Episodic memory arises as a function of dynamic interactions between the hippocampus and the neocortex, yet the mechanisms have remained elusive. Here, using human intracranial recordings during a mnemonic discrimination task, we report that 4-5 Hz (theta) power is differentially recruited during discrimination vs. overgeneralization, and its phase supports hippocampal-neocortical when memories are being formed and correctly retrieved. Interactions were largely bidirectional, with small but significant net directional biases; a hippocampus-to-neocortex bias during acquisition of new information that was subsequently correctly discriminated, and a neocortex-to-hippocampus bias during accurate discrimination of new stimuli from similar previously learned stimuli. The 4-5 Hz rhythm may facilitate the initial stages of information acquisition by neocortex during learning and the recall of stored information from cortex during retrieval. Future work should further probe these dynamics across different types of tasks and stimuli and computational models may need to be expanded accordingly to accommodate these findings. <<<

Laura K M Han, Richard Dinga, Tim Hahn, Christopher R K Ching, Lisa T Eyler, Lyubomir Aftanas, Moji Aghajani, André Aleman, Bernhard T Baune, Klaus Berger, Ivan Brak, Geraldo Busatto Filho, Angela Carballedo, Colm G Connolly, Baptiste Couvy-Duchesne, Kathryn R Cullen, Udo Dannlowski, Christopher G Davey, Danai Dima, Fabio L S Duran, Verena Enneking, Elena Filimonova, Stefan Frenzel, Thomas Frodl, Cynthia H Y Fu, Beata R Godlewska, Ian H Gotlib, Hans J Grabe, Nynke A Groenewold, Dominik Grotegerd, Oliver Gruber, Geoffrey B Hall, Ben J Harrison, Sean N Hatton, Marco Hermesdorf, Ian B Hickie, Tiffany C Ho, Norbert Hosten, Andreas Jansen, Claas Kähler, Tilo Kircher, Bonnie Klimes-Dougan, Bernd Krämer, Axel Krug, Jim Lagopoulos, Ramona Leenings, Frank P MacMaster, Glenda MacQueen, Andrew McIntosh, Quinn McLellan, Katie L McMahon, Sarah E Medland, Bryon A Mueller, Benson Mwangi, Evgeny Osipov, Maria J Portella, Elena Pozzi, Liesbeth Reneman, Jonathan Repple, Pedro G P Rosa, Matthew D Sacchet, Philipp G Sämann, Knut Schnell, Anouk Schrantee, Egle Simulionyte, Jair C Soares, Jens Sommer, Dan J Stein, Olaf Steinsträter, Lachlan T Strike, Sophia I Thomopoulos, Marie-José van Tol, Ilya M Veer, Robert R J M Vermeiren, Henrik Walter, Nic J A van der Wee, Steven J A van der Werff, Heather Whalley, Nils R Winter, Katharina Wittfeld, Margaret J Wright, Mon-Ju Wu, Henry Völzke, Tony T Yang, Vasileios Zannias, Greig I de Zubicaray, Giovana B Zunta-Soares, Christoph Abé, Martin Alda, Ole A Andreassen, Erlend Bøen, Caterina M Bonnin, Erick J Canales-Rodriguez, Dara Cannon, Xavier Caseras, Tiffany M Chaim-Avancini, Torbjørn Elvsåshagen, Pauline Favre, Sonya F Foley, Janice M Fullerton, Jose M Goikolea, Bartholomeus C M Haarman, Tomas Hajek, Chantal Henry, Josselin Houenou, Fleur M Howells, Martin Ingvar, Rayus Kuplicki, Beny Lafer, Mikael Landén, Rodrigo Machado-Vieira, Ulrik F Malt, Colm McDonald, Philip B Mitchell, Leila Nabulsi, Maria Concepcion Garcia Otaduy, Bronwyn J Overs, Mircea Polosan, Edith Pomarol-Clotet, Joaquim Radua, Maria M Rive, Gloria Roberts, Henricus G Ruhe, Raymond Salvador, Salvador Sarró, Theodore D Satterthwaite, Jonathan Savitz, Aart H Schene, Peter R Schofield, Mauricio H Serpa, Kang Sim, Marcio Gerhardt Soeiro-de-Souza, Ashley N Sutherland, Henk S Temmingh, Garrett M Timmons, Anne Uhlmann, Eduard Vieta, Daniel H Wolf, Marcus V Zanetti, Neda Jahanshad, Paul M Thompson, Dick J Veltman, Brenda W J H Penninx, Andre F Marquand, James H Cole, Lianne Schmaal <<<

