Assessment of induced rat mammary tumour response to chemotherapy using the apparent diffusion coefficient of tissue water as determined by diffusion-weighted 1H-NMR spectroscopy in vivo.

Chemosensitivity of N-methyl-N-nitrosourea-induced rat mammary tumours treated with 5-fluorouracil at a dose of 100 mg kg(-1) i.p. was assessed by using diffusion-weighted 1H-MRS to measure the average diffusion coefficient (ADC) of water in the tumour tissue. ADC measurements prior to any therapy correlated positively with necrotic fraction. Tumours with low initial ADC (< 0.95 x 10(9) m2 s(-1)) showed an increase in ADC 7 days after treatment, whereas tumours with a high initial ADC (> 1.2 x 10(9) m2 s(-1)) showed a decrease. All tumours decreased significantly in volume (P < 0.05) 2, 5 and 7 days after treatment. At day 7 post-treatment, tumours with a high pre-treatment ADC started to regrow. The initial ADC value, as well as changes after treatment predict tumour chemosensitivity, which could be clinically relevant

A polynomial eigenvalue approach for multiplex networks

We explore the block nature of the matrix representation of multiplex networks, introducing a new formalism to deal with its spectral properties as a function of the inter-layer coupling parameter. This approach allows us to derive interesting results based on an interpretation of the traditional eigenvalue problem. Specifically, our formalism is based on the reduction of the dimensionality of a matrix of interest but increasing the power of the characteristic polynomial, i.e, a polynomial eigenvalue problem. This approach may sound counterintuitive at first, but it enable us to relate the quadratic eigenvalue problem for a 2-Layer multiplex network with the spectra of its respective aggregated network. Additionally, it also allows us to derive bounds for the spectra, among many other interesting analytical insights. Furthermore, it also permits us to directly obtain analytical and numerical insights on the eigenvalue behavior as a function of the coupling between layers. Our study includes the supra-adjacency, supra-Laplacian and the probability transition matrices, which enables us to put our results under the perspective of structural phases in multiplex networks. We believe that this formalism and the results reported will make it possible to derive new results for multiplex networks in the future

Impact of the distribution of recovery rates on disease spreading in complex networks

We study a general epidemic model with arbitrary recovery rate distributions. This simple deviation from the standard setup is sufficient to prove that heterogeneity in the dynamical parameters can be as important as the more studied structural heterogeneity. Our analytical solution is able to predict the shift in the critical properties induced by heterogeneous recovery rates. We find that the critical value of infectivity tends to be smaller than the one predicted by quenched mean-field approaches in the homogeneous case and that it can be linked to the variance of the recovery rates. Our findings also illustrate the role of dynamical-structural correlations, where we allow a power-law network to dynamically behave as a homogeneous structure by an appropriate tuning of its recovery rates. Overall, our results demonstrate that heterogeneity in the recovery rates, eventually in all dynamical parameters, is as important as the structural heterogeneity

Disease localization in multilayer networks

We present a continuous formulation of epidemic spreading on multilayer networks using a tensorial representation, extending the models of monoplex networks to this context. We derive analytical expressions for the epidemic threshold of the susceptible-infected-susceptible (SIS) and susceptibleinfected- recovered dynamics, as well as upper and lower bounds for the disease prevalence in the steady state for the SIS scenario. Using the quasistationary state method, we numerically show the existence of disease localization and the emergence of two or more susceptibility peaks, which are characterized analytically and numerically through the inverse participation ratio. At variance with what is observed in single-layer networks, we show that disease localization takes place on the layers and not on the nodes of a given layer. Furthermore, when mapping the critical dynamics to an eigenvalue problem, we observe a characteristic transition in the eigenvalue spectra of the supra-contact tensor as a function of the ratio of two spreading rates: If the rate at which the disease spreads within a layer is comparable to the spreading rate across layers, the individual spectra of each layer merge with the coupling between layers. Finally, we report on an interesting phenomenon, the barrier effect; i.e., for a three-layer configuration, when the layer with the lowest eigenvalue is located at the center of the line, it can effectively act as a barrier to the disease. The formalism introduced here provides a unifying mathematical approach to disease contagion in multiplex systems, opening new possibilities for the study of spreading processes

A general Markov chain approach for disease and rumour spreading in complex networks

Spreading processes are ubiquitous in natural and artificial systems. They can be studied via a plethora of models, depending on the specific details of the phenomena under study. Disease contagion and rumour spreading are among the most important of these processes due to their practical relevance. However, despite the similarities between them, current models address both spreading dynamics separately. In this article, we propose a general spreading model that is based on discrete time Markov chains. The model includes all the transitions that are plausible for both a disease contagion process and rumour propagation. We show that our model not only covers the traditional spreading schemes but that it also contains some features relevant in social dynamics, such as apathy, forgetting, and lost/recovering of interest. The model is evaluated analytically to obtain the spreading thresholds and the early time dynamical behaviour for the contact and reactive processes in several scenarios. Comparison with Monte Carlo simulations shows that the Markov chain formalism is highly accurate while it excels in computational efficiency. We round off our work by showing how the proposed framework can be applied to the study of spreading processes occurring on social networks

