10 research outputs found
Comparison of chemical properties and toxicity of cooking aerosols (meat and fish)
No full textCooking activities are well-known for being the main source of indoor air pollution, particularly in terms of fine particulate matter (PM2.5). However, most existing research on cooking particles has primarily focused on understanding their physical and chemical properties, while studies investigating the health risks associated with these particles are lacking. Therefore, this study aimed to replicate the process of grilling pork and mackerel, commonly done in domestic settings, using a custom-made cooking chamber. The particles emitted during cooking were captured on filters, and their chemical composition (ions, elements, organic carbon, elemental carbon, water-soluble organic carbon, water-insoluble organic carbon), oxidative potential, and their effects on inflammatory responses, cell viability, and DNA damage were analyzed using lung cancer cells (A549).
Both pork and mackerel showed that organic carbon accounted for more than 90% of the total carbon content. The ratio of WISOC to organic carbon was 92% for pork and 78% for mackerel. The oxidative potential of mackerel was approximately 4.3 times higher than that of pork, with a value of 35.07 pmol/min/μg. There was no significant difference in cell toxicity between mackerel (86.82 μg/ml) and pork (79.60 μg/ml), and their cytotoxicity levels were comparable to those of particles emitted from other sources, such as automotive engine combustion (70.77~95.66 μg/ml). In terms of immune responses, both mackerel and pork exhibited a minimum 1.2 times to a maximum 2.4 times increase compared to the control when exposed to concentrations of 5-70 μg/ml.Maste
Building a collaborative relationship with a travel buddy for multimodal journey planning
No full textAs cities are cutting polluting cars off the picture and promoting the use of sustainable transport modes, the number of multimodal journeys that involve more than one transport mode will grow. Intelligent systems and smart assistants will fill in the gap and take their role to guide travelers along their way on the trail of mobility infrastructure weaved in cities., Smart travel assistants will not only enable seamless journeys through navigation, but also make many decisions on our behalf. How should we handle these tools to enrich our multimodal trips while not losing control of the relationship when they are equipped with capabilities to better understand and predict travelers’ needs and behavior? This thesis describes how multimodal travelers plan and make a journey and how to support this process with a personal travel assistant called a travel buddy. During the Mind Mapping session with users, it was found that they want smart assistants to know everything about themselves. However, since they believe machines can’t think like a human, they want to be more involved in customizing routes. On the other hand, there is a responsibility for such apps to deliver only accurate and necessary route options and travel information while making the interaction between a user and an app easy and intuitive. However, simplicity can bring negative consequences such as losing congruence with travel patterns of users by neglecting factors that are important determinants in human decision making. Aiming to establish a collaborative relationship between a traveler/user and a travel buddy, the final prototype depicts a future scenario in which a travel buddy actively learns, and asks for participation, and defines a travel type of a user to optimize the journey together. A defined travel type can be used as a tool for users to observe and reflect on their travel behavior. The impact of defining a travel type can change travel behavior when it’s treated well. This thesis concludes with a manifesto that delivers considerations when developing a travel buddy that will shape future mobility together with users.Design for Interactio
The development of high-performance PRO module and effects of operating condition on the performance of PRO module
No full textTopological flat bands in rhombohedral tetralayer and multilayer graphene on hexagonal boron nitride moire superlattices
No full textWe show that rhombohedral four-layer graphene (4LG) nearly aligned with a
hexagonal boron nitride (hBN) substrate often develops nearly flat isolated low
energy bands with non-zero valley Chern numbers. The bandwidths of the isolated
flatbands are controllable through an electric field and twist angle, becoming
as narrow as meV for interlayer potential differences between top and
bottom layers of meV and
at the graphene and boron nitride interface. The local density of states (LDOS)
analysis shows that the nearly flat band states are associated to the non-dimer
low energy sublattice sites at the top or bottom graphene layers and their
degree of localization in the moire superlattice is strongly gate tunable,
exhibiting at times large delocalization despite of the narrow bandwidth. We
verified that the first valence bands' valley Chern numbers are
, proportional to layer number for LG/BN systems
up to rhombohedral multilayers
Explainable AI for predicting oxidative potential of fine particles and key chemical drivers
No full textOxidative potential (OP) has emerged as promising health metric for ambient fine particles. Chemical components and OP of fine particles measured in China and Korea were used to develop OP prediction model with understanding the influence of chemical components and their interaction. Mn, Cu, Zn, Pb, and water-soluble organic carbon (WSOC) were selected as key chemical components to affect the OP. Various machine learning models incorporating explainable AI techniques were trained and evaluated. The best prediction model was found to be voting regression which aggregated individual predictions from random forest and gradient boosting models, explaining 74.9 % of OP variabilities across all measurement sites. Mn was the most important feature to affect the OP, followed by Pb, WSOC, Cu, and Zn. During OP event days at urban Gwangju, the Pb became the most important contributor, while at agricultural Gimje, the WSOC was the one to affect the OP. It was also found that the Cu above 0.004 µg/m³ with the WSOC had a strong antagonistic effect on the OP. The explainable AI methods should be so useful to predict the OP of ambient fine particles and to understand the important chemical components and their interaction. © 2025 Elsevier B.V., All rights reserved.FALSEsciescopu
SIX1, a glycolysis related gene, regulates tumor progression via c-Myc activation in breast cancer cells
No full textFigure S1. The RNA analysis of patient FFPE samples using nanostring analysis. (a) Eleven paired-tumor specimens were collected and formalin-fixed at the state of the diagnosis and surgery. Their RNA was extracted and analyzed with nanostring array. The normalized data were visualized with a volcano plot. (b) Gene enrichment scores of the high-MYC group (P01~P08, P11) were calculated compared with the low-MYC group (P09 and P10) using GSEA software.
