bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hsa_circ_0004872 mitigates proliferation, metastasis and immune escape of meningioma cells by suppressing PD-L1
Authors: Chen Kuo , Huang Zhengming , Liu Changsheng , Ouyang Qian , Yan Qing , Zheng Wei , Huang Yongkai 
Published: May 2024

AbstractMeningioma is a prevalent intracranial malignancy known for its aggressive growth. Circular RNAs (circRNAs) play a crucial role in the development of various cancers. However, their involvement in meningioma remains understudied. This study aimed to investigate the function and underlying mechanism of hsa_circ_0004872 in meningioma. The molecular expression of hsa_circ_0004872, PD-L1 and EIF4A3 was identified by RT-qPCR and/or western blot assays. Cell viability, migration, and invasion were assessed through CCK-8 and Transwell assays, respectively. Cytotoxicity was determined using an LDH assay, and cell apoptosis was monitored by flow cytometry. The RNA and protein interactions were assessed through RNA-protein immunoprecipitation (RIP) and RNA pull down analyses. Our findings revealed that hsa_circ_0004872 expression was significantly downregulated in both meningioma tissue samples and cells. Overexpression of hsa_circ_0004872 inhibited the proliferation, metastasis, and immune escape of meningioma cells, as well as enhanced the cytotoxicity of CD8+ T cells by suppressing PD-L1. Furthermore, hsa_circ_0004872 directly interacted with EIF4A3, leading to the degradation of PD-L1 mRNA. Finally, inhibiting EIF4A3 improved the proliferation, metastasis, and immune escape of meningioma cells, as well as the cytotoxicity of CD8+ T cells. Our study demonstrated that hsa_circ_0004872 mitigated the proliferation, metastasis,and immune escape of meningioma cells by targeting the EIF4A3/PD-L1 axis. These findings suggested that hsa_circ_0004872 and EIF4A3 might serve as promising biological markers and therapeutic targets for meningioma treatment.
On the existence of solutions to generalized Navier–Stokes–Fourier system with dissipative heating
Authors: Abbatiello Anna , Bulíček Miroslav , Lear Daniel 
Published: May 2024

AbstractWe consider a flow of non-Newtonian incompressible heat conducting fluids with dissipative heating. Such system can be obtained by scaling the classical Navier–Stokes–Fourier problem. As one possible singular limit may be obtained the so-called Oberbeck–Boussinesq system. However, this model is not suitable for studying the systems with high temperature gradient. These systems are described in much better way by completing the Oberbeck–Boussinesq system by an additional dissipative heating. The satisfactory existence result for such system was however not available. In this paper we show the large-data and the long-time existence of dissipative and suitable weak solution. This is the starting point for further analysis of the stability properties of such problems.
Distantly Supervised Relation Extraction Based on Residual Attention and Self Learning
Authors: Zheng Zhiyun , Xu Yamei , Liu Yun , Zhang Xingjin , Li Lun , Li Dun 
Published: May 2024

AbstractRelation extraction is an important task in information extraction, which aims to identify the relation between two given entities. The algorithm based on distant supervision can automatically generate a large amount of annotated data, which becomes the main method to deal with the task of relation extraction. However, previous studies rely too much on the precision of supervision information and ignore the effective supervision information hidden in the case of mislabeling, which leads to the loss of supervision information. To solve this problem, we propose the distantly supervised relation extraction model based on residual attention and self-learning. The model uses residual attention to extract features, and then uses self-learning idea to generate corrected labels for training data, which are added into the training process as supervisory signals to prevent parameter error updates caused by noisy labels. The model can not only reduce the problem of mislabeling caused by distant supervision, but also makes full use of the available supervisory information in the data to improve data utilization. Experiments show that compared with the existing mainstream baseline methods, the proposed model has higher precision and recall.
Bayesian cross-validation by parallel Markov chain Monte Carlo
Authors: Cooper Alex , Vehtari Aki , Forbes Catherine , Simpson Dan , Kennedy Lauren 
Published: May 2024

AbstractBrute force cross-validation (CV) is a method for predictive assessment and model selection that is general and applicable to a wide range of Bayesian models. Naive or ‘brute force’ CV approaches are often too computationally costly for interactive modeling workflows, especially when inference relies on Markov chain Monte Carlo (MCMC). We propose overcoming this limitation using massively parallel MCMC. Using accelerator hardware such as graphics processor units, our approach can be about as fast (in wall clock time) as a single full-data model fit. Parallel CV is flexible because it can easily exploit a wide range data partitioning schemes, such as those designed for non-exchangeable data. It can also accommodate a range of scoring rules. We propose MCMC diagnostics, including a summary of MCMC mixing based on the popular potential scale reduction factor ($$\widehat{\textrm{R}}$$

