The unremitting trends of increasing population, urbanization, diminishing water supply, and continuing climate change have contributed to declining stocks of arable land per person. As land resources for agriculture decrease, policy makers are faced with the challenge of sustainability and feeding the rapidly growing world population which is projected to reach approximately 9.7 billion in 2050. Solutions for improving future food production are exemplified by urban vertical farming which involves much greater use of technology and automation for land-use optimization. The vertical farm strategy aims to significantly increase productivity and reduce the environmental footprint within a framework of urban, indoor, climate-controlled high-rise buildings. It is claimed that such facilities offer many potential advantages as a clean and green source of food, along with biosecurity, freedom from pests, droughts, and reduced use of transportation and fossil fuels. In this article, the issues involved are evaluated together with potential advantages and disadvantages. Possible implications are identified for consideration by policy makers and to facilitate further economic analysis. ARTICLE HISTORY

As competition among industrial, agricultural, urban and environmental sectors for freshwater intensifies, wastewater is frequently being seen as a valuable resource rather than mere waste. Furthermore, wise reuse of this water alleviates environmental concerns attendant with its discharge to coastal environments and inland waterways. Globally, around 20 million ha of land are irrigated with wastewater, either neat or partially diluted. This figure is likely to increase markedly over the next few decades in response to rising levels of water stress in inhabited catchments -in 1995 around 2.3 billion people lived in river basins considered to be water stressed and this number is expected to increase to 3.5 billion by 2025. Here we review the current status of wastewater irrigation by providing an overview of the extent of the practice in different parts of the world and through an assessment of the current understanding of various issues relating to sustainable and safe management of irrigation with wastewater. A theme that emerges is that wastewater irrigation is not only more common in water stressed regions such as the Near East, but the rationale for the practice also tends to differ between the developing and developed worlds. In developing nations the prime drivers for wastewater irrigation appears to be livelihood dependence and food security, whereas environmental agenda appear to hold greater sway in the developed world. The following were identified as key areas requiring greater understanding for the long-term sustainability of wastewater irrigation: (i) accumulation of bio-available forms of heavy metals in soils, (ii) an understanding of the balance of various factors affecting the environmental fate of organics in wastewater irrigated soils (iii) the influence of reuse schemes on catchment hydrology, including transport of salt loads, (iv) risk models for helminth infections (pertinent to developing nations), (v) microbiological contamination risks for aquifers and surface waters, (vi) transfer efficiencies of chemical contaminants from soil to plant, (vii) effects of chronic exposure of people to chemical contaminants in wastewater, and (iix) strategies for engaging the public in wastewater irrigation schemes.

There is a bewildering range of estimates for the number of arthropods on Earth. Several measures are based on extrapolation from species specialized to tropical rain forest, each using specific assumptions and justifications. These approaches have not provided any sound measure of uncertainty associated with richness estimates. We present two models that account for parameter uncertainty by replacing point estimates with probability distributions. The models predict medians of 3.7 million and 2.5 million tropical arthropod species globally, with 90% confidence intervals of [2.0, 7.4] million and [1.1, 5.4] million, respectively. Estimates of 30 million or greater are predicted to have <0.00001 probability. Sensitivity analyses identified uncertainty in the proportion of canopy arthropod species that are beetles as the most influential parameter, although uncertainties associated with three other parameters were also important. Using the median estimates suggests that in spite of 250 years of taxonomy and around 855,000 species of arthropods already described, approximately 70% await description.

Artificial intelligence and automation are topics dominating global discussions on the future of professional employment, societal change, and economic performance. In this paper, we describe fundamental concepts underlying AI and Big Data and their significance to public health. We highlight issues involved and describe the potential impacts and challenges to medical professionals and diagnosticians. The possible benefits of advanced data analytics and machine learning are described in the context of recently reported research. Problems are identified and discussed with respect to ethical issues and the future roles of professionals and specialists in the age of artificial intelligence.

No abstract

A key challenge in the estimation of tropical arthropod species richness is the appropriate management of the large uncertainties associated with any model. Such uncertainties had largely been ignored until recently, when we attempted to account for uncertainty associated with model variables, using Monte Carlo analysis. This model is restricted by various assumptions. Here, we use a technique known as probability bounds analysis to assess the influence of assumptions about (1) distributional form and (2) dependencies between variables, and to construct probability bounds around the original model prediction distribution. The original Monte Carlo model yielded a median estimate of 6.1 million species, with a 90 % confidence interval of [3.6, 11.4]. Here we found that the probability bounds (p-bounds) surrounding this cumulative distribution were very broad, owing to uncertainties in distributional form and dependencies between variables. Replacing the implicit assumption of pure statistical independence between variables in the model with no dependency assumptions resulted in lower and upper p-bounds at 0.5 cumulative probability (i.e., at the median estimate) of 2.9-12.7 million. From here, replacing probability distributions with probability boxes, which represent classes of distributions, led to even wider bounds (2.4-20.0 million at 0.5 cumulative probability). Even the 100th percentile of the uppermost bound produced (i.e., the absolutely most conservative scenario) did not encompass the well-known hyper-estimate of 30 million species of tropical arthropods. This supports the lower estimates made by several authors over the last two decades.

No abstract

Purpose The purpose of this study was to summarize and evaluate artificial intelligence (AI) algorithms used in geographic atrophy (GA) diagnostic processes (e.g. isolating lesions or disease progression). Methods The search strategy and selection of publications were both conducted in accordance with the Preferred of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Web of Science were used to extract literary data. The algorithms were summarized by objective, performance, and scope of coverage of GA diagnosis (e.g. lesion automation and GA progression). Results Twenty-seven studies were identified for this review. A total of 18 publications focused on lesion segmentation only, 2 were designed to detect and classify GA, 2 were designed to predict future overall GA progression, 3 focused on prediction of future spatial GA progression, and 2 focused on prediction of visual function in GA. GA-related algorithms reported sensitivities from 0.47 to 0.98, specificities from 0.73 to 0.99, accuracies from 0.42 to 0.995, and Dice coefficients from 0.66 to 0.89. Conclusions Current GA-AI publications have a predominant focus on lesion segmentation and a minor focus on classification and progression analysis. AI could be applied to other facets of GA diagnoses, such as understanding the role of hyperfluorescent areas in GA. Using AI for GA has several advantages, including improved diagnostic accuracy and faster processing speeds. Translational Relevance AI can be used to quantify GA lesions and therefore allows one to impute visual function and quality-of-life. However, there is a need for the development of reliable and objective models and software to predict the rate of GA progression and to quantify improvements due to interventions.