Metformin: a modulator of bevacizumab activity in cancer? A case report.

Recurrent type I endometrial cancer ((EC)) has poor prognosis and demands novel therapeutic approaches. Bevacizumab, a VEGF-A neutralizing monoclonal antibody, has shown clinical activity in this setting. To our knowledge, however, although some diabetic cancer patients treated with bevacizumab may also take metformin, whether metformin modulates response to anti-VEGF therapy has not yet been investigated. Here, we report the case of a patient with advanced (EC) treated, among other drugs, with bevacizumab in combination with metformin. The patient affected by relapsed (EC) G3 type 1, presented in march 2010 with liver, lungs and mediastinic metastases. After six cycles of paclitaxel and cisplatin she underwent partial response. Later on, she had disease progression notwithstanding administration of multiple lines of chemotherapy. In march 2013, due to brain metastases with coma, she began steroid therapy with development of secondary diabetes. At this time, administration of Bevacizumab plus Metformin improved her performance status. CT scans performed in this time window showed reduced radiologic density of the lung and mediastinic lesions and of liver disease, suggestive of increased tumor necrosis. Strong F-18-FDG uptake by PET imaging along with high levels of monocarboxylate transporter 4 and lack of liver kinase B1 expression in liver metastasis, highlighted metabolic features previously associated with response to anti-VEGF therapy and phenformin in preclinical models. However, clinical benefit was transitory and was followed by rapid and fatal disease progression. These findingsalbeit limited to a single casesuggest that tumors lacking LKB1 expression and/or endowed with an highly glycolytic phenotype might develop large necrotic areas following combined treatment with metformin plus bevacizumab. As metformin is widely used among diabetes patients as well as in ongoing clinical trials in cancer patients, these results deserve further clinical investigation

Generalized Poisson difference autoregressive processes

This paper introduces a novel stochastic process with signed integer values. Its autoregressive dynamics effectively captures persistence in conditional moments, rendering it a valuable feature for forecasting applications. The increments follow a Generalized Poisson distribution, capable of accommodating over- and under-dispersion in the conditional distribution, thereby extending standard Poisson difference models. We derive key properties of the process, including stationarity conditions, the stationary distribution, and conditional and unconditional moments, which prove essential for accurate forecasting. We provide a Bayesian inference framework with an efficient posterior approximation based on Markov Chain Monte Carlo. This approach seamlessly incorporates inherent parameter uncertainty into predictive distributions. The effectiveness of the proposed model is demonstrated through applications to benchmark datasets on car accidents and an original dataset on cyber threats, highlighting its superior fitting and forecasting capabilities compared to standard Poisson model

Spatiotemporal Modeling Encounters 3D Medical Image Analysis: Slice-Shift UNet with Multi-View Fusion

As a fundamental part of computational healthcare, Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) provide volumetric data, making the development of algorithms for 3D image analysis a necessity. Despite being computationally cheap, 2D Convolutional Neural Networks can only extract spatial information. In contrast, 3D CNNs can extract three-dimensional features, but they have higher computational costs and latency, which is a limitation for clinical practice that requires fast and efficient models. Inspired by the field of video action recognition we propose a new 2D-based model dubbed Slice SHift UNet (SSH-UNet) which encodes three-dimensional features at 2D CNN's complexity. More precisely multi-view features are collaboratively learned by performing 2D convolutions along the three orthogonal planes of a volume and imposing a weights-sharing mechanism. The third dimension, which is neglected by the 2D convolution, is reincorporated by shifting a portion of the feature maps along the slices' axis. The effectiveness of our approach is validated in Multi-Modality Abdominal Multi-Organ Segmentation (AMOS) and Multi-Atlas Labeling Beyond the Cranial Vault (BTCV) datasets, showing that SSH-UNet is more efficient while on par in performance with state-of-the-art architectures

Focused Bayesian Prediction

We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defined over a class of plausible predictive models. After observing data, we update the prior to a posterior over these models, via a criterion that captures a user-specified measure of predictive accuracy. Under regularity, this update yields posterior concentration onto the element of the predictive class that maximizes the expectation of the accuracy measure. In a series of simulation experiments and empirical examples we find notable gains in predictive accuracy relative to conventional likelihood-based prediction

Fiscal Policy Regimes in Resource-Rich Economies

We analyse fiscal policy in resource-rich economies using a novel Bayesian regime-switching panel model. The identified regimes capture pro- or countercyclical fiscal behaviour, while the switches between the regimes have the interpretation of changes in fiscal policy. Applying the model to sixteen oil-producing economies, we show that fiscal policy has alternated between a procyclical and countercyclical regime multiple times over the sample. Furthermore, we find fiscal policy to be the most volatile in the procyclical regime and that the probability of being in the procyclical regime is higher for OPEC countries rather than non OPEC countries. We also show that following either an increase or decrease in oil revenues, the growth in government expenditure mostly increases, suggesting there is an upward bias in expenditures in oil-producing countries. These are new findings in the literature

