1,721,070 research outputs found
The global fight against trans fat: the potential role of international trade and law
Full text linkNon-communicable diseases in general and cardiovascular diseases in particular are a leading cause of death globally. Trans-fat consumption is a significant risk factor for cardiovascular diseases. The World Health Organization’s ‘REPLACE’ action package of 2018 aims to eliminate it completely in the global food supply by 2023. Legislative and other regulatory actions (i.e., banning trans-fat) are considered as effective means to achieve such a goal. Both wealthier and poorer countries are taking or considering action, as shown by the United States food regulations and Cambodian draft food legislation discussed in this paper. This paper reviews these actions and examines public and private stakeholders’ incentives to increase health-protecting or health-promoting standards and regulations at home and abroad, setting the ground for further research on the topic. It focuses on the potential of trade incentives as a potential driver of a ‘race to the top’. While it has been documented that powerful countries use international trade instruments to weaken other countries’ national regulations, at times these powerful countries may also be interested in more stringent regulations abroad to protect their exports from competition from third countries with less stringent regulations. This article explores practical and principled considerations on how such a dynamic may spread trans-fat restrictions globally. It argues that trade dynamics and public health considerations within powerful countries may help to promote anti-trans-fat regulation globally but will not be sufficient and is ethically questionable. True international regulatory cooperation is needed and could be facilitated by the World Health Organization. Nevertheless, the paper highlights that international trade and investment law offers opportunities for anti-trans-fat policy diffusion globally
Drone users’ and landowners' rights in Italy and the Netherlands: The medical use of drones
No full textBackground. Drones are increasingly integrated into recreational and economic activities, including for medical uses. In this scenario, drones carrying medical equipment or patients may fly over someone else’s property. This raises the question of how conflicts between drone users and landowners arising from the medical use of drones are resolved. This question predominantly revolves around the vertical extension of property rights. Aim and methodology. This article offers a comparative study of how these conflicts are tackled in Italy and the Netherlands, exploring the different operational solutions offered by their respective legal frameworks. In particular, the aim of the article is two-fold. First, the article intends to assess to which extent the Italian and Dutch operational solutions differ or converge, based on insights from the legislative, judicial, and doctrinal legal formants. Secondly, based on this comparative analysis, the article makes use of socio- economic considerations to assess the potential impact of the reconstructed Italian and Dutch operational solutions on the advancement of drone medical uses. Conclusions: The article argues that the current legal framework fails both to facilitate the use of drones, also for medical emergencies, and to protect landowners’ rights. A clear-cut height above which drones can fly (and under which drones cannot fly) can provide more clarity over the respective spheres of interest of the parties concerned
The Law of Off-label Uses of Medicines:Regulation and Litigation in the EU, UK and USA
No full textThis book examines the regulatory framework for untested and unapproved uses (off-label uses) of medicines in the EU, UK, and USA. Before reaching patients, medicines are extensively tested by manufacturers and approved by regulators to minimise the risk of adverse reactions. However, physicians can prescribe pharmaceuticals for off-label uses, widespread in paediatrics, oncology, rare diseases and, more recently, in treatment for Covid-19. While off-label uses may offer hope, they may also expose patients to risks and uncertainties. Clarification is therefore needed to improve the protection of patients’ rights while enhancing legal certainty for health actors. To this end, this work clarifies the regulatory mechanisms and litigation trends concerning off-licence prescriptions in these jurisdictions. It assesses how traditional, prevention-driven regulatory and civil liability rules are being adapted to tackle potential risks and scientific uncertainty. The book outlines the applicable regulations, as well as considering Brexit’s impact on off-label policies in the UK, and EU and national off-label policies in the context of the fight against the Covid-19 pandemic. It also explores under what conditions physicians, manufacturers, or regulators must compensate patients injured by untested prescriptions. The book will be an essential resource for researchers, academics and policy-makers working in the areas of medical law and ethics, public health law, pharmaceutical law and private comparative law
Covid, Teleworking and Inequality
No full textThis study presents an agent based model with two sectors differing for their location in the economy and workers sequentially searching for a job. In this framework we test the effects on workers’ disposable income of teleworking introduction, with different intensity by sectors, to verify if working organizational features may exacerbate the negative effects post-pandemic asymmetric shocks. Results show that workers employed in central locations of the economy gain a wage advantage compared to those finding job in peripheral positions. The differential in disposable income for the first group increases if in the central sector teleworking is introduced at higher intensity compared to the peripheral one. Sectors already penalized by closures and social distancing measures can face further inequalities in terms of disposable income for organizational and technological bounds
Forensic Handwriting Examination at IGS Conferences: A Review by Numbers
No full textWe review the number of contributions to the advancements in handwriting analysis for forensic applications that were presented at the biennial conferences of the International Graphonomics Society through its 20 editions. We introduce a taxonomy for the systematic analysis of the literature, propose a way to evaluate the overall interest and relevance of the topic in the context of the conference editions, as well as the interest and relevance of each category of the taxonomy. We discuss past and current trends emerging from the quantitative analysis and outline some future possible developments
Should We Look at Curvature or Velocity to Extract a Motor Program?
