bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of retinal vascularization in retinopathy of prematurity regressed after intravitreal ranibizumab monotherapy or without treatment based on fluorescein angiography
Authors: Wu Lei , Li Manhong , Wang Liang , Yan Hongxiang , Zhou Ziyi , Fan Jing , Zhou Yi , Gou Kaili , Guo Changmei , Wang Yusheng , Zhang Zifeng 
Published: November 2023

AbstractTo investigate the fluorescein angiography (FA) findings and compare the extent of retinal vascularization in retinopathy of prematurity (ROP), recovered after intravitreal ranibizumab (IVR) monotherapy and those regressed spontaneously. Infants with a history of ROP who underwent FA between April 2018 and November 2021 were retrospectively included. The patients were divided into two groups based on whether they had received IVR (IVR group) or had ROP that regressed spontaneously without treatment (untreated group). The differences between the two groups in zone II ROP were also compared, to equalize the subgroups as much as possible in terms of disease severity. FA findings were recorded. The extent of vascularization was measured by the ratio of the distance from the center of the disk to the border of the vascularized zone (DB) and the distance from the center of the disk to the center of the fovea (DF). The width of the persistent avascular retina (PAR) was counted by disc diameters (DD). One hundred and ten eyes of 55 infants were included in the IVR group and 76 eyes of 38 babies in the untreated group. The ratio of abnormal shape of vessels was significantly higher in the IVR group than in the untreated group (50.9% vs. 35.5%; P = 0.038), while the linear choroidal filling pattern, tortuosity of vessels over the posterior pole, dye leakage, anomalous branching of vessels, circumferential vessels, arteriovenous shunt, abnormal capillary bed, and macular abnormalities were similarly. There was a smaller temporal DB/DF ratio (4.48 vs. 4.63; P = 0.003) and greater PAR (2.63 vs. 1.76; P < 0.001) in the IVR group compared to the untreated group. In zone II ROP, the progression of retinal vascularization was significantly larger in the IVR group than that in the untreated group (P = 0.003), while no statistical differences were observed in FA features, the DB/DF ratio, and PAR between the two subgroups. The residual vascular abnormalities and PAR may be common results of ROP regression. The DB/DF ratio of 4.0 temporally and 3.3 nasally could be used as the preliminary indicators for safe retinal vascularization in the completion of ROP regression.
Tailoring the dispersion characteristics in planar arrays of discrete and coalesced split ring resonators
Authors: Spanos Ioannis , Stevens Christopher John , Solymar Laszlo , Shamonina Ekaterina 
Published: November 2023

AbstractIn this report, the coupling and dispersion characteristics of discrete and coalesced square resonators was investigated in the MHz regime. Resonators with one and three gaps were considered. When the resonators are not in direct contact, the number of gaps has little effect upon the total coupling, which is negative. When the resonators are connected so that they share one side, the coupling can change drastically depending on the number of gaps. In particular, when the shared side has a gap, the total coupling coefficient switches to positive values, making it possible for forward travelling waves to propagate on arrays. Experimental, numerical and analytical data verify this behaviour.
Prediction of ovulation: new insight into an old challenge
Authors: Maman Ettie , Adashi Eli Y. , Baum Micha , Hourvitz Ariel 
Published: November 2023

AbstractUltrasound monitoring and hormonal blood testing are considered by many as an accurate method to predict ovulation time. However, uniform and validated algorithms for predicting ovulation have yet to be defined. Daily hormonal tests and transvaginal ultrasounds were recorded to develop an algorithm for ovulation prediction. The rupture of the leading ovarian follicle was a marker for ovulation day. The model was validated retrospectively on natural cycles frozen embryo transfer cycles with documented ovulation. Circulating levels of LH or its relative variation failed, by themselves, to reliably predict ovulation. Any decrease in estrogen was 100% associated with ovulation emergence the same day or the next day. Progesterone levels > 2 nmol/L had low specificity to predict ovulation the next day (62.7%), yet its sensitivity was high (91.5%). A model for ovulation prediction, combining the three hormone levels and ultrasound was created with an accuracy of 95% to 100% depending on the combination of the hormone levels. Model validation showed correct ovulation prediction in 97% of these cycles. We present an accurate ovulation prediction algorithm. The algorithm is simple and user-friendly so both reproductive endocrinologists and general practitioners can use it to benefit their patients.
Quantum-anomalous-Hall current patterns and interference in thin slabs of chiral topological superconductors
Authors: Di Miceli Daniele , Serra Llorenç 
Published: November 2023

