Quantification of endocrine disruptors and pesticides in water by gas chromatography–tandem mass spectrometry. Method validation using weighted linear regression schemes

Amulti-residue methodology based on a solid phase extraction followed by gas chromatography–tandem mass spectrometry was developed for trace analysis of 32 compounds in water matrices, including estrogens and several pesticides from different chemical families, some of them with endocrine disrupting properties. Matrix standard calibration solutions were prepared by adding known amounts of the analytes to a residue-free sample to compensate matrix-induced chromatographic response enhancement observed for certain pesticides. Validation was done mainly according to the International Conference on Harmonisation recommendations, as well as some European and American validation guidelines with specifications for pesticides analysis and/or GC–MS methodology. As the assumption of homoscedasticity was not met for analytical data, weighted least squares linear regression procedure was applied as a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line, improving accuracy at the lower end of the calibration curve. The method was considered validated for 31 compounds after consistent evaluation of the key analytical parameters: specificity, linearity, limit of detection and quantification, range, precision, accuracy, extraction efficiency, stability and robustness.

Scalable and efficient data processing in networked control systems

Network control systems (NCSs) are spatially distributed systems in which the communication between sensors, actuators and controllers occurs through a shared band-limited digital communication network. However, the use of a shared communication network, in contrast to using several dedicated independent connections, introduces new challenges which are even more acute in large scale and dense networked control systems. In this paper we investigate a recently introduced technique of gathering information from a dense sensor network to be used in networked control applications. Obtaining efficiently an approximate interpolation of the sensed data is exploited as offering a good tradeoff between accuracy in the measurement of the input signals and the delay to the actuation. These are important aspects to take into account for the quality of control. We introduce a variation to the state-of-the-art algorithms which we prove to perform relatively better because it takes into account the changes over time of the input signal within the process of obtaining an approximate interpolation.

Evaluation of maternal and neonatal hospital care: quality index of completeness

OBJECTIVE Develop an index to evaluate the maternal and neonatal hospital care of the Brazilian Unified Health System.METHODS This descriptive cross-sectional study of national scope was based on the structure-process-outcome framework proposed by Donabedian and on comprehensive health care. Data from the Hospital Information System and the National Registry of Health Establishments were used. The maternal and neonatal network of Brazilian Unified Health System consisted of 3,400 hospitals that performed at least 12 deliveries in 2009 or whose number of deliveries represented 10.0% or more of the total admissions in 2009. Relevance and reliability were defined as criteria for the selection of variables. Simple and composite indicators and the index of completeness were constructed and evaluated, and the distribution of maternal and neonatal hospital care was assessed in different regions of the country.RESULTS A total of 40 variables were selected, from which 27 single indicators, five composite indicators, and the index of completeness of care were built. Composite indicators were constructed by grouping simple indicators and included the following variables: hospital size, level of complexity, delivery care practice, recommended hospital practice, and epidemiological practice. The index of completeness of care grouped the five variables and classified them in ascending order, thereby yielding five levels of completeness of maternal and neonatal hospital care: very low, low, intermediate, high, and very high. The hospital network was predominantly of small size and low complexity, with inadequate child delivery care and poor development of recommended and epidemiological practices. The index showed that more than 80.0% hospitals had a low index of completeness of care and that most qualified heath care services were concentrated in the more developed regions of the country.CONCLUSIONS The index of completeness proved to be of great value for monitoring the maternal and neonatal hospital care of Brazilian Unified Health System and indicated that the quality of health care was unsatisfactory. However, its application does not replace specific evaluations.

Benthic macroinvertebrates as bioindicators of water quality in an Atlantic forest fragment

