Neurohormonal, hemodynamic, and electrocardiographic evaluations of healthy dogs receiving long-term administration of doxorubicin

Objective - To evaluate diagnostic testing that could be used to establish an early diagnosis of cardiotoxicosis induced by long-term administration of doxorubicin. Animals - 13 adult mixed-breed dogs. Procedures - 7 dogs were administered doxorubicin chloride (30 mg/m2, IV, q 21 d for 168 days [cumulative dose, 240 mg/m2]), and 6 dogs received saline (0.9% NaCl) solution (5 mL, IV, q 21 d for 168 days; control group). Echocardiography, ECG, arterial blood pressure, plasma renin activity (PRA), and plasma concentrations of norepinephrine and brain natriuretic peptide (BNP) were assessed before each subsequent administration of doxorubicin and saline solution. Results - Dogs that received doxorubicin had a significant decrease in R-wave amplitude, compared with values for the control group, from 30 to 210 mg/m2. Doxorubicin-treated dogs had decreases in fractional shortening and left ventricular ejection fraction evident as early as 30 mg/m2, but significant differences between groups were not detected until 90 mg/m2 was reached. There was also a significant increase in PRA (≥ 120 mg/m2) and left ventricular end-systolic and end-diastolic dimensions (≥ 60 and ≥ 180 mg/m2, respectively). Systemic arterial pressure, remaining echocardiographic variables, and concentrations of norepinephrine and BNP had significant variations, but of no clinical importance, during doxorubicin administration. Conclusions and clinical relevance - Doxorubicin-induced cardiotoxicosis developed at 120 mg/m2, but there were no clinical signs of dilated cardiomyopathy or congestive heart failure. Echocardiography and determination of PRA were able to detect early cardiac alterations during the development of dilated cardiomyopathy, despite apparently differing degrees of sensitivity to development of doxorubicin-induced cardiotoxicosis.

Neural network approach for surface roughness prediction in surface grinding

Several systems are currently tested in order to obtain a feasible and safe method for automation and control of grinding process. This work aims to predict the surface roughness of the parts of SAE 1020 steel ground in a surface grinding machine. Acoustic emission and electrical power signals were acquired by a commercial data acquisition system. The former from a fixed sensor placed near the workpiece and the latter from the electric induction motor that drives the grinding wheel. Both signals were digitally processed through known statistics, which with the depth of cut composed three data sets implemented to the artificial neural networks. The neural network through its mathematical logical system interpreted the signals and successful predicted the workpiece roughness. The results from the neural networks were compared to the roughness values taken from the worpieces, showing high efficiency and applicability on monitoring and controlling the grinding process. Also, a comparison among the three data sets was carried out.

Risk prediction for weed infestation using classification rules

This paper proposes a fuzzy classification system for the risk of infestation by weeds in agricultural zones considering the variability of weeds. The inputs of the system are features of the infestation extracted from estimated maps by kriging for the weed seed production and weed coverage, and from the competitiveness, inferred from narrow and broad-leaved weeds. Furthermore, a Bayesian network classifier is used to extract rules from data which are compared to the fuzzy rule set obtained on the base of specialist knowledge. Results for the risk inference in a maize crop field are presented and evaluated by the estimated yield loss. © 2009 IEEE.

Modeling approach based on experimental results for prediction of measurement errors in energy meters

This paper presents a general modeling approach to investigate and to predict measurement errors in active energy meters both induction and electronic types. The measurement error modeling is based on Generalized Additive Model (GAM), Ridge Regression method and experimental results of meter provided by a measurement system. The measurement system provides a database of 26 pairs of test waveforms captured in a real electrical distribution system, with different load characteristics (industrial, commercial, agricultural, and residential), covering different harmonic distortions, and balanced and unbalanced voltage conditions. In order to illustrate the proposed approach, the measurement error models are discussed and several results, which are derived from experimental tests, are presented in the form of three-dimensional graphs, and generalized as error equations. © 2009 IEEE.

