bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bioinformatic analysis of the role of immune checkpoint genes and immune infiltration in the pathogenesis and development of premature ovarian insufficiency
Authors: Zhang Xiyan , Wang Ling , Yang Tongkun , Kong Li , Wei Luxiao , Du Jing 
Published: May 2024

Abstract
Purpose
With advances in immunology, increasing evidence suggests that immunity is involved in premature ovarian insufficiency (POI) pathogenesis. This study investigated the roles of immune checkpoint genes and immune cell infiltration in POI pathogenesis and development.

Methods
The GSE39501 dataset and immune checkpoint genes were obtained from the Gene Expression Omnibus database and related literature. The two datasets were intersected to obtain immune checkpoint-related differentially expressed genes (ICRDEGs), which were analyzed using Gene Ontology and Kyoto Encyclopedia of Gene and Genomes enrichment analysis, weighted correlation network analysis, protein–protein interaction and related microRNAs, transcription factors, and RNA binding proteins. The immune cell infiltration of ICRDEGs was explored, and receiver operating characteristic curves were used to validate the diagnostic value of ICRDEGs in POI.

Results
We performed ICRDEG functional enrichment analysis and found that these genes were closely related to immune processes, such as T cell activation. Specifically, they are enriched in various biological processes and pathways, such as cell adhesion molecule and T cell receptor signaling pathways. Weighted correlation network analysis identified seven hub genes: Cd200, Cd274, Cd28, neurociliary protein-1, Cd276, Cd40lg, and Cd47. Furthermore, we identified 112 microRNAs, 17 RNA-binding proteins, and 101 transcription factors. Finally, immune infiltration analysis showed a clear positive correlation between hub genes and multiple immune cell types.

Conclusion
Bioinformatic analysis identified seven potential ICRDEGs associated with POI, among which the immune checkpoint molecules CD200 and neurociliary protein-1 may be involved in the pathogenesis of POI.
A Genealogical Approach to Algorithmic Bias
Authors: Ziosi Marta , Watson David , Floridi Luciano 
Published: May 2024

AbstractThe Fairness, Accountability, and Transparency (FAccT) literature tends to focus on bias as a problem that requires ex post solutions (e.g. fairness metrics), rather than addressing the underlying social and technical conditions that (re)produce it. In this article, we propose a complementary strategy that uses genealogy as a constructive, epistemic critique to explain algorithmic bias in terms of the conditions that enable it. We focus on XAI feature attributions (Shapley values) and counterfactual approaches as potential tools to gauge these conditions and offer two main contributions. One is constructive: we develop a theoretical framework to classify these approaches according to their relevance for bias as evidence of social disparities. We draw on Pearl’s ladder of causation (Causality: models, reasoning, and inference. Cambridge University Press, Cambridge, 2000, Causality, 2nd edn. Cambridge University Press, Cambridge, 2009. https://doi.org/10.1017/CBO9780511803161) to order these XAI approaches concerning their ability to answer fairness-relevant questions and identify fairness-relevant solutions. The other contribution is critical: we evaluate these approaches in terms of their assumptions about the role of protected characteristics in discriminatory outcomes. We achieve this by building on Kohler-Hausmann’s (Northwest Univ Law Rev 113(5):1163–1227, 2019) constructivist theory of discrimination. We derive three recommendations for XAI practitioners to develop and AI policymakers to regulate tools that address algorithmic bias in its conditions and hence mitigate its future occurrence.
Metaheuristic-based energy-aware image compression for mobile app development
Authors: Mousavirad Seyed Jalaleddin , Alexandre Luís A. 
Published: May 2024

