bluuebunny/crossref_metadata_2025_split huggingface dataset:
A theory of emotion based on a universal model
Authors: Huang Yong 
Published: March 2024

AbstractThe complexity of emotions has thus far limited our understanding of them. To obtain a clear understanding of the nature of emotion, this paper proposes a novel emotion theory and establishes a universal model of the conscious world in the human brain, the substanguage and interaction model (SIM). Based on an analysis of the possibilities of the interaction process in the SIM, two basic emotions that are indecomposable factors within all emotions—hope and fear—are identified. A questionnaire survey reveals that this basic emotion exhibits high acceptability. Based on emotion theory, this paper reasonably explains the phenomena of facial attraction and infantile facial preference and discusses the psychological reasons for phonocentrism, the phenomenon of preferring the spoken word over the written word. In addition, this paper explores the possibility of artificial intelligence possessing self-emotions. Emotions are relevant to many areas of human knowledge, as well as to everyone’s daily lives, and the simple, clear way to understand emotions provided in this paper may be instructive for everyone.
High capacitance freestanding PEDOT:PSS electrodes for low-frequency electric field delivery
Authors: Jenkins Elise P. W. , Keene Scott T. , Dimov Ivan B. , Oldroyd Poppy , Malliaras George G. 
Published: March 2024

Since the 18th century, the use of electrical stimulation has been widely used to treat disease and control the development of cells, tissues, and organ growth. Electric fields and currents are used to mimic endogenous signals that regulate cell behavior to both better understand their mechanism of action and provide control for bio-engineering and therapeutics. While delivery of electric fields has proven useful, low frequency field delivery remains inaccessible with standard metal electrodes. This delivery of low frequency electric fields is limited by areal capacitance, resulting in low charge injection capacities or Faradaic reactions, which can generate cytotoxic by-products. Here, we demonstrate freestanding poly(3,4-ethylene dioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) electrodes, which conduct both ions and electrons, resulting in electrode capacitance that scales with volume rather than area. We show that these electrodes can be used to generate electric fields in an aqueous medium at frequencies as low as 0.1 Hz. Specifically, we demonstrate persistent electric fields in aqueous media without exceeding the electrochemical window of water, previously unattainable using standard electrodes. Finally, we show that thick PEDOT:PSS electrodes are not cytotoxic. This work opens the path toward delivery of low frequency stimulating electric fields to cells and tissues both in vitro and in vivo.
Mentoring as a way to the professional development of in-service physics teachers
Authors: Kozelková Katarína 
Published: March 2024

Abstract
Nowadays education is facing new challenges based on the changes in the world and society. The generation of pupils has changed and their needs are different to what the teachers were used to. The change not only in teaching and learning methods but also in the way of thinking is necessary. To help teachers to find a different point of view on education and implement the changes, the Regional teachers’ support centres were established. The first two launched in 2021 and within three years there should be 40 centres across Slovakia. The main idea is to provide individual support for primary and lower secondary teachers as well as training in five main topics -i) school curriculum, ii) assessment and evaluation of pupils’ learning outcomes, iii) educational process and teaching style, iv) personalization and inclusive education, v) digitalization. Individual support is provided by a mentor in the form of class observations and mentoring conversations. The physics teachers can be involved and work on professional self-development in the topics they feel they want to improve. The paper reports on the mentors’ activities with the main focus held on the possibility of the in-service physics teachers’ professional development.
Interactive hands-on laboratory for upper secondary students: What do the visitors appreciate?
Authors: Kácovský P , Snětinová M , Ryston M , Ceháková L , Legerská J 
Published: March 2024

Abstract
As the covid-19 pandemic wanes, events that take place outside of school are regaining popularity. One such event is a visit to the Interactive Physics Laboratory (IPL). The main goal of the IPL is to provide upper secondary student groups with the necessary space, equipment, and guidance for hands-on practical work in the form of structured inquiry. Since the spring of 2022, we have been requesting feedback from our student visitors through a paper-and-pencil questionnaire. The most crucial and sole research question addressed in this paper is: “What do students appreciate the most during their visit to the IPL?” To answer this question, we have employed a mixed methods research design, combining the ideological background of grounded theory with the utilization of statistical tools. In this contribution, we provide the results obtained from analysing answers from 250 student respondents. We present an inductively derived system of codes, categories, and coding rules obtained through open coding of the student responses. Additionally, we share elementary statistical findings regarding the frequency of occurrence of particular codes. This study serves as a pilot of the coding system that we plan to apply to a larger sample of respondents in the future.
Understanding and overcoming students’ difficulties in interpreting graphs: insights from a racing car problem
Authors: Susac A , Sajka M , Rosiek R , Šipuš Ž Milin 
Published: March 2024

