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In vivo preclinical X-ray micro-computed tomography (μCT) imaging is widely used to obtain three-dimensional anatomical information of small animals. However, the potential for radiation exposure to influence experimental outcomes necessitates accurate dose estimation. The aim of this study was to develop a robust and reproducible method for estimating and evaluating absorbed doses in small animal
The transverse momentum (pT) and centrality dependence of the nuclear modification factor RAA of prompt and non-prompt J/ψ, the latter originating from the weak decays of beauty hadrons, have been measured by the ALICE collaboration in Pb–Pb collisions at sNN = 5.02 TeV. The measurements are carried out through the e+e− decay channel at midrapidity (|y| < 0.9) in the transverse momentum region 1.5
INTRODUCTION: Plasma biomarkers’ utility for predicting incident mild cognitive impairment (MCI) remains unclear. We evaluated associations of plasma Alzheimer's disease (AD) biomarkers and amyloid positron emission tomography (PET) with transitions from cognitively unimpaired (CU) to MCI in the Mayo Clinic Study of Aging (MCSA) and BioFINDER-2 studies. METHODS: Associations of continuous baseline
Background and Aims: In primary sclerosing cholangitis (PSC), the risk for gallbladder malignancy is increased. Surveillance imaging is used for early diagnosis. The study aims to assess the reliability of ultrasound and magnetic resonance imaging (MRI) for the detection of gallbladder polyps in people with PSC and to define a polyp size as a cut-off at which cholecystectomy is indicated due to th
Newts have large genomes harbouring many repeat elements. How these elements shape the genome and relate to newts’ unique regeneration ability remains unknown. We present here the chromosome-scale assembly of the 20.3Gb genome of the Iberian ribbed newt, Pleurodeles waltl, with a hitherto unprecedented contiguity and completeness among giant genomes. We provide evidence that intronic repeat elemen
Background and Aims: Implantable cardioverter-defibrillators (ICDs) are critical for preventing sudden cardiac death (SCD) in arrhythmogenic right ventricular cardiomyopathy (ARVC). This study aims to identify cross-continental differences in utilization of primary prevention ICDs and survival free from sustained ventricular arrhythmia (VA) in ARVC. Methods: This was a retrospective analysis of AR
Label-free chemical and structural imaging of complex living tissue and biological systems is the holy grail of biomedical research and clinical diagnostics. The current analysis techniques are time-consuming and/or require extensive sample preparation, often due to the presence of interfering molecules such as water, making them unsuitable for the analysis of such systems. Here, we demonstrate a
Observations of low-mass stars have frequently shown a disagreement between observed stellar radii and radii predicted by theoretical stellar structure models. This ‘radius inflation’ problem could have an impact on both stellar and exoplanetary science. We present the final results of our observation programme with the CHaracterising ExOPlanet Satellite (CHEOPS) to obtain high-precision light cur
Deep Reinforcement Learning techniques are achieving state-of-the-art results in robust legged locomotion. While there exists a wide variety of legged platforms such as quadruped, humanoids, and hexapods, the field is still missing a single learning framework that can control all these different embodiments easily and effectively and possibly transfer, zero or few-shot, to unseen robot embodiments
A search for pair production of squarks or gluinos decaying via sleptons or weak bosons is reported. The search targets a final state with exactly two leptons with same-sign electric charge or at least three leptons without any charge requirement. The analysed data set corresponds to an integrated luminosity of 139 fb−1 of proton-proton collisions collected at a centre-of-mass energy of 13 TeV wit
Parallel-elastic joints can improve the efficiency and strength of robots by assisting the actuators with additional torques. For these benefits to be realized, a spring needs to be carefully designed. However, designing robots is an iterative and tedious process, often relying on intuition and heuristics. We introduce a design optimization framework that allows us to co-optimize a parallel elasti
Estimating collision probabilities between robots and environmental obstacles or other moving agents is crucial to ensure safety during path planning. This is an important building block of modern planning algorithms in many application scenarios such as autonomous driving, where noisy sensors perceive obstacles. While many approaches exist, they either provide too conservative estimates of the co
The advent of tactile sensors in robotics has sparked many ideas on how robots can leverage direct contact measurements of their environment interactions to improve manipulation tasks. An important line of research in this regard is grasp force control, which aims to manipulate objects safely by limiting the amount of force exerted on the object. While prior works have either hand-modeled their fo
Stateful policies play an important role in reinforcement learning, such as handling partially observable environments, enhancing robustness, or imposing an inductive bias directly into the policy structure. The conventional method for training stateful policies is Backpropagation Through Time (BPTT), which comes with significant drawbacks, such as slow training due to sequential gradient propagat
Safety is one of the key issues preventing the deployment of reinforcement learning techniques in real-world robots. While most approaches in the Safe Reinforcement Learning area do not require prior knowledge of constraints and robot kinematics and rely solely on data, it is often difficult to deploy them in complex real-world settings. Instead, model-based approaches that incorporate prior knowl
Safety is a fundamental property for the real-world deployment of robotic platforms. Any control policy should avoid dangerous actions that could harm the environment, humans, or the robot itself. In reinforcement learning (RL), safety is crucial when exploring a new environment to learn a new skill. This paper introduces a new formulation of safe exploration for robotic RL in the tangent space of
Adaptive control is often used for friction compensation in trajectory tracking tasks because it does not require torque sensors. However, it has some drawbacks: first, the most common certainty-equivalence adaptive control design is based on linearized parameterization of the friction model, therefore nonlinear effects, including the stiction and Stribeck effect, are usually omitted. Second, the
Integrating learning-based techniques, especially reinforcement learning, into robotics is promising for solving complex problems in unstructured environments. Most of the existing approaches rely on training in carefully calibrated simulators before being deployed on real robots, often without real-world fine-tuning. While effective in controlled settings, this framework falls short in applicatio
Hazardous situations involving radioactive, chemical, and other dangerous materials pose significant risks to emergency responders. In such situations, robots can support civil forces in reconnaissance and mitigation. However, this requires the development of robotic systems with novel AI-based assistance functions, operation procedures, and raises new ethical and legal questions. To address these
Trajectory planning under kinodynamic constraints is fundamental for advanced robotics applications that require dexterous, reactive, and rapid skills in complex environments. These constraints, which may represent task, safety, or actuator limitations, are essential for ensuring the proper functioning of robotic platforms and preventing unexpected behaviors. Recent advances in kinodynamic plannin
