Efficacy of Pseudomonas chlororaphis subsp. aureofaciens SH2 and Pseudomonas fluorescens RH43 isolates against root-knot nematodes (Meloidogyne spp.) in kiwifruit

The Root-knot nematodes, Meloidogyne spp., are parasites of many crops and orchards, including kiwifruit trees. The Islamic Republic of Iran is among the leading kiwifruit producers in the world and M. incognita has been found as the dominant species responsible for severe loss of this crop. In order to evaluate the eff ectiveness of antagonistic bacteria on larval mortality, number of galls per plant and egg masses of nematode reduction, fifty local bacterial strains were isolated from root surrounding soils of kiwifruit plants in the northern production areas in Iran. Bacterial antagonists were characterized by morphological, physiological, biochemical and molecular methods. Two representative strains, showing the best nematicidal activity, were identif ed as Pseudomonas chlororaphis subsp. aureofaciens (isolate Sh2) and Pseudomonas fluorescens (isolate Rh43). They increased the percentage of larval mortality to 56:38% and 54:28% respectively in assays in vitro and showed excellent performance also in vivo with consistent reduction of number of galls (67:31% and 55:63%, respectively) and egg mass (86:46% and 84:29%, respectively) in plants. This study indicates that Pseudomonas chlororaphis subsp. aureofaciens isolate Sh2 and Pseudomonas fluorescens isolate Rh43 are good potential biocontrol agents for containing root-knot nematodes in kiwifruit trees.peer-reviewe

High-Dimensional Dependency Structure Learning for Physical Processes

In this paper, we consider the use of structure learning methods for probabilistic graphical models to identify statistical dependencies in high-dimensional physical processes. Such processes are often synthetically characterized using PDEs (partial differential equations) and are observed in a variety of natural phenomena, including geoscience data capturing atmospheric and hydrological phenomena. Classical structure learning approaches such as the PC algorithm and variants are challenging to apply due to their high computational and sample requirements. Modern approaches, often based on sparse regression and variants, do come with finite sample guarantees, but are usually highly sensitive to the choice of hyper-parameters, e.g., parameter $\lambda$ for sparsity inducing constraint or regularization. In this paper, we present ACLIME-ADMM, an efficient two-step algorithm for adaptive structure learning, which estimates an edge specific parameter $\lambda_{ij}$ in the first step, and uses these parameters to learn the structure in the second step. Both steps of our algorithm use (inexact) ADMM to solve suitable linear programs, and all iterations can be done in closed form in an efficient block parallel manner. We compare ACLIME-ADMM with baselines on both synthetic data simulated by partial differential equations (PDEs) that model advection-diffusion processes, and real data (50 years) of daily global geopotential heights to study information flow in the atmosphere. ACLIME-ADMM is shown to be efficient, stable, and competitive, usually better than the baselines especially on difficult problems. On real data, ACLIME-ADMM recovers the underlying structure of global atmospheric circulation, including switches in wind directions at the equator and tropics entirely from the data.Comment: 21 pages, 8 figures, International Conference on Data Mining 201

Estimation of illuminance on the south facing surfaces for clear skies in Iran

Background: Daylight availability data are essential for designing effectively day lighted buildings. In respect to no available daylight availability data in Iran, illuminance data on the south facing vertical surfaces were estimated using a proper method. Methods: An illuminance measuring set was designed for measuring vertical illuminances for standard times over 15 days at one hour intervals from 9 a.m. to 3 p.m. at three measuring stations (Hamadan, Eshtehard and Kerman). Measuring data were used to confirm predicted by the IESNA method. Results: Measurement of respective illuminances on the south vertical surfaces resulted in minimum values of 10.5 KLx, mean values of 33.59 KLx and maximum values of 79.6 KLx. Conclusion: In this study was developed a regression model between measured and calculated data of south facing vertical illuminance. This model, have a good linear correlation between measured and calculated values (r= 0.892)