Abstract
The study investigates university students’ difficulties in interpreting graphs through the analysis of their responses to the modified PISA racing car problem. The impact of adding a distractor that is appealing to students and supplementary questions to enhance students’ graph understanding is explored. Eye-tracking technology was used to capture participants’ visual attention during the task. The findings highlight the challenges of treating graphs as pictures and the benefits of incorporating additional questions to improve students’ comprehension. The study emphasizes the need for instructional strategies that address intuitive reasoning and promote reflective thinking in graph interpretation.
Innovative technologies of physical and mathematical education implemented by using of fractal approach
Authors: Seben V , Yurkovych N , Mar’yan M 
Published: March 2024

Abstract
The implementation of the fractal approach to the teaching of physical and mathematical disciplines using computer modeling is substantiated.The formation of a fractal structure is established and iterations are defined that reflect the integrity and spontaneity of information presentation. The involvement of students of related specialties from two universities – University of Presov (Faculty of Humanities and Natural Sciences) and Uzhhorod National University (Faculty of Physics) – demonstrates the feasibility of using the fractal approach in training future teachers and scientists of natural sciences in higher educational institutions.
Affordances of technology-supported trails in education of pre-service primary teachers in physics
Authors: Valovicova L , Bockova V , Haringova S , Medova J 
Published: March 2024

Abstract
Outdoor activities focused on solving problems related to concrete objects, supported by technology, also known as trails, offer opportunities to develop not only content knowledge, but also the soft-skill of learners. The learner can be a pupil or a pre-service teacher, student of initial teacher education programme. Current students of initial teacher education programme are usually members of Generation Z and their education should be tailored to the needs of this generation. In this paper we designed an interdisciplinary trail connecting mathematics and science, particularly physics, knowledge and compared the solution process of pre-service teachers for primary schools and the primary pupils. The strategies almost did not differ, but pre-service teachers demonstrated deeper understanding of concept of measure.
Microfluidic-based preparation of artificial antigen-presenting gel droplets for integrated and minimalistic adoptive cell therapy strategies
Authors: Tian Yishen , Chen Wei , Du Guangshi , Gao Jie , Zhao Youbo , Wang Zhuli , Su Min , Hu Rong , Han Feng 
Published: March 2024

Abstract
Adoptive T-cell transfer for cancer therapy is limited by the inefficiency of in vitro T-cell expansion and the ability of in vivo T-cells to infiltrate tumors. The construction of multifunctional artificial antigen-presenting cells is a promising but challenging approach to achieve this goal. In this study, a multifunctional artificial antigen-presenting gel droplet (AAPGD) was designed. Its surface provides regulated T-cell receptor (TCR) stimulation and co-stimulation signals and is capable of slow release of mitogenic cytokines and collagen mimetic peptide. The highly uniform AAPGD are generated by a facile method based on standard droplet microfluidic devices. The results of the study indicate that, T-cell proliferated in vitro utilizing AAPGD have a fast rate and high activity. AAPGD increased the proportion of in vitro proliferating T cells low differentiation and specificity. The starting number of AAPGDs and the quality ratio of TCR-stimulated and co-stimulated signals on the surface have a large impact on the rapid proliferation of low-differentiated T cells in vitro. During reinfusion therapy, AAPGD also enhanced T-cell infiltration into the tumor site. In experiments using AAPGD for adoptive T cell therapy in melanoma mice, tumor growth was inhibited, eliciting a potent cytotoxic T-lymphocyte immune response and improving mouse survival. In conclusion, AAPGD promotes rapid low-differentiation proliferation of T cells in vitro and enhances T cell infiltration of tumors in vivo. It simplifies the preparation steps of adoptive cell therapy, improves the therapeutic effect, and provides a new pathway for overdosing T cells to treat solid tumors.
Generating human skeletal myoblast spheroids for vascular myogenic tissue engineering
Authors: Minne Mendy , Terrie Lisanne , Wüst Rebecca , Hasevoets Steffie , Vanden Kerchove Kato , Nimako Kakra , Lambrichts Ivo , Thorrez Lieven , Declercq Heidi 
Published: March 2024

