Multiresidue method for simultaneous analysis of aflatoxin M1, avermectins,organophosphate pesticides and milbemycin in milk by ultra-performance liquid chromatography coupled to tandem mass spectrometry

A method developed for the simultaneous analysis of aflatoxin M₁, abamectin, doramectin, eprinomectin, ivermectin, moxidectin, acephate, azinphos-ethyl, azinphos-methyl, diazinon, methamidophos, methidathion, mevinphos, pirimiphos-ethyl and pirimiphos-methyl in whole raw milk, based on the QuEChERS method for extraction and clean-up, with detection and quantification by ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) is described. The method was validated according to parameters of the Analytical Quality Assurance Manual from the Brazilian Ministry of Agriculture and Commission Decision 2002/657/EC, and proved suitable for analysis of these analytes within the proposed working range, with recovery values between 77% and 110%, a standard deviation lower than 20%, limits of detection between 0.05 and 0.99 µg l^?1, and limits of quantification between 0.15 and 1.98 µg l^?1. Samples from animals treated with abamectin, doramectin, ivermectin and diazinon were analysed by the validated method. Residues of aflatoxin M₁ were also found in field samples at levels below the established maximum residue limit.     

Determination of Trace Elements in Honey from Different Regions in Rio de Janeiro State (Brazil) by Total Reflection X‐Ray Fluorescence

Trace and minor elements in Brazilian honey were analyzed by total reflection X‐ray fluorescence spectroscopy. Up to 12 elements (K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, Se, Br, and Sr) were detected in 160 samples of honey from 4 regions of Rio de Janeiro State (Barra Mansa, Teresópolis, northern and southern Nova Friburgo). The results showed the samples from Teresópolis had higher rates of essential and nonessential elements than samples from the other regions, except for Ni. K and Ca were the most abundant elements in all samples, in the range of 116.5 to 987.0 μg g^?1. Ni, Cu, Zn, Se, and Sr were identified in small concentrations (0.01 to 12.08 μg g^?1) in all samples, indicating a low level of contamination in all the regions.     

Antilisterial activity of a Carnobacterium piscicola isolated from Brazilian smoked fish (surubim [Pseudoplatystoma sp.]) and its activity against a persistent strain of Listeria monocytogenes isolated from surubim

Data on the prevalence and growth of Listeria monocytogenes in lightly preserved fish products from subtropical and tropical regions are very scarce. Our research describes L. monocytogenes that was detected in 5% of the packages of cold-smoked surubim, a native Brazilian freshwater fish that we analyzed, and shows that the strains isolated were of the same random amplified polymorphic DNA subtype as the strains that were isolated from the same factory 4 years earlier. A bacteriocinogenic strain of Carnobacterium piscicola (strain C2), isolated from vacuum-packed cold-smoked surubim, and two C. piscicola strains, isolated from vacuum-packed, cold-smoked salmon, were capable of limiting or completely inhibiting the growth of an L. monocytogenes (strain V2) isolated from surubim in fish peptone model systems incubated at 10 degrees C. Monocultures of L. monocytogenes reached 108 CFU/ml (g), whereas the growth of L. monocytogenes was completely inhibited by C. piscicola C2. The bacteriocinogenic C. piscicola A9b+ and its nonbacteriocinogenic mutant A9b- reduced maximum Listeria levels by 2 to 3 log units. Both bacteriocinogenic C. piscicola strains prevented listerial growth in cold-smoked fish juices (surubim and salmon). Although the carnobacteria grew poorly on cold-smoked surubim at 10 degrees C, the strains were able to reduce maximum Listeria counts by 1 to 3 log units in an artificially inoculated product (surubim). We conclude that Brazilian smoked fish products harbor L. monocytogenes and should be stabilized against the growth of the organism. C. piscicola C2 has the potential for use as a bioprotective culture in surubim and other lightly preserved fish, but further studies are required to optimize its effect.     