AbstractThe widely applied JPEG standard has undergone recent efforts using population-based metaheuristic (PBMH) algorithms to optimise quantisation tables (QTs) for specific images. However, user preferences, like an Android developer’s preference for small-size images, are often overlooked, leading to high-quality images with large file sizes. Another limitation is the lack of comprehensive coverage in current QTs, failing to accommodate all possible combinations of file size and quality. Therefore, this paper aims to propose three distinct contributions. First, to include the user’s opinion in the compression process, the file size of the output image can be controlled by a user in advance. To this end, we propose a novel objective function for population-based JPEG image compression. Second, we suggest a novel representation to tackle the lack of comprehensive coverage. Our proposed representation can not only provide more comprehensive coverage but also find the proper value for the quality factor for a specific image without any background knowledge. Both representation and objective function changes are independent of the search strategies and can be used with any population-based metaheuristic (PBMH) algorithm. Therefore, as the third contribution, we also provide a comprehensive benchmark on 22 state-of-the-art and recently-introduced PBMH algorithms on our new formulation of JPEG image compression. Our extensive experiments on different benchmark images and in terms of different criteria show that our novel formulation for JPEG image compression can work effectively.
How do soil and topographic drivers determine tree diameter spatial distribution in even aged cork oak stands installed in average to high productivity areas
Authors: Firmino Paulo Neves , Paulo Joana Amaral , Lourenço António , Tomé Margarida , Campagnolo Manuel 
Published: May 2024

AbstractLocal terrain or microsite conditions influence the development of trees, particularly at early ages. These conditions might be described by edaphic or topographic variables. We mapped soil and topographic variables from four even-aged and even-spaced cork oak plantations located in two climatically distinct Portuguese regions. The major goal of this research was to understand the relation between soil and topographic fine-scale conditions and tree growth expressed by diameter without cork annual growth (idu). The methodology consisted in (1) analysing the spatial variability and autocorrelation of idu; (2) modelling idu with ordinary least squares (OLS) regressions; (3) comparing with spatial modelling of idu, incorporating spatial autocorrelation. The driest stands A and B, exhibited weaker spatial autocorrelation, distributed in smaller clusters (R2 < 0.03, OLS models), while stands C (R2 = 0.18, OLS models) and D (R2 = 0.11, OLS models) showed higher predictive capacity. Spatial models increased R2 scores, keeping most variables from OLS models and accounting for spatial autocorrelation. A + B + C + D OLS model obtained an R2 = 0.34 and respective spatial model R2 = 0.58. Apparent electrical conductivity at 0.5 (ECa0.5) and 1 m of soil depth, slope, elevation and topography position index were included as predictors (OLS), but only ECa0.5, slope and elevation were selected in the spatial model. Models were fitted using average to high productivity stands and should be used cautiously outside this range. Local terrain conditions determine the growth of young cork oak trees. Mapping soil and topographic variables before establishing new plantations may identify limiting microsite conditions where using cork oak species is not suitable due to low growth rates expectations.
A Pilot Study of Observation Poisoning on Selective Reincarnation in Multi-Agent Reinforcement Learning
Authors: Putla Harsha , Patibandla Chanakya , Singh Krishna Pratap , Nagabhushan P 
Published: May 2024