AbstractThe chiral topological superconductor, which supports propagating nontrivial edge modes while maintaining a gapped bulk, can be realized hybridizing a quantum-anomalous-Hall thin slab with an ordinary s-wave superconductor. We show that by sweeping the voltage bias in a normal-hybrid-normal double junction, the pattern of electric currents in the normal leads spans three main regimes. From single-mode edge-current quantization at low bias, to double-mode edge-current oscillations at intermediate voltages and up to diffusive bulk currents at larger voltages. Observing such patterns by resolving the spatial distribution of the local current in the thin slab could provide additional evidence, besides the global conductance, on the physics of chiral topological superconductors.
High-sensitivity deuterium metabolic MRI differentiates acute pancreatitis from pancreatic cancers in murine models
Authors: Montrazi Elton T. , Sasson Keren , Agemy Lilach , Peters Dana C. , Brenner Ori , Scherz Avigdor , Frydman Lucio 
Published: November 2023

AbstractDeuterium metabolic imaging (DMI) is a promising tool for investigating a tumor’s biology, and eventually contribute in cancer diagnosis and prognosis. In DMI, [6,6′-2H2]-glucose is taken up and metabolized by different tissues, resulting in the formation of HDO but also in an enhanced formation of [3,3′-2H2]-lactate at the tumor site as a result of the Warburg effect. Recent studies have shown DMI’s suitability to highlight pancreatic cancer in murine models by [3,3′-2H2]-lactate formation; an important question is whether DMI can also differentiate between these tumors and pancreatitis. This differentiation is critical, as these two diseases are hard to distinguish today radiologically, but have very different prognoses requiring distinctive treatments. Recent studies have shown the limitations that hyperpolarized MRI faces when trying to distinguish these pancreatic diseases by monitoring the [1-13C1]-pyruvate→[1-13C1]-lactate conversion. In this work, we explore DMI’s capability to achieve such differentiation. Initial tests used a multi-echo (ME) SSFP sequence, to identify any metabolic differences between tumor and acute pancreatitis models that had been previously elicited very similar [1-13C1]-pyruvate→[1-13C1]-lactate conversion rates. Although ME-SSFP provides approximately 5 times greater signal-to-noise ratio (SNR) than the standard chemical shift imaging (CSI) experiment used in DMI, no lactate signal was observed in the pancreatitis model. To enhance lactate sensitivity further, we developed a new, weighted-average, CSI-SSFP approach for DMI. Weighted-average CSI-SSFP improved DMI’s SNR by another factor of 4 over ME-SSFP—a sensitivity enhancement that sufficed to evidence natural abundance 2H fat in abdominal images, something that had escaped the previous approaches even at ultrahigh (15.2 T) MRI fields. Despite these efforts to enhance DMI’s sensitivity, no lactate signal could be detected in acute pancreatitis models (n = 10; [3,3′-2H2]-lactate limit of detection < 100 µM; 15.2 T). This leads to the conclusion that pancreatic tumors and acute pancreatitis may be clearly distinguished by DMI, based on their different abilities to generate deuterated lactate.
Prediction of alternative pre-mRNA splicing outcomes
Authors: Najjar Rayan , Mustelin Tomas 
Published: November 2023

