Molecular imaging in the development of a novel treatment paradigm for glioblastoma (GBM): an integrated multidisciplinary commentary.

Current therapeutic strategies against glioblastoma (GBM) have failed to prevent disease progression and recurrence effectively. The part played by molecular imaging (MI) in the development of novel therapies has gained increasing traction in recent years. For the first time, using expertise from an integrated multidisciplinary group of authors, herein we present a comprehensive evaluation of state-of-the-art GBM imaging and explore how advances facilitate the emergence of new treatment options. We propose a novel next-generation treatment paradigm based on the targeting of multiple hallmarks of cancer evolution that will heavily rely on MI.

Robust vaccine-elicited cellular immune responses in breast milk following systemic Simian Immunodeficiency Virus DNA prime and live virus vector boost vaccination of lactating rhesus monkeys.

Breast milk transmission of HIV remains an important mode of infant HIV acquisition. Enhancement of mucosal HIV-specific immune responses in milk of HIV-infected mothers through vaccination may reduce milk virus load or protect against virus transmission in the infant gastrointestinal tract. However, the ability of HIV/SIV strategies to induce virus-specific immune responses in milk has not been studied. In this study, five uninfected, hormone-induced lactating, Mamu A*01(+) female rhesus monkey were systemically primed and boosted with rDNA and the attenuated poxvirus vector, NYVAC, containing the SIVmac239 gag-pol and envelope genes. The monkeys were boosted a second time with a recombinant Adenovirus serotype 5 vector containing matching immunogens. The vaccine-elicited immunodominant epitope-specific CD8(+) T lymphocyte response in milk was of similar or greater magnitude than that in blood and the vaginal tract but higher than that in the colon. Furthermore, the vaccine-elicited SIV Gag-specific CD4(+) and CD8(+) T lymphocyte polyfunctional cytokine responses were more robust in milk than in blood after each virus vector boost. Finally, SIV envelope-specific IgG responses were detected in milk of all monkeys after vaccination, whereas an SIV envelope-specific IgA response was only detected in one vaccinated monkey. Importantly, only limited and transient increases in the proportion of activated or CCR5-expressing CD4(+) T lymphocytes in milk occurred after vaccination. Therefore, systemic DNA prime and virus vector boost of lactating rhesus monkeys elicits potent virus-specific cellular and humoral immune responses in milk and may warrant further investigation as a strategy to impede breast milk transmission of HIV.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Creative and Multidisciplinary Etching in the Digital Age

In our capacity as creative artists, researchers and fine arts professors, most part of our activity focuses on the relationships between people and creativity, technology, and resources, and, most frequently, what we aim to offer are new enjoyable and subversive ways of interacting with these three fields. As art teachers, we ask 'why and how' to teach dynamic bearing in mind that digital technology will undoubtedly impact on contemporary art practice.

EURECCA colorectal: multidisciplinary mission statement on better care for patients with colon and rectal cancer in Europe

Background: Care for patients with colon and rectal cancer has improved in the last twenty years however still considerable variation exists in cancer management and outcome between European countries. Therefore, EURECCA, which is the acronym of European Registration of cancer care, is aiming at defining core treatment strategies and developing a European audit structure in order to improve the quality of care for all patients with colon and rectal cancer. In December 2012 the first multidisciplinary consensus conference about colon and rectum was held looking for multidisciplinary consensus. The expert panel consisted of representatives of European scientific organisations involved in cancer care of patients with colon and rectal cancer and representatives of national colorectal registries. Methods: The expert panel had delegates of the European Society of Surgical Oncology (ESSO), European Society for Radiotherapy & Oncology (ESTRO), European Society of Pathology (ESP), European Society for Medical Oncology (ESMO), European Society of Radiology (ESR), European Society of Coloproctology (ESCP), European CanCer Organisation (ECCO), European Oncology Nursing Society (EONS) and the European Colorectal Cancer Patient Organisation (EuropaColon), as well as delegates from national registries or audits. Experts commented and voted on the two web-based online voting rounds before the meeting (between 4th and 25th October and between the 20th November and 3rd December 2012) as well as one online round after the meeting (4th20th March 2013) and were invited to lecture on the subjects during the meeting (13th15th December 2012). The sentences in the consensus document were available during the meeting and a televoting round during the conference by all participants was performed. All sentences that were voted on are available on the EURECCA website www.canceraudit.eu. The consensus document was divided in sections describing evidence based algorithms of diagnostics, pathology, surgery, medical oncology, radiotherapy, and follow-up where applicable for treatment of colon cancer, rectal cancer and stage IV separately. Consensus was achieved using the Delphi method. Results: The total number of the voted sentences was 465. All chapters were voted on by at least 75% of the experts. Of the 465 sentences, 84% achieved large consensus, 6% achieved moderate consensus, and 7% resulted in minimum consensus. Only 3% was disagreed by more than 50% of the members. Conclusions: It is feasible to achieve European Consensus on key diagnostic and treatment issues using the Delphi method. This consensus embodies the expertise of professionals from all disciplines involved in the care for patients with colon and rectal cancer. Diagnostic and treatment algorithms were developed to implement the current evidence and to define core treatment guidance for multidisciplinary team management of colon and rectal cancer throughout Europe.

