bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of Lookback Duration on the Performance of a Claims‐Based Frailty Proxy in Women With Stage I–III Breast Cancer
Authors: Duchesneau Emilie D. , Stürmer Til , Reeder‐Hayes Katherine , Kim Dae Hyun , Edwards Jessie K. , Faurot Keturah R. , Lund Jennifer L. 
Published: January 2025

ABSTRACTBackgroundFrailty is an important prognostic indicator in older women with breast cancer. The Faurot frailty index, a validated claims‐based frailty proxy measure, uses healthcare billing codes during a user‐specified ascertainment window to predict frailty. We assessed how the duration of frailty ascertainment affected the ability of the Faurot frailty index to predict one‐year mortality in women with stage I–II breast cancer.MethodsWe included 128 857 women (66+ years) with stage I–III breast cancer in the SEER‐Medicare database (2003–2019). The Faurot frailty index was calculated using 3‐, 6‐, 8‐, and 12‐month ascertainment windows prior to diagnosis or using all‐available lookback. Associations between the Faurot frailty index using each window and one‐year all‐cause mortality were estimated using Kaplan–Meier curves. Discrimination of one‐year mortality risk was assessed using C‐statistics.ResultsFive percent of women died during the year following diagnosis. Higher Faurot scores were associated with increased mortality risk for all frailty ascertainment windows. Differences in one‐year mortality risk for women with high vs. low Faurot frailty scores were reduced when using all‐available lookback (16% vs. 2%, difference = 15%, 95% CI 0.14–0.15) compared to shorter windows (e.g., 8 months: 25% vs. 2%, difference = 23%, 95% CI 0.22–0.24). C‐statistics ranged from 0.758 (all‐available lookback) to 0.770 (12 months) and were robust in subgroups defined by age, race, ethnicity, region, stage, and cancer subtype.ConclusionsThe Faurot frailty index performed well across 3‐ to 12‐month frailty ascertainment windows in women with breast cancer. Researchers should employ this index to address confounding by frailty in studies of cancer populations.
Generative Design of Thermoset Shape Memory Polymers Driven by Chemical Group: A Conditional Variational Autoencoder Approach
Authors: Das Borun , Peters Andrew , Li Guoqiang , Hei Xiali 
Published: January 2025

ABSTRACTThe discovery of novel thermoset shape memory polymers (TSMPs) for additive manufacturing can be accelerated through the use of a deep‐generative algorithm, minimizing the need for laborious traditional laboratory experiments. This study is the first to introduce an innovative approach that uses a deep generative learning model, namely the conditional variational autoencoder (CVAE), to discover novel TSMPs with lower glass transition temperature () and high recovery stress values (). In this study, specific chemical groups, such as epoxy, amine, thiol, and vinyl, are integrated as constraints to generate novel TSMPs while preserving the essential reaction properties. To address the challenges posed by a small dataset, the CVAE model is used with graph‐extracted features. Unlike previous studies focused on single‐polymer systems, this research extends to two‐monomer samples, discovering 22 novel TSMPs. This approach has practical implications in additive manufacturing, biomedical devices, aerospace, and robotics for the discovery of novel samples from limited data.
Evidence for Bxy‐npr‐21 on controlling juveniles’ growth and modulating male sexual arousal: from molecules to behaviors
Authors: Shao Hudie , Liu Wenyi , Hong Huan , Guo Kai , Chen Jing , Li Quan , Su Miao , Huang Xiaofang , Hu Jiafu 
Published: January 2025

