bluuebunny/crossref_metadata_2025_split huggingface dataset:
Energy absorption and resilience in quasi-static loading of foam-formed cellulose fibre materials
Authors: Pääkkönen Elina , Ketoja Jukka A. , Paltakari Jouni 
Published: July 2024

AbstractTo avoid microplastic pollution, there is an urgent need to replace fossil-based cushioning materials in packaging with easily recyclable alternatives. Here, we investigated the potential of lightweight cellulose fibre materials as a solution for mechanical protection. The quasi-static compression was studied among a vast set of 129 different foam-formed trial points with material density ranging from 21 kg/m3 to 123 kg/m3. The trial points included two different fibre types, bleached softwood kraft pulp (BSKP) and bleached chemithermomechanical pulp (CTMP), with varied refining level, pulp consistency, foaming conditions, surfactant type, strength additives, and final material density and thickness. Besides a correlation analysis of factors affecting compression stress and resilience, the results were reflected against a new theoretical prediction of energy absorption for an ideal low-density random fibre network. The theory predicts the initially-high cushion factor to rapidly drop down to the level of 4‒5 at 40‒80% compression. A similar behaviour was seen among the actual samples, despite their various non-ideal features. At 50% compression, the average cushion factor across the whole data set was 4.84 ± 0.10, being close to the theoretical prediction of 4.61 for the ideal case. The smallest cushion factor of 3.6 was found for a CTMP sample. The recovery from compression varied slightly among the samples and appeared highest for the material density of 60‒100 kg/m3, following the predicted proportion of non-buckled fibre segments. According to the results, cellulose fibre-based cushions have a soft initial response, which is preferable for fragile items.
Graphical abstract
Therapeutic Implications and Regulations of Protein Post-translational Modifications in Parkinsons Disease
Authors: Mishra Twinkle , Singh Shareen , Singh Thakur Gurjeet 
Published: July 2024

AbstractParkinsons disease (PD) is a neurodegenerative disorder characterized by dopaminergic neuron loss and alpha-synuclein aggregation. This comprehensive review examines the intricate role of post-translational modifications (PTMs) in PD pathogenesis, focusing on DNA methylation, histone modifications, phosphorylation, SUMOylation, and ubiquitination. Targeted PTM modulation, particularly in key proteins like Parkin, DJ1, and PINK1, emerges as a promising therapeutic strategy for mitigating dopaminergic degeneration in PD. Dysregulated PTMs significantly contribute to the accumulation of toxic protein aggregates and dopaminergic neuronal dysfunction observed in PD. Targeting PTMs, including epigenetic strategies, addressing aberrant phosphorylation events, and modulating SUMOylation processes, provides potential avenues for intervention. The ubiquitin–proteasome system, governed by enzymes like Parkin and Nedd4, offers potential targets for clearing misfolded proteins and developing disease-modifying interventions. Compounds like ginkgolic acid, SUMO E1 enzyme inhibitors, and natural compounds like Indole-3-carbinol illustrate the feasibility of modulating PTMs for therapeutic purposes in PD. This review underscores the therapeutic potential of PTM-targeted interventions in modulating PD-related pathways, emphasizing the need for further research in this promising area of Parkinsons disease therapeutics.
Specific sialylation of N-glycans and its novel regulatory mechanism
Authors: Gu Jianguo , Isaji Tomoya 
Published: July 2024

