bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of cross-linking units on the heat resistance and heat insulation properties of polysilsesquioxane-based materials
Authors: Tsutsui Kosuke , Koga Hiroyuki , Adachi Yohei , Ohshita Joji 
Published: August 2024

AbstractHeat insulators are key materials for efficient energy use and reduction of CO2 emissions. Recently, we examined cross-linked polymethylsilsesquioxane (MSQ) for use as the basic structure of heat-resistant insulation materials. In this study, we prepared MSQs with different cross-linking units and examined the effects of their structures on the heat resistance and heat insulation properties. Among those, MSQ linked by diethynylbenzene had sufficiently low thermal diffusivity and moderately high heat resistance. We also showed their structure–thermal property relationships. Although other influencing factors could exist, the rigidity, π‒π interactions, and bulkiness of the cross-linking units were found to predominantly influence the thermal properties. These findings will lead to new molecular designs for polysilsesquioxane (PSQ)-based heat-resistant heat insulators with high performance.
Becoming agents for genomic change: genetic counsellors’ views of patient care and implementation influences when genomics is mainstreamed
Authors: Do Trang Thu , Martyn Melissa , McClaren Belinda , McEwen Alison , Gaff Clara 
Published: August 2024

AbstractGenetic counsellors (GCs) across the world are increasingly transitioning beyond clinical genetics services to meet the growing demands for genomic healthcare. This presents a unique opportunity for GCs to be ‘genomic change agents’ as they work in alternative models of care. Through various innovative models of mainstream care funded through a change program, we explored the views of GCs regarding their position as ‘genomic change agents’ and what may hinder or drive the success of their evolving roles. Guided by the Diffusion of Innovation Theory, we conducted qualitative interviews with all twelve GCs employed by the change program in different models of providing genomics across five specialties in Australia. Audio-recordings of all interviews were transcribed verbatim and analysed using inductive content analysis. Findings show that early in these new roles, participants held varied descriptions of ‘genomics mainstreaming’: some envisioned it as an end state exclusive to medical specialists practicing genomics while others saw the involvement of GCs as crucial. Participants believed they were uniquely positioned to expedite patient access to genomic testing and counselling and enhance medical specialists’ capability to use genomics. Challenges included hesitancy of some medical specialists regarding the value of genomics in healthcare and potential tension arising from distinct perspectives and practice between genetic and non-genetic professionals. Participants anticipated a decline in the standard of care when non-genetic colleagues managed consent discussion and result disclosure. Our study underscores leadership support and peer connection with those in similar roles as essential elements for GCs’ success in mainstream settings.
Global ocean carbon uptake enhanced by rainfall
Authors: Parc Laetitia , Bellenger Hugo , Bopp Laurent , Perrot Xavier , Ho David T. 
Published: August 2024

AbstractRain alters local sea surface physical and biogeochemical properties but its spatiotemporal variability has led to its overlook in global ocean carbon uptake studies. Different physical and chemical processes in the gaseous and liquid phases control the transfer of carbon dioxide (CO2) between the atmosphere and ocean. Rain impacts the interfacial flux by (1) increasing turbulence in the ocean and (2) modulating the air–sea CO2 concentration gradient. Concurrently, raindrops inject CO2 absorbed during their fall into the ocean through wet deposition. Here this study presents a comprehensive estimate of these effects on the global ocean carbon uptake over 2008–2018 based on observational products (satellite and in situ) and reanalysis. Using various representations of the ocean surface response to rainfall and different rain products, we show that rain increases the ocean carbon sink by +0.14–0.19 PgC yr−1 over 2008–2018, representing an increase of 5–7% of the ocean carbon uptake (2.66 PgC yr−1). Rain-induced interfacial flux and wet deposition have comparable orders of magnitude. The former mainly increases the CO2 sink in the tropics because strong rain rates and weak winds induce noticeable salinity and CO2 dilution. The latter is important in the tropics, storm track regions and the Southern Ocean.
Neurospora intermedia from a traditional fermented food enables waste-to-food conversion
Authors: Maini Rekdal Vayu , Villalobos-Escobedo José Manuel , Rodriguez-Valeron Nabila , Olaizola Garcia Mikel , Prado Vásquez Diego , Rosales Alexander , Sörensen Pia M. , Baidoo Edward E. K. , Calheiros de Carvalho Ana , Riley Robert , Lipzen Anna , He Guifen , Yan Mi , Haridas Sajeet , Daum Christopher , Yoshinaga Yuko , Ng Vivian , Grigoriev Igor V. , Munk Rasmus , Wijaya Christofora Hanny , Nuraida Lilis , Damayanti Isty , Cruz-Morales Pablo , Keasling Jay. D. 
Published: August 2024