AbstractThis research explores the vulnerability of selective reincarnation, a concept in Multi-Agent Reinforcement Learning (MARL), in response to observation poisoning attacks. Observation poisoning is an adversarial strategy that subtly manipulates an agent’s observation space, potentially leading to a misdirection in its learning process. The primary aim of this paper is to systematically evaluate the robustness of selective reincarnation in MARL systems against the subtle yet potentially debilitating effects of observation poisoning attacks. Through assessing how manipulated observation data influences MARL agents, we seek to highlight potential vulnerabilities and inform the development of more resilient MARL systems. Our experimental testbed was the widely used HalfCheetah environment, utilizing the Independent Deep Deterministic Policy Gradient algorithm within a cooperative MARL setting. We introduced a series of triggers, namely Gaussian noise addition, observation reversal, random shuffling, and scaling, into the teacher dataset of the MARL system provided to the reincarnating agents of HalfCheetah. Here, the “teacher dataset” refers to the stored experiences from previous training sessions used to accelerate the learning of reincarnating agents in MARL. This approach enabled the observation of these triggers’ significant impact on reincarnation decisions. Specifically, the reversal technique showed the most pronounced negative effect for maximum returns, with an average decrease of 38.08% in Kendall’s tau values across all the agent combinations. With random shuffling, Kendall’s tau values decreased by 17.66%. On the other hand, noise addition and scaling aligned with the original ranking by only 21.42% and 32.66%, respectively. The results, quantified by Kendall’s tau metric, indicate the fragility of the selective reincarnation process under adversarial observation poisoning. Our findings also reveal that vulnerability to observation poisoning varies significantly among different agent combinations, with some exhibiting markedly higher susceptibility than others. This investigation elucidates our understanding of selective reincarnation’s robustness against observation poisoning attacks, which is crucial for developing more secure MARL systems and also for making informed decisions about agent reincarnation.
The effect of lateral thrust on the progressive slope failure under excavation and rainfall conditions
Authors: Yu Xiang , Zhao Tao , Gong Bin , Zhang Yongjun , Tang Chun’an , Luo Yu 
Published: May 2024

AbstractLarge landslides can involve the multiple failures of regional slopes. To understand the effect of lateral thrust caused by the failure of one slope on its surroundings, the failures of two adjacent highway slopes in Guangdong Province, China, were investigated in detail. The interactive failure processes and landslide morphological characteristics of the two slopes were first analyzed based on the on-site investigation. Then, a plane mechanical model of a large-scale slope was established to evaluate the significant influence of the lateral thrust generated by the west slope acting on the east excavated slope. Furthermore, the extrusion effect of the west slope was modelled under the alternate excavation disturbance and rainfall by transferring the thrust forces onto the interface elements, and the induced failure mechanism and instability mode of the east slope under lateral thrust were reproduced numerically. The results show that the compression-shear failure occurred at the middle and rear slope bodies because of the lateral thrust, which led to the formation of a thrust landslide and the final instability of the east slope.
Coconut-sourced MCT oil: its potential health benefits beyond traditional coconut oil
Authors: Duranova Hana , Kuzelova Lenka , Fialkova Veronika , Simora Veronika , Kovacikova Eva , Joanidis Patricia , Borotova Petra , Straka Dusan , Hoskin Roberta Targino , Moncada Marvin , de Medeiros Fábio Gonçalves Macêdo , Gabriny Lucia 
Published: May 2024

Abstract
Plant dietary lipids remain one of the most intriguing and complex classes of biological molecules. Among them, medium-chain triglycerides (MCTs) have garnered recognition for their unique physico-chemical properties and potential health benefits. Despite their classification as saturated fats, they stand apart from other saturated fatty acids due to their distinctive characteristics, positioning them as a valuable component in nutrition. While traditional dietary fats primarily contain long-chain triglycerides (LCTs), MCTs consist of medium-chain fatty acids (MCFAs), naturally found in coconut and palm oils. The structural dissimilarity grants MCTs advantageous attributes, encompassing rapid digestion and absorption, providing a swift source of energy. Importantly, MCT oil derived from coconuts surpasses traditional coconut oil in efficiency and speed of energy conversion due to its higher concentration of readily metabolizable MCTs, making it a superior choice in human nutrition. This comprehensive study delves deeply into the potential of coconut-derived MCT oil, illuminating its chemical constituents, production from coconut oil, distinctive physical and chemical properties, and metabolic characteristics. Additionally, it highlights a range of potential biological activities of the oil, including its efficacy in managing gastrointestinal disorders and promising roles in anticancer, neuroprotective, and antimicrobial effects. The report also discusses the extensive applications of MCT oil across diverse industrial and technological sectors, as well as its utilization in structured lipids, oleogels, and as a carrier for capsaicin. Addressing safety concerns and providing dosage guidelines, this paper emphasizes MCT oil as an alternative to coconut oil in various applications, offering a holistic perspective on its benefits.
Effect of intercropping maize and sunn hemp at different times and stand densities on soil properties and crop yield under in-field rainwater harvesting (IRWH) tillage in semi-arid South Africa
Authors: Dzvene Admire R. , Gura Isaac , Tesfuhuney Weldemichael , Walker Sue , Ceronio Gert 
Published: May 2024

