Study on different potato continuous cropping ways on rhizosphere soil nutrients and enzyme activities

To address the problem of food security, China produced potatoes as a staple food in 2015.However, there are increasing problems with continuous cropping production methods, potato continuous cropping has been inevitable.So it is necessary to research under the different potato continuous cropping ways, potato rhizosphere soil nutrients and enzyme activities which can direct potato fertilizer and ease potato continuous cropping obstacle. A two-growing season investigation was carried out during the spring and autumn of 2014 and 2015 to determine the different ways of potato continuous cropping on the overall growth of potatoes, soil nutrients, and enzyme activities. During continuous cropping nitrogen (N) content of rhizosphere soil was reduced; available potassium (Kav) was significantly reduced(p≤5%), especially in spring and autumn continuous cropping; and total phosphorus (Ptot) was reduced during the growth stage. However, the total potassium (Ktot), available phosphorus(Pav), and organic carbon (Ctot) increased before they decreased. For rhizosphere soil enzyme activities, urease initially increased and then decreased, and was lower in continuous cropping than multiple continuous cropping; in spring of 2015, invertase was the highest with continuous cropping. Catalase and polyphenol oxidase decreased initially before increasing. Continuous cropping in spring and autumn consumed more nutrients, especially potassium (K) than in spring. Therefore, potatoes planted in both spring and autumn enhanced the problems of continuous cropping. However, multiple continuous cropping that eased rhizosphere soil nutrient absorption and effectively improves soil nutrients and enzyme activities could provide an effective method for managing the negative impacts associated with continuous cropping

SMAC mimetic Debio 1143 synergizes with taxanes, topoisomerase inhibitors and bromodomain inhibitors to impede growth of lung adenocarcinoma cells

Targeting anti-apoptotic proteins can sensitize tumor cells to conventional chemotherapies or other targeted agents. Antagonizing the Inhibitor of Apoptosis Proteins (IAPs) with mimetics of the pro-apoptotic protein SMAC is one such approach. We used sensitization compound screening to uncover possible agents with the potential to further sensitize lung adenocarcinoma cells to the SMAC mimetic Debio 1143. Several compounds in combination with Debio 1143, including taxanes, topoisomerase inhibitors, and bromodomain inhibitors, super-additively inhibited growth and clonogenicity of lung adenocarcinoma cells. Co-treatment with Debio 1143 and the bromodomain inhibitor JQ1 suppresses the expression of c-IAP1, c-IAP2, and XIAP. Non-canonical NF-κB signaling is also activated following Debio 1143 treatment, and Debio 1143 induces the formation of the ripoptosome in Debio 1143-sensitive cell lines. Sensitivity to Debio 1143 and JQ1 co-treatment was associated with baseline caspase-8 expression. In vivo treatment of lung adenocarcinoma xenografts with Debio 1143 in combination with JQ1 or docetaxel reduced tumor volume more than either single agent alone. As Debio 1143-containing combinations effectively inhibited both in vitro and in vivo growth of lung adenocarcinoma cells, these data provide a rationale for Debio 1143 combinations currently being evaluated in ongoing clinical trials and suggest potential utility of other combinations identified here

CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes

ABSTRACT: A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) aims to collect available data from industry and academia which may be used for this purpose (www.csardock.org). Also, CSAR is charged with organizing community-wide exercises based on the collected data. The first of these exercises was aimed to gauge the overall state of docking and scoring, using a large and diverse data set of protein ligand complexes. Participants were asked to calculate the affinity of the complexes as provided and then recalculate with changes which may improve their specific method. This first data set was selected from existing PDB entries which had binding data (Kd or Ki) in Binding MOAD, augmented with entries from PDBbind. The final data set contains 343 diverse protein ligand complexes and spans 14 pKd. Sixteen proteins have three or more complexes in the data set, from which a user could start an inspection of congeneric series. Inherent experimental error limits the possible correlation between scores and measured affinity; R 2 is limited to ∼0.9 when fitting to the data set without over parametrizing. R 2 is limited to ∼0.8 when scoring the data set with a method trained on outside data. The details of how the data set was initially selected, and the process by which it matured t