Selective inhibition of toxic cyanobacteria by β-carboline-containing bacterium Bacillus flexus isolated from Saudi freshwaters

AbstractA bacterial strain SSZ01 isolated from a eutrophic lake in Saudi Arabia dominated by cyanobacterial blooms, showed an antialgal activity against cyanobacteria species. Based on the analysis of the 16S rDNA gene sequence, the isolated strain (SSZ01) most likely belonged to the genus Bacillus with a 99% similarity to Bacillus flexus strain EMGA5. The thin layer chromatography (TLC) analysis of the ethyl acetate extract of this bacterium revealed that this strain can produce harmine and norharmane compared to different β-carboline analog standards. Harmine and norharmane were also detected in considerable amounts in bacterial growth medium, indicating a potential excretion of these compounds into the aquatic environment. The crude extract of Bacillus flexus as well as pure materials of harmine and norharmane inhibited the growth of tested species of cyanobacteria. However, the bacterial crude extract has a higher toxicity against tested species of cyanobacteria than harmine and norharmane. In addition, harmine was more toxic to cyanobacteria than norharmane. On the other hand, neither pure compounds of harmine and norharmane nor crude bacterial extract showed any antialgal activity against tested species of green algae. The results of the present study suggest that B. flexus SSZ01 or its crude extract containing harmine and norharmane could be a candidate for the selective control of cyanobacterial blooms without affecting other algal species

Application of fluorescence in situ hybridization (FISH) to the analysis of sulfate reducing bacterial community in an oily bench scale reactor

Advances in the field of genomics and meta-genomics have led to rapid and accurate strategies for the monitoring of microbial biodiversity and have revealed its potential for biotechnological applications. In this study, fluorescent in situ hybridization (FISH) as a culture-independent molecular approach using specific CY3-labelled oligonucleotide probes was used to study the dynamics of the sulfate reduction bacterial community (SRB) of the activated sludge from an oily wastewater treatment system. The relative abundance of members of the dominant bacteria in the oily water reactor was determined by FISH for 16S rRNA using EUB338 probes, for detecting general eubacteria, and SRB385 for targeting SRBs and major species of delta-proteobacteria sulfate reducers. The percentage of cells hybridizing with probe EUB338 for the dominant bacteria decreased from 25.85 to 6.25%, while with the SRB385 probe for SRB bacteria, it increased from 7.21 to 10.20% of total cells during the reactor process. These data show that SRB bacteria dominated the active microbial community in the system. It is interesting that delta-proteobacterial SRBs occupied a high percentage and took place in an oily biological system under aerobic conditions.Keywords: Sulfate reducing bacteria, fluorescence in situ hybridization, 16S rRNA oligonucleotide probes, microbial community, dynamic

Scaling up stochastic gradient descent for non-convex optimisation

Stochastic gradient descent (SGD) is a widely adopted iterative method for optimizing differentiable objective functions. In this paper, we propose and discuss a novel approach to scale up SGD in applications involving non-convex functions and large datasets. We address the bottleneck problem arising when using both shared and distributed memory. Typically, the former is bounded by limited computation resources and bandwidth whereas the latter suffers from communication overheads. We propose a unified distributed and parallel implementation of SGD (named DPSGD) that relies on both asynchronous distribution and lock-free parallelism. By combining two strategies into a unified framework, DPSGD is able to strike a better trade-off between local computation and communication. The convergence properties of DPSGD are studied for non-convex problems such as those arising in statistical modelling and machine learning. Our theoretical analysis shows that DPSGD leads to speed-up with respect to the number of cores and number of workers while guaranteeing an asymptotic convergence rate of O(1/T) given that the number of cores is bounded by T1 / 4 and the number of workers is bounded by T1 / 2 where T is the number of iterations. The potential gains that can be achieved by DPSGD are demonstrated empirically on a stochastic variational inference problem (Latent Dirichlet Allocation) and on a deep reinforcement learning (DRL) problem (advantage actor critic - A2C) resulting in two algorithms: DPSVI and HSA2C. Empirical results validate our theoretical findings. Comparative studies are conducted to show the performance of the proposed DPSGD against the state-of-the-art DRL algorithms

Salicylic acid and proline enhance water use efficiency, antioxidant defense system and tissues' anatomy of wheat plants under field deficit irrigation stress

