A window into fungal endophytism in Salicornia europaea: deciphering fungal characteristics as plant growth promoting agents

Aim Plant-endophytic associations exist only when equilibrium is maintained between both partners. This study analyses the properties of endophytic fungi inhabiting a halophyte growing in high soil salinity and tests whether these fungi are beneficial or detrimental when non-host plants are inoculated. Method Fungi were isolated from Salicornia europaea collected from two sites differing in salinization history (anthropogenic and naturally saline) and analyzed for plant growth promoting abilities and non-host plant interactions. Results Most isolated fungi belonged to Ascomycota (96%) including dematiaceous fungi and commonly known plant pathogens and saprobes. The strains were metabolically active for siderophores, polyamines and indole-3-acetic acid (mainly Aureobasidium sp.) with very low activity for phosphatases. Many showed proteolytic, lipolytic, chitinolytic, cellulolytic and amylolytic activities but low pectolytic activity. Different activities between similar fungal species found in both sites were particularly seen for Epiccocum sp., Arthrinium sp. and Trichoderma sp. Inoculating the non-host Lolium perenne with selected fungi increased plant growth, mainly in the symbiont (Epichloë)-free variety. Arthrinium gamsii CR1-9 and Stereum gausapatum ISK3-11 were most effective for plant growth promotion. Conclusions This research suggests that host lifestyle and soil characteristics have a strong effect on endophytic fungi, and environmental stress could disturb the plant-fungi relations. In favourable conditions, these fungi may be effective in facilitating crop production in non-cultivable saline lands

Three-Class Mammogram Classification Based on Descriptive CNN Features

In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques

Pharmacometabolomic mapping of early biochemical changes induced by sertraline and placebo

In this study, we characterized early biochemical changes associated with sertraline and placebo administration and changes associated with a reduction in depressive symptoms in patients with major depressive disorder (MDD). MDD patients received sertraline or placebo in a double-blind 4-week trial; baseline, 1 week, and 4 weeks serum samples were profiled using a gas chromatography time of flight mass spectrometry metabolomics platform. Intermediates of TCA and urea cycles, fatty acids and intermediates of lipid biosynthesis, amino acids, sugars and gut-derived metabolites were changed after 1 and 4 weeks of treatment. Some of the changes were common to the sertraline- and placebo-treated groups. Changes after 4 weeks of treatment in both groups were more extensive. Pathway analysis in the sertraline group suggested an effect of drug on ABC and solute transporters, fatty acid receptors and transporters, G signaling molecules and regulation of lipid metabolism. Correlation between biochemical changes and treatment outcomes in the sertraline group suggested a strong association with changes in levels of branched chain amino acids (BCAAs), lower BCAAs levels correlated with better treatment outcomes; pathway analysis in this group revealed that methionine and tyrosine correlated with BCAAs. Lower levels of lactic acid, higher levels of TCA/urea cycle intermediates, and 3-hydroxybutanoic acid correlated with better treatment outcomes in placebo group. Results of this study indicate that biochemical changes induced by drug continue to evolve over 4 weeks of treatment and that might explain partially delayed response. Response to drug and response to placebo share common pathways but some pathways are more affected by drug treatment. BCAAs seem to be implicated in mechanisms of recovery from a depressed state following sertraline treatment