See the article, “Comparison of Normative Percentiles of Brain Volume Obtained from NeuroQuantⓇ vs. DeepBrainⓇ in the Korean Population: Correlation with Cranial Shape”, in volume 84 on page ...1080-1090 (https://doi.org/10.3348/jksr.2023.0006).
Long-lived “memory-like” NK cells have been identified in individuals infected by human cytomegalovirus (HCMV), but little is known about how the memory-like NK cell pool is formed. Here, we have ...shown that HCMV-infected individuals have several distinct subsets of memory-like NK cells that are often deficient for multiple transcription factors and signaling proteins, including tyrosine kinase SYK, for which the reduced expression was stable over time and correlated with epigenetic modification of the gene promoter. Deficient expression of these proteins was largely confined to the recently discovered FcRγ-deficient NK cells that display enhanced antibody-dependent functional activity. Importantly, FcRγ-deficient NK cells exhibited robust preferential expansion in response to virus-infected cells (both HCMV and influenza) in an antibody-dependent manner. These findings suggest that the memory-like NK cell pool is shaped and maintained by a mechanism that involves both epigenetic modification of gene expression and antibody-dependent expansion.
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•NK cells with multiple protein deficiencies are present in HCMV-infected individuals•SYK deficiency is associated with hyper-methylation of the gene promoter•Memory-like NK cells have protein deficiencies in combination with FcRγ deficiency•FcRγ-deficient NK cells expand preferentially in an antibody-dependent manner
Long-lived “memory-like” NK cells have been identified in HCMV-infected individuals at variable frequencies, but little is known about how this NK cell pool is formed. Kim and colleagues show data that support epigenetic modifications and antibody-dependent expansion as mechanisms underlying the formation of this memory-like NK cell pool.
This paper proposes a soft-switched single switch isolated converter. The proposed converter is able to offer low cost and high power density in step-up application due to the following features: ...zero-current switching (ZCS) turn-on and zero-voltage switching (ZVS) turn-off of switch and ZCS turn-off of diodes regardless of voltage and load variation; low rated lossless snubber; reduced transformer volume compared to flyback-based converters due to low magnetizing current. Experimental results on a 100 kHz, 250 W prototype are provided to validate the proposed concept.
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.
Objectives
To 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)
Methods
Radiomics 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).
Results
The 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.
Conclusion
Multiparametric 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.
Abstract
Recent work in psychology and neuroscience has revealed differences in impression updating across social distance and group membership. Observers tend to maintain prior impressions of close ...(vs. distant) and ingroup (vs. outgroup) others in light of new information, and this belief maintenance is at times accompanied by increased activity in Theory of Mind regions. It remains an open question whether differences in the strength of prior beliefs, in a context absent social motivation, contribute to neural differences during belief updating. We devised a functional magnetic resonance imaging study to isolate the impact of experimentally induced prior beliefs on mentalizing activity. Participants learned about targets who performed 2 or 4 same-valenced behaviors (leading to the formation of weak or strong priors), before performing 2 counter-valenced behaviors. We found a greater change in activity in dorsomedial prefrontal cortex (DMPFC) and right temporo-parietal junction following the violation of strong versus weak priors, and a greater change in activity in DMPFC and left temporo-parietal junction following the violation of positive versus negative priors. These results indicate that differences in neural responses to unexpected behaviors from close versus distant others, and ingroup versus outgroup members, may be driven in part by differences in the strength of prior beliefs.
Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics ...integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.
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Abstract
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.
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.
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.
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