AbstractBACKGROUNDBursaphelenchus xylophilus is considered a quarantine plant nematode species, that causes major damage to pine ecosystems globally. However, there are few reports on the identification and function of the sex pheromone receptors involved in mating. The function of Bxy‐npr‐21 as a potential sex pheromone receptor gene was verified from molecules to behaviors in this study.RESULTSHere, we firstly report that Bxy‐npr‐21 is a receptor gene involved in sexual attraction. The bioinformatic analysis indicated that the Bxy‐npr‐21 gene in B. xylophilus encodes a GPCR. The expression characterization for Bxy‐npr‐21 showed that it is widely expressed in whole body of larvae and sex organs of adults. The RNAi results suggested that the Bxy‐npr‐21 gene was involved mainly in movement, feeding, and mating. Sexual arousal experiments further validated that the Bxy‐npr‐21 gene was involved in the activation of males by female chemical signaling.CONCLUSIONSOur results strongly suggest that the Bxy‐npr‐21 gene is a key gene that regulates nematode growth, development and reproduction. The results of this study lay the foundation for revealing the molecular mechanisms of growth and reproduction of B. xylophilus. It can also provide an important basis for further control of B. xylophilus. © 2025 Society of Chemical Industry.
2D MXenes: Synthesis, Properties, and Applications in Silicon‐Based Optoelectronic Devices
Authors: Li Wei , Xu Zhiyuan , Yan Yu , Gao Qianfeng , Song Yaya , Wang Taiqiang , Dun Hongyu , Yang Mingyu , Huang Qian , Zhang Xiaodan , Zhao Ying , Hou Guofu 
Published: January 2025

AbstractMXenes, a rapidly emerging class of 2D transition metal carbides, nitrides, and carbonitrides, have attracted significant attention for their outstanding properties, including high electrical conductivity, tunable work function, and solution processability. These characteristics have made MXenes highly versatile and widely adopted in the next generation of optoelectronic devices, such as perovskite and organic solar cells. However, their integration into silicon‐based optoelectronic devices remains relatively underexplored, despite silicon's dominance in the semiconductor industry. In this review, a timely summary of the recent progress in utilizing Ti‐based MXenes, particularly Ti3C2Tx, in silicon‐based optoelectronic devices is provided. The composition, synthesis methods, and key properties of MXenes that contribute to their potential for enhanced device performance are focused on. Furthermore, the latest advancements in MXene applications in silicon‐based solar cells and photodetectors are discussed from fundamental and applied perspectives. Finally, the key challenges and future opportunities for the integration of MXenes in silicon‐based optoelectronic devices are outlined.
Pre‐Lithiated Silicon‐Based Composite Anode for High‐Performance All‐Solid‐State Batteries
Authors: Li Cheng , Wu Yuqi , Ren Fucheng , Liu Jun , Lin Ying , Xia Meng , Wang Huanran , Meng Chunyan , Wang Zhenyu , Gong Zhengliang , Yang Yong 
Published: January 2025

AbstractSilicon is widely recognized as a promising anode material for all‐solid‐state batteries (ASSBs) due to exceptional specific capacity, abundant availability, and environmental sustainability. However, the considerable volume expansion and particle fragmentation of Si during cycling lead to significant performance degradation, limiting its practical application. Herein, the development of a pre‐lithiated Si‐based composite anode (c‐Li1Si) is presented, designed to address the key challenges faced by Si‐based anodes, namely severe volume changes and low electrochemical stability. The c‐Li1Si anodes are prepared by incorporating Li₁Si powders with Li6PS5Cl (LPSCl) sulfide solid electrolyte (SSE), forming a dense structure that enhances conductivity and mitigates structural degradation. The ASSBs with c‐Li1Si‐60 anode exhibit outstanding electrochemical performance, including excellent rate capability and capacity retention of 84.4% after 1000 cycles at 1 C and exceptional performance even at low anode‐to‐cathode capacity ratios (N/P ratio) of 1.68. EIS and pressure measurements reveal improved reaction kinetics and reduced volume expansion. X‐ray micro‐CT and SEM further confirmed the introduction of LPSCl effectively alleviated volume changes and maintained electrode structural integrity, contributing to enhanced electrochemical performance. These results underscore the potential of the c‐Li1Si anode to overcome the intrinsic limitations of Si‐based anodes, offering a promising pathway toward high‐energy‐density ASSBs.
Non‐Stoichiometric Amorphous Calcium Carbonate Forms in Macromolecular Condensates via Interphase Diffusion
Authors: Paul Debojit , Varsano Neta , Biswas Protap , Kaplan‐Ashiri Ifat , Aram Lior , Gal Assaf 
Published: January 2025