AbstractTo understand the biological impact of alternative pre-mRNA splicing, it is vital to know which exons are involved, what protein domains they encode, and how the translated isoforms differ. Therefore, we developed a computational pipeline (RiboSplitter) focused on functional effect prediction. It builds on event-based alternative splicing detection with additional filtering steps leading to more efficient statistical testing, and with detection of isoform-specific protein changes. A key methodological advance is reading frame prediction by translating exonic DNA in all possible frames, then finding a single open reading frame, or a single frame with matches to known proteins of the gene. This allowed unambiguous translation in 93.9% of alternative splicing events when tested on RNA-sequencing data of B cells from Sjögren’s syndrome patients. RiboSplitter does not depend on reference annotations and translates events even when one or both isoform(s) are novel (unannotated). RiboSplitter’s visualizations illustrate each event with translation outcomes, show event location within the gene, and align exons to protein domains.
Modeling personalized heart rate response to exercise and environmental factors with wearables data
Authors: Nazaret Achille , Tonekaboni Sana , Darnell Gregory , Ren Shirley You , Sapiro Guillermo , Miller Andrew C. 
Published: November 2023

AbstractHeart rate (HR) response to workout intensity reflects fitness and cardiorespiratory health. Physiological models have been developed to describe such heart rate dynamics and characterize cardiorespiratory fitness. However, these models have been limited to small studies in controlled lab environments and are challenging to apply to noisy—but ubiquitous—data from wearables. We propose a hybrid approach that combines a physiological model with flexible neural network components to learn a personalized, multidimensional representation of fitness. The physiological model describes the evolution of heart rate during exercise using ordinary differential equations (ODEs). ODE parameters are dynamically derived via a neural network connecting personalized representations to external environmental factors, from area topography to weather and instantaneous workout intensity. Our approach efficiently fits the hybrid model to a large set of 270,707 workouts collected from wearables of 7465 users from the Apple Heart and Movement Study. The resulting model produces fitness representations that accurately predict full HR response to exercise intensity in future workouts, with a per-workout median error of 6.1 BPM [4.4–8.8 IQR]. We further demonstrate that the learned representations correlate with traditional metrics of cardiorespiratory fitness, such as VO2 max (explained variance 0.81 ± 0.003). Lastly, we illustrate how our model is naturally interpretable and explicitly describes the effects of environmental factors such as temperature and humidity on heart rate, e.g., high temperatures can increase heart rate by 10%. Combining physiological ODEs with flexible neural networks can yield interpretable, robust, and expressive models for health applications.
Population mobility data provides meaningful indicators of fast food intake and diet-related diseases in diverse populations
Authors: Horn Abigail L. , Bell Brooke M. , Bulle Bueno Bernardo García , Bahrami Mohsen , Bozkaya Burçin , Cui Yan , Wilson John P. , Pentland Alex , Moro Esteban , de la Haye Kayla 
Published: November 2023

AbstractThe characteristics of food environments people are exposed to, such as the density of fast food (FF) outlets, can impact their diet and risk for diet-related chronic disease. Previous studies examining the relationship between food environments and nutritional health have produced mixed findings, potentially due to the predominant focus on static food environments around people’s homes. As smartphone ownership increases, large-scale data on human mobility (i.e., smartphone geolocations) represents a promising resource for studying dynamic food environments that people have access to and visit as they move throughout their day. This study investigates whether mobility data provides meaningful indicators of diet, measured as FF intake, and diet-related disease, evaluating its usefulness for food environment research. Using a mobility dataset consisting of 14.5 million visits to geolocated food outlets in Los Angeles County (LAC) across a representative sample of 243,644 anonymous and opted-in adult smartphone users in LAC, we construct measures of visits to FF outlets aggregated over users living in neighborhood. We find that the aggregated measures strongly and significantly correspond to self-reported FF intake, obesity, and diabetes in a diverse, representative sample of 8,036 LAC adults included in a population health survey carried out by the LAC Department of Public Health. Visits to FF outlets were a better predictor of individuals’ obesity and diabetes than their self-reported FF intake, controlling for other known risks. These findings suggest mobility data represents a valid tool to study people’s use of dynamic food environments and links to diet and health.
Mixed quantum-classical dynamics for near term quantum computers
Authors: Bultrini Daniel , Vendrell Oriol 
Published: November 2023