AbstractAltered glycosylation is a common feature of cancer cells. Some subsets of glycans are found to be frequently enriched on the tumor cell surface and implicated in different tumor phenotypes. Among these, changes in sialylation have long been associated with metastatic cell behaviors such as invasion and enhanced cell survival. Sialylation typically exists in three prominent linkages: α2,3, α2,6, and α2,8, catalyzed by a group of sialyltransferases. The aberrant expression of all three linkages has been related to cancer progression. The increased α2,6 sialylation on N-glycans catalyzed by β-galactoside α2,6 sialyltransferase 1 (ST6Gal1) is frequently observed in many cancers. In contrast, functions of α2,3 sialylation on N-glycans catalyzed by at least three β-galactoside α2,3-sialyltransferases, ST3Gal3, ST3Gal4, and ST3Gal6 remain elusive due to a possibility of compensating for one another. In this minireview, we briefly describe functions of sialylation and recent findings that different α2,3 sialyltransferases specifically modify target proteins, as well as sialylation regulatory mechanisms vis a complex formation among integrin α3β1, Golgi phosphoprotein 3 (GOLPH3), phosphatidylinositol 4-kinase IIα (PI4KIIα), focal adhesion kinase (FAK) and sialyltransferase, which suggests a new concept for the regulation of glycosylation in cell biology.
Waiting for the revolution: how higher education institutions initially responded to ChatGPT
Authors: Korseberg Lene , Elken Mari 
Published: July 2024

AbstractAlthough debates regarding the integration of digital technologies in higher education are far from new, the launch of ChatGPT in November 2022 was considered by many as something different from the developments that had come before. This article explores how higher education institutions make sense of the potentiality inherent in artificial intelligence and the early responses to the proliferation of ChatGPT. Through a qualitative interview-based study carried out at three HEIs in Norway, and applying Scott’s (2005) three pillars of institutions as an analytical framework, the article examines the type of change pressure ChatGPT was perceived to represent in the period following its launch and the type of organizational response this perception warranted. The findings show that while it was expected that ChatGPT and related technologies not only could threaten — and potentially challenge — key norms and values in the long run, in the short term it was primarily perceived as a regulatory issue that needed to be controlled by higher education institutions. The article points to an epistemic and temporal imbalance in both the expectations and response to ChatGPT, coupled with a lack of technological competence to fully consider the kind of transformation that artificial intelligence technology potentially represents. Coupled with the sense of artificial intelligence being a “moving target”, this led higher education institutions to an initial state of organizational paralysis, in turn adopting a “wait and see” strategy.
Community-Engaged Data Science (CEDS): A Case Study of Working with Communities to Use Data to Inform Change
Authors: Olvera Ramona G. , Plagens Courtney , Ellison Sylvia , Klingler Kesla , Kuntz Amy K. , Chase Rachel P. 
Published: July 2024

AbstractData-informed decision making is a critical goal for many community-based public health research initiatives. However, community partners often encounter challenges when interacting with data. The Community-Engaged Data Science (CEDS) model offers a goal-oriented, iterative guide for communities to collaborate with research data scientists through data ambassadors. This study presents a case study of CEDS applied to research on the opioid epidemic in 18 counties in Ohio as part of the HEALing Communities Study (HCS). Data ambassadors provided a pivotal role in empowering community coalitions to translate data into action using key steps of CEDS which included: data landscapes identifying available data in the community; data action plans from logic models based on community data needs and gaps of data; data collection/sharing agreements; and data systems including portals and dashboards. Throughout the CEDS process, data ambassadors emphasized sustainable data workflows, supporting continued data engagement beyond the HCS. The implementation of CEDS in Ohio underscored the importance of relationship building, timing of implementation, understanding communities’ data preferences, and flexibility when working with communities. Researchers should consider implementing CEDS and integrating a data ambassador in community-based research to enhance community data engagement and drive data-informed interventions to improve public health outcomes.
Capturing Flow Experiences in Everyday Life: A Comparison of Recall and Momentary Measurement
Authors: Bartholomeyczik Karen , Knierim Michael T. , Weinhardt Christof , Oettingen Gabriele , Ebner-Priemer Ulrich 
Published: July 2024

