bluuebunny/crossref_metadata_2025_split huggingface dataset:
A comprehensive generalization of the Friendship Paradox to weights and attributes
Authors: Evtushenko Anna , Kleinberg Jon 
Published: June 2024

AbstractThe Friendship Paradox is a simple and powerful statement about node degrees in a graph. However, it only applies to undirected graphs with no edge weights, and the only node characteristic it concerns is degree. Since many social networks are more complex than that, it is useful to generalize this phenomenon, if possible, and a number of papers have proposed different generalizations. Here, we unify these generalizations in a common framework, retaining the focus on undirected graphs and allowing for weighted edges and for numeric node attributes other than degree to be considered, since this extension allows for a clean characterization and links to the original concepts most naturally. While the original Friendship Paradox and the Weighted Friendship Paradox hold for all graphs, considering non-degree attributes actually makes the extensions fail around 50% of the time, given random attribute assignment. We provide simple correlation-based rules to see whether an attribute-based version of the paradox holds. In addition to theory, our simulation and data results show how all the concepts can be applied to synthetic and real networks. Where applicable, we draw connections to prior work to make this an accessible and comprehensive paper that lets one understand the math behind the Friendship Paradox and its basic extensions.
Accelerating the design of lattice structures using machine learning
Authors: Gongora Aldair E. , Friedman Caleb , Newton Deirdre K. , Yee Timothy D. , Doorenbos Zachary , Giera Brian , Duoss Eric B. , Han Thomas Y.-J. , Sullivan Kyle , Rodriguez Jennifer N. 
Published: June 2024

AbstractLattices remain an attractive class of structures due to their design versatility; however, rapidly designing lattice structures with tailored or optimal mechanical properties remains a significant challenge. With each added design variable, the design space quickly becomes intractable. To address this challenge, research efforts have sought to combine computational approaches with machine learning (ML)-based approaches to reduce the computational cost of the design process and accelerate mechanical design. While these efforts have made substantial progress, significant challenges remain in (1) building and interpreting the ML-based surrogate models and (2) iteratively and efficiently curating training datasets for optimization tasks. Here, we address the first challenge by combining ML-based surrogate modeling and Shapley additive explanation (SHAP) analysis to interpret the impact of each design variable. We find that our ML-based surrogate models achieve excellent prediction capabilities (R2 > 0.95) and SHAP values aid in uncovering design variables influencing performance. We address the second challenge by utilizing active learning-based methods, such as Bayesian optimization, to explore the design space and report a 5 × reduction in simulations relative to grid-based search. Collectively, these results underscore the value of building intelligent design systems that leverage ML-based methods for uncovering key design variables and accelerating design.
Effects of switching from intravitreal injection of aflibercept to faricimab on ocular blood flow in patients with diabetic macular edema
Authors: Saima Yoshinari , Yokota Harumasa , Kushiyama Akifumi , Hanaguri Junya , Ohno Akira , Takase Koyo , Sugiyama Ruri , Muranaka Kimimasa , Yamagami Satoru , Nagaoka Taiji 
Published: June 2024

AbstractWe assessed the short-term effects of switching from intravitreal aflibercept (IVA) to intravitreal faricimab (IVF) on ocular blood flow in patients with treatment-resistant diabetic macular edema (DME). The medical records of 15 patients with DME who had received IVA injection ≥ 3 months before were retrospectively reviewed. The best-corrected visual acuity, central macular thickness (CMT) on optical coherence tomography, and mean blur rate (MBR) of all disc areas on laser speckle flowgraphy were measured before, 1 week after, and 4 weeks after IVA and IVF, respectively. The changes in visual acuity showed no significant difference after switching from IVA to IVF (P = 0.732). The mean CMT decreased significantly during the follow-up period (both P < 0.001). MBR showed no significant difference during the follow-up period (P = 0.26). However, it decreased significantly 4 weeks after IVF (P = 0.01) compared with the baseline value, but not 4 weeks after IVA (P = 0.074). A significant association was observed between decreased MBR and decreased CMT in patients who received IVF (correlation coefficient: 0.501, P = 0.005) but not in those who received IVA (P = 0.735). Thus, IVF maintained ocular blood flow reduction, although no significant differences in visual acuity and CMT changes were observed compared to IVA.
Dairy cows’ motivation to nurse their calves
Authors: Jensen Emma Hvidtfeldt , Bateson Melissa , Neave Heather W. , Rault Jean-Loup , Jensen Margit Bak 
Published: June 2024

