bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lipoprotein (a)-Related Inflammatory Imbalance: A Novel Horizon for the Development of Atherosclerosis
Authors: Qin Ting , Ma Tian-Yi , Huang Kang , Lu Shi-Juan , Zhong Jiang-Hua , Li Jian-Jun 
Published: June 2024

Abstract
Purpose of Review
The primary objective of this review is to explore the pathophysiological roles and clinical implications of lipoprotein(a) [Lp(a)] in the context of atherosclerotic cardiovascular disease (ASCVD). We seek to understand how Lp(a) contributes to inflammation and arteriosclerosis, aiming to provide new insights into the mechanisms of ASCVD progression.

Recent Findings
Recent research highlights Lp(a) as an independent risk factor for ASCVD. Studies show that Lp(a) not only promotes the inflammatory processes but also interacts with various cellular components, leading to endothelial dysfunction and smooth muscle cell proliferation. The dual role of Lp(a) in both instigating and, under certain conditions, mitigating inflammation is particularly noteworthy.

Summary
This review finds that Lp(a) plays a complex role in the development of ASCVD through its involvement in inflammatory pathways. The interplay between Lp(a) levels and inflammatory responses highlights its potential as a target for therapeutic intervention. These insights could pave the way for novel approaches in managing and preventing ASCVD, urging further investigation into Lp(a) as a therapeutic target.
Effect of hot water maceration, rehydration, and soft tissue presence on 3D geometry of bone
Authors: Bittner-Frank Martin , Strassl Andreas , Unger Ewald , Hirtler Lena , Kainberger Franz , Windhager Reinhard , Moscato Francesco , Benca Emir 
Published: June 2024

Abstract
Purpose
In forensic medicine, maceration is often essential for examining bone surfaces, serving purposes such as identifying cut marks, making geometric measurements, and determining the victim’s age. While hot water maceration removes soft tissue effectively, it is known to cause bone surface shrinkage. This raises the question of whether this effect is permanent or if it can be partially reversed through rehydration, considering the presence of soft tissue.

Methods
Computed tomography (CT) scans were conducted on the radii of 20 paired human anatomic forearm specimens. Subsequently, the radii were extracted, macerated in 60 °C water, CT-scanned in an air environment, rehydrated, re-implanted into the forearms, and CT-scanned again.

Results
Maceration resulted in a mean shrinkage of 0.12 mm on the outer bone surface. This shrinkage was nearly fully recoverable for the diaphysis after rehydration and accounting for soft tissue surrounding the bone. In contrast, the epiphysis showed permanent shrinkage, likely due to the loss of small bone fragments. Analysis of the inner bone surface indicated a smaller effect, but with significant standard deviations, especially for the epiphysis, possibly related to the less well-defined nature of the inner bone surface.

Conclusion
The epiphyseal surface of hot water-macerated bone will, on average, be approximately 0.15 mm deflated and cannot retain the original surface. On the other hand, the diaphyseal surface is less affected and can be nearly completely restored after rehydration and accounting for soft tissue surrounding the bone.
Top-down and bottom-up coupling effects of subsidies on recipient ecosystems
Authors: Osakpolor Stephen E. , Manfrin Alessandro , Leroux Shawn J. , Schäfer Ralf B. , McLeod Anne M. 
Published: June 2024

AbstractA subsidy can directly enter a recipient ecosystem by either being consumed or being recycled to the nutrient pool with both pathways causing multiple indirect and potentially conflicting effects. Subsidy pathways are prone to anthropogenic stressors, which can impact the relative strength of the recycling and direct consumption fluxes. Current studies have mainly focused on a single coupling pathway, limiting our knowledge of subsidy effects on recipient ecosystems. We derive a series of models to predict the relative and interactive effects between consumption and recycling subsidy coupling pathways on the biomass and functions of a recipient ecosystem. We solved the models for analytical equilibria, and compared model predictions with and without each subsidy pathway. Our results show that the direct consumption and recycling coupling pathways of subsidies interact antagonistically, as the feedbacks between both pathways led to lower stocks and functions of the recipient ecosystem than a model which omits these feedbacks. These subsidy effects are consistent for each trophic level of the recipient ecosystem. Our models also predicted that consumption and recycling pathways of subsidies had differential effects on the recipient ecosystem. Recycling coupling pathway always led to equal or higher stocks and functions across recipient ecosystem trophic levels, whereas consumption couplings had alternating positive and negative effects depending on trophic level and characteristic of a trophic cascade. We suggest that future empirical and theoretical research on subsidies should consider various coupling pathways to provide a more mechanistic understanding of the effects of subsidies.
Factors associated with the adjustment of typically developing siblings from single-incidence, multiple-incidence and non-autism spectrum disorders families
Authors: Şengül Erdem Hatice , Şengül Avşar Asiye 
Published: June 2024

