bluuebunny/crossref_metadata_2025_split huggingface dataset:
Single-cell transcriptomic analysis reveals dynamic activation of cellular signaling pathways regulating beige adipogenesis
Authors: Kyung Dong Soo , Lee Eunmin , Chae Sehyun , Son Yeonho , Moon Ye-Jin , Hwang Daehee , Kim Jong Kyoung , Lee Yun-Hee , Seong Je Kyung 
Published: October 2024

AbstractPDGFRA+ cells have been identified as adipocyte stem cells (ASCs) that differentiate into beige adipocytes in white adipose tissue (WAT) following thermogenic stimuli. To elucidate the molecular heterogeneity of ASCs, we conducted single-cell transcriptomic profiling of PDGFRA+ cells isolated from the inguinal WAT (iWAT) of mice treated with the beta3 adrenergic receptor agonist CL316243. Single-cell RNA-seq revealed nine major clusters, which were categorized into four groups: resting, proliferating, differentiating, and adipogenic factor-expressing cells (AFECs). Trajectory analysis revealed sequential activation of molecular pathways, including the Hedgehog and Notch signaling pathways, during beige adipogenesis. AFECs expressed Dpp4 and did not differentiate into adipocytes in culture or after transplantation. Furthermore, genetic lineage tracing studies indicated that DPP4+ cells did not differentiate into adipocytes in iWAT during CL316243-induced beige adipogenesis. However, high-fat diet feeding led to the recruitment of adipocytes from DPP4+ cells in iWAT. Overall, this study improved our understanding of the dynamic molecular basis of beige adipogenesis and the potential contribution of DPP4+ adipocyte lineages to the pathological expansion of WAT during diet-induced obesity.
Autophagy: a critical mechanism of N6-methyladenosine modification involved in tumor progression and therapy resistance
Authors: Wang Feiyang , Liao Qiudi , Qin Zihao , Li Jingyi , Wei Qingqing , Li Mengna , Deng Hongyu , Xiong Wei , Tan Ming , Zhou Ming 
Published: October 2024

AbstractN6-Methyladenosine (m6A) is an evolutionarily highly conserved epigenetic modification that affects eukaryotic RNAs, especially mRNAs, and m6A modification is commonly linked to tumor proliferation, progression, and therapeutic resistance by participating in RNA metabolism. Autophagy is an intracellular degradation and recycling biological process by which cells remove damaged organelles, protein aggregates, and other intracellular wastes, and release nutrients to maintain cell survival when energy is scarce. Recent studies have shown that m6A modification plays a critical role in the regulation of autophagy, affecting the initiation of autophagy, the formation and assembly of autophagosomes, and lysosomal function by regulating critical regulatory molecules involved in the process of autophagy. Moreover, autophagy can also affect the expression of the three types of regulators related to m6A, which in turn affects the levels of their target genes via m6A modification. Thus, m6A modification and autophagy form a sophisticated regulatory network through mutual regulation, which plays an important role in tumor progression and therapeutic resistance. In this manuscript, we reviewed the effects of m6A modification on autophagy as well as the effects of autophagy on m6A modification and the roles of the m6A-autophagy axis in tumor progression and therapy resistance. Additionally, we summarized the value and application prospects of key molecules in the m6A-autophagy axis in tumor diagnosis and therapy.
RAB4A is a master regulator of cancer cell stemness upstream of NUMB–NOTCH signaling
Authors: Karthikeyan Subbulakshmi , Casey Patrick J. , Wang Mei 
Published: October 2024

