bluuebunny/crossref_metadata_2025_split huggingface dataset:
Consequences of squash (Cucurbita argyrosperma) domestication for plant defence and herbivore interactions
Authors: Jaccard Charlyne , Ye Wenfeng , Bustos-Segura Carlos , Glauser Gaetan , Kaplan Ian , Benrey Betty 
Published: May 2023

Abstract
Main conclusion
Cucurbita argyrosperma domestication affected plant defence by downregulating the cucurbitacin synthesis-associated genes. However, tissue-specific suppression of defences made the cultivars less attractive to co-evolved herbivores Diabrotica balteata and Acalymma spp.

Abstract
Plant domestication reduces the levels of defensive compounds, increasing susceptibility to insects. In squash, the reduction of cucurbitacins has independently occurred several times during domestication. The mechanisms underlying these changes and their consequences for insect herbivores remain unknown. We investigated how Cucurbita argyrosperma domestication has affected plant chemical defence and the interactions with two herbivores, the generalist Diabrotica balteata and the specialist Acalymma spp. Cucurbitacin levels and associated genes in roots and cotyledons in three wild and four domesticated varieties were analysed. Domesticated varieties contained virtually no cucurbitacins in roots and very low amounts in cotyledons. Contrastingly, cucurbitacin synthesis-associated genes were highly expressed in the roots of wild populations. Larvae of both insects strongly preferred to feed on the roots of wild squash, negatively affecting the generalist’s performance but not that of the specialist. Our findings illustrate that domestication results in tissue-specific suppression of chemical defence, making cultivars less attractive to co-evolved herbivores. In the case of squash, this may be driven by the unique role of cucurbitacins in stimulating feeding in chrysomelid beetles.
Machine Learning applied to student attentiveness detection: Using emotional and non-emotional measures
Authors: Elbawab Mohamed , Henriques Roberto 
Published: May 2023

AbstractElectronic learning (e-learning) is considered the new norm of learning. One of the significant drawbacks of e-learning in comparison to the traditional classroom is that teachers cannot monitor the students' attentiveness. Previous literature used physical facial features or emotional states in detecting attentiveness. Other studies proposed combining physical and emotional facial features; however, a mixed model that only used a webcam was not tested. The study objective is to develop a machine learning (ML) model that automatically estimates students' attentiveness during e-learning classes using only a webcam. The model would help in evaluating teaching methods for e-learning. This study collected videos from seven students. The webcam of personal computers is used to obtain a video, from which we build a feature set that characterizes a student's physical and emotional state based on their face. This characterization includes eye aspect ratio (EAR), Yawn aspect ratio (YAR), head pose, and emotional states.A total of eleven variables are used in the training and validation of the model. ML algorithms are used to estimate individual students' attention levels. The ML models tested are decision trees, random forests, support vector machines (SVM), and extreme gradient boosting (XGBoost). Human observers' estimation of attention level is used as a reference. Our best attention classifier is the XGBoost, which achieved an average accuracy of 80.52%, with an AUROC OVR of 92.12%. The results indicate that a combination of emotional and non-emotional measures can generate a classifier with an accuracy comparable to other attentiveness studies. The study would also help assess the e-learning lectures through students' attentiveness. Hence will assist in developing the e-learning lectures by generating an attentiveness report for the tested lecture.
Cognitive Reflection and Decision-Making Accuracy: Examining Their Relation and Boundary Conditions in the Context of Evidence-based Management
Authors: Criado-Perez Christian , Jackson Chris , Minbashian Amirali , Collins Catherine G. 
Published: May 2023

Abstract
Evidence-based management (EBM) is an increasingly advocated yet rarely adopted framework to assist in making organizational decisions. We investigate how cognitive reflection – the ability or disposition to question an intuitive response and rely instead on a more analytical process—influences decision-making accuracy in the context of EBM. Across three experimental studies (N = 332, 166 and 99), we determine the effects of using actively sought and passively sought evidence, in boundary conditions of cognitive and emotional load. Results of study 1 show that cognitive reflection is generally associated with higher decision-making accuracy, particularly in passive pathways to EBM, and when exposed to low cognitive load. Results of studies 2 and 3 support that passive pathways to EBM will result in higher accuracy, and that negative emotion inducing stimuli can strengthen the effect of cognitive reflection on evidence collection. The findings demonstrate that a contingency approach to EBM is critical, with the impact of cognitive reflection differing for situational constraints and for actively and passively sought evidence.
Between Fiction, Reality, and Ideality: Virtual Objects as Computationally Grounded Intentional Objects
Authors: Skowron Bartłomiej , Stacewicz Paweł 
Published: May 2023

