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Single-cell omics technologies have revolutionized molecular profiling by providing high-resolution insights into cellular heterogeneity and complexity. Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine. Single-cell omics: revolutionizing molecular profiling with high-resolution insights Each cell is a bustling city of genetic material and proteins, but traditional methods often blur the individuality of each cell by averaging the data. Single-cell omics techniques, however, allow us to focus on individual cells, revealing the rich variety of cell types, states, and rare populations that make up our tissues and organs. In this review, researchers delve into the details of single-cell omics and multi-modal omics. They explore on how to isolate cells, add unique barcodes to keep track of them, and then analyze their genetic and molecular content. The integration of data across different molecular dimensions is a leap forward in the field, and it paves the way for future discoveries that could transform medicine. The potential implications of this research are vast, offering hope for more precise and personalized medical treatments in the future. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

The stratified structure of organic light-emitting diodes (OLEDs) confines much of the generated light within substrates or organic layers, limiting outcoupling to air. Macroscale half-ball lenses extract most substrate-trapped light but compromise the inherent planarity of OLEDs, while microlens arrays (MLAs), only tens of micrometres tall, preserve planarity yet provide only modest enhancements, particularly with limited lateral dimensions. Here, we introduce a systematic strategy to enhance outcoupling while maintaining near-planarity. By jointly tailoring the OLED device stack and the topography of near-planar outcoupling structures, we overcome the limited prompt extraction responsible for the modest performance of conventional MLAs. Our optimized devices reach an external quantum efficiency of 48.0% and a current efficiency of 192 cd A⁻¹, surpassing bare OLEDs (35.6%, 102 cd A⁻¹) and MLA-attached OLEDs (35.4%, 150 cd A⁻¹). This approach addresses trade-offs among intrusiveness, aperture ratio, and performance, providing a promising route to ultra-efficient, optical-crosstalk-free OLED displays. Microlens arrays preserve the planarity of organic light-emitting diodes but offer modest improvements in light outcoupling. Here, authors jointly tailor the device stack and topography of near-planar outcoupling structures, achieving an efficiency comparable to those obtained with a half-ball lens.

ObjectivesTo determine whether diffusion- and perfusion-weighted MRI–based radiomics features can improve prediction of isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in lower grade gliomas (LGGs)MethodsRadiomics features (n = 6472) were extracted from multiparametric MRI including conventional MRI, apparent diffusion coefficient (ADC), and normalized cerebral blood volume, acquired on 127 LGG patients with determined IDH mutation status and grade (WHO II or III). Radiomics models were constructed using machine learning–based feature selection and generalized linear model classifiers. Segmentation stability was calculated between two readers using concordance correlation coefficients (CCCs). Diagnostic performance to predict IDH mutation and tumor grade was compared between the multiparametric and conventional MRI radiomics models using the area under the receiver operating characteristics curve (AUC). The models were tested using a temporally independent validation set (n = 28).ResultsThe multiparametric MRI radiomics model was optimized with a random forest feature selector, with segmentation stability of a CCC threshold of 0.8. For IDH mutation, multiparametric MR radiomics showed similar performance (AUC 0.795) to the conventional radiomics model (AUC 0.729). In tumor grading, multiparametric model with ADC features showed higher performance (AUC 0.932) than the conventional model (AUC 0.555). The independent validation set showed the same trend with AUCs of 0.747 for IDH prediction and 0.819 for tumor grading with multiparametric MRI radiomics model.ConclusionMultiparametric MRI radiomics model showed improved diagnostic performance in tumor grading and comparable diagnostic performance in IDH mutation status, with ADC features playing a significant role.Key Points• The multiparametric MRI radiomics model was comparable with conventional MRI radiomics model in predicting IDH mutation.• The multiparametric MRI radiomics model outperformed conventional MRI in glioma grading.• Apparent diffusion coefficient played an important role in glioma grading and predicting IDH mutation status using radiomics.