AbstractCancer stem cells (CSCs) are a group of specially programmed tumor cells that possess the characteristics of perpetual cell renewal, increased invasiveness, and often, drug resistance. Hence, eliminating CSCs is a major challenge for cancer treatment. Understanding the cellular programs that maintain CSCs, and identifying the critical regulators for such programs, are major undertakings in both basic and translational cancer research. Recently, we have reported that RAB4A is a major regulator of epithelial-to-mesenchymal transition (EMT) and it does so mainly through regulating the activation of RAC1 GTPase. In the current study, we have delineated a new signaling circuitry through which RAB4A transmits its control of cancer stemness. Using in vitro and in vivo studies, we show that RAB4A, as the upstream regulator, relays signal stepwise to NUMB, NOTCH1, RAC1, and then SOX2 to control the self-renewal property of multiple cancer cells of diverse tissue origins. Knockdown of NUMB, or overexpression of NICD (the active fragment NOTCH1) or SOX2, rescued the in vitro sphere-forming and in vivo tumor-forming abilities that were lost upon RAB4A knockdown. Furthermore, we discovered that the chain of control is mostly through transcriptional regulation at every step of the pathway. The discovery of the novel signaling axis of RAB4A–NUMB–NOTCH–SOX2 opens the path for further expansion of the signaling chain and for the identification of new regulators and interacting proteins important for CSC functions, which can be explored to develop new and effective therapies.
Chromatic covalent organic frameworks enabling in-vivo chemical tomography
Authors: Wang Song , Han Yangyang , Reddy Vaishnavi Amarr , Ang Mervin Chun-Yi , Sánchez-Velázquez Gabriel , Saju Jolly Madathiparambil , Cao Yunteng , Khong Duc Thinh , Jayapal Praveen Kumar , Cheerlavancha Raju , Loh Suh In , Singh Gajendra Pratap , Urano Daisuke , Rajani Sarojam , Marelli Benedetto , Strano Michael S. 
Published: October 2024

AbstractCovalent organic frameworks designed as chromatic sensors offer opportunities to probe biological interfaces, particularly when combined with biocompatible matrices. Particularly compelling is the prospect of chemical tomography – or the 3D spatial mapping of chemical detail within the complex environment of living systems. Herein, we demonstrate a chromic Covalent Organic Framework (COF) integrated within silk fibroin (SF) microneedles that probe plant vasculature, sense the alkalization of vascular fluid as a biomarker for drought stress, and provide a 3D in-vivo mapping of chemical gradients using smartphone technology. A series of Schiff base COFs with tunable pKa ranging from 5.6 to 7.6 enable conical, optically transparent SF microneedles with COF coatings of 120 to 950 nm to probe vascular fluid and the surrounding tissues of tobacco and tomato plants. The conical design allows for 3D mapping of the chemical environment (such as pH) at standoff distances from the plant, enabling in-vivo chemical tomography. Chromatic COF sensors of this type will enable multidimensional chemical mapping of previously inaccessible and complex environments.
Protective effect of TCR-mediated MAIT cell activation during experimental autoimmune encephalomyelitis
Authors: Walkenhorst Mark , Sonner Jana K. , Meurs Nina , Engler Jan Broder , Bauer Simone , Winschel Ingo , Woo Marcel S. , Raich Lukas , Winkler Iris , Vieira Vanessa , Unger Lisa , Salinas Gabriela , Lantz Olivier , Friese Manuel A. , Willing Anne 
Published: October 2024

AbstractMucosal-associated invariant T (MAIT) cells express semi-invariant T cell receptors (TCR) for recognizing bacterial and yeast antigens derived from riboflavin metabolites presented on the non-polymorphic MHC class I-related protein 1 (MR1). Neuroinflammation in multiple sclerosis (MS) is likely initiated by autoreactive T cells and perpetuated by infiltration of additional immune cells, but the precise role of MAIT cells in MS pathogenesis remains unknown. Here, we use experimental autoimmune encephalomyelitis (EAE), a mouse model of MS, and find an accumulation of MAIT cells in the inflamed central nervous system (CNS) enriched for MAIT17 (RORγt+) and MAIT1/17 (T-bet+RORγt+) subsets with inflammatory and protective features. Results from transcriptome profiling and Nur77GFP reporter mice show that these CNS MAIT cells are activated via cytokines and TCR. Blocking TCR activation with an anti-MR1 antibody exacerbates EAE, whereas enhancing TCR activation with the cognate antigen, 5-(2-oxopropylideneamino)−6-D-ribitylaminouracil, ameliorates EAE severity, potentially via the induction of amphiregulin (AREG). In summary, our findings suggest that TCR-mediated MAIT cell activation is protective in CNS inflammation, likely involving an induction of AREG.
Formaldehyde initiates memory and motor impairments under weightlessness condition
Authors: Mei Tianhao , Chen Ying , Gao Yajuan , Zhao Hang , Lyu Xingzhou , Lin Jing , Niu Tianye , Han Hongbin , Tong Zhiqian 
Published: October 2024

