bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reporting completeness and methodological quality of guidelines for nutritional care of critically ill patients: A meta-research utilizing Reporting Items for practice Guidelines in HealTh Care (RIGHT) and Appraisal of Guidelines for Research & Evaluation (AGREE II)
Authors: Zucatti Kelly Pozzer , Cattani Aline , Silva Flávia Moraes 
Published: January 2025

Abstract
Studies have demonstrated that the quality and transparency of reporting Clinical Practice Guidelines (CPGs) in healthcare are low. This meta-research aimed to evaluate the adherence of nutrition CPGs for critically ill adults to the reporting RIGHT checklist and its association with the methodological quality assessed by AGREE II, along with other potential publication-related factors. A systematic search for CPGs until December 2024 was conducted. RIGHT and AGREE II were applied. Reporting completeness score was created using the data from the evaluations of whether each broken-down item of the RIGHT. Eleven CPGs were identified, none demonstrated adherence greater than 60% to the RIGHT checklist, and the mean of RIGHT score was 33.5±15.5%. There was a strong correlation between the RIGHT score and AGREE II (r=0.886). A development CPGs team including methodologist and/or statistician was associated with a higher RIGHT score (48.9±4.5 versus 27.2±11.0) and it was higher in CPGs recommended or recommended with modifications by AGREE II in comparison to those not recommended (50.1±4.6 versus 37.7±8.1 versus 17.0±6.8), and in those with acceptable and moderate compared to those with low methodological quality (50.1±4.6 versus 32.2±14.5 versus 19.3±6.2). It was also related to the language of publication, being higher in those published in English. The reporting completeness in CPGs for critically ill adults was low, with a strong correlation with the methodological quality. High values of reporting completeness scores were observed between CPGs recommended by AGREE II (with moderate or acceptable quality) and in those including a methodologist/statistician in the development team.
CONSERVATION AS TRANSLATION
Authors: FELLIN GIULIO , SCHUSTER PETER 
Published: January 2025

Abstract
Glivenko’s theorem says that classical provability of a propositional formula entails intuitionistic provability of the double negation of that formula. This stood right at the beginning of the success story of negative translations, indeed mainly designed for converting classically derivable formulae into intuitionistically derivable ones. We now generalise this approach: simultaneously from double negation to an arbitrary nucleus; from provability in a calculus to an inductively generated abstract consequence relation; and from propositional logic to any set of objects whatsoever. In particular, we give sharp criteria for the generalisation of classical logic to be a conservative extension of the one of intuitionistic logic with double negation.
The effects of maternal body weight on iodine concentration in breast milk and cord blood and infant growth
Authors: Şarahman Kahraman Ceren , Savaş Hasan Basri , Erdem Dilek , Yabancı Ayhan Nurcan 
Published: January 2025

Abstract
Breast milk (BM) is the only source of iodine and bioactive compounds that influence growth and development in infants. The content of BM may be influenced by maternal body mass index (BMI). The aim of this study was to investigate the effect of maternal weight on BM and cord blood iodine concentrations, growth-related hormones, infant anthropometric measurements. A total of 84 mother-infant pairs participated. Levels of leptin, adiponectin and insulin-like growth factor-I (IGF-I) in postnatal BM and cord blood were analysed by enzyme-linked immunosorbent assay (ELISA), iodine by Sandell-Kolthoff reaction. Dietary iodine intake of women was determined by food frequency questionnaire, and anthropometric measurements of infants at birth and 3 months were evaluated. Dietary iodine intake was found to be similar in normal weight (NW) and overweight/obese (OW/OB) women (p > 0.05). Breast milk iodine concentration (BMIC) was 17.4 μg in NW, 18.2 μg in OB/OW women. Adiponectin in cord blood and IGF-I in BM were higher OB/OW than NW women (p < 0.05). Positive correlations were found between the infant birth weight and adiponectin in BM, between the infant body weight at 3 months and leptin and adiponectin in BM, between the infant birth head circumference and IGF-I in BM (p < 0.05). In multiple linear regression model, leptin and adiponectin in BM had a positive effect on infant body weight (p < 0.05). Maternal BMI may influence infant body weight via leptin and adiponectin in BM and infant head circumference via IGF-I. No relationship was found between maternal BMI and iodine levels and anthropometric measurements of the infant. Longitudinal studies are recommended to understand the effect of BMIC on growth.
PARP4 deficiency enhances sensitivity to ATM inhibitor by impairing DNA damage repair in melanoma
Authors: Li Yuehua , Liu Yu , Ma Jingjing , Yang Yuqi , Yue Qiao , Zhu Guannan , Guo Weinan , Gao Tianwen , Shi Qiong , Li Chunying 
Published: January 2025

