Effect of nitrogen fertilizer on growth, flowering, fruiting and nodulation of three varieties of common bean in the arid region of aïn naga (Biskra, Algeria)

Field experiments were conducted to investigate the response of common bean (Phaseolus vulgaris L.) to nitrogen fertilizer. The main factor included the fertilizer (Granular Urea 46% N), was made up of two levels: no fertilization (0 kg/plot: control) and fertilization (0.4 kg/plot), while the secondary factor was the variety (three varieties of common bean: Djedida, Nelson and Jalila). The experimental design was a randomized complete block design with split plot arrangement and replicated four times. The effect of fertilization was evaluated during two development stages (flowering and fruiting) for each variety and this on the total dry biomass, the length of the aerial and root parts, as well as on the number of secondary roots. On the other hand, the combined effect of fertilization and variety was studied on some yield parameters per plant, such as the number of flowers, pods, seeds and the harvest index. In addition, the number of nodules at the end of seed maturation was evaluated. The results indicated that the nitrogen fertilizer application significantly reduced the root length, the number of nodules and secondary roots in most of the common bean varieties. However, fertilizer application significantly increased dry matter in both flowering and fruiting stages, for the three studied varieties. Pod number per plant and seed yield was increased by the application of N fertilizer, depending on varieties and the parameters being measured. Djedida and Jalila varieties gave the best yield and can therefore be recommended to farmers. According to our results, the percentage of improvement by N fertilization on one parameter or another does not exceed an average of 20%, compared to the control. Indeed, the effect of fertilization is positive and seems to increase the harvest index by 18% in Djedida and by 20% in Jalila, compared to non-fertilized plants. Unfertilized plants of the variety Nelson showed the highest ability to nodulate

The ocean sampling day consortium

Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world’s oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits

The Ocean Sampling Day Consortium

Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world’s oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits

Neural networks and principle component analysis approaches to predict pile capacity in sand

Determination of pile bearing capacity from the in-situ tests has developed considerably due to the significant development of their technology. The project presented in this paper is a combination of two approaches, artificial neural networks and main component analyses that allow the development of a neural network model that provides a more accurate prediction of axial load bearing capacity based on the SPT test data. The retropropagation multi-layer perceptron with Bayesian regularization (RB) was used in this model. This was established by the incorporation of about 260 data, obtained from the published literature, of experimental programs for large displacement driven piles. The PCA method is proposed for compression and suppression of the correlation between these data. This will improve the performance of generalization of the model

PREVALENCE AND ANTIBIOTIC RESISTANCE OF STAPHYLOCOCCI ISOLATED FROM KOLEA HOSPITAL (ALGERIA)

<p>The resurgence of multiple antibiotic resistances is a major public health problem, often causing therapeutic impasses. Our study was conducted at the Bacteriology Laboratory of Kolea (Tipaza, Algeria) Hospital. The aim of this study was to determine the prevalence of staphylococci in clinical purulent, sepsis and urinary samples, and to test the sensitivity of the selected strains against a various antibiotic (ATB) discs. The identification of <em>Staphylococcus</em> strains was performed by using classic biochemistry tests. Susceptibility against ATB was determined by the agar disk diffusion method. Among 1403 clinical samples obtained, only 61 was related to staphylococci infections (4.34%). <em>Staphylococcus</em> strains were found mainly in the purulent samples (60.65%). <em>S.aureus</em> was found to be the mainly isolated strains with a rate of 61.53% while the <em>S. epidermidis</em> strain was found predominantly in hospitalized patients (68.57%). The frequency of the overall resistance of <em>S.aureus</em> against penicillin G (80.6%) and gentamicin (61.53%) remains high. The multi-resistant <em>Staphylococcus</em> has become extremely widespread, hence the need for the accomplishment of a strategy to prevent the spread of this resistance.</p

Neural networks and principle component analysis approaches to predict pile capacity in sand

Determination of pile bearing capacity from the in-situ tests has developed considerably due to the significant development of their technology. The project presented in this paper is a combination of two approaches, artificial neural networks and main component analyses that allow the development of a neural network model that provides a more accurate prediction of axial load bearing capacity based on the SPT test data. The retropropagation multi-layer perceptron with Bayesian regularization (RB) was used in this model. This was established by the incorporation of about 260 data, obtained from the published literature, of experimental programs for large displacement driven piles. The PCA method is proposed for compression and suppression of the correlation between these data. This will improve the performance of generalization of the model