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Haematopoietic stem cells (HSCs) are a rare cell type that reconstitute the entire blood and immune systems after transplantation and can be used as a curative cell therapy for a variety of haematological diseases 1 , 2 . However, the low number of HSCs in the body makes both biological analyses and clinical application difficult, and the limited extent to which human HSCs can be expanded ex vivo remains a substantial barrier to the wider and safer therapeutic use of HSC transplantation 3 . Although various reagents have been tested in attempts to stimulate the expansion of human HSCs, cytokines have long been thought to be essential for supporting HSCs ex vivo 4 . Here we report the establishment of a culture system that allows the long-term ex vivo expansion of human HSCs, achieved through the complete replacement of exogenous cytokines and albumin with chemical agonists and a caprolactam-based polymer. A phosphoinositide 3-kinase activator, in combination with a thrombopoietin-receptor agonist and the pyrimidoindole derivative UM171, were sufficient to stimulate the expansion of umbilical cord blood HSCs that are capable of serial engraftment in xenotransplantation assays. Ex vivo HSC expansion was further supported by split-clone transplantation assays and single-cell RNA-sequencing analysis. Our chemically defined expansion culture system will help to advance clinical HSC therapies. A culture system allows the long-term expansion of human haematopoietic stem cells (HSCs) in vivo without the use of recombinant cytokines or albumin, with potential applications for clinical therapies involving HSCs.

In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provides a comprehensive comparison of vision transformers (ViTs) and convolutional neural networks (CNNs), the two leading techniques in the field of deep learning in medical imaging. We conducted a survey systematically. Particular attention was given to the robustness, computational efficiency, scalability, and accuracy of these models in handling complex medical datasets. The review incorporates findings from 36 studies and indicates a collective trend that transformer-based models, particularly ViTs, exhibit significant potential in diverse medical imaging tasks, showcasing superior performance when contrasted with conventional CNN models. Additionally, it is evident that pre-training is important for transformer applications. We expect this work to help researchers and practitioners select the most appropriate model for specific medical image analysis tasks, accounting for the current state of the art and future trends in the field.

Background Emerging infectious diseases require time before appropriate countermeasures can be established because of the uncertainty surrounding their infection characteristics. During the COVID-19 pandemic, this delay led to significant disruptions in both the education sector and society as a whole. However, it is challenging to impose uniformly strict infection control measures in educational settings because the importance of continuity of education and social stability must also be considered. Methods Characteristics of emerging infectious diseases, such as latent period, infectious period, transmission potential, and asymptomatic rate, gradually become clearer over time. This study therefore proposed a framework to evaluate effective infection control measures at different stages (early, middle, and late) of an emerging infectious disease, as more information becomes available. This framework enables decision-making about educational opportunities and social continuity to be adjusted to reflect the characteristics of the disease known at any given time. The value of this framework was verified through simulations. It was used to compare the effectiveness of four staggered school attendance strategies recommended by UNESCO, under various disease characteristics (latent period, infectious period, transmission potential, and asymptomatic rate). Results The most effective intervention varies with the characteristics of the infectious disease (latent period, infectious period, transmission potential, and asymptomatic rate), demonstrating the importance of our framework. This framework makes it possible to select appropriate school strategies based on the known characteristics of the disease at different stages of an outbreak, when considering the school environment and economic conditions. Conclusions This study proposed a framework for the phased adjustment of infection control measures in schools with increasing knowledge of the characteristics of an emerging infectious disease. Simulations demonstrated the effectiveness of the framework. This framework enables the selection of effective staggered school attendance methods that balance infection suppression with educational and social considerations. The framework could be extended to other environments, such as workplaces and public facilities, providing a comprehensive infection control strategy that mitigates social and educational burdens. Future research should explore the framework’s application to a broader range of infectious disease scenarios to enhance its feasibility for real-world policy implementation.

Ecologists have recently accepted the notion that species coexistence involves both niche and neutral processes, but few studies have explained how both of these opposite views can explain coexistence in the same community. Here we focus on competition among sessile organisms and explored first the extent to which species-based niche reflects local “matchups” between nearby individuals, using 726 saplings of 10 temperate tree species, and second the members engaging in the matchups, which have rarely been quantified despite the importance in mixedspecies forests. Growth responses to light showed considerable species-level differences, suggesting commonly seen regeneration niches. Outcomes of the individual matchups were basically predictable from the species mean response, but also with substantial contribution of within-species variation. We found strong imbalance in matchup frequencies, such that some individuals meet more individuals of differing species but others meet fewer, as well as many isolated, competition-free ones. The niche and neutral processes appear to reflect, respectively, between- and within-species differences, and our findings suggest that even when niche segregation is discernible, the role of stochasticity for the frequency of local competition, as well as its outcomes, cannot be discounted in species coexistence.

Key MessageIntegrative omics approaches revealed a crosstalk among phytohormones during tuberous root development in cassava.Tuberous root formation is a complex process consisting of phase changes as well as cell division and elongation for radial growth. We performed an integrated analysis to clarify the relationships among metabolites, phytohormones, and gene transcription during tuberous root formation in cassava (Manihot esculenta Crantz). We also confirmed the effects of the auxin (AUX), cytokinin (CK), abscisic acid (ABA), jasmonic acid (JA), gibberellin (GA), brassinosteroid (BR), salicylic acid, and indole-3-acetic acid conjugated with aspartic acid on tuberous root development. An integrated analysis of metabolites and gene expression indicated the expression levels of several genes encoding enzymes involved in starch biosynthesis and sucrose metabolism are up-regulated during tuberous root development, which is consistent with the accumulation of starch, sugar phosphates, and nucleotides. An integrated analysis of phytohormones and gene transcripts revealed a relationship among AUX signaling, CK signaling, and BR signaling, with AUX, CK, and BR inducing tuberous root development. In contrast, ABA and JA inhibited tuberous root development. These phenomena might represent the differences between stem tubers (e.g., potato) and root tubers (e.g., cassava). On the basis of these results, a phytohormonal regulatory model for tuberous root development was constructed. This model may be useful for future phytohormonal studies involving cassava.