AbstractThis study investigated the broader autism phenotype (BAP) in mothers and siblings and the mothers’ stress and perceived support levels as factors affecting typically developing siblings’ adjustment by introducing the number of children with autism spectrum disorders (n-ASD) as a factor. The sample consisted of 25 families with multiple-incidence autism spectrum disorder (ASD) children (multiplex families), 38 families with single-incidence ASD children (simplex families), and 46 families with non-ASD children. The data were collected via mothers by the Autism-Spectrum Quotient, Family Support Scale, Questionnaire on Resources and Stress, Social Communication Questionnaire, and Strengths and Difficulties Questionnaire. ANOVA, Kruskal-Wallis H-test, and multiple linear regression analysis were used to analyze the data. According to the findings, siblings and mothers’ BAP, maternal stress, and perceived support levels significantly differed depending on the n-ASD, and the n-ASD was a significant predictor of both siblings’ problem behaviors and prosocial behaviors. These findings were discussed, and limitations and suggestions were included.
The role of minimal residual disease and serum free light chain ratio in the management of multiple myeloma
Authors: Zhu Long-Ying , Hu Qi-Lei , Zhang Liang , Li Zuo-Jie 
Published: June 2024

AbstractMultiple myeloma (MM) denotes a cancerous growth characterized by abnormal proliferation of plasma cells. Growing evidence suggests that the complexity in addressing MM lies in the presence of minimal residual disease (MRD) within the body. MRD assessment is becoming increasingly important for risk assessment in patients with MM. Similarly, the levels of serum free protein light chain and their ratio play a crucial role in assessing the disease burden and changes in MM. In this paper, we review and explore the utilization of MRD and serum free light chain ratio in the treatment of MM, delving into their respective characteristics, advantages, disadvantages, and their interrelation.
Predicting properties of nodes via community-aware features
Authors: Kamiński Bogumił , Prałat Paweł , Théberge François , Zając Sebastian 
Published: June 2024

Abstract
This paper shows how information about the network’s community structure can be used to define node features with high predictive power for classification tasks. To do so, we define a family of community-aware node features and investigate their properties. Those features are designed to ensure that they can be efficiently computed even for large graphs. We show that community-aware node features contain information that cannot be completely recovered by classical node features or node embeddings (both classical and structural) and bring value in node classification tasks. This is verified for various classification tasks on synthetic and real-life networks.
Unveiling the secretome of Penicillium fuscoglaucum JAM-1 for efficient dual substrate degradation and waste valorization
Authors: Joshi Namrata , Grewal Jasneet , Stasiuk Robert , Drewniak Lukasz , Pranaw Kumar 
Published: June 2024

