Emerging roles of telomeric chromatin alterations in cancer

Telomeres, the nucleoprotein structures that cap the ends of eukaryotic chromosomes, play important and multiple roles in tumorigenesis. Functional telomeres need the establishment of a protective chromatin structure based on the interplay between the specific complex named shelterin and a tight nucleosomal organization. Telomere shortening in duplicating somatic cells leads eventually to the destabilization of the telomere capping structure and to the activation of a DNA damage response (DDR) signaling. The final outcome of this process is cell replicative senescence, which constitute a protective barrier against unlimited proliferation. Cells that can bypass senescence checkpoint continue to divide until a second replicative checkpoint, crisis, characterized by chromosome fusions and rearrangements leading to massive cell death by apoptosis. During crisis telomere dysfunctions can either inhibit cell replication or favor tumorigenesis by the accumulation of chromosomal rearrangements and neoplastic mutations. The acquirement of a telomere maintenance mechanism allows fixing the aberrant phenotype, and gives the neoplastic cell unlimited replicative potential, one of the main hallmarks of cancer. Despite the crucial role that telomeres play in cancer development, little is known about the epigenetic alterations of telomeric chromatin that affect telomere protection and are associated with tumorigenesis. Here we discuss the current knowledge on the role of telomeric chromatin in neoplastic transformation, with a particular focus on H3.3 mutations in alternative lengthening of telomeres (ALT) cancers and sirtuin deacetylases dysfunctions

Four-level N-scheme crossover resonances in Rb saturation spectroscopy in magnetic fields

We perform saturated absorption spectroscopy on the D$\_2$ line for room temperature rubidium atoms immersed in magnetic fields within the 0.05-0.13 T range. At those medium-high field values the hyperfine structure in the excited state is broken by the Zeeman effect, while in the ground state hyperfine structure and Zeeman shifts are comparable. The observed spectra are composed by a large number of absorption lines. We identify them as saturated absorptions on two-level systems, on three-level systems in a V configuration and on four-level systems in a N or double-N configuration where two optical transitions not sharing a common level are coupled by spontaneous emission decays. We analyze the intensity of all those transitions within a unified simple theoretical model. We concentrate our attention on the double-N crossovers signals whose intensity is very large because of the symmetry in the branching ratios of the four levels. We point out that these structures, present in all alkali atoms at medium-high magnetic fields, have interesting properties for electromagnetically induced transparency and slow light applications.Comment: Submitted to Physical Review

Auction-Based Task Allocation and Motion Planning for Multi-Robot Systems with Human Supervision

This paper presents a task allocation strategy for a multi-robot system with a human supervisor. The multi-robot system consists of a team of heterogeneous robots with different capabilities that operate in a dynamic scenario that can change in the robots’ capabilities or in the operational requirements. The human supervisor can intervene in the operation scenario by approving the final plan before its execution or forcing a robot to execute a specific task. The proposed task allocation strategy leverages an auction-based method in combination with a sampling-based multi-goal motion planning. The latter is used to evaluate the costs of execution of tasks based on realistic features of paths. The proposed architecture enables the allocation of tasks accounting for priorities and precedence constraints, as well as the quick re-allocation of tasks after a dynamic perturbation occurs –a crucial feature when the human supervisor preempts the outcome of the algorithm and makes manual adjustments. An extensive simulation campaign in a rescue scenario validates our approach in dynamic scenarios comprising a sensor failure of a robot, a total failure of a robot, and a human-driven re-allocation. We highlight the benefits of the proposed multi-goal strategy by comparing it with single-goal motion planning strategies at the state of the art. Finally, we provide evidence for the system efficiency by demonstrating the powerful synergistic combination of the auction-based allocation and the multi-goal motion planning approach

Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning

Abstract Fueled by advertising companies' need of accurately tracking users and their online habits, web fingerprinting practice has grown in recent years, with severe implications for users' privacy. In this paper, we design, engineer and evaluate a methodology which combines the analysis of JavaScript code and machine learning for the automatic detection of web fingerprinters. We apply our methodology on a dataset of more than 400, 000 JavaScript files accessed by about 1, 000 volunteers during a one-month long experiment to observe adoption of fingerprinting in a real scenario. We compare approaches based on both static and dynamic code analysis to automatically detect fingerprinters and show they provide different angles complementing each other. This demonstrates that studies based on either static or dynamic code analysis provide partial view on actual fingerprinting usage in the web. To the best of our knowledge we are the first to perform this comparison with respect to fingerprinting. Our approach achieves 94% accuracy in small decision time. With this we spot more than 840 fingerprinting services, of which 695 are unknown to popular tracker blockers. These include new actual trackers as well as services which use fingerprinting for purposes other than tracking, such as anti-fraud and bot recognition

