Outcome and patterns of failure in testicular lymphoma: a multicenter Rare Cancer Network study.

PURPOSE: To assess the outcome and patterns of failure in patients with testicular lymphoma treated by chemotherapy (CT) and/or radiation therapy (RT). METHODS AND MATERIALS: Data from a series of 36 adult patients with Ann Arbor Stage I (n = 21), II (n = 9), III (n = 3), or IV (n = 3) primary testicular lymphoma, consecutively treated between 1980 and 1999, were collected in a retrospective multicenter study by the Rare Cancer Network. Median age was 64 years (range: 21-91 years). Full staging workup (chest X-ray, testicular ultrasound, abdominal ultrasound, and/or thoracoabdominal computer tomography, bone marrow assessment, full blood count, lactate dehydrogenase, and cerebrospinal fluid evaluation) was completed in 18 (50%) patients. All but one patient underwent orchidectomy, and spermatic cord infiltration was found in 9 patients. Most patients (n = 29) had CT, consisting in most cases of cyclophosphamide, doxorubicin, vincristine, and prednisolone (CHOP) with (n = 17) or without intrathecal CT. External RT was delivered to scrotum alone (n = 12) or testicular, iliac, and para-aortic regions (n = 8). The median RT dose was 31 Gy (range: 20-44 Gy) in a median of 17 fractions (10-24), using a median of 1.8 Gy (range: 1.5-2.5 Gy) per fraction. The median follow-up period was 42 months (range: 6-138 months). RESULTS: After a median period of 11 months (range: 1-76 months), 14 patients presented lymphoma progression, mostly in the central nervous system (CNS) (n = 8). Among the 17 patients who received intrathecal CT, 4 had a CNS relapse (p = NS). No testicular, iliac, or para-aortic relapse was observed in patients receiving RT to these regions. The 5-year overall, lymphoma-specific, and disease-free survival was 47%, 66%, and 43%, respectively. In univariate analyses, statistically significant factors favorably influencing the outcome were early-stage and combined modality treatment. Neither RT technique nor total dose influenced the outcome. Multivariate analysis revealed that the most favorable independent factors predicting the outcome were younger age, early-stage disease, and combined modality treatment. CONCLUSIONS: In this multicenter retrospective study, CNS was found to be the principal site of relapse, and no extra-CNS lymphoma progression was observed in the irradiated volumes. More effective CNS prophylaxis, including combined modalities, should be prospectively explored in this uncommon site of extranodal lymphoma.

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

Dynamic simulation of regulatory networks using SQUAD.

BACKGROUND: The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. RESULTS: We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. CONCLUSION: The simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or determine the role of specific components within the network. The predictions can then be used to interpret and/or drive laboratory experiments. SQUAD provides a user-friendly graphical interface, accessible to both computational and experimental biologists for the fast qualitative simulation of large regulatory networks for which kinetic data is not necessarily available.

Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry.

Genetically engineered bioreporters are an excellent complement to traditional methods of chemical analysis. The application of fluorescence flow cytometry to detection of bioreporter response enables rapid and efficient characterization of bacterial bioreporter population response on a single-cell basis. In the present study, intrapopulation response variability was used to obtain higher analytical sensitivity and precision. We have analyzed flow cytometric data for an arsenic-sensitive bacterial bioreporter using an artificial neural network-based adaptive clustering approach (a single-layer perceptron model). Results for this approach are far superior to other methods that we have applied to this fluorescent bioreporter (e.g., the arsenic detection limit is 0.01 microM, substantially lower than for other detection methods/algorithms). The approach is highly efficient computationally and can be implemented on a real-time basis, thus having potential for future development of high-throughput screening applications.

Social network architecture and the maintenance of deleterious cultural traits.

How have changes in communications technology affected the way that misinformation spreads through a population and persists? To what extent do differences in the architecture of social networks affect the spread of misinformation, relative to the rates and rules by which individuals transmit or eliminate different pieces of information (cultural traits)? Here, we use analytical models and individual-based simulations to study how a 'cultural load' of misinformation can be maintained in a population under a balance between social transmission and selective elimination of cultural traits with low intrinsic value. While considerable research has explored how network architecture affects percolation processes, we find that the relative rates at which individuals transmit or eliminate traits can have much more profound impacts on the cultural load than differences in network architecture. In particular, the cultural load is insensitive to correlations between an individual's network degree and rate of elimination when these quantities vary among individuals. Taken together, these results suggest that changes in communications technology may have influenced cultural evolution more strongly through changes in the amount of information flow, rather than the details of who is connected to whom.

Computer Aided Detection and measurement of peripheral artery disease.

Computer-Aided Tomography Angiography (CTA) images are the standard for assessing Peripheral artery disease (PAD). This paper presents a Computer Aided Detection (CAD) and Computer Aided Measurement (CAM) system for PAD. The CAD stage detects the arterial network using a 3D region growing method and a fast 3D morphology operation. The CAM stage aims to accurately measure the artery diameters from the detected vessel centerline, compensating for the partial volume effect using Expectation Maximization (EM) and a Markov Random field (MRF). The system has been evaluated on phantom data and also applied to fifteen (15) CTA datasets, where the detection accuracy of stenosis was 88% and the measurement accuracy was with an 8% error.

ENFIN--A European network for integrative systems biology.

Integration of biological data of various types and the development of adapted bioinformatics tools represent critical objectives to enable research at the systems level. The European Network of Excellence ENFIN is engaged in developing an adapted infrastructure to connect databases, and platforms to enable both the generation of new bioinformatics tools and the experimental validation of computational predictions. With the aim of bridging the gap existing between standard wet laboratories and bioinformatics, the ENFIN Network runs integrative research projects to bring the latest computational techniques to bear directly on questions dedicated to systems biology in the wet laboratory environment. The Network maintains internally close collaboration between experimental and computational research, enabling a permanent cycling of experimental validation and improvement of computational prediction methods. The computational work includes the development of a database infrastructure (EnCORE), bioinformatics analysis methods and a novel platform for protein function analysis FuncNet.