Efeito dos polinizadores himenópteros em pomar de cereja

Dissertação apresentada à Escola Superior Agrária do Instituto Politécnico de Castelo Branco para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Fruticultura Integrada.

Hereditäre Thrombozytopathie beim Simmental Rind.

Der Wiederkäuerklinik oder dem Institut für Genetik der Universität Bern wurden zwi-schen 2012 und 2014 insgesamt 5 Rinder der Rasse Simmental vorgestellt, die je-weils nicht sistierende Blutungen nach Trauma zeigten. Alle betroffenen Tiere waren homozygote Träger für die seit 2007 bekannte RASGRP2 Mutation. Die verfügbaren Eltern wurden als heterozygote Anlageträger genotypisiert, was somit einen rezessi-ven Erbgang bestätigt. Drei erkrankte Tiere sind an den Folgen der unstillbaren Blu-tungen verstorben. Ein Tier konnte stabilisiert werden und wurde einen Monat nach der Entlassung aus der Klinik geschlachtet. Bei einem weiteren Fall wurden wieder-holt andauernde Blutungen sowie mehrmals Hämatome festgestellt und nach der genetischen Analyse wurde das Rind euthanasiert. Die Genotypisierung einer Stich-probe von 145 Stieren, die im Jahr 2013 in der Schweiz in der künstlichen Besamung zum Einsatz kamen, zeigte, dass 10% der getesteten Stiere in der Schweiz Anlage-träger für die assoziierte Mutation sind. Diese Stiere werden mit TP carrier gekenn-zeichnet und sollten zukünftig nicht mehr unkontrolliert eingesetzt werden. Die Zuchtverantwortlichen in der Schweiz nutzen heute den Gentest systematisch zur Selektion von anlagefreien Stieren.

Loran-C signal analysis : final report /

"Report no. CG-D-4-80."

Measurement of power density from marine radar /

Mode of access: Internet.

Loran-C display system test report /

"Report no. CG-D-47-80."

A standard for RF modulation factor /

Report No. FAA-RD-79-94." Microfiche.

Safe passage into the 21st century : modernizing NOAA's navigational services.

Mode of access: Internet.

Maneuvering board manual,

Mode of access: Internet.

The Sumner line of position furnished ready to lay down upon the chart by means of tables of simultaneous hour angle and azimuth of celestial bodies. Latitude 60æ N. to 60æ S.--declination 27æ N. to 27æ S.

"The plan of arrangement and scope of these tables, as well as the method of using them in navigation, were conceived by Mr. G.W. Littehales, C.E. Hydrographic office, Washington D.C."--Pref., p.iii.

Vessel bridge-to-bridge radiotelephone act : hearing before the Merchant Marine Subcommittee of the Committee on Commerce, United States Senate, Ninety-first Congress, second session, on H.R. 6971 ... H.R. 15549 ...

Mode of access: Internet.

Planting the southern pines /

Supersedes U.S. Dept. of Agriculture Technical bulletin 492. Artificial reforestation in the southern pine region, by P.C. Wakeley.

Combination of altimetry data from different satellite missions

Substantial altimetry datasets collected by different satellites have only become available during the past five years, but the future will bring a variety of new altimetry missions, both parallel and consecutive in time. The characteristics of each produced dataset vary with the different orbital heights and inclinations of the spacecraft, as well as with the technical properties of the radar instrument. An integral analysis of datasets with different properties offers advantages both in terms of data quantity and data quality. This thesis is concerned with the development of the means for such integral analysis, in particular for dynamic solutions in which precise orbits for the satellites are computed simultaneously. The first half of the thesis discusses the theory and numerical implementation of dynamic multi-satellite altimetry analysis. The most important aspect of this analysis is the application of dual satellite altimetry crossover points as a bi-directional tracking data type in simultaneous orbit solutions. The central problem is that the spatial and temporal distributions of the crossovers are in conflict with the time-organised nature of traditional solution methods. Their application to the adjustment of the orbits of both satellites involved in a dual crossover therefore requires several fundamental changes of the classical least-squares prediction/correction methods. The second part of the thesis applies the developed numerical techniques to the problems of precise orbit computation and gravity field adjustment, using the altimetry datasets of ERS-1 and TOPEX/Poseidon. Although the two datasets can be considered less compatible that those of planned future satellite missions, the obtained results adequately illustrate the merits of a simultaneous solution technique. In particular, the geographically correlated orbit error is partially observable from a dataset consisting of crossover differences between two sufficiently different altimetry datasets, while being unobservable from the analysis of altimetry data of both satellites individually. This error signal, which has a substantial gravity-induced component, can be employed advantageously in simultaneous solutions for the two satellites in which also the harmonic coefficients of the gravity field model are estimated.

