Hasil untuk "Science (General)"

Ruifeng Xiao, Cong Shen, Wen Shen et al.

Fyn is widely involved in diverse cellular physiological processes, including cell growth and survival, and has been implicated in the regulation of energy metabolism and the pathogenesis of diabetes mellitus through multiple pathways. Fyn plays a role in increasing fat accumulation and promoting insulin resistance, and it also contributes to the development of diabetic complications such as diabetic kidney disease and diabetic retinopathy. The primary mechanism by which Fyn modulates lipid metabolism is that it inhibits AMP-activated protein kinase (AMPK). Additionally, it affects energy homeostasis through regulating specific signal pathways affecting lipid metabolism including pathways related to CD36, through enhancement of adipocyte differentiation, and through modulating insulin signal transduction. Inflammatory stress is one of the fundamental mechanisms in diabetes mellitus and its complications. Fyn also plays a role in inflammatory stress-related signaling cascades such as the Akt/GSK-3β/Fyn/Nrf2 pathway, exacerbating inflammation in diabetes mellitus. Therefore, Fyn emerges as a promising therapeutic target for regulating glucolipid metabolism and alleviating type 2 diabetes mellitus. This review synthesizes research on the role of Fyn in the regulation of energy metabolism and the development of diabetes mellitus, while exploring its specific regulatory mechanisms.

Jiaxin Pei, Dustin Wright, Isabelle Augenstein et al.

Effectively engaging the public with science is vital for fostering trust and understanding in our scientific community. Yet, with an ever-growing volume of information, science communicators struggle to anticipate how audiences will perceive and interact with scientific news. In this paper, we introduce a computational framework that models public perception across twelve dimensions, such as newsworthiness, importance, and surprisingness. Using this framework, we create a large-scale science news perception dataset with 10,489 annotations from 2,101 participants from diverse US and UK populations, providing valuable insights into public responses to scientific information across domains. We further develop NLP models that predict public perception scores with a strong performance. Leveraging the dataset and model, we examine public perception of science from two perspectives: (1) Perception as an outcome: What factors affect the public perception of scientific information? (2) Perception as a predictor: Can we use the estimated perceptions to predict public engagement with science? We find that individuals' frequency of science news consumption is the driver of perception, whereas demographic factors exert minimal influence. More importantly, through a large-scale analysis and carefully designed natural experiment on Reddit, we demonstrate that the estimated public perception of scientific information has direct connections with the final engagement pattern. Posts with more positive perception scores receive significantly more comments and upvotes, which is consistent across different scientific information and for the same science, but are framed differently. Overall, this research underscores the importance of nuanced perception modeling in science communication, offering new pathways to predict public interest and engagement with scientific content.

Athanase Papadopoulos

Vincenzo Galilei and Constantijn Huygens were both humanists and eminent musicians, the former from the late Renaissance and the latter from the early Modern era. Their respective sons, Galileo and Christiaan, were scientists whose importance cannot be overestimated. My aim in this chapter is to set the scene for a parallel presentation of the legacy of the Galilei on the one hand, and the Huygens on the other. This will give us an opportunity to talk about mathematics, music and acoustics, but also about science in general, at this time of birth of the Modern era.

Yongxiao Li, Wanting Xu, Xinpei Wang et al.

In this study, we advance our exploration of Apolipoprotein A-I (apoA-I) peptide analogs (APAs) for their application in nanodisc (ND) assembly, focusing on the dynamic conformational characteristics and the potential for drug delivery. We explore APA-ND interactions with an emphasis on curcumin encapsulation, utilizing molecular dynamic simulations and in vitro assessments to evaluate the efficacy of various APA-ND formulations as drug carriers. The methodological approach involved the generation of three unique apoA-I α-11/3 helical mimics, resulting in fifteen distinct APAs. Their structural integrity was rigorously assessed using ColabFold-AF2, with particular attention to pLDDT and pTM scores. Extensive molecular dynamics simulations, covering 1.7 μs across 17 ND systems, were conducted to investigate the influence of APA sequence variations on ND stability and interactions. This study reveals that the composition of APAs, notably the presence of Proline, Serine, and Tryptophan, significantly impacts ND stability and morphology. Oligomeric APAs, in particular, demonstrated superior stability and distinct interaction patterns compared to their monomeric counterparts. Additionally, hydrodynamic diameter measurements over eight weeks indicated sequence-dependent stability, highlighting the potential of specific APA configurations for sustained colloidal stability. In vitro study successfully encapsulated curcumin in [AA]₃/DMPC ND formulations, revealing concentration-dependent stability and interaction dynamics. The findings underscore the remarkable capability of APA-NDs to maintain structural integrity and efficient drug encapsulation, positioning them as a promising platform for drug delivery. The study concludes by emphasizing the tunability and versatility of APA-NDs in drug formulation, potentially revolutionizing nanomedicine by enabling customized APA sequences and ND properties for targeted drug delivery.