No full textExperimental studies led by Lashley and Raibert in the early phase of human movement science highlighted the phenomenon of motor equivalence, according to which complex movements are represented in the brain abstractly, in a way that is independent of the effector used for the execution of the movement. This abstract representation is known as motor program and it defines the temporal sequence of target points the effector has to move towards to accomplish the desired movement. We present and compare two algorithms for the extraction of motor programs from handwriting samples. One algorithm considers that lognormal velocity profiles are an invariant characteristic of reaching movements and it identifies the position of the target points by analysing the velocity profile of samples. The other algorithm seeks target points by identifying the trajectory points corresponding to maximum curvature variations because experimental studies have shown that the activity of the primary motor cortex encodes the direction of the movement. We have compared the performance of the two algorithms in terms of the number of virtual target points extracted by handwriting samples generated by 32 subjects with their dominant and non-dominant hands. The results have shown that the two algorithms show a similar performance over 55% of samples but the extraction of motor programs by analysing the curvature variations is more robust to noise and unmodeled motor variability
Kaldorian Assumptions and Endogenous Fluctuations in the Dynamic Fixed-Price IS-LM Model
No full textWith the aim of better understanding the conditions which determine endogenous fluctuations at business
cycle frequencies, recent literature has revived interest in the Schinasi’s variant of the dynamic,
intermediate-run, IS-LM model (Schinasi 1981, 1982). Results, however, remain confined to Kaldoriantype
economies, namely to those economies which present a greater-than-unity marginal propensity to
spend out of income. This paper contributes to the debate by showing that, in the case of a negative interest
rate sensitivity of savings, stable endogenous cycles can actually emerge as equilibrium solutions of the
model also in the case of non Kaldorian-type economies. To this end, we combine the instruments of the
global analysis, specifically the homoclinic bifurcation Theorem of Kopell and Howard (1975), with
numerical methods
Cartesian genetic programming for diagnosis of Parkinson disease through handwriting analysis: Performance vs. interpretability issues
No full textIn the last decades, early disease identification through non-invasive and automatic methodologies has gathered increasing interest from the scientific community. Among others, Parkinson's disease (PD) has received special attention in that it is a severe and progressive neuro-degenerative disease. As a consequence, early diagnosis would provide more effective and prompt care strategies, that cloud successfully influence patients’ life expectancy. However, the most performing systems implement the so called black-box approach, which do not provide explicit rules to reach a decision. This lack of interpretability, has hampered the acceptance of those systems by clinicians and their deployment on the field. In this context, we perform a thorough comparison of different machine learning (ML) techniques, whose classification results are characterized by different levels of interpretability. Such techniques were applied for automatically identify PD patients through the analysis of handwriting and drawing samples. Results analysis shows that white-box approaches, such as Cartesian Genetic Programming and Decision Tree, allow to reach a twofold goal: support the diagnosis of PD and obtain explicit classification models, on which only a subset of features (related to specific tasks) were identified and exploited for classification. Obtained classification models provide important insights for the design of non-invasive, inexpensive and easy to administer diagnostic protocols. Comparison of different ML approaches (in terms of both accuracy and interpretability) has been performed on the features extracted from the handwriting and drawing samples included in the publicly available PaHaW and NewHandPD datasets. The experimental findings show that the Cartesian Genetic Programming outperforms the white-box methods in accuracy and the black-box ones in interpretability
Generation of Synthetic Drawing Samples to Diagnose Parkinson’s Disease
No full textThe state-of-the-art artificial intelligence tools for automatic diagnosis of Parkinson’s disease from handwriting require a lot of training samples from both healthy subjects and patients to exhibit impressive performance. Publicly available datasets include very few samples drawn by a small number of individuals and that limits the use of deep learning architectures. In this paper, we evaluate if the performance of a Convolutional Neural Network that recognizes the handwriting of Parkinson’s disease patients can be improved by adding synthetic samples to the training set. In the experimentation, we synthetically generated dynamic signals of spirals and meanders through the use of a Recurrent Neural Network. The performance of the system was evaluated on the NewHandPD dataset and the results showed that the use of synthetic samples increases the recognition accuracy of the convolutional neural network
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