Abstract
Engineered myogenic microtissues derived from human skeletal myoblasts offer unique opportunities for varying skeletal muscle tissue engineering applications, such as in vitro drug-testing and disease modelling. However, more complex models require the incorporation of vascular structures, which remains to be challenging. In this study, myogenic spheroids were generated using a high-throughput, non-adhesive micropatterned surface. Since monoculture spheroids containing human skeletal myoblasts were unable to remain their integrity, co-culture spheroids combining human skeletal myoblasts and human adipose-derived stem cells were created. When using the optimal ratio, uniform and viable spheroids with enhanced myogenic properties were achieved. Applying a pre-vascularization strategy, through addition of endothelial cells, resulted in the formation of spheroids containing capillary-like networks, lumina and collagen in the extracellular matrix, whilst retaining myogenicity. Moreover, sprouting of endothelial cells from the spheroids when encapsulated in fibrin was allowed. The possibility of spheroids, from different maturation stages, to assemble into a more large construct was proven by doublet fusion experiments. The relevance of using three-dimensional microtissues with tissue-specific microarchitecture and increased complexity, together with the high-throughput generation approach, makes the generated spheroids a suitable tool for in vitro drug-testing and human disease modeling.
Patient-specific finite element analysis for assessing hip fracture risk in aging populations
Authors: K N Chethan , Schmidt Genannt Waldschmidt Nadine , Corda John Valerian , Shenoy B Satish , Shetty Sawan , Keni Laxmikant G , Bhat N Shyamasunder , Nikam Nishant , Mihcin Senay 
Published: March 2024

Abstract
The femur is one of the most important bone in the human body, as it supports the body’s weight and helps with movement. The aging global population presents a significant challenge, leading to an increasing demand for artificial joints, particularly in knee and hip replacements, which are among the most prevalent surgical procedures worldwide. This study focuses on hip fractures, a common consequence of osteoporotic fractures in the elderly population. To accurately predict individual bone properties and assess fracture risk, patient-specific finite element models (FEM) were developed using CT data from healthy male individuals. The study employed ANSYS 2023 R2 software to estimate fracture loads under simulated single stance loading conditions, considering strain-based failure criteria. The FEM bone models underwent meticulous reconstruction, incorporating geometrical and mechanical properties crucial for fracture risk assessment. Results revealed an underestimation of the ultimate bearing capacity of bones, indicating potential fractures even during routine activities. The study explored variations in bone density, failure loads, and density/load ratios among different specimens, emphasizing the complexity of bone strength determination. Discussion of findings highlighted discrepancies between simulation results and previous studies, suggesting the need for optimization in modelling approaches. The strain-based yield criterion proved accurate in predicting fracture initiation but required adjustments for better load predictions. The study underscores the importance of refining density-elasticity relationships, investigating boundary conditions, and optimizing models through in vitro testing for enhanced clinical applicability in assessing hip fracture risk. In conclusion, this research contributes valuable insights into developing patient-specific FEM bone models for clinical hip fracture risk assessment, emphasizing the need for further refinement and optimization for accurate predictions and enhanced clinical utility.
Transforming two-dimensional tensor networks into quantum circuits for supervised learning
Authors: Song Zhihui , Xu Jinchen , Zhou Xin , Ding Xiaodong , Shan Zheng 
Published: March 2024

Abstract
There have been numerous quantum neural networks reported, but they struggle to match traditional neural networks in accuracy. Given the huge improvement of the neural network models’ accuracy by two-dimensional tensor network (TN) states in classical tensor network machine learning (TNML), it is promising to explore whether its application in quantum machine learning can extend the performance boundary of the models. Here, we transform two-dimensional TNs into quantum circuits for supervised learning. Specifically, we encode two-dimensional TNs into quantum circuits through rigorous mathematical proofs for constructing model ansätze, including string-bond states, entangled-plaquette states and isometric TN states. In addition, we propose adaptive data encoding methods and combine with TNs. We construct a tensor-network-inspired quantum circuit (TNQC) supervised learning framework for transferring TNML from classical to quantum, and build several novel two-dimensional TN-inspired quantum classifiers based on this framework. Finally, we propose a parallel quantum machine learning method for multi-class classification to construct 2D TNQC-based multi-class classifiers. Classical simulation results on the MNIST benchmark dataset show that our proposed models achieve the state-of-the-art accuracy performance, significantly outperforming other quantum classifiers on both binary and multi-class classification tasks, and beat simple convolutional classifiers on a fair track with identical inputs. The noise resilience of the models makes them successfully run and work in a real quantum computer.
Analysis of editor in chief gender and associated journal variables among 126 pathology journals
Authors: Wang Dayle K , Clark Landon M , Stephens Laura D , Adkins Brian D , Khan Shazia S , Booth Garrett S , Jacobs Jeremy W 
Published: March 2024

Abstract

Objectives
Gender equity studies have shown that women are underrepresented in journal editor in chief positions, which confer major professional opportunities and influence. We sought to systematically investigate editor in chief gender and journal attributes within pathology.