AbstractWhen weaning offspring, female mammals limit nursing opportunities. This study aimed to investigate whether imposing a gradual reduction in daily contact time, by separating cows from their calves as an attempt to stimulate weaning, reduced dairy cows’ motivation to nurse their calves. For seven weeks, 84 Holstein–Friesian cow-calf pairs were housed with either full-time (23 h contact/d), part-time (10 h contact/d), or no contact. In the following two weeks, half of full- and part-time pairs were subjected to reduced contact (50% of initial contact in week 8, 25% of initial contact in week 9), while the other half continued with unchanged contact. In weeks 8 and 9, cows’ motivation to obtain full contact to and opportunity to nurse their calves was measured using weighted push gates using a novel maximum price paid method providing an alternative choice to the cows to reduce frustration. Cows with reduced calf contact were more motivated than cows with unchanged contact; however, cows used the alternative choice less than expected. The results show that cows’ motivation for full calf contact and opportunity to nurse increases when daily calf contact is reduced, illustrating that dairy cows are motivated to continue nursing their 9- to 10-week-old calves.
Enhancing computer image recognition with improved image algorithms
Authors: Huang Lanqing , Yao Cheng , Zhang Lingyan , Luo Shijian , Ying Fangtian , Ying Weiqiang 
Published: June 2024

AbstractAdvances in computer image recognition have significantly impacted many industries, including healthcare, security and autonomous systems. This paper aims to explore the potential of improving image algorithms to enhance computer image recognition. Specifically, we will focus on regression methods as a means to improve the accuracy and efficiency of identifying images. In this study, we will analyze various regression techniques and their applications in computer image recognition, as well as the resulting performance improvements through detailed examples and data analysis. This paper deals with the problems related to visual image processing in outdoor unstructured environment. Finally, the heterogeneous patterns are converted into the same pattern, and the heterogeneous patterns are extracted from the fusion features of data modes. The simulation results show that the perception ability and recognition ability of outdoor image recognition in complex environment are improved.
C-reactive protein as robust laboratory value associated with prognosis in patients with stage III non-small cell lung cancer (NSCLC) treated with definitive radiochemotherapy
Authors: Richlitzki Cedric , Wiesweg Marcel , Metzenmacher Martin , Guberina Nika , Pöttgen Christoph , Hautzel Hubertus , Eberhardt Wilfried E. E. , Darwiche Kaid , Theegarten Dirk , Aigner Clemens , Bölükbas Servet , Schuler Martin , Stuschke Martin , Guberina Maja 
Published: June 2024

AbstractTo evaluate the prognostic value of biomarkers from peripheral blood obtained as routine laboratory assessment for overall survival in a cohort of stage III non-small cell lung cancer (NSCLC) patients treated with definitive radiochemotherapy at a high-volume cancer center. Seven blood biomarkers from 160 patients treated with definitive radiochemotherapy for stage III NSCLC were analyzed throughout the course treatment. Parameters were preselected using univariable and multivariable proportional hazards analysis and were assessed for internal validity using leave-one-out cross validation. Cross validated classifiers including biomarkers in addition to important clinical parameters were compared with classifiers containing the clinical parameters alone. An increased C-reactive protein (CRP) value in the final week of radiotherapy was found as a prognostic factor for overall survival, both as a continuous (HR 1.099 (1.038–1.164), p < 0.0012) as well as categorical variable splitting data at the median value of 1.2 mg/dl (HR 2.214 (1.388–3.531), p < 0.0008). In the multivariable analysis, the CRP value-maintained significance with an HR of 1.105 (1.040–1.173) and p-value of 0.0012. The cross validated classifier using CRP at the end of radiotherapy in addition to clinical parameters separated equally sized high and low risk groups more distinctly than a classifier containing the clinical parameters alone (HR = 2.786 (95% CI 1.686–4.605) vs. HR = 2.287 (95% CI 1.407–3.718)). Thus, the CRP value at the end of radiation therapy has successfully passed the crucial cross-validation test. The presented data on CRP levels suggests that inflammatory markers may become increasingly important during definitive radiochemotherapy, particularly with the growing utilization of immunotherapy as a consolidation therapy for stage III NSCLC.
Kidney cancer in the Middle East and North Africa region: a 30-year analysis (1990–2019)
Authors: Safiri Saeid , Hassanzadeh Kamaleddin , Ghaffari Jolfayi Amir , Mousavi Seyed Ehsan , Motlagh Asghari Kimia , Nejadghaderi Seyed Aria , Naghdi-Sedeh Nima , Noori Maryam , Sullman Mark J. M. , Collins Gary S. , Kolahi Ali-Asghar 
Published: June 2024