AbstractMixed quantum-classical dynamics is a set of methods often used to understand systems too complex to treat fully quantum mechanically. Many techniques exist for full quantum mechanical evolution on quantum computers, but mixed quantum-classical dynamics are less explored. We present a modular algorithm for general mixed quantum-classical dynamics where the quantum subsystem is coupled with the classical subsystem. We test it on a modified Shin-Metiu model in the first quantization through Ehrenfest propagation. We find that the Time-Dependent Variational Time Propagation algorithm performs well for short-time evolutions and retains qualitative results for longer-time evolutions.
Exciton formation dynamics at the SiO2/Si interface
Authors: Revuelta Sergio , Cánovas Enrique 
Published: November 2023

AbstractExcitons can operate as carriers for energy transduction in optoelectronics, and engineering their dynamics is of great interest. Here, we employ time-resolved terahertz spectroscopy to analyze exciton formation dynamics as a function of temperature for a (100) N-type silicon substrate passivated by native SiO2. By analyzing the frequency-resolved complex conductivity as a function of temperature we resolve the photophysics for the formation of free carriers into excitons. Notably, we observe a relatively long-lived ~300 ps transient population of free carriers at temperatures well below the Mott transition (4 K). We rationalize this by transient photophysics at the SiO2/N-type-Si surface, where holes localize and release under high injection conditions due to a transient surface photovoltage effect. We believe our results have implications for the design of excitonic-based electronic applications operating at cryogenic temperatures and accessed optically.
Power system risk assessment strategy based on weighted comprehensive allocation and improved BP neural network
Authors: Xiao Peng , Jiang Yixin , Liang Zhihong , Wang Hailin , Zhang Yunan 
Published: November 2023

AbstractDuring the operation and maintenance of the power system, power outages and supply‐demand imbalances can disrupt the normal power supply process. This issue must be mitigated or even resolved through the implementation of an appropriate power system risk warning. The article proposes a self‐assessment and early warning strategy for power system hazards based on an enhanced ant colony optimization algorithm (IACO) and a BP neural network. First, a combination of the Analytic Hierarchy Process (AHP) and the Entropy Weighting Method (EWM) is used to assign weights comprehensively to indicators that have a significant impact on the stability and safety of power system operation, thereby avoiding the negative impact of subjective experience or objective factors on the weight allocation results. Secondly, multiple regression analysis is used to calculate the risk assessment results of the selected indicators and weights corresponding to the power system. Training and testing samples for the BP neural network were calculated based on the weight allocation procedure described previously. Then, IACO is employed to global optimize the weights and thresholds of the BP neural network, and an enhanced BP neural network model for independent power system risk assessment is developed. The designed risk assessment and warning strategy was finally evaluated. The results indicate that the proposed power system risk assessment and early warning method can precisely predict the actual operating status of the power system based on weight values, thereby enhancing power supply quality by providing technical personnel with a data reference.
The GAPS programme at TNG
Authors: Mantovan G. , Malavolta L. , Desidera S. , Zingales T. , Borsato L. , Piotto G. , Maggio A. , Locci D. , Polychroni D. , Turrini D. , Baratella M. , Biazzo K. , Nardiello D. , Stassun K. , Nascimbeni V. , Benatti S. , Anna John A. , Watkins C. , Bieryla A. , Lissauer J. J. , Twicken J. D. , Lanza A. F. , Winn J. N. , Messina S. , Montalto M. , Sozzetti A. , Boffin H. , Cheryasov D. , Strakhov I. , Murgas F. , D’Arpa M. , Barkaoui K. , Benni P. , Bignamini A. , Bonomo A. S. , Borsa F. , Cabona L. , Cameron A. C. , Claudi R. , Cochran W. , Collins K. A. , Damasso M. , Dong J. , Endl M. , Fukui A. , Fűrész G. , Gandolfi D. , Ghedina A. , Jenkins J. , Kabáth P. , Latham D. W. , Lorenzi V. , Luque R. , Maldonado J. , McLeod K. , Molinaro M. , Narita N. , Nowak G. , Orell-Miquel J. , Pallé E. , Parviainen H. , Pedani M. , Quinn S. N. , Relles H. , Rowden P. , Scandariato G. , Schwarz R. , Seager S. , Shporer A. , Vanderburg A. , Wilson T. G. 
Published: November 2023