AbstractIn the pursuit of cost-effective and superior enzymes crucial for the efficient hydrolysis of diverse lignocellulosic biomasses, filamentous fungi have emerged as key candidates for bioprospecting endeavors. In our exploration for potent lignocellulosic biomass degraders, we have identified a strain of Penicillium fuscoglaucum JAM-1, showcasing multipurpose hydrolase capabilities in its secretome. During fermentation, P. fuscoglaucum JAM-1 effectively utilized rapeseed cake (RSC), resulting in improved enzymatic activities, including xylanase (612 U/gds), β-glucosidase (264 U/gds), endoglucanase (102 U/gds), FPase (21.3 U/gds), and exo-polygalacturonase (49.17 U/gds), as compared to pine sawdust (PSD). Secretome profiling revealed a protein abundance totaling 435 and 120 proteins during RSC and PSD utilization, respectively. The major component of carbohydrate-active enzymes (CAZymes) consists of cellulose-degrading proteins, including endoglucanases (GH5, GH7), β-glucosidases (GH1, GH3, GH17), and cellobiohydrolases (GH6, GH7). Correspondingly, hemicellulose-degrading enzymes were present, encompassing endo-1,4-xylanase (GH10), α/β-galactosidase (GH27, GH35, GH36), α/β-mannosidases (GH38, GH2, GH47, GH5), and α-l-arabinofuranosidase (GH43, GH62, GH51, GH54) and carbohydrate-active auxiliary activities enzymes, such as AA9 (formerly known as GH61) lytic polysaccharide monooxygenase (LPMO). Upon application to fruit waste, the crude enzyme demonstrated higher saccharification potential compared to commercial cellulase (Cellic CTec2). Specifically, the crude enzyme yielded 565 mg/g of reducing sugar within 72 h, outperforming Cellic CTec2, which yielded 352 mg/g under identical conditions. A comprehensive comparative analysis of enzyme workings, activities, and secretome profiling underscores P. fuscoglaucum JAM-1 as a potent cellulase producer, showcasing its potential to boost lignocellulose biodegradation. These findings highlight the practical applications of the P. fuscoglaucum JAM-1 in various industrial processes, suggesting its role as a valuable candidate for further exploration and exploitation in biotechnological applications.
Graphical abstract
Remote sensing for assessing the impact of forest fire severity on ecological and socio-economic activities in Kozan District, Turkey
Authors: Ibrahim Sa’ad , Kose Mustafa , Adamu Bashir , Jega Idris Mohammed 
Published: June 2024

AbstractMonitoring the ecological and socioeconomic impacts of wildfires using traditional approaches requires significant financial resources, time, and sampling expertise. However, not only are resources scarce, but the spatial and temporal extent of forest fires can also make it impractical to assess large areas over time. Thus, fire monitoring initiatives are often not realized. This has inevitably made the remote sensing approach an interesting option for fire protection managers and decision-makers due to its ability to measure large areas and its temporal capabilities. In this study, burn spectral indices derived from Landsat 8 (difference normalized vegetation index (dNDVI) and difference normalized burn ratio (dNBR)) were used to assess the ecological and socioeconomic impacts of forest fires based on an existing land use/land cover dataset. The relationships between estimated fire severity/area and environmental and anthropogenic factors were also evaluated. The results show that more than 700 hectares of forest and other land use categories were burned. Fires adversely affect high forests, thickets, degraded forests, and most cultivated and rural areas. The study also revealed a moderate positive relationship between burn severity and pre-fire vegetation (R2 = 0.48 and R2 = 0.49 for the dNDVI and dNBR, respectively). This result suggested that the fuel amount is the main driver of burn severity during the fire season in this particular ecosystem. Topography has been shown to affect fire behavior in the study area, where fires occur primarily at elevations averaging 400-800 meters above mean sea level. In contrast, there is a weak positive relationship between population density and burnt area. This phenomenon is commonly observed in specific regions, where the incidence of fire is directly proportional to the density of the population. However, the severity decreases when burning exceeds a threshold. This study has shown that Landsat 8 data-derived burn spectral indices (dNDVI and dNBR) have high potential for the spatial analysis of wildfires.
Period-like polynomials for L-series associated with half-integral weight cusp forms
Authors: Branch James , Diamantis Nikolaos , Raji Wissam , Rolen Larry 
Published: June 2024

AbstractGiven the L-series of a half-integral weight cusp form, we construct polynomials behaving similarly to the classical period polynomial of an integral weight cusp form. We also define a lift of half-integral weight cusp forms to integral weight cusp forms that are compatible with the L-series of the respective forms.
An iterative two-phase optimization method for heterogeneous multi-drone routing problem considering differentiated demands
Authors: Liu Huan , Wu Guohua , Yuan Yufei , Wang Dezhi , Zheng Long , Zhou Wei 
Published: June 2024

