bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hexagonal Prismatic Siloxanes Functionalized with Organosilyl Groups as Building Blocks of Nanoporous Materials
Authors: Hayashi Taiki , Kikuchi Miharu , Murase Nanako , Matsuno Takamichi , Sugimura Natsuhiko , Kuroda Kazuyuki , Shimojima Atsushi 
Published: January 2024

AbstractUtilization of well‐defined siloxane molecules allows for the construction of functional siloxane‐based nanoporous materials based on the molecular design. Herein, a novel class of siloxane‐based porous materials is synthesized via cross‐linking of dimethylsilyl‐ and dimethylvinylsilyl‐functionalized cage siloxanes with double‐6‐ring (D6R) geometry. Compared with the conventional double‐4‐ring cage siloxane, this study highlights the characteristics of D6R siloxanes as building blocks, demonstrating their high surface area and chemical stability. Furthermore, density functional theory calculations show their unique cation encapsulation ability.
A new control method of hybrid energy storage system for DC microgrid application
Authors: Taye Biks Alebachew , Choudhury Nalin Behari Dev 
Published: January 2024

AbstractEnergy storage system play a crucial role in safeguarding the reliability and steady voltage supply within microgrids. While batteries are the prevalent choice for energy storage in such applications, their limitation in handling high‐frequency discharging and charging necessitates the incorporation of high‐energy density and high‐power density storage devices to enhance power quality, maintain power balance, and reduce fluctuations. In this study, we introduce a hybrid energy storage system (HESS) solution, combining a battery and a supercapacitor, to address intermittent power supply challenges. The effective management of this HESS is pivotal for constant DC voltage and sustaining microgrid stability. To achieve this, we propose an innovative control strategy called the Adaptive Filter‐Based Method (AFBM) for DC microgrid operation, which prioritizes stable and smooth performance while addressing safety and degradation concerns related to the storage devices. Our control strategy determines the distribution of charging and discharging currents for each storage device within the DC microgrid based on their state of charge. We conducted simulations in MATLAB/Simulink, comparing the proposed AFBM method with the conventional filter‐based approach using identical configurations, parameters, and operational modes. The results demonstrate the superior performance of our proposed system, showcasing improved voltage stability, more equitable load distribution, and efficient charging and discharging of storage devices compared to the traditional method. This research contributes to the advancement of microgrid energy storage solutions, enhancing their reliability and performance.
What affects how Chinese patients wish to be involved in health care delivery? Perspectives of the Chinese policy and institutional contexts
Authors: Zhu Jingyan , Sui Ping 
Published: January 2024

AbstractIntroductionThe benefits of involving patients and carers include improving health outcomes and safety, reducing costs and enhancing an open, accountable and equitable relationship between service providers and users. However, the willingness for involvement and participation is largely affected by a range of factors, including those at the micro, meso and macro levels. As the previous studies have given much attention to the factors at the individual level, it is worthwhile to explore the factors at the broader levels of the policy and institutional contexts. The objective of the research is to understand how the policy and institutional contexts in China affect how patients wish to be involved in their health care delivery.MethodsAn abductive research strategy was used to generate emergent hypotheses in the first stage of data construction. In the second stage of fieldwork, hypotheses were tested using the deductive approach. Three local hospitals in Shandong Province, China, were selected. The case study method was designed with qualitative methods of policy documents and interviews. Interviews included health professionals, health board managers, local administrators and service users. Thematic analysis and framework analysis were conducted.ResultsFour policy and institutional contexts were identified: insufficient policy support, the current institutional design of involving users, the heterogeneity of organisational autonomy and resources, as well as the demography of the population of service users.ConclusionsAs a policy strategy at the state level, there is a lack of policy support for patient involvement and participation in the Chinese health system. The heterogeneity of the institutional context of health facilities plays a key role in affecting how patients wish to be involved in health care delivery.
Linc‐ROR inhibits NK cell‐killing activity by promoting RXRA ubiquitination and reducing MICB expression in gastric cancer patients
Authors: Niu Qingbin , Li Zongrui , Jiang He , Hu Baoguang 
Published: January 2024