Jiexing Qi, Chang Su, Zhixin Guo et al.

Generating SPARQL queries from natural language questions is challenging in Knowledge Base Question Answering (KBQA) systems. The current state-of-the-art models heavily rely on fine-tuning pretrained models such as T5. However, these methods still encounter critical issues such as triple-flip errors (e.g., (subject, relation, object) is predicted as (object, relation, subject)). To address this limitation, we introduce <b>TSET</b> (<b>T</b>riplet <b>S</b>tructure <b>E</b>nhanced <b>T</b>5), a model with a novel pretraining stage positioned between the initial T5 pretraining and the fine-tuning for the Text-to-SPARQL task. In this intermediary stage, we introduce a new objective called Triplet Structure Correction (TSC) to train the model on a SPARQL corpus derived from Wikidata. This objective aims to deepen the model’s understanding of the order of triplets. After this specialized pretraining, the model undergoes fine-tuning for SPARQL query generation, augmenting its query-generation capabilities. We also propose a method named “semantic transformation” to fortify the model’s grasp of SPARQL syntax and semantics without compromising the pre-trained weights of T5. Experimental results demonstrate that our proposed TSET outperforms existing methods on three well-established KBQA datasets: LC-QuAD 2.0, QALD-9 plus, and QALD-10, establishing a new state-of-the-art performance (95.0% <i>F</i>1 and 93.1% QM on LC-QuAD 2.0, 75.85% <i>F</i>1 and 61.76% QM on QALD-9 plus, 51.37% <i>F</i>1 and 40.05% QM on QALD-10).

Zijun Liu, Kaiming Liu, Yiqi Zhu et al.

Rapid development of artificial intelligence has drastically accelerated the development of scientific discovery. Trained with large-scale observation data, deep neural networks extract the underlying patterns in an end-to-end manner and assist human researchers with highly-precised predictions in unseen scenarios. The recent rise of Large Language Models (LLMs) and the empowered autonomous agents enable scientists to gain help through interaction in different stages of their research, including but not limited to literature review, research ideation, idea implementation, and academic writing. However, AI researchers instantiated by foundation model empowered agents with full-process autonomy are still in their infancy. In this paper, we study $\textbf{AI-Generated Science}$ (AIGS), where agents independently and autonomously complete the entire research process and discover scientific laws. By revisiting the definition of scientific research, we argue that $\textit{falsification}$ is the essence of both human research process and the design of an AIGS system. Through the lens of falsification, prior systems attempting towards AI-Generated Science either lack the part in their design, or rely heavily on existing verification engines that narrow the use in specialized domains. In this work, we propose Baby-AIGS as a baby-step demonstration of a full-process AIGS system, which is a multi-agent system with agents in roles representing key research process. By introducing FalsificationAgent, which identify and then verify possible scientific discoveries, we empower the system with explicit falsification. Experiments on three tasks preliminarily show that Baby-AIGS could produce meaningful scientific discoveries, though not on par with experienced human researchers. Finally, we discuss on the limitations of current Baby-AIGS, actionable insights, and related ethical issues in detail.

Nathalia Nascimento, Everton Guimaraes, Sai Sanjna Chintakunta et al.