AbstractDuring space flight, prolonged weightlessness stress exerts a range of detrimental impacts on the physiology and psychology of astronauts. These manifestations encompass depressive symptoms, anxiety, and impairments in both short-term memory and motor functions, albeit the precise underlying mechanisms remain elusive. Recent studies have revealed that hindlimb unloading (HU) animal models, which simulate space weightlessness, exhibited a disorder in memory and motor function associated with endogenous formaldehyde (FA) accumulation in the hippocampus and cerebellum, disruption of brain extracellular space (ECS), and blockage of interstitial fluid (ISF) drainage. Notably, the impairment of the blood-brain barrier (BBB) caused by space weightlessness elicits the infiltration of albumin and hemoglobin from the blood vessels into the brain ECS. However, excessive FA has the potential to form cross-links between these two proteins and amyloid-beta (Aβ), thereby obstructing ECS and inducing neuron death. Moreover, FA can inhibit N-methyl-D-aspartate (NMDA) currents by crosslinking NR1 and NR2B subunits, thus impairing memory. Additionally, FA has the ability to modulate the levels of certain microRNAs (miRNAs) such as miRNA-29b, which can affect the expression of aquaporin-4 (AQP4) so as to regulate ECS structure and ISF drainage. Especially, the accumulation of FA may inactivate the ataxia telangiectasia-mutated (ATM) protein kinase by forming cross-linking, a process that is associated with ataxia. Hence, this review presents that weightlessness stress-derived FA may potentially serve as a crucial catalyst in the deterioration of memory and motor abilities in the context of microgravity.
When combinations of humans and AI are useful: A systematic review and meta-analysis
Authors: Vaccaro Michelle , Almaatouq Abdullah , Malone Thomas 
Published: October 2024

AbstractInspired by the increasing use of artificial intelligence (AI) to augment humans, researchers have studied human–AI systems involving different tasks, systems and populations. Despite such a large body of work, we lack a broad conceptual understanding of when combinations of humans and AI are better than either alone. Here we addressed this question by conducting a preregistered systematic review and meta-analysis of 106 experimental studies reporting 370 effect sizes. We searched an interdisciplinary set of databases (the Association for Computing Machinery Digital Library, the Web of Science and the Association for Information Systems eLibrary) for studies published between 1 January 2020 and 30 June 2023. Each study was required to include an original human-participants experiment that evaluated the performance of humans alone, AI alone and human–AI combinations. First, we found that, on average, human–AI combinations performed significantly worse than the best of humans or AI alone (Hedges’ g = −0.23; 95% confidence interval, −0.39 to −0.07). Second, we found performance losses in tasks that involved making decisions and significantly greater gains in tasks that involved creating content. Finally, when humans outperformed AI alone, we found performance gains in the combination, but when AI outperformed humans alone, we found losses. Limitations of the evidence assessed here include possible publication bias and variations in the study designs analysed. Overall, these findings highlight the heterogeneity of the effects of human–AI collaboration and point to promising avenues for improving human–AI systems.
Dynamic of bifurcation, chaotic structure and multi soliton of fractional nonlinear Schrödinger equation arise in plasma physics
Authors: Alazman Ibtehal , Alkahtani Badr Saad Thaar , Mishra Manvendra Narayan 
Published: October 2024

AbstractIn this study, we examine the third-order fractional nonlinear Schrödinger equation (FNLSE) in $$(1+1)$$-dimensional, by employing the analytical methodology of the new extended direct algebraic method (NEDAM) alongside optical soliton solutions. In order to better understand high-order nonlinear wave behaviors in such systems, the researched model captures the physical and mathematical properties of nonlinear dispersive waves, with applications in plasma physics and optics. With the aid of above mentioned approach, we rigorously assess the novel optical soliton solutions in the form of dark, bright–dark, dark–bright, periodic, singular, rational, mixed trigonometric and hyperbolic forms. Additionally, stability assessments using conserved quantities, such as Hamiltonian property, and consistency checks were used to validate the solutions. The dynamic structure of the governing model is further examined using chaos, bifurcation, and sensitivity analysis. With the appropriate parameter values, 2D, 3D, and contour plots can all be utilized to graphically show the data. This work advances our knowledge of nonlinear wave propagation in Bose–Einstein condensates, ultrafast fibre optics, and plasma physics, among other areas with higher-order chromatic effects.
A generic self-learning emotional framework for machines
Authors: Hernández-Marcos Alberto , Ros Eduardo 
Published: October 2024