Biomedical events extraction using the hidden vector state model

Objective: Biomedical events extraction concerns about events describing changes on the state of bio-molecules from literature. Comparing to the protein-protein interactions (PPIs) extraction task which often only involves the extraction of binary relations between two proteins, biomedical events extraction is much harder since it needs to deal with complex events consisting of embedded or hierarchical relations among proteins, events, and their textual triggers. In this paper, we propose an information extraction system based on the hidden vector state (HVS) model, called HVS-BioEvent, for biomedical events extraction, and investigate its capability in extracting complex events. Methods and material: HVS has been previously employed for extracting PPIs. In HVS-BioEvent, we propose an automated way to generate abstract annotations for HVS training and further propose novel machine learning approaches for event trigger words identification, and for biomedical events extraction from the HVS parse results. Results: Our proposed system achieves an F-score of 49.57% on the corpus used in the BioNLP'09 shared task, which is only 2.38% lower than the best performing system by UTurku in the BioNLP'09 shared task. Nevertheless, HVS-BioEvent outperforms UTurku's system on complex events extraction with 36.57% vs. 30.52% being achieved for extracting regulation events, and 40.61% vs. 38.99% for negative regulation events. Conclusions: The results suggest that the HVS model with the hierarchical hidden state structure is indeed more suitable for complex event extraction since it could naturally model embedded structural context in sentences.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

Application of Naïve Bayesian sequential analysis to primary care optometry

The objective of this study was to investigate the effects of circularity, comorbidity, prevalence and presentation variation on the accuracy of differential diagnoses made in optometric primary care using a modified form of naïve Bayesian sequential analysis. No such investigation has ever been reported before. Data were collected for 1422 cases seen over one year. Positive test outcomes were recorded for case history (ethnicity, age, symptoms and ocular and medical history) and clinical signs in relation to each diagnosis. For this reason only positive likelihood ratios were used for this modified form of Bayesian analysis that was carried out with Laplacian correction and Chi-square filtration. Accuracy was expressed as the percentage of cases for which the diagnoses made by the clinician appeared at the top of a list generated by Bayesian analysis. Preliminary analyses were carried out on 10 diagnoses and 15 test outcomes. Accuracy of 100% was achieved in the absence of presentation variation but dropped by 6% when variation existed. Circularity artificially elevated accuracy by 0.5%. Surprisingly, removal of Chi-square filtering increased accuracy by 0.4%. Decision tree analysis showed that accuracy was influenced primarily by prevalence followed by presentation variation and comorbidity. Analysis of 35 diagnoses and 105 test outcomes followed. This explored the use of positive likelihood ratios, derived from the case history, to recommend signs to look for. Accuracy of 72% was achieved when all clinical signs were entered. The drop in accuracy, compared to the preliminary analysis, was attributed to the fact that some diagnoses lacked strong diagnostic signs; the accuracy increased by 1% when only recommended signs were entered. Chi-square filtering improved recommended test selection. Decision tree analysis showed that accuracy again influenced primarily by prevalence, followed by comorbidity and presentation variation. Future work will explore the use of likelihood ratios based on positive and negative test findings prior to considering naïve Bayesian analysis as a form of artificial intelligence in optometric practice.