AbstractFungal fermentation of food and agricultural by-products holds promise for improving food sustainability and security. However, the molecular basis of fungal waste-to-food upcycling remains poorly understood. Here we use a multi-omics approach to characterize oncom, a fermented food traditionally produced from soymilk by-products in Java, Indonesia. Metagenomic sequencing of samples from small-scale producers in Western Java indicated that the fungus Neurospora intermedia dominates oncom. Further transcriptomic, metabolomic and phylogenomic analysis revealed that oncom-derived N. intermedia utilizes pectin and cellulose degradation during fermentation and belongs to a genetically distinct subpopulation associated with human-generated by-products. Finally, we found that N. intermedia grew on diverse by-products such as fruit and vegetable pomace and plant-based milk waste, did not encode mycotoxins, and could create foods that were positively perceived by consumers outside Indonesia. These results showcase the traditional significance and future potential of fungal fermentation for creating delicious and nutritious foods from readily available by-products.
Learning integral operators via neural integral equations
Authors: Zappala Emanuele , Fonseca Antonio Henrique de Oliveira , Caro Josue Ortega , Moberly Andrew Henry , Higley Michael James , Cardin Jessica , Dijk David van 
Published: August 2024

AbstractNonlinear operators with long-distance spatiotemporal dependencies are fundamental in modelling complex systems across sciences; yet, learning these non-local operators remains challenging in machine learning. Integral equations, which model such non-local systems, have wide-ranging applications in physics, chemistry, biology and engineering. We introduce the neural integral equation, a method for learning unknown integral operators from data using an integral equation solver. To improve scalability and model capacity, we also present the attentional neural integral equation, which replaces the integral with self-attention. Both models are grounded in the theory of second-kind integral equations, where the indeterminate appears both inside and outside the integral operator. We provide a theoretical analysis showing how self-attention can approximate integral operators under mild regularity assumptions, further deepening previously reported connections between transformers and integration, as well as deriving corresponding approximation results for integral operators. Through numerical benchmarks on synthetic and real-world data, including Lotka–Volterra, Navier–Stokes and Burgers’ equations, as well as brain dynamics and integral equations, we showcase the models’ capabilities and their ability to derive interpretable dynamics embeddings. Our experiments demonstrate that attentional neural integral equations outperform existing methods, especially for longer time intervals and higher-dimensional problems. Our work addresses a critical gap in machine learning for non-local operators and offers a powerful tool for studying unknown complex systems with long-range dependencies.
Approximation for probability of coverage in softly inhibitive cellular networks
Authors: Chen Chunlin 
Published: August 2024

AbstractThe Strauss point process is a very popular model for describing the random cellular networks, yet several key statistical properties such as intensity, empty space function, and probability generating functional have remained elusive. This article addresses these issues by first leveraging the Poisson saddle point method to approximate the distance‐conditioned intensity for Strauss point processes. Subsequently, the author derives an analytically tractable expression for the distribution of empty space distance based on a conditional thinning mechanism. Additionally, the author establishes an upper bound for the probability generating functional in Strauss point processes, which is crucial for evaluating the Laplace transform of cumulative interference in relevant cellular networks. These findings facilitate the systematic derivation of spatially averaged probability of coverage, and the accuracy of analytic results is validated through simulations.
Embedded circuit analysis and design method for reconfigurable metasurface element
Authors: Wu Bincai , Xuan Xingqi , Chen Yuqi , Wen Jun , Hu Qijun , Zheng Shilie , Pratt Ian , Hui Xiaonan , Zhang Xianmin 
Published: August 2024