Abstract
In nature, intelligent living beings have developed emotions to modulate their behavior as a fundamental evolutionary advantage. However, researchers seeking to endow machines with this advantage lack a clear theory from cognitive neuroscience describing emotional elicitation from first principles, namely, from raw observations to specific affects. As a result, they often rely on case-specific solutions and arbitrary or hard-coded models that fail to generalize well to other agents and tasks. Here we propose that emotions correspond to distinct temporal patterns perceived in crucial values for living beings in their environment (like recent rewards, expected future rewards or anticipated world states) and introduce a fully self-learning emotional framework for Artificial Intelligence agents convincingly associating them with documented natural emotions. Applied in a case study, an artificial neural network trained on unlabeled agent’s experiences successfully learned and identified eight basic emotional patterns that are situationally coherent and reproduce natural emotional dynamics. Validation through an emotional attribution survey, where human observers rated their pleasure-arousal-dominance dimensions, showed high statistical agreement, distinguishability, and strong alignment with experimental psychology accounts. We believe that the framework’s generality and cross-disciplinary language defined, grounded on first principles from Reinforcement Learning, may lay the foundations for further research and applications, leading us toward emotional machines that think and act more like us.
Effects of neighbourhood characteristics on the distribution of vacant houses in Toda city, a satellite city of Tokyo metropolis of japan
Authors: Liu Qiyuan , Sho Kojiro , Corcoran Jonathan , Ando Naomi 
Published: October 2024

AbstractAs Japan faces a growing crisis of vacant housing, this study investigates the underexplored relationship between vacant house distribution and neighbourhood characteristics in Toda City, a satellite city of the Tokyo metropolis located in Saitama Prefecture. We selected 10 community features, including living convenience, housing supply, and policy support, to explore their influence on vacancy rates. Using a comprehensive dataset primarily derived from the PLATEAU data, we employed a two-step method combining global and local Moran’s I analyses to assess the spatial distribution normality of vacant houses. We then compared traditional statistical data analysis with spatial data analysis through multiscale geographically weighted regression to evaluate their effectiveness and identify the factors most closely associated with vacancy rates. Our findings reveal that spatial data analysis provides superior insights compared to traditional statistical methods. Both approaches consistently indicate a significant positive correlation between the supermarket area ratio and vacancy rates, and a significant negative correlation between road adjacency and vacancy rates. This study demonstrates the feasibility of using spatial data to analyse neighbourhood characteristics in the context of declining vacancy rates in satellite cities. The insights gained offer theoretical support for selecting data and methodologies in future research and policymaking that are aimed at mitigating the growth of vacant houses.
Mathematical modeling predicts that endemics by generalist insects are eradicated if nearly all plants produce constitutive defense
Authors: Chakraborty Suman , Dwivedi Shalu , Schuster Stefan 
Published: October 2024

AbstractPlants with constitutive defense chemicals exist widely in nature. The phenomenon is backed by abundant data from plant chemical ecology. Sufficient data are also available to conclude that plant defenses act as deterrent and repellent to attacking herbivores, particularly deleterious generalist insects. In the wild, generalist species are usually not endemic, meaning they are not restricted to certain plant species in a region. Therefore, our objective is to inspect theoretically whether evolution of chemical defenses in all plant species eradicate an endemic by any generalist species. The objective is addressed by developing deterministic ordinary differential equations under the following conditions: Plants without constitutive defenses are susceptible to oviposition by generalist insects, while they become defended against generalists by storing chemical defenses. From the models, we explicitly obtain that a generalist-free stable state is only possible if the vast majority of all plant individuals have chemical defenses. The model also allows one to predict the highest possible percentage of undefended plant individuals, which may be considered as free-riders.
Rationalised experiment design for parameter estimation with sensitivity clustering
Authors: Chhajer Harsh , Roy Rahul 
Published: October 2024

