Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes.     

Identifying the miRNA Signature Association with Aging-Related Senescence in Glioblastoma

Glioblastoma (GBM) is the most common malignant brain tumor and its malignant phenotypic characteristics are classified as grade IV tumors. Molecular interactions, such as protein–protein, protein–ncRNA, and protein–peptide interactions are crucial to transfer the signaling communications in cellular signaling pathways. Evidences suggest that signaling pathways of stem cells are also activated, which helps the propagation of GBM. Hence, it is important to identify a common signaling pathway that could be visible from multiple GBM gene expression data. microRNA signaling is considered important in GBM signaling, which needs further validation. We performed a high-throughput analysis using micro array expression profiles from 574 samples to explore the role of non-coding RNAs in the disease progression and unique signaling communication in GBM. A series of computational methods involving miRNA expression, gene ontology (GO) based gene enrichment, pathway mapping, and annotation from metabolic pathways databases, and network analysis were used for the analysis. Our study revealed the physiological roles of many known and novel miRNAs in cancer signaling, especially concerning signaling in cancer progression and proliferation. Overall, the results revealed a strong connection with stress induced senescence, significant miRNA targets for cell cycle arrest, and many common signaling pathways to GBM in the network.     

Mechanically durable superhydrophobic surfaces

Development of durable non-wetting surfaces is hindered by the fragility of the microscopic roughness features that are necessary for superhydrophobicity. Mechanical wear on superhydrophobic surfaces usually shows as increased sticking of water, leading to loss of non-wettability. Increased wear resistance has been demonstrated by exploiting hierarchical roughness where nanoscale roughness is protected to some degree by large scale features, and avoiding the use of hydrophilic bulk materials is shown to help prevent the formation of hydrophilic defects as a result of wear. Additionally, self-healing hydrophobic layers and roughness patterns have been suggested and demonstrated. Nevertheless, mechanical contact not only causes damage to roughness patterns but also surface contamination, which shortens the lifetime of superhydrophobic surfaces in spite of the self-cleaning effect. The use of photocatalytic effect and reduced electric resistance have been suggested to prevent the accumulation of surface contaminants. Resistance to organic contaminants is more challenging, however, oleophobic surface patterns which are non-wetting to organic liquids have been demonstrated. While the fragility of superhydrophobic surfaces currently limits their applicability, development of mechanically durable surfaces will enable a wide range of new applications in the future.     

Hydrophobic nanocellulose aerogels as floating, sustainable, reusable, and recyclable oil absorbents

Highly porous nanocellulose aerogels can be prepared by vacuum freeze-drying from microfibrillated cellulose hydrogels. Here we show that by functionalizing the native cellulose nanofibrils of the aerogel with a hydrophobic but oleophilic coating, such as titanium dioxide, a selectively oil-absorbing material capable of floating on water is achieved. Because of the low density and the ability to absorb nonpolar liquids and oils up to nearly all of its initial volume, the surface modified aerogels allow to collect organic contaminants from the water surface. The materials can be reused after washing, recycled, or incinerated with the absorbed oil. The cellulose is renewable and titanium dioxide is not environmentally hazardous, thus promoting potential in environmental applications.     

Healable,Stable and Stiff Hydrogels: Combining Conflicting Properties Using Dynamic and Selective Three‐Component Recognition with Reinforcing Cellulose Nanorods

Nanocomposite hydrogels are prepared combining polymer brush‐modified ‘hard’ cellulose nanocrystals (CNC) and ‘soft’ polymeric domains, and bound together by cucurbit[8]uril (CB[8]) supramolecular crosslinks, which allow dynamic host–guest interactions as well as selective and simultaneous binding of two guests, i.e., methyl viologen (the first guest) and naphthyl units (the second guest). CNCs are mechanically strong colloidal rods with nanometer‐scale lateral dimensions, which are functionalized by surface‐initiated atom transfer radical polymerization to yield a dense set of methacrylate polymer brushes bearing naphthyl units. They can then be non‐covalently cross‐linked through simple addition of poly(vinyl alcohol) polymers containing pendant viologen units as well as CB[8]s in aqueous media. The resulting supramolecular nanocomposite hydrogels combine three important criteria: high storage modulus (G′ > 10 kPa), rapid sol–gel transition (<6 s), and rapid self‐healing even upon aging for several months, as driven by balanced colloidal reinforcement as well as the selectivity and dynamics of the CB[8] three‐component supramolecular interactions. Such a new combination of properties for stiff and self‐healing hydrogel materials suggests new approaches for advanced dynamic materials from renewable sources.     

Droplet and Fluid Gating by Biomimetic Janus Membranes

The ability to gate (i.e., allow or block) droplet and fluid transport in a directional manner represents an important form of liquid manipulation and has tremendous application potential in fields involving intelligent liquid management. Inspired by passive transport across cell membranes which regulate permeability by transmembrane hydrophilic/hydrophobic interactions, macroscopic hydrophilic/hydrophobic Janus‐type membranes are prepared by facile vapor diffusion or plasma treatments for liquid gating. The resultant Janus membrane shows directional water droplet gating behavior in air‐water systems. Furthermore, membrane‐based directional gating of continuous water flow is demonstrated for the first time, enabling Janus membranes to act as facile fluid diodes for one‐way flow regulation. Additionally, in oil‐water systems, the Janus membranes show directional gating of droplets with integrated selectivity for either oil or water. The above remarkable gating properties of the Janus membranes could bring about novel applications in fluid rectifying, microchemical reaction manipulation, advanced separation, biomedical materials and smart textiles.