The role of a novel secretory peptidoglycan recognition protein with antibacterial ability from the Chinese Oak Silkworm Antheraea pernyi in humoral immunity.

Peptidoglycan recognition proteins (PGRPs) are a family of pattern recognition receptors that play a critical role in the immune response of invertebrates and vertebrates. Herein, the short ApPGRP-D gene was cloned from the model lepidopteran Antheraea pernyi. Quantitative PCR (qPCR) confirmed that ApPGRP-D is an immune-related protein and that the expression of ApPGRP-D can be induced by microorganisms. ApPGRP-D is a broad-spectrum pattern recognition protein that activates the prophenoloxidase cascade activation system and promotes the agglutination of microbial cells. Likely due to its amidase activity, ApPGRP-D can inhibit the growth of E. coli and S. aureus. In addition, we demonstrated for the first time that zinc ions, as important metal coenzymes, could promote multiple functions of ApPGRP-D but not its amidase activity.

Reconstructing Sparse Multiplex Networks with Application to Covert Networks.

Network structure provides critical information for understanding the dynamic behavior of complex systems. However, the complete structure of real-world networks is often unavailable, thus it is crucially important to develop approaches to infer a more complete structure of networks. In this paper, we integrate the configuration model for generating random networks into an Expectation-Maximization-Aggregation (EMA) framework to reconstruct the complete structure of multiplex networks. We validate the proposed EMA framework against the Expectation-Maximization (EM) framework and random model on several real-world multiplex networks, including both covert and overt ones. It is found that the EMA framework generally achieves the best predictive accuracy compared to the EM framework and the random model. As the number of layers increases, the performance improvement of EMA over EM decreases. The inferred multiplex networks can be leveraged to inform the decision-making on monitoring covert networks as well as allocating limited resources for collecting additional information to improve reconstruction accuracy. For law enforcement agencies, the inferred complete network structure can be used to develop more effective strategies for covert network interdiction.

OptoGap is an optogenetics-enabled assay for quantification of cell-cell coupling in multicellular cardiac tissue.

Intercellular electrical coupling is an essential means of communication between cells. It is important to obtain quantitative knowledge of such coupling between cardiomyocytes and non-excitable cells when, for example, pathological electrical coupling between myofibroblasts and cardiomyocytes yields increased arrhythmia risk or during the integration of donor (e.g., cardiac progenitor) cells with native cardiomyocytes in cell-therapy approaches. Currently, there is no direct method for assessing heterocellular coupling within multicellular tissue. Here we demonstrate experimentally and computationally a new contactless assay for electrical coupling, OptoGap, based on selective illumination of inexcitable cells that express optogenetic actuators and optical sensing of the response of coupled excitable cells (e.g., cardiomyocytes) that are light-insensitive. Cell-cell coupling is quantified by the energy required to elicit an action potential via junctional current from the light-stimulated cell(s). The proposed technique is experimentally validated against the standard indirect approach, GapFRAP, using light-sensitive cardiac fibroblasts and non-transformed cardiomyocytes in a two-dimensional setting. Its potential applicability to the complex three-dimensional setting of the native heart is corroborated by computational modelling and proper calibration. Lastly, the sensitivity of OptoGap to intrinsic cell-scale excitability is robustly characterized via computational analysis.

Semi-Analytical Model Based on the Volumetric Source Method to Production Simulation from Nonplanar Fracture Geometry in Tight Oil Reservoirs.

Fracturing measures are common practice for horizontal wells of tight oil reservoirs. Thus, production estimation is a significant problem that should be solved. However, previous models for the production of fractured horizontal wells of tight oil reservoirs have some problems. In this paper, we present a semi-analytical model based on the volumetric source method to simulate production from nonplanar fracture geometry in a tight oil reservoir. First, we developed an analytical model based on the volumetric source method in nonplanar fracture geometry with varying widths. Second, the model was coupled with fracture flow and solved by the Gauss-Seidel iteration. Third, the semi-analytical model was verified by a numerical reservoir simulator. Finally, sensitivity analysis was conducted for several critical parameters. Results of validations showed good agreement between this paper's model and the numerical reservoir simulator. The results from the sensitivity analysis showed that (1) production increases with an increased number of fracture segments; (2) production drops more quickly with a smaller fracture half-length in the first stage, and it drops slowly with a smaller fracture half-length in the second stage; (3) cumulative production increases more quickly with a bigger fracture conductivity; and (4) cumulative oil production from a fracture with a constant width and without stress sensitivity coefficient is smaller than that from a fracture with varying widths and with stress sensitivity coefficient. This research provides a basis and reference for production estimation in tight oil reservoirs.

Enzymatic-based cytometry, a sensitive single-cell cytometric method to assess BCR-ABL1 activity in CML.

We developed a simple, rapid and cost-effective enzymatic-based cytometry platform to measure intracellular signaling pathway activity. Our single-cell microwell array platform quantifies protein phosphorylation using enzymatic signal amplification and exploiting Michaelis-Menten kinetics. Our method provides a two-fold increase in resolution compared to conventional flow cytometry.

Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages.

