In vitro maintenance of Angiostrongylus costaricensis does not provide physiological conditions for egg laying

Fifteen pairs (male/female) of Angiostrongylus costaricensis were kept in vitro in Waymouth medium for three days to evaluate the amount and duration of egg laying. At 24, 48 and 72 hours, the mean egg counts were 321, 24 and 4 eggs/10 microliters, respectively. Most of the eggs were eliminated within the first 24 hours, suggesting they are expelled under non-physiological conditions. These results indicate that in vitro conditions are not appropriate for drug trials of egg-laying inhibitors for treatment of abdominal angiostrongylosis.

Human activity recognition with accelerometry: novel time and frequency features

Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.

Measuring male infertility: epidemiological aspects

Evidence suggests that human semen quality may have been deteriorating in recent years. Most of the evidence is retrospective, based on analysis of data sets collected for other purposes. Measures of male infertility are needed if we want to monitor the biological capacity for males to reproduce over time or between different populations. We also need these measures in analytical epidemiology if we want to identify risk indicators, risk factors, or even causes of an impaired male fecundity-that is, the male component in the biological ability to reproduce. The most direct evaluation of fecundity is to measure the time it takes to conceive. Since the time of conception may be missed in the case of an early abortion, time to get pregnant is often measured as the time it takes to obtain a conception that survives until a clinically recognized pregnancy or even a pregnancy that ends with a live born child occurs. A prolonged time required to produce pregnancy may therefore be due to a failure to conceive or a failure to maintain a pregnancy until clinical recognition. Studies that focus on quantitative changes in fecundity (that does not cause sterility) should in principle be possible in a pregnancy sample. The most important limitation in fertility studies is that the design requires equal persistency in trying to become pregnant and rather similar fertility desires and family planning methods in the groups to be compared. This design is probably achievable in exposure studies that make comparisons with reasonable comparable groups concerning social conditions and use of contraceptive methods.

Sperm tail flexibility test: a simple test for selecting viable spermatozoa for intracytoplasmic sperm injection from semen samples without motile spermatozoa

PURPOSE: The objective was to describe the results of the injection of immotile spermatozoa with flexible tails when only immotile spermatozoa are present in the semen sample. METHODS: A retrospective study was conducted to analyze the procedure results for 10 couples who participated in our intracytoplasmic sperm injection program. The sperm tail was considered flexible when it moved up and down independently of the head movement, and it was considered inflexible when the movement occurred together (tail plus head). The fertilization and pregnancy rate were analyzed. RESULTS: The normal fertilization rate (presence of 2 pronuclei) was 30.3% (40/132), and the abnormal fertilization rate (presence of less than or more than 2 pronuclei) was 6.81% (9/132). A total of 52 embryos were obtained with 9 transfer procedures performed (pregnancy rate: 11.12%). CONCLUSIONS: The sperm tail flexibility test (STFT) is an easy and cost-effective way for selecting viable immotile spermatozoa and can be used as an alternative method for determining the viability of spermatozoa. This test seems to be a simple and risk-free method when compared to the swelling test.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Vision-based portuguese sign language recognition system

Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.

Enrollment time as a requirement for biometric hand recognition systems

Biometric systems are increasingly being used as a means for authentication to provide system security in modern technologies. The performance of a biometric system depends on the accuracy, the processing speed, the template size, and the time necessary for enrollment. While much research has focused on the first three factors, enrollment time has not received as much attention. In this work, we present the findings of our research focused upon studying user’s behavior when enrolling in a biometric system. Specifically, we collected information about the user’s availability for enrollment in respect to the hand recognition systems (e.g., hand geometry, palm geometry or any other requiring positioning the hand on an optical scanner). A sample of 19 participants, chosen randomly apart their age, gender, profession and nationality, were used as test subjects in an experiment to study the patience of users enrolling in a biometric hand recognition system.

Communal egg laying by Gonatodes humeralis (Sauria, Gekkonidae) in Manaus primary and secondary forest areas

Communal nesting has been registered for a number of lizard species at different sites. Here it is described communal egg laying of Gonatodes humeralis at different sites near and in human buildings in the period between 1990 and 1998. All these communal nests have been found in the dry season, between April and July, suggesting that the nests of are more common in this season, when the activity of their predators is less intense and the reduction of humidity diminish the decomposition action of the fungi that may kill the eggs.