bluuebunny/crossref_metadata_2025_split huggingface dataset:
Expert consensus on the structure and content of an enhanced care pathway for psychological changes after stroke in the UK: A modified Delphi survey
Authors: Hobden Georgina , Tang Eugene Yee Hing , Demeyere Nele 
Published: July 2024

Abstract

Background
Enhancing long-term support for post-stroke cognitive impairment is a top research priority. Addressing current gaps in UK post-stroke cognitive care provision requires a pragmatic and scalable intervention that can be integrated in the existing stroke care pathway. This study aimed to develop consensus on core features of a UK-based monitoring and psychoeducational intervention for cognitive changes after stroke.
Methods
An expert panel of UK healthcare professionals and researchers participated in an online modified Delphi survey. Candidate intervention features were identified from clinical guidelines, existing literature, research team/collaborator expertise, and PPI group lived experience. Survey participants indicated whether they agreed/disagreed/had no opinion about including each candidate feature in the intervention and free-text responses were invited. We analysed responses for consensus (≥ 75% agreement) using descriptive statistics, with items not reaching consensus carried into subsequent rounds. Template analysis was used to identify similarities/differences in viewpoints for items that did not reach consensus.
Results
The survey rounds were completed by 36, 29 and 26 participants, respectively. Participants agreed reviews should include a stroke-specific cognitive screen (97% agree) and assessment of other psychological changes (low mood, anxiety, fatigue: 94%, 90%, 89% agree, respectively). They agreed stroke survivors should be offered at least one review, regardless of their psychological presentation in hospital. They agreed on the importance of various psychoeducation topics, and formal (100% agree) and informal (79% agree) training for those conducting reviews. Consensus was not reached on the review mode (in person/remote options: 67% agree), offering reviews one-year post-discharge to patients without impairments detected in hospital (68% disagree), or including a dementia screen (63% disagree) and/or neuropsychological assessment battery (58% disagree). However, there were similarities in participant viewpoints – for example, participants highlighted the importance of onwards referral where clinically indicated.
Conclusions
The UK-based post-stroke monitoring and psychoeducation intervention was originally conceptualised as a cognitive care pathway, but expert participants agreed on the importance of also addressing related psychological changes (eg low mood, anxiety). There was clear consensus on a minimum set of intervention features. Recommendations outlined here may usefully inform local service improvements. Future research will evaluate the impact of the intervention on patient outcomes.
MtL-NFW: A Meta-Learning Framework for Automated Noise Filter Selection and Hyperparameter Optimization in Auto-ML
Authors: Khan Irfan , Zhang Xianchao , Kumar Ramesh , Alhashmi Saadat M. , Ali Rahman 
Published: July 2024

Abstract

The extensive implementation of machine learning (ML) has transformed data analysis and decision-making processes. However, the process of choosing suitable ML algorithms for a specific task remains challenging, commonly referred to as the Algorithm Selection Problem (ASP) in academic literature. Automated machine learning (Auto-ML) tackles algorithm selection and hyperparameter adjustments by automating these processes, hence decreasing the required time and expertise. The current study is focused on the automated selection of noise filters for the task of classifying data with noisy labels. Class label noise, resulting from erroneous data labels, has a greater effect on the learning process compared to attribute-level noise. The existing Auto-ML approaches for selecting suitable noise filters overlook hyperparameter adjustment, which is important for the effectiveness of noise filters. Instead, it relies on computationally demanding techniques such as grid search. In addition, the current Auto-ML algorithms utilize random noise imputation techniques in their methodologies that are not 1 able to adequately capture the complex noise distributions typically present in real-world datasets. In order to overcome these limitations, we propose the introduction of Meta-learning for Noise-Filter Workflow Selection (MtL-NFW), an innovative framework that automate and integrate adequate hyperparameter selection along with noise filter for a certain task. This eliminates the need to rely on manual and computationally intensive methods for hyperparameters settings. Our second contribution is to leverage and employ the Neighbor-wise noise imputation technique to our framework, that induce noise based on proximity to class boundaries, generating more robust meta-training data. Our framework evaluates 114 classification datasets with six noise filtering algorithms and Random Forest as the base classifier, extracting six groups of meta-features and generating a meta-knowledge base of 51,642 configurations. XGBoost trained as meta-learner, predicts and recommends optimal noise filter and hyperparameter combinations for new datasets. Empirical validation on 114 OpenML datasets shows that MtL-NFW significantly improves the accuracy and robustness of noise filter recommendations, outperforming four state-of-the-art Auto-ML methods.
The effect of telomeres in cervical cancer
Authors: Xu Cong , Xu Yonghong , Cao Qing , Luo Guoling , Yu Jingwen , Wang Guangming 
Published: July 2024

