Iron (Fe) deficiency remains widespread among people in developing countries. To help solve this problem, breeders have been attempting to develop maize cultivars with high yields and high Fe concentrations in the kernels. We conducted a genome-wide association study and identified a gene, (), that regulates Fe concentrations in maize kernels. We cultivated maize varieties with both high yield and high Fe concentrations in their kernels by using a molecular marker developed from a 42-base pair insertion or deletion (indel) in the promoter of . expression is enriched in the basal endosperm transfer layer of kernels, and the ZmNAC78 protein directly regulates messenger RNA abundance of Fe transporters. Our results thus provide an approach to develop maize varieties with Fe-enriched kernels. <<<

Lei Chang, Yang Xie, Brett Taylor, Zhaoning Wang, Jiachen Sun, Tuyet R Tan, Rafael Bejar, Clark C Chen, Frank B Furnari, Ming Hu, Bing Ren <<<

Comprehensive analysis of chromatin architecture is crucial for understanding the gene regulatory programs during development and in disease pathogenesis, yet current methods often inadequately address the unique challenges presented by analysis of heterogeneous tissue samples. Here, we introduce Droplet Hi-C, which employs a commercial microfluidic device for high-throughput, single-cell chromatin conformation profiling in droplets. Using Droplet Hi-C, we mapped the chromatin architecture at single-cell resolution from the mouse cortex and analyzed gene regulatory programs in major cortical cell types. Additionally, we used this technique to detect copy number variation (CNV), structural variations (SVs) and extrachromosomal DNA (ecDNA) in cancer cells, revealing clonal dynamics and other oncogenic events during treatment. We further refined this technique to allow for joint profiling of chromatin architecture and transcriptome in single cells, facilitating a more comprehensive exploration of the links between chromatin architecture and gene expression in both normal tissues and tumors. Thus, Droplet Hi-C not only addresses critical gaps in chromatin analysis of heterogeneous tissues but also emerges as a versatile tool enhancing our understanding of gene regulation in health and disease. <<<

Transformers achieve remarkable performance in several tasks but due to theirquadratic complexity, with respect to the input's length, they areprohibitively slow for very long sequences. To address this limitation, weexpress the self-attention as a linear dot-product of kernel feature maps andmake use of the associativity property of matrix products to reduce thecomplexity from $\mathcal{O}\left(N^2\right)$ to $\mathcal{O}\left(N\right)$,where $N$ is the sequence length. We show that this formulation permits aniterative implementation that dramatically accelerates autoregressivetransformers and reveals their relationship to recurrent neural networks. Ourlinear transformers achieve similar performance to vanilla transformers andthey are up to 4000x faster on autoregressive prediction of very longsequences. <<<

AbstractThe rational targeting of RNA with small molecules is hampered by our still limited understanding of RNA structural and dynamic properties. Mostin silicotools for binding site identification rely on static structures and therefore cannot face the challenges posed by the dynamic nature of RNA molecules. Here, we present SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables exploring the conformational landscape of RNA with atomistic molecular dynamics and at the same time identifying RNA pockets in an efficient way with the aid of probes and enhanced-sampling techniques. In our benchmark composed of large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN successfully identified all the experimentally resolved pockets and ranked them among the most favorite probe hotspots. Overall, SHAMAN sets a solid foundation for future drug design efforts targeting RNA with small molecules, effectively addressing the long-standing challenges in the field. <<<

AbstractStable diffusion revolutionized image creation from descriptive text. GPT-2 (ref. 1), GPT-3(.5) (ref. 2) and GPT-4 (ref. 3) demonstrated high performance across a variety of language tasks. ChatGPT introduced such language models to the public. It is now clear that generative artificial intelligence (AI) such as large language models (LLMs) is here to stay and will substantially change the ecosystem of online text and images. Here we consider what may happen to GPT-{n} once LLMs contribute much of the text found online. We find that indiscriminate use of model-generated content in training causes irreversible defects in the resulting models, in which tails of the original content distribution disappear. We refer to this effect as ‘model collapse’ and show that it can occur in LLMs as well as in variational autoencoders (VAEs) and Gaussian mixture models (GMMs). We build theoretical intuition behind the phenomenon and portray its ubiquity among all learned generative models. We demonstrate that it must be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of LLM-generated content in data crawled from the Internet. <<<

Zhao Zhang, Hua Jiang, Li Huang, Sixi Liu, Xiaoya Zhou, Yun Cai, Ming Li, Fei Gao, Xiaoting Liang, Kam-Sze Tsang, Guangfu Chen, Chui-Yan Ma, Yuet-Hung Chai, Hongsheng Liu, Chen Yang, Mo Yang, Xiaoling Zhang, Shuo Han, Xin Du, Ling Chen, Wuh-Liang Hwu, Jiacai Zhuo, Qizhou Lian <<<