Improving breast cancer education: the case of an evolving multidisciplinary module for undergraduate medical students (lausanne medical school, 1993-2008).

Breast cancer is a public health issue in numerous countries. Multidisciplinary collaboration is required for patient care, research, and also education of future physicians. This paper uses Kern's framework for curriculum design to demonstrate how a breast diseases module for undergraduate medical students created in 1993 evolved over 15 years. The main outcomes of program refinements were better integrated course content, the development of electronic course documents, and implementation of computer-aided small group learning. A main future challenge is to further develop efficient instructional strategies in line with well-defined learning needs for undergraduate students.

Robust Detection of Incumbents in Cognitive Radio Networks Using Groups

Cognitive radio is a wireless technology aimed at improvingthe efficiency use of the radio-electric spectrum, thus facilitating a reductionin the load on the free frequency bands. Cognitive radio networkscan scan the spectrum and adapt their parameters to operate in the unoccupiedbands. To avoid interfering with licensed users operating on a givenchannel, the networks need to be highly sensitive, which is achieved byusing cooperative sensing methods. Current cooperative sensing methodsare not robust enough against occasional or continuous attacks. This articleoutlines a Group Fusion method that takes into account the behavior ofusers over the short and long term. On fusing the data, the method is basedon giving more weight to user groups that are more unanimous in their decisions.Simulations have been performed in a dynamic environment withinterferences. Results prove that when attackers are present (both reiterativeor sporadic), the proposed Group Fusion method has superior sensingcapability than other methods.

High capacity robust audio watermarking scheme based on FFT and linear regression

Peer-reviewed

A robust audio watermarking scheme based on MPEG 1 Layer 3 Compression

This paper describes an audio watermarking scheme based on lossy compression. The main idea is taken from an image watermarking approach where the JPEG compression algorithm is used to determine where and how the mark should be placed. Similarly, in the audio scheme suggested in this paper, an MPEG 1 Layer 3 algorithm is chosen for compression to determine the position of the mark bits and, thus, the psychoacoustic masking of the MPEG 1 Layer 3compression is implicitly used. This methodology provides with a high robustness degree against compression attacks. The suggested scheme is also shown to succeed against most of the StirMark benchmark attacks for audio.

The INTERMED questionnaire for predicting return to work after a multidisciplinary rehabilitation program for chronic low back pain.

OBJECTIVES: To evaluate the performance of the INTERMED questionnaire score, alone or combined with other criteria, in predicting return to work after a multidisciplinary rehabilitation program in patients with non-specific chronic low back pain. METHODS: The INTERMED questionnaire is a biopsychosocial assessment and clinical classification tool that separates heterogeneous populations into subgroups according to case complexity. We studied 88 patients with chronic low back pain who followed an intensive multidisciplinary rehabilitation program on an outpatient basis. Before the program, we recorded the INTERMED score, radiological abnormalities, subjective pain severity, and sick leave duration. Associations between these variables and return to full-time work within 3 months after the end of the program were evaluated using one-sided Fisher tests and univariate logistic regression followed by multivariate logistic regression. RESULTS: The univariate analysis showed a significant association between the INTERMED score and return to work (P<0.001; odds ratio, 0.90; 95% confidence interval, 0.86-0.96). In the multivariate analysis, prediction was best when the INTERMED score and sick leave duration were used in combination (P=0.03; odds ratio, 0.48; 95% confidence interval, 0.25-0.93). CONCLUSION: The INTERMED questionnaire is useful for evaluating patients with chronic low back pain. It could be used to improve the selection of patients for intensive multidisciplinary programs, thereby improving the quality of care, while reducing healthcare costs.