R
^

) and MCMC effective sample size ($$\widehat{\textrm{ESS}}$$

ESS
^

) measures. We also describe a method for determining whether an $$\widehat{\textrm{R}}$$

R
^

diagnostic indicates approximate stationarity of the chains, that may be of more general interest for applications beyond parallel CV. Finally, we show that parallel CV and its diagnostics can be implemented with online algorithms, allowing parallel CV to scale up to very large blocking designs on memory-constrained computing accelerators.
De Morgan-Płonka Sums
Authors: Randriamahazaka Thomas 
Published: May 2024

AbstractThis paper develops De Morgan-Płonka sums, which generalise Płonka sums to contexts in which negation is not topically transparent but still respects De Morgan duality. We give a general theory of De Morgan-Płonka sums, on the model of the general theory of Płonka sums. Additionally, we describe free De Morgan-Płonka sums and apply our construction to give an algebraic proof of completeness for Kit Fine’s truthmaker semantics for Angell’s logic of analytic containment.
Licensing and secrecy under imperfect intellectual property protection
Authors: Mallios Aineas Kostas 
Published: May 2024

AbstractI consider a technology holder that has to decide whether to file for a patent or rely on secrecy, and a competing firm that can enter the market through technology transfer or imitation. Acknowledging that imitation is uncertain, imperfect, and takes time to materialise, as well as that intellectual property protection is not absolute, I find that technology transfer will always occur if the technological efficiency to imitate is sufficiently low. This will happen regardless of the protection choice of the inventor. Conversely, highly efficient imitation technologies will lead to imitation instead of licensing. I also consider that a trade secret might accidentally leak and thus lose its economic value, thereby rendering secrecy less preferable to patenting. Additionally, the risk of leakage might also lead to more imitation than licensing. Finally, considering that, in practice, the probability of leakage might increase with the number of firms practising a secret, I suggest an increase in the attractiveness of the patent system.
Nanobodies in the fight against infectious diseases: repurposing nature's tiny weapons
Authors: Rizk Soha S. , Moustafa Dina M. , ElBanna Shahira A. , Nour El-Din Hanzada T. , Attia Ahmed S. 
Published: May 2024

AbstractNanobodies are the smallest known antigen-binding molecules to date. Their small size, good tissue penetration, high stability and solubility, ease of expression, refolding ability, and negligible immunogenicity in the human body have granted them excellence over conventional antibodies. Those exceptional attributes of nanobodies make them promising candidates for various applications in biotechnology, medicine, protein engineering, structural biology, food, and agriculture. This review presents an overview of their structure, development methods, advantages, possible challenges, and applications with special emphasis on infectious diseases-related ones. A showcase of how nanobodies can be harnessed for applications including neutralization of viruses and combating antibiotic-resistant bacteria is detailed. Overall, the impact of nanobodies in vaccine design, rapid diagnostics, and targeted therapies, besides exploring their role in deciphering microbial structures and virulence mechanisms are highlighted. Indeed, nanobodies are reshaping the future of infectious disease prevention and treatment.
Forecasting e-commerce consumer returns: a systematic literature review
Authors: Karl David 
Published: May 2024

AbstractThe substantial growth of e-commerce during the last years has led to a surge in consumer returns. Recently, research interest in consumer returns has grown steadily. The availability of vast customer data and advancements in machine learning opened up new avenues for returns forecasting. However, existing reviews predominantly took a broader perspective, focussing on reverse logistics and closed-loop supply chain management aspects. This paper addresses this gap by reviewing the state of research on returns forecasting in the realms of e-commerce. Methodologically, a systematic literature review was conducted, analyzing 25 relevant publications regarding methodology, required or employed data, significant predictors, and forecasting techniques, classifying them into several publication streams according to the papers’ main scope. Besides extending a taxonomy for machine learning in e-commerce, this review outlines avenues for future research. This comprehensive literature review contributes to several disciplines, from information systems to operations management and marketing research, and is the first to explore returns forecasting issues specifically from the e-commerce perspective.
Pathogenicity of Single and Combined Inoculations of Alternaria spp. on Potato
Authors: Baltzakis Ioannis , Nindya Syahida , Rahmayani Rahmayani , Thomas Jane E. , van der Werf Wopke , Struik Paul C. 
Published: May 2024