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates. <<<

Luchang Ming, Debao Fu, Zhaona Wu, Hu Zhao, Xingbing Xu, Tingting Xu, Xiaohu Xiong, Mu Li, Yi Zheng, Ge Li, Ling Yang, Chunjiao Xia, Rongfang Zhou, Keyan Liao, Qian Yu, Wenqi Chai, Sijia Li, Yinmeng Liu, Xiaokun Wu, Jianquan Mao, Julong Wei, Xu Li, Lei Wang, Changyin Wu, Weibo Xie <<<

Panicle architecture is a key determinant of rice grain yield and is mainly determined at the 1-2 mm young panicle stage. Here, we investigated the transcriptome of the 1-2 mm young panicles from 275 rice varieties and identified thousands of genes whose expression levels were associated with panicle traits. Multimodel association studies suggested that many small-effect genetic loci determine spikelet per panicle (SPP) by regulating the expression of genes associated with panicle traits. We found that alleles at cis-expression quantitative trait loci of SPP-associated genes underwent positive selection, with a strong preference for alleles increasing SPP. We further developed a method that integrates the associations of cis- and trans-expression components of genes with traits to identify causal genes at even small-effect loci and construct regulatory networks. We identified 36 putative causal genes of SPP, including SDT (MIR156j) and OsMADS17, and inferred that OsMADS17 regulates SDT expression, which was experimentally validated. Our study reveals the impact of regulatory variants on rice panicle architecture and provides new insights into the gene regulatory networks of panicle traits. <<<

Mammalian collagen has been widely used as a biomedical material. Nevertheless, there are still concerns about the variability between preparations, particularly with the possibility that the products may transmit animal-based diseases. Many groups have examined the possible application of bioengineered mammalian collagens. However, translating laboratory studies into large-scale manufacturing has often proved difficult, although certain yeast and plant systems seem effective. Production of full-length mammalian collagens, with the required secondary modification to give proline hydroxylation, has proved difficult in E. coli. However, recently, a new group of collagens, which have the characteristic triple helical structure of collagen, has been identified in bacteria. These proteins are stable without the need for hydroxyproline and are able to be produced and purified from E. coli in high yield. Initial studies indicate that they would be suitable for biomedical applications. <<<

Florianne O. L. Vehmeijer, Leanne K. Küpers, Gemma C. Sharp, Lucas A. Salas, Samantha Lent, Dereje D. Jima, Gwen Tindula, Sarah Reese, Cancan Qi, Olena Gruzieva, Christian Page, Faisal I. Rezwan, Philip E. Melton, Ellen Nohr, Geòrgia Escaramís, Peter Rzehak, Anni Heiskala, Tong Gong, Samuli T. Tuominen, Lu Gao, Jason P. Ross, Anne P. Starling, John W. Holloway, Paul Yousefi, Gunn Marit Aasvang, Lawrence J. Beilin, Anna Bergström, Elisabeth Binder, Leda Chatzi, Eva Corpeleijn, Darina Czamara, Brenda Eskenazi, Susan Ewart, Natalia Ferre, Veit Grote, Dariusz Gruszfeld, Siri E. Håberg, Cathrine Hoyo, Karen Huen, Robert Karlsson, Inger Kull, Jean-Paul Langhendries, Johanna Lepeule, Maria C. Magnus, Rachel L. Maguire, Peter L. Molloy, Claire Monnereau, Trevor A. Mori, Emily Oken, Katri Räikkönen, Sheryl Rifas-Shiman, Carlos Ruiz-Arenas, Sylvain Sebert, Vilhelmina Ullemar, Elvira Verduci, Judith M. Vonk, Cheng-jian Xu, Ivana V. Yang, Hongmei Zhang, Weiming Zhang, Wilfried Karmaus, Dana Dabelea, Beverly S. Muhlhausler, Carrie V. Breton, Jari Lahti, Catarina Almqvist, Marjo-Riitta Jarvelin, Berthold Koletzko, Martine Vrijheid, Thorkild I. A. Sørensen, Rae-Chi Huang, Syed Hasan Arshad, Wenche Nystad, Erik Melén, Gerard H. Koppelman, Stephanie J. London, Nina Holland, Mariona Bustamante, Susan K. Murphy, Marie-France Hivert, Andrea Baccarelli, Caroline L. Relton, Harold Snieder, Vincent W. V. Jaddoe, Janine F. Felix <<<