The adoption of Large Language Models (LLMs) for code generation in data science offers substantial potential for enhancing tasks such as data manipulation, statistical analysis, and visualization. However, the effectiveness of these models in the data science domain remains underexplored. This paper presents a controlled experiment that empirically assesses the performance of four leading LLM-based AI assistants-Microsoft Copilot (GPT-4 Turbo), ChatGPT (o1-preview), Claude (3.5 Sonnet), and Perplexity Labs (Llama-3.1-70b-instruct)-on a diverse set of data science coding challenges sourced from the Stratacratch platform. Using the Goal-Question-Metric (GQM) approach, we evaluated each model's effectiveness across task types (Analytical, Algorithm, Visualization) and varying difficulty levels. Our findings reveal that all models exceeded a 50% baseline success rate, confirming their capability beyond random chance. Notably, only ChatGPT and Claude achieved success rates significantly above a 60% baseline, though none of the models reached a 70% threshold, indicating limitations in higher standards. ChatGPT demonstrated consistent performance across varying difficulty levels, while Claude's success rate fluctuated with task complexity. Hypothesis testing indicates that task type does not significantly impact success rate overall. For analytical tasks, efficiency analysis shows no significant differences in execution times, though ChatGPT tended to be slower and less predictable despite high success rates. This study provides a structured, empirical evaluation of LLMs in data science, delivering insights that support informed model selection tailored to specific task demands. Our findings establish a framework for future AI assessments, emphasizing the value of rigorous evaluation beyond basic accuracy measures.

Enrico La Spina, Sebastiano Giallongo, Cesarina Giallongo et al.

Chronic myeloproliferative neoplasms encompass the BCR-ABL1-negative neoplasms polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). These are characterized by calreticulin (CALR), myeloproliferative leukemia virus proto-oncogene (MPL) and the tyrosine kinase Janus kinase 2 (JAK2) mutations, eventually establishing a hyperinflammatory tumor microenvironment (TME). Several reports have come to describe how constitutive activation of JAK-STAT and NFκB signaling pathways lead to uncontrolled myeloproliferation and pro-inflammatory cytokines secretion. In such a highly oxidative TME, the balance between Hematopoietic Stem Cells (HSCs) and Mesenchymal Stromal Cells (MSCs) has a crucial role in MPN development. For this reason, we sought to review the current literature concerning the interplay between HSCs and MSCs. The latter have been reported to play an outstanding role in establishing of the typical bone marrow (BM) fibrotic TME as a consequence of the upregulation of different fibrosis-associated genes including PDGF- β upon their exposure to the hyperoxidative TME characterizing MPNs. Therefore, MSCs might turn to be valuable candidates for niche-targeted targeting the synthesis of cytokines and oxidative stress in association with drugs eradicating the hematopoietic clone.

S. O. Buranok

The Monroe Doctrine occupies a unique place in the US history. It became one of the key foreign policy documents of its time and provided the basis for a wide variety of interpretations of the United States’ role and goals in the international arena at turning points of world history. One of these moments was the turn of the 1910s−1920s, when a new Versailles-Washington order of international relations was emerging. In the US public discourse, this period was marked by intense debates between supporters of the Democratic President V. Wilson and his isolationist opponents. Both Republicans and Democrats constantly referred to the Monroe Doctrine, on the one hand, to justify their own views on US foreign policy in the new conditions, and, on the other hand, to refute the arguments of their political opponents. The controversy surrounding the Monroe Doctrine has been reflected in publications in periodicals and analytical journals, as well as in cartoons. Studying these materials, it is possible to trace the evolution of the approaches of American politicians, experts, editors, and journalists to the Monroe Doctrine. The arguments of the Republicans against the ‘internationalist’ interpretation of the Monroe Doctrine that emerged in the face of the changing global context after the First World War are of particular interest. The study shows that at the initial stage of discussions (1920), the Monroe Doctrine was used by the Republicans primarily to criticize W. Wilson’s concept of international relations in general and his position on the League of Nations in particular. At the next stage (1921−1923), the debate focused around the need to revise the Monroe Doctrine itself, that aroused due to new trends in the development of international relations in the Far East and, in particular, because of the increasing competition between the United States and Japan. The author identifies several main approaches to the interpretation of the Monroe Doctrine formulated during the public debate in 1921−1923. It is shown that, despite significant divergences of view, both isolationists and internationalists eventually came to broader interpretations of the Monroe Doctrine, recognizing the need to extend its principles to the entire Asia-Pacific region.