AbstractLinc‐ROR plays an important role in gastric cancer (GC) development and progression. This study sought to determine how the aberrant expression of Linc‐ROR impacts GC progression and immune evasion, and to identify new targets for GC therapy. GC cells overexpressing Linc‐ROR and GSAGS cells were cocultured with NK‐92 cells, respectively, and Linc‐ROR expression was determined using reverse transcription polymerase chain reaction. Linc‐ROR overexpression experiments were used to measure the expression of MICB, a tumor protein that is recognized by natural killer (NK) cells. Bioinformatics analysis identified retinoid X receptor α (RXRA) and YY1 as MICB‐specific transcription factors. Cotransfection and ubiquitinated drug experiments found that Linc‐ROR promoted the ubiquitination and degradation of RXRA. Linc‐ROR was upregulated in GC tissue and high expression was associated with tumor escape from NK‐92 cell‐mediated immunity. Linc‐ROR overexpression inhibited the expression of MICB on the cell surface by degrading RXRA. These findings indicate that Linc‐ROR promotes the binding of RXRA and E3 ligase UBE4B, reducing RXRA and MICB expression, and limiting NK cell‐killing activity. Linc‐ROR is a critical long noncoding RNA with a tumor‐promoting function in GC and thus may serve as a potential therapeutic target.
Dental student application of artificial intelligence technology in detecting proximal caries lesions
Authors: Ayan Enes , Bayraktar Yusuf , Çelik Çiğdem , Ayhan Baturalp 
Published: January 2024

AbstractObjectivesThis study aimed to investigate the caries diagnosis performances of dental students after training with an artificial intelligence (AI) application utilizing deep learning techniques, a type of artificial neural network.MethodsA total of 1200 bitewing radiographs were obtained from the institution's database and two specialist dentists labeled the caries lesions in the images. Randomly selected 1000 images were used for training purposes and the remaining 200 radiographs were used to evaluate the caries diagnostic performance of the AI. Then, a convolutional neural network, a deep learning algorithm commonly employed to analyze visual imagery problems, called “You Only Look Once,” was modified and trained to detect enamel and dentin caries lesions in the radiographs. Forty dental students were selected voluntarily and randomly divided into two groups. The pre‐test results of dental caries diagnosis performances of both groups were recorded. After 1 week, group 2 students were trained using an AI application. Then, the post‐test results of both groups were recorded. The labeling duration of the students was also measured and analyzed.ResultsWhen both groups’ pre‐test and post‐test results were evaluated, a statistically significant improvement was found for all parameters examined except precision score (p < 0.05). However, the trained group's accuracy, sensitivity, specificity, and F1 scores were significantly higher than the non‐trained group in terms of post‐test scores (p < 0.05). In group 2 (trained group), the post‐test labeling time was considerably increased (p < 0.05).ConclusionsThe students trained by AI showed promising results in detecting caries lesions. The use of AI can also contribute to the clinical education of dental students.
Altered Iron and Microstructure in Huntington's Disease Subcortical Nuclei: Insight From 7T MRI
Authors: Yao Jingwen , Morrison Melanie A. , Jakary Angela , Avadiappan Sivakami , Rowley Paul , Glueck Julia , Driscoll Theresa , Geschwind Michael D. , Nelson Alexandra B. , Possin Kathrine L. , Xu Duan , Hess Christopher P. , Lupo Janine M. 
Published: January 2024