Abstract
Background
Evidence suggests that manipulating intercropping timing and stand density within intercropping systems could enhance crop yields. However, our current understanding of the effects of intercropping a cover crop on soil chemical properties and moisture still needs to be improved. This study investigates the effects of intercropping sunn hemp with maize at different timings and stand densities on selected soil properties and crop yield.

Materials and methods
A split-plot experiment was conducted under the in-field rainwater harvesting (IRWH) tillage. The trial had three intercropping times (simultaneously with maize planting, at V15 maize growth stage, and R1 maize growth stage) as the main plot factors and three stand densities (16, 32, and 48 plants m−2) as the subplot factors, with three replicates for both the 2019/20 and 2020/21 seasons. Changes in soil properties were assessed within the uppermost layer (0-30 cm). Soil moisture content was continuously monitored throughout the growing season and specific soil chemical properties were analyzed at harvest.

Results
The results showed that the interaction of sunn hemp intercropping period and stand densities did not significantly influence most of the measured soil properties. The early planting of sunn hemp had significantly 32.4% higher soil organic matter (SOM) than the last planting date at low stand density. After two growing seasons SOM, nitrogen, potassium, and manganese were significantly enhanced by 39.7%, 19.0%, 21% and 60.6% respectively. However, during the same period calcium, sodium and iron were significantly reduced by 13.4%, 46.1% and 78.0% respectively. The management of sunn hemp crop had significant effect on maize grain yield across the two seasons. The maize yields in the medium and high stand densities in the first season were significantly 15.3% and 34.3% higher than in the second season, respectively.

Conclusion
Due to the intercropping treatments, the retention of sunn hemp residues with varying quantities and qualities may have influenced the soil nutrient dynamics in the short-term. Significant changes in soil chemical properties and yield may need more time, and future research should be conducted out in agricultural regions with different soil mineral matrices.
Local News Reporting and Mass Attitudes on Infrastructure Investment
Authors: Trexler Andrew , Mullin Megan 
Published: May 2024

AbstractA growing body of research documents how shrinking local newsrooms undermine political accountability and local fiscal and policy performance in the United States. We extend this work to examine political impacts from the level of information content in local news, which has been jeopardized by reductions in newsroom staffing. To understand how information content affects public response to news coverage of a local issue, we focus on the case of preventive spending on infrastructure maintenance and repair. Inefficiently low levels of infrastructure investment are often attributed to low public knowledge about the risk of failure events. In a preregistered survey experiment, we test how the level and type of information in a news article affect support for infrastructure investment across two different types of infrastructure risk (repeated nuisance versus catastrophic failure). For both types of risk, we find that more information-rich reporting, whether investigative or event-driven, increases public support for preventive spending and imposes accountability penalties on local leaders who fail to invest in prevention.
Indefinite q-integrals from a method using q-Riccati equations
Authors: Heragy Gamela E. , Mansour Zeinab S. I. , Oraby Karima M. 
Published: May 2024

AbstractIn an earlier work, a method was introduced for obtaining indefinite q-integrals of q-special functions from the second-order linear q-difference equations that define them. In this paper, we reformulate the method in terms of q-Riccati equations, which are nonlinear and first order. We derive q-integrals using fragments of these Riccati equations, and here only two specific fragment types are examined in detail. The results presented here are for the q-Airy function, the Ramanujan function, the discrete q-Hermite I and II polynomials, the q-hypergeometric functions, the q-Laguerre polynomials, the Stieltjes-Wigert polynomial, the little q-Legendre and the big q-Legendre polynomials.
Using Reference Publication Year Spectroscopy (RPYS) to analyze the research and publication culture in immunology
Authors: Wray K. Brad , Paludan Søren R. , Bornmann Lutz , Haunschild Robin 
Published: May 2024

