This study investigate the removal efficiency of chromium (VI) from water by using (R-AgCl) Silver chloride resin (SCR) as selective sorbent material for removal chromium (VI) from aqueous solution; the study was carried out by sorption batch experiments at room temperature(25°C). Efficiency of Cr(VI) removal was determined by measuring the chromium concentration before and after the sorption process. Atomic absorption spectroscopy (AAS) method has been applied as analytical method for determination of Cr concentration in water. Governing factors for removal efficiency were proposed, analyzed and compared. It was conducted that the process is mostly affected by pH value of solution, mass of sorbent and concentration of Cr(VI) , which was the most efficient sorbent showed maximal efficiency and maximal sorption capacity, 1.5g of SCR after 60 min at 25 °C and pH 6.5 recovered 98% of Cr(VI) from 100 mL of water polluted with 20 mg of Cr(VI).

High antibiotic releases from manufacturing facilities have been identified as a risk factor for antibiotic resistance development in bacterial pathogens. However, the role of antibiotic pollution in selection and transferability of antibiotic resistance genes (ARGs) is still limited. In this study, we analyzed effluents from azithromycin-synthesis and veterinary-drug formulation facilities as well as sediments from receiving river and creek taken at the effluent discharge sites, upstream and downstream of discharge. Culturing showed that the effluent discharge significantly increased the proportion of antibiotic resistant bacteria in exposed sediments compared to the upstream ones. Quantitative real-time PCR revealed that effluents from both industries contained high and similar relative abundances of resistance genes [sul1, sul2, qacE/qacEΔ1, tet(A)], class 1 integrons (intI1) and IncP-1 plasmids (korB). Consequently, these genes significantly increased in relative abundances in receiving sediments, with more pronounced effects being observed for river than for creek sediments due to lower background levels of the investigated genes in the river. In addition, effluent discharge considerably increased transfer frequencies of captured ARGs from exposed sediments into Escherichia coli CV601 recipient as shown by biparental mating experiments. Most plasmids exogenously captured from effluent and polluted sediments belonged to the broad host range IncP-1ε plasmid group, conferred multiple antibiotic resistance and harbored class 1 integrons. Discharge of pharmaceutical waste from antibiotic manufacturing sites thus poses a risk for development and dissemination of multi-resistant bacteria, including pathogens.

Manure application to agricultural soil introduces antibiotic residues and increases the abundance of antibiotic-resistant bacteria (ARB) carrying antibiotic resistance genes (ARGs), often located on mobile genetic elements (MGEs). The rhizosphere is regarded as a hotspot of microbial activity and gene transfer, which can alter and prolong the effects of organic fertilizers containing antibiotics. However, not much is known about the influence of plants on the effects of doxycycline applied to soil via manure. In this study, the effects of manure spiked with or without doxycycline on the prokaryotic community composition as well as on the relative abundance of ARGs and MGEs in lettuce rhizosphere and bulk soil were investigated by means of a polyphasic cultivation-independent approach. Samples were taken 42 days after manure application, and total community DNA was extracted. Besides a pronounced manure effect, doxycycline spiking caused an additional enrichment of ARGs and MGEs. High-throughput quantitative PCR revealed an increase in tetracycline, aminoglycoside, and macrolide–lincosamide–streptogramin B (MLS_B) resistance genes associated with the application of manure spiked with doxycycline. This effect was unexpectedly lower in the rhizosphere than in bulk soil, suggesting a faster dissipation of the antibiotic and a more resilient prokaryotic community in the rhizosphere. Interestingly, the tetracycline resistance gene tetA(P) was highly enriched in manure-treated bulk soil and rhizosphere, with highest values observed in doxycycline-treated bulk soil, concurring with an enrichment of Clostridia. Thus, the gene tetA(P) might be a suitable marker of soil contamination by ARB, ARGs, and antibiotics of manure origin. These findings illustrate that the effects of manure and doxycycline on ARGs and MGEs differ between rhizosphere and bulk soil, which needs to be considered when assessing risks for human health connected to the spread of ARGs in the environment.

