Reliability analysis of body sensor networks with correlated isolation groups.

Body sensor networks (BSNs) are playing a crucial role in tackling arising challenges during the COVID-19 pandemic. This work contributes by modeling and analyzing the BSN reliability considering the effects of correlated functional dependence (FDEP) and random isolation time behavior. Particularly, the FDEP exists in BSNs where a relay is utilized to assist the communication between some biosensors and the sink device. When the relay malfunctions, the dependent biosensors may communicate directly with the sink for a limited, uncertain time. These biosensors then become isolated from the rest of the BSN when their remaining power depletes to the level insufficient to support the direct communication. Moreover, multiple biosensors sharing the same relay and a biosensor communicating with the sink via several alternative relays create correlations among different FDEP groups. In addition, the competition in the time domain exists between the local failure of the relay and the propagated failures of dependent biosensors. Both the correlation and competition complicate the reliability modeling and analysis of BSNs. This work proposes a combinatorial and analytical methodology to address both effects in the BSN reliability analysis. The proposed method is demonstrated using a detailed case study and verified using a continuous-time Markov chain method.

Gradient Material Strategies for Hydrogel Optimization in Tissue Engineering Applications.

Although a number of combinatorial/high-throughput approaches have been developed for biomaterial hydrogel optimization, a gradient sample approach is particularly well suited to identify hydrogel property thresholds that alter cellular behavior in response to interacting with the hydrogel due to reduced variation in material preparation and the ability to screen biological response over a range instead of discrete samples each containing only one condition. This review highlights recent work on cell-hydrogel interactions using a gradient material sample approach. Fabrication strategies for composition, material and mechanical property, and bioactive signaling gradient hydrogels that can be used to examine cell-hydrogel interactions will be discussed. The effects of gradients in hydrogel samples on cellular adhesion, migration, proliferation, and differentiation will then be examined, providing an assessment of the current state of the field and the potential of wider use of the gradient sample approach to accelerate our understanding of matrices on cellular behavior.

Neurite extension and neuronal differentiation of human induced pluripotent stem cell derived neural stem cells on polyethylene glycol hydrogels containing a continuous Young's Modulus gradient.

Mechanotransduction in neural cells involves multiple signaling pathways that are not fully understood. Differences in lineage and maturation state are suggested causes for conflicting reports on neural cell mechanosensitivity. To optimize matrices for use in stem cell therapy treatments transplanting human induced pluripotent stem cell derived neural stem cells (hNSC) into lesions after spinal cord injury, the effects of Young's Modulus changes on hNSC behavior must be understood. The present study utilizes polyethylene glycol hydrogels containing a continuous gradient in Young's modulus to examine changes in the Young's Modulus of the culture substrate on hNSC neurite extension and neural differentiation. Changes in the Young's Modulus of the polyethylene glycol hydrogels was found to affect neurite extension and cellular organization on the matrices. hNSC cultured on 907 Pa hydrogels were found to extend longer neurites than hNSC cultured on other tested Young's Moduli hydrogels. The gene expression of ß tubulin III and microtubule-associated protein 2 in hNSC was affected by changes in the Young's Modulus of the hydrogel. The combinatory method approach used in the present study demonstrates that hNSC are mechanosensitive and the matrix Young's Modulus should be a design consideration for hNSC transplant applications. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 824-833, 2017.

Design of selective gas sensors using additive-loaded In2O3 hollow spheres prepared by combinatorial hydrothermal reactions.

A combinatorial hydrothermal reaction has been used to prepare pure and additive (Sb, Cu, Nb, Pd, and Ni)-loaded In2O3 hollow spheres for gas sensor applications. The operation of Pd- and Cu-loaded In2O3 sensors at 371 °C leads to selective H2S detection. Selective detection of CO and NH3 was achieved by the Ni-In2O3 sensor at sensing temperatures of 371 and 440 °C, respectively. The gas responses of six different sensors to NH3, H2S, H2, CO and CH4 produced unique gas sensing patterns that can be used for the artificial recognition of these gases.