The naked mole rat (NMR; Heterocephalus glaber) is a small rodent species found in regions of Kenya, Ethiopia and Somalia. It has a high tolerance for hypoxia and is thus considered one of the most important natural models for studying hypoxia tolerance mechanisms. The various mechanisms underlying the NMR9s hypoxia tolerance are beginning to be understood at different levels of organization, and next-generation sequencing methods promise to expand this understanding to the level of gene expression. In this study, we examined the sequence and transcript abundance data of the muscle transcriptome of NMRs exposed to hypoxia using the Illumina HiSeq 2500 system to clarify the possible genomic adaptive responses to the hypoxic underground surroundings. The RNA-seq raw FastQ data were mapped against the NMR genome. We identified 2337 differentially expressed genes (DEGs) by comparison of the hypoxic and control groups. Functional annotation of the DEGs by gene ontology (GO) analysis revealed enrichment of hypoxia stress-related GO categories, including ‘biological regulation’, ‘cellular process’, ‘ion transport’ and ‘cell-cell signaling’. Enrichment of DEGs in signaling pathways was analyzed against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to identify possible interactions between DEGs. The results revealed significant enrichment of DEGs in focal adhesion, the mitogen-activated protein kinase (MAPK) signaling pathway and the glycine, serine and threonine metabolism pathway. Furthermore, inhibition of DEGs (STMN1, MAPK8IP1 and MAPK10) expression induced apoptosis and arrested cell growth in NMR fibroblasts following hypoxia. Thus, this global transcriptome analysis of NMRs can provide an important genetic resource for the study of hypoxia tolerance in mammals. Furthermore, the identified DEGs may provide important molecular targets for biomedical research into therapeutic strategies for stroke and cardiovascular diseases.

Over the past few decades, micro liquid dispensing technology has been widely used in biology, chemistry, material and environmental sciences due to its efficacy in processing multiple samples. For practical applications, precise and effective droplet generation is very important. Despite numerous droplet generation methods, the implementation of droplet-on-demand still faces challenges concerning system complexity, precision, cost, and robustness. In this work, a novel on-demand contacting droplet generation method incorporated with model-based feedback control with an image processing unit as a sensor was proposed. By studying droplet identification using image processing techniques, the model of droplet formation was simplified. Then model-based feedback control was implemented using volumes of dispensed samples as sensing signals by tuning related parameters adaptively to resist disturbances. The proposed method was integrated and applied to a homebuilt automated micro liquid dispensing system with droplets ranging from 20 nanoliter to 200 nanoliter. The experimental results demonstrated a high degree of accuracy and precision. Additionally, the proposed system’s practical utility was evaluated by analyzing mutations in genes associated with sensorineural hearing loss, verifying its effectiveness.

Wireless sensor networks (WSN) are becoming increasingly promising in practice. As the predeployment design and optimization are usually unpractical in random deployment scenarios, the global optimum of the WSN’s performance is achievable only if the topology dependent self-organizing process acquires the overview of the WSN, in which the boundary is the most important. The idea of this paper comes from the fact that contours only break on the geometrical boundary and the WSN are discrete sampling systems of real environments. By simulating a diffusion process in discrete form, the end point of semi-contours suggests the boundary nodes of a WSN. The simulation cases show the algorithm is well worked in WSN with average degree higher than 10. The boundary recognition could be very valuable for other algorithms dedicated to optimize the overall performance of WSN.

This paper presents a methodology to include financial risk management for the design of multiproduct, multi-echelon supply chain networks under uncertainty. The method is in the framework of two-stage stochastic programming. Definitions of financial risk and downside risk are adapted. Using these definitions, financial risk management constraints are introduced and a new two-stage stochastic programming model is established. Case studies illustrate the applicability of such financial risk management. Trade-offs between expected cost and risk are also analyzed.