A new biomimetic sensor based on molecularly imprinted polymers for highly sensitive and selective determination of hexazinone herbicide

A new selective sensor based on molecularly imprinted polymers (MIPs) was developed for the determination of hexazinone (HXZ) in environmental samples. MIPs were synthesized using a non-covalent approach, and selection of the monomers employed in the polymerization reaction was carried out by molecular modeling. Three functional monomers with high (2-vinylpyridine (MP17)) and intermediate (methacrylic acid (MP12) and acrylamide (MP5)) energies of binding to the template (HXZ) were selected for preparation of the MIPs, in order to conduct comparative studies and validate the theoretical data. For sensor construction, carbon pastes were modified with each MIP or NIP (non-imprinted polymer), and HXZ determination was performed using differential pulse adsorptive cathodic stripping voltammetry (DPAdCSV). All parameters affecting the sensor response were optimized. In HCl at pH 2.5, the sensor prepared with MP17 (5% w/w in the paste) showed a dynamic linear range between 1.9 × 10−11 and 1.1 × 10−10 mol L−1, and a detection limit of 2.6 × 10−12 mol L−1, under the following conditions: accumulation time of 200 s at a potential of −0.5V, scan rate of 50 mVs−1, pulse amplitude of 60 mV, and pulse width of 50 ms. The sensor was selective in the presence of other similar compounds, and was successfully applied to the analysis of HXZ in river water samples.

The benefits of soft sensor and multi-rate control for the implementation of wireless networked control systems

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Comparative study between powers of sigmoid functions, MLP-backpropagation and polynomials in function approximation problems

Function approximation is a very important task in environments where the computation has to be based on extracting information from data samples in real world processes. So, the development of new mathematical model is a very important activity to guarantee the evolution of the function approximation area. In this sense, we will present the Polynomials Powers of Sigmoid (PPS) as a linear neural network. In this paper, we will introduce one series of practical results for the Polynomials Powers of Sigmoid, where we will show some advantages of the use of the powers of sigmiod functions in relationship the traditional MLP-Backpropagation and Polynomials in functions approximation problems.

Classification of handwriting patterns in patients with Parkinson´s disease, using a biometric sensor

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Avaliação de um sensor de resistencia eletrica e sua correlação com atributos do solo

The electrical measures of the soil have been used as variables that correlate with its characteristics. This study aimed at developing an electrical capacitance sensor of low cost, to evaluate its performance on the field and verify the correlation between the measurements of electrical capacitance with physical properties (sand, silt and clay) and chemical properties of soil (pH, MO, P resin, H + Al, K, Ca, Mg, SB, CTC and V%) and the moisture content. The data sampling was performed at the farm named "Capão da Onça" which belongs to the State University of Ponta Grossa. The samples collection was conducted in an area of approximately 13 hectares, totalizing 81 samples. In each sampling the electrical capacitance of the soil was measured. After the sensor withdrawal, soil samples were collected and sent to be analysed in the laboratory of the College of Agronomics Science of the Paulista State University. The measuring instrument used to collect data on electric capacitance of the soil a digital multimeter was used. The data were submitted to the analysis of correlation and regression. The developed system presented a low cost and it was capable to measuring variation of the electrical capacitance of the soil. The obtained measures satisfactorily correlated with the levels of clay and sand, and weakly with the moisture content. This had demonstrated the possibility to use a sensor to verify the soil texture in not homogeneous areas. The measures of the electrical capacitance of the soil obtained by the sensor had significantly correlated with the soil attributes: calcium, magnesium, pH, SB and CTC. These results had demonstrated the possibility to use a sensor for soil fertility control.

Análise do potencial do sensor OLI (Operational Land Imager) a bordo do Landsat-8 em estimar a concentração de sólidos totais em suspensão no reservatório de Barra Bonita

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Design of a Wireless Vision Sensor for Object Tracking in Wireless Vision Sensor Networks

The integration of CMOS cameras with embedded processors and wireless communication devices has enabled the development of distributed wireless vision systems. Wireless Vision Sensor Networks (WVSNs), which consist of wirelessly connected embedded systems with vision and sensing capabilities, provide wide variety of application areas that have not been possible to realize with the wall-powered vision systems with wired links or scalar-data based wireless sensor networks. In this paper, the design of a middleware for a wireless vision sensor node is presented for the realization of WVSNs. The implemented wireless vision sensor node is tested through a simple vision application to study and analyze its capabilities, and determine the challenges in distributed vision applications through a wireless network of low-power embedded devices. The results of this paper highlight the practical concerns for the development of efficient image processing and communication solutions for WVSNs and emphasize the need for cross-layer solutions that unify these two so-far-independent research areas.

