Virus,protozoa and organic compounds decay in depurated oysters

Aims

(1) Evaluate the dynamic of the depuration process of Crassostrea gigas oysters using different ultraviolet doses with different amounts of contaminants (virus, protozoa and organic contaminants) and (2) investigate the morphological changes in the oysters' tissues produced by the depuration procedures.

Methods

The oysters were allocated in sites with different degrees of contamination and analyzed after 14 days. Some animals were used as positive controls by artificial bioaccumulation with HAdV2 and MNV1 and subjected to depuration assays using UV lamps (18 or 36 W) for 168 h. The following pollutants were researched in the naturally contaminated oysters, oysters after 14 days in sites and oysters during the depuration processes: virus (HAdV, HAV, HuNoV GI/GII and JCPyV), by (RT) qPCR; protozoa (Cryptosporidium and Giardia species), by immunomagnetic separation and immunofluorescence; and organic compounds (AHs, PAHs, LABs, PCBs and organochlorine pesticides—OCs), by chromatography. Changes in the oysters' tissues produced by the depuration processes were also evaluated using histochemical analysis by light microscopy. In the artificially bioaccumulated oysters, only HAdV2 and MNV1 were investigated by (RT) qPCR before the depuration procedures and after 96 and 168 h of these procedures.

Results

At 14 days post-allocation, HAdV was found in all the sites (6.2 × 105 to 4.4 × 107 GC g^− 1), and Giardia species in only one site. Levels of PCBs and OCs in the oyster's tissues were below the detection limit for all samples. AHs (3.5 to 4.4 μg g^− 1), PAHs (11 to 191 ng g^− 1) and LABs (57 to 751 ng g^− 1) were detected in the samples from 3 sites. During the depuration assays, we found HAdV, Giardia and Cryptosporidium species until 168 h, independent of UV treatment. AHs, PAHs and LABs were found also after 168 h of depuration (36 W and without UV lamp). The depuration procedures did not produce changes in the oysters' tissues. In the artificially contaminated and depurated oysters, we detected HAdV until 168 h and MNV1 until 96 h of depuration.

Conclusion

The applied depuration treatments were unable to eliminate the protozoa or to degrade the HAdV genomes but were able to degrade the MNV1 genomes. Similarly, the UV water treatment was not efficient for aliphatic hydrocarbons, PAHs and LABs, as their concentrations were equivalent or higher to the concentrations of the control samples and samples from depuration tanks without UV treatment.     

Exploring knowledge benchmarking using time-series directional distance functions and bibliometrics

For strategic reasons, benchmarking best practices from efficient competitors is not usual in many data envelopment analysis (DEA) applications. Even for industries composed of multiple branches, providing information about efficient practices for their peers can jeopardize results for those branches if they compete for market, resources or recognition by the central administration. In this work, a time-series adaptation for the DEA directional model is proposed as an alternative for coping with this problem. The methodological approach has three stages for this benchmarking to occur: Data, Information and Knowledge Extraction. In the first stage, we compare the same unit in different moments to identify efficient periods instead of efficient competitors. As a result, successful performance strategies are investigated using the bibliometric coupling of employees' relevant statements in the second and third stages. The application in a branch of the Brazilian Federal Savings Bank allowed an internal benchmarking of efficient periods when specific performance incentives, innovative processes, competitive strategies, and human resource changes were adopted for improving the unit's performance.     

Differential Sintering and Self-Stress Effects on YSZ Ionic Conductivity

Thermomechanical stress simulations are combined with experimental tests to assess the effects of rigid inclusions on the sintering of 8 mol% yttria-stabilized zirconia (8YSZ) green compacts and the phenomena of restricted and differential sintering on microstructure development and electrical properties are investigated. Rigid inclusions of sintered ceramic particles with different shapes (spherical and jagged) and compositions (alumina, 3YSZ, and 8YSZ) are added in different volume fractions (1, 5, and 15 vol%) to 8YSZ commercial powders, which are formed by isostatic pressing and sintered by conventional method. Restricted and differential sintering effects are observed in the development of the microstructure varying in function of volume fraction, shape, structural composition, and thermomechanical properties of the inclusions, resulting in different combinations of tensile and compressive strain states in the matrix, and varying electrical behaviors. The addition of 1 vol% of 8YSZ irregular rigid inclusions leads to an increase of 36% in total electrical conductivity and a 33% increase in power density under solid oxide fuel cells operation conditions compared to samples without inclusions.     

A decision support system for fraud detection in public procurement

Over the past few years, investigators in Brazil have been uncovering numerous corruption and money laundering schemes at all levels of government and in the country's largest corporations. It is estimated that between 2% and 5% of the global GDP is lost annually because of such practices, not only directly impacting public services and private sector development but also strengthening organized crime. However, most law enforcement agencies do not have the capability to carry out systematic corruption risk assessment leveraging on the availability of data related to public procurement. The currently prevailing approach employed by Brazilian law enforcement agencies to detect companies involved in potential cases of fraud consists in receiving circumstantial evidence or complaints from whistleblowers. As a result, a large number of companies involved in fraud remain undetected and unprosecuted. The decision support system (DSS) described in this work addresses these existing limitations by providing a tool for systematic analysis of public procurement. It allows the law enforcement agencies to establish priorities concerning the companies to be investigated. This DSS incorporates data mining algorithms for quantifying dozens of corruption risk patterns for all public contractors inside a specific jurisdiction, leading to improvements in the quality of public spending and to the identification of more cases of fraud. These algorithms combine operations research tools such as graph theory, clusterization, and regression analysis with advanced data science methods to allow the identification of the main risk patterns, such as collusion between bidders, conflicts of interest (e.g., a politician who owns a company contracted by the same government body where he or she was elected), and companies owned by a potentially straw person used for disguising its real owner (e.g., beneficiaries of cash conditional transfer programs). The DSS has already led to a detailed analysis of large public procurement datasets, which add up to more than 50 billion dollars. Moreover, the DSS provided strategic inputs to investigations conducted by federal and state agencies.     