Robust power allocation algorithms for MIMO OFDM systems with imperfect CSI

This paper presents a Bayesian approach to the design of transmit prefiltering matrices in closed-loop schemes robust to channel estimation errors. The algorithms are derived for a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Two different optimizationcriteria are analyzed: the minimization of the mean square error and the minimization of the bit error rate. In both cases, the transmitter design is based on the singular value decomposition (SVD) of the conditional mean of the channel response, given the channel estimate. The performance of the proposed algorithms is analyzed,and their relationship with existing algorithms is indicated. As withother previously proposed solutions, the minimum bit error rate algorithmconverges to the open-loop transmission scheme for very poor CSI estimates.

Robust beamforming for interference rejection in mobile communications

The problem of robust beamformer design for mobile communicationsapplications in the presence of moving co-channel sources isaddressed. A generalization of the optimum beamformer based on a statisticalmodel accounting for source movement is proposed. The new methodis easily implemented and is shown to offer dramatic improvements overconventional optimum beamforming for moving sources under a varietyof operating conditions.

Multidisciplinary practice vs. inter-firm collaboration in professional services market

Yritysten välinen yhteistyö kasvaa asiantuntijamarkkinoilla. Suppean palvelutarjooman omaavat yritykset muodostavat laajempia palveluita yhdistämällä osaamisiaan kumppaneidensa kanssa. Näin muodostuvat yritysryhmittymät uhkaavat alaa hallitsevia monipuolisen palvelutarjooman omaavia kansainvälisiä moniosaajayrityksiä. Tämän diplomityön tavoitteena on selvittää minkälaisia hyötyjä moniosaajayritys voi saada näitä ryhmittymiä vastaan lisäämällä omaa yhteistyötään. Tavoitteeseen pääsemiseksi markkinoilla olevat yritysryhmittymät tunnistetaan ja selvitetään minkälaisia asioita asiakas pitää tärkeänä ostaessaan asiantuntijapalveluita. Toimialan trendit ja aikaisemmat tutkimukset yritysten välisestä yhteistyöstä sekä asiakkaan ostokäyttäytymisestä osoittavat, että yhteistyön avulla yrityksellä on mahdollisuus saavuttaa monia hyötyjä. Tietoa olemassa olevista yritysryhmittymistä ja asiakkaiden ostokäyttäytymisestä kerättiin haastattelemalla yhden kansainvälisen moniosaajayrityksen henkilöstöä sekä asiakkaita. Tuloksena löytyi yritysryhmittymiä, joista osa uhkaa moniosaajayrityksen kilpailuetua. Asiakkaiden ostokäyttäyminen suosi hieman enemmän asiantuntijapalveluiden hankkimista yritysryhmittymältä moniosaajayrityksen sijaan. Tekemällä yhteistyötä ja tarjoamalla tiettyjä palveluita yhdessä kumppanin kanssa, moniosaajayritys voi saavuttaa hyötyjä yritysryhmittymiä vastaan ja vaikuttaa positiivisesti asiakkaan ostokäyttäytymiseen.

Robust Estimators of the Generalized Log-Gamma Distribution

We propose robust estimators of the generalized log-gamma distribution and, more generally, of location-shape-scale families of distributions. A (weighted) Q tau estimator minimizes a tau scale of the differences between empirical and theoretical quantiles. It is n(1/2) consistent; unfortunately, it is not asymptotically normal and, therefore, inconvenient for inference. However, it is a convenient starting point for a one-step weighted likelihood estimator, where the weights are based on a disparity measure between the model density and a kernel density estimate. The one-step weighted likelihood estimator is asymptotically normal and fully efficient under the model. It is also highly robust under outlier contamination. Supplementary materials are available online.