Estimation of striped bass (Morone saxatilis) diets using fatty acid signature analysis

Accurate estimates of diets are essential for the management of fisheries, especially when this information is used to construct food webs for a system. Traditionally, these studies have relied on examining the stomachs contents through direct observation of sacrificed fish, or using instruments to flush the items from the stomach. These methods only provide information on the recent feeding history. Fatty acid analysis is a biochemical technique that offers promise for examining diets in fish over a longer time scale than just the last few prey species consumed. The goal of this dissertation was to examine the feasibility and efficacy of using fatty acid signature analysis to record striped bass (Morone saxatilis) diets.;I examined how collection location (Upper or Lower Chesapeake bay), species, and season affected the fatty acid signature (a compilation of all fatty acids present) and lipid content of four common striped bass prey items: Atlantic menhaden (Brevoortia tyrannus), bay anchovy (Anchoa mitchilli), spot (Leiostomus xanthurus), and blue crab (Callinectes sapidus). Demersal species (spot and blue crab) were separated from pelagic species (menhaden and bay anchovy) based upon their fatty acid signature. Spot and blue crab were also grouped by season, with summer blue crab and spot distinct from fall blue crab and spot. Blue crab and spot within a season had very similar fatty acid signatures. Anchovy and menhaden did not show the same type of seasonal grouping as the demersal species. Anchovy and menhaden had the highest lipid content followed by spot, and blue crab had the lowest lipid content. Collection location did not appear to play a role in structuring the fatty acid signature. These results necessitate the collection of prey species at the same time as collection of predators for fatty acid signature analysis.;Fatty acids are deposited in tissues based upon the needs of that particular tissue. I assessed the diet history of striped bass based on two different tissues, adipose and liver tissue. Striped bass were held in flow through tanks at the NOAA Fisheries James J. Howard Laboratory in Sandy Hook, New Jersey, with water being pumped directly from the Sandy Hook Bay. Fish were fed a diet of spot for six weeks, at which point the spot diet was switched to a diet consisting of menhaden. Lipid levels in both tissues increased after the diet was switched to menhaden, a prey that had approximately twice the amount of lipid. The entire fatty acid signature did not change to mimic the prey as reported in a study in which the authors demonstrated that the fatty acid signature of cod (Gadus morhua) significantly changed to a squid signature in approximately three weeks. However, in the present study, certain marker fatty acids specific to the prey were able to distinguish the diet switch from spot to menhaden. The change in marker fatty acids and lipid levels was evident after a period of 31 days. Both liver tissue and adipose tissue demonstrated the change in diet, but adipose tissue may offer a more surgically feasible and non-lethal sample in striped bass.;The effect of striped bass size on fatty acid incorporation was analyzed for three different size classes; small (150 -- 200 mm), medium (300 -- 380 mm) and large (fish greater than 460 mm). Fish were housed in flow through tanks at the NOAA Laboratory in Oxford, Maryland, with water being pumped directly from the Tred Avon River. Striped bass were fed a diet of spot for four weeks, at which time the diet was switched to menhaden for four weeks. Lipid levels for these fish indicated that there was little to no deposition of lipids throughout the experimental feeding of menhaden. Fatty acid signatures also indicated that the entire fatty acid signatures, nor marker fatty acids, were able to determine the diet switch. Based upon these findings and negligible growth, the most likely cause was a lack of consumption by striped bass. Due to high turbidity, feeding was difficult to observe.;One of the most promising aspects of fatty acids analysis is the ability to estimate the proportional contribution of different prey items to the diet using prey fatty acids and their respective lipid levels. The statistical program, quantitative fatty acid signature analysis (QFASA), can perform this type of analysis, and also takes into account the effect of predator metabolism of each fatty acid by using calibration coefficients. I tested this model using striped bass fed diets containing mixtures of spot and menhaden and a control diet of just menhaden. In this experiment, the striped bass were fed spot for six weeks before the menhaden feeding experiment began to allow sufficient time for the fatty acids to become homogenized within the striped bass tissues. The model correctly quantified the contribution of spot after six weeks, but it was unable to correctly assess the inputs from the mixed diets or menhaden diet alone. Recent studies have shown that fatty acids may take 12 -- 14 weeks to stabilize in fish, which is twice as long as this experiment ran. Most of the work performed with QFASA has tested the model for homeotherms, e.g. marine mammals and seabirds. The fact that fish are poikilotherms may necessitate the duration of fatty acid incorporation to be on the scale of several months rather than weeks. Poikilotherms regulate the internal body temperature based upon ambient water temperatures, while homeotherms require a constant energy source to maintain a set body temperature. Fatty acids may be mobilized quicker and have a shorter retention time in homeotherm tissues. However, this situation is improbable for a generalist fish species like striped bass that will consume a variety of prey items and have the potential to be highly mobile.;Lastly, I tested the QFASA model on wild caught striped bass to determine the possibility of using this model on wild fish with prey items caught at the same time. Fish were caught during the fall when they are most likely to be consuming a high proportion of menhaden. Percent biomass of the stomach contents for these fish was compared to previous studies that collected similar sized fish (age-3) during the same season. The stomach contents of fish for this experiment, and fish from previous studies, showed that menhaden made the bulk of the diet (greater than 70%). The QFASA estimated the contribution of menhaden to be minimal (less than 2%). It is possible that striped bass were not feeding on menhaden for a long enough duration for the fatty acid signatures of menhaden to become predominant