AbstractIn a real-life study using time-based ambulatory assessment, we investigated how to unobtrusively capture within-subject and between-subject variations in flow in everyday life. We compared two observation approaches, momentary states, and coverage, which differed in the reference of flow reports and sampling frequency. Depending on condition, participants (N = 38) answered either ten or five queries per day that referred to the current state or the last two hours (n = 1442 observations in total). We found no effect of either approach on compliance, flow intensity, or flow reports over time. However, the approaches differed with respect to flow probability reports, within-subject variability in flow reports, and perceived burden. In addition, we introduced a reduced three-item version of the Flow Short Scale. Our results indicate acceptable to good reliability as well as concurrent, convergent and discriminant validity of this scale. Based on our findings, we recommend that the choice of observation approach for capturing everyday flow depends on the outcome of interest, the targeted comparison within- or between-subjects, and the expected task variability of the participants. Limitations regarding our sampling procedure and the retrospective assessment of flow experiences are discussed.
On Necessary Optimality Conditions for Sets of Points in Multiobjective Optimization
Authors: Cristofari Andrea , De Santis Marianna , Lucidi Stefano 
Published: July 2024

AbstractTaking inspiration from what is commonly done in single-objective optimization, most local algorithms proposed for multiobjective optimization extend the classical iterative scalar methods and produce sequences of points able to converge to single efficient points. Recently, a growing number of local algorithms that build sequences of sets has been devised, following the real nature of multiobjective optimization, where the aim is that of approximating the efficient set. This calls for a new analysis of the necessary optimality conditions for multiobjective optimization. We explore conditions for sets of points that share the same features of the necessary optimality conditions for single-objective optimization. On the one hand, from a theoretical point of view, these conditions define properties that are necessarily satisfied by the (weakly) efficient set. On the other hand, from an algorithmic point of view, any set that does not satisfy the proposed conditions can be easily improved by using first-order information on some objective functions. We analyse both the unconstrained and the constrained case, giving some examples.
Radiation sensitivity of biological systems, its modification by chemical modifiers and its quantitative evaluation
Authors: Múčka Viliam , Čuba Václav 
Published: July 2024

AbstractLarge number of sensitizers and protectors of cells, tissues, organs and organisms are being discussed in this review. Attention is paid to metals, nanoparticles or organic compounds as modifiers. They can modify the radiation sensitivity by 20–60%. In most cases, sensitization is caused by an increase in DNA damage or a decrease in the repair of radiation damage. The protection can be caused in many cases by repair of damaged DNA molecules, proteins or enzymes, or by reducing the ROS concentration. The most current research tasks in this area are formulated. A new quantitative and reproducible method of evaluating modifiers is discussed.
Unfairness in AI Anti-Corruption Tools: Main Drivers and Consequences
Authors: Odilla Fernanda 
Published: July 2024

AbstractThis article discusses the potential sources and consequences of unfairness in artificial intelligence (AI) predictive tools used for anti-corruption efforts. Using the examples of three AI-based anti-corruption tools from Brazil—risk estimation of corrupt behaviour in public procurement, among public officials, and of female straw candidates in electoral contests—it illustrates how unfairness can emerge at the infrastructural, individual, and institutional levels. The article draws on interviews with law enforcement officials directly involved in the development of anti-corruption tools, as well as academic and grey literature, including official reports and dissertations on the tools used as examples. Potential sources of unfairness include problematic data, statistical learning issues, the personal values and beliefs of developers and users, and the governance and practices within the organisations in which these tools are created and deployed. The findings suggest that the tools analysed were trained using inputs from past anti-corruption procedures and practices and based on common sense assumptions about corruption, which are not necessarily free from unfair disproportionality and discrimination. In designing the ACTs, the developers did not reflect on the risks of unfairness, nor did they prioritise the use of specific technological solutions to identify and mitigate this type of problem. Although the tools analysed do not make automated decisions and only support human action, their algorithms are not open to external scrutiny.
Technology-Enhanced Collaborative Inquiry in K–12 Classrooms: A Systematic Review of Empirical Studies
Authors: Chen Fan , Chen Gaowei 
Published: July 2024