Practical detection of spammers and content promoters in online video sharing systems

A number of online video sharing systems, out of which YouTube is the most popular, provide features that allow users to post a video as a response to a discussion topic. These features open opportunities for users to introduce polluted content, or simply pollution, into the system. For instance, spammers may post an unrelated video as response to a popular one, aiming at increasing the likelihood of the response being viewed by a larger number of users. Moreover, content promoters may try to gain visibility to a specific video by posting a large number of (potentially unrelated) responses to boost the rank of the responded video, making it appear in the top lists maintained by the system. Content pollution may jeopardize the trust of users on the system, thus compromising its success in promoting social interactions. In spite of that, the available literature is very limited in providing a deep understanding of this problem. In this paper, we address the issue of detecting video spammers and promoters. Towards that end, we first manually build a test collection of real YouTube users, classifying them as spammers, promoters, and legitimate users. Using our test collection, we provide a characterization of content, individual, and social attributes that help distinguish each user class. We then investigate the feasibility of using supervised classification algorithms to automatically detect spammers and promoters, and assess their effectiveness in our test collection. While our classification approach succeeds at separating spammers and promoters from legitimate users, the high cost of manually labeling vast amounts of examples compromises its full potential in realistic scenarios. For this reason, we further propose an active learning approach that automatically chooses a set of examples to label, which is likely to provide the highest amount of information, drastically reducing the amount of required training data while maintaining comparable classification effectiveness.     

Analytical Performance Models for MapReduce Workloads

MapReduce is a currently popular programming model to support parallel computations on large datasets. Among the several existing MapReduce implementations, Hadoop has attracted a lot of attention from both industry and research. In a Hadoop job, map and reduce tasks coordinate to produce a solution to the input problem, exhibiting precedence constraints and synchronization delays that are characteristic of a pipeline communication between maps (producers) and reduces (consumers). We here address the challenge of designing analytical models to estimate the performance of MapReduce workloads, notably Hadoop workloads, focusing particularly on the intra-job pipeline parallelism between map and reduce tasks belonging to the same job. We propose a hierarchical model that combines a precedence graph model and a queuing network model to capture the intra-job synchronization constraints. We first show how to build a precedence graph that represents the dependencies among multiple tasks of the same job. We then apply it jointly with an approximate Mean Value Analysis (aMVA) solution to predict mean job response time, throughput and resource utilization. We validate our solution against a queuing network simulator and a real setup in various scenarios, finding very close agreement in both cases. In particular, our model produces estimates of average job response time that deviate from measurements of a real setup by less than 15 %.     

Heavy Metals in Groundwater using Synchrotron Radiation Total Reflection X‐Ray Analysis

Abstract

The environmental pollution problem has increased due to industrial and population growth. São Paulo and Campinas are examples of large urban centers that have grown in a disorderly manner and, today, present contamination problems which have also reached the groundwater.

In order to obtain information about levels of heavy metals in groundwater, analyses were carried out with samples from well monitoring at Landfill Pirelli in Campinas and in some supplying wells in the Campinas region which are, in their majority, wells of industrial supply.

The analytical technique used for achievement of the analysis is total reflection X‐ray fluorescence with synchrotron radiation. All measurements were performed at Synchrotron Light Source Laboratory, located in Campinas, São Paulo, Brazil.

The determined elements in this research were Al, Fe, Mn, Cu, Cr, Ni, Zn, Ba, and Pb. The results were compared with the maximum permitted values (MPV) established by the Brazilian Health Department and, for Landfill Pirelli, the concentrations were higher than the permitted values for Ba and Pb, as was expected, because this landfill received residues during many years without control.

In well supply, as in State University of Campinas (UNICAMP), elements such as Cr and Pb were not expected, so the monitoring of these wells is necessary in order to verify if these elements continue to be detected with values above the permitted values. The same elements were also detected in other supplying wells in the Campinas metropolitan region. For lead (Pb), values above the maximum were observed in 15 wells.

The detection limits obtained varyied from 0.10 µg · L^?1 to 7.91 µg · L^?1 and were in agreement with the values presented by other analytical techniques.