The objective of this study was to evaluate benthic macroinvertebrate communities as bioindicators of water quality in five streams located in the "Reserva Particular do Patrimônio Natural" (RPPN) Mata Samuel de Paula and its surroundings, in the municipality of Nova Lima near the city of Belo Horizonte, Minas Gerais State, southeastern Brazil. This region has been strongly modified by human activities including mining and urbanization. Samples were collected in the field every three months between August 2004 and November 2005, totaling six samplings in the rainy and dry seasons. This assessment identified one area ecologically altered while the other sampling sites were found to be minimally disturbed systems, with well-preserved ecological conditions. However, according to the Biological Monitoring Work Party (BMWP) and the Average Score Per Taxon (ASPT) indices, all sampling sites had excellent water quality. A total of 14,952 organisms was collected, belonging to 155 taxa (148 Insecta, two Annelida, one Bivalvia, one Decapoda, one Planariidae, one Hydracarina, and one Entognatha). The most abundant benthic groups were Chironomidae (47.9%), Simuliidae (12.3%), Bivalvia (7.5%), Decapoda (6.1%), Oligochaeta (5.2%), Polycentropodidae (3.7%), Hydropsychidae (2.5%), Calamoceratidae (1.8%), Ceratopogonidae (1.7%), and Libellulidae (1.2%). The assessment of the benthic functional feeding groups showed that 34% of the macroinvertebrates were collector-gatherers, 29% predators, 24% collector-filterers, 8% shredders, and 5% scrapers. The RPPN Mata Samuel de Paula comprises diversified freshwater habitats that are of great importance for the conservation of many benthic taxa that are intolerant to organic pollution.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Structured output SVM for remote sensing image classification

In the recent years, kernel methods have revealed very powerful tools in many application domains in general and in remote sensing image classification in particular. The special characteristics of remote sensing images (high dimension, few labeled samples and different noise sources) are efficiently dealt with kernel machines. In this paper, we propose the use of structured output learning to improve remote sensing image classification based on kernels. Structured output learning is concerned with the design of machine learning algorithms that not only implement input-output mapping, but also take into account the relations between output labels, thus generalizing unstructured kernel methods. We analyze the framework and introduce it to the remote sensing community. Output similarity is here encoded into SVM classifiers by modifying the model loss function and the kernel function either independently or jointly. Experiments on a very high resolution (VHR) image classification problem shows promising results and opens a wide field of research with structured output kernel methods.

Textural classification of land cover using support vector machines: an empirical comparison with parametric, non parametric and hybrid classifiers in the Bolivian Amazon

Land cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims to establish an efficient classification approach to accurately map all broad land cover classes in a large, heterogeneous tropical area of Bolivia, as a basis for further studies (e.g., land cover-land use change). Specifically, we compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbour and four different support vector machines - SVM), and hybrid classifiers, using both hard and soft (fuzzy) accuracy assessments. In addition, we test whether the inclusion of a textural index (homogeneity) in the classifications improves their performance. We classified Landsat imagery for two dates corresponding to dry and wet seasons and found that non-parametric, and particularly SVM classifiers, outperformed both parametric and hybrid classifiers. We also found that the use of the homogeneity index along with reflectance bands significantly increased the overall accuracy of all the classifications, but particularly of SVM algorithms. We observed that improvements in producer’s and user’s accuracies through the inclusion of the homogeneity index were different depending on land cover classes. Earlygrowth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land cover classes were mapped with producer’s and user’s accuracies of around 90%. Our approach seems very well suited to accurately map land cover in tropical regions, thus having the potential to contribute to conservation initiatives, climate change mitigation schemes such as REDD+, and rural development policies.

Plasma membrane cyclic nucleotide gated calcium channels control land plant thermal sensing and acquired thermotolerance.

Typically at dawn on a hot summer day, land plants need precise molecular thermometers to sense harmless increments in the ambient temperature to induce a timely heat shock response (HSR) and accumulate protective heat shock proteins in anticipation of harmful temperatures at mid-day. Here, we found that the cyclic nucleotide gated calcium channel (CNGC) CNGCb gene from Physcomitrella patens and its Arabidopsis thaliana ortholog CNGC2, encode a component of cyclic nucleotide gated Ca(2+) channels that act as the primary thermosensors of land plant cells. Disruption of CNGCb or CNGC2 produced a hyper-thermosensitive phenotype, giving rise to an HSR and acquired thermotolerance at significantly milder heat-priming treatments than in wild-type plants. In an aequorin-expressing moss, CNGCb loss-of-function caused a hyper-thermoresponsive Ca(2+) influx and altered Ca(2+) signaling. Patch clamp recordings on moss protoplasts showed the presence of three distinct thermoresponsive Ca(2+) channels in wild-type cells. Deletion of CNGCb led to a total absence of one and increased the open probability of the remaining two thermoresponsive Ca(2+) channels. Thus, CNGC2 and CNGCb are expected to form heteromeric Ca(2+) channels with other related CNGCs. These channels in the plasma membrane respond to increments in the ambient temperature by triggering an optimal HSR, leading to the onset of plant acquired thermotolerance.

