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Agaricus subrufescens Peck is a well-known Basidiomycota fungus (Royal Sun Agaricus), with rising demand in consumption and production worldwide. This particular mushroom with high medical value has been used successfully in cancer therapy and in the treatment of some bacterial and viral diseases. Four strains of A. subrufescens (Si2.2, 853, 1105, and 2603) were cultivated, and 22 mineral elements of basidiomes (fruit bodies) were analyzed (caps and stipes separately). The data obtained about the mineral compositions were compared to the "reference" Agaricus bisporus (white button mushroom) and to the average of wild growing Agaricus species. The mineral composition of A. subrufescens strains can be characterized by the following: (1) high levels of valuable macroelements, i.e., potassium (28-30,000 mg/kg of dry matter), phosphorus (7-11,000 mg/kg of dry matter), and calcium and magnesium (for both elements, 1,000-1,500 mg/kg of dry matter); (2) significantly higher level of copper (compared to A. bisporus, 70-150 mg/kg of dry matter) and zinc (140-250 mg/kg of dry matter); (3) low quantity of sodium (140-180 mg/kg of dry matter); (4) attention should paid to the detectable amount of cadmium (2-17 mg/kg of dry matter) in strain Si2.2; (5) low or undetectable concentrations of some other poisonous microelements like As, Cr, and V; and (6) the distribution of elements in caps and stipes is characteristic-the majority of beneficial elements have higher contents in caps than in stipes, but some other elements, such as Ca, Fe, and Na, appear in an inverse proportion. In conclusion, it can be said that the mineral composition of A. subrufescens is definitely positive, with the exception of the above-mentioned Cd level.

Hoarded homes can pose a threat to public safety, with heightened risks of fire hazards, pest infestations and noxious odours in both the home and neighbouring dwellings. Communities across North America are responding to these public safety concerns through a harm reduction approach. This descriptive study explores the implementation and outcomes of the City of Vancouver's approach involving a partnership between fire prevention and public health. Data were collected from the team's 2016–2018 case tracking systems, consisting of health records and team intervention record, as well as notes taken from case briefing meetings. Study objectives included describing the intervention model, providing descriptive statistics on clients and their clutter volume, the interventions undertaken, and exploring predictors of clutter volume and case outcome through exploratory analyses. The sample included 82 cases involving severely hoarded conditions or more moderate hoarding conditions paired with additional client vulnerabilities (e.g. health conditions, frailty). Results from paired samples t‐tests and regression analysis, suggest the Hoarding Action Response Team's (HART) model of a community‐based intervention for hoarding was associated with clutter reduction and tenancy preservation. HART successfully maintained engagement with most clients, and most cases were closed within six home visits. Despite these successes, the team dealt with several barriers including client avoidance and limited resources. This paper provides guidance for communities who are working to develop a coordinated response to problems associated with hoarding and begins to establish expectations for what can be achieved through a community‐based hoarding intervention model. [ABSTRACT FROM AUTHOR]

This paper presents findings on mitigating the negative impact of renewable energy resources variability on the energy scheduling problem, in particular for island grids and microgrids. The methods and findings presented in this paper are twofold. First, data obtained from the City of Summerside in the province of Prince Edward Island, Canada, is leveraged to demonstrate the effectiveness of state-of-the-art time series predictors in mitigating energy scheduling inaccuracy. Second, the outcome of the time series prediction analysis is used to propose a novel data-driven battery energy storage system (BESS) sizing study for energy scheduling purposes. The proposed probabilistic method accounts for intra-interval variations of generation and demand, thus mitigating the trade-off between time resolution of the problem formulation and the solution accuracy. In addition, as part of the sizing study, a BESS management strategy is proposed to minimize energy scheduling inaccuracies, and is then used to obtain the optimal BESS size. Finally, the paper presents quantitative analyses of the impact of both the energy predictors and the BESS on the supplied energy cost using the actual data of the Summerside Electric grid. The paper reveals the significant potential for reducing energy cost in renewable-penetrated grids and microgrids through state-of-the-art predictors combined with applications of properly-sized energy storage systems. [ABSTRACT FROM AUTHOR]