Abstract
Besides the important pathogenic mechanisms of melanoma, including BRAF-driven and immunosuppressive microenvironment, genomic instability and abnormal DNA double-strand breaks (DSB) repair are significant driving forces for its occurrence and development. This suggests investigating novel therapeutic strategies from the synthetic lethality perspective. Poly (ADP-ribose) polymerase 4 (PARP4) is known to be a member of the PARP protein family. The low expression of PARP4 is significantly associated with defective DSB repair markers and poor prognosis in melanoma. Further research revealed that PARP4 plays a role in DSB repair by regulating the non-homologous end joining (NHEJ) pathway through its involvement in Ku80 mono-ADP-ribosylation. Moreover, from a synthetic lethality perspective, PARP4 expression is associated with ATM inhibitor sensitivity. Overall, our study provides new and valuable insights into the function of PARP4 and melanoma pathogenesis and suggests that ATM inhibitor may be a promising therapeutic approach for treating melanoma with low PARP4 expression.
Stroma-derived Dickkopf-1 contributes to the suppression of NK cell cytotoxicity in breast cancer
Authors: Lee Seunghyun , Ricci Biancamaria , Tran Jennifer , Eul Emily , Ye Jiayu , Ren Qihao , Clever David , Wang Julia , Wong Pamela , Haas Michael S. , Stewart Sheila A. , Ma Cynthia X. , Fehniger Todd A. , Faccio Roberta 
Published: January 2025

Abstract
Mechanisms related to tumor evasion from NK cell-mediated immune surveillance remain enigmatic. Dickkopf-1 (DKK1) is a Wnt/β-catenin inhibitor, whose levels correlate with breast cancer progression. We find DKK1 to be expressed by tumor cells and cancer-associated fibroblasts (CAFs) in patient samples and orthotopic breast tumors, and in bone. By using genetic approaches, we find that bone-derived DKK1 contributes to the systemic DKK1 elevation in tumor-bearing female mice, while CAFs contribute to DKK1 at primary tumor site. Systemic and bone-specific DKK1 targeting reduce tumor growth. Intriguingly, deletion of CAF-derived DKK1 also limits breast cancer progression, without affecting its levels in circulation, and regardless of DKK1 expression in the tumor cells. While not directly supporting tumor proliferation, stromal-DKK1 suppresses NK cell activation and cytotoxicity by downregulating AKT/ERK/S6 phosphorylation. Importantly, increased DKK1 levels and reduced cytotoxic NK cells are detected in women with progressive breast cancer. Our findings indicate that DKK1 represents a barrier to anti-tumor immunity through suppression of NK cells.
Monolithic electrostatic actuators with independent stiffness modulation
Authors: Xu Yuejun , Wen Jian , Burdet Etienne , Taghavi Majid 
Published: January 2025

