Several lipogenic genes have been shown to have effects on lipid metabolism: stearoyl CoA desaturase 1 (SCD1) catalyzes the desaturation of several fatty acids (FA) in the cis-Delta(9) position in mammary glands of ruminant animals, diacylglycerol acyltransferase 1 (DGAT1) is a key enzyme in triacylglycerol synthesis in the mammary gland, and sterol regulatory element binding protein (SREBP-1) is a transcription factor that regulates expression levels of the SCD1 gene and other genes relevant to lipid and FA metabolism in adipose tissue and mammary gland. In this work, 351 Italian Brown cows were genotyped for polymorphisms in the SCD1, SREBP-1, and DGAT1 genes to reveal the allelic distribution in the population. Subsequently, effects on individual milk FA composition and on cis-9 unsaturated/saturated FA ratios, a proxy of mammary stearoyl CoA desaturase activity, were investigated. The genotypes of SCD1 (A293V) and DGAT1 (K232A) were determined by an approach based on the ligation detection reaction and a universal array, whereas the genotype of SREBP-1 (84-bp insertion-deletion) was revealed by PCR amplification of intron 5. The genotype analysis showed an unbalanced distribution of alleles within all genes, being the allele with higher gene frequency at 82, 84, and 98% for SCD1, SREBP-1, and DGAT1, respectively. Significant associations between SCD1 and DGAT1 polymorphisms and milk FA composition were found, whereas SREBP-1 polymorphism was not associated with milk FA composition. In particular, SCD1 showed significant association with C14:1 cis-9 and C14:1 cis-9/C14:0, which is considered the best proxy of the desaturation activity in mammary gland. The DGAT1 polymorphism had the strongest association with milk FA composition, which confirmed the key role of DGAT1 in lipid metabolism of mammary gland. However, the unbalanced distribution of alleles in all polymorphisms investigated suggested that the size of population should be increased to confirm the results of the present study. <<<

This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni-/multi-modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software. <<<

Episodic memory enables humans to encode and later vividly retrieve information about our rich experiences, yet the neural representations that support this mental capacity are poorly understood. Using a large fMRI dataset ( = 468) of face-name associative memory tasks and principal component analysis to examine neural representational dimensionality (RD), we found that the human brain maintained a high-dimensional representation of faces through hierarchical representation within and beyond the face-selective regions. Critically, greater RD was associated with better subsequent memory performance both within and across participants, and this association was specific to episodic memory but not general cognitive abilities. Furthermore, the frontoparietal activities could suppress the shared low-dimensional fluctuations and reduce the correlations of local neural responses, resulting in greater RD. RD was not associated with the degree of item-specific pattern similarity, and it made complementary contributions to episodic memory. These results provide a mechanistic understanding of the role of RD in supporting accurate episodic memory. <<<

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation; analyses timings and patterns of tumour evolution; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity; and evaluates a range of more-specialized features of cancer genomes. <<<

Longitudinal fetal brain atlas is a powerful tool for understanding and characterizing the complex process of fetus brain development. Existing fetus brain atlases are typically constructed by averaged brain images on discrete time points independently over time. Due to the differences in onto-genetic trends among samples at different time points, the resulting atlases suffer from temporal inconsistency, which may lead to estimating error of the brain developmental characteristic parameters along the timeline. To this end, we proposed a multi-stage deep-learning framework to tackle the time inconsistency issue as a 4D (3D brain volume + 1D age) image data denoising task. Using implicit neural representation, we construct a continuous and noise-free longitudinal fetus brain atlas as a function of the 4D spatial-temporal coordinate. Experimental results on two public fetal brain atlases (CRL and FBA-Chinese atlases) show that the proposed method can significantly improve the atlas temporal consistency while maintaining good fetus brain structure representation. In addition, the continuous longitudinal fetus brain atlases can also be extensively applied to generate finer 4D atlases in both spatial and temporal resolution. <<<