AbstractImplementation of reconfigurable metasurfaces demands a complex structure. The design methodology for metasurface elements based on PIN diodes requires targeted optimisation tailored to the varying application environments and cannot be directly transplanted from one methodological approach to another. In addition, inclusion of varactors often imposes undesirable high‐power consumption with complex analog drive circuitry. Furthermore, the design method for the reconfigurable metasurface element has not been simplified and unified, hindering the rapid deployment of metasurface applications. The authors propose a general design model based on low‐power embedded circuitry for a multi‐function metasurface element and give the corresponding numerical analysis. The authors present a reconfigurable method based on the general design model and prove that the method achieves independent regulation of amplitude and phase for the reflected electromagnetic wave. Through analysis of the sensitive area at the bottom of the embedded circuit, a gradient descent algorithm is proposed to find the optimised phase points according to device sensitivity of the application scenario and suitable strategy. Finally, the authors demonstrate that the designed metasurface element possesses phase control capability and decent phase consistency obtained via far‐field scanning experiments. The proposed element design method will provide a theoretical reference for the integrated design of metasurface.
Multi‐data classification detection in smart grid under false data injection attack based on Inception network
Authors: Pan H. , Yang H. , Na C. N. , Jin J. Y. 
Published: August 2024

AbstractDuring operation, the smart grid is subject to different false data injection attacks (FDIA). If the different kinds of FDIAs and typical failures have been detected, the system operator can develop various defenses to protect the smart grid in multiple categories. Therefore, this article aims to propose a multi‐data classification detection model to differentiate the data of regular operation, faults, and FDIAs when the smart grid suffers FDIAs. Due to the unbalanced number of different kinds of samples in the dataset, Affinitive Borderlinen SMOTE is used to pre‐process the data by oversampling to improve the training accuracy. A multi‐data detection model based on the Inception network is established, and the overall structure of the network and the individual Inception modules are given. A small power system is an example of simulating a smart grid suffering from FDIAs. The designed classification detection model is simulated, validated, and compared with two‐dimensional convolutional neural networks and existing research results. The qualitative analysis of the evaluation metrics can show that the Inception network model has high accuracy and real‐time performance for detecting different data.
Gas-phase Fe/O and Fe/N abundances in star-forming regions
Authors: Méndez-Delgado J. E. , Kreckel K. , Esteban C. , García-Rojas J. , Carigi L. , Sander A. A. C. , Palla M. , Chruślińska M. , De Looze I. , Relaño M. , van der Giessen S. A. , Reyes-Rodríguez E. , Sánchez S. F. 
Published: August 2024

Context. In stars, metallicity is usually traced using Fe, while in nebulae, O serves as the preferred proxy. Both elements have different nucleosynthetic origins and are not directly comparable. Additionally, in ionized nebulae, Fe is heavily depleted onto dust grains.
Aims. We investigate the distribution of Fe gas abundances in a sample of 452 star-forming nebulae with [Fe III] λ4658 detections and their relationship with O and N abundances. Additionally, we analyze the depletion of Fe onto dust grains in photoionized environments.
Methods. We homogeneously determined the chemical abundances with direct determinations of electron temperature (Te), considering the effect of possible internal variations of this parameter. We adopted a sample of 300 Galactic stars to interpret the nebular findings.
Results. We find a moderate linear correlation (r = −0.59) between Fe/O and O/H. In turn, we report a stronger correlation (r = −0.80) between Fe/N and N/H. We interpret the tighter correlation as evidence that Fe and N are produced on similar timescales while Fe- dust depletion scales with the Fe availability. The apparently flat distribution between Fe/N and N/H in Milky Way stars supports this interpretation. We find that when 12+log(O/H)<7.6, the nebulae seem to reach a plateau value around log(Fe/O) ≈ −1.7. If this trend were confirmed, it would be consistent with a very small amount of Fe dust in these systems, similar to what is observed in high-z galaxies discovered by the James Webb Space Telescope (JWST). We derive a relationship that allows us to approximate the fraction of Fe trapped into dust in ionized nebulae. If the O-dust scales in the same way, its possible contribution in low-metallicity nebulae would be negligible. After analyzing the Fe/O abundances in J0811+4730 and J1631+4426, we do not see evidence of the presence of very massive stars with Minit > 300 M⊙ in these systems.
Conclusions. The close relation observed between the N and Fe abundances has the potential to serve as a link between stellar and nebular chemical studies. This requires an expansion of the number of abundance determinations for these elements in both stars and star-forming nebulae, especially at low metallicities.
PineTree: A generative, fast, and differentiable halo model for wide-field galaxy surveys
Authors: Ding Simon , Lavaux Guilhem , Jasche Jens 
Published: August 2024

