Conditional Creation and Rescue of Nipbl-Deficiency in Mice Reveals Multiple Determinants of Risk for Congenital Heart Defects

Elucidating the causes of congenital heart defects is made difficult by the complex morphogenesis of the mammalian heart, which takes place early in development, involves contributions from multiple germ layers, and is controlled by many genes. Here, we use a conditional/invertible genetic strategy to identify the cell lineage(s) responsible for the development of heart defects in a Nipbl-deficient mouse model of Cornelia de Lange Syndrome, in which global yet subtle transcriptional dysregulation leads to development of atrial septal defects (ASDs) at high frequency. Using an approach that allows for recombinase-mediated creation or rescue of Nipbl deficiency in different lineages, we uncover complex interactions between the cardiac mesoderm, endoderm, and the rest of the embryo, whereby the risk conferred by genetic abnormality in any one lineage is modified, in a surprisingly non-additive way, by the status of others. We argue that these results are best understood in the context of a model in which the risk of heart defects is associated with the adequacy of early progenitor cell populations relative to the sizes of the structures they must eventually form

Buildability of Mortar Feedstock in Material Extrusion Additive Manufacturing

Mortar feedstock is extruded to form bead and it is selectively placed line by line in the material extrusion additive manufacturing. With respects to part building process healthiness, load-supporting ability of overlaid beads is emphasized as buildability. Buildability is primarily dependent on thixotropic properties of feedstock and vertical overlapping schedule. In the present study, water-to-binder (w/b) ratio was chosen as material aspect to assess buildability. Uneven bead shape evolution and premature failure were highlighted owing to low yield stress of high w/b ratio feedstock. Feedstock with optimum w/b ratio showed good buildability even at the interval time of 19 sec

C2L: Causally Contrastive Learning for Robust Text Classification

Despite the super-human accuracy of recent deep models in NLP tasks, their robustness is reportedly limited due to their reliance on spurious patterns. We thus aim to leverage contrastive learning and counterfactual augmentation for robustness. For augmentation, existing work either requires humans to add counterfactuals to the dataset or machines to automatically matches near-counterfactuals already in the dataset. Unlike existing augmentation is affected by spurious correlations, ours, by synthesizing “a set” of counterfactuals, and making a collective decision on the distribution of predictions on this set, can robustly supervise the causality of each term. Our empirical results show that our approach, by collective decisions, is less sensitive to task model bias of attribution-based synthesis, and thus achieves significant improvements, in diverse dimensions: 1) counterfactual robustness, 2) cross-domain generalization, and 3) generalization from scarce data

Counterfactual Generative Smoothing for Imbalanced Natural Language Classification

© 2021 ACM.Classification datasets are often biased in observations, leaving onlya few observations for minority classes. Our key contribution is de-tecting and reducing Under-represented (U-) and Over-represented(O-) artifacts from dataset imbalance, by proposing a Counterfac-tual Generative Smoothing approach on both feature-space anddata-space, namely CGS_f and CGS_d. Our technical contribution issmoothing majority and minority observations, by sampling a ma-jority seed and transferring to minority. Our proposed approachesnot only outperform state-of-the-arts in both synthetic and real-lifedatasets, they effectively reduce both artifact types.N

Drug repositioning for enzyme modulator based on human metabolite-likeness

Abstract Background Recently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme’s metabolites and drugs. Methods We constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden’s index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness. Results In this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for their corresponding metabolites. In addition, we showed that drug repositioning results of 10 enzymes were concordant with the literature evidence. Conclusions This study introduced a method to predict the repositioning of known drugs to possible modulators of disease associated enzymes using human metabolite-likeness. We demonstrated that this approach works correctly with known antimetabolite drugs and showed that the proposed method has better performance compared to other drug target prediction methods in terms of enzyme modulators prediction. This study as a proof-of-concept showed how to apply metabolite-likeness to drug repositioning as well as potential in further expansion as we acquire more disease associated metabolite-target protein relations

Improving Printability of Digital-Light-Processing 3D Bioprinting via Photoabsorber Pigment Adjustment

Digital-light-processing (DLP) three-dimensional (3D) bioprinting, which has a rapid printing speed and high precision, requires optimized biomaterial ink to ensure photocrosslinking for successful printing. However, optimization studies on DLP bioprinting have yet to sufficiently explore the measurement of light exposure energy and biomaterial ink absorbance controls to improve the printability. In this study, we synchronized the light wavelength of the projection base printer with the absorption wavelength of the biomaterial ink. In this paper, we provide a stepwise explanation of the challenges associated with unsynchronized absorption wavelengths and provide appropriate examples. In addition to biomaterial ink wavelength synchronization, we introduce photorheological measurements, which can provide optimized light exposure conditions. The photorheological measurements provide precise numerical data on light exposure time and, therefore, are an effective alternative to the expendable and inaccurate conventional measurement methods for light exposure energy. Using both photorheological measurements and bioink wavelength synchronization, we identified essential printability optimization conditions for DLP bioprinting that can be applied to various fields of biological sciences

Enzyme-mediated film formation of melanin-like species from ortho-diphenols: Application to single-cell nanoencapsulation

Single-cell nanoencapsulation (SCNE) is a nanoarchitectonic strategy for creating cell-in-shell structures, in which the artificial shells, formed on individual living cells, protect the cells inside from otherwise lethal factors and also potentially provide them with advanced functions, such as exogenous biochemical reactions that are not attainable in wild-type cells. This work investigated enzymatic cascade systems for widening the substrate scope, beyond catecholamines, in the in-vitro formation of melanin-like films and shells and, ultimately, providing advanced building blocks and tools to the field of SCNE, inspired by the enzyme-derived structural diversification of melanin found in nature. The combination of glucose oxidase and horseradish peroxidase enabled the facilitated film formation of amine group-absent ortho-diphenols, such as protocatechuic aldehyde (PCA) and pyrocatechol. As a proof-of-demonstration, the developed reaction protocol was applied to the cytocompatible SCNE of Saccharomyces cerevisiae