Assessing the gain of biological data integration in gene networks inference

Background: A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods: We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions: This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.

Tokamak TCABR: Acquisition system, data analysis, and remote participation using MDSplus

Each plasma physics laboratory has a proprietary scheme to control and data acquisition system. Usually, it is different from one laboratory to another. It means that each laboratory has its own way to control the experiment and retrieving data from the database. Fusion research relies to a great extent on international collaboration and this private system makes it difficult to follow the work remotely. The TCABR data analysis and acquisition system has been upgraded to support a joint research programme using remote participation technologies. The choice of MDSplus (Model Driven System plus) is proved by the fact that it is widely utilized, and the scientists from different institutions may use the same system in different experiments in different tokamaks without the need to know how each system treats its acquisition system and data analysis. Another important point is the fact that the MDSplus has a library system that allows communication between different types of language (JAVA, Fortran, C, C++, Python) and programs such as MATLAB, IDL, OCTAVE. In the case of tokamak TCABR interfaces (object of this paper) between the system already in use and MDSplus were developed, instead of using the MDSplus at all stages, from the control, and data acquisition to the data analysis. This was done in the way to preserve a complex system already in operation and otherwise it would take a long time to migrate. This implementation also allows add new components using the MDSplus fully at all stages. (c) 2012 Elsevier B.V. All rights reserved.

Detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data

In this paper we discuss the detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data. The sensing units contained either lipase or glucose oxidase immobilized in layer-by-layer (LbL) films deposited onto interdigitated electrodes. The optimization consisted in identifying which part of the electrical response and combination of sensing units yielded the best distinguishing ability. It is shown that complete separation can be obtained for a range of concentrations of glucose and triglyceride when the interactive document map (IDMAP) technique is used to project the data into a two-dimensional plot. Most importantly, the optimization procedure can be extended to other types of biosensors, thus increasing the versatility of analysis provided by tailored molecular architectures exploited with various detection principles. (C) 2012 Elsevier B.V. All rights reserved.

STRUCTURAL CHARACTERIZATION OF CANOPIES OF Eucalyptus spp. USING RADIOMETRIC DATA FROM TM/Landsat 5

Empirical approaches and, more recently, physical approaches, have grounded the establishment of logical connections between radiometric variables derived from remote data and biophysical variables derived from vegetation cover. This study was aimed at evaluating correlations of dendrometric and density data from canopies of Eucalyptus spp., as collected in Capao Bonito forest unit, with radiometric data from imagery acquired by the TM/Landsat-5 sensor on two orbital passages over the study site (dates close to field data collection). Results indicate that stronger correlations were identified between crown dimensions and canopy height with near-infrared spectral band data (rho(s)4), irrespective of the satellite passage date. Estimates of spatial distribution of dendrometric data and canopy density (D) using spectral characterization were consistent with the spatial distribution of tree ages during the study period. Statistical tests were applied to evaluate performance disparities of empirical models depending on which date data were acquired. Results indicated a significant difference between models based on distinct data acquisition dates.

Analysis of the Organic Hydroperoxide Response of Chromobacterium violaceum Reveals That OhrR Is a Cys-Based Redox Sensor Regulated by Thioredoxin

Organic hydroperoxides are oxidants generated during bacterial-host interactions. Here, we demonstrate that the peroxidase OhrA and its negative regulator OhrR comprise a major pathway for sensing and detoxifying organic hydroperoxides in the opportunistic pathogen Chromobacterium violaceum. Initially, we found that an ohrA mutant was hypersensitive to organic hydroperoxides and that it displayed a low efficiency for decomposing these molecules. Expression of ohrA and ohrR was specifically induced by organic hydroperoxides. These genes were expressed as monocistronic transcripts and also as a bicistronic ohrR-ohrA mRNA, generating the abundantly detected ohrA mRNA and the barely detected ohrR transcript. The bicistronic transcript appears to be processed. OhrR repressed both the ohrA and ohrR genes by binding directly to inverted repeat sequences within their promoters in a redox-dependent manner. Site-directed mutagenesis of each of the four OhrR cysteine residues indicated that the conserved Cys21 is critical to organic hydroperoxide sensing, whereas Cys126 is required for disulfide bond formation. Taken together, these phenotypic, genetic and biochemical data indicate that the response of C. violaceum to organic hydroperoxides is mediated by OhrA and OhrR. Finally, we demonstrated that oxidized OhrR, inactivated by intermolecular disulfide bond formation, is specifically regenerated via thiol-disulfide exchange by thioredoxin (but not other thiol reducing agents such as glutaredoxin, glutathione and lipoamide), providing a physiological reducing system for this thiol-based redox switch.

