Comparative genomics of tadpole shrimps (Crustacea, Branchiopoda, Notostraca): Dynamic genome evolution against the backdrop of morphological stasis

This analysis presents five genome assemblies of four Notostraca taxa. Notostraca origin dates to the Permian/Upper Devonian and the extant forms show a striking morphological similarity to fossil taxa. The comparison of sequenced genomes with other Branchiopoda genomes shows that, despite the morphological stasis, Notostraca share a dynamic genome evolution with high turnover for gene families' expansion/contraction and a transposable elements content comparable to other branchiopods. While Notostraca substitutions rate appears similar or lower in comparison to other branchiopods, a subset of genes shows a faster evolutionary pace, highlighting the difficulty of generalizing about genomic stasis versus dynamism. Moreover, we found that the variation of Triops cancriformis transposable elements content appeared linked to reproductive strategies, in line with theoretical expectations. Overall, besides providing new genomic resources for the study of these organisms, which appear relevant for their ecology and evolution, we also confirmed the decoupling of morphological and molecular evolution

Transport of Potential Microbial Source Tracking Markers in Sandy Material

ABSTRACT TRANSPORT OF POTENTIAL MICROBIAL SOURCE TRACKING MARKERS IN SANDY MATERIALS by Jennifer J. Johanson The University of Wisconsin-Milwaukee 2016 Under the Supervision of Professor Shangping Xu Groundwater, a primary source of drinking water for nearly half the people in the United States, can be contaminated by pathogenic bacteria from fecal materials causing outbreaks of waterborne illness. Therefore, early identification of the presence of fecal contamination in groundwater can help prevent such outbreaks, and determining whether bacteria originate from human or animal feces can narrow down the location of potential pollution sources, allowing timely remediation and reduced potential for future outbreaks. Pathogens are found in relatively low concentration in feces leading to difficulties in their detection in groundwater samples. In addition, a wide variety of pathogenic bacteria and viruses may exist in feces making it costly to analyze groundwater directly for all potential pathogens. As a result, groundwater samples are routinely analyzed for non-pathogenic fecal indicator bacteria (FIB), which are used as a proxy for the potential contamination by fecal pathogens. An ideal FIB would be abundant in the source material, easy and inexpensive to analyze, mobile in the subsurface so that it does not lag behind the pathogens, and host-specific to help identify the contaminant source. Bacteria which can be identified as originating selectively from human vs nonhuman sources (animals) are especially helpful in determining the source of contamination when multiple potential sources are present. Escherichia coli (E. coli) has long been used as a FIB due to its abundance in fecal matter. However E. coli is found in many different hosts, which limits its use for source identification. Recent research has focused on identifying microbial source tracking (MST) bacteria which have markers that are specific to human or animal hosts, and these host-specific markers can be critical in early source identification efforts. This potential for MST is especially promising if combined with the other characteristics of an ideal FIB, such as abundance and mobility in the subsurface. This research focuses on evaluating the subsurface mobility of two bacteria, Enterococcus faecium (E. faecium) and Bacteriodes fragilis (B. fragilis), in order to better understand their potential use as source-tracking FIB. These bacteria are both abundant in fecal matter and they have shown promise as having human-specific markers. We performed column experiments to compare their subsurface transport through sandy material. Bacteria with relatively high attachment to sand have lower mobility in groundwater and may therefore be less effective as early tracers of fecal contamination The first part of our research compares two strains of E. faecium; one with and one without Enterococcal surface protein (Esp), a marker which recent research has linked to human sources, to evaluate whether the presence of Esp affects bacterial attachment to sand. The results indicate that in water with neutral pH (~7.2) the presence of Esp is linked to increased attachment to sand, thereby reducing the mobility of the Esp positive E. faecium. Because indicator bacteria should have relatively high mobility, this increased attachment potentially decreases the usefulness of Esp for MST. The results are consistent with calculations using the extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory of colloidal attachment, which predicts that attachment in bacteria with Esp should be greater than in those without Esp due to the presence of a higher energy barrier for the bacteria without Esp. The second part of this research compares the transport of the common aerobic fecal indicator bacteria E. coli, which has had limited success in source tracking, to the much more abundant anaerobic B. fragilis, which has shown promise as a potential MST bacteria. The results indicate that in water with neutral pH and low total ionic strength conditions, both E. coli and B. fragilis have similar attachment to sand, but at high ionic strength, such as may be found in areas near the source of contamination, the B. fragilis has lower attachment (and thus potentially higher mobility) than E. coli. The XDLVO calculations indicate a secondary energy minimum exists at higher ionic strength for both bacteria. This secondary minimum, which is absent at low ionic strength, occurs at a distance of 1 to 20 nm from the sand surface and appears to be the result of compression of the electrostatic double layer. The depth of this energy minimum is greater for E. coli than for B. fragilis, leading to greater attachment in the E. coli than the B. fragilis