Abstract
Robotic artificial muscles, inspired by the adaptability of biological muscles, outperform rigid robots in dynamic environments due to their flexibility. However, the intrinsic compliance of the soft actuators restricts force transmission capacity and dynamic response. Biological muscle modulates their stiffness and damping, varying viscoelastic properties and force in interaction with the surroundings. Here we replicate this function in the electro-stiffened ribbon actuator, a monolithic strong actuator capable of high contraction and stiffness modulation. electro-stiffened ribbon actuator employs dielectric-liquid-amplified electrostatic forces for contraction, and electrorheological fluid for rapid (<10 ms) stiffness and damping adjustments. This seamless integration enables contractile force modulation, extending its capability as a lightweight variable resistance passive spring by over 2.5 times, and improves its dynamic responses, with faster contractions and rapid attenuation of oscillations by more than 50%. We demonstrate electro-stiffened ribbon actuator’s versatility in active, passive and dual connection functions, including arm-bending wearable robotics, robotic arms with variable impact resistance and muscle-like stiffness and damping modulation.
Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification
Authors: Zhu Yu , Zhang Mingxu , Huang Qinchuan , Wu Xianbo , Wan Li , Huang Ju 
Published: January 2025

Abstract
The classification of chronic diseases has long been a prominent research focus in the field of public health, with widespread application of machine learning algorithms. Diabetes is one of the chronic diseases with a high prevalence worldwide and is considered a disease in its own right. Given the widespread nature of this chronic condition, numerous researchers are striving to develop robust machine learning algorithms for accurate classification. This study introduces a revolutionary approach for accurately classifying diabetes, aiming to provide new methodologies. An improved Secretary Bird Optimization Algorithm (QHSBOA) is proposed in combination with Kernel Extreme Learning Machine (KELM) for a diabetes classification prediction model. First, the Secretary Bird Optimization Algorithm (SBOA) is enhanced by integrating a particle swarm optimization search mechanism, dynamic boundary adjustments based on optimal individuals, and quantum computing-based t-distribution variations. The performance of QHSBOA is validated using the CEC2017 benchmark suite. Subsequently, QHSBOA is used to optimize the kernel penalty parameter
$$\:C$$
and bandwidth
$$\:c$$
of the KELM. Comparative experiments with other classification models are conducted on diabetes datasets. The experimental results indicate that the QHSBOA-KELM classification model outperforms other comparative models in four evaluation metrics: accuracy (ACC), Matthews correlation coefficient (MCC), sensitivity, and specificity. This approach offers an effective method for the early diagnosis and prediction of diabetes.
FL‐ADS: Federated learning anomaly detection system for distributed energy resource networks
Authors: Purohit Shaurya , Govindarasu Manimaran , Blakely Benjamin 
Published: January 2025

AbstractWith the ongoing development of Distributed Energy Resources (DER) communication networks, the imperative for strong cybersecurity and data privacy safeguards is increasingly evident. DER networks, which rely on protocols such as Distributed Network Protocol 3 and Modbus, are susceptible to cyberattacks such as data integrity breaches and denial of service due to their inherent security vulnerabilities. This paper introduces an innovative Federated Learning (FL)‐based anomaly detection system designed to enhance the security of DER networks while preserving data privacy. Our models leverage Vertical and Horizontal Federated Learning to enable collaborative learning while preserving data privacy, exchanging only non‐sensitive information, such as model parameters, and maintaining the privacy of DER clients' raw data. The effectiveness of the models is demonstrated through its evaluation on datasets representative of real‐world DER scenarios, showcasing significant improvements in accuracy and F1‐score across all clients compared to the traditional baseline model. Additionally, this work demonstrates a consistent reduction in loss function over multiple FL rounds, further validating its efficacy and offering a robust solution that balances effective anomaly detection with stringent data privacy needs.
Effective galactic dark matter: first order general relativistic corrections
Authors: Re Federico , Galoppo Marco 
Published: January 2025