Abstract

Globally, cervical cancer ranks as a prevalent cancer among women and stands as the fourth leading cause of mortality in gynecological cancers. Yet, it's still uncertain how telomeres impact cervical cancer. This research involved acquiring telomere associated genes (TRGs) from TelNet. Clinical data and TRGs expression levels of cervical cancer patients were acquired from the Cancer Genome Atlas (TCGA) database. Within the TCGA-CESC data collection, 327 TRGs were identified between cancerous and healthy tissues, with these genes, which differ in telomeres and are closely linked to cervical cancer, playing a role in various functional processes, predominantly in the cell cycle, DNA replication, and DNA replication. Key genes such as cellular aging, repair of double-strand breaks, and the Fanconi anemia pathway, among others, play a significant role in the cell's life cycle. Dysfunction in these genes could lead to irregularities in the body's cell synthesis and apoptosis processes, potentially hastening cervical cancer's advancement. Subsequently, the data was sequentially analyzed using single-factor cox regression, lasso regression, and multi-factor cox regression techniques, culminating in the creation of the TRGs risk model. Within the discovered TCGA group (p < 0.001), patients with cervical cancer in the group at high risk of TRGs experienced worse results. Furthermore, the TRGs risk score emerged as a standalone risk element for renal cancer. Furthermore, populations vulnerable to TRGs could gain advantages from the administration of specific therapeutic medications. To sum up, our team developed a genetic risk model linked to telomeres to forecast cervical cancer patients' outcomes, potentially aiding in choosing treatment medications for these patients.
Gum Arabic Magnetized Graphene Oxide Composite for the Removal of Fluorescent Dye Fluorescein from Aqueous Solutions: Equilibrium, Kinetic and Thermodynamic Studies
Authors: Ali Anwar , Khitab Fatima , Shah Jasmin , Jan Muhammad Rasul 
Published: July 2024

Abstract

In recent years, due to industrialization water pollution is the major issue. The aim of the present work was the synthesis of solid Gum Arabic Magnetized Graphene Oxide (GA-MGO) and its application for the removal of fluorescent dye, fluorescein from aqueous samples. The characterization of the composite was carried out through FTIR, SEM, EDX, point of zero charge, and surface area. Various parameters optimization such as pH, shaking time, adsorbent dose, adsorbate concentration, temperature, and their influence on the removal of fluorescein using GA-MGO composite during the adsorption process was studied. The synthesized GA-MGO exhibited high adsorption efficiency of 94.18 % in 60 mins (pH 2, fluorescein 20 µg mL-1, 0.08 g L-1 of adsorbent). The synthesized GA-MGO composite has a high capacity to remove fluorescein molecules from aqueous solution at 25 °C. The kinetics of adsorption was investigated and it was found that adsorption followed pseudo-second-order kinetics with the regression coefficient R2 value of 0.962. The R2 value of Langmuir (0.985) and Freundlich (0.894) showed that the Langmuir adsorption isotherm fitted best to the adsorption process. Different thermodynamic parameters, Gibb’s free energy and Van’t Hoff equation reveals tht the proposed reaction is spontaneous and exothermic. Regeneration of adsorbent/ desorption of dye and application of composite to the sample of sewage and industrial effluents results in better adsorption. The finding reveals the feasibility of GA-MGO composite as potential precursor for the effective adsorption of fluorescent dyes due to its high surface area.
Changes in various forms of fibronectin in patients undergoing coronary artery bypass grafting with cardiopulmonary bypass
Authors: Lemańska-Perek Anna , Krzyżanowska-Gołąb Dorota , Wysoczański Grzegorz , Barteczko-Grajek Barbara , Goździk Waldemar , Adamik Barbara 
Published: July 2024