AbstractCollaborative inquiry is an instructional approach that encourages student groups to engage in scientific inquiry processes, thereby enhancing their critical thinking and exploratory skills. The effective classroom implementation of this method requires ensuring student engagement in each inquiry stage, where technological tools and activity strategies play a crucial role in supporting their inquiry processes. However, the technological tools and activity strategies that contribute to designing classroom activities and fostering students’ engagement in collaborative inquiry remain unclear. This systematic review analyzed 58 empirical studies on technology-enhanced collaborative inquiry in K–12 classrooms. We examined the characteristics of these studies, the technological tools and activity strategies employed, and their reported outcomes and effects on student learning. The results indicate that most classroom practices featured diverse technological tools and activity strategies to support students in collaboratively performing stages of inquiry tasks. The findings highlight the need for further research into effectively integrating technological tools with activity strategies to provide adaptive support for specific inquiry processes, such as hypothesis generation and scientific argumentation. Our synthesis of previous outcome measurements revealed the positive impacts of technology-enhanced collaborative inquiry on students’ content knowledge, overall inquiry process, and scientific skills like argumentation. The implications of these findings are discussed for both researchers and practitioners, emphasizing the necessity of integrating appropriate technological tools and activity strategies to effectively facilitate collaborative inquiry in K–12 classrooms.
Constructing Social Vulnerability Indexes with Increased Data and Machine Learning Highlight the Importance of Wealth Across Global Contexts
Authors: Zhao Yuan , Paul Ronak , Reid Sean , Coimbra Vieira Carolina , Wolfe Chris , Zhang Yan , Chunara Rumi 
Published: July 2024

AbstractWe consider the availability of new harmonized data sources and novel machine learning methodologies in the construction of a social vulnerability index (SoVI), a multidimensional measure that defines how individuals’ and communities may respond to hazards including natural disasters, economic changes, and global health crises. The factors underpinning social vulnerability—namely, economic status, age, disability, language, ethnicity, and location—are well understood from a theoretical perspective, and existing indices are generally constructed based on specific data chosen to represent these factors. Further, the indices’ construction methods generally assume structured, linear relationships among input variables and may not capture subtle nonlinear patterns more reflective of the multidimensionality of social vulnerability. We compare a procedure which considers an increased number of variables to describe the SoVI factors with existing approaches that choose specific variables based on consensus within the social science community. Reproducing the analysis across eight countries, as well as leveraging deep learning methods which in recent years have been found to be powerful for finding structure in data, demonstrate that wealth-related factors consistently explain the largest variance and are the most common element in social vulnerability.
Integrated multi-view modeling for reliable machine learning-intensive software engineering
Authors: Husen Jati H. , Washizaki Hironori , Runpakprakun Jomphon , Yoshioka Nobukazu , Tun Hnin Thandar , Fukazawa Yoshiaki , Takeuchi Hironori 
Published: July 2024

AbstractDevelopment of machine learning (ML) systems differs from traditional approaches. The probabilistic nature of ML leads to a more experimentative development approach, which often results in a disparity between the quality of ML models with other aspects such as business, safety, and the overall system architecture. Herein the Multi-view Modeling Framework for ML Systems (M3S) is proposed as a solution to this problem. M3S provides an analysis framework that integrates different views. It is supported by an integrated metamodel to ensure the connection and consistency between different models. To facilitate the experimentative nature of ML training, M3S provides an integrated platform between the modeling environment and the ML training pipeline. M3S is validated through a case study and a controlled experiment. M3S shows promise, but future research needs to confirm its generality.
Singular concepts
Authors: Salmón Nathan 
Published: July 2024

AbstractAlonzo Church proposed a powerful and elegant theory of sequences of functions and their arguments as surrogates for Russellian singular propositions and singular concepts. Church’s proposed theory accords with his Alternative (0), the strictest of his three competing criteria for strict synonymy. The currently popular objection to strict criteria like (0) on the basis of the Russell–Myhill antinomy is here rebutted. Russell–Myhill is not a problem specifically for Alternative (0); it is a refutation of unrestrained concept comprehension. Unrestricted comprehension is also inconsistent with facts about sets of properties. Criteria more lax than (0) are philosophically inadequate. In particular, the rival conception of propositions as classes of possible worlds is subject to a fatal philosophical collapse. It follows on that conception, given that each of us is fallible, that everyone believes everything. It is shown, however, that Church’s proposed theory is vulnerable under (0) to a version of Russell’s notorious Gray’s Elegy objection. Some amendments to Church’s proposal are proffered, including an amendment, first proposed in the author’s Frege’s Puzzle (1986), that addresses Russell’s objection. Church’s response (personal correspondence) is considered.
Sequential Electrochemical and Chemical Multi-Polymerization of Catechol for Abatement of Environmental Pollutants
Authors: Altıncı Osman Cem , Körbahti Bahadır K. 
Published: July 2024

