Prostate carcinoma with neuroendocrine differentiation: case report and literature review

Neuroendocrine differentiation in prostatic carcinomas generally confers a more aggressive clinical behavior and less favorable prognosis than usual prostatic carcinomas. In this manuscript, we report a case of a 58-year-old man with prostatic carcinoma who died 1 year after initial diagnosis. Autopsy showed a disseminated prostatic carcinoma with neuroendocrine differentiation. There were metastasis to the spleen, an organ infrequently involved by disseminated epithelial neoplasms. Neuroendocrine differentiation was demonstrated by immunohistochemical studies in the biopsy and autopsy material.

`When I'm sixty-four': old age, family values, and private financial transfers

Private financial transfers are becoming more and more important as ageing levels increase in Europe, with elders acting as both givers and receivers. Our study is divided in two main parts. In the first part we analyse the determinants of private financial transfers, using the Survey of Health, Ageing, and Retirement in Europe (SHARE). In the second part we analyse the importance of family values for these transfers, combining SHARE with European Values Study. We show that family functions as the main agent of private transfers. We conclude that family values drive financial transfers, mainly gifts provided by elderly individuals. We find that receipts by old-aged people are more related with need cases, such as illness and poorness; moreover, for these particular cases, family network plays a very important role, working as a safety net.

Critical analysis of old and new vaccines against N. meningitidis serogroup C, considering the meningococcal disease epidemiology in Brazil

Worldwide, the impact of meningococcal disease is substantial, and the potential for the introduction and spread of more virulent strains of N. meningitidis or strains with increased resistance to current antibiotics causes concern, making prevention essential. OBJECTIVES: Review the indications for meningococcal disease vaccines, considering the epidemiological status in Brazil. METHODS: A critical literature review on this issue using the Medline and Lilacs databases. RESULTS: In Brazil, MenB and MenC were the most important serogroups identified in the 1990s. Polysaccharide vaccines available against those serogroups can offer only limited protection for infants, the group at highest risk for meningococcal disease. Additionally, polysaccharide vaccines may induce a hypo-responsive state to MenC. New meningococcal C conjugate vaccines could partially solve these problems, but it is unlikely that in the next few years a vaccine against MenB that can promote good protection against multiple strains of MenB responsible for endemic and epidemic diseases will become available. CONCLUSIONS: In order to make the best decision about recommendations on immunization practices, better quality surveillance data are required. In Brazil, MenC was responsible for about 2,000 cases per year during the last 10 years. New conjugate vaccines against MenC are very effective and immunogenic, and they should be recommended, especially for children less than 5 years old. Polysaccharide vaccines should be indicated only in epidemic situations and for high-risk groups. Until new vaccines against MenC and MenB are available for routine immunization programs, the most important measure for controlling meningococcal disease is early diagnosis of these infections in order to treat patients and to offer chemoprophylaxis to contacts.

New tools for old organelles

For hundreds of years biologists have studied the naturally occurring diversity in plant and animal species. The invention of the electron microscope in the rst half of the 1900's reveled that cells also can be incredible complex (and often stunningly beautiful). However, despite the fact that the eld of cell biology has existed for over 100 years we still lack a formal understanding of how cells evolve: It is unclear what the extents are in cell and organelle morphology, if and how diversity might be constrained, and how organelles change morphologically over time.(...)

Typology based method for choosing old masonry walls inspection procedures

The diagnosis of historic masonry walls is an intricate and complex field and has been an object of research for many years. This paper aims to propose practical methodologies for the diagnosis of historic masonry walls, specifically based on their typological characteristics. In order to develop such procedures, information relating to historic masonry typologies in Portugal, classified as rural, urban and military was gathered and techniques for the assessment of historic masonry were studied. All information was integrated to develop a pattern typology oriented methodology. Developed methodology was tested and validated in a small diagnosis campaign carried out in the Guimarães Castle. Methodology was proven to be advantageous and although the study is limited and focused on the Portuguese architectural specificities, it still holds global classifications, and therefore can be useful for any diagnosis procedure of a historic masonry wall.

Mechanical characterization of old chestnut beams

The main objective of this work is to evaluate, by non-destructive techniques, seven old Chestnut beams. For that, after the geometric assessment and the detailed visual inspection that allowed to strength grade the beams, a series of non-destructive tests was setup. In a first step, non-destructive bending tests, under the elastic limit, were performed to quantify the modulus of elasticity in bending (MoE) of the seven beams. Then, Resistograph® and Pilodyn® tests were done to assess the superficial decay and to have aclearer idea of the voids dimensions. Then, two beams were tested in bending until failure to evaluate the bending strength. In a second step, end parts were cut from the beams, one per end of the beams, to perform Resistograph®, Pilodyn® and ultrasound tests, to quantify the density of the beams and to extract meso-specimens to be used in tension parallel to the grain tests

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.

A VEP study in sleeping and awake one-month-old infants and its relation with social behavior

With the present study we aimed to analyze the relationship between infants' behavior and their visual evoked-potential (VEPs) response. Specifically, we want to verify differences regarding the VEP response in sleeping and awake infants and if an association between VEP components, in both groups, with neurobehavioral outcome could be identified. To do so, thirty-two full-term and healthy infants, approximately 1-month of age, were assessed through a VEP unpatterned flashlight stimuli paradigm, offered in two different intensities, and were assessed using a neurobehavioral scale. However, only 18 infants have both assessments, and therefore, these is the total included in both analysis. Infants displayed a mature neurobehavioral outcome, expected for their age. We observed that P2 and N3 components were present in both sleeping and awake infants. Differences between intensities were found regarding the P2 amplitude, but only in awake infants. Regression analysis showed that N3 amplitude predicted an adequate social interactive and internal regulatory behavior in infants who were awake during the stimuli presentation. Taking into account that social orientation and regulatory behaviors are fundamental keys for social-like behavior in 1-month-old infants, this study provides an important approach for assessing physiological biomarkers (VEPs) and its relation with social behavior, very early in postnatal development. Moreover, we evidence the importance of the infant's state when studying differences regarding visual threshold processing and its association with behavioral outcome.