AbstractVirtual objects, such as online shops, the elements that go to make up virtual life in computer games, virtual maps, e-books, avatars, cryptocurrencies, chatbots, holograms, etc., are a phenomenon we now encounter at every turn: they have become a part of our life and our world. Philosophers—and ontologists in particular—have sought to answer the question of what, exactly, they are. They fall into two camps: some, pointing to the chimerical character of virtuality, hold that virtual objects are like dreams, illusions and fictions, while others, citing the real impact of virtuality on our world, take them to be real—an actual part of the real world, just like other real objects. In this article, we defend the thesis that both sides are wrong. Using Roman Ingarden’s phenomenological ontology, we advocate a position according to which a virtual object is a computationally grounded intentional object that has its existential foundation in computational processes, which are compliant with a certain model of computation. We point out that virtuality is framed by some kind of ideal mathematical objects: i.e., mathematical models of computation, which in turn fall, each of them, under their respective ideas. We also refer to the idea of natural computation, which in conjunction with the ontological analysis carried out leads to the thesis that an object can be more or less virtual.
Atomically Dispersed Dual-Metal Sites Showing Unique Reactivity and Dynamism for Electrocatalysis
Authors: Wu Jun-Xi , Chen Wen-Xing , He Chun-Ting , Zheng Kai , Zhuo Lin-Ling , Zhao Zhen-Hua , Zhang Jie-Peng 
Published: May 2023

AbstractThe real structure and in situ evolution of catalysts under working conditions are of paramount importance, especially for bifunctional electrocatalysis. Here, we report asymmetric structural evolution and dynamic hydrogen-bonding promotion mechanism of an atomically dispersed electrocatalyst. Pyrolysis of Co/Ni-doped MAF-4/ZIF-8 yielded nitrogen-doped porous carbons functionalized by atomically dispersed Co–Ni dual-metal sites with an unprecedented N8V4 structure, which can serve as an efficient bifunctional electrocatalyst for overall water splitting. More importantly, the electrocatalyst showed remarkable activation behavior due to the in situ oxidation of the carbon substrate to form C–OH groups. Density functional theory calculations suggested that the flexible C–OH groups can form reversible hydrogen bonds with the oxygen evolution reaction intermediates, giving a bridge between elementary reactions to break the conventional scaling relationship.
Have You Ever Seen a Robot? An Analysis of Children’s Drawings Between Technology and Science Fiction
Authors: Giang Christian , Addimando Loredana , Botturi Luca , Negrini Lucio , Giusti Alessandro , Piatti Alberto 
Published: May 2023

AbstractTechnologies have become an essential part of the daily life of our children. Consequently, artifacts that imply the early adoption of abstract thinking affect the imagination of children and young people in relation to the world of technology, now much more than they did in the past. With the emerging importance of robots in many aspects of our everyday lives, the goal of this study is to investigate which mental representations children have about robots. To this end, drawings from 104 children aged between 7 and 12 years old were used as a map of representations, considering the drawings as a proxy capable of evoking learned or emerging mental frameworks. The drawings were analyzed in several steps: they were first labeled using binary descriptors and then classified using clustering methods based on Hamming distances between drawings. Finally, questionnaire items covering children’s perceptions about robots were analyzed for each of the resulting cluster separately to identify differences between them. The results show that there are relationships between the way children draw robots and their perception about robots’ capabilities as well as their aspirations to pursue a career in science. These findings can provide meaningful insights into how to design educational robots and learning activities for children to learn with and about robots.
The Beginning of the Early Upper Paleolithic in Poland
Authors: Picin Andrea , Stefański Damian , Cieśla Magda , Valde-Nowak Paweł 
Published: May 2023