Methods
We constructed a journal data set using the Scimago Journal & Country Rank and Clarivate Journal Citation Reports databases. We also included official journals of the major medical societies for the 12 pathology subspecialties recognized by the Association of American Medical Colleges. The final data set included 126 journals. We obtained editor in chief gender, impact factor, publication model (ie, hybrid access vs open access), year of founding, and geographic location for all included pathology journals.

Results
Women made up only 18% of the 141 total editor in chief positions. This inequity was present irrespective of all pathology journal variables studied. Among 10 journals with 2 editor in chief positions, 5 had only men and 5 had 1 man and 1 woman. All 3 journals with 3 editor in chief positions had 2 men and 1 woman.

Conclusions
Women are significantly underrepresented among editor in chiefs across pathology journals. Journals and affiliated members should advocate for diversity among these influential positions, given their impact on research, science, and medicine.
High-dimensional generalized median adaptive lasso with application to omics data
Authors: Liu Yahang , Gao Qian , Wei Kecheng , Huang Chen , Wang Ce , Yu Yongfu , Qin Guoyou , Wang Tong 
Published: March 2024

Abstract
Recently, there has been a growing interest in variable selection for causal inference within the context of high-dimensional data. However, when the outcome exhibits a skewed distribution, ensuring the accuracy of variable selection and causal effect estimation might be challenging. Here, we introduce the generalized median adaptive lasso (GMAL) for covariate selection to achieve an accurate estimation of causal effect even when the outcome follows skewed distributions. A distinctive feature of our proposed method is that we utilize a linear median regression model for constructing penalty weights, thereby maintaining the accuracy of variable selection and causal effect estimation even when the outcome presents extremely skewed distributions. Simulation results showed that our proposed method performs comparably to existing methods in variable selection when the outcome follows a symmetric distribution. Besides, the proposed method exhibited obvious superiority over the existing methods when the outcome follows a skewed distribution. Meanwhile, our proposed method consistently outperformed the existing methods in causal estimation, as indicated by smaller root-mean-square error. We also utilized the GMAL method on a deoxyribonucleic acid methylation dataset from the Alzheimer’s disease (AD) neuroimaging initiative database to investigate the association between cerebrospinal fluid tau protein levels and the severity of AD.
RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality
Authors: Bernard Clement , Postic Guillaume , Ghannay Sahar , Tahi Fariza 
Published: March 2024

Abstract
RNA is a complex macromolecule that plays central roles in the cell. While it is well known that its structure is directly related to its functions, understanding and predicting RNA structures is challenging. Assessing the real or predictive quality of a structure is also at stake with the complex 3D possible conformations of RNAs. Metrics have been developed to measure model quality while scoring functions aim at assigning quality to guide the discrimination of structures without a known and solved reference. Throughout the years, many metrics and scoring functions have been developed, and no unique assessment is used nowadays. Each developed assessment method has its specificity and might be complementary to understanding structure quality. Therefore, to evaluate RNA 3D structure predictions, it would be important to calculate different metrics and/or scoring functions. For this purpose, we developed RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions. In this paper, we present our RNAdvisor tool, as well as state-of-the-art existing metrics, scoring functions and a set of benchmarks we conducted for evaluating them. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr.
Benchmarking enrichment analysis methods with the disease pathway network
Authors: Buzzao Davide , Castresana-Aguirre Miguel , Guala Dimitri , Sonnhammer Erik L L 
Published: March 2024

Abstract
Enrichment analysis (EA) is a common approach to gain functional insights from genome-scale experiments. As a consequence, a large number of EA methods have been developed, yet it is unclear from previous studies which method is the best for a given dataset. The main issues with previous benchmarks include the complexity of correctly assigning true pathways to a test dataset, and lack of generality of the evaluation metrics, for which the rank of a single target pathway is commonly used. We here provide a generalized EA benchmark and apply it to the most widely used EA methods, representing all four categories of current approaches. The benchmark employs a new set of 82 curated gene expression datasets from DNA microarray and RNA-Seq experiments for 26 diseases, of which only 13 are cancers. In order to address the shortcomings of the single target pathway approach and to enhance the sensitivity evaluation, we present the Disease Pathway Network, in which related Kyoto Encyclopedia of Genes and Genomes pathways are linked. We introduce a novel approach to evaluate pathway EA by combining sensitivity and specificity to provide a balanced evaluation of EA methods. This approach identifies Network Enrichment Analysis methods as the overall top performers compared with overlap-based methods. By using randomized gene expression datasets, we explore the null hypothesis bias of each method, revealing that most of them produce skewed P-values.