Conditioning of stochastic 3-D fracture networks to hydrological and geophysical data

The geometry and connectivity of fractures exert a strong influence on the flow and transport properties of fracture networks. We present a novel approach to stochastically generate three-dimensional discrete networks of connected fractures that are conditioned to hydrological and geophysical data. A hierarchical rejection sampling algorithm is used to draw realizations from the posterior probability density function at different conditioning levels. The method is applied to a well-studied granitic formation using data acquired within two boreholes located 6 m apart. The prior models include 27 fractures with their geometry (position and orientation) bounded by information derived from single-hole ground-penetrating radar (GPR) data acquired during saline tracer tests and optical televiewer logs. Eleven cross-hole hydraulic connections between fractures in neighboring boreholes and the order in which the tracer arrives at different fractures are used for conditioning. Furthermore, the networks are conditioned to the observed relative hydraulic importance of the different hydraulic connections by numerically simulating the flow response. Among the conditioning data considered, constraints on the relative flow contributions were the most effective in determining the variability among the network realizations. Nevertheless, we find that the posterior model space is strongly determined by the imposed prior bounds. Strong prior bounds were derived from GPR measurements and helped to make the approach computationally feasible. We analyze a set of 230 posterior realizations that reproduce all data given their uncertainties assuming the same uniform transmissivity in all fractures. The posterior models provide valuable statistics on length scales and density of connected fractures, as well as their connectivity. In an additional analysis, effective transmissivity estimates of the posterior realizations indicate a strong influence of the DFN structure, in that it induces large variations of equivalent transmissivities between realizations. The transmissivity estimates agree well with previous estimates at the site based on pumping, flowmeter and temperature data.

Oral and general health-related quality of life in patients treated for oral cancer compared to control group.

BACKGROUND Health-related quality of life (HRQoL) is gaining importance as a valuable outcome measure in oral cancer area. The aim of this study was to assess the general and oral HRQoL of oral and oropharyngeal cancer patients 6 or more months after treatment and compare them with a population free from this disease. METHODS A cross-sectional study was carried out with patients treated for oral cancer at least 6 months post-treatment and a gender and age group matched control group. HRQoL was measured with the 12-Item Short Form Health Survey (SF-12); oral HRQoL (OHRQoL) was evaluated using the Oral Health Impact Profile (OHIP-14) and the Oral Impacts on Daily Performances (OIDP). Multivariable regression models assessed the association between the outcomes (SF-12, OHIP-14 and OIDP) and the exposure (patients versus controls), adjusting for sex, age, social class, functional tooth units and presence of illness. RESULTS For patients (n = 142) and controls (n = 142), 64.1% were males. The mean age was 65.2 (standard deviation (sd): 12.9) years in patients and 67.5 (sd: 13.7) years in controls. Patients had worse SF-12 Physical Component Summary scores than controls even in fully the adjusted model [β-coefficient = -0.11 (95% CI: -5.12-(-0.16)]. The differences in SF-12 Mental Component Summary were not statistically significant. Regarding OHRQoL patients had 11.63 (95% CI: 6.77-20.01) higher odds for the OHIP-14 and 21.26 (95% CI: 11.54-39.13) higher odds for OIDP of being in a worse category of OHRQoL compared to controls in the fully adjusted model. CONCLUSION At least 6 months after treatment, oral cancer patients had worse OHRQoL, worse physical HRQoL and similar psychological HRQoL than the general population.

Muchakinock Creek: Improving Water Quality for the Future, 2005

History has taken its toll on Muchakinock Creek. A number of problems over the years have led to the stream’s current state, one that’s landed it on Iowa’s list of impaired waters. However, the stream is also full of opportunity. The opportunity to improve water quality not only for the aquatic life and wildlife that live there, but also to pass along clean water to future generations of Iowans. But to act on this opportunity, we need your help.

Emerging Water Quality Issues, March 2001

Report produced by Iowa Department of Natural Resources about water quality issues.

Managing Iowa's Water Quality Information

A report by the Iowa Department of Natural Resources on how to manage water quality information.