AbstractIn Europe, early blight has become an increasingly severe disease in potato cultivation. We investigated the lesion sizes caused by infection with different Alternaria spp. predominantly from the UK in detached-leaf assays and whole-plant assays of different potato cultivars. The analysis reveals that whilst there is no significant interaction between the potato cultivars used and the Alternaria spp. tested, statistically significant differences were observed between the group means of small-spored vs. large-spored Alternaria spp. The small-spored species grouped together with no significant differences amongst them, whilst large-spored species were able to cause large expanding lesions in all tests, with Alternaria solani exhibiting the most extensive lesion growth. We also explored the effect of co-inoculation treatments of Alternaria spp., revealing no significant difference between the inoculation with Alternaria solani alone and co-inoculation with Alternaria solani and Alternaria alternata or Alternaria infectoria. The findings contribute to our understanding of the behaviour of Alternaria spp. and their impact on potato cultivars.
Optimization of methods for intrasplenic administration of human amniotic epithelial cells in order to perform safe and effective cell-based therapy for liver diseases
Authors: Czekaj Piotr , Król Mateusz , Kolanko Emanuel , Wieczorek Patrycja , Bogunia Edyta , Hermyt Mateusz , Grajoszek Aniela , Prusek Agnieszka 
Published: May 2024

AbstractIn animal experimental models the administration of stem cells into the spleen should ensure high effectiveness of their implantation in the liver due to a direct vascular connection between the two organs. The aim of this study was to update the methods of experimental intrasplenic cell transplantation using human amniotic epithelial cells (hAECs) which are promising cells in the treatment of liver diseases. BALB/c mice were administered intrasplenically with 0.5, 1, and 2 million hAECs by direct bolus injection (400 µl/min) and via a subcutaneous splenic port by fast (20 μl/min) and slow (10 μl/min) infusion. The port was prepared by translocating the spleen to the skin pocket. The spleen, liver, and lungs were collected at 3 h, 6 h, and 24 h after the administration of cells. The distribution of hAECs, histopathological changes in the organs, complete blood count, and biochemical markers of liver damage were assessed. It has been shown that the method of intrasplenic cell administration affects the degree of liver damage. The largest number of mice showing significant liver damage was observed after direct administration and the lowest after slow administration through a port. Liver damage increased with the number of administered cells, which, paradoxically, resulted in increased liver colonization efficiency. It was concluded that the administration of 1 × 106 hAECs by slow infusion via a subcutaneous splenic port reduces the incidence of complications at the expense of a slight decrease in the effectiveness of implantation of the transplanted cells in the liver.
A novel conditional survival nomogram for monitoring real-time prognosis of non-metastatic colorectal cancer
Authors: Luo Pei , Li Ying-ying , Huang Can , Guo Jun , Yao Xin 
Published: May 2024

Abstract
Aims
The aim of this study is to enhance the accuracy of monitoring and treatment information for patients diagnosed with colorectal cancer (CRC).

Methods
Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, a cohort of 335,948 eligible CRC patients was included in this investigation. Conditional survival probability and actuarial overall survival were employed as methodologies to investigate the association between clinicopathological characteristics and cancer prognosis.

Results
Among CRC patients, the 5-year survival rate was 59%, while the 10-year survival rate was 42%. Over time, conditional survival showed a consistent increase, with rates reaching 45% and 48% for individuals surviving 1 and 2 years, respectively. Notably, patients with unfavorable tumor stages exhibited substantial improvements in conditional survival, thereby narrowing the disparity with actuarial overall survival over time.

Conclusion
This study underscores the significance of time-dependent conditional survival probability, particularly for patients with a poorer prognosis. The findings suggest that long-term CRC survivors may experience improved cancer prognosis over time.
“European” Originality in Copyright Law in the English Case of THJ Systems Ltd. v. Sheridan
Authors: Rahmatian Andreas 
Published: May 2024

AbstractThe recent Court of Appeal case THJ Systems v. Sheridan (THJ Systems Ltd. v. Sheridan [2023] EWCA Civ 1354, [2024] E.C.D.R. 4, CA, 20 November 2023) is of great interest because it confirms the test for originality in copyright law in the UK after Brexit. The result of this decision is not too surprising, but the judicial clarification is nevertheless welcomed.
Investigation of additively manufactured lattice structures for refractive optical systems based on auxetic properties
Authors: Frasch Johannes , von Lukowicz Henrik , Risse Stefan 
Published: May 2024

AbstractThis paper reports on a novel concept of thermally stable lattice structures for next-generation refractive optical systems. The aim is to develop a metallic mount for optical lenses based on auxetic structures fabricated by additive manufacturing. Auxetic structures exhibit an effective negative Poisson ratio at the macroscopic level. This property is used to develop a mounting structure that compensates for thermal defocus between refractive optical elements. Numerical and experimental studies of the mechanical behavior of additively manufactured body-centered cubic cells provide the basis for the development of this application-specific mounting structure. Several static load cases are analyzed and deviations between numerical and experimental data are examined to quantify differences in dependence of element type, element size, and manufacturing-related geometrical inaccuracies. These results are used to compare the numerical analysis of the Poisson ratio of an auxetic unit cell with the analytical descriptions. This comparison is then applied to two-dimensional auxetic unit cell compounds. These studies provide the basis for the further development process of an auxetic mounting structure.
Influence maximization on temporal networks: a review
Authors: Yanchenko Eric , Murata Tsuyoshi , Holme Petter 
Published: May 2024