Eduardo Guendelman

Complexifying space time has many interesting applications, from the construction of higher dimensional unification, to provide a useful framework for quantum gravity and to better define some local symmetries that suffer singularities in real space time. In this context here spacetime is extended to complex spacetime and standard general coordinate invariance is also extended to complex holomorphic general coordinate transformations. This is possible by introducing a non Riemannian Measure of integration, which transforms avoiding non holomorphic behavior . Instead the measure transforms according to the inverse of the jacobian of the coordinate transformation and avoids the traditional square root of the determinant of the metric $\sqrt{-g}$. which is not globally holomorphic , or the determinant of the vierbein which is sensitive to the vierbein orientations and not invariant under local lorentz transformations with negative determinants. A contribution to the cosmological term appears as an integration constant in the equations of motion. A proposed action for Finsler geometry, which involves $-g$ rather than $\sqrt{-g}$ will also constitute an example of a Holomorphic General Coordinate Invariant Modified Measure Gravitational Theory.

J. Ernesto Solanes, Adolfo Muñoz, Luis Gracia et al.

This work proposes a new interface for the teleoperation of mobile robots based on virtual reality that allows a natural and intuitive interaction and cooperation between the human and the robot, which is useful for many situations, such as inspection tasks, the mapping of complex environments, etc. Contrary to previous works, the proposed interface does not seek the realism of the virtual environment but provides all the minimum necessary elements that allow the user to carry out the teleoperation task in a more natural and intuitive way. The teleoperation is carried out in such a way that the human user and the mobile robot cooperate in a synergistic way to properly accomplish the task: the user guides the robot through the environment in order to benefit from the intelligence and adaptability of the human, whereas the robot is able to automatically avoid collisions with the objects in the environment in order to benefit from its fast response. The latter is carried out using the well-known potential field-based navigation method. The efficacy of the proposed method is demonstrated through experimentation with the Turtlebot3 Burger mobile robot in both simulation and real-world scenarios. In addition, usability and presence questionnaires were also conducted with users of different ages and backgrounds to demonstrate the benefits of the proposed approach. In particular, the results of these questionnaires show that the proposed virtual reality based interface is intuitive, ergonomic and easy to use.

Sylvia L. R. Wood, Kyle T. Martins, Véronique Dumais-Lalonde et al.

Designing effective habitat and protected area networks, which sustain species-rich communities is a critical conservation challenge. Recent decades have witnessed the emergence of new computational methods for analyzing and prioritizing the connectivity needs of multiple species. We argue that the goal of prioritizing habitat for multispecies connectivity should be focused on long-term persistence of a set of species in a landscape or seascape. Here we present a review of the literature based on 77 papers published between 2010 and 2020, in which we assess the current state and recent advances in multispecies connectivity analysis in terrestrial ecosystems. We summarize the four most employed analytical methods, compare their data requirements, and provide an overview of studies comparing results from multiple methods. We explicitly look at approaches for integrating multiple species considerations into reserve design and identify novel approaches being developed to overcome computational and theoretical challenges posed by multispecies connectivity analyses. There is a lack of common metrics for multispecies connectivity. We suggest the index of metapopulation capacity as one metric by which to assess and compare the effectiveness of proposed network designs. We conclude that, while advances have been made over the past decade, the field remains nascent by its ability to integrate multiple species interactions into analytical approaches to connectivity. Furthermore, the field is hampered its ability to provide robust connectivity assessments for lack of a clear definition and goal for multispecies connectivity conservation.

Andi Kurniawan Nugroho, Dinar Mutiara Kusumo Nugraheni, Terawan Agus Putranto et al.

When the blood flow to the arteries in brain is blocked, its known as Ischemic stroke or blockage stroke. Ischemic stroke can occur due to the formation of blood clots in other parts of the body. Plaque buildup in arteries, on the other hand, can cause blockages because if it ruptures, it can form blood clots. The b-1000 Diffusion Weighted (DW) Magnetic Resonance Imaging (MRI) image was used in a general examination to obtain an image of the part of the brain that had a stroke. In this study, classifications used several variations of layer convolution to obtain high accuracy and high computational consumption using b-1000 Diffusion Weighted (DW) MR in ischemic stroke types: acute, sub-acute and chronic. Ischemic stroke was classified using five variants of the Convolutional Neural Network (CNN) architectural design, i.e., CNN1–CNN5. The test results show that the CNN5 architectural design provides the best ischemic stroke classification compared to other architectural designs tested, with an accuracy of 99.861%, precision 99.862%, recall 99.861, and F1-score 99.861%.