AbstractKidney cancer, a type of urogenital cancer, imposes a high burden on patients. Despite this, no recent research has evaluated the burden of this type of cancer in the Middle East and North Africa (MENA) region. This study explored the burden of kidney cancer from 1990 to 2019 according to age, sex and socio-demographic index (SDI). The Global Burden of Disease (GBD) 2019 data was utilized to estimate the incidence, death, and disability-adjusted life-years (DALYs) caused by kidney cancer. These estimates were reported as counts and as age-standardised rates with 95% uncertainty intervals (UIs). The estimated age-standardised incidence, mortality, and DALY rates of kidney cancer in 2019 were 3.2 (2.8–3.6), 1.4 (1.2–1.6), and 37.2 (32.0–42.6) per 100,000, respectively. Over the period from 1990 to 2019, these rates have increased by 98.0%, 48.9%, and 37.7%, respectively. In 2019, the United Arab Emirates, Qatar, and Lebanon had the largest age-standardised incidence, mortality, and DALY rates. The smallest age-standardised incidence rates were seen in Yemen, Afghanistan, and the Syrian Arab Republic. Additionally, the smallest age-standardised mortality and DALY rates were observed in the Syrian Arab Republic, Yemen, and Morocco. The highest incidence rates were found among individuals aged 75–79 in both males and females. In 2019, the MENA/Global DALY ratio exceeded one for females aged 5–19 age and males aged 5–14, compared to 1990age groups in males. The burden of kidney cancer consistently rose with increasing SDI levels from 1990 to 2019. The increasing burden of kidney cancer highlights the urgent need for interventions aimed at improving early diagnosis and treatment in the region.
Prediction of COD in industrial wastewater treatment plant using an artificial neural network
Authors: Çimen Mesutoğlu Özgül , Gök Oğuzhan 
Published: June 2024

AbstractIn this investigation, the modeling of the Aksaray industrial wastewater treatment plant was performed using artificial neural networks with various architectures in the MATLAB software. The dataset utilized in this study was collected from the Aksaray wastewater treatment plant over a 9-month period through daily records. The treatment efficiency of the plants was assessed based on the output values of chemical oxygen demand (COD) output. Principal component analysis (PCA) was applied to furnish input for the Feedforward Backpropagation Artificial Neural Networks (FFBANN). The model’s performance was evaluated using the Mean Squared Error (MSE), the Mean Absolute Error (MAE) and correlation coefficient (R2) parameters. The optimal architecture for the neural network model was determined through several trial and error iterations. According to the modeling results, the ANN exhibited a high predictive capability for plant performance, with an R2 reaching up to 0.9997 when comparing the observed and predicted output variables.
Development of a real-time cattle lameness detection system using a single side-view camera
Authors: Myint Bo Bo , Onizuka Tsubasa , Tin Pyke , Aikawa Masaru , Kobayashi Ikuo , Zin Thi Thi 
Published: June 2024