AbstractInfluence maximization (IM) is an important topic in network science where a small seed set is chosen to maximize the spread of influence on a network. Recently, this problem has attracted attention on temporal networks where the network structure changes with time. IM on such dynamically varying networks is the topic of this review. We first categorize methods into two main paradigms: single and multiple seeding. In single seeding, nodes activate at the beginning of the diffusion process, and most methods either efficiently estimate the influence spread and select nodes with a greedy algorithm, or use a node-ranking heuristic. Nodes activate at different time points in the multiple seeding problem, via either sequential seeding, maintenance seeding or node probing paradigms. Throughout this review, we give special attention to deploying these algorithms in practice while also discussing existing solutions for real-world applications. We conclude by sharing important future research directions and challenges.
Transnational companies in environmental conflicts: Rio Tinto, anti-mining resistance in Serbia, and the contradictions of Europeanization
Authors: Stuehlen Christin , Anderl Felix 
Published: May 2024

AbstractAfter decades of outsourcing mining activities, Global North governments have begun to domesticate production sites. This strategy aligns with decarbonization goals but also contributes to the emergence of new environmental conflicts and protest movements. The analysis of these movements provides an important insight into the changing shape of transnational rule in the context of resource extraction for the European Green Deal. Building on the approach to reconstruct rule from resistance, we reconstruct the anti-mining resistance movement against a lithium mine in Serbia. Tracing their tactics and addressees, we identify three main conflict actors that the movement is opposed to (the transnational mining company Rio Tinto, the Serbian government, and the European Union (EU)). By addressing these institutions, the protesters do not only constitute a multifaceted conflict constellation around the Jadar project, but they also submit an argument about who can and who is allowed to dictate to others how they ought to behave. While all involved parties play their part, the EU appears more as a structural force during the conflict, preconfiguring the possible conduct of others. Particularly, the accession process places contradictory expectations on Serbia, both enabling and restricting the agency of the government and domestic protest movements. Yet, transnational companies are likely profiting from this constellation in the long run, because the inbuilt contradiction between a commitment to green policies and a pressure to open markets to foreign investments in the context of the accession process is conducive to that end.
AlFloos: a balanced dataset for the sixth issue of the Saudi Arabian currency banknote*
Authors: Bati Ghassan F. 
Published: May 2024

AbstractArtificial Intelligence, especially machine learning, is considered by many researchers the new electricity and an important building block of the fourth industrial revolution. Saudi Arabia is amongst the first nations worldwide to realize this importance and apply it in its school systems at various levels. This interest motivates researchers to create Arabic datasets for research and educational purposes, particularly with the popularity of English sources and the absence of Arabic sources. This study attempts to lessen this gap by creating for the first time ever a dataset for the sixth issue of the Saudi Arabian currency and analyzing it using the famous machine learning tools: Orange Data Mining, Google Teachable Machine, and Liner.ai, which require no coding. The importance of the study to the community is as follows: 1—It provides the first work ever to establish a balanced dataset for the sixth issue of the Saudi Arabian currency banknote, which contains images for the banknote denominations and the tabular data generated using deep learning. 2—It is the first scientific work that uses shallow machine learning and deep learning models to create good-performing models for classifying the sixth issue of the Saudi Arabian currency without coding. This work paves the way for researchers and those interested from various fields and backgrounds to develop machine learning applications to classify the sixth issue of the Saudi Arabian currency, especially in mobile phones or in microcontrollers, to inspire multiple IoT and Tiny machine learning applications like currency recognition and classification for special needs people, automatic counting and sorting of banknotes in banks and stores, and fake currency detection.
Cognitive pairwise comparison forward feature selection with deep learning for astronomical object classification with sloan digital sky survey
Authors: Yuen Kevin Kam Fung 
Published: May 2024

AbstractThis paper proposes a hybrid approach integrating the expert knowledge judgment approach using the Cognitive Pairwise Comparison (CPC) to the Deep Learning, a modern classification approach, for astronomic object classification. The astronomic data with ten thousand samples retrieved from Sloan Digital Sky Survey Sky Server Data Release 15 (SDSS SkyServer DR 15) are used for this study. The CPC is an approach to elicit and encode expert knowledge in the format of a Pairwise Opposite Matrix (POM) to evaluate expert preferences for the features. A forward feature selection algorithm taking the expert choices using CPC for the ordered features is used for the feature selection for the deep learning algorithm to build a heuristic training model based on the astronomic data. Whilst the accuracy of the case of improper feature selection is just 37.1%, the proposed hybrid approach can obtain a very high accuracy of 97.9% for the classification of the astronomic object using the eight scaled features (u, g, r, i, z redshift, ra, dec). To extend this research, the proposed CPC can be used as a human-centered tool to be applied to other areas of data sciences.