AbstractResearch on the timing ofHomo sapiensdispersals in Central Europe is pivotal for understanding the behavioral trajectories of human adaptation to low biomass environments and cold climates. Previous studies on the Early Upper Paleolithic of Poland described a different scenario from the European panorama characterized by the local development of a laminar/lamellar technology from the foregoing Middle Paleolithic and the coexistence of different Aurignacian variants after 35 ka BP. In this paper, we examine this technical diversity by reassessing and revising the chronological and technological information of the key Aurignacian sites in Poland. Our study reveals that the distinctive techno-typological features of the different Aurignacian types are most likely the result of the mixing of artifacts from different chronologies. In our view, Poland was visited intermittently byHomosapiens since the Early Aurignacian. The deterioration of the climatic conditions during the second half of MIS 3 converted the Polish territories into a satellite area of the Aurignacian settlement system.
Similarity-Based Interference in Sentence Comprehension in Aphasia: a Computational Evaluation of Two Models of Cue-Based Retrieval
Authors: Lissón Paula , Paape Dario , Pregla Dorothea , Burchert Frank , Stadie Nicole , Vasishth Shravan 
Published: May 2023

AbstractSentence comprehension requires the listener to link incoming words with short-term memory representations in order to build linguistic dependencies. The cue-based retrieval theory of sentence processing predicts that the retrieval of these memory representations is affected by similarity-based interference. We present the first large-scale computational evaluation of interference effects in two models of sentence processing — the activation-based model and a modification of the direct-access model — in individuals with aphasia (IWA) and control participants in German. The parameters of the models are linked to prominent theories of processing deficits in aphasia, and the models are tested against two linguistic constructions in German: pronoun resolution and relative clauses. The data come from a visual-world eye-tracking experiment combined with a sentence-picture matching task. The results show that both control participants and IWA are susceptible to retrieval interference, and that a combination of theoretical explanations (intermittent deficiencies, slow syntax, and resource reduction) can explain IWA’s deficits in sentence processing. Model comparisons reveal that both models have a similar predictive performance in pronoun resolution, but the activation-based model outperforms the direct-access model in relative clauses.
Analytics Modules for Business Students
Authors: Carroll Paula 
Published: May 2023

AbstractData science is a relatively new requirement for business students. Historically, many business students shied away from business statistics and quantitative or operational research (OR) modules believing them to be boring and irrelevant. The high-profile use of analytics and modelling during the COVID pandemic has drawn awareness to the relevance of analytics. Greater availability of data and modelling tools afford business students an opportunity to re-engage with operational research and analytics and to enjoy the satisfaction of modelling and solving real-world problems, but the challenge of the mathematical modelling skills gap of business students remains. In this paper, we describe a learning pathway of modules in business analytics that can enhance business students’ confidence and capabilities in performing statistical and analytical business tasks. We recommend modelling tools and incremental innovative mathematical modelling teaching approaches that are pedagogically sound and suitable for business students with varying quantitative backgrounds.
Development of fibrotic gene signature and construction of a prognostic model in melanoma
Authors: Chen Siyu , Shi Congyu , Li Bo , Li Longjiang 
Published: May 2023

Abstract
Purpose
Skin cutaneous melanoma (SKCM) is a malignant tumor responsible for over 75% of skin cancer deaths, the relationship between fibrosis and cancer has been increasingly appreciated. The aim of this study is to investigate the fibrotic gene signature (FGS) in melanoma and construct a prognostic model based on FGS.

Methods
SKCM-related datasets were obtained from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. By weighted gene co-expression network analysis (WGCNA) of the TCGA-SKCM cohort and GSE65904 cohort, core modules and central genes highly associated with fibrotic features were identified and intersecting genes were defined as fibrotic gene signature (FGS). The least absolute shrinkage and selection operator (LASSO) regression analysis and the Akaike information criterion (AIC) method were conducted to construct a prognostic model based on the FGS gene set. The fibrotic gene signature enrichment score (FGES) and fibrotic gene signature risk score (FGRS) were used to analyze immune infiltration. For FGRS, the correlation between clinical characteristics and the expression of immune checkpoint genes between different risk groups was also analyzed in depth.