Metachromatic leukodystrophy (MLD) is an inherited disease caused by a deficiency of the enzyme arylsulfatase A (ARSA). Lentivirus-modified autologous hematopoietic stem cell gene therapy (HSCGT) has recently been approved for clinical use in pre- and early-symptomatic children with MLD to increase ARSA activity. Unfortunately, this advanced therapy is not available for most patients with MLD who have progressed to more advanced symptomatic stages at diagnosis. Patients with late-onset juvenile MLD typically present with a slower neurological progression of symptoms and represent a significant burden to the economy and healthcare system, whereas those with early-onset infantile MLD die within a few years of symptom onset. We conducted a pilot study to determine the safety and benefit of HSCGT in patients with post-symptomatic juvenile MLD and report preliminary results. The safety profile of HSCGT was favorable in this long-term follow-up over nine years. The most common adverse events (AEs) within two months of HSCGT were related to busulfan conditioning, and all AEs resolved. No HSCGT-related AEs and no evidence of distorted hematopoietic differentiation during long-term follow-up for up to 9.6 years. Importantly, to date, patients have maintained remarkably improved ARSA activity with a stable disease state, including increased Functional Independence Measure (FIM) score and decreased magnetic resonance imaging (MRI) lesion score. This long-term follow-up pilot study suggests that HSCGT is safe and provides clinical benefit to patients with post-symptomatic juvenile MLD. <<<

Terrains in a 3D world can be undulating. Yet, most prior research has exclusively investigated spatial representations on a flat surface, leaving a 2D cognitive map as the dominant model in the field. Here, we investigated whether humans represent a curved surface by building a dimension-reduced flattened 2D map or a full 3D map. Participants learned the location of objects positioned on a flat and curved surface in a virtual environment by driving on the concave side of the surface (Experiment 1), driving and looking vertically (Experiment 2), or flying (Experiment 3). Subsequently, they were asked to retrieve either the path distance or the 3D Euclidean distance between the objects. Path distance estimation was good overall, but we found a significant underestimation bias for the path distance on the curve, suggesting an influence of potential 3D shortcuts, even though participants were only driving on the surface. Euclidean distance estimation was better when participants were exposed more to the global 3D structure of the environment by looking and flying. These results suggest that the representation of the 2D manifold, embedded in a 3D world, is neither purely 2D nor 3D. Rather, it is flexible and dependent on the behavioral experience and demand. <<<

Alex C Y Chen, Sneha Jaiswal, Daniela Martinez, Cansu Yerinde, Keely Ji, Velita Miranda, Megan E Fung, Sarah A Weiss, Maria Zschummel, Kazuhiro Taguchi, Christopher S Garris, Thorsten R Mempel, Nir Hacohen, Debattama R Sen <<<

The etiology and effect of age-related immune dysfunction in cancer is not completely understood. Here we show that limited priming of CD8 T cells in the aged tumor microenvironment (TME) outweighs cell-intrinsic defects in limiting tumor control. Increased tumor growth in aging is associated with reduced CD8 T cell infiltration and function. Transfer of T cells from young mice does not restore tumor control in aged mice owing to rapid induction of T cell dysfunction. Cell-extrinsic signals in the aged TME drive a tumor-infiltrating age-associated dysfunctional (T) cell state that is functionally, transcriptionally and epigenetically distinct from canonical T cell exhaustion. Altered natural killer cell-dendritic cell-CD8 T cell cross-talk in aged tumors impairs T cell priming by conventional type 1 dendritic cells and promotes T cell formation. Aged mice are thereby unable to benefit from therapeutic tumor vaccination. Critically, myeloid-targeted therapy to reinvigorate conventional type 1 dendritic cells can improve tumor control and restore CD8 T cell immunity in aging. <<<

