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Abstract Progress in neuroscience research hinges on technical advances in visualizing living brain tissue with high fidelity and facility. Current neuroanatomical imaging approaches either require tissue fixation (electron microscopy), do not have cellular resolution (magnetic resonance imaging) or only give a fragmented view (fluorescence microscopy). Here, we show how regular light microscopy together with fluorescence labeling of the interstitial fluid in the extracellular space provide comprehensive optical access in real-time to the anatomical complexity and dynamics of living brain tissue at submicron scale. Using several common fluorescence microscopy modalities (confocal, light-sheet and 2-photon microscopy) in mouse organotypic and acute brain slices and the intact mouse brain in vivo, we demonstrate the value of this straightforward ‘shadow imaging’ approach by revealing neurons, microglia, tumor cells and blood capillaries together with their complete anatomical tissue contexts. In addition, we provide quantifications of perivascular spaces and the volume fraction of the extracellular space of brain tissue in vivo.

The COVID-19 pandemic has forced the digitalization of the majority of universities, prior to which they were largely operating using face-to-face modes of learning. Increased competition in the digital environment places universities under greater pressure to offer an innovative learning experience. The purpose of this paper is to understand the effects of the sudden pandemic on the ongoing process of digital transformation (DT) and how the learning value proposition of higher education institutions (HEIs) has been affected. The research is based on a single case study of a born digital university, focusing on the changes made to the learning value proposition, and particularly to the multimode learning offer. The paper uncovers the relation between multimodality and customized and personalized learning, all of which are dependent on the use of digital educational technology. The originality of this paper is its longitudinal look at a single case, observing how the significant DT process already underway prior to the pandemic has been impacted by it, accelerating the process, and clarifying the envisaged post-pandemic future for HEIs. Another distinctive aspect is the consideration of the learning proposition as a core element and part of a larger and interdependent value proposition within the overall HEIs business model.

The combination of two macro trends, Information and Communication Technologies' (ICT) proliferation and novel approaches in education, has resulted in a series of opportunities with no precedent in terms of content, channels and methods in education. The present contribution aims to describe the experience of using an automatic spreadsheet corrector, its perceived benefits as well as its impact on academic results. A total of 179 students coursing technical studies attended a basic course on business administration in the framework of which the field work was conducted. Perception assessment of students demonstrates a very good opinion about the learning experience using the proposed ICT tool. The user perception evaluation also shows that it is a valuable learning tool, while students who obtain a high final qualification are the ones that have been positively evaluated in the sessions using the automatic spreadsheet corrector. Beyond these highlighted benefits for students, teachers gain time, effort and workload reduction. The validity of the proposal is tested in the Polytechnic School of the University of Girona in Catalonia (Spain). [Paper presented at the International Conference on University Teaching and Innovation (CIDUI): Learning and Teaching Innovation Impacts (9th, Bellaterra, Barcelona, Spain, Jul 5-7 2016.)]

The COVID-19 pandemic has forced the digitalization of the majority of universities, prior to which they were largely operating using face-to-face modes of learning. Increased competition in the digital environment places universities under greater pressure to offer an innovative learning experience. The purpose of this paper is to understand the effects of the sudden pandemic on the ongoing process of digital transformation (DT) and how the learning value proposition of higher education institutions (HEIs) has been affected. The research is based on a single case study of a born digital university, focusing on the changes made to the learning value proposition, and particularly to the multimode learning offer. The paper uncovers the relation between multimodality and customized and personalized learning, all of which are dependent on the use of digital educational technology. The originality of this paper is its longitudinal look at a single case, observing how the significant DT process already underway prior to the pandemic has been impacted by it, accelerating the process, and clarifying the envisaged post-pandemic future for HEIs. Another distinctive aspect is the consideration of the learning proposition as a core element and part of a larger and interdependent value proposition within the overall HEIs business model.

The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference.