AbstractBackgroundDNA methylation has been shown to be associated with adiposity in adulthood. However, whether similar DNA methylation patterns are associated with childhood and adolescent body mass index (BMI) is largely unknown. More insight into this relationship at younger ages may have implications for future prevention of obesity and its related traits.MethodsWe examined whether DNA methylation in cord blood and whole blood in childhood and adolescence was associated with BMI in the age range from 2 to 18 years using both cross-sectional and longitudinal models. We performed meta-analyses of epigenome-wide association studies including up to 4133 children from 23 studies. We examined the overlap of findings reported in previous studies in children and adults with those in our analyses and calculated enrichment.ResultsDNA methylation at three CpGs (cg05937453, cg25212453, and cg10040131), each in a different age range, was associated with BMI at Bonferroni significance,P < 1.06 × 10−7, with a 0.96 standard deviation score (SDS) (standard error (SE) 0.17), 0.32 SDS (SE 0.06), and 0.32 BMI SDS (SE 0.06) higher BMI per 10% increase in methylation, respectively. DNA methylation at nine additional CpGs in the cross-sectional childhood model was associated with BMI at false discovery rate significance. The strength of the associations of DNA methylation at the 187 CpGs previously identified to be associated with adult BMI, increased with advancing age across childhood and adolescence in our analyses. In addition, correlation coefficients between effect estimates for those CpGs in adults and in children and adolescents also increased. Among the top findings for each age range, we observed increasing enrichment for the CpGs that were previously identified in adults (birthPenrichment = 1; childhoodPenrichment = 2.00 × 10−4; adolescencePenrichment = 2.10 × 10−7).ConclusionsThere were only minimal associations of DNA methylation with childhood and adolescent BMI. With the advancing age of the participants across childhood and adolescence, we observed increasing overlap with altered DNA methylation loci reported in association with adult BMI. These findings may be compatible with the hypothesis that DNA methylation differences are mostly a consequence rather than a cause of obesity. <<<

Quantum computers have been proposed to solve a number of important problems such as discovering new drugs, new catalysts for fertilizer production, breaking encryption protocols, optimizing financial portfolios, or implementing new artificial intelligence applications. Yet, to date, a simple task such as multiplying 3 by 5 is beyond existing quantum hardware. This article examines the difficulties that would need to be solved for quantum computers to live up to their promises. I discuss the whole stack of technologies that has been envisioned to build a quantum computer from the top layers (the actual algorithms and associated applications) down to the very bottom ones (the quantum hardware, its control electronics, cryogeny, etc.) while not forgetting the crucial intermediate layer of quantum error correction. <<<

Here we represent human lives in a way that shares structural similarity to language, and we exploit this similarity to adapt natural language processing techniques to examine the evolution and predictability of human lives based on detailed event sequences. We do this by drawing on a comprehensive registry dataset, which is available for Denmark across several years, and that includes information about life-events related to health, education, occupation, income, address and working hours, recorded with day-to-day resolution. We create embeddings of life-events in a single vector space, showing that this embedding space is robust and highly structured. Our models allow us to predict diverse outcomes ranging from early mortality to personality nuances, outperforming state-of-the-art models by a wide margin. Using methods for interpreting deep learning models, we probe the algorithm to understand the factors that enable our predictions. Our framework allows researchers to discover potential mechanisms that impact life outcomes as well as the associated possibilities for personalized interventions. <<<