Kateryna D Makova, Brandon D Pickett, Robert S Harris, Gabrielle A Hartley, Monika Cechova, Karol Pal, Sergey Nurk, DongAhn Yoo, Qiuhui Li, Prajna Hebbar, Barbara C McGrath, Francesca Antonacci, Margaux Aubel, Arjun Biddanda, Matthew Borchers, Erich Bornberg-Bauer, Gerard G Bouffard, Shelise Y Brooks, Lucia Carbone, Laura Carrel, Andrew Carroll, Pi-Chuan Chang, Chen-Shan Chin, Daniel E Cook, Sarah J C Craig, Luciana de Gennaro, Mark Diekhans, Amalia Dutra, Gage H Garcia, Patrick G S Grady, Richard E Green, Diana Haddad, Pille Hallast, William T Harvey, Glenn Hickey, David A Hillis, Savannah J Hoyt, Hyeonsoo Jeong, Kaivan Kamali, Sergei L Kosakovsky Pond, Troy M LaPolice, Charles Lee, Alexandra P Lewis, Yong-Hwee E Loh, Patrick Masterson, Kelly M McGarvey, Rajiv C McCoy, Paul Medvedev, Karen H Miga, Katherine M Munson, Evgenia Pak, Benedict Paten, Brendan J Pinto, Tamara Potapova, Arang Rhie, Joana L Rocha, Fedor Ryabov, Oliver A Ryder, Samuel Sacco, Kishwar Shafin, Valery A Shepelev, Viviane Slon, Steven J Solar, Jessica M Storer, Peter H Sudmant, Sweetalana, Alex Sweeten, Michael G Tassia, Françoise Thibaud-Nissen, Mario Ventura, Melissa A Wilson, Alice C Young, Huiqing Zeng, Xinru Zhang, Zachary A Szpiech, Christian D Huber, Jennifer L Gerton, Soojin V Yi, Michael C Schatz, Ivan A Alexandrov, Sergey Koren, Rachel J O'Neill, Evan E Eichler, Adam M Phillippy <<<

Apes possess two sex chromosomes-the male-specific Y chromosome and the X chromosome, which is present in both males and females. The Y chromosome is crucial for male reproduction, with deletions being linked to infertility. The X chromosome is vital for reproduction and cognition. Variation in mating patterns and brain function among apes suggests corresponding differences in their sex chromosomes. However, owing to their repetitive nature and incomplete reference assemblies, ape sex chromosomes have been challenging to study. Here, using the methodology developed for the telomere-to-telomere (T2T) human genome, we produced gapless assemblies of the X and Y chromosomes for five great apes (bonobo (Pan paniscus), chimpanzee (Pan troglodytes), western lowland gorilla (Gorilla gorilla gorilla), Bornean orangutan (Pongo pygmaeus) and Sumatran orangutan (Pongo abelii)) and a lesser ape (the siamang gibbon (Symphalangus syndactylus)), and untangled the intricacies of their evolution. Compared with the X chromosomes, the ape Y chromosomes vary greatly in size and have low alignability and high levels of structural rearrangements-owing to the accumulation of lineage-specific ampliconic regions, palindromes, transposable elements and satellites. Many Y chromosome genes expand in multi-copy families and some evolve under purifying selection. Thus, the Y chromosome exhibits dynamic evolution, whereas the X chromosome is more stable. Mapping short-read sequencing data to these assemblies revealed diversity and selection patterns on sex chromosomes of more than 100 individual great apes. These reference assemblies are expected to inform human evolution and conservation genetics of non-human apes, all of which are endangered species. <<<

Ioannis Sarropoulos, Mari Sepp, Robert Frömel, Kevin Leiss, Nils Trost, Evgeny Leushkin, Konstantin Okonechnikov, Piyush Joshi, Peter Giere, Lena M Kutscher, Margarida Cardoso-Moreira, Stefan M Pfister, Henrik Kaessmann <<<

Organ development is orchestrated by cell- and time-specific gene regulatory networks. In this study, we investigated the regulatory basis of mouse cerebellum development from early neurogenesis to adulthood. By acquiring snATAC-seq (single-nucleus assay for transposase accessible chromatin using sequencing) profiles for ~90,000 cells spanning 11 stages, we mapped cerebellar cell types and identified candidate cisregulatory elements (CREs). We detected extensive spatiotemporal heterogeneity among progenitor cells and a gradual divergence in the regulatory programs of cerebellar neurons during differentiation. Comparisons to vertebrate genomes and snATAC-seq profiles for ∼20,000 cerebellar cells from the marsupial opossum revealed a shared decrease in CRE conservation during development and differentiation as well as differences in constraint between cell types. Our work delineates the developmental and evolutionary dynamics of gene regulation in cerebellar cells and provides insights into mammalian organ development. <<<

This paper aims to generate materials for 3D meshes from text descriptions.Unlike existing methods that synthesize texture maps, we propose to generatesegment-wise procedural material graphs as the appearance representation, whichsupports high-quality rendering and provides substantial flexibility inediting. Instead of relying on extensive paired data, i.e., 3D meshes withmaterial graphs and corresponding text descriptions, to train a material graphgenerative model, we propose to leverage the pre-trained 2D diffusion model asa bridge to connect the text and material graphs. Specifically, our approachdecomposes a shape into a set of segments and designs a segment-controlleddiffusion model to synthesize 2D images that are aligned with mesh parts. Basedon generated images, we initialize parameters of material graphs and fine-tunethem through the differentiable rendering module to produce materials inaccordance with the textual description. Extensive experiments demonstrate thesuperior performance of our framework in photorealism, resolution, andeditability over existing methods. Project page: https://zju3dv.github.io/MaPa <<<

