The growing interest in algae is associated with current commercial products and future potential biofuel developments. The focus of this chapter is on commercial products from microalgae and macroalgae. This market is estimated to be of the order of billions of dollars per year with more than 20 different commercial products. The largest market is food products, including nutraceuticals and functional foods. There are many refined products; in some cases, there are multiple product streams associated with the separation process. Production occurs in fresh water for some microalgae such as Spirulina. Many macroalgae are grown in salt water environments; there are both managed production systems and harvests from natural areas of seas and oceans. Because of the diversity of growth environments and species, many more potential products are possible, and additional research is to be encouraged. There are many additional locations in the world that can be used to produce beneficial products. Keywords

Abstract. This study explores the spatial trends of cutaneous leishmaniasis (CL) and characterises the relationships between the observed spatial patterns and climate in Jordan, Syria, Iraq and Saudi Arabia in 2009. Areal interpolation revealed the presence of four major hotspots of relatively high incidence rates covering most parts of Syria, central parts of Iraq, and north-western, central, south-eastern and south-western parts of Saudi Arabia. The severity of these hotspots was seen to decrease from high to low latitudes. The spatial patterns could be partly linked to precipitation (the higher the precipitation, the higher the incidence rates) and to a lesser degree to temperature (the lower the temperature, the higher the incidence rates). No significant relationship was deduced between the observed spatial patterns of incidence rates and humidity. However, these three climatic factors could be used jointly as explanatory variables (ceteris paribus) to explain part of the spatial variations of the CL incidence rates in the study area by applying geographically weighted regression.

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