This paper presents $\textbf{R}$epresentation-$\textbf{C}$onditioned image$\textbf{G}$eneration (RCG), a simple yet effective image generation frameworkwhich sets a new benchmark in class-unconditional image generation. RCG doesnot condition on any human annotations. Instead, it conditions on aself-supervised representation distribution which is mapped from the imagedistribution using a pre-trained encoder. During generation, RCG samples fromsuch representation distribution using a representation diffusion model (RDM),and employs a pixel generator to craft image pixels conditioned on the sampledrepresentation. Such a design provides substantial guidance during thegenerative process, resulting in high-quality image generation. Tested onImageNet 256$\times$256, RCG achieves a Frechet Inception Distance (FID) of3.31 and an Inception Score (IS) of 253.4. These results not only significantlyimprove the state-of-the-art of class-unconditional image generation but alsorival the current leading methods in class-conditional image generation,bridging the long-standing performance gap between these two tasks. Code isavailable at https://github.com/LTH14/rcg. <<<

Preparing a single cell suspension is a critical step in any solid tissue flow cytometry experiment. Tissue dissection, enzymatic digestion, and mechanical dissociation are three significant steps leading to the degradation of the extracellular matrix and the isolation of single cells, allowing the generation of high-quality flow cytometry data. Cells and the extracellular matrix contain various proteins and other structures which must be considered when designing a tissue digestion protocol to preserve the viability of cells and the presence of relevant antigens while digesting matrix components and cleaving cell-cell junctions. Evaluation of the single cell suspension is essential before proceeding with the labeling of the cells as high viability and absence of cell debris and aggregates are critical for flow cytometry. The information presented should be used as a general guide of steps to consider when preparing a single cell suspension from solid tissues for flow cytometry experiments. © 2018 International Society for Advancement of Cytometry. <<<

Molecular circuits capable of processing temporal information are essential for complex decision making in response to both the presence and history of a molecular environment. A particular type of temporal information that has been recognized to be important is the relative timing of signals. Here we demonstrate the strategy of temporal memory combined with logic computation in DNA strand-displacement circuits capable of making decisions based on specific combinations of inputs as well as their relative timing. The circuit encodes the timing information on inputs in a set of memory strands, which allows for the construction of logic gates that act on current and historical signals. We show that mismatches can be employed to reduce the complexity of circuit design and that shortening specific toeholds can be useful for improving the robustness of circuit behavior. We also show that a detailed model can provide critical insights for guiding certain aspects of experimental investigations that an abstract model cannot. We envision that the design principles explored in this study can be generalized to more complex temporal logic circuits and incorporated into other types of circuit architectures, including DNA-based neural networks, enabling the implementation of timing-dependent learning rules and opening up new opportunities for embedding intelligent behaviors into artificial molecular machines. <<<

Large language models (LLMs) are central to modern natural languageprocessing, delivering exceptional performance in various tasks. However, theirintensive computational and memory requirements present challenges, especiallyfor devices with limited DRAM capacity. This paper tackles the challenge ofefficiently running LLMs that exceed the available DRAM capacity by storing themodel parameters on flash memory but bringing them on demand to DRAM. Ourmethod involves constructing an inference cost model that harmonizes with theflash memory behavior, guiding us to optimize in two critical areas: reducingthe volume of data transferred from flash and reading data in larger, morecontiguous chunks. Within this flash memory-informed framework, we introducetwo principal techniques. First, "windowing'" strategically reduces datatransfer by reusing previously activated neurons, and second, "row-columnbundling", tailored to the sequential data access strengths of flash memory,increases the size of data chunks read from flash memory. These methodscollectively enable running models up to twice the size of the available DRAM,with a 4-5x and 20-25x increase in inference speed compared to naive loadingapproaches in CPU and GPU, respectively. Our integration of sparsity awareness,context-adaptive loading, and a hardware-oriented design paves the way foreffective inference of LLMs on devices with limited memory. <<<

While machine learning (ML) research has recently grown more in popularity, its application in the omics domain is constrained by access to sufficiently large, high-quality datasets needed to train ML models. Federated Learning (FL) represents an opportunity to enable collaborative curation of such datasets among participating institutions. We compare the simulated performance of several models trained using FL against classically trained ML models on the task of multi-omics Parkinson's Disease prediction. We find that FL model performance tracks centrally trained ML models, where the most performant FL model achieves an AUC-PR of 0.876 ± 0.009, 0.014 ± 0.003 less than its centrally trained variation. We also determine that the dispersion of samples within a federation plays a meaningful role in model performance. Our study implements several open source FL frameworks and aims to highlight some of the challenges and opportunities when applying these collaborative methods in multi-omics studies. <<<