Predicting the future motion of road agents is a critical task in an autonomous driving pipeline. In this work, we address the problem of generating a set of scene-level, or joint, future trajectory predictions in multi-agent driving scenarios. To this end, we propose FJMP, a Factorized Joint Motion Prediction framework for multi-agent interactive driving scenarios. FJMP models the future scene interaction dynamics as a sparse directed interaction graph, where edges denote explicit interactions between agents. We then prune the graph into a directed acyclic graph (DAG) and decompose the joint prediction task into a sequence of marginal and conditional predictions according to the partial ordering of the DAG, where joint future trajectories are decoded using a directed acyclic graph neural network (DAGNN). We conduct experiments on the INTERACTION and Argoverse 2 datasets and demonstrate that FJMP produces more accurate and scene-consistent joint trajectory predictions than non-factorized approaches, especially on the most interactive and kinematically interesting agents. FJMP ranks 1st on the multi-agent test leaderboard of the INTERACTION dataset. <<<

Following a key developmental task of childhood-building a foundation of self-knowledge in the form of domain-specific self-concepts-adolescents begin to explore their emerging identities in ways that foster autonomy and connectedness. Neuroimaging studies of self-related processes demonstrate enhanced engagement of the ventromedial prefrontal cortex in adolescence, which may facilitate and reflect the development of identity by integrating the value of potential actions and choices. Drawing from neuroeconomic and social cognitive accounts, we propose that motivated behavior during adolescence can be modeled by a general value-based decision-making process centered around value accumulation in the ventromedial prefrontal cortex. This approach advances models of adolescent neurodevelopment that focus on reward sensitivity and cognitive control by considering more diverse value inputs, including contributions of developing self- and identity-related processes. It also considers adolescent decision making and behavior from adolescents' point of view rather than adults' perspectives on what adolescents should value or how they should behave. <<<

How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? This position paper proposes an architecture and training paradigms with which to construct autonomous intelligent agents. It combines concepts such as configurable predictive world model, behavior driven through intrinsic motivation, and hierarchical joint embedding architectures trained with self-supervised learning. <<<

Yongcai Huang, Haihai Wang, Yidong Zhu, Xing Huang, Shuai Li, Xingguo Wu, Yao Zhao, Zhigui Bao, Li Qin, Yongbo Jin, Yahui Cui, Guangjin Ma, Qiao Xiao, Qiong Wang, Jiechen Wang, Xuerong Yang, Hongjun Liu, Xiaoduo Lu, Brian A Larkins, Wenqin Wang, Yongrui Wu <<<

Teosinte, the wild ancestor of maize (Zea mays subsp. mays), has three times the seed protein content of most modern inbreds and hybrids, but the mechanisms that are responsible for this trait are unknown. Here we use trio binning to create a contiguous haplotype DNA sequence of a teosinte (Zea mays subsp. parviglumis) and, through map-based cloning, identify a major high-protein quantitative trait locus, TEOSINTE HIGH PROTEIN 9 (THP9), on chromosome 9. THP9 encodes an asparagine synthetase 4 enzyme that is highly expressed in teosinte, but not in the B73 inbred, in which a deletion in the tenth intron of THP9-B73 causes incorrect splicing of THP9-B73 transcripts. Transgenic expression of THP9-teosinte in B73 significantly increased the seed protein content. Introgression of THP9-teosinte into modern maize inbreds and hybrids greatly enhanced the accumulation of free amino acids, especially asparagine, throughout the plant, and increased seed protein content without affecting yield. THP9-teosinte seems to increase nitrogen-use efficiency, which is important for promoting a high yield under low-nitrogen conditions. <<<

Differential expression (DE) analysis and gene set enrichment (GSE) analysis are commonly applied in single cell RNA sequencing (scRNA-seq) studies. Here, we develop an integrative and scalable computational method, iDEA, to perform joint DE and GSE analysis through a hierarchical Bayesian framework. By integrating DE and GSE analyses, iDEA can improve the power and consistency of DE analysis and the accuracy of GSE analysis. Importantly, iDEA uses only DE summary statistics as input, enabling effective data modeling through complementing and pairing with various existing DE methods. We illustrate the benefits of iDEA with extensive simulations. We also apply iDEA to analyze three scRNA-seq data sets, where iDEA achieves up to five-fold power gain over existing GSE methods and up to 64% power gain over existing DE methods. The power gain brought by iDEA allows us to identify many pathways that would not be identified by existing approaches in these data. <<<