Salicylic acid or proline enhances plant performance and encourages resistance to abiotic stress in plants. This investigation tests the influence of soaking kernels in salicylic acid (0.1 mM) or proline (10 mM) on the growth and performance of wheat plants grown in open field under full irrigation (100% of ETc) or deficit irrigation (50% of ETc). The results revealed that plants under field deficit irrigation (FDI) stress showed a decline in growth, kernel yield, relative water content, total content of chlorophylls and carotenoids, as well as negative changes in the anatomy of leaf and stem. Addition of salicylic acid or proline notably increased water use efficiency (WUE) and mitigated the stress created by FDI. Field deficit irrigation stress greatly increased electrolyte leakage, total soluble phenols, proline, and total soluble sugar contents and activities of enzymes SOD, CAT, and POX. Salicylic acid was the more efficient in mitigating FDI stress than proline. The results conclude that salicylic acid, as a growth regulator, could be used to alleviate the negative effect of limited water-availability in soil on wheat as well as improving the growth and yield of the crop

Do the right thing : a privacy policy adherence analysis of over two million apps in Apple iOS App Store

Mobile app developers are often obliged by regulatory frameworks to provide a privacy policy in natural comprehensible language to describe their apps’ privacy practices. However, prior research has revealed that: (1) not all app developers offer links to their privacy policies; and (2) even if they do offer such access, it is difficult to determine if it is a valid link to a (valid) policy. While many prior studies looked at this issue in Google Play Store, Apple App Store, and particularly the iOS store, is much less clear. In this paper, we conduct the first and the largest study to investigate the previous issues in the iOS app store ecosystem. First, we introduce an App Privacy Policy Extractor (APPE), a system that embraces and analyses the metadata of over two million apps to give insightful information about the distribution of the supposed privacy policies, and the content of the provided privacy policy links, store-wide. The result shows that only 58.5% of apps provide links to purported privacy policies, while 39.3% do not provide policy links at all. Our investigation of the provided links shows that only 38.4% of those links were directed to actual privacy policies, while 61.6% failed to lead to a privacy policy. Further, for research purposes we introduce the App Privacy Policy Corpus (APPC-451K); the largest app privacy policy corpus consisting of data relating to more than 451K verified privacy policies

Isolation of Thermoalkalophilic-?-amylase Producing Bacteria and Optimization of Potato Waste Water Medium for Enhancement of ?-amylase Production

Sixty one thermoalkalophilic bacteria were isolated from soil samples in Saudi Arabia’s southern region. Isolate TA-38, obtained from the Tanomah region, showed the best performance for enzyme production and was submitted for further study. It was identified as Bacillus axarquiensis based on 16S rRNA gene sequencing studies. The feasibility of using potato waste water as a simple and cheap medium for the production of ?-amylase was evaluated compared with starch broth medium. The production of ?-amylase in the potato waste water medium was only 13.8% less than that of the starch medium. Maximum enzyme production was achieved after 48 hours of cultivation at the beginning of the stationary phase at pH 10.0 and 50 0C. The appropriate addition of starch; nitrogen; phosphate; and calcium to potato waste water significantly enhanced the production of ?-amylase. The enzyme production reached a maximum of 64.5 Uml-1 with the potato wastewater adding with 0.5 % starch; 0.4 % yeast extract; 0.04% CaCl2-2H2O and 0.05 % KH2PO4.  The optimization of the potato waste water medium led to an approximately 4.02 fold increase in the production of ?-amylase compared to starch broth medium. Data indicated that the potato waste water contained substrates which could be used by bacterial isolate for the production of ?-amylase production and the developed procedure was cost effective since it requires only a slightly addition of nutrients to the medium. Keywords: Isolation; ?-amylase; 16S rRNA; Production; Potato waste water; Thermoalkaliphilic bacteria

Trichoderma virens mitigates the root-knot disease progression in the chickpea plant

This study was planned to investigate the efficacy of various concentrations of Trichoderma virens against Meloidogyne incognita in vitro. The five concentrations viz., S, S/2, S/ 10, S/25, S/50 were prepared and planned for in vitro study to test the potential of T. virens against hatching and mortality of second-staged juveniles of M. incognita. It was observed a reduction in second-staged juveniles hatching within all tested aqueous concentrations of T. virens. The second-stage juvenile mortality was also recorded in the above-given concentrations of T. virens. The maximum decrease in second-stage juveniles hatching was found in standard aqueous fungal concentration (S). Moreover, in the same T. virens concentration (S), mortality of juveniles was also recorded as highest, and was followed by S/2, S/10, S/25 and S/50. Additionally, the application of T. virens as an individual, simultaneous, and sequential order with M. incognita was also investigated in pot-grown chickpea plants and found that its use was significantly effective in suppressing root-galling disease and improved the plants\u27 growth and physiological attributes. According to the correlation coefficient analysis, the root-knot index correlated significantly with the per cent reduction of the plants\u27 growth and physiological attributes

Synthesized copper oxide nanoparticles via the green route act as antagonists to pathogenic root-knot nematode, Meloidogyne incognita

