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Limb ischemia reperfusion (LIRI) injury is associated with serious local and systemic effects. Reperfusion may augment tissue injury in excess of that produced by ischemia alone. Calcium overloading and inflammation are considered to be two of the pathological mechanisms of limb ischemia/reperfusion (I/R) injury. Tao-Hong-Si-Wu decoction (THSWD) is a traditional Chinese herbal medicine with a powerful anti-inflammatory properties. We studied the probable restorative effect of THSWD on limb I/R-induced calcium overloading and inflammation in myoblast obtained from gastrocnemius muscle tissues of Sprague-Dawley rats (Frizzled Z5,a wnt5a blocker; KN-93, a calmodulin-dependent protein kinase II (CamkII) blocker; XeC, a IP3R blocker as positive controls). The simulated ischemia and reperfusion(I/R) solutions were used to imitate LIRI environment. The results showed that after I/R treatment, the secretion of proinflammatory factors (TNF-α and IL-1β) and Wnt5a/Ca2+ signal molecules (wnt5a, camkII, and IP3R) upregulated significantly, the Ca2+ concentration enhanced too in myoblast cells. THSWD pretreatment decreased the secretion of TNF-α and IL-1β, Ca2+ concentration; and abated the Wnt5a/Ca2+ signal molecules of wnt5a, camkII and IP3R expression activated by I/R injury; but could not abated the Wnt11 and protein kinase C (PKC) expression significantly, the results was similar with Frizzled Z5 treatment cells. Our research illustrated that THSWD may have a mitigating effect on LIRI targeting Wnt/IP3R/CAMKII but not Wnt/IP3R/PKC signaling pathway for the first time. This study may encourage the use of THSWD in the critical clinical settings with LIRI.

Convolutional neural networks (CNNs) have attracted increasing attention in the remote sensing community. Most CNNs only take the last fully-connected layers as features for the classification of remotely sensed images, discarding the other convolutional layer features which may also be helpful for classification purposes. In this paper, we propose a new adaptive deep pyramid matching (ADPM) model that takes advantage of the features from all of the convolutional layers for remote sensing image classification. To this end, the optimal fusing weights for different convolutional layers are learned from the data itself. In remotely sensed scenes, the objects of interest exhibit different scales in distinct scenes, and even a single scene may contain objects with different sizes. To address this issue, we select the CNN with spatial pyramid pooling (SPP-net) as the basic deep network, and further construct a multi-scale ADPM model to learn complementary information from multi-scale images. Our experiments have been conducted using two widely used remote sensing image databases, and the results show that the proposed method significantly improves the performance when compared to other state-of-the-art methods.

In this paper, the features extraction of passive sonar signals and classification recognition of underwater target are introduced. Due to the complexity and non-stationary of underwater signals, the zero-cross ratio is first used to initially classify the noise signal; then the LOFAR spectrum reflecting non-stationary signal is extracted, and during which the wavelet transform is carried out for some classes of signals. Finally, a fuzzy ART neural network is constructed to carry out the classification. Results of the experiment show that, for six-class target 147 running environments, 5000 realistic data of ship, the mean correct ratio achieves 89%. The result obtained is satisfactory.