AbstractTransient amorphous phases are known as functional precursors in the formation of crystalline materials, both in vivo and in vitro. A common route to regulate amorphous calcium carbonate (ACC) crystallization is via direct interactions with negatively charged macromolecules. However, a less explored phenomenon that can influence such systems is the electrostatically driven formation of Ca‐macromolecule dense phases. In this study, it is shown how Ca‐macromolecule condensates that form via liquid–liquid phase separation (LLPS) can be used to control the formation of metastable ACC via diffusion‐based mass transport. Contrary to the solid‐like ACC particles that form in the dilute phase via rapid nucleation and growth, the condensate ACC gradually forms via carbonate diffusion into the dense droplets. This yields transient phases with non‐stoichiometric compositions, similar to a solid solution. It is shown that the ability to control the concentration gradients across the phase boundary can be used to finely regulate the composition and stability of these amorphous precursors, offering new routes to control mineralization through transient phases.
Atomically Dispersed Ni‐N‐C Catalysts for Electrochemical CO2 Reduction
Authors: Weiss John C. , He Yanghua , Cullen David A. , Benavidez Angelica , Jernigen Jeremy D. , Zhang Hanguang , Osmieri Luigi , Zelenay Piotr 
Published: January 2025

AbstractThe atomic dispersion of nickel in Ni‐N‐C catalysts is key for the selective generation of carbon monoxide through the electrochemical carbon dioxide reduction reaction (CO2RR). Herein, the study reports a highly selective, atomically dispersed Ni1.0%‐N‐C catalyst with reduced Ni loading compared to previous reports. Extensive materials characterization fails to detect Ni crystalline phases, reveals the highest concentration of atomically dispersed Ni metal, and confirms the presence of the proposed Ni‐Nx active site at this reduced loading. The catalyst shows excellent activity and selectivity toward CO generation, with a faradaic efficiency for CO generation (FECO) of 97% and partial current density for CO (jco) of ‐9.0 mA cm−2 at ‐0.9 V in an electrochemical H‐type cell. CO2RR activity and selectivity are also studied by rotating disk electrode (RDE) measurements where transport limitations can be suppressed. It is expected that the utility of these Ni‐N‐C catalysts will lie with tandem CO2RR reaction schemes to multi‐carbon (C2+) products.
Bovine tuberculosis (bTB) eradication, brushtail possum control, and causal inference
Authors: Hone Jim 
Published: January 2025

AbstractBovine tuberculosis (bTB) in livestock (cattle and farmed deer) is the focus of an in‐progress eradication program in New Zealand. The program includes intensive brushtail possum population control as this species is a bTB host and could compromise eradication at a national level. Ten causal criteria (strength of association, experiment, temporality, plausible mechanism, biological gradient, consistency, coherence, specificity, analogy, validated causal predictions) were used to evaluate annual changes in bTB status in livestock herds relative to costs of possum population control and livestock bTB disease control. Hypotheses were evaluated using a model selection framework of the causal relationship between efforts (costs) and outcomes (bTB‐infected herds) following management. The 10 causal criteria were demonstrated empirically. Experiments used designs from the simplest pretest‐posttest comparison to a before‐after‐control‐impact design, though lacked random allocation of treatments to experimental units and some studies lacked statistical analyses. As hypothesised, the annual changes in bTB‐infected herds were positively related to annual costs. The predicted damped oscillations across years in annual changes in bTB‐infected herds were observed. The implications are that eradication of livestock bTB appears likely, though there is a risk that eradication may not be achieved by 2026, unless annual control expenditure is higher and/or more efficient in the future.
Establishment of a 3D-Printed Tissue-on-a-Chip Model for Live Imaging of Bacterial Infections
Authors: Fuglsang-Madsen Albert , Haagensen Janus Anders Juul , De Rudder Charlotte , Simões Filipa Bica , Molin Søren , Johansen Helle Krogh 
Published: January 2025

Abstract
Despite advances in healthcare, bacterial pathogens remain a severe global health threat, exacerbated by rising antibiotic resistance. Lower respiratory tract infections, with their high death toll, are of particular concern. Accurately replicating host-pathogen interactions in laboratory models is crucial for understanding these diseases and evaluating new therapies. In this communication, we briefly present existing in vivo models for cystic fibrosis and their limitations in replicating human respiratory infections. We then present a novel, 3D-printed, cytocompatible microfluidic lung-on-a-chip device, designed to simulate the human lung environment, and with possible use in recapitulating general infectious diseases.
Our device enables the colonisation of fully differentiated lung epithelia at an air-liquid interface with Pseudomonas aeruginosa, a key pathogen in many severe infections. By incorporating dynamic flow, we replicate the clearance of bacterial toxins and planktonic cells, simulating both acute and chronic infections. This platform supports real-time monitoring of therapeutic interventions, mimics repeated drug administrations as in clinical settings, and facilitates the analysis of colony-forming units and cytokine secretion over time. Our findings indicate that this lung-on-a-chip device has significant potential for advancing infectious disease research, in optimizing treatment strategies against infections and in developing novel treatments.
Machine Learning Promoting Sustainable Customer Behavior and Product Pricing
Authors: Pourmahdi Katayoon , Nyström Anna-Greta , Majd Amin 
Published: January 2025