Abstract

Coronary heart disease is one of the most common lifestyle diseases and one of the treatment methods is coronary artery bypass grafting. The use of cardiopulmonary bypass (CPB) during this procedure is associated with the transient activation of a systemic inflammatory response and may lead to several intra-and postoperative complications. Fibronectin (FN), an adhesive glycoprotein that is a key component of the extracellular matrix, is widely distributed in human body fluids. It is one of the endogenous mediators involved in both the inflammatory response and coagulation. We aimed to detect changes in the level of fibronectin and its possible fragmentation or forming of complexes with fibrine during coronary artery bypass grafting with cardiopulmonary bypass. Our results indicate that the CPB procedure is associated with significant changes in levels of fibronectin. Elevated levels after surgery, as well as slight increases in the amount of FN degradation products in relation to the duration of CPB may suggest that these increases are related to extracellular matrix remodeling resulting from cardiac surgery and the initiation of repair processes. The results indicate that fibronectin has clinical potential as a marker of repair processes.
YOLOX-SEH: Efficient Small Target Detection in Remote Sensing Images
Authors: Song Qingzeng , Zhang Honggeng , xue yongjiang 
Published: July 2024

Abstract

Object detection is a crucial and challenging problem in computer vision. In remote sensing image object detection, objects can appear in any orientation, complicating accurate detection. Additionally, the presence of small objects and environmental factors pose significant challenges to existing deep learning-based object detection algorithms. To address these issues, this paper proposes a YOLOX-based object detection algorithm named YOLOX-SEH. To enhance the detection accuracy of objects appearing in any orientation, a Swin Transformer structure is incorporated into the YOLO framework along with other optimizations. Specifically, the Swin Transformer employs sliding windows and hierarchical structures for more efficient and flexible computation, capturing global information from multiple scales with improved feature extraction capability. Additionally, an Explicit Visual Center (EVC) structure is introduced to capture global long-range dependencies through a lightweight MLP network, enhancing the accuracy and efficiency of object detection algorithms. To improve the detection of small objects, an additional prediction head is added to detect tiny objects, complementing YOLOX’s original three prediction heads for effective multi-scale object detection. Extensive experiments on the DOTA v1.0 and DIOR datasets demonstrate that our YOLOX-SEH performs well in remote sensing image detection with a small model size of only 8MB.
An image analysis method to study the relationship between seagrasses and their epiphytes
Authors: Huang Chi , Mehrubeoglu Mehrube , Piñón Carissa , Cammarata Kirk 
Published: July 2024

Abstract

Seagrasses are globally threatened due to increasing environmental stressors in coastal ecosystems. Excessive accumulation of algal epiphytes is suggested to be harmful to seagrasses, but an understanding of the quantitative relationship between epiphyte accumulation and effects on seagrass is still incomplete. The biomass, pigment and morphological measures widely used in understanding seagrass-epiphyte relationships provide limited spatiotemporal resolution of the dynamics of epiphyte colonization relative to host leaf growth and senescence. In this study, color scanning and image analysis methods were developed and validated to characterize epiphyte accumulation relative to seagrass host morphology. Thalassia testudinum collected monthly or bimonthly from July 2019 to April 2020 in Redfish Bay, Texas, was analyzed through traditional and image-based measures. Spectral Angle Mapper (SAM) algorithms within the ENVI Program distinguished the pixels of many epiphytes collectively from epiphyte-free leaf areas based on RGB color bands. Misclassified pixels were < 10% in 94% of the analyzed images. Classification accuracy was also evaluated by correlation of traditional biomass and morphology metrics vs. image-based metrics for more than 2,000 seagrass and epiphyte images collected across different seasons and environmental conditions. Image-derived seagrass metrics strongly correlated with traditional metrics, but linear regressions of epiphyte biomass vs. epiphyte area (pixels) and biomass ratio of epiphyte to seagrass vs. epiphyte coverage (epiphyte pixels/leaf pixels) were weaker. This was likely due to seasonal and environmental influences on both the epiphyte community composition and the seagrass-epiphyte relationship, since image analysis does not capture the depth of epiphyte growth on the leaf. The image analysis method provides insight into the spatiotemporal accumulation of epiphytes on the seagrass host and has the potential to be adapted to detect environmentally-induced changes in the community composition of epibiota.
The role of emotional stability in employees' job competence, job stress, and mental health: a moderated mediation model
Authors: Liang Zhengyan , Zhang Mingqiang , Tang Tian , Ye Hong , Yang Mei , Yao Min , Chen Jiayu , Li Hao , Zeng Yuqing 
Published: July 2024

