The role of FTO, a human RNA demethylase in perennial grass development and abiotic stress responses

The integration of the human fat mass and obesity associated (FTO) gene into turfgrass is a novel approach at improving cell proliferation and abiotic stress resistance. The FTO protein is an RNA demethylase responsible for epigenetic regulation of the genome. In related rice, the gene is associated with increased crop yield, tiller number, and aerial biomass. It is proposed to work via demethylation of repeat RNA associated with chromatin remodeling, causing widespread transcriptional activation. In this study, the feasibility of using FTO for plant trait modification in perennial grasses is being investigated. Potentially transformed embryogenic calli of creeping bentgrass with FTO gene have been developed and are awaiting regeneration into plants, which can be assessed for FTO gene expression as well as abiotic stress resistance. Responsible application of transgenic FTO turfgrass is also being explored via a novel sterility mechanism that involves knockout of the gene responsible for flowering

Empirical Phase Diagram Approach Toward Biophysical Characterization of Vaccine Candidates Against Shigella, Salmonella and Yersinia

Infectious diarrhea is an important public health problem and a major cause of morbidity all over the world. Infants and young children are the most vulnerable group. Vaccination is one of the most important public health tools for the prevention of infectious diseases, however, formulation of safe and effective vaccines against diarrheal disease can be challenging. Shigella, Salmonella and Yersinia enterocolitica are three highly virulent pathogens in urgent need of vaccine development. A common virulence factor is utilized by these three pathogens, the type III secretion system (T3SS), which is highly conserved across multiple serotypes within these groups. With the discovery that the Shigella T3SS proteins IpaD and IpaB are protective antigens, we constructed a novel IpaD-IpaB fusion protein to simplify the production and reduce the cost of vaccine production. Because of its hydrophobic IpaB portion, the DB fusion needs detergent to maintain solubility. A mild detergent called LDAO was identified and showed great promise for protein stabilization when compared to the detergent used previously (called OPOE). Inspired by the success of the DB fusion, we constructed a SipDB fusion using the homologous Salmonella T3SS proteins SipD and SipB. In addition to exploring the fusion strategy with regard to anti-Shigella and anti-Salmonella vaccines, a relatively new antigen delivery system called Bacterium-Like Particles (BLPs) was also explored and formulated as a potential means for delivering protective antigens from Shigella, Salmonella and Yersinia enterocolitica (LcrV and YopB). Derived from Lactococcus lactis, BLPs are peptidoglycan skeletons that are safe for newborns and can carry multiple antigens on their surface. The T3SS proteins were fused with a protein anchor domain for BLP attachment. The constructed fusion vaccine candidates and BLP-based vaccine candidates were biophysically characterized using multiple techniques including circular dichroism spectroscopy, intrinsic fluorescence spectroscopy, and static light scattering which allowed measuring the secondary, tertiary and quaternary structural changes, respectively, as a function of environmental stress. The resulting large dataset was summarized using a three-index empirical phase diagram (EPD), which is a colored representation of the overall structural integrity and conformational stability of the vaccine candidates in response to environmental conditions. The information acquired is used for identifying favorable states of protein and the proper detergent to be used for the formulation of the resulting vaccines. This approach can also be used in further studies on excipient screening for stabilizing final vaccine products, though that work was not done here.Microbiology, Cell, & Molecular Biolog

TE2Rules: Extracting Rule Lists from Tree Ensembles

Tree Ensemble (TE) models (e.g. Gradient Boosted Trees and Random Forests) often provide higher prediction performance compared to single decision trees. However, TE models generally lack transparency and interpretability, as humans have difficulty understanding their decision logic. This paper presents a novel approach to convert a TE trained for a binary classification task, to a rule list (RL) that is a global equivalent to the TE and is comprehensible for a human. This RL captures all necessary and sufficient conditions for decision making by the TE. Experiments on benchmark datasets demonstrate that, compared to state-of-the-art methods, (i) predictions from the RL generated by TE2Rules have high fidelity with respect to the original TE, (ii) the RL from TE2Rules has high interpretability measured by the number and the length of the decision rules, (iii) the run-time of TE2Rules algorithm can be reduced significantly at the cost of a slightly lower fidelity, and (iv) the RL is a fast alternative to the state-of-the-art rule-based instance-level outcome explanation techniques