Context. Accurate mock halo catalogues are indispensable data products for developing and validating cosmological inference pipelines. A major challenge in generating mock catalogues is modelling the halo or galaxy bias, which is the mapping from matter density to dark matter halos or observable galaxies. To this end, N-body codes produce state-of-the-art catalogues. However, generating large numbers of these N-body simulations for big volumes, especially if magnetohydrodynamics are included, requires significant computational time.
Aims. We introduce and benchmark a differentiable and physics-informed neural network that can generate mock halo catalogues of comparable quality to those obtained from full N-body codes. The model design is computationally efficient for the training procedure and the production of large mock catalogue suites.
Methods. We present a neural network, relying only on 18 to 34 trainable parameters, that produces halo catalogues from dark matter overdensity fields. The reduction in network weights was realised through incorporating symmetries motivated by first principles into our model architecture. We trained our model using dark-matter-only N-body simulations across different resolutions, redshifts, and mass bins. We validated the final mock catalogues by comparing them to N-body halo catalogues using different N-point correlation functions.
Results. Our model produces mock halo catalogues consistent with the reference simulations, showing that this novel network is a promising way to generate mock data for upcoming wide-field surveys due to its computational efficiency. Moreover, we find that the network can be trained on approximate overdensity fields to reduce the computational cost further. We also present how the trained network parameters can be interpreted to give insights into the physics of structure formation. Finally, we discuss the current limitations of our model as well as more general requirements and pitfalls of approximate halo mock generation that became evident from this study.
ChatGPT Performs Worse on USMLE-Style Ethics Questions Compared to Medical Knowledge Questions
Authors: Danehy Tessa , Hecht Jessica , Kentis Sabrina , Schechter Clyde B. , Jariwala Sunit P. 
Published: August 2024

Abstract
Objectives The main objective of this study is to evaluate the ability of the Large Language Model Chat Generative Pre-Trained Transformer (ChatGPT) to accurately answer the United States Medical Licensing Examination (USMLE) board-style medical ethics questions compared to medical knowledge-based questions. This study has the additional objectives of comparing the overall accuracy of GPT-3.5 to GPT-4 and assessing the variability of responses given by each version.
Methods Using AMBOSS, a third-party USMLE Step Exam test prep service, we selected one group of 27 medical ethics questions and a second group of 27 medical knowledge questions matched on question difficulty for medical students. We ran 30 trials asking these questions on GPT-3.5 and GPT-4 and recorded the output. A random-effects linear probability regression model evaluated accuracy and a Shannon entropy calculation evaluated response variation.
Results Both versions of ChatGPT demonstrated worse performance on medical ethics questions compared to medical knowledge questions. GPT-4 performed 18% points (p < 0.05) worse on medical ethics questions compared to medical knowledge questions and GPT-3.5 performed 7% points (p = 0.41) worse. GPT-4 outperformed GPT-3.5 by 22% points (p < 0.001) on medical ethics and 33% points (p < 0.001) on medical knowledge. GPT-4 also exhibited an overall lower Shannon entropy for medical ethics and medical knowledge questions (0.21 and 0.11, respectively) than GPT-3.5 (0.59 and 0.55, respectively) which indicates lower variability in response.
Conclusion Both versions of ChatGPT performed more poorly on medical ethics questions compared to medical knowledge questions. GPT-4 significantly outperformed GPT-3.5 on overall accuracy and exhibited a significantly lower response variability in answer choices. This underscores the need for ongoing assessment of ChatGPT versions for medical education.
Daily versus Alternate-Day Iron Supplementation for Pregnant Women with Iron Deficiency Anemia: A Randomized Controlled Trial
Authors: Lam Melissa Chu , Khandakar Binny , Heon Isaak , Hussain Farrah , Feldman Kristina , Kaplowitz Elianna , Overbey Jessica R. , Brustman Lois , Rosenn Barak 
Published: August 2024

