IL-15 modulates the effect of retinoic acid, promoting inflammation rather than oral tolerance to dietary antigens

3 páginas.-- Evaluation of: DePaolo RW, Abadie V, Tang F et al. Co-adjuvant effects of retinoic acid and IL-15 induce inflammatory immunity to dietary antigens. Nature 471(7337), 220–224 (2011).The physiological immune response in the intestine against dietary proteins and commensal flora is characterized by regulatory mechanisms (tolerance) that prevent harmful consequences. Intestinal dendritic cells (DCs) have a central role in the development of immunosuppressive regulatory T cells owing to their ability to produce TGF-b and retinoic acid (RA). However, the article under evaluation shows an unexpected effect of RA – that of promoting a proinflammatory phenotype in intestinal DCs involved in the generation of inflammatory immune responses to dietary antigens. By using a double transgenic murine model that resembles human celiac disease, it was demonstrated that RA synergizes with IL‑15 in promoting the breakdown of gluten tolerance and the development of enteropathy. The tissue microenvironment modulates DC function, and immune therapies that are based on RA aiming to restore oral tolerance should be used with caution because the presence of IL‑15 (and/or other proinflammatory cytokines) may have undesirable effects.Peer reviewe

Sampling Once…Using Data Multiple Times.

presentaciónMarine ecosystem variability shows large fluctuations on a wide variety of scales, from seconds to millennia and from local to global. This limits our ability to observe these systems and to develop good tools to predict how changes in the environment may affect their physical and biological properties. It also limits our ability to differentiate anthropogenic from natural processes. An example is how difficult it is to compare data collected in different sampling locations and at different times. Time series data help resolve both short- and longer-term scales of variability and provide context for traditional process-oriented studies. Time series projects focusing on biogeochemical and ecological observations have yielded important scientific results. They have helped to: (i) evaluate the statistical significance of the ranges of variability of many parameters and environmental variables and biological communities, and (ii) quantify and evaluate the dimension of the interactions between key physical/chemical oceanographic processes and biological rates in plankton communities. As a result, time series are helping estimate warming rates and trends as well as the effects of global change on biota. They have established reference baselines to evaluate the magnitude of environmental perturbations and estimate recovery times on biodiversity and productivity of specific trophic levels. In spite of their scientific value, marine time series are difficult to maintain over time because of costs and availability of trained personnel. Only a few survive beyond a decade. There is great potential in sharing and combining marine data sets from different time series programs from around the world. This allows for comparisons of changes occurring in distant locations, and helps detect changes that occur at broad scales, perhaps even global scales, and to distinguish them from local imbalances or fluctuation. Sharing data can have important economic and social benefits. For instance, efficient use of existing marine data represents a significant cost saving from the 2 billion Euro spent each year now in the EU collecting and accessing to marine data. From the social point of view, the demand from different stakeholders for answers to the challenges posed by changes in the marine environment is growing rapidly. Sharing and accessing time series data would reduce the uncertainties in the management of marine resources and ecosystem services. The UNESCO IOC advocates that: (i) an observation not made today is lost forever, (ii) existing observations are lost if not made accessible, (iii) the collective value of data sets is greater than its dispersed value, and (iv) open access to standardised time series data must be pursued as a common, coordinated international goal.IOC-UNESCO, IE

Fütterungseinflüsse auf das Fress- und Wiederkäuverhalten von Milchkühen auf einem Biobetrieb

In einer ökologischen Milchviehherde in der Schweiz wurden 23 Kühe mit verschiedenen Rationen gefüttert. Futteraufnahme und Wiederkäuverhalten wurden mit Kausensoren erfasst. Proteinkonzentrate sowie eine separate Heugabe zeigten signifikante Einflüsse auf das Fressen und Wiederkäuen während des Tages, jedoch nicht während der Nacht. Die Daten zeigen das Potential der Erhebung von Fress- und Wiederkäuverhalten zur Beurteilung von Fütterungssituationen mit grundfutterreichen Rationen

The DWD climate predictions website: Towards a seamless outlook based on subseasonal, seasonal and decadal predictions

