Inborn errors of metabolism are genetic disorders due to impaired activity of enzymes, transporters, or cofactors resulting in accumulation of abnormal metabolites proximal to the metabolic block, lack of essential products or accumulation of by-products. Many of these disorders have serious clinical consequences for affected neonates, and an early diagnosis allows presymptomatic treatment which can prevent severe permanent sequelae and in some cases death. Expanded newborn screening for these diseases is a promising field of targeted metabolomics. Here we report the application, between 2007 and 2014, of this approach to the identification of newborns in southern Italy at risk of developing a potentially fatal disease. The analysis of amino acids and acylcarnitines in dried blood spots by tandem mass spectrometry revealed 24 affected newborns among 45,466 infants evaluated between 48 and 72 hours of life (overall incidence: 1 : 1894). Diagnoses of newborns with elevated metabolites were confirmed by gas chromatography-mass spectrometry, biochemical studies, and genetic analysis. Five infants were diagnosed with medium-chain acyl CoA dehydrogenase deficiency, 1 with methylmalonic acidemia with homocystinuria type CblC, 2 with isolated methylmalonic acidemia, 1 with propionic acidemia, 1 with isovaleric academia, 1 with isobutyryl-CoA dehydrogenase deficiency, 1 with beta ketothiolase deficiency, 1 with short branched chain amino acid deficiency, 1 with 3-methlycrotonyl-CoA carboxylase deficiency, 1 with formimino-transferase cyclodeaminase deficiency, and 1 with cystathionine-beta-synthase deficiency. Seven cases of maternal vitamin B12 deficiency and 1 case of maternal carnitine uptake deficiency were detected. This study supports the widespread application of metabolomic-based newborn screening for these genetic diseases.

Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives. This paper studies how to learn variable impedance policies where both the Cartesian stiffness and the attractor can be learned from human demonstrations and corrections with a user-friendly interface. The presented framework, named ILoSA, uses Gaussian Processes for policy learning, identifying regions of uncertainty and allowing interactive corrections, stiffness modulation and active disturbance rejection. The experimental evaluation of the framework is carried out on a Franka-Emika Panda in four separate cases with unique force interaction properties: 1) pulling a plug wherein a sudden force discontinuity occurs upon successful removal of the plug, 2) pushing a box where a sustained force is required to keep the robot in motion, 3) wiping a whiteboard in which the force is applied perpendicular to the direction of movement, and 4) inserting a plug to verify the usability for precision-critical tasks in an experimental validation performed with non-expert users.

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This work investigates how the intricate task of a continuous pick & place (P&P) motion may be learned from humans based on demonstrations and corrections. Due to the complexity of the task, these demonstrations are often slow and even slightly flawed, particularly at moments when multiple aspects (i.e., end-effector movement, orientation, and gripper width) have to be demonstrated at once. Rather than training a person to give better demonstrations, non-expert users are provided with the ability to interactively modify the dynamics of their initial demonstration through teleoperated corrective feedback. This in turn allows them to teach motions outside of their own physical capabilities. In the end, the goal is to obtain a faster but reliable execution of the task. The presented framework learns the desired movement dynamics based on the current Cartesian position with Gaussian Processes (GPs), resulting in a reactive, time-invariant policy. Using GPs also allows online interactive corrections and active disturbance rejection through epistemic uncertainty minimization. The experimental evaluation of the framework is carried out on a Franka-Emika Panda. Tests were performed to determine i) the framework's effectiveness in successfully learning how to quickly pick & place an object, ii) ease of policy correction to environmental changes (i.e., different object sizes and mass), and iii) the framework's usability for non-expert users.

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