Artificial intelligence (AI) is rapidly advancing, aiming to mimic human cognitive abilities, and is addressing complex medical challenges in the field of biological science. Over the past decade, AI has experienced exponential growth and proven its effectiveness in processing massive datasets and optimizing decision-making. The main content of this review paper emphasizes the active utilization of AI in the field of stem cells. Stem cell therapies use diverse stem cells for drug development, disease modeling, and medical treatment research. However, cultivating and differentiating stem cells, along with demonstrating cell efficacy, require significant time and labor. In this review paper, convolutional neural networks (CNNs) are widely used to overcome these limitations by analyzing stem cell images, predicting cell types and differentiation efficiency, and enhancing therapeutic outcomes. In the biomedical sciences field, AI algorithms are used to automatically screen large compound databases, identify potential molecular structures and characteristics, and evaluate the efficacy and safety of candidate drugs for specific diseases. Also, AI aids in predicting disease occurrence by analyzing patients’ genetic data, medical images, and physiological signals, facilitating early diagnosis. The stem cell field also actively utilizes AI. Artificial intelligence has the potential to make significant advances in disease risk prediction, diagnosis, prognosis, and treatment and to reshape the future of healthcare. This review summarizes the applications and advancements of AI technology in fields such as drug development, regenerative medicine, and stem cell research.

Indirect reciprocity is a key mechanism that promotes cooperation in social dilemmas by means of reputation. Although it has been a common practice to represent reputations by binary values, either ‘good’ or ‘bad’, such a dichotomy is a crude approximation considering the complexity of reality. In this work, we studied norms with three different reputations, i.e., ‘good’, ‘neutral’, and ‘bad’. Through massive supercomputing for handling more than thirty billion possibilities, we fully identified which norms achieve cooperation and possess evolutionary stability against behavioural mutants. By systematically categorizing all these norms according to their behaviours, we found similarities and dissimilarities to their binary-reputation counterpart, the leading eight. We obtained four rules that should be satisfied by the successful norms, and the behaviour of the leading eight can be understood as a special case of these rules. A couple of norms that show counter-intuitive behaviours are also presented. We believe the findings are also useful for designing successful norms with more general reputation systems.

Evolutionary game theory assumes that players replicate a highly scored player’s strategy through genetic inheritance. However, when learning occurs culturally, it is often difficult to recognize someone’s strategy just by observing the behaviour. In this work, we consider players with memory-one stochastic strategies in the iterated Prisoner’s Dilemma, with an assumption that they cannot directly access each other’s strategy but only observe the actual moves for a certain number of rounds. Based on the observation, the observer has to infer the resident strategy in a Bayesian way and chooses his or her own strategy accordingly. By examining the best-response relations, we argue that players can escape from full defection into a cooperative equilibrium supported by Win-Stay-Lose-Shift in a self-confirming manner, provided that the cost of cooperation is low and the observational learning supplies sufficiently large uncertainty.

Background Prosthetic legs help individuals with an amputation regain locomotion. Recently, deep neural network (DNN)-based control methods, which take advantage of the end-to-end learning capability of the network, have been proposed. One prominent challenge for these learning-based approaches is obtaining data for the training, particularly for the training of a mid-level controller. In this study, we propose a method for generating synthetic gait patterns (vertical load and lower limb joint angles) using a generative adversarial network (GAN). This approach enables a mid-level controller to execute ambulation modes that are not included in the training datasets. Methods The conditional GAN is trained on benchmark datasets that contain the gait data of individuals without amputation; synthetic gait patterns are generated from the user input. Further, a DNN-based controller for the generation of impedance parameters is trained using the synthetic gait pattern and the corresponding synthetic stiffness and damping coefficients. Results The trained GAN generated synthetic gait patterns with a coefficient of determination of 0.97 and a structural similarity index of 0.94 relative to benchmark data that were not included in the training datasets. We trained a DNN-based controller using the GAN-generated synthetic gait patterns for level-ground walking, standing-to-sitting motion, and sitting-to-standing motion. Four individuals without amputation participated in bypass testing and demonstrated the ambulation modes. The model successfully generated control parameters for the knee and ankle based on thigh angle and vertical load. Conclusions This study demonstrates that synthetic gait patterns can be used to train DNN models for impedance control. We believe a conditional GAN trained on benchmark datasets can provide reliable gait data for ambulation modes that are not included in its training datasets. Thus, designing gait data using a conditional GAN could facilitate the efficient and effective training of controllers for prosthetic legs.