Seeking tools for image fusion between computed tomography, structural and functional magnetic resonance methods for applications in neurosurgery

OBJECTIVE: To evaluate tools for the fusion of images generated by tomography and structural and functional magnetic resonance imaging. METHODS: Magnetic resonance and functional magnetic resonance imaging were performed while a volunteer who had previously undergone cranial tomography performed motor and somatosensory tasks in a 3-Tesla scanner. Image data were analyzed with different programs, and the results were compared. RESULTS: We constructed a flow chart of computational processes that allowed measurement of the spatial congruence between the methods. There was no single computational tool that contained the entire set of functions necessary to achieve the goal. CONCLUSION: The fusion of the images from the three methods proved to be feasible with the use of four free-access software programs (OsiriX, Register, MRIcro and FSL). Our results may serve as a basis for building software that will be useful as a virtual tool prior to neurosurgery.

Processing of spectrally resolved x-ray images of inertial confinement fusion implosion cores recorded with multimonochromatic x-ray imagers

[EN] We discuss the processing of data recorded with multimonochromatic x-ray imagers (MMI) in inertial confinement fusion experiments. The MMI records hundreds of gated, spectrally resolved images that can be used to unravel the spatial structure of the implosion core. In particular, we present a new method to determine the centers in all the array of images, a better reconstruction technique of narrowband implosion core images, two algorithms to determine the shape and size of the implosion core volume based on reconstructed broadband images recorded along three-quasiorthogonal lines of sight, and the removal of artifacts from the space-integrated spectra.

The use of remote sensing imagery for monitoring recovery in the aftermath of a natural disaster: the Hurricane Katrina Case

Although Recovery is often defined as the less studied and documented phase of the Emergency Management Cycle, a wide literature is available for describing characteristics and sub-phases of this process. Previous works do not allow to gain an overall perspective because of a lack of systematic consistent monitoring of recovery utilizing advanced technologies such as remote sensing and GIS technologies. Taking into consideration the key role of Remote Sensing in Response and Damage Assessment, this thesis is aimed to verify the appropriateness of such advanced monitoring techniques to detect recovery advancements over time, with close attention to the main characteristics of the study event: Hurricane Katrina storm surge. Based on multi-source, multi-sensor and multi-temporal data, the post-Katrina recovery was analysed using both a qualitative and a quantitative approach. The first phase was dedicated to the investigation of the relation between urban types, damage and recovery state, referring to geographical and technological parameters. Damage and recovery scales were proposed to review critical observations on remarkable surge- induced effects on various typologies of structures, analyzed at a per-building level. This wide-ranging investigation allowed a new understanding of the distinctive features of the recovery process. A quantitative analysis was employed to develop methodological procedures suited to recognize and monitor distribution, timing and characteristics of recovery activities in the study area. Promising results, gained by applying supervised classification algorithms to detect localization and distribution of blue tarp, have proved that this methodology may help the analyst in the detection and monitoring of recovery activities in areas that have been affected by medium damage. The study found that Mahalanobis Distance was the classifier which provided the most accurate results, in localising blue roofs with 93.7% of blue roof classified correctly and a producer accuracy of 70%. It was seen to be the classifier least sensitive to spectral signature alteration. The application of the dissimilarity textural classification to satellite imagery has demonstrated the suitability of this technique for the detection of debris distribution and for the monitoring of demolition and reconstruction activities in the study area. Linking these geographically extensive techniques with expert per-building interpretation of advanced-technology ground surveys provides a multi-faceted view of the physical recovery process. Remote sensing and GIS technologies combined to advanced ground survey approach provides extremely valuable capability in Recovery activities monitoring and may constitute a technical basis to lead aid organization and local government in the Recovery management.