BackgroundPathophysiological changes of Huntington's disease (HD) can precede symptom onset by decades. Robust imaging biomarkers are needed to monitor HD progression, especially before the clinical onset.PurposeTo investigate iron dysregulation and microstructure alterations in subcortical regions as HD imaging biomarkers, and to associate such alterations with motor and cognitive impairments.Study TypeProspective.PopulationFourteen individuals with premanifest HD (38.0 ± 11.0 years, 9 females; far‐from‐onset N = 6, near‐onset N = 8), 21 manifest HD patients (49.1 ± 12.1 years, 11 females), and 33 age‐matched healthy controls (43.9 ± 12.2 years, 17 females).Field Strength/Sequence7 T, T1‐weighted imaging, quantitative susceptibility mapping, and diffusion tensor imaging.AssessmentVolume, susceptibility, fractional anisotropy (FA), and mean diffusivity (MD) within subcortical brain structures were compared across groups, used to establish HD classification models, and correlated to clinical measures and cognitive assessments.Statistical TestsGeneralized linear model, multivariate logistic regression, receiver operating characteristics with the area under the curve (AUC), and likelihood ratio test comparing a volumetric model to one that also includes susceptibility and diffusion metrics, Wilcoxon paired signed‐rank test, and Pearson's correlation. A P‐value <0.05 after Benjamini–Hochberg correction was considered statistically significant.ResultsSignificantly higher striatal susceptibility and FA were found in premanifest and manifest HD preceding atrophy, even in far‐from‐onset premanifest HD compared to controls (putamen susceptibility: 0.027 ± 0.022 vs. 0.018 ± 0.013 ppm; FA: 0.358 ± 0.048 vs. 0.313 ± 0.039). The model with additional susceptibility, FA, and MD features showed higher AUC compared to volume features alone when differentiating premanifest HD from HC (0.83 vs. 0.66), and manifest from premanifest HD (0.94 vs. 0.83). Higher striatal susceptibility significantly correlated with cognitive deterioration in HD (executive function: r = −0.600; socioemotional function: r = −0.486).Data Conclusion7 T MRI revealed iron dysregulation and microstructure alterations with HD progression, which could precede volume loss, provide added value to HD differentiation, and might be associated with cognitive changes.Evidence Level2Technical EfficacyStage 2
Comparison of Myocardial Blood Flow Quantification Models for Double ECG Gating Arterial Spin Labeling MRI: Reproducibility Assessment
Authors: Aramendía‐Vidaurreta Verónica , Solís‐Barquero Sergio M. , Vidorreta Marta , Ezponda Ana , Echeverria‐Chasco Rebeca , Bastarrika Gorka , Fernández‐Seara María A. 
Published: January 2024

BackgroundArterial spin labeling (ASL) allows non‐invasive quantification of myocardial blood flow (MBF). Double‐ECG gating (DG) ASL is more robust to heart rate variability than single‐ECG gating (SG), but its reproducibility requires further investigation. Moreover, the existence of multiple quantification models hinders its application. Frequency‐offset‐corrected‐inversion (FOCI) pulses provide sharper edge profiles than hyperbolic‐secant (HS), which could benefit myocardial ASL.PurposeTo assess the performance of MBF quantification models for DG compared to SG ASL, to evaluate their reproducibility and to compare the effects of HS and FOCI pulses.Study TypeProspective.SubjectsSixteen subjects (27 ± 8 years).Field Strength/Sequence1.5 T/DG and SG flow‐sensitive alternating inversion recovery ASL.AssessmentThree models for DG MBF quantification were compared using Monte Carlo simulations and in vivo experiments. Two models used a fitting approach (one using only a single label and control image pair per fit, the other using all available image pairs), while the third model used a T1 correction approach. Slice profile simulations were conducted for HS and FOCI pulses with varying B0 and B1. Temporal signal‐to‐noise ratio (tSNR) was computed for different acquisition/quantification strategies and inversion pulses. The number of images that minimized MBF error was investigated in the model with highest tSNR. Intra and intersession reproducibility were assessed in 10 subjects.Statistical TestsWithin‐subject coefficient of variation, analysis of variance. P‐value <0.05 was considered significant.ResultsMBF was not different across acquisition/quantification strategies (P = 0.27) nor pulses (P = 0.9). DG MBF quantification models exhibited significantly higher tSNR and superior reproducibility, particularly for the fitting model using multiple images (tSNR was 3.46 ± 2.18 in vivo and 3.32 ± 1.16 in simulations, respectively; wsCV = 16%). Reducing the number of ASL pairs to 13/15 did not increase MBF error (minimum = 0.22 mL/g/min).Data ConclusionReproducibility of MBF was better for DG than SG acquisitions, especially when employing a fitting model.Level of Evidence2Technical EfficacyStage 1
Special cases of the minimum spanning tree problem under explorable edge and vertex uncertainty
Authors: Mathwieser Corinna , Çela Eranda 
Published: January 2024