Context. Short-period giant planets (P≲ 10 days,Mp>0.1MJ) are frequently found to be solitary compared to other classes of exo-planets. Small inner companions to giant planets withP≲ 15 days are known only in five compact systems: WASP-47, Kepler-730, WASP-132, TOI-1130, and TOI-2000. Here, we report the confirmation of TOI-5398, the youngest known compact multi-planet system composed of a hot sub-Neptune (TOI-5398 c,Pc= 4.77271 days) orbiting interior to a short-period Saturn (TOI-5398 b,Pb= 10.590547 days) planet, both transiting around a 650 ± 150 Myr G-type star.Aims. As part of the Global Architecture of Planetary Systems (GAPS) Young Object project, we confirmed and characterised this compact system, measuring the radius and mass of both planets, thus constraining their bulk composition.Methods. Using multi-dimensional Gaussian processes, we simultaneously modelled stellar activity and planetary signals from the Transiting Exoplanet Survey Satellite (TESS) Sector 48 light curve and our High Accuracy Radial velocity Planet Searcher (HARPS-N) radial velocity (RV) time series. We confirmed the planetary nature of both planets, TOI-5398 b and TOI-5398 c, and obtained a precise estimation of their stellar parameters.Results. Through the use of astrometric, photometric, and spectroscopic observations, our findings indicate that TOI-5398 is a young, active G dwarf star (650 ± 150 Myr) with a rotational period ofProt= 7.34 days. The transit photometry and RV measurements enabled us to measure both the radius and mass of planets b,Rb= 10.30 ± 0.40R⊕,Mb= 58.7 ± 5.7M⊕, and c,Rc= 3.52 ± 0.19R⊕,Mc= 11.8 ± 4.8M⊕. TESS observed TOI-5398 during sector 48 and no further observations are planned in the current Extended Mission, making our ground-based light curves crucial for improvement of the ephemeris. With a transmission spectroscopy metric (TSM) value of around 300, TOI-5398 b is the most amenable warm giant (10 <P< 100 days) for JWST atmospheric characterisation.
Exploring the hypothesis of an inverted Z gradient inside Jupiter
Authors: Howard S. , Guillot T. , Markham S. , Helled R. , Müller S. , Stevenson D. J. , Lunine J. I. , Miguel Y. , Nettelmann N. 
Published: November 2023

Context. Reconciling models of Jupiter’s interior with measurements of the atmospheric composition still poses a significant challenge. Interior models favour a subsolar or solar abundance of heavy elements, Z, whereas atmospheric measurements suggest a supersolar abundance. One potential solution may be to account for the presence of an inverted Z gradient, namely, an inward decrease of Z, which implies a higher heavy-element abundance in the atmosphere than in the outer envelope.
Aims. We investigate two scenarios in which the inverted Z gradient is either located at levels where helium rain occurs (∼Mbar) or at higher levels (∼kbar) where a radiative region could exist. Here, we aim to assess the plausibility of these scenarios.
Methods. We calculated interior and evolution models of Jupiter with such an inverted Z gradient and we set constraints on its stability and formation.
Results. We find that an inverted Z gradient at the location of helium rain is not feasible, as it would require a late accretion and would involve too much material. We find interior models with an inverted Z gradient at upper levels due to a radiative zone preventing downward mixing, could satisfy the current gravitational field of the planet. However, our evolution models suggest that this second scenario cannot be validated.
Conclusions. We find that an inverted Z gradient in Jupiter could indeed be stable, however, its presence either at the Mbar or kbar levels is rather unlikely.