Nature of the Earth's earliest crust from hafnium isotopes in single detrital zircons

Continental crust forms from, and thus chemically depletes, the Earth's mantle. Evidence that the Earth's mantle was already chemically depleted by melting before the formation of today's oldest surviving crust has been presented in the form of Sm-Nd isotope studies of 3.8-4.0 billion years old rocks from Greenland(1-5) and Canada(5-7). But this interpretation has been questioned because of the possibility that subsequent perturbations may have re-equilibrated the neodymium-isotope compositions of these rocks(8). Independent and more robust evidence for the origin of the earliest crust and depletion of the Archaean mantle can potentially be provided by hafnium-isotope compositions of zircon, a mineral whose age can be precisely determined by U-Pb dating, and which can survive metamorphisms(4). But the amounts of hafnium in single zircon grains are too small for the isotopic composition to be precisely analysed by conventional methods. Here we report hafnium-isotope data, obtained using the new technique of multiple-collector plasma-source mass spectrometry(9), for 37 individual grains of the oldest known terrestrial zircons (from the Narryer Gneiss Complex, Australia, with U-Pb ages of up to 4.14 Gyr (refs 10-13)). We find that none of the grains has a depleted mantle signature, but that many were derived from a source with a hafnium-isotope composition similar to that of chondritic meteorites. Furthermore, more than half of the analysed grains seem to have formed by remelting of significantly older crust, indicating that crustal preservation and subsequent reworking might have been important processes from earliest times.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62681/1/399252a0.pd

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Genomic Designing for Climate-Smart Tomato

Tomato is the first vegetable consumed in the world. It is grown in very different conditions and areas, mainly in field for processing tomatoes while fresh-market tomatoes are often produced in greenhouses. Tomato faces many environmental stresses, both biotic and abiotic. Today many new genomic resources are available allowing an acceleration of the genetic progress. In this chapter, we will first present the main challenges to breed climate-smart tomatoes. The breeding objectives relative to productivity, fruit quality, and adaptation to environmental stresses will be presented with a special focus on how climate change is impacting these objectives. In the second part, the genetic and genomic resources available will be presented. Then, traditional and molecular breeding techniques will be discussed. A special focus will then be presented on ecophysiological modeling, which could constitute an important strategy to define new ideotypes adapted to breeding objectives. Finally, we will illustrate how new biotechnological tools are implemented and could be used to breed climate-smart tomatoes