Evaluation of the hypothalamus-pituitary axis response to exogenous GnRH, estradiol benzoate, And LH during the postpartum period in Nellore cows

The objective was to evaluate when the LH reserve was re-established in postpartum Nellore (Bos indicus) cows by evaluating the response of the hypothalamic-pituitary axis responsiveness to exogenous GnRH or estradiol benzoate (EB). Additionally, we tested the influence of dietary supplementation (SUPL) and calf removal (CR) on the duration of postpartum anestrus. Ninety multiparous lactating Nellore cows were randomly assigned to eight groups. The EB and GnRH groups received 1.0 mg EB (N = 7), and 50 μg lecireline (N = 16), respectively. Additional cows were given the same hormones, and subjected to either nutritional supplementation (EB-SUPL, N = 9; GnRH-SUPL, N = 16), or calf removal at 72 hours after calving (EB-CR, N = 4; GnRH-CR, N = 13). The remaining two groups were the LH (12.5 mg, N = 14) and control groups (saline, N = 11). Hormones were administered weekly from 7 (±5) days postpartum to first ovulation (detection of a CL during a weekly ultrasonographic examination). Blood samples were collected just before and 2 hours (GnRH, LH, and control groups) or 18 hours (EB groups) after hormone or saline (control) administration. Ovulation occurred as early as 15 days postpartum in the GnRH group. The mean ± SEM intervals (days) from calving to first ovulation were EB, 87.7 ± 4.2; EB-CR, 20.3 ± 1.2; EB-SUPL, 60.3 ± 3.2; GnRH, 40.4 ± 2.1; GnRH-CR, 21.0 ± 1.1; GnRH-SUPL, 26.4 ± 1.1; LH, 35.6 ± 1.1; and control, 60.9 ± 2.1. We concluded that there was sufficient LH in the pituitary gland (of Nellore cows) from the second week postpartum to induce ovulation in response to exogenous GnRH. Additionally, calf removal and nutritional supplementation reduced, by 2 to 4 weeks, the interval from calving to an LH increase and ovulation induced by GnRH or EB. © 2013.

Biomimetic sensor based on 5,10,15,20-tetrakis(pentafluorophenyl)-21H,23H- porphyrin iron (III) chloride and MWCNT for selective detection of 2,4-D

Exploitation of the electronic properties of carbon nanotubes for the development of voltammetric and amperometric sensors to monitor analytes of environmental relevance has increased in recent years. This work reports the development of a biomimetic sensor based on a carbon paste modified with 5,10,15,20-tetrakis(pentafluorophenyl)-21H,23H-porphyrin iron (III) chloride (a biomimetic catalyst of the P450 enzyme) and multi-wall carbon nanotubes (MWCNT), for the sensitive and selective detection of the herbicide 2,4- dichlorophenoxyacetic acid (2,4-D). The sensor was evaluated using cyclic voltammetry and amperometry, for electrochemical characterization and quantification purposes, respectively. Amperometric analyses were carried out at -100 mV vs. Ag/AgCl(KClsat), using a 0.1 mol L-1 phosphate buffer solution at pH 6.0 as the support electrolyte. Under these optimized analytical conditions, the sensor showed a linear response between 9.9 × 10-6 and 1.4 × 10-4 mol L-1, a sensitivity of 1.8 × 104 (±429) μA L mol -1, and limits of detection and quantification of 2.1 × 10 -6 and 6.8 × 10-6 mol L-1, respectively. The incorporation of functionalized MWCNT in the carbon paste resulted in a 10-fold increase in the response, compared to that of the biomimetic sensor without MWCNT. In addition, the low applied potential (-100 mV) used to obtain high sensitivity also contributed to the excellent selectivity of the proposed sensor. The viability of the application of this sensor for analysis of soil samples was confirmed by satisfactory recovery values, with a mean of 96% and RSD of 2.1% (n = 3). © 2013 Elsevier B.V.

Development of an electrochemical sensor based on nanostructured hausmannite-type manganese oxide for detection of sodium ions

The preparation and electrochemical characterization of hausmannite-type manganese oxide to use as a sensing material for sodium ion is described. This paper reports a new via synthetic to obtain of the hausmannite-type manganese oxide and its application in the construction of modified electrode as a voltammetric sensor. The electrochemical activity of hausmannite-type manganese oxide is controlled by intercalation/deintercalation of the sodium ions within the oxide lattice. The detection is based on the measurement of anodic current generated by oxidation of MnIII-MnIV at electrode surface. The best electrochemical response was obtained for a sensor composition of 20% (w/w) hausmannite oxide in the paste, a TRIS buffer solution of pH 6.0-7.0 and a scan rate of 50 mV s-1. A sensitive linear voltammetric response for sodium ions was obtained in the concentration range of 2.01 × 10 -5-2.09 × 10-4 mol L-1 with a slope of 355 μA L mmol-1 and a detection limit of 7.50 × 10 -6 mol L-1 using cyclic voltammetry. The use of hausmannite has significantly improved the selectivity of the sensor compared to the birnessite-type manganese oxide modified electrode. Under the working conditions, the proposed method was successfully applied to determination of sodium ions in urine samples. © 2013 Elsevier B.V.