Abstract
The emergence of sustainability concerns has significantly influenced company strategies. The growing importance of sustainability is particularly affecting marketing, especially product-line pricing strategies. Companies are being coerced into integrating sustainability goals and eco-friendly practices into their current operations; practices which also entail pricing strategies. Proposing optimal pricing strategies according to customer-perceived prices, to maximize sales optimization, has thus gained increased attention from both practitioners and scholars. This chapter explores sustainable consumer behavior and aims to investigate how organizations influence sustainable consumption through a pricing strategy that relies on descriptive and predictive analytics using machine learning methods. More precisely, the focus of this chapter is to provide a comprehensive overview of factors affecting sustainable consumer behavior and the role of price by using descriptive analytic methods (data summarization and visualization) and predictive analytic methods (logistic regression) as tools. Our empirical context features a Nordic retail conglomerate and covers data on sustainable consumer behavior from 2019 to 2024, to which descriptive and predictive analytics using machine learning methods are applied. We conclude the chapter by discussing how retailers can analyze and predict their customers’ purchasing behavior regarding choosing sustainable products over substituting ones.
Exploiting Machine Learning to Test Service Supply Scenarios: A Rescue Department Case
Authors: Immonen Mika , Huuskonen Heidi , Koivuniemi Jouni , Hallikas Jukka 
Published: January 2025

Abstract
This chapter assesses the use of machine learning (ML) to estimate service process performance and outlines questions concerning the modeling process. In particular, this study focuses on experimentation with service networks using trained artificial neural networks (ANNs). The theory is based on proactive supply chain risk management studies, the core of which is the utilization of big data, ML, and predictive analytics concerning supply networks. This article presents the case of a rescue service assessment in residential areas, where societal changes and healthcare reforms drive the fire station network. Here, the managerial discussion connects to healthcare politics and safety risk management considering challenges emerging from decentralized home care plans. The empirical study contains a description of the datasets and process for ANN-based modeling and presents the empirical results. The data include emergency register data that are enriched with geospatial variables and are utilized to train the ANN. The trained model is then exploited in a scenario experiment process for response time estimates of rescue services in a specific region. The analysis revealed statistically significant differences between the scenarios at both the general level and the local means of response times. In conclusion, the applied analysis protocol has the potential to be expanded for use as an assessment tool to test the supply networks of other time-critical services.
Sterols in fresh and preserved button mushroom (Agaricus bisporus) products
Authors: Sommer Katrin , Vetter Walter 
Published: January 2025

Abstract
Mushrooms are highly valued but also very perishable foods. As a consequence, various types of preserved mushroom products are available in retail stores. Common preservation techniques include pickling in brine and drying. Such processes might cause a partial transformation of unstable products like ergosterol, which is the most relevant sterol in mushrooms. In this study, gas chromatography with mass spectrometry (GC/MS) was used to examine the levels of ergosterol and other, lower abundant sterols in dried and pickled button mushrooms (Agaricus bisporus) from German retail. Multivariate analysis of the free sterol fraction was used to verify significantly lower ergosterol contents in the preserved products (pickled: 280 ± 30 mg/100 g dry weight, dried: 190 ± 20 mg/100 g dry weight) compared to fresh button mushrooms (610 ± 20 mg/100 g dry weight). In addition, the contribution of ergosterol to the total sterol content in dried button mushroom samples (84 ± 1%) was comparable to fresh samples (87 ± 1%) but significantly lower in the pickled products (78 ± 1%). Storage experiments with pure ergosterol standards in solution indicated a full degradation of ergosterol and the formation of seven transformation products at room temperature storage within 9 days.
First complete genome sequence of tulip mild mottle mosaic virus (Ophiovirus tulipae)
Authors: Neriya Yutaro , Hamamoto Kakeru , Kobayashi Tominari , Nakase Shunsuke , Kurosawa Rena , Suzuki Tomohiro , Nishigawa Hisashi , Morikawa Toshiyuki , Natsuaki Tomohide 
Published: January 2025

