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Assessing caregiver burden: the role of assistive devices and identification of assistive device needs
No full textPurposeThere is insufficient information on whether the use of assistive devices is associated with a lower burden on caregivers of individuals with disabilities. This study was conducted (1) to examine how care recipient-level factors, caregiver-level factors, and the use of assistive devices were associated with caregiver burden, and (2) to investigate the assistive device needs of caregivers.Materials and MethodsThis cross-sectional descriptive study used surveys. The participants were caregivers of people with disabilities living in Korea (n = 499). The independent variables were care recipient-level factors, caregiver-level factors, and utilization of assistive devices. The outcome variable, caregiver burden, was measured using the Korean Caregiver Burden Scale. For the data analysis, multivariate logistic regression models were built on the caregiver burden using variables that showed statistical significance in the univariate analysis.ResultsModel 1 included care recipient-level factors. Model 2 included caregiver-level factors in addition to care-recipient factors to examine caregiver factors associated with burden. Model 3 included the utilization of care devices, in addition to the variables used in Model 2, to investigate whether the use of care devices was associated with a lower burden. Caregivers who were married, were informal caregivers, or experienced greater psychological stress had an increased probability of experiencing caregiver burden. Moreover, not using assistive devices increases the odds of experiencing caregiver burden. The most desirable properties of devices are transfer, mobility, and bathing.ConclusionAs the use of assistive devices is associated with caregiver burden, such care devices should be developed and provided to caregivers based on their individual needs.N
Microsensor-Internalized Fibers as Autonomously Controllable Soft Actuators
No full textDespite their strengths in flexibility and miniaturization, the stable operation of soft actuators under ever-changing environmental and biological conditions is hindered by the lack of applicable methods using internal sensors to detect unintentional stimuli. Here, the integration of a microscale driving source and sensors in a single fiber via thermal drawing is presented as a strategy to scalably produce autonomously responsive, feedback-controllable soft actuators. The regulation of the input electrothermal stimuli via a closed loop control system that is based on completely coupled internal sensory components enables multimodal actuation of fiber-based actuators, which is further demonstrated through preservation of actuating conditions, actuation of selected devices in their bundles, and modulation of motion characteristics. The approach to manufacturing autonomously controllable soft actuators can expand applications of soft actuators in kaleidoscopic biomedical and bioengineering fields for transportation, robotics, and prosthetics.N
How does artificial intelligence improve human decision-making? Evidence from the AI-powered Go program
No full textResearch SummaryWe study how humans learn from artificial intelligence (AI), leveraging an introduction of an AI-powered Go program (APG) that unexpectedly outperformed the best professional player. We compare the move quality of professional players to APG's superior solutions around its public release. Our analysis of 749,190 moves demonstrates significant improvements in players' move quality, especially in the early stages of the game where uncertainty is highest. This improvement was accompanied by a higher alignment with AI's suggestions and a decreased number and magnitude of errors. Young players show greater improvement, suggesting potential inequality in learning from AI. Further, while players of all skill levels benefit, less skilled players gain higher marginal benefits. These findings have implications for managers seeking to adopt and utilize AI in their organizations.Managerial AbstractWe examine how professionals can learn from artificial intelligence (AI) by studying an AI-powered Go program (APG) that outperformed the best professional player. By analyzing 749,190 moves, we find that players' move quality improved significantly, closely aligning with the AI's recommendations. The number and magnitude of errors also decreased. This learning effect was particularly strong early in the game where decisions are more uncertain. Young players showed greater effect, suggesting that learning from AI may vary by age. While players of all skill levels benefited, those with less skill saw the greatest improvement. These findings highlight the instructional role of AI and offer guidance on how to effectively integrate AI into organizations to enhance worker performance across different age groups and skill levels.N
A Tutorial on Using Generative Models to Advance Psychological Science: Lessons From the Reliability Paradox