Investigations techniques carried out on the Qutb Minar, New Delhi, India

In the framework of the Eu-India Economic Cross Cultural Programme “Improving the Seismic Resistance of Cultural Heritage Buildings”, aimed at the preservation of ancient masonry structures with regard to the seismic risk, different NDT were applied to the Qutb Minar, New Delhi, India, in September 2005. The paper describes the different investigation techniques applied (Ambient Vibration and Pulse Sonic Velocity Tests), intended to define the dynamic response of the tower and to qualitatively define the masonry conditions. For the dynamic modal identification analysis different test equipments were used, in order to compare the data and to have more reliable results. The dynamic parameters resulted from the acquisition campaigns will be used to estimate the mechanical properties of the masonry walls and the boundary conditions of the structure, to be considered in successive seismic nonlinear analyses of the Qutb Minar, aimed at the assessment of the safety level of the construction

Dynamic identification of the Qutb Minar, New Delhi, India

Eu-India Economic Cross Cultural Programme “Improving the Seismic Resistance of Cultural Heritage Buildings” - Contract ALA-95-23-2003-077-122.Central Building Research Institute, Roorkee, India.Technical University of Catalonia, Barcelona, Spain.Archaeological Survey of India

Interacting Multiple Try Algorithms with Different Proposal Distributions

We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increasing the efficiency of a modified multiple-try Metropolis (MTM) algorithm. The extension with respect to the existing MCMC literature is twofold. The sampler proposed extends the basic MTM algorithm by allowing different proposal distributions in the multiple-try generation step. We exploit the structure of the MTM algorithm with different proposal distributions to naturally introduce an interacting MTM mechanism (IMTM) that expands the class of population Monte Carlo methods. We show the validity of the algorithm and discuss the choice of the selection weights and of the different proposals. We provide numerical studies which show that the new algorithm can perform better than the basic MTM algorithm and that the interaction mechanism allows the IMTM to efficiently explore the state space

PREFERÊNCIAS PAISAGÍSTICAS NO ENTORNO DE RIOS URBANOS: UMA REVISÃO

Under the practice of urban environmental planning, design and management, thewater bodies and their riverbanks is a challenge that cities have faced in the last decades due to new ecological paradigms towards a sustainable society. Research regarding landscape preference on urban riverbanks are important to understand the perception of user on of the interface between the natural and built environment, thus contributing to new practices on urban environmental planning, design and management. Therefore, this study aims to identify and systematize the attributes of landscape preference on urban riverbanks. The research method involved a Systematic Literature Review (RSL), in which the search was performed in four databases (Scopus, Science Direct, Springer and Scielo), resulting in a compilation of 339 journal articles, of which only 13 were selected for content analysis. The results showed that the most preferred attributes related to urban riverbanks are: recreation and leisure activities, riparian vegetation, naturalised riverbanks, but, in some contexts, there is the preference for artificialized riverbanks, physical and visual accessibility to water, water quality, between others. Such attributes could be considered guidelines for urban rivers restoration projects to recover the ecological quality related to the rivers and promote the quality of life in the cities.No âmbito da prática de planejamento, projeto e gestão ambiental urbana, os corpos d'água e suas margens são um desafio que as cidades têm enfrentado nas últimas décadas devido aos novos paradigmas ecológicos em direção a uma sociedade sustentável. Pesquisas relacionadas a preferências paisagísticas em margens de rios urbanos são importantes para compreender a percepção do usuário sobre a interface entre o ambiente natural e o construído, a fim de contribuir para novas práticas de planejamento, projeto e gestão ambiental urbana. Portanto, este estudo tem como objetivo identificar e sistematizar os atributos de preferência paisagística para o tratamento das bordas de rios urbanos. O método de pesquisa envolveu uma Revisão Sistemática da Literatura (RSL), cuja busca foi realizada em quatro bases de dados (Scopus, Science Direct, Springer e Scielo), resultando em uma compilação de 339 artigos de periódicos, dos quais 13 foram selecionados para análise de conteúdo após a aplicação dos critérios de inclusão e exclusão. Os resultados mostraram que os atributos preferenciais relacionados às margens de rios urbanos são, nesta ordem: atividades de recreação e lazer, vegetação ciliar, margens de rios naturalizadas, mas, em alguns contextos, há preferência por margens artificializadas, acessibilidade física e visual, entre outros