Abstract

This study focused on the role of emotional stability in employees' job competence, job stress, and mental health. The research utilized surveys conducted online and on-site, with 839 valid questionnaires collected by police officers in China. The findings revealed that job stressors had a full mediating effect on the relationship between job competence and mental health. Emotional stability not only moderates competence and stressors but also moderates stressors and mental health. However, the results of the multigroup structural equation model revealed that job competence was positively correlated with job stressors in civilian police officers but not in auxiliary police officers. Our study aids in predicting for whom will most likely be related to increased or decreased job stressors and mental health (i.e., individual low emotional stability); these findings have important theoretical and managerial implications.
A predictive model for identifying pulmonary lesions based on CT features of rimmed signs and satellite lesions: A multicentre study
Authors: cao zhenyu , Xu Gang , Xu Jianying , Tian Fengjuan , Shi Hengfeng , Yang Dengfa , Xie Zongyu , Wang Jian 
Published: July 2024

Abstract

Objectives
To verify a differential prediction model based on different lung pathology types utilizing the rimmed sign (RS) and satellite lesion (SL) methods to enhance identification efficiency.
Methods
From January 2015 to October 2023, the clinical data and chest CT images of 3030 patients with lung lesions were analysed. The lesions were divided into three groups: malignant, inflammatory, and benign. The pairwise identification models were constructed separately with (Models1-3) or without (Models1*-3*) rimmed signs and satellite lesions, and the developing and test group were divided by 7:3. Six models were built to distinguish between these groups (Model1 or Model1*: malignant vs. inflammatory; Model2 or Model2*: malignant vs. benign; Model3 or Model3*: inflammatory vs. benign). The curve (AUC) was calculated to evaluate the performance of these models. The Delong test was used to compare the differences between different models.
Results
In the test group, the AUC and Accuracy of Models1-3 and Models1-3* were 0.920/84.8%, 0.990/96.2%, 0.881/76.4%, and 0.900/73.5%, 0.989/90.1%, and 0.869/78.6%, respectively. The Delong test showed no significant difference between Models1-3 and Models1-3* (p > 0.05), Accuracy (T1, T2) > Accuracy (T1*, T2*), Accuracy (T3) < Accuracy (T3*).
Conclusions
The six prediction models in this study effectively differentiated among different types of lung lesions, with the rimmed sign and satellite lesion features improving the accuracy of Model1 and Model2.
Construction and Application of Early Stratification Dynamic Prediction Model for Bronchopulmonary Dysplasia in Extremely Premature / Very Low Birth Weight Infants
Authors: An Ning , Yang Jingwen , Zhang Rong , Han Wen , Zhou Xuchen , Yang Rong , Zhu Yanping , Zhao Ting , Li Mingxia 
Published: July 2024