Abstract
Tulip mild mottle mosaic disease, caused by tulip mild mottle mosaic virus (TMMMV, species Ophiovirus tulipae), was first reported in Japan in 1979. TMMMV has a negative-sense ssRNA genome and is closely related to ophioviruses such as Mirafiori lettuce big vein virus (MLBVV, Ophiovirus mirafioriense). However, its complete nucleotide sequence has not yet been reported. Therefore, in this study, we determined the complete nucleotide sequences of all four TMMMV segments. The 5′-terminal tetranucleotide sequences were highly conserved, and the five bases at the 5′ end were identical in all segments. Nucleotide sequence comparisons of the four segments showed the highest sequence identity to those of MLBVV (70–76% for the genomic RNA sequences and 81–87% for the amino acid sequences of each ORF, except for ORF2). The amino acid sequences of the coat proteins of TMMMV and MLBVV were found to be 83.1% identical, which is below the 85% threshold for assignment to the same species, indicating that these viruses belong to different species in the same genus.
Applications of artificial intelligence in orthodontics: a bibliometric and visual analysis
Authors: Polizzi Alessandro , Boato Mattia , Serra Sara , D’Antò Vincenzo , Leonardi Rosalia 
Published: January 2025

Abstract

Objectives
To conduct a comprehensive bibliometric analysis of the literature on artificial intelligence (AI) applications in orthodontics to provide a detailed overview of the current research trends, influential works, and future directions.

Materials and methods
A research strategy in The Web of Science Core Collection has been conducted to identify original articles regarding the use of AI in orthodontics. Articles were screened and selected by two independent reviewers and the following data were imported and processed for analysis: rankings, centrality metrics, publication trends, co-occurrence and clustering of keywords, journals, articles, authors, nations, and organizations. Data were analyzed using CiteSpace 6.3.R2 and VOSviewer.

Results
Almost 83% of the 381 chosen articles were released in the last three and a half years. Studies were published either in highly impacted orthodontic journals and also in journals related to informatics engineering, computer science, and medical imaging. Two-thirds of the available literature originated from China, the USA, and South Korea. AI-driven cephalometric landmarking and automatic segmentation were the main areas of research.

Conclusions
This report offers a thorough overview of the AI current trend in orthodontics and it highlights prominent research areas focused on increasing the speed and efficiency of orthodontic care. Furthermore, it offers insight into potential directions for future research.

Clinical relevance
Collaborative research efforts will be necessary to strengthen the maturity and robustness of AI models and to make AI-based clinical research sufficiently reliable for routine orthodontic clinical practice.
Particulate behaviour of soft granular materials: a case study on lentils
Authors: Singh Saurabh , Baudet Beatrice Anne , Coop Matthew Richard 
Published: January 2025

Abstract
In this article, lentils are used as a case study to characterise the particulate behaviour of soft granular materials. Experiments were carried out on a single lentil particle or a pair of lentils. The single particle or a pair of particles in contact were compressed vertically to crushing or to a fixed vertical load. Then, in inter-particle tests, pairs of particles were slid over each other at a constant vertical load, and the tangential stiffness and coefficient of friction were estimated. The pairs of lentil particles in contact were also subjected to repeated normal and tangential loading. The presence or absence of the cover of lentil particles (shell) was found to affect their behaviour significantly under these loading conditions. The lentil particles have a very compliant shell and stiff core in normal loading; the stiffness of the shell is constant, and the core follows Hertz contact law. The lentil particles show less variability in their crushing strength, with high Weibull modulus (~ 10), in comparison to other natural granular materials like sand. With repeated cycles of vertical loading, the contact between a lentil pairs of particles becomes more stiff and less damp. In tangential loading, the coefficient of friction between lentil particles decreases with normal force while the contact stiffness increases. Further, in cyclic tangential loading, the coefficient of friction decreases and the contact stiffness increases with cycles. A simple contact model is also proposed to use in discrete element simulations.

Graphical abstract