The energetic cost of maintenance in ruminants: From classical to new concepts and prediction systems

The cost of maintenance makes up a large part of total energy costs in ruminants. Metabolizable energy (ME) requirement for maintenance (MEm) is the daily ME intake that exactly balances heat energy (HE). The net energy requirement for maintenance (NEm) is estimated subtracting MEm from the HE produced by the processing of the diet. Men cannot be directly measured experimentally and is estimated by measuring basal metabolism in fasted animals or by regression measuring the recovered energy in fed animals. MEm and NEm usually, but not always, are expressed in terms of BW0.75. However, this scaling factor is substantially empirical and its exponent is often inadequate, especially for growing animals. MEm estimated by different feeding systems (AFRC, CNCPS, CSIRO, INRA, NRC) were compared by using dairy cattle data. The comparison showed that these systems differ in the approaches used to estimate MEm and for its quantification. The CSIRO system estimated the highest MEm, mostly because it includes a correction factor to increase ME as the feeding level increases. Relative to CSIRO estimates, those of NRC, INRA, CNCPS, and AFRC were on average 0.92, 0.86, 0.84, and 0.78, respectively. MEm is affected by the previous nutritional history of the animals. This phenomenon is best predicted by dynamic models, of which several have been published in the last decades. They are based either on energy flows or on nutrient flows. Some of the different approaches used were described and discussed.

Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks

The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidences that Artificial Neural Network ANN could be use to predict the traffic behavior in a metropolitan area such So Paulo (around 16 million inhabitants). The proposed methodology involves the application of Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing into a structured rule basis, without human expert aid. The dynamics of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP) and type Radial Basis Function (RBF), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule one. The results show that by making use of the Feature Reduction through RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown. © 2011 IEEE.

Anfis applied to the prediction of surface roughness in grinding of advanced ceramics

This paper introduces a methodology for predicting the surface roughness of advanced ceramics using Adaptive Neuro-Fuzzy Inference System (ANFIS). To this end, a grinding machine was used, equipped with an acoustic emission sensor and a power transducer connected to the electric motor rotating the diamond grinding wheel. The alumina workpieces used in this work were pressed and sintered into rectangular bars. Acoustic emission and cutting power signals were collected during the tests and digitally processed to calculate the mean, standard deviation, and two other statistical data. These statistics, as well the root mean square of the acoustic emission and cutting power signals were used as input data for ANFIS. The output values of surface roughness (measured during the tests) were implemented for training and validation of the model. The results indicated that an ANFIS network is an excellent tool when applied to predict the surface roughness of ceramic workpieces in the grinding process.

Effects of acute normovolemic anemia on hemodynamic parameters and acid-base balance in dogs

The aim of this study was to evaluate the hemodynamic and acid-base status of dogs subjected to acute normovolemic anemia. The dogs (n = 10) were evaluated 15 minutes and 24 hours after induction of anemia (hematocrit below 18) with blood withdrawal and simultaneously replacement of same volume of Ringer's lactate solution and hydroxyethyl starch-based solution in a 2:1 ratio. The cardiac output was measured by Doppler echocardiography and blood pressure by oscillometric device, and posteriorly hemodynamic parameters were calculated. The anemic groups had increase in cardiac index (P <.05) (3.82 ± 1.05 to 5.86 ± 1.49 and 5.81 ± 1.63 L/min m) and decreases (P <.05) in the indices of total peripheral resistance (6797.81 ± 3060.22 to 3220.14 ± 1275.02 and 3887.74 ± 1394.89 dinaseg/cm 5× m2) and oxygen delivery (7942.84 ± 3344.00 to 4021.68 ± 1627.00 and 4430.82 ± 1402.61 mL/min× m 2), respectively. There were no significant changes in pH, but PaO2 and SaO2 values were increased, and PaCO2 reduced in anemic dogs (P <.05). Therefore, acute normovolemic anemia can create significant hemodynamic changes and despite some hemogasometric changes, there were no changes in the acid-base status in dogs. Copyright © 2011 Tatiana Champion et al.

