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. <<<

Yunli Lai, Xiaofan Zhu, Sheng He, Zirui Dong, Yanqing Tang, Fuben Xu, Yun Chen, Lintao Meng, Yuli Tao, Shang Yi, Jiasun Su, Hongqian Huang, Jingsi Luo, Tak Yeung Leung, Hongwei Wei <<<

To evaluate the performance of noninvasive prenatal screening (NIPS) in the detection of common aneuploidies in a population-based study, a total of 86,262 single pregnancies referred for NIPS were prospectively recruited. Among 86,193 pregnancies with reportable results, follow-up was successfully conducted in 1160 fetuses reported with a high-risk result by NIPS and 82,511 cases (95.7%) with a low-risk result. The screen-positive rate (SPR) of common aneuploidies and sex chromosome abnormalities (SCAs) provided by NIPS were 0.7% (586/83,671) and 0.6% (505/83,671), respectively. The positive predictive values (PPVs) for Trisomy 21, Trisomy 18, Trisomy 13 and SCAs were calculated as 89.7%, 84.0%, 52.6% and 38.0%, respectively. In addition, less rare chromosomal abnormalities, including copy number variants (CNVs), were detected, compared with those reported by NIPS with higher read-depth. Among these rare abnormalities, only 23.2% (13/56) were confirmed by prenatal diagnosis. In total, four common trisomy cases were found to be false negative, resulting in a rate of 0.48/10,000 (4/83,671). In summary, this study conducted in an underdeveloped region with limited support for the new technology development and lack of cost-effective prenatal testing demonstrates the importance of implementing routine aneuploidy screening in the public sector for providing early detection and precise prognostic information. <<<

Joe Nassour, Lucia Gutierrez Aguiar, Adriana Correia, Tobias T Schmidt, Laura Mainz, Sara Przetocka, Candy Haggblom, Nimesha Tadepalle, April Williams, Maxim N Shokhirev, Semih C Akincilar, Vinay Tergaonkar, Gerald S Shadel, Jan Karlseder <<<

Cancers arise through the accumulation of genetic and epigenetic alterations that enable cells to evade telomere-based proliferative barriers and achieve immortality. One such barrier is replicative crisis-an autophagy-dependent program that eliminates checkpoint-deficient cells with unstable telomeres and other cancer-relevant chromosomal aberrations. However, little is known about the molecular events that regulate the onset of this important tumour-suppressive barrier. Here we identified the innate immune sensor Z-DNA binding protein 1 (ZBP1) as a regulator of the crisis program. A crisis-associated isoform of ZBP1 is induced by the cGAS-STING DNA-sensing pathway, but reaches full activation only when associated with telomeric-repeat-containing RNA (TERRA) transcripts that are synthesized from dysfunctional telomeres. TERRA-bound ZBP1 oligomerizes into filaments on the outer mitochondrial membrane of a subset of mitochondria, where it activates the innate immune adapter protein mitochondrial antiviral-signalling protein (MAVS). We propose that these oligomerization properties of ZBP1 serve as a signal amplification mechanism, where few TERRA-ZBP1 interactions are sufficient to launch a detrimental MAVS-dependent interferon response. Our study reveals a mechanism for telomere-mediated tumour suppression, whereby dysfunctional telomeres activate innate immune responses through mitochondrial TERRA-ZBP1 complexes to eliminate cells destined for neoplastic transformation. <<<

Mingrui Xia, Tianmei Si, Xiaoyi Sun, Qing Ma, Bangshan Liu, Li Wang, Jie Meng, Miao Chang, Xiaoqi Huang, Ziqi Chen, Yanqing Tang, Ke Xu, Qiyong Gong, Fei Wang, Jiang Qiu, Peng Xie, Lingjiang Li, Yong He, DIDA-Major Depressive Disorder Working Group <<<

Resting-state functional MRI (R-fMRI) studies have demonstrated widespread alterations in brain function in patients with major depressive disorder (MDD). However, a clear and consistent conclusion regarding a repeatable pattern of MDD-relevant alterations is still limited due to the scarcity of large-sample, multisite datasets. Here, we address this issue by including a large R-fMRI dataset with 1434 participants (709 patients with MDD and 725 healthy controls) from five centers in China. Individual functional activity maps that represent very local to long-range connections are computed using the amplitude of low-frequency fluctuations, regional homogeneity and distance-related functional connectivity strength. The reproducibility analyses involve different statistical strategies, global signal regression, across-center consistency, clinical variables, and sample size. We observed significant hypoactivity in the orbitofrontal, sensorimotor, and visual cortices and hyperactivity in the frontoparietal cortices in MDD patients compared to the controls. These alterations are not affected by different statistical analysis strategies, global signal regression and medication status and are generally reproducible across centers. However, these between-group differences are partially influenced by the episode status and the age of disease onset in patients, and the brain-clinical variable relationship exhibits poor cross-center reproducibility. Bootstrap analyses reveal that at least 400 subjects in each group are required to replicate significant alterations (an extent threshold of P < .05 and a height threshold of P < .001) at 50% reproducibility. Together, these results highlight reproducible patterns of functional alterations in MDD and relevant influencing factors, which provides crucial guidance for future neuroimaging studies of this disorder. <<<

