State University of Campinas

About: State University of Campinas is a education organization based out in Campinas, Brazil. It is known for research contribution in the topics: Population & Context (language use). The organization has 49454 authors who have published 104606 publications receiving 1841004 citations. The organization is also known as: UNICAMP & State University of Campinas.

From dry bonding to water-wet bonding to ethanol-wet bonding. A review of the interactions between dentin matrix and solvated resins using a macromodel of the hybrid layer.

Abstract: PURPOSE To review the use of a new resin-dentin bonding model called the macro-hybrid layer, to quantify resin uptake and matrix shrinkage during resin infiltration and solvent evaporation. A secondary purpose was to introduce the concept of ethanol-wet bonding where water-saturated acid-etched dentin is exchanged with ethanol to create ethanol-saturated dentin. Adhesive monomers seem to penetrate ethanol-saturated dentin more thoroughly than water-saturated dentin.

Randomized clinical trial of a levonorgestrel-releasing intrauterine system and a depot GnRH analogue for the treatment of chronic pelvic pain in women with endometriosis

Abstract: endometriosis-related pain over a period of six months. METHODS: Eighty-two women, 18 to 40 years of age (mean 30 years), with endometriosis, dysmenorrhoea and/or CPP, were randomized using a computer-generated system of sealed envelopes into either LNG-IUS (n 5 39) or GnRH analogue (n 5 43) treatment groups at three university centres. Daily scores of endometriosis-associated CPP were evaluated using the Visual Analogue Scale (VAS), daily bleeding score was calculated from bleeding calendars, and improvement in quality of life was evaluated using the Psychological General Well-Being Index Questionnaire (PGWBI). The pain score diary was based on the VAS in which women recorded the occurrence and intensity of pain on a daily basis. A monthly score was calculated from the result of the sum of the daily scores divided by the number of days in each observation period. RESULTS: CPP decreased significantly from the first month throughout the six months of therapy with both forms of treatment and there was no difference between the groups ( P> 0.999). In both treatment groups, women with stage III and IV endometriosis showed a more rapid improvement in the VAS pain score than women with stage I and II of the disease ( P< 0.002). LNG-IUS users had a higher bleeding score than GnRH-analogue users at all time points of observation with 34% and 71% of patients in the LNG-IUS and GnRH-analogue groups, respectively, reporting no bleeding during the first treatment month, and 70% and 98% reporting no bleeding during the sixth month. No difference was observed between groups with reference to improvement in quality of life. CONCLUSIONS: Both, the LNG-IUS and the GnRH-analogue were effective in the treatment of CPP-associated endometriosis, although no differences were observed between the two treatments. Among the additional advantages of the LNG-IUS is the fact that it does not provoke hypoestrogenism and that it requires only one medical intervention for its introduction every 5 years. This device could therefore become the treatment of choice for CPP-associated endometriosis in women who do not wish to conceive.

An Adapted Version of the U.S. Department of Agriculture Food Insecurity Module Is a Valid Tool for Assessing Household Food Insecurity in Campinas, Brazil

Abstract: Until recently, Brazil did not have a national instrument with which to assess household food insecurity (FI). The objectives of this study were as follows: 1) to describe the process of adaptation and validation of the 15-item USDA FI module, and 2) to assess its validity in the city of Campinas. The USDA scale was translated into Portuguese and subsequently tested for content and face validity through content expert and focus groups made up of community members. This was followed by a quantitative validation based on a convenience (n = 125) and a representative (n = 847) sample. Key adaptations involved replacing the term "balanced meal" with "healthy and varied diet," to construct items as questions rather than statements, and to ensure that respondents understood that information would not be used to determine program eligibility. Chronbach's alpha was 0.91 and the scale item response curves were parallel across the 4 household income strata. FI severity level was strongly associated in a dose-response manner (P < 0.001) with income strata and the probability of daily intake of fruits, vegetables, meat/fish, and dairy. These findings were replicated in the 2 independent survey samples. Results indicate that the adapted version of the USDA food insecurity module is valid for the population of Campinas. This validation methodology has now been replicated in urban and/or rural areas of 4 additional states with similar results. Thus, Brazil now has a household food insecurity instrument that can be used to set national goals, to follow progress, and to evaluate its national hunger and poverty eradication programs.

Fingerprint Liveness Detection Using Convolutional Neural Networks

Abstract: With the growing use of biometric authentication systems in the recent years, spoof fingerprint detection has become increasingly important. In this paper, we use convolutional neural networks (CNNs) for fingerprint liveness detection. Our system is evaluated on the data sets used in the liveness detection competition of the years 2009, 2011, and 2013, which comprises almost 50 000 real and fake fingerprints images. We compare four different models: two CNNs pretrained on natural images and fine-tuned with the fingerprint images, CNN with random weights, and a classical local binary pattern approach. We show that pretrained CNNs can yield the state-of-the-art results with no need for architecture or hyperparameter selection. Data set augmentation is used to increase the classifiers performance, not only for deep architectures but also for shallow ones. We also report good accuracy on very small training sets (400 samples) using these large pretrained networks. Our best model achieves an overall rate of 97.1% of correctly classified samples—a relative improvement of 16% in test error when compared with the best previously published results. This model won the first prize in the fingerprint liveness detection competition 2015 with an overall accuracy of 95.5%.

On the Generation of Spatiotemporal Datasets

Abstract: An efficient benchmarking environment for spatiotemporal access methods should at least include modules for generating synthetic datasets, storing datasets (real datasets included), collecting and running access structures, and visualizing experimental results. Focusing on the dataset repository module, a collection of synthetic data that would simulate a variety of real life scenarios is required. Several algorithms have been implemented in the past to generate static spatial (point or rectangular) data, for instance, following a predefined distribution in the workspace. However, by introducing motion, and thus temporal evolution in spatial object definition, generating synthetic data tends to be a complex problem. In this paper, we discuss the parameters to be considered by a generator for such type of data, propose an algorithm, called "Generate_Spatio_Temporal_Data" (GSTD), which generates sets of moving point or rectangular data that follow an extended set of distributions. Some actual generated datasets are also presented. The GSTD source code and several illustrative examples are currently available to all researchers through the Internet.