Abstract
Stationary, axisymmetric, dust sourced solutions of Einstein’s equations have been proposed as fully general relativistic models for disc galaxies. These models introduce a novel physical element, i.e., a non-negligible dragging vortex emerging from a full consideration of the essential self-interaction of matter and geometry in general relativity, which might demand a profound recalibration of the inferred amount of dark matter in disc galaxies. Within this framework, we identify the correct observables for redshift-inferred rotation curves of distant galaxies, correcting previously overlooked mistakes in the literature. We find that the presence of the dragging vortex introduces non-negligible corrective terms for the matter density required to maintain a stable physical system. We present the first estimate of the dragging speed which is required to explain a non-negligible fraction of dark matter in disc galaxies. In particular, we show that a sub-relativisitc dragging velocity of tens of kilometers per second in the neighbourhood of the Sun is sufficient to reduce the need of dark matter by 50% in the Milky Way. Finally, we find that the presence of such a dragging vortex also returns a net contribution to the strong gravitational lensing generated by the galaxy. Thus, we show that the considered class of general relativistic galaxy models, is not only physcially viable, but suggests the need for recalibration of the estimated dark matter content in disc galaxies, with far reaching consequences for astrophysics and cosmology.
Growth and characterization of LaMnOx thin films prepared by metalorganic aerosol deposition
Authors: Schüler Leonard , Moshnyaga Vasily 
Published: January 2025

Abstract
LaMnO
x
(LMO) thin films have been prepared by low-oxygen metal-organic aerosol deposition. The oxygen stoichiometry x was controlled by the oxygen partial pressure p(O2) during deposition which is set by the ratio of O2/N2 gas flow. The stoichiometry in terms of Mn4+/Mn3+ ratio is determined from structural, electrical and magnetic properties. Deposition in pure N2 gas leads to films with oxygen stoichiometry of x ≈ 3.01, comparable to state-of-the art pulsed laser deposition-grown LMO thin films. Overall, we show that MAD as a simple chemical vacuum-free thin-film deposition method is also a suitable technique for the synthesis of oxide thin films that need a low p(O2) like LMO.
AEFNet: a real-time network for detecting prohibited items in x-ray images across complex scenarios
Authors: Zhu Kaiyuan , Chang Xinyu , Zhang Qing 
Published: January 2025

Abstract
Accurate identification of prohibited items in x-ray security images is essential for ensuring public safety. However, current methodologies struggle to simultaneously address irregular deformation, multi-scale features, and background occlusion of prohibited items, leading to inadequate detection accuracy. To address these challenges, we propose an adaptive efficient focusing network (AEFNet) designed to target regions, thereby enhancing the automatic detection of prohibited items. Specifically, to accommodate the irregular deformation of target regions, we introduce the DACSP module, which dynamically adjusts sampling positions to enhance the network’s adaptability and focus on occluded targets. To address detail loss and managing multi-scale features, we propose a multi-scale focus feature module and a focusing diffusion pyramid network (FDPN), which enable the fusion of semantic and perceptual features, improving the use of contextual information at different detection scales. Additionally, detail-enhanced convolution improves the efficacy of feature utilization at different scales, while facilitating a lightweight network design. Finally, we employ the PIoUv2 function to optimize localization loss, resulting in significant performance enhancement. Experimental results show that AEFNet performs effectively across various x-ray security image datasets (PIDray, CLCXray, OPIXray) achieving 74.7%, 61.3%, and 89.2% mAP respectively, and AEFNet also demonstrates strong generalization capabilities on the PASCAL VOC dataset in non-prohibited item detection scenarios.
Effect of additive elements on plastic deformation: a three-dimensional discrete dislocation dynamics study
Authors: Wu Ronghai , Wang Zheng , Wu Xiaoxiang , Li Heng 
Published: January 2025