AbstractCatechol is a substance that is commonly found in wastewaters from a variety of sectors including paper, paint, petroleum, dyes, antioxidants, pesticides, iron and steel, solvents, nylon, detergent, textile, plastic, rubber, cosmetics, and medicine. In this study, sequential electrochemical and chemical multi-polymerization of catechol was investigated for environmental pollution abatement. The effect of operating parameters like catechol concentration (2–10 g/L), ammonium persulphate (APS) concentration (2–10 g/L) and reaction temperature (20–60 °C) were evaluated using response surface methodology. Catechol concentration was determined using HPLC in a gradient mobile phase. The electrochemical behavior of the polymer was investigated by cyclic voltammetry (CV). The structural and morphological properties of polycatechol were characterized by Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy with energy dispersive X-ray (SEM–EDX) analysis. It was observed from the SEM images a polymeric structure developed from a crystalline and heterogeneous structure when the APS concentration increased. Similarly, it was seen in SEM images that the polymers transitioned from a bulk and heterogeneous structure to a homogeneous structure as the temperature increased, and back to a heterogeneous structure as the catechol concentration increased. It was also found that catechol removal increased and reaction selectivity decreased by increasing the reaction temperature. The optimum operating conditions were found as 4 g/L catechol concentration, 9.5 g/L APS concentration, 30 °C reaction temperature with 100 cycles at 50 mV/s of electrochemical polymerization and 72 h of chemical polymerization. The results of this study show the potential of challenging new routes not only facile polymerization of organic monomers but also to decrease the undesirable pollutant concentration in the wastewater.
Transcriptomic Differences in Medullary Thyroid Carcinoma According to Grade
Authors: Ruz-Caracuel Ignacio , Caniego-Casas Tamara , Alonso-Gordoa Teresa , Carretero-Barrio Irene , Ariño-Palao Carmen , Santón Almudena , Rosas Marta , Pian Héctor , Molina-Cerrillo Javier , Luengo Patricia , Palacios José 
Published: July 2024

AbstractMedullary thyroid carcinoma (MTC) is a rare cancer derived from neuroendocrine C-cells of the thyroid. In contrast to other neuroendocrine tumors, a histological grading system was lacking until recently. A novel two-tier grading system based on the presence of high proliferation or necrosis is associated with prognosis. Transcriptomic analysis was conducted on 21 MTCs, including 9 high-grade tumors, with known mutational status, using the NanoString Tumor Signaling 360 Panel. This analysis, covering 760 genes, revealed upregulation of the genes EGLN3, EXO1, UBE2T, UBE2C, FOXM1, CENPA, DLL3, CCNA2, SOX2, KIF23, and CDCA5 in high-grade MTCs. Major pathways differentially expressed between high-grade and low-grade MTCs were DNA damage repair, p53 signaling, cell cycle, apoptosis, and Myc signaling. Validation through qRT-PCR in 30 MTCs demonstrated upregulation of ASCL1, DLL3, and SOX2 in high-grade MTCs, a gene signature akin to small-cell lung carcinoma, molecular subgroup A. Subsequently, DLL3 expression was validated by immunohistochemistry. MTCs with DLL3 overexpression (defined as ≥ 50% of positive tumor cells) were associated with significantly lower disease-free survival (p = 0.041) and overall survival (p = 0.01). Moreover, MTCs with desmoplasia had a significantly increased expression of DLL3. Our data supports the idea that DLL3 should be further explored as a predictor of aggressive disease and poor outcomes in MTC.