Marina Observation of Sea Turtles: Establishing a Database of Intracoastal Waterway Green Sea Turtles in Northeast Florida

As conservation efforts regarding green sea turtles, Chelonia mydas, continue, it is imperative to document behaviors and foraging habits/habitats of understudied populations. We have conducted an 18-month study dedicated to photographing the local population feeding alongside floating docks within the Guana Tolomato Matanzas estuary to determine the capability of matching head scale patterns efficiently through a pattern matching program: HotSpotter. To date, 195 unique sea turtles have been identified between two different marinas located in St. Augustine, FL. Of these, 98 were spotted more than once, with 39 of them being “tracked” for longer than a year. Temperature trends were also monitored in conjunction, showing that more individuals appeared during the warmer months of the year. The evidence, overall, indicates that these locations host a resident population of green sea turtles, leading to the need for a discussion on potential threats originating from the usage of these marinas by humans

Analysis of outsourcing of construction management services for the Naval Facilities Engineering Command

Through the past several years, the Navy has been reexamining the way that constructed facilities are delivered to internal customers. The Resident Officer In Charge of Construction (ROICC) offices, manned by Civil Service employees and Naval Officers, currently manages the construction contracts for the Naval Facilities Engineering Command (NAVFAC). The use of private sector construction management service by contract has not been examined in detail by the Navy as an alternative to this practice. This paper will examine whether outsourcing of traditional ROICC office duties to civilian contractors is feasible and what benefits and risks are found by doing so. Different contracting methods and approaches to implementing outsourcing of construction management will also be examinedhttp://archive.org/details/analysisofoutsou00mcg

Optimization of ring-stiffened cylindrical shells for practical hydrospace applications.

http://archive.org/details/optimizationofri00mcg

The Effects of Compression on the Detection of Atrial Fibrillation in ECG Signals

Although Atrial Fibrillation (AF) is the most frequent cause of cardioembolic stroke, the arrhythmia remains underdiagnosed, as it is often asymptomatic or intermittent. Automated detection of AF in ECG signals is important for patients with implantable cardiac devices, pacemakers or Holter systems. Such resource-constrained systems often operate by transmitting signals to a central server where diagnostic decisions are made. In this context, ECG signal compression is being increasingly investigated and employed to increase battery life, and hence the storage and transmission efficiency of these devices. At the same time, the diagnostic accuracy of AF detection must be preserved. This paper investigates the effects of ECG signal compression on an entropy-based AF detection algorithm that monitors R-R interval regularity. The compression and AF detection algorithms were applied to signals from the MIT-BIH AF database. The accuracy of AF detection on reconstructed signals is evaluated under varying degrees of compression using the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) compression algorithm. Results demonstrate that compression ratios (CR) of up to 90 can be obtained while maintaining a detection accuracy, expressed in terms of the area under the receiver operating characteristic curve, of at least 0.9. This highlights the potential for significant energy savings on devices that transmit/store ECG signals for AF detection applications, while preserving the diagnostic integrity of the signals, and hence the detection performance