Abstract Lactate is a central metabolite in brain physiology but also contributes to tumor development. Glioblastoma (GB) is the most common and malignant primary brain tumor in adults, recognized by angiogenic and invasive growth, in addition to its altered metabolism. We show herein that lactate fuels GB anaplerosis by replenishing the tricarboxylic acid (TCA) cycle in absence of glucose. Lactate dehydrogenases (LDHA and LDHB), which we found spatially expressed in GB tissues, catalyze the interconversion of pyruvate and lactate. However, ablation of both LDH isoforms, but not only one, led to a reduction in tumor growth and an increase in mouse survival. Comparative transcriptomics and metabolomics revealed metabolic rewiring involving high oxidative phosphorylation (OXPHOS) in the LDHA/B KO group which sensitized tumors to cranial irradiation, thus improving mouse survival. When mice were treated with the antiepileptic drug stiripentol, which targets LDH activity, tumor growth decreased. Our findings unveil the complex metabolic network in which both LDHA and LDHB are integrated and show that the combined inhibition of LDHA and LDHB strongly sensitizes GB to therapy.

Abstract Background Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy. Methods In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data. Results Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation. In addition, transcriptome and methylome analyses showed distinct clustering of the cell lines without genomic variation. Distinct signatures of tumor aggressiveness were revealed and validated in different patient cohorts. In particular, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. Machine learning and mathematical modeling confirmed the importance of CFB and SAA2 together, which had the highest impact on distant metastasis-free survival. From these data sets, a computational model predicting tumor progression and relapse was developed and validated. These results are of great translational significance. Conclusion A combination of experimental and mathematical modeling was able to generate meaningful data for the prediction of the clinical evolution of RCC.

Companies aiming to increase their firm performance implement specific practices known as High Performance Work Systems (HPWS). Uncertainty remains, however, about the structures included in them. The main purpose of this paper is to analysis the difference between implementing specific HPWP separately or as a HPWS, and their effect on performance aspects, using the Spanish subsample of the European Manufacturing Survey. The results show that these firms’ implementation rates are relatively low compared to the European average, despite over 50% of respondents implementing some of the practices studied (individually or as a bundle).

Lungs are the most frequent site of metastases growth. The amount and size of pulmonary metastases acquired from MRI imaging data are the important criteria to assess the efficacy of new drugs in preclinical models. While efficient solutions both for MR imaging and the downstream automatic segmentation have been proposed for human patients, both MRI lung imaging and segmentation in preclinical animal models remains challenging due to the physiological motion (respiratory and cardiac movements), to the low amount of protons in this organ and to the particular challenge of precise segmentation of metastases. As a consequence post-mortem analysis is currently required to obtain information on metastatic volume. In this work, we have developed a complete methodological pipeline for automated analysis of lungs and metastases in mice, consisting of an MR sequence for image acquisition and a deep learning method for automatic segmentation of both lungs and metastases. On one hand, we optimized an MR sequence for mouse lung imaging with high contrast for high detection sensitivity. On the other hand we developed DeepMeta, a multiclass U-Net 3+ deep learning model to automatically segment the images. To assess if the proposed deep learning pipeline is able to provide an accurate segmentation of both lungs and pulmonary metastases, we have longitudinally imaged mice with fast- and slow-growing metastasis. Fifty-five balb/c mice were injected with two different derivatives of renal carcinoma cells. Mice were imaged with a SG-bSSFP (self-gated balanced steady state free precession) sequence at different time points after the injection of cancer cells. Both lung and metastases segmentations were manually performed by experts. DeepMeta was trained to perform lung and metastases segmentation based on the resulting ground truth annotations. Volumes of lungs and of pulmonary metastases as well as the number of metastases per mouse were measured on a separate test dataset of MR images. Thanks to the SG method, the 3D bSSFP images of lungs were artifact-free, enabling the downstream detection and serial follow-up of metastases. Moreover, both lungs and metastases segmentation was accurately performed by DeepMeta as soon as they reached the volume of ∼0.02mm3. Thus we were able to distinguish two groups of mice in terms of number and volume of pulmonary metastases as well as in terms of the slow versus fast patterns of growth of metastases. We have shown that our methodology combining SG-bSSFP with deep learning, enables processing of the whole animal lungs and is thus a viable alternative to histology alone.