Shaohua Hu, Hao Xu, Xiaohua Meng, Xiangxiang Bai, Junli Xu, Jinru Ji, Chaoqun Ying, Yunbo Chen, Ping Shen, Yunxiao Zhou, Beiwen Zheng, Yonghong Xiao <<<

is an emerging species and has increasingly been reported to cause life-threatening infections and even outbreaks in humans. Nevertheless, there is little data regarding the geographical distribution, phylogenetic structure, and transmission across the globe, especially in Asia. We utilize whole-genome sequencing (WGS) data to define a global population framework, phylogenetic structure, geographical distribution, and transmission evaluation of pathogens. The geographical distribution diagram revealed the emerging pathogenic bacteria already distributed in various countries worldwide, especially in the USA and China. Strikingly, phylogenetic analysis showed a part of our China original shared the same ancestor with the USA outbreak strain, which implies the possibility of localized outbreaks and global spread. These closer related strains also contained ICEEaI, which might insert into a disrupted DNA repair gene and made the strain more liable to mutation and outbreak infection. BEAST analysis showed that the most recent common ancestor for ICEEaI was dated twelve years ago, and China might be the most likely recent source of this bacteria. Our study sheds light on the potential possibility of causing the large-scale outbreak and rapid global dissemination. Continued genomic surveillance of the dynamics of populations will generate further knowledge for optimizing future prevent global outbreak infections. <<<

Maria Hurskainen, Ivana Mižíková, David P Cook, Noora Andersson, Chanèle Cyr-Depauw, Flore Lesage, Emmi Helle, Laurent Renesme, Robert P Jankov, Markku Heikinheimo, Barbara C Vanderhyden, Bernard Thébaud <<<

During late lung development, alveolar and microvascular development is finalized to enable sufficient gas exchange. Impaired late lung development manifests as bronchopulmonary dysplasia (BPD) in preterm infants. Single-cell RNA sequencing (scRNA-seq) allows for assessment of complex cellular dynamics during biological processes, such as development. Here, we use MULTI-seq to generate scRNA-seq profiles of over 66,000 cells from 36 mice during normal or impaired lung development secondary to hyperoxia with validation of some of the findings in lungs from BPD patients. We observe dynamic populations of cells, including several rare cell types and putative progenitors. Hyperoxia exposure, which mimics the BPD phenotype, alters the composition of all cellular compartments, particularly alveolar epithelium, stromal fibroblasts, capillary endothelium and macrophage populations. Pathway analysis and predicted dynamic cellular crosstalk suggest inflammatory signaling as the main driver of hyperoxia-induced changes. Our data provides a single-cell view of cellular changes associated with late lung development in health and disease. <<<

Joseph M. Replogle , Reuben A. Saunders , Angela N. Pogson , Jeffrey A. Hussmann , Alexander Lenail , Alina Guna , Lauren Mascibroda , Eric J. Wagner , Karen Adelman , Gila Lithwick-Yanai , Nika Iremadze , Florian Oberstrass , Doron Lipson , Jessica L. Bonnar , Marco Jost , Thomas M. Norman , Jonathan S. Weissman <<<

A central goal of genetics is to define the relationships between genotypes and phenotypes. High-content phenotypic screens such as Perturb-seq (CRISPR-based screens with single-cell RNA-sequencing readouts) enable massively parallel functional genomic mapping but, to date, have been used at limited scales. Here, we perform genome-scale Perturb-seq targeting all expressed genes with CRISPR interference (CRISPRi) across >2.5 million human cells. We use transcriptional phenotypes to predict the function of poorly characterized genes, uncovering new regulators of ribosome biogenesis (including CCDC86, ZNF236, and SPATA5L1), transcription (C7orf26), and mitochondrial respiration (TMEM242). In addition to assigning gene function, single-cell transcriptional phenotypes allow for in-depth dissection of complex cellular phenomena—from RNA processing to differentiation. We leverage this ability to systematically identify genetic drivers and consequences of aneuploidy and to discover an unanticipated layer of stress-specific regulation of the mitochondrial genome. Our information-rich genotype-phenotype map reveals a multidimensional portrait of gene and cellular function. <<<

