A novel detrimental homozygous mutation in the WFS1 gene in two sisters from nonconsanguineous parents with untreated diabetes insipidus

Given the limited lifespan and with the recent progress in experimental treatments for WS, timely diagnosis and multidisciplinary treatment for DI/DM, hydronephrosis, and visual/psychiatric status-maintaining quality of life-are of crucial importance

SCALO: Scalability-Aware Parallelism Orchestration for Multi-Threaded Workloads

This article contributes a solution to orchestrate concurrent application execution to increase throughput. SCALO monitors co-executing applications at runtime to evaluate their scalability

Non-parametric data-driven background modelling using conditional probabilities

Background modelling is one of the main challenges in particle physics data analysis. Commonly employed strategies include the use of simulated events of the background processes, and the fitting of parametric background models to the observed data. However, reliable simulations are not always available or may be extremely costly to produce. As a result, in many cases, uncertainties associated with the accuracy or sample size of the simulation are the limiting factor in the analysis sensitivity. At the same time, parametric models are limited by the a priori unknown functional form and parameter values of the background distribution. These issues become ever more pressing when large datasets become available, as it is already the case at the CERN Large Hadron Collider, and when studying exclusive signatures involving hadronic backgrounds. Two novel and widely applicable non-parametric data-driven background modelling techniques are presented, which address these issues for a broad class of searches and measurements. The first, relying on ancestral sampling, uses data from a relaxed event selection to estimate a graph of conditional probability density functions of the variables used in the analysis, accounting for significant correlations. A background model is then generated by sampling events from this graph, before the full event selection is applied. In the second, a generative adversarial network is trained to estimate the joint probability density function of the variables used in the analysis. The training is performed on a relaxed event selection which excludes the signal region, and the network is conditioned on a blinding variable. Subsequently, the conditional probability density function is interpolated into the signal region to model the background. The application of each method on a benchmark analysis is presented in detail, and the performance is discussed.Comment: 33 pages, 18 figure

The geographical dimension of income and consumption inequality

This paper aims at examining interpersonal income and consumption inequality within the Attica Metropolitan Region, which includes Athens, the largest metropolis of Greece. It also aims to make comparisons between Attica and the rest of the country. The analysis is based on income and consumption microdata from Greek Household Budget Surveys (HBS) over the period 2008-2019, encapsulating the period from the commencement of the economic crisis until the year before the outset of the COVID-19 pandemic. Results indicate that income inequalities are systematically higher than consumption inequalities. From a spatial comparative perspective, the results show that the Attica Metropolitan Region exhibits a higher degree of income and consumption inequality relative to the rest of the country. Furthermore, the economic crisis increased income inequality in Athens and in the rest of the country, while consumption expenditure inequality increased in the Athens metropolitan area only. Finally, the distance between socio-economic groups, which stands as a measure of the degree of social polarization, increased during the economic crisis. However, this does not hold true for consumption inequality. Overall, the analysis demonstrates the sensitivity of inequality outcomes to the selection of the welfare indicator (income or consumption), as well as a number of noticeable differences in inequality outcomes between the Metropolitan region of Attica and the rest of the country. The paper unveils facets of inequality which necessitate the implementation of more people and place-targeted policies aimed at more inclusive and balanced welfare conditions in metropolitan regions and across the country

Sea trout Salmo trutta in the subarctic: home-bound but large variation in migratory behaviour between and within populations

publishedVersio

Decision and function problems based on boson sampling

Boson sampling is a mathematical problem that is strongly believed to be intractable for classical computers, whereas passive linear interferometers can produce samples efficiently. So far, the problem remains a computational curiosity, and the possible usefulness of boson-sampling devices is mainly limited to the proof of quantum supremacy. The purpose of this work is to investigate whether boson sampling can be used as a resource of decision and function problems that are computationally hard, and may thus have cryptographic applications. After the definition of a rather general theoretical framework for the design of such problems, we discuss their solution by means of a brute-force numerical approach, as well as by means of non-boson samplers. Moreover, we estimate the sample sizes required for their solution by passive linear interferometers, and it is shown that they are independent of the size of the Hilbert space.Comment: Close to the version published in PR

