The Duty to Warn in Toxic Tort Litigation

Subsequent to the landmark case of Greenman v. Yuba Power Products, Inc., the American judicial system has become encumbered by a staggering number of products liability actions. A significant number of these cases involve allegations of inadequate or nonexistent warnings. Given society\u27s increasing reliance on chemical products, the potential for additional claims from accidental exposure to or improper use of toxic chemicals in the home, the workplace, and the environment is immense, notwithstanding the best efforts of the chemical industry to minimize the risk of injury. The result is a huge cost to manufacturers -both from paying damage claims and incurring legal expenses in resisting claims. This Article begins with a description of the general elements of an adequate warning, utilizing the Restatement (Second) of Torts as a guide. It then focuses upon the learned intermediary doctrine, an exception to the general rules concerning adequate warning, to determine if its rationale permits application in other contexts. The discussion then shifts to an analysis of the duty to warn under the Uniform Product Liability Act (UPLA), drafted by the United States Department of Commerce, which suggests that the doctrine should be applied to hazardous chemical manufacturers. Finally, an analysis of the pertinent case law determines the parameters of this emerging doctrine and the extent to which these parameters have mirrored the guidelines set forth in the UPLA

Parallel Genetic Algorithms for calibrating Cellular Automata models: Application to lava flows

Cellular Automata are highly nonlinear dynamical systems which are suitable for simulating natural phenomena whose behaviour may be specified in terms of local interactions. The Cellular Automata model SCIARA, developed for the simulation of lava flows, demonstrated to be able to reproduce the behaviour of Etnean events. However, in order to apply the model for the prediction of future scenarios, a thorough calibrating phase is required. This work presents the application of Genetic Algorithms, general-purpose search algorithms inspired to natural selection and genetics, for the parameters optimisation of the modelSCIARA. Difficulties due to the elevated computational time suggested the adoption a Master-Slave Parallel Genetic Algorithm for the calibration of the model with respect to the 2001 Mt. Etna eruption. Results demonstrated the usefulness of the approach, both in terms of computing time and quality of performed simulations

The Catania 1669 lava eruptive crisis: simulation of a new possible eruption

SCIARA (Smart Cellular Interactive Automata for modeling the Rheology of Aetnean lava flows, to be read as “shea’rah”), our first two-dimensional Cellular Automata model for the simulation of lava flows, was tested and validated with success on several lava events like the 1986/87 Etnean eruption and the last phase of the 1991/93 Etnean one. Real and simulated events are satisfying within limits to forecast the surface covered by the lava flow. Moreover, improved versions have been adopted in testing other real lava flows of Mount Etna and of Reunion Island (Indian Ocean). The model has been applied with success in the determination of risk zones in the inhabited areas of Nicolosi, Pedara, S. Alfio and Zafferana (Sicily). The main goal of the present work has been the verification of the effects, in volcanic risk terms, in the Etnean area from Nicolosi to Catania, of a eruptive crisis similar to the event that occurred in 1669, as if the episode would happen nowadays. Catania has been severely interested in some major Etnean events in history, the most famous one being, namely, the 1669 eruption, involving 1 km3 of lava during 130 days. The simulation of lava tubes and the usage of different histories within the experiments have been crucial in the determination of a new risk area for Catania. In fact, simulations carried out without the introduction of lava tubes, never involved the city, proving the fact that lava tubes, played a fundamental role in the 1669 Catania lava crisis

Post-exposure prophylaxis with sotrovimab for Omicron (B.1.1.529) SARS-CoV-2 variant during the aplastic phase of autologous stem cell transplantation

Background To date, there is no information on the safety and efficacy of the novel anti-sarbecoviruses monoclonal antibody sotrovimab administered, as a post-exposure prophylactic measure, during the aplastic phase of autologous stem cell transplantation (ASCT). Methods We describe the outcomes of a Multiple Myeloma (MM) patient, who was threateningly exposed to the Omicron (B.1.1.529) SARS-CoV-2 variant, two days after having received a myeloablative regimen of high-dose melphalan. The patient fulfilled all CDC criteria for prolonged close contacts with an index patient who tested positive for a molecular nasopharyngeal swab (Omicron; B.1.1.529) soon after admission to the ward. Given the high risks of morbidity and mortality in the case of COVID-19 developing during the aplastic phase of transplantation, we adopted a post-exposure prophylaxis intervention based on intravenous (i.v.) sotrovimab

Precision medicine and machine learning towards the prediction of the outcome of potential celiac disease

Potential Celiac Patients (PCD) bear the Celiac Disease (CD) genetic predisposition, a significant production of antihuman transglutaminase antibodies, but no morphological changes in the small bowel mucosa. A minority of patients (17%) showed clinical symptoms and need a gluten free diet at time of diagnosis, while the majority progress over several years (up to a decade) without any clinical problem neither a progression of the small intestine mucosal damage even when they continued to assume gluten in their diet. Recently we developed a traditional multivariate approach to predict the natural history, on the base of the information at enrolment (time 0) by a discriminant analysis model. Still, the traditional multivariate model requires stringent assumptions that may not be answered in the clinical setting. Starting from a follow-up dataset available for PCD, we propose the application of Machine Learning (ML) methodologies to extend the analysis on available clinical data and to detect most influent features predicting the outcome. These features, collected at time of diagnosis, should be capable to classify patients who will develop duodenal atrophy from those who will remain potential. Four ML methods were adopted to select features predictive of the outcome; the feature selection procedure was indeed capable to reduce the number of overall features from 85 to 19. ML methodologies (Random Forests, Extremely Randomized Trees, and Boosted Trees, Logistic Regression) were adopted, obtaining high values of accuracy: all report an accuracy above 75%. The specificity score was always more than 75% also, with two of the considered methods over 98%, while the best performance of sensitivity was 60%. The best model, optimized Boosted Trees, was able to classify PCD starting from the selected 19 features with an accuracy of 0.80, sensitivity of 0.58 and specificity of 0.84. Finally, with this work, we are able to categorize PCD patients that can more likely develop overt CD using ML. ML techniques appear to be an innovative approach to predict the outcome of PCD, since they provide a step forward in the direction of precision medicine aimed to customize healthcare, medical therapies, decisions, and practices tailoring the clinical management of PCD children

A petro-chemical study of ancient mortars from the archaeological site of Kyme (Turkey)

Fourteen samples of ancient mortars (joint mortars and plasters) from the archaeological site of Kyme (Turkey) were studied by optical microscopy (OM), X-ray fluorescence (XRF), X-ray powder diffraction (XRPD), scanning electron microscopy (SEM-EDS) and micro- Raman spectroscopy to obtain information about their composition.The study allowed us to identify a new type of plaster inside the archaeological site of Kyme, not detected by previous studies of this site, in which vegetable fibers were intentionally added to the mixture. The combination of a petrographic analysis on thin sections by polarized light microscopy with a chemical analysis, has allowed us to highlight similarities and differences between the mortars and to get information about the evolution of constructive techniques in the archaeological area

Towards generalized measures grasping CA dynamics

In this paper we conceive Lyapunov exponents, measuring the rate of separation between two initially close configurations, and Jacobians, expressing the sensitivity of a CA's transition function to its inputs, for cellular automata (CA) based upon irregular tessellations of the n-dimensional Euclidean space. Further, we establish a relationship between both that enables us to derive a mean-field approximation of the upper bound of an irregular CA's maximum Lyapunov exponent. The soundness and usability of these measures is illustrated for a family of 2-state irregular totalistic CA