Abstract
Additive elements change plastic deformation in complicated manners, which may directly affect dislocation activities or indirectly affect plasticity associated microstructure. Clarifying such a complexity is still a challenge as it is not easy to decouple various effect mechanisms, especially in experiments. Previous investigations mainly focused on the indirect effect of additive elements on plasticity associated microstructure. In the present work, we try to clarify the direct effect of additive elements on dislocation activities by three-dimensional discrete dislocation dynamics, using the case of Ni-based single crystal superalloys with additive elements Ru, Cr, and Mo. The three additive elements are chosen because they are crucial for the mechanical properties of Ni-based single crystal superalloys. Moreover, their excess volumes are distinct and hence can be representative additive elements. The results show that both the excess volume and concentration of additive elements contribute to the strengthening of single crystal Ni-based superalloys. The strengthening effect enhances as the excess volume increases and the concentration increases, regardless of the loading conditions. Competition mechanisms of additive elements which cause different mechanical features under different loading conditions are also analyzed.
Effects of composition ratio and crystal orientation on nanoindentation behavior of monocrystal AuPt alloys
Authors: Lin Jiajun , Xu Hao , Tian Yuanyuan , Du Chunyang , Chen Shanyong , Luo Gangjie 
Published: January 2025

Abstract
The improvement of hardness and plasticity for material has been a significant challenge. This work employs molecular dynamics simulations to investigate the nanoindentation behavior of monocrystal AuPt alloys. The enhancement in both the hardness and plasticity of the alloy could be achieved by adjusting the crystal orientation and the composition ratio. For the alloy, the dislocation density notably rises with an increase in Au content, becoming pronounced when it reaches 73%. The high dislocation density at this specific composition ratio leads to the strong work hardening, thereby strengthening the hardness of the Au0.73Pt0.27 alloys. Moreover, the active dislocation motion in the Au0.73Pt0.27 alloy during plastic deformation implies its excellent plasticity. Notably, the hardness of the Au0.73Pt0.27 alloy with [001] crystal orientation is considerably greater relative to other crystal orientations. This is attributed to the propensity for twinning to occur in the Au0.73Pt0.27 alloy with the

[

001

]

crystal orientation, which triggers intensified twin boundary strengthening. This work provides valuable insights for the design of AuPt alloys with superior mechanical properties, broadening their scope for application.
Deep learning-based quick MLC sequencing for MRI-guided online adaptive radiotherapy: a feasibility study for pancreatic cancer patients
Authors: Ahunbay Ahmet , Paulson Eric , Ahunbay Ergun , Zhang Ying 
Published: January 2025

Abstract

Objective. One bottleneck of magnetic resonance imaging (MRI)-guided online adaptive radiotherapy is the time-consuming daily online replanning process. The current leaf sequencing method takes up to 10 min, with potential dosimetric degradation and small segment openings that increase delivery time. This work aims to replace this process with a fast deep learning-based method to provide deliverable MLC sequences almost instantaneously, potentially accelerating and enhancing online adaption. Approach. Daily MRIs and plans from 242 daily fractions of 49 abdomen cancer patients on a 1.5 T MR-Linac were used. The architecture included: (1) a recurrent conditional generative adversarial network model to predict segment shapes from a fluence map (FM), recurrently predicting each segment’s shape; and (2) a linear matrix equation module to optimize the monitor units (MUs) weights of segments. Multiple models with different segment numbers per beam (4–7) were trained. The final MLC sequences with the smallest relative absolute errors were selected. The predicted MLC sequence was imported into treatment planning system for dose calculation and compared with the original plans. Main results. The gamma passing rate for all fractions was 99.7 ± 0.2% (2%/2 mm criteria) and 92.7 ± 1.6% (1%/1 mm criteria). The average number of segments per beam in the proposed method was 6.0 ± 0.6 compared to 7.5 ± 0.3 in the original plan. The average total MUs were reduced from 1641 ± 262 to 1569.5 ± 236.7 in the predicted plans. The estimated delivery time was reduced from 9.7 min to 8.3 min, an average reduction of 14% and up to 25% for individual plans. Execution time for one plan was less than 10 s using a GTX1660TIGPU. Significance. The developed models can quickly and accurately generate an optimized, deliverable leaf sequence from a FM with fewer segments. This can seamlessly integrate into the current online replanning workflow, greatly expediting the daily plan adaptation process.