bluuebunny/crossref_metadata_2025_split huggingface dataset:
Maternal caffeine intake during pregnancy and the risk of childhood asthma by 10 years of age—Evidence from The Generation XXI birth cohort study
Authors: de Castro Mendes Francisca , Severo Milton , Paciência Inês , Lopes Carla , Santos Ana Cristina , Barros Henrique , Moreira André , Moreira Pedro 
Published: January 2025

AbstractBackgroundWe aimed to investigate the association between maternal caffeine intake during pregnancy and asthma in children by 10 years of age.MethodsWe considered 5585 mother–child pairs enrolled in a population‐based birth cohort. Consumption of regular and decaffeinated coffee, black and green tea, and cola beverages before and during pregnancy was obtained through face‐to‐face interviews within 72 h after giving birth, and total caffeine intake (mg/day) was estimated. Medical diagnosis of asthma was assessed and spirometry with bronchodilation was performed at 10 years of age. We used adjusted regression models to estimate the association between the caffeine intake/day during pregnancy with asthma by 10 years of age, and a quadratic relationship was verified between them. Consumption of caffeine before pregnancy, gestational age, maternal years of schooling, maternal self‐reported medical diagnosis of asthma, smoking status before and during pregnancy, and children's sex were considered as confounders. We used nonlinear least squares models to estimate the knot point and its respective confidence interval (CI).ResultsA higher intake of caffeine/day decreased the odds of having childhood asthma at 10 years of age (adjusted odds ratio [OR] = 0.60, 95% CI: 0.41; 0.88). The estimated knot point was 92.7 mg of caffeine/day (95% CI: 36.3, 163.3), where the risk was 7.2%, while for no intake (0 mg) the risk was 8.8%.ConclusionMaternal caffeine intake up to an estimated intake of approximately 93 mg/day during pregnancy decreased childhood asthma risk by 10 years of age. Nonetheless, further studies are required to confirm our results.Key MessageMaternal caffeine intake during pregnancy up to an estimated intake of approximately 93 mg/day decreased the risk of asthma in children by 10 years of age, but considering caffeine's potential adverse effects on other health outcomes, further studies are needed to explore its link to childhood asthma.
A cross‐scale transfer learning framework: prediction of SOD activity from leaf microstructure to macroscopic hyperspectral imaging
Authors: Hao Jie , Yan Yan , Zhang Yao , Zhang Yiyang , Cao Yune , Wu Longguo 
Published: January 2025

SummarySuperoxide dismutase (SOD) plays an important role to respond in the defence against damage when tomato leaves are under different types of adversity stresses. This work employed microhyperspectral imaging (MHSI) and visible near‐infrared (Vis–NIR) hyperspectral imaging (HSI) technologies to predict tomato leaf SOD activity. The macroscopic model of SOD activity in tomato leaves was constructed using the convolutional neural network in conjunction with the long and short‐term temporal memory (CNN‐LSTM) technique. Using heterogeneous two‐dimensional correlation spectra (H2D‐COS), the sensitive macroscopic and microscopic absorption peaks connected to tomato leaves' SOD activity were made clear. The combination of CNN‐LSTM algorithm and H2D‐COS analysis was used to research transfer learning between microscopic and macroscopic models based on sensitive wavelengths. The results demonstrated that the CNN‐LSTM model, which was based on the FD preprocessed spectra, had the best performance for the microscopic model, with RC and RP reaching 0.9311 and 0.9075, and RMSEC and RMSEP reaching 0.0109 U/mg and 0.0127 U/mg respectively. There were 10 macroscopic and 10 microscopic significant sensitivity peaks found. The transfer learning was carried out using sensitive wavelengths, and the model performed well with an RP value of 0.7549 and an RMSEP of 0.0725 U/mg. The combined CNN algorithm and H2D‐COS analysis demonstrated the viability of transfer learning across microscopic and macroscopic models for quantitative tomato leaf SOD prediction.
Prediction Models for Post‐Stroke Cognitive Impairment: A Systematic Review and Meta‐Analysis
Authors: Yang Yifang , Chen Yajing , Yang Yiyi , Yang Tingting , Wu Tingting , Chen Junbo , Yan Fanghong , Han Lin , Ma Yuxia 
Published: January 2025