The climate predictions website of the Deutscher Wetterdienst (DWD, https://www.dwd.de/climatepredictions) presents a consistent operational outlook for the coming weeks, months and years, focusing on the needs of German users. At global scale, subseasonal predictions from the European Centre of Medium-Range Weather Forecasts as well as seasonal and decadal predictions from the DWD are used. Statistical downscaling is applied to achieve high resolution over Germany. Lead-time dependent bias correction is performed on all time scales. Additionally, decadal predictions are recalibrated. The website offers ensemble mean and probabilistic predictions for temperature and precipitation combined with their skill (mean squared error skill score, ranked probability skill score). Two levels of complexity are offered: basic climate predictions display simple, regionally averaged information for Germany, German regions and cities as maps, time series and tables. The skill is presented as traffic light. Expert climate predictions show complex, gridded predictions for Germany (at high resolution), Europe and the world as maps and time series. The skill is displayed as the size of dots. Their color is related to the signal in the prediction. The website was developed in cooperation with users from different sectors via surveys, workshops and meetings to guarantee its understandability and usability. The users realize the potential of climate predictions, but some need advice in using probabilistic predictions and skill. Future activities will include the further development of predictions to improve skill (multi-model ensembles, teleconnections), the introduction of additional products (data provision, extremes) and the further clarification of the information (interactivity, video clips)

Test-time Unsupervised Domain Adaptation

Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain). This motivates the active field of domain adaptation. While some approaches to the problem require labeled data from the target domain, others adopt an unsupervised approach to domain adaptation (UDA). Evaluating UDA methods consists of measuring the model's ability to generalise to unseen data in the target domain. In this work, we argue that this is not as useful as adapting to the test set directly. We therefore propose an evaluation framework where we perform test-time UDA on each subject separately. We show that models adapted to a specific target subject from the target domain outperform a domain adaptation method which has seen more data of the target domain but not this specific target subject. This result supports the thesis that unsupervised domain adaptation should be used at test-time, even if only using a single target-domain subjectComment: Accepted at MICCAI 202

RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we cannot only form a decision on the spot, but also ponder, revisiting an initial guess from different angles, distilling relevant information, arriving at a better decision. Here, we propose RecycleNet, a latent feature recycling method, instilling the pondering capability for neural networks to refine initial decisions over a number of recycling steps, where outputs are fed back into earlier network layers in an iterative fashion. This approach makes minimal assumptions about the neural network architecture and thus can be implemented in a wide variety of contexts. Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision. We evaluate this across a variety of segmentation benchmarks and show consistent improvements even compared with top-performing segmentation methods. This allows trading increased computation time for improved performance, which can be beneficial, especially for safety-critical applications.Comment: Accepted at 2024 Winter Conference on Applications of Computer Vision (WACV

North Atlantic Ocean

RADIALES (IEO), with the support of the Korea Institute for Ocean Science and Technology (KIOST)Versión del edito

New light for time series: international collaboration in ship-based ecosystem monitoring.

Ship-based biogeochemical and ecological time series are one of the most valuable tools to characterize and quantify ocean ecosystems. These programs continuously provided major breakthroughs in understanding ecosystem variability, allow quantification of the ocean carbon cycle, and help understand the processes that link biodiversity, food webs, and changes in services that benefit human societies. A quantum jump in regional and global ocean ecosystem science can be gained by aggregating observations from individual time series that are distributed across different oceans and which are managed by different countries. The collective value of these data is greater than that provided by each time series individually. However, maintaining time series requires a commitment by the science community and sponsor agencies.. Based on the success of existing initiatives, e.g. ICES and SCOR working groups, IOC-UNESCO launched the International Group for Marine Ecological Time Series (IGMETS, http://igmets.net) to promote collaborations across different individual projects, and jointly look at holistic changes within different ocean regions. The effort explores the reasons and connections for changes in phytoplankton and zooplankton at a global level and identifies locations where particularly large changes may be ocurring. This compilation will facilitate better coordination, communication, and data intercomparability among time series.IEO (RADIALES) IOC-UNESC

Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

This paper introduces a novel methodology to integrate human brain connectomics and parcellation for brain tumor segmentation and survival prediction. For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1mm space and map this parcellation to each individual subject data. We use deep neural network architectures together with hard negative mining to achieve the final voxel level classification. For survival prediction, we present a new method for combining features from connectomics data, brain parcellation information, and the brain tumor mask. We leverage the average connectome information from the Human Connectome Project and map each subject brain volume onto this common connectome space. From this, we compute tractographic features that describe potential neural disruptions due to the brain tumor. These features are then used to predict the overall survival of the subjects. The main novelty in the proposed methods is the use of normalized brain parcellation data and tractography data from the human connectome project for analyzing MR images for segmentation and survival prediction. Experimental results are reported on the BraTS2018 dataset.Comment: 14 pages, 5 figures, 4 tables, accepted by BrainLes 2018 MICCAI worksho