Microalgal biodiesel is one of the most promising renewable fuels. The wet technique for lipids extraction has advantages over the dry method, such as energy-saving and shorter procedure. The cell disruption is a key factor in wet oil extraction to facilitate the intracellular oil release. Ultrasonication, high-pressure homogenization, enzymatic hydrolysis and the combination of enzymatic hydrolysis with high-pressure homogenization and ultrasonication were employed in this study to disrupt the cells of the microalga Neochloris oleoabundans. The cell disruption degree was investigated. The cell morphology before and after disruption was assessed with scanning and transmission electron microscopy. The energy requirements and the operation cost for wet cell disruption were also estimated. The highest disruption degree, up to 95.41%, assessed by accounting method was achieved by the combination of enzymatic hydrolysis and high-pressure homogenization. A lipid recovery of 92.6% was also obtained by the combined process. The combined process was found to be more efficient and economical compared with the individual process.

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Diabetic peripheral neuropathy (DPN) is a common complication of diabetes lacking of effective treatments. Enhanced excitability of dorsal root ganglion (DRG) neuron plays a crucial role in the progression of diabetic neuropathic hyperalgesia. Brain-derived neurotrophic factor (BDNF) is known as a neuromodulator of nociception, but whether and how BDNF modulates the excitability of DRG neurons in the development of DPN remain to be clarified. This study investigated the role of exogenous BDNF and its high-affinity tropomyosin receptor kinase B (TrkB) in rats with streptozotocin-induced diabetic neuropathic pain. The results showed that continued intrathecal administration of BDNF to diabetic rats dramatically alleviated mechanical and thermal hyperalgesia, as well as inhibited hyperexcitability of DRG neurons. These effects were blocked by pretreatment with TrkB Fc (a synthetic fusion protein consisting of the extracellular ligand-binding domain of the TrkB receptor). The expression of BDNF and TrkB was upregulated in the DRG of diabetic rats. Intrathecal administration of BDNF did not affect this upregulation. These data provide novel information that exogenous BDNF relieved pain symptoms of diabetic rats by reducing hyperexcitability of DRG neurons and might be the potential treatment of painful diabetic neuropathy.

Principal Component Analysis (PCA) as a tool for dimensionality reduction is widely used in many areas. In the area of bioinformatics, each involved variable corresponds to a specific gene. In order to improve the robustness of PCA-based method, this paper proposes a novel graph-Laplacian PCA algorithm by adopting L1/2 constraint (L1/2 gLPCA) on error function for feature (gene) extraction. The error function based on L1/2-norm helps to reduce the influence of outliers and noise. Augmented Lagrange Multipliers (ALM) method is applied to solve the subproblem. This method gets better results in feature extraction than other state-of-the-art PCA-based methods. Extensive experimental results on simulation data and gene expression data sets demonstrate that our method can get higher identification accuracies than others.

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BackgroundTraditional drug identification methods follow the “one drug-one target” thought. But those methods ignore the natural characters of human diseases. To overcome this limitation, many identification methods of drug-pathway association pairs have been developed, such as the integrative penalized matrix decomposition (iPaD) method. The iPaD method imposes the L1-norm penalty on the regularization term. However, lasso-type penalties have an obvious disadvantage, that is, the sparsity produced by them is too dispersive.ResultsTherefore, to improve the performance of the iPaD method, we propose a novel method named L2,1-iPaD to identify paired drug-pathway associations. In the L2,1-iPaD model, we use the L2,1-norm penalty to replace the L1-norm penalty since the L2,1-norm penalty can produce row sparsity.ConclusionsBy applying the L2,1-iPaD method to the CCLE and NCI-60 datasets, we demonstrate that the performance of L2,1-iPaD method is superior to existing methods. And the proposed method can achieve better enrichment in terms of discovering validated drug-pathway association pairs than the iPaD method by performing permutation test. The results on the two real datasets prove that our method is effective.