Second primitive malignant tumour in patients with gynaecological cancer

OBJECTIVE: The purpose of this study was to characterise the occurrence of multiple primitive gynaecological malignant neoplasias, restricted to the genital tract and breast or associated with other organs, and to detect which types of association are most frequent concerning location, histology and staging. POPULATION AND METHODS: The records of patients with gynaecological cancer at the Portuguese Institute of Oncology--Lisbon Centre, between 1986 and 1993 were used in this study. RESULTS: Of the 10,746 women with gynaecological cancer, 91 (0.8%) were found to have a second primitive malignant neoplasia. Of these neoplasias, 64% (58 cases) were also located at a gynaecological site. The most frequent associations were endometrium/breast (13 cases), bilateral breast (12 cases) and ovary/endometrium (11 cases). The majority of primitive multiple gynaecological tumours were synchronous. Regarding gynaecological cancer and non-gynaecological cancer, in 28 cases (31%), the most common non-gynaecological location was the colon/rectum. Five patients had triple tumours. CONCLUSION: Although these situations are relatively rare the possibility of multiple primitive cancers should be considered with the presence of malignant tumours in two or more organs. This distinction between multiple primitive or metastatic cancers could be important for treatment as well as prognosis.     

Qualitative and Quantitative Comparison of Plasma Exosomes from Neonates and Adults

Exosomes are extracellular vesicles that contain nucleic acids, lipids and metabolites, and play a critical role in health and disease as mediators of intercellular communication. The majority of extracellular vesicles in the blood are platelet-derived. Compared to adults, neonatal platelets are hyporeactive and show impaired granule release, associated with defects in Soluble N-ethylmaleimide-sensitive fusion Attachment protein REceptor (SNARE) proteins. Since these proteins participate in biogenesis of exosomes, we investigated the potential differences between newborn and adult plasma-derived exosomes. Plasma-derived exosomes were isolated by ultracentrifugation of umbilical cord blood from full-term neonates or peripheral blood from adults. Exosome characterization included size determination by transmission electron microscopy and quantitative proteomic analysis. Plasma-derived exosomes from neonates were significantly smaller and contained 65% less protein than those from adults. Remarkably, 131 proteins were found to be differentially expressed, 83 overexpressed and 48 underexpressed in neonatal (vs. adult) exosomes. Whereas the upregulated proteins in plasma exosomes from neonates are associated with platelet activation, coagulation and granule secretion, most of the underexpressed proteins are immunoglobulins. This is the first study showing that exosome size and content change with age. Our findings may contribute to elucidating the potential “developmental hemostatic mismatch risk” associated with transfusions containing plasma exosomes from adults.     

Modelling the dynamics of the students’ academic performance in the German region of the North Rhine-Westphalia: an epidemiological approach with uncertainty

Student academic underachievement is a concern of paramount importance in Europe, where around 15% of the students in the last high school courses do not achieve the minimum knowledge academic requirement. In this paper, we propose a model based on a system of differential equations to study the dynamics of the students’ academic performance in the German region of the North Rhine-Westphalia. This approach is supported by the idea that both good and bad study habits, are a mixture of personal decisions and influence of classmates. This model allows us to forecast the student academic performance by means of confidence intervals over the next few years.     

IaaSMon: Monitoring Architecture for Public Cloud Computing Data Centers

Monitoring of cloud computing infrastructures is an imperative necessity for cloud providers and administrators to analyze, optimize and discover what is happening in their own infrastructures. Current monitoring solutions do not fit well for this purpose mainly due to the incredible set of new requirements imposed by the particular requirements associated to cloud infrastructures. This paper describes in detail the main reasons why current monitoring solutions do not work well. Also, it provides an innovative monitoring architecture that enables the monitoring of the physical and virtual machines available within a cloud infrastructure in a non-invasive and transparent way making it suitable not only for private cloud computing but also for public cloud computing infrastructures. This architecture has been validated by means of a prototype integrating an existing enterprise-class monitoring solution, Nagios, with the control and data planes of OpenStack, a well-known stack for cloud infrastructures. As a result, our new monitoring architecture is able to extend the exiting Nagios functionalities to fit in the monitoring of cloud infrastructures. The proposed architecture has been designed, implemented and released as open source to the scientific community. The proposal has also been empirically validated in a production-level cloud computing infrastructure running a test bed with up to 128 VMs where overhead and responsiveness has been carefully analyzed.     

Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: A Case Study of an International Bank

The customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six‐step procedure. The back‐propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.     

Fast computation of minimal elementary decompositions of metabolic flux vectors

The concept of elementary flux vector is valuable in a number of applications of metabolic engineering. For instance, in metabolic flux analysis, each admissible flux vector can be expressed as a non-negative linear combination of a small number of elementary flux vectors. However a critical issue concerns the total number of elementary flux vectors which may be huge because it combinatorially increases with the size of the metabolic network. In this paper we present a fast algorithm that randomly computes a decomposition of admissible flux vectors in a minimal number of elementary flux vectors without explicitly enumerating all of them.     

CASANDRA: a prototype implementation of a system of network progressive transmission of medical digital images

In this paper, a prototype for progressive transmission of medical digital 2D images through the network, called CASANDRA, is presented. The prototype consists of the server part and the client part. In the server part, the images are acquired, stored, computed their wavelet transform and the wavelet coefficients stored, then transmitted progressively, when required, via TCP to the client. In the client part, with the inverse wavelet transform, the received wavelet coefficients are used to build successive improved reconstructions of the image.This prototype has been implemented and is being tested in the Radiotherapy Service of the Valencia University Hospital (Valencia, Spain).     

Exploring different technological platforms for supporting co-located collaborative games in the classroom

Computer Supported Collaborative Learning is a pedagogical approach that can be used for deploying educational games in the classroom. However, there is no clear understanding as to which technological platforms are better suited for deploying co-located collaborative games, nor the general affordances that are required. In this work we explore two different technological platforms for developing collaborative games in the classroom: one based on augmented reality technology and the other based on multiple-mice technology. In both cases, the same game was introduced to teach electrostatics and the results were compared experimentally using a real class.