Probing Lipid Coating Dynamics of Quantum Dot Core Micelles via Förster Resonance Energy Transfer

Lipid coated nanocrystal assemblies are among the most extensively investigated nanoparticle platforms for biomedical imaging and therapeutic purposes. However, very few efforts have been addressed to the lipid coating exchange dynamics in such systems, which is key to our understanding of the nanoparticles' coating stability and their interactions with the environment. Here, we apply the Förster resonance energy transfer (FRET) from quantum dot (QD) core to Cy5.5 dye labeled lipids at the surface to monitor the lipid exchange dynamics in situ and to study its dependence on concentration, temperature and solvent. A kinetic model is developed to describe the experimental data, allowing the rate constants and the activation energy for lipid exchange to be determined. The activation energy for lipid exchange on QD micelles is 155 kJ/mol in saline environment and 130 kJ/mol in pure water. The findings presented here provide basic knowledge on these self‐assembled structures and contribute to understanding their performance and to further design of nanomedicine.     

Robust mixture regression modeling based on scale mixtures of skew-normal distributions

The traditional estimation of mixture regression models is based on the assumption of normality (symmetry) of component errors and thus is sensitive to outliers, heavy-tailed errors and/or asymmetric errors. In this work we present a proposal to deal with these issues simultaneously in the context of the mixture regression by extending the classic normal model by assuming that the random errors follow a scale mixtures of skew-normal distributions. This approach allows us to model data with great flexibility, accommodating skewness and heavy tails. The main virtue of considering the mixture regression models under the class of scale mixtures of skew-normal distributions is that they have a nice hierarchical representation which allows easy implementation of inference. We develop a simple EM-type algorithm to perform maximum likelihood inference of the parameters of the proposed model. In order to examine the robust aspect of this flexible model against outlying observations, some simulation studies are also presented. Finally, a real data set is analyzed, illustrating the usefulness of the proposed method.     

Performance evaluation of vector implementations of combinatorial algorithms

We study the performance and the use of vector computers for the solution of combinatorial optimization problems, particularly dynamic programming and shortest path problems. A general model for performance evaluation and vector implementations for the problems described above are studied. These implementations were done on a CRAY-1 vector computer and the computational results obtained show (i) the adequacy of the performance evaluation model and (ii) very important gains concerning computing times, showing that vector computers will be of great importance in the field of combinatorial optimization.     

Back-scattered electron imaging for leakage analysis of four retrofilling materials

This study aimed to evaluate, ex vivo, the nanoleakage in dentinal tubules, the linear infiltration of silver nitrate in the dentin wall/root-end filling material interface, and the presence of gaps in this interface in root-end cavities filled with 4 filling materials. Forty-eight disto-buccal root canals of maxillary molars were instrumented and filled. Retrograde cavities were prepared with ultrasonic points (apical 2 mm). The samples were divided into 2 control groups (n = 4) and 4 experimental groups (n = 10): Group I--white mineral trioxide aggregate (MTA); Group II--Super EBA; Group III--Portland cement; and Group IV--Sealer 26. After 1 week, the specimens were subjected to silver nitrate and prepared for SEM (backscattered electrons). In the apical-apical segment, an area with significantly higher leakage was observed for Super EBA, followed by Portland cement, MTA, and Sealer 26 (P = 0.0054). In the medium and cervical segments, all materials showed the same leakage behavior (P = 0.1815 and P = 0.1723, respectively). The linear infiltration at the dentin wall/root-end filling material interface was higher with Super EBA than the other groups. No differences in the percentage of gaps along the 3 mm of dentin wall/root-end filling material interface between the 4 materials were evident (P > 0.05). Nanoleakage occurred mainly in the apical segment of the samples, and Super EBA showed the highest values. The area and linear leakage were lower in the middle and coronal segments, regardless of the root-end filling material. No material perfectly sealed the root-end cavities, which allowed for the leakage occurrence.     

Solving diameter‐constrained minimum spanning tree problems by constraint programming

The diameter‐constrained minimum spanning tree problem consists in finding a minimum spanning tree of a given graph, subject to the constraint that the maximum number of edges between any two vertices in the tree is bounded from above by a given constant. This problem typically models network design applications where all vertices communicate with each other at a minimum cost, subject to a given quality requirement. We propose alternative formulations using constraint programming that circumvent weak lower bounds yielded by most mixed‐integer programming formulations. Computational results show that the proposed formulation, combined with an appropriate search procedure, solves larger instances and is faster than other approaches in the literature.