ABSTRACTBackgroundStroke is one of the most serious illnesses worldwide and is the primary cause of acquired disability among adults. Post‐stroke cognitive impairment (PSCI) is a complication of stroke that significantly impacts patients' daily activities and social functions. Therefore, developing a risk prediction model for PSCI is essential for identifying and preventing disease progression.ObjectivesThis study systematically reviewed and analyzed PSCI prediction models, identifying the associated risk factors.MethodsWe systematically retrieved literature from PubMed, Cochrane Library, Embase, and other sources. Two researchers independently extracted the literature and assessed the risk of bias using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist and The Prediction Model Risk of Bias Assessment Tool (PROBAST).ResultsA total of 20 articles describe the PSCI prediction model, with an incidence rate ranging from 8% to 75%. The area under the receiver operating characteristic curve (AUC) value for the development models ranged from 0.66 to 0.969, while the validation models ranged from 0.763 to 0.893. Age, diabetes, hypersensitive C‐reactive protein (hs‐CRP), hypertension, and homocysteine (hcy) were identified as the strongest predictors.ConclusionIn this systematic review, several PSCI prediction models demonstrate promising prediction performance, although they often lack external validation and exhibit high heterogeneity in some predictive factors. Therefore, we recommend that medical practitioners utilize a comprehensive set of predictive factors to screen for high‐risk PSCI patients. Furthermore, future research should prioritize refining and validating existing models by incorporating novel variables and methodologies.
Emergence of Well‐Ordering and Clustering for a First‐Order Nonlinear Consensus Model
Authors: Byeon Junhyeok , Ha Seung‐Yeal , Kang Myeongju , Yoon Wook 
Published: January 2025

ABSTRACTWe study the predictability of asymptotic clustering patterns in a first‐order nonlinear consensus model on receiver network on the real line. Nonlinear couplings between particles (agents) are characterized by an odd, locally Lipschitz, and increasing function. The proposed consensus model and its clustering dynamics is motivated by the one‐dimensional Cucker–Smale flocking model. Despite the complexity registered by heterogeneous couplings, we provide a sufficient framework to predict asymptotic dynamics such as particles' aggregation, segregation, and clustering patterns. We also verify the robustness of clustering patterns to structural changes such as relativistic effects implemented by the suitable composition of functions.
Cutaneous disorders in captive psittacines, a retrospective study of 1454 cases at a university veterinary teaching hospital (1988–2021)
Authors: White Stephen D. , Beaufrère Hugues , Guzman David S.‐M. , Affolter Verena K. , Tell Lisa A. , Paul‐Murphy Joanne , Hawkins Michelle G. 
Published: January 2025

AbstractBackgroundPsittacines (parrots and their allies) are kept under human care as companion animals, live exhibit specimens in zoological institutions and occasionally as research subjects. Cutaneous disorders such as feather destructive behaviour (FDB) and pododermatitis are commonly noted in clinical reviews, case reports and text book chapters.Hypothesis/ObjectivesTo document the type, signalment associations and prevalence of cutaneous disorders in a large number of captive psittacines in an academic referral teaching hospital population.AnimalsCase population at a university veterinary teaching hospital.Methods and MaterialsRetrospective study using computerised medical records of birds (psittacine and non‐psittacine) examined between 1 January 1988 and 31 December 2021. Inclusion criteria were records of psittacines with dermatological diagnoses.ResultsOf 3472 psittacines, 1454 (41.9%) had cutaneous disease. Age was strongly associated with an increased risk of cutaneous diseases. The most common cutaneous disorders were pododermatitis (n = 729) and FDB (n = 528). Female sex and increased age were associated with significantly higher odds of FDB, as were the genera Cacatua (cockatoos), Psittacus (African grey parrots), Ara (macaws) and Agapornis (lovebirds). Increased age and the genus Agapornis also were associated with significantly higher odds of pododermatitis. The most common infectious disease was mite (presumed Knemidocoptes) infestation, almost exclusively seen in budgerigars; increased age was associated with significantly lower odds of having mites.Conclusions and Clinical RelevancePododermatitis and FDB were the most common cutaneous disorders in psittacines. As 50% of psittacines with skin disease presented for nonskin concerns, clinicians should perform a dermatological examination regardless of the reason for presentation.
Linear independence of coherent systems associated to discrete subgroups
Authors: Enstad Ulrik , van Velthoven Jordy Timo 
Published: January 2025