AbstractRecent advancements in machine learning and deep learning have revolutionized various computer vision applications, including object detection, tracking, and classification. This research investigates the application of deep learning for cattle lameness detection in dairy farming. Our study employs image processing techniques and deep learning methods for cattle detection, tracking, and lameness classification. We utilize two powerful object detection algorithms: Mask-RCNN from Detectron2 and the popular YOLOv8. Their performance is compared to identify the most effective approach for this application. Bounding boxes are drawn around detected cattle to assign unique local IDs, enabling individual tracking and isolation throughout the video sequence. Additionally, mask regions generated by the chosen detection algorithm provide valuable data for feature extraction, which is crucial for subsequent lameness classification. The extracted cattle mask region values serve as the basis for feature extraction, capturing relevant information indicative of lameness. These features, combined with the local IDs assigned during tracking, are used to compute a lameness score for each cattle. We explore the efficacy of various established machine learning algorithms, such as Support Vector Machines (SVM), AdaBoost and so on, in analyzing the extracted lameness features. Evaluation of the proposed system was conducted across three key domains: detection, tracking, and lameness classification. Notably, the detection module employing Detectron2 achieved an impressive accuracy of 98.98%. Similarly, the tracking module attained a high accuracy of 99.50%. In lameness classification, AdaBoost emerged as the most effective algorithm, yielding the highest overall average accuracy (77.9%). Other established machine learning algorithms, including Decision Trees (DT), Support Vector Machines (SVM), and Random Forests, also demonstrated promising performance (DT: 75.32%, SVM: 75.20%, Random Forest: 74.9%). The presented approach demonstrates the successful implementation for cattle lameness detection. The proposed system has the potential to revolutionize dairy farm management by enabling early lameness detection and facilitating effective monitoring of cattle health. Our findings contribute valuable insights into the application of advanced computer vision methods for livestock health management.
Risk prediction of cholangitis after stent implantation based on machine learning
Authors: Zhao Rui , Gu Lin , Ke Xiquan , Deng Xiaojing , Li Dapeng , Ma Zhenzeng , Wang Qizhi , Zheng Hailun , Yang Yong 
Published: June 2024

AbstractThe risk of cholangitis after ERCP implantation in malignant obstructive jaundice patients remains unknown. To develop models based on artificial intelligence methods to predict cholangitis risk more accurately, according to patients after stent implantation in patients’ MOJ clinical data. This retrospective study included 218 patients with MOJ undergoing ERCP surgery. A total of 27 clinical variables were collected as input variables. Seven models (including univariate analysis and six machine learning models) were trained and tested for classified prediction. The model’ performance was measured by AUROC. The RFT model demonstrated excellent performances with accuracies up to 0.86 and AUROC up to 0.87. Feature selection in RF and SHAP was similar, and the choice of the best variable subset produced a high performance with an AUROC up to 0.89. We have developed a hybrid machine learning model with better predictive performance than traditional LR prediction models, as well as other machine learning models for cholangitis based on simple clinical data. The model can assist doctors in clinical diagnosis, adopt reasonable treatment plans, and improve the survival rate of patients.
Assessing the causal relationship between circulating immune cells and abdominal aortic aneurysm by bi-directional Mendelian randomization analysis
Authors: Ruan Weiqiang , Zhou Xiaoqin , Wang Ting , Liu Huizhen , Zhang Guiying , Sun Jiaoyan , Lin Ke 
Published: June 2024

AbstractAlthough there is an association between abdominal aortic aneurysm (AAA) and circulating immune cell phenotypes, the exact causal relationship remains unclear. This study aimed to explore the causal relationships between immune cell phenotypes and AAA risk using a bidirectional two-sample Mendelian randomization approach. Data from genome-wide association studies pertaining to 731 immune cell traits and AAA were systematically analyzed. Using strict selection criteria, we identified 339 immune traits that are associated with at least 3 single nucleotide polymorphisms. A comprehensive MR analysis was conducted using several methods including Inverse Variance Weighted, Weighted Median Estimator, MR-Egger regression, Weighted Mode, and Simple Median methods. CD24 on switched memory cells (OR = 0.922, 95% CI 0.914–0.929, P = 2.62e−79) at the median fluorescence intensities level, and SSC-A on HLA-DR + natural killer cells (OR = 0.873, 95% CI 0.861–0.885, P = 8.96e−81) at the morphological parameter level, exhibited the strongest causal associations with AAA. In the reverse analysis, no significant causal effects of AAA on immune traits were found. The study elucidates the causal involvement of multiple circulating immune cell phenotypes in AAA development, signifying their potential as diagnostic markers or therapeutic targets. These identified immune traits may be crucial in modulating AAA-related inflammatory pathways.
Weakened continental lithosphere beneath the northern Red Sea inferred from elastic thickness
Authors: Jallouli Chokri , Al-Dini Waleed , Mogren Saad , Alzahrani Hassan 
Published: June 2024