The ability to accurately measure recovery rate of infrastructure systems and communities impacted by disasters is vital to ensure effective response and resource allocation before, during, and after a disruption. However, a challenge in quantifying such measures resides in the lack of data as community recovery information is seldom recorded. To provide accurate community recovery measures, a hierarchical Bayesian kernel model (HBKM) is developed to predict the recovery rate of communities experiencing power outages during storms. The performance of the proposed method is evaluated using cross-validation and compared with two models, the hierarchical Bayesian regression model and the Poisson generalized linear model. A case study focusing on the recovery of communities in Shelby County, Tennessee after severe storms between 2007 and 2017 is presented to illustrate the proposed approach. The predictive accuracy of the models is evaluated using the log-likelihood and root mean squared error. The HBKM yields on average the highest out-of-sample predictive accuracy. This approach can help assess the recoverability of a community when data are scarce and inform decision making in the aftermath of a disaster. An illustrative example is presented demonstrating how accurate measures of community resilience can help reduce the cost of infrastructure restoration.

OptoDyCE as an automated system for high-throughput all-optical dynamic cardiac electrophysiology.

The improvement of preclinical cardiotoxicity testing, discovery of new ion-channel-targeted drugs, and phenotyping and use of stem cell-derived cardiomyocytes and other biologics all necessitate high-throughput (HT), cellular-level electrophysiological interrogation tools. Optical techniques for actuation and sensing provide instant parallelism, enabling contactless dynamic HT testing of cells and small-tissue constructs, not affordable by other means. Here we show, computationally and experimentally, the limits of all-optical electrophysiology when applied to drug testing, then implement and validate OptoDyCE, a fully automated system for all-optical cardiac electrophysiology. We validate optical actuation by virally introducing optogenetic drivers in rat and human cardiomyocytes or through the modular use of dedicated light-sensitive somatic 'spark' cells. We show that this automated all-optical approach provides HT means of cellular interrogation, that is, allows for dynamic testing of >600 multicellular samples or compounds per hour, and yields high-content information about the action of a drug over time, space and doses.

Inscribing Optical Excitability to Non-Excitable Cardiac Cells: Viral Delivery of Optogenetic Tools in Primary Cardiac Fibroblasts.

We describe in detail a method to introduce optogenetic actuation tools, a mutant version of channelrhodopsin-2, ChR2(H134R), and archaerhodopsin (ArchT), into primary cardiac fibroblasts (cFB) in vitro by adenoviral infection to yield quick, robust, and consistent expression. Instructions on adjusting infection parameters such as the multiplicity of infection and virus incubation duration are provided to generalize the method for different lab settings or cell types. Specific conditions are discussed to create hybrid co-cultures of the optogenetically modified cFB and non-transformed cardiomyocytes to obtain light-sensitive excitable cardiac syncytium, including stencil-patterned cell growth. We also describe an all-optical framework for the functional testing of responsiveness of these opsins in cFB. The presented methodology provides cell-specific tools for the mechanistic investigation of the functional bioelectric contribution of different non-excitable cells in the heart and their electrical coupling to cardiomyocytes under different conditions.

Cardiac Optogenetics: Enhancement by All-trans-Retinal.

All-trans-Retinal (ATR) is a photosensitizer, serving as the chromophore for depolarizing and hyperpolarizing light-sensitive ion channels and pumps (opsins), recently employed as fast optical actuators. In mammalian optogenetic applications (in brain and heart), endogenous ATR availability is not considered a limiting factor, yet it is unclear how ATR modulation may affect the response to optical stimulation. We hypothesized that exogenous ATR may improve light responsiveness of cardiac cells modified by Channelrhodopsin2 (ChR2), hence lowering the optical pacing energy. In virally-transduced (Ad-ChR2(H134R)-eYFP) light-sensitive cardiac syncytium in vitro, ATR supplements ≤2 µM improved cardiomyocyte viability and augmented ChR2 membrane expression several-fold, while >4 µM was toxic. Employing integrated optical actuation (470 nm) and optical mapping, we found that 1-2 µM ATR dramatically reduced optical pacing energy (over 30 times) to several µW/mm(2), lowest values reported to date, but also caused action potential prolongation, minor changes in calcium transients and no change in conduction. Theoretical analysis helped explain ATR-caused reduction of optical excitation threshold in cardiomyocytes. We conclude that cardiomyocytes operate at non-saturating retinal levels, and carefully-dosed exogenous ATR can enhance the performance of ChR2 in cardiac cells and yield energy benefits over orders of magnitude for optogenetic stimulation.

Cardiac applications of optogenetics.

In complex multicellular systems, such as the brain or the heart, the ability to selectively perturb and observe the response of individual components at the cellular level and with millisecond resolution in time, is essential for mechanistic understanding of function. Optogenetics uses genetic encoding of light sensitivity (by the expression of microbial opsins) to provide such capabilities for manipulation, recording, and control by light with cell specificity and high spatiotemporal resolution. As an optical approach, it is inherently scalable for remote and parallel interrogation of biological function at the tissue level; with implantable miniaturized devices, the technique is uniquely suitable for in vivo tracking of function, as illustrated by numerous applications in the brain. Its expansion into the cardiac area has been slow. Here, using examples from published research and original data, we focus on optogenetics applications to cardiac electrophysiology, specifically dealing with the ability to manipulate membrane voltage by light with implications for cardiac pacing, cardioversion, cell communication, and arrhythmia research, in general. We discuss gene and cell delivery methods of inscribing light sensitivity in cardiac tissue, functionality of the light-sensitive ion channels within different types of cardiac cells, utility in probing electrical coupling between different cell types, approaches and design solutions to all-optical electrophysiology by the combination of optogenetic sensors and actuators, and specific challenges in moving towards in vivo cardiac optogenetics.