Ultra-Tough Self-Healing Hydrogel via Hierarchical Energy Associative Dissipation

Owing to high water content and homogeneous texture, conventional hydrogels hardly reach satisfactory mechanical performance. Tensile-resistant groups and structural heterogeneity are employed to fabricate tough hydrogels. However, those techniques significantly increase the complexity and cost of material synthesis, and have only limited applicability. Here, it is shown that ultra-tough hydrogels can be obtained via a unique hierarchical architecture composed of chemically coupled self-assembly units. The associative energy dissipation among them may be rationally engineered to yield libraries of tough gels with self-healing capability. Tunable tensile strength, fracture strain, and toughness of up to 19.6 MPa, 20 000%, and 135.7 MJ cm⁻3 are achieved, all of which exceed the best known records. The results demonstrate a universal strategy to prepare desired ultra-tough hydrogels in predictable and controllable manners.

A Hierarchical Network with Fault Tolerance by a Multi-Factor Method for Neighborhood Area Network in Smart Grid

The neighborhood area network of a smart grid usually has hierarchical wireless communication. Due to forwarding and processing more data, the upper-layer nodes are more likely to suffer congestion and energy exhaustion. This phenomenon leads to the failure of uploading data to the control center. To solve this problem, this paper proposes a scheme for constructing a multi-factor fault-tolerant hierarchical network. This scheme firstly defines a criterion for the generation of redundant links by multi-factor method in a hierarchical wireless network with the characteristics of the neighborhood area network. Then the redundant links are used to reconstruct the existing topology of the neighborhood area network for improving fault tolerance. Finally, a greedy routing algorithm is put forward to select a proper data transmission path by bypassing low energy nodes, further reducing the failure of uploading data to the control center. The simulation results show that the proposed scheme can effectively improve the fault tolerance of the network topology of the wireless neighborhood area network and balance the network energy consumption. Compared with the original scheme, the proposed scheme improves the fault tolerance by 35% and the relative transmission rate by 21%.

Network medicine in ovarian cancer: topological properties to drug discovery

Network medicine provides network theoretical tools, methods and properties to study underlying laws governing human interactome to identify disease states and disease complexity leading to drug discovery. Within this framework, we investigated the topological properties of ovarian cancer network (OCN) and the roles of hubs to understand OCN organization to address disease states and complexity. The OCN constructed from the experimentally verified genes exhibits fractal nature in the topological properties with deeply rooted functional communities indicating self-organizing behavior. The network properties at all levels of organization obey one parameter scaling law which lacks centrality lethality rule. We showed that $\langle k\rangle $ can be taken as a scaling parameter, where, power law exponent can be estimated from the ratio of network diameters. The betweenness centrality $C_B$ shows two distinct behaviors one shown by high degree hubs and the other by segregated low degree nodes. The $C_B$ power law exponent is found to connect the exponents of distributions of high and low degree nodes. OCN showed the absence of rich-club formation which leads to the missing of a number of attractors in the network causing formation of weakly tied diverse functional modules to keep optimal network efficiency. In OCN, provincial and connector hubs, which includes identified key regulators, take major responsibility to keep the OCN integrity and organization. Further, most of the key regulators are found to be over expressed and positively correlated with immune infiltrates. Finally, few potential drugs are identified related to the key regulators.

3D-Printable Hierarchical Nanogel-GelMA Composite Hydrogel System

The strength of the extracellular matrix (ECM) is that it is hierarchical in terms of matrix built-up, matrix density and fiber structure, which allows for hormones, cytokines, and other small biomolecules to be stored within its network. The ECM-like hydrogels that are currently used do not possess this ability, and long-term storage, along with the need for free diffusion of small molecules, are generally incompatible requirements. Nanogels are able to fulfill the additional requirements upon successful integration. Herein, a stable hierarchical nanogel–gelatin methacryloyl (GelMA) composite hydrogel system is provided by covalently embedding nanogels inside the micropore network of GelMA hydrogel to allow a controlled local functionality that is not found in a homogenous GelMA hydrogel. Nanogels have emerged as a powerful tool in nanomedicine and are highly versatile, due to their simplicity of chemical control and biological compatibility. In this study, an N-isopropylacrylamide-based nanogel with primary amine groups on the surface was modified with methacryloyl groups to obtain a photo-cross-linking ability similar to GelMA. The nanogel-GelMA composite hydrogel was formed by mixing the GelMA and the photo-initiator within the nanogel solution through UV irradiation. The morphology of the composite hydrogel was observed by scanning electron microscopy, which clearly showed the nanogel wrapped within the GelMA network and covering the surface of the pore wall. A release experiment was conducted to prove covalent bonding and the stability of the nanogel inside the GelMA hydrogel. In addition, 3D printability studies showed that the nanogel-GelMA composite ink is printable. Therefore, the suggested stable hierarchical nanogel-GelMA composite hydrogel system has great potential to achieve the in situ delivery and controllable release of bioactive molecules in 3D cell culture systems.

