Aim: To examine the effects of pioglitazone, a PPARγ agonist, on memory performance and brain amyloidogenesis in streptozotocin (STZ)-induced diabetic mice. Methods: ICR male mice were injected with STZ (150 mg/kg, iv) to induce experimental diabetes. Pioglitazone (9 and 18 mg·kg, po) was administered for 6 weeks. Passive avoidance and Morris water maze (MWM) tests were used to evaluate cognitive function. The blood glucose and serum insulin levels were detected using the glucose oxidase method and an ELISA assay, respectively. β-amyloid (Aβ), β-amyloid precursor protein (APP), β-amyloid precursor protein cleaving enzyme 1 (BACE1), NF-κB p65, the receptor for advanced glycation end products (RAGE) and PPARγ in the brains were analyzed using Western blotting assays. Results: The STZ-induced diabetic mice characterized by hyperglycemia and hypoinsulinemia performed poorly in both the passive avoidance and MWM tests, accompanied by increased Aβ 1-40 /Aβ 1-42 , APP, BACE1, NF-κB p65 and RAGE levels and decreased PPARγ level in the hippocampus and cortex. Chronic pioglitazone treatment significantly ameliorated the memory deficits and amyloidogenesis of STZ-induced diabetic mice, and suppressed expression of APP, BACE1, RAGE and NF-κB p65, and activated PPARγ in the hippocampus and cortex. However, pioglitazone did not significantly affect blood glucose and insulin levels. Conclusion: Pioglitazone ameliorates memory deficits in STZ-induced diabetic mice by reducing brain Aβ level via activation of PPARγ, which is independent of its effects on blood glucose and insulin levels. The results suggest that pioglitazone may be used for treating the cognitive dysfunction in type 1 diabetes mellitus.

To plan the data collecting path for the mobile collector in wireless sensor network (WSN), an efficient energy-aware distributed intelligent data gathering algorithm (DIDGA) is proposed, which includes cluster formation and path formation phases. In cluster formation phase, an energy-efficient distributed clustering scheme is proposed to form a coverage-efficient WSN, which constructs a minimum connected dominating set (MCDS) based on maximal independent sets (MISs) in distributed and localized manner, and the node with more power is selected to be the cluster head in turn to prolong the network lifetime. In path formation phase, a path formation optimized algorithm (PFOA) is proposed to resolve the path formation NP problem with dynamic requirements. Then DIDGA uses the cluster head relay mechanism for planning the data gathering path. Compared with existed algorithms, detailed simulation results show that the proposed DIDGA can reduce average hop counts, average data gathering time, energy consumption, increase the efficiency of event detection ratio and prolong the network lifetime.

Fabric prints may contain intricate and nesting color patterns. To evaluate colors on such a fabric, regions of different colors must be measured individually. Therefore, precise separation of colored patterns is paramount in analyzing fabric colors for digital printing, and in assessing the colorfastness of a printed fabric after a laundering or abrasion process. This paper presents a self-organizing-map (SOM) based clustering algorithm used to automatically classify colors on printed fabrics and to accurately partition the regions of different colors for color measurement. The main color categories of an image are firstly identified and flagged using the SOM’s density map and U-matrix. Then, the region of each color category is located by divining the U-matrix map with an adaptive threshold, which is determined by recursively decreasing it from a high threshold until all the flagged neurons are assigned to different regions in the divided map. Finally, the regions with high color similarity are merged to avoid possible over-segmentation. Unlike many other clustering algorithms, this algorithm does not need to pre-define the number of clusters (e.g. main colors) and can automatically select a distance threshold to partition the U-matrix map. The experimental results show that the intricate color patterns can be precisely separated into individual regions representing different colors.

No abstract

No abstract