AbstractOwing to low cost, high flexibility and delivery efficiency, effectively addressing the challenges of “last-mile” delivery. While collaborative truck-drone delivery systems have been proposed to overcome limitations such as limited battery life and payload capacity, they are not well-suited for large and heavy parcel delivery. To solve the issue, a pioneering heterogeneous multi-drone delivery system. In this system, the mother drone handles the delivery of large and heavy parcels, releasing small drones to manage the delivery of smaller and lighter parcels. To address the complexities of this multi-drone delivery system, we introduce a divide-and-conquer framework consisting of two integral phases. The first phase, the task allocation phase, generates multiple task allocation schemes, while the second phase, the single-drone route planning phase, produces high-quality routes for each individual drone. Two phases are performed in an iterative manner until the predefined stopping criteria are satisfied. In the task allocation phase, we propose a simulated annealing algorithm (SA) to facilitate task allocation among multiple drones, utilizing transfer and recombination operators to generate high-quality solutions. After obtaining the task allocation scheme, a satisfactory route of a mother drone is generated by a variable neighborhood descent algorithm (VND). A desirable route for each single small drone is produced by dynamic programming (DP).Extensive experiments are conducted, demonstrating the outstanding optimization and time efficiency of the proposed two-phase optimization method by the fact that it obtains within a 4.89% gap from the optimal solution generated by CPLEX in 15.48 s for instance up to 125 nodes.
Joint data augmentations for automated graph contrastive learning and forecasting
Authors: Liu Jiaqi , Chen Yifu , Ren Qianqian , Gao Yang 
Published: June 2024

AbstractGraph augmentation plays a crucial role in graph contrastive learning. However, existing methods primarily optimize augmentations specific to particular datasets, which limits their robustness and generalization capabilities. To overcome these limitations, many studies have explored automated graph data augmentations. However, these approaches face challenges due to weak labels and data incompleteness. To tackle these challenges, we propose an innovative framework called Joint Data Augmentations for Automated Graph Contrastive Learning (JDAGCL). The proposed model first integrates two augmenters: a feature-level augmenter and an edge-level augmenter. The two augmenters learn whether to drop an edge or node to obtain optimized graph structures and enrich the information available for modeling and forecasting task. Moreover, we introduce two stage training strategy to further process the features extracted by the encoder and enhance their effectiveness for forecasting downstream task. The experimental results demonstrate that our proposed model JDAGCL achieves state-of-the-art performance compared to the latest baseline methods, with an average improvement of 14% in forecasting accuracy across multiple benchmark datasets.
Grounding rod hanging and removing robot with hand-eye self-calibration capability in substation
Authors: Lin Yunhan , Wang Jiahui , Liu Kaibo , Min Huasong 
Published: June 2024

AbstractIn this paper, a robot system for hanging and removing grounding rods is designed by integrating 3D recognition technology, hand-eye self-calibration technology, and automatic operation technology. Specifically, a novel hand-eye self-calibration algorithm is proposed that only uses common objects in the actual scene, which differs from traditional robot hand-eye calibration in that it requires a dedicated calibration board to assist with offline completion. Addressing the problem that existing self-calibration methods cannot be optimized as a whole, resulting in low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Experimental verifications are conducted using both public dataset and practical robot system. In the dataset experiment, our method demonstrates superior accuracy and robustness compared to existing self-calibration methods. Furthermore, the practical robot platform experiment confirms the feasibility and efficacy of our approach across various wind speeds and lighting conditions.
Two-stage many-objective evolutionary algorithm: enhanced dominance relations and control mechanisms for separated balance
Authors: Li Wei , Niliang Qilin , Wang Lei , Jiang Qiaoyong 
Published: June 2024