Arash Jamshidi, Minetta C Liu, Eric A Klein, Oliver Venn, Earl Hubbell, John F Beausang, Samuel Gross, Collin Melton, Alexander P Fields, Qinwen Liu, Nan Zhang, Eric T Fung, Kathryn N Kurtzman, Hamed Amini, Craig Betts, Daniel Civello, Peter Freese, Robert Calef, Konstantin Davydov, Saniya Fayzullina, Chenlu Hou, Roger Jiang, Byoungsok Jung, Susan Tang, Vasiliki Demas, Joshua Newman, Onur Sakarya, Eric Scott, Archana Shenoy, Seyedmehdi Shojaee, Kristan K Steffen, Virgil Nicula, Tom C Chien, Siddhartha Bagaria, Nathan Hunkapiller, Mohini Desai, Zhao Dong, Donald A Richards, Timothy J Yeatman, Allen L Cohn, David D Thiel, Donald A Berry, Mohan K Tummala, Kristi McIntyre, Mikkael A Sekeres, Alan Bryce, Alexander M Aravanis, Michael V Seiden, Charles Swanton <<<

In the Circulating Cell-free Genome Atlas (NCT02889978) substudy 1, we evaluate several approaches for a circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) test by defining clinical limit of detection (LOD) based on circulating tumor allele fraction (cTAF), enabling performance comparisons. Among 10 machine-learning classifiers trained on the same samples and independently validated, when evaluated at 98% specificity, those using whole-genome (WG) methylation, single nucleotide variants with paired white blood cell background removal, and combined scores from classifiers evaluated in this study show the highest cancer signal detection sensitivities. Compared with clinical stage and tumor type, cTAF is a more significant predictor of classifier performance and may more closely reflect tumor biology. Clinical LODs mirror relative sensitivities for all approaches. The WG methylation feature best predicts cancer signal origin. WG methylation is the most promising technology for MCED and informs development of a targeted methylation MCED test. <<<

While spontaneous yawning is common across all vertebrate classes, contagious yawning is less common and has been observed only in a few species of social animals. Interspecific contagious yawning in response to yawning by humans has been observed only by chimpanzees and dogs. After confirming additional occurrences of intraspecific contagious yawning in a group of captive African elephants previously studied, we further investigated the potential for the same group of elephants to engage in interspecific contagious yawning with familiar human handlers. Ten captive African elephants, most of whom had been previously studied, were observed over 13 nights for evidence of intraspecific contagious yawning. Seven of these elephants were also involved in trials where familiar handlers performed staged yawns, as well as trials with staged non-yawning gapes, or trials with no yawns or gapes. Incorporating previously collected contagious yawning data, we describe nine instances of intraspecific contagious yawning in the elephants. Three of the seven elephants yawned contagiously in response to humans during the interspecific yawning trials. This is the first report of interspecific contagious yawning by elephants in response to yawns by familiar humans. <<<

Face processing supports our ability to recognize friend from foe, form tribes and understand the emotional implications of changes in facial musculature. This skill relies on a distributed network of brain regions, but how these regions interact is poorly understood. Here we integrate anatomical and functional connectivity measurements with behavioural assays to create a global model of the face connectome. We dissect key features, such as the network topology and fibre composition. We propose a neurocognitive model with three core streams; face processing along these streams occurs in a parallel and reciprocal manner. Although long-range fibre paths are important, the face network is dominated by short-range fibres. Finally, we provide evidence that the well-known right lateralization of face processing arises from imbalanced intra- and interhemispheric connections. In summary, the face network relies on dynamic communication across highly structured fibre tracts, enabling coherent face processing that underpins behaviour and cognition. <<<