Development and application of an electronic tongue for detection and monitoring of nitrate, nitrite and ammonium levels in waters

This work presents the first study and development of an electronic tongue analysis system for the monitoring of nitrogen stable species: nitrate, nitrite and ammonium in water. The electronic tongue was composed of an array of 15 potentiometric poly(vinyl chloride) membrane sensors sensitive to cations and anions plus an artificial neural network (ANN) response model. The building of the ANN model was performed in a medium containing sodium, potassium, and chloride as interfering ions, thus simulating real environmental samples. The correlation coefficient in the cross-validation of nitrate, nitrite and ammonium was satisfactory in the three cases with values higher than 0.92. Finally, the utility of the proposed system is shown in the monitoring of the photoelectrocatalytic treatment of nitrate. © 2013 Elsevier B.V.

Damage detection in nonlinear structures using discrete-time volterra series

Structural damage identification is basically a nonlinear phenomenon; however, nonlinear procedures are not used currently in practical applications due to the complexity and difficulty for implementation of such techniques. Therefore, the development of techniques that consider the nonlinear behavior of structures for damage detection is a research of major importance since nonlinear dynamical effects can be erroneously treated as damage in the structure by classical metrics. This paper proposes the discrete-time Volterra series for modeling the nonlinear convolution between the input and output signals in a benchmark nonlinear system. The prediction error of the model in an unknown structural condition is compared with the values of the reference structure in healthy condition for evaluating the method of damage detection. Since the Volterra series separate the response of the system in linear and nonlinear contributions, these indexes are used to show the importance of considering the nonlinear behavior of the structure. The paper concludes pointing out the main advantages and drawbacks of this damage detection methodology. © (2013) Trans Tech Publications.

Damage Detection Based on Electromechanical Impedance Principle and Principal Components

This paper presents a novel time domain approach for Structural Health Monitoring (SHM) systems based on Electromechanical Impedance (EMI) principle and Principal Component Coefficients (PCC), also known as loadings. Differently of typical applications of EMI applied to SHM, which are based on computing the Frequency Response Function (FRF), in this work the procedure is based on the EMI principle but all analysis is conducted directly in time-domain. For this, the PCC are computed from the time response of PZT (Lead Zirconate Titanate) transducers bonded to the monitored structure, which act as actuator and sensor at the same time. The procedure is carried out exciting the PZT transducers using a wide band chirp signal and getting their time responses. The PCC are obtained in both healthy and damaged conditions and used to compute statistics indexes. Tests were carried out on an aircraft aluminum plate and the results have demonstrated the effectiveness of the proposed method making it an excellent approach for SHM applications. Finally, the results using EMI signals in both frequency and time responses are obtained and compared. © The Society for Experimental Mechanics 2014.

Genetic and biochemical markers of hydroxyurea therapeutic response in sickle cell anemia

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A trade off between separation, detection and sustainability in liquid chromatographic fingerprinting

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

Evaporative respiratory cooling augments pit organ thermal detection in rattlesnakes

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

A Silsesquioxane Organically Modified with 4-Amino-5-(4-pyridyl)-4H-1,2,4-triazole-3-thiol: Thermal Behavior and Its Electrochemical Detection of Sulfhydryl Compounds

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

Electroanalytical determination of bumetanide employing a biomimetic sensor for detection of doping in sports

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

Development and application of a screen-printed electrode modified with MWCNT for the electrocatalytic detection of thiram

Interest in the electronic properties of carbon nanotubes has increased in recent years. These materials can be used in the development of electrochemical sensors for the measurement and monitoring of analytes of environmental interest, such as pharmaceuticals, dyes, and pesticides. This work describes the use of homemade screen-printed electrodes modified with multi-walled carbon nanotubes (MWCNT) for the electrochemical detection of the fungicide thiram. The electrochemical characteristics of the proposed system were evaluated using cyclic voltammetry, with investigation of the electrochemical behavior of the sensor in the presence of the analyte, and estimation of electrochemical parameters including the diffusion coefficient, electron transfer coefficient (α), and number of electrons transferred in the catalytic electro-oxidation. The sensor response was optimized using amperometry. The best sensor performance was obtained in 0.1 mol L-1 phosphate buffer solution at pH 8.0, where a detection limit of 7.9 x 10-6 mol L-1 was achieved. Finally, in order to improve the sensitivity of the sensor, square wave voltammetry (SWV) was used for thiram quantification, instead of amperometry. Using SWV, a response range for thiram from 9.9 x 10-6 to 9.1 x 10-5 mol L-1 was obtained, with a sensitivity of 30948 µA mol L-1, and limits of detection and quantification of 1.6 x 10-6 and 5.4 x 10-6 mol L-1, respectively. The applicability of this efficient new alternative methodology for thiram detection was demonstrated using analyses of enriched soil samples.

Development of biomimetic sensor for fast and sensitive detection of Norfloxacin

A biomimetic sensor is proposed as a promising new analytical method for determination of norfloxacin (NF) in pharmaceuticals. The sensor was prepared by modifying a glassy carbon electrode surface with a Nafion® membrane doped with poly(copper phthalocyanine) complex [poly-CuPc]. Amperometric measurements carried out with the sensor under an applied potential of -0.05 V vs Ag|AgCl in 0.1 mol L-1 acetic acid containing 1.5 × 10-3 mol L-1 hydrogen peroxide showed a linear response range from 2.0 × 10-4 to 1.2 × 10-3 mol L-1. Selectivity and interference studies were also performed. A sensor response mechanism is proposed, based on the experimental evidence. Recovery studies were carried out using environmental samples, in order to evaluate the sensor’s potential for use with these sample classes. Finally, sensor performance was evaluated using analyses of commercial formulations.

Multi-walled carbon nanotubes modified screen-printed electrodes for cisplatin detection

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)