Distant metastasis-free survival (DMFS) curves are widely used in oncology. They are classically analyzed using the Kaplan-Meier estimator or agnostic statistical models from survival analysis. Here we report on a method to extract more information from DMFS curves using a mathematical model of primary tumor growth and metastatic dissemination. The model depends on two parameters, α and μ, respectively quantifying tumor growth and dissemination. We assumed these to be lognormally distributed in a patient population. We propose a method for identification of the parameters of these distributions based on least-squares minimization between the data and the simulated survival curve. We studied the practical identifiability of these parameters and found that including the percentage of patients with metastasis at diagnosis was critical to ensure robust estimation. We also studied the impact and identifiability of covariates and their coefficients in α and μ, either categorical or continuous, including various functional forms for the latter (threshold, linear or a combination of both). We found that both the functional form and the coefficients could be determined from DMFS curves. We then applied our model to a clinical dataset of metastatic relapse from kidney cancer with individual data of 105 patients. We show that the model was able to describe the data and illustrate our method to disentangle the impact of three covariates on DMFS: a categorical one (Führman grade) and two continuous ones (gene expressions of the macrophage mannose receptor 1 (MMR) and the G Protein-Coupled Receptor Class C Group 5 Member A (GPRC5a) gene). We found that all had an influence in metastasis dissemination (μ), but not on growth (α).

La preocupante situación ambiental actual a nivel mundial ha puesto a reflexionar a empresas del sector manufacturero, al igual que a sus clientes, sobre la necesidad de desarrollar y adquirir, respectivamente, un producto innovador verde (PIV). Algunas de las razones por parte de los consumidores están sustentadas en una mayor conciencia ambiental, preocupaciones éticas, motivos morales, así como la sensibilización sobre qué planeta se proveerá para las futuras generaciones. Por parte de las empresas, muchas aún no se encuentran convencidas de los beneficios que obtendrían al incursionar en el desarrollo de productos verdes. Del mismo modo, más allá de contribuir al cuidado del medio ambiente, las empresas también requieren de otras motivaciones que las impulse a destinar recursos y esfuerzos en innovación ecológica. Con la intención de avanzar en la comprensión de las empresas y de los clientes con relación a la necesidad de articular tanto sus requerimientos como sus deseos, a la vez, como un impulso a los académicos para el progreso de la investigación del PIV. En este artículo, sobre la base de la literatura, se exploran alternativas sobre cómo abordar en el ámbito organizacional el paradigma del PIV, así como qué inspira a las empresas y a clientes para su obtención. Los resultados del estudio detectaron una serie de impulsores relacionados con orientaciones en función comprender las necesidades del cliente ecológico, dinámicas para llevar a cabo a nivel de la organización y motivaciones tanto del cliente como de las empresas en procura del alcance del PIV, presentando una asociación, que establece la propuesta de un marco orientado la consecución y compra del PIV. En virtud de lo anterior, los investigadores y los gerentes corporativos y operativos tienen la oportunidad para generar aportes importantes en esta temática bajo sus diferentes líneas de actuación, tanto en el ámbito académico como en el gerencial.

Dufies et al. find high Plk1 expression levels in aggressive clear cell renal cell carcinoma and discover that Plk1 is transcriptionally upregulated in a manner dependent on HIF-2. They also find that high Plk1 expression is correlated to a poor prognosis and resistance to tyrosine kinase inhibitor against VEGF receptor, suggesting a critical role for hypoxia/HIF-2-induced Plk1 in disease progression.