Leonardo Tozzi, Xue Zhang, Adam Pines, Alisa M Olmsted, Emily S Zhai, Esther T Anene, Megan Chesnut, Bailey Holt-Gosselin, Sarah Chang, Patrick C Stetz, Carolina A Ramirez, Laura M Hack, Mayuresh S Korgaonkar, Max Wintermark, Ian H Gotlib, Jun Ma, Leanne M Williams <<<

There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or 'biotypes' to enable stratification of patients with depression and anxiety. We used task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol conducted across multiple studies in patients with depression and anxiety when treatment free (n = 801) and after randomization to pharmacotherapy or behavioral therapy (n = 250). From these patients, we derived personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy. Participants were subdivided into six biotypes defined by distinct profiles of intrinsic task-free functional connectivity within the default mode, salience and frontoparietal attention circuits, and of activation and connectivity within frontal and subcortical regions elicited by emotional and cognitive tasks. The six biotypes showed consistency with our theoretical taxonomy and were distinguished by symptoms, behavioral performance on general and emotional cognitive computerized tests, and response to pharmacotherapy as well as behavioral therapy. Our results provide a new, theory-driven, clinically validated and interpretable quantitative method to parse the biological heterogeneity of depression and anxiety. Thus, they represent a promising approach to advance precision clinical care in psychiatry. <<<

Sankaraleengam Alagapan, Ki Sueng Choi, Stephen Heisig, Patricio Riva-Posse, Andrea Crowell, Vineet Tiruvadi, Mosadoluwa Obatusin, Ashan Veerakumar, Allison C Waters, Robert E Gross, Sinead Quinn, Lydia Denison, Matthew O'Shaughnessy, Marissa Connor, Gregory Canal, Jungho Cha, Rachel Hershenberg, Tanya Nauvel, Faical Isbaine, Muhammad Furqan Afzal, Martijn Figee, Brian H Kopell, Robert Butera, Helen S Mayberg, Christopher J Rozell <<<

Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) can provide long-term symptom relief for treatment-resistant depression (TRD). However, achieving stable recovery is unpredictable, typically requiring trial-and-error stimulation adjustments due to individual recovery trajectories and subjective symptom reporting. We currently lack objective brain-based biomarkers to guide clinical decisions by distinguishing natural transient mood fluctuations from situations requiring intervention. To address this gap, we used a new device enabling electrophysiology recording to deliver SCC DBS to ten TRD participants (ClinicalTrials.gov identifier NCT01984710). At the study endpoint of 24 weeks, 90% of participants demonstrated robust clinical response, and 70% achieved remission. Using SCC local field potentials available from six participants, we deployed an explainable artificial intelligence approach to identify SCC local field potential changes indicating the patient's current clinical state. This biomarker is distinct from transient stimulation effects, sensitive to therapeutic adjustments and accurate at capturing individual recovery states. Variable recovery trajectories are predicted by the degree of preoperative damage to the structural integrity and functional connectivity within the targeted white matter treatment network, and are matched by objective facial expression changes detected using data-driven video analysis. Our results demonstrate the utility of objective biomarkers in the management of personalized SCC DBS and provide new insight into the relationship between multifaceted (functional, anatomical and behavioural) features of TRD pathology, motivating further research into causes of variability in depression treatment. <<<

Conventional deformable registration methods aim at solving an optimizationmodel carefully designed on image pairs and their computational costs areexceptionally high. In contrast, recent deep learning based approaches canprovide fast deformation estimation. These heuristic network architectures arefully data-driven and thus lack explicit geometric constraints, e.g.,topology-preserving, which are indispensable to generate plausibledeformations. We design a new deep learning based framework to optimize adiffeomorphic model via multi-scale propagation in order to integrateadvantages and avoid limitations of these two categories of approaches.Specifically, we introduce a generic optimization model to formulatediffeomorphic registration and develop a series of learnable architectures toobtain propagative updating in the coarse-to-fine feature space. Moreover, wepropose a novel bilevel self-tuned training strategy, allowing efficient searchof task-specific hyper-parameters. This training strategy increases theflexibility to various types of data while reduces computational and humanburdens. We conduct two groups of image registration experiments on 3D volumedatasets including image-to-atlas registration on brain MRI data andimage-to-image registration on liver CT data. Extensive results demonstrate thestate-of-the-art performance of the proposed method with diffeomorphicguarantee and extreme efficiency. We also apply our framework to challengingmulti-modal image registration, and investigate how our registration to supportthe down-streaming tasks for medical image analysis including multi-modalfusion and image segmentation. <<<

AbstractIn this article, we will introduce the basic concept and the quantum feature of a novel computing system, coherent Ising machines, and describe their theoretical and experimental performance. We start with the discussion how to construct such physical devices as the quantum analog of classical neuron and synapse, and end with the performance comparison against various classical neural networks implemented in CPU and supercomputers. <<<