AbstractThis article studies the Minimum Spanning Tree Problem under Explorable Uncertainty as well as a related vertex uncertainty version of the problem. We particularly consider special instance types, including cactus graphs, for which we provide randomized algorithms. We introduce the problem of finding a minimum weight spanning star under uncertainty for which we show that no algorithm can achieve constant competitive ratio.
Predictors of rewarded caregiving for family caregivers of person with dementia: A longitudinal study
Authors: Liu Hsin‐Yun , Kuo Li‐Min , Shyu Yea‐Ing L. , Hsu Wen‐Chuin 
Published: January 2024

AbstractThe purpose of this study was to explore variables associated with rewarded caregiving for family caregivers of persons living with dementia over a 2‐year follow‐up. This correlational longitudinal study was comprised of 200 family caregivers of persons living with dementia from neurological clinics of a medical center was conducted. Dichotomous scoring of the Rewards of Caregiving Scale of the Family Caregiving Inventory resulted in 61 (30.5%) caregivers being assigned to the well‐rewarded group and 139 (69.5%) to the poorly reward group at baseline. Variables included characteristics of family caregivers and their care receivers and assessments with validated scales of caregivers' social support and dyadic relational variables of mutuality, preparedness, and balance. Analysis at baseline showed significant predictors of well‐rewarded family caregivers were being an adult child of the care receiver, having a high perceived level mutuality and having a high perceived level of preparedness. These three variables remained as significant predictors at the 1‐ and 2‐year follow‐up. Caregivers with high levels of perceived mutuality and preparedness at 2‐years were seven times more likely to perceive themselves as well‐rewarded. Based on the findings, developing clinical interventions that focus on promoting mutuality and preparedness for family caregivers of persons with dementia could allow caregivers to perceive their role as rewarding. Attention should also be paid to the relationship between the caregiver and care receiver to enhance the family caregiver's feelings of mutuality and preparedness.
Factors influencing older women's decision‐making related to treatment of operable breast cancer: A qualitative systematic review
Authors: Malcolm Francesca Ligori , Howard Penny , Klukowska Anita Maria , Minhas Nikita , Parks Ruth Mary , Cheung Kwok Leung 
Published: January 2024

AbstractObjectiveThere is variation in practice in the treatment of older women with breast cancer. International guidelines highlight the importance of patient autonomy in treatment decision‐making. The aim of this study is to identify factors which influence decision‐making in older women with operable breast cancer, which will enable us to further understand how to support these patients.MethodsSystematic review in accordance with the PRISMA guidelines was performed to identify factors which influence treatment decision‐making in older women with operable breast cancer. Medline, Web of Science and SCOPUS were searched.ResultsThe search yielded 5840 results; 13 articles met the inclusion criteria and reported on a total of 1118 women. Thematic analysis identified three key themes in which decision‐making factors could be categorised. These were healthcare‐related factors, patient‐related factors and impact of treatment. Healthcare‐related factors included communication with clinicians and provision of information. Patient‐related factors were age, pre‐existing knowledge, preconceptions of breast cancer and treatment, decision‐making style and co‐morbidities. The impact of treatment considerations included body image and effect on quality of life. Decision‐making style was frequently reported; older women did not demonstrate one preferred style.ConclusionsThe findings have highlighted the complex interplay of factors which influence how older women make breast cancer treatment‐decisions. Clinicians should have an awareness of the factors highlighted to maximise their ability to provide support and personalised care to older women with breast cancer whilst treatment decisions are made.
Recent Advances in Interface Engineering for Enhanced Open‐Circuit Voltage Regulation in Perovskite Solar Cells
Authors: Zhang Siqi , Ren Fumeng , Sun Zhenxing , Liu Xiaoxuan , Tan Zhengtian , Liu Wenguang , Chen Rui , Liu Zonghao , Chen Wei 
Published: January 2024

AbstractIn recent years, perovskite solar cells (PSCs) have attracted significant attention due to their excellent photoelectric properties. However, several key performance parameters of these devices still fall short of their theoretical limits. Among these parameters, the regulation of open‐circuit voltage (VOC) has been a focal point of intensive research efforts, playing a pivotal role in advancing the efficiency of PSCs. This review first provides an overview of the generation and loss mechanism of VOC. It then discusses the significance of interface engineering in VOC regulation. Recent developments in high‐efficiency PSCs realized via interface engineering have been summarized and categorized into three key areas: surface modification, interface structure optimization, and surface dimensional engineering. Finally, a comprehensive summary of past research in this domain and offered insights into the future prospects of enhancing VOC in PSCs is provided.
Markedly Enhanced Analysis of Mass Spectrometry Images Using Weakly Supervised Machine Learning
Authors: Gardner Wil , Winkler David A. , Bamford Sarah E. , Muir Benjamin W. , Pigram Paul J. 
Published: January 2024