Assessing reliability of protein-protein interactions by integrative analysis of data in model organisms

Background: Protein-protein interactions play vital roles in nearly all cellular processes and are involved in the construction of biological pathways such as metabolic and signal transduction pathways. Although large-scale experiments have enabled the discovery of thousands of previously unknown linkages among proteins in many organisms, the high-throughput interaction data is often associated with high error rates. Since protein interaction networks have been utilized in numerous biological inferences, the inclusive experimental errors inevitably affect the quality of such prediction. Thus, it is essential to assess the quality of the protein interaction data. Results: In this paper, a novel Bayesian network-based integrative framework is proposed to assess the reliability of protein-protein interactions. We develop a cross-species in silico model that assigns likelihood scores to individual protein pairs based on the information entirely extracted from model organisms. Our proposed approach integrates multiple microarray datasets and novel features derived from gene ontology. Furthermore, the confidence scores for cross-species protein mappings are explicitly incorporated into our model. Applying our model to predict protein interactions in the human genome, we are able to achieve 80% in sensitivity and 70% in specificity. Finally, we assess the overall quality of the experimentally determined yeast protein-protein interaction dataset. We observe that the more high-throughput experiments confirming an interaction, the higher the likelihood score, which confirms the effectiveness of our approach. Conclusion: This study demonstrates that model organisms certainly provide important information for protein-protein interaction inference and assessment. The proposed method is able to assess not only the overall quality of an interaction dataset, but also the quality of individual protein-protein interactions. We expect the method to continually improve as more high quality interaction data from more model organisms becomes available and is readily scalable to a genome-wide application

Which Positive Feedback Matters? The Role of Language Concreteness and Temporal Effect in Continuous Contribution in Open Innovation Community

The feedback mechanism is the basis for motivating users to make continuous contributions in the Open Innovation Community (OIC). Although previous studies have revealed the overall role of positive feedback in promoting continuous user contribution, it is not clear which type of positive feedback is more effective and how it changes over time. To solve these problems, we constructed a research model based on reinforcement theory and took Lego Ideas, a typical OIC, as the research object to crawl users’ ideas and feedback data for empirical analysis. The results confirmed the effect of positive feedback and further demonstrated that, the effectiveness of positive feedback varies based on feedback concreteness and the tenure of the focal user. Our study contributes to the literature on how feedback affects user contributions in online communities by refining the classifications of feedback, and provide practical guidance for companies to motivate users to contributing ideas continuously

Voter Coalitions in Decentralized Autonomous Organization (DAO): Evidence from MakerDAO

Decentralized Autonomous Organization (DAO) provides a decentralized governance solution through blockchain, where decision-making process relies on on-chain voting and follows majority rule. This paper focuses on MakerDAO, and we find five voter coalitions after applying clustering algorithm to voting history. The emergence of a dominant voter coalition is a signal of governance centralization in DAO, and voter coalitions have complicated influence on Maker protocol, which is governed by MakerDAO. This paper presents empirical evidence of multicoalition democracy in DAO and further contributes to the contemporary debate on whether decentralized governance is possible

An Operational Perspective to Fairness Interventions: Where and How to Intervene

As AI-based decision systems proliferate, their successful operationalization requires balancing multiple desiderata: predictive performance, disparity across groups, safeguarding sensitive group attributes (e.g., race), and engineering cost. We present a holistic framework for evaluating and contextualizing fairness interventions with respect to the above desiderata. The two key points of practical consideration are \emph{where} (pre-, in-, post-processing) and \emph{how} (in what way the sensitive group data is used) the intervention is introduced. We demonstrate our framework with a case study on predictive parity. In it, we first propose a novel method for achieving predictive parity fairness without using group data at inference time via distibutionally robust optimization. Then, we showcase the effectiveness of these methods in a benchmarking study of close to 400 variations across two major model types (XGBoost vs. Neural Net), ten datasets, and over twenty unique methodologies. Methodological insights derived from our empirical study inform the practical design of ML workflow with fairness as a central concern. We find predictive parity is difficult to achieve without using group data, and despite requiring group data during model training (but not inference), distributionally robust methods we develop provide significant Pareto improvement. Moreover, a plain XGBoost model often Pareto-dominates neural networks with fairness interventions, highlighting the importance of model inductive bias