A taxonomy of task-based parallel programming technologies for high-performance computing

Task-based programming models for shared memory -- such as Cilk Plus and OpenMP 3 -- are well established and documented. However, with the increase in parallel, many-core and heterogeneous systems, a number of research-driven projects have developed more diversified task-based support, employing various programming and runtime features. Unfortunately, despite the fact that dozens of different task-based systems exist today and are actively used for parallel and high-performance computing (HPC), no comprehensive overview or classification of task-based technologies for HPC exists. In this paper, we provide an initial task-focused taxonomy for HPC technologies, which covers both programming interfaces and runtime mechanisms. We demonstrate the usefulness of our taxonomy by classifying state-of-the-art task-based environments in use today

Carbon export in the seasonal sea ice zone north of Svalbard from winter to late summer

Phytoplankton blooms in the Arctic Ocean's seasonal sea ice zone are expected to start earlier and occur further north with retreating and thinning sea ice cover. The current study is the first compilation of phytoplankton bloom development and fate in the seasonally variable sea ice zone north of Svalbard from winter to late summer, using short-term sediment trap deployments. Clear seasonal patterns were discovered, with low winter and pre-bloom phytoplankton standing stocks and export fluxes, a short and intense productive season in May and June, and low Chl a standing stocks but moderate carbon export fluxes in the autumn post-bloom conditions. We observed intense phytoplankton blooms with Chl a standing stocks of >350 mg m−2 below consolidated sea ice cover, dominated by the prymnesiophyte Phaeocystis pouchetii. The largest vertical organic carbon export fluxes to 100 m, of up to 513 mg C m−2 day−1, were recorded at stations dominated by diatoms, while those dominated by P. pouchetii recorded carbon export fluxes up to 310 mg C m−2 day−1. Fecal pellets from krill and copepods contributed a substantial fraction to carbon export in certain areas, especially where blooms of P. pouchetii dominated and Atlantic water advection was prominent. The interplay between the taxonomic composition of protist assemblages, large grazers, distance to open water, and Atlantic water advection was found to be crucial in determining the fate of the blooms and the magnitude of organic carbon exported out of the surface water column. Previously, the marginal ice zone was considered the most productive region in the area, but our study reveals intense blooms and high export events in ice-covered waters. This is the first comprehensive study on carbon export fluxes for under-ice phytoplankton blooms, a phenomenon suggested to have increased in importance under the new Arctic sea ice regime

Micro- and macro-habitat selection of Atlantic salmon, (Salmo salar), post-smolts in relation to marine environmental cues

Atlantic salmon is an economically and culturally important species. The species encounters several natural and man-made threats during its migration between fresh water and the ocean, which in combination may explain its ongoing decline. With the aim to better understand whether post-smolt behaviour is influenced by physical oceanographic conditions, the migratory behaviour of 173 post-smolts in a high-latitude Norwegian fjord was investigated, combining acoustic telemetry with site- and time-specific environmental variables from an oceanographic model. Most post-smolts (94%) performed a unidirectional migration out the fjord. Progression rates were relatively high (0.42–2.41 km h−1; 0.84–3.78 BL s−1) and increased with distance from the river. While post-smolts had an affinity for lower salinities in the inner fjord, statistical models failed to detect any significant relationship between the small-scale (within arrays) migratory behaviour and salinity, temperature, or coastal surface currents within the fjord. In the outer part, the post-smolts predominantly exited the fjord system through the strait with the highest surface salinities and lowest temperatures, independently of the current direction. Our findings indicate that the macro-habitat selection of the Atlantic salmon post-smolts was influenced by environmental factors: the post-smolts directed their migration towards “ocean cues.” However, this was not confirmed on the micro-habitat level.publishedVersio