Technologies and Methodologies for the Detection of Harmful Algae and Their Toxins, St Petersburg, Florida, Oct 22-24 2008 : workshop proceedings

The Alliance for Coastal Technologies (ACT) Workshop "Technologies and Methodologies for the Detection of Harmful Algae and their Toxins" convened in St. Petersburg, Florida, October 22- 24, 2008 and was co-sponsored by ACT (http://act-us.info); the Cooperative Institute for Coastal and Estuarine Environmental Technology (CICEET, http://ciceet.unh.edu); and the Florida Fish and Wildlife Conservation Commission (FWC, http://www.myfwc.com). Participants from various sectors, including researchers, coastal decision makers, and technology vendors, collaborated to exchange information and build consensus. They focused on the status of currently available detection technologies and methodologies for harmful algae (HA) and their toxins, provided direction for developing operational use of existing technology, and addressed requirements for future technology developments in this area. Harmful algal blooms (HABs) in marine and freshwater systems are increasingly common worldwide and are known to cause extensive ecological, economic, and human health problems. In US waters, HABs are encountered in a growing number of locations and are also increasing in duration and severity. This expansion in HABs has led to elevated incidences of poisonous seafood, toxin-contaminated drinking water, mortality of fish and other animals dependent upon aquatic resources (including protected species), public health and economic impacts in coastal and lakeside communities, losses to aquaculture enterprises, and long-term aquatic ecosystem changes. This meeting represented the fourth ACT sponsored workshop that has addressed technology developments for improved monitoring of water-born pathogens and HA species in some form. A primary motivation was to assess the need and community support for an ACT-led Performance Demonstration of Harmful Algae Detection Technologies and Methodologies in order to facilitate their integration into regional ocean observing systems operations. The workshop focused on the identification of region-specific monitoring needs and available technologies and methodologies for detection/quantification of harmful algal species and their toxins along the US marine and freshwater coasts. To address this critical environmental issue, several technologies and methodologies have been, or are being, developed to detect and quantify various harmful algae and their associated toxins in coastal marine and freshwater environments. There are many challenges to nationwide adoption of HAB detection as part of a core monitoring infrastructure: the geographic uniqueness of primary algal species of concern around the country, the variety of HAB impacts, and the need for a clear vision of the operational requirements for monitoring the various species. Nonetheless, it was a consensus of the workshop participants that ACT should support the development of HA detection technology performance demonstrations but that these would need to be tuned regionally to algal species and toxins of concern in order to promote the adoption of state of the art technologies into HAR monitoring networks. [PDF contains 36 pages]

Biologically-inspired data decorrelation for hyper-spectral imaging

Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification

A statistical investigation of fish prices in Nigeria

The main objective of this study is to describe and characterize the behaviour of fish prices in Nigeria. Drawing upon aspects of the data from a nationwide fish survey in 1980/81 and on various secondary data, the study analyses the pattern of fish price movement and makes projections of fish prices in Nigeria till 2002 A.D. It is concluded that unless efforts are directed at stemming inflation in fish prices, prices paid by fish consumers in Nigeria will be more than doubled within the next two decades

A combined statistical model for multiple motifs search

Transcription factor binding sites (TFBS) play key roles in genebior 6.8 wavelet expression and regulation. They are short sequence segments with de¯nite structure and can be recognized by the corresponding transcription factors correctly. From the viewpoint of statistics, the candidates of TFBS should be quite di®erent from the segments that are randomly combined together by nucleotide. This paper proposes a combined statistical model for ¯nding over- represented short sequence segments in di®erent kinds of data set. While the over-represented short sequence segment is described by position weight matrix, the nucleotide distribution at most sites of the segment should be far from the background nucleotide distribution. The central idea of this approach is to search for such kind of signals. This algorithm is tested on 3 data sets, including binding sites data set of cyclic AMP receptor protein in E.coli, PlantProm DB which is a non-redundant collection of proximal promoter sequences from di®erent species, collection of the intergenic sequences of the whole genome of E.Coli. Even though the complexity of these three data sets is quite di®erent, the results show that this model is rather general and sensible.

Fisheries statistical bulletin, Kainji Lake, northern Nigeria, 1997

The bulletin presents summary tables and charts on levels of fishing activity, fishing effort, yields and economic values of yields for the fisheries of Kainji Lake, Nigeria for the year 1997. Frame survey data and fishing gear measurements are also included. (PDF contains 34 pages)

Fisheries statistical bulletin, Kainji Lake, Nigeria, 1998

A tabulated summary is presented of the main fisheries data collected to date (1998) by the Nigerian-German Kainji Lake Fisheries Promotion Project, together with a current overview of the fishery. The data are given under the following sections: 1) Fishing localities and types; 2) Frame survey data; 3) Number of licensed fishermen by state; 4) Mesh size distribution; 5) Fishing net characteristics; 6) Fish yield; 7) Total annual fishing effort by gear type; 8) Total annual value of fish landed by gear type; 9) Graphs of effort and CPUE by gear type. (PDF contains 36 pages)