No full textTheories of individual differences are foundational to psychological and brain sciences, yet they are traditionally developed and tested using superficial summaries of data (e.g., mean response times) that are disconnected from our otherwise rich conceptual theories of behavior. To resolve this theory-description gap, we review the generative modeling approach, which involves formally specifying how behavior is generated within individuals, and in turn how generative mechanisms vary across individuals. Generative modeling shifts our focus away from estimating descriptive statistical "effects" toward estimating psychologically interpretable parameters, while simultaneously enhancing the reliability and validity of our measures. We demonstrate the utility of generative modeling in the context of the "reliability paradox," a phenomenon wherein replicable group effects (e.g., Stroop effect) fail to capture individual differences (e.g., low test-retest reliability). Simulations and empirical data from the Implicit Association Test and Stroop, Flanker, Posner, and delay discounting tasks show that generative models yield (a) more theoretically informative parameters, and (b) higher test-retest reliability estimates relative to traditional approaches, illustrating their potential for enhancing theory development.Y
I2-SLAM: Inverting Imaging Process for Robust Photorealistic Dense SLAM
No full textWe present an inverse image-formation module that can enhance the robustness of existing visual SLAM pipelines for casually captured scenarios. Casual video captures often suffer from motion blur and varying appearances, which degrade the final quality of coherent 3D visual representation. We propose integrating the physical imaging into the SLAM system, which employs linear HDR radiance maps to collect measurements. Specifically, individual frames aggregate images of multiple poses along the camera trajectory to explain prevalent motion blur in hand-held videos. Additionally, we accommodate per-frame appearance variation by dedicating explicit variables for image formation steps, namely white balance, exposure time, and camera response function. Through joint optimization of additional variables, the SLAM pipeline produces high-quality images with more accurate trajectories. Extensive experiments demonstrate that our approach can be incorporated into recent visual SLAM pipelines using various scene representations, such as neural radiance fields or Gaussian splatting. Project website.N
Unravelling the evolution of mycetophagy and phytophagy in fungus weevils (Curculionoidea: Anthribidae): Phylogenomic insights into Anthribinae paraphyly and tribal non-monophyly
No full textFungus weevils (family Anthribidae) are morphologically and ecologically diverse, with highly varied feeding habits, mainly mycetophagy but also phytophagy, palynophagy and entomophagy. The phylogeny of the family is virtually unexplored, its evolutionary history obscure; thus, the existing classification is controversial and likely artificial. We generated the first multi-gene higher-level phylogeny estimate of Anthribidae using DNA data from 400 nuclear genes obtained via anchored hybrid enrichment from 40 species representing 17 tribes plus genera incertae sedis. As in previous studies, the family Anthribidae was consistently recovered as the sister group of Nemonychidae. We recovered two main clades in Anthribidae as sister groups with strong statistical support, viz. a monophyletic subfamily Urodontinae and the traditionally recognized Anthribinae, which was rendered paraphyletic by the subfamily Choraginae. Paraphyly and polyphyly among tribes of Anthribinae indicate that current tribal concepts-all based on morphology and without phylogenetic analysis-are artificial. Based on our results, we subsume the subfamily Choraginae into Anthribinae and place its six current tribes (Apolectini, Araecerini, Choragini, Cisanthribini, Valenfriesiini and Xenorchestini) in an expanded subfamily Anthribinae. We also transfer three genera currently treated as Anthribinae incertae sedis to three generally recognized tribes, namely Pleosporius Holloway to Sintorini, Xylanthribus Kuschel to Proscoporhinini and Anthribidus F & aring;hraeus to Platystomini. The phylogenetic positions of Urodontinae and Trigonorhinini suggest that phytophagy is the ancestral feeding mode of Anthribidae, with a few taxa of Anthribinae having secondarily evolved plant-feeding from mycetophagy, the predominant feeding habit of the subfamily. Overall, our results provide the first molecular phylogenetic context for research on Anthribidae and a first step towards reconstructing a natural tribal classification of the Anthribinae. Our study highlights the need for a phylogenetic approach, sampling of type genera and deeper taxon sampling to identify natural tribal-level groupings.N
Impact of Heterocore Atoms on CO2 Electroreduction in Atomically Precise Silver Nanoclusters
No full textUnderstanding the effect of internal atoms in metal nanoparticles on heterogeneous catalytic processes is crucial for achieving high activity and selectivity. This requires meticulous synthetic control over the size, composition, and atomic arrangement of nanoparticles. Here, we report the design of ligand-exchange-induced structure transformation and nanomolecule-templated atomic-level galvanic exchange strategies to synthesize PtAg24(IPBT)18 (denoted as PtAg24) and AuAg24(IPBT)18 (denoted as AuAg24) nanoclusters (NCs). Both NCs exhibit identical total metal atom and ligand (IPBT: 2-isopropylbenzenethiolate) counts, as well as atomic-level structure, except for the difference in the core atom (Pt and Au). Using these model NCs, we uncover the impact of heterocore atoms on the electrochemical CO2 reduction reaction (eCO(2)RR) activity and selectivity. The central Pt atom in PtAg24 is less favorable for eCO(2)RR activity, with an activity approximately 4 times smaller than that of Au in AuAg24. The eCO(2)RR product CO selectivity is <30% for PtAg24, while it exceeds 70% for AuAg24, revealing the critical role of the central atom in surface catalytic pathways. Furthermore, AuAg24 exhibits high activity, with a CO partial current density of -202.2 mA cm-2, and stability over 24 h, retaining 90% CO selectivity in a membrane electrode assembly configuration. Operando spectroscopy and density functional theory calculations suggest the weaker adsorption of *CO intermediates and smaller energy barrier facilitate CO production on AuAg24 compared to PtAg24, providing valuable atomistic insights into the reaction intermediates and mechanism. The findings in this work will inspire the design of more atomically precise model nanocatalysts to explore the role of their remarkable features in the catalytic activity and selectivity for renewable energy conversion and storage.N