This investigation explains the green synthesis, characterization and biocontrol potential of copper oxide nanoparticles (CuONPs) against second-stage juveniles (J2s) of root-knot nematode, Meloidogyne incognita infesting chickpea. Mono-disperse, spherical, pure CuONPs were synthesized from Jatropha curcas leaf with particle sizes ranging from 5 to 15 nm in diameter. Antagonistic activities of synthesized CuONPs were studied against Meloidogyne incognita. The highest mortality of J2s was found in the 200 ppm concentration of CuONPs at 24 h of exposure. The exact concentration also showed maximum inhibition of J2s hatching from egg masses after six days of exposure. It was worth noting that 25 ppm concentration was the least effective. The pot experiment showed that CuONPs significantly reduced the root infection caused by M. incognita and enhanced chickpea plants’ growth and physiological attributes (Chlorophyll and carotenoid content). The results depicted when the concentration of CuONPs was increased, J2s mortality rate was also increased. We highlighted the antinematode influence of green synthesized CuONPs. Thus, it will offer an excellent eco-friendly strategy to optimize yield under pathogens attack and provide prospects of green synthesized-based nanoparticles development for pests control. Plants mediated CuONPs will also help in resolving the current toxicity concerns and future challenges in the agriculture

Integration of radiometric ground-based data and high-resolution quickbird imagery with multivariate modeling to estimate maize traits in the nile delta of Egypt

In site-specific management, rapid and accurate identification of crop stress at a large scale is critical. Radiometric ground-based data and satellite imaging with advanced spatial and spectral resolution allow for a deeper understanding of crop stress and the level of stress in a given area. This research aimed to assess the potential of radiometric ground-based data and high-resolution QuickBird satellite imagery to determine the leaf area index (LAI), biomass fresh weight (BFW) and chlorophyll meter (Chlm) of maize across well-irrigated, water stress and salinity stress areas in the Nile Delta of Egypt. Partial least squares regression (PLSR) and multiple linear regression (MLR) were evaluated to estimate the three measured traits based on vegetation spectral indices (vegetation-SRIs) derived from these methods and their combination. Maize field visits were conducted during the summer seasons from 28 to 30 July 2007 to collect ground reference data concurrent with the acquisition of radiometric ground-based measurements and QuickBird satellite imagery. The results showed that the majority of vegetation-SRIs extracted from radiometric ground-based data and high-resolution satellite images were more effective in estimating LAI, BFW, and Chlm. In general, the vegetation-SRIs of radiometric ground-based data showed higher R2 with measured traits compared to the vegetation-SRIs extracted from high-resolution satellite imagery. The coefficient of determination (R2) of the significant relationships between vegetation-SRIs of both methods and three measured traits varied from 0.64 to 0.89. For example, with QuickBird high-resolution satellite images, the relationships of the green normalized difference vegetation index (GNDVI) with LAI and BFW showed the highest R2 of 0.80 and 0.84, respectively. Overall, the ground-based vegetation-SRIs and the satellite-based indices were found to be in good agreement to assess the measured traits of maize. Both the calibration (Cal.) and validation (Val.) models of PLSR and MLR showed the highest performance in predicting the three measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery. For example, validation (Val.) models of PLSR and MLR showed the highest performance in predicting the measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery with R2 (0.91) of both methods for LAI, R2 (0.91–0.93) for BFW respectively, and R2 (0.82) of both methods for Chlm. The models of PLSR and MLR showed approximately the same performance in predicting the three measured traits and no clear difference was found between them and their combinations. In conclusion, the results obtained from this study showed that radiometric ground-based measurements and high spectral resolution remote-sensing imagery have the potential to offer necessary crop monitoring information across well-irrigated, water stress and salinity stress in regions suffering lack of freshwater resources

Social and Economic Factors that Influence Health Outcomes in Family Medicine

Over the last twenty years, the social determinants of health (SDOH) have gained more and more attention in the public health community. SDOH are non-medical elements that may be significantly impacted by social policies and have an impact on health. The increasing variety that exists within our societies makes it absolutely necessary for us to incorporate into undergraduate medical education social determinants of health such as racial factors, financial instability, partner violence, insufficient accessibility to transportation, and inadequate social supports, as well as the crucial role of health education.  Nonetheless, a growing body of research indicates that a variety of health outcomes are fundamentally caused by socioeconomic variables including wealth, income, and education together. In this review we highlight the influence of socio-economic factors on health outcomes, approaches to incorporate social determinants of health in family medicine practice, and we also talk about challenges and solutions in addressing social determinants in family medicine. The objective of this research was to assess the impact of social determinants of health on family medicine practice in which socioeconomic determinants of health might have an effect on health outcomes and healthcare delivery within the context of a family medicine clinical environment