AbstractThe northern Red Sea (NRS) is considered an extended continental region that has resulted in a rift system. Gravity and bathymetry data were used to estimate the Moho depth and the elastic thickness Te of the lithosphere beneath the NRS region to characterize its flexural rigidity and understand its mechanical behavior. Focusing on the Mabahiss Deep in NRS, we analyzed the lithosphere's flexural rigidity. The observed long-wavelength positive Bouguer anomaly is attributed to crustal thinning and lithospheric mantle uplift. The crustal thickness varies from 28 km in coastal areas to 24 km beneath the axial rift, supporting a regional compensation model over the Airy model. Forward modeling suggests that the optimal model explaining the regional Bouguer anomaly is a flexural model with Te equal to 7 km, indicating a weak and irregular continental crust. The primary factor contributing to this weakness is heating activity. Given the weakened state of the crust and the ongoing extension in the region, the NRS rift could evolve into a rupture, potentially leading to the formation of oceanic crust.
Molecular basis for receptor recognition and broad host tropism for merbecovirus MjHKU4r-CoV-1
Authors: Zhao Zhennan , Li Xin , Chai Yan , Liu Zhifeng , Wang Qihui , Gao George F 
Published: June 2024

AbstractA novel pangolin-origin MERS-like coronavirus (CoV), MjHKU4r-CoV-1, was recently identified. It is closely related to bat HKU4-CoV, and is infectious in human organs and transgenic mice. MjHKU4r-CoV-1 uses the dipeptidyl peptidase 4 (DPP4 or CD26) receptor for virus entry and has a broad host tropism. However, the molecular mechanism of its receptor binding and determinants of host range are not yet clear. Herein, we determine the structure of the MjHKU4r-CoV-1 spike (S) protein receptor-binding domain (RBD) complexed with human CD26 (hCD26) to reveal the basis for its receptor binding. Measuring binding capacity toward multiple animal receptors for MjHKU4r-CoV-1, mutagenesis analyses, and homology modeling highlight that residue sites 291, 292, 294, 295, 336, and 344 of CD26 are the crucial host range determinants for MjHKU4r-CoV-1. These results broaden our understanding of this potentially high-risk virus and will help us prepare for possible outbreaks in the future.
Detecting Hearing Loss Through Targeted Surveillance: Risk Registry and Surveillance Timeframe Recommendations
Authors: Fitzgibbons E. Jane , Driscoll Carlie , Traves Lia , Beswick Rachael 
Published: June 2024

Purpose:
The purpose of this study was to inform the revision of a targeted surveillance risk registry by identifying which risk factors predict postnatally identified hearing loss (PNIHL) in children who pass newborn hearing screening and to determine whether hearing surveillance beyond the age of 1 year is warranted.

Method:
We used retrospective analysis of the audiological outcomes of children born in the state of Queensland, Australia, between January 1, 2010, and December 31, 2019, who passed the newborn hearing screen with risk factors.

Results:
Approximately one third of children were lost to follow-up and could not be included in the analysis. Risk factors that predicted PNIHL in the analyzed cohort were as follows: syndromes associated with hearing loss, craniofacial anomalies, perinatal infections, and family history of permanent childhood hearing loss. Severe asphyxia did not predict PNIHL but yielded some cases of significant bilateral hearing loss. Hearing loss in children with a history of prolonged ventilation was mild and/or unilateral in nature (except in cases where the hearing loss was due to an unrelated etiology). There were no cases of PNIHL in children with hyperbilirubinemia or neonatal bacterial meningitis. For the risk factors that predicted PNIHL, nearly all hearing losses were detected by 1 year of age, except for children with family history where one quarter of hearing losses had a later onset.

Conclusions:
The four risk factors recommended for efficient postnatal identification of hearing loss are as follows: syndromes associated with hearing loss, craniofacial anomalies, perinatal infection, and family history of permanent childhood hearing loss. Hearing surveillance through to 1 year old is sufficient except for children with a family history, where a second phase assessment is indicated. Alternative targeted surveillance protocols and models of care are required to minimize loss to follow-up.