Effects of altered epididymal sperm transit time on sperm quality

The epididymal sperm transit time seems to have an important role in the process of sperm maturation, and it seems that alterations to the transit can harm the process. The aim of the present work was to evaluate the influence of altered sperm transit time through the epididymis on sperm parameters and fertility of rats, as well as the role of testosterone in the alterations. Sprague-Dawley adult male rats were randomly assigned to four different groups and were treated for 12 days: (i) 10 mu g/rat/day DES, to accelerate the transit; (ii) 6.25 mg/kg/day guanethidine sulphate, to delay the transit; (iii) same treatment as group 1, plus androgen supplementation; (iv) control animals received the vehicles. Guanethidine treatment delayed the sperm transit time through the epididymal cauda, provoking increased sperm reserves in this region. Animals exposed to DES showed an acceleration of sperm transit time in the epididymis, and consequently decreased sperm density in both epididymal regions, the caput-corpus and cauda, and diminished sperm motility. In both cases sperm production was not altered. Testosterone supplementation was able to restore the transit time to values close to normality, as they were higher than in the control rats. The same occurred in relation to sperm motility. Rats exposed to DES presented lower fertility after in utero artificial insemination using sperm collected from the proximal cauda epididymis. Therefore, it was concluded that the acceleration of rat sperm transit time appeared to harm normal sperm maturation, thus decreasing sperm quality and fertility capacity, in an androgen-dependent way.

Orchidopexy restores morphometric-stereologic changes in the caput epididymis and daily sperm production in cryptorchidic mice, although sperm transit time and fertility parameters remain impaired

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

Artificial immune systems for classification of petroleum well drilling operations

This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.

Pharyngeal clearance and pharyngeal transit time determined by a biomagnetic method in normal humans

Clearance and transit time are parameters of great value in studies of digestive transit. Such parameters are nowadays obtained by means of scintigraphy and videofluoroscopy, with each technique having advantages and disadvantages. In this study we present a new, noninvasive method to study swallowing pharyngeal clearance (PC) and pharyngeal transit time (PTT). This new method is based on variations of magnetic flux produced by a magnetic bolus passing through the pharynx and detected by an AC biosusceptometer (ACB). These measurements may be performed in a simple way. cause no discomfort. and do not use radiation. We measured PC in 8 volunteers (7 males and I female. 23-33 years old) and PTT in 8 other volunteers (7 males and I female. 21-29 years old). PC was 0.82 +/- 0.10 s (mean +/- SD) and PTT was 0.75 +/- 0.03 s. The results were similar for PC but longer for PTT than those determined by means of other techniques. We conclude that the biomagnetic method can be used to evaluate PC and PTT.

Estimation of water transit time in soils under Amazon forest cover using variations in sigma18O values.

In an attempt to estimate the soil-water transit time using the variation in 18O values, a statistical model was used. This model is based on linear regression analysis applied to the values observed for soil water and rain water. The time obtained from these correlations represents the mean time necessary for the water to run from one collecting point to the next.-from Authors

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.