Uterine and luteal hemodynamic evaluation of the non pregnant mare

Background: Studies with Doppler ultrassonography started at the end of the 90s for the determination of physiological and pathological alterations in the reproductive tract of the mare. Uterine alterations caused by inflammation, response from seminal plasma infusion, hormonal variations during estrous and diestrus, pregnancy and action of various vasoactive factors influence on the vascular perfusion detected by Doppler ultrasound. The development of efficient methods for uterine quality evaluation is of big importance for field equine reproduction veterinarians, once uterine environment is responsible for pregnancy maintenance. Review: Nowadays, the most used methods of uterine evaluation are the mode B ultrassonography, cytology, culture and biopsy. Hemodynamic evaluation of the uterus can be done by spectral data collected from large vessels, as A. uterine and its ramifications, or from subjective or objective evaluations from endometrium, miometrium and mesometrium attachment, which provide data referent to local and specific alterations of the evaluated area. Alterations in uterine vascular perfusion has been detected during estrous cycle, during pregnancy and in cases of infusion of inflammatory substances. These alterations happen because of vasoactive substances that act in the uterus during these events, however, most of these vasoactive substances are probably not even known. Also, important hemodynamic alterations in old mares, as an increase in vascular resistance, have been described. This increase might result from fibrosis of the uterus and in women it is considered to be a cause of infertility. In mares, periglandular fibrosis of the endometrium is considered to be the major diagnosable cause of embryonic and fetal loss in older mares. For the CL, ovarian artery of the mare supplies the ovary as well as the oviduct and therefore can be used for evaluation of these areas. The CL evaluation can also be done by the percentage of luteum area with colored signals as an indicator of the extent of blood flow. The percentage of the CL area with colored signals is determined subjectively by images observations in real time and/or by a freezing Power Doppler cross-section image with the maximum number of color pixels taped and the total number of color pixels is assessed by a computer analysis system. Therefore, a high correlation between plasmatic progesterone and CL vascularization also allows the CL evalution by this technique. In a first report, CL circulation reached its maximum on D5, the progesterone concentration in peripheral blood increased until D7 and in a posterior report, maximum perfusion was achieved two days after the maximum progesterone concentration (D8). Blood flow reduced between D10-D14 some days before the plasma progesterone decrease and, during the luteolytic period (D15-D17), the decline in CL blood-flow area was greater than blood flow decrease. Conclusion: Doppler ultrassonography add knowledge about uterine viability and CL functionality can be easily used by veterinarians in the field. It is a noninvasive method that provides real time results. However, because of the short time studies in this area have been done, many other answers still need to be found until normal and pathological patterns will be established.

Cluster analysis and artificial neural network on the superovulatory response prediction in mice

Complex biological systems require sophisticated approach for analysis, once there are variables with distinct measure levels to be analyzed at the same time in them. The mouse assisted reproduction, e.g. superovulation and viable embryos production, demand a multidisciplinary control of the environment, endocrinologic and physiologic status of the animals, of the stressing factors and the conditions which are favorable to their copulation and subsequently oocyte fertilization. In the past, analyses with a simplified approach of these variables were not well succeeded to predict the situations that viable embryos were obtained in mice. Thereby, we suggest a more complex approach with association of the Cluster Analysis and the Artificial Neural Network to predict embryo production in superovulated mice. A robust prediction could avoid the useless death of animals and would allow an ethic management of them in experiments requiring mouse embryo.

Spin stabilized satellite's attitude analytical prediction

An analytical approach for spin stabilized attitude propagation is presented, considering the coupled effect of the aerodynamic torque and the gravity gradient torque. A spherical coordination system fixed in the satellite is used to locate the satellite spin axis in relation to the terrestrial equatorial system. The spin axis direction is specified by its right ascension and the declination angles and the equation of motion are described by these two angles and the magnitude of the spin velocity. An analytical averaging method is applied to obtain the mean torques over an orbital period. To compute the average components of both aerodynamic torque and the gravity gradient torque in the satellite body frame reference system, an average time in the fast varying orbit element, the mean anomaly, is utilized. Afterwards, the inclusion of such torques on the rotational motion differential equations of spin stabilized satellites yields conditions to derive an analytical solution. The pointing deviation evolution, that is, the deviation between the actual spin axis and the computed spin axis, is also availed. In order to validate the analytical approach, the theory developed has been applied for spin stabilized Brazilian satellite SCD1, which are quite appropriated for verification and comparison of the data generated and processed by the Satellite Control Center of the Brazil National Research Institute (INPE). Numerical simulations performed with data of Brazilian Satellite SCD1 show the period that the analytical solution can be used to the attitude propagation, within the dispersion range of the attitude determination system performance of Satellite Control Center of the Brazilian Research Institute.

