Protein C (PC) deficiency is a rare but life-threatening bleeding disorder that can present in the immediate neonatal period. This article presents the case of a baby girl with acute and progressive neonatal purpura fulminans as the presenting feature of PC deficiency. Other common complications of this disease include ophthalmic problems and central nervous system (CNS) changes. Management consists of correcting the coagulopathy, intensive wound care including negative-pressure dressings and skin grafting, and supportive care for the ophthalmic and CNS issues. Long-term follow-up consists of lifelong anticoagulant therapy to avoid recurrence of these complications.

Chemical resistance in pest organisms threatens global food security and human health, yet resistance issues are mostly dealt with reactively. Predictive models of resistance risk provide an avenue for field practitioners to implement proactive pest management but require knowledge of factors that drive resistance evolution. Despite the importance of chemical selection pressure on resistance evolution, poor availability of chemical usage data has limited the use of a general multi-species measure of selection pressure in predictive models. We demonstrate the use of pesticide product registrations as a predictor of resistance status and potential proxy of chemical selection pressure. Pesticide product registrations were obtained for 427 USA and 209 Australian agricultural arthropod pests, for 42 and 39 chemical Mode of Action (MoA) groups, respectively. We constructed Bayesian logistic regressions of resistance status as a function of the number of pesticide product registrations and two ecological traits, phagy, and voltinism. Our models were well-supported with demonstrated power to discriminate between resistant and susceptible observations in both USA and Australian species sets using cross-validation. Importantly, we observed strong support for a positive association between pesticide products and resistance status. Our work expands the horizon for proactive management by quantitatively linking a proxy for selection pressure on pest species to different chemical MoAs, which can be combined with ecological information to build models of resistance evolution risk. Because pesticide product registrations can typically be obtained from publicly available data, we believe they have broad applicability for risk predictions in other agricultural pests, such as weeds and fungi, and to other geographical regions beyond the USA and Australia.

The evolution of chemical resistance in pests is a global threat to food security and human health, yet resistance issues are mostly dealt with after they occur. Predictive models of resistance risk provide an opportunity for field practitioners to implement proactive strategies to manage pest species more effectively. These models require identification of variables that are associated with resistance risk. Phagy and voltinism, for example, have been previously shown as useful predictors of resistance evolution. Yet despite the importance of chemical selection pressure in resistance evolution, this predictor has been difficult to incorporate into predictive models due to incomplete and (or) poor datasets on chemical usage patterns. Here we test whether chemical registrations of pesticide products can act as a proxy for selection pressures in predictive models. We obtained pesticide product registrations from publicly available chemical databases for 427 agricultural arthropod pest species in the USA and 209 Australian species. We constructed Bayesian logistic regressions of resistance status as a function of the number of pesticide product registrations for 42 chemical Mode of Action (MoA) groups, as well as phagy and voltinism. These models were well supported by our data and cross-validation analyses and could discriminate between observations of resistance and susceptibility in both USA and Australian species sets. Moreover, we found strong support for a positive correlation between pesticide product registrations and resistance status. We propose a “registration–selection–resistance” hypothesis to explain this positive association, involving a positive feedback loop between marketing and social factors of pesticide use with the evolutionary processes of selection and adaptation. Pesticide product registrations provide a predictive variable that is specific to both pest species and chemical MoA groups. Our work aids the development of predictive models for proactive resistance management, combining ecological and operational factors, with broad applicability across a suite of agricultural pests, including weeds and fungi.

In 2004, the Ontario Ministry of the Environment proposed the 'Drinking water source protection act' which stipulated that, in the development of water protection plans, significant direct threats to source watersheds are to be identified. Examination of the major risk factors threatening water resources proved there are insufficient scientific data available to regulators to accomplish this task. Research showed E.coli O157:H7, Salmonella, Giardia lamblia, and Cryptosporidium parvum, and the sources of these pathogens in the environment are, qualitatively, significant threats to water resources. However, a quantitative characterization of significance depends of the failure probabilities of pathogen sources. Using the Ontario Spills Action Centre data, the occurrence of failure was found to have a high non-zero probability. However, considerable uncertainties revealed in these data suggest that a better understanding of failure is critical to accurately characterize significant threats to drinking water resources.

In 2004, the Ontario Ministry of the Environment proposed the 'Drinking water source protection act' which stipulated that, in the development of water protection plans, significant direct threats to source watersheds are to be identified. Examination of the major risk factors threatening water resources proved there are insufficient scientific data available to regulators to accomplish this task. Research showed E.coli O157:H7, Salmonella, Giardia lamblia, and Cryptosporidium parvum, and the sources of these pathogens in the environment are, qualitatively, significant threats to water resources. However, a quantitative characterization of significance depends of the failure probabilities of pathogen sources. Using the Ontario Spills Action Centre data, the occurrence of failure was found to have a high non-zero probability. However, considerable uncertainties revealed in these data suggest that a better understanding of failure is critical to accurately characterize significant threats to drinking water resources.