AbstractSupervised and unsupervised machine learning algorithms are routinely applied to time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) imaging data and, more broadly, to mass spectrometry imaging (MSI). These algorithms have accelerated large‐scale, single‐pixel analysis, classification, and regression. However, there is relatively little research on methods suited for so‐called weakly supervised problems, where ground‐truth class labels exist at the image level, but not at the individual pixel level. Unsupervised learning methods are usually applied to these problems. However, these methods cannot make use of available labels. Here a novel method specifically designed for weakly supervised MSI data is presented. A dual‐stream multiple instance learning (MIL) approach is adapted from computational pathology that reveals the spatial‐spectral characteristics distinguishing different classes of MSI images. The method uses an information entropy‐regularized attention mechanism to identify characteristic class pixels that are then used to extract characteristic mass spectra. This work provides a proof‐of‐concept exemplification using printed ink samples imaged by ToF‐SIMS. A second application‐oriented study is also presented, focusing on the analysis of a mixed powder sample type. Results demonstrate the potential of the MIL method for broader application in MSI, with implications for understanding subtle spatial‐spectral characteristics in various applications and contexts.
Automatic detection of building surface cracks using UAV and deep learning‐combined approach
Authors: Wang Jiehui , Wang Pujin , Qu Lei , Pei Zheng , Ueda Tamon 
Published: January 2024

AbstractConcrete cracking is one of the most significant damage types in reinforced concrete structures due to its potential to adversely affect durability, safety, and serviceability and even reduce the bearing capacity during operation. Thus, damage inspection of damage caused by concrete cracking is important for management, maintenance, and structural assessment for both damaged and undamaged existing buildings but with concrete cracking after a long time of use that needs reconstruction or renovation. This study provides an improved building damage inspection approach by applying Unmanned Aerial Vehicles (UAVs) and state‐of‐the‐art deep learning algorithms to detect concrete cracks on building surfaces. Two distinct architectures for Convolutional Neural Networks (CNNs), namely ResNet50 and YOLOv8 based on classification, and object detection approaches to create a total of 11 models are established, trained, and compared. The classification models attained accuracy levels exceeding 99%, whereas the object detection models achieved approximately 85%. All models effectively identified and accurately located concrete cracks on building surfaces. Besides, the CNN models' capacity to detect cracks is influenced by a variety of model hyperparameters, encompassing factors such as model architecture, the number of network layers, different training dataset sizes, and the quantity of trainable parameters necessary to learn the specific features of detection targets during the training process. The results of this study ultimately demonstrate that the proposed approach can yield accurate detection results and holds high potential for application in crack inspection to advance automatic damage inspection in building structures to a greater extent. In addition, it is important to note that a universal rule cannot be established rule as a larger and more complex model, or an increased number of trainable parameters, necessarily leads to improved detection performance. Models that are trained from scratch using local datasets might not necessarily result in enhanced performance in comparison to the improvements gained through fine‐tuning via transfer learning. Therefore, an appropriate training type, dataset size, task complexity, computational resources, and time demands to achieve a balance between accuracy and efficiency should be considered for specific application scenarios.
Maximal D = 2 supergravities from higher dimensions
Authors: Bossard Guillaume , Ciceri Franz , Inverso Gianluca , Kleinschmidt Axel 
Published: January 2024

Abstract
We develop in detail the general framework of consistent Kaluza-Klein truncations from D=11 and type II supergravities to gauged maximal supergravities in two dimensions. In particular, we unveil the complete bosonic dynamics of all gauged maximal supergravities that admit a geometric uplift. Our construction relies on generalised Scherk-Schwarz reductions of E9 exceptional field theory. The application to the reduction of D=11 supergravity on S8 × S1 to SO(9) gauged supergravity is presented in a companion paper.