Gastrodin Alleviates Vascular Dementia in a 2-VO-Vascular Dementia Rat Model by Altering Amyloid and Tau Levels.

Vascular dementia (VaD) is the second most common type of dementia and has become a major public health challenge as the global population ages. VaD is caused by cerebrovascular disease, and most patients with VaD have been reported to also have Alzheimer's pathologies, which is the formation of neurofibrillary tangles and amyloid plaques that are mainly composed of hyperphosphorylated Tau and amyloid ß (Aß) respectively. However, the mechanisms of VaD are not completely understood, and very few drugs are available to treat this condition. Gastrodin (Gas) is the main bioactive component of the traditional Chinese herbal plant named Tian Ma (Gastrodia elata), and it has been used to treat neurasthenia in the clinical practice of Chinese Medicine for many years. Here, we hypothesize that Gas alleviates VaD in a rat model of permanent bilateral common carotid artery occlusion (2-VO)-induced VaD. Based on the results of the Morris water maze test and attention set shift test, either 22.5 or 90 mg/kg/day Gas improved the executive dysfunction and memory impairment of 2-VO rats following an intragastric administration for 4 weeks. Both 22.5 and 90 mg/kg/day Gas reduced Aß1-40 and Aß1-42 plaques in plasma and hippocampus of 2-VO rats. Mechanistically, in 2-VO rats, treatment with Gas (90 mg/kg/day) suppressed Aß plaque deposition by decreasing the hippocampus levels of phosphorylated Tau. Thus, Gas ameliorated the cognitive deficits of 2-VO rats by inhibiting the abnormal phosphorylation of Aß and Tau.

A Preliminary Study on the Effectiveness of K-Nearest Neighbor Algorithm Based on Local Mean for Classification of Bioinformatics Data.

The preliminary classification of biological class data is of great importance for bioinformatics. One can quickly classify object data by comparing their existing features with known traits. k-nearest neighbor algorithm is easy to apply in this field, but its drawbacks make it less meaningful to improve the efficiency of the algorithm by simply changing the distance model, so this study uses a local mean-based k-nearest neighbor classifier and compares the accuracy of the predicted classification of six different distance models used. The prediction accuracies in the experimental results were all greater than 70%, and the highest accuracy was achieved in different data sets for all distance models with K=2; the prediction accuracy of Minkowski distance with different parameters had the highest volatility in the test.and the experimental results can be used as a reference for related practitioners.

An automated pipeline for mitochondrial segmentation on ATUM-SEM stacks.

It is possible now to look more closely into mitochondrial physical structures due to the rapid development of electron microscope (EM). Mitochondrial physical structures play important roles in both cellular physiology and neuronal functions. Unfortunately, the segmentation of mitochondria from EM images has proven to be a difficult and challenging task, due to the presence of various subcellular structures, as well as image distortions in the sophisticated background. Although the current state-of-the-art algorithms have achieved some promising results, they have demonstrated poor performances on these mitochondria which are in close proximity to vesicles or various membranes. In order to overcome these limitations, this study proposes explicitly modelling the mitochondrial double membrane structures, and acquiring the image edges by way of ridge detection rather than by image gradient. In addition, this study also utilizes group-similarity in context to further optimize the local misleading segmentation. Then, the experimental results determined from the images acquired by automated tape-collecting ultramicrotome scanning electron microscopy (ATUM-SEM) demonstrate the effectiveness of this study's proposed algorithm.

[Effects of Phenol on Activity and Microfauna Community Structure of Activated Sludge].

In order to investigate the effects of phenol on activity and microfauna community structure of activated sludge, phenol with different concentrations were added into the wastewater for activated sludge culturing in a SBR reactor, then the activity parameters of TTC-ETS and INT-ETS of activated sludge were measured, and the dynamics of microfauna community was analyzed. The results indicated that TTC-ETS activity was more applicable to evaluate the effects of phenol on sludge activity than INT-ETS activity. The higher the concentration of phenol was, the more seriously the sludge activity was inhibited. When 50 mg·L-1 of phenol was added into the influent, the inhibition rate of sludge activity was (20.75±10.43)%, while it increased to (39.73±26.92)% with a great fluctuation at the phenol concentration of 100 mg·L-1. The inhibition rate of sludge activity fluctuated at about 40% in the later period of sludge culturing with 300 mg·L-1 of phenol. The inhibitory effects on microfauna community in activated sludge increased with increasing phenol concentration. Phenol had different effects on various groups of microfauna community, i.e., only one group (testate amoebae) was inhibited significantly at a low feeding concentration of phenol, many groups (sessile ciliates, testate amoebae, crawling ciliates, carnivorous ciliates) were inhibited at the phenol concentrations of 100 mg·L-1 and 300 mg·L-1, whereas a few groups (flagellates, nematodes, etc.) were promoted at the phenol concentration of 300 mg·L-1. There was a certain degree of relationship between sludge activity and microfauna under the influence of phenol, which means that Centropyxis aculeata, Plagiocampa mutabilis etc., may be bio-indicators for low sludge activity, and Epistylis lacustris, Bodo lens and Pleuromonas jaculans can be used as bio-indicators for high sludge activity in the activated sludge used to treat phenolic wastewater.