Figure S2. The mRNA expression of SIX1 were analyzed in LTED cells, which were analyzed with next-generation sequencing and qRT-PCR. (a) Gene enrichment scores were calculated using GSEA software and were visualized as the clustering heatmap. (b) the mRNA expression of SIX1 and c-Myc was also measured with qRT-PCR in MCF7 parent and LTED cell
Subjective optimality in finite sequential decision-making
No full textAuthor summaryIn many real-life decisions, such as hiring an employee, the current candidate is the only option decision-makers can choose among sequentially revealed options, while past options are forgone and future options are unknown. To make the best choice in such problems, decision-makers should set appropriate criteria considering the distribution of values and remaining chances. Here, we provide behavioral and physiological evidence for subjective valuation that explains how individuals set criteria deviating from optimality. The extent to which individuals expect from candidates, how sensitive they are to the value of candidates, and how costly it is to examine each candidate determine the way how individuals make choices. Our results suggest that seemingly suboptimal decision strategies in finite sequential decisions may be optimal in subjective valuation.
Many decisions in life are sequential and constrained by a time window. Although mathematically derived optimal solutions exist, it has been reported that humans often deviate from making optimal choices. Here, we used a secretary problem, a classic example of finite sequential decision-making, and investigated the mechanisms underlying individuals' suboptimal choices. Across three independent experiments, we found that a dynamic programming model comprising subjective value function explains individuals' deviations from optimality and predicts the choice behaviors under fewer and more opportunities. We further identified that pupil dilation reflected the levels of decision difficulty and subsequent choices to accept or reject the stimulus at each opportunity. The value sensitivity, a model-based estimate that characterizes each individual's subjective valuation, correlated with the extent to which individuals' physiological responses tracked stimuli information. Our results provide model-based and physiological evidence for subjective valuation in finite sequential decision-making, rediscovering human suboptimality in subjectively optimal decision-making processes
Single-Step Synthesis of Mesoporous Vinyl Polymers via Hierarchical Assembly of Stereocontrolled Chains and Their Unique Properties
No full textMesoporous materials, vital in energy, environmental, and medical applications, require resource-intensive production. Mesoporous vinyl polymers, p-alkyl-N-phenyl-acrylamide (APAA) polymers is introduced, which offer unprecedented advantages in mass production. APAA monomers swiftly form syndiotactic chains through Monomer Aggregation-mediated Rapid, Radical Stereocontrolled (MARRS) polymerization, spontaneously creating mesoporous structures. Structural analyses (X-ray, NMR, DSC, FTIR, TEM, and Brunauer-Emmett-Teller measurements) and molecular mechanics simulations suggest the formation of Y-shaped clusters via intermolecular hydrogen bonding between the syndiotactic chains, which then form either a lamellar or hexagonal cylindrical structure containing mesopores. APAA polymers are highly processable, enabling the straightforward production of microfibers, films, and microparticles. They exhibit significant blue light emission (Quantum Yield: 7–18%) while maintaining exceptional transparency in the visible range. UV-crosslinked APAA polymer fibers, which have both superhydrophobicity and strong water adhesion (petal effect), along with crosslinked APAA polymer microparticles, are highly effective at absorbing liquid-phase volatile organic compounds (VOCs) such as chloroform, tetrahydrofuran, benzene, and toluene. They achieve both rapid absorption (<10 s) and high absorption capacity. Their remarkably simple and economical synthesis, together with their unique physicochemical properties, position APAA polymers as promising, commercially sustainable mesoporous materials with diverse applications such as UV absorbers, transparent blue-emitting films, anti-counterfeiting materials, and water remediation. © 2025 The Author(s). Small published by Wiley-VCH GmbH.TRUEsciescopu
Whole-brain annotation and multi-connectome cell typing of Drosophila
No full textThe fruit fly Drosophila melanogaster has emerged as a key model organism in neuroscience, in large part due to the concentration of collaboratively generated molecular, genetic and digital resources available for it. Here we complement the approximately 140,000 neuron FlyWire whole-brain connectome1 with a systematic and hierarchical annotation of neuronal classes, cell types and developmental units (hemilineages). Of 8,453 annotated cell types, 3,643 were previously proposed in the partial hemibrain connectome2, and 4,581 are new types, mostly from brain regions outside the hemibrain subvolume. Although nearly all hemibrain neurons could be matched morphologically in FlyWire, about one-third of cell types proposed for the hemibrain could not be reliably reidentified. We therefore propose a new definition of cell type as groups of cells that are each quantitatively more similar to cells in a different brain than to any other cell in the same brain, and wevalidate this definition through joint analysis of FlyWire and hemibrain connectomes. Further analysis defined simple heuristics for the reliability of connections between brains, revealed broad stereotypy and occasional variability in neuron count and connectivity, and provided evidence for functional homeostasis in the mushroom body through adjustments of the absolute amount of excitatory input while maintaining the excitation/inhibition ratio. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open-source toolchain for brain-scale comparative connectomics. © The Author(s) 2024.TRUEsciescopu