Objective This study aimed to evaluate the most effective regimen to raise hemoglobin (Hb) by comparing alternate-day dosing of iron to daily dosing in pregnant women with iron deficiency anemia.
Study Design Women with Hb < 11.0 g/dL and ferritin ≤ 25 µg/L between 120/7 and 340/7 weeks' gestation were recruited. A total of 88 patients were randomized using block randomization with 1:1 allocation to receive either 1 tablet of 325 mg ferrous sulfate on consecutive days or 2 tablets every other day. The primary outcome, the change in Hb after 6 weeks of treatment was assessed using an analysis of covariance to adjust for baseline level. Secondary outcomes included change in ferritin, hepcidin, side effects, and compliance. Patients completed a questionnaire to assess for adverse symptoms and adherence was monitored by installing a pill reminder app on smartphones of patients.
Results A total of 88 patients were consented. The daily iron group had a greater proportion of nulliparous women (40 vs. 7%). Most patients (98%) had mild anemia (Hb: 9–10.9 g/dL) at recruitment, with a median gestational age of 28.1 weeks (interquartile range [IQR]: 25.6, 30.9) and median duration of treatment of 42 days (IQR: 35, 45). At 6 weeks, the daily iron group had a mean increase in Hb of 0.8 ± 0.9 g/dL, whereas the alternate-day iron group had a mean increase of 0.5 ± 1.0 g/dL (baseline adjusted difference of means: −0.3 [95% confidence interval: −0.7, 0.1], p = 0.15). Frequency of adverse effects attributable to iron were similar between groups. Patient self-reported compliance to treatment was also similar between groups. Among those that used the app, compliance was higher among the daily group compared with the alternate daily group (median: 95.5% [IQR: 75, 100] vs. 85% [IQR: 40, 92]), although this difference was not statistically significant (p = 0.07).
Conclusion This trial suggests that there are no significant differences between alternate-day iron supplementation and daily iron supplementation for treating iron deficiency anemia.
Key Points
Predictors of Vaginal Delivery among Patients Admitted with Severe Preeclampsia
Authors: Myers Sabrena , Pyne Abigail , Darling Alice , Al-Shibli Noor , Cate Jennifer J.M. , Grace Matthew R. , Wheeler Sarahn , Dotters-Katz Sarah K. 
Published: August 2024

Objective This study aimed to assess the rates of vaginal delivery (VD) and the predictors of VD in a cohort of patients with early (<34 weeks) preeclampsia with severe features (Early Severe PreEClampsia [ESPEC]).
Study Design We conducted a retrospective cohort study of patients with ESPEC admitted to a single center from 2013 to 2019. Exclusion criteria included patients with contraindications to labor, multifetal gestation, or presenting in spontaneous labor. Patient characteristics were abstracted. The primary outcome was rate of VD. Secondary outcome was factors associated with VD. Secondary analysis performed including only primiparous patients. Bivariate statistics and logistic regression were used to analyze data.
Results Of 229 patients with ESPEC, 184 (80%) were candidates for labor. Of those, 74 (40%) underwent prelabor cesarean delivery (CD). Among the 110 remaining patients who attempted VD, 47 (43%) were successful. No significant differences in characteristics between VD and CD patients were found on bivariate analysis. In regression models, BMI ≥ 40 was associated with increased odds of CD (adjusted odds ratio [aOR]: 2.83, 95% confidence interval [CI]: 1.01, 7.95), whereas private insurance was associated with reduced odds of CD (aOR: 0.37, 95% CI: 0.16, 0.86). In planned secondary analysis of primiparous patients, 101/123 (82%) were candidates for labor. Of those, 29 underwent prelabor CD. The VD rate among primiparous patients attempting labor was 40% (29/72). In this subgroup, private insurance was associated with VD (71 vs. 46%, p = 0.03). In regression models, only private insurance remained associated with CD (aOR: 0.30, 95% CI: 0.10, 0.92).
Conclusion Patients with ESPEC who attempted VD were successful less than half of the time, with similar rates among the subset of primiparous patients. BMI ≥ 40 was associated with increased odds of CD, whereas private insurance was associated with reduced odds of CD. These data may aid providers in counseling patients with ESPEC on the likelihood of successful VD.
Key Points