Human perceptual decisions can be repelled away from (repulsive adaptation) or attracted towards recent visual experience (attractive serial dependence). It is currently unclear whether and how these repulsive and attractive biases interact during visual processing and what computational principles underlie these history dependencies. Here we disentangle repulsive and attractive biases by exploring their respective timescales. We find that perceptual decisions are concurrently attracted towards the short-term perceptual history and repelled from stimuli experienced up to minutes into the past. The temporal pattern of short-term attraction and long-term repulsion cannot be captured by an ideal Bayesian observer model alone. Instead, it is well captured by an ideal observer model with efficient encoding and Bayesian decoding of visual information in a slowly changing environment. Concurrent attractive and repulsive history biases in perceptual decisions may thus be the consequence of the need for visual processing to simultaneously satisfy constraints of efficiency and stability. <<<

Classical reinforcement learning (CRL) has been widely applied in neuroscience and psychology; however, quantum reinforcement learning (QRL), which shows superior performance in computer simulations, has never been empirically tested on human decision-making. Moreover, all current successful quantum models for human cognition lack connections to neuroscience. Here we studied whether QRL can properly explain value-based decision-making. We compared 2 QRL and 12 CRL models by using behavioural and functional magnetic resonance imaging data from healthy and cigarette-smoking subjects performing the Iowa Gambling Task. In all groups, the QRL models performed well when compared with the best CRL models and further revealed the representation of quantum-like internal-state-related variables in the medial frontal gyrus in both healthy subjects and smokers, suggesting that value-based decision-making can be illustrated by QRL at both the behavioural and neural levels. <<<

Molecular forces generated by cell receptors are infrequent and transient, and hence difficult to detect. Here we report an assay that leverages the CRISPR-associated protein 12a (Cas12a) to amplify the detection of cellular traction forces generated by as few as 50 adherent cells. The assay involves the immobilization of a DNA duplex modified with a ligand specific for a cell receptor. Traction forces of tens of piconewtons trigger the dehybridization of the duplex, exposing a cryptic Cas12-activating strand that sets off the indiscriminate Cas12-mediated cleavage of a fluorogenic reporter strand. We used the assay to perform hundreds of force measurements using human platelets from a single blood draw to extract individualized dose-response curves and half-maximal inhibitory concentrations for a panel of antiplatelet drugs. For seven patients who had undergone cardiopulmonary bypass, platelet dysfunction strongly correlated with the need for platelet transfusion to limit bleeding. The Cas12a-mediated detection of cellular traction forces may be used to assess cell state, and to screen for genes, cell-adhesion ligands, drugs or metabolites that modulate cell mechanics. <<<

Yutao Liu, Yundi Zhang, Wenchuan Xie, Jing Zhao, Yiting Dong, Chunwei Xu, Yanan Wang, Man Li, Guoqiang Wang, Xin Zhu, Wenxian Wang, Kequan Lin, Huafei Lu, Yusheng Han, Leo Li, Jianchun Duan, Shangli Cai, Jie Wang, Zhijie Wang <<<

An increased interest in longitudinal neurodevelopment during the first few years after birth has emerged in recent years. Noninvasive magnetic resonance imaging (MRI) can provide crucial information about the development of brain structures in the early months of life. Despite the success of MRI collections and analysis for adults, it remains a challenge for researchers to collect high-quality multimodal MRIs from developing infant brains because of their irregular sleep pattern, limited attention, inability to follow instructions to stay still during scanning. In addition, there are limited analytic approaches available. These challenges often lead to a significant reduction of usable MRI scans and pose a problem for modeling neurodevelopmental trajectories. Researchers have explored solving this problem by synthesizing realistic MRIs to replace corrupted ones. Among synthesis methods, the convolutional neural network-based (CNN-based) generative adversarial networks (GANs) have demonstrated promising performance. In this study, we introduced a novel 3D MRI synthesis framework- pyramid transformer network (PTNet3D)- which relies on attention mechanisms through transformer and performer layers. We conducted extensive experiments on high-resolution Developing Human Connectome Project (dHCP) and longitudinal Baby Connectome Project (BCP) datasets. Compared with CNN-based GANs, PTNet3D consistently shows superior synthesis accuracy and superior generalization on two independent, large-scale infant brain MRI datasets. Notably, we demonstrate that PTNet3D synthesized more realistic scans than CNN-based models when the input is from multi-age subjects. Potential applications of PTNet3D include synthesizing corrupted or missing images. By replacing corrupted scans with synthesized ones, we observed significant improvement in infant whole brain segmentation. <<<