Abstract

Objective
To investigate the independent risk factors for Bronchopulmonary Dysplasia (BPD) at different time points within the first week in extremely premature/very low birth weight infants and to construct an early stratification dynamic prediction model for BPD through machine learning, aiming to achieve dynamic prediction of BPD for the early identification of high-risk groups and preemptive prevention.
Methods
A retrospective collection of clinical data was conducted on premature infants admitted to the Neonatology Department of the First Affiliated Hospital of Xinjiang Medical University from January 2017 to December 2022, with gestational age (GA) < 32 weeks or birth weight (BW) < 1500g. Eligible subjects were randomly divided into training and validation sets in a 7:3 ratio for model building and internal validation. Prospective clinical data from preterm infants admitted to six neonatal rescue centers in various districts of Xinjiang from January to October 2023 were independently collected to validate the practical application value of each model. Clinical parameters were collected, and study participants were divided into three groups: no BPD, mild BPD, and moderate to severe BPD (msBPD). Machine learning predictive models for BPD stratification employing logistic regression (LR), random forest (RF), XGBoost (XGB), and gradient boosting decision tree (GBDT) were constructed for postnatal days 1, 3, and 7. Comprehensive evaluation was performed to select the optimal model at each time point and proceed to external validation.
Results
The study retrospectively gathered data from 554 preterm infants (286 no BPD, 212 mild, and 56 msBPD cases). Prospectively, 387 preterm infants (208 no BPD, 138 mild, and 41 msBPD cases). On ordinal logistic regression, GA, BW, prenatal steroids, interruption of umbilical blood flow, severe preeclampsia, FIO2, CRP, RBC, systemic inflammatory response index (SIRI), prognostic nutritional index, platelet mass index, alveolar-arterial oxygen difference, and oxygenation index were independent risk factors for BPD severity at different times after birth. After comprehensive evaluation, the LR and XGB models were identified as better BPD stratification prediction models for postnatal days 1, 3, and 7 (AUC = 0.810,0.837 and 0.813 respectively).
Conclusion
Early stratification dynamic prediction machine learning models for BPD have been constructed for postnatal days 1, 3, and 7 in extremely premature/very low birth weight infants. These may serve as effective tools for the screening of high-risk BPD populations.
Quantifying the Non-isomorphism of Global Urban Road Networks using GNNs and Graph Kernels
Authors: Tian Linfang , Rao Weixiong , Zhao Kai , Vo Huy T. 
Published: July 2024

Abstract

The concept of quantifying graph non-isomorphism aims to establish a more flexible framework, overcoming the stringent limitations inherent in graph isomorphism. This approach enables the measurement of the degree of difference between two graphs using appropriate metrics, making it broadly applicable in scenarios where strict graph isomorphism is not required. In this paper, we employ Graph Neural Networks (GNNs) and graph kernels to quantify the non-isomorphism of road networks, analyzing 10,361 road networks from 30 cities worldwide. Our results indicate that Edge Convolutional Neural Network (EdgeCNN) outperforms the Weisfeiler-Lehman (WL) kernel.Therefore, we challenge the assertion that "GNNs are at most as powerful as the WL test in distinguishing graph structures," highlighting that this claim overlooks the diversity and significance of node attributes and is limited to homogeneous graphs. In practice, node attributes are crucial in graph evaluation. Compared to graph kernels, GNNs can more effectively and comprehensively leverage node attributes, thereby enhancing the accuracy of heterogeneous graph classification. By quantifying graph non-isomorphism, we not only gain deeper insights into the differences in urban road networks but also provide quantitative evidence for urban road network planning, promoting more scientifically sound urban development strategies.
Histograms of projected normal vector distribution for 3D local surface description
Authors: Sa Jiming , Zhang Xuecheng , Zhang Chi , Song Yuyan , Ding Liwei , Huang Yechen 
Published: July 2024

Abstract

Existing methods for 3D local feature description often struggle to achieve a good balance in distinctiveness, robustness, and computational efficiency. To address this challenge, a novel 3D local feature descriptor named Histograms of Projected Normal Vector Distribution (HPNVD) is proposed. The HPNVD descriptor consists of two main components. First, a Local Reference Frame (LRF) is constructed based on the covariance matrix and neighborhood projection to achieve invariance to rigid transformations. Then, the local surface normals are projected onto three coordinate planes within the LRF, which allows for effective encoding of the local shape information. The projection plane is further divided into multiple regions, and a histogram is computed for each plane to generate the final HPNVD descriptor. Experimental results demonstrate that the proposed HPNVD descriptor achieves a good balance among distinctiveness, robustness, and computational efficiency. Moreover, the HPNVD-based point cloud registration algorithm shows excellent performance, further validating the effectiveness of the descriptor. The source code for the proposed method and the dataset used in the paper can be accessed at: https://github.com/zhangxuecheng1998/HPNVD-descriptor-and-datasets. The DOI for this repository is 10.5281/zenodo.12567370.