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This study assessed differences in the physical, chemical, and microbial properties of soils under trees of the native species Prosopis cineraria and the invasive species Prosopis juliflora trees, focusing on implications for ecosystem management and restoration. At the start of the growing season, 30 trees of each species with a trunk diameter of 15–30 cm were randomly selected. Soil samples were taken from the top 20 cm of soil profiles east of each tree, under the tree crowns and from control plots in open areas. Three soil samples per site were pooled for chemical and microbial analysis. Soil moisture was highest under P. cineraria (14.64 ± 0.3) and lowest in control plots (9.04 ± 0.65). Soil pH was highest in control soils (7.91 ± 0.09), slightly lower under P. cineraria (7.77 ± 0.06), and lowest under P. juliflora (7.49 ± 0.0). Electrical conductivity, soil salinity was highest under P. juliflora (2.25 ± 0.12). Microbial activity indicators (basal respiration and microbial biomass carbon) were greater under P. cineraria than under P. juliflora trees. Native P. cineraria trees enhance soil conditions, benefiting ecosystem management. In contrast, invasive P. juliflora trees raise soil salinity, threatening soil quality, biodiversity, and ecosystem services in the Sahara-Sahel region. Managing the spread of P. juliflora is crucial to maintaining ecosystem functions.

The influence of drying air temperatures, drying air flow rate and feed flow rate on percentage survival rate (% SR) of starter and probiotic bacteria, moisture content, water activity and colour of probiotic yoghurt powder produced using a pilot-scale spray dryer was investigated. Optimisation of spray drying conditions was achieved based on the desirability value. Results showed that inlet air temperature, drying air flow rate, feed flow rate and outlet air temperature of 150 °C, 478 m3/h, 2 L/h and 63.3 °C, respectively, were optimal conditions to produce powder that meets quality specifications and with a satisfactory% SR. [ABSTRACT FROM AUTHOR]

The article presents research which dealt with the optimization of the response to surveillance alerts in automated systems. The researchers examined the potential outcomes of dynamic decision making models when applied to outbreak identification methods. They assessed the level to which adaptive optimization can tolerate noise theoretically and the importance of the model to show the realization of the derived error bounds in practice.

Existing population health indicators tend to be out-of-date, not fully available at local levels of geography, and not developed in a coherent/consistent manner, which hinders their use in public health. The PopHR platform aims to deliver an electronic repository that contains multiple aggregated clinical, administrative, and environmental data sources to provide a coherent view of the health status of populations in the province of Quebec, Canada. This platform is designed to provide representative information in near-real time with high geographical resolution, thereby assisting public health professionals, analysts, clinicians and the public in decision-making. This paper presents our ongoing efforts to develop an integrated population health indicator ontology (PHIO) that captures the knowledge required for calculation and interpretation of health indicators within a PopHR semantic framework. [ABSTRACT FROM AUTHOR]

A wide variety of disease outbreak detection methods has been developed in automated public health surveillance systems. The choice of outbreak detection method results in large changes in performance under different circumstances. In this paper, we investigate how outbreak detection methods can be combined in order to improve the overall detection performance. We used Hierarchical Mixture of Experts, which is a probabilistic model for combining classification methods, for fusion of detection methods. Simulated surveillance data for waterborne disease outbreaks are used in this research to train and evaluate a Hierarchical Mixture of Experts model. Performance evaluation of our approach with respect to sensitivity-specificity trade-off and detection timeliness is provided in comparison with several other detection methods.

Worldwide developments concerning infectious diseases and bioterrorism are driving forces for improving aberrancy detection in public health surveillance. The performance of an aberrancy detection algorithm can be measured in terms of sensitivity, specificity and timeliness. However, these metrics are probabilistically dependent variables and there is always a trade-off between them. This situation raises the question of how to quantify this tradeoff. The answer to this question depends on the characteristics of the specific disease under surveillance, the characteristics of data used for surveillance, and the algorithmic properties of detection methods. In practice, the evidence describing the relative performance of different algorithms remains fragmented and mainly qualitative. In this paper, we consider the development and evaluation of a Bayesian network framework for analysis of performance measures of aberrancy detection algorithms. This framework enables principled comparison of algorithms and identification of suitable algorithms for use in specific public health surveillance settings.

This commentary discusses Tzountzouris and Gilbert's article on issues related to emerging health human resources (HHR) requirements and the role of educational institutions in anticipating and meeting them. It is apparent that educational institutions are influenced by and, in turn, influence a number of elements of HHR. They therefore have a unique role in responding to emerging HHR needs, from assessing the needs to evaluating responses. While this commentary shares the general idea presented by the authors of the lead paper, it outlines some of the issues that should be taken into consideration in formulating these roles.

The potentially catastrophic impact of an epidemic specially these due to bioterrorist attack, makes developing effective detection methods essential for public health. Current detection methods trade off reliability of alarms for early detection of outbreaks. The performance of these methods can be improved by disease-specific modeling techniques that take into account the potential costs and effects of an attack to provide optimal warnings and the cost and effectiveness of interventions. We study this optimization problem in the framework of sequential decision making under uncertainty. Our approach relies on estimating the future benefit of true alarms and the costs of false alarms. Using these quantities it identifies optimal decisions regarding the credibility of outputs from a traditional detection method at each point in time. The key contribution of this paper is to apply Partially Observable Markov Decision Processes (POMDPs) on outbreak detection methods for improving alarm function in the case of anthrax. We present empirical evidence illustrating that at a fixed specificity, the performance of detection methods with respect to sensitivity and timeliness is improved significantly by utilizing POMDPs in detection of anthrax attacks.