Licheng Wen, Xuemeng Yang, Daocheng Fu, Xiaofeng Wang, Pinlong Cai, Xin Li, Tao Ma, Yingxuan Li, Linran Xu, Dengke Shang, Zheng Zhu, Shaoyan Sun, Yeqi Bai, Xinyu Cai, Min Dou, Shuanglu Hu, Botian Shi, Yu Qiao <<<

The pursuit of autonomous driving technology hinges on the sophisticatedintegration of perception, decision-making, and control systems. Traditionalapproaches, both data-driven and rule-based, have been hindered by theirinability to grasp the nuance of complex driving environments and theintentions of other road users. This has been a significant bottleneck,particularly in the development of common sense reasoning and nuanced sceneunderstanding necessary for safe and reliable autonomous driving. The advent ofVisual Language Models (VLM) represents a novel frontier in realizing fullyautonomous vehicle driving. This report provides an exhaustive evaluation ofthe latest state-of-the-art VLM, GPT-4V(ision), and its application inautonomous driving scenarios. We explore the model's abilities to understandand reason about driving scenes, make decisions, and ultimately act in thecapacity of a driver. Our comprehensive tests span from basic scene recognitionto complex causal reasoning and real-time decision-making under varyingconditions. Our findings reveal that GPT-4V demonstrates superior performancein scene understanding and causal reasoning compared to existing autonomoussystems. It showcases the potential to handle out-of-distribution scenarios,recognize intentions, and make informed decisions in real driving contexts.However, challenges remain, particularly in direction discernment, trafficlight recognition, vision grounding, and spatial reasoning tasks. Theselimitations underscore the need for further research and development. Projectis now available on GitHub for interested parties to access and utilize:\url{https://github.com/PJLab-ADG/GPT4V-AD-Exploration} <<<

Time and space are primary dimensions of human experience. Separate lines of investigation have identified neural correlates of time and space, yet little is known about how these representations converge during self-guided experience. Here, 10 subjects with intracranially implanted microelectrodes play a timed, virtual navigation game featuring object search and retrieval tasks separated by fixed delays. Time cells and place cells activate in parallel during timed navigation intervals, whereas a separate time cell sequence spans inter-task delays. The prevalence, firing rates, and behavioral coding strengths of time cells and place cells are indistinguishable-yet time cells selectively remap between search and retrieval tasks, while place cell responses remain stable. Thus, the brain can represent time and space as overlapping but dissociable dimensions. Time cells and place cells may constitute a biological basis for the cognitive map of spatiotemporal context onto which memories are written. <<<

Double-negative (DN) T cells are present at relatively low frequencies in human peripheral blood, and are characterized as expressing the alpha-beta or gamma-delta T-cell receptor (TCR), but not the CD4 nor the CD8 co-receptors. Despite their low frequencies, these cells are potent producers of cytokines and, thus, are key orchestrators of immune responses. DN T cells were initially associated with induction of peripheral immunological tolerance and immunomodulatory activities related to disease prevention. However, other studies demonstrated that these cells can also display effector functions associated with pathology development. This apparent contradiction highlighted the heterogeneity of the DN T-cell population. Here, we review phenotypic and functional characteristics of DN T cells, emphasizing their role in human diseases. The need for developing biomarkers to facilitate the translation of studies from animal models to humans will also be discussed. Finally, we will examine DN T cells as promising therapeutic targets to prevent or inhibit human disease development. <<<