Heather Brindley

Reframing Tomorrow presents the Design for the Mosaic Web Open Knowledge Initiative. This is a new opportunity for delivery of the United Nations' Sustainable Development Goals in an ethical, politically-acceptable, cost-effective and timely manner.

B. Escher, J. Hermens

Nataliia Khoma, Oleksii Kokoriev

This article analyses compliance of the post-Soviet Baltic States with the EU liberal-democratic standards, at both institutional and value levels. The authors prove that fulfilment of the Copenhagen criteria for EU accession did not determine an enhancement of the quality of democracy in Estonia, Latvia and Lithuania. This study highlights that, in recent years, the Baltic States have entered a phase of stagnation of liberal-democratic transformations and that they need a more active position of the state on institutional reforms and resocialization of citizens to strengthen adherence to the political and legal values that the EU is based on. The article emphasises how the global financial crisis of 2008, the European migration crisis (2015) and the current coronavirus pandemic have all had an impact on the quality of democracy in the Baltic States. The authors focus on the incomplete process of value reforming among the Baltic population against the EU liberal-democratic standards. The article highlights that the post-totalitarian rotation of values in Estonia, Latvia and Lithuania is slow and faces rejection of European liberal-democratic values to a greater or lesser extent. It underlines the preservation of the totalitarian (Soviet) vestiges of political culture, which contradict the EU paradigm of values and prevent the Baltic States from improving the quality of democracy. It is noted that, in terms of the radicalization level in defending national interests, the Baltic countries take the intermediate position between the Nordic and the V4 countries, particularly Hungary and Poland that develop illiberal democracy patterns.

Iñigo Martinez, Elisabeth Viles, Igor G. Olaizola

Data science has employed great research efforts in developing advanced analytics, improving data models and cultivating new algorithms. However, not many authors have come across the organizational and socio-technical challenges that arise when executing a data science project: lack of vision and clear objectives, a biased emphasis on technical issues, a low level of maturity for ad-hoc projects and the ambiguity of roles in data science are among these challenges. Few methodologies have been proposed on the literature that tackle these type of challenges, some of them date back to the mid-1990, and consequently they are not updated to the current paradigm and the latest developments in big data and machine learning technologies. In addition, fewer methodologies offer a complete guideline across team, project and data & information management. In this article we would like to explore the necessity of developing a more holistic approach for carrying out data science projects. We first review methodologies that have been presented on the literature to work on data science projects and classify them according to the their focus: project, team, data and information management. Finally, we propose a conceptual framework containing general characteristics that a methodology for managing data science projects with a holistic point of view should have. This framework can be used by other researchers as a roadmap for the design of new data science methodologies or the updating of existing ones.

Aviv Keren

Objects are a centerpiece of the mathematical realm and our interaction with and reasoning about it, just as they are of the physical one (if not more). And humans' mathematical reasoning must ultimately be grounded in our general intelligence. Yet in contemporary cognitive science and A.I., the physical and mathematical domains are customarily explored separately, which allows for baking in assumptions for what objects are for the system - and missing potential connections. In this paper, I put the issue into its philosophical and cognitive context. I then describe an abstract theoretical framework for learning object representations, that makes room for mathematical objects on par with non-mathematical ones. Finally, I describe a case study that builds on that view to show how our general ability for integrating different aspects of objects effects our conception of the natural numbers.

Lingfei Wu, Aniket Kittur, Hyejin Youn et al.

What science does, what science could do, and how to make science work? If we want to know the answers to these questions, we need to be able to uncover the mechanisms of science, going beyond metrics that are easily collectible and quantifiable. In this perspective piece, we link metrics to mechanisms by demonstrating how emerging metrics of science not only offer complementaries to existing ones, but also shed light on the hidden structure and mechanisms of science. Based on fundamental properties of science, we classify existing theories and findings into: hot and cold science referring to attention shift between scientific fields, fast and slow science reflecting productivity of scientists and teams, soft and hard science revealing reproducibility of scientific research. We suggest that interest about mechanisms of science since Derek J. de Solla Price, Robert K. Merton, Eugene Garfield, and many others complement the zeitgeist in pursuing new, complex metrics without understanding the underlying processes. We propose that understanding and modeling the mechanisms of science condition effective development and application of metrics.

Herman Aguinis, R. Gottfredson