Conference on Planetary Volatiles

Initial and present volatile inventories and distributions in the Earth, other planets, meteorites, and comets; observational evidence on the time history of volatile transfer among reservoirs; and volatiles in planetary bodies, their mechanisms of transport, and their relation to thermal, chemical, geological and biological evolution were addressed

Mitochondrial DNA mutations in renal cell carcinomas revealed no general impact on energy metabolism

Previously, renal cell carcinoma tissues were reported to display a marked reduction of components of the respiratory chain. To elucidate a possible relationship between tumourigenesis and alterations of oxidative phosphorylation, we screened for mutations of the mitochondrial DNA (mtDNA) in renal carcinoma tissues and patient-matched normal kidney cortex. Seven of the 15 samples investigated revealed at least one somatic heteroplasmic mutation as determined by denaturating HPLC analysis (DHPLC). No homoplasmic somatic mutations were observed. Actually, half of the mutations presented a level of heteroplasmy below 25%, which could be easily overlooked by automated sequence analysis. The somatic mutations included four known D-loop mutations, four so far unreported mutations in ribosomal genes, one synonymous change in the ND4 gene and four nonsynonymous base changes in the ND2, COI, ND5 and ND4L genes. One renal cell carcinoma tissue showed a somatic A3243G mutation, which is a known frequent cause of MELAS syndrome (mitochondrial encephalomyopathy, lactic acidosis, stroke-like episode) and specific compensatory alterations of enzyme activities of the respiratory chain in the tumour tissue. No difference between histopathology and clinical progression compared to the other tumour tissues was observed. In conclusion, the low abundance as well as the frequently observed low level of heteroplasmy of somatic mtDNA mutations indicates that the decreased aerobic energy capacity in tumour tissue seems to be mediated by a general nuclear regulated mechanism

Unrolled three-operator splitting for parameter-map learning in Low Dose X-ray CT reconstruction

We propose a method for fast and automatic estimation of spatially dependent regularization maps for total variation-based (TV) tomography reconstruction. The estimation is based on two distinct sub-networks, with the first sub-network estimating the regularization parameter-map from the input data while the second one unrolling T iterations of the Primal-Dual Three-Operator Splitting (PD3O) algorithm. The latter approximately solves the corresponding TV-minimization problem incorporating the previously estimated regularization parameter-map. The overall network is then trained end-to-end in a supervised learning fashion using pairs of clean-corrupted data but crucially without the need of having access to labels for the optimal regularization parameter-maps

Quantum models of classical mechanics: maximum entropy packets

In a previous paper, a project of constructing quantum models of classical properties has been started. The present paper concludes the project by turning to classical mechanics. The quantum states that maximize entropy for given averages and variances of coordinates and momenta are called ME packets. They generalize the Gaussian wave packets. A non-trivial extension of the partition-function method of probability calculus to quantum mechanics is given. Non-commutativity of quantum variables limits its usefulness. Still, the general form of the state operators of ME packets is obtained with its help. The diagonal representation of the operators is found. A general way of calculating averages that can replace the partition function method is described. Classical mechanics is reinterpreted as a statistical theory. Classical trajectories are replaced by classical ME packets. Quantum states approximate classical ones if the product of the coordinate and momentum variances is much larger than Planck constant. Thus, ME packets with large variances follow their classical counterparts better than Gaussian wave packets.Comment: 26 pages, no figure. Introduction and the section on classical limit are extended, new references added. Definitive version accepted by Found. Phy

Learning Regularization Parameter-Maps for Variational Image Reconstruction Using Deep Neural Networks and Algorithm Unrolling

We introduce a method for the fast estimation of data-adapted, spatially and temporally dependent regularization parameter-maps for variational image reconstruction, focusing on total variation (TV) minimization. The proposed approach is inspired by recent developments in algorithm unrolling using deep neural networks (NNs) and relies on two distinct subnetworks. The first subnetwork estimates the regularization parameter-map from the input data. The second subnetwork unrolls iterations of an iterative algorithm which approximately solves the corresponding TV-minimization problem incorporating the previously estimated regularization parameter-map. The overall network is then trained end-to-end in a supervised learning fashion using pairs of clean and corrupted data but crucially without the need for access to labels for the optimal regularization parameter-maps. We first prove consistency of the unrolled scheme by showing that the unrolled minimizing energy functional used for the supervised learning -converges, as tends to infinity, to the corresponding functional that incorporates the exact solution map of the TV-minimization problem. Then, we apply and evaluate the proposed method on a variety of large-scale and dynamic imaging problems with retrospectively simulated measurement data for which the automatic computation of such regularization parameters has been so far challenging using the state-of-the-art methods: a 2D dynamic cardiac magnetic resonance imaging (MRI) reconstruction problem, a quantitative brain MRI reconstruction problem, a low-dose computed tomography problem, and a dynamic image denoising problem. The proposed method consistently improves the TV reconstructions using scalar regularization parameters, and the obtained regularization parameter-maps adapt well to imaging problems and data by leading to the preservation of detailed features. Although the choice of the regularization parameter-maps is data-driven and based on NNs, the subsequent reconstruction algorithm is interpretable since it inherits the properties (e.g., convergence guarantees) of the iterative reconstruction method from which the network is implicitly defined

Seventy-five mosses and liverworts found frozen with the late Neolithic Tyrolean Iceman: Origins, taphonomy and the Iceman’s last journey

The Iceman site is unique in the bryology of the Quaternary. Only 21 bryophytes (mosses and liverworts) grow now in the immediate vicinity of the 5,300 year old Iceman discovery site at 3,210m above sea level in the Ötztal Alps, Italy. By contrast 75 or more species including at least ten liverworts were recovered as subfossils frozen in, on and around the Iceman from before, at and after his time. About two thirds of the species grow in the nival zone (above 3,000m above sea level) now while about one third do not. A large part of this third can be explained by the Iceman having both deliberately and inadvertently carried bryophytes during his last, fatal journey. Multivariate analyses (PCA, RDA) provide a variety of explanations for the arrivals of the bryophytes in the rocky hollow where the mummy was discovered. This is well into the nival zone of perennial snow and ice with a very sparse, non-woody flora and very low vegetation cover. Apart from the crucial anthropochory (extra-local plants), both hydrochory (local species) and zoochory (by wild game such as ibex of both local and extra-local species) have been important. Anemochory of mainly local species was of lesser importance and of extra-local species probably of little or no importance. The mosses Neckera complanata and several other ecologically similar species as well as a species of Sphagnum (bogmoss) strongly support the claim that the Iceman, took northwards up Schnalstal, South Tyrol, as the route of the last journey. A different species of bogmoss, taken from his colon is another indication the Iceman’s presence at low altitude south of Schnalstal during his last hours when he was first high up, low down and finally at over 3,000m