Understanding diversity is important in our inclusive society to hedge against ignorance and accommodate plural perspectives. Diversity nowadays can be observed in online social spaces. People from different backgrounds (e.g. gender, age, culture, expertise) are interacting every day around online digital objects (e.g. videos, images and web articles) leaving their social content in different format, commonly as textual comments and profiles. The social clouds around digital objects (i.e. user comments, user profiles and other metadata of digital objects) offer rich source of information about the users and their perspectives on different domains. Although, researchers from disparate disciplines have been working on understanding and measuring diversity from different perspectives, little has been done to automatically measure diversity in social clouds. This is the main objective of this research. This research proposes a semantic driven computational model to systematically represent and automatically measure diversity in a social cloud. Definitions from a prominent diversity framework and Semantic Web techniques underpin the proposed model. Diversity is measured based on four diversity indices - variety, balance, coverage and (within and across) disparity with regards to two perspectives – (a) domain, which is captured in user comments and represented by domain ontologies, and (b) user, which is captured in profiles of users who made the comments and represented by a proposed User Diversity Ontology. The proposed model is operationalised resulting in a Semantic Driven Diversity Analytics Tool (SeDDAT), which is responsible for diversity profiling based on the diversity indices. The proposed approach of applying the model is illustrated on social clouds from two social spaces - open (YouTube) and closed (Active Video Watching (AVW-Space)). The open social cloud shows the applicability of the model to generate diversity profiles of a large pool of videos (600) with thousands of users and comments. Closed social clouds of two user groups around same set of videos illustrate transferability and further utility of the model. A list of possible diversity patterns within social clouds is provided, which in turn deepen the understanding of diversity and open doors for further utilities of the diversity profiles. The proposed model is applicable in similar scenarios, such as in the social clouds around MOOCs and news articles.

Age prediction is an active study field that can substantially affect many computer vision problems like object recognition. In this paper, an accurate system with extensive experiments is proposed to provide an efficient and accurate approach for age range prediction of people from their facial images. Histogram Equalization technique is used to reduce the illumination effects on all facial images taken from FG-NET and UTD aging databases, and image resizing is used to unify all image sizes. Moreover, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are used to extract the features of these images. Then, Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) are used for the classification processes. To evaluate the performance of the proposed system, both Leave-One-Out (LOO) and Confusion Matrix (CM) are used. The extensive and intensified experiments improved the age range predicting performance up to 98.6%.

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Produce is increasingly recognized as a reservoir of human pathogens and transferable antibiotic resistance genes. This study aimed to explore methods to characterize the transferable resistome of bacteria associated with produce. Mixed salad, arugula, and cilantro purchased from supermarkets in Germany were analyzed by means of cultivation- and DNA-based methods. Before and after a nonselective enrichment step, tetracycline (TET)-resistant Escherichia coli were isolated and plasmids conferring TET resistance were captured by exogenous plasmid isolation. TET-resistant E. coli isolates, transconjugants, and total community DNA (TC-DNA) from the microbial fraction detached from leaves or after enrichment were analyzed for the presence of resistance genes, class 1 integrons, and various plasmids by real-time PCR and PCR-Southern blot hybridization. Real-time PCR primers were developed for IncI and IncF plasmids. TET-resistant E. coli isolated from arugula and cilantro carried IncF, IncI1, IncN, IncHI1, IncU, and IncX1 plasmids. Three isolates from cilantro were positive for IncN plasmids and bla_CTX-M-1 From mixed salad and cilantro, IncF, IncI1, and IncP-1β plasmids were captured exogenously. Importantly, whereas direct detection of IncI and IncF plasmids in TC-DNA failed, these plasmids became detectable in DNA extracted from enrichment cultures. This confirms that cultivation-independent DNA-based methods are not always sufficiently sensitive to detect the transferable resistome in the rare microbiome. In summary, this study showed that an impressive diversity of self-transmissible multiple resistance plasmids was detected in bacteria associated with produce that is consumed raw, and exogenous capturing into E. coli suggests that they could transfer to gut bacteria as well.

ABSTRACT To our knowledge all the previous researches and studies for extracting the likely cause of emotion spikes was for comments and reviews with non-Arabic languages. Although, According to a study performed by Semiocast , Arabic was the fastest growing language on Twitter in 2011, and was the 6th most used language on Twitter in 2012. While a wide range of Arabic opinionated posts are broadcasted, research in the area of Arabic sentiment analysis remain sparse and show a very slow progress compared to that being carried out in other languages, mainly in English . For that, our work will be to identifying the likely causes of strong and sudden change of emotions within the temporal dimension of influential users’ emotion flow in Arabic Twitter we chose twitter because Twitter as a microblogging platform, receives over 500 million tweets worldwide every day as per 2016.These emotion spikes are the reaction of users toward certain events. Hence, our system will try to extracts keyphrases, which associated with each identified emotion spike, and passes them to an analyze step. Then the system will detect the named-entities and events or topics identification since the extracted keyphrases indicate a change on user’s emotions, and represent the causes of a particular emotion spike. 

Modelling diversity is especially valuable in soft skills learning, where contextual awareness and understanding of different perspectives are crucial. This paper presents an application of a diversity analytics pipeline to generate domain diversity profiles for learners as captured in their comments while watching videos for learning a soft skill. The datasets for analysis were collected from a series of studies on learning presentation skills with Active Video Watching system (AVW-Space). Two user studies (with 37 postgraduates and 140 undergraduates, respectively) were compared. The learners’ diversity and personal profiles are used to further understand and highlight any notable patterns about their domain coverage on presentation skills.