Aligning biodiversity conservation and agricultural production in heterogeneous landscapes

Understanding the trade-offs between biodiversity conservation and agricultural production has become a fundamental question in sustainability science. Substantial research has focused on how species’ populations respond to agricultural intensification, with the goal to understand whether conservation policies that spatially separate agriculture and conservation or, alternatively, integrate the two are more beneficial. Spatial heterogeneity in both species abundance and agricultural productivity have been largely left out of this discussion, although these patterns are ubiquitous from local to global scales due to varying land capacity. Here, we address the question of how to align agricultural production and biodiversity conservation in heterogeneous landscapes. Using model simulations of species abundance and agricultural yields, we show that trade-offs between agricultural production and species’ abundance can be reduced by minimizing the cost (in terms of species abundance) of agricultural production. We find that when species’ abundance and agricultural yields vary across landscapes, the optimal strategy to minimize trade-offs is rarely pure land sparing or land sharing. Instead, landscapes that combine elements of both strategies are optimal. Additionally, we show how the reference population of a species is defined has important influences on optimization results. Our findings suggest that in the real world, understanding the impact of heterogeneous land capacity on biodiversity and agricultural production is crucial to designing multi-use landscapes that jointly maximize conservation and agricultural benefits.Fil: Butsic, Van. University of California at Berkeley; Estados Unidos. Berkeley University; Estados UnidosFil: Kuemmerle, Tobias. Universität zu Berlin; AlemaniaFil: Pallud, Leo. ENSTA ParisTech; FranciaFil: Helmstedt, Kate J.. Queensland University of Technology; AustraliaFil: Macchi, Leandro. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; Argentina. Universität zu Berlin; AlemaniaFil: Potts, Matthew D.. University of California at Berkeley; Estados Unido

Expression of Bcl-2 in node-negative breast cancer is associated with various prognostic factors, but does not predict response to one course of perioperative chemotherapy.

The aim of this study was to assess relationships between Bcl-2 expression, response to chemotherapy and a number of pathological and biological tumour parameters in premenopausal, lymph node-negative breast cancer patients. Expression of Bcl-2 was determined using immunohistochemistry on paraffin-embedded sections in a series of 441 premenopausal, lymph node-negative breast cancers of patients randomised to receive perioperative chemotherapy (5-fluorouracil, doxorubicin, cyclophosphamide) or no perioperative chemotherapy. Immunohistochemistry of Bcl-2 was evaluated by scoring both staining intensity (0-3) and number of positive cells (0-2). Using these scores tumours were grouped into categories 0-6. It was found that 9.2% of the tumours were completely negative (0), 17.2% weakly (1 + 2), 41.6% moderately (3 + 4) and 31.9% strongly positive (5 + 6) for Bcl-2. A positive correlation was found between high Bcl-2 expression and oestrogen (P < 0.001) and progesterone receptor positivity (P < 0.001) and low tumour grade (P < 0.001), whereas high Bcl-2 expression was negatively correlated with p53 (P < 0.001) and c-erb-B-2 positively (P < 0.001), high Ki-67 index (P < 0.001), mitotic index (P < 0.001) and large tumour size (P = 0.006). Patients with tumours expressing high levels of Bcl-2 (overall score 3-6) had a significantly better disease-free (P = 0.004) and overall (P = 0.009) survival. However, in a multivariate model this association no longer remained significant. There was a trend for an effect of adjuvant chemotherapy on disease-free survival both for patients with Bcl-2-positive (HR-0.61, 95% CI 0.35-1.06, P = 0.07) and negative (HR = 0.55, 95% CI 0.27-1.12, P = 0.09) breast tumours at a median follow-up of 49 months. The level of Bcl-2 expression does not seem to predict response to perioperative chemotherapy in premenopausal, lymph node-negative breast cancer patients. High levels of Bcl-2 are preferentially expressed in well-differentiated tumours and are associated with favourable prognosis. However, Bcl-2 expression is not an independent prognostic factor in this patient series

A generative approach for image-based modeling of tumor growth

22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. ProceedingsExtensive imaging is routinely used in brain tumor patients to monitor the state of the disease and to evaluate therapeutic options. A large number of multi-modal and multi-temporal image volumes is acquired in standard clinical cases, requiring new approaches for comprehensive integration of information from different image sources and different time points. In this work we propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.German Academy of Sciences Leopoldina (Fellowship Programme LPDS 2009-10)Academy of Finland (133611)National Institutes of Health (U.S.) (NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (NCRR NAC P41- RR13218)National Institutes of Health (U.S.) (NINDS R01-NS051826)National Institutes of Health (U.S.) (NIH R01-NS052585)National Institutes of Health (U.S.) (NIH R01-EB006758)National Institutes of Health (U.S.) (NIH R01-EB009051)National Institutes of Health (U.S.) (NIH P41-RR014075)National Science Foundation (U.S.) (CAREER Award 0642971

Chemotherapy and diffuse low-grade gliomas: a survey within the European Low-Grade Glioma Network.

Diffuse low-grade gliomas (DLGGs) are rare and incurable tumors. Whereas maximal safe, functional-based surgical resection is the first-line treatment, the timing and choice of further treatments (chemotherapy, radiation therapy, or combined treatments) remain controversial. An online survey on the management of DLGG patients was sent to 28 expert centers from the European Low-Grade Glioma Network (ELGGN) in May 2015. It contained 40 specific questions addressing the modalities of use of chemotherapy in these patients. The survey demonstrated a significant heterogeneity in practice regarding the initial management of DLGG patients and the use of chemotherapy. Interestingly, radiation therapy combined with the procarbazine, CCNU (lomustine), and vincristine regimen has not imposed itself as the gold-standard treatment after surgery, despite the results of the Radiation Therapy Oncology Group 9802 study. Temozolomide is largely used as first-line treatment after surgical resection for high-risk DLGG patients, or at progression. The heterogeneity in the management of patients with DLGG demonstrates that many questions regarding the postoperative strategy and the use of chemotherapy remain unanswered. Our survey reveals a high recruitment potential within the ELGGN for retrospective or prospective studies to generate new data regarding these issues

A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle

The prediction of the outcome of individual patients with glioblastoma would be of great significance for monitoring responses to therapy. We hypothesise that, although a large number of genetic-metabolic abnormalities occur upstream, there are two ‘final common pathways' dominating glioblastoma growth – net rates of proliferation (ρ) and dispersal (D). These rates can be estimated from features of pretreatment MR images and can be applied in a mathematical model to predict tumour growth, impact of extent of tumour resection and patient survival. Only the pre-operative gadolinium-enhanced T1-weighted (T1-Gd) and T2-weighted (T2) volume data from 70 patients with previously untreated glioblastoma were used to derive a ratio D/ρ for each patient. We developed a ‘virtual control' for each patient with the same size tumour at the time of diagnosis, the same ratio of net invasion to proliferation (D/ρ) and the same extent of resection. The median durations of survival and the shapes of the survival curves of actual and ‘virtual' patients subjected to biopsy or subtotal resection (STR) superimpose exactly. For those actually receiving gross total resection (GTR), as shown by post-operative CT, the actual survival curve lies between the ‘virtual' results predicted for 100 and 125% resection of the T1-Gd volume. The concordance between predicted (virtual) and actual survivals suggests that the mathematical model is realistic enough to allow precise definition of the effectiveness of individualised treatments and their site(s) of action on proliferation (ρ) and/or dispersal (D) of the tumour cells without knowledge of any other clinical or pathological information