AbstractThis note considers the finite linear independence of coherent systems associated to discrete subgroups. We show by simple arguments that such coherent systems of amenable groups are linearly independent whenever the associated twisted group ring does not contain any nontrivial zero divisors. We verify the latter for discrete subgroups in nilpotent Lie groups. For the particular case of time‐frequency translates of Euclidean space, our approach provides a simple and self‐contained proof of the Heil–Ramanathan–Topiwala conjecture for subsets of arbitrary discrete subgroups.
The Resistance of Sandwich Cylinders With Density-Graded Foam Cores to Internal Blast Loadings
Authors: Su Binhao , Cui Tianning , Peng Hui , Pei Xiaoyang , Li Jianfeng , Guo Kai , Zhang Wei , Qin Qinghua 
Published: January 2025

Abstract
The resistance of sandwich cylinders with density-graded foam cores to internal blast loadings is investigated theoretically and numerically. Four kinds of density-graded foam core are designed, such as positive, negative, middle–high, and middle–low gradients. The deformation process of the sandwich cylinders is assumed to be split into three phases, i.e., fluid–structure interaction, combined deformation of core and inner wall, and sandwich stage of motion. Employing a rigid perfectly-plastic locking model of density-graded foam core, analytical models are proposed to predict the dynamic response of gradient sandwich cylinders. Finite element simulations of gradient sandwich cylinders subjected to internal blast loadings are carried out and agree well with the analytical predictions. Furthermore, the effects of the core density gradients and the wall thickness distributions on the blast resistance are explored and identified. It is shown that the thicker inner wall design can enhance the blast resistance of sandwich cylinders with the same mass.
Design and Validation Strategy for an X-Ray Target Subject to Ultra High Heat Flux Loading
Authors: Ravichandran Mahadevan , Winter Johanna , Dimroth Anton , Bartzsch Stefan , Kraus Kim Melanie , Zimmermann Markus 
Published: January 2025

Abstract
Microbeam X-ray therapy is a promising cancer therapy that uses a high-power electron beam hitting a metallic target. For a clinical microbeam therapy X-ray source, an electron beam of a power of 1 MW onto a focal spot of 0.05 mm × 20 mm size is needed, with a penetration depth of 0.1 mm. This means a heat flux input of 1 TW/m2, an order of magnitude higher than nuclear and medical applications. Numerical simulations based on surface and volumetric heat loading for such an electron beam are presented in this work. The local temperature around the focal spot is modeled in a lower-scale model with an element size of 10 µm and volumetric heat loading. This differs from the state-of-the-art simulations, in which electron beam loading is modeled as surface heat flux loads. The simulated temperature agrees with the mathematical estimates within an error of 10% while proving feasibility. A novel validation strategy is proposed to address the lack of available test facilities to replicate this extreme heat flux. The critical parameters describing the high-heat-flux-loading are identified as temperature, thermal strain, thermal stress, and strain rate. Scaled-down test specifications are determined to use a test facility of power less than 100 kW. With the verified simulation using the scaled-down test, it is proposed to establish the material's capability to withstand the concentrated 1 MW heat load without a 1 MW test facility.
A Generalized Shear Lag Model for the “Brick-Mortar” Staggered Composite With Arbitrary Soft Phase Contents
Authors: Zuo Randong , Cheng Yonggui , Peng Zhilong , Yao Yin , Zhang Bo , Chen Shaohua 
Published: January 2025

Abstract
Although the mechanical mechanisms of staggered biological composites are widely investigated, each existing model has its own application scope. In order to comprehensively characterize the mechanical properties of the staggered composite with arbitrary soft phase content, a generalized shear lag model is established, in which the soft phase may simultaneously bear the tension and shear loads. A novel approach of considering the soft phase as virtual layered composites composed of sub-soft and sub-hard phases is developed. The sub-hard phase has zero thickness and undergoes tensile load, while the sub-soft phase endures shear load. Accurate stress fields in the staggered composite can be well achieved with the new approach. Two simplified models are further proposed based on the generalized shear lag model. Comparison with the finite element results shows that the shear stress in the end soft phase has little effect on the mechanical performance of the staggered composite; while the normal stress in the interlayer soft phase and the variation of shear stress in the thickness direction of interlayer soft phase must be considered for the staggered composite with high interlayer soft phase content. Furthermore, the applicable scopes of the existing typical partition models are determined by comparing the effective Young's modulus predicted by the generalized model, the finite element calculation, and the partition models. The obtained results could be helpful for the precise design of composite materials with high mechanical properties.
Data-Driven Digital Twins for Real-Time Machine Monitoring: A Case Study on a Rotating Machine
Authors: Cheruku Suryapavan , Balaji Suryanarayan , Delgado Adolfo , Krishnamurthy Vinayak R. 
Published: January 2025

