Automatic Human Sperm Concentrartion in microscopic videos

  Background: Human sperm cell counting analysis is of significant interest to biologists studying sperm function and to medical practitioners evaluating male infertility. Currently the analysis of this assessment is done manually by looking at the sperm samples through a phase-contrast microscope using expert knowledge to do a subjective judgement of the quality. Aims: to eliminate the subjective and error prone of the manual semen analysis and to avoid inter and intra-laboratory inconsistencies in semen analysis test results Methods: In this paper we introduce a technique for human sperm concentration. Its principle is based on the execution of three steps: The first step in unavoidable. It concerns the pretreatment of the human sperm microscopic videos which consists of a conversion of the RGB color space into the YCbCr space, the “Gaussian filtering” and the “discrete wavelet filtering”. The second step is devoted to the segmentation of the image into two classes: spermatozoas and the background. To achieve this, we used an edge detection technique “Sobel Contour detector”. The third step is to separate true sperm from false ones. It uses a machine learning technique of type decision trees that consist on two classes classification based on invariant characteristics that are the dimensions of the bounding ellipse of the spermatozoid head as well as its surface. Results: To test the robustness of our system, we compared our results with those performed manually by andrologists. After results analysis, we can conclude that our system brings a real improvement of precision as well as treatment time which make it might be useful for groups who intend to design new CASA systems. Conclusion: In this study, we designed and implemented a system for automatic concentration assessment based on machine learning method and image processing techniques

MALDI-TOF MS Detection of Endophytic Bacteria Associated with Great Nettle (Urtica dioica L.), Grown in Algeria

Any plant with a vascular system has a specific endophytic microflora. The identification of bacteria is essential in plant pathology. Although identification methods are effective, they are costly and time consuming. The purpose of this work is to isolate and to identify the different bacteria from the internal tissues of Urtica dioica L. and to study their diversity. This last is based on the different parts of the plant (stems, leaves and roots) and the harvest regions (Dellys and Tlamcen). The identification of bacteria is done by biochemical tests and confirmed by MALDI-TOF MS. Seven genus and eleven species were isolated from the Great Nettle. They belong to the genera Bacillus, Escherichia, Pantoea, Enterobacter, Staphylococcus, Enterococcus and Paenibacillus. The majority of these bacteria were isolated from Tlemcen which makes this region the richest in endophytic bacteria compared to that harvested from Dellys. The results show also that the leaves are the most diversified in endophytic bacteria. Bacillus pumilus-ME is the common species of the three parts of the plant harvested in both regions. From this work, it emerges that the Great Nettle can be settled by various endophytic bacteria which are differently distributed within the same plant harvested in different regions