The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of proteins. Along with novel architectures for generative modeling and sequence analysis, they have revolutionized the protein design field in the past few years remarkably by improving the accuracy and ability to identify novel protein sequences and structures. Deep neural networks can now learn and extract the fundamental features of protein structures, predict how they interact with other biomolecules, and have the potential to create new effective drugs for treating disease. As their applicability in protein design is rapidly growing, we review the recent developments and technology in deep learning methods and provide examples of their performance to generate novel functional proteins. <<<

Variational autoencoders are powerful algorithms for identifying dominantlatent structure in a single dataset. In many applications, however, we areinterested in modeling latent structure and variation that are enriched in atarget dataset compared to some background---e.g. enriched in patients comparedto the general population. Contrastive learning is a principled framework tocapture such enriched variation between the target and background, butstate-of-the-art contrastive methods are limited to linear models. In thispaper, we introduce the contrastive variational autoencoder (cVAE), whichcombines the benefits of contrastive learning with the power of deep generativemodels. The cVAE is designed to identify and enhance salient latent features.The cVAE is trained on two related but unpaired datasets, one of which hasminimal contribution from the salient latent features. The cVAE explicitlymodels latent features that are shared between the datasets, as well as thosethat are enriched in one dataset relative to the other, which allows thealgorithm to isolate and enhance the salient latent features. The algorithm isstraightforward to implement, has a similar run-time to the standard VAE, andis robust to noise and dataset purity. We conduct experiments across diversetypes of data, including gene expression and facial images, showing that thecVAE effectively uncovers latent structure that is salient in a particularanalysis. <<<

Danillo G Augusto, Lawton D Murdolo, Demetra S M Chatzileontiadou, Joseph J Sabatino, Tasneem Yusufali, Noah D Peyser, Xochitl Butcher, Kerry Kizer, Karoline Guthrie, Victoria W Murray, Vivian Pae, Sannidhi Sarvadhavabhatla, Fiona Beltran, Gurjot S Gill, Kara L Lynch, Cassandra Yun, Colin T Maguire, Michael J Peluso, Rebecca Hoh, Timothy J Henrich, Steven G Deeks, Michelle Davidson, Scott Lu, Sarah A Goldberg, J Daniel Kelly, Jeffrey N Martin, Cynthia A Vierra-Green, Stephen R Spellman, David J Langton, Michael J Dewar-Oldis, Corey Smith, Peter J Barnard, Sulggi Lee, Gregory M Marcus, Jeffrey E Olgin, Mark J Pletcher, Martin Maiers, Stephanie Gras, Jill A Hollenbach <<<

Studies have demonstrated that at least 20% of individuals infected with SARS-CoV-2 remain asymptomatic. Although most global efforts have focused on severe illness in COVID-19, examining asymptomatic infection provides a unique opportunity to consider early immunological features that promote rapid viral clearance. Here, postulating that variation in the human leukocyte antigen (HLA) loci may underly processes mediating asymptomatic infection, we enrolled 29,947 individuals, for whom high-resolution HLA genotyping data were available, in a smartphone-based study designed to track COVID-19 symptoms and outcomes. Our discovery cohort (n = 1,428) comprised unvaccinated individuals who reported a positive test result for SARS-CoV-2. We tested for association of five HLA loci with disease course and identified a strong association between HLA-B*15:01 and asymptomatic infection, observed in two independent cohorts. Suggesting that this genetic association is due to pre-existing T cell immunity, we show that T cells from pre-pandemic samples from individuals carrying HLA-B*15:01 were reactive to the immunodominant SARS-CoV-2 S-derived peptide NQKLIANQF. The majority of the reactive T cells displayed a memory phenotype, were highly polyfunctional and were cross-reactive to a peptide derived from seasonal coronaviruses. The crystal structure of HLA-B*15:01-peptide complexes demonstrates that the peptides NQKLIANQF and NQKLIANAF (from OC43-CoV and HKU1-CoV) share a similar ability to be stabilized and presented by HLA-B*15:01. Finally, we show that the structural similarity of the peptides underpins T cell cross-reactivity of high-affinity public T cell receptors, providing the molecular basis for HLA-B*15:01-mediated pre-existing immunity. <<<