Results
A total of 301 genes were defined as FGS, and a robust eight-gene prediction model was constructed based on FGS, these 8 genes are SV2A, HEYL, OLFML2A, PROX1, ACOX2, PRRX1, PHACTR1 and LHX6. On the basis of the model, a nomogram consisting of FGRS could accurately predict prognosis. In addition, patients in the high-risk group showed immunosuppression, while patients in the low-risk group may benefit more from immunotherapy. However, there was no significant difference between the immune infiltration of different FGES groups.

Conclusion
In this study, taken together, we developed a fibrotic gene signature in melanoma, and construct an eight-gene prognostic model based on the FGS to provide a reference for prognosis estimation and treatment selection for melanoma patients.
Theoretical Study of Interhemispheric Electron Bouncing Within Pulsating Aurora
Authors: Khazanov George V. , Chu Mike , Samara Marilia , Michell Robert G. 
Published: May 2023

AbstractWave‐particle interaction processes in the equatorial magnetosphere initiate time‐dependent electron precipitation in the pulsating aurora. These electrons enter loss‐cone and bounce between the two magnetically conjugate hemispheres, collide with the atmospheric constituents, and introduce additional time scales in electron precipitation dynamics. In this letter we present preliminary results of pulsating aurora formation using the time‐dependent SuperThermal Electron Transport code, which considers the magnetosphere‐ionosphere‐atmosphere energy coupling between the two magnetically conjugate regions and discuss their contribution to the peculiarities of electron distribution function formation within the pulsating aurora.
Trends and prospects in mitochondrial genome editing
Authors: Phan Hong Thi Lam , Lee Hyunji , Kim Kyoungmi 
Published: May 2023

AbstractMitochondria are of fundamental importance in programmed cell death, cellular metabolism, and intracellular calcium concentration modulation, and inheritable mitochondrial disorders via mitochondrial DNA (mtDNA) mutation cause several diseases in various organs and systems. Nevertheless, mtDNA editing, which plays an essential role in the treatment of mitochondrial disorders, still faces several challenges. Recently, programmable editing tools for mtDNA base editing, such as cytosine base editors derived from DddA (DdCBEs), transcription activator-like effector (TALE)-linked deaminase (TALED), and zinc finger deaminase (ZFD), have emerged with considerable potential for correcting pathogenic mtDNA variants. In this review, we depict recent advances in the field, including structural biology and repair mechanisms, and discuss the prospects of using base editing tools on mtDNA to broaden insight into their medical applicability for treating mitochondrial diseases.
Candesartan, an angiotensin-II receptor blocker, ameliorates insulin resistance and hepatosteatosis by reducing intracellular calcium overload and lipid accumulation
Authors: Lee Jin Wook , Gu Hyun-Oh , Jung Yunshin , Jung YunJae , Seo Seung-Yong , Hong Jeong-Hee , Hong In-Sun , Lee Dae Ho , Kim Ok-Hee , Oh Byung-Chul 
Published: May 2023

AbstractInsulin resistance is a major contributor to the pathogenesis of several human diseases, including type 2 diabetes, hypertension, and hyperlipidemia. Notably, insulin resistance and hypertension share common abnormalities, including increased oxidative stress, inflammation, and organelle dysfunction. Recently, we showed that excess intracellular Ca2+, a known pathogenic factor in hypertension, acts as a critical negative regulator of insulin signaling by forming Ca2+-phosphoinositides that prevent the membrane localization of AKT, a key serine/threonine kinase signaling molecule. Whether preventing intracellular Ca2+ overload improves insulin sensitivity, however, has not yet been investigated. Here, we show that the antihypertensive agent candesartan, compared with other angiotensin-II receptor blockers, has previously unrecognized beneficial effects on attenuating insulin resistance. We found that candesartan markedly reduced palmitic acid (PA)-induced intracellular Ca2+ overload and lipid accumulation by normalizing dysregulated store-operated channel (SOC)-mediated Ca2+ entry into cells, which alleviated PA-induced insulin resistance by promoting insulin-stimulated AKT membrane localization and increased the phosphorylation of AKT and its downstream substrates. As pharmacological approaches to attenuate intracellular Ca2+ overload in vivo, administering candesartan to obese mice successfully decreased insulin resistance, hepatic steatosis, dyslipidemia, and tissue inflammation by inhibiting dysregulated SOC-mediated Ca2+ entry and ectopic lipid accumulation. The resulting alterations in the phosphorylation of key signaling molecules consequently alleviate impaired insulin signaling by increasing the postprandial membrane localization and phosphorylation of AKT. Thus, our findings provide robust evidence for the pleiotropic contribution of intracellular Ca2+ overload in the pathogenesis of insulin resistance and suggest that there are viable approved drugs that can be repurposed for the treatment of insulin resistance and hypertension.
An MRTF-A–ZEB1–IRF9 axis contributes to fibroblast–myofibroblast transition and renal fibrosis
Authors: Zhao Qianwen , Shao Tinghui , Zhu Yuwen , Zong Gengjie , Zhang Junjie , Tang Shifan , Lin Yanshan , Ma Hongzhen , Jiang Zhifan , Xu Yong , Wu Xiaoyan , Zhang Tao 
Published: May 2023