Indoor positioning system with smart wi-fi antennas

The advancements of the indoor positioning system (IPS) in the recent years have been immense, yet we do not see the standardization of any solution. The system which is being used in many public scenarios is the Wi-Fi technology and the solution which we propose in this dissertation would not require change of infrastructure, but rather reusing existing one and simply enhancing it. This solution is low-cost and with relatively high precision considering current precision of Global Positioning System (GPS) of five meters. In this dissertation prototype is designed with motorized directional antennas using signal strength of two ESP8266. These position measurements are calculated and presented via micro controller and a Wi-Fi enabled device – ESP8266. Together with Yagi antenna, this solution has shown extremely good IPS characteristics and possibility to be implemented in real-case scenarios

Report of the Planning Group on Commercial Catches, Discards and Biological Sampling (PGCCDBS) [1-5 March 2010 Lisbon, Portugal]

Contributors: Kjell Harald Nedreaas (co-chair) and Jon Helge Vølsta

Prediction Of Heart Failure Decompensations Using Artificial Intelligence - Machine Learning Techniques

Los apartados 4.41, 4.4.2 y 4.4.3 del capítulo 4 están sujetos a confidencialidad por la autora. 203 p.Heart failure (HF) is a major concern in public health. Its total impact is increased by its high incidence and prevalence and its unfavourable medium-term prognosis. In addition, HF leads to huge health care resource consumption. Moreover, efforts to develop a deterministic understanding of rehospitalization have been difficult, as no specific patient or hospital factors have been shown to consistently predict 30-day readmission after hospitalization for HF.Taking all these facts into account, we wanted to develop a project to improve the assistance care of patients with HF. Up to know, we were using telemonitoring with a codification system that generated alarms depending on the received values. However, these simple rules generated large number of false alerts being, hence, not trustworthy. The final aims of this work are: (i) asses the benefits of remote patient telemonitoring (RPT), (ii) improve the results obtained with RPT using ML techniques, detecting which parameters measured by telemonitoring best predict HF decompensations and creating predictive models that will reduce false alerts and detect early decompensations that otherwise will lead to hospital admissions and (iii) determine the influence of environmental factors on HF decompensations.All in all, the conclusions of this study are:1. Asses the benefits of RPT: Telemonitoring has not shown a statistically significant reduction in the number of HF-related hospital admissions. Nevertheless, we have observed a statistically significant reduction in mortality in the intervention group with a considerable percentage of deaths from non-cardiovascular causes. Moreover, patients have considered the RPT programme as a tool that can help them in the control of their chronic disease and in the relationship with health professionals.2. Improve the results obtained with RPT using machine learning techniques: Significant weight increases, desaturation below 90%, perception of clinical worsening, including development of oedema, worsening of functional class and orthopnoea are good predictors of heart failure decompensation. In addition, machine learning techniques have improved the current alerts system implemented in our hospital. The system reduces the number of false alerts notably although it entails a decrement on sensitivity values. The best results are achieved with the predictive model built by applying NB with Bernoulli to the combination of telemonitoring alerts and questionnaire alerts (Weight + Ankle + well-being plus the yellow alerts of systolic blood pressure, diastolic blood pressure, O2Sat and heart rate). 3. Determine the influence of environmental factors on HF decompensations: Air temperature is the most significant environmental factor (negative correlation) in our study, although some other attributes, such as precipitation, are also relevant. This work also shows a consistent association between increasing levels SO2 and NOX air and HF hospitalizations

Prediction Of Heart Failure Decompensations Using Artificial Intelligence - Machine Learning Techniques