Porous Alumina Ceramics with Multimodal Pore Size Distributions

Pore networks with multimodal pore size distributions combining advantages from isotropic and anisotropic shaped pores of different sizes are highly attractive to optimize the physical properties of porous ceramics. Multimodal porous Al₂O₃ ceramics were manufactured using pyrolyzed cellulose fibers (l = 150 µm, d = 8 µm) and two types of isotropic phenolic resin spheres (d = 30 and 300 µm) as sacrificial templates. The sacrificial templates were homogeneously distributed in the Al₂O₃ matrix, compacted by uniaxial pressing and extracted by a burnout and sintering process up to 1700 °C in air. The amount of sacrificial templates was varied up to a volume content of 67 Vol% to form pore networks with porosities of 0-60 Vol%. The mechanical and thermal properties were measured by 4-point-bending and laser flash analysis (LFA) resulting in bending strengths of 173 MPa to 14 MPa and heat conductivities of 22.5 Wm^-1K^-1 to 4.6 Wm^-1K^-1. Based on µCT-measurements, the representative volume-of-interest (VOI) of the samples digital twin was determined for further analysis. The interconnectivity, tortuosity, permeability, the local and global stress distribution as well as strut and cell size distribution were evaluated on the digital twin's VOI. Based on the experimental and simulation results, the samples pore network can be tailored by changing the fiber to sphere ratio and the overall sacrificial template volume. The presence pore formers significantly influenced the mechanical and thermal properties, resulting in higher strengths for samples containing fibrous templates and lower heat conductivities for samples containing spherical templates.

Privacy-preserving model learning on a blockchain network-of-networks

Objective

To facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a “flattened” topology, while real-world research networks may consist of “network-of-networks” which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks.

Materials and Methods

We propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology.

Results

HierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level.

Discussion

HierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns.

Conclusion

We demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction.

Hierarchical network modeling with multidimensional information for aquatic safety management in the cold chain

The cold-chain information has characterized by the loss and dispersion according to the different collecting methods. The description for the quality decay factors of aquatic products can be defined as the multidimensional information. A series of nodes with multidimensional information are assembled to be hierarchies aiming at describing the environment conditions and locations in the supply chain. Each of the single hierarchy levels constitutes a sequence of node information in a network, which is applied as internal information analysis. The cross-layer information structure is defined as "bridge" information which is able to record the information transmissions among every hierarchy from the point of view of the whole chain. The study has established a novel structured modeling to describe the cold chain of aquatic products based on a network-hierarchy framework. An organized and sustainable transmission process can be built and recorded by the multidimensional attributes for the whole course of cold chain of aquatic products. In addition, seamless connections among every hierarchy are attainable by the environmental information records continuously to monitor the quality of aquatic products. The quality assessments and shelf life predictions are estimated properly as the risk control in order to monitor and trace the safety of aquatic products under the supply chain perspective.

Macroscopic complexity from an autonomous network of networks of theta neurons

We examine the emergence of collective dynamical structures and complexity in a network of interacting populations of neuronal oscillators. Each population consists of a heterogeneous collection of globally-coupled theta neurons, which are a canonical representation of Type-1 neurons. For simplicity, the populations are arranged in a fully autonomous driver-response configuration, and we obtain a full description of the asymptotic macroscopic dynamics of this network. We find that the collective macroscopic behavior of the response population can exhibit equilibrium and limit cycle states, multistability, quasiperiodicity, and chaos, and we obtain detailed bifurcation diagrams that clarify the transitions between these macrostates. Furthermore, we show that despite the complexity that emerges, it is possible to understand the complicated dynamical structure of this system by building on the understanding of the collective behavior of a single population of theta neurons. This work is a first step in the construction of a mathematically-tractable network-of-networks representation of neuronal network dynamics.

Object recognition with hierarchical discriminant saliency networks

The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and computer vision literatures. This demonstrates benefits for all the functional enhancements of the HDSN, the class tuning inherent to discriminant saliency, and saliency layers based on templates of increasing target selectivity and invariance. Altogether, these experiments suggest that there are non-trivial benefits in integrating attention and recognition.