AbstractMyofibroblasts, characterized by the expression of the matricellular protein periostin (Postn), mediate the profibrogenic response during tissue repair and remodeling. Previous studies have demonstrated that systemic deficiency in myocardin-related transcription factor A (MRTF-A) attenuates renal fibrosis in mice. In the present study, we investigated the myofibroblast-specific role of MRTF-A in renal fibrosis and the underlying mechanism. We report that myofibroblast-specific deletion of MRTF-A, achieved through crossbreeding Mrtfa-flox mice with Postn-CreERT2 mice, led to amelioration of renal fibrosis. RNA-seq identified zinc finger E-Box binding homeobox 1 (Zeb1) as a downstream target of MRTF-A in renal fibroblasts. MRTF-A interacts with TEA domain transcription factor 1 (TEAD1) to bind to the Zeb1 promoter and activate Zeb1 transcription. Zeb1 knockdown retarded the fibroblast–myofibroblast transition (FMyT) in vitro and dampened renal fibrosis in mice. Transcriptomic assays showed that Zeb1 might contribute to FMyT by repressing the transcription of interferon regulatory factor 9 (IRF9). IRF9 knockdown overcame the effect of Zeb1 depletion and promoted FMyT, whereas IRF9 overexpression antagonized TGF-β-induced FMyT. In conclusion, our data unveil a novel MRTF-A–Zeb1–IRF9 axis that can potentially contribute to fibroblast–myofibroblast transition and renal fibrosis. Screening for small-molecule compounds that target this axis may yield therapeutic options for the mollification of renal fibrosis.
Association between fatty acids and the risk of impaired glucose tolerance and type 2 diabetes mellitus in American adults: NHANES 2005−2016
Authors: Zhu Xiaoqiong , Chen Liu , Lin Jiansheng , Ba Mingqin , Liao Junqiu , Zhang Ping , Zhao Cunxi 
Published: May 2023

Abstract
Background
Fatty acids (FAs) play a major role in regulating insulin sensitivity. However, owing to dietary quantitative tools, it has been challenging to study the dietary FAs in previous studies. There is a lack of knowledge regarding the associations between dietary FAs and the risk of impaired glucose tolerance (IGT) and type 2 diabetes mellitus (T2DM).

Methods
Dietary FAs, adjustment of variables including age, sex, race, educational level, poverty to income ratio, body mass index, smoking, hypertension, physical activity, and diabetes data were extracted from the National Health and Nutrition Examination Survey 2005–2016. A multivariate logistic regression model was used to determine the associations between FA intake and the risk of IGT and T2DM.

Results
This serial cross-sectional study included 9082 samples. After adjusting all the variables, a negative correlation was observed between total saturated FA and the risk of IGT (OR = 0.991, 95% (CI): 0.985–0.998, P = 0.024). Total FA at quintile 4 was negatively correlated with T2DM (OR = 0.714, 95% CI: 0.532–0.959, P = 0.025) compared with quintile 1. Factor analysis identified four factors of which F4 was negatively associated with the risk of T2DM (OR = 0.824, 95% CI: 0.715–0.949, P = 0.029). Based on this factor, we identified an unsaturated FA signature (n = 4 FAs, including octadecenoic acid (18:1), octadecadienoic acid (18:2), octadecatrienoic acid (18:3), and eicosenoic acid (20:1)).

Conclusions
Several unsaturated FAs with high proportions in natural oils may reduce the risk of T2DM.