Correlations between climate network and relief data

In the last few years, the scientific community has witnessed an ongoing trend of using ideas developed in the study of complex networks to analyze climate dynamics. This powerful combination, usually called climate networks, can be used to uncover non-trivial patterns of weather changes throughout the years. Here we investigate the temperature network of the North American region and show that two network characteristics, namely degree and clustering, have marked differences between the eastern and western regions. We show that such differences are a reflection of the presence of a large network community on the western side of the continent. Moreover, we provide evidence that this large community is a consequence of the peculiar characteristics of the western relief of North America

Analysis of 13 C and 14 C labeling in pyruvate and lactate in tumor and blood of lymphoma-bearing mice injected with 13 C- and 14 C-labeled pyruvate

Measurements of hyperpolarized 13C label exchange between injected [1‐13C]pyruvate and the endogenous tumor lactate pool can give an apparent first‐order rate constant for the exchange. The determination of the isotope flux, however, requires an estimate of the labeled pyruvate concentration in the tumor. This was achieved here by measurement of the tumor uptake of [1‐14C]pyruvate, which showed that <2% of the injected pyruvate reached the tumor site. Multiplication of this estimated labeled pyruvate concentration in the tumor with the apparent first‐order rate constant for hyperpolarized 13C label exchange gave an isotope flux that showed good agreement with a flux determined directly by the injection of non‐polarized [3‐13C]pyruvate, rapid excision of the tumor after 30 s and measurement of 13C‐labeled lactate concentrations in tumor extracts. The distribution of labeled lactate between intra‐ and extracellular compartments and the blood pool was investigated by imaging, by measurement of the labeled lactate concentration in blood and tumor, and by examination of the effects of a gadolinium contrast agent and a lactate transport inhibitor on the intensity of the hyperpolarized [1‐13C]lactate signal. These measurements showed that there was significant export of labeled lactate from the tumor, but that labeled lactate in the blood pool produced by the injection of hyperpolarized [1‐13C]pyruvate showed only relatively low levels of polarization. This study shows that measurements of hyperpolarized 13C label exchange between pyruvate and lactate in a murine tumor model can provide an estimate of the true isotope flux if the concentration of labeled pyruvate that reaches the tumor can be determined

Leishmania amazonensis promastigotes in 3D Collagen I culture: an in vitro physiological environment for the study of extracellular matrix and host cell interactions

Leishmania amazonensis is the causative agent of American cutaneous leishmaniasis, an important neglected tropical disease. Once Leishmania amazonensis is inoculated into the human host, promastigotes are exposed to the extracellular matrix (ECM) of the dermis. However, little is known about the interaction between the ECM and Leishmania promastigotes. In this study we established L. amazonensis promastigote culture in a three-dimensional (3D) environment mainly composed of Collagen I (COL I). This 3D culture recreates in vitro some aspects of the human host infection site, enabling the study of the interaction mechanisms of L. amazonensis with the host ECM. Promastigotes exhibited “freeze and run” migration in the 3D COL I matrix, which is completely different from the conventional in vitro swimming mode of migration. Moreover, L. amazonensis promastigotes were able to invade, migrate inside, and remodel the 3D COL I matrix. Promastigote trans-matrix invasion and the freeze and run migration mode were also observed when macrophages were present in the matrix. At least two classes of proteases, metallo- and cysteine proteases, are involved in the 3D COL I matrix degradation caused by Leishmania. Treatment with a mixture of protease inhibitors significantly reduced promastigote invasion and migration through this matrix. Together our results demonstrate that L. amazonensis promastigotes release proteases and actively remodel their 3D environment, facilitating their migration. This raises the possibility that promastigotes actively interact with their 3D environment during the search for their cellular “home”—macrophages. Supporting this hypothesis, promastigotes migrated faster than macrophages in a novel 3D co-culture model

Overall Picture Of Expressed Heat Shock Factors In Glycine Max, Lotus Japonicusand Medicago Truncatula

Heat shock (HS) leads to the activation of molecular mechanisms, known as HS-response, that prevent damage and enhance survival under stress. Plants have a flexible and specialized network of Heat Shock Factors (HSFs), which are transcription factors that induce the expression of heat shock proteins. The present work aimed to identify and characterize the Glycine maxHSF repertory in the Soybean Genome Project (GENOSOJA platform), comparing them with other legumes (Medicago truncatulaand Lotus japonicus) in view of current knowledge of Arabidopsis thaliana. The HSF characterization in leguminous plants led to the identification of 25, 19 and 21 candidate ESTs in soybean, Lotusand Medicago, respectively. A search in the SuperSAGE libraries revealed 68 tags distributed in seven HSF gene types. 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