On the influence of the mode-shapes of a marine propulsion shafting system on the prediction of torsional stresses

Torsional vibration predictions and measurements of a marine propulsion system, which has both damping and a highly flexible coupling, are presented in this paper. Using the conventional approach to stress prediction in the shafting system, the numerical predictions and the experimental torsional vibration stress curves in some parts of the shafting system are found to be quite different. The free torsional vibration characteristics and forced torsional vibration response of the system are analyzed in detail to investigate this phenomenon. It is found that the second to fourth natural modes of the shafting system have significant local deformation. This results in large torsional resonant responses in different sections of the system corresponding to different engine speeds. The results show that when there is significant local deformation in the shafting system for different modes, then multi-point measurements should be made, rather than the conventional method of using a single measurement at the free end of the shaft, to obtain the full torsional vibration characteristics of the shafting system.

Cephalometric analysis of prediction tracings: A comparison of three different methods

The purpose of this study was to compare-using cephalometric analysis (McNamara, and Legan and Burstone)-prediction tracings performed using three different methods, that is, manual and using the Dentofacial Planner Plus and Dolphin Image computer programs, with postoperative outcomes. Pre- and postoperative (6 months after surgery) lateral cephalometric radiographs were selected from 25 long-faced patients treated with combined surgery. Prediction tracings were made with each method and compared cephalometrically with the postoperative results. This protocol was repeated once more for method error evaluation. Statistical analysis was made by ANOVA and the Tukey test. The results showed superior predictability when the manual method was applied (50% similarity to postoperative results), followed by Dentofacial Planner Plus (31.2%) and Dolphin Image (18.8%). The experimental condition suggests that the manual method provides greater accuracy, although the predictability of the digital methods proved quite satisfactory. © 2013 World Federation of Orthodontists.

Mean surface temperature prediction models for broiler chickens-a study of sensible heat flow

Body surface temperature can be used to evaluate thermal equilibrium in animals. The bodies of broiler chickens, like those of all birds, are partially covered by feathers. Thus, the heat flow at the boundary layer between broilers' bodies and the environment differs between feathered and featherless areas. The aim of this investigation was to use linear regression models incorporating environmental parameters and age to predict the surface temperatures of the feathered and featherless areas of broiler chickens. The trial was conducted in a climate chamber, and 576 broilers were distributed in two groups. In the first trial, 288 broilers were monitored after exposure to comfortable or stressful conditions during a 6-week rearing period. Another 288 broilers were measured under the same conditions to test the predictive power of the models. Sensible heat flow was calculated, and for the regions covered by feathers, sensible heat flow was predicted based on the estimated surface temperatures. The surface temperatures of the feathered and featherless areas can be predicted based on air, black globe or operative temperatures. According to the sensible heat flow model, the broilers' ability to maintain thermal equilibrium by convection and radiation decreased during the rearing period. Sensible heat flow estimated based on estimated surface temperatures can be used to predict animal responses to comfortable and stressful conditions. © 2013 ISB.

Mild cognitive impairment (part 2): Biological markers for diagnosis and prediction of dementia in Alzheimer's disease

To present a critical review of publications reporting on the rationale and clinical implications of the use of biomarkers for the early diagnosis of Alzheimer's disease (AD). Methods: We conducted a systematic search of the PubMed and Web of Science electronic databases, limited to articles published in English between 1999 and 2012, and based on the following terms: mild cognitive impairment, Alzheimer's disease OR dementia, biomarkers. We retrieved 1,130 articles, of which 175 were reviews. Overall, 955 original articles were eligible. Results: The following points were considered relevant for the present review: a) rationale for biomarkers research in AD and mild cognitive impairment (MCI); b) usefulness of distinct biomarkers for the diagnosis and prediction of AD; c) the role of multimodality biomarkers for the diagnosis and prediction of AD; d) the role of biomarkers in clinical trials of patients with AD and MCI; and e) current limitations to the widespread use of biomarkers in research and clinical settings. Conclusion: Different biomarkers are useful for the early diagnosis and prediction of AD in at-risk subjects. Nonetheless, important methodological limitations need to be overcome for widespread use of biomarkers in research and clinical settings. © 2013 Associação Brasileira de Psiquiatria.