Los apartados 4.41, 4.4.2 y 4.4.3 del capítulo 4 están sujetos a confidencialidad por la autora. 203 p.Heart failure (HF) is a major concern in public health. Its total impact is increased by its high incidence and prevalence and its unfavourable medium-term prognosis. In addition, HF leads to huge health care resource consumption. Moreover, efforts to develop a deterministic understanding of rehospitalization have been difficult, as no specific patient or hospital factors have been shown to consistently predict 30-day readmission after hospitalization for HF.Taking all these facts into account, we wanted to develop a project to improve the assistance care of patients with HF. Up to know, we were using telemonitoring with a codification system that generated alarms depending on the received values. However, these simple rules generated large number of false alerts being, hence, not trustworthy. The final aims of this work are: (i) asses the benefits of remote patient telemonitoring (RPT), (ii) improve the results obtained with RPT using ML techniques, detecting which parameters measured by telemonitoring best predict HF decompensations and creating predictive models that will reduce false alerts and detect early decompensations that otherwise will lead to hospital admissions and (iii) determine the influence of environmental factors on HF decompensations.All in all, the conclusions of this study are:1. Asses the benefits of RPT: Telemonitoring has not shown a statistically significant reduction in the number of HF-related hospital admissions. Nevertheless, we have observed a statistically significant reduction in mortality in the intervention group with a considerable percentage of deaths from non-cardiovascular causes. Moreover, patients have considered the RPT programme as a tool that can help them in the control of their chronic disease and in the relationship with health professionals.2. Improve the results obtained with RPT using machine learning techniques: Significant weight increases, desaturation below 90%, perception of clinical worsening, including development of oedema, worsening of functional class and orthopnoea are good predictors of heart failure decompensation. In addition, machine learning techniques have improved the current alerts system implemented in our hospital. The system reduces the number of false alerts notably although it entails a decrement on sensitivity values. The best results are achieved with the predictive model built by applying NB with Bernoulli to the combination of telemonitoring alerts and questionnaire alerts (Weight + Ankle + well-being plus the yellow alerts of systolic blood pressure, diastolic blood pressure, O2Sat and heart rate). 3. Determine the influence of environmental factors on HF decompensations: Air temperature is the most significant environmental factor (negative correlation) in our study, although some other attributes, such as precipitation, are also relevant. This work also shows a consistent association between increasing levels SO2 and NOX air and HF hospitalizations

Generic Development Concept and Costing for Marginal Field

The objectives of this study is to identify the latest approaches and technical advances associated with development of marginal offshore field and the innovations used to reduce overall field development cost. Also to develop a costing basis to evaluate quick estimation of development cost of a marginal field. Increasing oil demand pushes oil companies to find concepts, which considerably reduce the costs of marginal field developments and consequently make these developments economically feasible. The methodology adopted for this study is literature review. Review and analysis of actual marginal field development concepts, novel facilities, and criteria used for options selection and development strategies around the world. A Generic development concept of marginal offshore field development were reviewed in order to identify the suitable alternative options, project management strategies and innovative technology that can be used for conceptual development phase for marginal prospects. A definition and understanding of marginal fields was established, drilling development approach conventional and innovative methods were identified, and also conventional and novel facilities development concepts were reviewed. The development basic cost estimation method is carried out. The findings showed that factors such as the reserve, environmental and regulations conditions, market conditions, field development cost and proximity to host existing process platform determine the commercial viability of marginal prospects. The success of such prospects was found to be dependent on development strategy, applied technology and project execution. Cost, schedule and existing infrastructure were identified as the main drivers influencing the strategy selection and facilities viability. Strategies involving fast track developments, tie-back to host facilities, leasing of facilities and stand alone developments were highlighted as the preferred choices. The life –cycle cost is an important method in assessing the impact of new technology on marginal field economics. A guideline for selecting a marginal field development strategy was proposed

Cross-layer design and optimization of medium access control protocols for wlans

This thesis provides a contribution to the field of Medium Access Control (MAC) layer protocol design for wireless networks by proposing and evaluating mechanisms that enhance different aspects of the network performance. These enhancements are achieved through the exchange of information between different layers of the traditional protocol stack, a concept known as Cross-Layer (CL) design. The main thesis contributions are divided into two parts. The first part of the thesis introduces a novel MAC layer protocol named Distributed Queuing Collision Avoidance (DQCA). DQCA behaves as a reservation scheme that ensures collision-free data transmissions at the majority of the time and switches automatically to an Aloha-like random access mechanism when the traffic load is low. DQCA can be enriched by more advanced scheduling algorithms based on a CL dialogue between the MAC and other protocol layers, to provide higher throughput and Quality of Service (QoS) guarantees. The second part of the thesis explores a different challenge in MAC layer design, related to the ability of multiple antenna systems to offer point-to-multipoint communications. Some modifications to the recently approved IEEE 802.11n standard are proposed in order to handle simultaneous multiuser downlink transmissions. A number of multiuser MAC schemes that handle channel access and scheduling issues and provide mechanisms for feedback acquisition have been presented and evaluated. The obtained performance enhancements have been demonstrated with the help of both theoretical analysis and simulation obtained results