Pulsed high magnetic field measurement via a Rubidium vapor sensor

We present a new technique to measure pulsed magnetic fields based on the use of Rubidium in gas phase as a metrological standard. We have therefore developed an instrument based on laser inducing transitions at about 780~nm (D2 line) in a Rubidium gas contained in a mini-cell of 3~mm~x~3~mm cross section. To be able to insert such a cell in a standard high field pulsed magnet we have realized a fibred probe kept at a fixed temperature. Transition frequencies for both the $\pi$ (light polarization parallel to the magnetic field) and $\sigma$ (light polarization perpendicular to the magnetic field) configurations are measured by a commercial wavemeter. One innovation of our sensor is that in addition of monitoring the light transmitted by the Rb cell, which is usual, we also monitor the fluorescence emission of the gas sample from a very small volume with the advantage of reducing the impact of the field inhomogeneity on the field measurement. Our sensor has been tested up to about 58~T.Comment: Submitted to Review Scientific Instrument

Ranking Models for the Temporal Dimension of Text

Temporal features of text have been shown to improve clustering and organization of documents, text classification, visualization, and ranking. Temporal ranking models consider the temporal expressions found in text (e.g., “in 2021” or “last year”) as time units, rather than as keywords, to define a temporal relevance and improve ranking. This paper introduces a new class of ranking models called Temporal Metric Space Models (TMSM), based on a new domain for representing temporal information found in documents and queries, where each temporal expression is represented as a time interval. Furthermore, we introduce a new frequency-based baseline called Temporal BM25 (TBM25). We evaluate the effectiveness of each proposed metric against a purely textual baseline, as well as several variations of the metrics themselves, where we change the aggregate function, the time granularity and the combination weight. Our extensive experiments on five test collections show statistically significant improvements of TMSM and TBM25 over state-of-the-art temporal ranking models. Combining the temporal similarity scores with the text similarity scores always improves the results, when the combination weight is between 2% and 6% for the temporal scores. This is true also for test collections where only 5% of queries contain explicit temporal expressions

Ground Risk Map for Unmanned Aircraft in Urban Environments

The large diversity of unmanned aircraft requires a suitable and proper risk assessment. In this paper, we propose the use of risk map to define the risk associated to unmanned aircraft. It is a two-dimensional location-based map that quantifies the risk to the population on ground of flight operations over a specified area. The risk map is generated by a probabilistic approach and it combines several layers, including population density, sheltering factor, no-fly zones, and obstacles. Each element of the risk map has associated a risk value that quantifies the risk of flying over a specific location. The risk values are defined by a risk assessment process using different uncontrolled descent events, the drone parameters, environmental characteristics, as well as uncertainties on parameters. The risk map is able to quantify the risk of large areas, such as urban environments, and allows for easy identification of which locations the flight has high and low risk. The map is a tool for informed decision making, and results report some examples of risk map with different aircraft in a realistic urban environment

A Risk-based Path Planning Strategy to Compute Optimum Risk Path for Unmanned Aircraft Systems over Populated Areas

The large diffusion of Unmanned Aircraft Systems (UAS) requires a suitable strategy to design safe flight missions. In this paper, we propose a novel path planning strategy to compute optimum risk path for UAS over populated areas. The proposed strategy is based on a variant of the RRT* (Rapidly-exploring Random Tree "Star") algorithm, performing a risk assessment during the path planning phase. Like other RRT-based algorithms, the proposed path planning explores the state space by constructing a graph. Each time a new node is added to the graph, the algorithm estimates the risk level involved by the new node, evaluating the flight direction and velocity of the UAS placed in the analyzed node. The risk level quantifies the risk of flying over a specific location and it is defined using a probabilistic risk assessment approach taking into account the drone parameters and environmental characteristics. Then, the proposed algorithm computes an asymptotically optimal path by minimizing the overall risk and flight time. Simulation results in realistic environments corroborate the proposed approach proving how the proposed risk-based path planning is able to compute an effective and safe path in urban areas

Noncommutative geometry inspired charged black holes

We find a new, non-commutative geometry inspired, solution of the coupled Einstein-Maxwell field equations describing a variety of charged, self-gravitating objects, including extremal and non-extremal black holes. The metric smoothly interpolates between deSitter geometry, at short distance, and Reissner-Nordstroem geometry far away from the origin. Contrary to the ordinary Reissner-Nordstroem spacetime there is no curvature singularity in the origin neither "naked" nor shielded by horizons. We investigate both the Hawking process and pair creation in this new scenario.Comment: 14 pages, 9 figure, LaTe

Auction-based Task Allocation for Safe and Energy Efficient UAS Parcel Transportation

In this paper, two greedy auction-based algorithms are proposed for the allocation of heterogeneous tasks to a heterogeneous fleet of UAVs. The tasks set is composed of parcel delivery tasks and charge tasks, the latter to guarantee service persistency. An optimization problem is solved by each agent to determine its bid for each task. When considering delivery tasks, the bidder aims at minimizing the energy consumption, while the minimization of the flight time is adopted for charge tasks bids. The algorithms include a path planner that computes the minimum risk path for each task-UAV bid exploiting a 2D risk map of the operational area, defined in an urban environment. Each solution approach is implemented by means of two auction strategies: single-item and multiple-item. Considerations about complexity and efficiency of the algorithms are drawn from Monte Carlo simulations