AbstractQuantitative experiments are essential for investigating, uncovering, and confirming our understanding of complex systems, necessitating the use of effective and robust experimental designs. Despite generally outperforming other approaches, the broader adoption of model-based design of experiments (MBDoE) has been hindered by oversimplified assumptions and computational overhead. To address this, we present PARameter SEnsitivity Clustering (PARSEC), an MBDoE framework that identifies informative measurable combinations through parameter sensitivity (PS) clustering. We combined PARSEC with a new variant of Approximate Bayesian Computation-based parameter estimation for rapid, automated assessment and ranking of experiment designs. Using two kinetic model systems with distinct dynamical features, we show that PARSEC-based experiments improve the parameter estimation of a complex system. By its inherent formulation, PARSEC can account for experimental restrictions and parameter variability. Moreover, we demonstrate that there is a strong correlation between sample size and the optimal number of PS clusters in PARSEC, offering a novel method to determine the ideal sampling for experiments. This validates our argument for employing parameter sensitivity in experiment design and illustrates the potential to leverage both model architecture and system dynamics to effectively explore the experimental design space.
Dynamics of the human bile acid metabolome during weight loss
Authors: Schmid Andreas , Liebisch Gerhard , Burkhardt Ralph , Belikan Hannah , Köhler Sebastian , Steger Daniel , Schweitzer Leonie , Pons-Kühnemann Jörn , Karrasch Thomas , Schäffler Andreas 
Published: October 2024

AbstractBile acids (BA) are supposed to cause metabolic alterations after bariatric surgery (BS). Here we report the longitudinal dynamics of the human BA metabolome by LC–MS/MS after BS versus low calory diet (LCD) in two obesity cohorts over 12 months. Rapid and persistent oscillations of 23 BA subspecies could be identified with highly specific patterns in BS vs. LCD. TCDCA, GLCA, and TLCA represent most promising candidates for drug development.
Exploring the potential of biosurfactants produced by fungi found in soil contaminated with petrochemical wastes
Authors: Mahmoud Yehia A.-G. , El-Halmouch Yasser H. , Nasr Ebtsam E. , Al-Sodany Yassin M. , El-Nogoumy Baher A. , Ali Sameh S. 
Published: October 2024

AbstractBiosurfactants are a diverse group of compounds derived from microorganisms, possessing various structures and applications. The current study was seeking to isolate and identify a new biosurfactant-producing fungus from soil contaminated with petrochemical waste. The bioprocess conditions were optimized to maximize biosurfactant production for Aspergillus carneus OQ152507 using a glucose peptone culture medium with a pH of 7.0 and a temperature of 35 °C. The carbon source was glucose (3%), and ammonium sulfate (0.25%) was utilized as the nitrogen source. For Aspergillus niger OQ195934, the optimized conditions involved a starch nitrate culture medium with a pH of 7.0 and a temperature of 30 °C. The carbon source used was sucrose (3.5%), and ammonium sulfate (0.25%) served as the nitrogen source. The phenol-H2SO4 and phosphate tests showed that the biosurfactants that were extracted did contain glycolipid and/or phospholipid molecules. They showed considerable antimicrobial activity against certain microbes. The obtained biosurfactants increased the solubility of tested polyaromatic hydrocarbons, including fluoranthene, pyrene, anthracene, and fluorine, and successfully removed the lubricating oil from contaminated soil and aqueous media surface tension reduction. Based on the obtained results, A. carneus and A. niger biosurfactants could be potential candidates for environmental oil remediation processes.
Machine learning to improve the understanding of rabies epidemiology in low surveillance settings
Authors: Keshavamurthy Ravikiran , Boutelle Cassandra , Nakazawa Yoshinori , Joseph Haim , Joseph Dady W. , Dilius Pierre , Gibson Andrew D. , Wallace Ryan M. 
Published: October 2024

AbstractIn low and middle-income countries, a large proportion of animal rabies investigations end without a conclusive diagnosis leading to epidemiologic interpretations informed by clinical, rather than laboratory data. We compared Extreme Gradient Boosting (XGB) with Logistic Regression (LR) for their ability to estimate the probability of rabies in animals investigated as part of an Integrated Bite Case Management program (IBCM). To balance our training data, we used Random Oversampling (ROS) and Synthetic Minority Oversampling Technique. We developed a risk stratification framework based on predicted rabies probabilities. XGB performed better at predicting rabies cases than LR. Oversampling strategies enhanced the model sensitivity making them the preferred technique to predict rare events like rabies in a biting animal. XGB-ROS classified most of the confirmed rabies cases and only a small proportion of non-cases as either high (confirmed cases = 85.2%, non-cases = 0.01%) or moderate (confirmed cases = 8.4%, non-cases = 4.0%) risk. Model-based risk stratification led to a 3.2-fold increase in epidemiologically useful data compared to a routine surveillance strategy using IBCM case definitions. Our study demonstrates the application of machine learning to strengthen zoonotic disease surveillance under resource-limited settings.