Early in vivo studies demonstrated the involvement of a tumor-suppressing transcription factor, p53, into cellular droplets such as Cajal and promyelocytic leukemia protein bodies, suggesting that the liquid-liquid phase separation (LLPS) might be involved in the cellular functions of p53. To examine this possibility, we conducted extensive investigations on the droplet formation of p53 in vitro . First, p53 itself was found to form liquid-like droplets at neutral and slightly acidic pH and at low salt concentrations. Truncated p53 mutants modulated droplet formation, suggesting the importance of multivalent electrostatic interactions among the N-terminal and C-terminal domains. Second, FRET efficiency measurements for the dimer mutants of p53 revealed that distances between the core domains and between the C-terminal domains were modulated in an opposite manner within the droplets. Third, the molecular crowding agents were found to promote droplet formation, whereas ssDNA, dsDNA, and ATP, to suppress it. Finally, the p53 mutant mimicking posttranslational phosphorylation did not form the droplets. We conclude that p53 itself has a potential to form droplets that can be controlled by cellular molecules and by posttranslational modifications, suggesting that LLPS might be involved in p53 function.

In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

Key messageSuppression of starch branching enzymes 1 and 2 in cassava leads to increased resistant starch content through the production of high-amylose and modification of the amylopectin structure.Cassava (Manihot esculenta Crantz) is a starchy root crop used for human consumption as a staple food and industrial applications. Starch is synthesized by various isoforms of several enzymes. However, the function of starch branching enzymes (SBEs) in starch biosynthesis and mechanisms of starch regulation in cassava have not been understood well. In this study, we aimed to suppress the expression of SBEs in cassava to generate starches with a range of distinct properties, in addition to verifying the functional characteristics of the SBEs. One SBE1, two SBE2, and one SBE3 genes were classified by phylogenetic analysis and amino acid alignment. Quantitative real-time RT-PCR revealed tissue-specific expression of SBE genes in the tuberous roots and leaves of cassava. We introduced RNAi constructs containing fragments of SBE1, SBE2, or both genes into cassava by Agrobacterium-mediated transformation, and assessed enzymatic activity of SBE using tuberous roots and leaves from these transgenic plants. Simultaneous suppression of SBE1 and SBE2 rendered an extreme starch phenotype compared to suppression of SBE2 alone. Degree of polymerization of 6–13 chains in amylopectin was markedly reduced by suppression of both SBE1 and SBE2 in comparison to the SBE2 suppression; however, no change in chain-length profiles was observed in the SBE1 suppression alone. The role of SBE1 and SBE2 may have functional overlap in the storage tissue of cassava. Simultaneous suppression of SBE1 and SBE2 resulted in highly resistant starch with increased apparent amylose content compared to suppression of SBE2 alone. This study provides valuable information for understanding starch biosynthesis and suggests targets for altering starch quality.

Key messageEthanol priming induces heat stress tolerance by the stimulation of unfolded protein response.Global warming increases the risk of heat stress-related yield losses in agricultural crops. Chemical priming, using safe agents, that can flexibly activate adaptive regulatory responses to adverse conditions, is a complementary approach to genetic improvement for stress adaptation. In the present study, we demonstrated that pretreatment of Arabidopsis with a low concentration of ethanol enhances heat tolerance without suppressing plant growth. We also demonstrated that ethanol pretreatment improved leaf growth in lettuce (Lactuca sativa L.) plants grown in the field conditions under high temperatures. Transcriptome analysis revealed a set of genes that were up-regulated in ethanol-pretreated plants, relative to water-pretreated controls. Binding Protein 3 (BIP3), an endoplasmic reticulum (ER)-stress marker chaperone gene, was among the identified up-regulated genes. The expression levels of BIP3 were confirmed by RT-qPCR. Root-uptake of ethanol was metabolized to organic acids, nucleic acids, amines and other molecules, followed by an increase in putrescine content, which substantially promoted unfolded protein response (UPR) signaling and high-temperature acclimation. We also showed that inhibition of polyamine production and UPR signaling negated the heat stress tolerance induced by ethanol pretreatment. These findings collectively indicate that ethanol priming activates UPR signaling via putrescine accumulation, leading to enhanced heat stress tolerance. The information gained from this study will be useful for establishing ethanol-mediated chemical priming strategies that can be used to help maintain crop production under heat stress conditions.

Induced-fit or conformational selection is of profound significance in biological regulation. Biological receptors alter their conformation to respond to the shape and electrostatic surfaces of guest molecules. Here we report a water-soluble artificial molecular host that can sensitively respond to the size, shape, and charged state of guest molecules. The molecular host, i.e. nanocube, is an assembled structure consisting of six gear-shaped amphiphiles (GSAs). This nanocube can expand or contract its size upon the encapsulation of neutral and anionic guest molecules with a volume ranging from 74 to 535 Å3 by induced-fit. The responding property of this nanocube, reminiscent of a feature of biological molecules, arises from the fact that the GSAs in the nanocubes are connected to each other only through the hydrophobic effect and very weak intermolecular interactions such as van der Waals and cation-π interactions.