short‐term memory model is more accurate than the group method of data handling and adaptive neuro‐fuzzy inference system models. However, this method requires much better hardware requirements.
The historical dialectology of stative morphology in Zapotecan
Authors: Beam de Azcona Rosemary G. 
Published: August 2022

Abstract
This paper updates the reconstruction of the stative aspect prefix in Proto-Zapotecan as *n- and tracks
innovations in stative marking. An early change is proposed to have deleted preconsonantal nasals, rendering segmentally unmarked
stative forms of consonant-initial verbs in varieties of Zapotec then spoken in and around the city of Monte Albán. Contact with
Chatino may be a factor in the retention of preconsonantal *n in Zapotec varieties spoken to the south. A fuller stative prefix,
usually *na-, arose later from a grammaticalized form of the stative-marked copula (Munro
2007; Uchihara 2021). *na- is more productive than *n- and
provides the basis for a new proposed “Eastern Zapotec” genetic grouping. However, the isogloss for *na- crosscuts the earlier
isogloss for preconsonantal nasal deletion, showing that any model of Zapotecan linguistic history needs to address not only
divergence but also convergence. Ethnohistorical and archaeological evidence provide a social context to the linguistic changes
discussed.
Microinstability simulations for stellarators involving kinetic electrons and realistic profiles
Authors: Riemann J , Kleiber R , Borchardt M , Slaby C 
Published: August 2022

Abstract
In this work, linear simulation results obtained with the global particle-in-cell code EUTERPE are applied to plasmas with both adiabatic and kinetic electrons in a Wendelstein 7-X magnetic configuration. Special attention is paid to challenges arising from the need to design realistic and consistent gradient scans on realistic profiles used in global simulations. Results obtained in a pressure-preserving gradient scan performed with kinetic electrons suggest a strong impact of a finite density gradient on the growth of electron-driven instabilities below the ion-temperature-gradient stability limit. Power transfer diagnostics are used to identify the nature of the coexisting driving mechanisms.
Cosmic birefringence from monodromic axion dark energy
Authors: Gasparotto Silvia , Obata Ippei 
Published: August 2022

Abstract
The recently reported non-zero isotropic birefringence angle in Planck 2018 polarization
data provides a tantalizing hint for new physics of axions. In this paper, we explain this by a
string theory motivated axion with a monodromy potential that plays the role of dark energy. Upon
using the birefringence measurement and the constraint on the equation of state for dark energy in
this scenario, we find an upper bound on the axion decay constant as fa
≲ 1016
GeV. This naturally gives an energy scale of order GUT and can resolve the theoretical issue of
super-Planckian field range of the conventional axion dark energy model. We further study the
implications of cosmic birefringence for the underlying theory and its consequences for the string
swampland conjectures. We finally discuss oscillatory features in the dark energy sector and the
expected cosmic birefringence tomography.