MagiCapture: High-Resolution Multi-Concept Portrait Customization

Large-scale text-to-image models including Stable Diffusion are capable of generating high-fidelity photorealistic portrait images. There is an active research area dedicated to personalizing these models, aiming to synthesize specific subjects or styles using provided sets of reference images. However, despite the plausible results from these personalization methods, they tend to produce images that often fall short of realism and are not yet on a commercially viable level. This is particularly noticeable in portrait image generation, where any unnatural artifact in human faces is easily discernible due to our inherent human bias. To address this, we introduce MagiCapture, a personalization method for integrating subject and style concepts to generate high-resolution portrait images using just a few subject and style references. For instance, given a handful of random selfies, our fine-tuned model can generate high-quality portrait images in specific styles, such as passport or profile photos. The main challenge with this task is the absence of ground truth for the composed concepts, leading to a reduction in the quality of the final output and an identity shift of the source subject. To address these issues, we present a novel Attention Refocusing loss coupled with auxiliary priors, both of which facilitate robust learning within this weakly supervised learning setting. Our pipeline also includes additional post-processing steps to ensure the creation of highly realistic outputs. MagiCapture outperforms other baselines in both quantitative and qualitative evaluations and can also be generalized to other non-human objects.Comment: 8 pages, 7 figure

A network-based comparative framework to study conservation and divergence of proteomes in plant phylogenies

Comparative functional genomics offers a powerful approach to study species evolution. To date, the majority of these studies have focused on the transcriptome in mammalian and yeast phylogenies. Here, we present a novel multi-species proteomic dataset and a computational pipeline to systematically compare the protein levels across multiple plant species. Globally we find that protein levels diverge according to phylogenetic distance but is more constrained than the mRNA level. Module-level comparative analysis of groups of proteins shows that proteins that are more highly expressed tend to be more conserved. To interpret the evolutionary patterns of conservation and divergence, we develop a novel network-based integrative analysis pipeline that combines publicly available transcriptomic datasets to define co-expression modules. Our analysis pipeline can be used to relate the changes in protein levels to different species-specific phenotypic traits. We present a case study with the rhizobia-legume symbiosis process that supports the role of autophagy in this symbiotic association

Co-Inheritance Analysis within the Domains of Life Substantially Improves Network Inference by Phylogenetic Profiling.

Phylogenetic profiling, a network inference method based on gene inheritance profiles, has been widely used to construct functional gene networks in microbes. However, its utility for network inference in higher eukaryotes has been limited. An improved algorithm with an in-depth understanding of pathway evolution may overcome this limitation. In this study, we investigated the effects of taxonomic structures on co-inheritance analysis using 2,144 reference species in four query species: Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, and Homo sapiens. We observed three clusters of reference species based on a principal component analysis of the phylogenetic profiles, which correspond to the three domains of life-Archaea, Bacteria, and Eukaryota-suggesting that pathways inherit primarily within specific domains or lower-ranked taxonomic groups during speciation. Hence, the co-inheritance pattern within a taxonomic group may be eroded by confounding inheritance patterns from irrelevant taxonomic groups. We demonstrated that co-inheritance analysis within domains substantially improved network inference not only in microbe species but also in the higher eukaryotes, including humans. Although we observed two sub-domain clusters of reference species within Eukaryota, co-inheritance analysis within these sub-domain taxonomic groups only marginally improved network inference. Therefore, we conclude that co-inheritance analysis within domains is the optimal approach to network inference with the given reference species. The construction of a series of human gene networks with increasing sample sizes of the reference species for each domain revealed that the size of the high-accuracy networks increased as additional reference species genomes were included, suggesting that within-domain co-inheritance analysis will continue to expand human gene networks as genomes of additional species are sequenced. Taken together, we propose that co-inheritance analysis within the domains of life will greatly potentiate the use of the expected onslaught of sequenced genomes in the study of molecular pathways in higher eukaryotes

Within-domain phylogenetic profiling improves the human co-functional network as more genomes are used.