AbstractAlthough the multiobjective evolutionary algorithms (MOEAs) have been proved to bring promising prospects for solving multiobjective optimization problems (MOPs), the performance of the algorithm deteriorates sharply in high-dimensional objective space due to the weak selection pressure and the unregulated balance, which is caused by the increase of objective space dimension. Some current MOEAs with two-stage strategy (TS) strive to address above issues by dividing the evolutionary process into two independent stages, in which convergence and diversity are handled separately within successive generations of different stages. However, TS-MOEAs have some weaknesses, such as sensitivity to stage division, and incomplete separation of convergence and diversity. In this paper, TS/KW-MaOEA is proposed for solving many-objective optimization problems (MaOPs), which keeps TS as the central and equips a perfect control mechanism for separated balance. More specifically, TS/KW-MaOEA can automatically adjust the balance trend and provide appropriate selection pressure for MaOPs according to the Kondratiev wave (KW) search model and the objective space dimension. To verify the effectiveness of the proposed algorithm, a series of experiments are carried out against seven state-of-the-art many-objective optimization algorithms on 15 benchmark problems with up to 30 objectives. Experimental results indicate that the proposed algorithm is highly competitive against peer competitors.
Enhancing learning on uncertain pixels in self-distillation for object segmentation
Authors: Chen Lei , Cao Tieyong , Zheng Yunfei , Wang Yang , Zhang Bo , Yang Jibin 
Published: June 2024

AbstractSelf-distillation method guides the model learning via transferring knowledge of the model itself, which has shown the advantages in object segmentation. However, it has been proved that uncertain pixels with predicted probability close to 0.5 will restrict the model performance. The existing self-distillation methods cannot guide the model to enhance its learning ability for uncertain pixels, so the improvement is limited. To boost the student model’s learning ability for uncertain pixels, a novel self-distillation method is proposed. Firstly, the predicted probability in the current training sample and the ground truth label are fused to construct the teacher knowledge, as the current predicted information can express the performance of student models and represent the uncertainty of pixels more accurately. Secondly, a quadratic mapping function between the predicted probabilities of the teacher and student model is proposed. Theoretical analysis shows that the proposed method using the mapping function can guide the model to enhance the learning ability for uncertain pixels. Finally, the essential difference of utilizing the predicted probability of the student model in self-distillation is discussed in detail. Extensive experiments were conducted on models with convolutional neural networks and Transformer architectures as the backbone networks. The results on four public datasets demonstrate that the proposed method can effectively improve the student model performance.
Research on coal-rock identification method and data augmentation algorithm of comprehensive working face based on FL-Segformer
Authors: Zhang Yun , Tong Liang , Lai Xingping , Cao Shenggen , Yan Baoxu , Yang Yanbin , Liu Yongzi , He Wei 
Published: June 2024

AbstractCoal-rock interface identification technology was pivotal in automatically adjusting the shearer’s cutting drum during coal mining. However, it also served as a technical bottleneck hindering the advancement of intelligent coal mining. This study aimed to address the poor accuracy of current coal-rock identification technology on comprehensive working faces, coupled with the limited availability of coal-rock datasets. The loss function of the SegFormer model was enhanced, the model’s hyperparameters and learning rate were adjusted, and an automatic recognition method was proposed for coal-rock interfaces based on FL-SegFormer. Additionally, an experimental platform was constructed to simulate the dusty environment during coal-rock cutting by the shearer, enabling the collection of coal-rock test image datasets. The morphology-based algorithms were employed to expand the coal-rock image datasets through image rotation, color dithering, and Gaussian noise injection so as to augment the diversity and applicability of the datasets. As a result, a coal-rock image dataset comprising 8424 samples was generated. The findings demonstrated that the FL-SegFormer model achieved a Mean Intersection over Union (MIoU) and mean pixel accuracy (MPA) of 97.72% and 98.83%, respectively. The FL-SegFormer model outperformed other models in terms of recognition accuracy, as evidenced by an MIoU exceeding 95.70% of the original image. Furthermore, the FL-SegFormer model using original coal-rock images was validated from No. 15205 working face of the Yulin test mine in northern Shaanxi. The calculated average error was only 1.77%, and the model operated at a rate of 46.96 frames per second, meeting the practical application and deployment requirements in underground settings. These results provided a theoretical foundation for achieving automatic and efficient mining with coal mining machines and the intelligent development of coal mines.