Objectives We aimed to develop multiparametric physiologic MRI-based spatial habitats and to evaluate whether temporal changes in these habitats help to distinguish tumor progression from treatment-related change in post-treatment glioblastoma. Methods This retrospective, single-institution study included patients with glioblastoma treated by concurrent chemoradiotherapy who had newly developed or enlarging, measurable contrast-enhancing mass. Contrast-enhancing mass was divided into three spatial habitats by K-means clustering of voxel-wise ADC and CBV values. Temporal changes of these habitats between two consecutive examinations prior to the diagnosis of tumor progression or treatment-related change were assessed. Predictors were selected using logistic regression and the performance was measured with an area under the receiver operating characteristics curve (AUC). Spatiotemporal habitats were further analyzed for correlation with the site of tumor progression. Results There were 75 patients (mean, 58 years; range, 26−81 years; 43 men) with 48 cases of tumor progression and 39 cases of treatment-related change including 12 patient overlaps at different time points. Three spatial habitats of hypervascular cellular, hypovascular cellular, and nonviable tissue were identified. Increase in the hypervascular cellular (OR 4.55, p = .002) and hypovascular cellular habitat (OR 1.22, p < .001) was predictive of tumor progression. Combination of spatiotemporal habitats yielded a high diagnostic performance with an AUC of 0.89 (95% CI, 0.87–0.92). An increase in hypovascular cellular habitat predicted the site of tumor progression in 84% [21/25] of cases with tumor progression. Conclusions Temporal changes in spatial habitats derived from multiparametric physiologic MRI provided diagnostic value in distinguishing tumor progression from treatment-related change and predicted site of tumor progression in post-treatment glioblastoma. Key Points • In post-treatment glioblastoma, three spatial habitats of hypervascular cellular, hypovascular cellular, and nonviable tissue were identified, and an increase in the hypervascular cellular (OR 4.55, p = .002) and hypovascular cellular habitat (OR 1.22, p < .001) was predictive of tumor progression. • Combination of spatiotemporal habitats yielded a high diagnostic performance with an AUC of 0.89 (95% CI, 0.87–0.92). • An increase in hypovascular cellular habitat predicted the site of tumor progression in 84% (21/25) of cases with tumor progression.

Finding d -electron heavy fermion states has been an important topic as the diversity in d -electron materials can lead to many exotic Kondo effect-related phenomena or new states of matter such as correlation-driven topological Kondo insulator. Yet, obtaining direct spectroscopic evidence for a d-electron heavy fermion system has been elusive to date. Here, we report the observation of Kondo lattice behavior in an antiferromagnetic metal, FeTe, via angle-resolved photoemission spectroscopy, scanning tunneling spectroscopy and transport property measurements. The Kondo lattice behavior is represented by the emergence of a sharp quasiparticle and Fano-type tunneling spectra at low temperatures. The transport property measurements confirm the low-temperature Fermi liquid behavior and reveal successive coherent-incoherent crossover upon increasing temperature. We interpret the Kondo lattice behavior as a result of hybridization between localized Fe 3d xy and itinerant Te 5p z orbitals. Our observations strongly suggest unusual cooperation between Kondo lattice behavior and long-range magnetic order. The Kondo hybridization typically occurs in heavy-fermion systems containing f electrons, although recently it has been reported in d-electron systems. Kim et al. report spectroscopic evidence of the Kondo hybridization in FeTe and discuss it role in the mechanism of the magnetic order.

This study investigates the effects of an air layer on drag reduction and turbulence dynamics in channel flow over a superhydrophobic surface (SHS). Employing the OpenFOAM platform, direct numerical simulation was conducted to investigate turbulent channel flow with an air layer over an SHS. The simulations, which take into account the interaction between water and air, analyze various parameters such as velocity distribution, drag reduction (DR), Reynolds stress, turbulent kinetic energy (TKE), and coherent structures near the water–air interface. The presence of an air layer significantly alters the velocity distribution, leading to higher velocities at the interface compared to simulations without the air layer. Notably, the thickness of the air layer emerges as an important factor, with larger thicknesses resulting in increased velocities and drag reduction. This study underscores the substantial impact of the air layer on TKE near the superhydrophobic surface, emphasizing its role in understanding and optimizing drag reduction. Furthermore, the nonlinear relationship between slip velocity, Q contours, and coherent structures near the SHS are investigated.