Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions.

We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host-microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network's topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/ .

Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions.

Technological advances have generated tremendous amounts of high-throughput omics data. Integrating data from multiple cohorts and diverse omics types from new and previously published studies can offer a holistic view of a biological system and aid in deciphering its critical players and key mechanisms. In this protocol, we describe how to use Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that can perform meta-analysis of cohorts and detect master regulators among measured parameters that govern pathological or physiological responses of host-microbiota (or any multi-omic data) interactions in a particular condition or disease. TkNA first reconstructs the network that represents a statistical model capturing the complex relationships between the different omics of the biological system. Here, it selects differential features and their per-group correlations by identifying robust and reproducible patterns of fold change direction and sign of correlation across several cohorts. Next, a causality-sensitive metric, statistical thresholds, and a set of topological criteria are used to select the final edges that form the transkingdom network. The second part of the analysis involves interrogating the network. Using the network's local and global topology metrics, it detects nodes that are responsible for control of given subnetwork or control of communication between kingdoms and/or subnetworks. The underlying basis of the TkNA approach involves fundamental principles including laws of causality, graph theory and information theory. Hence, TkNA can be used for causal inference via network analysis of any host and/or microbiota multi-omics data. This quick and easy-to-run protocol requires very basic familiarity with the Unix command-line environment.

Miniaturization of Non-Assembly Metallic Pin-Joints by LPBF-Based Additive Manufacturing as Perfect Pivots for Pantographic Metamaterials.

This work introduced additively manufactured non-assembly, miniaturized pin-joints for pantographic metamaterials as perfect pivots. The titanium alloy Ti6Al4V was utilized with laser powder bed fusion technology. The pin-joints were produced using optimized process parameters required for manufacturing miniaturized joints, and they were printed at a particular angle to the build platform. Additionally, this process optimization will eliminate the requirement to geometrically compensate the computer-aided design model, allowing for even further miniaturization. In this work, pin-joint lattice structures known as pantographic metamaterials were taken into consideration. The mechanical behavior of the metamaterial was characterized by bias extension tests and cyclic fatigue experiments, showing superior levels of performance (no sign of fatigue for 100 cycles of an elongation of approximately 20%) in comparison to classic pantographic metamaterials made with rigid pivots. The individual pin-joints, with a pin diameter of 350 to 670 µm, were analyzed using computed tomography scans, indicating that the mechanism of the rotational joint functions well even though the clearance of 115 to 132 µm between the moving parts is comparable to the nominal spatial resolution of the printing process. Our findings emphasize new possibilities to develop novel mechanical metamaterials with actual moving joints on a small scale. The results will also support stiffness-optimized metamaterials with variable-resistance torque for non-assembly pin-joints in the future.