The Antimicrobial Effect of a Low-Frequency Square Wave Compared to Chlorhexidine
No full textBackground/Objectives: Oral health is critical for overall health, particularly in hospitalized patients whose weakened physical state can lead to oral changes, such as dry mouth and gingivitis due to anxiety and stress. Neglected oral hygiene can lead to infections and systemic complications. This study aims to evaluate the antibacterial efficacy of low-frequency square-wave positive voltage electrical stimulation compared to chlorhexidine and to assess its potential as a next-generation solution for preventing hospital-acquired infections. Methods: Sixty-three tooth specimens were randomly assigned to seven groups, including various concentrations of chlorhexidine and electrical stimulation with or without brushing. Biofilm formation was induced using saliva from healthy donors and standard strains of Streptococcus mutans and Aggregatibactor actinomycetemcomitans. Bacterial colony-forming units (CFU) and absorbance changes were measured post-treatment. Results: Significant reductions in CFU counts were observed in both the chlorhexidine and electrical stimulation groups compared to the control, with the 5V2H group showing superior antibacterial efficacy over 0.12% chlorhexidine. Chlorhexidine-treated specimens demonstrated a dose-dependent response and minimal bacterial presence, while electrical stimulation showed effectiveness but with re-growth observed after 4 h. Scanning electron microscopy revealed substantial biofilm on untreated and electrically stimulated specimens, whereas chlorhexidine-treated specimens exhibited minimal bacterial presence. Conclusions: Intermittent electrical stimulation shows promise as an alternative to chlorhexidine for oral hygiene management in critical care settings, though an optimization of electrical parameters is necessary for sustained effects. This approach could reduce hospital-acquired infections by providing an effective, non-chemical method for maintaining oral hygiene.Y
Robust genome editing activity and the applications of enhanced miniature CRISPR-Cas12f1
No full textWith recent advancements in gene editing technology using the CRISPR/Cas system, there is a demand for more effective gene editors. A key factor facilitating efficient gene editing is effective CRISPR delivery into cells, which is known to be associated with the size of the CRISPR system. Accordingly, compact CRISPR-Cas systems derived from various strains are discovered, among which Un1Cas12f1 is 2.6 times smaller than SpCas9, providing advantages for gene therapy research. Despite extensive engineering efforts to improve Un1Cas12f1, the editing efficiency of Un1Cas12f1 is still shown to be low depending on the target site. To overcome this limitation, we develop enhanced Cas12f1 (eCas12f1), which exhibits gene editing activity similar to SpCas9 and AsCpf1, even in gene targets where previously improved Un1Cas12f1 variants showed low gene editing efficiency. Furthermore, we demonstrate that eCas12f1 efficiently induces apoptosis in cancer cells and is compatible with base editing and regulation of gene expression, verifying its high utility and applicability in gene therapy research.Y
Identification of genetic factors influencing flavonoid biosynthesis through pooled transcriptome analysis in mungbean sprouts
No full textIntroduction Mungbean (Vigna radiata L.) is gaining increasing interest among legume crops because of its nutritional value. Various secondary metabolites that act as antioxidants and bioactive compounds are beneficial for human health. The secondary metabolite content in plants is easily influenced by environmental conditions, and this influence varies depending on the genotype.Materials and Methods Here, we screened six genotypes with consistently high and low content of major secondary metabolites (gallic acid, chlorogenic acid, neo-chlorogenic acid, genistin, formononetin, catechin, syringic acid, and resveratrol) across environmental replicates. Transcriptome data obtained from the individual genotypes were pooled into two groups: high and low levels of secondary metabolites.Results and Discussion Of the 200 differentially expressed genes identified using stringent criteria, 23 were annotated in the secondary metabolite pathway. By combining the results of the secondary metabolite and transcriptome data, we identified six key genes encoding four enzymes (CCoAOMT1; Caffeoyl-CoA O-methyltransferase, CYP81E1; 4'-methoxyisoflavone 2'-hydroxylase, DFR; dihydroflavonol-4-reductase, and HCT; shikimate O-hydroxycinnamoyltransferase) that commonly influence the content of secondary metabolites (catechin, chlorogenic acid, formononetin, and genistin) in mungbeans. Through regulatory network analysis, NAC042 and MYB74 transcription factors were identified. These transcription factors regulate the expression of four key genes in mungbean, CCoAOMT1(Vradi02g00000724.1), CYP81E1(Vradi09g00002897.1), DFR(Vradi07g00001336.1), and HCT(Vradi07g00000614.1) leading to high flavonoid content.Conclusion These results provide information on the common genetic factors involved in the production of secondary metabolites, which can improve the nutritional value of mungbeans and contribute to the development of elite mungbean cultivars.Y