AbstractReference Publication Year Spectroscopy (RPYS) is a method to reveal the historical roots and landmark papers of a research field based on cited references data. RPYS visualizes how frequently cited references occur in the cited reference lists of papers published in the research field. Previous RPYS studies revealed various historical aspects of the research and publication culture in various fields of study (e.g., economy and philosophy of science). Employing RPYS, we examine a dataset of citing publications in immunology. We identify and analyze 20 cited publications occurring very frequently in the cited reference lists of these citing papers. The 20 publications shed light on the epistemic culture and the development of the field of immunology. Whereas 65% of the publications were contributions to the theoretical and empirical understanding of immunology, 35% were contributions to methodology.
What is Legal Reasoning?
Authors: Siliquini-Cinelli Luca 
Published: May 2024

Abstract
Pursuant to the aims and scope of the Special Issue it is part of, this invited contribution seeks to shed new light on the nature and working logic of legal reasoning. It does so by engaging with two of the most authoritative views on the subject which have recently been put forward in the Common law world—namely, Lord Hoffmann’s, and Larry Alexander and Emily Sherwin’s. A key-concern of the Anglophone debate on legal reasoning is whether it is a specialistic type of reasoning requiring ad hoc education and training, or ordinary reasoning subject to ordinary rules of language (i.e. sentence construction, interpretation, etc.). The article argues that compelling though they are, these sorts of enquiries do not help to understand what legal reasoning really is and how it operates. In particular, it argues that if we are to understand what legal reasoning is and how it works, we ought to examine the propositions it aims to craft and support. In so arguing, the article further shows that exploring law’s nature and operations as an intellectual means for social ordering also helps to understand how law works as a regulatory phenomenon more generally.
Predicting Social Inequality in Poland Using Price Dispersion on the Real Estate Market
Authors: Stachurski Tomasz , Ża̧dło Tomasz , Wolny-Dominiak Alicja 
Published: May 2024

AbstractMeasuring and predicting price dispersion on the real estate market is an important issue for both investors and policymakers. Price dispersion in the housing market can be seen as an additional dimension for measuring social inequality and one of the main goals of public policies that focus on life satisfaction and the accumulation of permanent wealth. The article considers the Polish real estate market and proposes the prediction methods of four measures of dispersion. They are based on quantiles and allow overall measurement of dispersion. In the prediction of dispersion measures the plug-in predictors utylizing longitudinal mixed models are proposed. Furthermore, the ex ante prediction accuracy measure called the quantile of absolute prediction errors (QAPE) is assessed using the residual bootstrap estimators. QAPE allows for a comprehensive description of the distribution of prediction errors, thus fostering discussion of possible various market scenarios.
Pattern matching algorithms in blockchain for network fees reduction
Authors: Susik Robert , Nowotniak Robert 
Published: May 2024

AbstractBlockchain received a vast amount of attention in recent years and is still growing. The second generation of blockchain, such as Ethereum, allows execution of almost any program in EVM, making it a global protocol for distributed applications. The code deployment and each operation performed in EVM cost the network fee called gas, whose price varies and can be significant. That is why code optimization and well-chosen algorithms are crucial in programming on the blockchain. This paper evaluates the gas usage of several exact pattern matching algorithms on the EVM. We also propose an efficient implementation of the algorithms in the Solidity/YUL language. We evaluate the gas fees of all the algorithms for different parameters (such as pattern length, alphabet size, and text size). We show a significant gas fee and execution time reduction with up to 22-fold lower gas usage and 55-fold speed-up compared to StringUtils (a popular Solidity string library).