Oil sands process-affected water (OSPW), generated by surface mining in Canada’s oil sands, require treatment of environmentally persistent dissolved organic compounds before release to the watershed. Conventional chemical and mechanical treatments have not proved suitable for treating the large quantities of stored OSPW, and the biological recalcitrance of some dissolved organics may not be adequately addressed by conventional passive treatment systems. Previous work has evaluated photocatalytic treatment as a passive advanced oxidation process (P-AOP) for OSPW remediation. This work expands upon this prior research to further characterize the effects of water chemistry on the treatment rate and detoxification threshold. Under artificial sunlight, buoyant photocatalysts (BPCs) detoxified all OSPW samples within 1 week of treatment time with simultaneous treatment of polycyclic aromatic hydrocarbons, naphthenic acid fraction components (NAFCs), and un-ionized ammonia. Overall, these results further demonstrate passive photocatalysis as an effective method for treatment of OSPW contaminants of potential concern (COPCs).

Rare paediatric eye diseases (RPEDs) threaten both vision and life. Recently, rare diseases were recognised as a global public health agenda, with children specified as a priority in the World Health Organization's VISION 2020 against avoidable visual loss. We conducted a review through a query of online databases (PubMed, Embase and Cochrane Library). Articles related to RPEDs were selected based on relevance by 2 authors, with any disagreements adjudicated by the third author. We synthesise the current state of knowledge regarding RPEDs, barriers to their care, and recommendations for the future. RPEDs often result in significant visual loss, profoundly impacting the way children comprehend and participate in the world. These diseases may also reduce life expectancy and even be life-threatening. Barriers to the care of RPEDs include an unclear definition of "rare diseases", missed or delayed diagnosis, inadequate knowledge and expertise in management, and challenging research environments. Our findings provide an update on the diagnosis and management of RPEDs, which is of relevance to ophthalmologists, paediatricians, healthcare policymakers and social workers. We propose supportive policies and adequate resource allocation to these diseases, comprehensive and patient-centred care, alongside improved education and training, enhanced research capabilities and continued collaboration across institutions.

AbstractSuccessful cumulative effects management is fundamental for conservation policy and practice. We investigated the application of a carrying capacity (CC) model as a cumulative effects management tool for bighorn sheep (Ovis canadensis canadensis) in British Columbia, Canada, where CC is defined as the natural limit of a sustainable population that is set by the availability of resources in the environment. We estimated winter CC using forage availability across winter ranges, weighted by relative selection by sheep and a safe use factor, and divided by overwinter forage requirements to determine how many sheep the landscape can support. We explored application of our model to decision‐making about new industrial projects or conservation activities in a cumulative effects context. Cumulative effects include both positive and negative contributions to animal populations and we simulated the potential positive outcomes of burning to increase bighorn sheep carrying capacity in our study area. Our results show that carefully planned conservation actions could generate a 5% increase in CC (i.e., from 493 to 519 sheep). Robust tools and scientific techniques that are capable of quantifying multiple impacts and conservation actions and that consider spatial processes over long temporal scales, such as the CC model presented, should be applied to help inform decisions about how to better manage cumulative habitat change and achieve conservation objectives.

The skin microbiome is an extensive community of bacteria, fungi, mites, and viruses colonizing the skin. Fluctuations in the composition of the skin microbiome have been observed in atopic dermatitis (AD) and food allergy (FA), particularly in early life, established disease, and associated with therapeutics. However, AD is a multifactorial disease characterized by skin barrier aberrations modulated by genetics, immunology, and environmental influences, thus the skin microbiome is not the sole feature of this disease. Future research should focus on mechanistic understanding of how early life skin microbial shifts may influence AD and FA onset, to guide potential early intervention strategies or as microbial biomarkers to identify high-risk infants who may benefit from possible microbiome-based biotherapeutic strategies. Harnessing skin microbes as AD biotherapeutics is an emerging field, but more work is needed to investigate whether this approach can lead to sustained clinical responses.

Abstract The development of evidence-based COVID-19 vaccination policies for new birth cohorts is hindered by the lack of systematic examination of the protective capacity of elements of adaptive immunity besides neutralising antibodies. To address this knowledge gap, we conducted a longitudinal cohort study of healthy 5- to 12-year-olds vaccinated with BNT162b2. We serially measured binding and neutralising antibody titres, Spike (S)-specific memory B cell (MBC) and S-reactive T cell responses over 1 year; SARS-CoV-2 infections were ascertained using antigen rapid tests, and serological and cellular responses against nucleocapsid. We found that children mounted antibody, MBC and T cell responses after 2 doses, with continual expansion of SARS-CoV-2 variant neutralising capacity up to 1 year in the absence of wild-type infection. A booster (third) dose only improved neutralising antibody titres without impacting MBC and T cell responses. Hybrid immunity was greater in those infected after 2 vaccine doses than those infected before dose 2. Binding IgG titres, MBC and T cell responses were predictive of protection against symptomatic infection prior to attaining hybrid immunity; neutralising antibody titres only correlated with protection after hybrid immunity. The stable MBC and T cell responses over time suggest sustained protection against symptomatic SARS-CoV-2 infection, even when antibody levels wane.

This review is about the ethical use of sensors to monitor occupational exposure to hazards. It considers whether the same or different, ethical measures apply to using sensors, compared to conventional hazard monitoring surveys. To undertake the review, subject experts developed a research question, identified suitable search terms, and set the scope of these searches. Candidate research papers dating from 2000 to mid-2022 that met inclusion criteria were identified and reviewed by each author. Ethical concerns were identified by the authors of studies in which sensors were used to monitor employee health and well-being, but most of the studies that used them to monitor employee exposure to hazards focused on the technical aspects of their deployment. These ethical concerns included questions about employee rights and privacy, the anonymity of the data collected with sensors, and how the security of this data is managed. The review considers ethical standards and codes of practice for occupational hygiene work and the ethical risks when sensors are used to gather data. Sensors may provide insight into occupational exposure to hazards, but their use is not always adequately explained to employees by those managing this monitoring work. The ethical concerns identified were relevant to many areas of industrial hygiene work, but more studies are required that consider the ethical use of sensors in workplaces. Studies that monitored employee health, well-being, and productivity, identified ethical risks in using sensors to monitor these endpoints. An ethical framework and checklist for hygienists are proposed including a set of questions that consider the risks of using sensors to monitor occupational hazards. Industrial hygiene professional bodies provide ethical codes of practice for their members but may also need to consider the implications of using sensors in workplaces. Ethical standards support the collection of industrial hygiene exposure data whilst maintaining the privacy rights of employees.

In this paper, a data-driven smart optimization algorithm is presented to identify the optimal operating point of the process and the path from the current operating condition to the optimal operating condition. To model the plant dynamics, a locally weighted semi-supervised probabilistic principal component regression (PPCR) model that is robust to missing data and can handle the time-invariant delay is developed in this paper. Being a locally weighted linear model, a nonlinearity index is utilized to control the accuracy of approximation of the local model. In addition, an updating strategy on the delay range is proposed to reduce the computational effort of real-time modeling. To account for the model–plant mismatch, a penalty term in the form of a robust Gaussian process regression is incorporated into the optimization problem. Finally, to ensure that the algorithm reaches different regions of search space and avoid convergence to local optimum, an exploration step with the aid of acquisition functions is utilized. The performance of the developed approach is illustrated through an experimental case study on a hybrid tank system, and the practical applicability of the algorithm is demonstrated through an industrial example.