Abstract
In this work, we present a framework for data-driven digital twins for real-time machine monitoring. Data-driven digital twins are gaining prominence in a variety of industrial applications owing to their ability to capture complex relationships between sensor data and system behavior. The computational efficiency gained using such twins is critical for real-time machine monitoring and diagnostics with timely and interactive human intervention. One of the fundamental challenges in the current data-driven digital twins is a lack of understanding of how different data synthesis strategies of the same sensor data affect the predictive power of the twin models typically obtained through statistical learning. As a result, the interactive support for enabling human intervention and machine health monitoring is not generalized for different machine configurations and fault conditions. Using turbomachinery as a concrete demonstrative context, we investigate two fundamentally different data synthesis strategies, namely, integrated and combinatorial, as digital twins for a rotating machine. Specifically, we consider a rotor kit as a machine component, develop a synthetic dataset using simulations, and conduct systematic studies on the predictive performance of reduced-order models trained using the different data synthesis strategies. Our experiments show that the combinatorial dataset offers higher prediction accuracy in comparison to randomized data generation. Moreover, we created a cloud-based augmented reality (AR) mobile tool to show the feasibility of our methodology in developing potential machine monitoring applications with human-in-the-loop components.
Induction of macrophage efferocytosis in pancreatic cancer via PI3Kγ inhibition and radiotherapy promotes tumour control
Authors: Russell Shannon Nicole , Demetriou Constantinos , Valenzano Giampiero , Evans Alice , Go Simei , Stanly Tess , Hazini Ahmet , Willenbrock Frances , Gordon-Weeks Alex Nicolas , Mukherjee Somnath , Tesson Matthias , Morton Jennifer P , O'Neill Eric , Jones Keaton Ian 
Published: January 2025

BackgroundThe immune suppression mechanisms in pancreatic ductal adenocarcinoma (PDAC) remain unknown, but preclinical studies have implicated macrophage-mediated immune tolerance. Hence, pathways that regulate macrophage phenotype are of strategic interest, with reprogramming strategies focusing on inhibitors of phosphoinositide 3-kinase-gamma (PI3Kγ) due to restricted immune cell expression. Inhibition of PI3Kγ alone is ineffective in PDAC, despite increased infiltration of CD8+ T cells.ObjectiveWe hypothesised that the immune stimulatory effects of radiation, and its ability to boost tumour antigen availability could synergise with PI3Kγ inhibition to augment antitumour immunity.DesignWe used orthoptic and genetically engineered mouse models of pancreatic cancer (LSL-KrasG12D/+;Trp53R172H/+;Pdx1-Cre). Stereotactic radiotherapy was delivered using contrast CT imaging, and PI3Kγ inhibitors by oral administration. Changes in the tumour microenvironment were quantified by flow cytometry, multiplex immunohistochemistry and RNA sequencing. Tumour-educated macrophages were used to investigate efferocytosis, antigen presentation and CD8+ T cell activation. Single-cell RNA sequencing data and fresh tumour samples with autologous macrophages to validate our findings.ResultsTumour-associated macrophages that employ efferocytosis to eradicate apoptotic cells can be redirected to present tumour antigens, stimulate CD8+ T cell responses and increase local tumour control. Specifically, we demonstrate how PI3Kγ signalling restricts inflammatory macrophages and that inhibition supports MERTK-dependent efferocytosis. We further find that the combination of PI3Kγ inhibition with targeted radiotherapy stimulates inflammatory macrophages to invoke a pathogen-induced like efferocytosis that switches from immune tolerant to antigen presenting.ConclusionsOur data supports a new immunotherapeutic approach and a translational rationale to improve survival in PDAC.
LGR5 as a diagnostic marker for dysplasia in serrated polyps
Authors: Yilmaz Osman , Arora Kshtij , Lee Soo Hyun , Hosseini Sahar , Chen Feidi , Padmanabha Nandan , Eng George , Kantekure Kanchan , Yilmaz Omer , Deshpande Vikram 
Published: January 2025