<p>The construction of human co-functional networks with sub-sampling of the reference species genomes demonstrated that the size of the high-accuracy networks inferred by the within-domain co-inheritance analysis is directly proportional to the growth in the number of sequenced genomes in terms of both (A) the genome coverage and (B) the number of network links. Human gene networks were constructed using subsets of the reference genomes. First, networks were constructed for the Archaea, Bacteria, and Eukaryota domains separately using all genomes available for each domain (122, 1,626, and 396, respectively); next, three subsets of randomly selected genomes were used for network inference by phylogenetic profiling for each domain (sets of 15, 30, and 60 genomes for Archaea; 200, 400, and 800 genomes for Bacteria; and 50, 100, and 200 genomes for Eukaryota). The lines connect the median performance scores of the triplicated test results.</p

Network inference by co-inheritance analysis within sub-domain taxonomic groups.

<p>(A) The PCA biplot analysis for the 396 eukaryotic reference species revealed two clusters of reference species, one for an in-group kingdom and the other for out-group kingdoms, in the three eukaryotic query species. The description of these plots is the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139006#pone.0139006.g001" target="_blank">Fig 1</a>. (B) The performance curves of the networks inferred based on 396 eukaryotic reference species genomes, as for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139006#pone.0139006.g002" target="_blank">Fig 2C</a>. The networks inferred from a profile by an in-group kingdom, an out-group kingdom, a single profile of all the reference species (i.e., all-genomes profile), or by a divide-and-integrate approach with the two clusters are shown for each query species.</p

Network inference by within-domain co-inheritance analysis.

<p>(A) A schematic illustration of the three classes of co-inheritance patterns within the domains of life. Each rectangle represent the presence (filled) or absence (empty) of a homolog of the given query gene in the reference species. The presence of homologs might indicate that the ancestor of the query gene also was inherited in the reference species. If two query genes have been co-inherited in a reference species, then both of their homologs are present in the reference species. For example, gene A and B have been co-inherited in Archaea, but not in either Bacteria or Eukaryota. The co-inheritance patterns between A and B, C and D, and E and F are evident only within specific domains (Archaea, Bacteria, and Eukaryota, respectively). (B) The co-functional networks inferred by within-domain co-inheritance analysis in the four query species. The network in red was inferred by co-inheritance analysis within archaeal species only, the network in green within bacterial species only, the network in blue within eukaryotic species only, and the network in black within all species. Note that most links were inferred by co-inheritance analysis within each domain. (C) The performance curves of networks inferred by phylogenetic profiling in four species. The accuracy of each network is depicted by the <i>precision</i> for the given coverage of the coding genome. For each query species, the co-functional networks inferred from a profile consisting of the Archaea, Bacteria, or Eukaryota genomes; a profile of all the reference species (All-species); or by integrating the three networks inferred from each domain-specific profile (Divide-and-integrate) are shown. The divide-and-integrate network outperformed the other networks in all the query species. In contrast, the network inferred from the all-genomes profile performed poorly, especially in higher eukaryotes.</p

Sources of protein sequence data used in this study.

<p>Sources of protein sequence data used in this study.</p

3D Imaging of Human Chromosome at Nanoscale Using Coherent X-Rays

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Pathway-specific protein domains are predictive for human diseases.

Protein domains are basic functional units of proteins. Many protein domains are pervasive among diverse biological processes, yet some are associated with specific pathways. Human complex diseases are generally viewed as pathway-level disorders. Therefore, we hypothesized that pathway-specific domains could be highly informative for human diseases. To test the hypothesis, we developed a network-based scoring scheme to quantify specificity of domain-pathway associations. We first generated domain profiles for human proteins, then constructed a co-pathway protein network based on the associations between domain profiles. Based on the score, we classified human protein domains into pathway-specific domains (PSDs) and non-specific domains (NSDs). We found that PSDs contained more pathogenic variants than NSDs. PSDs were also enriched for disease-associated mutations that disrupt protein-protein interactions (PPIs) and tend to have a moderate number of domain interactions. These results suggest that mutations in PSDs are likely to disrupt within-pathway PPIs, resulting in functional failure of pathways. Finally, we demonstrated the prediction capacity of PSDs for disease-associated genes with experimental validations in zebrafish. Taken together, the network-based quantitative method of modeling domain-pathway associations presented herein suggested underlying mechanisms of how protein domains associated with specific pathways influence mutational impacts on diseases via perturbations in within-pathway PPIs, and provided a novel genomic feature for interpreting genetic variants to facilitate the discovery of human disease genes