Deep generative models have been an upsurge in the deep learning community since they were proposed. These models are designed for generating new synthetic data including images, videos and texts by fitting the data approximate distributions. In the last few years, deep generative models have shown superior performance in drug discovery especially de novo molecular design. In this study, deep generative models are reviewed to witness the recent advances of de novo molecular design for drug discovery. In addition, we divide those models into two categories based on molecular representations in silico. Then these two classical types of models are reported in detail and discussed about both pros and cons. We also indicate the current challenges in deep generative models for de novo molecular design. De novo molecular design automatically is promising but a long road to be explored. <<<

: The ability to engineer mammalian genomes in a quick and cost-effective way has led to rapid adaptation of CRISPR technology in biomedical research. CRISPR-based engineering has the potential to accelerate drug discovery, to support the reduction of high attrition rate in drug development and to enhance development of cell and gene-based therapies.: How CRISPR technology is transforming drug discovery is discussed in this review. From target identification to target validation in both and models, CRISPR technology is positively impacting the early stages of drug development by providing a straightforward way to genome engineering. This property also attracted attention for CRISPR application in the cell and gene therapy area.: CRISPR technology is rapidly becoming the preferred tool for genome engineering and nowadays it is hard to imagine the drug discovery pipeline without this technology. With the years to come, CRISPR technology will undoubtedly be further refined and will flourish into a mature technology that will play a key role in supporting genome engineering requirements in the drug discovery pipeline as well as in cell and gene therapy development. <<<

Deformable medical image registration is widely used in medical image processing with the invertible and one-to-one mapping between images. While state-of-the-art image registration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on computer vision tasks. Existing models neglect to employ attention mechanisms to handle the long-range cross-image relevance in embedding learning, limiting such approaches to identify the semantically meaningful correspondence of anatomical structures. These methods also ignore the topology preservation and invertibility of the transformation although they achieve fast image registration. In this paper, we propose a novel, symmetric unsupervised learning network Swin-VoxelMorph based on the Swin Transformer which minimizes the dissimilarity between images and estimates both forward and inverse transformations simultaneously. Specifically, we propose 3D Swin-UNet, which applies hierarchical Swin Transformer with shifted windows as the encoder to extract context features. And a symmetric Swin Transformer-based decoder with patch expanding layer is designed to perform the up-sampling operation to estimate the registration fields. Besides, our objective loss functions can guarantee substantial diffeomorphic properties of the predicted transformations. We verify our method on two datasets including ADNI and PPMI, and it achieves state-of-the-art registration accuracy while maintaining desirable diffeomorphic properties. <<<

Fengling Hu, Sarah M Weinstein, Erica B Baller, Alessandra M Valcarcel, Azeez Adebimpe, Armin Raznahan, David R Roalf, Timothy E Robert-Fitzgerald, Virgilio Gonzenbach, Ruben C Gur, Raquel E Gur, Simon Vandekar, John A Detre, Kristin A Linn, Aaron Alexander-Bloch, Theodore D Satterthwaite, Russell T Shinohara <<<

When individual subjects are imaged with multiple modalities, biological information is present not only within each modality, but also between modalities - that is, in how modalities covary at the voxel level. Previous studies have shown that local covariance structures between modalities, or intermodal coupling (IMCo), can be summarized for two modalities, and that two-modality IMCo reveals otherwise undiscovered patterns in neurodevelopment and certain diseases. However, previous IMCo methods are based on the slopes of local weighted linear regression lines, which are inherently asymmetric and limited to the two-modality setting. Here, we present a generalization of IMCo estimation which uses local covariance decompositions to define a symmetric, voxel-wise coupling coefficient that is valid for two or more modalities. We use this method to study coupling between cerebral blood flow, amplitude of low frequency fluctuations, and local connectivity in 803 subjects ages 8 through 22. We demonstrate that coupling is spatially heterogeneous, varies with respect to age and sex in neurodevelopment, and reveals patterns that are not present in individual modalities. As availability of multi-modal data continues to increase, principal-component-based IMCo (pIMCo) offers a powerful approach for summarizing relationships between multiple aspects of brain structure and function. An R package for estimating pIMCo is available at: https://github.com/hufengling/pIMCo. <<<