AimsWNT signalling pathway dysregulation is often a critical early component in colorectal neoplasia, particularly the chromosomal instability pathway. Using two WNT reporters,LGR5andAXIN2, we sought to assess whether these polyps demonstrate predictable expression patterns and if these patterns show diagnostic value.MethodsWe evaluated 23 adenomas (TA), 23 sessile serrated lesions (SSLs), 14 SSL with dysplasia and 38 traditional serrated adenomas (TSA). Chromogenic in situ hybridisation stains (ISH) forLGR5andAXIN2were performed. Reactivity was defined as strong, intermediate or weak. Upper third crypt reactivity was defined as full-thickness staining. Accentuation within ectopic crypts (ECF) was recorded.ResultsTAs (91%) showed strong reactivity and full-thickness staining withLGR5. TSAs showed full-thickness and weak to intermediateLGR5reactivity (79%) and ECF withLGR5accentuation was exclusively seen in TSA. SSL showed weakLGR5reactivity confined to the basal crypt region (100%). SSL with dysplasia also showed weak or intermediate (100%)LGR5reactivity, but the reactivity pattern was full thickness (88%).AXIN2expression parallelsLGR5expression (Pearson coefficient=0.63) regarding signal intensity for the examined polyp groups.ConclusionsQualitative and quantitative differences inAXIN2andLGR5expression assist in the diagnosis of SSL with dysplasia.
Timing-based mass measurement of exotic long-lived particles at the FCC-ee
Authors: Aleksan R. , Perez E. , Polesello G. , Valle N. 
Published: January 2025

AbstractThe very high luminosity run foreseen at the Z-pole for the FCC-ee will allow the detection in Z decays of new particles with very low couplings to the Standard Model. These particles can have measurable flight paths before they decay. If the timing and the position of the decay vertex can be measured with high precision, the mass of such particles can be measured by exploiting the constrained kinematics of an $$e^+e^-$$

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collider. The mass resolution achievable with this technique is studied through a detailed analysis in the framework of a parametrised simulation of the performance of the IDEA detector. The adopted benchmark model is the production of Heavy Neutral Leptons, which is one of the key channels for new physics discovery at the FCC-ee.
A Machine Learning Approach to Resolving Conflicts in Physical Human–Robot Interaction
Authors: Dincer Enes Ulas , Al-Saadi Zaid , Hamad Yahya M. , Aydin Yusuf , Kucukyilmaz Ayse , Basdogan Cagatay 
Published: January 2025

As artificial intelligence techniques become more sophisticated, we anticipate that robots collaborating with humans will develop their own intentions, leading to potential conflicts in interaction. This development calls for advanced conflict resolution strategies in physical human–robot interaction (pHRI), a key focus of our research. We use a machine learning (ML) classifier to detect conflicts during co-manipulation tasks to adapt the robot’s behavior accordingly using an admittance controller. In our approach, we focus on two groups of interactions, namely “harmonious” and “conflicting,” corresponding respectively to the cases of the human and the robot working in harmony to transport an object when they aim for the same target, and human and robot are in conflict when human changes the manipulation plan, e.g. due to a change in the direction of movement or parking location of the object.
Co-manipulation scenarios were designed to investigate the efficacy of the proposed ML approach, involving 20 participants. Task performance achieved by the ML approach was compared against three alternative approaches: (a) a rule-based (RB) Approach, where interaction behaviors were rule-derived from statistical distributions of haptic features; (b) an unyielding robot that is proactive during harmonious interactions but does not resolve conflicts otherwise, and (c) a passive robot which always follows the human partner. This mode of cooperation is known as “hand guidance” in pHRI literature and is frequently used in industrial settings for so-called “teaching” a trajectory to a collaborative robot.
The results show that the proposed ML approach is superior to the others in task performance. However, a detailed questionnaire administered after the experiments, which contains several metrics, covering a spectrum of dimensions to measure the subjective opinion of the participants, reveals that the most preferred mode of interaction with the robot is surprisingly passive. This preference indicates a strong inclination toward an interaction mode that gives more control to humans and offers less demanding interaction, even if it is not the most efficient in task performance. Hence, there is a clear trade-off between task performance and the preferred mode of interaction of humans with a robot, and a well-balanced approach is necessary for designing effective pHRI systems in the future.