The effects of compression on ultra wideband radar signals

Over the past ten years, Ultra Wideband (UWB) Radar has been widely investigated as a biomedical imaging modality, used to detect early-stage breast cancer and to continuously monitor vital signs using both wearable and contactless devices. The advantages of the technology in terms of low-power requirements and non-ionising radiation are well recognised, with the technology being applied to a range of non-invasive medical applications, from respiration to heart monitoring. Across all these applications, there is a strong necessity to efficiently manage the large quantities of UWB data which will be captured. For wearable devices in particular, the efficient compression of UWB data allows the monitoring system to conserve limited resources such as memory and battery capacity, by reducing data storage and in some cases transmission requirements. In contrast to lossless compression techniques, lossy compression algorithms can achieve higher compression ratios and consequently greater power savings, at the expense of a marginal degradation of the reconstructed signal. This paper compares the lossy JPEG2000 and Set Partitioning In Hierarchical Trees (SPIHT) algorithms for UWB signal compression. This study examines the effects of lossy signal compression on an UWB breast cancer classification algorithm. This particular application was chosen because the classification algorithm relies heavily on shape and surface texture detail embedded in the Radar Target Signature (RTS) of the tumour, and therefore will provide both a robust and easily quantifiable test platform for the compression algorithms. The study will evaluate the performance of the classification algorithm as a function of Compression Ratio (CR) and Percentage Root-mean-square Difference (PRD) between the original and reconstructed UWB signals

Spiking neural networks for breast cancer classification in a dielectrically heterogeneous breast

The considerable overlap in the dielectric properties of benign and malignant tissue at microwave frequencies means that breast tumour classification using traditional UWB Radar imaging algorithms could be very problematic. Several studies have examined the possibility of using the Radar Target Signature (RTS) of a tumour to classify the tumour as either benign or malignant, since the RTS has been shown to be influenced by the size, shape and surface texture of tumours. The main weakness of existing studies is that they mainly consider tumours in a 3D dielectrically homogenous or 2D heterogeneous breast model. In this paper, the effects of dielectric heterogeneity on a novel Spiking Neural Network (SNN) classifier are examined in terms of both sensitivity and specificity, using a 3D dielectrically heterogeneous breast model. The performance of the SNN classifier is compared to an existing LDA classifier. The effect of combining conflicting classification readings in a multi-antenna system is also considered. Finally and importantly, misclassified tumours are analysed and suggestions for future work are discussed

The effects of compression on ultra wideband radar signals

Over the past ten years, Ultra Wideband (UWB) Radar has been widely investigated as a biomedical imaging modality, used to detect early-stage breast cancer and to continuously monitor vital signs using both wearable and contactless devices. The advantages of the technology in terms of low-power requirements and non-ionising radiation are well recognised, with the technology being applied to a range of non-invasive medical applications, from respiration to heart monitoring. Across all these applications, there is a strong necessity to efficiently manage the large quantities of UWB data which will be captured. For wearable devices in particular, the efficient compression of UWB data allows the monitoring system to conserve limited resources such as memory and battery capacity, by reducing data storage and in some cases transmission requirements. In contrast to lossless compression techniques, lossy compression algorithms can achieve higher compression ratios and consequently greater power savings, at the expense of a marginal degradation of the reconstructed signal. This paper compares the lossy JPEG2000 and Set Partitioning In Hierarchical Trees (SPIHT) algorithms for UWB signal compression. This study examines the effects of lossy signal compression on an UWB breast cancer classification algorithm. This particular application was chosen because the classification algorithm relies heavily on shape and surface texture detail embedded in the Radar Target Signature (RTS) of the tumour, and therefore will provide both a robust and easily quantifiable test platform for the compression algorithms. The study will evaluate the performance of the classification algorithm as a function of Compression Ratio (CR) and Percentage Root-mean-square Difference (PRD) between the original and reconstructed UWB signals

Spiking neural networks for breast cancer classification in a dielectrically heterogeneous breast

The considerable overlap in the dielectric properties of benign and malignant tissue at microwave frequencies means that breast tumour classification using traditional UWB Radar imaging algorithms could be very problematic. Several studies have examined the possibility of using the Radar Target Signature (RTS) of a tumour to classify the tumour as either benign or malignant, since the RTS has been shown to be influenced by the size, shape and surface texture of tumours. The main weakness of existing studies is that they mainly consider tumours in a 3D dielectrically homogenous or 2D heterogeneous breast model. In this paper, the effects of dielectric heterogeneity on a novel Spiking Neural Network (SNN) classifier are examined in terms of both sensitivity and specificity, using a 3D dielectrically heterogeneous breast model. The performance of the SNN classifier is compared to an existing LDA classifier. The effect of combining conflicting classification readings in a multi-antenna system is also considered. Finally and importantly, misclassified tumours are analysed and suggestions for future work are discussed